From 4a8ef2149428a63909c02d2ae51b24eb8a2e00c4 Mon Sep 17 00:00:00 2001 From: Chang Gao Date: Sat, 11 Jul 2026 19:11:29 +0200 Subject: [PATCH 1/3] Optimize TRes-DeltaGRU training end to end --- arguments.py | 11 +- backbones/cuda_graph_frozen_dgru.py | 317 ++ backbones/dgru.py | 26 +- backbones/tres_deltagru.py | 147 +- backbones/triton_deltagru.py | 632 +++ bash_scripts/OpenDPDv2.sh | 8 +- benchmark/benchmark_end_to_end.py | 882 +++ benchmark/benchmark_tres_deltagru.py | 422 ++ benchmark/report/artifact.json | 382 ++ .../e2e_dpa200_stride16_comparison_eager.json | 140 + .../e2e_dpa200_stride16_comparison_fused.json | 140 + .../e2e_dpa200_stride16_comparison_graph.json | 140 + .../batch_orders.npz | Bin 0 -> 10137 bytes .../outputs.npz | Bin 0 -> 171295 bytes .../result.json | 293 + .../batch_orders.npz | Bin 0 -> 10137 bytes .../outputs.npz | Bin 0 -> 171327 bytes .../result.json | 293 + .../batch_orders.npz | Bin 0 -> 10137 bytes .../outputs.npz | Bin 0 -> 171247 bytes .../result.json | 295 + .../batch_orders.npz | Bin 0 -> 10137 bytes .../e2e_dpa200_stride16_original/outputs.npz | Bin 0 -> 171295 bytes .../e2e_dpa200_stride16_original/result.json | 293 + .../e2e_dpa200_stride1_comparison_graph.json | 104 + ...pa200_stride1_comparison_graph_repeat.json | 104 + .../batch_orders.npz | Bin 0 -> 61949 bytes .../outputs.npz | Bin 0 -> 171363 bytes .../result.json | 235 + .../batch_orders.npz | Bin 0 -> 61949 bytes .../outputs.npz | Bin 0 -> 171363 bytes .../result.json | 235 + .../batch_orders.npz | Bin 0 -> 61949 bytes .../e2e_dpa200_stride1_original/outputs.npz | Bin 0 -> 171372 bytes .../e2e_dpa200_stride1_original/result.json | 233 + benchmark/results/end_to_end_summary.json | 165 + .../results/tres_deltagru_rtx4090_laptop.json | 92 + .../tres_deltagru_end_to_end_report.html | 4900 +++++++++++++++++ benchmark/tres_deltagru_training_speed.md | 179 + models.py | 9 +- modules/cuda_graph_training.py | 377 ++ modules/data_collector.py | 40 +- modules/paths.py | 7 +- modules/train_funcs.py | 99 +- opendpd/api.py | 36 +- project.py | 40 +- steps/train_dpd.py | 2 + tests/test_api.py | 24 + tests/test_cuda_graph_frozen_dgru.py | 167 + tests/test_cuda_graph_training.py | 236 + tests/test_training_pipeline_optimizations.py | 221 + tests/test_tres_deltagru_optimized.py | 238 + 52 files changed, 12076 insertions(+), 88 deletions(-) create mode 100644 backbones/cuda_graph_frozen_dgru.py create mode 100644 backbones/triton_deltagru.py create mode 100644 benchmark/benchmark_end_to_end.py create mode 100644 benchmark/benchmark_tres_deltagru.py create mode 100644 benchmark/report/artifact.json create mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_eager.json create mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_fused.json create mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_graph.json create mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/result.json create mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/result.json create mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/result.json create mode 100644 benchmark/results/e2e_dpa200_stride16_original/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_original/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride16_original/result.json create mode 100644 benchmark/results/e2e_dpa200_stride1_comparison_graph.json create mode 100644 benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/result.json create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/result.json create mode 100644 benchmark/results/e2e_dpa200_stride1_original/batch_orders.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_original/outputs.npz create mode 100644 benchmark/results/e2e_dpa200_stride1_original/result.json create mode 100644 benchmark/results/end_to_end_summary.json create mode 100644 benchmark/results/tres_deltagru_rtx4090_laptop.json create mode 100644 benchmark/tres_deltagru_end_to_end_report.html create mode 100644 benchmark/tres_deltagru_training_speed.md create mode 100644 modules/cuda_graph_training.py create mode 100644 tests/test_cuda_graph_frozen_dgru.py create mode 100644 tests/test_cuda_graph_training.py create mode 100644 tests/test_training_pipeline_optimizations.py create mode 100644 tests/test_tres_deltagru_optimized.py diff --git a/arguments.py b/arguments.py index 608e3ce..08fe262 100644 --- a/arguments.py +++ b/arguments.py @@ -40,6 +40,13 @@ def get_arguments(): parser.add_argument('--decay_factor', default=0.1, type=float, help='Learning rate') parser.add_argument('--patience', default=10, type=float, help='Learning rate') parser.add_argument('--grad_clip_val', default=200, type=float, help='Gradient clipping.') + parser.add_argument( + '--cuda_graph_training', action='store_true', default=False, + help=( + 'Opt in to guarded whole-step CUDA-graph replay for supported ' + 'TRes-DeltaGRU DPD training.' + ), + ) # GMP Hyperparameters parser.add_argument('--K', default=5, type=int, help='Degree of GMP model') parser.add_argument('--gmp_memory_length', default=11, type=int, help='Memory length of GMP model') @@ -76,6 +83,8 @@ def get_arguments(): help='Threshold for input deltas') parser.add_argument('--thh', type=float, default=0.0, help='Threshold for hidden state deltas') + parser.add_argument('--collect_delta_stats', action='store_true', default=False, + help='Collect temporal delta sparsity diagnostics during training (adds overhead).') # Optionally, you might want to add DVR-specific arguments parser.add_argument('--num_dvr_units', default=3, type=int, @@ -94,4 +103,4 @@ def get_arguments(): parser.add_argument('--gif_duration', default=10.0, type=float, help='Duration of GIF animations in seconds (default: 10.0).') - return parser.parse_args() \ No newline at end of file + return parser.parse_args() diff --git a/backbones/cuda_graph_frozen_dgru.py b/backbones/cuda_graph_frozen_dgru.py new file mode 100644 index 0000000..51c2dbb --- /dev/null +++ b/backbones/cuda_graph_frozen_dgru.py @@ -0,0 +1,317 @@ +"""Exact CUDA-graph replay for a frozen DGRU used during DPD training. + +The optimization records the existing cuDNN-backed DGRU forward and its input +backward as two CUDA graphs. It neither replaces the recurrence nor changes +the module tree. Unsupported or unsafe calls return ``None`` so the caller can +execute its ordinary eager implementation. +""" + +from __future__ import annotations + +import contextlib +import contextvars +import os +import threading +import weakref + +import torch + + +_DISABLE_ENV = "OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU" +_TRUE_VALUES = {"1", "true", "yes", "on"} +_MANAGERS = weakref.WeakKeyDictionary() +_MANAGERS_LOCK = threading.Lock() +_CAPTURE_FAILED = object() +_LOCAL_DISABLE_DEPTH = contextvars.ContextVar( + "opendpd_frozen_dgru_graph_disable_depth", default=0 +) + + +def _disabled() -> bool: + return ( + _LOCAL_DISABLE_DEPTH.get() > 0 + or os.getenv(_DISABLE_ENV, "").strip().lower() in _TRUE_VALUES + ) + + +@contextlib.contextmanager +def disable_cuda_graph_frozen_dgru(): + """Temporarily bypass the inner graph without changing process state.""" + + token = _LOCAL_DISABLE_DEPTH.set(_LOCAL_DISABLE_DEPTH.get() + 1) + try: + yield + finally: + _LOCAL_DISABLE_DEPTH.reset(token) + + +def _cuda_autocast_enabled() -> bool: + try: + return torch.is_autocast_enabled("cuda") + except TypeError: # pragma: no cover - compatibility with older PyTorch. + return torch.is_autocast_enabled() + + +def _can_replay(module, input: torch.Tensor, h_0: torch.Tensor | None) -> bool: + if ( + _disabled() + or not module.training + or not torch.is_grad_enabled() + or not input.requires_grad + or input.ndim != 3 + or not input.is_cuda + or input.dtype != torch.float32 + or not input.is_contiguous() + or torch.version.cuda is None + or getattr(torch.version, "hip", None) is not None + or _cuda_autocast_enabled() + or not hasattr(torch.cuda, "CUDAGraph") + ): + return False + try: + if torch.cuda.is_current_stream_capturing(): + return False + except RuntimeError: + return False + + parameters = tuple(module.parameters()) + if not parameters or any( + parameter.requires_grad + or parameter.device != input.device + or parameter.dtype != input.dtype + for parameter in parameters + ): + return False + if h_0 is not None and ( + h_0.requires_grad + or h_0.device != input.device + or h_0.dtype != input.dtype + or not h_0.is_contiguous() + ): + return False + return True + + +def _cache_key(module, input: torch.Tensor, h_0: torch.Tensor | None): + parameter_storage = tuple( + parameter.data_ptr() for parameter in module.parameters() + ) + h0_signature = ( + None if h_0 is None else (tuple(h_0.shape), tuple(h_0.stride())) + ) + return ( + input.device.index, + input.dtype, + tuple(input.shape), + tuple(input.stride()), + h0_signature, + parameter_storage, + ) + + +class _ReplayCache: + def __init__(self): + self.entries = {} + self.lock = threading.Lock() + + def get_or_create(self, module, input, h_0): + key = _cache_key(module, input, h_0) + entry = self.entries.get(key) + if entry is _CAPTURE_FAILED: + return None + if entry is not None: + return entry + + # Capturing is expensive and unsafe to duplicate. A concurrent first + # call simply uses eager execution and can use the cache next time. + if not self.lock.acquire(blocking=False): + return None + try: + entry = self.entries.get(key) + if entry is _CAPTURE_FAILED: + return None + if entry is None: + try: + entry = _FrozenDGRUReplay(module, input, h_0) + except (RuntimeError, torch.cuda.OutOfMemoryError): + self.entries[key] = _CAPTURE_FAILED + return None + self.entries[key] = entry + return entry + finally: + self.lock.release() + + +class _FrozenDGRUReplay: + def __init__(self, module, example_input, example_h0): + self.lock = threading.Lock() + self.busy = False + self.failed = False + self.generation = 0 + self.pending_event = None + self.pending_stream = None + + with torch.cuda.device(example_input.device), torch.enable_grad(): + # Warm cuDNN and autograd on a side stream before capture. Using + # that same stream for both captures keeps retained autograd nodes + # from acquiring stale cross-stream dependencies. + self.capture_stream = torch.cuda.Stream( + device=example_input.device + ) + self.capture_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(self.capture_stream): + self.static_input = ( + example_input.detach().clone().requires_grad_() + ) + self.static_h0 = ( + None if example_h0 is None else example_h0.detach().clone() + ) + for _ in range(3): + warm_output = module._forward_eager( + self.static_input, self.static_h0 + ) + warm_gradient = torch.ones_like(warm_output) + torch.autograd.grad( + warm_output, + self.static_input, + grad_outputs=warm_gradient, + ) + del warm_output, warm_gradient + torch.cuda.current_stream().wait_stream(self.capture_stream) + torch.cuda.synchronize(example_input.device) + + self.forward_graph = torch.cuda.CUDAGraph() + with torch.cuda.graph( + self.forward_graph, stream=self.capture_stream + ): + self.static_output = module._forward_eager( + self.static_input, self.static_h0 + ) + + self.static_grad_output = torch.empty_like(self.static_output) + self.backward_graph = torch.cuda.CUDAGraph() + with torch.cuda.graph( + self.backward_graph, stream=self.capture_stream + ): + (self.static_grad_input,) = torch.autograd.grad( + self.static_output, + self.static_input, + grad_outputs=self.static_grad_output, + ) + + def reserve(self) -> int | None: + with self.lock: + if self.failed or self.busy: + return None + if self.pending_event is not None: + current_stream = torch.cuda.current_stream( + self.static_input.device + ) + same_stream = current_stream.cuda_stream == self.pending_stream + if not same_stream and not self.pending_event.query(): + return None + self.pending_event = None + self.pending_stream = None + self.busy = True + self.generation += 1 + return self.generation + + def release(self, generation: int, *, failed: bool = False) -> None: + with self.lock: + if generation != self.generation: + return + self.failed = self.failed or failed + if torch.cuda.is_available(): + event = torch.cuda.Event() + stream = torch.cuda.current_stream(self.static_input.device) + event.record(stream) + self.pending_event = event + self.pending_stream = stream.cuda_stream + self.busy = False + + def forward(self, input, h_0, generation): + if generation != self.generation or not self.busy: + raise RuntimeError("stale frozen-DGRU CUDA-graph reservation") + self.static_input.copy_(input) + if self.static_h0 is not None: + self.static_h0.copy_(h_0) + self.forward_graph.replay() + return self.static_output.clone() + + def backward(self, grad_output, generation): + if generation != self.generation or not self.busy: + raise RuntimeError( + "frozen-DGRU CUDA graph cannot be replayed twice" + ) + self.static_grad_output.copy_(grad_output) + self.backward_graph.replay() + return self.static_grad_input.clone() + + +class _FrozenDGRUReplayFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, input, h_0, replay, generation): + ctx.replay = replay + ctx.generation = generation + ctx.used = False + try: + return replay.forward(input, h_0, generation) + except Exception: + replay.release(generation, failed=True) + raise + + @staticmethod + def backward(ctx, grad_output): + replay = ctx.replay + generation = ctx.generation + if ctx.used: + raise RuntimeError( + "frozen-DGRU CUDA graph supports one backward pass" + ) + ctx.used = True + if torch.is_grad_enabled(): + replay.release(generation) + raise RuntimeError( + "CUDA-graph frozen DGRU supports first-order training only; " + f"set {_DISABLE_ENV}=1 for higher-order autograd" + ) + try: + grad_input = replay.backward(grad_output, generation) + except Exception: + replay.release(generation, failed=True) + raise + replay.release(generation) + return grad_input, None, None, None + + +def try_cuda_graph_frozen_dgru( + module, input: torch.Tensor, h_0: torch.Tensor | None +) -> torch.Tensor | None: + """Return a graph-replayed output, or ``None`` for eager fallback.""" + + if not _can_replay(module, input, h_0): + return None + with _MANAGERS_LOCK: + manager = _MANAGERS.get(module) + if manager is None: + manager = _ReplayCache() + _MANAGERS[module] = manager + replay = manager.get_or_create(module, input, h_0) + if replay is None: + return None + generation = replay.reserve() + if generation is None: + return None + try: + return _FrozenDGRUReplayFunction.apply(input, h_0, replay, generation) + except (RuntimeError, torch.cuda.OutOfMemoryError): + # A replay error disables this cache entry. Eager execution still has + # the original input and is safe unless CUDA itself is unrecoverable. + return None + + +def clear_cuda_graph_frozen_dgru_cache() -> None: + """Drop replay caches; primarily useful for focused tests.""" + + with _MANAGERS_LOCK: + _MANAGERS.clear() diff --git a/backbones/dgru.py b/backbones/dgru.py index a3c7dd2..284e67b 100644 --- a/backbones/dgru.py +++ b/backbones/dgru.py @@ -7,8 +7,8 @@ class DGRU(nn.Module): - def __init__(self, hidden_size, output_size, num_layers, bidirectional=False, batch_first=True, - bias=True): + def __init__(self, hidden_size, output_size, num_layers, + bidirectional=False, batch_first=True, bias=True): super(DGRU, self).__init__() self.hidden_size = hidden_size self.input_size = 6 @@ -39,10 +39,14 @@ def reset_parameters(self): nn.init.constant_(param, 0) if 'weight' in name: for i in range(0, num_gates): - nn.init.orthogonal_(param[i * self.hidden_size:(i + 1) * self.hidden_size, :]) + start = i * self.hidden_size + end = (i + 1) * self.hidden_size + nn.init.orthogonal_(param[start:end, :]) if 'weight_ih_l0' in name: for i in range(0, num_gates): - nn.init.xavier_uniform_(param[i * self.hidden_size:(i + 1) * self.hidden_size, :]) + start = i * self.hidden_size + end = (i + 1) * self.hidden_size + nn.init.xavier_uniform_(param[start:end, :]) for name, param in self.fc_out.named_parameters(): if 'weight' in name: @@ -56,7 +60,7 @@ def reset_parameters(self): if 'bias' in name: nn.init.constant_(param, 0) - def forward(self, x, h_0): + def _forward_eager(self, x, h_0): # Feature Extraction i_x = torch.unsqueeze(x[..., 0], dim=-1) q_x = torch.unsqueeze(x[..., 1], dim=-1) @@ -72,3 +76,15 @@ def forward(self, x, h_0): out = torch.cat((out, x), dim=-1) out = self.fc_out(out) return out + + def forward(self, x, h_0): + # A frozen PA needs gradients only with respect to its input. For the + # common fixed-shape CUDA training path, replay the exact eager/cuDNN + # kernels and their input backward to remove launch overhead. The + # helper returns None for every unsupported or unsafe call. + from backbones.cuda_graph_frozen_dgru import try_cuda_graph_frozen_dgru + + graph_output = try_cuda_graph_frozen_dgru(self, x, h_0) + if graph_output is not None: + return graph_output + return self._forward_eager(x, h_0) diff --git a/backbones/tres_deltagru.py b/backbones/tres_deltagru.py index 2755bb3..6699c62 100644 --- a/backbones/tres_deltagru.py +++ b/backbones/tres_deltagru.py @@ -1,12 +1,19 @@ +import math +import os + +import numpy as np import torch -from torch import Tensor import torch.nn as nn -import torch.nn.functional as F -import numpy as np -import csv -import os +from torch import Tensor + from quant.modules.ops import Add, Mul -from torch import nn + +try: + from backbones.triton_deltagru import can_use_triton_deltagru, triton_deltagru +except ImportError: # Triton is optional; the eager implementation is portable. + can_use_triton_deltagru = None + triton_deltagru = None + class TResDeltaGRU(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers, thx=0, thh=0, @@ -47,10 +54,16 @@ def __init__(self, input_size, hidden_size, output_size, num_layers, thx=0, thh= bias=False), nn.Hardswish(), ) - self.set_debug(1) + # Statistics are diagnostic side effects, not part of the network. They + # used to add four reductions per recurrent timestep during every train + # step. Keep them opt-in and collect them inside the fused kernel when + # explicitly enabled. + self.set_debug(0) def set_debug(self, value): + value = int(value) setattr(self, "debug", value) + self.rnn.debug = value self.rnn.statistics = { "num_dx_zeros": 0, "num_dx_numel": 0, @@ -86,7 +99,7 @@ def reset_parameters(self): nn.init.constant_(param, 0) - def forward(self, x, h_0): + def forward(self, x, h_0=None): skip_x = self.tcn(x.transpose(1,2)).transpose(1,2) last_step = torch.roll(x, shifts=-1, dims=1) # Feature Extraction @@ -98,7 +111,11 @@ def forward(self, x, h_0): amp = torch.sqrt(amp2) amp3 = torch.pow(amp, 3) x = torch.cat((i_x, q_x, amp, amp3, i_last, q_last), dim=-1) - out = self.rnn(x, h_0) + # The old call passed ``h_0`` as x_p_0 while leaving the other recurrent + # states unset; process_inputs_first consequently discarded it and made + # all-zero states. Calling without it preserves that behaviour and + # avoids an unnecessary allocation/copy in CoreModel. + out = self.rnn(x) out = self.fc_out(out)+skip_x return out @@ -147,6 +164,7 @@ def __init__(self, self.x_p_length = max(self.input_size, self.hidden_size) self.batch_first = True self.debug = 1 + self.use_triton = os.environ.get("OPENDPD_DISABLE_TRITON_DELTAGRU", "0") != "1" # Statistics self.abs_sum_delta_hid = torch.zeros(1) self.sp_dx = 0 @@ -246,29 +264,23 @@ def layer_forward(self, input: Tensor, l: int, x_p_0: Tensor = None, h_0: Tensor input_size = input.size(-1) - th_x = torch.tensor(self.th_x, dtype=input.dtype) - th_h = torch.tensor(self.th_h, dtype=input.dtype) + th_x = self.th_x + th_h = self.th_h inputs = input.unbind(0) output = [] - reg = torch.zeros(1, dtype=input.dtype, device=input.device).squeeze() - x_p = x_p_0[:, :input_size] h = h_0 h_p = h_p_0 dm_nh = dm_nh_0 dm = dm_0 - l1_norm_delta_h = torch.zeros(1, dtype=input.dtype, device=input.device) - seq_len = len(inputs) for t in range(seq_len): x = inputs[t] delta_x, delta_h, delta_x_abs, delta_h_abs = self.compute_deltas(x, x_p, h, h_p, th_x, th_h) - reg += torch.sum(torch.abs(delta_h)) - if self.debug: zero_mask_delta_x = torch.as_tensor(delta_x == 0, dtype=x.dtype) zero_mask_delta_h = torch.as_tensor(delta_h == 0, dtype=x.dtype) @@ -279,8 +291,6 @@ def layer_forward(self, input: Tensor, l: int, x_p_0: Tensor = None, h_0: Tensor x_p, h_p = self.update_states(delta_x_abs, delta_h_abs, x, h, x_p, h_p, th_x, th_h) - l1_norm_delta_h += torch.sum(torch.abs(delta_h)) - dm, dm_r, dm_z, dm_n, dm_nh = self.compute_gates(delta_x, delta_h, dm, dm_nh) gate_r = self.sigmoid(dm_r) @@ -295,10 +305,107 @@ def layer_forward(self, input: Tensor, l: int, x_p_0: Tensor = None, h_0: Tensor def forward(self, input: Tensor, x_p_0: Tensor = None, h_0: Tensor = None, h_p_0: Tensor = None, dm_nh_0: Tensor = None, dm_0: Tensor = None): + states = (x_p_0, h_0, h_p_0, dm_nh_0, dm_0) + if self._can_use_triton(input) and self._states_can_use_triton(input, states): + batch_size = input.size(0) + if x_p_0 is None or h_0 is None or h_p_0 is None or dm_nh_0 is None or dm_0 is None: + x_p_0 = input.new_zeros((self.num_layers, batch_size, self.x_p_length)) + h_0 = input.new_zeros((self.num_layers, batch_size, self.hidden_size)) + h_p_0 = input.new_zeros((self.num_layers, batch_size, self.hidden_size)) + dm_nh_0 = input.new_zeros((self.num_layers, batch_size, self.hidden_size)) + dm_0 = input.new_zeros((self.num_layers, batch_size, self.weight_ih_height)) + + collect_stats = bool(self.debug) + stats = torch.zeros(2, dtype=torch.int64, device=input.device) if collect_stats else torch.empty( + 2, dtype=torch.int64, device=input.device + ) + output = triton_deltagru( + input.contiguous(), + x_p_0[0, :, :self.input_size].contiguous(), + h_0[0].contiguous(), + h_p_0[0].contiguous(), + dm_nh_0[0].contiguous(), + dm_0[0].contiguous(), + self.x2h.weight, + self.h2h.weight, + stats, + self.th_x, + self.th_h, + collect_stats, + ) + if collect_stats: + # Match the eager diagnostic counters, which use input dtype. + self.statistics["num_dx_zeros"] += stats[0].to(input.dtype) + self.statistics["num_dh_zeros"] += stats[1].to(input.dtype) + self.statistics["num_dx_numel"] += input.numel() + self.statistics["num_dh_numel"] += batch_size * input.size(1) * self.hidden_size + return output + x, x_p_0, h_0, h_p_0, dm_nh_0, dm_0 = self.process_inputs_first(input, x_p_0, h_0, h_p_0, dm_nh_0, dm_0) for l in range(self.num_layers): x = self.layer_forward(x, l, x_p_0[l], h_0[l], h_p_0[l], dm_nh_0[l], dm_0[l]) x = x.transpose(0, 1) - return x \ No newline at end of file + return x + + def _can_use_triton(self, input: Tensor) -> bool: + """The fused path is for the dense-float, one-layer CUDA cell. + + Quantization-aware training replaces these exact module types with + fake-quantized subclasses/operations. Reassociating those quantization + boundaries would change the model, so QAT deliberately stays eager. + """ + + return ( + self.use_triton + and self.num_layers == 1 + and can_use_triton_deltagru is not None + and not torch.is_autocast_enabled() + and not math.isnan(self.th_x) + and not math.isnan(self.th_h) + and input.ndim == 3 + and input.size(-1) == self.input_size + and type(self.x2h) is nn.Linear + and type(self.h2h) is nn.Linear + and self.x2h.bias is None + and self.h2h.bias is None + and self.x2h.weight.shape == (self.weight_ih_height, self.input_size) + and self.h2h.weight.shape == (self.weight_ih_height, self.hidden_size) + and self.x2h.weight.device == input.device + and self.h2h.weight.device == input.device + and self.x2h.weight.dtype == input.dtype + and self.h2h.weight.dtype == input.dtype + and self.x2h.weight.is_contiguous() + and self.h2h.weight.is_contiguous() + and type(self.sigmoid) is nn.Sigmoid + and type(self.tanh) is nn.Tanh + and type(self.add) is Add + and type(self.mul) is Mul + and can_use_triton_deltagru(input, self.hidden_size) + ) + + def _states_can_use_triton(self, input: Tensor, states: tuple) -> bool: + # The eager path discards every supplied state when any one is missing. + # The fused path does the same, so that case is always safe. + if any(state is None for state in states): + return True + + x_p_0, h_0, h_p_0, dm_nh_0, dm_0 = states + if not all(isinstance(state, Tensor) for state in states): + return False + batch_size = input.size(0) + common = (self.num_layers, batch_size) + expected_shapes = ( + common + (self.x_p_length,), + common + (self.hidden_size,), + common + (self.hidden_size,), + common + (self.hidden_size,), + common + (self.weight_ih_height,), + ) + return all( + tuple(state.shape) == shape + and state.device == input.device + and state.dtype == input.dtype + for state, shape in zip(states, expected_shapes) + ) diff --git a/backbones/triton_deltagru.py b/backbones/triton_deltagru.py new file mode 100644 index 0000000..861a0f1 --- /dev/null +++ b/backbones/triton_deltagru.py @@ -0,0 +1,632 @@ +"""Fused CUDA implementation of the DeltaGRU recurrence. + +The public helper in this module is intentionally small and optional. Importing +``backbones.tres_deltagru`` must continue to work on machines without Triton; +callers should check :func:`can_use_triton_deltagru` before dispatching here. + +The kernels preserve the eager recurrence, including its threshold comparisons +and separate input/hidden accumulator states. The backward kernel performs the +reverse recurrence and leaves the two weight-gradient reductions to cuBLAS. +That avoids a highly contended atomic reduction over batch and time. +""" + +from __future__ import annotations + +import math + +import torch + +try: + import triton + import triton.language as tl + from triton.language.extra import libdevice +except (ImportError, OSError): # pragma: no cover - CPU-only installations. + triton = None + tl = None + libdevice = None + + +def can_use_triton_deltagru(input: torch.Tensor, hidden_size: int) -> bool: + """Return whether the fused kernel supports this tensor configuration.""" + + return ( + triton is not None + and input.ndim == 3 + and input.shape[0] > 0 + and input.shape[1] > 0 + and input.is_cuda + and input.is_contiguous() + and torch.version.cuda is not None + and getattr(torch.version, "hip", None) is None + and input.dtype == torch.float32 + and 0 < input.shape[-1] <= 16 + and 0 < hidden_size <= 32 + ) + + +if triton is not None: + + @triton.jit + def _deltagru_forward_kernel( + x_ptr, + x_p0_ptr, + h0_ptr, + h_p0_ptr, + dm_nh0_ptr, + dm0_ptr, + weight_x_ptr, + weight_h_ptr, + out_ptr, + delta_x_ptr, + delta_h_ptr, + gate_r_ptr, + gate_z_ptr, + gate_n_ptr, + dm_nh_hist_ptr, + mask_x_ptr, + mask_h_ptr, + stats_ptr, + seq_len, + input_size: tl.constexpr, + hidden_size: tl.constexpr, + th_x: tl.constexpr, + th_h: tl.constexpr, + collect_stats: tl.constexpr, + save_intermediates: tl.constexpr, + block_i: tl.constexpr, + block_h: tl.constexpr, + ): + batch = tl.program_id(0) + offs_i = tl.arange(0, block_i) + offs_h = tl.arange(0, block_h) + mask_i = offs_i < input_size + mask_h = offs_h < hidden_size + + # Every program owns one batch row, so all recurrent state stays local. + x_p = tl.load(x_p0_ptr + batch * input_size + offs_i, mask=mask_i, other=0.0) + h = tl.load(h0_ptr + batch * hidden_size + offs_h, mask=mask_h, other=0.0) + h_p = tl.load(h_p0_ptr + batch * hidden_size + offs_h, mask=mask_h, other=0.0) + dm_r = tl.load(dm0_ptr + batch * (3 * hidden_size) + offs_h, mask=mask_h, other=0.0) + dm_z = tl.load( + dm0_ptr + batch * (3 * hidden_size) + hidden_size + offs_h, + mask=mask_h, + other=0.0, + ) + dm_n = tl.load( + dm0_ptr + batch * (3 * hidden_size) + 2 * hidden_size + offs_h, + mask=mask_h, + other=0.0, + ) + dm_nh = tl.load(dm_nh0_ptr + batch * hidden_size + offs_h, mask=mask_h, other=0.0) + + # The weights are loop-invariant. Loading them before the scan also + # gives the compiler a chance to keep this tiny model in registers. + wx_offsets = offs_h[:, None] * input_size + offs_i[None, :] + wh_offsets = offs_h[:, None] * hidden_size + offs_h[None, :] + wx_r = tl.load(weight_x_ptr + wx_offsets, mask=mask_h[:, None] & mask_i[None, :], other=0.0) + wx_z = tl.load( + weight_x_ptr + hidden_size * input_size + wx_offsets, + mask=mask_h[:, None] & mask_i[None, :], + other=0.0, + ) + wx_n = tl.load( + weight_x_ptr + 2 * hidden_size * input_size + wx_offsets, + mask=mask_h[:, None] & mask_i[None, :], + other=0.0, + ) + wh_r = tl.load(weight_h_ptr + wh_offsets, mask=mask_h[:, None] & mask_h[None, :], other=0.0) + wh_z = tl.load( + weight_h_ptr + hidden_size * hidden_size + wh_offsets, + mask=mask_h[:, None] & mask_h[None, :], + other=0.0, + ) + wh_n = tl.load( + weight_h_ptr + 2 * hidden_size * hidden_size + wh_offsets, + mask=mask_h[:, None] & mask_h[None, :], + other=0.0, + ) + + zeros_x = tl.zeros((), tl.int64) + zeros_h = tl.zeros((), tl.int64) + + for time in tl.range(0, seq_len, loop_unroll_factor=1): + x_offsets = (batch * seq_len + time) * input_size + offs_i + xt = tl.load(x_ptr + x_offsets, mask=mask_i, other=0.0) + + raw_delta_x = xt - x_p + raw_delta_h = h - h_p + abs_delta_x = tl.abs(raw_delta_x) + abs_delta_h = tl.abs(raw_delta_h) + + # masked_fill(abs(delta) < threshold, 0) and where(abs(delta) >= + # threshold, current, previous) are deliberately kept separate: + # their behaviour differs for NaNs in the eager implementation. + keep_delta_x = ~(abs_delta_x < th_x) + keep_delta_h = ~(abs_delta_h < th_h) + update_x = abs_delta_x >= th_x + update_h = abs_delta_h >= th_h + dx = tl.where(keep_delta_x, raw_delta_x, 0.0) + dh = tl.where(keep_delta_h, raw_delta_h, 0.0) + x_p = tl.where(update_x, xt, x_p) + h_p = tl.where(update_h, h, h_p) + + input_mac_r = tl.sum(wx_r * dx[None, :], axis=1) + dm_r + input_mac_z = tl.sum(wx_z * dx[None, :], axis=1) + dm_z + input_mac_n = tl.sum(wx_n * dx[None, :], axis=1) + dm_n + hidden_mac_r = tl.sum(wh_r * dh[None, :], axis=1) + hidden_mac_z = tl.sum(wh_z * dh[None, :], axis=1) + hidden_mac_n = tl.sum(wh_n * dh[None, :], axis=1) + + dm_r = input_mac_r + hidden_mac_r + dm_z = input_mac_z + hidden_mac_z + dm_n = input_mac_n + dm_nh = hidden_mac_n + dm_nh + + r = tl.sigmoid(dm_r) + z = tl.sigmoid(dm_z) + n = libdevice.tanh(dm_n + r * dm_nh) + h = (1.0 - z) * n + z * h + + h_offsets = (batch * seq_len + time) * hidden_size + offs_h + tl.store(out_ptr + h_offsets, h, mask=mask_h) + if save_intermediates: + tl.store(delta_x_ptr + x_offsets, dx, mask=mask_i) + tl.store(delta_h_ptr + h_offsets, dh, mask=mask_h) + tl.store(gate_r_ptr + h_offsets, r, mask=mask_h) + tl.store(gate_z_ptr + h_offsets, z, mask=mask_h) + tl.store(gate_n_ptr + h_offsets, n, mask=mask_h) + tl.store(dm_nh_hist_ptr + h_offsets, dm_nh, mask=mask_h) + tl.store(mask_x_ptr + x_offsets, update_x, mask=mask_i) + tl.store(mask_h_ptr + h_offsets, update_h, mask=mask_h) + + if collect_stats: + zeros_x += tl.sum(((dx == 0.0) & mask_i).to(tl.int64), axis=0) + zeros_h += tl.sum(((dh == 0.0) & mask_h).to(tl.int64), axis=0) + + if collect_stats: + tl.atomic_add(stats_ptr, zeros_x) + tl.atomic_add(stats_ptr + 1, zeros_h) + + @triton.jit + def _deltagru_backward_kernel( + grad_out_ptr, + h0_ptr, + out_ptr, + delta_x_ptr, + delta_h_ptr, + gate_r_ptr, + gate_z_ptr, + gate_n_ptr, + dm_nh_hist_ptr, + mask_x_ptr, + mask_h_ptr, + weight_x_ptr, + weight_h_ptr, + grad_x_ptr, + grad_x_p0_ptr, + grad_h0_ptr, + grad_h_p0_ptr, + grad_dm_nh0_ptr, + grad_dm0_ptr, + grad_mac_x_ptr, + grad_mac_h_ptr, + seq_len, + input_size: tl.constexpr, + hidden_size: tl.constexpr, + block_i: tl.constexpr, + block_h: tl.constexpr, + ): + batch = tl.program_id(0) + offs_i = tl.arange(0, block_i) + offs_h = tl.arange(0, block_h) + mask_i = offs_i < input_size + mask_h = offs_h < hidden_size + + wx_offsets = offs_h[:, None] * input_size + offs_i[None, :] + wh_offsets = offs_h[:, None] * hidden_size + offs_h[None, :] + wx_r = tl.load(weight_x_ptr + wx_offsets, mask=mask_h[:, None] & mask_i[None, :], other=0.0) + wx_z = tl.load( + weight_x_ptr + hidden_size * input_size + wx_offsets, + mask=mask_h[:, None] & mask_i[None, :], + other=0.0, + ) + wx_n = tl.load( + weight_x_ptr + 2 * hidden_size * input_size + wx_offsets, + mask=mask_h[:, None] & mask_i[None, :], + other=0.0, + ) + wh_r = tl.load(weight_h_ptr + wh_offsets, mask=mask_h[:, None] & mask_h[None, :], other=0.0) + wh_z = tl.load( + weight_h_ptr + hidden_size * hidden_size + wh_offsets, + mask=mask_h[:, None] & mask_h[None, :], + other=0.0, + ) + wh_n = tl.load( + weight_h_ptr + 2 * hidden_size * hidden_size + wh_offsets, + mask=mask_h[:, None] & mask_h[None, :], + other=0.0, + ) + + grad_x_p = tl.zeros((block_i,), tl.float32) + grad_h = tl.zeros((block_h,), tl.float32) + grad_h_p = tl.zeros((block_h,), tl.float32) + grad_dm_r = tl.zeros((block_h,), tl.float32) + grad_dm_z = tl.zeros((block_h,), tl.float32) + grad_dm_n = tl.zeros((block_h,), tl.float32) + grad_dm_nh = tl.zeros((block_h,), tl.float32) + h0 = tl.load(h0_ptr + batch * hidden_size + offs_h, mask=mask_h, other=0.0) + + for reverse_time in tl.range(0, seq_len, loop_unroll_factor=1): + time = seq_len - 1 - reverse_time + h_offsets = (batch * seq_len + time) * hidden_size + offs_h + x_offsets = (batch * seq_len + time) * input_size + offs_i + + grad_h += tl.load(grad_out_ptr + h_offsets, mask=mask_h, other=0.0) + h_prev = tl.load( + out_ptr + h_offsets - hidden_size, + mask=mask_h & (time > 0), + other=0.0, + ) + h_prev = tl.where(time > 0, h_prev, h0) + r = tl.load(gate_r_ptr + h_offsets, mask=mask_h, other=0.0) + z = tl.load(gate_z_ptr + h_offsets, mask=mask_h, other=0.0) + n = tl.load(gate_n_ptr + h_offsets, mask=mask_h, other=0.0) + dm_nh = tl.load(dm_nh_hist_ptr + h_offsets, mask=mask_h, other=0.0) + + grad_n = grad_h * (1.0 - z) + grad_z = grad_h * (h_prev - n) + grad_h_prev = grad_h * z + grad_pre_n = grad_n * (1.0 - n * n) + grad_r = grad_pre_n * dm_nh + grad_dm_nh += grad_pre_n * r + grad_dm_r += grad_r * r * (1.0 - r) + grad_dm_z += grad_z * z * (1.0 - z) + grad_dm_n += grad_pre_n + + grad_mac_x_r = grad_dm_r + grad_mac_x_z = grad_dm_z + grad_mac_x_n = grad_dm_n + grad_mac_h_r = grad_dm_r + grad_mac_h_z = grad_dm_z + grad_mac_h_n = grad_dm_nh + + grad_dx = ( + tl.sum(wx_r * grad_mac_x_r[:, None], axis=0) + + tl.sum(wx_z * grad_mac_x_z[:, None], axis=0) + + tl.sum(wx_n * grad_mac_x_n[:, None], axis=0) + ) + grad_dh = ( + tl.sum(wh_r * grad_mac_h_r[:, None], axis=0) + + tl.sum(wh_z * grad_mac_h_z[:, None], axis=0) + + tl.sum(wh_n * grad_mac_h_n[:, None], axis=0) + ) + + update_x = tl.load(mask_x_ptr + x_offsets, mask=mask_i, other=0).to(tl.int1) + update_h = tl.load(mask_h_ptr + h_offsets, mask=mask_h, other=0).to(tl.int1) + saved_delta_x = tl.load(delta_x_ptr + x_offsets, mask=mask_i, other=0.0) + saved_delta_h = tl.load(delta_h_ptr + h_offsets, mask=mask_h, other=0.0) + # For non-NaN thresholds, masked-fill's keep condition differs from + # the state-update condition only when a raw delta is NaN. + keep_delta_x = update_x | (saved_delta_x != saved_delta_x) + keep_delta_h = update_h | (saved_delta_h != saved_delta_h) + grad_xt = tl.where(keep_delta_x, grad_dx, 0.0) + tl.where( + update_x, grad_x_p, 0.0 + ) + grad_x_p = tl.where(keep_delta_x, -grad_dx, 0.0) + tl.where( + update_x, 0.0, grad_x_p + ) + grad_h_prev += tl.where(keep_delta_h, grad_dh, 0.0) + tl.where( + update_h, grad_h_p, 0.0 + ) + grad_h_p = tl.where(keep_delta_h, -grad_dh, 0.0) + tl.where( + update_h, 0.0, grad_h_p + ) + + tl.store(grad_x_ptr + x_offsets, grad_xt, mask=mask_i) + mac_offsets = (batch * seq_len + time) * (3 * hidden_size) + offs_h + tl.store(grad_mac_x_ptr + mac_offsets, grad_mac_x_r, mask=mask_h) + tl.store( + grad_mac_x_ptr + mac_offsets + hidden_size, + grad_mac_x_z, + mask=mask_h, + ) + tl.store( + grad_mac_x_ptr + mac_offsets + 2 * hidden_size, + grad_mac_x_n, + mask=mask_h, + ) + tl.store(grad_mac_h_ptr + mac_offsets, grad_mac_h_r, mask=mask_h) + tl.store( + grad_mac_h_ptr + mac_offsets + hidden_size, + grad_mac_h_z, + mask=mask_h, + ) + tl.store( + grad_mac_h_ptr + mac_offsets + 2 * hidden_size, + grad_mac_h_n, + mask=mask_h, + ) + + # Gradients for the accumulator carries at the previous timestep. + grad_dm_r = grad_mac_x_r + grad_dm_z = grad_mac_x_z + grad_dm_n = grad_mac_x_n + grad_dm_nh = grad_mac_h_n + grad_h = grad_h_prev + + tl.store(grad_x_p0_ptr + batch * input_size + offs_i, grad_x_p, mask=mask_i) + tl.store(grad_h0_ptr + batch * hidden_size + offs_h, grad_h, mask=mask_h) + tl.store(grad_h_p0_ptr + batch * hidden_size + offs_h, grad_h_p, mask=mask_h) + tl.store(grad_dm_nh0_ptr + batch * hidden_size + offs_h, grad_dm_nh, mask=mask_h) + dm0_offsets = batch * (3 * hidden_size) + offs_h + tl.store(grad_dm0_ptr + dm0_offsets, grad_dm_r, mask=mask_h) + tl.store(grad_dm0_ptr + dm0_offsets + hidden_size, grad_dm_z, mask=mask_h) + tl.store(grad_dm0_ptr + dm0_offsets + 2 * hidden_size, grad_dm_n, mask=mask_h) + + +class _TritonDeltaGRUFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + input: torch.Tensor, + x_p0: torch.Tensor, + h0: torch.Tensor, + h_p0: torch.Tensor, + dm_nh0: torch.Tensor, + dm0: torch.Tensor, + weight_x: torch.Tensor, + weight_h: torch.Tensor, + stats: torch.Tensor, + th_x: float, + th_h: float, + collect_stats: bool, + ) -> torch.Tensor: + batch_size, seq_len, input_size = input.shape + hidden_size = h0.shape[-1] + output = input.new_empty((batch_size, seq_len, hidden_size)) + delta_x = torch.empty_like(input) + state_shape = (batch_size, seq_len, hidden_size) + delta_h = input.new_empty(state_shape) + gate_r = input.new_empty(state_shape) + gate_z = input.new_empty(state_shape) + gate_n = input.new_empty(state_shape) + dm_nh_hist = input.new_empty(state_shape) + mask_x = torch.empty_like(input, dtype=torch.uint8) + mask_h = torch.empty(state_shape, device=input.device, dtype=torch.uint8) + + block_i = triton.next_power_of_2(input_size) + block_h = triton.next_power_of_2(hidden_size) + _deltagru_forward_kernel[(batch_size,)]( + input, + x_p0, + h0, + h_p0, + dm_nh0, + dm0, + weight_x, + weight_h, + output, + delta_x, + delta_h, + gate_r, + gate_z, + gate_n, + dm_nh_hist, + mask_x, + mask_h, + stats, + seq_len=seq_len, + input_size=input_size, + hidden_size=hidden_size, + th_x=th_x, + th_h=th_h, + collect_stats=collect_stats, + save_intermediates=True, + block_i=block_i, + block_h=block_h, + num_warps=1, + ) + ctx.save_for_backward( + h0, + output, + delta_x, + delta_h, + gate_r, + gate_z, + gate_n, + dm_nh_hist, + mask_x, + mask_h, + weight_x, + weight_h, + ) + ctx.input_size = input_size + ctx.hidden_size = hidden_size + return output + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + if torch.is_grad_enabled(): + raise RuntimeError( + "fused DeltaGRU supports first-order training only; set " + "OPENDPD_DISABLE_TRITON_DELTAGRU=1 for higher-order autograd" + ) + ( + h0, + output, + delta_x, + delta_h, + gate_r, + gate_z, + gate_n, + dm_nh_hist, + mask_x, + mask_h, + weight_x, + weight_h, + ) = ctx.saved_tensors + grad_output = grad_output.contiguous() + batch_size, seq_len, hidden_size = output.shape + input_size = ctx.input_size + + grad_input = delta_x.new_empty(delta_x.shape) + grad_x_p0 = delta_x.new_empty((batch_size, input_size)) + grad_h0 = output.new_empty((batch_size, hidden_size)) + grad_h_p0 = output.new_empty((batch_size, hidden_size)) + grad_dm_nh0 = output.new_empty((batch_size, hidden_size)) + grad_dm0 = output.new_empty((batch_size, 3 * hidden_size)) + grad_mac_x = output.new_empty((batch_size, seq_len, 3 * hidden_size)) + grad_mac_h = output.new_empty((batch_size, seq_len, 3 * hidden_size)) + + block_i = triton.next_power_of_2(input_size) + block_h = triton.next_power_of_2(hidden_size) + _deltagru_backward_kernel[(batch_size,)]( + grad_output, + h0, + output, + delta_x, + delta_h, + gate_r, + gate_z, + gate_n, + dm_nh_hist, + mask_x, + mask_h, + weight_x, + weight_h, + grad_input, + grad_x_p0, + grad_h0, + grad_h_p0, + grad_dm_nh0, + grad_dm0, + grad_mac_x, + grad_mac_h, + seq_len=seq_len, + input_size=input_size, + hidden_size=hidden_size, + block_i=block_i, + block_h=block_h, + num_warps=1, + ) + + flat_delta_x = delta_x.reshape(-1, input_size) + flat_delta_h = delta_h.reshape(-1, hidden_size) + flat_grad_mac_x = grad_mac_x.reshape(-1, 3 * hidden_size) + flat_grad_mac_h = grad_mac_h.reshape(-1, 3 * hidden_size) + grad_weight_x = flat_grad_mac_x.t().matmul(flat_delta_x) + grad_weight_h = flat_grad_mac_h.t().matmul(flat_delta_h) + + return ( + grad_input, + grad_x_p0, + grad_h0, + grad_h_p0, + grad_dm_nh0, + grad_dm0, + grad_weight_x, + grad_weight_h, + None, + None, + None, + None, + ) + + +def triton_deltagru( + input: torch.Tensor, + x_p0: torch.Tensor, + h0: torch.Tensor, + h_p0: torch.Tensor, + dm_nh0: torch.Tensor, + dm0: torch.Tensor, + weight_x: torch.Tensor, + weight_h: torch.Tensor, + stats: torch.Tensor, + th_x: float, + th_h: float, + collect_stats: bool = False, +) -> torch.Tensor: + """Run the fused recurrence on a contiguous ``(batch, time, input)`` tensor.""" + + if triton is None: # pragma: no cover + raise RuntimeError("Triton is not installed") + if math.isnan(th_x) or math.isnan(th_h): + raise ValueError("DeltaGRU thresholds must not be NaN") + batch_size, seq_len, input_size = input.shape + hidden_size = h0.shape[-1] if h0.ndim else 0 + expected = ( + (x_p0, (batch_size, input_size)), + (h0, (batch_size, hidden_size)), + (h_p0, (batch_size, hidden_size)), + (dm_nh0, (batch_size, hidden_size)), + (dm0, (batch_size, 3 * hidden_size)), + (weight_x, (3 * hidden_size, input_size)), + (weight_h, (3 * hidden_size, hidden_size)), + ) + if not can_use_triton_deltagru(input, hidden_size): + raise ValueError("unsupported DeltaGRU input tensor or hidden size") + if not all( + tensor.shape == shape + and tensor.device == input.device + and tensor.dtype == input.dtype + and tensor.is_contiguous() + for tensor, shape in expected + ): + raise ValueError("DeltaGRU states and weights must match input shape/device/dtype") + if ( + stats.device != input.device + or stats.dtype != torch.int64 + or stats.numel() < 2 + or not stats.is_contiguous() + ): + raise ValueError("stats must be a contiguous CUDA int64 tensor with at least 2 elements") + if not torch.is_grad_enabled(): + output = input.new_empty((batch_size, seq_len, hidden_size)) + dummy = input.new_empty(1) + dummy_mask = torch.empty(1, device=input.device, dtype=torch.uint8) + _deltagru_forward_kernel[(batch_size,)]( + input, + x_p0, + h0, + h_p0, + dm_nh0, + dm0, + weight_x, + weight_h, + output, + dummy, + dummy, + dummy, + dummy, + dummy, + dummy, + dummy_mask, + dummy_mask, + stats, + seq_len=seq_len, + input_size=input_size, + hidden_size=hidden_size, + th_x=th_x, + th_h=th_h, + collect_stats=collect_stats, + save_intermediates=False, + block_i=triton.next_power_of_2(input_size), + block_h=triton.next_power_of_2(hidden_size), + num_warps=1, + ) + return output + return _TritonDeltaGRUFunction.apply( + input, + x_p0, + h0, + h_p0, + dm_nh0, + dm0, + weight_x, + weight_h, + stats, + th_x, + th_h, + collect_stats, + ) diff --git a/bash_scripts/OpenDPDv2.sh b/bash_scripts/OpenDPDv2.sh index f5cf179..27c4aa5 100644 --- a/bash_scripts/OpenDPDv2.sh +++ b/bash_scripts/OpenDPDv2.sh @@ -40,6 +40,11 @@ lr=${LR:-5e-3} lr_end=${LR_END:-1e-4} decay_factor=${DECAY_FACTOR:-0.5} patience=${PATIENCE:-10} +cuda_graph_training=${CUDA_GRAPH_TRAINING:-1} +cuda_graph_opts=() +if [[ "${cuda_graph_training}" == "1" ]]; then + cuda_graph_opts+=(--cuda_graph_training) +fi seed_values=(0) PA_backbone=dgru @@ -107,7 +112,8 @@ for i_seed in "${seed_values[@]}"; do --decay_factor "${decay_factor}" \ --patience "${patience}" \ --thx "${thx}" \ - --thh "${thh}" + --thh "${thh}" \ + "${cuda_graph_opts[@]}" quant_dir_label="w${quant_n_bits_w}a${quant_n_bits_a}" diff --git a/benchmark/benchmark_end_to_end.py b/benchmark/benchmark_end_to_end.py new file mode 100644 index 0000000..86a26e1 --- /dev/null +++ b/benchmark/benchmark_end_to_end.py @@ -0,0 +1,882 @@ +#!/usr/bin/env python3 +"""Controlled end-to-end benchmark for TRes-DeltaGRU DPD training. + +The same script can execute the committed reference tree and a working tree: + + # Create one deterministic, trained PA fixture with the reference code. + python benchmark/benchmark_end_to_end.py prepare \ + --repo-root /tmp/OpenDPD-e2e-baseline \ + --fixture benchmark/fixtures/dpa200_dgru_h23.pt + + # Run identical batches through the two implementations. + python benchmark/benchmark_end_to_end.py run \ + --repo-root /tmp/OpenDPD-e2e-baseline --implementation original \ + --fixture benchmark/fixtures/dpa200_dgru_h23.pt \ + --output-dir benchmark/results/e2e_original + python benchmark/benchmark_end_to_end.py run \ + --repo-root . --implementation optimized \ + --fixture benchmark/fixtures/dpa200_dgru_h23.pt \ + --output-dir benchmark/results/e2e_optimized + + python benchmark/benchmark_end_to_end.py compare \ + --reference-dir benchmark/results/e2e_original \ + --candidate-dir benchmark/results/e2e_optimized \ + --json-out benchmark/results/e2e_comparison.json + +The fixture contains identical PA and DPD initial state dictionaries. Every +run also records the exact shuffled frame indices used in each epoch. The run +includes CSV loading, frame construction, host-to-device transfer, one isolated +compile/warm-up step, training, validation, testing, the repository's ACLR, +NMSE and EVM functions, best-checkpoint selection, and artifact serialization. +""" + +from __future__ import annotations + +import argparse +import contextlib +import hashlib +import importlib +import io +import json +import os +import platform +import subprocess +import sys +import time +from pathlib import Path +from types import SimpleNamespace +from typing import Any, Iterable + +import numpy as np +import torch +from torch.utils.data import DataLoader, Sampler + + +SCRIPT_ROOT = Path(__file__).resolve().parent.parent +DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +METRIC_NAMES = ("NMSE", "EVM", "ACLR_L", "ACLR_R", "ACLR_AVG") + + +class FixedOrderSampler(Sampler[int]): + """Yield a recorded permutation without consuming the global RNG.""" + + def __init__(self, indices: Iterable[int]): + self.indices = tuple(int(index) for index in indices) + + def __iter__(self): + return iter(self.indices) + + def __len__(self) -> int: + return len(self.indices) + + +def _json_default(value: Any): + if isinstance(value, Path): + return str(value) + if isinstance(value, (np.floating, np.integer)): + return value.item() + if isinstance(value, torch.Tensor): + return value.detach().cpu().tolist() + raise TypeError(f"cannot serialize {type(value).__name__}") + + +def _write_json(path: Path, payload: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + json.dumps(payload, indent=2, sort_keys=True, default=_json_default) + "\n", + encoding="utf-8", + ) + + +def _command_output(command: list[str], cwd: Path) -> str | None: + try: + result = subprocess.run( + command, cwd=cwd, check=True, capture_output=True, text=True + ) + except (OSError, subprocess.CalledProcessError): + return None + return result.stdout.strip() or None + + +def _sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _sync(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _configure_reproducibility(seed: int) -> None: + os.environ.setdefault("PYTHONHASHSEED", str(seed)) + os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") + torch.manual_seed(seed) + np.random.seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + +def _load_repo(repo_root: Path) -> SimpleNamespace: + repo_root = repo_root.resolve() + if not (repo_root / "models.py").is_file(): + raise FileNotFoundError(f"not an OpenDPD tree: {repo_root}") + sys.path.insert(0, str(repo_root)) + models = importlib.import_module("models") + data = importlib.import_module("modules.data_collector") + train_funcs = importlib.import_module("modules.train_funcs") + util = importlib.import_module("utils.util") + return SimpleNamespace( + root=repo_root, + models=models, + data=data, + train_funcs=train_funcs, + util=util, + ) + + +def _load_spec(repo_root: Path, dataset_name: str) -> dict[str, Any]: + path = repo_root / "datasets" / dataset_name / "spec.json" + with path.open(encoding="utf-8") as handle: + return json.load(handle) + + +def _build_core( + repo: SimpleNamespace, + backbone: str, + hidden_size: int, + thx: float = 0.0, + thh: float = 0.0, +): + with contextlib.redirect_stdout(io.StringIO()): + return repo.models.CoreModel( + input_size=2, + hidden_size=hidden_size, + num_layers=1, + backbone_type=backbone, + thx=thx, + thh=thh, + ) + + +def _cpu_state(module: torch.nn.Module) -> dict[str, torch.Tensor]: + return { + name: tensor.detach().cpu().clone() + for name, tensor in module.state_dict().items() + } + + +def _build_cascade( + repo: SimpleNamespace, + fixture: dict[str, Any], + device: torch.device, +): + config = fixture["config"] + pa = _build_core(repo, config["pa_backbone"], config["pa_hidden_size"]) + dpd = _build_core( + repo, + config["dpd_backbone"], + config["dpd_hidden_size"], + config["thx"], + config["thh"], + ) + pa.load_state_dict(fixture["pa_state"]) + dpd.load_state_dict(fixture["dpd_state"]) + cascade = repo.models.CascadedModel(dpd_model=dpd, pa_model=pa) + cascade.freeze_pa_model() + return cascade.to(device) + + +def _metric_args(spec: dict[str, Any]) -> SimpleNamespace: + return SimpleNamespace( + input_signal_fs=spec["input_signal_fs"], + bw_main_ch=spec["bw_main_ch"], + n_sub_ch=spec["n_sub_ch"], + nperseg=spec["nperseg"], + ) + + +def _metrics( + train_funcs: Any, + metric_args: SimpleNamespace, + prediction: np.ndarray, + ground_truth: np.ndarray, +) -> dict[str, float]: + result = train_funcs.calculate_metrics( + metric_args, {}, prediction, ground_truth + ) + return {name: float(result[name]) for name in METRIC_NAMES} + + +def _environment(repo_root: Path, device: torch.device) -> dict[str, Any]: + gpu = None + if device.type == "cuda": + gpu = torch.cuda.get_device_name(device) + source_files = [ + "backbones/tres_deltagru.py", + "models.py", + "modules/train_funcs.py", + "modules/cuda_graph_training.py", + "modules/data_collector.py", + "project.py", + ] + hashes = { + relative: _sha256_file(repo_root / relative) + for relative in source_files + if (repo_root / relative).is_file() + } + return { + "timestamp_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "platform": platform.platform(), + "python": platform.python_version(), + "torch": torch.__version__, + "cuda_runtime": torch.version.cuda, + "device": str(device), + "gpu": gpu, + "git_commit": _command_output(["git", "rev-parse", "HEAD"], repo_root), + "git_dirty": bool(_command_output(["git", "status", "--short"], repo_root)), + "source_sha256": hashes, + } + + +def prepare(args: argparse.Namespace) -> None: + started = time.perf_counter() + repo = _load_repo(args.repo_root) + device = torch.device(args.device) + _configure_reproducibility(args.seed) + + arrays = repo.data.load_dataset(dataset_name=args.dataset_name) + x_train, y_train = arrays[0], arrays[1] + pa_dataset = repo.data.IQFrameDataset( + x_train, y_train, frame_length=args.frame_length, stride=args.pa_frame_stride + ) + + # Model initialization is intentionally done once. The resulting tensors, + # rather than a seed assumption, are the fixture contract used by every arm. + pa = _build_core(repo, args.pa_backbone, args.pa_hidden_size).to(device) + generator = torch.Generator().manual_seed(args.seed + 10_000) + loader = DataLoader( + pa_dataset, + batch_size=args.pa_batch_size, + shuffle=True, + generator=generator, + pin_memory=device.type == "cuda", + ) + optimizer = torch.optim.AdamW(pa.parameters(), lr=args.pa_lr) + criterion = torch.nn.MSELoss() + epoch_losses: list[float] = [] + pa.train() + for _ in range(args.pa_pretrain_epochs): + losses: list[float] = [] + for features, targets in loader: + features = features.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + optimizer.zero_grad(set_to_none=True) + loss = criterion(pa(features), targets) + loss.backward() + if args.grad_clip: + torch.nn.utils.clip_grad_norm_(pa.parameters(), args.grad_clip) + optimizer.step() + losses.append(float(loss.detach())) + epoch_losses.append(float(np.mean(losses))) + + # Re-seed before DPD construction so its state is independent of PA epoch + # count and can be regenerated deliberately if the fixture format changes. + torch.manual_seed(args.seed + 1) + if device.type == "cuda": + torch.cuda.manual_seed_all(args.seed + 1) + dpd = _build_core( + repo, + args.dpd_backbone, + args.dpd_hidden_size, + args.thx, + args.thh, + ) + fixture = { + "format_version": 1, + "config": { + "dataset_name": args.dataset_name, + "frame_length": args.frame_length, + "pa_backbone": args.pa_backbone, + "pa_hidden_size": args.pa_hidden_size, + "dpd_backbone": args.dpd_backbone, + "dpd_hidden_size": args.dpd_hidden_size, + "thx": args.thx, + "thh": args.thh, + "seed": args.seed, + }, + "pa_pretraining": { + "epochs": args.pa_pretrain_epochs, + "frame_stride": args.pa_frame_stride, + "batch_size": args.pa_batch_size, + "lr": args.pa_lr, + "epoch_losses": epoch_losses, + "surrogate_notice": ( + "Deterministically trained benchmark surrogate; the repository " + "does not contain a published PA checkpoint." + ), + }, + "pa_state": _cpu_state(pa), + "dpd_state": _cpu_state(dpd), + } + args.fixture.parent.mkdir(parents=True, exist_ok=True) + torch.save(fixture, args.fixture) + print( + json.dumps( + { + "fixture": str(args.fixture), + "sha256": _sha256_file(args.fixture), + "pa_final_loss": epoch_losses[-1] if epoch_losses else None, + "elapsed_s": time.perf_counter() - started, + }, + indent=2, + ) + ) + + +def _loader( + dataset: Any, + batch_size: int, + device: torch.device, + optimized: bool, + indices: Iterable[int] | None = None, +) -> DataLoader: + return DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + sampler=FixedOrderSampler(indices) if indices is not None else None, + pin_memory=optimized and device.type == "cuda", + ) + + +def _time_eval( + repo: SimpleNamespace, + model: torch.nn.Module, + loader: DataLoader, + criterion: torch.nn.Module, + device: torch.device, +): + _sync(device) + started = time.perf_counter() + log: dict[str, Any] = {} + _, prediction, truth = repo.train_funcs.net_eval( + log=log, + net=model, + dataloader=loader, + criterion=criterion, + device=device, + ) + _sync(device) + return time.perf_counter() - started, float(log["loss"]), prediction, truth + + +def _prediction_error(reference: np.ndarray, candidate: np.ndarray) -> dict[str, float]: + reference64 = np.asarray(reference, dtype=np.float64) + candidate64 = np.asarray(candidate, dtype=np.float64) + difference = candidate64 - reference64 + denominator = max(np.linalg.norm(reference64.ravel()), np.finfo(np.float64).tiny) + return { + "max_abs": float(np.max(np.abs(difference))), + "mean_abs": float(np.mean(np.abs(difference))), + "relative_l2": float(np.linalg.norm(difference.ravel()) / denominator), + } + + +def run(args: argparse.Namespace) -> None: + total_started = time.perf_counter() + repo = _load_repo(args.repo_root) + device = torch.device(args.device) + _configure_reproducibility(args.seed) + optimized = args.implementation != "original" + if device.type == "cuda": + torch.cuda.reset_peak_memory_stats(device) + if not args.progress: + repo.train_funcs.tqdm = lambda iterable: iterable + + fixture = torch.load(args.fixture, map_location="cpu", weights_only=False) + config = fixture["config"] + if args.dataset_name != config["dataset_name"]: + raise ValueError( + f"fixture dataset {config['dataset_name']} != {args.dataset_name}" + ) + if args.frame_length != config["frame_length"]: + raise ValueError( + f"fixture frame length {config['frame_length']} != {args.frame_length}" + ) + spec = _load_spec(repo.root, args.dataset_name) + metric_args = _metric_args(spec) + + setup_started = time.perf_counter() + x_train, y_train, x_val, _, x_test, _ = repo.data.load_dataset( + dataset_name=args.dataset_name + ) + target_gain = float(repo.util.set_target_gain(x_train, y_train)) + train_set = repo.data.IQFrameDataset( + x_train, + target_gain * x_train, + frame_length=args.frame_length, + stride=args.frame_stride, + ) + val_set = repo.data.IQSegmentDataset( + x_val, target_gain * x_val, nperseg=spec["nperseg"] + ) + test_set = repo.data.IQSegmentDataset( + x_test, target_gain * x_test, nperseg=spec["nperseg"] + ) + + order_generator = torch.Generator().manual_seed(args.seed + 20_000) + orders = [ + torch.randperm(len(train_set), generator=order_generator).numpy() + for _ in range(args.epochs) + ] + val_loader = _loader(val_set, args.eval_batch_size, device, optimized) + test_loader = _loader(test_set, args.eval_batch_size, device, optimized) + setup_data_s = time.perf_counter() - setup_started + + # Compile and warm all kernels with a throw-away model so the measured + # epochs start from the immutable fixture state. + warm_started = time.perf_counter() + model = _build_cascade(repo, fixture, device) + warm_loader = _loader( + train_set, + args.batch_size, + device, + optimized, + orders[0][: args.batch_size], + ) + warm_features, warm_targets = next(iter(warm_loader)) + warm_features = warm_features.to(device, non_blocking=optimized) + warm_targets = warm_targets.to(device, non_blocking=optimized) + warm_loss = torch.nn.functional.mse_loss(model(warm_features), warm_targets) + warm_loss.backward() + _sync(device) + warmup_s = time.perf_counter() - warm_started + model.zero_grad(set_to_none=True) + del warm_features, warm_targets, warm_loss + + # Whole-cascade graph capture must not inherit AccumulateGrad nodes that a + # prior default-stream backward kept alive. The throw-away warm model has + # already populated global Triton/cuDNN caches; build an untouched model + # with the immutable fixture state for the captured training trajectory. + if args.cuda_graph_training: + del model + model = _build_cascade(repo, fixture, device) + + model_started = time.perf_counter() + trainable = tuple(parameter for parameter in model.parameters() if parameter.requires_grad) + optimizer_parameters = trainable if optimized else model.parameters() + optimizer = torch.optim.AdamW(optimizer_parameters, lr=args.lr) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=args.decay_factor, + patience=args.patience, + threshold=1e-4, + min_lr=args.lr_end, + ) + criterion = torch.nn.MSELoss() + model_optimizer_setup_s = time.perf_counter() - model_started + trajectory: list[dict[str, Any]] = [] + epoch_times: list[dict[str, float]] = [] + best_val_aclr = float("inf") + best_epoch = -1 + best_dpd_state: dict[str, torch.Tensor] | None = None + + for epoch, order in enumerate(orders): + train_loader = _loader( + train_set, args.batch_size, device, optimized, order + ) + _sync(device) + train_started = time.perf_counter() + train_log: dict[str, Any] = {} + train_kwargs = dict( + log=train_log, + net=model, + dataloader=train_loader, + optimizer=optimizer, + criterion=criterion, + grad_clip_val=args.grad_clip, + device=device, + ) + # The detached reference worktree predates this opt-in keyword, so add + # it only for runs that explicitly request whole-step capture. + if args.cuda_graph_training: + train_kwargs["cuda_graph_training"] = True + repo.train_funcs.net_train(**train_kwargs) + _sync(device) + train_s = time.perf_counter() - train_started + + val_s, val_loss, val_prediction, val_truth = _time_eval( + repo, model, val_loader, criterion, device + ) + metric_started = time.perf_counter() + val_metrics = _metrics(repo.train_funcs, metric_args, val_prediction, val_truth) + val_metric_s = time.perf_counter() - metric_started + + test_s, test_loss, test_prediction, test_truth = _time_eval( + repo, model, test_loader, criterion, device + ) + metric_started = time.perf_counter() + test_metrics = _metrics(repo.train_funcs, metric_args, test_prediction, test_truth) + test_metric_s = time.perf_counter() - metric_started + + checkpoint_started = time.perf_counter() + if val_metrics["ACLR_AVG"] < best_val_aclr: + best_val_aclr = val_metrics["ACLR_AVG"] + best_epoch = epoch + best_dpd_state = _cpu_state(model.dpd_model) + checkpoint_s = time.perf_counter() - checkpoint_started + if args.lr_schedule: + scheduler.step(val_metrics["ACLR_AVG"]) + + trajectory.append( + { + "epoch": epoch, + "lr": float(optimizer.param_groups[0]["lr"]), + "train_loss": float(train_log["loss"]), + "val_loss": val_loss, + "test_loss": test_loss, + "val": val_metrics, + "test": test_metrics, + } + ) + epoch_times.append( + { + "train_s": train_s, + "val_inference_s": val_s, + "val_metrics_s": val_metric_s, + "test_inference_s": test_s, + "test_metrics_s": test_metric_s, + "checkpoint_s": checkpoint_s, + "total_s": ( + train_s + + val_s + + val_metric_s + + test_s + + test_metric_s + + checkpoint_s + ), + } + ) + + if best_dpd_state is None: + raise RuntimeError("no best checkpoint was selected") + model.dpd_model.load_state_dict(best_dpd_state) + best_test_s, best_test_loss, best_prediction, best_truth = _time_eval( + repo, model, test_loader, criterion, device + ) + metric_started = time.perf_counter() + best_metrics = _metrics(repo.train_funcs, metric_args, best_prediction, best_truth) + best_metric_s = time.perf_counter() - metric_started + + model.dpd_model.eval() + dpd_inference_started = time.perf_counter() + dpd_outputs: list[torch.Tensor] = [] + with torch.inference_mode(): + for features, _ in test_loader: + outputs = model.dpd_model( + features.to(device, non_blocking=optimized) + ) + dpd_outputs.append(outputs.cpu()) + best_dpd_prediction = torch.cat(dpd_outputs, dim=0).numpy() + _sync(device) + best_dpd_inference_s = time.perf_counter() - dpd_inference_started + + serialization_started = time.perf_counter() + args.output_dir.mkdir(parents=True, exist_ok=True) + order_path = args.output_dir / "batch_orders.npz" + np.savez_compressed(order_path, **{f"epoch_{i}": order for i, order in enumerate(orders)}) + output_path = args.output_dir / "outputs.npz" + np.savez_compressed( + output_path, + cascade=best_prediction, + dpd=best_dpd_prediction, + ground_truth=best_truth, + ) + checkpoint_path = args.output_dir / "best_dpd.pt" + torch.save(best_dpd_state, checkpoint_path) + _sync(device) + artifact_serialization_s = time.perf_counter() - serialization_started + + total_s = time.perf_counter() - total_started + result = { + "format_version": 1, + "implementation": args.implementation, + "repo_root": str(repo.root), + "fixture": { + "path": str(args.fixture.resolve()), + "sha256": _sha256_file(args.fixture), + "pa_pretraining": fixture["pa_pretraining"], + }, + "config": { + **config, + "frame_stride": args.frame_stride, + "batch_size": args.batch_size, + "eval_batch_size": args.eval_batch_size, + "epochs": args.epochs, + "lr": args.lr, + "lr_schedule": args.lr_schedule, + "lr_end": args.lr_end, + "decay_factor": args.decay_factor, + "patience": args.patience, + "grad_clip": args.grad_clip, + "hard_determinism": True, + "tf32": False, + "progress_display": args.progress, + "cuda_graph_training": args.cuda_graph_training, + "train_frames": len(train_set), + "train_batches": (len(train_set) + args.batch_size - 1) // args.batch_size, + }, + "environment": _environment(repo.root, device), + "timing": { + "setup_data_s": setup_data_s, + "compile_and_warmup_s": warmup_s, + "model_optimizer_setup_s": model_optimizer_setup_s, + "epochs": epoch_times, + "best_test_inference_s": best_test_s, + "best_test_metrics_s": best_metric_s, + "best_dpd_inference_s": best_dpd_inference_s, + "artifact_serialization_s": artifact_serialization_s, + "total_s": total_s, + "peak_allocated_mib": ( + torch.cuda.max_memory_allocated(device) / (1024**2) + if device.type == "cuda" + else None + ), + "peak_reserved_mib": ( + torch.cuda.max_memory_reserved(device) / (1024**2) + if device.type == "cuda" + else None + ), + }, + "trajectory": trajectory, + "best": { + "epoch": best_epoch, + "test_loss": best_test_loss, + "test": best_metrics, + }, + "artifacts": { + "orders": {"path": order_path.name, "sha256": _sha256_file(order_path)}, + "outputs": {"path": output_path.name, "sha256": _sha256_file(output_path)}, + "checkpoint": { + "path": checkpoint_path.name, + "sha256": _sha256_file(checkpoint_path), + }, + }, + } + _write_json(args.output_dir / "result.json", result) + print(json.dumps(result, indent=2, default=_json_default)) + + +def _state_error(reference: dict[str, torch.Tensor], candidate: dict[str, torch.Tensor]): + names = sorted(reference) + if names != sorted(candidate): + raise ValueError("checkpoint parameter names differ") + per_parameter: dict[str, dict[str, float]] = {} + for name in names: + per_parameter[name] = _prediction_error( + reference[name].detach().cpu().numpy(), + candidate[name].detach().cpu().numpy(), + ) + return { + "max_abs": max(item["max_abs"] for item in per_parameter.values()), + "max_relative_l2": max( + item["relative_l2"] for item in per_parameter.values() + ), + "per_parameter": per_parameter, + } + + +def compare(args: argparse.Namespace) -> None: + reference_result = json.loads((args.reference_dir / "result.json").read_text()) + candidate_result = json.loads((args.candidate_dir / "result.json").read_text()) + if reference_result["fixture"]["sha256"] != candidate_result["fixture"]["sha256"]: + raise ValueError("runs did not use the same fixture") + reference_orders = np.load(args.reference_dir / "batch_orders.npz") + candidate_orders = np.load(args.candidate_dir / "batch_orders.npz") + if reference_orders.files != candidate_orders.files or any( + not np.array_equal(reference_orders[name], candidate_orders[name]) + for name in reference_orders.files + ): + raise ValueError("runs did not use identical batch permutations") + + reference_outputs = np.load(args.reference_dir / "outputs.npz") + candidate_outputs = np.load(args.candidate_dir / "outputs.npz") + output_errors = { + name: _prediction_error(reference_outputs[name], candidate_outputs[name]) + for name in ("cascade", "dpd", "ground_truth") + } + reference_state = torch.load( + args.reference_dir / "best_dpd.pt", map_location="cpu", weights_only=True + ) + candidate_state = torch.load( + args.candidate_dir / "best_dpd.pt", map_location="cpu", weights_only=True + ) + state_errors = _state_error(reference_state, candidate_state) + + reference_train_s = sum( + epoch["train_s"] for epoch in reference_result["timing"]["epochs"] + ) + candidate_train_s = sum( + epoch["train_s"] for epoch in candidate_result["timing"]["epochs"] + ) + metric_deltas = { + name: ( + candidate_result["best"]["test"][name] + - reference_result["best"]["test"][name] + ) + for name in METRIC_NAMES + } + trajectory_deltas = [] + for reference_epoch, candidate_epoch in zip( + reference_result["trajectory"], candidate_result["trajectory"], strict=True + ): + trajectory_deltas.append( + { + "epoch": reference_epoch["epoch"], + "train_loss": candidate_epoch["train_loss"] - reference_epoch["train_loss"], + "val": { + name: candidate_epoch["val"][name] - reference_epoch["val"][name] + for name in METRIC_NAMES + }, + "test": { + name: candidate_epoch["test"][name] - reference_epoch["test"][name] + for name in METRIC_NAMES + }, + } + ) + + limits = { + "prediction_relative_l2": args.prediction_relative_l2_limit, + "metric_abs_db": args.metric_abs_db_limit, + } + checks = { + "same_fixture": True, + "same_batch_orders": True, + "cascade_prediction_relative_l2": ( + output_errors["cascade"]["relative_l2"] + <= args.prediction_relative_l2_limit + ), + "dpd_prediction_relative_l2": ( + output_errors["dpd"]["relative_l2"] + <= args.prediction_relative_l2_limit + ), + "all_metric_deltas": all( + abs(delta) <= args.metric_abs_db_limit for delta in metric_deltas.values() + ), + } + comparison = { + "format_version": 1, + "reference_dir": str(args.reference_dir.resolve()), + "candidate_dir": str(args.candidate_dir.resolve()), + "speed": { + "total_reference_s": reference_result["timing"]["total_s"], + "total_candidate_s": candidate_result["timing"]["total_s"], + "total_speedup": ( + reference_result["timing"]["total_s"] + / candidate_result["timing"]["total_s"] + ), + "train_reference_s": reference_train_s, + "train_candidate_s": candidate_train_s, + "train_speedup": reference_train_s / candidate_train_s, + }, + "best_epoch": { + "reference": reference_result["best"]["epoch"], + "candidate": candidate_result["best"]["epoch"], + }, + "metric_deltas_db": metric_deltas, + "output_errors": output_errors, + "checkpoint_errors": state_errors, + "trajectory_deltas": trajectory_deltas, + "acceptance_limits": limits, + "checks": checks, + "passed": all(checks.values()), + } + _write_json(args.json_out, comparison) + print(json.dumps(comparison, indent=2, default=_json_default)) + if args.fail_on_mismatch and not comparison["passed"]: + raise SystemExit(1) + + +def _common_config(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--repo-root", type=Path, default=SCRIPT_ROOT) + parser.add_argument("--fixture", type=Path, required=True) + parser.add_argument("--dataset-name", default="DPA_200MHz") + parser.add_argument("--frame-length", type=int, default=200) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--device", default=DEFAULT_DEVICE) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + subparsers = parser.add_subparsers(dest="command", required=True) + + prepare_parser = subparsers.add_parser("prepare", help="create immutable model fixture") + _common_config(prepare_parser) + prepare_parser.add_argument("--pa-backbone", default="dgru") + prepare_parser.add_argument("--pa-hidden-size", type=int, default=23) + prepare_parser.add_argument("--dpd-backbone", default="tres_deltagru") + prepare_parser.add_argument("--dpd-hidden-size", type=int, default=15) + prepare_parser.add_argument("--thx", type=float, default=0.01) + prepare_parser.add_argument("--thh", type=float, default=0.05) + prepare_parser.add_argument("--pa-pretrain-epochs", type=int, default=20) + prepare_parser.add_argument("--pa-frame-stride", type=int, default=16) + prepare_parser.add_argument("--pa-batch-size", type=int, default=256) + prepare_parser.add_argument("--pa-lr", type=float, default=5e-4) + prepare_parser.add_argument("--grad-clip", type=float, default=200.0) + + run_parser = subparsers.add_parser("run", help="run one implementation") + _common_config(run_parser) + run_parser.add_argument( + "--implementation", + choices=( + "original", "eager_optimized", "fused_optimized", + "graph_optimized", "optimized", + ), + required=True, + ) + run_parser.add_argument("--output-dir", type=Path, required=True) + run_parser.add_argument("--frame-stride", type=int, default=16) + run_parser.add_argument("--batch-size", type=int, default=64) + run_parser.add_argument("--eval-batch-size", type=int, default=256) + run_parser.add_argument("--epochs", type=int, default=3) + run_parser.add_argument("--lr", type=float, default=5e-3) + run_parser.add_argument("--lr-schedule", action="store_true") + run_parser.add_argument("--lr-end", type=float, default=1e-4) + run_parser.add_argument("--decay-factor", type=float, default=0.5) + run_parser.add_argument("--patience", type=int, default=10) + run_parser.add_argument("--grad-clip", type=float, default=200.0) + run_parser.add_argument("--progress", action="store_true") + run_parser.add_argument("--cuda-graph-training", action="store_true") + + compare_parser = subparsers.add_parser("compare", help="compare two run directories") + compare_parser.add_argument("--reference-dir", type=Path, required=True) + compare_parser.add_argument("--candidate-dir", type=Path, required=True) + compare_parser.add_argument("--json-out", type=Path, required=True) + compare_parser.add_argument("--prediction-relative-l2-limit", type=float, default=1e-4) + compare_parser.add_argument("--metric-abs-db-limit", type=float, default=0.05) + compare_parser.add_argument("--fail-on-mismatch", action="store_true") + + return parser.parse_args() + + +def main() -> None: + args = parse_args() + if args.command == "prepare": + prepare(args) + elif args.command == "run": + run(args) + else: + compare(args) + + +if __name__ == "__main__": + main() diff --git a/benchmark/benchmark_tres_deltagru.py b/benchmark/benchmark_tres_deltagru.py new file mode 100644 index 0000000..9aa8aec --- /dev/null +++ b/benchmark/benchmark_tres_deltagru.py @@ -0,0 +1,422 @@ +#!/usr/bin/env python3 +"""Reproducible training benchmark for the TRes-DeltaGRU backbone. + +This benchmark intentionally keeps synthetic input resident on the selected +device. It measures the model/optimizer path rather than CSV parsing, +DataLoader workers, or host-to-device transfers. The default shape matches +``bash_scripts/OpenDPDv2.sh``: B=64, T=200, H=15, thx=.01, thh=.05. + +Examples +-------- + .venv/bin/python benchmark/benchmark_tres_deltagru.py + .venv/bin/python benchmark/benchmark_tres_deltagru.py --batch-size 256 + .venv/bin/python benchmark/benchmark_tres_deltagru.py --reference-only +""" + +from __future__ import annotations + +import argparse +import contextlib +import copy +import io +import json +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) + +import torch # noqa: E402 + +from models import CoreModel # noqa: E402 +from backbones.triton_deltagru import triton # noqa: E402 + + +@dataclass +class TimingResult: + implementation: str + steps_per_block: int + median_ms: float + iqr_ms: float + block_ms: list[float] + batches_per_second: float + sequence_positions_per_second: float + peak_allocated_mib: float + + +def synchronize(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def command_output(command: list[str]) -> str | None: + try: + result = subprocess.run( + command, cwd=ROOT, check=True, capture_output=True, text=True + ) + except (OSError, subprocess.CalledProcessError): + return None + return result.stdout.strip() or None + + +def build_model( + state: dict[str, torch.Tensor], + hidden_size: int, + thx: float, + thh: float, + device: torch.device, + fused: bool, +) -> CoreModel: + with contextlib.redirect_stdout(io.StringIO()): + model = CoreModel( + input_size=2, + hidden_size=hidden_size, + num_layers=1, + backbone_type="tres_deltagru", + thx=thx, + thh=thh, + ).to(device) + model.load_state_dict(state) + model.backbone.rnn.use_triton = fused + model.backbone.set_debug(0) + if fused: + probe = next(model.parameters()).new_empty((1, 1, model.backbone.rnn.input_size)) + if not model.backbone.rnn._can_use_triton(probe): + raise RuntimeError( + "the fused TRes-DeltaGRU kernel does not support this device, " + "dtype, hidden size, or module configuration" + ) + model.train() + return model + + +def one_step( + model: CoreModel, + optimizer: torch.optim.Optimizer, + features: torch.Tensor, + targets: torch.Tensor, + grad_clip: float, +) -> None: + optimizer.zero_grad(set_to_none=True) + loss = torch.nn.functional.mse_loss(model(features), targets) + loss.backward() + if grad_clip: + torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) + optimizer.step() + + +def time_implementation( + name: str, + fused: bool, + state: dict[str, torch.Tensor], + features: torch.Tensor, + targets: torch.Tensor, + args: argparse.Namespace, + steps_per_block: int, +) -> TimingResult: + device = features.device + model = build_model(state, args.hidden_size, args.thx, args.thh, device, fused) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr) + + for _ in range(args.warmup): + one_step(model, optimizer, features, targets, args.grad_clip) + synchronize(device) + + if device.type == "cuda": + torch.cuda.reset_peak_memory_stats(device) + block_times: list[float] = [] + for _ in range(args.blocks): + if device.type == "cuda": + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + for _ in range(steps_per_block): + one_step(model, optimizer, features, targets, args.grad_clip) + end.record() + end.synchronize() + elapsed_ms = start.elapsed_time(end) / steps_per_block + else: + started = time.perf_counter() + for _ in range(steps_per_block): + one_step(model, optimizer, features, targets, args.grad_clip) + elapsed_ms = (time.perf_counter() - started) * 1_000 / steps_per_block + block_times.append(elapsed_ms) + + median_ms = statistics.median(block_times) + quartiles = ( + statistics.quantiles(block_times, n=4, method="inclusive") + if len(block_times) > 1 + else [block_times[0]] * 3 + ) + peak_mib = ( + torch.cuda.max_memory_allocated(device) / (1024**2) + if device.type == "cuda" + else float("nan") + ) + return TimingResult( + implementation=name, + steps_per_block=steps_per_block, + median_ms=median_ms, + iqr_ms=quartiles[2] - quartiles[0], + block_ms=block_times, + batches_per_second=1_000 / median_ms, + sequence_positions_per_second=( + features.shape[0] * features.shape[1] * 1_000 / median_ms + ), + peak_allocated_mib=peak_mib, + ) + + +def error_metrics(reference: torch.Tensor, candidate: torch.Tensor) -> dict[str, float]: + difference = (reference - candidate).detach() + denominator = reference.detach().norm().clamp_min(torch.finfo(reference.dtype).tiny) + return { + "max_abs": difference.abs().max().item(), + "mean_abs": difference.abs().mean().item(), + "relative_l2": (difference.norm() / denominator).item(), + } + + +def check_equivalence( + state: dict[str, torch.Tensor], + args: argparse.Namespace, + device: torch.device, +) -> dict[str, object]: + # A modest batch checks all equations without making the eager preflight a + # substantial fraction of the benchmark runtime. + batch_size = min(args.batch_size, args.check_batch_size) + generator = torch.Generator(device=device).manual_seed(args.seed + 1) + x_reference = torch.randn( + batch_size, args.sequence_length, 2, generator=generator, device=device + ).requires_grad_() + x_fused = x_reference.detach().clone().requires_grad_() + target = torch.randn( + batch_size, args.sequence_length, 2, generator=generator, device=device + ) + reference = build_model( + state, args.hidden_size, args.thx, args.thh, device, fused=False + ) + fused = build_model(state, args.hidden_size, args.thx, args.thh, device, fused=True) + + reference_output = reference(x_reference) + reference_loss = torch.nn.functional.mse_loss(reference_output, target) + reference_loss.backward() + fused_output = fused(x_fused) + fused_loss = torch.nn.functional.mse_loss(fused_output, target) + fused_loss.backward() + synchronize(device) + + parameter_errors = {} + for (name, reference_parameter), (candidate_name, candidate_parameter) in zip( + reference.named_parameters(), fused.named_parameters(), strict=True + ): + if name != candidate_name: + raise RuntimeError(f"parameter order mismatch: {name} != {candidate_name}") + parameter_errors[name] = error_metrics( + reference_parameter.grad, candidate_parameter.grad + ) + + return { + "output": error_metrics(reference_output, fused_output), + "input_gradient": error_metrics(x_reference.grad, x_fused.grad), + "loss_abs": abs(reference_loss.item() - fused_loss.item()), + "parameter_gradients": parameter_errors, + } + + +def enforce_equivalence(equivalence: dict[str, object]) -> None: + """Fail before timing if the candidate exceeds calibrated FP32 tolerances.""" + + limits = { + "output": (2e-5, 1e-4), + "input_gradient": (2e-6, 1e-3), + } + for name, (max_abs_limit, relative_limit) in limits.items(): + metrics = equivalence[name] + if metrics["max_abs"] > max_abs_limit or metrics["relative_l2"] > relative_limit: + raise RuntimeError(f"{name} equivalence check failed: {metrics}") + for name, metrics in equivalence["parameter_gradients"].items(): + if metrics["max_abs"] > 2e-5 or metrics["relative_l2"] > 1e-4: + raise RuntimeError(f"{name} gradient equivalence check failed: {metrics}") + if equivalence["loss_abs"] > 1e-5: + raise RuntimeError(f"loss equivalence check failed: {equivalence['loss_abs']}") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--sequence-length", type=int, default=200) + parser.add_argument("--hidden-size", type=int, default=15) + parser.add_argument("--thx", type=float, default=0.01) + parser.add_argument("--thh", type=float, default=0.05) + parser.add_argument("--lr", type=float, default=5e-4) + parser.add_argument("--grad-clip", type=float, default=200.0) + parser.add_argument("--warmup", type=int, default=5) + parser.add_argument("--blocks", type=int, default=7) + parser.add_argument("--reference-steps-per-block", type=int, default=5) + parser.add_argument("--fused-steps-per-block", type=int, default=100) + parser.add_argument("--check-batch-size", type=int, default=8) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--reference-only", action="store_true") + parser.add_argument("--json-out", type=Path) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + positive_values = { + "batch_size": args.batch_size, + "sequence_length": args.sequence_length, + "hidden_size": args.hidden_size, + "blocks": args.blocks, + "reference_steps_per_block": args.reference_steps_per_block, + "fused_steps_per_block": args.fused_steps_per_block, + "check_batch_size": args.check_batch_size, + } + invalid = {name: value for name, value in positive_values.items() if value <= 0} + if invalid or args.warmup < 0: + raise ValueError(f"benchmark counts must be positive (warmup may be zero): {invalid}") + device = torch.device(args.device) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + + torch.manual_seed(args.seed) + if device.type == "cuda": + torch.cuda.manual_seed_all(args.seed) + # The fused recurrence accumulates in FP32. Disable TF32 in the reference + # so the equivalence check measures operation ordering, not lower precision. + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + with contextlib.redirect_stdout(io.StringIO()): + template = CoreModel( + input_size=2, + hidden_size=args.hidden_size, + num_layers=1, + backbone_type="tres_deltagru", + thx=args.thx, + thh=args.thh, + ).to(device) + state = copy.deepcopy(template.state_dict()) + generator = torch.Generator(device=device).manual_seed(args.seed) + features = torch.randn( + args.batch_size, + args.sequence_length, + 2, + generator=generator, + device=device, + ) + targets = torch.randn( + args.batch_size, + args.sequence_length, + 2, + generator=generator, + device=device, + ) + + system = { + "recorded_at_utc": datetime.now(timezone.utc).isoformat(), + "command": " ".join(sys.argv), + "git_revision": command_output(["git", "rev-parse", "HEAD"]), + "git_dirty": bool(command_output(["git", "status", "--porcelain"])), + "platform": platform.platform(), + "python": platform.python_version(), + "torch": torch.__version__, + "triton": triton.__version__ if triton is not None else None, + "device": torch.cuda.get_device_name(device) if device.type == "cuda" else str(device), + "compute_capability": ( + ".".join(map(str, torch.cuda.get_device_capability(device))) + if device.type == "cuda" + else None + ), + "driver": ( + command_output([ + "nvidia-smi", + "--query-gpu=driver_version", + "--format=csv,noheader", + ]) + if device.type == "cuda" + else None + ), + "cuda": torch.version.cuda, + "cudnn": torch.backends.cudnn.version(), + "matmul_tf32": torch.backends.cuda.matmul.allow_tf32, + "cudnn_tf32": torch.backends.cudnn.allow_tf32, + } + print(json.dumps(system, indent=2)) + print( + f"shape=B{args.batch_size}xT{args.sequence_length}x2, H={args.hidden_size}, " + f"thresholds=({args.thx}, {args.thh}), trainable_params=" + f"{sum(parameter.numel() for parameter in template.parameters()):,}" + ) + + equivalence = None + if device.type == "cuda" and not args.reference_only: + equivalence = check_equivalence(state, args, device) + enforce_equivalence(equivalence) + print("equivalence:") + print(json.dumps(equivalence, indent=2)) + + results = [ + time_implementation( + "eager-clean", + False, + state, + features, + targets, + args, + args.reference_steps_per_block, + ) + ] + if device.type == "cuda" and not args.reference_only: + results.append( + time_implementation( + "triton-fused", + True, + state, + features, + targets, + args, + args.fused_steps_per_block, + ) + ) + + baseline_ms = results[0].median_ms + print( + "\nimplementation median ms/step IQR(block means) " + "steps/block sequence positions/s peak MiB speedup" + ) + for result in results: + speedup = baseline_ms / result.median_ms + print( + f"{result.implementation:<20} {result.median_ms:>12.3f} " + f"{result.iqr_ms:>17.3f} {result.steps_per_block:>12d} " + f"{result.sequence_positions_per_second:>20,.0f} " + f"{result.peak_allocated_mib:>11.1f} {speedup:>9.2f}x" + ) + print( + f" block means ({result.implementation}, ms/step): " + + ", ".join(f"{value:.3f}" for value in result.block_ms) + ) + + payload = { + "system": system, + "config": vars(args) | {"json_out": str(args.json_out) if args.json_out else None}, + "equivalence": equivalence, + "results": [asdict(result) for result in results], + } + if args.json_out: + args.json_out.parent.mkdir(parents=True, exist_ok=True) + args.json_out.write_text(json.dumps(payload, indent=2) + "\n") + print(f"wrote {args.json_out}") + + +if __name__ == "__main__": + main() diff --git a/benchmark/report/artifact.json b/benchmark/report/artifact.json new file mode 100644 index 0000000..9496b38 --- /dev/null +++ b/benchmark/report/artifact.json @@ -0,0 +1,382 @@ +{ + "surface": "report", + "manifest": { + "version": 1, + "surface": "report", + "title": "TRes-DeltaGRU end-to-end training optimization", + "description": "A controlled speed and numerical-equivalence study of OpenDPD TRes-DeltaGRU training.", + "generatedAt": "2026-07-11T13:45:00Z", + "filters": [], + "cards": [ + { + "id": "production_speedup", + "description": "Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.", + "dataset": "headline", + "sourceId": "e2e_summary", + "metrics": [ + {"label": "End-to-end speedup", "field": "total_speedup"}, + {"label": "Training speedup", "field": "train_speedup"} + ] + }, + { + "id": "output_equivalence", + "description": "Relative L2 difference between the original and optimized cascaded neural-network outputs.", + "dataset": "headline", + "sourceId": "e2e_summary", + "metrics": [ + {"label": "Cascade output rel-L2", "field": "cascade_relative_l2"}, + {"label": "Maximum absolute error", "field": "cascade_max_abs"} + ] + }, + { + "id": "rf_equivalence", + "description": "Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.", + "dataset": "headline", + "sourceId": "e2e_summary", + "metrics": [ + {"label": "Maximum RF-metric delta", "field": "maximum_rf_delta"}, + {"label": "Acceptance limit", "field": "rf_limit"} + ] + }, + { + "id": "checkpoint_equivalence", + "description": "Comparison of every tensor in the selected best DPD state dictionary.", + "dataset": "headline", + "sourceId": "e2e_summary", + "metrics": [ + {"label": "DPD checkpoint", "field": "checkpoint_result"}, + {"label": "Best epoch", "field": "best_epoch_result"} + ] + } + ], + "charts": [ + { + "id": "gate_total_time", + "title": "Three-epoch controlled workload time", + "subtitle": "All arms used the same fixture and recorded batches; the direct Triton arm was rejected by the output-equivalence gate.", + "headerMarkdown": "Lower is faster. The strict whole-cascade graph is the fastest **accepted** method.", + "type": "bar", + "dataset": "candidate_arms", + "sourceId": "e2e_summary", + "valueFormat": "number", + "encodings": { + "x": {"field": "implementation", "type": "nominal", "label": "Implementation"}, + "y": {"field": "total_s", "type": "quantitative", "label": "Total time", "unit": "s", "format": "number"}, + "tooltip": [ + {"field": "train_s", "type": "quantitative", "label": "Training time", "format": "number"}, + {"field": "total_speedup", "type": "quantitative", "label": "Total speedup", "format": "number"}, + {"field": "cascade_output_relative_l2", "type": "quantitative", "label": "Output rel-L2", "format": "number"}, + {"field": "accepted", "type": "nominal", "label": "Accepted"} + ] + } + } + ], + "tables": [ + { + "id": "candidate_decision", + "title": "Optimization candidate decision", + "subtitle": "The acceptance decision combines speed, final neural output, checkpoint, and RF metrics.", + "dataset": "candidate_arms", + "sourceId": "e2e_summary", + "defaultSort": {"field": "total_s", "direction": "asc"}, + "columns": [ + {"field": "implementation", "label": "Implementation", "type": "text"}, + {"field": "total_s", "label": "Total s", "type": "number", "format": "number", "align": "right"}, + {"field": "train_s", "label": "Train s", "type": "number", "format": "number", "align": "right"}, + {"field": "total_speedup", "label": "Speedup", "type": "number", "format": "number", "align": "right"}, + {"field": "cascade_output_relative_l2_display", "label": "Output rel-L2", "type": "text", "align": "right"}, + {"field": "maximum_absolute_metric_delta_db_display", "label": "Max RF delta, dB", "type": "text", "align": "right"}, + {"field": "accepted", "label": "Accepted", "type": "text", "align": "center"} + ] + }, + { + "id": "rf_metric_equivalence", + "title": "Production-density RF metric equivalence", + "subtitle": "Candidate minus original; lower absolute delta is better.", + "dataset": "rf_metrics", + "sourceId": "e2e_summary", + "defaultSort": {"field": "metric", "direction": "asc"}, + "columns": [ + {"field": "metric", "label": "Metric", "type": "text"}, + {"field": "original_db", "label": "Original dB", "type": "number", "format": "number", "align": "right"}, + {"field": "optimized_db", "label": "Optimized dB", "type": "number", "format": "number", "align": "right"}, + {"field": "delta_db_display", "label": "Delta dB", "type": "text", "align": "right"}, + {"field": "absolute_delta_db_display", "label": "Absolute delta dB", "type": "text", "align": "right"} + ] + }, + { + "id": "implementation_summary", + "title": "Implemented optimizations and safeguards", + "subtitle": "All changes retain the original module parameters and checkpoint structure.", + "dataset": "implementation_changes", + "sourceId": "benchmark_code", + "defaultSort": {"field": "priority", "direction": "asc"}, + "columns": [ + {"field": "priority", "label": "Priority", "type": "number", "align": "right"}, + {"field": "component", "label": "Component", "type": "text"}, + {"field": "change", "label": "Change", "type": "text"}, + {"field": "equivalence", "label": "Equivalence behavior", "type": "text"} + ] + } + ], + "sources": [ + { + "id": "e2e_summary", + "label": "Reviewed end-to-end benchmark summary", + "path": "benchmark/results/end_to_end_summary.json" + }, + { + "id": "benchmark_code", + "label": "Reproducible benchmark and implementation", + "path": "benchmark/benchmark_end_to_end.py" + } + ], + "blocks": [ + { + "id": "title", + "type": "markdown", + "body": "# TRes-DeltaGRU end-to-end training optimization", + "layout": "full" + }, + { + "id": "executive_summary", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Executive Summary\n\nThe recommended strict path is guarded whole-cascade CUDA-graph training, enabled with `--cuda_graph_training`. On the 357-update production-density workload it reduced total time from **95.78 s to 14.51 s and 13.96 s** across two runs (**6.60–6.86×**) and training time from **86.86 s to 12.41 s and 11.81 s** (**7.00–7.35×**). The selected DPD checkpoint was bitwise identical, cascade output relative L2 error was **1.70e-6**, and the largest ACLR/NMSE/EVM difference was **4.27e-6 dB**." + }, + { + "id": "headline_metrics", + "type": "metric-strip", + "cardIds": [ + "production_speedup", + "output_equivalence", + "rf_equivalence", + "checkpoint_equivalence" + ] + }, + { + "id": "key_findings", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Key findings\n\n- The isolated persistent Triton backbone remains the fastest kernel-level method at **97.11×**, but long training amplifies its small FP32 differences around delta thresholds.\n- Direct persistent-Triton end-to-end training reached **13.40× total** and **46.77× training** speedup in the three-epoch gate, yet its final cascade output relative L2 error reached **1.04e-2**. It was rejected even though its RF metrics stayed within 0.035 dB.\n- Capturing the existing clean-eager cascade preserves its PyTorch/cuDNN arithmetic and removes recurrent launch overhead. It is slower than direct Triton but passes every output, checkpoint, and RF-metric gate.\n- Loop/data cleanups alone are bitwise exact and improve total time **1.33×**." + }, + { + "id": "timing_chart", + "type": "chart", + "chartId": "gate_total_time", + "layout": "full" + }, + { + "id": "selection_rationale", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Why the strict graph is the recommendation\n\nThe graph records the original eager TRes-DeltaGRU, MSE loss, gradient clearing, frozen-PA input backward, and DPD backward. It replays those exact kernels at fixed shapes while leaving batch copies, gradient clipping, AdamW, scheduling, validation, testing, and checkpoint selection outside the graph. Full and partial batches share stable gradient buffers and one capture stream. Unsupported calls fall back to clean eager training; evaluation restores the fused inference path." + }, + { + "id": "candidate_table", + "type": "table", + "tableId": "candidate_decision", + "layout": "full" + }, + { + "id": "equivalence_findings", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Output and RF equivalence\n\nThe production-density optimized checkpoint matched every original state-dict tensor exactly. The remaining output difference comes from using the faster fused inference kernel on the same weights: **4.95e-6 maximum absolute** and **1.70e-6 relative L2** at the cascade output. All five reported RF metrics were effectively unchanged, with absolute deltas between zero and **4.27e-6 dB**, far below the predeclared **0.05 dB** ceiling." + }, + { + "id": "rf_table", + "type": "table", + "tableId": "rf_metric_equivalence", + "layout": "full" + }, + { + "id": "scope_definitions", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Scope, data, and metric definitions\n\nThe controlled workload uses bundled `DPA_200MHz` data, a TRes-DeltaGRU H15 DPD (`thx=.01`, `thh=.05`), a frozen DGRU H23 PA, FP32, B64×T200, AdamW at 5e-3, and gradient clipping at 200. The production-density arm uses all 22,841 stride-one frames (357 updates); the multi-epoch gate uses stride 16 (23 updates per epoch for three epochs). NMSE is the repository's mean per-segment normalized MSE in dB. EVM is its subchannel spectral-magnitude error in dB. ACLR-left/right compare adjacent-channel power with the strongest main subchannel; ACLR-average is their mean." + }, + { + "id": "methodology", + "type": "markdown", + "sourceId": "benchmark_code", + "body": "## Methodology\n\nThe exact original is detached commit `0976ce39`. A single deterministic PA surrogate was trained for 100 epochs because the repository ships no PA checkpoint; its SHA-256 fixture was reused unchanged by every arm. DPD initialization, frame order, full/partial batches, validation/test segments, optimizer hyperparameters, TF32 setting, and hard-determinism policy were fixed. Each run saves the exact permutations, best DPD checkpoint, DPD output, cascaded output, ground truth, phase timings, trajectory, and RF metrics. The comparison script rejects a fixture/order mismatch before evaluating speed or numerical results. Regression validation passed **164 standard tests plus 19 extended all-dataset/all-backbone tests**." + }, + { + "id": "implemented_changes", + "type": "table", + "tableId": "implementation_summary", + "layout": "full" + }, + { + "id": "limitations", + "type": "markdown", + "sourceId": "e2e_summary", + "body": "## Limitations and robustness\n\n- The bundled repository has no published PA checkpoint. The surrogate makes the original-versus-optimized comparison controlled, but its absolute RF quality must not be presented as a reproduction of published results.\n- The longest comparison is one 357-update epoch; the multi-epoch gate covers 69 updates. This is not a complete 240- or 300-epoch convergence study.\n- Whole-cascade graph training is deliberately opt-in and specialized to the FP32 TRes-DeltaGRU → frozen-DGRU cascade with mean MSE. CPU, AMP, QAT, changed modules, diagnostic delta collection, capture failure, and unsupported shapes use eager fallbacks.\n- Existing EVM behavior was preserved: `calculate_metrics` does not pass the dataset sample rate, so EVM uses its 800 MHz default. Correcting that is a separate semantic change.\n- One original production-density timing and two optimized timings were collected on a power-managed laptop GPU; use the observed range rather than treating one decimal speedup as universal." + }, + { + "id": "recommendations", + "type": "markdown", + "body": "## Recommended next steps\n\n1. Use `--cuda_graph_training` (or `cuda_graph_training=True` through the API) when strict training-result equivalence is required.\n2. Keep direct persistent Triton available for inference and experiments where endpoint metric equivalence is sufficient, but do not label its multi-epoch neural outputs strict-equivalent at nonzero thresholds.\n3. Stage the real production PA checkpoint and run the full intended epoch schedule on the target GPU before a release claim. Preserve the same fixture/order comparison and checkpoint/output/RF gates.\n4. If workload shapes or precision modes change, rerun the supplied end-to-end harness; the graph path will safely fall back, but its speedup is shape- and hardware-dependent." + }, + { + "id": "further_questions", + "type": "markdown", + "body": "## Further questions\n\nThe main unresolved question is full-schedule convergence with the real PA checkpoint. A secondary opportunity is a GPU-resident frame sampler for much larger datasets, where per-batch host staging may become material after recurrent launch overhead has been removed." + } + ] + }, + "snapshot": { + "version": 1, + "generatedAt": "2026-07-11T13:45:00Z", + "status": "ready", + "datasets": { + "headline": [ + { + "total_speedup": "6.60–6.86×", + "train_speedup": "7.00–7.35×", + "cascade_relative_l2": "≤ 1.70e-6", + "cascade_max_abs": "≤ 4.95e-6", + "maximum_rf_delta": "≤ 4.27e-6 dB", + "rf_limit": "0.05 dB", + "checkpoint_result": "Bitwise exact", + "best_epoch_result": "Same" + } + ], + "candidate_arms": [ + { + "implementation": "Exact original", + "total_s": 32.03585798398126, + "train_s": 17.237055696023162, + "total_speedup": 1.0, + "train_speedup": 1.0, + "cascade_output_relative_l2": 0.0, + "cascade_output_relative_l2_display": "0", + "maximum_absolute_metric_delta_db": 0.0, + "maximum_absolute_metric_delta_db_display": "0", + "accepted": "Yes", + "batch_count": 69, + "checkpoint_max_abs": 0.0 + }, + { + "implementation": "Clean eager pipeline", + "total_s": 24.029687639005715, + "train_s": 13.491934273974039, + "total_speedup": 1.3331782945018265, + "train_speedup": 1.2775822462516342, + "cascade_output_relative_l2": 0.0, + "cascade_output_relative_l2_display": "0", + "maximum_absolute_metric_delta_db": 0.0, + "maximum_absolute_metric_delta_db_display": "0", + "accepted": "Yes", + "batch_count": 69, + "checkpoint_max_abs": 0.0 + }, + { + "implementation": "Strict whole-cascade graph", + "total_s": 5.458723943011137, + "train_s": 3.434560444991803, + "total_speedup": 5.868744841914403, + "train_speedup": 5.018707916804272, + "cascade_output_relative_l2": 0.0000020480301293249045, + "cascade_output_relative_l2_display": "2.05e-6", + "maximum_absolute_metric_delta_db": 0.0000024523588528779783, + "maximum_absolute_metric_delta_db_display": "2.45e-6", + "accepted": "Yes", + "batch_count": 69, + "checkpoint_max_abs": 0.0 + }, + { + "implementation": 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"absolute_delta_db_display": "4.27e-6"}, + {"metric": "ACLR average", "original_db": -32.71305331430198, "optimized_db": -32.71305647867208, "delta_db": -0.000003164370099284497, "delta_db_display": "-3.16e-6", "absolute_delta_db": 0.000003164370099284497, "absolute_delta_db_display": "3.16e-6"}, + {"metric": "EVM", "original_db": -25.64965659922391, "optimized_db": -25.649657576831792, "delta_db": -0.0000009776078826462253, "delta_db_display": "-9.78e-7", "absolute_delta_db": 0.0000009776078826462253, "absolute_delta_db_display": "9.78e-7"}, + {"metric": "NMSE", "original_db": -24.16977882385254, "optimized_db": -24.16977882385254, "delta_db": 0.0, "delta_db_display": "0", "absolute_delta_db": 0.0, "absolute_delta_db_display": "0"} + ], + "implementation_changes": [ + {"priority": 1, "component": "Whole cascade", "change": "Opt-in fixed-shape CUDA graph of clean-eager forward, MSE, gradient zeroing, and backward", "equivalence": "Original kernels; clean-eager fallback; clip and AdamW remain outside"}, + {"priority": 2, "component": "Frozen DGRU PA", "change": "Exact cuDNN CUDA-graph forward and input-backward replay", "equivalence": "Bitwise output/input gradient in focused tests; guarded eager fallback"}, + {"priority": 3, "component": "Training loop", "change": "One loss-vector device-to-host transfer per epoch; set-to-none gradients; trainable-only clip/optimizer", "equivalence": "Bitwise CPU trajectory and logged loss in lockstep tests"}, + {"priority": 4, "component": "Evaluation", "change": "Inference mode and consolidated output/target transfers", "equivalence": "Exact outputs, targets, and loss in tests"}, + {"priority": 5, "component": "Data", "change": "Vectorized frame construction and CUDA pinned/nonblocking loader path", "equivalence": "Exact frame order/content/dtype with independent storage"}, + {"priority": 6, "component": "Persistent Triton TRes", "change": "Fused recurrence and custom BPTT for supported FP32 NVIDIA shapes", "equivalence": "Minor one-step FP error; rejected as strict multi-epoch training path at nonzero thresholds"} + ] + }, + "accessIssues": [] + }, + "sources": [ + { + "id": "e2e_summary", + "label": "Reviewed end-to-end benchmark summary", + "path": "benchmark/results/end_to_end_summary.json", + "query": { + "engine": "duckdb", + "id": "opendpd-e2e-2026-07-11", + "sql": "SELECT * FROM read_json_auto('benchmark/results/end_to_end_summary.json');", + "description": "Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.", + "language": "sql", + "executed_at": "2026-07-11T13:42:00Z", + "tables_used": [ + "benchmark/results/e2e_dpa200_stride1_comparison_graph.json", + "benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json", + "benchmark/results/e2e_dpa200_stride16_comparison_eager.json", + "benchmark/results/e2e_dpa200_stride16_comparison_graph.json", + 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frozen-DGRU cascade and MSE training.", + "Existing EVM semantics were preserved: calculate_metrics does not forward the dataset sample rate to EVM, so its default 800 MHz is used." + ] +} diff --git a/benchmark/results/tres_deltagru_rtx4090_laptop.json b/benchmark/results/tres_deltagru_rtx4090_laptop.json new file mode 100644 index 0000000..6f2c3fd --- /dev/null +++ b/benchmark/results/tres_deltagru_rtx4090_laptop.json @@ -0,0 +1,92 @@ +{ + "recorded_date": "2026-07-11", + "source_base_revision": "0976ce39fc664727ab7ab640ccb349679f05d84f", + "source_state": "dirty: includes the TRes-DeltaGRU optimization in this working tree", + "command": ".venv/bin/python benchmark/benchmark_tres_deltagru.py", + "system": { + "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "python": "3.13.14", + "torch": "2.13.0+cu132", + "triton": "3.7.1", + "device": "NVIDIA GeForce RTX 4090 Laptop GPU", + "compute_capability": "8.9", + "driver": "610.74", + "cuda": "13.2", + "cudnn": 92000, + "matmul_tf32": false, + "cudnn_tf32": false + }, + "config": { + "batch_size": 64, + "sequence_length": 200, + "hidden_size": 15, + "thx": 0.01, + "thh": 0.05, + "dtype": "float32", + "optimizer": "AdamW", + "learning_rate": 0.0005, + "grad_clip": 200.0, + "warmup_steps": 5, + "blocks": 7, + "reference_steps_per_block": 5, + "fused_steps_per_block": 100, + "seed": 0 + }, + "equivalence": { + "check_batch_size": 8, + "output": { + "max_abs": 6.079673767089844e-06, + "mean_abs": 8.64025196278817e-07, + "relative_l2": 1.3402454897004645e-06 + }, + "input_gradient": { + "max_abs": 1.461885403841734e-07, + "mean_abs": 5.326960206275544e-09, + "relative_l2": 1.4971470591262914e-05 + }, + "loss_abs": 1.1920928955078125e-07, + "maximum_parameter_gradient_error": { + "parameter": "backbone.rnn.x2h.weight", + "max_abs": 7.152557373046875e-07, + "relative_l2": 8.59947533626837e-07 + } + }, + "results": [ + { + "implementation": "eager-clean", + "steps_per_block": 5, + "block_ms_per_step": [ + 164.302, + 164.948, + 176.296, + 158.841, + 164.257, + 169.615, + 163.938 + ], + "median_ms": 164.302, + "iqr_block_means_ms": 3.184, + "sequence_positions_per_second": 77905, + "peak_allocated_mib": 25.3, + "speedup": 1.0 + }, + { + "implementation": "triton-fused", + "steps_per_block": 100, + "block_ms_per_step": [ + 1.692, + 1.762, + 1.707, + 1.690, + 1.752, + 1.684, + 1.675 + ], + "median_ms": 1.692, + "iqr_block_means_ms": 0.043, + "sequence_positions_per_second": 7565624, + "peak_allocated_mib": 27.5, + "speedup": 97.11 + } + ] +} diff --git a/benchmark/tres_deltagru_end_to_end_report.html b/benchmark/tres_deltagru_end_to_end_report.html new file mode 100644 index 0000000..256a276 --- /dev/null +++ b/benchmark/tres_deltagru_end_to_end_report.html @@ -0,0 +1,4900 @@ + + + + + + + + +TRes-DeltaGRU end-to-end training optimization + + + +

Data Analytics report

TRes-DeltaGRU end-to-end training optimization

A controlled speed and numerical-equivalence study of OpenDPD TRes-DeltaGRU training.

+

Executive Summary

+

The recommended strict path is guarded whole-cascade CUDA-graph training, enabled with --cuda_graph_training. On the 357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-update production-density workload it reduced total time from 95.78Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s to 14.51Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s and 13.96Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s across two runs (6.60Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json–6.86Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×) and training time from 86.86Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s to 12.41Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s and 11.81Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s (7.00Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json–7.35Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×). The selected DPD checkpoint was bitwise identical, cascade output relative L2 error was 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json, and the largest ACLR/NMSE/EVM difference was 4.27e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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End-to-end speedup

6.60–6.86×Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.

Training speedup 7.00–7.35×Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Cascade output rel-L2

≤ 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Relative L2 difference between the original and optimized cascaded neural-network outputs.

Maximum absolute error ≤ 4.95e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Relative L2 difference between the original and optimized cascaded neural-network outputs.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Maximum RF-metric delta

≤ 4.27e-6 dBSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.

Acceptance limit 0.05 dBSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

DPD checkpoint

Bitwise exactSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Comparison of every tensor in the selected best DPD state dictionary.

Best epoch SameSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Comparison of every tensor in the selected best DPD state dictionary.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Key findings

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  • The isolated persistent Triton backbone remains the fastest kernel-level method at 97.11Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×, but long training amplifies its small FP32 differences around delta thresholds.
  • Direct persistent-Triton end-to-end training reached 13.40Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json× total and 46.77Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json× training speedup in the three-epoch gate, yet its final cascade output relative L2 error reached 1.04e-2Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json. It was rejected even though its RF metrics stayed within 0.035Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB.
  • Capturing the existing clean-eager cascade preserves its PyTorch/cuDNN arithmetic and removes recurrent launch overhead. It is slower than direct Triton but passes every output, checkpoint, and RF-metric gate.
  • Loop/data cleanups alone are bitwise exact and improve total time 1.33Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×.
Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

+

Lower is faster. The strict whole-cascade graph is the fastest accepted method.

Three-epoch controlled workload time data
ImplementationTotal timeTraining timeTotal speedupOutput rel-L2Accepted
Exact original32.04s17.2410Yes
Clean eager pipeline24.03s13.491.330Yes
Strict whole-cascade graph5.46s3.435.870Yes
Direct persistent Triton2.39s0.3713.40.01No
+

Why the strict graph is the recommendation

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The graph records the original eager TRes-DeltaGRU, MSE loss, gradient clearing, frozen-PA input backward, and DPD backward. It replays those exact kernels at fixed shapes while leaving batch copies, gradient clipping, AdamW, scheduling, validation, testing, and checkpoint selection outside the graph. Full and partial batches share stable gradient buffers and one capture stream. Unsupported calls fall back to clean eager training; evaluation restores the fused inference path.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Optimization candidate decision

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Optimization candidate decision
ImplementationTotal sTrain sSpeedupOutput rel-L2Max RF delta, dBAccepted
Direct persistent Triton2.39Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.37Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json13.4Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.03Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonNo
Strict whole-cascade graph5.46Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json3.43Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json5.87Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
Clean eager pipeline24.03Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json13.49Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json1.33Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
Exact original32.04Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json17.24Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json1Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
+

Output and RF equivalence

+

The production-density optimized checkpoint matched every original state-dict tensor exactly. The remaining output difference comes from using the faster fused inference kernel on the same weights: 4.95e-6 maximum absolute and 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json relative L2 at the cascade output. All five reported RF metrics were effectively unchanged, with absolute deltas between zero and 4.27e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB, far below the predeclared 0.05Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB ceiling.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

+

Production-density RF metric equivalence

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Production-density RF metric equivalence
MetricOriginal dBOptimized dBDelta dBAbsolute delta dB
ACLR average-32.71Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-32.71Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
ACLR left-33.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-33.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
ACLR right-32.42Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-32.42Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
EVM-25.65Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-25.65Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
NMSE-24.17Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-24.17Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
+

Scope, data, and metric definitions

+

The controlled workload uses bundled DPA_200MHz data, a TRes-DeltaGRU H15 DPD (thx=.01, thh=.05), a frozen DGRU H23 PA, FP32, B64×T200, AdamW at 5e-3Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json, and gradient clipping at 200Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json. The production-density arm uses all 22,841Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json stride-one frames (357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates); the multi-epoch gate uses stride 16Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json (23Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates per epoch for three epochs). NMSE is the repository's mean per-segment normalized MSE in dB. EVM is its subchannel spectral-magnitude error in dB. ACLR-left/right compare adjacent-channel power with the strongest main subchannel; ACLR-average is their mean.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

+

Methodology

+

The exact original is detached commit 0976ce39. A single deterministic PA surrogate was trained for 100Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py epochs because the repository ships no PA checkpoint; its SHA-256Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py fixture was reused unchanged by every arm. DPD initialization, frame order, full/partial batches, validation/test segments, optimizer hyperparameters, TF32 setting, and hard-determinism policy were fixed. Each run saves the exact permutations, best DPD checkpoint, DPD output, cascaded output, ground truth, phase timings, trajectory, and RF metrics. The comparison script rejects a fixture/order mismatch before evaluating speed or numerical results. Regression validation passed 164Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py standard tests plus 19Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py extended all-dataset/all-backbone tests.

Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py

Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

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Implemented optimizations and safeguards

Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py

Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

Implemented optimizations and safeguards
PriorityComponentChangeEquivalence behavior
1Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyWhole cascadeOpt-in fixed-shape CUDA graph of clean-eager forward, MSE, gradient zeroing, and backwardOriginal kernels; clean-eager fallback; clip and AdamW remain outside
2Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyFrozen DGRU PAExact cuDNN CUDA-graph forward and input-backward replayBitwise output/input gradient in focused tests; guarded eager fallback
3Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyTraining loopOne loss-vector device-to-host transfer per epoch; set-to-none gradients; trainable-only clip/optimizerBitwise CPU trajectory and logged loss in lockstep tests
4Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyEvaluationInference mode and consolidated output/target transfersExact outputs, targets, and loss in tests
5Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyDataVectorized frame construction and CUDA pinned/nonblocking loader pathExact frame order/content/dtype with independent storage
6Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyPersistent Triton TResFused recurrence and custom BPTT for supported FP32 NVIDIA shapesMinor one-step FP error; rejected as strict multi-epoch training path at nonzero thresholds
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Limitations and robustness

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  • The bundled repository has no published PA checkpoint. The surrogate makes the original-versus-optimized comparison controlled, but its absolute RF quality must not be presented as a reproduction of published results.
  • The longest comparison is one 357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-update epoch; the multi-epoch gate covers 69Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates. This is not a complete 240Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json- or 300Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-epoch convergence study.
  • Whole-cascade graph training is deliberately opt-in and specialized to the FP32 TRes-DeltaGRU → frozen-DGRU cascade with mean MSE. CPU, AMP, QAT, changed modules, diagnostic delta collection, capture failure, and unsupported shapes use eager fallbacks.
  • Existing EVM behavior was preserved: calculate_metrics does not pass the dataset sample rate, so EVM uses its 800 MHz default. Correcting that is a separate semantic change.
  • One original production-density timing and two optimized timings were collected on a power-managed laptop GPU; use the observed range rather than treating one decimal speedup as universal.
Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Recommended next steps

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  1. Use --cuda_graph_training (or cuda_graph_training=True through the API) when strict training-result equivalence is required.
  2. Keep direct persistent Triton available for inference and experiments where endpoint metric equivalence is sufficient, but do not label its multi-epoch neural outputs strict-equivalent at nonzero thresholds.
  3. Stage the real production PA checkpoint and run the full intended epoch schedule on the target GPU before a release claim. Preserve the same fixture/order comparison and checkpoint/output/RF gates.
  4. If workload shapes or precision modes change, rerun the supplied end-to-end harness; the graph path will safely fall back, but its speedup is shape- and hardware-dependent.
+

Further questions

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The main unresolved question is full-schedule convergence with the real PA checkpoint. A secondary opportunity is a GPU-resident frame sampler for much larger datasets, where per-batch host staging may become material after recurrent launch overhead has been removed.

Sources

  1. Reviewed end-to-end benchmark summarybenchmark/results/end_to_end_summary.json · duckdb · 2026-07-11T13:42:00Z

    Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

    SQL query
    SELECT * FROM read_json_auto('benchmark/results/end_to_end_summary.json');
  2. Reproducible benchmark and implementationbenchmark/benchmark_end_to_end.py · duckdb · 2026-07-11T13:42:00Z

    Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

    SQL query
    SELECT content FROM read_text('benchmark/benchmark_end_to_end.py');
+ + + + + + + diff --git a/benchmark/tres_deltagru_training_speed.md b/benchmark/tres_deltagru_training_speed.md new file mode 100644 index 0000000..42daaef --- /dev/null +++ b/benchmark/tres_deltagru_training_speed.md @@ -0,0 +1,179 @@ +# TRes-DeltaGRU training-speed benchmark + +## Result + +In the documented production-shape run, the TRes-DeltaGRU backbone (`B=64`, +`T=200`, `H=15`, `thx=.01`, `thh=.05`, FP32) was **97.1x faster** with the fused implementation: +**164.30 ms -> 1.692 ms per training step**. The timed step includes forward, +MSE backward, gradient clipping, and AdamW. The recurrent layer by itself +improved from **176.69 ms -> 0.721 ms** in a separate forward/backward +microbenchmark (245x). + +The implementation has the same 999 parameters and unchanged state-dict keys. +It preserves the feature extractor, thresholds, delta state updates, gate +equations, TCN residual, and output layer. Floating-point reduction order, +FMA contraction, and sigmoid/tanh implementations differ. + +This headline applies to dense FP32, first-order training on a supported NVIDIA +CUDA/Triton system. CPU, AMP, QAT, higher-order autograd, and unsupported shapes +take or should take the eager path; see the limitations below. + +## Test system and method + +| Field | Value | +|---|---| +| GPU | NVIDIA GeForce RTX 4090 Laptop GPU, compute capability 8.9 | +| Software | Python 3.13.14, PyTorch 2.13.0+cu132, CUDA 13.2, cuDNN 9.20, Triton 3.7.1 | +| Host | WSL2, Linux 6.6.114.1 | +| Precision | FP32; cuBLAS and cuDNN TF32 disabled | +| Step | `zero_grad(set_to_none=True)`, forward, MSE, backward, clip norm 200, AdamW | +| Data | Seeded synthetic IQ and targets, resident on GPU | +| Timing | 5 warmups; 7 synchronized CUDA-event blocks; median and IQR reported | +| Eager block | 5 steps per timing block | +| Fused block | 100 steps per timing block | + +The table reports the IQR of block-average step times. Because eager blocks +average 5 steps and fused blocks average 100, those IQRs describe stability +within each implementation and should not be compared as equal-size samples. +The script runs eager before fused; repeated runs on this power-managed laptop +produced roughly 81x-99x, so the defensible conclusion is "about two orders of +magnitude," not a universal exact 98.5x. + +This isolates backbone compute and optimizer cost. It intentionally excludes +CSV loading, frame construction, DataLoader work, and host-to-device transfer. +The real DPD pipeline also backpropagates through its frozen PA model; that is a +separate cost and can cap end-to-end gains even after this backbone is fast. + +## Production-shape result + +| Implementation | Median ms/step | IQR of block means | Sequence positions/s | Peak allocated | Speedup | +|---|---:|---:|---:|---:|---:| +| Clean eager reference | 164.302 | 3.184 | 77,905 | 25.3 MiB | 1.00x | +| Persistent Triton | **1.692** | 0.043 | **7,565,624** | 27.5 MiB | **97.11x** | + +Raw block means, environment details, and the equivalence summary are archived +in [`results/tres_deltagru_rtx4090_laptop.json`](results/tres_deltagru_rtx4090_laptop.json). + +The original implementation was slower than the clean eager reference because +it collected delta statistics and performed two unused hidden-delta reductions +at every timestep. An exploratory pre-change measurement was about 275 ms for +forward plus MSE backward, but it is not included in the comparable table +because it used a different step boundary. + +## Scaling experiments + +These shorter runs used five timing blocks (three for `T=1000`). B=16 used +3/100 eager/fused steps per block, B=256 used 3/50, T=50 used 5/100, and T=1000 +used 1/20 with two warmups. Their IQRs are omitted because the block settings +differ. + +### Batch scaling at T=200 + +| Batch | Clean eager ms | Fused ms | Speedup | +|---:|---:|---:|---:| +| 16 | 165.060 | 1.694 | 97.45x | +| 64 | 164.302 | 1.692 | 97.11x | +| 256 | 168.910 | 1.939 | 87.12x | + +Eager latency is nearly flat across these batch sizes because fixed Python and +kernel-launch overhead dominates, so its throughput still rises with batch. +The persistent-kernel latency is also nearly flat across this measured range. + +### Sequence scaling at B=64 + +| Sequence | Clean eager ms | Fused ms | Speedup | +|---:|---:|---:|---:| +| 50 | 42.754 | 1.727 | 24.75x | +| 200 | 164.302 | 1.692 | 97.11x | +| 1000 | 1,041.712 | 3.657 | 284.86x | + +The fused runtime grows with arithmetic rather than with Python and CUDA-launch +overhead, so its advantage increases for longer sequences. + +## Numerical-equivalence result + +The production-length check used identical weights, `B=8`, the production +thresholds, MSE, and TF32 disabled. + +| Quantity | Maximum absolute error | Relative L2 error | +|---|---:|---:| +| Output | 6.08e-6 | 1.34e-6 | +| Input gradient | 1.46e-7 | 1.50e-5 | +| `x2h.weight` gradient | 7.15e-7 | 8.60e-7 | +| `h2h.weight` gradient | 1.07e-7 | 6.16e-7 | +| `fc_out.weight` gradient | 4.77e-7 | 4.48e-7 | +| TCN parameter gradients | <=1.49e-8 | <=1.28e-7 | + +The absolute loss difference was `1.19e-7`. Additional checks covered sequence +lengths 1, 17, 50, 200, and 1000; thresholds `(0,0)` and `(.01,.05)`; custom +initial recurrent states; recurrent-state gradients; delta-statistics equality; +and a 19,662-sample inference sequence. At `T=1000`, output relative L2 error +was `2.44e-6` and all parameter-gradient relative errors remained below +`2.9e-6`. These differences come from reduction/activation ordering in FP32. + +## What was tried + +Rows other than the selected kernel are exploratory scratch experiments; the +committed benchmark directly reproduces the eager-versus-fused full-backbone +rows, while the candidate observations explain why those routes were not kept. + +| Candidate | Experiment | Decision | +|---|---|---| +| Remove diagnostic/dead work | Fixed `set_debug(0)`, removed two unused reductions, Python scalar thresholds, and the discarded outer hidden-state allocation. | Kept as output/gradient-equivalent portable cleanup. Diagnostic collection is now explicit. | +| Algebraic held-state rewrite | Mathematically telescoped delta accumulators into held-state projections. About 1.66x faster in the recurrent experiment; FP32 max output error about 5.2e-6. | Useful fallback idea, but far below the fused kernel's gain and not valid across QAT fake-quant boundaries. | +| `torch.compile(..., fullgraph=True, mode="reduce-overhead")` | The 200-step Python loop produced an enormous unrolled graph; compilation did not finish in a practical window on this host. PyTorch's prototype `scan` has no Inductor lowering in this version. | Rejected for this implementation. | +| cuDNN `nn.GRU` fast path | With both thresholds exactly zero, the delta accumulators telescope to a bias-free GRU. RNN-only: about 178.2 -> 1.12 ms (159x), max FP32 output error 5.84e-6 with TF32 off. | Excellent special case, but it cannot represent the production nonzero thresholds. The persistent kernel is faster here and supports both cases. | +| BF16/TF32 | Lower precision can accelerate dense GEMMs, but this tiny cell is launch-bound and threshold decisions can change near the boundary. | Not needed; fused FP32 already gives the large gain while minimizing trajectory changes. | +| Persistent custom recurrence | One Triton program owns each batch sequence; a reverse program implements BPTT. | **Selected.** Highest speed and supports nonzero thresholds. | + +## Implementation notes + +The forward kernel keeps `x_p`, `h`, `h_p`, `dm`, and `dm_nh` local while it +scans time. It fuses thresholding, both small projections, accumulator updates, +sigmoid/tanh gates, and the hidden update. It saves only the intermediates +needed by BPTT. + +The backward kernel scans time in reverse and emits input gradients plus the two +linear-output adjoints. Two final matrix multiplications reduce weight +gradients over batch and time. Inference under `no_grad` or `inference_mode` +uses a forward-only path and does not allocate the training workspace. Initial +Triton compilation is cache-dependent and took roughly 0.6 seconds on this +machine; it is a one-time cost per kernel specialization and is excluded from +steady-state timings. + +The fused path currently dispatches only for: + +- NVIDIA CUDA with Triton available; +- FP32 dense-float modules; +- one recurrent layer; +- input width up to 16 and hidden width up to 32. + +CPU, unsupported shapes/dtypes, multi-layer cells, and quantization-aware models +use the cleaned eager recurrence. QAT deliberately stays eager because its +fake-quantized Linear/Sigmoid/Tanh/Add/Mul boundaries are part of that model's +actual computation; bypassing or reassociating them would not be equivalent. +Temporal sparsity counters are disabled by default; pass +`--collect_delta_stats` to restore `SP_T_DX`, `SP_T_DH`, `SP_T_DV`, and +`HW_PARAM` training-log fields when that diagnostic overhead is wanted. +CUDA autocast deliberately falls back to eager so AMP keeps its original +fake/mixed-precision boundaries. The custom backward is explicitly +first-order-only; use `OPENDPD_DISABLE_TRITON_DELTAGRU=1` for higher-order +autograd, JVP/vmap-style transforms, or gradient-penalty/meta-learning code. + +## Reproduction + +```bash +.venv/bin/python benchmark/benchmark_tres_deltagru.py +``` + +Useful variants: + +```bash +.venv/bin/python benchmark/benchmark_tres_deltagru.py --batch-size 256 +.venv/bin/python benchmark/benchmark_tres_deltagru.py --sequence-length 1000 \ + --blocks 3 --reference-steps-per-block 1 --fused-steps-per-block 20 +``` + +Set `OPENDPD_DISABLE_TRITON_DELTAGRU=1` for normal application runs that should +force the portable eager path. The benchmark itself explicitly chooses each +implementation so it can compare them in one process. diff --git a/models.py b/models.py index 76da3b7..f1e2ad4 100644 --- a/models.py +++ b/models.py @@ -147,11 +147,14 @@ def __init__(self, input_size, hidden_size, num_layers, backbone_type, window_si pass def forward(self, x, h_0=None): - device = x.device batch_size = x.size(0) # NOTE: dim of x must be (batch, time, feat)/(N, T, F) - if h_0 is None: # Create initial hidden states if necessary - h_0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device) + if h_0 is None and self.backbone_type != 'tres_deltagru': + # Create directly on the input device. TRes-DeltaGRU owns five + # recurrent states internally and historically discarded this one. + h_0 = torch.zeros( + self.num_layers, batch_size, self.hidden_size, device=x.device + ) # Forward Propagate through the RNN out = self.backbone(x, h_0) diff --git a/modules/cuda_graph_training.py b/modules/cuda_graph_training.py new file mode 100644 index 0000000..cdcb3c7 --- /dev/null +++ b/modules/cuda_graph_training.py @@ -0,0 +1,377 @@ +"""Opt-in CUDA-graph training for the supported DPD cascade. + +Only the existing clean-eager forward, MSE loss, gradient zeroing, and backward +are captured. Batch copies, clipping, and optimizer updates remain ordinary +PyTorch operations so parameters and optimizer state can change in place +between replays. Every unsupported condition returns ``None`` for a strict +clean-eager fallback. +""" + +from __future__ import annotations + +import contextlib +import os +import threading +import weakref +from dataclasses import dataclass + +import torch +from torch import nn + +from backbones.cuda_graph_frozen_dgru import disable_cuda_graph_frozen_dgru + + +_DISABLE_ENV = "OPENDPD_DISABLE_CUDA_GRAPH_TRAINING" +_TRUE_VALUES = {"1", "true", "yes", "on"} +_CACHES = weakref.WeakKeyDictionary() +_CACHES_LOCK = threading.Lock() +_CAPTURE_FAILED = object() + + +@dataclass(frozen=True) +class CudaGraphTrainingResult: + """Result of one captured forward/loss/backward replay.""" + + loss: torch.Tensor + output: torch.Tensor + + +def _disabled() -> bool: + return os.getenv(_DISABLE_ENV, "").strip().lower() in _TRUE_VALUES + + +def _cuda_autocast_enabled() -> bool: + try: + return torch.is_autocast_enabled("cuda") + except TypeError: # pragma: no cover - compatibility with older PyTorch. + return torch.is_autocast_enabled() + + +def _tres_layers(module: nn.Module) -> tuple[nn.Module, ...]: + """Return only actual TRes recurrence layers, not lookalike attributes.""" + + from backbones.tres_deltagru import DeltaGRULayer + + return tuple( + child for child in module.modules() + if isinstance(child, DeltaGRULayer) + ) + + +@contextlib.contextmanager +def force_clean_eager_tres(module: nn.Module): + """Temporarily disable fused TRes arithmetic, restoring it afterward.""" + + layers = _tres_layers(module) + previous = tuple(layer.use_triton for layer in layers) + for layer in layers: + layer.use_triton = False + try: + yield + finally: + for layer, enabled in zip(layers, previous, strict=True): + layer.use_triton = enabled + + +def _supported_cascade(module: nn.Module) -> bool: + """Limit capture to the measured, state-free DPD/PA architecture.""" + + from backbones.dgru import DGRU + from backbones.tres_deltagru import TResDeltaGRU + from models import CascadedModel, CoreModel + + if not isinstance(module, CascadedModel): + return False + dpd = module.dpd_model + pa = module.pa_model + if not isinstance(dpd, CoreModel) or not isinstance(pa, CoreModel): + return False + if ( + dpd.backbone_type != "tres_deltagru" + or pa.backbone_type != "dgru" + or not isinstance(dpd.backbone, TResDeltaGRU) + or not isinstance(pa.backbone, DGRU) + or dpd.backbone.rnn.debug + or any(parameter.requires_grad for parameter in pa.parameters()) + ): + return False + return tuple(_tres_layers(module)) == (dpd.backbone.rnn,) + + +def _can_capture( + module: nn.Module, + features: torch.Tensor, + targets: torch.Tensor, + criterion: nn.Module, + parameters: tuple[torch.Tensor, ...], +) -> bool: + if ( + _disabled() + or not module.training + or not torch.is_grad_enabled() + or not isinstance(criterion, nn.MSELoss) + or criterion.reduction != "mean" + or not _supported_cascade(module) + or not parameters + or features.requires_grad + or targets.requires_grad + or not features.is_cuda + or not targets.is_cuda + or features.device != targets.device + or features.dtype != torch.float32 + or targets.dtype != torch.float32 + or not features.is_contiguous() + or not targets.is_contiguous() + or torch.version.cuda is None + or getattr(torch.version, "hip", None) is not None + or _cuda_autocast_enabled() + or not hasattr(torch.cuda, "CUDAGraph") + ): + return False + try: + if torch.cuda.is_current_stream_capturing(): + return False + except RuntimeError: + return False + + model_parameters = tuple(module.parameters()) + if any( + child._forward_hooks + or child._forward_pre_hooks + or child._backward_hooks + or child._backward_pre_hooks + for child in module.modules() + ): + return False + trainable = tuple( + parameter for parameter in model_parameters + if parameter.requires_grad + ) + if parameters != trainable: + return False + return all( + parameter.device == features.device + and parameter.dtype == torch.float32 + for parameter in model_parameters + ) + + +def _cache_key( + module: nn.Module, + features: torch.Tensor, + targets: torch.Tensor, + parameters: tuple[torch.Tensor, ...], +): + return ( + features.device.index, + features.dtype, + tuple(features.shape), + tuple(features.stride()), + tuple(targets.shape), + tuple(targets.stride()), + tuple(parameter.data_ptr() for parameter in module.parameters()), + tuple(parameter.data_ptr() for parameter in parameters), + ) + + +class _GraphCache: + def __init__(self): + self.entries = {} + self.capture_streams = {} + self.lock = threading.Lock() + + def get_or_create( + self, module, features, targets, criterion, parameters + ): + key = _cache_key(module, features, targets, parameters) + entry = self.entries.get(key) + if entry is _CAPTURE_FAILED: + return None + if entry is not None and entry.failed: + return None + if entry is not None and entry.is_valid(): + return entry + + # A simultaneous first call falls back rather than attempting a second + # capture of the same module and parameter storage. + if not self.lock.acquire(blocking=False): + return None + try: + entry = self.entries.get(key) + if entry is _CAPTURE_FAILED: + return None + if entry is not None and entry.failed: + return None + if entry is None or not entry.is_valid(): + try: + device_index = features.device.index + capture_stream = self.capture_streams.get(device_index) + if capture_stream is None: + capture_stream = torch.cuda.Stream( + device=features.device + ) + self.capture_streams[device_index] = capture_stream + entry = _CapturedTrainingStep( + module, features, targets, criterion, parameters, + capture_stream, + ) + except (RuntimeError, torch.cuda.OutOfMemoryError): + self.entries[key] = _CAPTURE_FAILED + return None + self.entries[key] = entry + return entry + finally: + self.lock.release() + + +class _CapturedTrainingStep: + def __init__( + self, module, example_features, example_targets, + criterion, parameters, capture_stream + ): + self.parameters = parameters + self.parameter_pointers = tuple( + parameter.data_ptr() for parameter in parameters + ) + self.lock = threading.Lock() + self.busy = False + self.failed = False + self.pending_event = None + self.pending_stream = None + + with torch.cuda.device(example_features.device), torch.enable_grad(): + self.capture_stream = capture_stream + self.capture_stream.wait_stream(torch.cuda.current_stream()) + with ( + force_clean_eager_tres(module), + disable_cuda_graph_frozen_dgru(), + torch.cuda.stream(self.capture_stream), + ): + self.static_features = example_features.detach().clone() + self.static_targets = example_targets.detach().clone() + # Initialize cuDNN/cuBLAS and persistent parameter-grad buffers + # on the exact stream that will own the capture. + for _ in range(2): + for parameter in parameters: + if parameter.grad is not None: + parameter.grad.zero_() + warm_output = module(self.static_features) + warm_loss = criterion(warm_output, self.static_targets) + warm_loss.backward() + del warm_output, warm_loss + + torch.cuda.current_stream().wait_stream(self.capture_stream) + torch.cuda.synchronize(example_features.device) + if any(parameter.grad is None for parameter in parameters): + raise RuntimeError( + "captured parameters must all receive gradients" + ) + + self.graph = torch.cuda.CUDAGraph() + with ( + force_clean_eager_tres(module), + disable_cuda_graph_frozen_dgru(), + torch.cuda.graph(self.graph, stream=self.capture_stream), + ): + for parameter in parameters: + parameter.grad.zero_() + self.static_output = module(self.static_features) + self.static_loss = criterion( + self.static_output, self.static_targets + ) + self.static_loss.backward() + + self.gradient_pointers = tuple( + parameter.grad.data_ptr() for parameter in parameters + ) + + def is_valid(self) -> bool: + return ( + not self.failed + and self.parameter_pointers == tuple( + parameter.data_ptr() for parameter in self.parameters + ) + and all( + parameter.grad is not None for parameter in self.parameters + ) + and self.gradient_pointers == tuple( + parameter.grad.data_ptr() for parameter in self.parameters + ) + ) + + def reserve(self) -> bool: + with self.lock: + if not self.is_valid() or self.busy: + return False + if self.pending_event is not None: + stream = torch.cuda.current_stream( + self.static_features.device + ) + same_stream = stream.cuda_stream == self.pending_stream + if not same_stream and not self.pending_event.query(): + return False + self.pending_event = None + self.pending_stream = None + self.busy = True + return True + + def release(self, *, failed: bool = False) -> None: + with self.lock: + self.failed = self.failed or failed + stream = torch.cuda.current_stream(self.static_features.device) + event = torch.cuda.Event() + event.record(stream) + self.pending_event = event + self.pending_stream = stream.cuda_stream + self.busy = False + + def run(self, module, features, targets): + if not self.reserve(): + return None + try: + self.static_features.copy_(features) + self.static_targets.copy_(targets) + with force_clean_eager_tres(module): + self.graph.replay() + # Each logged loss needs independent storage because replay updates + # the static scalar on the next batch. + result = CudaGraphTrainingResult( + loss=self.static_loss.detach().clone(), + output=self.static_output.detach(), + ) + except (RuntimeError, torch.cuda.OutOfMemoryError): + self.release(failed=True) + return None + self.release() + return result + + +def try_cuda_graph_training_step( + module: nn.Module, + features: torch.Tensor, + targets: torch.Tensor, + criterion: nn.Module, + parameters: tuple[torch.Tensor, ...], +) -> CudaGraphTrainingResult | None: + """Replay one captured training body, or return ``None`` for fallback.""" + + if not _can_capture(module, features, targets, criterion, parameters): + return None + with _CACHES_LOCK: + cache = _CACHES.get(module) + if cache is None: + cache = _GraphCache() + _CACHES[module] = cache + step = cache.get_or_create( + module, features, targets, criterion, parameters + ) + if step is None: + return None + return step.run(module, features, targets) + + +def clear_cuda_graph_training_cache() -> None: + """Drop all captured training steps; intended primarily for tests.""" + + with _CACHES_LOCK: + _CACHES.clear() diff --git a/modules/data_collector.py b/modules/data_collector.py index e0ec4e5..d65f55e 100644 --- a/modules/data_collector.py +++ b/modules/data_collector.py @@ -233,18 +233,40 @@ def __getitem__(self, idx): class IQFrameDataset(Dataset): def __init__(self, features, targets, frame_length, stride=1): # Convert segments into frames - self.features = torch.Tensor(self.get_frames(features, frame_length, stride)) - self.targets = torch.Tensor(self.get_frames(targets, frame_length, stride)) + # ``torch.Tensor(data)`` historically converts every input dtype to + # float32. Keep that behavior while forcing independent storage. + self.features = torch.tensor( + self.get_frames(features, frame_length, stride), dtype=torch.float32 + ) + self.targets = torch.tensor( + self.get_frames(targets, frame_length, stride), dtype=torch.float32 + ) @staticmethod def get_frames(sequence, frame_length, stride_length): - frames = [] - sequence_length = len(sequence) - num_frames = (sequence_length - frame_length) // stride_length + 1 - for i in range(num_frames): - frame = sequence[i * stride_length: i * stride_length + frame_length] - frames.append(frame) - return np.stack(frames) + sequence = np.asarray(sequence) + if frame_length <= 0: + raise ValueError("frame_length must be positive") + if stride_length <= 0: + raise ValueError("stride_length must be positive") + if len(sequence) < frame_length: + raise ValueError("frame_length cannot exceed the sequence length") + + # sliding_window_view creates every overlapping frame at once. For a + # two-dimensional IQ sequence it initially places the window axis last + # (N, IQ, frame); move it behind the sample axis to recover the exact + # historical (N, frame, IQ) order, then apply the requested stride. + frames = np.lib.stride_tricks.sliding_window_view( + sequence, window_shape=frame_length, axis=0 + ) + if sequence.ndim > 1: + frames = np.moveaxis(frames, -1, 1) + frames = frames[::stride_length] + + # Window arrays alias the source by construction. Return a contiguous + # copy so neither the dataset nor callers observe storage aliasing. + return np.array(frames, copy=True, order='C') + def __len__(self): return len(self.features) diff --git a/modules/paths.py b/modules/paths.py index 5c45ccd..f205b26 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -51,8 +51,9 @@ def gen_log_stat(args: argparse.Namespace, elapsed_time, net, optimizer, epoch, log_stat['THX'] = net.dpd_model.backbone.thx log_stat['THH'] = net.dpd_model.backbone.thh - # Add sparsity metrics if available - if 'delta' in net.dpd_model.backbone_type: + # Statistics are opt-in because collecting them in an eager + # recurrent cell adds reductions at every timestep. + if getattr(net.dpd_model.backbone.rnn, 'debug', 0): sparsity_metrics = net.dpd_model.backbone.get_temporal_sparsity() sparsity_log = {f'{k}': v for k, v in sparsity_metrics.items()} log_stat.update(sparsity_log) @@ -115,4 +116,4 @@ def gen_pa_model_id(args): list_pamodel_id += list(item) pa_model_id = '_'.join(list_pamodel_id) pa_model_id = 'PA_' + pa_model_id - return pa_model_id \ No newline at end of file + return pa_model_id diff --git a/modules/train_funcs.py b/modules/train_funcs.py index 1fa1419..a1962ee 100644 --- a/modules/train_funcs.py +++ b/modules/train_funcs.py @@ -2,6 +2,8 @@ __license__ = "Apache-2.0 License" __email__ = "yizhuo.wu@tudelft.nl, chang.gao@tudelft.nl" +import contextlib + import numpy as np import torch import torch.nn as nn @@ -13,41 +15,82 @@ import argparse +def _mean_losses(losses): + """Return the historical NumPy mean with one device-to-host transfer. + + ``Tensor.item()`` used to synchronize the accelerator once per batch. A + detached scalar keeps exactly the same stored value, so we can transfer all + batch losses together and still let NumPy perform the same float mean as + before. + """ + if not losses: + # Preserve NumPy's historical empty-loader result (NaN). + return np.mean([]) + return np.mean(torch.stack(losses).cpu().tolist()) + + def net_train(log: Dict[str, Any], net: nn.Module, dataloader: DataLoader, optimizer: Optimizer, criterion: Callable, grad_clip_val: float, - device: torch.device): + device: torch.device, + cuda_graph_training: bool = False): # Set Network to Training Mode net = net.train() # Statistics losses = [] + # The optimizer contains only trainable parameters in the standard + # pipeline. Materialize this tuple once per epoch rather than walking the + # complete cascaded model for every batch. + trainable_params = tuple( + parameter + for group in optimizer.param_groups + for parameter in group['params'] + if parameter.requires_grad + ) + non_blocking = device.type == 'cuda' + if cuda_graph_training: + from modules.cuda_graph_training import ( + force_clean_eager_tres, + try_cuda_graph_training_step, + ) # Iterate through batches for features, targets in tqdm(dataloader): # Move features and targets to the proper device - features = features.to(device) - targets = targets.to(device) - # Initialize all gradients to zero - optimizer.zero_grad() - # Forward Propagation - out = net(features) - # Calculate the Loss Function - loss = criterion(out, targets) - # Backward propagation - loss.backward() + features = features.to(device, non_blocking=non_blocking) + targets = targets.to(device, non_blocking=non_blocking) + graph_result = None + if cuda_graph_training: + graph_result = try_cuda_graph_training_step( + net, features, targets, criterion, trainable_params + ) + if graph_result is None: + # Strict opt-in fallback keeps the same clean-eager TRes + # arithmetic as the captured body. The original dispatch flag is + # restored immediately afterward, including for evaluation. + context = ( + force_clean_eager_tres(net) + if cuda_graph_training else contextlib.nullcontext() + ) + with context: + optimizer.zero_grad(set_to_none=True) + out = net(features) + loss = criterion(out, targets) + loss.backward() + else: + loss = graph_result.loss # Gradient clipping if grad_clip_val != 0: - nn.utils.clip_grad_norm_(net.parameters(), grad_clip_val) + nn.utils.clip_grad_norm_(trainable_params, grad_clip_val) # Update parameters optimizer.step() - # Detach loss from the graph indicating the end of forward propagation - loss.detach() - # Get losses - losses.append(loss.item()) + # Keep only the scalar value, not its graph. The complete vector is + # copied to the host once after the epoch to avoid a sync per batch. + losses.append(loss.detach()) # Average loss - loss = np.mean(losses) + loss = _mean_losses(losses) # Save Statistics log['loss'] = loss # End of Training Epoch @@ -60,30 +103,32 @@ def net_eval(log: Dict, criterion: Callable, device: torch.device): net = net.eval() - with torch.no_grad(): + with torch.inference_mode(): # Statistics losses = [] prediction = [] ground_truth = [] + non_blocking = device.type == 'cuda' # Batch Iteration for features, targets in tqdm(dataloader): # Move features and targets to the proper device - features = features.to(device) - targets = targets.to(device) + features = features.to(device, non_blocking=non_blocking) + targets = targets.to(device, non_blocking=non_blocking) # Forward Propagation outputs = net(features) # Calculate loss function loss = criterion(outputs, targets) # Collect prediction and ground truth for metric calculation - prediction.append(outputs.cpu()) - ground_truth.append(targets.cpu()) + prediction.append(outputs) + ground_truth.append(targets) # Collect losses to calculate the average loss per epoch - losses.append(loss.item()) + losses.append(loss) # Average loss per epoch - avg_loss = np.mean(losses) - # Prediction and Ground Truth - prediction = torch.cat(prediction, dim=0).numpy() - ground_truth = torch.cat(ground_truth, dim=0).numpy() + avg_loss = _mean_losses(losses) + # Concatenate before copying so evaluation performs one host transfer per + # result tensor instead of one transfer per batch. + prediction = torch.cat(prediction, dim=0).cpu().numpy() + ground_truth = torch.cat(ground_truth, dim=0).cpu().numpy() # Save Statistics log['loss'] = avg_loss # End of Evaluation Epoch diff --git a/opendpd/api.py b/opendpd/api.py index 43348c9..df437f6 100644 --- a/opendpd/api.py +++ b/opendpd/api.py @@ -25,6 +25,21 @@ from arguments import get_arguments +def _append_cli_kwargs(kwargs: Dict[str, Any]) -> None: + """Append keyword options while respecting argparse boolean flags.""" + + boolean_flags = { + 'use_segments', 'quant', 'collect_delta_stats', + 'cuda_graph_training', 'plot', + } + for key, value in kwargs.items(): + if isinstance(value, bool) and key in boolean_flags: + if value: + sys.argv.append(f'--{key}') + elif value is not None: + sys.argv.extend([f'--{key}', str(value)]) + + def train_pa( dataset_name: Optional[str] = None, dataset_path: Optional[str] = None, @@ -98,8 +113,7 @@ def train_pa( sys.argv.extend(['--plot_every', str(plot_every)]) # Add any additional keyword arguments - for key, value in kwargs.items(): - sys.argv.extend([f'--{key}', str(value)]) + _append_cli_kwargs(kwargs) # Create project and run training proj = Project() @@ -129,6 +143,8 @@ def train_dpd( thh: float = 0.0, plot: bool = False, plot_every: int = 1, + collect_delta_stats: bool = False, + cuda_graph_training: bool = False, **kwargs ) -> Dict[str, Any]: """ @@ -151,6 +167,8 @@ def train_dpd( thh: Threshold for hidden state deltas (for delta-based models) plot: Enable plot generation during training plot_every: Generate per-epoch plots every N epochs + collect_delta_stats: Collect temporal sparsity diagnostics during training + cuda_graph_training: Opt in to guarded CUDA-graph DPD training **kwargs: Additional arguments passed to the training configuration Returns: @@ -188,13 +206,16 @@ def train_dpd( sys.argv.extend(['--seed', str(seed)]) sys.argv.extend(['--thx', str(thx)]) sys.argv.extend(['--thh', str(thh)]) + if collect_delta_stats: + sys.argv.append('--collect_delta_stats') + if cuda_graph_training: + sys.argv.append('--cuda_graph_training') if plot: sys.argv.append('--plot') sys.argv.extend(['--plot_every', str(plot_every)]) # Add any additional keyword arguments - for key, value in kwargs.items(): - sys.argv.extend([f'--{key}', str(value)]) + _append_cli_kwargs(kwargs) # Create project and run training proj = Project() @@ -258,8 +279,7 @@ def run_dpd( sys.argv.append('--plot') # Add any additional keyword arguments - for key, value in kwargs.items(): - sys.argv.extend([f'--{key}', str(value)]) + _append_cli_kwargs(kwargs) # Create project and run DPD proj = Project() @@ -318,8 +338,7 @@ def plot_dpd( sys.argv.extend(['--DPD_hidden_size', str(DPD_hidden_size)]) sys.argv.extend(['--accelerator', accelerator]) - for key, value in kwargs.items(): - sys.argv.extend([f'--{key}', str(value)]) + _append_cli_kwargs(kwargs) proj = Project() plot_module.main(proj) @@ -571,4 +590,3 @@ def run(self, **kwargs): config['dataset_path'] = self.dataset_path return run_dpd(**config) - diff --git a/project.py b/project.py index 9f2878c..1823270 100644 --- a/project.py +++ b/project.py @@ -216,9 +216,19 @@ def build_dataloaders(self): test_set = IQSegmentDataset(X_test, y_test, nperseg=self.args.nperseg) # Define PyTorch Dataloaders - train_loader = DataLoader(train_set, batch_size=self.batch_size, shuffle=True) - val_loader = DataLoader(val_set, batch_size=self.batch_size_eval, shuffle=False) - test_loader = DataLoader(test_set, batch_size=self.batch_size_eval, shuffle=False) + pin_memory = self.device.type == 'cuda' + train_loader = DataLoader( + train_set, batch_size=self.batch_size, shuffle=True, + pin_memory=pin_memory + ) + val_loader = DataLoader( + val_set, batch_size=self.batch_size_eval, shuffle=False, + pin_memory=pin_memory + ) + test_loader = DataLoader( + test_set, batch_size=self.batch_size_eval, shuffle=False, + pin_memory=pin_memory + ) return (train_loader, val_loader, test_loader), input_size @@ -255,18 +265,27 @@ def build_criterion(self): raise AttributeError('Please use a valid loss function. Check argument.py.') def build_optimizer(self, net: nn.Module): + # Frozen PA parameters in a cascaded DPD model never receive gradients; + # excluding them avoids needless optimizer and clipping traversal while + # leaving the DPD updates unchanged. + trainable_params = tuple( + parameter for parameter in net.parameters() if parameter.requires_grad + ) + if not trainable_params: + raise ValueError("Cannot build an optimizer without trainable parameters") + # Optimizer if self.opt_type == 'adam': - optimizer = optim.Adam(net.parameters(), lr=self.lr) + optimizer = optim.Adam(trainable_params, lr=self.lr) elif self.opt_type == 'sgd': - optimizer = optim.SGD(net.parameters(), lr=self.lr, momentum=0.9) + optimizer = optim.SGD(trainable_params, lr=self.lr, momentum=0.9) elif self.opt_type == 'rmsprop': - optimizer = optim.RMSprop(net.parameters(), lr=self.lr) + optimizer = optim.RMSprop(trainable_params, lr=self.lr) elif self.opt_type == 'adamw': - optimizer = optim.AdamW(net.parameters(), lr=self.lr) + optimizer = optim.AdamW(trainable_params, lr=self.lr) elif self.opt_type == 'adabound': import adabound # Run pip install adabound (https://github.com/Luolc/AdaBound) - optimizer = adabound.AdaBound(net.parameters(), lr=self.lr, final_lr=0.1) + optimizer = adabound.AdaBound(trainable_params, lr=self.lr, final_lr=0.1) else: raise RuntimeError('Please use a valid optimizer.') @@ -352,7 +371,8 @@ def train(self, net: nn.Module, criterion: Callable, optimizer: optim.Optimizer, criterion=criterion, dataloader=train_loader, grad_clip_val=self.grad_clip_val, - device=self.device) + device=self.device, + cuda_graph_training=self.cuda_graph_training) # ----------- # Validation @@ -560,4 +580,4 @@ def train(self, net: nn.Module, criterion: Callable, optimizer: optim.Optimizer, print(f"Warning: GIF generation failed: {e}") print("Training Completed...") - print(" ") \ No newline at end of file + print(" ") diff --git a/steps/train_dpd.py b/steps/train_dpd.py index 3cfefd3..be92811 100644 --- a/steps/train_dpd.py +++ b/steps/train_dpd.py @@ -50,6 +50,8 @@ def main(proj: Project): thh=proj.thh) net_dpd = get_quant_model(proj, net_dpd) + if proj.collect_delta_stats and hasattr(net_dpd.backbone, 'set_debug'): + net_dpd.backbone.set_debug(1) print("::: DPD Model: ", net_dpd) n_net_dpd_params = count_net_params(net_dpd) diff --git a/tests/test_api.py b/tests/test_api.py index 04e794f..86b65a0 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -84,6 +84,30 @@ def test_rejects_wrong_columns(self, tmp_path): class TestApiTraining: + def test_cuda_graph_training_flag_is_explicit_opt_in(self, preserved_argv): + from arguments import get_arguments + + sys.argv = ['opendpd'] + assert get_arguments().cuda_graph_training is False + sys.argv = ['opendpd', '--cuda_graph_training'] + assert get_arguments().cuda_graph_training is True + + def test_boolean_kwargs_are_emitted_as_flags(self, preserved_argv): + from opendpd import api + + sys.argv = ['opendpd'] + api._append_cli_kwargs({ + "collect_delta_stats": True, + "cuda_graph_training": True, + "plot": False, + "frame_stride": 16, + "unused": None, + }) + assert sys.argv == [ + 'opendpd', '--collect_delta_stats', '--cuda_graph_training', + '--frame_stride', '16' + ] + def test_train_pa_smoke(self, tmp_path, monkeypatch, preserved_argv): from pathlib import Path diff --git a/tests/test_cuda_graph_frozen_dgru.py b/tests/test_cuda_graph_frozen_dgru.py new file mode 100644 index 0000000..18ee09a --- /dev/null +++ b/tests/test_cuda_graph_frozen_dgru.py @@ -0,0 +1,167 @@ +"""Correctness and fallback tests for frozen-DGRU CUDA-graph replay.""" + +import copy + +import pytest +import torch + +from backbones.cuda_graph_frozen_dgru import ( + clear_cuda_graph_frozen_dgru_cache, +) +from backbones.dgru import DGRU + + +requires_nvidia_cuda = pytest.mark.skipif( + not torch.cuda.is_available() + or torch.version.cuda is None + or getattr(torch.version, "hip", None) is not None, + reason="requires NVIDIA CUDA", +) + + +def _build_frozen(device="cpu"): + model = DGRU(hidden_size=23, output_size=2, num_layers=1) + model = model.to(device).train() + model.reset_parameters() + for parameter in model.parameters(): + parameter.requires_grad_(False) + return model + + +def _is_graph_replay(output): + return "FrozenDGRUReplayFunction" in type(output.grad_fn).__name__ + + +def test_cpu_fallback_preserves_module_and_state_dict(monkeypatch): + clear_cuda_graph_frozen_dgru_cache() + monkeypatch.delenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", raising=False) + model = _build_frozen() + state_keys = tuple(model.state_dict()) + module_names = tuple(name for name, _ in model.named_modules()) + features = torch.randn(2, 7, 2, requires_grad=True) + initial_state = torch.zeros(1, 2, 23) + + output = model(features, initial_state) + output.square().mean().backward() + + assert not _is_graph_replay(output) + assert tuple(model.state_dict()) == state_keys + assert tuple(name for name, _ in model.named_modules()) == module_names + assert torch.isfinite(features.grad).all() + + +def test_trainable_dgru_uses_eager_fallback(): + model = DGRU(hidden_size=5, output_size=2, num_layers=1).train() + features = torch.randn(2, 4, 2, requires_grad=True) + output = model(features, torch.zeros(1, 2, 5)) + assert not _is_graph_replay(output) + output.sum().backward() + assert all(parameter.grad is not None for parameter in model.parameters()) + + +@requires_nvidia_cuda +def test_cuda_replay_is_bitwise_exact_and_state_dict_stable(monkeypatch): + torch.manual_seed(123) + clear_cuda_graph_frozen_dgru_cache() + model = _build_frozen("cuda") + state_before = copy.deepcopy(model.state_dict()) + module_names = tuple(name for name, _ in model.named_modules()) + initial_state = torch.randn(1, 4, 23, device="cuda") * 0.1 + + for _ in range(2): + features = torch.randn(4, 37, 2, device="cuda") + output_gradient = torch.randn(4, 37, 2, device="cuda") + monkeypatch.setenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", "1") + reference_input = features.clone().requires_grad_() + reference_output = model(reference_input, initial_state) + reference_output.backward(output_gradient) + + monkeypatch.delenv( + "OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", raising=False + ) + replay_input = features.clone().requires_grad_() + replay_output = model(replay_input, initial_state) + replay_output.backward(output_gradient) + torch.cuda.synchronize() + + assert _is_graph_replay(replay_output) + assert torch.equal(replay_output, reference_output) + assert torch.equal(replay_input.grad, reference_input.grad) + + assert tuple(model.state_dict()) == tuple(state_before) + assert tuple(name for name, _ in model.named_modules()) == module_names + for name, value in model.state_dict().items(): + assert torch.equal(value, state_before[name]) + + +@requires_nvidia_cuda +def test_busy_replay_and_opt_out_fall_back_safely(monkeypatch): + torch.manual_seed(321) + clear_cuda_graph_frozen_dgru_cache() + monkeypatch.delenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", raising=False) + model = _build_frozen("cuda") + initial_state = torch.zeros(1, 2, 23, device="cuda") + + first_input = torch.randn(2, 17, 2, device="cuda", requires_grad=True) + first_output = model(first_input, initial_state) + assert _is_graph_replay(first_output) + + # The first graph still owns its training reserve until backward. A + # second in-flight call must use independent eager buffers. + second_input = torch.randn(2, 17, 2, device="cuda", requires_grad=True) + second_output = model(second_input, initial_state) + assert not _is_graph_replay(second_output) + second_output.sum().backward() + first_output.sum().backward() + + monkeypatch.setenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", "true") + opt_out_input = torch.randn(2, 17, 2, device="cuda", requires_grad=True) + opt_out_output = model(opt_out_input, initial_state) + assert not _is_graph_replay(opt_out_output) + opt_out_output.sum().backward() + assert torch.isfinite(first_input.grad).all() + assert torch.isfinite(second_input.grad).all() + assert torch.isfinite(opt_out_input.grad).all() + + +@requires_nvidia_cuda +def test_autocast_and_eval_use_eager_fallback(monkeypatch): + clear_cuda_graph_frozen_dgru_cache() + monkeypatch.delenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", raising=False) + model = _build_frozen("cuda") + initial_state = torch.zeros(1, 2, 23, device="cuda") + features = torch.randn(2, 7, 2, device="cuda", requires_grad=True) + + with torch.autocast("cuda", dtype=torch.float16): + autocast_output = model(features, initial_state) + assert not _is_graph_replay(autocast_output) + + noncontiguous = torch.randn( + 2, 2, 7, device="cuda", requires_grad=True + ).transpose(1, 2) + noncontiguous_output = model(noncontiguous, initial_state) + assert not _is_graph_replay(noncontiguous_output) + + with torch.no_grad(): + no_grad_output = model(features, initial_state) + assert not no_grad_output.requires_grad + + model.eval() + eval_output = model(features, initial_state) + assert not _is_graph_replay(eval_output) + + +@requires_nvidia_cuda +def test_higher_order_gradient_fails_loudly(monkeypatch): + clear_cuda_graph_frozen_dgru_cache() + monkeypatch.delenv("OPENDPD_DISABLE_CUDA_GRAPH_FROZEN_DGRU", raising=False) + model = _build_frozen("cuda") + features = torch.randn(1, 3, 2, device="cuda", requires_grad=True) + initial_state = torch.zeros(1, 1, 23, device="cuda") + + with pytest.raises(RuntimeError, match="first-order training only"): + torch.autograd.grad( + model(features, initial_state).sum(), + features, + create_graph=True, + ) diff --git a/tests/test_cuda_graph_training.py b/tests/test_cuda_graph_training.py new file mode 100644 index 0000000..594e0c2 --- /dev/null +++ b/tests/test_cuda_graph_training.py @@ -0,0 +1,236 @@ +"""Tests for guarded whole-cascade CUDA-graph training.""" + +import contextlib +import copy +import io + +import pytest +import torch +from torch.utils.data import DataLoader, TensorDataset + +import modules.cuda_graph_training as graph_training +from backbones.cuda_graph_frozen_dgru import disable_cuda_graph_frozen_dgru +from models import CascadedModel, CoreModel +from modules.cuda_graph_training import ( + clear_cuda_graph_training_cache, + force_clean_eager_tres, + try_cuda_graph_training_step, +) +from modules.train_funcs import net_train + + +requires_nvidia_cuda = pytest.mark.skipif( + not torch.cuda.is_available() + or torch.version.cuda is None + or getattr(torch.version, "hip", None) is not None, + reason="requires NVIDIA CUDA", +) + + +def _build_cascade(device="cpu"): + with contextlib.redirect_stdout(io.StringIO()): + dpd = CoreModel( + 2, 15, 1, "tres_deltagru", thx=0.01, thh=0.05 + ) + pa = CoreModel(2, 23, 1, "dgru") + cascade = CascadedModel(dpd, pa) + cascade.freeze_pa_model() + return cascade.to(device).train() + + +def _trainable(module): + return tuple( + parameter for parameter in module.parameters() + if parameter.requires_grad + ) + + +def test_cpu_opt_in_falls_back_cleanly_and_restores_dispatch(): + torch.manual_seed(301) + clear_cuda_graph_training_cache() + reference = _build_cascade() + candidate = copy.deepcopy(reference) + state_keys = tuple(candidate.state_dict()) + module_names = tuple(name for name, _ in candidate.named_modules()) + features = torch.randn(4, 5, 2) + targets = torch.randn(4, 5, 2) + loader = DataLoader( + TensorDataset(features, targets), batch_size=2, shuffle=False + ) + reference_optimizer = torch.optim.AdamW(_trainable(reference), lr=5e-4) + candidate_optimizer = torch.optim.AdamW(_trainable(candidate), lr=5e-4) + criterion = torch.nn.MSELoss() + + reference_log = {} + with force_clean_eager_tres(reference): + net_train( + reference_log, reference, loader, reference_optimizer, + criterion, 200.0, torch.device("cpu") + ) + candidate_log = {} + original_dispatch = candidate.dpd_model.backbone.rnn.use_triton + observed_dispatch = [] + hook = candidate.dpd_model.backbone.rnn.register_forward_pre_hook( + lambda recurrent, _inputs: observed_dispatch.append( + recurrent.use_triton + ) + ) + net_train( + candidate_log, candidate, loader, candidate_optimizer, + criterion, 200.0, torch.device("cpu"), + cuda_graph_training=True, + ) + hook.remove() + + assert candidate_log == reference_log + assert observed_dispatch and not any(observed_dispatch) + assert candidate.dpd_model.backbone.rnn.use_triton is original_dispatch + assert tuple(candidate.state_dict()) == state_keys + assert tuple(name for name, _ in candidate.named_modules()) == module_names + for expected, actual in zip( + reference.parameters(), candidate.parameters(), strict=True + ): + assert torch.equal(actual, expected) + + +def _snapshot(module, parameters, optimizer, output, loss): + return { + "output": output.detach().clone(), + "loss": loss.detach().clone(), + "gradients": [ + parameter.grad.detach().clone() for parameter in parameters + ], + "parameters": [parameter.detach().clone() for parameter in parameters], + "optimizer": [ + { + key: ( + value.detach().clone() + if torch.is_tensor(value) else value + ) + for key, value in optimizer.state[parameter].items() + } + for parameter in parameters + ], + "state_keys": tuple(module.state_dict()), + } + + +def _run_eager_trajectory(module, batches, targets): + parameters = _trainable(module) + optimizer = torch.optim.AdamW(parameters, lr=5e-4) + criterion = torch.nn.MSELoss() + output = loss = None + for features, target in zip(batches, targets, strict=True): + optimizer.zero_grad(set_to_none=True) + with force_clean_eager_tres(module), disable_cuda_graph_frozen_dgru(): + output = module(features) + loss = criterion(output, target) + loss.backward() + torch.nn.utils.clip_grad_norm_(parameters, 200.0) + optimizer.step() + return _snapshot(module, parameters, optimizer, output, loss) + + +def _run_graph_trajectory(module, batches, targets): + parameters = _trainable(module) + optimizer = torch.optim.AdamW(parameters, lr=5e-4) + criterion = torch.nn.MSELoss() + output = loss = None + original_dispatch = module.dpd_model.backbone.rnn.use_triton + for features, target in zip(batches, targets, strict=True): + result = try_cuda_graph_training_step( + module, features, target, criterion, parameters + ) + assert result is not None + assert module.dpd_model.backbone.rnn.use_triton is original_dispatch + output = result.output.clone() + loss = result.loss + torch.nn.utils.clip_grad_norm_(parameters, 200.0) + optimizer.step() + return _snapshot(module, parameters, optimizer, output, loss) + + +def _max_abs(left, right): + return max( + (first - second).abs().max().item() + for first, second in zip(left, right, strict=True) + ) + + +def _optimizer_max_abs(left, right): + differences = [] + for first, second in zip(left, right, strict=True): + assert first.keys() == second.keys() + for key in first: + if torch.is_tensor(first[key]): + differences.append( + (first[key] - second[key]).abs().max().item() + ) + else: + assert first[key] == second[key] + return max(differences, default=0.0) + + +@requires_nvidia_cuda +def test_cuda_ten_step_drift_is_within_eager_repeat_envelope(monkeypatch): + torch.manual_seed(401) + clear_cuda_graph_training_cache() + base = _build_cascade("cuda") + eager_first = copy.deepcopy(base) + eager_second = copy.deepcopy(base) + graphed = copy.deepcopy(base) + for module in (eager_first, eager_second, graphed): + module.pa_model.backbone.rnn.flatten_parameters() + generator = torch.Generator(device="cuda").manual_seed(402) + batch_sizes = [4, 3] * 5 + batches = [ + torch.randn(size, 17, 2, device="cuda", generator=generator) + for size in batch_sizes + ] + targets = [ + torch.randn(size, 17, 2, device="cuda", generator=generator) + for size in batch_sizes + ] + + first = _run_eager_trajectory(eager_first, batches, targets) + second = _run_eager_trajectory(eager_second, batches, targets) + capture_count = 0 + original_init = graph_training._CapturedTrainingStep.__init__ + + def counted_init(instance, *args, **kwargs): + nonlocal capture_count + capture_count += 1 + original_init(instance, *args, **kwargs) + + monkeypatch.setattr( + graph_training._CapturedTrainingStep, "__init__", counted_init + ) + candidate = _run_graph_trajectory(graphed, batches, targets) + torch.cuda.synchronize() + + assert candidate["state_keys"] == first["state_keys"] + assert capture_count == 2 + comparisons = ( + ( + (first["output"] - candidate["output"]).abs().max().item(), + (first["output"] - second["output"]).abs().max().item(), + ), + ( + abs((first["loss"] - candidate["loss"]).item()), + abs((first["loss"] - second["loss"]).item()), + ), + ( + _max_abs(first["gradients"], candidate["gradients"]), + _max_abs(first["gradients"], second["gradients"]), + ), + ( + _max_abs(first["parameters"], candidate["parameters"]), + _max_abs(first["parameters"], second["parameters"]), + ), + ( + _optimizer_max_abs(first["optimizer"], candidate["optimizer"]), + _optimizer_max_abs(first["optimizer"], second["optimizer"]), + ), + ) + for candidate_error, eager_repeat_error in comparisons: + assert candidate_error <= eager_repeat_error diff --git a/tests/test_training_pipeline_optimizations.py b/tests/test_training_pipeline_optimizations.py new file mode 100644 index 0000000..c48dc83 --- /dev/null +++ b/tests/test_training_pipeline_optimizations.py @@ -0,0 +1,221 @@ +"""Equivalence checks for the optimized data and epoch plumbing.""" + +from copy import deepcopy +from types import SimpleNamespace + +import numpy as np +import pytest +import torch +from torch import nn +from torch.utils.data import DataLoader, TensorDataset + +from modules.data_collector import IQFrameDataset +from modules.train_funcs import net_eval, net_train +from project import Project + + +def _reference_frames(sequence, frame_length, stride): + num_frames = (len(sequence) - frame_length) // stride + 1 + return np.stack([ + sequence[index * stride:index * stride + frame_length] + for index in range(num_frames) + ]) + + +@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.int32]) +@pytest.mark.parametrize("stride", [1, 2, 4]) +def test_vectorized_frames_match_reference_and_do_not_alias(dtype, stride): + sequence = np.arange(34, dtype=dtype).reshape(17, 2) + expected = _reference_frames(sequence, frame_length=5, stride=stride) + + actual = IQFrameDataset.get_frames( + sequence, frame_length=5, stride_length=stride + ) + + assert np.array_equal(actual, expected) + assert actual.flags.c_contiguous + assert not np.shares_memory(actual, sequence) + + sequence[...] = 0 + assert np.array_equal(actual, expected) + + +def test_frame_dataset_preserves_values_order_and_independent_storage(): + features = np.arange(40, dtype=np.float64).reshape(20, 2) / 7 + targets = -features.copy() + expected_features = torch.Tensor(_reference_frames(features, 6, 3)) + expected_targets = torch.Tensor(_reference_frames(targets, 6, 3)) + + dataset = IQFrameDataset(features, targets, frame_length=6, stride=3) + features[...] = 99 + targets[...] = 99 + + assert dataset.features.dtype == torch.float32 + assert dataset.targets.dtype == torch.float32 + assert torch.equal(dataset.features, expected_features) + assert torch.equal(dataset.targets, expected_targets) + + +class _FrozenTail(nn.Module): + """Tiny analogue of a trainable DPD followed by a frozen PA.""" + + def __init__(self): + super().__init__() + self.trainable = nn.Linear(3, 4) + self.frozen = nn.Linear(4, 2) + for parameter in self.frozen.parameters(): + parameter.requires_grad_(False) + + def forward(self, inputs): + return self.frozen(torch.tanh(self.trainable(inputs))) + + +def _reference_train( + log, net, dataloader, optimizer, criterion, grad_clip_val +): + """The pre-optimization epoch loop, retained only as a test oracle.""" + net.train() + losses = [] + for features, targets in dataloader: + features = features.to(torch.device("cpu")) + targets = targets.to(torch.device("cpu")) + optimizer.zero_grad() + loss = criterion(net(features), targets) + loss.backward() + if grad_clip_val != 0: + nn.utils.clip_grad_norm_(net.parameters(), grad_clip_val) + optimizer.step() + loss.detach() + losses.append(loss.item()) + log["loss"] = np.mean(losses) + return net + + +def _build_project_optimizer(net): + project = Project.__new__(Project) + project.opt_type = "adamw" + project.lr = 2e-3 + project.decay_factor = 0.5 + project.patience = 2 + project.lr_end = 1e-7 + return project.build_optimizer(net)[0] + + +@pytest.mark.parametrize("num_batches", [1, 4]) +def test_train_epoch_matches_original_updates_and_logged_loss(num_batches): + torch.manual_seed(71) + features = torch.randn(num_batches * 3, 3) + targets = torch.randn(num_batches * 3, 2) + dataloader = DataLoader( + TensorDataset(features, targets), batch_size=3, shuffle=False + ) + + reference_net = _FrozenTail() + optimized_net = deepcopy(reference_net) + reference_optimizer = torch.optim.AdamW( + reference_net.parameters(), lr=2e-3 + ) + optimized_optimizer = _build_project_optimizer(optimized_net) + + optimized_parameters = { + id(parameter) + for group in optimized_optimizer.param_groups + for parameter in group["params"] + } + assert optimized_parameters == { + id(parameter) for parameter in optimized_net.parameters() + if parameter.requires_grad + } + + reference_log = {} + optimized_log = {} + criterion = nn.MSELoss() + _reference_train( + reference_log, reference_net, dataloader, reference_optimizer, + criterion, grad_clip_val=0.7 + ) + net_train( + optimized_log, optimized_net, dataloader, optimized_optimizer, + criterion, grad_clip_val=0.7, device=torch.device("cpu") + ) + + assert optimized_log["loss"] == reference_log["loss"] + for reference, optimized in zip( + reference_net.parameters(), optimized_net.parameters(), strict=True + ): + assert torch.equal(optimized, reference) + + +def _reference_eval(log, net, dataloader, criterion): + net.eval() + losses = [] + prediction = [] + ground_truth = [] + with torch.no_grad(): + for features, targets in dataloader: + outputs = net(features) + loss = criterion(outputs, targets) + prediction.append(outputs.cpu()) + ground_truth.append(targets.cpu()) + losses.append(loss.item()) + log["loss"] = np.mean(losses) + return ( + torch.cat(prediction, dim=0).numpy(), + torch.cat(ground_truth, dim=0).numpy(), + ) + + +def test_inference_eval_matches_original_outputs_targets_and_loss(): + torch.manual_seed(101) + net = _FrozenTail() + features = torch.randn(11, 3) + targets = torch.randn(11, 2) + dataloader = DataLoader( + TensorDataset(features, targets), batch_size=4, shuffle=False + ) + criterion = nn.L1Loss() + + reference_log = {} + reference_prediction, reference_targets = _reference_eval( + reference_log, net, dataloader, criterion + ) + optimized_log = {} + _, optimized_prediction, optimized_targets = net_eval( + optimized_log, net, dataloader, criterion, torch.device("cpu") + ) + + assert optimized_log["loss"] == reference_log["loss"] + assert np.array_equal(optimized_prediction, reference_prediction) + assert np.array_equal(optimized_targets, reference_targets) + + +@pytest.mark.parametrize( + "device_type, expected", [("cpu", False), ("cuda", True)] +) +def test_cuda_loaders_pin_memory(monkeypatch, device_type, expected): + features = np.arange(48, dtype=np.float64).reshape(24, 2) / 10 + targets = features * 2 + + def fake_load_dataset(**_kwargs): + return ( + features, targets, + features[:12], targets[:12], + features[:12], targets[:12], + ) + + monkeypatch.setattr( + "modules.data_collector.load_dataset", fake_load_dataset + ) + project = Project.__new__(Project) + project.dataset_name = "unused" + project.step = "train_pa" + project.frame_length = 4 + project.frame_stride = 2 + project.batch_size = 3 + project.batch_size_eval = 2 + project.args = SimpleNamespace(nperseg=6) + project.device = torch.device(device_type) + + loaders, _ = project.build_dataloaders() + + assert all(loader.pin_memory is expected for loader in loaders) diff --git a/tests/test_tres_deltagru_optimized.py b/tests/test_tres_deltagru_optimized.py new file mode 100644 index 0000000..0c2568c --- /dev/null +++ b/tests/test_tres_deltagru_optimized.py @@ -0,0 +1,238 @@ +"""Correctness tests for the fused TRes-DeltaGRU recurrence.""" + +import contextlib +import copy +import io +from types import SimpleNamespace + +import pytest +import torch + +from models import CoreModel +from backbones.tres_deltagru import DeltaGRULayer +from backbones.triton_deltagru import triton +from modules.paths import gen_log_stat + + +requires_fused_cuda = pytest.mark.skipif( + not torch.cuda.is_available() or triton is None, + reason="requires CUDA and Triton", +) + + +def build_model(device, *, fused, thx=0.01, thh=0.05): + with contextlib.redirect_stdout(io.StringIO()): + model = CoreModel( + input_size=2, + hidden_size=15, + num_layers=1, + backbone_type="tres_deltagru", + thx=thx, + thh=thh, + ).to(device) + model.backbone.rnn.use_triton = fused + model.backbone.set_debug(0) + return model + + +def test_debug_setting_reaches_recurrent_layer(): + model = build_model("cpu", fused=False) + model.backbone.set_debug(1) + assert model.backbone.debug == 1 + assert model.backbone.rnn.debug == 1 + model.backbone.set_debug(0) + assert model.backbone.debug == 0 + assert model.backbone.rnn.debug == 0 + + +def test_opt_in_statistics_are_logged_and_reset(): + model = build_model("cpu", fused=False) + model.backbone.set_debug(1) + model(torch.randn(2, 3, 2)) + wrapper = SimpleNamespace( + dpd_model=model, + named_parameters=model.named_parameters, + ) + args = SimpleNamespace( + step="train_dpd", + n_epochs=2, + batch_size=2, + frame_length=3, + DPD_backbone="tres_deltagru", + DPD_hidden_size=15, + ) + + log = gen_log_stat(args, 0.0, wrapper, None, 0) + + assert {"SP_T_DX", "SP_T_DH", "SP_T_DV", "HW_PARAM"} <= log.keys() + assert model.backbone.rnn.statistics == { + "num_dx_zeros": 0, + "num_dx_numel": 0, + "num_dh_zeros": 0, + "num_dh_numel": 0, + } + + +@requires_fused_cuda +@pytest.mark.parametrize("thresholds", [(0.0, 0.0), (0.01, 0.05)]) +@pytest.mark.parametrize("sequence_length", [1, 17, 200]) +def test_fused_output_and_gradients_match_eager(thresholds, sequence_length): + torch.manual_seed(7) + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + thx, thh = thresholds + reference = build_model("cuda", fused=False, thx=thx, thh=thh) + fused = build_model("cuda", fused=True, thx=thx, thh=thh) + fused.load_state_dict(copy.deepcopy(reference.state_dict())) + + reference_input = torch.randn( + 2, sequence_length, 2, device="cuda", requires_grad=True + ) + fused_input = reference_input.detach().clone().requires_grad_() + target = torch.randn(2, sequence_length, 2, device="cuda") + + reference_output = reference(reference_input) + reference_loss = torch.nn.functional.mse_loss(reference_output, target) + reference_loss.backward() + fused_output = fused(fused_input) + fused_loss = torch.nn.functional.mse_loss(fused_output, target) + fused_loss.backward() + + torch.testing.assert_close(fused_output, reference_output, rtol=3e-5, atol=2e-5) + torch.testing.assert_close(fused_input.grad, reference_input.grad, rtol=5e-4, atol=2e-6) + for (name, reference_parameter), (fused_name, fused_parameter) in zip( + reference.named_parameters(), fused.named_parameters(), strict=True + ): + assert name == fused_name + torch.testing.assert_close( + fused_parameter.grad, + reference_parameter.grad, + rtol=5e-4, + atol=2e-6, + msg=lambda message, parameter=name: f"{parameter}: {message}", + ) + + +@requires_fused_cuda +def test_fused_statistics_match_eager(): + torch.manual_seed(11) + reference = build_model("cuda", fused=False) + fused = build_model("cuda", fused=True) + fused.load_state_dict(copy.deepcopy(reference.state_dict())) + reference.backbone.set_debug(1) + fused.backbone.set_debug(1) + features = torch.randn(4, 17, 2, device="cuda") + + reference(features) + fused(features) + + for key in ("num_dx_zeros", "num_dx_numel", "num_dh_zeros", "num_dh_numel"): + reference_value = torch.as_tensor(reference.backbone.rnn.statistics[key]) + fused_value = torch.as_tensor(fused.backbone.rnn.statistics[key]) + torch.testing.assert_close(fused_value.cpu(), reference_value.cpu()) + + +@requires_fused_cuda +def test_custom_initial_state_gradients_match_eager(): + torch.manual_seed(13) + reference = DeltaGRULayer(6, 15, 1, thx=0.01, thh=0.05).cuda() + fused = copy.deepcopy(reference) + reference.use_triton = False + fused.use_triton = True + reference.debug = 0 + fused.debug = 0 + + reference_input = torch.randn(2, 17, 6, device="cuda", requires_grad=True) + fused_input = reference_input.detach().clone().requires_grad_() + + def make_state(width): + return (torch.randn(1, 2, width, device="cuda") * 0.1).requires_grad_() + + reference_states = [ + make_state(15), # x_p_0 uses x_p_length=max(input_size, hidden_size) + make_state(15), + make_state(15), + make_state(15), + make_state(45), + ] + fused_states = [ + state.detach().clone().requires_grad_() for state in reference_states + ] + output_gradient = torch.randn(2, 17, 15, device="cuda") + + reference_output = reference(reference_input, *reference_states) + (reference_output * output_gradient).sum().backward() + fused_output = fused(fused_input, *fused_states) + (fused_output * output_gradient).sum().backward() + + torch.testing.assert_close(fused_output, reference_output, rtol=3e-5, atol=2e-5) + torch.testing.assert_close(fused_input.grad, reference_input.grad, rtol=5e-4, atol=2e-6) + for reference_state, fused_state in zip( + reference_states, fused_states, strict=True + ): + torch.testing.assert_close( + fused_state.grad, reference_state.grad, rtol=5e-4, atol=2e-6 + ) + + +@requires_fused_cuda +def test_inference_does_not_save_training_workspace(): + model = build_model("cuda", fused=True).eval() + features = torch.randn(4, 512, 2, device="cuda") + with torch.inference_mode(): + output = model(features) + assert output.shape == (4, 512, 2) + assert not output.requires_grad + assert torch.isfinite(output).all() + + +@requires_fused_cuda +def test_cuda_autocast_uses_equivalent_eager_fallback(): + torch.manual_seed(17) + reference = build_model("cuda", fused=False) + candidate = build_model("cuda", fused=True) + candidate.load_state_dict(copy.deepcopy(reference.state_dict())) + features = torch.randn(2, 17, 2, device="cuda") + + with torch.autocast("cuda", dtype=torch.float16): + reference_output = reference(features) + candidate_output = candidate(features) + probe = torch.empty(1, 1, 6, device="cuda") + assert not candidate.backbone.rnn._can_use_triton(probe) + + torch.testing.assert_close(candidate_output, reference_output, rtol=0, atol=0) + + +@requires_fused_cuda +def test_quantization_aware_model_uses_eager_fallback(): + from quant.quant_envs import AttrDict, Base_GRUQuantEnv + + float_model = build_model("cpu", fused=True) + with contextlib.redirect_stdout(io.StringIO()): + environment = Base_GRUQuantEnv( + float_model, + AttrDict( + n_bits_w=16, + n_bits_a=16, + pretrained_model="", + quant_dir_label="", + ), + ) + quantized_model = environment.q_model.cuda() + probe = torch.empty(1, 1, 6, device="cuda") + assert not quantized_model.backbone.rnn._can_use_triton(probe) + + features = torch.randn(2, 4, 2, device="cuda") + output = quantized_model(features) + output.square().mean().backward() + assert torch.isfinite(output).all() + + +@requires_fused_cuda +def test_higher_order_gradient_fails_loudly(): + model = build_model("cuda", fused=True) + features = torch.randn(1, 2, 2, device="cuda", requires_grad=True) + with pytest.raises(RuntimeError, match="first-order training only"): + torch.autograd.grad( + model(features).sum(), features, create_graph=True + ) From 3df35e081e6e41463fa46f21778c72a748823274 Mon Sep 17 00:00:00 2001 From: Chang Gao Date: Fri, 24 Jul 2026 12:42:20 +0200 Subject: [PATCH 2/3] Add benchmark verification findings, fix GMP/QR path, add TRes-GRU Full experimental re-run of benchmark/benchmark_report.md. Eight of the ten reported DPD numbers reproduce (QR rows to <=0.05 dB, SGD rows to <=0.5 dB), but the verification exposed three defects: - benchmark_volterra_qr.py never branched on --model, so --model gmp silently ran the Memory Polynomial basis. Present since the script was first added; the report's GMP rows cannot have come from it as committed. Fixed here, and with the fix both GMP rows reproduce. - The APA_200MHz PA NMSE of -43.52 dB in section 4 is unattainable. Re-running the report's own command gives -31.48 dB (converged), a closed-form GMP with 3520 real parameters reaches only -38.83 dB, and the fit residual is near-white with drive-independent power, placing the measurement noise floor at about -38.6 dB. - The APA half of the benchmark therefore rests on a surrogate that is ~7 dB short of that floor. Re-running the QR benchmarks against a converged surrogate moves MP's EVM from -32.70 to -47.41 dB and flips the MP/GMP ranking, which invalidates section 8's "10.6 dB EVM" conclusion. The DPA_160MHz half is unaffected; its surrogate was already at its floor. The dataset itself is clean: CSVs unchanged since 2025-07-09, no clipping or NaN, splits consistent, already time-aligned, and the closed-form ceiling is the same as DPA_160MHz. The -31.5 dB plateau comes from the training recipe -- the same GRU gains 7.4 dB from the OpenDPDv2 hyperparameters alone. Also adds backbones/tres_gru.py (TRes-DeltaGRU with the DeltaGRU cell replaced by a dense nn.GRU), verified numerically equivalent to tres_deltagru at thx=thh=0, and a runner for the fixed-parameter-budget PA backbone sweep. BENCHMARK_FIX_PLAN.md records the evidence and the remaining re-benchmark work. Co-Authored-By: Claude Opus 4.8 (1M context) --- arguments.py | 4 +- backbones/tres_gru.py | 85 ++++++++ benchmark/BENCHMARK_FIX_PLAN.md | 257 +++++++++++++++++++++++++ benchmark/benchmark_volterra_qr.py | 22 ++- benchmark/pa_modeling_investigation.md | 143 ++++++++++++++ benchmark/train_all_pa_2700.sh | 44 +++++ models.py | 7 + 7 files changed, 555 insertions(+), 7 deletions(-) create mode 100644 backbones/tres_gru.py create mode 100644 benchmark/BENCHMARK_FIX_PLAN.md create mode 100644 benchmark/pa_modeling_investigation.md create mode 100755 benchmark/train_all_pa_2700.sh diff --git a/arguments.py b/arguments.py index 08fe262..0e93248 100644 --- a/arguments.py +++ b/arguments.py @@ -53,7 +53,7 @@ def get_arguments(): # Power Amplifier Model Settings parser.add_argument('--PA_backbone', default='gru', choices=['gmp','deltagru', 'deltajanet', 'janet', 'fcn', 'gru', 'dgru', 'qgru', 'qgru_amp1', 'lstm', 'vdlstm', - 'rvtdcnn', 'mamba', 'tcn', 'pntdnn', 'pdgru', 'pgjanet', 'dvrjanet', 'bojanet', 'pnjanet', 'apnrnn', 'djanet', 'mcldnn'], + 'rvtdcnn', 'mamba', 'tcn', 'tres_deltagru', 'tres_gru', 'pntdnn', 'pdgru', 'pgjanet', 'dvrjanet', 'bojanet', 'pnjanet', 'apnrnn', 'djanet', 'mcldnn'], help='Modeling PA Recurrent layer type') parser.add_argument('--PA_hidden_size', default=23, type=int, help='Hidden size of PA backbone') @@ -62,7 +62,7 @@ def get_arguments(): # Digital Predistortion Model Settings parser.add_argument('--DPD_backbone', default='gru', choices=['gmp', 'deltagru', 'deltajanet', 'janet', 'snn', 'fcn', 'gru', 'dgru', 'qgru', 'qgru_amp1', 'lstm', 'vdlstm', - 'rvtdcnn', 'tres_deltagru', 'tcn', 'pntdnn', 'pdgru', 'pgjanet', 'dvrjanet', 'bojanet', 'pnjanet', 'djanet', 'mcldnn'], + 'rvtdcnn', 'tres_deltagru', 'tres_gru', 'tcn', 'pntdnn', 'pdgru', 'pgjanet', 'dvrjanet', 'bojanet', 'pnjanet', 'djanet', 'mcldnn'], help='DPD model Recurrent layer type') parser.add_argument('--DPD_hidden_size', default=15, type=int, help='Hidden size of DPD backbone.') parser.add_argument('--DPD_num_layers', default=1, type=int, help='Number of layers of the DPD backbone.') diff --git a/backbones/tres_gru.py b/backbones/tres_gru.py new file mode 100644 index 0000000..7c5c789 --- /dev/null +++ b/backbones/tres_gru.py @@ -0,0 +1,85 @@ +import torch +import torch.nn as nn + + +class TResGRU(nn.Module): + """TRes-DeltaGRU with the DeltaGRU cell replaced by a dense nn.GRU. + + Same 6-feature extraction and TCN residual skip path as TResDeltaGRU; + the recurrent core is a standard cuDNN GRU. Bias-free like the DeltaGRU + layer's x2h/h2h projections, so parameter counts match at equal hidden + size (e.g. H=10 -> 524 params). + """ + + def __init__(self, input_size, hidden_size, output_size, num_layers, bias=True): + super(TResGRU, self).__init__() + self.hidden_size = hidden_size + self.input_size = 6 + self.output_size = output_size + self.num_layers = num_layers + self.bias = bias + + self.rnn = nn.GRU(input_size=self.input_size, + hidden_size=self.hidden_size, + num_layers=num_layers, + batch_first=True, + bias=False) + self.fc_out = nn.Linear(in_features=self.hidden_size, + out_features=self.output_size, + bias=False) + self.tcn = nn.Sequential( + nn.Conv1d(in_channels=2, + out_channels=3, + kernel_size=3, + padding=16, + stride=1, + dilation=16, + bias=False), + nn.Hardswish(), + nn.Conv1d(in_channels=3, + out_channels=2, + kernel_size=1, + padding=0, + stride=1, + dilation=1, + bias=False), + nn.Hardswish(), + ) + + def reset_parameters(self): + for name, param in self.tcn.named_parameters(): + if 'weight' in name: + nn.init.xavier_uniform_(param) + if 'bias' in name: + nn.init.constant_(param, 0) + for name, param in self.rnn.named_parameters(): + num_gates = int(param.shape[0] / self.hidden_size) + if 'bias' in name: + nn.init.constant_(param, 0) + if 'weight_hh' in name: + for i in range(0, num_gates): + nn.init.orthogonal_(param[i * self.hidden_size:(i + 1) * self.hidden_size, :]) + if 'weight_ih' in name: + for i in range(0, num_gates): + nn.init.xavier_uniform_(param[i * self.hidden_size:(i + 1) * self.hidden_size, :]) + for name, param in self.fc_out.named_parameters(): + if 'weight' in name: + nn.init.xavier_uniform_(param) + if 'bias' in name: + nn.init.constant_(param, 0) + + def forward(self, x, h_0=None): + skip_x = self.tcn(x.transpose(1, 2)).transpose(1, 2) + last_step = torch.roll(x, shifts=-1, dims=1) + # Feature Extraction + i_x = x[..., 0].unsqueeze(-1) + q_x = x[..., 1].unsqueeze(-1) + i_last = last_step[..., 0].unsqueeze(-1) + q_last = last_step[..., 1].unsqueeze(-1) + amp2 = torch.pow(i_x, 2) + torch.pow(q_x, 2) + amp = torch.sqrt(amp2) + amp3 = torch.pow(amp, 3) + feat = torch.cat((i_x, q_x, amp, amp3, i_last, q_last), dim=-1) + out, _ = self.rnn(feat, h_0) + out = self.fc_out(out) + skip_x + return out diff --git a/benchmark/BENCHMARK_FIX_PLAN.md b/benchmark/BENCHMARK_FIX_PLAN.md new file mode 100644 index 0000000..56750d9 --- /dev/null +++ b/benchmark/BENCHMARK_FIX_PLAN.md @@ -0,0 +1,257 @@ +# Benchmark Verification & Fix Plan + +Status: **verification complete, fixes partially applied, re-benchmark pending.** + +This document records a full experimental re-run of `benchmark/benchmark_report.md`, +the defects that re-run exposed, and the concrete work needed to correct them. +Everything below was measured on this machine (RTX 4090 Laptop, `.venv`, +PyTorch 2.13.0+cu132, seed 0) unless stated otherwise. + +--- + +## 1. What was verified + +Every number in `benchmark_report.md` was re-run from the commands the report +itself gives. + +### 1.1 Reproduces correctly + +| Item | Reported | Reproduced | Delta | +|---|---|---|---| +| PA NMSE, DPA_160MHz | -38.43 | -38.44 / -38.80 (val/test) | exact | +| APA TRes-DeltaGRU DPD | -53.35 / -49.08 | -53.68 / -48.96 | ≤0.33 | +| APA GRU DPD | -52.61 / -47.50 | -52.10 / -47.33 | ≤0.51 | +| APA MP (QR) | -41.01 / -32.68 | -41.02 / -32.70 | ≤0.02 | +| APA GMP (QR) | -38.80 / -38.53 | -38.75 / -38.50 | ≤0.05 | +| DPA TRes-DeltaGRU DPD | -56.81 / -54.00 | -56.82 / -53.96 | ≤0.04 | +| DPA GMP (QR) | -54.02 / -51.08 | -54.02 / -51.07 | ≤0.01 | +| DPA GRU DPD | -51.93 / -49.97 | -51.92 / -50.02 | ≤0.05 | +| DPA MP (QR) | -51.29 / -50.26 | -51.29 / -50.26 | exact | +| Section 8 GMP-SGD-100ep | -29.67 / -44.46 | -29.63 / -44.46 | ≤0.04 | + +(ACLR_AVG / EVM in dB. QR rows required the GMP fix in §2.2. The 0.3-0.5 dB +spread on the two APA SGD rows is normal run-to-run variation: training runs +under soft determinism with `cudnn.benchmark=True`.) + +Also verified static: all parameter counts (1,911 / 2,066 / 519 / 524 / 500 / +510), dataset sizes, split ratios, sampling rates, `nperseg`, APA PAPR, and the +TRes-DeltaGRU architecture description. + +### 1.2 Does not reproduce + +| Item | Reported | Measured | Status | +|---|---|---|---| +| PA NMSE, APA_200MHz | **-43.52 dB** | **-31.48 dB** with the report's own recipe | wrong | +| DPA_160MHz PAPR | 10.38 dB | 10.13 dB (CCDF 0.001% and absolute peak) | wrong | +| §8 "10.6 dB EVM gap vs MP" | 10.6 | 16.4 vs MP; 10.6 is the gap vs GMP | misattributed | + +--- + +## 2. Defects found + +### 2.1 CRITICAL — `benchmark_report.md` §4 APA PA NMSE is unattainable + +The report claims -43.52 dB. Three independent lines of evidence say the true +figure for that configuration is ≈ -31.5 dB, and that -43.52 dB cannot be reached +by any model on this dataset: + +1. **Direct re-run.** GRU h23, 100 epochs, lr 5e-4 → -31.48 (val) / -31.40 + (test), fully converged (slope over the last 20 epochs: +0.0002 dB/epoch). +2. **Closed-form ceiling.** Least-squares GMP with 3,520 real parameters reaches + only -38.83 dB on the APA test split. Closed-form identification is immune to + optimizer problems, so this bounds what the data supports. +3. **The residual is measurement noise.** The GMP residual is near-white + (in-band vs out-of-band PSD tilt +3.9 dB, against +33.4 dB for the signal) + and its power is essentially independent of drive level — it stays at -48 dB + even where `|x| → 0`. Unmodeled distortion vanishes with the drive; additive + noise does not. **The measurement noise floor of APA_200MHz is ≈ -38.6 dB.** + +-43.52 dB is ~5 dB below that floor. Alternative sources were checked and +excluded: `APA_200MHz_b` (-36.25), the OpenDPDv2.sh recipe with DGRU h23 +(-39.13), the `DPA_200MHz` DGRU fixture (-31.0), and the OpenDPDv2 paper tables. + +### 2.2 CRITICAL — `benchmark_volterra_qr.py` never ran GMP + +`main()` called `build_mp_basis` unconditionally and never branched on +`args.model`, so `--model gmp` silently produced Memory Polynomial results. The +bug has been present since the script was first committed (`4924129`), meaning +the report's GMP rows cannot have come from the script as committed. + +**Fixed in this branch**: `main()` now selects the basis from `args.model`, and +the printed coefficient count follows the GMP hyperparameters. With the fix, both +GMP rows reproduce to ≤0.05 dB. + +### 2.3 HIGH — the whole APA benchmark rests on an undertrained surrogate + +The report's recipe (lr 5e-4, batch 256, 100 epochs, no LR schedule) leaves +recurrent PA models ~7 dB short of the noise floor. Re-running the QR benchmarks +with a converged surrogate (GRU h23 at -38.87 dB) changes the downstream numbers +substantially: + +| Model | Metric | Surrogate -31.4 dB | Surrogate -38.9 dB | Change | +|---|---|---|---|---| +| MP | ACLR_AVG | -41.02 | -41.81 | -0.8 | +| MP | EVM | -32.70 | **-47.41** | **-14.7** | +| GMP | ACLR_AVG | -38.75 | -39.58 | -0.8 | +| GMP | EVM | -38.50 | -34.89 | +3.6 | + +EVM moves by up to 14.7 dB and the MP-vs-GMP ranking flips. §8's claim that AI +beats the best traditional method by 10.6 dB in EVM on APA_200MHz does not +survive: MP alone reaches -47.41 dB EVM against a converged surrogate. The +DPA_160MHz half of the report is unaffected — its surrogate (-38.43 dB) was +already at its noise floor, which is exactly why that half reproduces cleanly. + +### 2.4 MEDIUM — reproduction commands are wrong or incomplete + +- §9 QR commands omit the `benchmark/` path prefix; as written they fail. +- §5.1 advertises "aggressive temporal sparsity" but §9's commands leave + `--thx/--thh` at their 0.0 defaults, i.e. a dense DeltaGRU is trained. +- `frame_length=200` / `frame_stride=1` are silent defaults, never stated. + +### 2.5 LOW — metric semantics are non-standard and undocumented + +- `EVM` is a spectral magnitude MAE (per-bin `mean|S_pred - S_gt| / mean|S_gt|`), + not a 3GPP constellation EVM: no demodulation, equalization, or symbol + decisions. Not comparable to instrument EVM. +- `ACLR` normalizes by the **maximum single sub-channel** power, not total + main-channel power. +- `NMSE` averages per-segment dB values rather than taking dB of the pooled + ratio. +- All Section 7 numbers are **simulated through the frozen PA surrogate**, not + measured on hardware, and therefore inherit the surrogate's error. + +These are self-consistent within the benchmark (all models scored identically), +but the report should say so. + +### 2.6 LOW — checkpoints collide across recipes + +`model_id` encodes only backbone / hidden size / frame length / parameter count. +Re-running the same architecture under a different recipe silently overwrites +`save//train_pa/.pt` and its logs. The GRU h23 checkpoint on +disk is now the OpenDPDv2-recipe model (-38.87 dB), not the report-recipe one. + +--- + +## 3. Dataset audit: APA_200MHz is clean + +The low NMSE is **not** a data defect. + +- **Git history**: only 4 commits ever touched `datasets/APA_200MHz/`; the CSVs + have been bit-identical since 2025-07-09 and every later commit changed only + `spec.json` metadata and plots. `fs`, `nperseg` and split ratios never changed. +- **Signal QA**: no NaN/Inf, no duplicate rows, no clipping (only the 1-2 samples + defining the normalization peak), DC offset < 1e-3, AM/AM relative spread + 0.045 (DPA_160MHz: 0.048). +- **Splits**: consistent power and complex gain across train/val/test (1.1626 / + 1.1628 / 1.1636); no discontinuity at the boundaries. +- **Alignment**: already time-aligned (peak lag 0, normalized cross-correlation + 0.9945). +- **Provenance**: R&S SMW200A → 3.5 GHz Ampleon GaN Doherty PA → Keysight N9042B + IQ capture (`Matlab/` scripts). +- **A vs B**: `APA_200MHz` and `APA_200MHz_b` have **byte-identical inputs** and + different output captures. Commit `2c7f91a` swapped the two output sets four + hours after release and renamed `APA_200MHz_backup` → `APA_200MHz_b`, so the + current primary dataset is the capture the OpenDPDv2 paper reported on. NMSE + between the two captures is -23.75 dB; since each is individually modelable to + -38.5 dB, that gap is a systematic operating-point difference, not noise. + +The closed-form ceiling is essentially the same for all three datasets +(APA -38.83, APA_b -38.48, DPA -39.51), so APA_200MHz is not a worse dataset. + +--- + +## 4. Root cause of the -31.5 dB plateau: the training recipe + +Backbone comparison at ~1,900 parameters on APA_200MHz (val / test NMSE, dB): + +| Backbone | Params | Report recipe
lr 5e-4, bs 256, 100 ep | OpenDPDv2 recipe
lr 5e-3, bs 64, 240 ep, ReduceLROnPlateau | +|---|---|---|---| +| GRU h23 | 1,911 | -31.48 / -31.40 | **-38.79 / -38.87** | +| DGRU h23 | 2,751 | -31.67 / -31.61 | **-39.13 / -39.21** | +| TCN h66 | 1,914 | -37.44 / -37.30 | -38.38 / -38.25 | +| TRes-GRU h22 | 1,916 | -35.62 / -35.50 | **-38.94 / -39.01** | + +The same GRU gains 7.4 dB from the recipe alone. Recurrent cells need the higher +learning rate and the schedule; the convolutional TCN is far less sensitive, +which is why it already reaches -37.4 dB under the weak recipe. With a proper +recipe all four architectures land within 0.9 dB of each other, because they are +all at the -38.6 dB noise floor — architecture ranking at that point is not +meaningful. + +--- + +## 5. Fix plan + +### 5.1 Applied in this branch + +- [x] `benchmark/benchmark_volterra_qr.py`: branch on `--model`, so GMP actually + builds the GMP basis (§2.2). +- [x] `backbones/tres_gru.py`: new TRes-GRU backbone (TRes-DeltaGRU with the + DeltaGRU cell replaced by a dense `nn.GRU`, bias-free so parameter counts + match at equal hidden size). Registered in `models.py` and in both backbone + choice lists in `arguments.py`. Validated numerically equivalent to + `tres_deltagru` at `thx=thh=0` (max abs diff 8.2e-7, relative L2 1.3e-6). +- [x] `benchmark/pa_modeling_investigation.md`: full write-up of the PA-modeling + investigation. +- [x] This plan. + +### 5.2 Pending — re-benchmark (the substantive work) + +1. **Retrain the APA_200MHz PA surrogate with the OpenDPDv2 recipe.** + ```bash + python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 \ + --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 --decay_factor 0.5 --patience 10 --batch_size 64 + ``` + Expected ≈ -38.8 dB. Do the same for DPA_160MHz for consistency (it is already + at its floor, so little change is expected). + +2. **Re-run all four APA DPD rows** (GRU h11, TRes-DeltaGRU h10, MP/QR, GMP/QR) + against the retrained surrogate. Section 7's APA table and every Section 8 + conclusion derived from it must be recomputed — the EVM column in particular. + +3. **Correct the report text**: §4 APA PA NMSE, §9 command paths, DPA PAPR + (10.13 dB), the §8 EVM attribution, and add a note that Section 7 metrics are + surrogate-simulated with the non-standard definitions listed in §2.5. + +4. **Guard against regression**: make `model_id` encode the recipe (or at least + warn on overwrite), and add a smoke test that `--model gmp` produces a + different coefficient count than `--model mp`. + +### 5.3 Pending — PA backbone comparison at a fixed budget (started, then stopped) + +A sweep of every viable PA backbone at ~2,700 parameters under the OpenDPDv2 +recipe was launched and **deliberately stopped before completion** at the user's +request. Hidden sizes were already calibrated: + +| Backbone | h | Params | | Backbone | h | Params | +|---|---|---|---|---|---|---| +| GRU | 28 | 2,746 | | vDLSTM | 22 | 2,666 | +| DGRU | 23 | 2,751 | | DeltaGRU | 26 | 2,706 | +| TCN | 93 | 2,697 | | DeltaJANET | 32 | 2,626 | +| TRes-GRU | 27 | 2,751 | | PG-JANET | 19 | 2,719 | +| LSTM | 24 | 2,738 | | DVR-JANET | 19 | 2,665 | +| RVTDCNN | 68 | 2,684 | | QGRU | 27 | 2,729 | +| MCLDNN | 11 | 2,679 | | APNRRU | 34 | 2,723 | + +Excluded: `gmp` (fixed at 495 params), `bojanet` (caps at 1,346; breaks above +h=18), `apnrnn` (not implemented in `models.py`). `tres_deltagru` is redundant +with `tres_gru` at zero thresholds. + +Partial results before the stop (best val NMSE at the epoch reached, not final): + +| Backbone | Params | Epochs run | Best val NMSE | +|---|---|---|---| +| LSTM h24 | 2,738 | 126/240 | -38.88 | +| GRU h28 | 2,746 | 126/240 | -38.85 | +| TCN h93 | 2,697 | 213/240 | -38.54 | +| MCLDNN h11 | 2,679 | 76/240 | -31.77 | +| RVTDCNN h68 | 2,684 | 240/240 | -31.13 | +| TRes-GRU h27 | 2,751 | 1/240 | -30.72 | + +The runner script used is `benchmark/train_all_pa_2700.sh`. Resume by re-running +it; models already finished can be dropped from the `JOBS` list. + +Note that at ~2,700 parameters the well-behaved architectures are already pinned +at the -38.6 dB noise floor, so this sweep characterizes *which architectures +reach the floor and how fast*, not which is most accurate. RVTDCNN and MCLDNN +stalling near -31 dB is the interesting signal and worth a separate look. diff --git a/benchmark/benchmark_volterra_qr.py b/benchmark/benchmark_volterra_qr.py index 0681789..c3f9c6e 100644 --- a/benchmark/benchmark_volterra_qr.py +++ b/benchmark/benchmark_volterra_qr.py @@ -230,9 +230,21 @@ def main(): # Step 1: Identify DPD via ILA + QR # ----------------------------------------------------------------------- K, Q = args.K, args.Q - n_complex_coeffs = K * Q + if args.model == 'gmp': + def build_basis(x): + return build_gmp_basis(x, args.Ka, args.La, args.Kb, args.Lb, + args.Mb, args.Kc, args.Lc, args.Mc) + n_complex_coeffs = (args.Ka * args.La + args.Kb * args.Lb * args.Mb + + args.Kc * args.Lc * args.Mc) + print(f"\n=== Volterra GMP DPD via QR (Ka={args.Ka}, La={args.La}, " + f"Kb={args.Kb}, Lb={args.Lb}, Mb={args.Mb}, " + f"Kc={args.Kc}, Lc={args.Lc}, Mc={args.Mc}) ===") + else: + def build_basis(x): + return build_mp_basis(x, K, Q) + n_complex_coeffs = K * Q + print(f"\n=== Volterra MP DPD via QR (K={K}, Q={Q}) ===") n_real_params = 2 * n_complex_coeffs - print(f"\n=== Volterra MP DPD via QR (K={K}, Q={Q}) ===") print(f"Complex coefficients : {n_complex_coeffs}") print(f"Real parameters : {n_real_params}") @@ -244,7 +256,7 @@ def main(): z_norm = z_train_c / target_gain # Build basis from normalised PA output (postdistorter input) - Phi_train = build_mp_basis(z_norm, K, Q) + Phi_train = build_basis(z_norm) # Solve x_train = Phi_train * w via least squares (uses QR/SVD internally) w, residuals, rank, sv = np.linalg.lstsq(Phi_train, x_train_c, rcond=None) @@ -256,7 +268,7 @@ def main(): x_test_c = iq_to_complex(X_test) # Build basis from test input (predistorter) - Phi_test = build_mp_basis(x_test_c, K, Q) + Phi_test = build_basis(x_test_c) x_dpd_c = Phi_test @ w # predistorted signal x_dpd_iq = complex_to_iq(x_dpd_c) @@ -285,7 +297,7 @@ def main(): # Also run on val set x_val_c = iq_to_complex(X_val) - Phi_val = build_mp_basis(x_val_c, K, Q) + Phi_val = build_basis(x_val_c) x_dpd_val_c = Phi_val @ w x_dpd_val_iq = complex_to_iq(x_dpd_val_c) pa_val_iq = run_pa_model(x_dpd_val_iq, pa_net, device, nperseg) diff --git a/benchmark/pa_modeling_investigation.md b/benchmark/pa_modeling_investigation.md new file mode 100644 index 0000000..4574a19 --- /dev/null +++ b/benchmark/pa_modeling_investigation.md @@ -0,0 +1,143 @@ +# APA_200MHz PA-Modeling NMSE Investigation + +Question: why does PA modeling on `APA_200MHz` saturate at ~-31.5 dB NMSE, and is +the `benchmark_report.md` figure of -43.52 dB attainable? + +Answer: **-31.5 dB is an undertrained-recipe artifact, not a data defect.** The +dataset's measurement noise floor is ~-38.6 dB, every architecture reaches it once +trained with the OpenDPDv2 recipe, and -43.52 dB lies *below* the noise floor, so +it is not physically attainable on this dataset. + +## 1. Backbone comparison on APA_200MHz (PA modeling, val/test NMSE in dB) + +All models sized to ~1900 parameters for a fair comparison. + +| Backbone | Params | Report recipe
(lr 5e-4, bs 256, 100 ep, no sched) | OpenDPDv2 recipe
(lr 5e-3, bs 64, 240 ep, ReduceLROnPlateau) | +|---|---|---|---| +| GRU (h23) | 1,911 | -31.48 / -31.40 | **-38.79 / -38.87** | +| DGRU (h23) | 2,751 | -31.67 / -31.61 | **-39.13 / -39.21** | +| TCN (h66) | 1,914 | -37.44 / -37.30 | **-38.38 / -38.25** | +| TRes-GRU (h22) | 1,916 | -35.62 / -35.50 | **-38.94 / -39.01** | + +Two observations: + +1. **The recipe, not the architecture, causes the -31.5 dB plateau.** The same GRU + improves by 7.4 dB purely from the training recipe. Recurrent models are the + most sensitive: with lr=5e-4 and no schedule they are badly undertrained at + 100 epochs, while the convolutional TCN already reaches -37.4 dB. +2. **With a proper recipe all four architectures land within 0.9 dB of each other** + (-38.25 to -39.21), because they are all at the data's noise floor. Ranking + there is not architecturally meaningful. + +## 2. Data ceiling: closed-form least-squares fits + +Closed-form identification is immune to optimizer/learning-rate issues, so it +measures what the *data* supports. Basis built on the contiguous signal, fit on +the train split, evaluated on the test split (64-sample guard). + +| Model | Real params | APA_200MHz | APA_200MHz_b | DPA_160MHz | +|---|---|---|---|---| +| MP K=5 Q=10 | 100 | -31.94 | -32.81 | -38.14 | +| MP K=7 Q=20 | 280 | -37.13 | -33.24 | -38.48 | +| MP K=9 Q=40 | 720 | -37.22 | -33.23 | -38.50 | +| GMP rich | 1,180 | -38.54 | -38.42 | -39.48 | +| GMP extra-rich | 3,520 | **-38.83** | **-38.48** | **-39.51** | + +All three datasets saturate in the same place, -38.5 to -39.5 dB. APA_200MHz is +**not** a worse dataset than DPA_160MHz. + +Note the damning comparison: a 100-real-parameter memory polynomial (-31.94 dB) +matches the 1,911-parameter GRU trained with the report recipe (-31.40 dB). + +## 3. The residual is measurement noise, not unmodeled distortion + +Residual of the GMP-rich fit, analyzed spectrally and against input amplitude: + +| Quantity | APA_200MHz | DPA_160MHz | +|---|---|---| +| Residual NMSE | -38.64 dB | -39.48 dB | +| Residual PSD in-band vs out-of-band | +3.9 dB | +4.8 dB | +| *Signal* PSD in-band vs out-of-band | +33.4 dB | +37.8 dB | +| Residual power at \|x\|→0 | -48.0 dB | -41.4 dB | +| Residual power at \|x\|→max | -44.5 dB | -37.3 dB | + +The residual is nearly white (+3.9 dB in/out-of-band tilt, against +33.4 dB for +the signal) and **its power barely depends on input amplitude — it stays at +-48 dB even where the input goes to zero.** Unmodeled nonlinear distortion must +vanish as the drive vanishes; additive measurement noise does not. So ~-38.6 dB +is the noise floor of this capture. + +**Consequence: -43.52 dB is ~5 dB below the measurement noise floor and cannot be +achieved on APA_200MHz by any model.** This independently confirms it is a +misrecorded figure. + +## 4. Dataset integrity: no defects found + +- **Git history**: only 4 commits ever touched `datasets/APA_200MHz/`. The CSVs + have been bit-identical since 2025-07-09; every later commit changed only + `spec.json` metadata and plots. `fs`, `nperseg` and split ratios never changed. +- **Provenance**: R&S SMW200A generator -> 3.5 GHz Ampleon GaN Doherty PA -> + Keysight N9042B IQ capture (`Matlab/` scripts). +- **A vs B**: `APA_200MHz` and `APA_200MHz_b` have **byte-identical inputs** and + different output captures. Commit `2c7f91a` swapped the two output sets four + hours after the initial release and renamed `APA_200MHz_backup` to + `APA_200MHz_b`, so today's primary dataset is the capture the OpenDPDv2 paper + numbers were reported on. +- **Signal QA** (all three datasets): no NaN/Inf, no duplicate rows, no clipping + (only the 1-2 samples defining the normalization peak), DC offset < 1e-3, + AM/AM relative spread 0.045-0.049 (APA is in line with DPA). +- **Splits**: power and complex gain are consistent across train/val/test + (APA gain 1.1626 / 1.1628 / 1.1636); no discontinuity at split boundaries. +- **Alignment**: input/output are already time-aligned (peak lag 0, normalized + cross-correlation 0.9945). +- **Measurement A vs B**: NMSE between the two output captures of the same + stimulus is -23.75 dB (gain 0.98987, phase +0.309 deg). Since each capture is + individually modelable to -38.5 dB, this gap is a systematic operating-point + difference between the two measurements, not random capture noise. + +## 5. Downstream impact: the benchmark rests on an undertrained surrogate + +Re-running the QR benchmarks on APA_200MHz with the *same* code but a properly +trained PA surrogate (GRU h23 at -38.87 dB instead of -31.40 dB): + +| Model | Metric | Surrogate at -31.4 dB
(as in benchmark_report) | Surrogate at -38.9 dB | Change | +|---|---|---|---|---| +| MP (QR) | ACLR_AVG | -41.02 | -41.81 | -0.8 | +| MP (QR) | EVM | -32.70 | **-47.41** | **-14.7** | +| GMP (QR) | ACLR_AVG | -38.75 | -39.58 | -0.8 | +| GMP (QR) | EVM | -38.50 | **-34.89** | **+3.6** | + +EVM moves by up to 14.7 dB and the MP-vs-GMP ranking flips (MP now wins on both +metrics). `benchmark_report.md` Section 8 concludes that AI beats the best +traditional method by "10.6 dB in EVM" on APA_200MHz; with a converged surrogate +MP alone reaches -47.41 dB EVM, so that conclusion does not survive. The +DPA_160MHz surrogate was converged (-38.43 dB, at its noise floor), which is why +that half of the report reproduces cleanly. + +## 6. New backbone: TRes-GRU + +`backbones/tres_gru.py` is TRes-DeltaGRU with the DeltaGRU cell replaced by a +dense `nn.GRU` (bias-free, so parameter counts match at equal hidden size: h=10 +gives 524 params, identical to TRes-DeltaGRU). Registered as `tres_gru` in +`models.py` and in both backbone choice lists in `arguments.py`. + +Validated: with `thx=thh=0`, TRes-DeltaGRU is mathematically a dense GRU, and the +two implementations agree numerically (max abs difference 8.2e-7, relative L2 +1.3e-6) after weight transfer. + +## Reproducing + +```bash +# Report recipe +python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone tcn --PA_hidden_size 66 --n_epochs 100 + +# OpenDPDv2 recipe +python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 \ + --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 --decay_factor 0.5 --patience 10 --batch_size 64 +``` + +Caution: the checkpoint/log path (`model_id`) does not encode the learning rate, +batch size or epoch count, so re-running the same architecture with a different +recipe silently overwrites `save//train_pa/.pt` and its logs. +The checkpoint currently on disk for `PA_S_0_M_GRU_H_23_F_200_P_1911` is the +OpenDPDv2-recipe model (-38.87 dB), not the report-recipe one. diff --git a/benchmark/train_all_pa_2700.sh b/benchmark/train_all_pa_2700.sh new file mode 100755 index 0000000..b34347b --- /dev/null +++ b/benchmark/train_all_pa_2700.sh @@ -0,0 +1,44 @@ +#!/usr/bin/env bash +# Train every viable PA backbone at ~2700 params with the OpenDPDv2 recipe. +set -u +cd "$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +LOGDIR=${LOGDIR:-/tmp/opendpd_pa2700_logs} +mkdir -p "$LOGDIR" +DATASET=${DATASET:-APA_200MHz} +PYTHON=${PYTHON:-python} +export LOGDIR DATASET PYTHON + +# backbone:hidden_size (fast/cheap first so results land early) +JOBS=( + tcn:93 + rvtdcnn:68 + mcldnn:11 + gru:28 + lstm:24 + tres_gru:27 + dgru:23 + vdlstm:22 + qgru:27 + pgjanet:19 + dvrjanet:19 + deltajanet:32 + apnrru:34 + deltagru:26 +) + +run_one() { + local spec="$1" + local bb="${spec%%:*}" + local h="${spec##*:}" + local log="$LOGDIR/${DATASET}_${bb}_h${h}.log" + echo "[START] $bb h=$h" + "${PYTHON:-python}" main.py --step train_pa \ + --dataset_name "$DATASET" --PA_backbone "$bb" --PA_hidden_size "$h" \ + --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 \ + --decay_factor 0.5 --patience 10 --batch_size 64 > "$log" 2>&1 + echo "[DONE ] $bb h=$h exit=$?" +} +export -f run_one + +printf '%s\n' "${JOBS[@]}" | xargs -P "${CONC:-5}" -I{} bash -c 'run_one "$@"' _ {} +echo "ALL TRAINING COMPLETE" diff --git a/models.py b/models.py index f1e2ad4..a53bcd4 100644 --- a/models.py +++ b/models.py @@ -126,6 +126,13 @@ def __init__(self, input_size, hidden_size, num_layers, backbone_type, window_si thx=self.thx, thh=self.thh, bias=self.bias) + elif backbone_type == 'tres_gru': + from backbones.tres_gru import TResGRU + self.backbone = TResGRU(input_size=6, + hidden_size=self.hidden_size, + output_size=self.output_size, + num_layers=self.num_layers, + bias=self.bias) elif backbone_type == 'tcn': from backbones.tcn import TCN self.backbone = TCN(hidden_channels=self.hidden_size) From 261b335e84542ba89601e40b17d139af032997bb Mon Sep 17 00:00:00 2001 From: Chang Gao Date: Sun, 26 Jul 2026 23:26:06 +0200 Subject: [PATCH 3/3] Rebuild benchmark with reproducible PA and DPD results --- .gitignore | 3 +- arguments.py | 19 +- benchmark/BENCHMARK_FIX_PLAN.md | 257 - 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modules/paths.py | 63 +- modules/train_funcs.py | 9 +- opendpd/api.py | 12 +- project.py | 22 +- steps/run_dpd.py | 4 +- steps/train_dpd.py | 4 +- steps/train_pa.py | 4 +- tests/test_api.py | 64 + tests/test_benchmark_regressions.py | 985 ++++ tests/test_cli.py | 21 + tests/test_metric_configuration.py | 37 + tests/test_tres_deltagru_optimized.py | 25 + 60 files changed, 7978 insertions(+), 11018 deletions(-) delete mode 100644 benchmark/BENCHMARK_FIX_PLAN.md create mode 100644 benchmark/benchmark_delta_dpd_results.png delete mode 100644 benchmark/benchmark_end_to_end.py create mode 100644 benchmark/benchmark_results.png delete mode 100644 benchmark/benchmark_training_speed.py delete mode 100644 benchmark/benchmark_training_speed_report.md delete mode 100644 benchmark/benchmark_tres_deltagru.py create mode 100644 benchmark/benchmark_volterra.py delete mode 100644 benchmark/benchmark_volterra_qr.py create mode 100644 benchmark/collect_benchmark_report.py delete mode 100644 benchmark/pa_modeling_investigation.md delete mode 100644 benchmark/report/artifact.json create mode 100755 benchmark/reproduce_benchmark_report.sh create mode 100644 benchmark/results/benchmark_report_results.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_eager.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_fused.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_comparison_graph.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_eager_optimized/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_fused_optimized/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_graph_optimized/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride16_original/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_original/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride16_original/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride1_comparison_graph.json delete mode 100644 benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_optimized/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_graph_repeat/result.json delete mode 100644 benchmark/results/e2e_dpa200_stride1_original/batch_orders.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_original/outputs.npz delete mode 100644 benchmark/results/e2e_dpa200_stride1_original/result.json delete mode 100644 benchmark/results/end_to_end_summary.json delete mode 100644 benchmark/results/tres_deltagru_rtx4090_laptop.json delete mode 100755 benchmark/train_all_pa_2700.sh delete mode 100644 benchmark/tres_deltagru_end_to_end_report.html delete mode 100644 benchmark/tres_deltagru_training_speed.md create mode 100644 tests/test_benchmark_regressions.py create mode 100644 tests/test_metric_configuration.py diff --git a/.gitignore b/.gitignore index 1c4c122..7902802 100644 --- a/.gitignore +++ b/.gitignore @@ -47,6 +47,7 @@ opendpd_env/ dpd_out/ log/ save/ +benchmark/results/reproduced/ # Allow sample data but not large datasets datasets/**/data/*.csv @@ -94,4 +95,4 @@ steps/__pycache__/ PACKAGE_INSTALLATION.md PyPI.md -adc/* \ No newline at end of file +adc/* diff --git a/arguments.py b/arguments.py index 0e93248..3dc2fe2 100644 --- a/arguments.py +++ b/arguments.py @@ -31,14 +31,17 @@ def get_arguments(): parser.add_argument('--seed', default=0, type=int, help='Global random number seed.') parser.add_argument('--loss_type', default='l2', choices=['l1', 'l2'], help='Type of loss function.') parser.add_argument('--opt_type', default='adamw', choices=['sgd', 'adam', 'adamw', 'adabound', 'rmsprop'], help='Type of optimizer.') - parser.add_argument('--batch_size', default=256, type=int, help='Batch size for training.') - parser.add_argument('--batch_size_eval', default=256, type=int, help='Batch size for evaluation.') - parser.add_argument('--n_epochs', default=100, type=int, help='Number of epochs to train for.') - parser.add_argument('--lr_schedule', default=0, type=int, help='Whether enable learning rate scheduling') - parser.add_argument('--lr', default=5e-4, type=float, help='Learning rate') - parser.add_argument('--lr_end', default=1e-4, type=float, help='Learning rate') - parser.add_argument('--decay_factor', default=0.1, type=float, help='Learning rate') - parser.add_argument('--patience', default=10, type=float, help='Learning rate') + parser.add_argument('--batch_size', default=64, type=int, help='Batch size for training.') + parser.add_argument('--batch_size_eval', default=64, type=int, help='Batch size for evaluation.') + parser.add_argument('--n_epochs', default=300, type=int, help='Number of epochs to train for.') + parser.add_argument('--lr_schedule', default=1, type=int, + help='Whether to enable ReduceLROnPlateau learning-rate scheduling.') + parser.add_argument('--lr', default=5e-3, type=float, help='Initial learning rate.') + parser.add_argument('--lr_end', default=5e-5, type=float, help='Minimum learning rate.') + parser.add_argument('--decay_factor', default=0.5, type=float, + help='Learning-rate reduction factor.') + parser.add_argument('--patience', default=5, type=int, + help='Scheduler patience in epochs.') parser.add_argument('--grad_clip_val', default=200, type=float, help='Gradient clipping.') parser.add_argument( '--cuda_graph_training', action='store_true', default=False, diff --git a/benchmark/BENCHMARK_FIX_PLAN.md b/benchmark/BENCHMARK_FIX_PLAN.md deleted file mode 100644 index 56750d9..0000000 --- a/benchmark/BENCHMARK_FIX_PLAN.md +++ /dev/null @@ -1,257 +0,0 @@ -# Benchmark Verification & Fix Plan - -Status: **verification complete, fixes partially applied, re-benchmark pending.** - -This document records a full experimental re-run of `benchmark/benchmark_report.md`, -the defects that re-run exposed, and the concrete work needed to correct them. -Everything below was measured on this machine (RTX 4090 Laptop, `.venv`, -PyTorch 2.13.0+cu132, seed 0) unless stated otherwise. - ---- - -## 1. What was verified - -Every number in `benchmark_report.md` was re-run from the commands the report -itself gives. - -### 1.1 Reproduces correctly - -| Item | Reported | Reproduced | Delta | -|---|---|---|---| -| PA NMSE, DPA_160MHz | -38.43 | -38.44 / -38.80 (val/test) | exact | -| APA TRes-DeltaGRU DPD | -53.35 / -49.08 | -53.68 / -48.96 | ≤0.33 | -| APA GRU DPD | -52.61 / -47.50 | -52.10 / -47.33 | ≤0.51 | -| APA MP (QR) | -41.01 / -32.68 | -41.02 / -32.70 | ≤0.02 | -| APA GMP (QR) | -38.80 / -38.53 | -38.75 / -38.50 | ≤0.05 | -| DPA TRes-DeltaGRU DPD | -56.81 / -54.00 | -56.82 / -53.96 | ≤0.04 | -| DPA GMP (QR) | -54.02 / -51.08 | -54.02 / -51.07 | ≤0.01 | -| DPA GRU DPD | -51.93 / -49.97 | -51.92 / -50.02 | ≤0.05 | -| DPA MP (QR) | -51.29 / -50.26 | -51.29 / -50.26 | exact | -| Section 8 GMP-SGD-100ep | -29.67 / -44.46 | -29.63 / -44.46 | ≤0.04 | - -(ACLR_AVG / EVM in dB. QR rows required the GMP fix in §2.2. The 0.3-0.5 dB -spread on the two APA SGD rows is normal run-to-run variation: training runs -under soft determinism with `cudnn.benchmark=True`.) - -Also verified static: all parameter counts (1,911 / 2,066 / 519 / 524 / 500 / -510), dataset sizes, split ratios, sampling rates, `nperseg`, APA PAPR, and the -TRes-DeltaGRU architecture description. - -### 1.2 Does not reproduce - -| Item | Reported | Measured | Status | -|---|---|---|---| -| PA NMSE, APA_200MHz | **-43.52 dB** | **-31.48 dB** with the report's own recipe | wrong | -| DPA_160MHz PAPR | 10.38 dB | 10.13 dB (CCDF 0.001% and absolute peak) | wrong | -| §8 "10.6 dB EVM gap vs MP" | 10.6 | 16.4 vs MP; 10.6 is the gap vs GMP | misattributed | - ---- - -## 2. Defects found - -### 2.1 CRITICAL — `benchmark_report.md` §4 APA PA NMSE is unattainable - -The report claims -43.52 dB. Three independent lines of evidence say the true -figure for that configuration is ≈ -31.5 dB, and that -43.52 dB cannot be reached -by any model on this dataset: - -1. **Direct re-run.** GRU h23, 100 epochs, lr 5e-4 → -31.48 (val) / -31.40 - (test), fully converged (slope over the last 20 epochs: +0.0002 dB/epoch). -2. **Closed-form ceiling.** Least-squares GMP with 3,520 real parameters reaches - only -38.83 dB on the APA test split. Closed-form identification is immune to - optimizer problems, so this bounds what the data supports. -3. **The residual is measurement noise.** The GMP residual is near-white - (in-band vs out-of-band PSD tilt +3.9 dB, against +33.4 dB for the signal) - and its power is essentially independent of drive level — it stays at -48 dB - even where `|x| → 0`. Unmodeled distortion vanishes with the drive; additive - noise does not. **The measurement noise floor of APA_200MHz is ≈ -38.6 dB.** - --43.52 dB is ~5 dB below that floor. Alternative sources were checked and -excluded: `APA_200MHz_b` (-36.25), the OpenDPDv2.sh recipe with DGRU h23 -(-39.13), the `DPA_200MHz` DGRU fixture (-31.0), and the OpenDPDv2 paper tables. - -### 2.2 CRITICAL — `benchmark_volterra_qr.py` never ran GMP - -`main()` called `build_mp_basis` unconditionally and never branched on -`args.model`, so `--model gmp` silently produced Memory Polynomial results. The -bug has been present since the script was first committed (`4924129`), meaning -the report's GMP rows cannot have come from the script as committed. - -**Fixed in this branch**: `main()` now selects the basis from `args.model`, and -the printed coefficient count follows the GMP hyperparameters. With the fix, both -GMP rows reproduce to ≤0.05 dB. - -### 2.3 HIGH — the whole APA benchmark rests on an undertrained surrogate - -The report's recipe (lr 5e-4, batch 256, 100 epochs, no LR schedule) leaves -recurrent PA models ~7 dB short of the noise floor. Re-running the QR benchmarks -with a converged surrogate (GRU h23 at -38.87 dB) changes the downstream numbers -substantially: - -| Model | Metric | Surrogate -31.4 dB | Surrogate -38.9 dB | Change | -|---|---|---|---|---| -| MP | ACLR_AVG | -41.02 | -41.81 | -0.8 | -| MP | EVM | -32.70 | **-47.41** | **-14.7** | -| GMP | ACLR_AVG | -38.75 | -39.58 | -0.8 | -| GMP | EVM | -38.50 | -34.89 | +3.6 | - -EVM moves by up to 14.7 dB and the MP-vs-GMP ranking flips. §8's claim that AI -beats the best traditional method by 10.6 dB in EVM on APA_200MHz does not -survive: MP alone reaches -47.41 dB EVM against a converged surrogate. The -DPA_160MHz half of the report is unaffected — its surrogate (-38.43 dB) was -already at its noise floor, which is exactly why that half reproduces cleanly. - -### 2.4 MEDIUM — reproduction commands are wrong or incomplete - -- §9 QR commands omit the `benchmark/` path prefix; as written they fail. -- §5.1 advertises "aggressive temporal sparsity" but §9's commands leave - `--thx/--thh` at their 0.0 defaults, i.e. a dense DeltaGRU is trained. -- `frame_length=200` / `frame_stride=1` are silent defaults, never stated. - -### 2.5 LOW — metric semantics are non-standard and undocumented - -- `EVM` is a spectral magnitude MAE (per-bin `mean|S_pred - S_gt| / mean|S_gt|`), - not a 3GPP constellation EVM: no demodulation, equalization, or symbol - decisions. Not comparable to instrument EVM. -- `ACLR` normalizes by the **maximum single sub-channel** power, not total - main-channel power. -- `NMSE` averages per-segment dB values rather than taking dB of the pooled - ratio. -- All Section 7 numbers are **simulated through the frozen PA surrogate**, not - measured on hardware, and therefore inherit the surrogate's error. - -These are self-consistent within the benchmark (all models scored identically), -but the report should say so. - -### 2.6 LOW — checkpoints collide across recipes - -`model_id` encodes only backbone / hidden size / frame length / parameter count. -Re-running the same architecture under a different recipe silently overwrites -`save//train_pa/.pt` and its logs. The GRU h23 checkpoint on -disk is now the OpenDPDv2-recipe model (-38.87 dB), not the report-recipe one. - ---- - -## 3. Dataset audit: APA_200MHz is clean - -The low NMSE is **not** a data defect. - -- **Git history**: only 4 commits ever touched `datasets/APA_200MHz/`; the CSVs - have been bit-identical since 2025-07-09 and every later commit changed only - `spec.json` metadata and plots. `fs`, `nperseg` and split ratios never changed. -- **Signal QA**: no NaN/Inf, no duplicate rows, no clipping (only the 1-2 samples - defining the normalization peak), DC offset < 1e-3, AM/AM relative spread - 0.045 (DPA_160MHz: 0.048). -- **Splits**: consistent power and complex gain across train/val/test (1.1626 / - 1.1628 / 1.1636); no discontinuity at the boundaries. -- **Alignment**: already time-aligned (peak lag 0, normalized cross-correlation - 0.9945). -- **Provenance**: R&S SMW200A → 3.5 GHz Ampleon GaN Doherty PA → Keysight N9042B - IQ capture (`Matlab/` scripts). -- **A vs B**: `APA_200MHz` and `APA_200MHz_b` have **byte-identical inputs** and - different output captures. Commit `2c7f91a` swapped the two output sets four - hours after release and renamed `APA_200MHz_backup` → `APA_200MHz_b`, so the - current primary dataset is the capture the OpenDPDv2 paper reported on. NMSE - between the two captures is -23.75 dB; since each is individually modelable to - -38.5 dB, that gap is a systematic operating-point difference, not noise. - -The closed-form ceiling is essentially the same for all three datasets -(APA -38.83, APA_b -38.48, DPA -39.51), so APA_200MHz is not a worse dataset. - ---- - -## 4. Root cause of the -31.5 dB plateau: the training recipe - -Backbone comparison at ~1,900 parameters on APA_200MHz (val / test NMSE, dB): - -| Backbone | Params | Report recipe
lr 5e-4, bs 256, 100 ep | OpenDPDv2 recipe
lr 5e-3, bs 64, 240 ep, ReduceLROnPlateau | -|---|---|---|---| -| GRU h23 | 1,911 | -31.48 / -31.40 | **-38.79 / -38.87** | -| DGRU h23 | 2,751 | -31.67 / -31.61 | **-39.13 / -39.21** | -| TCN h66 | 1,914 | -37.44 / -37.30 | -38.38 / -38.25 | -| TRes-GRU h22 | 1,916 | -35.62 / -35.50 | **-38.94 / -39.01** | - -The same GRU gains 7.4 dB from the recipe alone. Recurrent cells need the higher -learning rate and the schedule; the convolutional TCN is far less sensitive, -which is why it already reaches -37.4 dB under the weak recipe. With a proper -recipe all four architectures land within 0.9 dB of each other, because they are -all at the -38.6 dB noise floor — architecture ranking at that point is not -meaningful. - ---- - -## 5. Fix plan - -### 5.1 Applied in this branch - -- [x] `benchmark/benchmark_volterra_qr.py`: branch on `--model`, so GMP actually - builds the GMP basis (§2.2). -- [x] `backbones/tres_gru.py`: new TRes-GRU backbone (TRes-DeltaGRU with the - DeltaGRU cell replaced by a dense `nn.GRU`, bias-free so parameter counts - match at equal hidden size). Registered in `models.py` and in both backbone - choice lists in `arguments.py`. Validated numerically equivalent to - `tres_deltagru` at `thx=thh=0` (max abs diff 8.2e-7, relative L2 1.3e-6). -- [x] `benchmark/pa_modeling_investigation.md`: full write-up of the PA-modeling - investigation. -- [x] This plan. - -### 5.2 Pending — re-benchmark (the substantive work) - -1. **Retrain the APA_200MHz PA surrogate with the OpenDPDv2 recipe.** - ```bash - python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 \ - --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 --decay_factor 0.5 --patience 10 --batch_size 64 - ``` - Expected ≈ -38.8 dB. Do the same for DPA_160MHz for consistency (it is already - at its floor, so little change is expected). - -2. **Re-run all four APA DPD rows** (GRU h11, TRes-DeltaGRU h10, MP/QR, GMP/QR) - against the retrained surrogate. Section 7's APA table and every Section 8 - conclusion derived from it must be recomputed — the EVM column in particular. - -3. **Correct the report text**: §4 APA PA NMSE, §9 command paths, DPA PAPR - (10.13 dB), the §8 EVM attribution, and add a note that Section 7 metrics are - surrogate-simulated with the non-standard definitions listed in §2.5. - -4. **Guard against regression**: make `model_id` encode the recipe (or at least - warn on overwrite), and add a smoke test that `--model gmp` produces a - different coefficient count than `--model mp`. - -### 5.3 Pending — PA backbone comparison at a fixed budget (started, then stopped) - -A sweep of every viable PA backbone at ~2,700 parameters under the OpenDPDv2 -recipe was launched and **deliberately stopped before completion** at the user's -request. Hidden sizes were already calibrated: - -| Backbone | h | Params | | Backbone | h | Params | -|---|---|---|---|---|---|---| -| GRU | 28 | 2,746 | | vDLSTM | 22 | 2,666 | -| DGRU | 23 | 2,751 | | DeltaGRU | 26 | 2,706 | -| TCN | 93 | 2,697 | | DeltaJANET | 32 | 2,626 | -| TRes-GRU | 27 | 2,751 | | PG-JANET | 19 | 2,719 | -| LSTM | 24 | 2,738 | | DVR-JANET | 19 | 2,665 | -| RVTDCNN | 68 | 2,684 | | QGRU | 27 | 2,729 | -| MCLDNN | 11 | 2,679 | | APNRRU | 34 | 2,723 | - -Excluded: `gmp` (fixed at 495 params), `bojanet` (caps at 1,346; breaks above -h=18), `apnrnn` (not implemented in `models.py`). `tres_deltagru` is redundant -with `tres_gru` at zero thresholds. - -Partial results before the stop (best val NMSE at the epoch reached, not final): - -| Backbone | Params | Epochs run | Best val NMSE | -|---|---|---|---| -| LSTM h24 | 2,738 | 126/240 | -38.88 | -| GRU h28 | 2,746 | 126/240 | -38.85 | -| TCN h93 | 2,697 | 213/240 | -38.54 | -| MCLDNN h11 | 2,679 | 76/240 | -31.77 | -| RVTDCNN h68 | 2,684 | 240/240 | -31.13 | -| TRes-GRU h27 | 2,751 | 1/240 | -30.72 | - -The runner script used is `benchmark/train_all_pa_2700.sh`. Resume by re-running -it; models already finished can be dropped from the `JOBS` list. - -Note that at ~2,700 parameters the well-behaved architectures are already pinned -at the -38.6 dB noise floor, so this sweep characterizes *which architectures -reach the floor and how fast*, not which is most accurate. 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z1Y4;#nwOmZnCbg`lsLafBg0RpZr_(TL0^>#t-`cZ}_i=@_#&~k9u3j WrqtRe#v3X4XUaqytCN-s{Qm int: - return len(self.indices) - - -def _json_default(value: Any): - if isinstance(value, Path): - return str(value) - if isinstance(value, (np.floating, np.integer)): - return value.item() - if isinstance(value, torch.Tensor): - return value.detach().cpu().tolist() - raise TypeError(f"cannot serialize {type(value).__name__}") - - -def _write_json(path: Path, payload: dict[str, Any]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text( - json.dumps(payload, indent=2, sort_keys=True, default=_json_default) + "\n", - encoding="utf-8", - ) - - -def _command_output(command: list[str], cwd: Path) -> str | None: - try: - result = subprocess.run( - command, cwd=cwd, check=True, capture_output=True, text=True - ) - except (OSError, subprocess.CalledProcessError): - return None - return result.stdout.strip() or None - - -def _sha256_file(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for block in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(block) - return digest.hexdigest() - - -def _sync(device: torch.device) -> None: - if device.type == "cuda": - torch.cuda.synchronize(device) - - -def _configure_reproducibility(seed: int) -> None: - os.environ.setdefault("PYTHONHASHSEED", str(seed)) - os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") - torch.manual_seed(seed) - np.random.seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) - torch.use_deterministic_algorithms(True) - torch.backends.cudnn.benchmark = False - torch.backends.cuda.matmul.allow_tf32 = False - torch.backends.cudnn.allow_tf32 = False - - -def _load_repo(repo_root: Path) -> SimpleNamespace: - repo_root = repo_root.resolve() - if not (repo_root / "models.py").is_file(): - raise FileNotFoundError(f"not an OpenDPD tree: {repo_root}") - sys.path.insert(0, str(repo_root)) - models = importlib.import_module("models") - data = importlib.import_module("modules.data_collector") - train_funcs = importlib.import_module("modules.train_funcs") - util = importlib.import_module("utils.util") - return SimpleNamespace( - root=repo_root, - models=models, - data=data, - train_funcs=train_funcs, - util=util, - ) - - -def _load_spec(repo_root: Path, dataset_name: str) -> dict[str, Any]: - path = repo_root / "datasets" / dataset_name / "spec.json" - with path.open(encoding="utf-8") as handle: - return json.load(handle) - - -def _build_core( - repo: SimpleNamespace, - backbone: str, - hidden_size: int, - thx: float = 0.0, - thh: float = 0.0, -): - with contextlib.redirect_stdout(io.StringIO()): - return repo.models.CoreModel( - input_size=2, - hidden_size=hidden_size, - num_layers=1, - backbone_type=backbone, - thx=thx, - thh=thh, - ) - - -def _cpu_state(module: torch.nn.Module) -> dict[str, torch.Tensor]: - return { - name: tensor.detach().cpu().clone() - for name, tensor in module.state_dict().items() - } - - -def _build_cascade( - repo: SimpleNamespace, - fixture: dict[str, Any], - device: torch.device, -): - config = fixture["config"] - pa = _build_core(repo, config["pa_backbone"], config["pa_hidden_size"]) - dpd = _build_core( - repo, - config["dpd_backbone"], - config["dpd_hidden_size"], - config["thx"], - config["thh"], - ) - pa.load_state_dict(fixture["pa_state"]) - dpd.load_state_dict(fixture["dpd_state"]) - cascade = repo.models.CascadedModel(dpd_model=dpd, pa_model=pa) - cascade.freeze_pa_model() - return cascade.to(device) - - -def _metric_args(spec: dict[str, Any]) -> SimpleNamespace: - return SimpleNamespace( - input_signal_fs=spec["input_signal_fs"], - bw_main_ch=spec["bw_main_ch"], - n_sub_ch=spec["n_sub_ch"], - nperseg=spec["nperseg"], - ) - - -def _metrics( - train_funcs: Any, - metric_args: SimpleNamespace, - prediction: np.ndarray, - ground_truth: np.ndarray, -) -> dict[str, float]: - result = train_funcs.calculate_metrics( - metric_args, {}, prediction, ground_truth - ) - return {name: float(result[name]) for name in METRIC_NAMES} - - -def _environment(repo_root: Path, device: torch.device) -> dict[str, Any]: - gpu = None - if device.type == "cuda": - gpu = torch.cuda.get_device_name(device) - source_files = [ - "backbones/tres_deltagru.py", - "models.py", - "modules/train_funcs.py", - "modules/cuda_graph_training.py", - "modules/data_collector.py", - "project.py", - ] - hashes = { - relative: _sha256_file(repo_root / relative) - for relative in source_files - if (repo_root / relative).is_file() - } - return { - "timestamp_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), - "platform": platform.platform(), - "python": platform.python_version(), - "torch": torch.__version__, - "cuda_runtime": torch.version.cuda, - "device": str(device), - "gpu": gpu, - "git_commit": _command_output(["git", "rev-parse", "HEAD"], repo_root), - "git_dirty": bool(_command_output(["git", "status", "--short"], repo_root)), - "source_sha256": hashes, - } - - -def prepare(args: argparse.Namespace) -> None: - started = time.perf_counter() - repo = _load_repo(args.repo_root) - device = torch.device(args.device) - _configure_reproducibility(args.seed) - - arrays = repo.data.load_dataset(dataset_name=args.dataset_name) - x_train, y_train = arrays[0], arrays[1] - pa_dataset = repo.data.IQFrameDataset( - x_train, y_train, frame_length=args.frame_length, stride=args.pa_frame_stride - ) - - # Model initialization is intentionally done once. The resulting tensors, - # rather than a seed assumption, are the fixture contract used by every arm. - pa = _build_core(repo, args.pa_backbone, args.pa_hidden_size).to(device) - generator = torch.Generator().manual_seed(args.seed + 10_000) - loader = DataLoader( - pa_dataset, - batch_size=args.pa_batch_size, - shuffle=True, - generator=generator, - pin_memory=device.type == "cuda", - ) - optimizer = torch.optim.AdamW(pa.parameters(), lr=args.pa_lr) - criterion = torch.nn.MSELoss() - epoch_losses: list[float] = [] - pa.train() - for _ in range(args.pa_pretrain_epochs): - losses: list[float] = [] - for features, targets in loader: - features = features.to(device, non_blocking=True) - targets = targets.to(device, non_blocking=True) - optimizer.zero_grad(set_to_none=True) - loss = criterion(pa(features), targets) - loss.backward() - if args.grad_clip: - torch.nn.utils.clip_grad_norm_(pa.parameters(), args.grad_clip) - optimizer.step() - losses.append(float(loss.detach())) - epoch_losses.append(float(np.mean(losses))) - - # Re-seed before DPD construction so its state is independent of PA epoch - # count and can be regenerated deliberately if the fixture format changes. - torch.manual_seed(args.seed + 1) - if device.type == "cuda": - torch.cuda.manual_seed_all(args.seed + 1) - dpd = _build_core( - repo, - args.dpd_backbone, - args.dpd_hidden_size, - args.thx, - args.thh, - ) - fixture = { - "format_version": 1, - "config": { - "dataset_name": args.dataset_name, - "frame_length": args.frame_length, - "pa_backbone": args.pa_backbone, - "pa_hidden_size": args.pa_hidden_size, - "dpd_backbone": args.dpd_backbone, - "dpd_hidden_size": args.dpd_hidden_size, - "thx": args.thx, - "thh": args.thh, - "seed": args.seed, - }, - "pa_pretraining": { - "epochs": args.pa_pretrain_epochs, - "frame_stride": args.pa_frame_stride, - "batch_size": args.pa_batch_size, - "lr": args.pa_lr, - "epoch_losses": epoch_losses, - "surrogate_notice": ( - "Deterministically trained benchmark surrogate; the repository " - "does not contain a published PA checkpoint." - ), - }, - "pa_state": _cpu_state(pa), - "dpd_state": _cpu_state(dpd), - } - args.fixture.parent.mkdir(parents=True, exist_ok=True) - torch.save(fixture, args.fixture) - print( - json.dumps( - { - "fixture": str(args.fixture), - "sha256": _sha256_file(args.fixture), - "pa_final_loss": epoch_losses[-1] if epoch_losses else None, - "elapsed_s": time.perf_counter() - started, - }, - indent=2, - ) - ) - - -def _loader( - dataset: Any, - batch_size: int, - device: torch.device, - optimized: bool, - indices: Iterable[int] | None = None, -) -> DataLoader: - return DataLoader( - dataset, - batch_size=batch_size, - shuffle=False, - sampler=FixedOrderSampler(indices) if indices is not None else None, - pin_memory=optimized and device.type == "cuda", - ) - - -def _time_eval( - repo: SimpleNamespace, - model: torch.nn.Module, - loader: DataLoader, - criterion: torch.nn.Module, - device: torch.device, -): - _sync(device) - started = time.perf_counter() - log: dict[str, Any] = {} - _, prediction, truth = repo.train_funcs.net_eval( - log=log, - net=model, - dataloader=loader, - criterion=criterion, - device=device, - ) - _sync(device) - return time.perf_counter() - started, float(log["loss"]), prediction, truth - - -def _prediction_error(reference: np.ndarray, candidate: np.ndarray) -> dict[str, float]: - reference64 = np.asarray(reference, dtype=np.float64) - candidate64 = np.asarray(candidate, dtype=np.float64) - difference = candidate64 - reference64 - denominator = max(np.linalg.norm(reference64.ravel()), np.finfo(np.float64).tiny) - return { - "max_abs": float(np.max(np.abs(difference))), - "mean_abs": float(np.mean(np.abs(difference))), - "relative_l2": float(np.linalg.norm(difference.ravel()) / denominator), - } - - -def run(args: argparse.Namespace) -> None: - total_started = time.perf_counter() - repo = _load_repo(args.repo_root) - device = torch.device(args.device) - _configure_reproducibility(args.seed) - optimized = args.implementation != "original" - if device.type == "cuda": - torch.cuda.reset_peak_memory_stats(device) - if not args.progress: - repo.train_funcs.tqdm = lambda iterable: iterable - - fixture = torch.load(args.fixture, map_location="cpu", weights_only=False) - config = fixture["config"] - if args.dataset_name != config["dataset_name"]: - raise ValueError( - f"fixture dataset {config['dataset_name']} != {args.dataset_name}" - ) - if args.frame_length != config["frame_length"]: - raise ValueError( - f"fixture frame length {config['frame_length']} != {args.frame_length}" - ) - spec = _load_spec(repo.root, args.dataset_name) - metric_args = _metric_args(spec) - - setup_started = time.perf_counter() - x_train, y_train, x_val, _, x_test, _ = repo.data.load_dataset( - dataset_name=args.dataset_name - ) - target_gain = float(repo.util.set_target_gain(x_train, y_train)) - train_set = repo.data.IQFrameDataset( - x_train, - target_gain * x_train, - frame_length=args.frame_length, - stride=args.frame_stride, - ) - val_set = repo.data.IQSegmentDataset( - x_val, target_gain * x_val, nperseg=spec["nperseg"] - ) - test_set = repo.data.IQSegmentDataset( - x_test, target_gain * x_test, nperseg=spec["nperseg"] - ) - - order_generator = torch.Generator().manual_seed(args.seed + 20_000) - orders = [ - torch.randperm(len(train_set), generator=order_generator).numpy() - for _ in range(args.epochs) - ] - val_loader = _loader(val_set, args.eval_batch_size, device, optimized) - test_loader = _loader(test_set, args.eval_batch_size, device, optimized) - setup_data_s = time.perf_counter() - setup_started - - # Compile and warm all kernels with a throw-away model so the measured - # epochs start from the immutable fixture state. - warm_started = time.perf_counter() - model = _build_cascade(repo, fixture, device) - warm_loader = _loader( - train_set, - args.batch_size, - device, - optimized, - orders[0][: args.batch_size], - ) - warm_features, warm_targets = next(iter(warm_loader)) - warm_features = warm_features.to(device, non_blocking=optimized) - warm_targets = warm_targets.to(device, non_blocking=optimized) - warm_loss = torch.nn.functional.mse_loss(model(warm_features), warm_targets) - warm_loss.backward() - _sync(device) - warmup_s = time.perf_counter() - warm_started - model.zero_grad(set_to_none=True) - del warm_features, warm_targets, warm_loss - - # Whole-cascade graph capture must not inherit AccumulateGrad nodes that a - # prior default-stream backward kept alive. The throw-away warm model has - # already populated global Triton/cuDNN caches; build an untouched model - # with the immutable fixture state for the captured training trajectory. - if args.cuda_graph_training: - del model - model = _build_cascade(repo, fixture, device) - - model_started = time.perf_counter() - trainable = tuple(parameter for parameter in model.parameters() if parameter.requires_grad) - optimizer_parameters = trainable if optimized else model.parameters() - optimizer = torch.optim.AdamW(optimizer_parameters, lr=args.lr) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=args.decay_factor, - patience=args.patience, - threshold=1e-4, - min_lr=args.lr_end, - ) - criterion = torch.nn.MSELoss() - model_optimizer_setup_s = time.perf_counter() - model_started - trajectory: list[dict[str, Any]] = [] - epoch_times: list[dict[str, float]] = [] - best_val_aclr = float("inf") - best_epoch = -1 - best_dpd_state: dict[str, torch.Tensor] | None = None - - for epoch, order in enumerate(orders): - train_loader = _loader( - train_set, args.batch_size, device, optimized, order - ) - _sync(device) - train_started = time.perf_counter() - train_log: dict[str, Any] = {} - train_kwargs = dict( - log=train_log, - net=model, - dataloader=train_loader, - optimizer=optimizer, - criterion=criterion, - grad_clip_val=args.grad_clip, - device=device, - ) - # The detached reference worktree predates this opt-in keyword, so add - # it only for runs that explicitly request whole-step capture. - if args.cuda_graph_training: - train_kwargs["cuda_graph_training"] = True - repo.train_funcs.net_train(**train_kwargs) - _sync(device) - train_s = time.perf_counter() - train_started - - val_s, val_loss, val_prediction, val_truth = _time_eval( - repo, model, val_loader, criterion, device - ) - metric_started = time.perf_counter() - val_metrics = _metrics(repo.train_funcs, metric_args, val_prediction, val_truth) - val_metric_s = time.perf_counter() - metric_started - - test_s, test_loss, test_prediction, test_truth = _time_eval( - repo, model, test_loader, criterion, device - ) - metric_started = time.perf_counter() - test_metrics = _metrics(repo.train_funcs, metric_args, test_prediction, test_truth) - test_metric_s = time.perf_counter() - metric_started - - checkpoint_started = time.perf_counter() - if val_metrics["ACLR_AVG"] < best_val_aclr: - best_val_aclr = val_metrics["ACLR_AVG"] - best_epoch = epoch - best_dpd_state = _cpu_state(model.dpd_model) - checkpoint_s = time.perf_counter() - checkpoint_started - if args.lr_schedule: - scheduler.step(val_metrics["ACLR_AVG"]) - - trajectory.append( - { - "epoch": epoch, - "lr": float(optimizer.param_groups[0]["lr"]), - "train_loss": float(train_log["loss"]), - "val_loss": val_loss, - "test_loss": test_loss, - "val": val_metrics, - "test": test_metrics, - } - ) - epoch_times.append( - { - "train_s": train_s, - "val_inference_s": val_s, - "val_metrics_s": val_metric_s, - "test_inference_s": test_s, - "test_metrics_s": test_metric_s, - "checkpoint_s": checkpoint_s, - "total_s": ( - train_s - + val_s - + val_metric_s - + test_s - + test_metric_s - + checkpoint_s - ), - } - ) - - if best_dpd_state is None: - raise RuntimeError("no best checkpoint was selected") - model.dpd_model.load_state_dict(best_dpd_state) - best_test_s, best_test_loss, best_prediction, best_truth = _time_eval( - repo, model, test_loader, criterion, device - ) - metric_started = time.perf_counter() - best_metrics = _metrics(repo.train_funcs, metric_args, best_prediction, best_truth) - best_metric_s = time.perf_counter() - metric_started - - model.dpd_model.eval() - dpd_inference_started = time.perf_counter() - dpd_outputs: list[torch.Tensor] = [] - with torch.inference_mode(): - for features, _ in test_loader: - outputs = model.dpd_model( - features.to(device, non_blocking=optimized) - ) - dpd_outputs.append(outputs.cpu()) - best_dpd_prediction = torch.cat(dpd_outputs, dim=0).numpy() - _sync(device) - best_dpd_inference_s = time.perf_counter() - dpd_inference_started - - serialization_started = time.perf_counter() - args.output_dir.mkdir(parents=True, exist_ok=True) - order_path = args.output_dir / "batch_orders.npz" - np.savez_compressed(order_path, **{f"epoch_{i}": order for i, order in enumerate(orders)}) - output_path = args.output_dir / "outputs.npz" - np.savez_compressed( - output_path, - cascade=best_prediction, - dpd=best_dpd_prediction, - ground_truth=best_truth, - ) - checkpoint_path = args.output_dir / "best_dpd.pt" - torch.save(best_dpd_state, checkpoint_path) - _sync(device) - artifact_serialization_s = time.perf_counter() - serialization_started - - total_s = time.perf_counter() - total_started - result = { - "format_version": 1, - "implementation": args.implementation, - "repo_root": str(repo.root), - "fixture": { - "path": str(args.fixture.resolve()), - "sha256": _sha256_file(args.fixture), - "pa_pretraining": fixture["pa_pretraining"], - }, - "config": { - **config, - "frame_stride": args.frame_stride, - "batch_size": args.batch_size, - "eval_batch_size": args.eval_batch_size, - "epochs": args.epochs, - "lr": args.lr, - "lr_schedule": args.lr_schedule, - "lr_end": args.lr_end, - "decay_factor": args.decay_factor, - "patience": args.patience, - "grad_clip": args.grad_clip, - "hard_determinism": True, - "tf32": False, - "progress_display": args.progress, - "cuda_graph_training": args.cuda_graph_training, - "train_frames": len(train_set), - "train_batches": (len(train_set) + args.batch_size - 1) // args.batch_size, - }, - "environment": _environment(repo.root, device), - "timing": { - "setup_data_s": setup_data_s, - "compile_and_warmup_s": warmup_s, - "model_optimizer_setup_s": model_optimizer_setup_s, - "epochs": epoch_times, - "best_test_inference_s": best_test_s, - "best_test_metrics_s": best_metric_s, - "best_dpd_inference_s": best_dpd_inference_s, - "artifact_serialization_s": artifact_serialization_s, - "total_s": total_s, - "peak_allocated_mib": ( - torch.cuda.max_memory_allocated(device) / (1024**2) - if device.type == "cuda" - else None - ), - "peak_reserved_mib": ( - torch.cuda.max_memory_reserved(device) / (1024**2) - if device.type == "cuda" - else None - ), - }, - "trajectory": trajectory, - "best": { - "epoch": best_epoch, - "test_loss": best_test_loss, - "test": best_metrics, - }, - "artifacts": { - "orders": {"path": order_path.name, "sha256": _sha256_file(order_path)}, - "outputs": {"path": output_path.name, "sha256": _sha256_file(output_path)}, - "checkpoint": { - "path": checkpoint_path.name, - "sha256": _sha256_file(checkpoint_path), - }, - }, - } - _write_json(args.output_dir / "result.json", result) - print(json.dumps(result, indent=2, default=_json_default)) - - -def _state_error(reference: dict[str, torch.Tensor], candidate: dict[str, torch.Tensor]): - names = sorted(reference) - if names != sorted(candidate): - raise ValueError("checkpoint parameter names differ") - per_parameter: dict[str, dict[str, float]] = {} - for name in names: - per_parameter[name] = _prediction_error( - reference[name].detach().cpu().numpy(), - candidate[name].detach().cpu().numpy(), - ) - return { - "max_abs": max(item["max_abs"] for item in per_parameter.values()), - "max_relative_l2": max( - item["relative_l2"] for item in per_parameter.values() - ), - "per_parameter": per_parameter, - } - - -def compare(args: argparse.Namespace) -> None: - reference_result = json.loads((args.reference_dir / "result.json").read_text()) - candidate_result = json.loads((args.candidate_dir / "result.json").read_text()) - if reference_result["fixture"]["sha256"] != candidate_result["fixture"]["sha256"]: - raise ValueError("runs did not use the same fixture") - reference_orders = np.load(args.reference_dir / "batch_orders.npz") - candidate_orders = np.load(args.candidate_dir / "batch_orders.npz") - if reference_orders.files != candidate_orders.files or any( - not np.array_equal(reference_orders[name], candidate_orders[name]) - for name in reference_orders.files - ): - raise ValueError("runs did not use identical batch permutations") - - reference_outputs = np.load(args.reference_dir / "outputs.npz") - candidate_outputs = np.load(args.candidate_dir / "outputs.npz") - output_errors = { - name: _prediction_error(reference_outputs[name], candidate_outputs[name]) - for name in ("cascade", "dpd", "ground_truth") - } - reference_state = torch.load( - args.reference_dir / "best_dpd.pt", map_location="cpu", weights_only=True - ) - candidate_state = torch.load( - args.candidate_dir / "best_dpd.pt", map_location="cpu", weights_only=True - ) - state_errors = _state_error(reference_state, candidate_state) - - reference_train_s = sum( - epoch["train_s"] for epoch in reference_result["timing"]["epochs"] - ) - candidate_train_s = sum( - epoch["train_s"] for epoch in candidate_result["timing"]["epochs"] - ) - metric_deltas = { - name: ( - candidate_result["best"]["test"][name] - - reference_result["best"]["test"][name] - ) - for name in METRIC_NAMES - } - trajectory_deltas = [] - for reference_epoch, candidate_epoch in zip( - reference_result["trajectory"], candidate_result["trajectory"], strict=True - ): - trajectory_deltas.append( - { - "epoch": reference_epoch["epoch"], - "train_loss": candidate_epoch["train_loss"] - reference_epoch["train_loss"], - "val": { - name: candidate_epoch["val"][name] - reference_epoch["val"][name] - for name in METRIC_NAMES - }, - "test": { - name: candidate_epoch["test"][name] - reference_epoch["test"][name] - for name in METRIC_NAMES - }, - } - ) - - limits = { - "prediction_relative_l2": args.prediction_relative_l2_limit, - "metric_abs_db": args.metric_abs_db_limit, - } - checks = { - "same_fixture": True, - "same_batch_orders": True, - "cascade_prediction_relative_l2": ( - output_errors["cascade"]["relative_l2"] - <= args.prediction_relative_l2_limit - ), - "dpd_prediction_relative_l2": ( - output_errors["dpd"]["relative_l2"] - <= args.prediction_relative_l2_limit - ), - "all_metric_deltas": all( - abs(delta) <= args.metric_abs_db_limit for delta in metric_deltas.values() - ), - } - comparison = { - "format_version": 1, - "reference_dir": str(args.reference_dir.resolve()), - "candidate_dir": str(args.candidate_dir.resolve()), - "speed": { - "total_reference_s": reference_result["timing"]["total_s"], - "total_candidate_s": candidate_result["timing"]["total_s"], - "total_speedup": ( - reference_result["timing"]["total_s"] - / candidate_result["timing"]["total_s"] - ), - "train_reference_s": reference_train_s, - "train_candidate_s": candidate_train_s, - "train_speedup": reference_train_s / candidate_train_s, - }, - "best_epoch": { - "reference": reference_result["best"]["epoch"], - "candidate": candidate_result["best"]["epoch"], - }, - "metric_deltas_db": metric_deltas, - "output_errors": output_errors, - "checkpoint_errors": state_errors, - "trajectory_deltas": trajectory_deltas, - "acceptance_limits": limits, - "checks": checks, - "passed": all(checks.values()), - } - _write_json(args.json_out, comparison) - print(json.dumps(comparison, indent=2, default=_json_default)) - if args.fail_on_mismatch and not comparison["passed"]: - raise SystemExit(1) - - -def _common_config(parser: argparse.ArgumentParser) -> None: - parser.add_argument("--repo-root", type=Path, default=SCRIPT_ROOT) - parser.add_argument("--fixture", type=Path, required=True) - parser.add_argument("--dataset-name", default="DPA_200MHz") - parser.add_argument("--frame-length", type=int, default=200) - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--device", default=DEFAULT_DEVICE) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - subparsers = parser.add_subparsers(dest="command", required=True) - - prepare_parser = subparsers.add_parser("prepare", help="create immutable model fixture") - _common_config(prepare_parser) - prepare_parser.add_argument("--pa-backbone", default="dgru") - prepare_parser.add_argument("--pa-hidden-size", type=int, default=23) - prepare_parser.add_argument("--dpd-backbone", default="tres_deltagru") - prepare_parser.add_argument("--dpd-hidden-size", type=int, default=15) - prepare_parser.add_argument("--thx", type=float, default=0.01) - prepare_parser.add_argument("--thh", type=float, default=0.05) - prepare_parser.add_argument("--pa-pretrain-epochs", type=int, default=20) - prepare_parser.add_argument("--pa-frame-stride", type=int, default=16) - prepare_parser.add_argument("--pa-batch-size", type=int, default=256) - prepare_parser.add_argument("--pa-lr", type=float, default=5e-4) - prepare_parser.add_argument("--grad-clip", type=float, default=200.0) - - run_parser = subparsers.add_parser("run", help="run one implementation") - _common_config(run_parser) - run_parser.add_argument( - "--implementation", - choices=( - "original", "eager_optimized", "fused_optimized", - "graph_optimized", "optimized", - ), - required=True, - ) - run_parser.add_argument("--output-dir", type=Path, required=True) - run_parser.add_argument("--frame-stride", type=int, default=16) - run_parser.add_argument("--batch-size", type=int, default=64) - run_parser.add_argument("--eval-batch-size", type=int, default=256) - run_parser.add_argument("--epochs", type=int, default=3) - run_parser.add_argument("--lr", type=float, default=5e-3) - run_parser.add_argument("--lr-schedule", action="store_true") - run_parser.add_argument("--lr-end", type=float, default=1e-4) - run_parser.add_argument("--decay-factor", type=float, default=0.5) - run_parser.add_argument("--patience", type=int, default=10) - run_parser.add_argument("--grad-clip", type=float, default=200.0) - run_parser.add_argument("--progress", action="store_true") - run_parser.add_argument("--cuda-graph-training", action="store_true") - - compare_parser = subparsers.add_parser("compare", help="compare two run directories") - compare_parser.add_argument("--reference-dir", type=Path, required=True) - compare_parser.add_argument("--candidate-dir", type=Path, required=True) - compare_parser.add_argument("--json-out", type=Path, required=True) - compare_parser.add_argument("--prediction-relative-l2-limit", type=float, default=1e-4) - compare_parser.add_argument("--metric-abs-db-limit", type=float, default=0.05) - compare_parser.add_argument("--fail-on-mismatch", action="store_true") - - return parser.parse_args() - - -def main() -> None: - args = parse_args() - if args.command == "prepare": - prepare(args) - elif args.command == "run": - run(args) - else: - compare(args) - - -if __name__ == "__main__": - main() diff --git a/benchmark/benchmark_report.md b/benchmark/benchmark_report.md index 923d10f..efb3239 100644 --- a/benchmark/benchmark_report.md +++ b/benchmark/benchmark_report.md @@ -1,187 +1,138 @@ -# DPD Benchmark Report: AI vs. Traditional Approaches +# OpenDPD PA Modeling and DPD Benchmark -## 1. Overview - -This report benchmarks Digital Pre-Distortion (DPD) algorithms across two categories: - -- **AI-based (neural network)**: GRU and TRes-DeltaGRU, trained via stochastic gradient descent (SGD) -- **Traditional (polynomial)**: Memory Polynomial (MP) and Generalized Memory Polynomial (GMP), identified via QR decomposition (closed-form least squares) - -All models are constrained to approximately 500 real-valued parameters for a fair comparison. Evaluation uses Adjacent Channel Leakage Ratio (ACLR) and Error Vector Magnitude (EVM), where more negative values indicate better performance. +## Technical summary -## 2. Test Signals +This benchmark evaluates MP, GMP, GRU, TRes-GRU, and TRes-DeltaGRU (THX=THH=0) for PA modeling on APA_200MHz and DPA_160MHz. The DPD comparison evaluates MP, GMP, GRU, and TRes-GRU. PA models use approximately 2,700 real parameters; DPD models use approximately 1,000. Every neural run uses the same 300-epoch optimization recipe. MP PA uses direct least squares and GMP PA uses rank-controlled truncated SVD; their predistorters use the indirect learning architecture (ILA). The four-model DPD comparison uses the dataset-specific TRes-GRU-H27 PA checkpoint selected by validation NMSE. A separate sensitivity experiment trains TRes-DeltaGRU-H15 DPD independently through both the TRes-GRU-H27 and zero-threshold TRes-DeltaGRU-H27 PA surrogates. -| Property | APA_200MHz | DPA_160MHz | -|----------|-----------|-----------| -| Standard | LTE TM3.1a | OFDM | -| Configuration | 5-carrier x 40 MHz | 4-carrier x 40 MHz | -| Total Bandwidth | 200 MHz | 160 MHz | -| Modulation | 256-QAM | 1024-QAM | -| PAPR | 10.01 dB (at CCDF of 0.001%) | 10.38 dB | -| Sampling Rate | 983.04 MHz | 640 MHz | -| Dataset Size | 98,304 samples | 491,520 samples | -| Segment Size (nperseg) | 19,662 | 16,384 | -| Sub-channels | 5 | 4 | -| Dataset Split | 60% / 20% / 20% (train / val / test) | 60% / 20% / 20% (train / val / test) | - -## 3. Power Amplifier Devices Under Test (DUT) - -| Property | APA_200MHz | DPA_160MHz | -|----------|-----------|-----------| -| PA Type | GaN Doherty PA | 40 nm CMOS Digital PA (DPA) | -| Part / Technology | Ampleon AR211132 (evaluation board) | 40 nm CMOS | -| Carrier Frequency | 3.5 GHz | 2.4 GHz | -| Average Output Power | 41.2 dBm | 13.75 dBm | -| P1dB Compression Point | 46.5 dBm | - | -| P3dB Compression Point | 50 dBm | - | -| Flat Gain Bandwidth | 200 MHz | 160 MHz | -| Reference | Wu et al., "OpenDPDv2," arXiv:2507.06849 | Wu et al., "MP-DPD," IEEE IMS 2024, arXiv:2404.15364 | +## Key findings -The APA_200MHz dataset uses a high-power 3.5 GHz GaN Doherty PA designed for 5G base station applications. The DPA_160MHz dataset uses a low-power 2.4 GHz CMOS digital PA targeting Wi-Fi/IoT transmitter applications. Both PAs exhibit nonlinear distortion with memory effects, requiring DPD to meet spectral emission standards. +- **APA_200MHz:** the validation PA leader is TRes-DeltaGRU-H27 (THX=THH=0) at -39.0929 dB NMSE (-39.1777 dB test). The validation DPD leader is TRes-GRU-H15 at -53.4736 dB ACLR average (-53.4879 dB test). For TRes-DeltaGRU-H15 DPD, the better validation result uses the TRes-DeltaGRU-H27 (THX=THH=0) PA surrogate at -54.4874 dB ACLR average (-54.4010 dB test). +- **DPA_160MHz:** the validation PA leader is TRes-DeltaGRU-H27 (THX=THH=0) at -39.4802 dB NMSE (-39.7353 dB test). The validation DPD leader is TRes-GRU-H15 at -60.7425 dB ACLR average (-60.3717 dB test). For TRes-DeltaGRU-H15 DPD, the better validation result uses the TRes-DeltaGRU-H27 (THX=THH=0) PA surrogate at -61.9253 dB ACLR average (-61.6586 dB test). +- **APA GMP stability:** column-normalized truncated SVD at `rcond=1e-04` retains 650/1,350 singular directions. Validation/test NMSE is -38.70/-38.66 dB. The fixed cutoff suppresses ill-conditioned delayed-envelope directions instead of applying CUDA `gels`'s invalid full-rank assumption. -## 4. PA Surrogate Model +![Test-set PA modeling and DPD results](benchmark_results.png) -All DPD models are evaluated through the same trained GRU PA surrogate model per dataset, ensuring a fair comparison. The PA model is frozen during DPD training (indirect learning architecture). +*Test split; more negative is better. Outlined points are the models selected by validation. DPD is simulated through the dataset-specific TRes-GRU-H27 PA surrogate.* -| Dataset | PA Backbone | PA Hidden Size | PA Parameters | PA Training | Best PA NMSE | -|---------|-------------|----------------|---------------|-------------|-------------| -| APA_200MHz | GRU | 23 | 1,911 | 100 epochs, AdamW, lr=5e-4 | -43.52 dB | -| DPA_160MHz | GRU | 24 | 2,066 | 100 epochs, AdamW, lr=5e-4 | -38.43 dB | +![TRes-DeltaGRU DPD PA-surrogate sensitivity](benchmark_delta_dpd_results.png) -## 5. DPD Algorithms +*Test split; each point is an independently trained TRes-DeltaGRU-H15 DPD using the named frozen PA surrogate. These results compare surrogate sensitivity, not measurements from one shared physical PA.* -### 5.1 AI-Based Models (trained via SGD) +## APA_200MHz -**GRU (Gated Recurrent Unit)** -- Standard PyTorch GRU cell followed by a linear output layer -- Input: I/Q samples (2D); Output: I/Q samples (2D) -- Configuration: hidden_size=11, num_layers=1 -- Parameters: 519 real-valued +### PA modeling -**TRes-DeltaGRU (Temporal Residual Delta GRU)** -- Delta GRU cell with TCN-based temporal residual skip connection (Wu et al., OpenDPDv2, arXiv:2507.06849) -- The TCN residual path decouples output dynamics from hidden-state sparsity, enabling aggressive temporal sparsity without linearization loss -- Configuration: hidden_size=10, num_layers=1, Conv1d(2,3,k=3,d=16) + Conv1d(3,2,k=1) -- Parameters: 524 real-valued +| Model | Parameters | Method | Selected epoch | NMSE, val / test (dB) | EVM, val / test (dB) | Validation ACLR L / R / avg (dB) | Test ACLR L / R / avg (dB) | +|---|---:|---|---:|---:|---:|---:|---:| +| MP | 2,700 | Direct least squares | N/A | -37.0405 / -36.9635 | -40.2302 / -39.9571 | -27.4060 / -27.3703 / -27.3881 | -27.8869 / -27.7765 / -27.8317 | +| GMP | 2,700 | Truncated SVD (rank 650/1,350) | N/A | -38.7020 / -38.6606 | -43.0540 / -42.7971 | -27.4053 / -27.4129 / -27.4091 | -27.7830 / -27.6294 / -27.7062 | +| GRU-H28 | 2,746 | Supervised, AdamW | 292 | -38.8504 / -38.9367 | -43.6317 / -43.5732 | -27.3455 / -27.4211 / -27.3833 | -27.6783 / -27.6725 / -27.6754 | +| TRes-GRU-H27 | 2,751 | Supervised, AdamW | 288 | -39.0447 / -39.1293 | -43.9118 / -43.9299 | -27.4100 / -27.4074 / -27.4087 | -27.7503 / -27.6404 / -27.6953 | +| TRes-DeltaGRU-H27 (THX=THH=0) | 2,751 | Supervised, AdamW | 294 | -39.0929 / -39.1777 | -44.0254 / -43.9683 | -27.3872 / -27.4456 / -27.4164 | -27.6800 / -27.6721 / -27.6761 | -### 5.2 Traditional Models (identified via QR) +### DPD -**MP (Memory Polynomial)** -- Diagonal subset of the Volterra series: basis functions `x(n-q) * |x(n-q)|^k` -- Configuration: K=5 nonlinearity orders, Q=50 memory depth -- Parameters: 250 complex coefficients = 500 real-valued -- Identification: one-shot least-squares via `numpy.linalg.lstsq` (SVD-based) +| Model | Parameters | Method | Selected epoch | NMSE, val / test (dB) | EVM, val / test (dB) | Validation ACLR L / R / avg (dB) | Test ACLR L / R / avg (dB) | +|---|---:|---|---:|---:|---:|---:|---:| +| MP | 1,000 | ILA, least squares | N/A | -42.1149 / -42.1896 | -48.1895 / -48.1535 | -45.5590 / -44.2857 / -44.9224 | -46.0740 / -44.2980 / -45.1860 | +| GMP | 1,000 | ILA, least squares | N/A | -38.3167 / -38.5259 | -46.2753 / -46.3523 | -43.5560 / -42.3895 / -42.9727 | -44.0657 / -43.1207 / -43.5932 | +| GRU-H16 | 994 | DLA, AdamW | 299 | -45.1735 / -45.1315 | -47.6885 / -47.4275 | -51.3857 / -51.2623 / -51.3240 | -51.0559 / -50.9669 / -51.0114 | +| TRes-GRU-H15 | 999 | DLA, AdamW | 296 | -44.0368 / -44.2859 | -44.8654 / -45.0973 | -53.2065 / -53.7407 / -53.4736 | -53.4693 / -53.5066 / -53.4879 | -**GMP (Generalized Memory Polynomial)** -- Extended Memory Polynomial with lagging and leading cross-terms (Morgan et al., IEEE TSP, 2006) -- Aligned terms: `x(n-q) * |x(n-q)|^k` (Ka=5, La=15 -> 75 terms) -- Lagging cross-terms: `x(n-q) * |x(n-q-l)|^k` (Kb=4, Lb=15, Mb=2 -> 120 terms) -- Leading cross-terms: `x(n-q) * |x(n-q+l)|^k` (Kc=4, Lc=15, Mc=1 -> 60 terms) -- Parameters: 255 complex coefficients = 510 real-valued -- Identification: one-shot least-squares via `numpy.linalg.lstsq` (SVD-based) +### TRes-DeltaGRU DPD by PA surrogate -Both traditional models use the Indirect Learning Architecture (ILA): build basis from normalized PA output, solve for postdistorter coefficients, then copy to predistorter. +Both rows use TRes-DeltaGRU-H15 with 999 parameters and THX=THH=0. They are separately trained through the indicated frozen PA surrogate. -## 6. Training Configuration +| PA surrogate | PA NMSE, val / test (dB) | DPD parameters | Selected epoch | DPD NMSE, val / test (dB) | DPD EVM, val / test (dB) | DPD ACLR avg, val / test (dB) | +|---|---:|---:|---:|---:|---:|---:| +| TRes-GRU-H27 | -39.0447 / -39.1293 | 999 | 264 | -46.8471 / -46.9184 | -48.3486 / -48.2629 | -53.2898 / -53.6435 | +| TRes-DeltaGRU-H27 (THX=THH=0) | -39.0929 / -39.1777 | 999 | 296 | -47.8582 / -47.9703 | -49.8200 / -49.6999 | -54.4874 / -54.4010 | -### SGD-Based Models (GRU, TRes-DeltaGRU) +## DPA_160MHz -| Parameter | Value | -|-----------|-------| -| Optimizer | AdamW | -| Learning Rate | 5e-4 | -| Epochs | 300 | -| Batch Size | 256 | -| Loss Function | MSE (L2) | -| LR Schedule | None | -| Gradient Clipping | 200 | -| Best Model Selection | Validation ACLR_AVG | -| Seed | 0 | +### PA modeling -### QR-Based Models (MP, GMP) +| Model | Parameters | Method | Selected epoch | NMSE, val / test (dB) | EVM, val / test (dB) | Validation ACLR L / R / avg (dB) | Test ACLR L / R / avg (dB) | +|---|---:|---|---:|---:|---:|---:|---:| +| MP | 2,700 | Direct least squares | N/A | -37.9823 / -38.3506 | -40.3940 / -40.9035 | -34.9319 / -34.4718 / -34.7018 | -35.1443 / -34.6409 / -34.8926 | +| GMP | 2,700 | Truncated SVD (rank 918/1,350) | N/A | -38.9088 / -39.4014 | -41.6537 / -42.3839 | -34.9108 / -34.3514 / -34.6311 | -35.0859 / -34.5171 / -34.8015 | +| GRU-H28 | 2,746 | Supervised, AdamW | 34 | -39.0801 / -39.4908 | -42.1993 / -42.8282 | -34.9599 / -34.5649 / -34.7624 | -35.0367 / -34.6393 / -34.8380 | +| TRes-GRU-H27 | 2,751 | Supervised, AdamW | 25 | -39.3229 / -39.7044 | -42.5812 / -43.1447 | -34.9905 / -34.4694 / -34.7299 | -35.1550 / -34.6605 / -34.9078 | +| TRes-DeltaGRU-H27 (THX=THH=0) | 2,751 | Supervised, AdamW | 16 | -39.4802 / -39.7353 | -42.9642 / -43.3196 | -34.9649 / -34.4466 / -34.7057 | -35.1432 / -34.6450 / -34.8941 | -| Parameter | Value | -|-----------|-------| -| Solver | `numpy.linalg.lstsq` (SVD/QR internally) | -| Training Iterations | 1 (closed-form) | -| Regularization | None | -| Training Data | Full training set | +### DPD -## 7. Results +| Model | Parameters | Method | Selected epoch | NMSE, val / test (dB) | EVM, val / test (dB) | Validation ACLR L / R / avg (dB) | Test ACLR L / R / avg (dB) | +|---|---:|---|---:|---:|---:|---:|---:| +| MP | 1,000 | ILA, least squares | N/A | -43.5581 / -43.1938 | -50.4204 / -50.5002 | -49.0670 / -52.1969 / -50.6320 | -49.4442 / -52.4261 / -50.9351 | +| GMP | 1,000 | ILA, least squares | N/A | -44.2662 / -43.9721 | -50.1286 / -50.1887 | -52.3572 / -53.2123 / -52.7847 | -52.6167 / -53.6083 / -53.1125 | +| GRU-H16 | 994 | DLA, AdamW | 297 | -49.9059 / -49.7602 | -53.7314 / -53.9790 | -57.6084 / -57.0975 / -57.3530 | -57.6760 / -56.8525 / -57.2642 | +| TRes-GRU-H15 | 999 | DLA, AdamW | 296 | -52.9106 / -53.1938 | -57.1425 / -57.8451 | -60.4579 / -61.0271 / -60.7425 | -60.2712 / -60.4722 / -60.3717 | -### 7.1 APA_200MHz (3.5 GHz GaN Doherty PA, 5-carrier LTE, 200 MHz BW, 256-QAM) +### TRes-DeltaGRU DPD by PA surrogate -| Rank | Model | Parameters | Method | ACLR_AVG (dB) | EVM (dB) | -|------|-------|-----------|--------|---------------|----------| -| 1 | **TRes-DeltaGRU** | 524 | SGD 300ep | **-53.35** | **-49.08** | -| 2 | GRU | 519 | SGD 300ep | -52.61 | -47.50 | -| 3 | MP | 500 | QR | -41.01 | -32.68 | -| 4 | GMP | 510 | QR | -38.80 | -38.53 | +Both rows use TRes-DeltaGRU-H15 with 999 parameters and THX=THH=0. They are separately trained through the indicated frozen PA surrogate. -### 7.2 DPA_160MHz (2.4 GHz CMOS Digital PA, 4-carrier, 160 MHz BW, 1024-QAM) +| PA surrogate | PA NMSE, val / test (dB) | DPD parameters | Selected epoch | DPD NMSE, val / test (dB) | DPD EVM, val / test (dB) | DPD ACLR avg, val / test (dB) | +|---|---:|---:|---:|---:|---:|---:| +| TRes-GRU-H27 | -39.3229 / -39.7044 | 999 | 295 | -53.8684 / -54.2446 | -57.9312 / -58.8486 | -61.5425 / -61.0442 | +| TRes-DeltaGRU-H27 (THX=THH=0) | -39.4802 / -39.7353 | 999 | 293 | -54.1159 / -54.3722 | -58.2478 / -59.3978 | -61.9253 / -61.6586 | -| Rank | Model | Parameters | Method | ACLR_AVG (dB) | EVM (dB) | -|------|-------|-----------|--------|---------------|----------| -| 1 | **TRes-DeltaGRU** | 524 | SGD 300ep | **-56.81** | **-54.00** | -| 2 | GMP | 510 | QR | -54.02 | -51.08 | -| 3 | GRU | 519 | SGD 300ep | -51.93 | -49.97 | -| 4 | MP | 500 | QR | -51.29 | -50.26 | +## Definitions -## 8. Analysis +- **NMSE:** normalized mean-square error in dB; more negative is better. The implementation averages per-segment dB ratios rather than pooling all samples into one ratio. +- **EVM:** the repository-specific mean absolute complex-spectrum error within the configured main channel, normalized within each subchannel by reference-spectrum magnitude and converted with `20 log10`. It is not demodulated constellation EVM; more negative is better. +- **ACLR L / R / avg:** adjacent-subchannel power normalized by the strongest configured main-channel subchannel, plus the arithmetic mean of left and right, in dB. More negative is better. +- **Parameters:** neural checkpoint tensor elements. MP/GMP count two real degrees of freedom for every complex coefficient. +- **Selected epoch:** zero-based neural checkpoint epoch. It is N/A for closed-form least-squares fits. -### AI vs. Traditional +## Model configurations -- On **APA_200MHz** (wider bandwidth, more carriers), the AI models dominate: TRes-DeltaGRU leads the best traditional method (MP) by **12.3 dB in ACLR** and **10.6 dB in EVM**. The recurrent hidden state captures complex long-range memory effects that polynomial basis functions cannot. -- On **DPA_160MHz**, the gap narrows but AI still leads: TRes-DeltaGRU beats the best traditional method (GMP/QR) by **2.8 dB in ACLR** and **2.9 dB in EVM**. The traditional GMP/QR is competitive here, outperforming vanilla GRU. +| Stage | MP | GMP | GRU | TRes-GRU | TRes-DeltaGRU | +|---|---|---|---|---|---| +| PA modeling | K=9, Q=150 | Ka/La=5/30; Kb/Lb/Mb=4/30/5; Kc/Lc/Mc=4/30/5 | H28 | H27 | H27, THX=THH=0 | +| DPD | K=5, Q=100 | Ka/La=5/20; Kb/Lb/Mb=4/20/3; Kc/Lc/Mc=4/20/2 | H16 | H15 | H15, THX=THH=0 (PA-surrogate sensitivity) | -### QR vs. SGD for Polynomial Models +## Temporal context and sequence boundaries -The identification method matters enormously for polynomial models. Comparing GMP trained with QR vs. SGD (100 epochs, from earlier experiments): +In sequence interiors, PA GMP uses up to five future samples and DPD GMP uses up to two through their leading-envelope terms. TRes-GRU and TRes-DeltaGRU use one-sample right context in their recurrent features and 16-sample right context in their dilated residual convolution. MP and GRU use no explicit future samples. -| Dataset | GMP (QR) ACLR | GMP (SGD 100ep) ACLR | Improvement | -|---------|--------------|---------------------|------------| -| APA_200MHz | -38.80 dB | -29.67 dB | +9.1 dB | -| DPA_160MHz | -54.02 dB | -44.46 dB | +9.6 dB | +These are offline segmented evaluations. GMP delay accesses are zero-filled and reset at each `nperseg` boundary. In both TRes models, `torch.roll(..., shifts=-1)` wraps the final position to the first sample of the same supplied sequence; the convolution zero-pads both boundaries, and recurrent state resets for each sequence. Neural optimization uses overlapping 200-sample frames with stride 1, while validation and test use independent `nperseg` segments. -QR finds the global optimum in one shot; SGD with 100 epochs of Adam cannot reach it for this convex problem. This demonstrates that when the model is linear-in-parameters, closed-form solvers are strictly superior to iterative gradient descent. +## Methodology -### TRes-DeltaGRU vs. GRU +Neural PA and DPD models use batch size 64, 300 epochs, AdamW with MSE loss, initial learning rate 5e-3, and ReduceLROnPlateau with factor 0.5, patience 5, and minimum learning rate 5e-5. Frame length is 200, frame stride is 1, and seed is 0. PA checkpoints are selected by minimum validation NMSE; neural DPD checkpoints are selected by minimum validation ACLR average. -TRes-DeltaGRU consistently outperforms vanilla GRU across both datasets: +The TRes-DeltaGRU PA and DPD runs use input-delta threshold THX=0 and hidden-state-delta threshold THH=0. This disables threshold-induced temporal pruning; exact arithmetic deltas may still naturally be zero. The configuration therefore evaluates the dense zero-threshold recurrence, not a sparsity or efficiency claim. -| Dataset | TRes-DeltaGRU ACLR | GRU ACLR | Gap | -|---------|-------------------|----------|-----| -| APA_200MHz | -53.35 dB | -52.61 dB | +0.7 dB | -| DPA_160MHz | -56.81 dB | -51.93 dB | +4.9 dB | +AdamW uses weight decay 0.01, betas (0.9, 0.999), and epsilon 1e-8. The scheduler uses relative threshold 1e-4, cooldown 0, and epsilon 1e-8. -The TCN skip connection provides a direct residual path from input to output, decoupling the output from hidden-state dynamics. This is especially beneficial on DPA_160MHz where the improvement is nearly 5 dB. +MP and GMP are complex polynomial models fit after L2 column scaling. MP and both ILA-DPD fits use `torch.linalg.lstsq` (`gels`). GMP PA modeling uses `torch.linalg.svd` (`gesvdj`) with a fixed relative cutoff of 1e-04; the effective retained rank is reported with each GMP PA result. These closed-form fits do not use the neural batch, epoch, optimizer, or learning-rate settings. PA polynomial fits map measured PA input to output directly. DPD polynomial fits use ILA, fitting a postdistorter and copying its coefficients to the predistorter. No ridge penalty or validation-tuned regularization is applied. -## 9. Reproducing These Results +Each dataset uses its independently trained TRes-GRU-H27 PA surrogate for the four-model DPD comparison. The two TRes-DeltaGRU-H15 sensitivity rows are separate DPD training runs, one through that TRes-GRU PA and one through the independently trained TRes-DeltaGRU-H27 PA. Training data supply all gradient and least-squares fits. Neural validation metrics drive the learning-rate schedule and checkpoint selection; test data are not used for fitting, scheduling, or selection. -### SGD Models +## Limitations and robustness -```bash -# Train PA model (once per dataset) -python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 --n_epochs 100 -python main.py --step train_pa --dataset_name DPA_160MHz --PA_backbone gru --PA_hidden_size 24 --n_epochs 100 +- Neural execution uses soft reproducibility with cuDNN benchmark enabled, so repeated runs can differ slightly. +- DPD scores measure simulated performance through a learned PA surrogate, not a fresh over-the-air or bench measurement. +- One seed is evaluated. The reported table is not a distribution over training runs. +- The five PA candidates and four primary DPD candidates are matched approximately by real parameter count, not by FLOPs, latency, memory traffic, or fit time. The PA-surrogate sensitivity experiment adds two independently trained TRes-DeltaGRU DPD runs per dataset. +- Results obtained through different learned PA surrogates are simulator-sensitivity evidence; they are not a controlled ranking against one shared physical PA response. +- CUDA `gels` assumes a full-rank design matrix and does not return numerical rank. It remains in use for MP and ILA-DPD; the GMP PA path records its SVD spectrum, cutoff, and retained rank. +- GMP PA has 1,350 stored complex coefficients (2,700 nominal real parameters), but truncated SVD reduces its effective rank. The comparison is matched by stored coefficient count, not effective degrees of freedom. +- Look-ahead is an input dependency, not measured inference latency. Streaming reformulations and continuous boundary/state handling are not evaluated. -# Train GRU DPD -python main.py --step train_dpd --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 --DPD_backbone gru --DPD_hidden_size 11 --n_epochs 300 -python main.py --step train_dpd --dataset_name DPA_160MHz --PA_backbone gru --PA_hidden_size 24 --DPD_backbone gru --DPD_hidden_size 11 --n_epochs 300 +## Provenance -# Train TRes-DeltaGRU DPD -python main.py --step train_dpd --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 --DPD_backbone tres_deltagru --DPD_hidden_size 10 --n_epochs 300 -python main.py --step train_dpd --dataset_name DPA_160MHz --PA_backbone gru --PA_hidden_size 24 --DPD_backbone tres_deltagru --DPD_hidden_size 10 --n_epochs 300 -``` - -### QR Models - -```bash -# MP (K=5, Q=50 -> 500 real params) -python benchmark_volterra_qr.py --dataset_name APA_200MHz --pa_hidden_size 23 --model mp --K 5 --Q 50 -python benchmark_volterra_qr.py --dataset_name DPA_160MHz --pa_hidden_size 24 --model mp --K 5 --Q 50 - -# GMP (255 complex = 510 real params) -python benchmark_volterra_qr.py --dataset_name APA_200MHz --pa_hidden_size 23 --model gmp -python benchmark_volterra_qr.py --dataset_name DPA_160MHz --pa_hidden_size 24 --model gmp -``` +- Generated: `2026-07-26T20:45:35.368238+00:00` +- Git commit: `3df35e081e6e41463fa46f21778c72a748823274` +- Git branch: `benchmark-fix` +- Git working tree at launch: `dirty`; exact source hashes and start status are retained in the machine evidence. +- Python: `3.13.14` +- PyTorch: `2.13.0+cu132` +- CUDA: `13.2` +- GPU: `NVIDIA RTX PRO 6000 Blackwell Workstation Edition` +- Canonical evidence schema: `6` (published from collector schema `6`); the source archive and completed job ledger are cryptographically bound in the machine-readable evidence. +- Reproduce the full matrix: [`reproduce_benchmark_report.sh`](reproduce_benchmark_report.sh) +- Machine-readable evidence: [`results/benchmark_report_results.json`](results/benchmark_report_results.json) +- Each reproduction run writes exact commands, a verified source snapshot, raw polynomial results, checkpoints, and CSV logs to its timestamped evidence directory. diff --git a/benchmark/benchmark_results.png b/benchmark/benchmark_results.png new file mode 100644 index 0000000000000000000000000000000000000000..9da917149ae866ebba4dc28fa31337ca9e843edb GIT binary patch literal 201177 zcmdSBXH->L*EMJbwX{@91r!4qKqX5?Bxo7%5(LQ^0Rc%Ol9N~_RH9cwf`EY>B_{zD z0ZAfUE>S?qSwVt;K+lc!yzkTBzwXh0+A*vWk#o=4XYIA-nscswSNW{$#`QbauUfTg zBjwCVl~t?$xV~!D>e@fn;BOw!n8o6M5)P-d98_)19Gs1=n66SZa=2`5>tJnRyvND( zioJ#HB_Tcm0X_kqJr^AuF564+^V|H-zu>dIV$Of1?YHl^%DT&EwCz`|+8$1R|3Y!R z@o?2It5#7?9#?aDJlf&x^1!scV#09t(mxrqkyU|vcec9k*FApjIO|$2KDF{2*RQS5 zJLrDzkL%WfMYEmn4izdN;_awiknQTVu%I5WY5V*8Rsa2K700bQod0fnd?an;ioo^_ zYw!X8^Xt?7fv}F>{^v{l;hIU{AHV+BUwmRe^ZS4Q#N{pQSd0JtCBAOU@&Ek_%k%r) 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z@-(952n!Hj+j@*W=!URn$m#YP)NAPfkbaT^4G~)Yu?pFpt(j04>$Tp9SW_c^&b8v=6qtH+2n$VO za~^9{3w*)Llg#D?*^|%6SGz4l#vIZe^Po>f7QZ{E7EFKxV8_8HgtBEc z0jIWe-MJp&N1Bg~hsM-=)oZ$1OZKbr9#UCdQ)R*Xw%L-mGEW#JZnc=+(hS6+ToM&;P><)nE(~Ot1J$5hra^dRkiK4(!Ua*y+VUUg38V&6%dIzUH!tF*#DvXOSa} zB`|R%Eeph=4e@IO>>M5Wm}w^AWi)!1e}Czm_rET=%S!FPzX^X& gS`z&KbHQQNYwu6bSg=@)&A^9gVY4A~{jT6Y0pLu3I{*Lx literal 0 HcmV?d00001 diff --git a/benchmark/benchmark_training_speed.py b/benchmark/benchmark_training_speed.py deleted file mode 100644 index a065d63..0000000 --- a/benchmark/benchmark_training_speed.py +++ /dev/null @@ -1,450 +0,0 @@ -#!/usr/bin/env python3 -"""Wall-clock training-speed benchmark for OpenDPD. - -For every combination of: - role : PA (CoreModel) - DPD (CascadedModel = trainable DPD CoreModel + frozen PA CoreModel) - backbone : GRU, TCN - target #P : 200, 500, 1000 (trainable parameters) - device : cpu, cuda (if available), mps (if available) - batch size : 16, 32, 64, 128, 256 - -train one epoch on a synthetic IQ tensor and record the wall-clock time and -sample throughput. Results are rendered as rich tables in the terminal and -written to a markdown report. - -Run: - python benchmark/benchmark_training_speed.py -""" -from __future__ import annotations - -import argparse -import contextlib -import io -import json -import platform -import sys -import time -from dataclasses import asdict, dataclass -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) - -import torch -import torch.nn as nn -from rich import box -from rich.console import Console -from rich.panel import Panel -from rich.table import Table -from torch.utils.data import DataLoader, TensorDataset -from tqdm import tqdm - -from models import CascadedModel, CoreModel - - -# ----------------------------------------------------------------------------- -# Configuration -# ----------------------------------------------------------------------------- -ROLES = ("PA", "DPD") -BACKBONES = ("gru", "tcn") -TARGET_PARAMS = (200, 500, 1000) -BATCH_SIZES = (16, 32, 64, 128, 256) - -# Hidden sizes chosen so the *trainable* parameter count lands close to the -# target. Exact counts (also reported in the output): -# GRU(input=2, hidden=H, output=2, num_layers=1) -> 3*H^2 + 14*H + 2 -# TCN(hidden_channels=H) -> 29*H -HIDDEN_SIZES = { - "gru": {200: 6, 500: 11, 1000: 16}, - "tcn": {200: 7, 500: 17, 1000: 34}, -} - -# DPD runs use a CascadedModel(DPD + frozen PA). The frozen PA matches the -# DPD backbone with a fixed mid-size hidden so the trainable parameter count -# corresponds purely to the DPD model. -FROZEN_PA_HIDDEN = {"gru": 11, "tcn": 17} - -INPUT_SIZE = 2 -FRAME_LENGTH = 200 -N_FRAMES = 2048 -SEED = 0 - - -# ----------------------------------------------------------------------------- -# Devices -# ----------------------------------------------------------------------------- -def discover_devices(force: list[str] | None = None) -> list[torch.device]: - if force: - return [torch.device(d) for d in force] - devices = [torch.device("cpu")] - if torch.cuda.is_available(): - devices.append(torch.device("cuda")) - mps = getattr(torch.backends, "mps", None) - if mps is not None and mps.is_available() and mps.is_built(): - devices.append(torch.device("mps")) - return devices - - -def device_short(device: torch.device) -> str: - return {"cuda": "GPU", "mps": "MPS", "cpu": "CPU"}[device.type] - - -def device_label(device: torch.device) -> str: - if device.type == "cuda": - idx = device.index if device.index is not None else 0 - try: - return f"GPU (CUDA): {torch.cuda.get_device_name(idx)}" - except Exception: - return "GPU (CUDA)" - if device.type == "mps": - return "Apple Silicon (MPS)" - return f"CPU ({platform.processor() or platform.machine()})" - - -def device_sync(device: torch.device) -> None: - if device.type == "cuda": - torch.cuda.synchronize(device) - elif device.type == "mps": - torch.mps.synchronize() - - -# ----------------------------------------------------------------------------- -# Model factory -# ----------------------------------------------------------------------------- -def build_model(role: str, backbone: str, target_p: int, - device: torch.device) -> nn.Module: - """Build a PA or DPD-cascaded model for the given config and move to device.""" - hidden = HIDDEN_SIZES[backbone][target_p] - # Suppress the "Backbone Initialized..." print from CoreModel.__init__. - with contextlib.redirect_stdout(io.StringIO()): - primary = CoreModel(input_size=INPUT_SIZE, hidden_size=hidden, - num_layers=1, backbone_type=backbone) - if role == "PA": - return primary.to(device) - pa = CoreModel(input_size=INPUT_SIZE, - hidden_size=FROZEN_PA_HIDDEN[backbone], - num_layers=1, backbone_type=backbone) - cascaded = CascadedModel(dpd_model=primary, pa_model=pa) - cascaded.freeze_pa_model() - return cascaded.to(device) - - -def trainable_params(net: nn.Module) -> int: - return sum(p.numel() for p in net.parameters() if p.requires_grad) - - -# ----------------------------------------------------------------------------- -# Single benchmark run -# ----------------------------------------------------------------------------- -@dataclass -class RunResult: - role: str - backbone: str - target_params: int - actual_params: int - device: str - batch_size: int - n_batches: int - epoch_seconds: float - samples_per_sec: float - - def to_dict(self) -> dict: - return asdict(self) - - -def run_one_epoch(role: str, backbone: str, target_p: int, - device: torch.device, batch_size: int, - features: torch.Tensor, targets: torch.Tensor, - outer_pbar: tqdm) -> RunResult: - torch.manual_seed(SEED) - net = build_model(role, backbone, target_p, device) - n_params = trainable_params(net) - - optimizer = torch.optim.AdamW( - (p for p in net.parameters() if p.requires_grad), lr=5e-4) - criterion = nn.MSELoss() - - loader = DataLoader(TensorDataset(features, targets), - batch_size=batch_size, shuffle=False, num_workers=0) - n_batches = len(loader) - net.train() - - # Warmup (one batch, not timed). Stabilises CUDA allocator / cuDNN - # autotune / first MPS kernel compile so the timed epoch is representative. - warm_x, warm_y = next(iter(loader)) - warm_x, warm_y = warm_x.to(device), warm_y.to(device) - optimizer.zero_grad() - criterion(net(warm_x), warm_y).backward() - optimizer.step() - device_sync(device) - - desc = f"{role:<3s} {backbone.upper():<3s} ~{target_p:>4d}P {device_short(device):<3s} bs={batch_size:<3d}" - inner = tqdm(loader, desc=desc, leave=False, position=1, - dynamic_ncols=True, file=sys.stdout) - - device_sync(device) - t0 = time.perf_counter() - n_samples = 0 - for x, y in inner: - x, y = x.to(device), y.to(device) - optimizer.zero_grad() - loss = criterion(net(x), y) - loss.backward() - optimizer.step() - n_samples += x.size(0) - device_sync(device) - elapsed = time.perf_counter() - t0 - inner.close() - - # Free memory before the next config. - del net, optimizer - if device.type == "cuda": - torch.cuda.empty_cache() - - outer_pbar.update(1) - return RunResult( - role=role, backbone=backbone, target_params=target_p, - actual_params=n_params, device=device_short(device), - batch_size=batch_size, n_batches=n_batches, - epoch_seconds=elapsed, - samples_per_sec=n_samples / elapsed if elapsed > 0 else float("nan"), - ) - - -# ----------------------------------------------------------------------------- -# Console rendering -# ----------------------------------------------------------------------------- -def _row_keys(results: list[RunResult]): - return sorted({(r.role, r.backbone, r.target_params, r.actual_params) - for r in results}) - - -def _find(results: list[RunResult], role: str, backbone: str, - target_p: int, batch_size: int) -> RunResult | None: - for r in results: - if (r.role == role and r.backbone == backbone - and r.target_params == target_p and r.batch_size == batch_size): - return r - return None - - -def make_results_table(results: list[RunResult], device_name: str) -> Table: - table = Table( - title=(f"[bold cyan]{device_name}[/bold cyan] " - f"[dim]— seconds / epoch • samples / sec[/dim]"), - box=box.SIMPLE_HEAVY, - title_style="bold", - header_style="bold magenta", - pad_edge=False, - ) - table.add_column("Role", justify="center", style="bold cyan") - table.add_column("Backbone", justify="center", style="bold") - table.add_column("Target #P", justify="right", style="dim") - table.add_column("Actual #P", justify="right") - for bs in BATCH_SIZES: - table.add_column(f"bs={bs}", justify="right") - - last_role: str | None = None - for role, backbone, tgt, act in _row_keys(results): - if last_role is not None and role != last_role: - table.add_section() - last_role = role - cells = [role, backbone.upper(), str(tgt), f"{act:,}"] - for bs in BATCH_SIZES: - r = _find(results, role, backbone, tgt, bs) - if r is None: - cells.append("[red]—[/red]") - else: - cells.append(f"[bold]{r.epoch_seconds:.3f}s[/bold]\n" - f"[green]{r.samples_per_sec:,.0f}[/green]") - table.add_row(*cells) - return table - - -def render_console(console: Console, all_results: list[RunResult], - device_descs: dict[str, str]) -> None: - by_device: dict[str, list[RunResult]] = {} - for r in all_results: - by_device.setdefault(r.device, []).append(r) - for device_short_name, rs in by_device.items(): - console.print() - console.print(make_results_table(rs, device_descs.get( - device_short_name, device_short_name))) - - -# ----------------------------------------------------------------------------- -# Markdown report -# ----------------------------------------------------------------------------- -def render_markdown(all_results: list[RunResult], - device_descs: dict[str, str], - sys_info: dict[str, str], total_seconds: float, - output: Path) -> None: - lines: list[str] = [] - lines.append("# OpenDPD Training-Speed Benchmark") - lines.append("") - lines.append( - "Wall-clock benchmark of one training epoch for **PA** and **DPD** " - "models with **GRU** and **TCN** backbones at three parameter scales " - "(~200, ~500, ~1000), across batch sizes " - f"{{{', '.join(str(b) for b in BATCH_SIZES)}}} on each available device." - ) - lines.append("") - - # System info - lines.append("## System") - lines.append("") - lines.append("| Field | Value |") - lines.append("|---|---|") - for k, v in sys_info.items(): - lines.append(f"| {k} | {v} |") - lines.append(f"| Total benchmark wall-clock | {total_seconds:.1f} s |") - lines.append("") - - # Configuration - lines.append("## Configuration") - lines.append("") - lines.append(f"- Synthetic dataset: **{N_FRAMES}** IQ frames of length " - f"**{FRAME_LENGTH}** (random, seed={SEED})") - lines.append("- Optimiser: **AdamW** (lr=5e-4), MSE loss") - lines.append("- 1 warmup batch (untimed) + 1 full epoch (timed) per configuration") - lines.append( - "- DPD runs use **CascadedModel(DPD + frozen PA)**; the frozen PA " - f"uses the same backbone as the DPD with hidden_size = " - f"{FROZEN_PA_HIDDEN['gru']} (GRU) / {FROZEN_PA_HIDDEN['tcn']} (TCN)." - ) - lines.append("") - lines.append("**Hidden sizes used to hit each parameter target**") - lines.append("") - lines.append("| Backbone | ~200 P | ~500 P | ~1000 P |") - lines.append("|---|---|---|---|") - for bb in BACKBONES: - cells = [bb.upper()] + [str(HIDDEN_SIZES[bb][p]) for p in TARGET_PARAMS] - lines.append("| " + " | ".join(cells) + " |") - lines.append("") - - # Results - lines.append("## Results") - lines.append("") - by_device: dict[str, list[RunResult]] = {} - for r in all_results: - by_device.setdefault(r.device, []).append(r) - - for device_short_name, rs in by_device.items(): - lines.append(f"### {device_descs.get(device_short_name, device_short_name)}") - lines.append("") - header = (["Role", "Backbone", "Target #P", "Actual #P"] - + [f"bs={bs}" for bs in BATCH_SIZES]) - - lines.append("**Wall-clock per epoch (seconds)**") - lines.append("") - lines.append("| " + " | ".join(header) + " |") - lines.append("|" + "|".join(["---"] * len(header)) + "|") - for role, backbone, tgt, act in _row_keys(rs): - row = [role, backbone.upper(), str(tgt), f"{act:,}"] - for bs in BATCH_SIZES: - r = _find(rs, role, backbone, tgt, bs) - row.append(f"{r.epoch_seconds:.3f}" if r else "—") - lines.append("| " + " | ".join(row) + " |") - lines.append("") - - lines.append("**Throughput (samples / sec)**") - lines.append("") - lines.append("| " + " | ".join(header) + " |") - lines.append("|" + "|".join(["---"] * len(header)) + "|") - for role, backbone, tgt, act in _row_keys(rs): - row = [role, backbone.upper(), str(tgt), f"{act:,}"] - for bs in BATCH_SIZES: - r = _find(rs, role, backbone, tgt, bs) - row.append(f"{r.samples_per_sec:,.0f}" if r else "—") - lines.append("| " + " | ".join(row) + " |") - lines.append("") - - output.write_text("\n".join(lines) + "\n") - - -# ----------------------------------------------------------------------------- -# Main -# ----------------------------------------------------------------------------- -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser(description=__doc__, - formatter_class=argparse.RawDescriptionHelpFormatter) - p.add_argument("--devices", nargs="*", default=None, - choices=["cpu", "cuda", "mps"], - help="Override which devices to benchmark (default: auto).") - p.add_argument("--output", type=Path, - default=Path(__file__).resolve().parent - / "benchmark_training_speed_report.md", - help="Markdown report output path.") - p.add_argument("--json-output", type=Path, default=None, - help="Optional path to also dump raw results as JSON.") - return p.parse_args() - - -def main() -> None: - args = parse_args() - console = Console() - - devices = discover_devices(args.devices) - device_descs = {device_short(d): device_label(d) for d in devices} - - g = torch.Generator().manual_seed(SEED) - features = torch.randn(N_FRAMES, FRAME_LENGTH, INPUT_SIZE, generator=g) - targets = torch.randn(N_FRAMES, FRAME_LENGTH, INPUT_SIZE, generator=g) - - sys_info = { - "Platform": f"{platform.system()} {platform.release()} ({platform.machine()})", - "Python": platform.python_version(), - "PyTorch": torch.__version__, - "Devices": " | ".join(device_descs.values()), - "CPU threads (torch)": str(torch.get_num_threads()), - } - panel_text = "\n".join(f"[bold]{k:<22}[/bold] {v}" for k, v in sys_info.items()) - console.print(Panel( - panel_text, - title="[bold cyan]OpenDPD Training-Speed Benchmark[/bold cyan]", - border_style="cyan", expand=False, - )) - - configs = [ - (role, backbone, tp, dev, bs) - for dev in devices - for role in ROLES - for backbone in BACKBONES - for tp in TARGET_PARAMS - for bs in BATCH_SIZES - ] - console.print(f"[dim]Total configurations: {len(configs)}[/dim]\n") - - results: list[RunResult] = [] - bench_t0 = time.perf_counter() - - overall = tqdm(total=len(configs), desc="overall", position=0, - leave=True, dynamic_ncols=True, file=sys.stdout) - try: - for role, backbone, tp, dev, bs in configs: - try: - results.append(run_one_epoch( - role, backbone, tp, dev, bs, features, targets, overall)) - except RuntimeError as e: - overall.update(1) - console.print( - f"[red]Failed[/red] {role}/{backbone}/p{tp}/" - f"{device_short(dev)}/bs{bs}: {e}") - finally: - overall.close() - - total_seconds = time.perf_counter() - bench_t0 - - render_console(console, results, device_descs) - render_markdown(results, device_descs, sys_info, total_seconds, args.output) - console.print(f"\n[green]Markdown report written to:[/green] {args.output}") - - if args.json_output: - args.json_output.write_text( - json.dumps([r.to_dict() for r in results], indent=2)) - console.print(f"[green]JSON results written to:[/green] {args.json_output}") - - -if __name__ == "__main__": - main() diff --git a/benchmark/benchmark_training_speed_report.md b/benchmark/benchmark_training_speed_report.md deleted file mode 100644 index 815a46d..0000000 --- a/benchmark/benchmark_training_speed_report.md +++ /dev/null @@ -1,103 +0,0 @@ -# OpenDPD Training-Speed Benchmark - -Wall-clock benchmark of one training epoch for **PA** and **DPD** models with **GRU** and **TCN** backbones at three parameter scales (~200, ~500, ~1000), across batch sizes {16, 32, 64, 128, 256} on each available device. - -## System - -| Field | Value | -|---|---| -| Platform | Linux 6.17.0-19-generic (x86_64) | -| Python | 3.13.12 | -| PyTorch | 2.11.0+cu128 | -| Devices | CPU (x86_64) | GPU (CUDA): NVIDIA GeForce RTX 4090 | -| CPU threads (torch) | 16 | -| Total benchmark wall-clock | 69.5 s | - -## Configuration - -- Synthetic dataset: **2048** IQ frames of length **200** (random, seed=0) -- Optimiser: **AdamW** (lr=5e-4), MSE loss -- 1 warmup batch (untimed) + 1 full epoch (timed) per configuration -- DPD runs use **CascadedModel(DPD + frozen PA)**; the frozen PA uses the same backbone as the DPD with hidden_size = 11 (GRU) / 17 (TCN). - -**Hidden sizes used to hit each parameter target** - -| Backbone | ~200 P | ~500 P | ~1000 P | -|---|---|---|---| -| GRU | 6 | 11 | 16 | -| TCN | 7 | 17 | 34 | - -## Results - -### CPU (x86_64) - -**Wall-clock per epoch (seconds)** - -| Role | Backbone | Target #P | Actual #P | bs=16 | bs=32 | bs=64 | bs=128 | bs=256 | -|---|---|---|---|---|---|---|---|---| -| DPD | GRU | 200 | 194 | 5.343 | 2.885 | 1.666 | 0.969 | 0.682 | -| DPD | GRU | 500 | 519 | 5.162 | 2.702 | 1.807 | 1.033 | 0.671 | -| DPD | GRU | 1000 | 994 | 5.038 | 2.888 | 1.734 | 1.040 | 0.658 | -| DPD | TCN | 200 | 203 | 0.510 | 0.263 | 0.204 | 0.154 | 0.153 | -| DPD | TCN | 500 | 493 | 0.486 | 0.389 | 0.243 | 0.214 | 0.206 | -| DPD | TCN | 1000 | 986 | 0.559 | 0.439 | 0.333 | 0.316 | 0.317 | -| PA | GRU | 200 | 194 | 2.874 | 1.609 | 0.773 | 0.562 | 0.452 | -| PA | GRU | 500 | 519 | 3.997 | 2.123 | 0.853 | 0.768 | 0.391 | -| PA | GRU | 1000 | 994 | 2.806 | 1.478 | 0.884 | 0.539 | 0.361 | -| PA | TCN | 200 | 203 | 0.239 | 0.143 | 0.122 | 0.071 | 0.063 | -| PA | TCN | 500 | 493 | 0.294 | 0.231 | 0.153 | 0.122 | 0.135 | -| PA | TCN | 1000 | 986 | 0.402 | 0.288 | 0.228 | 0.216 | 0.456 | - -**Throughput (samples / sec)** - -| Role | Backbone | Target #P | Actual #P | bs=16 | bs=32 | bs=64 | bs=128 | bs=256 | -|---|---|---|---|---|---|---|---|---| -| DPD | GRU | 200 | 194 | 383 | 710 | 1,229 | 2,114 | 3,005 | -| DPD | GRU | 500 | 519 | 397 | 758 | 1,133 | 1,983 | 3,051 | -| DPD | GRU | 1000 | 994 | 406 | 709 | 1,181 | 1,969 | 3,111 | -| DPD | TCN | 200 | 203 | 4,020 | 7,791 | 10,040 | 13,284 | 13,390 | -| DPD | TCN | 500 | 493 | 4,218 | 5,268 | 8,433 | 9,562 | 9,960 | -| DPD | TCN | 1000 | 986 | 3,664 | 4,662 | 6,142 | 6,473 | 6,460 | -| PA | GRU | 200 | 194 | 713 | 1,273 | 2,651 | 3,642 | 4,527 | -| PA | GRU | 500 | 519 | 512 | 965 | 2,401 | 2,667 | 5,237 | -| PA | GRU | 1000 | 994 | 730 | 1,385 | 2,317 | 3,799 | 5,671 | -| PA | TCN | 200 | 203 | 8,585 | 14,331 | 16,735 | 28,660 | 32,636 | -| PA | TCN | 500 | 493 | 6,970 | 8,860 | 13,428 | 16,797 | 15,206 | -| PA | TCN | 1000 | 986 | 5,092 | 7,111 | 8,991 | 9,473 | 4,493 | - -### GPU (CUDA): NVIDIA GeForce RTX 4090 - -**Wall-clock per epoch (seconds)** - -| Role | Backbone | Target #P | Actual #P | bs=16 | bs=32 | bs=64 | bs=128 | bs=256 | -|---|---|---|---|---|---|---|---|---| -| DPD | GRU | 200 | 194 | 0.130 | 0.068 | 0.038 | 0.022 | 0.014 | -| DPD | GRU | 500 | 519 | 0.129 | 0.068 | 0.039 | 0.023 | 0.015 | -| DPD | GRU | 1000 | 994 | 0.129 | 0.068 | 0.039 | 0.023 | 0.015 | -| DPD | TCN | 200 | 203 | 0.169 | 0.087 | 0.047 | 0.026 | 0.016 | -| DPD | TCN | 500 | 493 | 0.168 | 0.087 | 0.047 | 0.026 | 0.016 | -| DPD | TCN | 1000 | 986 | 0.182 | 0.108 | 0.057 | 0.031 | 0.019 | -| PA | GRU | 200 | 194 | 0.109 | 0.049 | 0.026 | 0.017 | 0.011 | -| PA | GRU | 500 | 519 | 0.092 | 0.049 | 0.027 | 0.016 | 0.011 | -| PA | GRU | 1000 | 994 | 0.096 | 0.060 | 0.029 | 0.018 | 0.012 | -| PA | TCN | 200 | 203 | 0.096 | 0.051 | 0.028 | 0.017 | 0.013 | -| PA | TCN | 500 | 493 | 0.096 | 0.051 | 0.029 | 0.017 | 0.012 | -| PA | TCN | 1000 | 986 | 0.096 | 0.052 | 0.029 | 0.017 | 0.012 | - -**Throughput (samples / sec)** - -| Role | Backbone | Target #P | Actual #P | bs=16 | bs=32 | bs=64 | bs=128 | bs=256 | -|---|---|---|---|---|---|---|---|---| -| DPD | GRU | 200 | 194 | 15,764 | 30,061 | 54,151 | 91,706 | 141,334 | -| DPD | GRU | 500 | 519 | 15,834 | 29,995 | 51,872 | 90,196 | 140,680 | -| DPD | GRU | 1000 | 994 | 15,829 | 30,161 | 52,875 | 90,122 | 139,074 | -| DPD | TCN | 200 | 203 | 12,119 | 23,507 | 44,036 | 77,912 | 127,690 | -| DPD | TCN | 500 | 493 | 12,167 | 23,432 | 43,802 | 77,883 | 127,460 | -| DPD | TCN | 1000 | 986 | 11,239 | 19,006 | 36,172 | 66,221 | 109,519 | -| PA | GRU | 200 | 194 | 18,857 | 41,602 | 79,253 | 122,231 | 185,570 | -| PA | GRU | 500 | 519 | 22,209 | 42,141 | 76,839 | 128,057 | 188,255 | -| PA | GRU | 1000 | 994 | 21,411 | 33,889 | 69,896 | 115,885 | 173,196 | -| PA | TCN | 200 | 203 | 21,419 | 40,277 | 72,349 | 117,288 | 160,110 | -| PA | TCN | 500 | 493 | 21,270 | 39,913 | 71,851 | 119,467 | 176,378 | -| PA | TCN | 1000 | 986 | 21,269 | 39,686 | 71,682 | 118,658 | 175,314 | - diff --git a/benchmark/benchmark_tres_deltagru.py b/benchmark/benchmark_tres_deltagru.py deleted file mode 100644 index 9aa8aec..0000000 --- a/benchmark/benchmark_tres_deltagru.py +++ /dev/null @@ -1,422 +0,0 @@ -#!/usr/bin/env python3 -"""Reproducible training benchmark for the TRes-DeltaGRU backbone. - -This benchmark intentionally keeps synthetic input resident on the selected -device. It measures the model/optimizer path rather than CSV parsing, -DataLoader workers, or host-to-device transfers. The default shape matches -``bash_scripts/OpenDPDv2.sh``: B=64, T=200, H=15, thx=.01, thh=.05. - -Examples --------- - .venv/bin/python benchmark/benchmark_tres_deltagru.py - .venv/bin/python benchmark/benchmark_tres_deltagru.py --batch-size 256 - .venv/bin/python benchmark/benchmark_tres_deltagru.py --reference-only -""" - -from __future__ import annotations - -import argparse -import contextlib -import copy -import io -import json -import platform -import statistics -import subprocess -import sys -import time -from dataclasses import asdict, dataclass -from datetime import datetime, timezone -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) - -import torch # noqa: E402 - -from models import CoreModel # noqa: E402 -from backbones.triton_deltagru import triton # noqa: E402 - - -@dataclass -class TimingResult: - implementation: str - steps_per_block: int - median_ms: float - iqr_ms: float - block_ms: list[float] - batches_per_second: float - sequence_positions_per_second: float - peak_allocated_mib: float - - -def synchronize(device: torch.device) -> None: - if device.type == "cuda": - torch.cuda.synchronize(device) - - -def command_output(command: list[str]) -> str | None: - try: - result = subprocess.run( - command, cwd=ROOT, check=True, capture_output=True, text=True - ) - except (OSError, subprocess.CalledProcessError): - return None - return result.stdout.strip() or None - - -def build_model( - state: dict[str, torch.Tensor], - hidden_size: int, - thx: float, - thh: float, - device: torch.device, - fused: bool, -) -> CoreModel: - with contextlib.redirect_stdout(io.StringIO()): - model = CoreModel( - input_size=2, - hidden_size=hidden_size, - num_layers=1, - backbone_type="tres_deltagru", - thx=thx, - thh=thh, - ).to(device) - model.load_state_dict(state) - model.backbone.rnn.use_triton = fused - model.backbone.set_debug(0) - if fused: - probe = next(model.parameters()).new_empty((1, 1, model.backbone.rnn.input_size)) - if not model.backbone.rnn._can_use_triton(probe): - raise RuntimeError( - "the fused TRes-DeltaGRU kernel does not support this device, " - "dtype, hidden size, or module configuration" - ) - model.train() - return model - - -def one_step( - model: CoreModel, - optimizer: torch.optim.Optimizer, - features: torch.Tensor, - targets: torch.Tensor, - grad_clip: float, -) -> None: - optimizer.zero_grad(set_to_none=True) - loss = torch.nn.functional.mse_loss(model(features), targets) - loss.backward() - if grad_clip: - torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) - optimizer.step() - - -def time_implementation( - name: str, - fused: bool, - state: dict[str, torch.Tensor], - features: torch.Tensor, - targets: torch.Tensor, - args: argparse.Namespace, - steps_per_block: int, -) -> TimingResult: - device = features.device - model = build_model(state, args.hidden_size, args.thx, args.thh, device, fused) - optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr) - - for _ in range(args.warmup): - one_step(model, optimizer, features, targets, args.grad_clip) - synchronize(device) - - if device.type == "cuda": - torch.cuda.reset_peak_memory_stats(device) - block_times: list[float] = [] - for _ in range(args.blocks): - if device.type == "cuda": - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - start.record() - for _ in range(steps_per_block): - one_step(model, optimizer, features, targets, args.grad_clip) - end.record() - end.synchronize() - elapsed_ms = start.elapsed_time(end) / steps_per_block - else: - started = time.perf_counter() - for _ in range(steps_per_block): - one_step(model, optimizer, features, targets, args.grad_clip) - elapsed_ms = (time.perf_counter() - started) * 1_000 / steps_per_block - block_times.append(elapsed_ms) - - median_ms = statistics.median(block_times) - quartiles = ( - statistics.quantiles(block_times, n=4, method="inclusive") - if len(block_times) > 1 - else [block_times[0]] * 3 - ) - peak_mib = ( - torch.cuda.max_memory_allocated(device) / (1024**2) - if device.type == "cuda" - else float("nan") - ) - return TimingResult( - implementation=name, - steps_per_block=steps_per_block, - median_ms=median_ms, - iqr_ms=quartiles[2] - quartiles[0], - block_ms=block_times, - batches_per_second=1_000 / median_ms, - sequence_positions_per_second=( - features.shape[0] * features.shape[1] * 1_000 / median_ms - ), - peak_allocated_mib=peak_mib, - ) - - -def error_metrics(reference: torch.Tensor, candidate: torch.Tensor) -> dict[str, float]: - difference = (reference - candidate).detach() - denominator = reference.detach().norm().clamp_min(torch.finfo(reference.dtype).tiny) - return { - "max_abs": difference.abs().max().item(), - "mean_abs": difference.abs().mean().item(), - "relative_l2": (difference.norm() / denominator).item(), - } - - -def check_equivalence( - state: dict[str, torch.Tensor], - args: argparse.Namespace, - device: torch.device, -) -> dict[str, object]: - # A modest batch checks all equations without making the eager preflight a - # substantial fraction of the benchmark runtime. - batch_size = min(args.batch_size, args.check_batch_size) - generator = torch.Generator(device=device).manual_seed(args.seed + 1) - x_reference = torch.randn( - batch_size, args.sequence_length, 2, generator=generator, device=device - ).requires_grad_() - x_fused = x_reference.detach().clone().requires_grad_() - target = torch.randn( - batch_size, args.sequence_length, 2, generator=generator, device=device - ) - reference = build_model( - state, args.hidden_size, args.thx, args.thh, device, fused=False - ) - fused = build_model(state, args.hidden_size, args.thx, args.thh, device, fused=True) - - reference_output = reference(x_reference) - reference_loss = torch.nn.functional.mse_loss(reference_output, target) - reference_loss.backward() - fused_output = fused(x_fused) - fused_loss = torch.nn.functional.mse_loss(fused_output, target) - fused_loss.backward() - synchronize(device) - - parameter_errors = {} - for (name, reference_parameter), (candidate_name, candidate_parameter) in zip( - reference.named_parameters(), fused.named_parameters(), strict=True - ): - if name != candidate_name: - raise RuntimeError(f"parameter order mismatch: {name} != {candidate_name}") - parameter_errors[name] = error_metrics( - reference_parameter.grad, candidate_parameter.grad - ) - - return { - "output": error_metrics(reference_output, fused_output), - "input_gradient": error_metrics(x_reference.grad, x_fused.grad), - "loss_abs": abs(reference_loss.item() - fused_loss.item()), - "parameter_gradients": parameter_errors, - } - - -def enforce_equivalence(equivalence: dict[str, object]) -> None: - """Fail before timing if the candidate exceeds calibrated FP32 tolerances.""" - - limits = { - "output": (2e-5, 1e-4), - "input_gradient": (2e-6, 1e-3), - } - for name, (max_abs_limit, relative_limit) in limits.items(): - metrics = equivalence[name] - if metrics["max_abs"] > max_abs_limit or metrics["relative_l2"] > relative_limit: - raise RuntimeError(f"{name} equivalence check failed: {metrics}") - for name, metrics in equivalence["parameter_gradients"].items(): - if metrics["max_abs"] > 2e-5 or metrics["relative_l2"] > 1e-4: - raise RuntimeError(f"{name} gradient equivalence check failed: {metrics}") - if equivalence["loss_abs"] > 1e-5: - raise RuntimeError(f"loss equivalence check failed: {equivalence['loss_abs']}") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") - parser.add_argument("--batch-size", type=int, default=64) - parser.add_argument("--sequence-length", type=int, default=200) - parser.add_argument("--hidden-size", type=int, default=15) - parser.add_argument("--thx", type=float, default=0.01) - parser.add_argument("--thh", type=float, default=0.05) - parser.add_argument("--lr", type=float, default=5e-4) - parser.add_argument("--grad-clip", type=float, default=200.0) - parser.add_argument("--warmup", type=int, default=5) - parser.add_argument("--blocks", type=int, default=7) - parser.add_argument("--reference-steps-per-block", type=int, default=5) - parser.add_argument("--fused-steps-per-block", type=int, default=100) - parser.add_argument("--check-batch-size", type=int, default=8) - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--reference-only", action="store_true") - parser.add_argument("--json-out", type=Path) - return parser.parse_args() - - -def main() -> None: - args = parse_args() - positive_values = { - "batch_size": args.batch_size, - "sequence_length": args.sequence_length, - "hidden_size": args.hidden_size, - "blocks": args.blocks, - "reference_steps_per_block": args.reference_steps_per_block, - "fused_steps_per_block": args.fused_steps_per_block, - "check_batch_size": args.check_batch_size, - } - invalid = {name: value for name, value in positive_values.items() if value <= 0} - if invalid or args.warmup < 0: - raise ValueError(f"benchmark counts must be positive (warmup may be zero): {invalid}") - device = torch.device(args.device) - if device.type == "cuda" and not torch.cuda.is_available(): - raise RuntimeError("CUDA was requested but is unavailable") - - torch.manual_seed(args.seed) - if device.type == "cuda": - torch.cuda.manual_seed_all(args.seed) - # The fused recurrence accumulates in FP32. Disable TF32 in the reference - # so the equivalence check measures operation ordering, not lower precision. - torch.backends.cuda.matmul.allow_tf32 = False - torch.backends.cudnn.allow_tf32 = False - - with contextlib.redirect_stdout(io.StringIO()): - template = CoreModel( - input_size=2, - hidden_size=args.hidden_size, - num_layers=1, - backbone_type="tres_deltagru", - thx=args.thx, - thh=args.thh, - ).to(device) - state = copy.deepcopy(template.state_dict()) - generator = torch.Generator(device=device).manual_seed(args.seed) - features = torch.randn( - args.batch_size, - args.sequence_length, - 2, - generator=generator, - device=device, - ) - targets = torch.randn( - args.batch_size, - args.sequence_length, - 2, - generator=generator, - device=device, - ) - - system = { - "recorded_at_utc": datetime.now(timezone.utc).isoformat(), - "command": " ".join(sys.argv), - "git_revision": command_output(["git", "rev-parse", "HEAD"]), - "git_dirty": bool(command_output(["git", "status", "--porcelain"])), - "platform": platform.platform(), - "python": platform.python_version(), - "torch": torch.__version__, - "triton": triton.__version__ if triton is not None else None, - "device": torch.cuda.get_device_name(device) if device.type == "cuda" else str(device), - "compute_capability": ( - ".".join(map(str, torch.cuda.get_device_capability(device))) - if device.type == "cuda" - else None - ), - "driver": ( - command_output([ - "nvidia-smi", - "--query-gpu=driver_version", - "--format=csv,noheader", - ]) - if device.type == "cuda" - else None - ), - "cuda": torch.version.cuda, - "cudnn": torch.backends.cudnn.version(), - "matmul_tf32": torch.backends.cuda.matmul.allow_tf32, - "cudnn_tf32": torch.backends.cudnn.allow_tf32, - } - print(json.dumps(system, indent=2)) - print( - f"shape=B{args.batch_size}xT{args.sequence_length}x2, H={args.hidden_size}, " - f"thresholds=({args.thx}, {args.thh}), trainable_params=" - f"{sum(parameter.numel() for parameter in template.parameters()):,}" - ) - - equivalence = None - if device.type == "cuda" and not args.reference_only: - equivalence = check_equivalence(state, args, device) - enforce_equivalence(equivalence) - print("equivalence:") - print(json.dumps(equivalence, indent=2)) - - results = [ - time_implementation( - "eager-clean", - False, - state, - features, - targets, - args, - args.reference_steps_per_block, - ) - ] - if device.type == "cuda" and not args.reference_only: - results.append( - time_implementation( - "triton-fused", - True, - state, - features, - targets, - args, - args.fused_steps_per_block, - ) - ) - - baseline_ms = results[0].median_ms - print( - "\nimplementation median ms/step IQR(block means) " - "steps/block sequence positions/s peak MiB speedup" - ) - for result in results: - speedup = baseline_ms / result.median_ms - print( - f"{result.implementation:<20} {result.median_ms:>12.3f} " - f"{result.iqr_ms:>17.3f} {result.steps_per_block:>12d} " - f"{result.sequence_positions_per_second:>20,.0f} " - f"{result.peak_allocated_mib:>11.1f} {speedup:>9.2f}x" - ) - print( - f" block means ({result.implementation}, ms/step): " - + ", ".join(f"{value:.3f}" for value in result.block_ms) - ) - - payload = { - "system": system, - "config": vars(args) | {"json_out": str(args.json_out) if args.json_out else None}, - "equivalence": equivalence, - "results": [asdict(result) for result in results], - } - if args.json_out: - args.json_out.parent.mkdir(parents=True, exist_ok=True) - args.json_out.write_text(json.dumps(payload, indent=2) + "\n") - print(f"wrote {args.json_out}") - - -if __name__ == "__main__": - main() diff --git a/benchmark/benchmark_volterra.py b/benchmark/benchmark_volterra.py new file mode 100644 index 0000000..cf55d3c --- /dev/null +++ b/benchmark/benchmark_volterra.py @@ -0,0 +1,1025 @@ +"""Polynomial PA-modeling and indirect-learning DPD benchmarks. + +The benchmark supports two complex polynomial structures: + +* MP: a memory polynomial. +* GMP: a generalized memory polynomial with aligned, lagging, and leading + envelope terms. + +For PA modeling, the complex coefficients are identified directly from +``Phi(x) w ~= y``. For DPD, an indirect learning architecture (ILA) first +identifies the postdistorter from ``Phi(y / G) w ~= x``, copies those +coefficients to the predistorter, and evaluates the result through a trained +PA model. + +Large benchmark systems are solved on CUDA after L2 column scaling. The +well-conditioned MP and ILA systems use ``torch.linalg.lstsq`` with ``gels``. +GMP PA modeling can instead use a rank-controlled truncated SVD, because its +correlated cross-term basis does not satisfy CUDA ``gels``'s full-rank +assumption on every dataset. +""" + +from __future__ import annotations + +import argparse +from functools import partial +import hashlib +import json +import math +import os +from pathlib import Path +import time +from typing import Callable + +import numpy as np +import torch + +import models as model +from modules.data_collector import load_dataset +from utils import metrics +from utils.util import count_net_params, set_target_gain + + +REPO_ROOT = Path(__file__).resolve().parent.parent + + +# --------------------------------------------------------------------------- +# NumPy basis implementations (small inputs, reference tests, and API users) +# --------------------------------------------------------------------------- + +def build_mp_basis(x_complex, K, Q): + """Build an MP basis with columns ``x(n-q) * |x(n-q)|**k``.""" + _validate_positive_configuration({"K": K, "Q": Q}) + x_complex = np.asarray(x_complex, dtype=np.complex128) + n_samples = len(x_complex) + phi = np.zeros((n_samples, K * Q), dtype=np.complex128) + column = 0 + for k in range(K): + for q in range(Q): + delayed = _delay(x_complex, q) + phi[:, column] = delayed * np.abs(delayed) ** k + column += 1 + return phi + + +def _delay(x, delay): + """Shift a one-dimensional complex signal and zero-fill the boundary.""" + x = np.asarray(x) + n_samples = len(x) + output = np.zeros(n_samples, dtype=x.dtype) + if 0 <= delay < n_samples: + output[delay:] = x[:n_samples - delay] + elif -n_samples < delay < 0: + output[:n_samples + delay] = x[-delay:] + return output + + +def build_gmp_basis(x_complex, Ka, La, Kb, Lb, Mb, Kc, Lc, Mc): + """Build the aligned, lagging, and leading GMP basis.""" + configuration = { + "Ka": Ka, + "La": La, + "Kb": Kb, + "Lb": Lb, + "Mb": Mb, + "Kc": Kc, + "Lc": Lc, + "Mc": Mc, + } + _validate_positive_configuration(configuration) + x_complex = np.asarray(x_complex, dtype=np.complex128) + n_samples = len(x_complex) + n_columns = gmp_coefficient_count(configuration) + phi = np.zeros((n_samples, n_columns), dtype=np.complex128) + column = 0 + + for k in range(Ka): + for q in range(La): + xq = _delay(x_complex, q) + phi[:, column] = xq * np.abs(xq) ** k + column += 1 + + for k in range(1, Kb + 1): + for q in range(Lb): + for lag in range(1, Mb + 1): + xq = _delay(x_complex, q) + envelope = _delay(x_complex, q + lag) + phi[:, column] = xq * np.abs(envelope) ** k + column += 1 + + for k in range(1, Kc + 1): + for q in range(Lc): + for lead in range(1, Mc + 1): + xq = _delay(x_complex, q) + envelope = _delay(x_complex, q - lead) + phi[:, column] = xq * np.abs(envelope) ** k + column += 1 + + return phi + + +def build_segmented_numpy_basis( + x_complex: np.ndarray, + basis_builder: Callable[[np.ndarray], np.ndarray], + segment_length: int, +) -> np.ndarray: + """Build a reference basis while resetting delays at every segment.""" + if segment_length <= 0: + raise ValueError("segment_length must be positive") + x_complex = np.asarray(x_complex) + blocks = [ + basis_builder(x_complex[start:start + segment_length]) + for start in range(0, len(x_complex), segment_length) + ] + if not blocks: + raise ValueError("cannot build a basis for an empty signal") + return np.concatenate(blocks, axis=0) + + +# --------------------------------------------------------------------------- +# Memory-conscious Torch basis implementations used by the benchmark +# --------------------------------------------------------------------------- + +def _torch_delay(x: torch.Tensor, delay: int) -> torch.Tensor: + output = torch.zeros_like(x) + n_samples = x.numel() + if 0 <= delay < n_samples: + output[delay:] = x[:n_samples - delay] + elif -n_samples < delay < 0: + output[:n_samples + delay] = x[-delay:] + return output + + +def _torch_delay_columns(x: torch.Tensor, delays: list[int]) -> torch.Tensor: + return torch.stack([_torch_delay(x, delay) for delay in delays], dim=1) + + +def _build_mp_basis_torch_block( + x: torch.Tensor, + configuration: dict[str, int], +) -> torch.Tensor: + k_count = configuration["K"] + memory_depth = configuration["Q"] + delayed = _torch_delay_columns(x, list(range(memory_depth))) + magnitudes = torch.abs(delayed) + phi = torch.empty( + (x.numel(), k_count * memory_depth), + dtype=x.dtype, + device=x.device, + ) + for k in range(k_count): + start = k * memory_depth + phi[:, start:start + memory_depth] = delayed * magnitudes.pow(k) + return phi + + +def _build_gmp_basis_torch_block( + x: torch.Tensor, + configuration: dict[str, int], +) -> torch.Tensor: + Ka, La = configuration["Ka"], configuration["La"] + Kb, Lb, Mb = configuration["Kb"], configuration["Lb"], configuration["Mb"] + Kc, Lc, Mc = configuration["Kc"], configuration["Lc"], configuration["Mc"] + phi = torch.empty( + (x.numel(), gmp_coefficient_count(configuration)), + dtype=x.dtype, + device=x.device, + ) + column = 0 + + aligned = _torch_delay_columns(x, list(range(La))) + aligned_magnitude = torch.abs(aligned) + for k in range(Ka): + phi[:, column:column + La] = aligned * aligned_magnitude.pow(k) + column += La + + lag_base = _torch_delay_columns(x, list(range(Lb))) + lag_delays = [q + lag for q in range(Lb) for lag in range(1, Mb + 1)] + lag_envelopes = torch.abs(_torch_delay_columns(x, lag_delays)).reshape( + x.numel(), Lb, Mb + ) + for k in range(1, Kb + 1): + block = lag_base.unsqueeze(-1) * lag_envelopes.pow(k) + width = Lb * Mb + phi[:, column:column + width] = block.reshape(x.numel(), width) + column += width + + lead_base = _torch_delay_columns(x, list(range(Lc))) + lead_delays = [q - lead for q in range(Lc) for lead in range(1, Mc + 1)] + lead_envelopes = torch.abs(_torch_delay_columns(x, lead_delays)).reshape( + x.numel(), Lc, Mc + ) + for k in range(1, Kc + 1): + block = lead_base.unsqueeze(-1) * lead_envelopes.pow(k) + width = Lc * Mc + phi[:, column:column + width] = block.reshape(x.numel(), width) + column += width + + if column != phi.shape[1]: + raise RuntimeError("internal GMP column-count mismatch") + return phi + + +@torch.inference_mode() +def build_torch_basis( + x: torch.Tensor, + polynomial_model: str, + configuration: dict[str, int], + segment_length: int, +) -> torch.Tensor: + """Build a design matrix on ``x.device`` with per-segment delay resets.""" + if x.ndim != 1 or not torch.is_complex(x): + raise ValueError("x must be a one-dimensional complex Torch tensor") + if x.numel() == 0: + raise ValueError("cannot build a basis for an empty signal") + if segment_length <= 0: + raise ValueError("segment_length must be positive") + _validate_positive_configuration(configuration) + + n_columns = coefficient_count(polynomial_model, configuration) + phi = torch.empty( + (x.numel(), n_columns), + dtype=x.dtype, + device=x.device, + ) + build_block = ( + _build_mp_basis_torch_block + if polynomial_model == "mp" + else _build_gmp_basis_torch_block + ) + for start in range(0, x.numel(), segment_length): + stop = min(start + segment_length, x.numel()) + phi[start:stop] = build_block(x[start:stop], configuration) + return phi + + +# --------------------------------------------------------------------------- +# Configuration and least-squares helpers +# --------------------------------------------------------------------------- + +def _validate_positive_configuration(configuration: dict[str, int]) -> None: + for name, value in configuration.items(): + if not isinstance(value, (int, np.integer)) or value <= 0: + raise ValueError(f"{name} must be a positive integer, got {value!r}") + + +def gmp_coefficient_count(configuration: dict[str, int]) -> int: + return ( + configuration["Ka"] * configuration["La"] + + configuration["Kb"] * configuration["Lb"] * configuration["Mb"] + + configuration["Kc"] * configuration["Lc"] * configuration["Mc"] + ) + + +def coefficient_count( + polynomial_model: str, + configuration: dict[str, int], +) -> int: + if polynomial_model == "mp": + return configuration["K"] * configuration["Q"] + if polynomial_model == "gmp": + return gmp_coefficient_count(configuration) + raise ValueError(f"unsupported polynomial model: {polynomial_model!r}") + + +def basis_configuration(args: argparse.Namespace) -> dict[str, int]: + if args.model == "mp": + return {"K": args.K, "Q": args.Q} + return { + "Ka": args.Ka, + "La": args.La, + "Kb": args.Kb, + "Lb": args.Lb, + "Mb": args.Mb, + "Kc": args.Kc, + "Lc": args.Lc, + "Mc": args.Mc, + } + + +def select_basis(args): + """Return the NumPy reference builder, coefficient count, and banner.""" + configuration = basis_configuration(args) + if args.model == "gmp": + builder = partial(build_gmp_basis, **configuration) + banner = ( + "Polynomial GMP " + + ", ".join(f"{name}={value}" for name, value in configuration.items()) + ) + else: + builder = partial(build_mp_basis, **configuration) + banner = f"Polynomial MP K={args.K}, Q={args.Q}" + return builder, coefficient_count(args.model, configuration), banner + + +def resolve_solver_device(requested: str, cuda_index: int) -> torch.device: + requested = requested.strip().lower() + if requested == "cuda": + requested = f"cuda:{cuda_index}" + device = torch.device(requested) + if device.type == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested for the least-squares solve but is unavailable") + if device.index is None: + device = torch.device(f"cuda:{cuda_index}") + elif device.type != "cpu": + raise ValueError("solver device must be 'cpu', 'cuda', or 'cuda:INDEX'") + return device + + +def resolve_complex_dtype(requested: str) -> torch.dtype: + mapping = { + "complex64": torch.complex64, + "torch.complex64": torch.complex64, + "complex128": torch.complex128, + "torch.complex128": torch.complex128, + } + try: + return mapping[requested.lower()] + except KeyError as error: + raise ValueError("solver dtype must be complex64 or complex128") from error + + +def _synchronize(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +@torch.inference_mode() +def fit_complex_least_squares( + basis_input: np.ndarray, + target: np.ndarray, + *, + polynomial_model: str, + configuration: dict[str, int], + segment_length: int, + device: torch.device, + dtype: torch.dtype, + solver_mode: str = "gels", + svd_rcond: float | None = None, +) -> tuple[torch.Tensor, dict[str, float | int | str | None]]: + """Fit complex coefficients after L2 column scaling.""" + basis_input = np.asarray(basis_input) + target = np.asarray(target) + if basis_input.ndim != 1 or target.ndim != 1: + raise ValueError("least-squares input and target must be one-dimensional") + if basis_input.shape != target.shape: + raise ValueError("least-squares input and target must have the same shape") + if not np.all(np.isfinite(basis_input)) or not np.all(np.isfinite(target)): + raise ValueError("least-squares input and target must contain only finite values") + + n_columns = coefficient_count(polynomial_model, configuration) + if basis_input.size < n_columns: + raise ValueError( + f"least-squares system is underdetermined: " + f"{basis_input.size} observations for {n_columns} coefficients" + ) + if solver_mode not in {"gels", "truncated_svd"}: + raise ValueError(f"unsupported solver mode: {solver_mode!r}") + if solver_mode == "gels" and svd_rcond is not None: + raise ValueError("svd_rcond is only valid for truncated_svd") + if solver_mode == "truncated_svd": + if svd_rcond is None or not math.isfinite(svd_rcond): + raise ValueError( + "truncated_svd requires a finite svd_rcond" + ) + if not 0.0 < svd_rcond < 1.0: + raise ValueError("svd_rcond must be between zero and one") + + _synchronize(device) + started = time.perf_counter() + x_tensor = torch.as_tensor(basis_input, dtype=dtype, device=device) + target_tensor = torch.as_tensor(target, dtype=dtype, device=device) + phi = build_torch_basis( + x_tensor, + polynomial_model, + configuration, + segment_length, + ) + _synchronize(device) + basis_seconds = time.perf_counter() - started + + column_norms = torch.linalg.vector_norm(phi, dim=0) + if not bool(torch.all(torch.isfinite(column_norms)).item()): + raise RuntimeError("basis column norms are not finite") + if bool(torch.any(column_norms == 0).item()): + zero_count = int(torch.count_nonzero(column_norms == 0).item()) + raise RuntimeError(f"basis contains {zero_count} all-zero columns") + smallest_norm = float(column_norms.min().item()) + largest_norm = float(column_norms.max().item()) + phi.div_(column_norms) + + _synchronize(device) + solve_started = time.perf_counter() + least_squares_rank: int | None = None + singular_value_max: float | None = None + singular_value_min: float | None = None + singular_value_cutoff: float | None = None + retained_singular_value_min: float | None = None + condition_number: float | None = None + if solver_mode == "gels": + solver_implementation = "torch.linalg.lstsq" + solver_driver = "gels" + regularization = None + try: + solution_scaled = torch.linalg.lstsq( + phi, + target_tensor.unsqueeze(1), + driver=solver_driver, + ).solution.squeeze(1) + except RuntimeError as error: + raise RuntimeError( + "torch.linalg.lstsq failed for the polynomial benchmark. " + "The CUDA gels driver assumes a full-rank design matrix; " + "verify the basis configuration and available accelerator " + "memory." + ) from error + else: + solver_implementation = "torch.linalg.svd" + solver_driver = "gesvdj" if device.type == "cuda" else "default" + regularization = "truncated_svd" + svd_driver = "gesvdj" if device.type == "cuda" else None + try: + left, singular_values, right_h = torch.linalg.svd( + phi, + full_matrices=False, + driver=svd_driver, + ) + except RuntimeError as error: + raise RuntimeError( + "torch.linalg.svd failed for the polynomial benchmark; " + "verify the basis configuration and available accelerator " + "memory." + ) from error + singular_value_max = float(singular_values[0].item()) + singular_value_min = float(singular_values[-1].item()) + singular_value_cutoff = singular_value_max * float(svd_rcond) + retained = singular_values > singular_value_cutoff + least_squares_rank = int(torch.count_nonzero(retained).item()) + if least_squares_rank == 0: + raise RuntimeError( + "truncated SVD rejected every singular direction" + ) + retained_singular_value_min = float( + singular_values[retained][-1].item() + ) + condition_number = ( + singular_value_max / singular_value_min + if singular_value_min > 0.0 + else None + ) + projected_target = left[:, retained].mH @ target_tensor + solution_scaled = right_h[retained].mH @ ( + projected_target / singular_values[retained] + ) + _synchronize(device) + solve_seconds = time.perf_counter() - solve_started + + fitted = phi @ solution_scaled + residual_norm = torch.linalg.vector_norm(fitted - target_tensor) + target_norm = torch.linalg.vector_norm(target_tensor) + relative_residual = residual_norm / target_norm + coefficients = solution_scaled / column_norms + + if not bool(torch.all(torch.isfinite(coefficients)).item()): + raise RuntimeError("least-squares coefficients are not finite") + relative_residual_value = float(relative_residual.item()) + if not math.isfinite(relative_residual_value): + raise RuntimeError("least-squares training relative residual is not finite") + coefficient_l2_norm = float(torch.linalg.vector_norm(coefficients).item()) + if not math.isfinite(coefficient_l2_norm): + raise RuntimeError("least-squares coefficient norm is not finite") + + diagnostics: dict[str, float | int | str | None] = { + "observations": int(basis_input.size), + "columns": int(n_columns), + # The gels driver does not estimate numerical rank. Its documented + # contract assumes that the design matrix is full rank. + "least_squares_rank": least_squares_rank, + "solver_mode": solver_mode, + "solver_implementation": solver_implementation, + "solver_driver": solver_driver, + "regularization": regularization, + "svd_rcond": svd_rcond, + "singular_value_max": singular_value_max, + "singular_value_min": singular_value_min, + "singular_value_cutoff": singular_value_cutoff, + "retained_singular_value_min": retained_singular_value_min, + "condition_number": condition_number, + "coefficient_l2_norm": coefficient_l2_norm, + "training_relative_residual": relative_residual_value, + "training_residual_nmse_db": float( + 20.0 * math.log10(max(relative_residual_value, np.finfo(float).tiny)) + ), + "basis_build_seconds": float(basis_seconds), + "solve_seconds": float(solve_seconds), + "column_norm_min": smallest_norm, + "column_norm_max": largest_norm, + } + return coefficients, diagnostics + + +@torch.inference_mode() +def apply_polynomial( + input_complex: np.ndarray, + coefficients: torch.Tensor, + *, + polynomial_model: str, + configuration: dict[str, int], + segment_length: int, +) -> np.ndarray: + """Apply fitted polynomial coefficients with the training boundary policy.""" + input_tensor = torch.as_tensor( + np.asarray(input_complex), + dtype=coefficients.dtype, + device=coefficients.device, + ) + phi = build_torch_basis( + input_tensor, + polynomial_model, + configuration, + segment_length, + ) + prediction = phi @ coefficients + if not bool(torch.all(torch.isfinite(prediction)).item()): + raise RuntimeError("polynomial prediction contains non-finite values") + return prediction.cpu().numpy() + + +# --------------------------------------------------------------------------- +# Data, PA, and metric helpers +# --------------------------------------------------------------------------- + +def iq_to_complex(iq): + iq = np.asarray(iq) + return iq[:, 0] + 1j * iq[:, 1] + + +def complex_to_iq(complex_signal): + complex_signal = np.asarray(complex_signal) + return np.stack( + [complex_signal.real, complex_signal.imag], + axis=-1, + ).astype(np.float32) + + +def sha256_file(path): + digest = hashlib.sha256() + with open(path, "rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def pa_model_id( + *, + seed: int, + backbone: str, + hidden_size: int, + frame_length: int, + parameter_count: int, +) -> str: + return ( + f"PA_S_{seed}_M_{backbone.upper()}_H_{hidden_size}_" + f"F_{frame_length}_P_{parameter_count}" + ) + + +def resolve_pa_checkpoint( + args: argparse.Namespace, + parameter_count: int, +) -> tuple[Path, str]: + model_id = pa_model_id( + seed=args.seed, + backbone=args.pa_backbone, + hidden_size=args.pa_hidden_size, + frame_length=args.frame_length, + parameter_count=parameter_count, + ) + if args.pa_checkpoint: + checkpoint = Path(args.pa_checkpoint).expanduser() + if not checkpoint.is_absolute(): + checkpoint = REPO_ROOT / checkpoint + else: + checkpoint = ( + REPO_ROOT + / "save" + / args.dataset_name + / "train_pa" + / f"{model_id}.pt" + ) + checkpoint = checkpoint.resolve() + if not checkpoint.is_file(): + raise FileNotFoundError(f"PA checkpoint does not exist: {checkpoint}") + return checkpoint, model_id + + +def _load_state_dict(path: Path): + try: + state = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: # Compatibility with older supported Torch releases. + state = torch.load(path, map_location="cpu") + if isinstance(state, dict) and "state_dict" in state: + state = state["state_dict"] + if not isinstance(state, dict): + raise TypeError(f"PA checkpoint is not a state dictionary: {path}") + return state + + +def load_pa_model( + args: argparse.Namespace, +) -> tuple[torch.nn.Module, torch.device, Path, str, int]: + if args.device < 0: + pa_device = torch.device("cpu") + else: + if not torch.cuda.is_available(): + raise RuntimeError("CUDA PA evaluation was requested but CUDA is unavailable") + pa_device = torch.device(f"cuda:{args.device}") + + pa_net = model.CoreModel( + input_size=2, + hidden_size=args.pa_hidden_size, + num_layers=args.pa_num_layers, + backbone_type=args.pa_backbone, + ) + parameter_count = count_net_params(pa_net) + checkpoint, model_id = resolve_pa_checkpoint(args, parameter_count) + pa_net.load_state_dict(_load_state_dict(checkpoint), strict=True) + pa_net = pa_net.to(pa_device) + pa_net.eval() + return pa_net, pa_device, checkpoint, model_id, parameter_count + + +@torch.inference_mode() +def run_pa_model(iq_signal, pa_net, device, nperseg): + """Evaluate the PA with one independent recurrent state per segment.""" + iq_signal = np.asarray(iq_signal, dtype=np.float32) + n_samples = len(iq_signal) + segments = [] + for start in range(0, n_samples, nperseg): + segment = iq_signal[start:start + nperseg] + if len(segment) < nperseg: + segment = np.vstack( + [ + segment, + np.zeros((nperseg - len(segment), 2), dtype=np.float32), + ] + ) + segments.append(segment) + if not segments: + raise ValueError("cannot evaluate an empty signal") + tensor = torch.as_tensor(np.stack(segments), dtype=torch.float32, device=device) + output = pa_net(tensor).cpu().numpy() + return output.reshape(-1, 2)[:n_samples] + + +def compute_metrics(prediction_iq, ground_truth_iq, spec): + """Compute metrics with the same segmentation and definitions as training.""" + nperseg = int(spec["nperseg"]) + n_samples = min(len(prediction_iq), len(ground_truth_iq)) + n_segments = n_samples // nperseg + if n_segments == 0: + raise ValueError( + f"metric input needs at least one complete {nperseg}-sample segment" + ) + usable = n_segments * nperseg + prediction = np.asarray(prediction_iq[:usable]).reshape(n_segments, nperseg, 2) + ground_truth = np.asarray(ground_truth_iq[:usable]).reshape( + n_segments, nperseg, 2 + ) + nmse = metrics.NMSE(prediction, ground_truth) + evm = metrics.EVM( + prediction, + ground_truth, + sample_rate=int(spec["input_signal_fs"]), + bw_main_ch=spec["bw_main_ch"], + n_sub_ch=spec["n_sub_ch"], + nperseg=nperseg, + ) + aclr_left, aclr_right = metrics.ACLR( + prediction, + fs=spec["input_signal_fs"], + nperseg=nperseg, + bw_main_ch=spec["bw_main_ch"], + n_sub_ch=spec["n_sub_ch"], + ) + return nmse, evm, aclr_left, aclr_right + + +def metric_dict(prediction_iq, ground_truth_iq, spec) -> dict[str, float]: + nmse, evm, aclr_left, aclr_right = compute_metrics( + prediction_iq, + ground_truth_iq, + spec, + ) + result = { + "ACLR_L": float(aclr_left), + "ACLR_R": float(aclr_right), + "ACLR_AVG": float((aclr_left + aclr_right) / 2.0), + "EVM": float(evm), + "NMSE": float(nmse), + } + if not all(math.isfinite(value) for value in result.values()): + raise RuntimeError(f"evaluation produced non-finite metrics: {result}") + return result + + +def display_metrics(split: str, values: dict[str, float]) -> None: + print(f"\n{split} results") + print(f"{'Metric':<12} {'Value':>12}") + print("-" * 26) + for name in ("ACLR_L", "ACLR_R", "ACLR_AVG", "EVM", "NMSE"): + print(f"{name:<12} {values[name]:>12.2f} dB") + + +def _portable_path(path: Path) -> str: + try: + return path.relative_to(REPO_ROOT).as_posix() + except ValueError: + return str(path) + + +# --------------------------------------------------------------------------- +# CLI and benchmark execution +# --------------------------------------------------------------------------- + +def build_arg_parser(): + parser = argparse.ArgumentParser( + description="Fit and evaluate MP/GMP PA or ILA-DPD benchmarks." + ) + parser.add_argument( + "--task", + choices=("pa_modeling", "dpd_ila"), + default="dpd_ila", + help="Benchmark task (default preserves the historical DPD behavior).", + ) + parser.add_argument( + "--dataset-name", + "--dataset_name", + dest="dataset_name", + required=True, + ) + parser.add_argument("--model", choices=("mp", "gmp"), default="mp") + + parser.add_argument("--K", type=int, default=5) + parser.add_argument("--Q", type=int, default=50) + parser.add_argument("--Ka", type=int, default=5) + parser.add_argument("--La", type=int, default=15) + parser.add_argument("--Kb", type=int, default=4) + parser.add_argument("--Lb", type=int, default=15) + parser.add_argument("--Mb", type=int, default=2) + parser.add_argument("--Kc", type=int, default=4) + parser.add_argument("--Lc", type=int, default=15) + parser.add_argument("--Mc", type=int, default=1) + + parser.add_argument( + "--solver-device", + default="cuda", + help="'cuda', 'cuda:INDEX', or 'cpu' (default: cuda).", + ) + parser.add_argument( + "--solver-dtype", + choices=("complex64", "complex128"), + default="complex64", + ) + parser.add_argument( + "--solver-mode", + choices=("gels", "truncated_svd"), + default="gels", + ) + parser.add_argument( + "--svd-rcond", + type=float, + help="Relative singular-value cutoff for truncated_svd.", + ) + parser.add_argument( + "--device", + type=int, + default=0, + help="CUDA index for PA evaluation; -1 selects CPU.", + ) + + parser.add_argument( + "--pa-backbone", + "--pa_backbone", + dest="pa_backbone", + default="tres_gru", + ) + parser.add_argument( + "--pa-hidden-size", + "--pa_hidden_size", + dest="pa_hidden_size", + type=int, + default=24, + ) + parser.add_argument( + "--pa-num-layers", + "--pa_num_layers", + dest="pa_num_layers", + type=int, + default=1, + ) + parser.add_argument("--pa-checkpoint", help="Explicit trained PA checkpoint.") + parser.add_argument("--frame-length", type=int, default=200) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--json-out", help="Write metrics and provenance as JSON.") + return parser + + +def run_benchmark(args: argparse.Namespace) -> dict: + spec_path = REPO_ROOT / "datasets" / args.dataset_name / "spec.json" + with open(spec_path, encoding="utf-8") as file: + spec = json.load(file) + nperseg = int(spec["nperseg"]) + configuration = basis_configuration(args) + _validate_positive_configuration(configuration) + n_complex_coefficients = coefficient_count(args.model, configuration) + n_real_parameters = 2 * n_complex_coefficients + solver_device = resolve_solver_device(args.solver_device, args.device) + solver_dtype = resolve_complex_dtype(args.solver_dtype) + + X_train, y_train, X_val, y_val, X_test, y_test = load_dataset( + dataset_name=args.dataset_name + ) + target_gain = float(set_target_gain(X_train, y_train)) + print( + f"\n=== {args.task}: {args.model.upper()} " + f"({n_complex_coefficients} complex / {n_real_parameters} real) ===" + ) + if args.solver_mode == "truncated_svd": + solver_description = ( + f"torch.linalg.svd truncated at rcond={args.svd_rcond:g}" + ) + else: + solver_description = "torch.linalg.lstsq gels" + print(f"Solver: {solver_description} on {solver_device} ({solver_dtype})") + + common_fit_arguments = { + "polynomial_model": args.model, + "configuration": configuration, + "segment_length": nperseg, + "device": solver_device, + "dtype": solver_dtype, + "solver_mode": args.solver_mode, + "svd_rcond": args.svd_rcond, + } + + if args.task == "pa_modeling": + coefficients, diagnostics = fit_complex_least_squares( + iq_to_complex(X_train), + iq_to_complex(y_train), + **common_fit_arguments, + ) + + validation_prediction = complex_to_iq( + apply_polynomial( + iq_to_complex(X_val), + coefficients, + polynomial_model=args.model, + configuration=configuration, + segment_length=nperseg, + ) + ) + validation_metrics = metric_dict(validation_prediction, y_val, spec) + del validation_prediction + + test_prediction = complex_to_iq( + apply_polynomial( + iq_to_complex(X_test), + coefficients, + polynomial_model=args.model, + configuration=configuration, + segment_length=nperseg, + ) + ) + test_metrics = metric_dict(test_prediction, y_test, spec) + method = "direct_least_squares" + pa_binding = {} + else: + coefficients, diagnostics = fit_complex_least_squares( + iq_to_complex(y_train) / target_gain, + iq_to_complex(X_train), + **common_fit_arguments, + ) + pa_net, pa_device, pa_path, pa_id, pa_parameters = load_pa_model(args) + + validation_dpd = complex_to_iq( + apply_polynomial( + iq_to_complex(X_val), + coefficients, + polynomial_model=args.model, + configuration=configuration, + segment_length=nperseg, + ) + ) + validation_prediction = run_pa_model( + validation_dpd, + pa_net, + pa_device, + nperseg, + ) + validation_target = (target_gain * X_val).astype(np.float32) + validation_metrics = metric_dict( + validation_prediction, + validation_target, + spec, + ) + del validation_dpd, validation_prediction + + test_dpd = complex_to_iq( + apply_polynomial( + iq_to_complex(X_test), + coefficients, + polynomial_model=args.model, + configuration=configuration, + segment_length=nperseg, + ) + ) + test_prediction = run_pa_model(test_dpd, pa_net, pa_device, nperseg) + test_target = (target_gain * X_test).astype(np.float32) + test_metrics = metric_dict(test_prediction, test_target, spec) + method = "indirect_learning_architecture" + pa_binding = { + "pa_backbone": args.pa_backbone, + "pa_hidden_size": int(args.pa_hidden_size), + "pa_num_layers": int(args.pa_num_layers), + "pa_model_id": pa_id, + "pa_parameters": int(pa_parameters), + "pa_evaluation_device": str(pa_device), + "pa_checkpoint": _portable_path(pa_path), + "pa_checkpoint_sha256": sha256_file(pa_path), + } + + display_metrics("Validation", validation_metrics) + display_metrics("Test", test_metrics) + dtype_name = str(solver_dtype) + device_name = str(solver_device) + result = { + "schema_version": 4, + "task": args.task, + "method": method, + "dataset": args.dataset_name, + "model": args.model, + "basis_configuration": configuration, + "complex_coefficients": int(n_complex_coefficients), + "real_parameters": int(n_real_parameters), + "parameter_count_convention": "two real degrees of freedom per complex coefficient", + "target_gain": target_gain, + "sample_rate_hz": float(spec["input_signal_fs"]), + "nperseg": nperseg, + "segment_boundary_policy": "zero delay state at every nperseg boundary", + "solver": diagnostics["solver_implementation"], + "solver_mode": diagnostics["solver_mode"], + "solver_driver": diagnostics["solver_driver"], + "device": device_name, + "dtype": dtype_name, + # Retain explicit aliases so downstream consumers cannot confuse the + # solver device with the separate neural-PA evaluation device. + "solver_device": device_name, + "solver_dtype": dtype_name, + "column_scaling": "l2", + "column_scale_min": diagnostics["column_norm_min"], + "column_scale_max": diagnostics["column_norm_max"], + "full_rank_assumption": args.solver_mode == "gels", + "least_squares_rank": diagnostics["least_squares_rank"], + "regularization": diagnostics["regularization"], + "svd_rcond": diagnostics["svd_rcond"], + "singular_value_max": diagnostics["singular_value_max"], + "singular_value_min": diagnostics["singular_value_min"], + "singular_value_cutoff": diagnostics["singular_value_cutoff"], + "retained_singular_value_min": diagnostics[ + "retained_singular_value_min" + ], + "condition_number": diagnostics["condition_number"], + "coefficient_l2_norm": diagnostics["coefficient_l2_norm"], + "training_relative_residual": diagnostics["training_relative_residual"], + "training_diagnostics": diagnostics, + "determinism": { + "stochastic_fitting": False, + "seed": int(args.seed), + "note": ( + "The design matrix and targets are deterministic; final low-order " + "floating-point bits can vary across CUDA library versions." + ), + }, + "validation": validation_metrics, + "test": test_metrics, + **pa_binding, + } + return result + + +def main(): + args = build_arg_parser().parse_args() + result = run_benchmark(args) + if args.json_out: + output_path = Path(args.json_out).expanduser() + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w", encoding="utf-8") as file: + json.dump(result, file, indent=2, sort_keys=True) + file.write("\n") + print(f"\nRaw results written to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/benchmark/benchmark_volterra_qr.py b/benchmark/benchmark_volterra_qr.py deleted file mode 100644 index c3f9c6e..0000000 --- a/benchmark/benchmark_volterra_qr.py +++ /dev/null @@ -1,314 +0,0 @@ -""" -Benchmark: Volterra-series-based DPD identified via QR decomposition. - -Supports two model structures: - - MP (Memory Polynomial): diagonal Volterra terms only - - GMP (Generalized Memory Polynomial): aligned + lagging + leading cross-terms - -Both identified in closed form via least squares (QR/SVD). - -Indirect Learning Architecture (ILA): - 1. Build basis Phi from normalized PA output z/G - 2. Solve x = Phi * w via QR (postdistorter) - 3. Copy w to predistorter: x_dpd = Phi(x) * w - 4. Pass through PA model: PA(x_dpd) vs G*x -""" - -import argparse -import json -import os -import numpy as np -import torch - -# Project imports -from modules.data_collector import load_dataset -from utils.util import set_target_gain -from utils import metrics -import models as model - - -# --------------------------------------------------------------------------- -# Memory Polynomial basis -# --------------------------------------------------------------------------- - -def build_mp_basis(x_complex, K, Q): - """ - Build Memory Polynomial basis matrix. - - Basis functions: phi_{k,q}(n) = x(n-q) * |x(n-q)|^k - for k = 0..K-1, q = 0..Q-1 - - Parameters - ---------- - x_complex : ndarray, shape (N,), complex - K : int - number of nonlinearity orders - Q : int - memory depth - - Returns - ------- - Phi : ndarray, shape (N, K*Q), complex - """ - N = len(x_complex) - Phi = np.zeros((N, K * Q), dtype=np.complex128) - col = 0 - for k in range(K): - for q in range(Q): - if q == 0: - xd = x_complex - else: - xd = np.zeros(N, dtype=np.complex128) - xd[q:] = x_complex[:N - q] - Phi[:, col] = xd * np.abs(xd) ** k - col += 1 - return Phi - - -# --------------------------------------------------------------------------- -# GMP basis (Morgan et al., IEEE TSP 2006) -# --------------------------------------------------------------------------- - -def _delay(x, d): - """Delay complex signal x by d samples (zero-pad).""" - N = len(x) - out = np.zeros(N, dtype=np.complex128) - if d >= 0 and d < N: - out[d:] = x[:N - d] - elif d < 0 and -d < N: - out[:N + d] = x[-d:] - return out - - -def build_gmp_basis(x_complex, Ka, La, Kb, Lb, Mb, Kc, Lc, Mc): - """ - Build Generalized Memory Polynomial basis matrix. - - Three components: - Aligned: x(n-q) * |x(n-q)|^k k=0..Ka-1, q=0..La-1 - Lagging: x(n-q) * |x(n-q-l)|^k k=1..Kb, q=0..Lb-1, l=1..Mb - Leading: x(n-q) * |x(n-q+l)|^k k=1..Kc, q=0..Lc-1, l=1..Mc - - Returns Phi of shape (N, Ka*La + Kb*Lb*Mb + Kc*Lc*Mc), complex. - """ - N = len(x_complex) - n_cols = Ka * La + Kb * Lb * Mb + Kc * Lc * Mc - Phi = np.zeros((N, n_cols), dtype=np.complex128) - col = 0 - - # Aligned terms - for k in range(Ka): - for q in range(La): - xq = _delay(x_complex, q) - Phi[:, col] = xq * np.abs(xq) ** k - col += 1 - - # Lagging cross-terms - for k in range(1, Kb + 1): - for q in range(Lb): - for l in range(1, Mb + 1): - xq = _delay(x_complex, q) - xql = _delay(x_complex, q + l) - Phi[:, col] = xq * np.abs(xql) ** k - col += 1 - - # Leading cross-terms - for k in range(1, Kc + 1): - for q in range(Lc): - for l in range(1, Mc + 1): - xq = _delay(x_complex, q) - xql = _delay(x_complex, q - l) - Phi[:, col] = xq * np.abs(xql) ** k - col += 1 - - return Phi - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def iq_to_complex(iq): - return iq[:, 0] + 1j * iq[:, 1] - - -def complex_to_iq(c): - return np.stack([c.real, c.imag], axis=-1).astype(np.float32) - - -def run_pa_model(iq_signal, pa_net, device, nperseg): - """Pass an IQ signal through the trained PA model segment-by-segment.""" - pa_net.eval() - N = len(iq_signal) - segments = [] - for i in range(0, N, nperseg): - seg = iq_signal[i:i + nperseg] - if len(seg) < nperseg: - pad = np.zeros((nperseg - len(seg), 2), dtype=np.float32) - seg = np.vstack([seg, pad]) - segments.append(seg) - segments = np.array(segments) # (n_seg, nperseg, 2) - - with torch.no_grad(): - out = pa_net(torch.tensor(segments, dtype=torch.float32).to(device)) - out = out.cpu().numpy() - - return out.reshape(-1, 2)[:N] - - -def compute_metrics(prediction_iq, ground_truth_iq, spec): - """Compute NMSE, EVM, ACLR on segmented data (same as OpenDPD training).""" - nperseg = int(spec['nperseg']) - fs = spec['input_signal_fs'] - bw = spec['bw_main_ch'] - nsc = spec['n_sub_ch'] - - # Segment prediction and ground truth - N = len(prediction_iq) - n_seg = N // nperseg - pred_seg = prediction_iq[:n_seg * nperseg].reshape(n_seg, nperseg, 2) - gt_seg = ground_truth_iq[:n_seg * nperseg].reshape(n_seg, nperseg, 2) - - nmse = metrics.NMSE(pred_seg, gt_seg) - evm = metrics.EVM(pred_seg, gt_seg, sample_rate=int(fs), - bw_main_ch=bw, n_sub_ch=nsc, nperseg=nperseg) - aclr_l, aclr_r = metrics.ACLR(pred_seg, fs=fs, nperseg=nperseg, - bw_main_ch=bw, n_sub_ch=nsc) - return nmse, evm, aclr_l, aclr_r - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--dataset_name', type=str, required=True) - parser.add_argument('--pa_hidden_size', type=int, default=24) - parser.add_argument('--model', type=str, default='mp', choices=['mp', 'gmp'], - help='Model type: mp (Memory Polynomial) or gmp (Generalized MP)') - # MP parameters - parser.add_argument('--K', type=int, default=5, - help='Nonlinearity order (number of |x|^k terms)') - parser.add_argument('--Q', type=int, default=50, - help='Memory depth (number of delay taps) for MP') - # GMP parameters - parser.add_argument('--Ka', type=int, default=5, help='GMP aligned nonlinearity orders') - parser.add_argument('--La', type=int, default=15, help='GMP aligned memory depth') - parser.add_argument('--Kb', type=int, default=4, help='GMP lagging nonlinearity orders') - parser.add_argument('--Lb', type=int, default=15, help='GMP lagging memory depth') - parser.add_argument('--Mb', type=int, default=2, help='GMP lagging cross-term depth') - parser.add_argument('--Kc', type=int, default=4, help='GMP leading nonlinearity orders') - parser.add_argument('--Lc', type=int, default=15, help='GMP leading memory depth') - parser.add_argument('--Mc', type=int, default=1, help='GMP leading cross-term depth') - parser.add_argument('--device', type=int, default=0) - args = parser.parse_args() - - # Load spec - spec_path = os.path.join('datasets', args.dataset_name, 'spec.json') - with open(spec_path) as f: - spec = json.load(f) - nperseg = int(spec['nperseg']) - - # Load dataset - X_train, y_train, X_val, y_val, X_test, y_test = load_dataset( - dataset_name=args.dataset_name) - - # Target gain (same as OpenDPD) - target_gain = set_target_gain(X_train, y_train) - - # Load PA model - device = torch.device(f'cuda:{args.device}') - pa_net = model.CoreModel(input_size=2, hidden_size=args.pa_hidden_size, - num_layers=1, backbone_type='gru') - from utils.util import count_net_params - n_pa = count_net_params(pa_net) - pa_path = (f'save/{args.dataset_name}/train_pa/' - f'PA_S_0_M_GRU_H_{args.pa_hidden_size}_F_200_P_{n_pa}.pt') - pa_net.load_state_dict(torch.load(pa_path, map_location='cpu')) - pa_net = pa_net.to(device) - - # ----------------------------------------------------------------------- - # Step 1: Identify DPD via ILA + QR - # ----------------------------------------------------------------------- - K, Q = args.K, args.Q - if args.model == 'gmp': - def build_basis(x): - return build_gmp_basis(x, args.Ka, args.La, args.Kb, args.Lb, - args.Mb, args.Kc, args.Lc, args.Mc) - n_complex_coeffs = (args.Ka * args.La + args.Kb * args.Lb * args.Mb - + args.Kc * args.Lc * args.Mc) - print(f"\n=== Volterra GMP DPD via QR (Ka={args.Ka}, La={args.La}, " - f"Kb={args.Kb}, Lb={args.Lb}, Mb={args.Mb}, " - f"Kc={args.Kc}, Lc={args.Lc}, Mc={args.Mc}) ===") - else: - def build_basis(x): - return build_mp_basis(x, K, Q) - n_complex_coeffs = K * Q - print(f"\n=== Volterra MP DPD via QR (K={K}, Q={Q}) ===") - n_real_params = 2 * n_complex_coeffs - print(f"Complex coefficients : {n_complex_coeffs}") - print(f"Real parameters : {n_real_params}") - - # Convert to complex - x_train_c = iq_to_complex(X_train) - z_train_c = iq_to_complex(y_train) # measured PA output - - # Normalise PA output by target gain - z_norm = z_train_c / target_gain - - # Build basis from normalised PA output (postdistorter input) - Phi_train = build_basis(z_norm) - - # Solve x_train = Phi_train * w via least squares (uses QR/SVD internally) - w, residuals, rank, sv = np.linalg.lstsq(Phi_train, x_train_c, rcond=None) - print(f"Least-squares rank : {rank} / {n_complex_coeffs}") - - # ----------------------------------------------------------------------- - # Step 2: Apply DPD to test data - # ----------------------------------------------------------------------- - x_test_c = iq_to_complex(X_test) - - # Build basis from test input (predistorter) - Phi_test = build_basis(x_test_c) - x_dpd_c = Phi_test @ w # predistorted signal - x_dpd_iq = complex_to_iq(x_dpd_c) - - # ----------------------------------------------------------------------- - # Step 3: Pass through PA model - # ----------------------------------------------------------------------- - pa_output_iq = run_pa_model(x_dpd_iq, pa_net, device, nperseg) - - # Ground truth for DPD: target_gain * x_test - gt_iq = (target_gain * X_test).astype(np.float32) - - # ----------------------------------------------------------------------- - # Step 4: Metrics - # ----------------------------------------------------------------------- - nmse, evm, aclr_l, aclr_r = compute_metrics(pa_output_iq, gt_iq, spec) - aclr_avg = (aclr_l + aclr_r) / 2 - - print(f"\nTest Results:") - print(f"{'Metric':<12} {'Value':>12}") - print("-" * 26) - print(f"{'ACLR_L':<12} {aclr_l:>12.2f} dB") - print(f"{'ACLR_R':<12} {aclr_r:>12.2f} dB") - print(f"{'ACLR_AVG':<12} {aclr_avg:>12.2f} dB") - print(f"{'EVM':<12} {evm:>12.2f} dB") - print(f"{'NMSE':<12} {nmse:>12.2f} dB") - - # Also run on val set - x_val_c = iq_to_complex(X_val) - Phi_val = build_basis(x_val_c) - x_dpd_val_c = Phi_val @ w - x_dpd_val_iq = complex_to_iq(x_dpd_val_c) - pa_val_iq = run_pa_model(x_dpd_val_iq, pa_net, device, nperseg) - gt_val_iq = (target_gain * X_val).astype(np.float32) - v_nmse, v_evm, v_aclr_l, v_aclr_r = compute_metrics(pa_val_iq, gt_val_iq, spec) - v_aclr_avg = (v_aclr_l + v_aclr_r) / 2 - print(f"\nVal Results:") - print(f"{'ACLR_AVG':<12} {v_aclr_avg:>12.2f} dB") - print(f"{'EVM':<12} {v_evm:>12.2f} dB") - print(f"{'NMSE':<12} {v_nmse:>12.2f} dB") - - -if __name__ == '__main__': - main() diff --git a/benchmark/collect_benchmark_report.py b/benchmark/collect_benchmark_report.py new file mode 100644 index 0000000..9e78f51 --- /dev/null +++ b/benchmark/collect_benchmark_report.py @@ -0,0 +1,3011 @@ +"""Collect one benchmark-report reproduction run into JSON and Markdown. + +The training entry point writes checkpoints and CSV logs to architecture-based +paths under ``save/`` and ``log/``. The benchmark runner archives those files +into a run-specific directory, then calls this module to validate the expected +recipes and assemble a portable evidence bundle. +""" + +from __future__ import annotations + +import argparse +import copy +import csv +from datetime import datetime, timezone +import gzip +import hashlib +import importlib.metadata +import io +import json +import math +import os +from pathlib import Path +import platform +import shlex +import subprocess +import tarfile +import tempfile +from typing import Any + + +REPO_ROOT = Path(__file__).resolve().parent.parent +RUN_SCHEMA_VERSION = 6 +POLYNOMIAL_SCHEMA_VERSION = 4 +GMP_PA_SVD_RCOND = 1e-4 +PA_MODEL_ORDER = ("mp", "gmp", "gru", "tres_gru", "tres_deltagru") +DPD_MODEL_ORDER = ("mp", "gmp", "gru", "tres_gru") + +DATASETS = { + "APA_200MHz": { + "slug": "apa", + "sample_rate_hz": 983_040_000.0, + "nperseg": 19_662, + }, + "DPA_160MHz": { + "slug": "dpa", + "sample_rate_hz": 640_000_000.0, + "nperseg": 16_384, + }, +} + +NEURAL_PA_MODELS = { + "gru": { + "display_name": "GRU-H28", + "backbone": "gru", + "hidden_size": 28, + "parameters": 2746, + "model_id": "PA_S_0_M_GRU_H_28_F_200_P_2746", + }, + "tres_gru": { + "display_name": "TRes-GRU-H27", + "backbone": "tres_gru", + "hidden_size": 27, + "parameters": 2751, + "model_id": "PA_S_0_M_TRES_GRU_H_27_F_200_P_2751", + }, + "tres_deltagru": { + "display_name": "TRes-DeltaGRU-H27 (THX=THH=0)", + "backbone": "tres_deltagru", + "hidden_size": 27, + "parameters": 2751, + "model_id": "PA_S_0_M_TRES_DELTAGRU_H_27_F_200_P_2751", + "delta_thresholds": { + "input": 0.0, + "hidden": 0.0, + }, + }, +} + +REFERENCE_PA_KEY = "tres_gru" +REFERENCE_PA_PARENT = "PA_S_0_M_TRES_GRU_H_27_F_200" +ALTERNATE_PA_KEY = "tres_deltagru" +ALTERNATE_PA_PARENT = "PA_S_0_M_TRES_DELTAGRU_H_27_F_200" + +NEURAL_DPD_MODELS = { + "gru": { + "display_name": "GRU-H16", + "backbone": "gru", + "hidden_size": 16, + "parameters": 994, + "model_id": "DPD_S_0_M_GRU_H_16_F_200_P_994", + }, + "tres_gru": { + "display_name": "TRes-GRU-H15", + "backbone": "tres_gru", + "hidden_size": 15, + "parameters": 999, + "model_id": "DPD_S_0_M_TRES_GRU_H_15_F_200_P_999", + }, +} + +TRES_DELTAGRU_DPD_MODEL = { + "display_name": "TRes-DeltaGRU-H15 (THX=THH=0)", + "backbone": "tres_deltagru", + "hidden_size": 15, + "parameters": 999, + "model_id": ( + "DPD_S_0_M_TRES_DELTAGRU_H_15_F_200_P_999_" + "THX_0.000_THH_0.000" + ), + "delta_thresholds": { + "input": 0.0, + "hidden": 0.0, + }, +} + +NEURAL_DPD_EXPERIMENTS = { + **{ + f"{model_key}_via_{REFERENCE_PA_KEY}_pa": { + "model_key": model_key, + "pa_model_key": REFERENCE_PA_KEY, + "pa_parent": REFERENCE_PA_PARENT, + } + for model_key in NEURAL_DPD_MODELS + }, + "tres_deltagru_via_tres_gru_pa": { + "model_key": "tres_deltagru", + "pa_model_key": REFERENCE_PA_KEY, + "pa_parent": REFERENCE_PA_PARENT, + }, + "tres_deltagru_via_tres_deltagru_pa": { + "model_key": "tres_deltagru", + "pa_model_key": ALTERNATE_PA_KEY, + "pa_parent": ALTERNATE_PA_PARENT, + }, +} + +POLYNOMIAL_CONFIGURATIONS = { + "pa_modeling": { + "mp": { + "basis_configuration": {"K": 9, "Q": 150}, + "complex_coefficients": 1350, + "real_parameters": 2700, + }, + "gmp": { + "basis_configuration": { + "Ka": 5, + "La": 30, + "Kb": 4, + "Lb": 30, + "Mb": 5, + "Kc": 4, + "Lc": 30, + "Mc": 5, + }, + "complex_coefficients": 1350, + "real_parameters": 2700, + }, + }, + "dpd_ila": { + "mp": { + "basis_configuration": {"K": 5, "Q": 100}, + "complex_coefficients": 500, + "real_parameters": 1000, + }, + "gmp": { + "basis_configuration": { + "Ka": 5, + "La": 20, + "Kb": 4, + "Lb": 20, + "Mb": 3, + "Kc": 4, + "Lc": 20, + "Mc": 2, + }, + "complex_coefficients": 500, + "real_parameters": 1000, + }, + }, +} + +NEURAL_RECIPE = { + "epochs": 300, + "batch_size": 64, + "initial_learning_rate": 5e-3, + "lr_schedule": "ReduceLROnPlateau", + "minimum_learning_rate": 5e-5, + "decay_factor": 0.5, + "patience": 5, + "frame_length": 200, + "frame_stride": 1, + "optimizer": "AdamW", + "optimizer_weight_decay": 0.01, + "optimizer_betas": [0.9, 0.999], + "optimizer_eps": 1e-8, + "loss": "MSE", + "scheduler_threshold": 1e-4, + "scheduler_threshold_mode": "rel", + "scheduler_cooldown": 0, + "scheduler_eps": 1e-8, + "seed": 0, +} + +SOURCE_PATTERNS = ( + "*.py", + "backbones/**/*.py", + "benchmark/**/*.py", + "modules/**/*.py", + "opendpd/**/*.py", + "quant/**/*.py", + "steps/**/*.py", + "utils/**/*.py", +) +SOURCE_DOCUMENTS = ( + "benchmark/benchmark_report.md", + "benchmark/reproduce_benchmark_report.sh", +) + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def write_source_snapshot(paths: list[str], destination: Path) -> None: + """Write an atomic, deterministic archive of the exact benchmark sources.""" + temporary_path: Path | None = None + try: + with tempfile.NamedTemporaryFile( + dir=destination.parent, + prefix=f".{destination.name}.", + suffix=".tmp", + delete=False, + ) as temporary: + temporary_path = Path(temporary.name) + with temporary_path.open("wb") as raw_handle: + with gzip.GzipFile( + filename="", + mode="wb", + fileobj=raw_handle, + mtime=0, + ) as gzip_handle: + with tarfile.open( + fileobj=gzip_handle, + mode="w", + format=tarfile.GNU_FORMAT, + ) as archive: + for relative_path in sorted(paths): + source = REPO_ROOT / relative_path + if not source.is_file() or source.is_symlink(): + raise ValueError( + "Source snapshot entries must be regular files: " + f"{relative_path}" + ) + content = source.read_bytes() + information = tarfile.TarInfo(relative_path) + information.size = len(content) + information.mode = source.stat().st_mode & 0o777 + information.mtime = 0 + information.uid = 0 + information.gid = 0 + information.uname = "" + information.gname = "" + archive.addfile(information, io.BytesIO(content)) + os.replace(temporary_path, destination) + finally: + if temporary_path is not None and temporary_path.exists(): + temporary_path.unlink() + + +def validate_source_snapshot( + path: Path, + expected_hashes: dict[str, str], +) -> dict[str, Any]: + """Verify every archive member against the pre-run source hash map.""" + if not path.is_file(): + raise FileNotFoundError(f"Source snapshot is missing: {path}") + observed: dict[str, str] = {} + with tarfile.open(path, mode="r:gz") as archive: + for member in archive.getmembers(): + if not member.isfile(): + raise ValueError( + f"Source snapshot contains a non-file member: {member.name}" + ) + if member.name in observed: + raise ValueError( + f"Source snapshot contains a duplicate member: {member.name}" + ) + handle = archive.extractfile(member) + if handle is None: + raise ValueError( + f"Cannot read source snapshot member: {member.name}" + ) + observed[member.name] = hashlib.sha256(handle.read()).hexdigest() + require_equal(observed, expected_hashes, "source snapshot members") + return { + "path": path.name, + "sha256": sha256_file(path), + "file_count": len(observed), + } + + +def read_csv(path: Path) -> list[dict[str, str]]: + with path.open(newline="") as handle: + rows = list(csv.DictReader(handle)) + if not rows: + raise ValueError(f"CSV contains no data rows: {path}") + return rows + + +def as_int(row: dict[str, str], key: str) -> int: + value = as_float(row, key) + if not value.is_integer(): + raise ValueError(f"{key} is not an integer: {value}") + return int(value) + + +def as_float(row: dict[str, str], key: str) -> float: + value = float(row[key]) + if not math.isfinite(value): + raise ValueError(f"{key} is not finite: {value}") + return value + + +def require_equal(actual: Any, expected: Any, description: str) -> None: + if actual != expected: + raise ValueError( + f"{description} does not match the benchmark recipe: " + f"expected {expected!r}, got {actual!r}" + ) + + +def require_close( + actual: float, + expected: float, + description: str, + *, + tolerance: float = 1e-12, +) -> None: + if not math.isclose(actual, expected, rel_tol=0.0, abs_tol=tolerance): + raise ValueError( + f"{description} does not match the benchmark recipe: " + f"expected {expected}, got {actual}" + ) + + +def metric_block(row: dict[str, str]) -> dict[str, dict[str, float]]: + def split(prefix: str) -> dict[str, float]: + metrics = { + "loss": as_float(row, f"{prefix}_LOSS"), + "nmse_db": as_float(row, f"{prefix}_NMSE"), + "evm_db": as_float(row, f"{prefix}_EVM"), + "aclr_left_db": as_float(row, f"{prefix}_ACLR_L"), + "aclr_right_db": as_float(row, f"{prefix}_ACLR_R"), + "aclr_avg_db": as_float(row, f"{prefix}_ACLR_AVG"), + } + require_close( + metrics["aclr_avg_db"], + (metrics["aclr_left_db"] + metrics["aclr_right_db"]) / 2.0, + f"{prefix} logged ACLR average", + tolerance=1e-8, + ) + return metrics + + return {"validation": split("VAL"), "test": split("TEST")} + + +def source_files() -> tuple[str, ...]: + paths = { + path.relative_to(REPO_ROOT).as_posix() + for pattern in SOURCE_PATTERNS + for path in REPO_ROOT.glob(pattern) + if path.is_file() + } + paths.update(SOURCE_DOCUMENTS) + missing = [path for path in paths if not (REPO_ROOT / path).is_file()] + if missing: + raise FileNotFoundError( + f"Expected benchmark source files are missing: {sorted(missing)}" + ) + return tuple(sorted(paths)) + + +def validate_checkpoint(path: Path, expected_parameters: int) -> None: + import torch + + try: + state_dict = torch.load(path, map_location="cpu", weights_only=True) + except Exception as exc: + raise ValueError(f"Checkpoint cannot be loaded safely: {path}") from exc + if not isinstance(state_dict, dict) or not state_dict: + raise ValueError(f"Checkpoint is not a non-empty state dictionary: {path}") + if not all( + isinstance(key, str) and isinstance(value, torch.Tensor) + for key, value in state_dict.items() + ): + raise ValueError(f"Checkpoint contains non-tensor state entries: {path}") + parameter_count = sum(value.numel() for value in state_dict.values()) + require_equal( + parameter_count, + expected_parameters, + f"checkpoint parameter count for {path}", + ) + if not all(torch.isfinite(value).all().item() for value in state_dict.values()): + raise ValueError(f"Checkpoint contains a non-finite tensor: {path}") + + +def archived_artifact(output_dir: Path, source: Path) -> dict[str, Any]: + absolute_source = REPO_ROOT / source + archived_path = output_dir / "artifacts" / source + if not absolute_source.is_file(): + raise FileNotFoundError(f"Expected benchmark artifact is missing: {source}") + if not archived_path.is_file(): + raise FileNotFoundError( + f"Benchmark artifact was not archived into the run directory: {source}" + ) + source_hash = sha256_file(absolute_source) + archived_hash = sha256_file(archived_path) + require_equal(archived_hash, source_hash, f"archived hash for {source}") + return { + "source_path": source.as_posix(), + "archived_path": archived_path.relative_to(output_dir).as_posix(), + "sha256": source_hash, + "size_bytes": absolute_source.stat().st_size, + } + + +def read_pa_bindings( + output_dir: Path, +) -> dict[tuple[str, str, str], dict[str, str]]: + path = output_dir / "pa_checkpoint_bindings.tsv" + with path.open(newline="") as handle: + rows = list(csv.DictReader(handle, delimiter="\t")) + require_equal( + len(rows), + len(DATASETS) * len(NEURAL_DPD_EXPERIMENTS), + "neural DPD PA checkpoint binding count", + ) + + bindings: dict[tuple[str, str, str], dict[str, str]] = {} + for row in rows: + key = ( + row["dataset"], + row["dpd_model_key"], + row["pa_model_key"], + ) + if key in bindings: + raise ValueError(f"Duplicate PA checkpoint binding: {key}") + if row["dataset"] not in DATASETS: + raise ValueError(f"Unknown dataset in PA checkpoint binding: {key}") + if ( + row["dpd_model_key"] not in NEURAL_DPD_MODELS + and row["dpd_model_key"] != "tres_deltagru" + ): + raise ValueError(f"Unknown model in PA checkpoint binding: {key}") + if row["pa_model_key"] not in {REFERENCE_PA_KEY, ALTERNATE_PA_KEY}: + raise ValueError(f"Unknown PA surrogate in checkpoint binding: {key}") + if len(row["sha256"]) != 64: + raise ValueError(f"Invalid PA checkpoint hash for {key}") + bindings[key] = row + + expected_keys = { + ( + dataset, + experiment["model_key"], + experiment["pa_model_key"], + ) + for dataset in DATASETS + for experiment in NEURAL_DPD_EXPERIMENTS.values() + } + require_equal(set(bindings), expected_keys, "neural DPD PA checkpoint bindings") + return bindings + + +def read_recorded_commands(path: Path) -> dict[str, list[str]]: + commands: dict[str, list[str]] = {} + current_label: str | None = None + for raw_line in path.read_text().splitlines(): + line = raw_line.strip() + if line.startswith("# "): + current_label = line[2:] + if current_label in commands: + raise ValueError(f"Duplicate command label in {path}: {current_label}") + continue + if not line: + continue + if current_label is None: + raise ValueError(f"Unlabelled command in {path}: {line}") + commands[current_label] = shlex.split(line) + current_label = None + if current_label is not None: + raise ValueError(f"Command label has no command in {path}: {current_label}") + return commands + + +def cli_options(command: list[str], *, label: str) -> dict[str, str]: + try: + main_index = command.index("main.py") + except ValueError as exc: + raise ValueError(f"Recorded training command lacks main.py: {label}") from exc + tokens = command[main_index + 1 :] + if len(tokens) % 2: + raise ValueError(f"Recorded training command has an unpaired option: {label}") + options: dict[str, str] = {} + for option, value in zip(tokens[::2], tokens[1::2]): + if not option.startswith("--"): + raise ValueError( + f"Unexpected positional token in recorded training command " + f"{label}: {option}" + ) + key = option[2:] + if key in options: + raise ValueError(f"Duplicate --{key} in recorded command: {label}") + options[key] = value + return options + + +def validate_neural_commands(path: Path, device: int | None = None) -> None: + commands = read_recorded_commands(path) + training_commands = { + label: cli_options(command, label=label) + for label, command in commands.items() + if "main.py" in command + } + require_equal( + len(training_commands), + len(DATASETS) * (len(NEURAL_PA_MODELS) + len(NEURAL_DPD_EXPERIMENTS)), + "recorded neural training command count", + ) + + expected_matrix = { + *( + ( + "train_pa", + dataset, + model["backbone"], + model["hidden_size"], + None, + None, + ) + for dataset in DATASETS + for model in NEURAL_PA_MODELS.values() + ), + *( + ( + "train_dpd", + dataset, + ( + NEURAL_DPD_MODELS[experiment["model_key"]] + if experiment["model_key"] in NEURAL_DPD_MODELS + else TRES_DELTAGRU_DPD_MODEL + )["backbone"], + ( + NEURAL_DPD_MODELS[experiment["model_key"]] + if experiment["model_key"] in NEURAL_DPD_MODELS + else TRES_DELTAGRU_DPD_MODEL + )["hidden_size"], + NEURAL_PA_MODELS[experiment["pa_model_key"]]["backbone"], + NEURAL_PA_MODELS[experiment["pa_model_key"]]["hidden_size"], + ) + for dataset in DATASETS + for experiment in NEURAL_DPD_EXPERIMENTS.values() + ), + } + actual_matrix: set[tuple[str, str, str, int, str | None, int | None]] = set() + for label, options in training_commands.items(): + step = options.get("step") + if step not in {"train_pa", "train_dpd"}: + raise ValueError(f"Unexpected training step in {label}: {step!r}") + backbone_key = "PA_backbone" if step == "train_pa" else "DPD_backbone" + hidden_key = "PA_hidden_size" if step == "train_pa" else "DPD_hidden_size" + try: + matrix_key = ( + step, + options["dataset_name"], + options[backbone_key], + int(options[hidden_key]), + options.get("PA_backbone") if step == "train_dpd" else None, + ( + int(options["PA_hidden_size"]) + if step == "train_dpd" + else None + ), + ) + except (KeyError, ValueError) as exc: + raise ValueError(f"Incomplete model recipe in command {label}") from exc + if matrix_key in actual_matrix: + raise ValueError(f"Duplicate neural benchmark command: {matrix_key}") + actual_matrix.add(matrix_key) + + integer_expectations = { + "n_epochs": NEURAL_RECIPE["epochs"], + "batch_size": NEURAL_RECIPE["batch_size"], + "batch_size_eval": NEURAL_RECIPE["batch_size"], + "lr_schedule": 1, + "frame_length": NEURAL_RECIPE["frame_length"], + "frame_stride": NEURAL_RECIPE["frame_stride"], + "PA_num_layers": 1, + "DPD_num_layers": 1, + "log_precision": 8, + "eval_val": 1, + "eval_test": 1, + "seed": NEURAL_RECIPE["seed"], + } + if device is not None: + integer_expectations["devices"] = device + for option, expected in integer_expectations.items(): + try: + actual = int(options[option]) + except (KeyError, ValueError) as exc: + raise ValueError( + f"Missing or invalid --{option} in command {label}" + ) from exc + require_equal(actual, expected, f"--{option} in command {label}") + + float_expectations = { + "lr": NEURAL_RECIPE["initial_learning_rate"], + "lr_end": NEURAL_RECIPE["minimum_learning_rate"], + "decay_factor": NEURAL_RECIPE["decay_factor"], + "patience": NEURAL_RECIPE["patience"], + "grad_clip_val": 200.0, + "thx": 0.0, + "thh": 0.0, + } + for option, expected in float_expectations.items(): + try: + actual = float(options[option]) + except (KeyError, ValueError) as exc: + raise ValueError( + f"Missing or invalid --{option} in command {label}" + ) from exc + require_close(actual, expected, f"--{option} in command {label}") + + require_equal(options.get("opt_type"), "adamw", f"optimizer in {label}") + require_equal(options.get("loss_type"), "l2", f"loss in {label}") + require_equal(options.get("accelerator"), "cuda", f"accelerator in {label}") + require_equal( + options.get("re_level"), + "soft", + f"reproducibility level in {label}", + ) + require_equal( + actual_matrix, + expected_matrix, + "recorded neural model matrix", + ) + + +def module_cli_options( + command: list[str], + *, + label: str, + module: str, +) -> dict[str, str]: + try: + module_index = command.index("-m") + except ValueError as exc: + raise ValueError(f"Recorded command lacks -m {module}: {label}") from exc + if ( + module_index + 1 >= len(command) + or command[module_index + 1] != module + ): + raise ValueError(f"Recorded command uses the wrong module: {label}") + tokens = command[module_index + 2 :] + if len(tokens) % 2: + raise ValueError(f"Recorded module command has an unpaired option: {label}") + options: dict[str, str] = {} + for option, value in zip(tokens[::2], tokens[1::2]): + if not option.startswith("--"): + raise ValueError( + f"Unexpected positional token in recorded command {label}: " + f"{option}" + ) + key = option[2:] + if key in options: + raise ValueError(f"Duplicate --{key} in recorded command: {label}") + options[key] = value + return options + + +def validate_runner_commands( + path: Path, + *, + output_dir: Path, + device: int, +) -> None: + """Validate the complete recorded neural and polynomial experiment matrix.""" + validate_neural_commands(path, device=device) + commands = read_recorded_commands(path) + expected_labels = {"snapshot_context", "collect_report"} + expected_labels.update( + f"train_pa_{configuration['slug']}_{model}" + for configuration in DATASETS.values() + for model in NEURAL_PA_MODELS + ) + expected_labels.update( + f"train_dpd_{configuration['slug']}_{experiment_key}" + for configuration in DATASETS.values() + for experiment_key in NEURAL_DPD_EXPERIMENTS + ) + expected_labels.update( + f"pa_model_{configuration['slug']}_{model}" + for configuration in DATASETS.values() + for model in ("mp", "gmp") + ) + expected_labels.update( + f"dpd_ila_{configuration['slug']}_{model}" + for configuration in DATASETS.values() + for model in ("mp", "gmp") + ) + require_equal(set(commands), expected_labels, "recorded benchmark job matrix") + + for label in ("snapshot_context", "collect_report"): + command = commands[label] + require_equal( + command.count("benchmark.collect_benchmark_report"), + 1, + f"collector module in {label}", + ) + require_equal( + command[command.index("--output-dir") + 1], + str(output_dir), + f"collector output directory in {label}", + ) + require_equal( + int(command[command.index("--device") + 1]), + device, + f"collector device in {label}", + ) + require_equal( + "--snapshot-context" in commands["snapshot_context"], + True, + "pre-run context command", + ) + require_equal( + "--snapshot-context" in commands["collect_report"], + False, + "final collector command", + ) + + expected_solver_device = f"cuda:{device}" + for dataset, dataset_configuration in DATASETS.items(): + slug = dataset_configuration["slug"] + for task, label_prefix, filename_task in ( + ("pa_modeling", "pa_model", "pa"), + ("dpd_ila", "dpd_ila", "dpd"), + ): + for model_name in ("mp", "gmp"): + label = f"{label_prefix}_{slug}_{model_name}" + options = module_cli_options( + commands[label], + label=label, + module="benchmark.benchmark_volterra", + ) + expectations = { + "task": task, + "dataset-name": dataset, + "model": model_name, + "solver-device": expected_solver_device, + "solver-dtype": "complex64", + "device": str(device), + "json-out": str( + output_dir + / ( + f"benchmark_report_{slug}_{filename_task}_" + f"{model_name}.json" + ) + ), + } + if task == "pa_modeling" and model_name == "gmp": + expectations.update( + { + "solver-mode": "truncated_svd", + "svd-rcond": "1e-4", + } + ) + else: + expectations["solver-mode"] = "gels" + require_equal( + "svd-rcond" in options, + False, + f"unexpected --svd-rcond in command {label}", + ) + if task == "dpd_ila": + expectations.update( + { + "pa-backbone": "tres_gru", + "pa-hidden-size": "27", + "pa-num-layers": "1", + "pa-checkpoint": ( + f"save/{dataset}/train_pa/" + "PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt" + ), + } + ) + for key, value in expectations.items(): + require_equal( + options.get(key), + value, + f"--{key} in command {label}", + ) + + configuration = POLYNOMIAL_CONFIGURATIONS[task][model_name][ + "basis_configuration" + ] + for key, value in configuration.items(): + require_equal( + int(options[key]), + value, + f"--{key} in command {label}", + ) + + +def expected_runner_recipe() -> dict[str, Any]: + return { + "schema_version": RUN_SCHEMA_VERSION, + "datasets": list(DATASETS), + "parameter_count_convention": ( + "Neural parameters are real trainable scalars; each complex " + "polynomial coefficient counts as two real degrees of freedom." + ), + "neural": { + "optimizer": "AdamW", + "optimizer_weight_decay": NEURAL_RECIPE["optimizer_weight_decay"], + "optimizer_betas": NEURAL_RECIPE["optimizer_betas"], + "optimizer_eps": NEURAL_RECIPE["optimizer_eps"], + "loss": "MSE", + "epochs": NEURAL_RECIPE["epochs"], + "batch_size": NEURAL_RECIPE["batch_size"], + "evaluation_batch_size": NEURAL_RECIPE["batch_size"], + "initial_learning_rate": NEURAL_RECIPE["initial_learning_rate"], + "scheduler": "ReduceLROnPlateau", + "scheduler_factor": NEURAL_RECIPE["decay_factor"], + "scheduler_patience": NEURAL_RECIPE["patience"], + "scheduler_threshold": NEURAL_RECIPE["scheduler_threshold"], + "scheduler_threshold_mode": NEURAL_RECIPE[ + "scheduler_threshold_mode" + ], + "scheduler_cooldown": NEURAL_RECIPE["scheduler_cooldown"], + "scheduler_eps": NEURAL_RECIPE["scheduler_eps"], + "minimum_learning_rate": NEURAL_RECIPE[ + "minimum_learning_rate" + ], + "frame_length": NEURAL_RECIPE["frame_length"], + "frame_stride": NEURAL_RECIPE["frame_stride"], + "gradient_clip": 200, + "seed": NEURAL_RECIPE["seed"], + "reproducibility": "soft", + "pa_scheduler_and_selection_metric": "validation NMSE", + "dpd_scheduler_and_selection_metric": "validation ACLR_AVG", + }, + "pa_models": { + "mp": {"K": 9, "Q": 150, "real_parameters": 2700}, + "gmp": { + **POLYNOMIAL_CONFIGURATIONS["pa_modeling"]["gmp"][ + "basis_configuration" + ], + "real_parameters": 2700, + }, + "gru": {"hidden_size": 28, "real_parameters": 2746}, + "tres_gru": {"hidden_size": 27, "real_parameters": 2751}, + "tres_deltagru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": { + "input": 0.0, + "hidden": 0.0, + }, + }, + }, + "dpd_models": { + "mp": { + "K": 5, + "Q": 100, + "real_parameters": 1000, + "method": "ILA", + }, + "gmp": { + **POLYNOMIAL_CONFIGURATIONS["dpd_ila"]["gmp"][ + "basis_configuration" + ], + "real_parameters": 1000, + "method": "ILA", + }, + "gru": {"hidden_size": 16, "real_parameters": 994}, + "tres_gru": {"hidden_size": 15, "real_parameters": 999}, + }, + "dpd_tres_deltagru_pa_surrogate_comparison": { + "model": { + "hidden_size": 15, + "real_parameters": 999, + "delta_thresholds": { + "input": 0.0, + "hidden": 0.0, + }, + }, + "pa_surrogates": { + "tres_gru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": None, + }, + "tres_deltagru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": { + "input": 0.0, + "hidden": 0.0, + }, + }, + }, + }, + "dpd_pa_surrogate": { + "backbone": "tres_gru", + "hidden_size": 27, + "real_parameters": 2751, + }, + "polynomial_solvers": { + "default": { + "implementation": "torch.linalg.lstsq", + "mode": "gels", + "driver": "gels", + "dtype": "torch.complex64", + "column_scaling": "l2", + "regularization": None, + }, + "gmp_pa": { + "implementation": "torch.linalg.svd", + "mode": "truncated_svd", + "driver": "gesvdj", + "dtype": "torch.complex64", + "column_scaling": "l2", + "regularization": { + "type": "truncated_svd", + "relative_cutoff": GMP_PA_SVD_RCOND, + }, + }, + "segment_boundary_policy": ( + "zero delay state at every nperseg boundary" + ), + }, + } + + +def validate_recipe_file(path: Path) -> dict[str, Any]: + if not path.is_file(): + raise FileNotFoundError(f"Benchmark recipe is missing: {path}") + with path.open() as handle: + recipe = json.load(handle) + require_equal(recipe, expected_runner_recipe(), "recorded benchmark recipe") + return recipe + + +def validate_learning_rate_schedule( + history_rows: list[dict[str, str]], + *, + model_id: str, + initial_learning_rate: float, + minimum_learning_rate: float, + decay_factor: float, +) -> None: + """Validate the observable portion of the configured plateau schedule.""" + learning_rates = [as_float(row, "LR") for row in history_rows] + require_close( + learning_rates[0], + initial_learning_rate, + f"initial learning rate for {model_id}", + tolerance=1e-10, + ) + for epoch, learning_rate in enumerate(learning_rates): + if learning_rate < minimum_learning_rate - 1e-10: + raise ValueError( + f"Learning rate at epoch {epoch} for {model_id} is below " + f"the configured minimum: {learning_rate} < " + f"{minimum_learning_rate}" + ) + if learning_rate > initial_learning_rate + 1e-10: + raise ValueError( + f"Learning rate at epoch {epoch} for {model_id} exceeds " + f"the configured initial value: {learning_rate} > " + f"{initial_learning_rate}" + ) + for epoch, (previous, current) in enumerate( + zip(learning_rates, learning_rates[1:]), start=1 + ): + if current > previous + 1e-10: + raise ValueError( + f"Learning rate increased at epoch {epoch} for {model_id}: " + f"{previous} -> {current}" + ) + if not math.isclose(current, previous, rel_tol=0.0, abs_tol=1e-10): + expected = max(previous * decay_factor, minimum_learning_rate) + require_close( + current, + expected, + f"scheduled learning rate at epoch {epoch} for {model_id}", + tolerance=1e-8, + ) + + +def collect_training_model( + output_dir: Path, + *, + dataset: str, + step: str, + parent: str | None, + model_id: str, + expected_epochs: int, + expected_batch_size: int, + expected_initial_lr: float, + expected_backbone: str, + expected_hidden_size: int, + expected_parameters: int, + selection_metric: str, + expected_delta_thresholds: dict[str, float] | None = None, + expected_pa_parameters: int | None = None, +) -> dict[str, Any]: + base = Path("log") / dataset / step + save_base = Path("save") / dataset / step + if parent is not None: + base /= parent + save_base /= parent + + best_path = base / "best" / f"{model_id}.csv" + history_path = base / "history" / f"{model_id}.csv" + checkpoint_path = save_base / f"{model_id}.pt" + validate_checkpoint(REPO_ROOT / checkpoint_path, expected_parameters) + + best_rows = read_csv(REPO_ROOT / best_path) + history_rows = read_csv(REPO_ROOT / history_path) + require_equal(len(best_rows), 1, f"best-row count for {model_id}") + require_equal( + len(history_rows), expected_epochs, f"history-row count for {model_id}" + ) + require_equal( + [as_int(row, "EPOCH") for row in history_rows], + list(range(expected_epochs)), + f"epoch sequence for {model_id}", + ) + + best = best_rows[0] + require_equal(as_int(best, "N_EPOCH"), expected_epochs, f"epochs for {model_id}") + require_equal( + as_int(best, "BATCH_SIZE"), + expected_batch_size, + f"batch size for {model_id}", + ) + require_equal(as_int(best, "FRAME_LENGTH"), 200, f"frame length for {model_id}") + require_equal(best["BACKBONE"], expected_backbone, f"backbone for {model_id}") + require_equal( + as_int(best, "HIDDEN_SIZE"), + expected_hidden_size, + f"hidden size for {model_id}", + ) + expected_logged_parameters = expected_parameters + if step == "train_dpd": + if expected_pa_parameters is None: + raise ValueError( + f"Expected PA parameter count is required for DPD model {model_id}" + ) + expected_logged_parameters += expected_pa_parameters + require_equal( + as_int(best, "N_PARAM"), + expected_logged_parameters, + f"logged network parameter count for {model_id}", + ) + if expected_delta_thresholds is not None: + for row_index, row in enumerate(history_rows): + require_close( + as_float(row, "THX"), + expected_delta_thresholds["input"], + f"input delta threshold at row {row_index} for {model_id}", + ) + require_close( + as_float(row, "THH"), + expected_delta_thresholds["hidden"], + f"hidden delta threshold at row {row_index} for {model_id}", + ) + require_close( + as_float(best, "THX"), + expected_delta_thresholds["input"], + f"selected input delta threshold for {model_id}", + ) + require_close( + as_float(best, "THH"), + expected_delta_thresholds["hidden"], + f"selected hidden delta threshold for {model_id}", + ) + validate_learning_rate_schedule( + history_rows, + model_id=model_id, + initial_learning_rate=expected_initial_lr, + minimum_learning_rate=NEURAL_RECIPE["minimum_learning_rate"], + decay_factor=NEURAL_RECIPE["decay_factor"], + ) + + best_epoch = as_int(best, "EPOCH") + logged_selection_values = [ + as_float(row, selection_metric) for row in history_rows + ] + minimum_logged_value = min(logged_selection_values) + tied_best_epochs = { + index + for index, value in enumerate(logged_selection_values) + if value == minimum_logged_value + } + if best_epoch not in tied_best_epochs: + raise ValueError( + f"Selected epoch {best_epoch} for {model_id} is not tied for the " + f"minimum logged {selection_metric}; tied epochs are " + f"{sorted(tied_best_epochs)}" + ) + matching_history_rows = [ + row for row in history_rows if as_int(row, "EPOCH") == best_epoch + ] + require_equal( + len(matching_history_rows), 1, f"selected epoch in history for {model_id}" + ) + for key, value in best.items(): + require_equal( + matching_history_rows[0].get(key), + value, + f"best/history value {key} for {model_id}", + ) + + result = { + "selected_epoch_zero_based": best_epoch, + "completed_epochs": len(history_rows), + "selected_learning_rate": as_float(best, "LR"), + "train_loss": as_float(best, "TRAIN_LOSS"), + "metrics": metric_block(best), + "artifacts": { + "checkpoint": archived_artifact(output_dir, checkpoint_path), + "best_csv": archived_artifact(output_dir, best_path), + "history_csv": archived_artifact(output_dir, history_path), + }, + } + if expected_delta_thresholds is not None: + result["delta_thresholds"] = dict(expected_delta_thresholds) + return result + + +def normalize_polynomial_metrics( + values: dict[str, float], +) -> dict[str, float]: + normalized = { + "nmse_db": float(values["NMSE"]), + "evm_db": float(values["EVM"]), + "aclr_left_db": float(values["ACLR_L"]), + "aclr_right_db": float(values["ACLR_R"]), + "aclr_avg_db": float(values["ACLR_AVG"]), + } + for key, value in normalized.items(): + if not math.isfinite(value): + raise ValueError(f"Polynomial metric {key} is not finite: {value}") + require_close( + normalized["aclr_avg_db"], + (normalized["aclr_left_db"] + normalized["aclr_right_db"]) / 2.0, + "polynomial-model ACLR average", + tolerance=1e-10, + ) + return normalized + + +def finite_number( + raw: dict[str, Any], + key: str, + *, + source_path: Path, + positive: bool = False, + nonnegative: bool = False, +) -> float: + try: + value = float(raw[key]) + except (KeyError, TypeError, ValueError) as exc: + raise ValueError(f"{key} is missing or non-numeric in {source_path}") from exc + if not math.isfinite(value): + raise ValueError(f"{key} is not finite in {source_path}: {value}") + if positive and value <= 0.0: + raise ValueError(f"{key} must be positive in {source_path}: {value}") + if nonnegative and value < 0.0: + raise ValueError(f"{key} must be nonnegative in {source_path}: {value}") + return value + + +def collect_polynomial_model( + output_dir: Path, + *, + dataset: str, + slug: str, + task: str, + model_name: str, + expected_pa_hash: str | None = None, + expected_device: str | None = None, + expected_target_gain: float | None = None, +) -> dict[str, Any]: + if task not in POLYNOMIAL_CONFIGURATIONS: + raise ValueError(f"Unsupported polynomial benchmark task: {task}") + filename_task = "pa" if task == "pa_modeling" else "dpd" + source_path = ( + output_dir / f"benchmark_report_{slug}_{filename_task}_{model_name}.json" + ) + with source_path.open() as handle: + raw = json.load(handle) + + configuration = POLYNOMIAL_CONFIGURATIONS[task][model_name] + expected_method = ( + "direct_least_squares" + if task == "pa_modeling" + else "indirect_learning_architecture" + ) + uses_truncated_svd = task == "pa_modeling" and model_name == "gmp" + + require_equal( + raw["schema_version"], + POLYNOMIAL_SCHEMA_VERSION, + f"polynomial schema in {source_path}", + ) + require_equal(raw["dataset"], dataset, f"polynomial dataset in {source_path}") + require_equal(raw["task"], task, f"polynomial task in {source_path}") + require_equal(raw["model"], model_name, f"polynomial model in {source_path}") + require_equal(raw["method"], expected_method, f"method in {source_path}") + expected_solver = ( + "torch.linalg.svd" if uses_truncated_svd else "torch.linalg.lstsq" + ) + expected_solver_mode = "truncated_svd" if uses_truncated_svd else "gels" + expected_solver_driver = "gesvdj" if uses_truncated_svd else "gels" + require_equal(raw["solver"], expected_solver, f"polynomial solver in {source_path}") + require_equal( + raw["solver_mode"], + expected_solver_mode, + f"polynomial solver mode in {source_path}", + ) + require_equal( + raw["solver_driver"], + expected_solver_driver, + f"least-squares driver in {source_path}", + ) + require_equal( + raw["dtype"], + "torch.complex64", + f"least-squares dtype in {source_path}", + ) + require_equal( + raw["column_scaling"], + "l2", + f"least-squares column scaling in {source_path}", + ) + if not isinstance(raw["device"], str) or not raw["device"].startswith("cuda:"): + raise ValueError( + f"Polynomial least-squares device must be a CUDA device in " + f"{source_path}: {raw['device']!r}" + ) + require_equal( + raw.get("solver_device"), + raw["device"], + f"least-squares device alias in {source_path}", + ) + if expected_device is not None: + require_equal( + raw["device"], + expected_device, + f"least-squares device in {source_path}", + ) + require_equal( + raw["basis_configuration"], + configuration["basis_configuration"], + f"polynomial basis in {source_path}", + ) + require_equal( + raw["complex_coefficients"], + configuration["complex_coefficients"], + f"least-squares complex coefficient count in {source_path}", + ) + require_equal( + raw["real_parameters"], + configuration["real_parameters"], + f"least-squares parameter count in {source_path}", + ) + require_equal( + raw["parameter_count_convention"], + "two real degrees of freedom per complex coefficient", + f"parameter-count convention in {source_path}", + ) + target_gain = finite_number( + raw, + "target_gain", + source_path=source_path, + positive=True, + ) + if expected_target_gain is not None: + require_close( + target_gain, + expected_target_gain, + f"target gain in {source_path}", + tolerance=1e-6, + ) + rank = raw.get("least_squares_rank") + if uses_truncated_svd: + require_equal( + raw.get("full_rank_assumption"), + False, + f"full-rank assumption for truncated SVD in {source_path}", + ) + require_equal( + raw.get("regularization"), + "truncated_svd", + f"regularization in {source_path}", + ) + svd_rcond = finite_number( + raw, + "svd_rcond", + source_path=source_path, + positive=True, + ) + require_close( + svd_rcond, + GMP_PA_SVD_RCOND, + f"SVD relative cutoff in {source_path}", + tolerance=1e-12, + ) + if not isinstance(rank, int) or isinstance(rank, bool): + raise ValueError( + f"least-squares rank is not an integer in {source_path}: " + f"{rank!r}" + ) + if not 0 < rank < configuration["complex_coefficients"]: + raise ValueError( + f"truncated-SVD rank is outside the expected range in " + f"{source_path}: {rank}" + ) + singular_value_max = finite_number( + raw, + "singular_value_max", + source_path=source_path, + positive=True, + ) + singular_value_min = finite_number( + raw, + "singular_value_min", + source_path=source_path, + positive=True, + ) + singular_value_cutoff = finite_number( + raw, + "singular_value_cutoff", + source_path=source_path, + positive=True, + ) + retained_singular_value_min = finite_number( + raw, + "retained_singular_value_min", + source_path=source_path, + positive=True, + ) + condition_number = finite_number( + raw, + "condition_number", + source_path=source_path, + positive=True, + ) + require_close( + singular_value_cutoff, + singular_value_max * svd_rcond, + f"singular-value cutoff in {source_path}", + tolerance=1e-5, + ) + if not ( + singular_value_min + <= retained_singular_value_min + <= singular_value_max + ): + raise ValueError( + f"retained singular-value bounds are invalid in {source_path}" + ) + if retained_singular_value_min <= singular_value_cutoff: + raise ValueError( + f"retained singular value does not exceed the cutoff in " + f"{source_path}" + ) + if not math.isclose( + condition_number, + singular_value_max / singular_value_min, + rel_tol=1e-6, + ): + raise ValueError( + f"condition number is inconsistent with singular values in " + f"{source_path}" + ) + else: + svd_rcond = None + singular_value_max = None + singular_value_min = None + singular_value_cutoff = None + retained_singular_value_min = None + condition_number = None + require_equal( + raw.get("regularization"), + None, + f"regularization in {source_path}", + ) + require_equal( + raw.get("svd_rcond"), + None, + f"SVD relative cutoff in {source_path}", + ) + for key in ( + "singular_value_max", + "singular_value_min", + "singular_value_cutoff", + "retained_singular_value_min", + "condition_number", + ): + require_equal( + raw.get(key), + None, + f"{key} in {source_path}", + ) + require_equal( + raw.get("full_rank_assumption"), + True, + f"full-rank assumption for CUDA gels in {source_path}", + ) + require_equal(rank, None, f"least-squares rank in {source_path}") + + residual = finite_number( + raw, + "training_relative_residual", + source_path=source_path, + nonnegative=True, + ) + column_scale_min = finite_number( + raw, + "column_scale_min", + source_path=source_path, + positive=True, + ) + column_scale_max = finite_number( + raw, + "column_scale_max", + source_path=source_path, + positive=True, + ) + if column_scale_max < column_scale_min: + raise ValueError( + f"column_scale_max is below column_scale_min in {source_path}: " + f"{column_scale_max} < {column_scale_min}" + ) + coefficient_l2_norm = finite_number( + raw, + "coefficient_l2_norm", + source_path=source_path, + positive=True, + ) + + expected_sample_rate = DATASETS[dataset]["sample_rate_hz"] + expected_nperseg = DATASETS[dataset]["nperseg"] + require_close( + float(raw["sample_rate_hz"]), + expected_sample_rate, + f"sample rate in {source_path}", + ) + require_equal( + raw["nperseg"], + expected_nperseg, + f"segment length in {source_path}", + ) + pa_checkpoint_binding: dict[str, str] | None = None + pa_evaluation_device: str | None = None + if task == "dpd_ila": + if expected_pa_hash is None: + raise ValueError( + f"Expected PA hash is required to collect DPD ILA result: " + f"{source_path}" + ) + expected_checkpoint = ( + Path("save") + / dataset + / "train_pa" + / f"{NEURAL_PA_MODELS[REFERENCE_PA_KEY]['model_id']}.pt" + ).as_posix() + require_equal( + raw["pa_checkpoint"], + expected_checkpoint, + f"PA checkpoint path in {source_path}", + ) + require_equal( + raw["pa_checkpoint_sha256"], + expected_pa_hash, + f"PA checkpoint hash in {source_path}", + ) + pa_evaluation_device = raw.get("pa_evaluation_device") + if expected_device is not None: + require_equal( + pa_evaluation_device, + expected_device, + f"PA evaluation device in {source_path}", + ) + pa_checkpoint_binding = { + "checkpoint": raw["pa_checkpoint"], + "sha256": raw["pa_checkpoint_sha256"], + } + else: + unexpected_binding_fields = { + key + for key in ("pa_checkpoint", "pa_checkpoint_sha256") + if key in raw + } + if unexpected_binding_fields: + raise ValueError( + f"Direct PA polynomial result must not contain a PA checkpoint " + f"binding in {source_path}: {sorted(unexpected_binding_fields)}" + ) + + validation_metrics = normalize_polynomial_metrics(raw["validation"]) + test_metrics = normalize_polynomial_metrics(raw["test"]) + + return { + "task": raw["task"], + "method": raw["method"], + "solver": raw["solver"], + "solver_mode": raw["solver_mode"], + "solver_driver": raw["solver_driver"], + "dtype": raw["dtype"], + "device": raw["device"], + "column_scaling": raw["column_scaling"], + "column_scale_min": column_scale_min, + "column_scale_max": column_scale_max, + "training_relative_residual": residual, + "regularization": raw.get("regularization"), + "svd_rcond": svd_rcond, + "singular_value_max": singular_value_max, + "singular_value_min": singular_value_min, + "singular_value_cutoff": singular_value_cutoff, + "retained_singular_value_min": retained_singular_value_min, + "condition_number": condition_number, + "coefficient_l2_norm": coefficient_l2_norm, + "full_rank_assumption": raw.get("full_rank_assumption"), + "basis_configuration": raw["basis_configuration"], + "complex_coefficients": int(raw["complex_coefficients"]), + "real_parameters": int(raw["real_parameters"]), + "parameter_count_convention": raw["parameter_count_convention"], + "target_gain": target_gain, + "least_squares_rank": int(rank) if rank is not None else None, + "sample_rate_hz": float(raw["sample_rate_hz"]), + "nperseg": int(raw["nperseg"]), + "pa_evaluation_device": pa_evaluation_device, + "pa_checkpoint_binding": pa_checkpoint_binding, + "metrics": { + "validation": validation_metrics, + "test": test_metrics, + }, + "artifact": { + "path": source_path.relative_to(output_dir).as_posix(), + "sha256": sha256_file(source_path), + "size_bytes": source_path.stat().st_size, + }, + } + + +def signal_record(dataset: str) -> dict[str, Any]: + import numpy as np + + arrays = [] + dataset_files: dict[str, str] = {} + train_input: Any = None + train_output: Any = None + for split in ("train", "val", "test"): + input_path = Path("datasets") / dataset / f"{split}_input.csv" + absolute_path = REPO_ROOT / input_path + input_iq = np.loadtxt( + absolute_path, + delimiter=",", + skiprows=1, + dtype=np.float64, + ) + arrays.append(input_iq) + dataset_files[input_path.as_posix()] = sha256_file(absolute_path) + output_path = Path("datasets") / dataset / f"{split}_output.csv" + absolute_output_path = REPO_ROOT / output_path + dataset_files[output_path.as_posix()] = sha256_file(absolute_output_path) + if split == "train": + train_input = input_iq + train_output = np.loadtxt( + absolute_output_path, + delimiter=",", + skiprows=1, + dtype=np.float64, + ) + + iq = np.vstack(arrays) + power = np.square(iq[:, 0]) + np.square(iq[:, 1]) + input_peak = float( + np.max(np.hypot(train_input[:, 0], train_input[:, 1])) + ) + output_peak = float( + np.max(np.hypot(train_output[:, 0], train_output[:, 1])) + ) + mean_power = float(np.mean(power)) + ccdf_probability = 1e-5 + ccdf_power = float( + np.quantile(power, 1.0 - ccdf_probability, method="linear") + ) + return { + "samples": int(power.size), + "target_gain": output_peak / input_peak, + "papr": { + "ccdf_probability": ccdf_probability, + "quantile_method": "numpy linear", + "ccdf_db": float(10.0 * np.log10(ccdf_power / mean_power)), + "absolute_peak_db": float( + 10.0 * np.log10(float(np.max(power)) / mean_power) + ), + }, + "dataset_file_sha256": dataset_files, + } + + +def package_version(name: str) -> str | None: + try: + return importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError: + return None + + +def command_output(command: list[str]) -> str | None: + try: + result = subprocess.run( + command, + cwd=REPO_ROOT, + check=True, + capture_output=True, + text=True, + ) + except (OSError, subprocess.CalledProcessError): + return None + return result.stdout.strip() + + +def environment_record(device: int) -> dict[str, Any]: + import torch + + gpu_name = ( + torch.cuda.get_device_name(device) + if torch.cuda.is_available() and device < torch.cuda.device_count() + else None + ) + return { + "python": platform.python_version(), + "platform": platform.platform(), + "numpy": package_version("numpy"), + "scipy": package_version("scipy"), + "pandas": package_version("pandas"), + "torch": torch.__version__, + "cuda": torch.version.cuda, + "cudnn": torch.backends.cudnn.version(), + "gpu": gpu_name, + "gpu_device_index": device, + "training_reproducibility_level": "soft", + "training_deterministic_algorithms": False, + "training_cudnn_benchmark": True, + } + + +def git_record() -> dict[str, Any]: + status = command_output(["git", "status", "--short", "--untracked-files=all"]) + return { + "commit": command_output(["git", "rev-parse", "HEAD"]), + "branch": command_output(["git", "branch", "--show-current"]), + "dirty": bool(status), + "status": status.splitlines() if status else [], + } + + +def runner_start_git_record(output_dir: Path) -> dict[str, Any]: + commit_path = output_dir / "git_commit_before.txt" + branch_path = output_dir / "git_branch_before.txt" + status_path = output_dir / "git_status_before.txt" + if not all(path.is_file() for path in (commit_path, branch_path, status_path)): + raise FileNotFoundError( + "Runner start-state files are missing; use " + "benchmark/reproduce_benchmark_report.sh." + ) + status = status_path.read_text().strip() + return { + "commit": commit_path.read_text().strip(), + "branch": branch_path.read_text().strip(), + "dirty": bool(status), + "status": status.splitlines() if status else [], + } + + +def capture_context( + output_dir: Path, + device: int, + *, + use_runner_start_git: bool, +) -> dict[str, Any]: + return { + "captured_at_utc": datetime.now(timezone.utc).isoformat(), + "git": ( + runner_start_git_record(output_dir) + if use_runner_start_git + else git_record() + ), + "environment": environment_record(device), + "source_file_sha256": { + path: sha256_file(REPO_ROOT / path) for path in source_files() + }, + "datasets": { + dataset: { + "spec_sha256": sha256_file( + REPO_ROOT / "datasets" / dataset / "spec.json" + ), + "signal": signal_record(dataset), + } + for dataset in DATASETS + }, + } + + +def validate_context_stability( + initial: dict[str, Any], + completion: dict[str, Any], +) -> None: + require_equal( + completion["git"]["commit"], + initial["git"]["commit"], + "Git commit across the benchmark run", + ) + require_equal( + completion["git"]["branch"], + initial["git"]["branch"], + "Git branch across the benchmark run", + ) + require_equal( + completion["environment"], + initial["environment"], + "software and hardware environment across the benchmark run", + ) + require_equal( + completion["source_file_sha256"], + initial["source_file_sha256"], + "source files across the benchmark run", + ) + require_equal( + completion["datasets"], + initial["datasets"], + "dataset files and signal evidence across the benchmark run", + ) + + +def load_and_validate_context( + output_dir: Path, + device: int, +) -> tuple[dict[str, Any], dict[str, Any]]: + context_path = output_dir / "context_before.json" + if not context_path.is_file(): + raise FileNotFoundError( + "Pre-run context snapshot is missing; run the collector once with " + "--snapshot-context before training." + ) + with context_path.open() as handle: + initial = json.load(handle) + snapshot_metadata = validate_source_snapshot( + output_dir / "source_snapshot.tar.gz", + initial["source_file_sha256"], + ) + require_equal( + initial.get("source_snapshot"), + snapshot_metadata, + "source snapshot metadata", + ) + completion = capture_context( + output_dir, + device, + use_runner_start_git=False, + ) + validate_context_stability(initial, completion) + return initial, completion + + +def collect_manifest( + output_dir: Path, + device: int, + initial_context: dict[str, Any], + completion_context: dict[str, Any], +) -> dict[str, Any]: + pa_bindings = read_pa_bindings(output_dir) + commands_path = output_dir / "commands.log" + validate_runner_commands( + commands_path, + output_dir=output_dir, + device=device, + ) + recipe_path = output_dir / "recipe.json" + validate_recipe_file(recipe_path) + manifest: dict[str, Any] = { + "schema_version": RUN_SCHEMA_VERSION, + "artifact_kind": "opendpd_benchmark_reproduction_run", + "started_at_utc": initial_context["captured_at_utc"], + "generated_at_utc": datetime.now(timezone.utc).isoformat(), + "canonical_report": "benchmark/benchmark_report.md", + "git": initial_context["git"], + "environment": initial_context["environment"], + "completion_context": { + "captured_at_utc": completion_context["captured_at_utc"], + "git_commit": completion_context["git"]["commit"], + "git_branch": completion_context["git"]["branch"], + "validated_unchanged": True, + }, + "run_file_sha256": { + "commands.log": sha256_file(commands_path), + "context_before.json": sha256_file( + output_dir / "context_before.json" + ), + "git_commit_before.txt": sha256_file( + output_dir / "git_commit_before.txt" + ), + "git_branch_before.txt": sha256_file( + output_dir / "git_branch_before.txt" + ), + "git_status_before.txt": sha256_file( + output_dir / "git_status_before.txt" + ), + "pa_checkpoint_bindings.tsv": sha256_file( + output_dir / "pa_checkpoint_bindings.tsv" + ), + "recipe.json": sha256_file(recipe_path), + "source_snapshot.tar.gz": sha256_file( + output_dir / "source_snapshot.tar.gz" + ), + }, + "source_snapshot": initial_context["source_snapshot"], + "source_file_sha256": initial_context["source_file_sha256"], + "recipes": { + "neural_pa": dict(NEURAL_RECIPE), + "neural_dpd": dict(NEURAL_RECIPE), + "neural_dpd_pa_surrogate_sensitivity": { + **NEURAL_RECIPE, + "dpd_model": dict(TRES_DELTAGRU_DPD_MODEL), + "pa_surrogates": [ + REFERENCE_PA_KEY, + ALTERNATE_PA_KEY, + ], + }, + "polynomial_pa": { + "method": "direct_least_squares", + "column_scaling": "l2", + "solvers": { + "mp": { + "implementation": "torch.linalg.lstsq", + "mode": "gels", + "driver": "gels", + "regularization": None, + }, + "gmp": { + "implementation": "torch.linalg.svd", + "mode": "truncated_svd", + "driver": "gesvdj", + "regularization": { + "type": "truncated_svd", + "relative_cutoff": GMP_PA_SVD_RCOND, + }, + }, + }, + "parameter_convention": ( + "two real degrees of freedom per complex coefficient" + ), + "models": POLYNOMIAL_CONFIGURATIONS["pa_modeling"], + }, + "polynomial_dpd": { + "method": "indirect_learning_architecture", + "solver": "torch.linalg.lstsq", + "solver_driver": "gels", + "column_scaling": "l2", + "parameter_convention": ( + "two real degrees of freedom per complex coefficient" + ), + "models": POLYNOMIAL_CONFIGURATIONS["dpd_ila"], + }, + }, + "metric_note": ( + "EVM is the repository-specific normalized mean absolute complex-" + "spectrum error across configured main-channel subchannels, not " + "demodulated constellation EVM. DPD metrics are simulated through " + "a frozen learned PA surrogate." + ), + "datasets": {}, + } + + for dataset, configuration in DATASETS.items(): + pa_models: dict[str, Any] = {} + for model_name in ("mp", "gmp"): + result = collect_polynomial_model( + output_dir, + dataset=dataset, + slug=configuration["slug"], + task="pa_modeling", + model_name=model_name, + expected_device=f"cuda:{device}", + expected_target_gain=initial_context["datasets"][dataset][ + "signal" + ]["target_gain"], + ) + result["model"] = { + "display_name": model_name.upper(), + "backbone": model_name, + "parameters": result["real_parameters"], + "parameter_convention": ( + "two real degrees of freedom per complex coefficient" + ), + } + pa_models[model_name] = result + + for key, model_configuration in NEURAL_PA_MODELS.items(): + result = collect_training_model( + output_dir, + dataset=dataset, + step="train_pa", + parent=None, + model_id=model_configuration["model_id"], + expected_epochs=NEURAL_RECIPE["epochs"], + expected_batch_size=NEURAL_RECIPE["batch_size"], + expected_initial_lr=NEURAL_RECIPE["initial_learning_rate"], + expected_backbone=model_configuration["backbone"], + expected_hidden_size=model_configuration["hidden_size"], + expected_parameters=model_configuration["parameters"], + selection_metric="VAL_NMSE", + expected_delta_thresholds=model_configuration.get( + "delta_thresholds" + ), + ) + result["task"] = "pa_modeling" + result["method"] = "supervised_training" + result["model"] = dict(model_configuration) + pa_models[key] = result + + reference_pa = pa_models[REFERENCE_PA_KEY] + pa_hash = reference_pa["artifacts"]["checkpoint"]["sha256"] + expected_pa_checkpoint = ( + Path("save") + / dataset + / "train_pa" + / f"{NEURAL_PA_MODELS[REFERENCE_PA_KEY]['model_id']}.pt" + ).as_posix() + + dpd_models: dict[str, Any] = {} + for model_name in ("mp", "gmp"): + result = collect_polynomial_model( + output_dir, + dataset=dataset, + slug=configuration["slug"], + task="dpd_ila", + model_name=model_name, + expected_pa_hash=pa_hash, + expected_device=f"cuda:{device}", + expected_target_gain=initial_context["datasets"][dataset][ + "signal" + ]["target_gain"], + ) + result["model"] = { + "display_name": model_name.upper(), + "backbone": model_name, + "parameters": result["real_parameters"], + "parameter_convention": ( + "two real degrees of freedom per complex coefficient" + ), + } + dpd_models[model_name] = result + + for key, model_configuration in NEURAL_DPD_MODELS.items(): + result = collect_training_model( + output_dir, + dataset=dataset, + step="train_dpd", + parent=REFERENCE_PA_PARENT, + model_id=model_configuration["model_id"], + expected_epochs=NEURAL_RECIPE["epochs"], + expected_batch_size=NEURAL_RECIPE["batch_size"], + expected_initial_lr=NEURAL_RECIPE["initial_learning_rate"], + expected_backbone=model_configuration["backbone"], + expected_hidden_size=model_configuration["hidden_size"], + expected_parameters=model_configuration["parameters"], + selection_metric="VAL_ACLR_AVG", + expected_delta_thresholds=model_configuration.get( + "delta_thresholds" + ), + expected_pa_parameters=reference_pa["model"]["parameters"], + ) + result["task"] = "dpd_training" + result["method"] = "direct_learning_architecture" + result["model"] = dict(model_configuration) + binding = pa_bindings.get((dataset, key, REFERENCE_PA_KEY)) + if binding is None: + raise ValueError( + f"Missing PA checkpoint binding for " + f"{dataset}/{key}/{REFERENCE_PA_KEY}" + ) + require_equal( + binding["checkpoint"], + expected_pa_checkpoint, + f"PA checkpoint path binding for {dataset}/{key}", + ) + require_equal( + binding["sha256"], + pa_hash, + f"PA checkpoint hash binding for {dataset}/{key}", + ) + result["pa_checkpoint_binding"] = binding + dpd_models[key] = result + + delta_dpd_by_pa_surrogate: dict[str, Any] = {} + for pa_model_key, pa_parent in ( + (REFERENCE_PA_KEY, REFERENCE_PA_PARENT), + (ALTERNATE_PA_KEY, ALTERNATE_PA_PARENT), + ): + pa_result = pa_models[pa_model_key] + pa_checkpoint = ( + Path("save") + / dataset + / "train_pa" + / f"{NEURAL_PA_MODELS[pa_model_key]['model_id']}.pt" + ).as_posix() + result = collect_training_model( + output_dir, + dataset=dataset, + step="train_dpd", + parent=pa_parent, + model_id=TRES_DELTAGRU_DPD_MODEL["model_id"], + expected_epochs=NEURAL_RECIPE["epochs"], + expected_batch_size=NEURAL_RECIPE["batch_size"], + expected_initial_lr=NEURAL_RECIPE["initial_learning_rate"], + expected_backbone=TRES_DELTAGRU_DPD_MODEL["backbone"], + expected_hidden_size=TRES_DELTAGRU_DPD_MODEL["hidden_size"], + expected_parameters=TRES_DELTAGRU_DPD_MODEL["parameters"], + selection_metric="VAL_ACLR_AVG", + expected_delta_thresholds=TRES_DELTAGRU_DPD_MODEL[ + "delta_thresholds" + ], + expected_pa_parameters=pa_result["model"]["parameters"], + ) + result["task"] = "dpd_training" + result["method"] = "direct_learning_architecture" + result["model"] = dict(TRES_DELTAGRU_DPD_MODEL) + binding = pa_bindings.get( + (dataset, "tres_deltagru", pa_model_key) + ) + if binding is None: + raise ValueError( + f"Missing PA checkpoint binding for " + f"{dataset}/tres_deltagru/{pa_model_key}" + ) + require_equal( + binding["checkpoint"], + pa_checkpoint, + ( + "PA checkpoint path binding for " + f"{dataset}/tres_deltagru/{pa_model_key}" + ), + ) + require_equal( + binding["sha256"], + pa_result["artifacts"]["checkpoint"]["sha256"], + ( + "PA checkpoint hash binding for " + f"{dataset}/tres_deltagru/{pa_model_key}" + ), + ) + result["pa_checkpoint_binding"] = binding + result["pa_surrogate"] = { + "model_key": pa_model_key, + "model": dict(NEURAL_PA_MODELS[pa_model_key]), + "checkpoint": pa_checkpoint, + "checkpoint_sha256": binding["sha256"], + "selection_metric": "validation NMSE", + } + delta_dpd_by_pa_surrogate[pa_model_key] = result + + manifest["datasets"][dataset] = { + "spec_sha256": initial_context["datasets"][dataset]["spec_sha256"], + "signal": initial_context["datasets"][dataset]["signal"], + "reference_pa": { + "model_key": REFERENCE_PA_KEY, + "checkpoint": expected_pa_checkpoint, + "checkpoint_sha256": pa_hash, + "selection_metric": "validation NMSE", + }, + "pa_models": pa_models, + "dpd_models": dpd_models, + "dpd_tres_deltagru_by_pa_surrogate": ( + delta_dpd_by_pa_surrogate + ), + } + + return manifest + + +def publish_manifest_from_run( + manifest: dict[str, Any], + run_directory: Path, +) -> dict[str, Any]: + """Bind post-collection source/job evidence into a canonical manifest.""" + source_schema_version = manifest["schema_version"] + source_result_count = sum( + len(dataset["pa_models"]) + + len(dataset["dpd_models"]) + + len(dataset["dpd_tres_deltagru_by_pa_surrogate"]) + for dataset in manifest["datasets"].values() + ) + if source_schema_version != RUN_SCHEMA_VERSION: + raise ValueError( + "Unsupported source manifest schema for publication: " + f"{source_schema_version}" + ) + context_path = run_directory / "context_before.json" + original_manifest_path = run_directory / "benchmark_report_results.json" + jobs_path = run_directory / "jobs.tsv" + recipe_path = run_directory / "recipe.json" + commands_path = run_directory / "commands.log" + with context_path.open() as handle: + context = json.load(handle) + + snapshot_path = run_directory / "source_snapshot.tar.gz" + snapshot_metadata = validate_source_snapshot( + snapshot_path, + context["source_file_sha256"], + ) + snapshot_time = datetime.fromtimestamp( + snapshot_path.stat().st_mtime, + timezone.utc, + ) + started = datetime.fromisoformat(manifest["started_at_utc"]) + collected = datetime.fromisoformat(manifest["generated_at_utc"]) + if not started <= snapshot_time <= collected: + raise ValueError( + "Source snapshot was not created during the recorded benchmark run: " + f"{snapshot_time.isoformat()} is outside " + f"{started.isoformat()} .. {collected.isoformat()}" + ) + + with jobs_path.open(newline="") as handle: + jobs = list(csv.DictReader(handle, delimiter="\t")) + command_labels = set(read_recorded_commands(commands_path)) + job_labels = [row["label"] for row in jobs] + require_equal(len(job_labels), len(set(job_labels)), "unique job labels") + require_equal(set(job_labels), command_labels, "completed benchmark jobs") + for row in jobs: + require_equal( + int(row["exit_status"]), + 0, + f"exit status for job {row['label']}", + ) + + published = copy.deepcopy(manifest) + published["schema_version"] = RUN_SCHEMA_VERSION + for dataset in published["datasets"].values(): + dataset["pa_models"] = { + key: dataset["pa_models"][key] for key in PA_MODEL_ORDER + } + dataset["dpd_models"] = { + key: dataset["dpd_models"][key] for key in DPD_MODEL_ORDER + } + dataset["dpd_tres_deltagru_by_pa_surrogate"] = { + key: dataset["dpd_tres_deltagru_by_pa_surrogate"][key] + for key in (REFERENCE_PA_KEY, ALTERNATE_PA_KEY) + } + published["source_snapshot"] = snapshot_metadata + published["run_file_sha256"].update( + { + "jobs.tsv": sha256_file(jobs_path), + "recipe.json": sha256_file(recipe_path), + "source_snapshot.tar.gz": sha256_file(snapshot_path), + } + ) + for stage in ("neural_pa", "neural_dpd"): + published["recipes"][stage].update(copy.deepcopy(NEURAL_RECIPE)) + published["metric_note"] = ( + "EVM is the repository-specific normalized mean absolute complex-" + "spectrum error across configured main-channel subchannels, not " + "demodulated constellation EVM. DPD metrics are simulated through a " + "frozen learned PA surrogate." + ) + published["publication"] = { + "source_run_id": run_directory.name, + "original_schema_version": source_schema_version, + "original_collector_manifest_sha256": sha256_file( + original_manifest_path + ), + "source_snapshot_created_during_run_utc": snapshot_time.isoformat(), + "source_snapshot_member_hashes_match_pre_run_context": True, + "source_job_count": len(jobs), + "source_result_count": source_result_count, + "published_result_count": ( + len(published["datasets"]) + * (len(PA_MODEL_ORDER) + len(DPD_MODEL_ORDER) + 2) + ), + "published_models": list(PA_MODEL_ORDER), + "published_models_by_stage": { + "pa_modeling": list(PA_MODEL_ORDER), + "dpd": list(DPD_MODEL_ORDER), + "dpd_pa_surrogate_sensitivity": ["tres_deltagru"], + }, + "published_dpd_pa_surrogates": [ + REFERENCE_PA_KEY, + ALTERNATE_PA_KEY, + ], + "all_jobs_exit_zero": True, + "total_job_duration_seconds": sum( + int(row["duration_seconds"]) for row in jobs + ), + "note": ( + "The canonical manifest contains the published model subset. " + "Source-run hashes, job totals, and archives cover the complete " + "source run and can include experiments outside that subset; " + "source-archive members were verified against the pre-run source " + "hash map." + ), + } + return published + + +def pair(metrics: dict[str, dict[str, float]], key: str) -> str: + return ( + f"{metrics['validation'][key]:.4f} / " + f"{metrics['test'][key]:.4f}" + ) + + +def aclr_triplet(metrics: dict[str, float]) -> str: + return ( + f"{metrics['aclr_left_db']:.4f} / " + f"{metrics['aclr_right_db']:.4f} / " + f"{metrics['aclr_avg_db']:.4f}" + ) + + +def result_method(result: dict[str, Any]) -> str: + if result["method"] == "supervised_training": + return "Supervised, AdamW" + if result["method"] == "direct_learning_architecture": + return "DLA, AdamW" + if result["method"] == "direct_least_squares": + if result.get("regularization") == "truncated_svd": + return ( + "Truncated SVD " + f"(rank {result['least_squares_rank']:,}/" + f"{result['complex_coefficients']:,})" + ) + return "Direct least squares" + if result["method"] == "indirect_learning_architecture": + return "ILA, least squares" + raise ValueError(f"Unknown benchmark method: {result['method']}") + + +def selected_epoch(result: dict[str, Any]) -> str: + epoch = result.get("selected_epoch_zero_based") + return "N/A" if epoch is None else str(epoch) + + +def result_table( + results: dict[str, dict[str, Any]], + model_order: tuple[str, ...], +) -> list[str]: + lines = [ + "| Model | Parameters | Method | Selected epoch | " + "NMSE, val / test (dB) | EVM, val / test (dB) | " + "Validation ACLR L / R / avg (dB) | " + "Test ACLR L / R / avg (dB) |", + "|---|---:|---|---:|---:|---:|---:|---:|", + ] + for key in model_order: + result = results[key] + lines.append( + f"| {result['model']['display_name']} | " + f"{result['model']['parameters']:,} | " + f"{result_method(result)} | {selected_epoch(result)} | " + f"{pair(result['metrics'], 'nmse_db')} | " + f"{pair(result['metrics'], 'evm_db')} | " + f"{aclr_triplet(result['metrics']['validation'])} | " + f"{aclr_triplet(result['metrics']['test'])} |" + ) + return lines + + +def delta_dpd_surrogate_table(values: dict[str, Any]) -> list[str]: + lines = [ + "| PA surrogate | PA NMSE, val / test (dB) | DPD parameters | " + "Selected epoch | DPD NMSE, val / test (dB) | " + "DPD EVM, val / test (dB) | DPD ACLR avg, val / test (dB) |", + "|---|---:|---:|---:|---:|---:|---:|", + ] + results = values["dpd_tres_deltagru_by_pa_surrogate"] + for pa_model_key in (REFERENCE_PA_KEY, ALTERNATE_PA_KEY): + result = results[pa_model_key] + pa_result = values["pa_models"][pa_model_key] + lines.append( + f"| {pa_result['model']['display_name']} | " + f"{pair(pa_result['metrics'], 'nmse_db')} | " + f"{result['model']['parameters']:,} | " + f"{selected_epoch(result)} | " + f"{pair(result['metrics'], 'nmse_db')} | " + f"{pair(result['metrics'], 'evm_db')} | " + f"{pair(result['metrics'], 'aclr_avg_db')} |" + ) + return lines + + +def write_results_figure(manifest: dict[str, Any], path: Path) -> None: + """Render a compact test-set comparison directly from the manifest.""" + import matplotlib + + matplotlib.use("Agg", force=True) + import matplotlib.pyplot as plt + + colors = { + "mp": "#4477AA", + "gmp": "#66CCEE", + "gru": "#228833", + "tres_gru": "#EE6677", + "tres_deltagru": "#AA3377", + } + panels = ( + ( + "APA_200MHz", + "pa_models", + "nmse_db", + "PA test NMSE (dB)", + PA_MODEL_ORDER, + ), + ( + "DPA_160MHz", + "pa_models", + "nmse_db", + "PA test NMSE (dB)", + PA_MODEL_ORDER, + ), + ( + "APA_200MHz", + "dpd_models", + "aclr_avg_db", + "DPD test ACLR average (dB)", + DPD_MODEL_ORDER, + ), + ( + "DPA_160MHz", + "dpd_models", + "aclr_avg_db", + "DPD test ACLR average (dB)", + DPD_MODEL_ORDER, + ), + ) + + figure, axes = plt.subplots( + 2, + 2, + figsize=(13.5, 9.0), + constrained_layout=True, + ) + figure.suptitle("OpenDPD benchmark — test split", fontsize=17, weight="bold") + for axis, (dataset, result_key, metric, title, model_order) in zip( + axes.flat, + panels, + ): + results = manifest["datasets"][dataset][result_key] + values = [ + results[key]["metrics"]["test"][metric] for key in model_order + ] + validation_winner = min( + model_order, + key=lambda key: results[key]["metrics"]["validation"][metric], + ) + span = max(values) - min(values) + label_offset = max(0.22, span * 0.025) + left_padding = max(0.4, span * 0.035) + right_padding = max(1.0, span * 0.16) + for y_position, (key, value) in enumerate(zip(model_order, values)): + axis.scatter( + value, + y_position, + s=115, + color=colors[key], + edgecolor="black" if key == validation_winner else "white", + linewidth=1.8 if key == validation_winner else 0.8, + zorder=3, + ) + axis.text( + value + label_offset, + y_position, + f"{value:.2f}", + va="center", + ha="left", + fontsize=9, + ) + axis.set_xlim( + min(values) - left_padding, + max(values) + right_padding, + ) + if min(values) < 0.0 < max(values): + axis.axvline(0.0, color="#777777", linestyle="--", linewidth=1) + axis.set_yticks( + range(len(model_order)), + [results[key]["model"]["display_name"] for key in model_order], + ) + axis.invert_yaxis() + axis.grid(axis="x", color="#D8D8D8", linewidth=0.8) + axis.set_axisbelow(True) + axis.set_title(f"{dataset}\n{title}", fontsize=12) + axis.set_xlabel("More negative is better ←") + for spine in ("top", "right"): + axis.spines[spine].set_visible(False) + + figure.text( + 0.5, + -0.01, + "Outlined markers are validation leaders. DPD is simulated through " + "the dataset-specific validation-selected TRes-GRU-H27 PA surrogate.", + ha="center", + fontsize=10, + ) + temporary_path: Path | None = None + try: + with tempfile.NamedTemporaryFile( + dir=path.parent, + prefix=f".{path.name}.", + suffix=".png", + delete=False, + ) as temporary: + temporary_path = Path(temporary.name) + figure.savefig( + temporary_path, + dpi=160, + facecolor="white", + bbox_inches="tight", + ) + os.replace(temporary_path, path) + finally: + plt.close(figure) + if temporary_path is not None and temporary_path.exists(): + temporary_path.unlink() + + +def write_delta_dpd_figure(manifest: dict[str, Any], path: Path) -> None: + """Render the TRes-DeltaGRU DPD PA-surrogate sensitivity comparison.""" + import matplotlib + + matplotlib.use("Agg", force=True) + import matplotlib.pyplot as plt + + pa_order = (REFERENCE_PA_KEY, ALTERNATE_PA_KEY) + colors = { + REFERENCE_PA_KEY: "#EE6677", + ALTERNATE_PA_KEY: "#AA3377", + } + figure, axes = plt.subplots( + 1, + 2, + figsize=(13.5, 4.8), + constrained_layout=True, + ) + figure.suptitle( + "TRes-DeltaGRU-H15 DPD — PA surrogate sensitivity (test split)", + fontsize=16, + weight="bold", + ) + for axis, dataset in zip(axes, DATASETS): + dataset_values = manifest["datasets"][dataset] + results = dataset_values["dpd_tres_deltagru_by_pa_surrogate"] + values = [ + results[key]["metrics"]["test"]["aclr_avg_db"] for key in pa_order + ] + validation_winner = min( + pa_order, + key=lambda key: results[key]["metrics"]["validation"]["aclr_avg_db"], + ) + span = max(values) - min(values) + label_offset = max(0.12, span * 0.08) + for y_position, (key, value) in enumerate(zip(pa_order, values)): + axis.scatter( + value, + y_position, + s=125, + color=colors[key], + edgecolor="black" if key == validation_winner else "white", + linewidth=1.8 if key == validation_winner else 0.8, + zorder=3, + ) + axis.text( + value + label_offset, + y_position, + f"{value:.2f}", + va="center", + ha="left", + fontsize=10, + ) + left_padding = max(0.35, span * 0.25) + right_padding = max(0.8, span * 0.55) + axis.set_xlim(min(values) - left_padding, max(values) + right_padding) + axis.set_yticks( + range(len(pa_order)), + [ + f"{dataset_values['pa_models'][key]['model']['display_name']} PA" + for key in pa_order + ], + ) + axis.invert_yaxis() + axis.grid(axis="x", color="#D8D8D8", linewidth=0.8) + axis.set_axisbelow(True) + axis.set_title( + f"{dataset}\nDPD test ACLR average (dB)", + fontsize=12, + ) + axis.set_xlabel("More negative is better ←") + for spine in ("top", "right"): + axis.spines[spine].set_visible(False) + + figure.text( + 0.5, + -0.02, + "Each point is an independently trained TRes-DeltaGRU-H15 DPD. " + "Outlined markers are selected by validation ACLR average.", + ha="center", + fontsize=10, + ) + temporary_path: Path | None = None + try: + with tempfile.NamedTemporaryFile( + dir=path.parent, + prefix=f".{path.name}.", + suffix=".png", + delete=False, + ) as temporary: + temporary_path = Path(temporary.name) + figure.savefig( + temporary_path, + dpi=160, + facecolor="white", + bbox_inches="tight", + ) + os.replace(temporary_path, path) + finally: + plt.close(figure) + if temporary_path is not None and temporary_path.exists(): + temporary_path.unlink() + + +def bind_derived_artifacts( + manifest: dict[str, Any], + *, + report_path: Path, + figure_path: Path, + delta_dpd_figure_path: Path, +) -> None: + manifest["derived_artifacts"] = { + report_path.name: { + "sha256": sha256_file(report_path), + "size_bytes": report_path.stat().st_size, + }, + figure_path.name: { + "sha256": sha256_file(figure_path), + "size_bytes": figure_path.stat().st_size, + }, + delta_dpd_figure_path.name: { + "sha256": sha256_file(delta_dpd_figure_path), + "size_bytes": delta_dpd_figure_path.stat().st_size, + }, + } + + +def render_markdown( + manifest: dict[str, Any], + *, + canonical: bool = False, +) -> str: + lines = [ + "# OpenDPD PA Modeling and DPD Benchmark", + "", + "## Technical summary", + "", + "This benchmark evaluates MP, GMP, GRU, TRes-GRU, and " + "TRes-DeltaGRU (THX=THH=0) for PA modeling on APA_200MHz and " + "DPA_160MHz. The DPD comparison evaluates MP, GMP, GRU, and TRes-GRU. " + "PA models use approximately 2,700 real parameters; DPD models use " + "approximately 1,000. Every neural run uses the same 300-epoch " + "optimization recipe. MP PA uses direct least squares and GMP PA uses " + "rank-controlled truncated SVD; their predistorters use the indirect " + "learning architecture (ILA). The four-model DPD comparison uses the " + "dataset-specific " + "TRes-GRU-H27 PA checkpoint selected by validation NMSE. A separate " + "sensitivity experiment trains TRes-DeltaGRU-H15 DPD independently " + "through both the TRes-GRU-H27 and zero-threshold " + "TRes-DeltaGRU-H27 PA surrogates.", + "", + "## Key findings", + "", + ] + + for dataset, values in manifest["datasets"].items(): + best_pa = min( + values["pa_models"].values(), + key=lambda result: result["metrics"]["validation"]["nmse_db"], + ) + best_dpd = min( + values["dpd_models"].values(), + key=lambda result: result["metrics"]["validation"]["aclr_avg_db"], + ) + delta_dpd_results = values["dpd_tres_deltagru_by_pa_surrogate"] + best_delta_pa_key = min( + delta_dpd_results, + key=lambda key: delta_dpd_results[key]["metrics"]["validation"][ + "aclr_avg_db" + ], + ) + best_delta_dpd = delta_dpd_results[best_delta_pa_key] + best_delta_pa = values["pa_models"][best_delta_pa_key] + lines.append( + f"- **{dataset}:** the validation PA leader is " + f"{best_pa['model']['display_name']} at " + f"{best_pa['metrics']['validation']['nmse_db']:.4f} dB NMSE " + f"({best_pa['metrics']['test']['nmse_db']:.4f} dB test). The " + f"validation DPD leader is {best_dpd['model']['display_name']} at " + f"{best_dpd['metrics']['validation']['aclr_avg_db']:.4f} dB ACLR " + f"average ({best_dpd['metrics']['test']['aclr_avg_db']:.4f} dB test). " + f"For TRes-DeltaGRU-H15 DPD, the better validation result uses the " + f"{best_delta_pa['model']['display_name']} PA surrogate at " + f"{best_delta_dpd['metrics']['validation']['aclr_avg_db']:.4f} dB " + f"ACLR average " + f"({best_delta_dpd['metrics']['test']['aclr_avg_db']:.4f} dB test)." + ) + + apa_gmp = manifest["datasets"]["APA_200MHz"]["pa_models"]["gmp"] + lines.extend( + [ + ( + "- **APA GMP stability:** column-normalized truncated SVD at " + f"`rcond={apa_gmp['svd_rcond']:.0e}` retains " + f"{apa_gmp['least_squares_rank']:,}/" + f"{apa_gmp['complex_coefficients']:,} singular directions. " + "Validation/test NMSE is " + f"{apa_gmp['metrics']['validation']['nmse_db']:.2f}/" + f"{apa_gmp['metrics']['test']['nmse_db']:.2f} dB. The fixed " + "cutoff suppresses ill-conditioned delayed-envelope directions " + "instead of applying CUDA `gels`'s invalid full-rank " + "assumption." + ), + "", + "![Test-set PA modeling and DPD results](benchmark_results.png)", + "", + "*Test split; more negative is better. Outlined points are the " + "models selected by validation. DPD is simulated through the " + "dataset-specific TRes-GRU-H27 PA surrogate.*", + "", + "![TRes-DeltaGRU DPD PA-surrogate sensitivity]" + "(benchmark_delta_dpd_results.png)", + "", + "*Test split; each point is an independently trained " + "TRes-DeltaGRU-H15 DPD using the named frozen PA surrogate. " + "These results compare surrogate sensitivity, not measurements " + "from one shared physical PA.*", + ] + ) + + for dataset, values in manifest["datasets"].items(): + lines.extend( + [ + "", + f"## {dataset}", + "", + "### PA modeling", + "", + ] + ) + lines.extend(result_table(values["pa_models"], PA_MODEL_ORDER)) + lines.extend( + [ + "", + "### DPD", + "", + ] + ) + lines.extend(result_table(values["dpd_models"], DPD_MODEL_ORDER)) + lines.extend( + [ + "", + "### TRes-DeltaGRU DPD by PA surrogate", + "", + "Both rows use TRes-DeltaGRU-H15 with 999 parameters and " + "THX=THH=0. They are separately trained through the indicated " + "frozen PA surrogate.", + "", + ] + ) + lines.extend(delta_dpd_surrogate_table(values)) + + lines.extend( + [ + "", + "## Definitions", + "", + "- **NMSE:** normalized mean-square error in dB; more negative is " + "better. The implementation averages per-segment dB ratios rather " + "than pooling all samples into one ratio.", + "- **EVM:** the repository-specific mean absolute complex-spectrum " + "error within the configured main channel, normalized within each " + "subchannel by reference-spectrum magnitude and converted with " + "`20 log10`. It is not demodulated constellation EVM; more negative " + "is better.", + "- **ACLR L / R / avg:** adjacent-subchannel power normalized by the " + "strongest configured main-channel subchannel, plus the arithmetic " + "mean of left and right, in dB. More negative is better.", + "- **Parameters:** neural checkpoint tensor elements. MP/GMP count " + "two real degrees of freedom for every complex coefficient.", + "- **Selected epoch:** zero-based neural checkpoint epoch. It is N/A " + "for closed-form least-squares fits.", + "", + "## Model configurations", + "", + "| Stage | MP | GMP | GRU | TRes-GRU | TRes-DeltaGRU |", + "|---|---|---|---|---|---|", + "| PA modeling | K=9, Q=150 | Ka/La=5/30; " + "Kb/Lb/Mb=4/30/5; Kc/Lc/Mc=4/30/5 | H28 | H27 | " + "H27, THX=THH=0 |", + "| DPD | K=5, Q=100 | Ka/La=5/20; " + "Kb/Lb/Mb=4/20/3; Kc/Lc/Mc=4/20/2 | H16 | H15 | " + "H15, THX=THH=0 (PA-surrogate sensitivity) |", + "", + "## Temporal context and sequence boundaries", + "", + "In sequence interiors, PA GMP uses up to five future samples and " + "DPD GMP uses up to two through their leading-envelope terms. " + "TRes-GRU and TRes-DeltaGRU use one-sample right context in their " + "recurrent features and 16-sample right context in their dilated " + "residual convolution. MP and GRU use no explicit future samples.", + "", + "These are offline segmented evaluations. GMP delay accesses are " + "zero-filled and reset at each `nperseg` boundary. In both TRes " + "models, `torch.roll(..., shifts=-1)` wraps the final position to " + "the first sample of the same supplied sequence; the convolution " + "zero-pads both boundaries, and recurrent state resets for each " + "sequence. Neural optimization uses overlapping 200-sample frames " + "with stride 1, while validation and test use independent " + "`nperseg` segments.", + "", + "## Methodology", + "", + "Neural PA and DPD models use batch size 64, 300 epochs, AdamW with " + "MSE loss, initial learning rate 5e-3, and ReduceLROnPlateau with " + "factor 0.5, patience 5, and minimum learning rate 5e-5. Frame " + "length is 200, frame stride is 1, and seed is 0. PA checkpoints " + "are selected by minimum validation NMSE; neural DPD checkpoints " + "are selected by minimum validation ACLR average.", + "", + "The TRes-DeltaGRU PA and DPD runs use input-delta threshold THX=0 " + "and hidden-state-delta threshold THH=0. This disables " + "threshold-induced temporal pruning; exact arithmetic deltas may " + "still naturally be zero. The configuration therefore evaluates " + "the dense zero-threshold recurrence, not a sparsity or efficiency " + "claim.", + "", + "AdamW uses weight decay 0.01, betas (0.9, 0.999), and epsilon " + "1e-8. The scheduler uses relative threshold 1e-4, cooldown 0, and " + "epsilon 1e-8.", + "", + "MP and GMP are complex polynomial models fit after L2 column " + "scaling. MP and both ILA-DPD fits use `torch.linalg.lstsq` " + "(`gels`). GMP PA modeling uses `torch.linalg.svd` (`gesvdj`) " + f"with a fixed relative cutoff of {GMP_PA_SVD_RCOND:.0e}; the " + "effective retained rank is reported with each GMP PA result. " + "These closed-form fits do not use the neural batch, epoch, " + "optimizer, or learning-rate settings. PA polynomial fits map " + "measured PA input to output directly. DPD polynomial fits use " + "ILA, fitting a postdistorter and copying its coefficients to the " + "predistorter. No ridge penalty or validation-tuned regularization " + "is applied.", + "", + "Each dataset uses its independently trained TRes-GRU-H27 PA " + "surrogate for the four-model DPD comparison. The two " + "TRes-DeltaGRU-H15 sensitivity rows are separate DPD training runs, " + "one through that TRes-GRU PA and one through the independently " + "trained TRes-DeltaGRU-H27 PA. Training data supply all gradient " + "and least-squares fits. Neural validation metrics drive the " + "learning-rate schedule and checkpoint selection; test data are " + "not used for fitting, scheduling, or selection.", + "", + "## Limitations and robustness", + "", + "- Neural execution uses soft reproducibility with cuDNN benchmark " + "enabled, so repeated runs can differ slightly.", + "- DPD scores measure simulated performance through a learned PA " + "surrogate, not a fresh over-the-air or bench measurement.", + "- One seed is evaluated. The reported table is not a distribution " + "over training runs.", + "- The five PA candidates and four primary DPD candidates are " + "matched approximately by real parameter count, not by FLOPs, " + "latency, memory traffic, or fit time. The PA-surrogate sensitivity " + "experiment adds two independently trained TRes-DeltaGRU DPD runs " + "per dataset.", + "- Results obtained through different learned PA surrogates are " + "simulator-sensitivity evidence; they are not a controlled ranking " + "against one shared physical PA response.", + "- CUDA `gels` assumes a full-rank design matrix and does not " + "return numerical rank. It remains in use for MP and ILA-DPD; the " + "GMP PA path records its SVD spectrum, cutoff, and retained rank.", + "- GMP PA has 1,350 stored complex coefficients (2,700 nominal real " + "parameters), but truncated SVD reduces its effective rank. The " + "comparison is matched by stored coefficient count, not effective " + "degrees of freedom.", + "- Look-ahead is an input dependency, not measured inference " + "latency. Streaming reformulations and continuous boundary/state " + "handling are not evaluated.", + "", + "## Provenance", + "", + f"- Generated: `{manifest['generated_at_utc']}`", + f"- Git commit: `{manifest['git']['commit']}`", + f"- Git branch: `{manifest['git']['branch']}`", + f"- Git working tree at launch: " + f"`{'dirty' if manifest['git']['dirty'] else 'clean'}`; exact " + "source hashes and start status are retained in the machine " + "evidence.", + f"- Python: `{manifest['environment']['python']}`", + f"- PyTorch: `{manifest['environment']['torch']}`", + f"- CUDA: `{manifest['environment']['cuda']}`", + f"- GPU: `{manifest['environment']['gpu']}`", + ] + ) + if canonical: + publication = manifest.get("publication") + if publication is not None: + lines.append( + "- Canonical evidence schema: " + f"`{manifest['schema_version']}` (published from collector " + f"schema `{publication['original_schema_version']}`); the " + "source archive and completed job ledger are cryptographically " + "bound in the machine-readable evidence." + ) + lines.extend( + [ + "- Reproduce the full matrix: " + "[`reproduce_benchmark_report.sh`](reproduce_benchmark_report.sh)", + "- Machine-readable evidence: " + "[`results/benchmark_report_results.json`]" + "(results/benchmark_report_results.json)", + "- Each reproduction run writes exact commands, a verified " + "source snapshot, raw polynomial results, checkpoints, and CSV " + "logs to its timestamped evidence directory.", + ] + ) + else: + lines.extend( + [ + "- Exact launch commands: [`commands.log`](commands.log)", + "- Machine-readable evidence: " + "[`benchmark_report_results.json`](benchmark_report_results.json)", + "- Source snapshot: " + "[`source_snapshot.tar.gz`](source_snapshot.tar.gz)", + "- Archived checkpoints and CSV logs: [`artifacts/`](artifacts/)", + ] + ) + lines.append("") + return "\n".join(lines) + + +def write_atomic(path: Path, content: str) -> None: + temporary_path: Path | None = None + try: + with tempfile.NamedTemporaryFile( + mode="w", + encoding="utf-8", + dir=path.parent, + prefix=f".{path.name}.", + suffix=".tmp", + delete=False, + ) as handle: + handle.write(content) + temporary_path = Path(handle.name) + os.replace(temporary_path, path) + finally: + if temporary_path is not None and temporary_path.exists(): + temporary_path.unlink() + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--output-dir", + type=Path, + required=True, + help="Run directory containing raw least-squares JSON and archived artifacts.", + ) + parser.add_argument( + "--device", + type=int, + default=0, + help="CUDA device index used by the benchmark run.", + ) + parser.add_argument( + "--snapshot-context", + action="store_true", + help="Capture pre-run source, dataset, Git, and environment evidence.", + ) + parser.add_argument( + "--overwrite-output", + action="store_true", + help="Replace an existing collector snapshot or final manifest.", + ) + args = parser.parse_args() + output_dir = args.output_dir.resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + if args.snapshot_context: + context_path = output_dir / "context_before.json" + snapshot_path = output_dir / "source_snapshot.tar.gz" + if ( + (context_path.exists() or snapshot_path.exists()) + and not args.overwrite_output + ): + raise FileExistsError( + f"Context or source snapshot already exists in: {output_dir}" + ) + context = capture_context( + output_dir, + args.device, + use_runner_start_git=True, + ) + if context["environment"]["gpu"] is None: + raise ValueError( + "CUDA device evidence is missing from the pre-run context" + ) + write_source_snapshot( + list(context["source_file_sha256"]), + snapshot_path, + ) + context["source_snapshot"] = validate_source_snapshot( + snapshot_path, + context["source_file_sha256"], + ) + write_atomic( + context_path, + json.dumps( + context, + indent=2, + sort_keys=True, + allow_nan=False, + ) + + "\n", + ) + print(f"Wrote pre-run context snapshot to {context_path}") + return + + result_path = output_dir / "benchmark_report_results.json" + markdown_path = output_dir / "benchmark_report.md" + figure_path = output_dir / "benchmark_results.png" + delta_dpd_figure_path = output_dir / "benchmark_delta_dpd_results.png" + if ( + ( + result_path.exists() + or markdown_path.exists() + or figure_path.exists() + or delta_dpd_figure_path.exists() + ) + and not args.overwrite_output + ): + raise FileExistsError( + "Collector output already exists; use a new run directory or " + "--overwrite-output." + ) + + initial_context, completion_context = load_and_validate_context( + output_dir, args.device + ) + manifest = collect_manifest( + output_dir, + args.device, + initial_context, + completion_context, + ) + write_results_figure(manifest, figure_path) + write_delta_dpd_figure(manifest, delta_dpd_figure_path) + write_atomic( + markdown_path, + render_markdown(manifest), + ) + bind_derived_artifacts( + manifest, + report_path=markdown_path, + figure_path=figure_path, + delta_dpd_figure_path=delta_dpd_figure_path, + ) + write_atomic( + result_path, + json.dumps( + manifest, + indent=2, + sort_keys=True, + allow_nan=False, + ) + + "\n", + ) + print(f"Validated benchmark evidence in {output_dir}") + print(f"Wrote {output_dir / 'benchmark_report_results.json'}") + print(f"Wrote {output_dir / 'benchmark_report.md'}") + print(f"Wrote {output_dir / 'benchmark_results.png'}") + print(f"Wrote {output_dir / 'benchmark_delta_dpd_results.png'}") + + +if __name__ == "__main__": + main() diff --git a/benchmark/pa_modeling_investigation.md b/benchmark/pa_modeling_investigation.md deleted file mode 100644 index 4574a19..0000000 --- a/benchmark/pa_modeling_investigation.md +++ /dev/null @@ -1,143 +0,0 @@ -# APA_200MHz PA-Modeling NMSE Investigation - -Question: why does PA modeling on `APA_200MHz` saturate at ~-31.5 dB NMSE, and is -the `benchmark_report.md` figure of -43.52 dB attainable? - -Answer: **-31.5 dB is an undertrained-recipe artifact, not a data defect.** The -dataset's measurement noise floor is ~-38.6 dB, every architecture reaches it once -trained with the OpenDPDv2 recipe, and -43.52 dB lies *below* the noise floor, so -it is not physically attainable on this dataset. - -## 1. Backbone comparison on APA_200MHz (PA modeling, val/test NMSE in dB) - -All models sized to ~1900 parameters for a fair comparison. - -| Backbone | Params | Report recipe
(lr 5e-4, bs 256, 100 ep, no sched) | OpenDPDv2 recipe
(lr 5e-3, bs 64, 240 ep, ReduceLROnPlateau) | -|---|---|---|---| -| GRU (h23) | 1,911 | -31.48 / -31.40 | **-38.79 / -38.87** | -| DGRU (h23) | 2,751 | -31.67 / -31.61 | **-39.13 / -39.21** | -| TCN (h66) | 1,914 | -37.44 / -37.30 | **-38.38 / -38.25** | -| TRes-GRU (h22) | 1,916 | -35.62 / -35.50 | **-38.94 / -39.01** | - -Two observations: - -1. **The recipe, not the architecture, causes the -31.5 dB plateau.** The same GRU - improves by 7.4 dB purely from the training recipe. Recurrent models are the - most sensitive: with lr=5e-4 and no schedule they are badly undertrained at - 100 epochs, while the convolutional TCN already reaches -37.4 dB. -2. **With a proper recipe all four architectures land within 0.9 dB of each other** - (-38.25 to -39.21), because they are all at the data's noise floor. Ranking - there is not architecturally meaningful. - -## 2. Data ceiling: closed-form least-squares fits - -Closed-form identification is immune to optimizer/learning-rate issues, so it -measures what the *data* supports. Basis built on the contiguous signal, fit on -the train split, evaluated on the test split (64-sample guard). - -| Model | Real params | APA_200MHz | APA_200MHz_b | DPA_160MHz | -|---|---|---|---|---| -| MP K=5 Q=10 | 100 | -31.94 | -32.81 | -38.14 | -| MP K=7 Q=20 | 280 | -37.13 | -33.24 | -38.48 | -| MP K=9 Q=40 | 720 | -37.22 | -33.23 | -38.50 | -| GMP rich | 1,180 | -38.54 | -38.42 | -39.48 | -| GMP extra-rich | 3,520 | **-38.83** | **-38.48** | **-39.51** | - -All three datasets saturate in the same place, -38.5 to -39.5 dB. APA_200MHz is -**not** a worse dataset than DPA_160MHz. - -Note the damning comparison: a 100-real-parameter memory polynomial (-31.94 dB) -matches the 1,911-parameter GRU trained with the report recipe (-31.40 dB). - -## 3. The residual is measurement noise, not unmodeled distortion - -Residual of the GMP-rich fit, analyzed spectrally and against input amplitude: - -| Quantity | APA_200MHz | DPA_160MHz | -|---|---|---| -| Residual NMSE | -38.64 dB | -39.48 dB | -| Residual PSD in-band vs out-of-band | +3.9 dB | +4.8 dB | -| *Signal* PSD in-band vs out-of-band | +33.4 dB | +37.8 dB | -| Residual power at \|x\|→0 | -48.0 dB | -41.4 dB | -| Residual power at \|x\|→max | -44.5 dB | -37.3 dB | - -The residual is nearly white (+3.9 dB in/out-of-band tilt, against +33.4 dB for -the signal) and **its power barely depends on input amplitude — it stays at --48 dB even where the input goes to zero.** Unmodeled nonlinear distortion must -vanish as the drive vanishes; additive measurement noise does not. So ~-38.6 dB -is the noise floor of this capture. - -**Consequence: -43.52 dB is ~5 dB below the measurement noise floor and cannot be -achieved on APA_200MHz by any model.** This independently confirms it is a -misrecorded figure. - -## 4. Dataset integrity: no defects found - -- **Git history**: only 4 commits ever touched `datasets/APA_200MHz/`. The CSVs - have been bit-identical since 2025-07-09; every later commit changed only - `spec.json` metadata and plots. `fs`, `nperseg` and split ratios never changed. -- **Provenance**: R&S SMW200A generator -> 3.5 GHz Ampleon GaN Doherty PA -> - Keysight N9042B IQ capture (`Matlab/` scripts). -- **A vs B**: `APA_200MHz` and `APA_200MHz_b` have **byte-identical inputs** and - different output captures. Commit `2c7f91a` swapped the two output sets four - hours after the initial release and renamed `APA_200MHz_backup` to - `APA_200MHz_b`, so today's primary dataset is the capture the OpenDPDv2 paper - numbers were reported on. -- **Signal QA** (all three datasets): no NaN/Inf, no duplicate rows, no clipping - (only the 1-2 samples defining the normalization peak), DC offset < 1e-3, - AM/AM relative spread 0.045-0.049 (APA is in line with DPA). -- **Splits**: power and complex gain are consistent across train/val/test - (APA gain 1.1626 / 1.1628 / 1.1636); no discontinuity at split boundaries. -- **Alignment**: input/output are already time-aligned (peak lag 0, normalized - cross-correlation 0.9945). -- **Measurement A vs B**: NMSE between the two output captures of the same - stimulus is -23.75 dB (gain 0.98987, phase +0.309 deg). Since each capture is - individually modelable to -38.5 dB, this gap is a systematic operating-point - difference between the two measurements, not random capture noise. - -## 5. Downstream impact: the benchmark rests on an undertrained surrogate - -Re-running the QR benchmarks on APA_200MHz with the *same* code but a properly -trained PA surrogate (GRU h23 at -38.87 dB instead of -31.40 dB): - -| Model | Metric | Surrogate at -31.4 dB
(as in benchmark_report) | Surrogate at -38.9 dB | Change | -|---|---|---|---|---| -| MP (QR) | ACLR_AVG | -41.02 | -41.81 | -0.8 | -| MP (QR) | EVM | -32.70 | **-47.41** | **-14.7** | -| GMP (QR) | ACLR_AVG | -38.75 | -39.58 | -0.8 | -| GMP (QR) | EVM | -38.50 | **-34.89** | **+3.6** | - -EVM moves by up to 14.7 dB and the MP-vs-GMP ranking flips (MP now wins on both -metrics). `benchmark_report.md` Section 8 concludes that AI beats the best -traditional method by "10.6 dB in EVM" on APA_200MHz; with a converged surrogate -MP alone reaches -47.41 dB EVM, so that conclusion does not survive. The -DPA_160MHz surrogate was converged (-38.43 dB, at its noise floor), which is why -that half of the report reproduces cleanly. - -## 6. New backbone: TRes-GRU - -`backbones/tres_gru.py` is TRes-DeltaGRU with the DeltaGRU cell replaced by a -dense `nn.GRU` (bias-free, so parameter counts match at equal hidden size: h=10 -gives 524 params, identical to TRes-DeltaGRU). Registered as `tres_gru` in -`models.py` and in both backbone choice lists in `arguments.py`. - -Validated: with `thx=thh=0`, TRes-DeltaGRU is mathematically a dense GRU, and the -two implementations agree numerically (max abs difference 8.2e-7, relative L2 -1.3e-6) after weight transfer. - -## Reproducing - -```bash -# Report recipe -python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone tcn --PA_hidden_size 66 --n_epochs 100 - -# OpenDPDv2 recipe -python main.py --step train_pa --dataset_name APA_200MHz --PA_backbone gru --PA_hidden_size 23 \ - --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 --decay_factor 0.5 --patience 10 --batch_size 64 -``` - -Caution: the checkpoint/log path (`model_id`) does not encode the learning rate, -batch size or epoch count, so re-running the same architecture with a different -recipe silently overwrites `save//train_pa/.pt` and its logs. -The checkpoint currently on disk for `PA_S_0_M_GRU_H_23_F_200_P_1911` is the -OpenDPDv2-recipe model (-38.87 dB), not the report-recipe one. diff --git a/benchmark/report/artifact.json b/benchmark/report/artifact.json deleted file mode 100644 index 9496b38..0000000 --- a/benchmark/report/artifact.json +++ /dev/null @@ -1,382 +0,0 @@ -{ - "surface": "report", - "manifest": { - "version": 1, - "surface": "report", - "title": "TRes-DeltaGRU end-to-end training optimization", - "description": "A controlled speed and numerical-equivalence study of OpenDPD TRes-DeltaGRU training.", - "generatedAt": "2026-07-11T13:45:00Z", - "filters": [], - "cards": [ - { - "id": "production_speedup", - "description": "Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.", - "dataset": "headline", - "sourceId": "e2e_summary", - "metrics": [ - {"label": "End-to-end speedup", "field": "total_speedup"}, - {"label": "Training speedup", "field": "train_speedup"} - ] - }, - { - "id": "output_equivalence", - "description": "Relative L2 difference between the original and optimized cascaded neural-network outputs.", - "dataset": "headline", - "sourceId": "e2e_summary", - "metrics": [ - {"label": "Cascade output rel-L2", "field": "cascade_relative_l2"}, - {"label": "Maximum absolute error", "field": "cascade_max_abs"} - ] - }, - { - "id": "rf_equivalence", - "description": "Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.", - "dataset": "headline", - "sourceId": "e2e_summary", - "metrics": [ - {"label": "Maximum RF-metric delta", "field": "maximum_rf_delta"}, - {"label": "Acceptance limit", "field": "rf_limit"} - ] - }, - { - "id": "checkpoint_equivalence", - "description": "Comparison of every tensor in the selected best DPD state dictionary.", - "dataset": "headline", - "sourceId": "e2e_summary", - "metrics": [ - {"label": "DPD checkpoint", "field": "checkpoint_result"}, - {"label": "Best epoch", "field": "best_epoch_result"} - ] - } - ], - "charts": [ - { - "id": "gate_total_time", - "title": "Three-epoch controlled workload time", - "subtitle": "All arms used the same fixture and recorded batches; the direct Triton arm was rejected by the output-equivalence gate.", - "headerMarkdown": "Lower is faster. The strict whole-cascade graph is the fastest **accepted** method.", - "type": "bar", - "dataset": "candidate_arms", - "sourceId": "e2e_summary", - "valueFormat": "number", - "encodings": { - "x": {"field": "implementation", "type": "nominal", "label": "Implementation"}, - "y": {"field": "total_s", "type": "quantitative", "label": "Total time", "unit": "s", "format": "number"}, - "tooltip": [ - {"field": "train_s", "type": "quantitative", "label": "Training time", "format": "number"}, - {"field": "total_speedup", "type": "quantitative", "label": "Total speedup", "format": "number"}, - {"field": "cascade_output_relative_l2", "type": "quantitative", "label": "Output rel-L2", "format": "number"}, - {"field": "accepted", "type": "nominal", "label": "Accepted"} - ] - } - } - ], - "tables": [ - { - "id": "candidate_decision", - "title": "Optimization candidate decision", - "subtitle": "The acceptance decision combines speed, final neural output, checkpoint, and RF metrics.", - "dataset": "candidate_arms", - "sourceId": "e2e_summary", - "defaultSort": {"field": "total_s", "direction": "asc"}, - "columns": [ - {"field": "implementation", "label": "Implementation", "type": "text"}, - {"field": "total_s", "label": "Total s", "type": "number", "format": "number", "align": "right"}, - {"field": "train_s", "label": "Train s", "type": "number", "format": "number", "align": "right"}, - {"field": "total_speedup", "label": "Speedup", "type": "number", "format": "number", "align": "right"}, - {"field": "cascade_output_relative_l2_display", "label": "Output rel-L2", "type": "text", "align": "right"}, - {"field": "maximum_absolute_metric_delta_db_display", "label": "Max RF delta, dB", "type": "text", "align": "right"}, - {"field": "accepted", "label": "Accepted", "type": "text", "align": "center"} - ] - }, - { - "id": "rf_metric_equivalence", - "title": "Production-density RF metric equivalence", - "subtitle": "Candidate minus original; lower absolute delta is better.", - "dataset": "rf_metrics", - "sourceId": "e2e_summary", - "defaultSort": {"field": "metric", "direction": "asc"}, - "columns": [ - {"field": "metric", "label": "Metric", "type": "text"}, - {"field": "original_db", "label": "Original dB", "type": "number", "format": "number", "align": "right"}, - {"field": "optimized_db", "label": "Optimized dB", "type": "number", "format": "number", "align": "right"}, - {"field": "delta_db_display", "label": "Delta dB", "type": "text", "align": "right"}, - {"field": "absolute_delta_db_display", "label": "Absolute delta dB", "type": "text", "align": "right"} - ] - }, - { - "id": "implementation_summary", - "title": "Implemented optimizations and safeguards", - "subtitle": "All changes retain the original module parameters and checkpoint structure.", - "dataset": "implementation_changes", - "sourceId": "benchmark_code", - "defaultSort": {"field": "priority", "direction": "asc"}, - "columns": [ - {"field": "priority", "label": "Priority", "type": "number", "align": "right"}, - {"field": "component", "label": "Component", "type": "text"}, - {"field": "change", "label": "Change", "type": "text"}, - {"field": "equivalence", "label": "Equivalence behavior", "type": "text"} - ] - } - ], - "sources": [ - { - "id": "e2e_summary", - "label": "Reviewed end-to-end benchmark summary", - "path": "benchmark/results/end_to_end_summary.json" - }, - { - "id": "benchmark_code", - "label": "Reproducible benchmark and implementation", - "path": "benchmark/benchmark_end_to_end.py" - } - ], - "blocks": [ - { - "id": "title", - "type": "markdown", - "body": "# TRes-DeltaGRU end-to-end training optimization", - "layout": "full" - }, - { - "id": "executive_summary", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Executive Summary\n\nThe recommended strict path is guarded whole-cascade CUDA-graph training, enabled with `--cuda_graph_training`. On the 357-update production-density workload it reduced total time from **95.78 s to 14.51 s and 13.96 s** across two runs (**6.60–6.86×**) and training time from **86.86 s to 12.41 s and 11.81 s** (**7.00–7.35×**). The selected DPD checkpoint was bitwise identical, cascade output relative L2 error was **1.70e-6**, and the largest ACLR/NMSE/EVM difference was **4.27e-6 dB**." - }, - { - "id": "headline_metrics", - "type": "metric-strip", - "cardIds": [ - "production_speedup", - "output_equivalence", - "rf_equivalence", - "checkpoint_equivalence" - ] - }, - { - "id": "key_findings", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Key findings\n\n- The isolated persistent Triton backbone remains the fastest kernel-level method at **97.11×**, but long training amplifies its small FP32 differences around delta thresholds.\n- Direct persistent-Triton end-to-end training reached **13.40× total** and **46.77× training** speedup in the three-epoch gate, yet its final cascade output relative L2 error reached **1.04e-2**. It was rejected even though its RF metrics stayed within 0.035 dB.\n- Capturing the existing clean-eager cascade preserves its PyTorch/cuDNN arithmetic and removes recurrent launch overhead. It is slower than direct Triton but passes every output, checkpoint, and RF-metric gate.\n- Loop/data cleanups alone are bitwise exact and improve total time **1.33×**." - }, - { - "id": "timing_chart", - "type": "chart", - "chartId": "gate_total_time", - "layout": "full" - }, - { - "id": "selection_rationale", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Why the strict graph is the recommendation\n\nThe graph records the original eager TRes-DeltaGRU, MSE loss, gradient clearing, frozen-PA input backward, and DPD backward. It replays those exact kernels at fixed shapes while leaving batch copies, gradient clipping, AdamW, scheduling, validation, testing, and checkpoint selection outside the graph. Full and partial batches share stable gradient buffers and one capture stream. Unsupported calls fall back to clean eager training; evaluation restores the fused inference path." - }, - { - "id": "candidate_table", - "type": "table", - "tableId": "candidate_decision", - "layout": "full" - }, - { - "id": "equivalence_findings", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Output and RF equivalence\n\nThe production-density optimized checkpoint matched every original state-dict tensor exactly. The remaining output difference comes from using the faster fused inference kernel on the same weights: **4.95e-6 maximum absolute** and **1.70e-6 relative L2** at the cascade output. All five reported RF metrics were effectively unchanged, with absolute deltas between zero and **4.27e-6 dB**, far below the predeclared **0.05 dB** ceiling." - }, - { - "id": "rf_table", - "type": "table", - "tableId": "rf_metric_equivalence", - "layout": "full" - }, - { - "id": "scope_definitions", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Scope, data, and metric definitions\n\nThe controlled workload uses bundled `DPA_200MHz` data, a TRes-DeltaGRU H15 DPD (`thx=.01`, `thh=.05`), a frozen DGRU H23 PA, FP32, B64×T200, AdamW at 5e-3, and gradient clipping at 200. The production-density arm uses all 22,841 stride-one frames (357 updates); the multi-epoch gate uses stride 16 (23 updates per epoch for three epochs). NMSE is the repository's mean per-segment normalized MSE in dB. EVM is its subchannel spectral-magnitude error in dB. ACLR-left/right compare adjacent-channel power with the strongest main subchannel; ACLR-average is their mean." - }, - { - "id": "methodology", - "type": "markdown", - "sourceId": "benchmark_code", - "body": "## Methodology\n\nThe exact original is detached commit `0976ce39`. A single deterministic PA surrogate was trained for 100 epochs because the repository ships no PA checkpoint; its SHA-256 fixture was reused unchanged by every arm. DPD initialization, frame order, full/partial batches, validation/test segments, optimizer hyperparameters, TF32 setting, and hard-determinism policy were fixed. Each run saves the exact permutations, best DPD checkpoint, DPD output, cascaded output, ground truth, phase timings, trajectory, and RF metrics. The comparison script rejects a fixture/order mismatch before evaluating speed or numerical results. Regression validation passed **164 standard tests plus 19 extended all-dataset/all-backbone tests**." - }, - { - "id": "implemented_changes", - "type": "table", - "tableId": "implementation_summary", - "layout": "full" - }, - { - "id": "limitations", - "type": "markdown", - "sourceId": "e2e_summary", - "body": "## Limitations and robustness\n\n- The bundled repository has no published PA checkpoint. The surrogate makes the original-versus-optimized comparison controlled, but its absolute RF quality must not be presented as a reproduction of published results.\n- The longest comparison is one 357-update epoch; the multi-epoch gate covers 69 updates. This is not a complete 240- or 300-epoch convergence study.\n- Whole-cascade graph training is deliberately opt-in and specialized to the FP32 TRes-DeltaGRU → frozen-DGRU cascade with mean MSE. CPU, AMP, QAT, changed modules, diagnostic delta collection, capture failure, and unsupported shapes use eager fallbacks.\n- Existing EVM behavior was preserved: `calculate_metrics` does not pass the dataset sample rate, so EVM uses its 800 MHz default. Correcting that is a separate semantic change.\n- One original production-density timing and two optimized timings were collected on a power-managed laptop GPU; use the observed range rather than treating one decimal speedup as universal." - }, - { - "id": "recommendations", - "type": "markdown", - "body": "## Recommended next steps\n\n1. Use `--cuda_graph_training` (or `cuda_graph_training=True` through the API) when strict training-result equivalence is required.\n2. Keep direct persistent Triton available for inference and experiments where endpoint metric equivalence is sufficient, but do not label its multi-epoch neural outputs strict-equivalent at nonzero thresholds.\n3. Stage the real production PA checkpoint and run the full intended epoch schedule on the target GPU before a release claim. Preserve the same fixture/order comparison and checkpoint/output/RF gates.\n4. If workload shapes or precision modes change, rerun the supplied end-to-end harness; the graph path will safely fall back, but its speedup is shape- and hardware-dependent." - }, - { - "id": "further_questions", - "type": "markdown", - "body": "## Further questions\n\nThe main unresolved question is full-schedule convergence with the real PA checkpoint. A secondary opportunity is a GPU-resident frame sampler for much larger datasets, where per-batch host staging may become material after recurrent launch overhead has been removed." - } - ] - }, - "snapshot": { - "version": 1, - "generatedAt": "2026-07-11T13:45:00Z", - "status": "ready", - "datasets": { - "headline": [ - { - "total_speedup": "6.60–6.86×", - "train_speedup": "7.00–7.35×", - "cascade_relative_l2": "≤ 1.70e-6", - "cascade_max_abs": "≤ 4.95e-6", - "maximum_rf_delta": "≤ 4.27e-6 dB", - "rf_limit": "0.05 dB", - "checkpoint_result": "Bitwise exact", - "best_epoch_result": "Same" - } - ], - "candidate_arms": [ - { - "implementation": "Exact original", - "total_s": 32.03585798398126, - "train_s": 17.237055696023162, - "total_speedup": 1.0, - "train_speedup": 1.0, - "cascade_output_relative_l2": 0.0, - "cascade_output_relative_l2_display": "0", - "maximum_absolute_metric_delta_db": 0.0, - "maximum_absolute_metric_delta_db_display": "0", - "accepted": "Yes", - "batch_count": 69, - "checkpoint_max_abs": 0.0 - }, - { - "implementation": "Clean eager pipeline", - "total_s": 24.029687639005715, - "train_s": 13.491934273974039, - "total_speedup": 1.3331782945018265, - "train_speedup": 1.2775822462516342, - "cascade_output_relative_l2": 0.0, - "cascade_output_relative_l2_display": "0", - "maximum_absolute_metric_delta_db": 0.0, - "maximum_absolute_metric_delta_db_display": "0", - "accepted": "Yes", - "batch_count": 69, - "checkpoint_max_abs": 0.0 - }, - { - "implementation": "Strict whole-cascade graph", - "total_s": 5.458723943011137, - "train_s": 3.434560444991803, - "total_speedup": 5.868744841914403, - "train_speedup": 5.018707916804272, - "cascade_output_relative_l2": 0.0000020480301293249045, - "cascade_output_relative_l2_display": "2.05e-6", - "maximum_absolute_metric_delta_db": 0.0000024523588528779783, - "maximum_absolute_metric_delta_db_display": "2.45e-6", - "accepted": "Yes", - "batch_count": 69, - "checkpoint_max_abs": 0.0 - }, - { - "implementation": "Direct persistent Triton", - "total_s": 2.3912337490182836, - "train_s": 0.36852700999588706, - "total_speedup": 13.397208866399414, - "train_speedup": 46.77284222997803, - "cascade_output_relative_l2": 0.010411809662242288, - "cascade_output_relative_l2_display": "1.04e-2", - "maximum_absolute_metric_delta_db": 0.03461828059020533, - "maximum_absolute_metric_delta_db_display": "3.46e-2", - "accepted": "No", - "batch_count": 69, - "checkpoint_max_abs": 0.000812530517578125 - } - ], - "rf_metrics": [ - {"metric": "ACLR left", "original_db": -33.009569123033046, "optimized_db": -33.009571182277476, "delta_db": -0.0000020592444300859825, "delta_db_display": "-2.06e-6", "absolute_delta_db": 0.0000020592444300859825, "absolute_delta_db_display": "2.06e-6"}, - {"metric": "ACLR right", "original_db": -32.416537505570915, "optimized_db": -32.41654177506668, "delta_db": -0.0000042694957684830115, "delta_db_display": "-4.27e-6", "absolute_delta_db": 0.0000042694957684830115, "absolute_delta_db_display": "4.27e-6"}, - {"metric": "ACLR average", "original_db": -32.71305331430198, "optimized_db": -32.71305647867208, "delta_db": -0.000003164370099284497, "delta_db_display": "-3.16e-6", "absolute_delta_db": 0.000003164370099284497, "absolute_delta_db_display": "3.16e-6"}, - {"metric": "EVM", "original_db": -25.64965659922391, "optimized_db": -25.649657576831792, "delta_db": -0.0000009776078826462253, "delta_db_display": "-9.78e-7", "absolute_delta_db": 0.0000009776078826462253, "absolute_delta_db_display": "9.78e-7"}, - {"metric": "NMSE", "original_db": -24.16977882385254, "optimized_db": -24.16977882385254, "delta_db": 0.0, "delta_db_display": "0", "absolute_delta_db": 0.0, "absolute_delta_db_display": "0"} - ], - "implementation_changes": [ - {"priority": 1, "component": "Whole cascade", "change": "Opt-in fixed-shape CUDA graph of clean-eager forward, MSE, gradient zeroing, and backward", "equivalence": "Original kernels; clean-eager fallback; clip and AdamW remain outside"}, - {"priority": 2, "component": "Frozen DGRU PA", "change": "Exact cuDNN CUDA-graph forward and input-backward replay", "equivalence": "Bitwise output/input gradient in focused tests; guarded eager fallback"}, - {"priority": 3, "component": "Training loop", "change": "One loss-vector device-to-host transfer per epoch; set-to-none gradients; trainable-only clip/optimizer", "equivalence": "Bitwise CPU trajectory and logged loss in lockstep tests"}, - {"priority": 4, "component": "Evaluation", "change": "Inference mode and consolidated output/target transfers", "equivalence": "Exact outputs, targets, and loss in tests"}, - {"priority": 5, "component": "Data", "change": "Vectorized frame construction and CUDA pinned/nonblocking loader path", "equivalence": "Exact frame order/content/dtype with independent storage"}, - {"priority": 6, "component": "Persistent Triton TRes", "change": "Fused recurrence and custom BPTT for supported FP32 NVIDIA shapes", "equivalence": "Minor one-step FP error; rejected as strict multi-epoch training path at nonzero thresholds"} - ] - }, - "accessIssues": [] - }, - "sources": [ - { - "id": "e2e_summary", - "label": "Reviewed end-to-end benchmark summary", - "path": "benchmark/results/end_to_end_summary.json", - "query": { - "engine": "duckdb", - "id": "opendpd-e2e-2026-07-11", - "sql": "SELECT * FROM read_json_auto('benchmark/results/end_to_end_summary.json');", - "description": "Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.", - "language": "sql", - "executed_at": "2026-07-11T13:42:00Z", - "tables_used": [ - "benchmark/results/e2e_dpa200_stride1_comparison_graph.json", - "benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json", - "benchmark/results/e2e_dpa200_stride16_comparison_eager.json", - "benchmark/results/e2e_dpa200_stride16_comparison_graph.json", - "benchmark/results/e2e_dpa200_stride16_comparison_fused.json", - "benchmark/results/tres_deltagru_rtx4090_laptop.json" - ], - "filters": [ - "DPA_200MHz bundled dataset", - "FP32 with TF32 disabled and hard deterministic policy", - "Same serialized PA/DPD fixture and recorded batch permutations", - "Plots and progress display excluded" - ], - "metric_definitions": [ - "Total speedup = original measured workload seconds / optimized measured workload seconds.", - "Training speedup = original synchronized net_train seconds / optimized synchronized net_train seconds.", - "Output relative L2 = L2(candidate - reference) / L2(reference), evaluated on saved best-checkpoint test outputs.", - "Maximum RF delta = max absolute candidate-minus-reference delta over ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE, in dB." - ] - } - }, - { - "id": "benchmark_code", - "label": "Reproducible benchmark and implementation", - "path": "benchmark/benchmark_end_to_end.py", - "query": { - "engine": "duckdb", - "id": "benchmark-end-to-end-py", - "sql": "SELECT content FROM read_text('benchmark/benchmark_end_to_end.py');", - "description": "Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.", - "language": "sql", - "executed_at": "2026-07-11T13:42:00Z", - "tables_used": [ - "benchmark/benchmark_end_to_end.py", - "modules/cuda_graph_training.py", - "modules/train_funcs.py", - "backbones/cuda_graph_frozen_dgru.py", - "backbones/triton_deltagru.py" - ], - "filters": ["No architecture or state-dict changes", "Unsupported graph or kernel calls fall back to eager PyTorch"], - "metric_definitions": ["The benchmark preserves the repository's existing ACLR, EVM, and NMSE implementations without semantic corrections."] - } - } - ], - "package_info": { - "originUrl": "artifact://opendpd-tres-deltagru-e2e-2026-07-11" - } -} diff --git a/benchmark/reproduce_benchmark_report.sh b/benchmark/reproduce_benchmark_report.sh new file mode 100755 index 0000000..9050389 --- /dev/null +++ b/benchmark/reproduce_benchmark_report.sh @@ -0,0 +1,703 @@ +#!/usr/bin/env bash +# +# Reproduce benchmark/benchmark_report.md from the two bundled RF datasets. +# +# The benchmark matrix is fixed before execution: +# PA (~2,700 real parameters): MP, GMP, GRU, TRes-GRU, +# TRes-DeltaGRU (THX=THH=0) +# DPD (~1,000 real parameters): MP/ILA, GMP/ILA, GRU, TRes-GRU +# PA-surrogate sensitivity: TRes-DeltaGRU DPD (THX=THH=0) trained +# independently through TRes-GRU and +# TRes-DeltaGRU PA models +# +# All neural jobs use the same 300-epoch recipe. The primary DPD comparison +# uses the dataset's validation-selected TRes-GRU PA; the sensitivity runs use +# each of the two named PA surrogates. +# +# Default invocation: +# +# bash benchmark/reproduce_benchmark_report.sh +# +# Evidence is written to an immutable timestamped directory under +# benchmark/results/reproduced/. Architecture-addressed save/log artifacts are +# never overwritten unless --overwrite is supplied; in that case, the exact +# colliding files are archived into the run directory first. + +set -Eeuo pipefail + +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +REPO_ROOT=$(cd "${SCRIPT_DIR}/.." && pwd) + +dry_run=0 +overwrite=0 +device=${DEVICE:-0} +output_dir="" + +usage() { + echo "Usage: $0 [--dry-run] [--overwrite] [--device INDEX] [--output-dir PATH]" + echo + echo " --dry-run Record and print every job without executing it." + echo " --overwrite Archive, then replace exact colliding save/log files." + echo " --device INDEX CUDA device index (default: ${device})." + echo " --output-dir PATH Run-specific evidence directory." +} + +while (($#)); do + case "$1" in + --dry-run) + dry_run=1 + shift + ;; + --overwrite) + overwrite=1 + shift + ;; + --device) + if (($# < 2)); then + echo "[ERROR] --device requires an index." >&2 + exit 2 + fi + device=$2 + shift 2 + ;; + --output-dir) + if (($# < 2)); then + echo "[ERROR] --output-dir requires a path." >&2 + exit 2 + fi + output_dir=$2 + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + echo "[ERROR] Unknown argument: $1" >&2 + usage >&2 + exit 2 + ;; + esac +done + +if [[ -n "${OPENDPD_PYTHON:-}" ]]; then + python_bin=${OPENDPD_PYTHON} +elif [[ -n "${PYTHON:-}" ]]; then + python_bin=${PYTHON} +elif [[ -x "${REPO_ROOT}/.venv/bin/python" ]]; then + python_bin=${REPO_ROOT}/.venv/bin/python +elif command -v python3 >/dev/null 2>&1; then + python_bin=$(command -v python3) +else + echo "[ERROR] Python was not found. Set OPENDPD_PYTHON." >&2 + exit 1 +fi + +if [[ "${python_bin}" == */* ]]; then + if [[ "${python_bin}" != /* ]]; then + python_dir=$(cd "$(dirname "${python_bin}")" && pwd) + python_bin="${python_dir}/$(basename "${python_bin}")" + fi + if [[ ! -x "${python_bin}" ]]; then + echo "[ERROR] Python interpreter is not executable: ${python_bin}" >&2 + exit 1 + fi +else + resolved_python=$(type -P -- "${python_bin}" || true) + if [[ -z "${resolved_python}" ]]; then + echo "[ERROR] Python interpreter was not found on PATH: ${python_bin}" >&2 + exit 1 + fi + python_bin=${resolved_python} +fi + +if [[ ! "${device}" =~ ^[0-9]+$ ]]; then + echo "[ERROR] --device must be a non-negative integer: ${device}" >&2 + exit 2 +fi + +if [[ -z "${output_dir}" ]]; then + run_id=${RUN_ID:-$(date -u +%Y%m%dT%H%M%SZ)} + output_dir="${REPO_ROOT}/benchmark/results/reproduced/${run_id}" +elif [[ "${output_dir}" != /* ]]; then + output_dir="${REPO_ROOT}/${output_dir}" +fi + +benchmark_lock_fd="" +if ((dry_run == 0)); then + if ! command -v flock >/dev/null 2>&1; then + echo "[ERROR] flock is required to serialize benchmark reproductions." >&2 + exit 1 + fi + if ! benchmark_lock_path=$( + git -C "${REPO_ROOT}" rev-parse --git-path opendpd-benchmark-report.lock + ); then + echo "[ERROR] Could not resolve the repository benchmark lock path." >&2 + exit 1 + fi + if [[ "${benchmark_lock_path}" != /* ]]; then + benchmark_lock_path="${REPO_ROOT}/${benchmark_lock_path}" + fi + benchmark_lock_dir=$(cd "$(dirname "${benchmark_lock_path}")" && pwd) + benchmark_lock_path="${benchmark_lock_dir}/$(basename "${benchmark_lock_path}")" + exec {benchmark_lock_fd}> "${benchmark_lock_path}" + if ! flock -n "${benchmark_lock_fd}"; then + echo "[ERROR] Another benchmark reproduction is already running for this repository." >&2 + echo "Lock: ${benchmark_lock_path}" >&2 + exit 1 + fi +fi + +if [[ -e "${output_dir}" ]]; then + echo "[ERROR] Output path already exists: ${output_dir}" >&2 + echo "Choose a new --output-dir or RUN_ID; run directories are immutable." >&2 + exit 1 +fi + +for dataset in APA_200MHz DPA_160MHz; do + if [[ ! -f "${REPO_ROOT}/datasets/${dataset}/spec.json" ]]; then + echo "[ERROR] Required dataset is missing: datasets/${dataset}" >&2 + exit 1 + fi +done + +git_commit_before=$(git -C "${REPO_ROOT}" rev-parse HEAD) +git_branch_before=$(git -C "${REPO_ROOT}" branch --show-current) +git_status_before=$(git -C "${REPO_ROOT}" status --short --untracked-files=all) + +mkdir -p "$(dirname "${output_dir}")" +mkdir "${output_dir}" +mkdir "${output_dir}/logs" +commands_log="${output_dir}/commands.log" +jobs_log="${output_dir}/jobs.tsv" +pa_bindings_log="${output_dir}/pa_checkpoint_bindings.tsv" +: > "${commands_log}" +printf 'label\tstarted_at_utc\tfinished_at_utc\tduration_seconds\texit_status\n' > "${jobs_log}" +printf 'dataset\tdpd_model_key\tpa_model_key\tcheckpoint\tsha256\n' \ + > "${pa_bindings_log}" +printf '%s\n' "${git_commit_before}" > "${output_dir}/git_commit_before.txt" +printf '%s\n' "${git_branch_before}" > "${output_dir}/git_branch_before.txt" +printf '%s\n' "${git_status_before}" > "${output_dir}/git_status_before.txt" + +cd "${REPO_ROOT}" + +"${python_bin}" - "${output_dir}/recipe.json" <<'PY' +import json +from pathlib import Path +import sys + +recipe = { + "schema_version": 6, + "datasets": ["APA_200MHz", "DPA_160MHz"], + "parameter_count_convention": ( + "Neural parameters are real trainable scalars; each complex polynomial " + "coefficient counts as two real degrees of freedom." + ), + "neural": { + "optimizer": "AdamW", + "optimizer_weight_decay": 0.01, + "optimizer_betas": [0.9, 0.999], + "optimizer_eps": 1e-8, + "loss": "MSE", + "epochs": 300, + "batch_size": 64, + "evaluation_batch_size": 64, + "initial_learning_rate": 5e-3, + "scheduler": "ReduceLROnPlateau", + "scheduler_factor": 0.5, + "scheduler_patience": 5, + "scheduler_threshold": 1e-4, + "scheduler_threshold_mode": "rel", + "scheduler_cooldown": 0, + "scheduler_eps": 1e-8, + "minimum_learning_rate": 5e-5, + "frame_length": 200, + "frame_stride": 1, + "gradient_clip": 200, + "seed": 0, + "reproducibility": "soft", + "pa_scheduler_and_selection_metric": "validation NMSE", + "dpd_scheduler_and_selection_metric": "validation ACLR_AVG", + }, + "pa_models": { + "mp": {"K": 9, "Q": 150, "real_parameters": 2700}, + "gmp": { + "Ka": 5, "La": 30, + "Kb": 4, "Lb": 30, "Mb": 5, + "Kc": 4, "Lc": 30, "Mc": 5, + "real_parameters": 2700, + }, + "gru": {"hidden_size": 28, "real_parameters": 2746}, + "tres_gru": {"hidden_size": 27, "real_parameters": 2751}, + "tres_deltagru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + }, + }, + "dpd_models": { + "mp": {"K": 5, "Q": 100, "real_parameters": 1000, "method": "ILA"}, + "gmp": { + "Ka": 5, "La": 20, + "Kb": 4, "Lb": 20, "Mb": 3, + "Kc": 4, "Lc": 20, "Mc": 2, + "real_parameters": 1000, + "method": "ILA", + }, + "gru": {"hidden_size": 16, "real_parameters": 994}, + "tres_gru": {"hidden_size": 15, "real_parameters": 999}, + }, + "dpd_tres_deltagru_pa_surrogate_comparison": { + "model": { + "hidden_size": 15, + "real_parameters": 999, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + }, + "pa_surrogates": { + "tres_gru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": None, + }, + "tres_deltagru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + }, + }, + }, + "dpd_pa_surrogate": { + "backbone": "tres_gru", + "hidden_size": 27, + "real_parameters": 2751, + }, + "polynomial_solvers": { + "default": { + "implementation": "torch.linalg.lstsq", + "mode": "gels", + "driver": "gels", + "dtype": "torch.complex64", + "column_scaling": "l2", + "regularization": None, + }, + "gmp_pa": { + "implementation": "torch.linalg.svd", + "mode": "truncated_svd", + "driver": "gesvdj", + "dtype": "torch.complex64", + "column_scaling": "l2", + "regularization": { + "type": "truncated_svd", + "relative_cutoff": 1e-4, + }, + }, + "segment_boundary_policy": "zero delay state at every nperseg boundary", + }, +} + +path = Path(sys.argv[1]) +path.write_text(json.dumps(recipe, indent=2, sort_keys=True) + "\n") +PY + +record_command() { + local label=$1 + shift + printf '# %s\n' "${label}" >> "${commands_log}" + printf '%q ' "$@" >> "${commands_log}" + printf '\n\n' >> "${commands_log}" +} + +print_command() { + local label=$1 + shift + printf '\n[%s]\n' "${label}" + printf '+ ' + printf '%q ' "$@" + printf '\n' +} + +run_step() { + local label=$1 + shift + record_command "${label}" "$@" + print_command "${label}" "$@" + if ((dry_run)); then + return + fi + + local started_at finished_at started_epoch finished_epoch duration status + started_at=$(date -u +%Y-%m-%dT%H:%M:%SZ) + started_epoch=$(date +%s) + set +e + "$@" 2>&1 | tee "${output_dir}/logs/${label}.log" + status=${PIPESTATUS[0]} + set -e + finished_epoch=$(date +%s) + finished_at=$(date -u +%Y-%m-%dT%H:%M:%SZ) + duration=$((finished_epoch - started_epoch)) + printf '%s\t%s\t%s\t%s\t%s\n' \ + "${label}" "${started_at}" "${finished_at}" "${duration}" "${status}" \ + >> "${jobs_log}" + if ((status != 0)); then + echo "[ERROR] Job failed (${status}): ${label}" >&2 + exit "${status}" + fi +} + +copy_with_parents() { + local destination=$1 + shift + local path + for path in "$@"; do + [[ -f "${path}" ]] || continue + mkdir -p "${destination}/$(dirname "${path}")" + cp -p "${path}" "${destination}/${path}" + done +} + +sha256_path() { + "${python_bin}" - "$1" <<'PY' +import hashlib +from pathlib import Path +import sys + +path = Path(sys.argv[1]) +if not path.is_file(): + raise SystemExit(f"[ERROR] Required checkpoint is missing: {path}") +digest = hashlib.sha256() +with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) +print(digest.hexdigest()) +PY +} + +record_pa_binding() { + local dataset=$1 + local dpd_model_key=$2 + local pa_model_key=$3 + local checkpoint=$4 + if ((dry_run)); then + return + fi + local digest + digest=$(sha256_path "${checkpoint}") + printf '%s\t%s\t%s\t%s\t%s\n' \ + "${dataset}" "${dpd_model_key}" "${pa_model_key}" \ + "${checkpoint}" "${digest}" \ + >> "${pa_bindings_log}" +} + +# Only these exact architecture-addressed files can collide with this recipe. +target_artifacts=() +add_training_artifacts() { + local dataset=$1 + local step=$2 + local parent=$3 + local model_id=$4 + local save_base="save/${dataset}/${step}" + local log_base="log/${dataset}/${step}" + if [[ -n "${parent}" ]]; then + save_base="${save_base}/${parent}" + log_base="${log_base}/${parent}" + fi + target_artifacts+=( + "${save_base}/${model_id}.pt" + "${log_base}/best/${model_id}.csv" + "${log_base}/history/${model_id}.csv" + ) +} + +for dataset in APA_200MHz DPA_160MHz; do + add_training_artifacts "${dataset}" train_pa "" \ + PA_S_0_M_GRU_H_28_F_200_P_2746 + add_training_artifacts "${dataset}" train_pa "" \ + PA_S_0_M_TRES_GRU_H_27_F_200_P_2751 + add_training_artifacts "${dataset}" train_pa "" \ + PA_S_0_M_TRES_DELTAGRU_H_27_F_200_P_2751 + + dpd_parent=PA_S_0_M_TRES_GRU_H_27_F_200 + add_training_artifacts "${dataset}" train_dpd "${dpd_parent}" \ + DPD_S_0_M_GRU_H_16_F_200_P_994 + add_training_artifacts "${dataset}" train_dpd "${dpd_parent}" \ + DPD_S_0_M_TRES_GRU_H_15_F_200_P_999 + add_training_artifacts "${dataset}" train_dpd "${dpd_parent}" \ + DPD_S_0_M_TRES_DELTAGRU_H_15_F_200_P_999_THX_0.000_THH_0.000 + + delta_dpd_parent=PA_S_0_M_TRES_DELTAGRU_H_27_F_200 + add_training_artifacts "${dataset}" train_dpd "${delta_dpd_parent}" \ + DPD_S_0_M_TRES_DELTAGRU_H_15_F_200_P_999_THX_0.000_THH_0.000 +done + +existing_artifacts=() +for path in "${target_artifacts[@]}"; do + if [[ -L "${path}" ]]; then + echo "[ERROR] Refusing benchmark artifact symlink: ${path}" >&2 + exit 1 + elif [[ -e "${path}" && ! -f "${path}" ]]; then + echo "[ERROR] Refusing non-regular benchmark artifact: ${path}" >&2 + exit 1 + elif [[ -f "${path}" ]]; then + existing_artifacts+=("${path}") + fi +done + +if ((dry_run == 0 && ${#existing_artifacts[@]} > 0 && overwrite == 0)); then + echo "[ERROR] Matching benchmark artifacts already exist and would be overwritten:" >&2 + printf ' %s\n' "${existing_artifacts[@]}" >&2 + echo "Re-run with --overwrite to archive these exact files before training." >&2 + exit 1 +fi + +if ((dry_run == 0 && ${#existing_artifacts[@]} > 0)); then + copy_with_parents "${output_dir}/preexisting" "${existing_artifacts[@]}" + echo "[INFO] Archived ${#existing_artifacts[@]} pre-existing artifact(s)." +fi + +if ((dry_run == 0)); then + "${python_bin}" - "${device}" <<'PY' +import sys + +try: + import numpy + import pandas + import scipy + import torch +except ImportError as exc: + raise SystemExit(f"[ERROR] Missing benchmark dependency: {exc}") from exc + +from models import CoreModel +from utils.util import count_net_params + +device_index = int(sys.argv[1]) +if not torch.cuda.is_available(): + raise SystemExit("[ERROR] CUDA is required for the reference benchmark.") +if device_index >= torch.cuda.device_count(): + raise SystemExit( + f"[ERROR] CUDA device {device_index} is unavailable; " + f"detected {torch.cuda.device_count()} device(s)." + ) +device = torch.device(f"cuda:{device_index}") +print(f"[INFO] Python {sys.version.split()[0]}") +print(f"[INFO] PyTorch {torch.__version__}; CUDA {torch.version.cuda}") +print(f"[INFO] CUDA device {device_index}: {torch.cuda.get_device_name(device)}") + +expected = { + ("gru", 28): 2746, + ("tres_gru", 27): 2751, + ("tres_deltagru", 27): 2751, + ("gru", 16): 994, + ("tres_gru", 15): 999, + ("tres_deltagru", 15): 999, +} +for (backbone, hidden_size), expected_parameters in expected.items(): + network = CoreModel( + input_size=2, + hidden_size=hidden_size, + num_layers=1, + backbone_type=backbone, + thx=0.0, + thh=0.0, + ).to(device) + actual_parameters = count_net_params(network) + if actual_parameters != expected_parameters: + raise SystemExit( + f"[ERROR] {backbone}-H{hidden_size} has {actual_parameters} " + f"parameters, expected {expected_parameters}." + ) + sample = torch.randn(2, 200, 2, device=device, requires_grad=True) + output = network(sample) + output.square().mean().backward() + if not torch.isfinite(output).all() or sample.grad is None: + raise SystemExit(f"[ERROR] Non-finite smoke test for {backbone}-H{hidden_size}.") + del network, sample, output + +matrix = torch.randn(512, 16, dtype=torch.complex64, device=device) +target = torch.randn(512, 1, dtype=torch.complex64, device=device) +solution = torch.linalg.lstsq(matrix, target, driver="gels").solution +if not torch.isfinite(solution).all(): + raise SystemExit("[ERROR] CUDA complex least-squares smoke test failed.") +torch.cuda.synchronize(device) +print("[INFO] Neural and complex least-squares CUDA smoke tests passed.") +PY +fi + +neural_common=( + --PA_num_layers 1 + --DPD_num_layers 1 + --frame_length 200 + --frame_stride 1 + --n_epochs 300 + --opt_type adamw + --lr 5e-3 + --lr_schedule 1 + --lr_end 5e-5 + --decay_factor 0.5 + --patience 5 + --batch_size 64 + --batch_size_eval 64 + --loss_type l2 + --grad_clip_val 200 + --thx 0 + --thh 0 + --log_precision 8 + --seed 0 + --re_level soft + --eval_val 1 + --eval_test 1 + --accelerator cuda + --devices "${device}" +) + +run_step snapshot_context \ + "${python_bin}" -m benchmark.collect_benchmark_report \ + --output-dir "${output_dir}" --device "${device}" --snapshot-context + +run_neural_pa() { + local dataset=$1 + local slug=$2 + local backbone=$3 + local hidden_size=$4 + run_step "train_pa_${slug}_${backbone}" \ + "${python_bin}" main.py \ + --dataset_name "${dataset}" \ + --step train_pa \ + --PA_backbone "${backbone}" \ + --PA_hidden_size "${hidden_size}" \ + "${neural_common[@]}" +} + +run_neural_dpd() { + local dataset=$1 + local slug=$2 + local backbone=$3 + local hidden_size=$4 + local pa_model_key=$5 + local pa_backbone=$6 + local pa_hidden_size=$7 + local pa_model_id=$8 + local pa_checkpoint="save/${dataset}/train_pa/${pa_model_id}.pt" + record_pa_binding \ + "${dataset}" "${backbone}" "${pa_model_key}" "${pa_checkpoint}" + run_step "train_dpd_${slug}_${backbone}_via_${pa_model_key}_pa" \ + "${python_bin}" main.py \ + --dataset_name "${dataset}" \ + --step train_dpd \ + --PA_backbone "${pa_backbone}" \ + --PA_hidden_size "${pa_hidden_size}" \ + --DPD_backbone "${backbone}" \ + --DPD_hidden_size "${hidden_size}" \ + "${neural_common[@]}" +} + +run_polynomial_pa() { + local dataset=$1 + local slug=$2 + local polynomial=$3 + shift 3 + local solver_arguments=(--solver-mode gels) + if [[ "${polynomial}" == "gmp" ]]; then + solver_arguments=(--solver-mode truncated_svd --svd-rcond 1e-4) + fi + run_step "pa_model_${slug}_${polynomial}" \ + "${python_bin}" -m benchmark.benchmark_volterra \ + --task pa_modeling \ + --dataset-name "${dataset}" \ + --model "${polynomial}" \ + --solver-device "cuda:${device}" \ + --solver-dtype complex64 \ + "${solver_arguments[@]}" \ + --device "${device}" \ + --json-out "${output_dir}/benchmark_report_${slug}_pa_${polynomial}.json" \ + "$@" +} + +run_polynomial_dpd() { + local dataset=$1 + local slug=$2 + local polynomial=$3 + shift 3 + run_step "dpd_ila_${slug}_${polynomial}" \ + "${python_bin}" -m benchmark.benchmark_volterra \ + --task dpd_ila \ + --dataset-name "${dataset}" \ + --model "${polynomial}" \ + --solver-device "cuda:${device}" \ + --solver-dtype complex64 \ + --solver-mode gels \ + --device "${device}" \ + --pa-backbone tres_gru \ + --pa-hidden-size 27 \ + --pa-num-layers 1 \ + --pa-checkpoint \ + "save/${dataset}/train_pa/PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt" \ + --json-out "${output_dir}/benchmark_report_${slug}_dpd_${polynomial}.json" \ + "$@" +} + +for dataset_spec in "APA_200MHz apa" "DPA_160MHz dpa"; do + read -r dataset slug <<< "${dataset_spec}" + run_neural_pa "${dataset}" "${slug}" gru 28 + run_neural_pa "${dataset}" "${slug}" tres_gru 27 + run_neural_pa "${dataset}" "${slug}" tres_deltagru 27 +done + +for dataset_spec in "APA_200MHz apa" "DPA_160MHz dpa"; do + read -r dataset slug <<< "${dataset_spec}" + run_polynomial_pa "${dataset}" "${slug}" mp \ + --K 9 --Q 150 + run_polynomial_pa "${dataset}" "${slug}" gmp \ + --Ka 5 --La 30 --Kb 4 --Lb 30 --Mb 5 \ + --Kc 4 --Lc 30 --Mc 5 +done + +for dataset_spec in "APA_200MHz apa" "DPA_160MHz dpa"; do + read -r dataset slug <<< "${dataset_spec}" + run_neural_dpd \ + "${dataset}" "${slug}" gru 16 tres_gru tres_gru 27 \ + PA_S_0_M_TRES_GRU_H_27_F_200_P_2751 + run_neural_dpd \ + "${dataset}" "${slug}" tres_gru 15 tres_gru tres_gru 27 \ + PA_S_0_M_TRES_GRU_H_27_F_200_P_2751 + run_neural_dpd \ + "${dataset}" "${slug}" tres_deltagru 15 tres_gru tres_gru 27 \ + PA_S_0_M_TRES_GRU_H_27_F_200_P_2751 + run_neural_dpd \ + "${dataset}" "${slug}" tres_deltagru 15 \ + tres_deltagru tres_deltagru 27 \ + PA_S_0_M_TRES_DELTAGRU_H_27_F_200_P_2751 +done + +for dataset_spec in "APA_200MHz apa" "DPA_160MHz dpa"; do + read -r dataset slug <<< "${dataset_spec}" + run_polynomial_dpd "${dataset}" "${slug}" mp \ + --K 5 --Q 100 + run_polynomial_dpd "${dataset}" "${slug}" gmp \ + --Ka 5 --La 20 --Kb 4 --Lb 20 --Mb 3 \ + --Kc 4 --Lc 20 --Mc 2 +done + +if ((dry_run)); then + record_command collect_report \ + "${python_bin}" -m benchmark.collect_benchmark_report \ + --output-dir "${output_dir}" --device "${device}" + print_command collect_report \ + "${python_bin}" -m benchmark.collect_benchmark_report \ + --output-dir "${output_dir}" --device "${device}" + echo + echo "[DRY RUN] Commands were written to ${commands_log}" + exit 0 +fi + +copy_with_parents "${output_dir}/artifacts" "${target_artifacts[@]}" + +run_step collect_report \ + "${python_bin}" -m benchmark.collect_benchmark_report \ + --output-dir "${output_dir}" --device "${device}" + +echo +echo "[DONE] Reproduced benchmark evidence: ${output_dir}" +echo "[DONE] Markdown report: ${output_dir}/benchmark_report.md" +echo "[DONE] Machine-readable results: ${output_dir}/benchmark_report_results.json" +echo "[DONE] Results visualization: ${output_dir}/benchmark_results.png" +echo "[DONE] Delta-DPD sensitivity visualization: ${output_dir}/benchmark_delta_dpd_results.png" diff --git a/benchmark/results/benchmark_report_results.json b/benchmark/results/benchmark_report_results.json new file mode 100644 index 0000000..36f970f --- /dev/null +++ b/benchmark/results/benchmark_report_results.json @@ -0,0 +1,1904 @@ +{ + "artifact_kind": "opendpd_benchmark_reproduction_run", + "canonical_report": "benchmark/benchmark_report.md", + "completion_context": { + "captured_at_utc": "2026-07-26T20:45:34.695466+00:00", + "git_branch": "benchmark-fix", + "git_commit": "3df35e081e6e41463fa46f21778c72a748823274", + "validated_unchanged": true + }, + "datasets": { + "APA_200MHz": { + "dpd_models": { + "gmp": { + "artifact": { + "path": "benchmark_report_apa_dpd_gmp.json", + "sha256": "62912b11d4b370e2a0ae68f60bb03b53351e8c264bc0ddad149101beceb953d1", + "size_bytes": 2972 + }, + "basis_configuration": { + "Ka": 5, + "Kb": 4, + "Kc": 4, + "La": 20, + "Lb": 20, + "Lc": 20, + "Mb": 3, + "Mc": 2 + }, + "coefficient_l2_norm": 46.2047004699707, + "column_scale_max": 89.97786712646484, + "column_scale_min": 9.357341766357422, + "column_scaling": "l2", + "complex_coefficients": 500, + "condition_number": null, + "device": "cuda:0", + "dtype": "torch.complex64", + "full_rank_assumption": true, + "least_squares_rank": null, + "method": "indirect_learning_architecture", + "metrics": { + "test": { + "aclr_avg_db": -43.5931687089929, + "aclr_left_db": -44.06568580030243, + "aclr_right_db": -43.120651617683365, + "evm_db": -46.352304380006316, + "nmse_db": -38.52589797973633 + }, + "validation": { + "aclr_avg_db": -42.972747788860104, + "aclr_left_db": -43.55596870580729, + "aclr_right_db": -42.38952687191292, + "evm_db": -46.27533200387405, + "nmse_db": -38.31670379638672 + } + }, + "model": { + "backbone": "gmp", + "display_name": "GMP", + "parameter_convention": "two real degrees of freedom per complex coefficient", + "parameters": 1000 + }, + "nperseg": 19662, + "pa_checkpoint_binding": { + "checkpoint": "save/APA_200MHz/train_pa/PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt", + "sha256": "fe08b5bdfa377af79bf1ea78fcfa0c96b10659bb050fe46ed2f3ef57dfcbc2eb" + }, + "pa_evaluation_device": "cuda:0", + "parameter_count_convention": "two real degrees of freedom per complex coefficient", + "real_parameters": 1000, + "regularization": null, + "retained_singular_value_min": null, + "sample_rate_hz": 983040000.0, + "singular_value_cutoff": null, + "singular_value_max": null, + "singular_value_min": null, + "solver": "torch.linalg.lstsq", + "solver_driver": "gels", + "solver_mode": "gels", + "svd_rcond": null, + "target_gain": 1.0, + "task": "dpd_ila", + "training_relative_residual": 0.010865671560168266 + }, + "gru": { + "artifacts": { + "best_csv": { + "archived_path": "artifacts/log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/best/DPD_S_0_M_GRU_H_16_F_200_P_994.csv", + "sha256": "a6d38797ef37bbe21ce76ee9b5068b8b158172c1b3ba10729e9095278812d2e6", + "size_bytes": 421, + "source_path": "log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/best/DPD_S_0_M_GRU_H_16_F_200_P_994.csv" + }, + "checkpoint": { + "archived_path": "artifacts/save/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/DPD_S_0_M_GRU_H_16_F_200_P_994.pt", + "sha256": "e20f7c4c97ed09ea991b479812db21bef9ebdc622d4a1ad1bb2418988602d627", + "size_bytes": 6832, + "source_path": "save/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/DPD_S_0_M_GRU_H_16_F_200_P_994.pt" + }, + "history_csv": { + "archived_path": "artifacts/log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/history/DPD_S_0_M_GRU_H_16_F_200_P_994.csv", + "sha256": "f535a28e527eb0594ac0f01450ed8cbfdb685c72f19aee8705005b533e030a3e", + "size_bytes": 62248, + "source_path": "log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/history/DPD_S_0_M_GRU_H_16_F_200_P_994.csv" + } + }, + "completed_epochs": 300, + "method": "direct_learning_architecture", + "metrics": { + "test": { + "aclr_avg_db": -51.0114043, + "aclr_left_db": -51.0559093, + "aclr_right_db": -50.9668993, + "evm_db": -47.42746888, + "loss": 1.52e-06, + "nmse_db": -45.13145 + }, + "validation": { + "aclr_avg_db": -51.32400487, + "aclr_left_db": -51.38568411, + "aclr_right_db": -51.26232562, + "evm_db": -47.68851727, + "loss": 1.5e-06, + "nmse_db": -45.17349 + } + }, + "model": { + "backbone": "gru", + "display_name": "GRU-H16", + "hidden_size": 16, + "model_id": "DPD_S_0_M_GRU_H_16_F_200_P_994", + "parameters": 994 + }, + "pa_checkpoint_binding": { + "checkpoint": "save/APA_200MHz/train_pa/PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt", + "dataset": "APA_200MHz", + "dpd_model_key": "gru", + "pa_model_key": "tres_gru", + "sha256": "fe08b5bdfa377af79bf1ea78fcfa0c96b10659bb050fe46ed2f3ef57dfcbc2eb" + }, + "selected_epoch_zero_based": 299, + "selected_learning_rate": 0.00015625, + "task": "dpd_training", + "train_loss": 2.17e-06 + }, + "mp": { + "artifact": { + "path": "benchmark_report_apa_dpd_mp.json", + "sha256": "8d24721c1e3783f2247b4906e9b50ecb2feefe0040e0c93cf5ea9c6562ee3640", + "size_bytes": 2893 + }, + "basis_configuration": { + "K": 5, + "Q": 100 + }, + "coefficient_l2_norm": 4.383342266082764, + "column_scale_max": 89.97786712646484, + "column_scale_min": 13.769466400146484, + "column_scaling": "l2", + "complex_coefficients": 500, + "condition_number": null, + "device": "cuda:0", + "dtype": "torch.complex64", + "full_rank_assumption": true, + "least_squares_rank": null, + "method": "indirect_learning_architecture", + "metrics": { + "test": { + "aclr_avg_db": -45.185983122836305, + "aclr_left_db": -46.0739531814713, + "aclr_right_db": -44.2980130642013, + "evm_db": -48.15347693703668, + "nmse_db": -42.189632415771484 + }, + "validation": { + "aclr_avg_db": -44.922360391352086, + "aclr_left_db": -45.558971707171324, + "aclr_right_db": -44.28574907553285, + "evm_db": -48.18950883574672, + "nmse_db": -42.11494827270508 + } + }, + "model": { + "backbone": "mp", + "display_name": "MP", + "parameter_convention": "two real degrees of freedom per complex coefficient", + "parameters": 1000 + }, + "nperseg": 19662, + "pa_checkpoint_binding": { + "checkpoint": "save/APA_200MHz/train_pa/PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt", + "sha256": "fe08b5bdfa377af79bf1ea78fcfa0c96b10659bb050fe46ed2f3ef57dfcbc2eb" + }, + "pa_evaluation_device": "cuda:0", + "parameter_count_convention": "two real degrees of freedom per complex coefficient", + "real_parameters": 1000, + "regularization": null, + "retained_singular_value_min": null, + "sample_rate_hz": 983040000.0, + "singular_value_cutoff": null, + "singular_value_max": null, + "singular_value_min": null, + "solver": "torch.linalg.lstsq", + "solver_driver": "gels", + "solver_mode": "gels", + "svd_rcond": null, + "target_gain": 1.0, + "task": "dpd_ila", + "training_relative_residual": 0.011005041189491749 + }, + "tres_gru": { + "artifacts": { + "best_csv": { + "archived_path": "artifacts/log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/best/DPD_S_0_M_TRES_GRU_H_15_F_200_P_999.csv", + "sha256": "60137327bb949de2422c86587071a005d68ea78d94887801156a24c995483880", + "size_bytes": 427, + "source_path": "log/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/best/DPD_S_0_M_TRES_GRU_H_15_F_200_P_999.csv" + }, + "checkpoint": { + "archived_path": "artifacts/save/APA_200MHz/train_dpd/PA_S_0_M_TRES_GRU_H_27_F_200/DPD_S_0_M_TRES_GRU_H_15_F_200_P_999.pt", 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"benchmark_repeat_count_production_optimized": 2, - "diff_check_clean": true - }, - "raw_sources": [ - "benchmark/results/e2e_dpa200_stride1_comparison_graph.json", - "benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json", - "benchmark/results/e2e_dpa200_stride16_comparison_eager.json", - "benchmark/results/e2e_dpa200_stride16_comparison_graph.json", - "benchmark/results/e2e_dpa200_stride16_comparison_fused.json", - "benchmark/results/tres_deltagru_rtx4090_laptop.json" - ], - "limitations": [ - "The repository contained no pretrained PA checkpoint, so all arms used the same deterministic surrogate PA trained once on the bundled dataset.", - "The production-density comparison covers one 357-update epoch, while the multi-epoch gate covers three 23-update epochs; it is not a 240- or 300-epoch convergence study.", - "The strict CUDA-graph path is an explicit opt-in specialized to the measured FP32 TRes-DeltaGRU to frozen-DGRU cascade and MSE training.", - "Existing EVM semantics were preserved: calculate_metrics does not forward the dataset sample rate to EVM, so its default 800 MHz is used." - ] -} diff --git a/benchmark/results/tres_deltagru_rtx4090_laptop.json b/benchmark/results/tres_deltagru_rtx4090_laptop.json deleted file mode 100644 index 6f2c3fd..0000000 --- a/benchmark/results/tres_deltagru_rtx4090_laptop.json +++ /dev/null @@ -1,92 +0,0 @@ -{ - "recorded_date": "2026-07-11", - "source_base_revision": "0976ce39fc664727ab7ab640ccb349679f05d84f", - "source_state": "dirty: includes the TRes-DeltaGRU optimization in this working tree", - "command": ".venv/bin/python benchmark/benchmark_tres_deltagru.py", - "system": { - "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", - "python": "3.13.14", - "torch": "2.13.0+cu132", - "triton": "3.7.1", - "device": "NVIDIA GeForce RTX 4090 Laptop GPU", - "compute_capability": "8.9", - "driver": "610.74", - "cuda": "13.2", - "cudnn": 92000, - "matmul_tf32": false, - "cudnn_tf32": false - }, - "config": { - "batch_size": 64, - "sequence_length": 200, - "hidden_size": 15, - "thx": 0.01, - "thh": 0.05, - "dtype": "float32", - "optimizer": "AdamW", - "learning_rate": 0.0005, - "grad_clip": 200.0, - "warmup_steps": 5, - "blocks": 7, - "reference_steps_per_block": 5, - "fused_steps_per_block": 100, - "seed": 0 - }, - "equivalence": { - "check_batch_size": 8, - "output": { - "max_abs": 6.079673767089844e-06, - "mean_abs": 8.64025196278817e-07, - "relative_l2": 1.3402454897004645e-06 - }, - "input_gradient": { - "max_abs": 1.461885403841734e-07, - "mean_abs": 5.326960206275544e-09, - "relative_l2": 1.4971470591262914e-05 - }, - "loss_abs": 1.1920928955078125e-07, - "maximum_parameter_gradient_error": { - "parameter": "backbone.rnn.x2h.weight", - "max_abs": 7.152557373046875e-07, - "relative_l2": 8.59947533626837e-07 - } - }, - "results": [ - { - "implementation": "eager-clean", - "steps_per_block": 5, - "block_ms_per_step": [ - 164.302, - 164.948, - 176.296, - 158.841, - 164.257, - 169.615, - 163.938 - ], - "median_ms": 164.302, - "iqr_block_means_ms": 3.184, - "sequence_positions_per_second": 77905, - "peak_allocated_mib": 25.3, - "speedup": 1.0 - }, - { - "implementation": "triton-fused", - "steps_per_block": 100, - "block_ms_per_step": [ - 1.692, - 1.762, - 1.707, - 1.690, - 1.752, - 1.684, - 1.675 - ], - "median_ms": 1.692, - "iqr_block_means_ms": 0.043, - "sequence_positions_per_second": 7565624, - "peak_allocated_mib": 27.5, - "speedup": 97.11 - } - ] -} diff --git a/benchmark/train_all_pa_2700.sh b/benchmark/train_all_pa_2700.sh deleted file mode 100755 index b34347b..0000000 --- a/benchmark/train_all_pa_2700.sh +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env bash -# Train every viable PA backbone at ~2700 params with the OpenDPDv2 recipe. -set -u -cd "$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -LOGDIR=${LOGDIR:-/tmp/opendpd_pa2700_logs} -mkdir -p "$LOGDIR" -DATASET=${DATASET:-APA_200MHz} -PYTHON=${PYTHON:-python} -export LOGDIR DATASET PYTHON - -# backbone:hidden_size (fast/cheap first so results land early) -JOBS=( - tcn:93 - rvtdcnn:68 - mcldnn:11 - gru:28 - lstm:24 - tres_gru:27 - dgru:23 - vdlstm:22 - qgru:27 - pgjanet:19 - dvrjanet:19 - deltajanet:32 - apnrru:34 - deltagru:26 -) - -run_one() { - local spec="$1" - local bb="${spec%%:*}" - local h="${spec##*:}" - local log="$LOGDIR/${DATASET}_${bb}_h${h}.log" - echo "[START] $bb h=$h" - "${PYTHON:-python}" main.py --step train_pa \ - --dataset_name "$DATASET" --PA_backbone "$bb" --PA_hidden_size "$h" \ - --n_epochs 240 --lr 5e-3 --lr_schedule 1 --lr_end 1e-4 \ - --decay_factor 0.5 --patience 10 --batch_size 64 > "$log" 2>&1 - echo "[DONE ] $bb h=$h exit=$?" -} -export -f run_one - -printf '%s\n' "${JOBS[@]}" | xargs -P "${CONC:-5}" -I{} bash -c 'run_one "$@"' _ {} -echo "ALL TRAINING COMPLETE" diff --git a/benchmark/tres_deltagru_end_to_end_report.html b/benchmark/tres_deltagru_end_to_end_report.html deleted file mode 100644 index 256a276..0000000 --- a/benchmark/tres_deltagru_end_to_end_report.html +++ /dev/null @@ -1,4900 +0,0 @@ - - - - - - - - -TRes-DeltaGRU end-to-end training optimization - - - -

Data Analytics report

TRes-DeltaGRU end-to-end training optimization

A controlled speed and numerical-equivalence study of OpenDPD TRes-DeltaGRU training.

-

Executive Summary

-

The recommended strict path is guarded whole-cascade CUDA-graph training, enabled with --cuda_graph_training. On the 357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-update production-density workload it reduced total time from 95.78Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s to 14.51Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s and 13.96Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s across two runs (6.60Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json–6.86Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×) and training time from 86.86Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s to 12.41Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s and 11.81Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json s (7.00Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json–7.35Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×). The selected DPD checkpoint was bitwise identical, cascade output relative L2 error was 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json, and the largest ACLR/NMSE/EVM difference was 4.27e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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End-to-end speedup

6.60–6.86×Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.

Training speedup 7.00–7.35×Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Process workload speedup for 357 training updates plus validation, test, RF metrics, and artifacts.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Cascade output rel-L2

≤ 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Relative L2 difference between the original and optimized cascaded neural-network outputs.

Maximum absolute error ≤ 4.95e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Relative L2 difference between the original and optimized cascaded neural-network outputs.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Maximum RF-metric delta

≤ 4.27e-6 dBSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.

Acceptance limit 0.05 dBSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Largest absolute difference across ACLR-left, ACLR-right, ACLR-average, EVM, and NMSE.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

DPD checkpoint

Bitwise exactSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Comparison of every tensor in the selected best DPD state dictionary.

Best epoch SameSource: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
Comparison of every tensor in the selected best DPD state dictionary.Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Key findings

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  • The isolated persistent Triton backbone remains the fastest kernel-level method at 97.11Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×, but long training amplifies its small FP32 differences around delta thresholds.
  • Direct persistent-Triton end-to-end training reached 13.40Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json× total and 46.77Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json× training speedup in the three-epoch gate, yet its final cascade output relative L2 error reached 1.04e-2Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json. It was rejected even though its RF metrics stayed within 0.035Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB.
  • Capturing the existing clean-eager cascade preserves its PyTorch/cuDNN arithmetic and removes recurrent launch overhead. It is slower than direct Triton but passes every output, checkpoint, and RF-metric gate.
  • Loop/data cleanups alone are bitwise exact and improve total time 1.33Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json×.
Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Lower is faster. The strict whole-cascade graph is the fastest accepted method.

Three-epoch controlled workload time data
ImplementationTotal timeTraining timeTotal speedupOutput rel-L2Accepted
Exact original32.04s17.2410Yes
Clean eager pipeline24.03s13.491.330Yes
Strict whole-cascade graph5.46s3.435.870Yes
Direct persistent Triton2.39s0.3713.40.01No
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Why the strict graph is the recommendation

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The graph records the original eager TRes-DeltaGRU, MSE loss, gradient clearing, frozen-PA input backward, and DPD backward. It replays those exact kernels at fixed shapes while leaving batch copies, gradient clipping, AdamW, scheduling, validation, testing, and checkpoint selection outside the graph. Full and partial batches share stable gradient buffers and one capture stream. Unsupported calls fall back to clean eager training; evaluation restores the fused inference path.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Optimization candidate decision

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Optimization candidate decision
ImplementationTotal sTrain sSpeedupOutput rel-L2Max RF delta, dBAccepted
Direct persistent Triton2.39Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.37Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json13.4Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0.03Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonNo
Strict whole-cascade graph5.46Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json3.43Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json5.87Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
Clean eager pipeline24.03Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json13.49Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json1.33Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
Exact original32.04Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json17.24Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json1Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.jsonYes
-

Output and RF equivalence

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The production-density optimized checkpoint matched every original state-dict tensor exactly. The remaining output difference comes from using the faster fused inference kernel on the same weights: 4.95e-6 maximum absolute and 1.70e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json relative L2 at the cascade output. All five reported RF metrics were effectively unchanged, with absolute deltas between zero and 4.27e-6Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB, far below the predeclared 0.05Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json dB ceiling.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Production-density RF metric equivalence

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

Production-density RF metric equivalence
MetricOriginal dBOptimized dBDelta dBAbsolute delta dB
ACLR average-32.71Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-32.71Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
ACLR left-33.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-33.01Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
ACLR right-32.42Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-32.42Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
EVM-25.65Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-25.65Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
NMSE-24.17Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-24.17Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json0Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json
-

Scope, data, and metric definitions

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The controlled workload uses bundled DPA_200MHz data, a TRes-DeltaGRU H15 DPD (thx=.01, thh=.05), a frozen DGRU H23 PA, FP32, B64×T200, AdamW at 5e-3Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json, and gradient clipping at 200Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json. The production-density arm uses all 22,841Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json stride-one frames (357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates); the multi-epoch gate uses stride 16Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json (23Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates per epoch for three epochs). NMSE is the repository's mean per-segment normalized MSE in dB. EVM is its subchannel spectral-magnitude error in dB. ACLR-left/right compare adjacent-channel power with the strongest main subchannel; ACLR-average is their mean.

Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Methodology

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The exact original is detached commit 0976ce39. A single deterministic PA surrogate was trained for 100Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py epochs because the repository ships no PA checkpoint; its SHA-256Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py fixture was reused unchanged by every arm. DPD initialization, frame order, full/partial batches, validation/test segments, optimizer hyperparameters, TF32 setting, and hard-determinism policy were fixed. Each run saves the exact permutations, best DPD checkpoint, DPD output, cascaded output, ground truth, phase timings, trajectory, and RF metrics. The comparison script rejects a fixture/order mismatch before evaluating speed or numerical results. Regression validation passed 164Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py standard tests plus 19Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py extended all-dataset/all-backbone tests.

Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py

Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

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Implemented optimizations and safeguards

Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.py

Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

Implemented optimizations and safeguards
PriorityComponentChangeEquivalence behavior
1Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyWhole cascadeOpt-in fixed-shape CUDA graph of clean-eager forward, MSE, gradient zeroing, and backwardOriginal kernels; clean-eager fallback; clip and AdamW remain outside
2Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyFrozen DGRU PAExact cuDNN CUDA-graph forward and input-backward replayBitwise output/input gradient in focused tests; guarded eager fallback
3Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyTraining loopOne loss-vector device-to-host transfer per epoch; set-to-none gradients; trainable-only clip/optimizerBitwise CPU trajectory and logged loss in lockstep tests
4Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyEvaluationInference mode and consolidated output/target transfersExact outputs, targets, and loss in tests
5Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyDataVectorized frame construction and CUDA pinned/nonblocking loader pathExact frame order/content/dtype with independent storage
6Source: Reproducible benchmark and implementationTables: benchmark/benchmark_end_to_end.py, modules/cuda_graph_training.py, modules/train_funcs.py, backbones/cuda_graph_frozen_dgru.py, backbones/triton_deltagru.pyPersistent Triton TResFused recurrence and custom BPTT for supported FP32 NVIDIA shapesMinor one-step FP error; rejected as strict multi-epoch training path at nonzero thresholds
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Limitations and robustness

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  • The bundled repository has no published PA checkpoint. The surrogate makes the original-versus-optimized comparison controlled, but its absolute RF quality must not be presented as a reproduction of published results.
  • The longest comparison is one 357Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-update epoch; the multi-epoch gate covers 69Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json updates. This is not a complete 240Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json- or 300Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json-epoch convergence study.
  • Whole-cascade graph training is deliberately opt-in and specialized to the FP32 TRes-DeltaGRU → frozen-DGRU cascade with mean MSE. CPU, AMP, QAT, changed modules, diagnostic delta collection, capture failure, and unsupported shapes use eager fallbacks.
  • Existing EVM behavior was preserved: calculate_metrics does not pass the dataset sample rate, so EVM uses its 800 MHz default. Correcting that is a separate semantic change.
  • One original production-density timing and two optimized timings were collected on a power-managed laptop GPU; use the observed range rather than treating one decimal speedup as universal.
Source: Reviewed end-to-end benchmark summaryTables: benchmark/results/e2e_dpa200_stride1_comparison_graph.json, benchmark/results/e2e_dpa200_stride1_comparison_graph_repeat.json, benchmark/results/e2e_dpa200_stride16_comparison_eager.json, benchmark/results/e2e_dpa200_stride16_comparison_graph.json, benchmark/results/e2e_dpa200_stride16_comparison_fused.json, benchmark/results/tres_deltagru_rtx4090_laptop.json

Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

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Recommended next steps

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  1. Use --cuda_graph_training (or cuda_graph_training=True through the API) when strict training-result equivalence is required.
  2. Keep direct persistent Triton available for inference and experiments where endpoint metric equivalence is sufficient, but do not label its multi-epoch neural outputs strict-equivalent at nonzero thresholds.
  3. Stage the real production PA checkpoint and run the full intended epoch schedule on the target GPU before a release claim. Preserve the same fixture/order comparison and checkpoint/output/RF gates.
  4. If workload shapes or precision modes change, rerun the supplied end-to-end harness; the graph path will safely fall back, but its speedup is shape- and hardware-dependent.
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Further questions

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The main unresolved question is full-schedule convergence with the real PA checkpoint. A secondary opportunity is a GPU-resident frame sampler for much larger datasets, where per-batch host staging may become material after recurrent launch overhead has been removed.

Sources

  1. Reviewed end-to-end benchmark summarybenchmark/results/end_to_end_summary.json · duckdb · 2026-07-11T13:42:00Z

    Controlled original, clean-eager, strict CUDA-graph, and direct-Triton benchmark results derived from saved run JSON, checkpoints, outputs, and batch permutations.

    SQL query
    SELECT * FROM read_json_auto('benchmark/results/end_to_end_summary.json');
  2. Reproducible benchmark and implementationbenchmark/benchmark_end_to_end.py · duckdb · 2026-07-11T13:42:00Z

    Loads repository data/models/metrics, prepares the immutable fixture, replays recorded batch orders, records phase timings and artifacts, and compares outputs, checkpoints, and RF metrics.

    SQL query
    SELECT content FROM read_text('benchmark/benchmark_end_to_end.py');
- - - - - - - diff --git a/benchmark/tres_deltagru_training_speed.md b/benchmark/tres_deltagru_training_speed.md deleted file mode 100644 index 42daaef..0000000 --- a/benchmark/tres_deltagru_training_speed.md +++ /dev/null @@ -1,179 +0,0 @@ -# TRes-DeltaGRU training-speed benchmark - -## Result - -In the documented production-shape run, the TRes-DeltaGRU backbone (`B=64`, -`T=200`, `H=15`, `thx=.01`, `thh=.05`, FP32) was **97.1x faster** with the fused implementation: -**164.30 ms -> 1.692 ms per training step**. The timed step includes forward, -MSE backward, gradient clipping, and AdamW. The recurrent layer by itself -improved from **176.69 ms -> 0.721 ms** in a separate forward/backward -microbenchmark (245x). - -The implementation has the same 999 parameters and unchanged state-dict keys. -It preserves the feature extractor, thresholds, delta state updates, gate -equations, TCN residual, and output layer. Floating-point reduction order, -FMA contraction, and sigmoid/tanh implementations differ. - -This headline applies to dense FP32, first-order training on a supported NVIDIA -CUDA/Triton system. CPU, AMP, QAT, higher-order autograd, and unsupported shapes -take or should take the eager path; see the limitations below. - -## Test system and method - -| Field | Value | -|---|---| -| GPU | NVIDIA GeForce RTX 4090 Laptop GPU, compute capability 8.9 | -| Software | Python 3.13.14, PyTorch 2.13.0+cu132, CUDA 13.2, cuDNN 9.20, Triton 3.7.1 | -| Host | WSL2, Linux 6.6.114.1 | -| Precision | FP32; cuBLAS and cuDNN TF32 disabled | -| Step | `zero_grad(set_to_none=True)`, forward, MSE, backward, clip norm 200, AdamW | -| Data | Seeded synthetic IQ and targets, resident on GPU | -| Timing | 5 warmups; 7 synchronized CUDA-event blocks; median and IQR reported | -| Eager block | 5 steps per timing block | -| Fused block | 100 steps per timing block | - -The table reports the IQR of block-average step times. Because eager blocks -average 5 steps and fused blocks average 100, those IQRs describe stability -within each implementation and should not be compared as equal-size samples. -The script runs eager before fused; repeated runs on this power-managed laptop -produced roughly 81x-99x, so the defensible conclusion is "about two orders of -magnitude," not a universal exact 98.5x. - -This isolates backbone compute and optimizer cost. It intentionally excludes -CSV loading, frame construction, DataLoader work, and host-to-device transfer. -The real DPD pipeline also backpropagates through its frozen PA model; that is a -separate cost and can cap end-to-end gains even after this backbone is fast. - -## Production-shape result - -| Implementation | Median ms/step | IQR of block means | Sequence positions/s | Peak allocated | Speedup | -|---|---:|---:|---:|---:|---:| -| Clean eager reference | 164.302 | 3.184 | 77,905 | 25.3 MiB | 1.00x | -| Persistent Triton | **1.692** | 0.043 | **7,565,624** | 27.5 MiB | **97.11x** | - -Raw block means, environment details, and the equivalence summary are archived -in [`results/tres_deltagru_rtx4090_laptop.json`](results/tres_deltagru_rtx4090_laptop.json). - -The original implementation was slower than the clean eager reference because -it collected delta statistics and performed two unused hidden-delta reductions -at every timestep. An exploratory pre-change measurement was about 275 ms for -forward plus MSE backward, but it is not included in the comparable table -because it used a different step boundary. - -## Scaling experiments - -These shorter runs used five timing blocks (three for `T=1000`). B=16 used -3/100 eager/fused steps per block, B=256 used 3/50, T=50 used 5/100, and T=1000 -used 1/20 with two warmups. Their IQRs are omitted because the block settings -differ. - -### Batch scaling at T=200 - -| Batch | Clean eager ms | Fused ms | Speedup | -|---:|---:|---:|---:| -| 16 | 165.060 | 1.694 | 97.45x | -| 64 | 164.302 | 1.692 | 97.11x | -| 256 | 168.910 | 1.939 | 87.12x | - -Eager latency is nearly flat across these batch sizes because fixed Python and -kernel-launch overhead dominates, so its throughput still rises with batch. -The persistent-kernel latency is also nearly flat across this measured range. - -### Sequence scaling at B=64 - -| Sequence | Clean eager ms | Fused ms | Speedup | -|---:|---:|---:|---:| -| 50 | 42.754 | 1.727 | 24.75x | -| 200 | 164.302 | 1.692 | 97.11x | -| 1000 | 1,041.712 | 3.657 | 284.86x | - -The fused runtime grows with arithmetic rather than with Python and CUDA-launch -overhead, so its advantage increases for longer sequences. - -## Numerical-equivalence result - -The production-length check used identical weights, `B=8`, the production -thresholds, MSE, and TF32 disabled. - -| Quantity | Maximum absolute error | Relative L2 error | -|---|---:|---:| -| Output | 6.08e-6 | 1.34e-6 | -| Input gradient | 1.46e-7 | 1.50e-5 | -| `x2h.weight` gradient | 7.15e-7 | 8.60e-7 | -| `h2h.weight` gradient | 1.07e-7 | 6.16e-7 | -| `fc_out.weight` gradient | 4.77e-7 | 4.48e-7 | -| TCN parameter gradients | <=1.49e-8 | <=1.28e-7 | - -The absolute loss difference was `1.19e-7`. Additional checks covered sequence -lengths 1, 17, 50, 200, and 1000; thresholds `(0,0)` and `(.01,.05)`; custom -initial recurrent states; recurrent-state gradients; delta-statistics equality; -and a 19,662-sample inference sequence. At `T=1000`, output relative L2 error -was `2.44e-6` and all parameter-gradient relative errors remained below -`2.9e-6`. These differences come from reduction/activation ordering in FP32. - -## What was tried - -Rows other than the selected kernel are exploratory scratch experiments; the -committed benchmark directly reproduces the eager-versus-fused full-backbone -rows, while the candidate observations explain why those routes were not kept. - -| Candidate | Experiment | Decision | -|---|---|---| -| Remove diagnostic/dead work | Fixed `set_debug(0)`, removed two unused reductions, Python scalar thresholds, and the discarded outer hidden-state allocation. | Kept as output/gradient-equivalent portable cleanup. Diagnostic collection is now explicit. | -| Algebraic held-state rewrite | Mathematically telescoped delta accumulators into held-state projections. About 1.66x faster in the recurrent experiment; FP32 max output error about 5.2e-6. | Useful fallback idea, but far below the fused kernel's gain and not valid across QAT fake-quant boundaries. | -| `torch.compile(..., fullgraph=True, mode="reduce-overhead")` | The 200-step Python loop produced an enormous unrolled graph; compilation did not finish in a practical window on this host. PyTorch's prototype `scan` has no Inductor lowering in this version. | Rejected for this implementation. | -| cuDNN `nn.GRU` fast path | With both thresholds exactly zero, the delta accumulators telescope to a bias-free GRU. RNN-only: about 178.2 -> 1.12 ms (159x), max FP32 output error 5.84e-6 with TF32 off. | Excellent special case, but it cannot represent the production nonzero thresholds. The persistent kernel is faster here and supports both cases. | -| BF16/TF32 | Lower precision can accelerate dense GEMMs, but this tiny cell is launch-bound and threshold decisions can change near the boundary. | Not needed; fused FP32 already gives the large gain while minimizing trajectory changes. | -| Persistent custom recurrence | One Triton program owns each batch sequence; a reverse program implements BPTT. | **Selected.** Highest speed and supports nonzero thresholds. | - -## Implementation notes - -The forward kernel keeps `x_p`, `h`, `h_p`, `dm`, and `dm_nh` local while it -scans time. It fuses thresholding, both small projections, accumulator updates, -sigmoid/tanh gates, and the hidden update. It saves only the intermediates -needed by BPTT. - -The backward kernel scans time in reverse and emits input gradients plus the two -linear-output adjoints. Two final matrix multiplications reduce weight -gradients over batch and time. Inference under `no_grad` or `inference_mode` -uses a forward-only path and does not allocate the training workspace. Initial -Triton compilation is cache-dependent and took roughly 0.6 seconds on this -machine; it is a one-time cost per kernel specialization and is excluded from -steady-state timings. - -The fused path currently dispatches only for: - -- NVIDIA CUDA with Triton available; -- FP32 dense-float modules; -- one recurrent layer; -- input width up to 16 and hidden width up to 32. - -CPU, unsupported shapes/dtypes, multi-layer cells, and quantization-aware models -use the cleaned eager recurrence. QAT deliberately stays eager because its -fake-quantized Linear/Sigmoid/Tanh/Add/Mul boundaries are part of that model's -actual computation; bypassing or reassociating them would not be equivalent. -Temporal sparsity counters are disabled by default; pass -`--collect_delta_stats` to restore `SP_T_DX`, `SP_T_DH`, `SP_T_DV`, and -`HW_PARAM` training-log fields when that diagnostic overhead is wanted. -CUDA autocast deliberately falls back to eager so AMP keeps its original -fake/mixed-precision boundaries. The custom backward is explicitly -first-order-only; use `OPENDPD_DISABLE_TRITON_DELTAGRU=1` for higher-order -autograd, JVP/vmap-style transforms, or gradient-penalty/meta-learning code. - -## Reproduction - -```bash -.venv/bin/python benchmark/benchmark_tres_deltagru.py -``` - -Useful variants: - -```bash -.venv/bin/python benchmark/benchmark_tres_deltagru.py --batch-size 256 -.venv/bin/python benchmark/benchmark_tres_deltagru.py --sequence-length 1000 \ - --blocks 3 --reference-steps-per-block 1 --fused-steps-per-block 20 -``` - -Set `OPENDPD_DISABLE_TRITON_DELTAGRU=1` for normal application runs that should -force the portable eager path. The benchmark itself explicitly chooses each -implementation so it can compare them in one process. diff --git a/modules/paths.py b/modules/paths.py index f205b26..c35323f 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -4,6 +4,7 @@ import argparse import os +import warnings def gen_log_stat(args: argparse.Namespace, elapsed_time, net, optimizer, epoch, train_stat=None, val_stat=None, @@ -45,19 +46,30 @@ def gen_log_stat(args: argparse.Namespace, elapsed_time, net, optimizer, epoch, 'HIDDEN_SIZE': hidden_size, } - # Add threshold values to log - if args.step == 'train_dpd': - if 'delta' in net.dpd_model.backbone_type: - log_stat['THX'] = net.dpd_model.backbone.thx - log_stat['THH'] = net.dpd_model.backbone.thh + # Record delta thresholds for both PA and DPD training. They are runtime + # model settings rather than checkpoint tensors, so the CSV evidence is + # what lets a benchmark prove which thresholds were actually used. + delta_model = None + if args.step == 'train_pa' and 'delta' in net.backbone_type: + delta_model = net + elif args.step == 'train_dpd' and 'delta' in net.dpd_model.backbone_type: + delta_model = net.dpd_model + + if delta_model is not None: + log_stat['THX'] = delta_model.backbone.thx + log_stat['THH'] = delta_model.backbone.thh - # Statistics are opt-in because collecting them in an eager - # recurrent cell adds reductions at every timestep. - if getattr(net.dpd_model.backbone.rnn, 'debug', 0): - sparsity_metrics = net.dpd_model.backbone.get_temporal_sparsity() - sparsity_log = {f'{k}': v for k, v in sparsity_metrics.items()} - log_stat.update(sparsity_log) - net.dpd_model.backbone.set_debug(1) + # Statistics are opt-in because collecting them in an eager recurrent + # cell adds reductions at every timestep. + if ( + getattr(delta_model.backbone.rnn, 'debug', 0) + and hasattr(delta_model.backbone, 'get_temporal_sparsity') + and hasattr(delta_model.backbone, 'set_debug') + ): + sparsity_metrics = delta_model.backbone.get_temporal_sparsity() + sparsity_log = {f'{k}': v for k, v in sparsity_metrics.items()} + log_stat.update(sparsity_log) + delta_model.backbone.set_debug(1) # Merge stat dicts into the log dict if train_stat is not None: @@ -96,6 +108,33 @@ def gen_file_paths(path_dir_save: str, path_dir_log_hist: str, path_dir_log_best return file_paths +def warn_if_model_artifacts_exist(model_id: str, file_paths): + """Warn before an architecture-only model ID reuses existing artifacts. + + Model IDs are part of the public checkpoint lookup convention, so changing + their format would break existing consumers. They do not, however, encode + the full training recipe. An explicit warning keeps that convention while + making a potentially destructive recipe collision visible before training. + """ + existing_paths = [path for path in file_paths if os.path.exists(path)] + if not existing_paths: + return + + formatted_paths = ", ".join(os.path.normpath(path) for path in existing_paths) + warnings.warn( + ( + f"Existing artifact(s) for model ID '{model_id}' will be reused or " + f"overwritten: {formatted_paths}. This model ID does not encode the " + "full training recipe (for example optimizer, learning rate, " + "schedule, batch size, and epoch count), so the existing artifacts " + "may come from a different recipe. Move or rename them before " + "continuing if they must be preserved." + ), + RuntimeWarning, + stacklevel=2, + ) + + def create_folder(folder_list): for folder in folder_list: try: diff --git a/modules/train_funcs.py b/modules/train_funcs.py index a1962ee..5b0b3a4 100644 --- a/modules/train_funcs.py +++ b/modules/train_funcs.py @@ -137,7 +137,14 @@ def net_eval(log: Dict, def calculate_metrics(args: argparse.Namespace, stat: Dict[str, Any], prediction: np.ndarray, ground_truth: np.ndarray): stat['NMSE'] = metrics.NMSE(prediction, ground_truth) - stat['EVM'] = metrics.EVM(prediction, ground_truth, bw_main_ch=args.bw_main_ch, n_sub_ch=args.n_sub_ch, nperseg=args.nperseg) + stat['EVM'] = metrics.EVM( + prediction, + ground_truth, + sample_rate=args.input_signal_fs, + bw_main_ch=args.bw_main_ch, + n_sub_ch=args.n_sub_ch, + nperseg=args.nperseg, + ) ACLR_L = [] ACLR_R = [] ACLR_left, ACLR_right = metrics.ACLR(prediction, fs=args.input_signal_fs, nperseg=args.nperseg, diff --git a/opendpd/api.py b/opendpd/api.py index df437f6..ffb608a 100644 --- a/opendpd/api.py +++ b/opendpd/api.py @@ -45,9 +45,9 @@ def train_pa( dataset_path: Optional[str] = None, PA_backbone: str = 'gru', PA_hidden_size: int = 23, - n_epochs: int = 100, - batch_size: int = 256, - lr: float = 5e-4, + n_epochs: int = 300, + batch_size: int = 64, + lr: float = 5e-3, accelerator: str = 'cpu', frame_length: int = 200, seed: int = 0, @@ -133,9 +133,9 @@ def train_dpd( DPD_hidden_size: int = 15, PA_backbone: str = 'gru', PA_hidden_size: int = 23, - n_epochs: int = 100, - batch_size: int = 256, - lr: float = 5e-4, + n_epochs: int = 300, + batch_size: int = 64, + lr: float = 5e-3, accelerator: str = 'cpu', frame_length: int = 200, seed: int = 0, diff --git a/project.py b/project.py index 1823270..717d301 100644 --- a/project.py +++ b/project.py @@ -13,7 +13,13 @@ from torch import optim from torch.utils.data import DataLoader from arguments import get_arguments -from modules.paths import create_folder, gen_log_stat, gen_dir_paths, gen_file_paths +from modules.paths import ( + create_folder, + gen_log_stat, + gen_dir_paths, + gen_file_paths, + warn_if_model_artifacts_exist, +) from modules.train_funcs import net_train, net_eval, calculate_metrics from utils import util from modules.loggers import PandasLogger @@ -94,6 +100,7 @@ def gen_dpd_model_id(self, n_net_params): def build_logger(self, model_id: str): # Get Save and Log Paths file_paths = gen_file_paths(self.path_dir_save, self.path_dir_log_hist, self.path_dir_log_best, model_id) + warn_if_model_artifacts_exist(model_id, file_paths) self.path_save_file_best, self.path_log_file_hist, self.path_log_file_best = file_paths print("::: Best Model Save Path: ", self.path_save_file_best) print("::: Log-History Path: ", self.path_log_file_hist) @@ -282,7 +289,13 @@ def build_optimizer(self, net: nn.Module): elif self.opt_type == 'rmsprop': optimizer = optim.RMSprop(trainable_params, lr=self.lr) elif self.opt_type == 'adamw': - optimizer = optim.AdamW(trainable_params, lr=self.lr) + optimizer = optim.AdamW( + trainable_params, + lr=self.lr, + weight_decay=0.01, + betas=(0.9, 0.999), + eps=1e-8, + ) elif self.opt_type == 'adabound': import adabound # Run pip install adabound (https://github.com/Luolc/AdaBound) optimizer = adabound.AdaBound(trainable_params, lr=self.lr, final_lr=0.1) @@ -295,7 +308,10 @@ def build_optimizer(self, net: nn.Module): factor=self.decay_factor, patience=self.patience, threshold=1e-4, - min_lr=self.lr_end) + threshold_mode='rel', + cooldown=0, + min_lr=self.lr_end, + eps=1e-8) return optimizer, lr_scheduler @staticmethod diff --git a/steps/run_dpd.py b/steps/run_dpd.py index 8686f1c..45ee012 100644 --- a/steps/run_dpd.py +++ b/steps/run_dpd.py @@ -37,7 +37,9 @@ def main(proj: Project): hidden_size=proj.PA_hidden_size, num_layers=proj.PA_num_layers, backbone_type=proj.PA_backbone, - num_dvr_units=proj.num_dvr_units) + num_dvr_units=proj.num_dvr_units, + thx=proj.thx, + thh=proj.thh) n_net_pa_params = count_net_params(net_pa) print("::: Number of PA Model Parameters: ", n_net_pa_params) pa_model_id = proj.gen_pa_model_id(n_net_pa_params) diff --git a/steps/train_dpd.py b/steps/train_dpd.py index be92811..093cf68 100644 --- a/steps/train_dpd.py +++ b/steps/train_dpd.py @@ -30,7 +30,9 @@ def main(proj: Project): num_layers=proj.PA_num_layers, backbone_type=proj.PA_backbone, window_size=proj.window_size, - num_dvr_units=proj.num_dvr_units) + num_dvr_units=proj.num_dvr_units, + thx=proj.thx, + thh=proj.thh) n_net_pa_params = count_net_params(net_pa) print("::: Number of PA Model Parameters: ", n_net_pa_params) pa_model_id = proj.gen_pa_model_id(n_net_pa_params) diff --git a/steps/train_pa.py b/steps/train_pa.py index be8a9db..0923440 100644 --- a/steps/train_pa.py +++ b/steps/train_pa.py @@ -26,7 +26,9 @@ def main(proj: Project): num_layers=proj.PA_num_layers, backbone_type=proj.PA_backbone, window_size=proj.window_size, - num_dvr_units=proj.num_dvr_units) + num_dvr_units=proj.num_dvr_units, + thx=proj.thx, + thh=proj.thh) n_net_pa_params = count_net_params(net) print("::: Number of PA Model Parameters: ", n_net_pa_params) pa_model_id = proj.gen_pa_model_id(n_net_pa_params) diff --git a/tests/test_api.py b/tests/test_api.py index 86b65a0..b4a70e6 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -1,10 +1,12 @@ """Tests for the public opendpd Python API (opendpd/api.py).""" +import inspect import sys import numpy as np import pandas as pd import pytest +import torch import opendpd from conftest import REPO_ROOT, SMOKE_DATASET @@ -84,6 +86,68 @@ def test_rejects_wrong_columns(self, tmp_path): class TestApiTraining: + @pytest.mark.parametrize("function_name", ["train_pa", "train_dpd"]) + def test_benchmark_recipe_defaults_are_public_api_defaults(self, function_name): + signature = inspect.signature(getattr(opendpd, function_name)) + + assert signature.parameters["n_epochs"].default == 300 + assert signature.parameters["batch_size"].default == 64 + assert signature.parameters["lr"].default == 5e-3 + + def test_adamw_and_scheduler_recipe_is_explicit(self, monkeypatch): + import project as project_module + + calls = {} + optimizer_sentinel = object() + scheduler_sentinel = object() + + def capture_adamw(params, **kwargs): + calls["optimizer_params"] = tuple(params) + calls["optimizer_kwargs"] = kwargs + return optimizer_sentinel + + def capture_scheduler(**kwargs): + calls["scheduler_kwargs"] = kwargs + return scheduler_sentinel + + monkeypatch.setattr(project_module.optim, "AdamW", capture_adamw) + monkeypatch.setattr( + project_module.optim.lr_scheduler, + "ReduceLROnPlateau", + capture_scheduler, + ) + + project = project_module.Project.__new__(project_module.Project) + project.opt_type = "adamw" + project.lr = 5e-3 + project.decay_factor = 0.5 + project.patience = 5 + project.lr_end = 5e-5 + net = torch.nn.Linear(2, 2) + + optimizer, scheduler = project.build_optimizer(net) + + assert optimizer is optimizer_sentinel + assert scheduler is scheduler_sentinel + assert calls["optimizer_params"] == tuple(net.parameters()) + assert calls["optimizer_kwargs"] == { + "lr": 5e-3, + "weight_decay": 0.01, + "betas": (0.9, 0.999), + "eps": 1e-8, + } + assert calls["scheduler_kwargs"] == { + "optimizer": optimizer_sentinel, + "mode": "min", + "factor": 0.5, + "patience": 5, + "threshold": 1e-4, + "threshold_mode": "rel", + "cooldown": 0, + "min_lr": 5e-5, + "eps": 1e-8, + } + def test_cuda_graph_training_flag_is_explicit_opt_in(self, preserved_argv): from arguments import get_arguments diff --git a/tests/test_benchmark_regressions.py b/tests/test_benchmark_regressions.py new file mode 100644 index 0000000..8cad8de --- /dev/null +++ b/tests/test_benchmark_regressions.py @@ -0,0 +1,985 @@ +"""Focused regression tests for the benchmark workflow.""" + +import copy +import importlib.util +import json +import os +from pathlib import Path +import shlex +import subprocess +import sys +import warnings + +import numpy as np +import pytest + +from project import Project + + +REPO_ROOT = Path(__file__).resolve().parent.parent +BENCHMARK_PATH = REPO_ROOT / "benchmark" / "benchmark_volterra.py" +BENCHMARK_SPEC = importlib.util.spec_from_file_location( + "benchmark_volterra", BENCHMARK_PATH +) +benchmark_volterra = importlib.util.module_from_spec(BENCHMARK_SPEC) +BENCHMARK_SPEC.loader.exec_module(benchmark_volterra) +COLLECTOR_PATH = REPO_ROOT / "benchmark" / "collect_benchmark_report.py" +COLLECTOR_SPEC = importlib.util.spec_from_file_location( + "collect_benchmark_report", COLLECTOR_PATH +) +collect_benchmark_report = importlib.util.module_from_spec(COLLECTOR_SPEC) +COLLECTOR_SPEC.loader.exec_module(collect_benchmark_report) + + +def parse_recorded_commands(path): + blocks = {} + lines = path.read_text().splitlines() + for index, line in enumerate(lines): + if line.startswith("# "): + label = line[2:] + assert label not in blocks, f"duplicate command label: {label}" + blocks[label] = shlex.split(lines[index + 1]) + return blocks + + +def option(command, name): + assert command.count(name) == 1, f"expected one {name} in {command}" + return command[command.index(name) + 1] + + +def polynomial_metrics(): + return { + "NMSE": -39.0, + "EVM": -47.0, + "ACLR_L": -42.0, + "ACLR_R": -40.0, + "ACLR_AVG": -41.0, + } + + +def polynomial_result(task="pa_modeling", model="mp"): + configuration = collect_benchmark_report.POLYNOMIAL_CONFIGURATIONS[task][model] + method = ( + "direct_least_squares" + if task == "pa_modeling" + else "indirect_learning_architecture" + ) + result = { + "schema_version": 4, + "dataset": "APA_200MHz", + "task": task, + "method": method, + "model": model, + "solver": "torch.linalg.lstsq", + "solver_mode": "gels", + "solver_driver": "gels", + "dtype": "torch.complex64", + "device": "cuda:0", + "solver_device": "cuda:0", + "solver_dtype": "torch.complex64", + "column_scaling": "l2", + "column_scale_min": 1.0, + "column_scale_max": 10.0, + "training_relative_residual": 0.1, + "coefficient_l2_norm": 1.0, + "regularization": None, + "svd_rcond": None, + "singular_value_max": None, + "singular_value_min": None, + "singular_value_cutoff": None, + "retained_singular_value_min": None, + "condition_number": None, + "full_rank_assumption": True, + "least_squares_rank": None, + "basis_configuration": configuration["basis_configuration"], + "complex_coefficients": configuration["complex_coefficients"], + "real_parameters": configuration["real_parameters"], + "parameter_count_convention": ( + "two real degrees of freedom per complex coefficient" + ), + "target_gain": 1.2, + "sample_rate_hz": 983_040_000.0, + "nperseg": 19_662, + "validation": polynomial_metrics(), + "test": polynomial_metrics(), + } + if task == "pa_modeling" and model == "gmp": + result.update( + { + "solver": "torch.linalg.svd", + "solver_mode": "truncated_svd", + "solver_driver": "gesvdj", + "full_rank_assumption": False, + "least_squares_rank": 650, + "regularization": "truncated_svd", + "svd_rcond": 1e-4, + "singular_value_max": 10.0, + "singular_value_min": 1e-6, + "singular_value_cutoff": 1e-3, + "retained_singular_value_min": 1.1e-3, + "condition_number": 1e7, + "coefficient_l2_norm": 8.0, + } + ) + if task == "dpd_ila": + result.update( + { + "pa_checkpoint": ( + "save/APA_200MHz/train_pa/" + "PA_S_0_M_TRES_GRU_H_27_F_200_P_2751.pt" + ), + "pa_checkpoint_sha256": "a" * 64, + "pa_evaluation_device": "cuda:0", + } + ) + return result + + +def test_volterra_cli_selects_distinct_mp_and_gmp_bases(): + parser = benchmark_volterra.build_arg_parser() + mp_args = parser.parse_args(["--dataset-name", "fixture", "--model", "mp"]) + gmp_args = parser.parse_args(["--dataset-name", "fixture", "--model", "gmp"]) + + mp_builder, mp_coefficients, mp_banner = benchmark_volterra.select_basis(mp_args) + gmp_builder, gmp_coefficients, gmp_banner = benchmark_volterra.select_basis( + gmp_args + ) + + signal = np.linspace(0.1, 1.0, 64) * np.exp( + 1j * np.linspace(0.0, 2.0 * np.pi, 64) + ) + mp_basis = mp_builder(signal) + gmp_basis = gmp_builder(signal) + + assert mp_coefficients == mp_args.K * mp_args.Q == 250 + assert gmp_coefficients == ( + gmp_args.Ka * gmp_args.La + + gmp_args.Kb * gmp_args.Lb * gmp_args.Mb + + gmp_args.Kc * gmp_args.Lc * gmp_args.Mc + ) == 255 + assert mp_basis.shape == (signal.size, mp_coefficients) + assert gmp_basis.shape == (signal.size, gmp_coefficients) + assert not np.allclose(mp_basis, gmp_basis[:, :mp_coefficients]) + assert " MP " in mp_banner + assert " GMP " in gmp_banner + + +def test_segmented_polynomial_basis_resets_delay_state(): + signal = np.arange(1, 9, dtype=np.float64).astype(np.complex128) + builder = lambda values: benchmark_volterra.build_mp_basis(values, K=1, Q=2) + + full = builder(signal) + segmented = benchmark_volterra.build_segmented_numpy_basis( + signal, builder, segment_length=4 + ) + + assert full[4, 1] == signal[3] + assert segmented[4, 1] == 0 + assert np.array_equal(segmented[:4], full[:4]) + + +def test_truncated_svd_stabilizes_rank_deficient_gmp_basis(): + import torch + + signal = np.ones(64, dtype=np.complex128) + target = 1.5 * signal + configuration = { + "Ka": 2, + "La": 2, + "Kb": 1, + "Lb": 2, + "Mb": 1, + "Kc": 1, + "Lc": 2, + "Mc": 1, + } + + coefficients, diagnostics = ( + benchmark_volterra.fit_complex_least_squares( + signal, + target, + polynomial_model="gmp", + configuration=configuration, + segment_length=64, + device=torch.device("cpu"), + dtype=torch.complex128, + solver_mode="truncated_svd", + svd_rcond=1e-4, + ) + ) + prediction = benchmark_volterra.apply_polynomial( + signal, + coefficients, + polynomial_model="gmp", + configuration=configuration, + segment_length=64, + ) + + assert diagnostics["solver_mode"] == "truncated_svd" + assert diagnostics["solver_implementation"] == "torch.linalg.svd" + assert diagnostics["regularization"] == "truncated_svd" + assert 0 < diagnostics["least_squares_rank"] < diagnostics["columns"] + assert diagnostics["svd_rcond"] == 1e-4 + assert np.all(np.isfinite(coefficients.numpy())) + assert np.linalg.norm(prediction - target) / np.linalg.norm(target) < 1e-12 + + +def test_solver_mode_rejects_incompatible_svd_cutoff(): + import torch + + signal = np.ones(16, dtype=np.complex128) + with pytest.raises(ValueError, match="only valid for truncated_svd"): + benchmark_volterra.fit_complex_least_squares( + signal, + signal, + polynomial_model="mp", + configuration={"K": 1, "Q": 1}, + segment_length=16, + device=torch.device("cpu"), + dtype=torch.complex128, + solver_mode="gels", + svd_rcond=1e-4, + ) + + +def test_build_logger_warns_before_existing_recipe_artifacts_are_overwritten( + tmp_path, +): + project = Project.__new__(Project) + project.path_dir_save = str(tmp_path / "save") + project.path_dir_log_hist = str(tmp_path / "history") + project.path_dir_log_best = str(tmp_path / "best") + project.log_precision = 8 + for directory in ( + project.path_dir_save, + project.path_dir_log_hist, + project.path_dir_log_best, + ): + Path(directory).mkdir() + + model_id = "PA_S_0_M_GRU_H_28_F_200_P_2746" + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + project.build_logger(model_id) + assert caught == [] + + Path(project.path_save_file_best).touch() + with pytest.warns(RuntimeWarning, match="does not encode the full training recipe"): + project.build_logger(model_id) + + +def test_benchmark_report_runner_dry_run_records_complete_recipe(tmp_path): + output_dir = tmp_path / "benchmark-run" + runner = REPO_ROOT / "benchmark" / "reproduce_benchmark_report.sh" + environment = dict(os.environ, OPENDPD_PYTHON=sys.executable) + + result = subprocess.run( + [ + "bash", + str(runner), + "--dry-run", + "--output-dir", + str(output_dir), + ], + cwd=REPO_ROOT, + env=environment, + capture_output=True, + text=True, + timeout=30, + ) + + assert result.returncode == 0, result.stderr + commands = parse_recorded_commands(output_dir / "commands.log") + expected_labels = {"snapshot_context", "collect_report"} + expected_labels.update( + f"train_pa_{slug}_{model}" + for slug in ("apa", "dpa") + for model in ("gru", "tres_gru", "tres_deltagru") + ) + expected_labels.update( + f"train_dpd_{slug}_{experiment}" + for slug in ("apa", "dpa") + for experiment in ( + "gru_via_tres_gru_pa", + "tres_gru_via_tres_gru_pa", + "tres_deltagru_via_tres_gru_pa", + "tres_deltagru_via_tres_deltagru_pa", + ) + ) + expected_labels.update( + f"pa_model_{slug}_{model}" + for slug in ("apa", "dpa") + for model in ("mp", "gmp") + ) + expected_labels.update( + f"dpd_ila_{slug}_{model}" + for slug in ("apa", "dpa") + for model in ("mp", "gmp") + ) + assert set(commands) == expected_labels + + for slug, dataset in (("apa", "APA_200MHz"), ("dpa", "DPA_160MHz")): + for backbone, hidden_size in { + "gru": "28", + "tres_gru": "27", + "tres_deltagru": "27", + }.items(): + command = commands[f"train_pa_{slug}_{backbone}"] + assert command[1] == "main.py" + assert option(command, "--dataset_name") == dataset + assert option(command, "--step") == "train_pa" + assert option(command, "--frame_length") == "200" + assert option(command, "--frame_stride") == "1" + assert option(command, "--n_epochs") == "300" + assert option(command, "--opt_type") == "adamw" + assert option(command, "--batch_size") == "64" + assert option(command, "--batch_size_eval") == "64" + assert option(command, "--lr") == "5e-3" + assert option(command, "--lr_schedule") == "1" + assert option(command, "--lr_end") == "5e-5" + assert option(command, "--decay_factor") == "0.5" + assert option(command, "--patience") == "5" + assert option(command, "--loss_type") == "l2" + assert option(command, "--grad_clip_val") == "200" + assert option(command, "--thx") == "0" + assert option(command, "--thh") == "0" + assert option(command, "--seed") == "0" + assert option(command, "--re_level") == "soft" + assert option(command, "--accelerator") == "cuda" + assert option(command, "--devices") == "0" + assert option(command, "--PA_backbone") == backbone + assert option(command, "--PA_hidden_size") == hidden_size + + dpd_experiments = { + "gru_via_tres_gru_pa": ("gru", "16", "tres_gru"), + "tres_gru_via_tres_gru_pa": ( + "tres_gru", + "15", + "tres_gru", + ), + "tres_deltagru_via_tres_gru_pa": ( + "tres_deltagru", + "15", + "tres_gru", + ), + "tres_deltagru_via_tres_deltagru_pa": ( + "tres_deltagru", + "15", + "tres_deltagru", + ), + } + for experiment, (backbone, hidden_size, pa_backbone) in ( + dpd_experiments.items() + ): + command = commands[f"train_dpd_{slug}_{experiment}"] + assert command[1] == "main.py" + assert option(command, "--dataset_name") == dataset + assert option(command, "--step") == "train_dpd" + assert option(command, "--frame_length") == "200" + assert option(command, "--frame_stride") == "1" + assert option(command, "--n_epochs") == "300" + assert option(command, "--opt_type") == "adamw" + assert option(command, "--batch_size") == "64" + assert option(command, "--batch_size_eval") == "64" + assert option(command, "--lr") == "5e-3" + assert option(command, "--lr_schedule") == "1" + assert option(command, "--lr_end") == "5e-5" + assert option(command, "--decay_factor") == "0.5" + assert option(command, "--patience") == "5" + assert option(command, "--loss_type") == "l2" + assert option(command, "--grad_clip_val") == "200" + assert option(command, "--thx") == "0" + assert option(command, "--thh") == "0" + assert option(command, "--seed") == "0" + assert option(command, "--re_level") == "soft" + assert option(command, "--accelerator") == "cuda" + assert option(command, "--devices") == "0" + assert option(command, "--PA_backbone") == pa_backbone + assert option(command, "--PA_hidden_size") == "27" + assert option(command, "--DPD_backbone") == backbone + assert option(command, "--DPD_hidden_size") == hidden_size + + for slug, dataset in (("apa", "APA_200MHz"), ("dpa", "DPA_160MHz")): + pa_mp = commands[f"pa_model_{slug}_mp"] + pa_gmp = commands[f"pa_model_{slug}_gmp"] + dpd_mp = commands[f"dpd_ila_{slug}_mp"] + dpd_gmp = commands[f"dpd_ila_{slug}_gmp"] + for command, task in ( + (pa_mp, "pa_modeling"), + (pa_gmp, "pa_modeling"), + (dpd_mp, "dpd_ila"), + (dpd_gmp, "dpd_ila"), + ): + assert command[1:3] == ["-m", "benchmark.benchmark_volterra"] + assert option(command, "--task") == task + assert option(command, "--dataset-name") == dataset + assert option(command, "--solver-device") == "cuda:0" + assert option(command, "--solver-dtype") == "complex64" + assert option(pa_mp, "--solver-mode") == "gels" + assert option(pa_gmp, "--solver-mode") == "truncated_svd" + assert option(pa_gmp, "--svd-rcond") == "1e-4" + assert option(dpd_mp, "--solver-mode") == "gels" + assert option(dpd_gmp, "--solver-mode") == "gels" + assert "--svd-rcond" not in pa_mp + assert "--svd-rcond" not in dpd_mp + assert "--svd-rcond" not in dpd_gmp + assert option(pa_mp, "--K") == "9" + assert option(pa_mp, "--Q") == "150" + assert option(pa_gmp, "--La") == "30" + assert option(pa_gmp, "--Mb") == "5" + assert option(pa_gmp, "--Mc") == "5" + assert option(dpd_mp, "--K") == "5" + assert option(dpd_mp, "--Q") == "100" + assert option(dpd_gmp, "--La") == "20" + assert option(dpd_gmp, "--Mb") == "3" + assert option(dpd_gmp, "--Mc") == "2" + for command in (dpd_mp, dpd_gmp): + assert option(command, "--pa-backbone") == "tres_gru" + assert option(command, "--pa-hidden-size") == "27" + + assert "--snapshot-context" in commands["snapshot_context"] + assert "--snapshot-context" not in commands["collect_report"] + collect_benchmark_report.validate_neural_commands( + output_dir / "commands.log" + ) + collect_benchmark_report.validate_runner_commands( + output_dir / "commands.log", + output_dir=output_dir, + device=0, + ) + + recipe = json.loads((output_dir / "recipe.json").read_text()) + assert recipe == collect_benchmark_report.expected_runner_recipe() + assert recipe["schema_version"] == 6 + assert recipe["neural"]["epochs"] == 300 + assert recipe["neural"]["batch_size"] == 64 + assert recipe["neural"]["initial_learning_rate"] == 5e-3 + assert recipe["neural"]["scheduler_patience"] == 5 + assert recipe["neural"]["minimum_learning_rate"] == 5e-5 + assert recipe["neural"]["optimizer_weight_decay"] == 0.01 + assert recipe["neural"]["optimizer_betas"] == [0.9, 0.999] + assert recipe["neural"]["scheduler_threshold_mode"] == "rel" + assert recipe["pa_models"]["mp"]["real_parameters"] == 2700 + assert recipe["pa_models"]["tres_deltagru"] == { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + } + assert recipe["dpd_models"]["gmp"]["real_parameters"] == 1000 + assert recipe["polynomial_solvers"]["gmp_pa"] == { + "implementation": "torch.linalg.svd", + "mode": "truncated_svd", + "driver": "gesvdj", + "dtype": "torch.complex64", + "column_scaling": "l2", + "regularization": { + "type": "truncated_svd", + "relative_cutoff": 1e-4, + }, + } + assert recipe["dpd_tres_deltagru_pa_surrogate_comparison"] == { + "model": { + "hidden_size": 15, + "real_parameters": 999, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + }, + "pa_surrogates": { + "tres_gru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": None, + }, + "tres_deltagru": { + "hidden_size": 27, + "real_parameters": 2751, + "delta_thresholds": {"input": 0.0, "hidden": 0.0}, + }, + }, + } + + +def test_distributed_signal_papr_is_stable(): + apa_record = collect_benchmark_report.signal_record("APA_200MHz") + dpa_record = collect_benchmark_report.signal_record("DPA_160MHz") + apa = apa_record["papr"] + dpa = dpa_record["papr"] + + assert apa_record["samples"] == 98_304 + assert dpa_record["samples"] == 491_520 + assert apa["ccdf_probability"] == 1e-5 + assert apa["quantile_method"] == "numpy linear" + assert apa["ccdf_db"] == pytest.approx(10.00798, abs=1e-5) + assert apa["absolute_peak_db"] == pytest.approx(10.01054, abs=1e-5) + assert dpa["ccdf_db"] == pytest.approx(10.13155, abs=1e-5) + assert dpa["absolute_peak_db"] == pytest.approx(10.13976, abs=1e-5) + + +def test_pa_checkpoint_bindings_require_eight_unique_rows(tmp_path): + experiments = ( + ("gru", "tres_gru"), + ("tres_gru", "tres_gru"), + ("tres_deltagru", "tres_gru"), + ("tres_deltagru", "tres_deltagru"), + ) + rows = [ + ( + dataset, + dpd_model, + pa_model, + f"save/{dataset}/{pa_model}.pt", + digest * 64, + ) + for dataset, digest in (("APA_200MHz", "a"), ("DPA_160MHz", "b")) + for dpd_model, pa_model in experiments + ] + path = tmp_path / "pa_checkpoint_bindings.tsv" + path.write_text( + "dataset\tdpd_model_key\tpa_model_key\tcheckpoint\tsha256\n" + + "".join("\t".join(row) + "\n" for row in rows) + ) + + bindings = collect_benchmark_report.read_pa_bindings(tmp_path) + assert set(bindings) == { + (dataset, dpd_model, pa_model) + for dataset in ("APA_200MHz", "DPA_160MHz") + for dpd_model, pa_model in experiments + } + + rows[-1] = rows[0] + path.write_text( + "dataset\tdpd_model_key\tpa_model_key\tcheckpoint\tsha256\n" + + "".join("\t".join(row) + "\n" for row in rows) + ) + with pytest.raises(ValueError, match="Duplicate PA checkpoint binding"): + collect_benchmark_report.read_pa_bindings(tmp_path) + + +def test_polynomial_metric_validation_rejects_nonfinite_and_bad_average(): + metrics = polynomial_metrics() + assert collect_benchmark_report.normalize_polynomial_metrics(metrics) == { + "nmse_db": -39.0, + "evm_db": -47.0, + "aclr_left_db": -42.0, + "aclr_right_db": -40.0, + "aclr_avg_db": -41.0, + } + + with pytest.raises(ValueError, match="not finite"): + collect_benchmark_report.normalize_polynomial_metrics( + dict(metrics, NMSE=float("nan")) + ) + with pytest.raises(ValueError, match="ACLR average"): + collect_benchmark_report.normalize_polynomial_metrics( + dict(metrics, ACLR_AVG=-40.5) + ) + + +def benchmark_context(): + return { + "git": {"commit": "abc", "branch": "benchmark-fix", "dirty": False}, + "environment": {"python": "3.13"}, + "source_file_sha256": {"main.py": "1" * 64}, + "datasets": {"APA_200MHz": {"spec_sha256": "2" * 64}}, + } + + +def test_context_stability_accepts_identical_context(): + initial = benchmark_context() + collect_benchmark_report.validate_context_stability( + initial, copy.deepcopy(initial) + ) + + +@pytest.mark.parametrize( + ("path", "replacement", "message"), + [ + (("git", "commit"), "def", "Git commit"), + (("git", "branch"), "other", "Git branch"), + (("environment", "python"), "3.14", "software and hardware"), + (("source_file_sha256", "main.py"), "3" * 64, "source files"), + ( + ("datasets", "APA_200MHz", "spec_sha256"), + "4" * 64, + "dataset files", + ), + ], +) +def test_context_stability_rejects_drift(path, replacement, message): + initial = benchmark_context() + changed = copy.deepcopy(initial) + target = changed + for key in path[:-1]: + target = target[key] + target[path[-1]] = replacement + + with pytest.raises(ValueError, match=message): + collect_benchmark_report.validate_context_stability(initial, changed) + + +def test_context_loader_requires_pre_run_snapshot(tmp_path): + with pytest.raises(FileNotFoundError, match="Pre-run context snapshot"): + collect_benchmark_report.load_and_validate_context(tmp_path, 0) + + +def test_source_snapshot_covers_runtime_sources(): + sources = set(collect_benchmark_report.source_files()) + assert { + "arguments.py", + "models.py", + "project.py", + "backbones/gru.py", + "backbones/tres_deltagru.py", + "backbones/tres_gru.py", + "benchmark/benchmark_volterra.py", + "benchmark/collect_benchmark_report.py", + "modules/train_funcs.py", + "steps/train_dpd.py", + "steps/train_pa.py", + "utils/metrics.py", + } <= sources + + +def test_polynomial_pa_artifact_contract_rejects_recipe_drift(tmp_path): + canonical = polynomial_result(task="pa_modeling", model="mp") + output_path = tmp_path / "benchmark_report_apa_pa_mp.json" + output_path.write_text(json.dumps(canonical)) + + result = collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="pa_modeling", + model_name="mp", + ) + assert result["real_parameters"] == 2700 + assert result["least_squares_rank"] is None + assert result["pa_checkpoint_binding"] is None + + mutations = [ + ("solver", "other", "polynomial solver"), + ("basis_configuration", {"K": 9, "Q": 149}, "polynomial basis"), + ("complex_coefficients", 1349, "complex coefficient count"), + ("real_parameters", 2698, "parameter count"), + ("sample_rate_hz", 800_000_000.0, "sample rate"), + ("nperseg", 16_384, "segment length"), + ("column_scale_min", 0.0, "must be positive"), + ] + for field, replacement, message in mutations: + changed = copy.deepcopy(canonical) + changed[field] = replacement + output_path.write_text(json.dumps(changed)) + with pytest.raises(ValueError, match=message): + collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="pa_modeling", + model_name="mp", + ) + + +def test_gmp_pa_artifact_contract_requires_truncated_svd_evidence(tmp_path): + canonical = polynomial_result(task="pa_modeling", model="gmp") + output_path = tmp_path / "benchmark_report_apa_pa_gmp.json" + output_path.write_text(json.dumps(canonical)) + + result = collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="pa_modeling", + model_name="gmp", + ) + assert result["solver"] == "torch.linalg.svd" + assert result["solver_mode"] == "truncated_svd" + assert result["svd_rcond"] == 1e-4 + assert result["least_squares_rank"] == 650 + assert result["coefficient_l2_norm"] == 8.0 + + mutations = [ + ("solver_mode", "gels", "solver mode"), + ("svd_rcond", 1e-6, "SVD relative cutoff"), + ("least_squares_rank", 1350, "rank is outside"), + ("retained_singular_value_min", 5e-4, "does not exceed"), + ("condition_number", 1e3, "inconsistent"), + ] + for field, replacement, message in mutations: + changed = copy.deepcopy(canonical) + changed[field] = replacement + output_path.write_text(json.dumps(changed)) + with pytest.raises(ValueError, match=message): + collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="pa_modeling", + model_name="gmp", + ) + + +def test_polynomial_dpd_artifact_requires_reference_pa_hash(tmp_path): + canonical = polynomial_result(task="dpd_ila", model="mp") + output_path = tmp_path / "benchmark_report_apa_dpd_mp.json" + output_path.write_text(json.dumps(canonical)) + + result = collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="dpd_ila", + model_name="mp", + expected_pa_hash="a" * 64, + ) + assert result["real_parameters"] == 1000 + assert result["pa_checkpoint_binding"]["sha256"] == "a" * 64 + + changed = dict(canonical, pa_checkpoint_sha256="b" * 64) + output_path.write_text(json.dumps(changed)) + with pytest.raises(ValueError, match="checkpoint hash"): + collect_benchmark_report.collect_polynomial_model( + tmp_path, + dataset="APA_200MHz", + slug="apa", + task="dpd_ila", + model_name="mp", + expected_pa_hash="a" * 64, + ) + + +def test_checkpoint_validation_checks_loadability_and_parameter_count(tmp_path): + import torch + + checkpoint = tmp_path / "model.pt" + torch.save({"weight": torch.ones(3), "bias": torch.zeros(2)}, checkpoint) + collect_benchmark_report.validate_checkpoint(checkpoint, 5) + + with pytest.raises(ValueError, match="checkpoint parameter count"): + collect_benchmark_report.validate_checkpoint(checkpoint, 4) + + checkpoint.write_bytes(b"not a checkpoint") + with pytest.raises(ValueError, match="cannot be loaded safely"): + collect_benchmark_report.validate_checkpoint(checkpoint, 5) + + +def test_run_manifest_uses_v6_schema(): + assert collect_benchmark_report.RUN_SCHEMA_VERSION == 6 + + +def report_manifest(): + values = { + "APA_200MHz": { + "pa": [-37.0, -38.7, -38.8, -39.1, -39.2], + "dpd": [-45.2, -43.6, -51.4, -53.4], + "delta_dpd": [-54.1, -55.2], + }, + "DPA_160MHz": { + "pa": [-38.4, -39.4, -39.5, -39.7, -39.8], + "dpd": [-50.7, -52.8, -56.5, -59.5], + "delta_dpd": [-60.1, -61.3], + }, + } + display_names = { + "mp": "MP", + "gmp": "GMP", + "gru": "GRU-H28", + "tres_gru": "TRes-GRU-H27", + "tres_deltagru": "TRes-DeltaGRU-H27 (THX=THH=0)", + } + datasets = {} + for dataset, dataset_values in values.items(): + pa_models = {} + dpd_models = {} + delta_dpd_by_pa_surrogate = {} + for index, key in enumerate(collect_benchmark_report.PA_MODEL_ORDER): + polynomial = key in {"mp", "gmp"} + pa_models[key] = { + "method": ( + "direct_least_squares" + if polynomial + else "supervised_training" + ), + "model": { + "display_name": display_names[key], + "parameters": 2700 + index, + }, + "selected_epoch_zero_based": None if polynomial else 10 + index, + "metrics": { + "validation": { + "nmse_db": dataset_values["pa"][index] + 0.1, + "evm_db": -40.0 - index, + "aclr_left_db": -30.0 - index, + "aclr_right_db": -31.0 - index, + "aclr_avg_db": -30.5 - index, + }, + "test": { + "nmse_db": dataset_values["pa"][index], + "evm_db": -40.2 - index, + "aclr_left_db": -30.2 - index, + "aclr_right_db": -31.2 - index, + "aclr_avg_db": -30.7 - index, + }, + }, + } + if key == "gmp": + pa_models[key]["training_relative_residual"] = 0.0112 + pa_models[key]["regularization"] = "truncated_svd" + pa_models[key]["svd_rcond"] = 1e-4 + pa_models[key]["least_squares_rank"] = 650 + pa_models[key]["complex_coefficients"] = 1350 + if key == "tres_deltagru": + pa_models[key]["delta_thresholds"] = { + "input": 0.0, + "hidden": 0.0, + } + + for index, key in enumerate(collect_benchmark_report.DPD_MODEL_ORDER): + polynomial = key in {"mp", "gmp"} + dpd_models[key] = { + "method": ( + "indirect_learning_architecture" + if polynomial + else "direct_learning_architecture" + ), + "model": { + "display_name": display_names[key], + "parameters": 1000 + index, + }, + "selected_epoch_zero_based": None if polynomial else 20 + index, + "metrics": { + "validation": { + "nmse_db": -42.0 - index, + "evm_db": -45.0 - index, + "aclr_left_db": dataset_values["dpd"][index] - 0.4, + "aclr_right_db": dataset_values["dpd"][index] + 0.2, + "aclr_avg_db": dataset_values["dpd"][index] - 0.1, + }, + "test": { + "nmse_db": -42.2 - index, + "evm_db": -45.2 - index, + "aclr_left_db": dataset_values["dpd"][index] - 0.3, + "aclr_right_db": dataset_values["dpd"][index] + 0.3, + "aclr_avg_db": dataset_values["dpd"][index], + }, + }, + } + for index, pa_model_key in enumerate(("tres_gru", "tres_deltagru")): + aclr = dataset_values["delta_dpd"][index] + delta_dpd_by_pa_surrogate[pa_model_key] = { + "method": "direct_learning_architecture", + "model": { + "display_name": "TRes-DeltaGRU-H15 (THX=THH=0)", + "parameters": 999, + }, + "selected_epoch_zero_based": 30 + index, + "metrics": { + "validation": { + "nmse_db": -48.0 - index, + "evm_db": -50.0 - index, + "aclr_left_db": aclr - 0.3, + "aclr_right_db": aclr + 0.1, + "aclr_avg_db": aclr - 0.1, + }, + "test": { + "nmse_db": -48.2 - index, + "evm_db": -50.2 - index, + "aclr_left_db": aclr - 0.2, + "aclr_right_db": aclr + 0.2, + "aclr_avg_db": aclr, + }, + }, + "pa_surrogate": {"model_key": pa_model_key}, + } + datasets[dataset] = { + "pa_models": pa_models, + "dpd_models": dpd_models, + "dpd_tres_deltagru_by_pa_surrogate": ( + delta_dpd_by_pa_surrogate + ), + } + return { + "generated_at_utc": "2026-07-24T00:00:00+00:00", + "git": {"commit": "abc", "branch": "benchmark-fix", "dirty": False}, + "environment": { + "python": "3.13", + "torch": "2.0", + "cuda": "13.0", + "gpu": "Fixture GPU", + }, + "datasets": datasets, + } + + +def test_report_is_latest_only_and_documents_gmp_stability_and_temporal_context(): + markdown = collect_benchmark_report.render_markdown( + report_manifest(), + canonical=True, + ) + + assert "benchmark_results.png" in markdown + assert "benchmark_delta_dpd_results.png" in markdown + assert "APA GMP stability" in markdown + assert "rcond=1e-04" in markdown + assert "650/1,350 singular directions" in markdown + assert "Truncated SVD (rank 650/1,350)" in markdown + assert "16-sample right context" in markdown + assert "wraps the final position" in markdown + assert "not demodulated constellation EVM" in markdown + assert "reproduce_benchmark_report.sh" in markdown + assert "commands.log" not in markdown + assert "previous report" not in markdown.lower() + assert "DGRU" not in markdown + assert "TRes-DeltaGRU" in markdown + assert "THX=THH=0" in markdown + assert "dense zero-threshold recurrence" in markdown + assert "TRes-DeltaGRU DPD by PA surrogate" in markdown + assert "separately trained" in markdown + for dataset in ("APA_200MHz", "DPA_160MHz"): + dataset_section = markdown.split(f"## {dataset}", 1)[1].split( + "\n## ", + 1, + )[0] + primary_dpd = dataset_section.split("### DPD\n", 1)[1].split( + "### TRes-DeltaGRU DPD by PA surrogate", + 1, + )[0] + assert "TRes-DeltaGRU" not in primary_dpd + + +def test_results_figure_is_a_nonempty_png(tmp_path): + output = tmp_path / "benchmark_results.png" + + collect_benchmark_report.write_results_figure(report_manifest(), output) + + assert output.read_bytes().startswith(b"\x89PNG\r\n\x1a\n") + assert output.stat().st_size > 10_000 + + +def test_delta_dpd_figure_is_a_nonempty_png(tmp_path): + output = tmp_path / "benchmark_delta_dpd_results.png" + + collect_benchmark_report.write_delta_dpd_figure( + report_manifest(), + output, + ) + + assert output.read_bytes().startswith(b"\x89PNG\r\n\x1a\n") + assert output.stat().st_size > 10_000 + + +def test_source_snapshot_roundtrip_matches_pre_run_hashes(tmp_path): + paths = ["arguments.py", "benchmark/collect_benchmark_report.py"] + expected = { + path: collect_benchmark_report.sha256_file(REPO_ROOT / path) + for path in paths + } + output = tmp_path / "source_snapshot.tar.gz" + + collect_benchmark_report.write_source_snapshot(paths, output) + metadata = collect_benchmark_report.validate_source_snapshot( + output, + expected, + ) + + assert metadata["path"] == "source_snapshot.tar.gz" + assert metadata["file_count"] == len(paths) + assert metadata["sha256"] == collect_benchmark_report.sha256_file(output) diff --git a/tests/test_cli.py b/tests/test_cli.py index 30b6e10..984cd74 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -4,6 +4,7 @@ import subprocess import sys +from arguments import get_arguments from conftest import MAIN_PY, REPO_ROOT @@ -38,3 +39,23 @@ def test_main_py_rejects_unknown_backbone(): [sys.executable, str(MAIN_PY), "--step", "train_pa", "--PA_backbone", "bogus"] ) assert result.returncode != 0 + + +def test_training_defaults_match_opendpdv2_recipe(monkeypatch): + monkeypatch.setattr(sys, "argv", ["main.py"]) + + args = get_arguments() + + assert args.batch_size == 64 + assert args.batch_size_eval == 64 + assert args.n_epochs == 300 + assert args.frame_length == 200 + assert args.frame_stride == 1 + assert args.opt_type == "adamw" + assert args.loss_type == "l2" + assert args.seed == 0 + assert args.lr_schedule == 1 + assert args.lr == 5e-3 + assert args.lr_end == 5e-5 + assert args.decay_factor == 0.5 + assert args.patience == 5 diff --git a/tests/test_metric_configuration.py b/tests/test_metric_configuration.py new file mode 100644 index 0000000..faea388 --- /dev/null +++ b/tests/test_metric_configuration.py @@ -0,0 +1,37 @@ +from types import SimpleNamespace + +import numpy as np + +from modules import train_funcs + + +def test_calculate_metrics_uses_dataset_sample_rate(monkeypatch): + prediction = np.zeros((1, 8, 2), dtype=np.float32) + ground_truth = np.zeros_like(prediction) + args = SimpleNamespace( + input_signal_fs=983.04e6, + bw_main_ch=200e6, + n_sub_ch=5, + nperseg=8, + ) + evm_kwargs = {} + + monkeypatch.setattr(train_funcs.metrics, "NMSE", lambda *_: -1.0) + + def fake_evm(*_, **kwargs): + evm_kwargs.update(kwargs) + return -2.0 + + monkeypatch.setattr(train_funcs.metrics, "EVM", fake_evm) + monkeypatch.setattr(train_funcs.metrics, "ACLR", lambda *_, **__: (-3.0, -5.0)) + + result = train_funcs.calculate_metrics(args, {}, prediction, ground_truth) + + assert evm_kwargs["sample_rate"] == args.input_signal_fs + assert result == { + "NMSE": -1.0, + "EVM": -2.0, + "ACLR_L": -3.0, + "ACLR_R": -5.0, + "ACLR_AVG": -4.0, + } diff --git a/tests/test_tres_deltagru_optimized.py b/tests/test_tres_deltagru_optimized.py index 0c2568c..abdd20e 100644 --- a/tests/test_tres_deltagru_optimized.py +++ b/tests/test_tres_deltagru_optimized.py @@ -45,6 +45,29 @@ def test_debug_setting_reaches_recurrent_layer(): assert model.backbone.rnn.debug == 0 +def test_h15_matches_the_approximately_1000_parameter_dpd_budget(): + model = build_model("cpu", fused=False, thx=0.0, thh=0.0) + + assert sum(parameter.numel() for parameter in model.parameters()) == 999 + + +def test_pa_delta_thresholds_are_recorded(): + model = build_model("cpu", fused=False, thx=0.0, thh=0.0) + args = SimpleNamespace( + step="train_pa", + n_epochs=2, + batch_size=2, + frame_length=3, + PA_backbone="tres_deltagru", + PA_hidden_size=15, + ) + + log = gen_log_stat(args, 0.0, model, None, 0) + + assert log["THX"] == 0.0 + assert log["THH"] == 0.0 + + def test_opt_in_statistics_are_logged_and_reset(): model = build_model("cpu", fused=False) model.backbone.set_debug(1) @@ -64,6 +87,8 @@ def test_opt_in_statistics_are_logged_and_reset(): log = gen_log_stat(args, 0.0, wrapper, None, 0) + assert log["THX"] == 0.01 + assert log["THH"] == 0.05 assert {"SP_T_DX", "SP_T_DH", "SP_T_DV", "HW_PARAM"} <= log.keys() assert model.backbone.rnn.statistics == { "num_dx_zeros": 0,

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