From 152c7849bcc30ef43baf997436c42cf7f75437b1 Mon Sep 17 00:00:00 2001 From: Nachi Gargi Date: Mon, 31 Aug 2026 16:19:01 -0700 Subject: [PATCH 01/11] feat(ops): add linear_gluact_linear cuTile kernel Co-authored-by: Jinman Xie Co-authored-by: Yinuo Liu Co-authored-by: Hannah Li Co-authored-by: Zhengyi Zhang Co-authored-by: Anmol Gupta Co-authored-by: Thomas Schmid --- src/tilegym/ops/cutile/__init__.py | 2 + .../ops/cutile/linear_gluact_linear.py | 402 ++++++++++++++++++ src/tilegym/ops/ops.py | 38 ++ tests/ops/test_linear_gluact_linear.py | 187 ++++++++ 4 files changed, 629 insertions(+) create mode 100644 src/tilegym/ops/cutile/linear_gluact_linear.py create mode 100644 tests/ops/test_linear_gluact_linear.py diff --git a/src/tilegym/ops/cutile/__init__.py b/src/tilegym/ops/cutile/__init__.py index 443a4cc3..391597c6 100644 --- a/src/tilegym/ops/cutile/__init__.py +++ b/src/tilegym/ops/cutile/__init__.py @@ -24,6 +24,7 @@ from . import gemma_attention_decode from . import group_gemm from . import layer_norm_legacy + from . import linear_gluact_linear from . import matmul from . import mla from . import mla_decoding @@ -103,6 +104,7 @@ "matmul", "group_gemm", "mhc", + "linear_gluact_linear", "chunk_gated_delta_rule", "recurrent_gated_delta_rule", "sparse_mla", diff --git a/src/tilegym/ops/cutile/linear_gluact_linear.py b/src/tilegym/ops/cutile/linear_gluact_linear.py new file mode 100644 index 00000000..e00ec3c3 --- /dev/null +++ b/src/tilegym/ops/cutile/linear_gluact_linear.py @@ -0,0 +1,402 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +"""cuTile implementation of fused Linear + GLU activation + Linear.""" + +from math import ceil +from types import SimpleNamespace + +import cuda.tile as ct +import torch +from cuda.tile import RoundingMode as RMd +from cuda.tile.tune import exhaustive_search + +from tilegym.backend import register_impl +from tilegym.logger import get_logger + +logger = get_logger(__name__) + +# Module-level tune cache: (M, N1, K, act_type_id, dtype, device) -> (best_cfg, tuned_kernel) +_linear_gluact_tune_cache: dict = {} + +# Activation type constants +ACT_SILU = 0 +ACT_RELU = 1 +ACT_GELU = 2 + +activation_type_map = {"silu": ACT_SILU, "relu": ACT_RELU, "gelu": ACT_GELU, "gelu-tanh": ACT_GELU} + + +def _sigmoid_ct(x, BLOCK_M: ct.Constant[int], BLOCK_N: ct.Constant[int]): + """Sigmoid activation: 1 / (1 + exp(-x))""" + one = ct.full((BLOCK_M, BLOCK_N), 1.0, dtype=ct.float32) + neg_x = -x + exp_neg_x = ct.exp(neg_x) + denom = one + exp_neg_x + return ct.truediv(one, denom, flush_to_zero=True, rounding_mode=RMd.APPROX) + + +def _silu_fwd_ct(x, BLOCK_M: ct.Constant[int], BLOCK_N: ct.Constant[int]): + """SiLU (Swish) activation: x * sigmoid(x)""" + sigmoid_x = _sigmoid_ct(x, BLOCK_M, BLOCK_N) + return x * sigmoid_x + + +def _relu_fwd_ct(x, BLOCK_M: ct.Constant[int], BLOCK_N: ct.Constant[int]): + """ReLU activation: max(0, x)""" + zeros = ct.full((BLOCK_M, BLOCK_N), 0.0, dtype=ct.float32) + return ct.maximum(x, zeros) + + +def _gelu_tanh_fwd_ct(x, BLOCK_M: ct.Constant[int], BLOCK_N: ct.Constant[int]): + """GELU activation using tanh approximation""" + sqrt_2_over_pi = ct.full((BLOCK_M, BLOCK_N), 0.7978845608028654, dtype=ct.float32) + const_044715 = ct.full((BLOCK_M, BLOCK_N), 0.044715, dtype=ct.float32) + half = ct.full((BLOCK_M, BLOCK_N), 0.5, dtype=ct.float32) + one = ct.full((BLOCK_M, BLOCK_N), 1.0, dtype=ct.float32) + + x_squared = x * x + x_cubed = x_squared * x + inner_arg = x + const_044715 * x_cubed + tanh_arg = sqrt_2_over_pi * inner_arg + tanh_result = ct.tanh(tanh_arg) + inner_term = half * (one + tanh_result) + return x * inner_term + + +# AUTOTUNING CONFIGURATIONS +def _linear_gluact_autotune_configs(): + """ + Iterator of autotune configurations for linear_gluact kernel. + """ + gpu_capability = torch.cuda.get_device_capability() + + if gpu_capability[0] >= 10: + # sm100+ (Blackwell): supports 2CTA and larger blocks + # 2CTA (medium-large matrices) + yield SimpleNamespace(BLOCK_M=256, BLOCK_N=256, BLOCK_K=64, GROUP_M=8, num_ctas=2, occupancy=1) + yield SimpleNamespace(BLOCK_M=256, BLOCK_N=128, BLOCK_K=128, GROUP_M=8, num_ctas=2, occupancy=1) + yield SimpleNamespace(BLOCK_M=256, BLOCK_N=256, BLOCK_K=128, GROUP_M=8, num_ctas=2, occupancy=1) + # 1CTA (fallback + small matrices) + yield SimpleNamespace(BLOCK_M=256, BLOCK_N=128, BLOCK_K=64, GROUP_M=8, num_ctas=1, occupancy=1) + yield SimpleNamespace(BLOCK_M=128, BLOCK_N=128, BLOCK_K=64, GROUP_M=8, num_ctas=1, occupancy=1) + else: + # Older GPUs: only NUM_CTAS=1 is supported + yield SimpleNamespace(BLOCK_M=128, BLOCK_N=128, BLOCK_K=64, GROUP_M=8, num_ctas=1, occupancy=1) + yield SimpleNamespace(BLOCK_M=128, BLOCK_N=128, BLOCK_K=32, GROUP_M=8, num_ctas=1, occupancy=1) + yield SimpleNamespace(BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, GROUP_M=8, num_ctas=1, occupancy=1) + + # Conservative fallback for small matrices and unsupported architectures. + yield SimpleNamespace(BLOCK_M=32, BLOCK_N=32, BLOCK_K=32, GROUP_M=8, num_ctas=1, occupancy=1) + yield SimpleNamespace(BLOCK_M=32, BLOCK_N=64, BLOCK_K=32, GROUP_M=8, num_ctas=1, occupancy=1) + + +@ct.kernel +def _linear_gluact_fwd_kernel( + Input, # Input tensor [M, K] + Weight_act, # Weight for activation branch [N1, K] + Weight_noact, # Weight for non-activation branch [N1, K] + Act_out, # Output of activation branch [M, N1] + Noact_out, # Output of non-activation branch [M, N1] + Mul_out, # Output after GLU gating [M, N1] + M: ct.Constant[int], + N1: ct.Constant[int], + K: ct.Constant[int], + BLOCK_M: ct.Constant[int], + BLOCK_N: ct.Constant[int], + BLOCK_K: ct.Constant[int], + GROUP_M: ct.Constant[int], + ACT_TYPE: ct.Constant[int], +): + """ + cuTile kernel for fused Linear + GLU Activation forward pass. + + Computes: + 1. act_in = input @ weight_act^T + 2. act_out = activation(act_in) + 3. noact_out = input @ weight_noact^T + 4. mul_out = act_out * noact_out [GLU gating] + """ + pid = ct.bid(0) + + # Calculate grid dimensions + grid_m = ct.cdiv(M, BLOCK_M) + grid_n = ct.cdiv(N1, BLOCK_N) + + # Grid reordering for better cache locality + width = GROUP_M * grid_n + group_id = pid // width + group_size = ct.minimum(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // group_size + + # Initialize accumulators for both branches + acc_act = ct.full((BLOCK_M, BLOCK_N), 0.0, dtype=ct.float32) + acc_noact = ct.full((BLOCK_M, BLOCK_N), 0.0, dtype=ct.float32) + + # Matrix multiplication loop over K dimension + num_k_blocks = ct.cdiv(K, BLOCK_K) + + for k_block in range(num_k_blocks): + # Load input tile [BLOCK_M, BLOCK_K] - keep in native dtype (fp16) + input_tile = ct.load(Input, index=(pid_m, k_block), shape=(BLOCK_M, BLOCK_K)) + + # Load weight_act tile [BLOCK_N, BLOCK_K] and transpose - keep in native dtype (fp16) + weight_act_tile = ct.load(Weight_act, index=(pid_n, k_block), shape=(BLOCK_N, BLOCK_K)) + weight_act_tile_T = ct.transpose(weight_act_tile) + + # Load weight_noact tile [BLOCK_N, BLOCK_K] and transpose - keep in native dtype (fp16) + weight_noact_tile = ct.load(Weight_noact, index=(pid_n, k_block), shape=(BLOCK_N, BLOCK_K)) + weight_noact_tile_T = ct.transpose(weight_noact_tile) + + # Accumulate: input @ weight^T (tensor cores handle fp16 input -> fp32 accumulation) + acc_act = ct.mma(input_tile, weight_act_tile_T, acc=acc_act) + acc_noact = ct.mma(input_tile, weight_noact_tile_T, acc=acc_noact) + + # Convert to output dtype before activation (fp16 activation is as accurate as fp32) + acc_act = ct.astype(acc_act, Input.dtype) + acc_noact = ct.astype(acc_noact, Input.dtype) + + # Apply activation (activation functions compute in fp32 internally for precision) + if ACT_TYPE == ACT_SILU: + act_out_tile = _silu_fwd_ct(acc_act, BLOCK_M, BLOCK_N) + elif ACT_TYPE == ACT_RELU: + act_out_tile = _relu_fwd_ct(acc_act, BLOCK_M, BLOCK_N) + elif ACT_TYPE == ACT_GELU: + act_out_tile = _gelu_tanh_fwd_ct(acc_act, BLOCK_M, BLOCK_N) + else: + act_out_tile = acc_act + + # Convert activation output back to input dtype + act_out_tile = ct.astype(act_out_tile, Input.dtype) + + mul_out_tile = act_out_tile * acc_noact + + # Store all outputs + ct.store(Act_out, index=(pid_m, pid_n), tile=act_out_tile) + ct.store(Noact_out, index=(pid_m, pid_n), tile=acc_noact) + ct.store(Mul_out, index=(pid_m, pid_n), tile=mul_out_tile) + + +def _cutile_autotune_linear_gluact( + stream, + input_flat, + weight_act, + weight_noact, + act_out, + noact_out, + mul_out, + M, + N1, + K, + act_type_id, +): + """ + Autotuned launch for linear_gluact_fwd_kernel. + """ + cache_key = (M, N1, K, act_type_id, input_flat.dtype, str(input_flat.device)) + if cache_key not in _linear_gluact_tune_cache: + result = exhaustive_search( + list(_linear_gluact_autotune_configs()), + stream, + lambda cfg: (ceil(M / cfg.BLOCK_M) * ceil(N1 / cfg.BLOCK_N), 1, 1), + _linear_gluact_fwd_kernel, + lambda cfg: ( + input_flat, + weight_act, + weight_noact, + act_out, + noact_out, + mul_out, + M, + N1, + K, + cfg.BLOCK_M, + cfg.BLOCK_N, + cfg.BLOCK_K, + cfg.GROUP_M, + act_type_id, + ), + lambda cfg: {"num_ctas": cfg.num_ctas, "occupancy": cfg.occupancy}, + ) + best_cfg = result.best.config + _linear_gluact_tune_cache[cache_key] = ( + best_cfg, + _linear_gluact_fwd_kernel.replace_hints(num_ctas=best_cfg.num_ctas, occupancy=best_cfg.occupancy), + ) + best_cfg, tuned_kernel = _linear_gluact_tune_cache[cache_key] + ct.launch( + stream, + (ceil(M / best_cfg.BLOCK_M) * ceil(N1 / best_cfg.BLOCK_N), 1, 1), + tuned_kernel, + ( + input_flat, + weight_act, + weight_noact, + act_out, + noact_out, + mul_out, + M, + N1, + K, + best_cfg.BLOCK_M, + best_cfg.BLOCK_N, + best_cfg.BLOCK_K, + best_cfg.GROUP_M, + act_type_id, + ), + ) + + +def _linear_gluact_linear_cutile_impl( + input: torch.Tensor, + weight_act: torch.Tensor, + weight_noact: torch.Tensor, + weight2: torch.Tensor, + act_type: str = "silu", + kernel_configs: dict = None, + use_autotune: bool = True, +): + """ + cuTile implementation of Linear + GLU Activation + Linear. + + Computation Flow (GLU - Gated Linear Unit): + 1. input -> Linear1_act : act_in = input @ weight_act^T + 2. act_in -> activation : act_out = activation(act_in) + 3. input -> Linear1_noact : noact_out = input @ weight_noact^T + 4. element-wise multiply : mul_out = act_out * noact_out [gating] + 5. mul_out -> Linear2 : output = mul_out @ weight2^T + + Args: + input: Input tensor (*, in_features) + weight_act: Weight for activation branch (out_features, in_features) + weight_noact: Weight for non-activation branch (out_features, in_features) + weight2: Weight for final linear (final_features, out_features) + act_type: Activation type ('silu', 'relu', 'gelu', 'gelu-tanh') + kernel_configs: Kernel configuration dict (ignored if use_autotune=True) + use_autotune: Whether to use autotuning (default: True) + + Returns: + output: Output tensor (*, final_features) + """ + # Validate activation type + assert act_type in activation_type_map, f"Unsupported activation type: {act_type}" + act_type_id = activation_type_map[act_type] + + # Get dimensions + input_shape = input.shape + if input.dim() > 2: + input_flat = input.view(-1, input_shape[-1]) + else: + input_flat = input + + M, K = input_flat.shape + N1, K_weight = weight_act.shape + N2, N1_weight2 = weight2.shape + + assert K == K_weight, f"Input/weight dimension mismatch: {K} != {K_weight}" + assert N1 == N1_weight2, f"Weight dimension mismatch: {N1} != {N1_weight2}" + assert weight_act.shape == weight_noact.shape, "weight_act and weight_noact must have same shape" + + # Ensure contiguous + assert input_flat.is_contiguous(), "Input must be contiguous" + assert weight_act.is_contiguous(), "weight_act must be contiguous" + assert weight_noact.is_contiguous(), "weight_noact must be contiguous" + assert weight2.is_contiguous(), "weight2 must be contiguous" + + # Allocate intermediate tensors + act_out = torch.empty((M, N1), device=input.device, dtype=input.dtype) + noact_out = torch.empty((M, N1), device=input.device, dtype=input.dtype) + mul_out = torch.empty((M, N1), device=input.device, dtype=input.dtype) + + stream = torch.cuda.current_stream() + + if use_autotune: + # Launch autotuned kernel + _cutile_autotune_linear_gluact( + stream, + input_flat, + weight_act, + weight_noact, + act_out, + noact_out, + mul_out, + M, + N1, + K, + act_type_id, + ) + else: + # Use manual kernel configuration + default_configs = { + "BLOCK_M": 128, + "BLOCK_N": 128, + "BLOCK_K": 64, + "GROUP_M": 8, + } + if kernel_configs is not None: + default_configs.update(kernel_configs) + + BLOCK_M = default_configs["BLOCK_M"] + BLOCK_N = default_configs["BLOCK_N"] + BLOCK_K = default_configs["BLOCK_K"] + GROUP_M = default_configs["GROUP_M"] + + grid = (ceil(M / BLOCK_M) * ceil(N1 / BLOCK_N), 1, 1) + + ct.launch( + stream, + grid, + _linear_gluact_fwd_kernel, + ( + input_flat, + weight_act, + weight_noact, + act_out, + noact_out, + mul_out, + M, + N1, + K, + BLOCK_M, + BLOCK_N, + BLOCK_K, + GROUP_M, + act_type_id, + ), + ) + + # Final linear transformation using cuTile matmul + from tilegym.ops.cutile.matmul import matmul as cutile_matmul + + output = cutile_matmul(mul_out, weight2, trans_b=True, static_persistent=True) + + # Reshape output if needed + if input.dim() > 2: + output = output.view(*input_shape[:-1], N2) + + return output + + +@register_impl("linear_gluact_linear", backend="cutile") +def linear_gluact_linear( + input: torch.Tensor, + weight_act: torch.Tensor, + weight_noact: torch.Tensor, + weight2: torch.Tensor, + act_type: str = "silu", + kernel_configs: dict = None, +): + """ + Registered cuTile implementation for linear_gluact_linear dispatch. + + Args: + input: Input tensor (*, in_features) + weight_act: Weight for activation branch (out_features, in_features) + weight_noact: Weight for non-activation branch (out_features, in_features) + weight2: Weight for final linear (final_features, out_features) + act_type: Activation type ('silu', 'relu', 'gelu', 'gelu-tanh') + kernel_configs: Kernel configuration dict (ignored if use_autotune=True) + """ + return _linear_gluact_linear_cutile_impl(input, weight_act, weight_noact, weight2, act_type, kernel_configs) diff --git a/src/tilegym/ops/ops.py b/src/tilegym/ops/ops.py index d262eb28..e0c83ae4 100644 --- a/src/tilegym/ops/ops.py +++ b/src/tilegym/ops/ops.py @@ -941,6 +941,44 @@ def bmm( raise NotImplementedError(f"BMM is not implemented for this backend: {get_current_backend()}") +@dispatch( + "linear_gluact_linear", +) +def linear_gluact_linear( + input: torch.Tensor, + weight_act: torch.Tensor, + weight_noact: torch.Tensor, + weight2: torch.Tensor, + act_type: str = "silu", + **kwargs: Any, +): + """ + Fused Linear + GLU Activation + Linear operation that automatically selects implementation based on current backend. + + Computation Flow (GLU - Gated Linear Unit): + 1. input -> Linear1_act : act_in = input @ weight_act^T + 2. act_in -> activation : act_out = activation(act_in) + 3. input -> Linear1_noact : noact_out = input @ weight_noact^T + 4. element-wise multiply : mul_out = act_out * noact_out [gating mechanism] + 5. mul_out -> Linear2 : output = mul_out @ weight2^T + + Mathematical Expression: + output = activation(input @ W1_act^T) ⊙ (input @ W1_noact^T) @ W2^T + + Args: + input: Input tensor (*, in_features) + weight_act: Weight for activation branch (out_features, in_features) + weight_noact: Weight for non-activation branch (out_features, in_features) + weight2: Weight for final linear (final_features, out_features) + act_type: Activation type. Supported: 'silu', 'relu', 'gelu', 'gelu-tanh'. Default: 'silu' + **kwargs: Additional arguments, including kernel_configs if needed + + Returns: + torch.Tensor: Output tensor (*, final_features) + """ + raise NotImplementedError(f"linear_gluact_linear is not implemented for this backend: {get_current_backend()}") + + @dispatch( "recurrent_gated_delta_rule", ) diff --git a/tests/ops/test_linear_gluact_linear.py b/tests/ops/test_linear_gluact_linear.py new file mode 100644 index 00000000..f431e006 --- /dev/null +++ b/tests/ops/test_linear_gluact_linear.py @@ -0,0 +1,187 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +import pytest +import torch + +import tilegym + +from .. import common + + +class Test_LinearGluactLinear(common.PyTestCase): + _backends = ["cutile"] + _perf_backends = _backends + ["pytorch"] + + def _prepare_data( + self, + m, + n, + k, + dtype, + ): + """Create test tensors for the linear_gluact_linear tests.""" + self.setUp() + device = torch.device("cuda") + a = torch.rand(m, k, device=device, dtype=dtype, requires_grad=True) + c = torch.rand(k, n, device=device, dtype=dtype, requires_grad=True) + # Separate weights for the activation and non-activation branches + b_act = torch.rand(n, k, device=device, dtype=dtype, requires_grad=True) + b_noact = torch.rand(n, k, device=device, dtype=dtype, requires_grad=True) + return a, b_act, b_noact, c, None, None + + @staticmethod + def reference( + input, + weight_act, + weight_noact, + weight2, + bias_act, + bias_noact, + act_type, + dropout_prob=0.0, + ): + """Reference implementation (separate weights)""" + noact_out = torch.nn.functional.linear(input, weight_noact, bias=bias_noact) + act_in = torch.nn.functional.linear(input, weight_act, bias=bias_act) + + if act_type == "relu": + act_out = torch.nn.functional.relu(act_in) + elif act_type == "gelu": + act_out = torch.nn.functional.gelu(act_in, approximate="none") + elif act_type == "gelu-tanh": + act_out = torch.nn.functional.gelu(act_in, approximate="tanh") + elif act_type == "silu": + act_out = torch.nn.functional.silu(act_in) + else: + raise ValueError(f"Unsupported activation type: {act_type}") + + act_out = act_out * noact_out + if dropout_prob > 0.0: + act_out = torch.nn.functional.dropout(act_out, p=dropout_prob) + return torch.nn.functional.linear(act_out, weight2) + + @pytest.mark.parametrize( + "m, n, k, act_type, dtype", + [ + (1024, 1024, 512, "gelu-tanh", torch.float32), + (1024, 1024, 1023, "silu", torch.float32), + (1024, 1024, 1024, "silu", torch.float32), + (1024, 1024, 1024, "relu", torch.float32), + (512, 512, 256, "silu", torch.float16), + ], + ) + @pytest.mark.parametrize("backend", _backends) + def test_op(self, m, n, k, act_type, dtype, backend, arch): + """Test dispatched linear_gluact_linear implementation with different backends""" + if k == 1023: + pytest.skip("Skip 1023 because strides must be 16-byte aligned when creating tensor descriptors") + + if arch in ["sm120", "sm121"]: + pytest.skip("Skip on sm120, sm121: limited shared memory size.") + + # Set backend + if tilegym.is_backend_available(backend): + tilegym.set_backend(backend) + else: + pytest.skip(f"Backend {backend} is not available") + + a, b_act, b_noact, c, _, _ = self._prepare_data(m, n, k, dtype) + + # Dispatched implementation doesn't support bias and dropout yet + bias_act = None + bias_noact = None + + def dispatch_adapter( + input, + weight_act, + weight_noact, + weight2, + bias_act, + bias_noact, + act_type, + dropout_prob, + ): + # Implementation doesn't support bias and dropout yet + if bias_act is not None or bias_noact is not None: + pytest.skip("Implementation doesn't support bias yet") + if dropout_prob > 0.0: + pytest.skip("Implementation doesn't support dropout yet") + # Use the dispatch interface + return tilegym.ops.linear_gluact_linear(input, weight_act, weight_noact, weight2, act_type) + + self.assertCorrectness( + dispatch_adapter, + self.reference, + { + "input": a, + "weight_act": b_act, + "weight_noact": b_noact, + "weight2": c, + "bias_act": bias_act, + "bias_noact": bias_noact, + "act_type": act_type, + "dropout_prob": 0.0, + }, + rtol=5e-3 if dtype == torch.float16 else 3e-3, + atol=1e-3 if dtype == torch.float16 else 1e-5, + ) + + @pytest.mark.parametrize( + "m, n, k, act_type, dtype", + [ + (1024, 1024, 1024, "silu", torch.float16), + (2048, 2048, 2048, "silu", torch.float16), + (4096, 4096, 4096, "silu", torch.float16), + ], + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), + ) + @pytest.mark.parametrize("backend", _perf_backends) + def test_perf(self, m, n, k, act_type, dtype, backend, arch, record_property): + """Performance test with backend comparison""" + self.setUp() + device = torch.device("cuda") + + # Create tensors with proper scaling to avoid fp16 overflow + # Use randn (normal distribution) with small scale like real neural networks + scale = 0.02 # Similar to typical weight initialization + a = torch.randn(m, k, device=device, dtype=dtype) * scale + c = torch.randn(k, n, device=device, dtype=dtype) * scale + b_act = torch.randn(n, k, device=device, dtype=dtype) * scale + b_noact = torch.randn(n, k, device=device, dtype=dtype) * scale + + if backend != "pytorch": + if tilegym.is_backend_available(backend): + tilegym.set_backend(backend) + else: + pytest.skip(f"Backend {backend} is not available") + + with torch.no_grad(): + if backend == "pytorch": + backend_fn = lambda: self.reference(a, b_act, b_noact, c, None, None, act_type) + else: + kernel_kwargs = { + "act_type": act_type, + } + + backend_fn = lambda: tilegym.ops.linear_gluact_linear(a, b_act, b_noact, c, **kernel_kwargs) + + if backend != "pytorch": + self.assertCorrectness( + backend_fn, + lambda: self.reference(a, b_act, b_noact, c, None, None, act_type), + kwargs={}, + rtol=5e-3 if dtype == torch.float16 else 3e-3, + atol=1e-3 if dtype == torch.float16 else 1e-5, + ) + + res = common.benchmark_framework(backend, backend_fn, use_cudagraph=True) + record_property("benchmark", res) + + # Explicit cleanup to prevent OOM + del a, b_act, b_noact, c, backend_fn + torch.cuda.empty_cache() + import gc + + gc.collect() From a8c512bb5b6d4e9113d0f6811163c9043f0dd270 Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Mon, 31 Aug 2026 20:30:58 -0700 Subject: [PATCH 02/11] Optimize cuTile cross entropy for release qualification --- .../unsloth/cutile/cross_entropy_loss.py | 23 ++++++------------- 1 file changed, 7 insertions(+), 16 deletions(-) diff --git a/src/tilegym/suites/unsloth/cutile/cross_entropy_loss.py b/src/tilegym/suites/unsloth/cutile/cross_entropy_loss.py index c9367c01..b92411e6 100644 --- a/src/tilegym/suites/unsloth/cutile/cross_entropy_loss.py +++ b/src/tilegym/suites/unsloth/cutile/cross_entropy_loss.py @@ -64,10 +64,8 @@ def _cross_entropy_forward_ct( # tensor_view/partition_view abstraction that causes predicate explosion) # Bounds-check elimination when BLOCK_SIZE == VOCAB_SIZE (power-of-2 vocab) no_padding = BLOCK_SIZE == VOCAB_SIZE - logits_row = ct.gather(logits, (row_idx, col_offsets), check_bounds=not no_padding, padding_value=0) + logits_row = ct.gather(logits, (row_idx, col_offsets), check_bounds=not no_padding, padding_value=-math.inf) logits_row = ct.astype(logits_row, ct.float32) - if BLOCK_SIZE > VOCAB_SIZE: - logits_row = ct.where(col_offsets < VOCAB_SIZE, logits_row, -math.inf) # Apply logit scaling (Cohere): t * x if DO_LOGIT_SCALING: @@ -77,9 +75,8 @@ def _cross_entropy_forward_ct( if DO_SOFTCAPPING: logits_row = SOFTCAP * ct.tanh(logits_row / SOFTCAP) - # Numerically stable logsumexp: c + log(sum(exp(x - c))) c = ct.max(logits_row, 0) - lse = c + ct.log(ct.sum(ct.exp(logits_row - c), 0)) + lse = c + ct.log(ct.sum(ct.exp2((logits_row - c) * 1.4426950408889634, flush_to_zero=True), 0)) # Compute loss = logsumexp - x_label # Direct O(1) gather from original logits + re-apply transforms (not O(BLOCK_SIZE) scan) @@ -170,7 +167,7 @@ def _chunked_cross_entropy_forward_ct( # ---- CuTile kernel: cross-entropy backward ---- -@ct.kernel +@ct.kernel(num_ctas=2) def _cross_entropy_backward_ct( logits, # (n_rows, vocab_size) — 2D, read-only: original logits from forward grad_logits, # (n_rows, vocab_size) — 2D, write-only: output gradient buffer @@ -204,8 +201,7 @@ def _cross_entropy_backward_ct( if label_idx != -100: dloss_val = ct.gather(dloss, (row_idx,), padding_value=0).item() - # Load logits chunk via gather (read-only access to saved logits) - x = ct.gather(logits, (row_idx, col_offsets), check_bounds=True, padding_value=0) + x = ct.gather(logits, (row_idx, col_offsets), check_bounds=True, padding_value=0, latency=10) x = ct.astype(x, ct.float32) # Apply logit scaling @@ -222,9 +218,7 @@ def _cross_entropy_backward_ct( # Load saved logsumexp lse = ct.gather(logsumexp_in, (row_idx,), padding_value=0).item() - - # Compute softmax: exp(x - logsumexp) - y = ct.exp(x - lse) + y = ct.exp2((x - lse) * 1.4426950408889634, flush_to_zero=True) # Subtract 1 at label position: softmax - 1_{label} y = ct.where(col_offsets == label_idx, y - 1.0, y) @@ -239,7 +233,7 @@ def _cross_entropy_backward_ct( # Store gradient to separate output buffer, masked to valid vocab positions result = ct.astype(dloss_val * y, grad_logits.dtype) - ct.scatter(grad_logits, (row_idx, col_offsets), result, check_bounds=True) + ct.scatter(grad_logits, (row_idx, col_offsets), result, check_bounds=True, latency=10) # ---- Autograd Function ---- @@ -326,13 +320,10 @@ def backward(ctx, dlosses): logits, logsumexp, labels = ctx.saved_tensors n_rows, vocab_size = logits.shape - BLOCK_SIZE = 4096 + BLOCK_SIZE = 8192 if 8192 < vocab_size <= MAX_FUSED_SIZE else 4096 div, mod = divmod(vocab_size, BLOCK_SIZE) n_blocks = div + (mod != 0) - # Ensure dlosses is contiguous for gather access - dlosses = dlosses.contiguous() - # Allocate separate output buffer to avoid in-place modification of # saved tensors (violates PyTorch autograd version tracking) grad_logits = torch.empty_like(logits) From 0142e4800d6466db5df35478fb8427c08f9c946a Mon Sep 17 00:00:00 2001 From: Zhiwei Fang Date: Mon, 31 Aug 2026 20:37:16 -0700 Subject: [PATCH 03/11] perf(flashinfer/cutile): optimize MLA paged decode split-KV pipeline --- .../flashinfer/cutile/fmha_decode_bsr.py | 210 ++++++++++++++---- 1 file changed, 172 insertions(+), 38 deletions(-) diff --git a/src/tilegym/suites/flashinfer/cutile/fmha_decode_bsr.py b/src/tilegym/suites/flashinfer/cutile/fmha_decode_bsr.py index d59cdeae..e6ae4d2f 100644 --- a/src/tilegym/suites/flashinfer/cutile/fmha_decode_bsr.py +++ b/src/tilegym/suites/flashinfer/cutile/fmha_decode_bsr.py @@ -19,6 +19,19 @@ # Module-level tune caches for paged decode and MLA decode _decode_kv_paged_tune_cache: dict = {} _decode_mla_paged_tune_cache: dict = {} +_mla_scratch_cache: dict = {} + + +def _mla_dummy_lse(device): + key = str(device) + t = _mla_dummy_lse_cache.get(key) + if t is None: + t = torch.zeros(1, device=device, dtype=torch.float32) + _mla_dummy_lse_cache[key] = t + return t + + +_mla_dummy_lse_cache: dict = {} INV_LOG_2 = 1.0 / math.log(2) @@ -104,6 +117,8 @@ def _splitk_reduce_kernel( out_all = ct.load(attn_splitk_out, (0, batch_id, head_id, 0), shape=(NUM_KV_SPLITS_POW2, 1, 1, BLOCK_D)) out_all = ct.reshape(out_all, (NUM_KV_SPLITS_POW2, BLOCK_D)) out_all = ct.astype(out_all, ct.float32) + valid_2d = ct.reshape(valid_mask, (NUM_KV_SPLITS_POW2, 1)) + out_all = ct.where(valid_2d, out_all, ct.zeros((NUM_KV_SPLITS_POW2, BLOCK_D), dtype=ct.float32)) weights_row = ct.reshape(weights, (1, NUM_KV_SPLITS_POW2)) acc = ct.mma(weights_row, out_all, ct.zeros((1, BLOCK_D), dtype=ct.float32)) @@ -113,6 +128,60 @@ def _splitk_reduce_kernel( ct.store(attn_out, (batch_id, head_id, 0), ct.reshape(result, (1, 1, BLOCK_D))) +@ct.kernel +def _splitk_reduce_kernel_htile( + attn_splitk_out, + lse_splitk_out, + attn_out, + actual_seq_lens, + NUM_KV_LEN_PER_SPLIT: ConstInt, + NUM_KV_SPLITS: ConstInt, + NUM_KV_SPLITS_POW2: ConstInt, + BLOCK_H: ConstInt, + BLOCK_D: ConstInt, +): + # Head-tiled variant of _splitk_reduce_kernel: one CTA merges BLOCK_H heads + # with a broadcast multiply + axis-sum instead of a degenerate + # [1, S] x [S, D] mma per (batch, head). + batch_id = ct.bid(0) + head_block = ct.bid(1) + dtype = attn_out.dtype + + # Scratch buffers are uninitialized (cached torch.empty); rows at or beyond + # this batch's actual split count are garbage and must be masked out with a + # select (a plain multiply-by-zero would propagate NaN from the garbage). + seq_len_tile = ct.load(actual_seq_lens, (batch_id,), shape=(1,)) + seq_len = seq_len_tile.item() + actual_num_splits = (seq_len + NUM_KV_LEN_PER_SPLIT - 1) // NUM_KV_LEN_PER_SPLIT + actual_num_splits = ct.minimum(actual_num_splits, NUM_KV_SPLITS) + + lse = ct.reshape( + ct.load(lse_splitk_out, (batch_id, head_block, 0), shape=(1, BLOCK_H, NUM_KV_SPLITS_POW2)), + (BLOCK_H, NUM_KV_SPLITS_POW2), + ) + split_indices = ct.arange(NUM_KV_SPLITS_POW2, dtype=ct.int32) + valid_row = ct.reshape(split_indices, (1, NUM_KV_SPLITS_POW2)) < actual_num_splits + lse = ct.where(valid_row, lse, ct.full((BLOCK_H, NUM_KV_SPLITS_POW2), -1e30, dtype=ct.float32)) + lse_max = ct.max(lse, axis=1, keepdims=True) + weights = ct.exp2(lse - lse_max) + weights = weights / ct.sum(weights, axis=1, keepdims=True) + + x = ct.load( + attn_splitk_out, + (0, batch_id, head_block, 0), + shape=(NUM_KV_SPLITS_POW2, 1, BLOCK_H, BLOCK_D), + latency=10, + ) + x = ct.astype(ct.reshape(x, (NUM_KV_SPLITS_POW2, BLOCK_H, BLOCK_D)), ct.float32) + valid_x = ct.reshape(split_indices, (NUM_KV_SPLITS_POW2, 1, 1)) < actual_num_splits + x = ct.where(valid_x, x, ct.zeros((NUM_KV_SPLITS_POW2, BLOCK_H, BLOCK_D), dtype=ct.float32)) + w = ct.reshape(ct.transpose(weights), (NUM_KV_SPLITS_POW2, BLOCK_H, 1)) + acc = ct.sum(x * w, axis=0, keepdims=False) + + result = ct.astype(acc, dtype) + ct.store(attn_out, (batch_id, head_block, 0), ct.reshape(result, (1, BLOCK_H, BLOCK_D))) + + def _splitk_reduce_with_seq_len(attn_splitk_out, lse_splitk_out, actual_seq_lens, num_kv_len_per_split, attn_out=None): NUM_KV_SPLITS, B, num_heads, head_dim = attn_splitk_out.shape @@ -123,17 +192,6 @@ def _splitk_reduce_with_seq_len(attn_splitk_out, lse_splitk_out, actual_seq_lens attn_out.copy_(attn_splitk_out[0]) return attn_out - if NUM_KV_SPLITS == 2: - lse_0, lse_1 = lse_splitk_out[:, :, 0], lse_splitk_out[:, :, 1] - lse_max = torch.maximum(lse_0, lse_1) - w0, w1 = torch.exp2(lse_0 - lse_max), torch.exp2(lse_1 - lse_max) - w_sum = w0 + w1 - result = ( - attn_splitk_out[0].float() * w0.unsqueeze(-1) + attn_splitk_out[1].float() * w1.unsqueeze(-1) - ) / w_sum.unsqueeze(-1) - attn_out.copy_(result.to(attn_out.dtype)) - return attn_out - NUM_KV_SPLITS_POW2 = next_power_of_2(NUM_KV_SPLITS) BLOCK_D = next_power_of_2(head_dim) @@ -145,6 +203,26 @@ def _splitk_reduce_with_seq_len(attn_splitk_out, lse_splitk_out, actual_seq_lens else: lse_padded = lse_splitk_out + BLOCK_H_R = 4 if num_heads % 4 == 0 else 1 + if BLOCK_H_R > 1: + ct.launch( + torch.cuda.current_stream(), + (B, num_heads // BLOCK_H_R, 1), + _splitk_reduce_kernel_htile, + ( + attn_splitk_out, + lse_padded, + attn_out, + actual_seq_lens, + num_kv_len_per_split, + NUM_KV_SPLITS, + NUM_KV_SPLITS_POW2, + BLOCK_H_R, + BLOCK_D, + ), + ) + return attn_out + ct.launch( torch.cuda.current_stream(), (B, num_heads, 1), @@ -823,10 +901,18 @@ def _decode_mla_kv_paged_kernel( NUM_PAGES_PER_BLOCK: ConstInt, TRANS_QK: ConstBool, USE_GATHER_PAGE_LOAD: ConstBool, + DV_SPLITS: ConstInt, ): batch_id = ct.bid(0) head_block_id = ct.bid(1) - kv_split_id = ct.bid(2) + if DV_SPLITS > 1: + _z = ct.bid(2) + kv_split_id = _z // DV_SPLITS + dv_id = _z % DV_SPLITS + else: + kv_split_id = ct.bid(2) + dv_id = 0 + DV_CHUNK = BLOCK_D // DV_SPLITS seq_len_tile = ct.gather(actual_seq_lens, (batch_id,), padding_value=0) seq_len = seq_len_tile.item() @@ -877,9 +963,9 @@ def _decode_mla_kv_paged_kernel( # TRANS_QK=True: swap Q/K operands in MMA so the M-dim = BLOCK_N (128) instead of BLOCK_H (16/32), # matching the WGMMA tile size on B200 and achieving full tensor-core utilisation. if TRANS_QK: - acc = ct.full((BLOCK_D, BLOCK_H), 0.0, dtype=ct.float32) + acc = ct.full((DV_CHUNK, BLOCK_H), 0.0, dtype=ct.float32) else: - acc = ct.full((BLOCK_H, BLOCK_D), 0.0, dtype=ct.float32) + acc = ct.full((BLOCK_H, DV_CHUNK), 0.0, dtype=ct.float32) for iter_idx in range(num_iters): curr_n = start_n + iter_idx * BLOCK_N @@ -921,13 +1007,13 @@ def _decode_mla_kv_paged_kernel( v_tile = ct.reshape( ct.load( v_cache, - index=(page_id, token_block_idx, 0), - shape=(1, BLOCK_N, BLOCK_D), + index=(page_id, token_block_idx, dv_id), + shape=(1, BLOCK_N, DV_CHUNK), order=(0, 1, 2), allow_tma=True, latency=4, ), - (BLOCK_N, BLOCK_D), + (BLOCK_N, DV_CHUNK), ) elif USE_GATHER_PAGE_LOAD: # Multi-page on datacenter Blackwell: gather TMA, page_ids computed once @@ -958,10 +1044,10 @@ def _decode_mla_kv_paged_kernel( v_tile = ct.reshape( ct.load_advanced_indexing( v_cache, - (page_ids, ct.Slice(token, LOAD_BLOCK_N), ct.Slice(0, BLOCK_D)), + (page_ids, ct.Slice(token, LOAD_BLOCK_N), ct.Slice(dv_id * DV_CHUNK, DV_CHUNK)), padding_mode=PAD_ZERO, ), - (BLOCK_N, BLOCK_D), + (BLOCK_N, DV_CHUNK), ) else: # Multi-page on sm120/sm121/A100: per-page TMA loads (gather regresses the @@ -1025,13 +1111,13 @@ def _decode_mla_kv_paged_kernel( v_tile = ct.reshape( ct.load( v_cache, - index=(page_id, token_block_idx, 0), - shape=(1, BLOCK_N, BLOCK_D), + index=(page_id, token_block_idx, dv_id), + shape=(1, BLOCK_N, DV_CHUNK), order=(0, 1, 2), allow_tma=True, latency=2, ), - (BLOCK_N, BLOCK_D), + (BLOCK_N, DV_CHUNK), ) else: v_tile = _load_page_mla( @@ -1072,15 +1158,15 @@ def _decode_mla_kv_paged_kernel( if TRANS_QK: # acc: [BLOCK_D, BLOCK_H] → divide by l_i, transpose to [BLOCK_H, BLOCK_D], then store acc = ct.truediv((acc * V_SCALE), ct.reshape(l_i, (1, BLOCK_H)), flush_to_zero=True, rounding_mode=RMd.APPROX) - acc_out = ct.astype(ct.transpose(acc), output.dtype) # [BLOCK_H, BLOCK_D] + acc_out = ct.astype(ct.transpose(acc), output.dtype) # [BLOCK_H, DV_CHUNK] else: acc = ct.truediv((acc * V_SCALE), ct.reshape(l_i, (BLOCK_H, 1)), flush_to_zero=True, rounding_mode=RMd.APPROX) - acc_out = ct.astype(acc, output.dtype) # [BLOCK_H, BLOCK_D] + acc_out = ct.astype(acc, output.dtype) # [BLOCK_H, DV_CHUNK] - acc_4d = ct.reshape(acc_out, (1, 1, BLOCK_H, BLOCK_D)) + acc_4d = ct.reshape(acc_out, (1, 1, BLOCK_H, DV_CHUNK)) ct.store( output, - index=(kv_split_id, batch_id, head_block_idx, 0), + index=(kv_split_id, batch_id, head_block_idx, dv_id), tile=acc_4d, order=(0, 1, 2, 3), allow_tma=True, @@ -1088,6 +1174,8 @@ def _decode_mla_kv_paged_kernel( ) if HAS_LSE_OUT: + if DV_SPLITS > 1 and dv_id != 0: + return lse = m_i + ct.log2(l_i) offs_h = ct.arange(BLOCK_H, dtype=ct.int32) lse_indices = (batch_id, head_offset + offs_h, kv_split_id) @@ -1389,6 +1477,7 @@ def _mla_decode_autotune_base( stride_block_table, TRANS_QK, USE_GATHER_PAGE_LOAD, + DV_SPLITS, ): mla_cache_key = ( num_batch, @@ -1402,6 +1491,7 @@ def _mla_decode_autotune_base( HAS_LSE_OUT, TRANS_QK, USE_GATHER_PAGE_LOAD, + DV_SPLITS, q.dtype, str(q.device), ) @@ -1409,7 +1499,7 @@ def _mla_decode_autotune_base( result = exhaustive_search( list(_mla_decode_autotune_configs(trans_qk=TRANS_QK)), stream, - lambda cfg: (num_batch, (num_qo_heads + cfg.BLOCK_H - 1) // cfg.BLOCK_H, NUM_KV_SPLITS), + lambda cfg: (num_batch, (num_qo_heads + cfg.BLOCK_H - 1) // cfg.BLOCK_H, NUM_KV_SPLITS * DV_SPLITS), _decode_mla_kv_paged_kernel, lambda cfg: ( q, @@ -1437,6 +1527,7 @@ def _mla_decode_autotune_base( max(cfg.BLOCK_N // page_size, 1), TRANS_QK, USE_GATHER_PAGE_LOAD, + DV_SPLITS, ), lambda cfg: {"occupancy": cfg.occupancy}, ) @@ -1448,7 +1539,7 @@ def _mla_decode_autotune_base( best_cfg, tuned_kernel = _decode_mla_paged_tune_cache[mla_cache_key] ct.launch( stream, - (num_batch, (num_qo_heads + best_cfg.BLOCK_H - 1) // best_cfg.BLOCK_H, NUM_KV_SPLITS), + (num_batch, (num_qo_heads + best_cfg.BLOCK_H - 1) // best_cfg.BLOCK_H, NUM_KV_SPLITS * DV_SPLITS), tuned_kernel, ( q, @@ -1476,6 +1567,7 @@ def _mla_decode_autotune_base( max(best_cfg.BLOCK_N // page_size, 1), TRANS_QK, USE_GATHER_PAGE_LOAD, + DV_SPLITS, ), ) return Att_Out @@ -1528,28 +1620,65 @@ def decode_mla_kv_paged( kv_len_per_split = max(1 << (kv_len_per_split - 1).bit_length() if kv_len_per_split > 0 else 128, 128) NUM_KV_SPLITS = (estimated_seq_len + kv_len_per_split - 1) // kv_len_per_split max_seq_len = estimated_seq_len + if NUM_KV_SPLITS <= 1: + # A single split degenerates to the non-split path; writing straight + # into `outputs` avoids the scratch buffer and the trailing copy. + should_use_split_kv = False + NUM_KV_SPLITS = 1 + kv_len_per_split = -1 else: should_use_split_kv = False NUM_KV_SPLITS = 1 kv_len_per_split = -1 if should_use_split_kv: - # Initialize to 0 and -inf so empty splits contribute nothing - Att_Out = torch.zeros((NUM_KV_SPLITS, num_batch, num_qo_heads, head_dim_qk), device=q.device, dtype=q.dtype) - LSE_Out = torch.full( - (num_batch, num_qo_heads, NUM_KV_SPLITS), float("-inf"), device=q.device, dtype=torch.float32 + # Scratch buffers are cached and left uninitialized: the reduce kernel + # masks rows at/beyond each batch's actual split count, so no zero / + # -inf fill kernels are needed per call. Buffers use the pow2 width + # the reduce kernel loads (narrower would be an OOB read). + _splits_pow2 = next_power_of_2(NUM_KV_SPLITS) + # Keyed by stream as well: concurrent calls on different streams must + # not share split scratch, or their writes would overlap. + _skey = ( + _splits_pow2, + num_batch, + num_qo_heads, + head_dim_qk, + q.dtype, + str(q.device), + torch.cuda.current_stream().cuda_stream, ) + _scratch = _mla_scratch_cache.get(_skey) + if _scratch is None: + _scratch = ( + torch.empty((_splits_pow2, num_batch, num_qo_heads, head_dim_qk), device=q.device, dtype=q.dtype), + torch.empty((num_batch, num_qo_heads, _splits_pow2), device=q.device, dtype=torch.float32), + ) + _mla_scratch_cache[_skey] = _scratch + Att_Out, LSE_Out = _scratch else: outputs = torch.empty_like(q) if outputs is None else outputs Att_Out = outputs.reshape(1, num_batch, num_qo_heads, head_dim_qk) - LSE_Out = torch.zeros(1, device=q.device, dtype=torch.float32) + LSE_Out = _mla_dummy_lse(q.device) actual_seq_lens_flat = actual_seq_lens.reshape(-1).contiguous() block_tables_flat = block_tables.reshape(-1).contiguous() stride_block_table = block_tables.shape[1] if block_tables.dim() > 1 else 1 HAS_LSE_OUT = should_use_split_kv - LSE_Out_arg = LSE_Out if should_use_split_kv else torch.zeros(1, device=q.device, dtype=torch.float32) + LSE_Out_arg = LSE_Out if should_use_split_kv else _mla_dummy_lse(q.device) + + # Head-dim split: when the launch would leave most SMs idle, split V's + # head_dim across extra CTAs. K is re-read by each dv CTA (in MLA, K is + # the V latent), so this only pays off at low CTA counts. + DV_SPLITS = 1 + if USE_GATHER_PAGE_LOAD and head_dim_qk % 4 == 0: + # Budget with the smallest BLOCK_H the autotuner may pick (16), an + # upper bound on head blocks, so DV never over-splits the grid. + _head_blocks_ub = max(-(-QUERY_GROUP_SIZE // 16), 1) + _total_ctas = num_batch * _head_blocks_ub * NUM_KV_SPLITS + while DV_SPLITS < 4 and _total_ctas * DV_SPLITS * 2 <= NUM_SMS: + DV_SPLITS *= 2 _mla_decode_autotune_base( torch.cuda.current_stream(), @@ -1576,6 +1705,7 @@ def decode_mla_kv_paged( stride_block_table, TRANS_QK, USE_GATHER_PAGE_LOAD, + DV_SPLITS, ) if should_use_split_kv: @@ -1651,10 +1781,13 @@ def decode_mla_kv_paged( kv_len_per_split = estimated_seq_len if should_use_split_kv: - # Initialize to 0 and -inf so empty splits contribute nothing - Att_Out = torch.zeros((NUM_KV_SPLITS, num_batch, num_qo_heads, head_dim_qk), device=q.device, dtype=q.dtype) + # Initialize to 0 and -inf so empty splits contribute nothing. Buffers are + # sized to the pow2 row count the reduce kernel loads; narrower would be + # an out-of-bounds read for non-power-of-two split counts. + _splits_pow2 = next_power_of_2(NUM_KV_SPLITS) + Att_Out = torch.zeros((_splits_pow2, num_batch, num_qo_heads, head_dim_qk), device=q.device, dtype=q.dtype) LSE_Out = torch.full( - (num_batch, num_qo_heads, NUM_KV_SPLITS), float("-inf"), device=q.device, dtype=torch.float32 + (num_batch, num_qo_heads, _splits_pow2), float("-inf"), device=q.device, dtype=torch.float32 ) grid = (num_batch, num_head_blocks, NUM_KV_SPLITS) else: @@ -1666,7 +1799,7 @@ def decode_mla_kv_paged( block_tables_flat = block_tables.reshape(-1).contiguous() stride_block_table = block_tables.shape[1] if block_tables.dim() > 1 else 1 - LSE_Out_arg = LSE_Out if LSE_Out is not None else torch.zeros(1, device=q.device, dtype=torch.float32) + LSE_Out_arg = LSE_Out if LSE_Out is not None else _mla_dummy_lse(q.device) HAS_LSE_OUT = LSE_Out is not None num_ctas = 2 if (num_batch >= 16 and BLOCK_H >= 64) else None @@ -1706,6 +1839,7 @@ def decode_mla_kv_paged( NUM_PAGES_PER_BLOCK, TRANS_QK, USE_GATHER_PAGE_LOAD, + 1, ), ) From ced08a8223487fa82ced286861fd34956c2d27c4 Mon Sep 17 00:00:00 2001 From: Rundong Li Date: Mon, 31 Aug 2026 22:34:18 -0700 Subject: [PATCH 04/11] feat(kernel-inventory): migrate runtime workloads to Kernel Factory JSONL --- .gitattributes | 1 + modeling/transformers/pyproject.toml | 2 +- modeling/transformers/uv.lock | 1511 +---------------- src/tilegym/kernel_inventory/__init__.py | 10 + .../_workload_schema_compat.py | 222 +++ src/tilegym/kernel_inventory/layout.py | 148 +- src/tilegym/kernel_inventory/schema.py | 58 + src/tilegym/kernel_inventory/workloads.py | 526 ++++++ .../olmo3_dual_rms_norm.json | 2 + .../olmo3_dual_rms_norm/workload.jsonl | 1 + .../workload.jsonl | 1 + .../olmoe_dual_rms_norm.json | 2 + .../olmoe_dual_rms_norm/workload.jsonl | 1 + .../workload.jsonl | 1 + .../qwen3_5_causal_conv1d_update_silu.json | 3 +- .../workload.jsonl | 1 + .../workload.jsonl | 1 + .../qwen3_5_gdr_preprocess/workload.jsonl | 1 + .../workload.jsonl | 1 + .../workload.jsonl | 1 + .../qwen3_5_sigmoid_mul/workload.jsonl | 1 + .../workload.jsonl | 1 + tests/kernel_inventory/conftest.py | 12 +- .../kernel_inventory/kernel_runtime_utils.py | 388 ++--- tests/kernel_inventory/runtime_inputs.py | 441 ----- tests/kernel_inventory/runtime_inputs.yaml | 16 - .../test_hierarchical_layout.py | 84 +- ...test_kernel_definition_solution_runtime.py | 368 ++-- tests/kernel_inventory/test_runtime_inputs.py | 499 ------ .../kernel_inventory/test_workload_schema.py | 154 ++ tests/kernel_inventory/test_workloads.py | 332 ++++ 31 files changed, 1964 insertions(+), 2826 deletions(-) create mode 100644 .gitattributes create mode 100644 src/tilegym/kernel_inventory/_workload_schema_compat.py create mode 100644 src/tilegym/kernel_inventory/schema.py create mode 100644 src/tilegym/kernel_inventory/workloads.py create mode 100644 src/tilegym/transformers/olmo3/kernel_workloads/olmo3_dual_rms_norm/workload.jsonl create mode 100644 src/tilegym/transformers/olmo3/kernel_workloads/olmo3_rms_norm_residual_add/workload.jsonl create mode 100644 src/tilegym/transformers/olmoe/kernel_workloads/olmoe_dual_rms_norm/workload.jsonl create mode 100644 src/tilegym/transformers/olmoe/kernel_workloads/olmoe_residual_add_rms_norm/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_prefill_silu/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_update_silu/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_gdr_preprocess/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_residual_add_gemma_rms_norm/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_rms_norm_gated_silu/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_sigmoid_mul/workload.jsonl create mode 100644 src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_silu_and_mul_separate/workload.jsonl delete mode 100644 tests/kernel_inventory/runtime_inputs.py delete mode 100644 tests/kernel_inventory/runtime_inputs.yaml delete mode 100644 tests/kernel_inventory/test_runtime_inputs.py create mode 100644 tests/kernel_inventory/test_workload_schema.py create mode 100644 tests/kernel_inventory/test_workloads.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..30d400cb --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +**/kernel_workloads/**/*.safetensors filter=lfs diff=lfs merge=lfs -text diff --git a/modeling/transformers/pyproject.toml b/modeling/transformers/pyproject.toml index 3ccb570c..0efcb124 100644 --- a/modeling/transformers/pyproject.toml +++ b/modeling/transformers/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" name = "tilegym-hf-bench" version = "0.1.0" description = "Hugging Face inference benchmarks and profiler tooling for TileGym" -requires-python = ">=3.10" +requires-python = ">=3.12" dependencies = [ "accelerate==1.13.0", "cuda-bindings>=13.2.0", diff --git a/modeling/transformers/uv.lock b/modeling/transformers/uv.lock index 84e40ba2..df736204 100644 --- a/modeling/transformers/uv.lock +++ b/modeling/transformers/uv.lock @@ -1,17 +1,13 @@ version = 1 revision = 3 -requires-python = ">=3.10" +requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - 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All rights reserved. +# +# SPDX-License-Identifier: MIT + +"""Public compatibility models for the Kernel Factory Workload contract. + +Internal builds validate against the pinned canonical schema directly. +Public TileGym releases cannot depend on that private package, so this module +implements the documented Workload subset used by the checked-in inventory. +Project-specific file and Definition compatibility rules intentionally live +outside these schema models. +""" + +from __future__ import annotations + +import math +from enum import Enum +from typing import Annotated +from typing import Any +from typing import Literal +from typing import TypeAlias + +from pydantic import BaseModel +from pydantic import Field +from pydantic import StrictBool +from pydantic import StrictFloat +from pydantic import StrictInt +from pydantic import field_validator +from pydantic import model_serializer +from pydantic import model_validator + + +class EvalMode(str, Enum): + """Evaluation phases requested for one Workload.""" + + FULL = "full" + CORRECTNESS_ONLY = "correctness_only" + BENCHMARK_ONLY = "benchmark_only" + + +class SamplingStrategy(str, Enum): + """Sampling strategy for correctness-only dynamic axes.""" + + RANDOM = "random" + LINEAR = "linear" + + +class DynamicAxis(BaseModel): + """Correctness-only dynamic shape sampling policy for one axis.""" + + min: int | str + max: int | str + multiple_of: int | str = 1 + sampling_strategy: SamplingStrategy = SamplingStrategy.RANDOM + intermediate_samples: int = Field(default=0, ge=0) + + @field_validator("min", "max") + @classmethod + def _validate_bound(cls, value: int | str) -> int | str: + if isinstance(value, bool) or isinstance(value, int) and value < 0: + raise ValueError("dynamic axis bounds must be non-negative integers or const-axis names") + if isinstance(value, str) and not value: + raise ValueError("dynamic axis const-axis names must be non-empty") + return value + + @field_validator("multiple_of") + @classmethod + def _validate_multiple_of(cls, value: int | str) -> int | str: + if isinstance(value, bool) or isinstance(value, int) and value <= 0: + raise ValueError("dynamic axis multiple_of must be positive") + if isinstance(value, str) and not value: + raise ValueError("dynamic axis multiple_of const-axis name must be non-empty") + return value + + @model_validator(mode="after") + def _validate_numeric_bounds(self) -> "DynamicAxis": + if isinstance(self.min, int) and isinstance(self.max, int) and self.min > self.max: + raise ValueError("dynamic axis min must not exceed max") + return self + + +class RandomInput(BaseModel): + """Random tensor input descriptor.""" + + type: Literal["random"] = "random" + + +ScalarValue: TypeAlias = StrictInt | StrictFloat | StrictBool + + +class ScalarInput(BaseModel): + """Python numeric scalar input descriptor.""" + + type: Literal["scalar"] = "scalar" + value: ScalarValue + + +class SafetensorsShard(BaseModel): + """One rank-local tensor locator.""" + + path: str = Field(min_length=1) + tensor_key: str = Field(min_length=1) + + +class SafetensorsInput(BaseModel): + """Replicated or rank-sharded safetensors input descriptor.""" + + type: Literal["safetensors"] = "safetensors" + path: str | None = None + tensor_key: str | None = None + shards: list[SafetensorsShard] | None = None + + @model_validator(mode="after") + def _validate_locator(self) -> "SafetensorsInput": + has_path = self.path is not None + has_key = self.tensor_key is not None + has_shards = self.shards is not None + if has_path != has_key: + raise ValueError("safetensors path and tensor_key must be specified together") + if has_shards and (has_path or has_key): + raise ValueError("safetensors replicated locator and shards are mutually exclusive") + if not has_shards and not has_path: + raise ValueError("safetensors input requires path/tensor_key or shards") + if has_path and (not self.path or not self.tensor_key): + raise ValueError("safetensors path and tensor_key must be non-empty") + if has_shards and not self.shards: + raise ValueError("safetensors shards must be non-empty") + return self + + @model_serializer(mode="wrap") + def _serialize_without_unused_locators(self, handler: Any) -> dict[str, Any]: + return {key: value for key, value in handler(self).items() if value is not None} + + +class NullInput(BaseModel): + """Absent optional input descriptor.""" + + type: Literal["null"] = "null" + + +class StringInput(BaseModel): + """Python string input descriptor.""" + + type: Literal["string"] = "string" + value: str + + +class CustomInput(BaseModel): + """Definition-provided custom input descriptor.""" + + type: Literal["custom"] = "custom" + + +InputSpec: TypeAlias = Annotated[ + RandomInput | ScalarInput | SafetensorsInput | NullInput | StringInput | CustomInput, + Field(discriminator="type"), +] + + +class ToleranceSpec(BaseModel): + """Numerical correctness bounds for one Workload.""" + + max_atol: float = Field(default=0.01, ge=0.0, allow_inf_nan=False) + max_rtol: float = Field(default=0.01, ge=0.0, allow_inf_nan=False) + required_matched_ratio: float = Field(default=0.99, ge=0.0, le=1.0, allow_inf_nan=False) + max_error_cap: float | None = Field(default=None, ge=0.0, allow_inf_nan=False) + allow_negative_inf: bool = False + + +def _validate_finite_json(value: Any, path: str = "custom_correctness_kwargs") -> None: + if value is None or isinstance(value, str | bool | int): + return + if isinstance(value, float): + if not math.isfinite(value): + raise ValueError(f"{path} must contain only finite JSON numbers") + return + if isinstance(value, list): + for index, item in enumerate(value): + _validate_finite_json(item, f"{path}[{index}]") + return + if isinstance(value, dict): + for key, item in value.items(): + if not isinstance(key, str): + raise ValueError(f"{path} object keys must be strings") + _validate_finite_json(item, f"{path}.{key}") + return + raise ValueError(f"{path} contains a non-JSON value") + + +class Workload(BaseModel): + """Concrete Kernel Factory-compatible workload configuration.""" + + axes: dict[str, Annotated[int, Field(ge=0)] | DynamicAxis] + inputs: dict[str, InputSpec] + uuid: str = Field(min_length=1) + tolerance: ToleranceSpec = Field(default_factory=ToleranceSpec) + custom_correctness_kwargs: dict[str, Any] = Field(default_factory=dict) + eval_mode: EvalMode = EvalMode.FULL + weight: float | None = Field(default=None, gt=0.0, allow_inf_nan=False) + + @field_validator("axes") + @classmethod + def _validate_axis_names(cls, axes: dict[str, int | DynamicAxis]) -> dict[str, int | DynamicAxis]: + if any(not name for name in axes): + raise ValueError("workload axis names must be non-empty") + return axes + + @field_validator("custom_correctness_kwargs") + @classmethod + def _validate_custom_correctness_kwargs(cls, value: dict[str, Any]) -> dict[str, Any]: + _validate_finite_json(value) + return value + + @model_validator(mode="after") + def _validate_cross_field_contract(self) -> "Workload": + has_dynamic_axes = any(isinstance(value, DynamicAxis) for value in self.axes.values()) + if has_dynamic_axes and self.eval_mode is not EvalMode.CORRECTNESS_ONLY: + raise ValueError("dynamic axes require eval_mode='correctness_only'") + custom_count = sum(isinstance(value, CustomInput) for value in self.inputs.values()) + if custom_count and custom_count != len(self.inputs): + raise ValueError("custom inputs cannot be mixed with other input descriptor types") + return self diff --git a/src/tilegym/kernel_inventory/layout.py b/src/tilegym/kernel_inventory/layout.py index 3e3ad1a3..481f9f92 100644 --- a/src/tilegym/kernel_inventory/layout.py +++ b/src/tilegym/kernel_inventory/layout.py @@ -12,14 +12,15 @@ from typing import Literal InventoryLevel = Literal["legacy", "public", "wrapper", "leaf"] +InventoryKind = Literal["definition", "solution", "workload"] @dataclass(frozen=True) class InventoryCoordinate: - """Canonical, path-derived coordinate for one inventory JSON file.""" + """Canonical, path-derived coordinate for one inventory metadata file.""" inventory_root: Path - kind: Literal["definition", "solution"] + kind: InventoryKind path: Path relative_path: Path operation: str | None @@ -45,9 +46,28 @@ def inventory_coordinate(path: str | Path) -> InventoryCoordinate: """Classify an inventory path using only its checked-in hierarchy.""" path = Path(path) anchor = _inventory_anchor(path) - kind: Literal["definition", "solution"] = "definition" if anchor.name == "kernel_definitions" else "solution" - relative = path.relative_to(anchor) - parts = relative.parts + kind_by_anchor: dict[str, InventoryKind] = { + "kernel_definitions": "definition", + "kernel_solutions": "solution", + "kernel_workloads": "workload", + } + kind = kind_by_anchor[anchor.name] + if kind == "workload": + if path.name != "workload.jsonl": + raise ValueError(f"Expected workload.jsonl for workload inventory path: {path}") + storage_relative = path.relative_to(anchor) + semantic_parts = storage_relative.parent.parts + if not semantic_parts: + raise ValueError(f"Workload must live in a semantic coordinate directory: {path}") + relative = Path(*semantic_parts).with_suffix(".jsonl") + parts = semantic_parts + local_name = semantic_parts[-1] + else: + if path.suffix != ".json": + raise ValueError(f"Expected .json for {kind} inventory path: {path}") + relative = path.relative_to(anchor) + parts = relative.parts + local_name = path.stem if len(parts) == 1: operation = None backend = None @@ -57,14 +77,14 @@ def inventory_coordinate(path: str | Path) -> InventoryCoordinate: backend = parts[0] level = "legacy" elif len(parts) == 2: - if path.stem != parts[0]: - raise ValueError(f"Hierarchical public Definition must be named after its operation directory: {path}") + if local_name != parts[0]: + raise ValueError(f"Hierarchical public inventory file must be named after its operation directory: {path}") operation = parts[0] backend = None level = "public" elif len(parts) == 3: operation, backend = parts[:2] - level = "wrapper" if Path(parts[2]).stem == operation else "leaf" + level = "wrapper" if local_name == operation else "leaf" else: raise ValueError(f"Unsupported kernel inventory hierarchy: {path}") return InventoryCoordinate( @@ -74,13 +94,15 @@ def inventory_coordinate(path: str | Path) -> InventoryCoordinate: relative_path=relative, operation=operation, backend=backend, - local_name=path.stem, + local_name=local_name, level=level, ) def iter_inventory_json_paths(root: str | Path, directory_name: str) -> Iterator[Path]: """Yield JSON files recursively below every matching inventory directory.""" + if directory_name not in {"kernel_definitions", "kernel_solutions"}: + raise ValueError(f"Expected kernel_definitions or kernel_solutions, got {directory_name!r}") root = Path(root) paths = set() for directory in root.glob(f"src/tilegym/**/{directory_name}"): @@ -93,13 +115,29 @@ def iter_inventory_json_paths(root: str | Path, directory_name: str) -> Iterator yield from sorted(paths) +def iter_inventory_workload_paths(root: str | Path) -> Iterator[Path]: + """Yield Workload JSONL files from active Definition/Solution inventories.""" + root = Path(root) + paths = set() + for directory in root.glob("src/tilegym/**/kernel_workloads"): + if not directory.is_dir() or directory.name != "kernel_workloads": + continue + if not (directory.parent / "kernel_definitions").is_dir(): + continue + if not (directory.parent / "kernel_solutions").is_dir(): + continue + for path in directory.rglob("*.jsonl"): + inventory_coordinate(path) + paths.add(path) + yield from sorted(paths) + + def solution_paths_for_definition(definition_path: str | Path) -> Iterator[Path]: """Resolve legacy or hierarchical Solution pairings for one Definition. - Hierarchical public and wrapper Definitions implement the literal acceptance - matrix: all three wrapper-level semantic contracts are checked against both - backend wrapper Solutions. Leaf Definitions pair only with the mirrored - backend leaf Solution. + Public Definitions pair with every registered wrapper entrance. Wrapper + Definitions pair only with their same-backend entrance, and leaf + Definitions pair only with the exact mirrored leaf Solution. """ definition = Path(definition_path) coordinate = inventory_coordinate(definition) @@ -119,26 +157,58 @@ def solution_paths_for_definition(definition_path: str | Path) -> Iterator[Path] assert coordinate.operation is not None operation = coordinate.operation - if coordinate.level in {"public", "wrapper"}: - for backend in ("triton", "cutile"): - solution = solution_root / operation / backend / f"{operation}.json" - if solution.is_file(): - yield solution + if coordinate.level == "public": + operation_solution_root = solution_root / operation + candidates = [ + path + for path in operation_solution_root.glob(f"*/{operation}.json") + if inventory_coordinate(path).level == "wrapper" + ] + yield from sorted(candidates, key=_wrapper_solution_sort_key) return assert coordinate.backend is not None + if coordinate.level == "wrapper": + solution = solution_root / operation / coordinate.backend / f"{operation}.json" + if solution.is_file(): + yield solution + return + solution = solution_root / operation / coordinate.backend / definition.name if solution.is_file(): yield solution -def mirrored_definition_path(solution_path: str | Path) -> Path: - """Return the exact mirrored Definition for a hierarchical leaf Solution.""" - solution = Path(solution_path) - coordinate = inventory_coordinate(solution) - if coordinate.kind != "solution": - raise ValueError(f"Expected a Solution path: {solution}") - return coordinate.inventory_root / "kernel_definitions" / coordinate.relative_path +def mirrored_workload_path(definition_path: str | Path) -> Path: + """Return the adjacent Workload path for one Definition.""" + definition = Path(definition_path) + coordinate = inventory_coordinate(definition) + if coordinate.kind != "definition": + raise ValueError(f"Expected a Definition path: {definition}") + semantic_directory = coordinate.relative_path.with_suffix("") + return coordinate.inventory_root / "kernel_workloads" / semantic_directory / "workload.jsonl" + + +def mirrored_definition_path(inventory_path: str | Path) -> Path: + """Return the exact mirrored Definition for a Solution or Workload.""" + path = Path(inventory_path) + coordinate = inventory_coordinate(path) + if coordinate.kind not in {"solution", "workload"}: + raise ValueError(f"Expected a Solution or Workload path: {path}") + return coordinate.inventory_root / "kernel_definitions" / coordinate.relative_path.with_suffix(".json") + + +def definition_solution_paths_for_workload(workload_path: str | Path) -> Iterator[tuple[Path, Path]]: + """Yield the adjacent Definition and its accepted Solutions for a Workload.""" + workload = Path(workload_path) + coordinate = inventory_coordinate(workload) + if coordinate.kind != "workload": + raise ValueError(f"Expected a Workload path: {workload}") + definition = mirrored_definition_path(workload) + if not definition.is_file(): + raise ValueError(f"Missing mirrored Definition for Workload {workload}: {definition}") + for solution in solution_paths_for_definition(definition): + yield definition, solution def validate_hierarchical_operation_topology( @@ -146,7 +216,7 @@ def validate_hierarchical_operation_topology( operation: str, backends: tuple[str, ...] | list[str], ) -> None: - """Require the literal three-level topology and wrapper acceptance matrix. + """Require the three-level topology and accepted local pairings. ``inventory_root`` is the package or suite directory containing sibling ``kernel_definitions`` and ``kernel_solutions`` directories. Backends must @@ -184,18 +254,30 @@ def validate_hierarchical_operation_topology( f"solutions_only={sorted(leaf_solutions - leaf_definitions)}" ) - expected_matrix = set(wrapper_solutions) - for definition in (public, *wrapper_definitions): + expected_public = set(wrapper_solutions) + actual_public = set(solution_paths_for_definition(public)) + if actual_public != expected_public: + raise ValueError( + f"Incomplete public entrance pairing for {public}: " + f"expected={sorted(expected_public)}, actual={sorted(actual_public)}" + ) + for definition, solution in zip(wrapper_definitions, wrapper_solutions, strict=True): actual = set(solution_paths_for_definition(definition)) - if actual != expected_matrix: + if actual != {solution}: raise ValueError( - f"Incomplete wrapper acceptance matrix for {definition}: " - f"expected={sorted(expected_matrix)}, actual={sorted(actual)}" + f"Invalid backend-local wrapper pairing for {definition}: " + f"expected={[solution]}, actual={sorted(actual)}" ) def _inventory_anchor(path: Path) -> Path: for parent in (path.parent, *path.parents): - if parent.name in {"kernel_definitions", "kernel_solutions"}: + if parent.name in {"kernel_definitions", "kernel_solutions", "kernel_workloads"}: return parent - raise ValueError(f"Path does not live below kernel_definitions or kernel_solutions: {path}") + raise ValueError(f"Path does not live below kernel_definitions, kernel_solutions, or kernel_workloads: {path}") + + +def _wrapper_solution_sort_key(path: Path) -> tuple[int, str]: + backend = path.parent.name + preferred_order = {"triton": 0, "cutile": 1, "cutile_rs": 2} + return preferred_order.get(backend, len(preferred_order)), backend diff --git a/src/tilegym/kernel_inventory/schema.py b/src/tilegym/kernel_inventory/schema.py new file mode 100644 index 00000000..571cf720 --- /dev/null +++ b/src/tilegym/kernel_inventory/schema.py @@ -0,0 +1,58 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +"""Stable schema boundary for kernel-inventory Workloads. + +Internal builds use the pinned canonical contract. The public release strips +that private import block and uses TileGym's documented compatibility models. +Callers must import Workload symbols from this module rather than either +backend directly. +""" + +from __future__ import annotations + +from typing import Any + +USING_CANONICAL_WORKLOAD_SCHEMA = False + +if not USING_CANONICAL_WORKLOAD_SCHEMA: + from tilegym.kernel_inventory._workload_schema_compat import CustomInput + from tilegym.kernel_inventory._workload_schema_compat import EvalMode + from tilegym.kernel_inventory._workload_schema_compat import InputSpec + from tilegym.kernel_inventory._workload_schema_compat import NullInput + from tilegym.kernel_inventory._workload_schema_compat import RandomInput + from tilegym.kernel_inventory._workload_schema_compat import SafetensorsInput + from tilegym.kernel_inventory._workload_schema_compat import SafetensorsShard + from tilegym.kernel_inventory._workload_schema_compat import ScalarInput + from tilegym.kernel_inventory._workload_schema_compat import StringInput + from tilegym.kernel_inventory._workload_schema_compat import ToleranceSpec + from tilegym.kernel_inventory._workload_schema_compat import Workload + + +def workload_model_validate(payload: Any) -> Workload: + """Validate one Workload payload through the selected schema backend.""" + return Workload.model_validate(payload) + + +def workload_model_dump(workload: Workload) -> dict[str, Any]: + """Return normalized JSON-compatible Workload data.""" + return workload.model_dump(mode="json") + + +__all__ = [ + "USING_CANONICAL_WORKLOAD_SCHEMA", + "CustomInput", + "EvalMode", + "InputSpec", + "NullInput", + "RandomInput", + "SafetensorsInput", + "SafetensorsShard", + "ScalarInput", + "StringInput", + "ToleranceSpec", + "Workload", + "workload_model_dump", + "workload_model_validate", +] diff --git a/src/tilegym/kernel_inventory/workloads.py b/src/tilegym/kernel_inventory/workloads.py new file mode 100644 index 00000000..319a8b0a --- /dev/null +++ b/src/tilegym/kernel_inventory/workloads.py @@ -0,0 +1,526 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +"""Loading and Definition-aware validation for inventory Workload JSONL.""" + +from __future__ import annotations + +import ast +import json +import math +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any +from typing import Iterator +from uuid import UUID + +from pydantic import ValidationError + +from tilegym.kernel_inventory.layout import definition_solution_paths_for_workload +from tilegym.kernel_inventory.layout import inventory_coordinate +from tilegym.kernel_inventory.layout import iter_inventory_json_paths +from tilegym.kernel_inventory.layout import iter_inventory_workload_paths +from tilegym.kernel_inventory.layout import mirrored_definition_path +from tilegym.kernel_inventory.layout import mirrored_workload_path +from tilegym.kernel_inventory.schema import CustomInput +from tilegym.kernel_inventory.schema import NullInput +from tilegym.kernel_inventory.schema import RandomInput +from tilegym.kernel_inventory.schema import SafetensorsInput +from tilegym.kernel_inventory.schema import ScalarInput +from tilegym.kernel_inventory.schema import StringInput +from tilegym.kernel_inventory.schema import Workload +from tilegym.kernel_inventory.schema import workload_model_validate + +_WORKLOAD_FIELDS = { + "axes", + "inputs", + "uuid", + "tolerance", + "custom_correctness_kwargs", + "eval_mode", + "weight", +} +_REQUIRED_EXPLICIT_FIELDS = {"axes", "inputs", "uuid", "tolerance", "eval_mode"} +_TOLERANCE_FIELDS = { + "max_atol", + "max_rtol", + "required_matched_ratio", + "max_error_cap", + "allow_negative_inf", +} +_EXPECTED_TOLERANCE = { + "max_atol": 0.02, + "max_rtol": 0.02, + "required_matched_ratio": 1.0, + "max_error_cap": None, + "allow_negative_inf": False, +} + + +class KernelWorkloadError(ValueError): + """Raised when checked-in Workload metadata is invalid.""" + + +@dataclass(frozen=True) +class WorkloadRecord: + """One parsed Workload plus its physical JSONL source coordinate.""" + + path: Path + line_number: int + workload: Workload + + @property + def source_label(self) -> str: + """Return a path-and-line label for diagnostics.""" + return f"{self.path}:{self.line_number}" + + +def load_workload_jsonl(path: str | Path) -> tuple[WorkloadRecord, ...]: + """Load a strict one-object-per-line Workload JSONL file.""" + path = Path(path) + try: + text = path.read_text(encoding="utf-8") + except OSError as exc: + raise KernelWorkloadError(f"{path}: unable to read Workload JSONL: {exc}") from exc + if not text: + raise KernelWorkloadError(f"{path}: Workload JSONL must not be empty") + + records = [] + for line_number, line in enumerate(text.splitlines(), start=1): + label = f"{path}:{line_number}" + if not line.strip(): + raise KernelWorkloadError(f"{label}: blank lines are not allowed") + if line.lstrip().startswith("#"): + raise KernelWorkloadError(f"{label}: comments are not allowed") + try: + payload = json.loads(line) + except json.JSONDecodeError as exc: + raise KernelWorkloadError(f"{label}: invalid JSON: {exc.msg}") from exc + if not isinstance(payload, dict): + raise KernelWorkloadError(f"{label}: expected one JSON object") + _validate_checked_in_policy(payload, label) + try: + workload = workload_model_validate(payload) + except ValidationError as exc: + raise KernelWorkloadError(f"{label}: Workload schema invalid: {exc}") from exc + records.append(WorkloadRecord(path=path, line_number=line_number, workload=workload)) + + if not records: + raise KernelWorkloadError(f"{path}: Workload JSONL must contain at least one row") + return tuple(records) + + +def validate_workload_against_definition( + record: WorkloadRecord, + definition: dict[str, Any], + definition_path: str | Path, +) -> None: + """Validate one Workload row against its adjacent Definition contract.""" + label = record.source_label + axes = _require_mapping(definition.get("axes"), f"{definition_path}: Definition.axes") + inputs = _require_mapping(definition.get("inputs"), f"{definition_path}: Definition.inputs") + variable_axes = {name for name, spec in axes.items() if _require_mapping(spec, f"axis {name}").get("type") == "var"} + workload_axes = set(record.workload.axes) + if workload_axes != variable_axes: + missing = sorted(variable_axes - workload_axes) + unknown = sorted(workload_axes - variable_axes) + raise KernelWorkloadError(f"{label}: Workload axes mismatch: missing={missing}, unknown_or_constant={unknown}") + if any(type(value) is not int or value < 0 for value in record.workload.axes.values()): + raise KernelWorkloadError(f"{label}: full-evaluation Workload axes must be concrete non-negative integers") + + workload_inputs = set(record.workload.inputs) + definition_inputs = set(inputs) + if workload_inputs != definition_inputs: + missing = sorted(definition_inputs - workload_inputs) + unknown = sorted(workload_inputs - definition_inputs) + raise KernelWorkloadError(f"{label}: Workload inputs mismatch: missing={missing}, unknown={unknown}") + + none_defaults = _reference_none_defaults(definition.get("reference", "")) + for name, descriptor in record.workload.inputs.items(): + spec = _require_mapping(inputs[name], f"{definition_path}: Definition.inputs.{name}") + shape = spec.get("shape") + dtype = spec.get("dtype") + if isinstance(descriptor, RandomInput | SafetensorsInput): + if shape is None: + raise KernelWorkloadError(f"{label}: input {name!r} uses a tensor descriptor for scalar TensorSpec") + if isinstance(descriptor, SafetensorsInput): + locators = descriptor.shards if descriptor.shards is not None else (descriptor,) + for locator in locators: + _resolve_safetensors_path(record, locator.path) + elif isinstance(descriptor, ScalarInput): + if shape is not None: + raise KernelWorkloadError(f"{label}: input {name!r} uses a scalar descriptor for tensor TensorSpec") + _validate_scalar_dtype(descriptor.value, dtype, f"{label}: input {name!r}") + elif isinstance(descriptor, NullInput): + if name not in none_defaults: + raise KernelWorkloadError( + f"{label}: null input {name!r} requires an explicit None default in Definition.reference.run" + ) + elif isinstance(descriptor, StringInput): + if shape is not None: + raise KernelWorkloadError(f"{label}: input {name!r} uses a string descriptor for tensor TensorSpec") + elif isinstance(descriptor, CustomInput): + if not definition.get("custom_inputs_entrypoint"): + raise KernelWorkloadError(f"{label}: custom inputs require Definition.custom_inputs_entrypoint") + + _validate_resolvable_constraints(record, definition, axes) + + +def validate_workload_catalog(root: str | Path, *, require_complete: bool) -> None: + """Validate active Workload topology, rows, UUIDs, and adjacent contracts.""" + root = Path(root) + definition_paths = set(iter_inventory_json_paths(root, "kernel_definitions")) + workload_paths = set(iter_inventory_workload_paths(root)) + definition_by_workload = {mirrored_workload_path(path): path for path in definition_paths} + + stale = sorted(workload_paths - set(definition_by_workload)) + if stale: + raise KernelWorkloadError(f"Stale Workload files without active Definitions: {stale}") + if require_complete: + missing = sorted(set(definition_by_workload) - workload_paths) + if missing: + raise KernelWorkloadError(f"Active Definitions missing mirrored Workloads: {missing}") + + uuid_sources: dict[str, str] = {} + asset_owners: dict[Path, set[Path]] = {} + for workload_path in sorted(workload_paths): + definition_path = mirrored_definition_path(workload_path) + definition = _load_json_object(definition_path) + targets = list(definition_solution_paths_for_workload(workload_path)) + if not targets: + raise KernelWorkloadError(f"{workload_path}: adjacent Definition has no accepted Solution target") + for record in load_workload_jsonl(workload_path): + previous = uuid_sources.get(record.workload.uuid) + if previous is not None: + raise KernelWorkloadError( + f"{record.source_label}: duplicate Workload UUID {record.workload.uuid!r}; first seen at {previous}" + ) + uuid_sources[record.workload.uuid] = record.source_label + validate_workload_against_definition(record, definition, definition_path) + for asset_path in _safetensors_paths(record): + asset_owners.setdefault(asset_path, set()).add(workload_path) + + shared_assets = {path: owners for path, owners in asset_owners.items() if len(owners) > 1} + if shared_assets: + details = {str(path): [str(owner) for owner in sorted(owners)] for path, owners in shared_assets.items()} + raise KernelWorkloadError(f"Safetensors assets must have exactly one owning Workload: {details}") + + inventory_roots = {inventory_coordinate(path).inventory_root for path in definition_paths} + checked_in_assets = { + path.resolve() + for inventory_root in inventory_roots + for path in (inventory_root / "kernel_workloads").rglob("*.safetensors") + } + orphan_assets = sorted(checked_in_assets - set(asset_owners)) + if orphan_assets: + raise KernelWorkloadError(f"Orphan safetensors files without Workload references: {orphan_assets}") + + +def iter_workload_records(root: str | Path) -> Iterator[WorkloadRecord]: + """Yield every parsed Workload row in deterministic path/line order.""" + for path in iter_inventory_workload_paths(root): + yield from load_workload_jsonl(path) + + +def materialize_workload_inputs( + record: WorkloadRecord, + definition: dict[str, Any], + *, + torch: Any, + device: Any, +) -> tuple[dict[str, int], dict[str, Any]]: + """Materialize concrete axes and inputs for one validated Workload row.""" + definition_axes = _require_mapping(definition.get("axes"), "Definition.axes") + axes = { + name: spec["value"] + for name, spec in definition_axes.items() + if isinstance(spec, dict) and spec.get("type") == "const" + } + axes.update({name: int(value) for name, value in record.workload.axes.items()}) + + definition_inputs = _require_mapping(definition.get("inputs"), "Definition.inputs") + materialized = {} + for name, descriptor in record.workload.inputs.items(): + spec = _require_mapping(definition_inputs[name], f"Definition.inputs.{name}") + if isinstance(descriptor, RandomInput): + materialized[name] = _materialize_random_tensor(spec, axes, torch, device) + elif isinstance(descriptor, SafetensorsInput): + materialized[name] = _materialize_safetensors_tensor(record, descriptor, spec, axes, torch, device) + elif isinstance(descriptor, ScalarInput | StringInput): + materialized[name] = descriptor.value + elif isinstance(descriptor, NullInput): + materialized[name] = None + elif isinstance(descriptor, CustomInput): + raise KernelWorkloadError( + f"{record.source_label}: TileGym runtime materialization does not yet support custom inputs" + ) + else: # pragma: no cover - selected schema union prevents this + raise KernelWorkloadError(f"{record.source_label}: unsupported input descriptor for {name!r}") + return axes, materialized + + +def resolve_torch_dtype(dtype: str, torch: Any) -> Any: + """Resolve an inventory dtype string to its torch dtype.""" + supported = { + "float64", + "float32", + "float16", + "bfloat16", + "float8_e4m3fn", + "float8_e5m2", + "int64", + "int32", + "int16", + "int8", + "uint64", + "uint32", + "uint16", + "uint8", + "bool", + } + if dtype not in supported: + raise KernelWorkloadError(f"Unsupported inventory dtype: {dtype}") + try: + return getattr(torch, dtype) + except AttributeError as exc: + raise KernelWorkloadError(f"PyTorch does not provide inventory dtype: {dtype}") from exc + + +def _materialize_random_tensor(spec: dict[str, Any], axes: dict[str, int], torch: Any, device: Any) -> Any: + shape = _resolved_shape(spec, axes) + dtype_name = spec["dtype"] + dtype = resolve_torch_dtype(dtype_name, torch) + if dtype_name == "bool": + return torch.randint(0, 2, shape, dtype=torch.bool, device=device) + if dtype_name.startswith("uint"): + return torch.randint(0, 4, shape, dtype=torch.int64, device=device).to(dtype) + if dtype_name.startswith("int"): + return torch.randint(-4, 4, shape, dtype=dtype, device=device) + return torch.randn(shape, dtype=torch.float32, device=device).to(dtype) + + +def _materialize_safetensors_tensor( + record: WorkloadRecord, + descriptor: SafetensorsInput, + spec: dict[str, Any], + axes: dict[str, int], + torch: Any, + device: Any, +) -> Any: + if descriptor.shards is not None: + rank = int(os.environ.get("RANK", "0")) + if rank < 0 or rank >= len(descriptor.shards): + raise KernelWorkloadError( + f"{record.source_label}: rank {rank} has no safetensors shard among {len(descriptor.shards)} locators" + ) + locator = descriptor.shards[rank] + raw_path, tensor_key = locator.path, locator.tensor_key + else: + assert descriptor.path is not None and descriptor.tensor_key is not None + raw_path, tensor_key = descriptor.path, descriptor.tensor_key + + tensor_path = _resolve_safetensors_path(record, raw_path) + + try: + from safetensors.torch import load_file + + values = load_file(tensor_path.as_posix(), device="cpu") + except (OSError, RuntimeError, ValueError) as exc: + raise KernelWorkloadError(f"{record.source_label}: unable to load safetensors file {raw_path}: {exc}") from exc + if tensor_key not in values: + raise KernelWorkloadError(f"{record.source_label}: safetensors key {tensor_key!r} not found in {raw_path}") + value = values[tensor_key] + expected_shape = _resolved_shape(spec, axes) + expected_dtype = resolve_torch_dtype(spec["dtype"], torch) + if tuple(value.shape) != expected_shape: + raise KernelWorkloadError( + f"{record.source_label}: safetensors input shape {tuple(value.shape)} does not match {expected_shape}" + ) + if value.dtype != expected_dtype: + raise KernelWorkloadError( + f"{record.source_label}: safetensors input dtype {value.dtype} does not match {expected_dtype}" + ) + return value.to(device=device).contiguous() + + +def _resolve_safetensors_path(record: WorkloadRecord, raw_path: str) -> Path: + owning_directory = record.path.parent.resolve() + relative_path = Path(raw_path) + if relative_path.is_absolute(): + raise KernelWorkloadError(f"{record.source_label}: safetensors path must be relative: {raw_path}") + if raw_path != relative_path.name or relative_path.suffix != ".safetensors": + raise KernelWorkloadError( + f"{record.source_label}: safetensors path must be a .safetensors basename beside workload.jsonl: {raw_path}" + ) + tensor_path = (owning_directory / relative_path).resolve() + try: + tensor_path.relative_to(owning_directory) + except ValueError as exc: + raise KernelWorkloadError( + f"{record.source_label}: safetensors path escapes Workload directory: {raw_path}" + ) from exc + if not tensor_path.is_file(): + raise KernelWorkloadError(f"{record.source_label}: safetensors file does not exist: {raw_path}") + return tensor_path + + +def _safetensors_paths(record: WorkloadRecord) -> Iterator[Path]: + for descriptor in record.workload.inputs.values(): + if not isinstance(descriptor, SafetensorsInput): + continue + locators = descriptor.shards if descriptor.shards is not None else (descriptor,) + for locator in locators: + yield _resolve_safetensors_path(record, locator.path) + + +def _resolved_shape(spec: dict[str, Any], axes: dict[str, int]) -> tuple[int, ...]: + shape = spec.get("shape") + if not isinstance(shape, list): + raise KernelWorkloadError(f"Tensor input requires a list shape, got {shape!r}") + try: + return tuple(axes[dimension] for dimension in shape) + except KeyError as exc: + raise KernelWorkloadError(f"Tensor shape references unresolved axis {exc.args[0]!r}") from exc + + +def _validate_checked_in_policy(payload: dict[str, Any], label: str) -> None: + _validate_finite_json(payload, label) + non_string_fields = [key for key in payload if not isinstance(key, str)] + if non_string_fields: + raise KernelWorkloadError(f"{label}: Workload field names must be strings: {non_string_fields!r}") + unknown = sorted(set(payload) - _WORKLOAD_FIELDS) + if unknown: + raise KernelWorkloadError(f"{label}: unsupported Workload fields: {unknown}") + missing = sorted(_REQUIRED_EXPLICIT_FIELDS - set(payload)) + if missing: + raise KernelWorkloadError(f"{label}: missing explicit Workload fields: {missing}") + + tolerance = _require_mapping(payload["tolerance"], f"{label}: tolerance") + tolerance_fields = set(tolerance) + if tolerance_fields != _TOLERANCE_FIELDS: + missing_tolerance = sorted(_TOLERANCE_FIELDS - tolerance_fields) + unknown_tolerance = sorted(tolerance_fields - _TOLERANCE_FIELDS) + raise KernelWorkloadError( + f"{label}: tolerance fields mismatch: missing={missing_tolerance}, unknown={unknown_tolerance}" + ) + if tolerance != _EXPECTED_TOLERANCE: + raise KernelWorkloadError( + f"{label}: tolerance must equal the checked-in Workload migration policy {_EXPECTED_TOLERANCE}" + ) + if payload["eval_mode"] != "full": + raise KernelWorkloadError(f"{label}: eval_mode must be 'full'") + + try: + uuid = UUID(payload.get("uuid", "")) + except (AttributeError, TypeError, ValueError) as exc: + raise KernelWorkloadError(f"{label}: uuid must be an RFC 4122 UUIDv4 string") from exc + if uuid.version != 4 or str(uuid) != payload["uuid"].lower(): + raise KernelWorkloadError(f"{label}: uuid must be a canonical RFC 4122 UUIDv4 string") + + +def _validate_finite_json(value: Any, label: str) -> None: + if isinstance(value, float) and not math.isfinite(value): + raise KernelWorkloadError(f"{label}: Workload JSON numbers must be finite") + if isinstance(value, list): + for item in value: + _validate_finite_json(item, label) + elif isinstance(value, dict): + for item in value.values(): + _validate_finite_json(item, label) + + +def _validate_scalar_dtype(value: Any, dtype: Any, label: str) -> None: + if dtype == "bool": + valid = type(value) is bool + elif isinstance(dtype, str) and dtype.startswith(("int", "uint")): + valid = type(value) is int + elif isinstance(dtype, str) and dtype.startswith(("float", "bfloat")): + valid = type(value) in {int, float} + else: + valid = isinstance(value, str) if dtype in {"str", "string"} else False + if not valid: + raise KernelWorkloadError(f"{label} value {value!r} is incompatible with scalar dtype {dtype!r}") + + +def _reference_none_defaults(reference: Any) -> set[str]: + if not isinstance(reference, str): + return set() + try: + tree = ast.parse(reference) + except SyntaxError: + return set() + run = next((node for node in tree.body if isinstance(node, ast.FunctionDef) and node.name == "run"), None) + if run is None: + return set() + positional = [*run.args.posonlyargs, *run.args.args] + positional_defaults = [None] * (len(positional) - len(run.args.defaults)) + list(run.args.defaults) + defaults = {argument.arg: default for argument, default in zip(positional, positional_defaults, strict=True)} + defaults.update(dict(zip((argument.arg for argument in run.args.kwonlyargs), run.args.kw_defaults, strict=True))) + return {name for name, default in defaults.items() if isinstance(default, ast.Constant) and default.value is None} + + +def _validate_resolvable_constraints( + record: WorkloadRecord, + definition: dict[str, Any], + definition_axes: dict[str, Any], +) -> None: + context = { + name: spec["value"] + for name, spec in definition_axes.items() + if isinstance(spec, dict) and spec.get("type") == "const" + } + context.update(record.workload.axes) + context.update( + { + name: descriptor.value + for name, descriptor in record.workload.inputs.items() + if isinstance(descriptor, ScalarInput | StringInput) + } + ) + context.update( + {name: None for name, descriptor in record.workload.inputs.items() if isinstance(descriptor, NullInput)} + ) + for constraint in definition.get("constraints", []): + if not isinstance(constraint, str): + continue + try: + expression = ast.parse(constraint, mode="eval") + except SyntaxError: + continue + names = {node.id for node in ast.walk(expression) if isinstance(node, ast.Name)} + if not names <= set(context): + continue + try: + holds = eval(compile(expression, "", "eval"), {"__builtins__": {}}, context) + except (NameError, TypeError, ValueError): + continue + if not holds: + raise KernelWorkloadError(f"{record.source_label}: Workload violates Definition constraint: {constraint}") + + +def _require_mapping(value: Any, label: str) -> dict[str, Any]: + if not isinstance(value, dict): + raise KernelWorkloadError(f"{label} must be an object") + return value + + +def _load_json_object(path: Path) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + raise KernelWorkloadError(f"{path}: unable to load Definition: {exc}") from exc + return _require_mapping(value, str(path)) + + +__all__ = [ + "KernelWorkloadError", + "WorkloadRecord", + "iter_workload_records", + "load_workload_jsonl", + "materialize_workload_inputs", + "resolve_torch_dtype", + "validate_workload_against_definition", + "validate_workload_catalog", +] diff --git a/src/tilegym/transformers/olmo3/kernel_definitions/olmo3_dual_rms_norm.json b/src/tilegym/transformers/olmo3/kernel_definitions/olmo3_dual_rms_norm.json index d3c3b3b4..2fc615b1 100644 --- a/src/tilegym/transformers/olmo3/kernel_definitions/olmo3_dual_rms_norm.json +++ b/src/tilegym/transformers/olmo3/kernel_definitions/olmo3_dual_rms_norm.json @@ -17,6 +17,7 @@ }, "k": { "dtype": "bfloat16", + "inplace_output": true, "shape": [ "N", "D" @@ -30,6 +31,7 @@ }, "q": { "dtype": "bfloat16", + "inplace_output": true, "shape": [ "N", "D" diff --git a/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_dual_rms_norm/workload.jsonl b/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_dual_rms_norm/workload.jsonl new file mode 100644 index 00000000..6c6e9ca9 --- /dev/null +++ b/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_dual_rms_norm/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"cf61f2fb-9ea8-4baf-b584-469043c078d4","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"k":{"type":"random"},"k_weight":{"type":"random"},"q":{"type":"random"},"q_weight":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_rms_norm_residual_add/workload.jsonl b/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_rms_norm_residual_add/workload.jsonl new file mode 100644 index 00000000..14588395 --- /dev/null +++ b/src/tilegym/transformers/olmo3/kernel_workloads/olmo3_rms_norm_residual_add/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"37d7b71d-9446-4a95-b545-d8e8b2570150","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"residual":{"type":"random"},"weight":{"type":"random"},"x":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/olmoe/kernel_definitions/olmoe_dual_rms_norm.json b/src/tilegym/transformers/olmoe/kernel_definitions/olmoe_dual_rms_norm.json index 1a877846..587d1000 100644 --- a/src/tilegym/transformers/olmoe/kernel_definitions/olmoe_dual_rms_norm.json +++ b/src/tilegym/transformers/olmoe/kernel_definitions/olmoe_dual_rms_norm.json @@ -17,6 +17,7 @@ }, "k": { "dtype": "bfloat16", + "inplace_output": true, "shape": [ "N", "D" @@ -30,6 +31,7 @@ }, "q": { "dtype": "bfloat16", + "inplace_output": true, "shape": [ "N", "D" diff --git a/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_dual_rms_norm/workload.jsonl b/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_dual_rms_norm/workload.jsonl new file mode 100644 index 00000000..0a6c821b --- /dev/null +++ b/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_dual_rms_norm/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"d2634e08-5d5a-4ddc-90d4-c4576e144df9","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"k":{"type":"random"},"k_weight":{"type":"random"},"q":{"type":"random"},"q_weight":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_residual_add_rms_norm/workload.jsonl b/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_residual_add_rms_norm/workload.jsonl new file mode 100644 index 00000000..8fee37e4 --- /dev/null +++ b/src/tilegym/transformers/olmoe/kernel_workloads/olmoe_residual_add_rms_norm/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"5373038a-ba4f-47c5-a855-fb06b215caae","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"residual":{"type":"random"},"weight":{"type":"random"},"x":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_definitions/qwen3_5_causal_conv1d_update_silu.json b/src/tilegym/transformers/qwen3_5/kernel_definitions/qwen3_5_causal_conv1d_update_silu.json index 2b5977d4..ff29eba6 100644 --- a/src/tilegym/transformers/qwen3_5/kernel_definitions/qwen3_5_causal_conv1d_update_silu.json +++ b/src/tilegym/transformers/qwen3_5/kernel_definitions/qwen3_5_causal_conv1d_update_silu.json @@ -34,7 +34,8 @@ "D", "K_state" ], - "dtype": "bfloat16" + "dtype": "bfloat16", + "inplace_output": true }, "hidden_states": { "shape": [ diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_prefill_silu/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_prefill_silu/workload.jsonl new file mode 100644 index 00000000..72fb70d8 --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_prefill_silu/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"a8c940c5-8d2e-4a3f-a63d-c2fe8ba79651","axes":{"D":64,"T":5,"T_padded":8},"inputs":{"seq_len":{"type":"scalar","value":5},"weight":{"type":"random"},"x":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_update_silu/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_update_silu/workload.jsonl new file mode 100644 index 00000000..0b7307d7 --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_causal_conv1d_update_silu/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"f408cd90-e29e-43e3-939b-f1ca533daf4c","axes":{"D":64},"inputs":{"conv_state":{"type":"random"},"hidden_states":{"type":"random"},"weight":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_gdr_preprocess/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_gdr_preprocess/workload.jsonl new file mode 100644 index 00000000..685d61d6 --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_gdr_preprocess/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"ea32ae5e-ddac-4916-96e2-eb3bc31ff6dd","axes":{"H":64,"N":3},"inputs":{"A_log":{"type":"random"},"a":{"type":"random"},"b":{"type":"random"},"dt_bias":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_residual_add_gemma_rms_norm/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_residual_add_gemma_rms_norm/workload.jsonl new file mode 100644 index 00000000..b928ae75 --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_residual_add_gemma_rms_norm/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"6cd35c15-9925-4340-ae53-9b3a7b47c8a7","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"offset":{"type":"scalar","value":1.0},"residual":{"type":"random"},"weight":{"type":"random"},"x":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_rms_norm_gated_silu/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_rms_norm_gated_silu/workload.jsonl new file mode 100644 index 00000000..7e11ed82 --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_rms_norm_gated_silu/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"d449ec22-da0b-4200-9f43-f911cfc73669","axes":{"D":64,"N":3},"inputs":{"eps":{"type":"scalar","value":1e-06},"gate":{"type":"random"},"hidden_states":{"type":"random"},"weight":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_sigmoid_mul/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_sigmoid_mul/workload.jsonl new file mode 100644 index 00000000..8af932ae --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_sigmoid_mul/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"5f1a21cf-36b0-428f-8729-ecfc71d7469b","axes":{"D":64,"N":3},"inputs":{"gate":{"type":"random"},"x":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_silu_and_mul_separate/workload.jsonl b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_silu_and_mul_separate/workload.jsonl new file mode 100644 index 00000000..1571435f --- /dev/null +++ b/src/tilegym/transformers/qwen3_5/kernel_workloads/qwen3_5_silu_and_mul_separate/workload.jsonl @@ -0,0 +1 @@ +{"uuid":"6374bbe2-6389-42df-9d8e-a18a1014cf45","axes":{"D":64,"N":3},"inputs":{"gate":{"type":"random"},"up":{"type":"random"}},"tolerance":{"allow_negative_inf":false,"max_atol":0.02,"max_error_cap":null,"max_rtol":0.02,"required_matched_ratio":1.0},"eval_mode":"full"} diff --git a/tests/kernel_inventory/conftest.py b/tests/kernel_inventory/conftest.py index 6fb30c90..29053135 100644 --- a/tests/kernel_inventory/conftest.py +++ b/tests/kernel_inventory/conftest.py @@ -11,15 +11,15 @@ def pytest_addoption(parser): action="store", default=None, help=( - "Limit kernel Definition/Solution runtime checks to one directory " - "under src/tilegym that contains kernel_definitions and kernel_solutions." + "Limit Workload/Definition/Solution runtime checks to one directory " + "under src/tilegym that contains kernel_workloads, kernel_definitions, and kernel_solutions." ), ) def pytest_generate_tests(metafunc): - if {"definition_path", "solution_path"}.issubset(metafunc.fixturenames): - from tests.kernel_inventory.kernel_runtime_utils import definition_solution_cases_for_submodule + if {"workload_record", "definition_path", "solution_path"}.issubset(metafunc.fixturenames): + from tests.kernel_inventory.kernel_runtime_utils import workload_runtime_cases_for_submodule - cases = definition_solution_cases_for_submodule(metafunc.config.getoption("--kernel-submodule")) - metafunc.parametrize(("definition_path", "solution_path"), cases) + cases = workload_runtime_cases_for_submodule(metafunc.config.getoption("--kernel-submodule")) + metafunc.parametrize(("workload_record", "definition_path", "solution_path"), cases) diff --git a/tests/kernel_inventory/kernel_runtime_utils.py b/tests/kernel_inventory/kernel_runtime_utils.py index 66eabb65..230c1417 100644 --- a/tests/kernel_inventory/kernel_runtime_utils.py +++ b/tests/kernel_inventory/kernel_runtime_utils.py @@ -7,7 +7,6 @@ import ast import importlib.util import inspect -import itertools import os import sys import types @@ -17,10 +16,6 @@ import pytest -from tests.kernel_inventory.runtime_inputs import RuntimeInputCatalog -from tests.kernel_inventory.runtime_inputs import make_runtime_override -from tests.kernel_inventory.runtime_inputs import resolve_torch_dtype - # Correctness coverage should select a stable config before importing cuda.tile. os.environ.setdefault("DISABLE_TUNE", "1") os.environ.setdefault("TILEGYM_DISABLE_AUTOTUNE", "1") @@ -35,12 +30,15 @@ tilegym_pkg.__path__ = [str(REPO_ROOT / "src/tilegym")] sys.modules["tilegym"] = tilegym_pkg try: - from tilegym.kernel_inventory import iter_kernel_definition_paths - from tilegym.kernel_inventory import iter_solution_paths_for_definition + from tilegym.kernel_inventory import WorkloadRecord from tilegym.kernel_inventory import load_json + from tilegym.kernel_inventory import load_workload_jsonl + from tilegym.kernel_inventory import materialize_workload_inputs + from tilegym.kernel_inventory import resolve_torch_dtype from tilegym.kernel_inventory import validate_definition from tilegym.kernel_inventory import validate_solution from tilegym.kernel_inventory import validate_solution_entry_point + from tilegym.kernel_inventory import validate_workload_against_definition from tilegym.kernel_inventory.composition import installed_reference_modules from tilegym.kernel_inventory.launch import RawKernelLaunch from tilegym.kernel_inventory.launch import allocate_definition_outputs @@ -48,7 +46,9 @@ from tilegym.kernel_inventory.launch import make_launch_context from tilegym.kernel_inventory.launch import materialize_launch_arguments from tilegym.kernel_inventory.launch import resolve_grid + from tilegym.kernel_inventory.layout import definition_solution_paths_for_workload from tilegym.kernel_inventory.layout import inventory_coordinate + from tilegym.kernel_inventory.layout import iter_inventory_workload_paths from tilegym.kernel_inventory.return_contract import CAPTURE_RETURN_NAME from tilegym.kernel_inventory.return_contract import instrument_reference_returns from tilegym.kernel_inventory.triton_backend import get_available_triton_backend @@ -56,14 +56,6 @@ if _original_tilegym is None: sys.modules.pop("tilegym", None) -DEFAULT_AXIS_VALUES = { - "N": 3, - "D": 64, - "H": 64, - "T": 5, -} -RUNTIME_INPUT_CATALOG = RuntimeInputCatalog.from_path() - class _CapturedReferenceReturn: """Reference value paired with the executed AST-derived output-name tree.""" @@ -73,42 +65,50 @@ def __init__(self, value: Any, contract: Any): self.contract = contract -def all_definition_solution_cases() -> list[Any]: - """Return runtime parameter pairs for every discoverable inventory Definition.""" - return definition_solution_cases_for_submodule(None) +def all_workload_runtime_cases() -> list[Any]: + """Return runtime triples for every discoverable inventory Workload row.""" + return workload_runtime_cases_for_submodule(None) -def definition_solution_cases_for_submodule(submodule: str | Path | None) -> list[Any]: - """Return runtime parameter pairs for one inventory submodule or the full catalog.""" +def workload_runtime_cases_for_submodule(submodule: str | Path | None) -> list[Any]: + """Return Workload/Definition/Solution triples for one inventory submodule.""" search_root = _kernel_submodule_root(submodule) cases = [] - definition_paths = ( - iter_kernel_definition_paths(REPO_ROOT) + workload_paths = ( + iter_inventory_workload_paths(REPO_ROOT) if search_root == REPO_ROOT - else sorted((search_root / "kernel_definitions").rglob("*.json")) + else sorted((search_root / "kernel_workloads").rglob("workload.jsonl")) ) - for definition_path in definition_paths: - solution_paths = list(iter_solution_paths_for_definition(definition_path)) - if not solution_paths: - raise ValueError(f"Definition has no checked-in Solutions: {definition_path}") - for solution_path in solution_paths: - solution = load_json(solution_path) - cases.append( - pytest.param(definition_path, solution_path, id=_runtime_case_id(definition_path, solution_path)) - ) + for workload_path in workload_paths: + targets = list(definition_solution_paths_for_workload(workload_path)) + if not targets: + raise ValueError(f"Workload Definition has no checked-in Solutions: {workload_path}") + for record in load_workload_jsonl(workload_path): + for definition_path, solution_path in targets: + cases.append( + pytest.param( + record, + definition_path, + solution_path, + id=_runtime_case_id(record, definition_path, solution_path), + ) + ) if search_root != REPO_ROOT and not cases: - raise ValueError(f"Kernel submodule has no Definition/Solution pairs: {search_root}") + raise ValueError(f"Kernel submodule has no Workload/Definition/Solution triples: {search_root}") return cases -def _runtime_case_id(definition_path: str | Path, solution_path: str | Path) -> str: - """Return an unambiguous Definition-coordinate plus Solution-backend ID.""" +def _runtime_case_id(record: WorkloadRecord, definition_path: str | Path, solution_path: str | Path) -> str: + """Return an unambiguous row, Definition-coordinate, and backend ID.""" definition_coordinate = inventory_coordinate(definition_path) solution_coordinate = inventory_coordinate(solution_path) backend = solution_coordinate.backend if backend is None: backend = load_json(solution_path)["spec"]["language"] - return f"{definition_coordinate.canonical_id}::{backend}" + return ( + f"{definition_coordinate.canonical_id}::line={record.line_number}::" + f"uuid={record.workload.uuid}::backend={backend}" + ) def _kernel_submodule_root(submodule: str | Path | None) -> Path: @@ -140,12 +140,12 @@ def _kernel_submodule_root(submodule: str | Path | None) -> Path: raise ValueError(f"Kernel submodule does not exist: {submodule}") missing_directories = [ directory - for directory in ("kernel_definitions", "kernel_solutions") + for directory in ("kernel_definitions", "kernel_solutions", "kernel_workloads") if not (submodule_root / directory).is_dir() ] if missing_directories: raise ValueError( - f"Kernel submodule must contain kernel_definitions and kernel_solutions: {submodule_root} " + f"Kernel submodule must contain kernel_definitions, kernel_solutions, and kernel_workloads: {submodule_root} " f"(missing {', '.join(missing_directories)})" ) return submodule_root @@ -181,19 +181,28 @@ def _isolated_solution_modules(): sys.modules.update(saved_modules) -def run_definition_solution_runtime(definition_path: Path, solution_path: Path) -> None: - """Validate one Solution against its Definition reference or error contract.""" +def run_definition_solution_workload_runtime( + workload_record: WorkloadRecord, + definition_path: Path, + solution_path: Path, +) -> None: + """Validate one Solution against one adjacent Definition/Workload row.""" with _isolated_solution_modules(): - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) -def _run_definition_solution_runtime(definition_path: Path, solution_path: Path) -> None: - """Run one Definition/Solution check with package imports isolated.""" +def _run_definition_solution_workload_runtime( + workload_record: WorkloadRecord, + definition_path: Path, + solution_path: Path, +) -> None: + """Run one Workload/Definition/Solution check with imports isolated.""" definition = load_json(definition_path) solution = load_json(solution_path) validate_definition(definition, definition_path) validate_solution(solution, repo_root=REPO_ROOT, definition=definition) validate_solution_entry_point(solution, repo_root=REPO_ROOT) + validate_workload_against_definition(workload_record, definition, definition_path) if solution.get("launch") is None: _assert_matching_entry_signatures_static(definition, solution, definition_path) @@ -212,25 +221,27 @@ def _run_definition_solution_runtime(definition_path: Path, solution_path: Path) device = torch.device("cuda") torch.manual_seed(2026) - axes = _axis_values(definition, definition_path) - base_inputs = _make_inputs(definition, torch, device, axes, definition_path) - mutated_inputs = RUNTIME_INPUT_CATALOG.case_for_definition(definition_path).mutated_inputs - unknown_mutations = sorted(set(mutated_inputs) - set(definition["inputs"])) - if unknown_mutations: - raise ValueError(f"{definition_path}: runtime input catalog mutates unknown inputs: {unknown_mutations}") - for boolean_assignment in _boolean_branch_assignments(definition, axes, base_inputs): - inputs = dict(base_inputs) - inputs.update(boolean_assignment) - _run_runtime_branch( - definition, - reference_fn, - solution, - solution_fn, - inputs, - axes, - torch, - mutated_inputs, - ) + axes, inputs = materialize_workload_inputs( + workload_record, + definition, + torch=torch, + device=device, + ) + mutated_inputs = tuple( + name for name, spec in definition["inputs"].items() if spec.get("inplace_output") is True + ) + _run_runtime_branch( + definition, + reference_fn, + solution, + solution_fn, + inputs, + axes, + torch, + mutated_inputs, + tolerance=workload_record.workload.tolerance, + case_label=workload_record.source_label, + ) def _require_solution_runtime_dependencies(solution: dict[str, Any]) -> None: @@ -251,13 +262,18 @@ def _run_runtime_branch( axes: dict[str, int], torch: Any, mutated_inputs: tuple[str, ...], + *, + tolerance: Any | None = None, + case_label: str | None = None, ) -> None: - """Compare reference and Solution for one concrete Boolean branch.""" + """Compare reference and Solution for one concrete Workload row.""" reference_inputs = {name: _clone_value(value) for name, value in inputs.items()} solution_inputs = {name: _clone_value(value) for name, value in inputs.items()} reference_before = {name: _clone_value(value) for name, value in reference_inputs.items()} solution_before = {name: _clone_value(value) for name, value in solution_inputs.items()} - branch = _boolean_branch_label(inputs) + branch = case_label or _boolean_branch_label(inputs) + rtol = float(tolerance.max_rtol) if tolerance is not None else 2e-2 + atol = float(tolerance.max_atol) if tolerance is not None else 2e-2 if "runtime:unsupported" in definition.get("tags", []): _assert_unsupported_solution_matches_reference( @@ -288,14 +304,16 @@ def _run_runtime_branch( axes, torch, f"{definition['name']} {branch}", + rtol=rtol, + atol=atol, ) for name in mutated_inputs: torch.testing.assert_close( solution_inputs[name], reference_inputs[name], - rtol=2e-2, - atol=2e-2, + rtol=rtol, + atol=atol, msg=lambda msg: f"{definition['name']} {branch} mutated input {name} mismatch\n{msg}", ) for name in sorted(set(definition["inputs"]) - set(mutated_inputs)): @@ -473,148 +491,12 @@ def _capture_runtime_error(call: Any, label: str) -> Exception: pytest.fail(f"{label} must raise because the Definition is tagged runtime:unsupported") -def _axis_values(definition: dict[str, Any], definition_path: Path | None = None) -> dict[str, int]: - """Build deterministic concrete axis values for a Definition runtime case.""" - values = {name: axis["value"] for name, axis in definition["axes"].items() if axis.get("type") == "const"} - for name, axis in definition["axes"].items(): - if axis.get("type") == "var": - defaults = DEFAULT_AXIS_VALUES - values[name] = defaults.get(name, 4) - if "T_padded" in values and "T" in values and "K" in values: - values["T_padded"] = values["T"] + values["K"] - 1 - if definition_path is not None: - overrides = RUNTIME_INPUT_CATALOG.case_for_definition(definition_path).axes - unknown = sorted(set(overrides) - set(definition["axes"])) - if unknown: - raise ValueError(f"{definition_path}: runtime input catalog overrides unknown axes: {unknown}") - values.update(overrides) - return values - - -def _make_inputs( - definition: dict[str, Any], - torch: Any, - device: Any, - axes: dict[str, int], - definition_path: Path | None = None, -) -> dict[str, Any]: - """Create seeded representative inputs that conform to a Definition.""" - inputs = {} - overrides = RUNTIME_INPUT_CATALOG.case_for_definition(definition_path).inputs if definition_path else {} - unknown = sorted(set(overrides) - set(definition["inputs"])) - if unknown: - raise ValueError(f"{definition_path}: runtime input catalog overrides unknown inputs: {unknown}") - for name, spec in definition["inputs"].items(): - config = overrides.get(name) - if config is None or config["kind"] == "default": - inputs[name] = _make_input(name, spec, axes, torch, device) - else: - inputs[name] = make_runtime_override(config, spec, axes, torch, device) - _satisfy_boolean_constraints(definition, inputs, axes) - return inputs - - -def _boolean_branch_assignments( - definition: dict[str, Any], - axes: dict[str, int] | None = None, - inputs: dict[str, Any] | None = None, -) -> list[dict[str, bool]]: - """Enumerate scalar Boolean assignments satisfying applicable constraints.""" - all_boolean_inputs = [ - name for name, spec in definition["inputs"].items() if spec["shape"] is None and spec["dtype"] == "bool" - ] - boolean_inputs = list(all_boolean_inputs) - if inputs is not None: - boolean_inputs = [name for name in boolean_inputs if name not in inputs or inputs[name] is not None] - constraints = [ - constraint - for constraint in definition.get("constraints", ()) - if set(all_boolean_inputs) & _constraint_names(constraint) - ] - assignments = [] - for values in itertools.product((False, True), repeat=len(boolean_inputs)): - assignment = dict(zip(boolean_inputs, values, strict=True)) - candidate = dict(inputs or {}) - candidate.update(assignment) - if _constraints_hold(constraints, candidate, axes or {}): - assignments.append(assignment) - if not assignments: - pytest.fail(f"{definition['name']}: no Boolean input assignment satisfies Definition.constraints") - return assignments - - def _boolean_branch_label(inputs: dict[str, Any]) -> str: """Describe the concrete scalar Boolean branch in assertion messages.""" values = [f"{name}={value}" for name, value in inputs.items() if isinstance(value, bool)] return f"[{', '.join(values)}]" if values else "[no Boolean inputs]" -def _satisfy_boolean_constraints(definition: dict[str, Any], inputs: dict[str, Any], axes: dict[str, int]) -> None: - """Choose scalar Boolean inputs that satisfy evaluable Definition constraints.""" - inputs.update(_boolean_branch_assignments(definition, axes, inputs)[0]) - - -def _constraint_names(constraint: str) -> set[str]: - """Return variable names in one valid Python constraint expression.""" - try: - tree = ast.parse(constraint, mode="eval") - except SyntaxError: - return set() - return {node.id for node in ast.walk(tree) if isinstance(node, ast.Name)} - - -def _constraints_hold(constraints: Any, inputs: dict[str, Any], axes: dict[str, int]) -> bool: - """Evaluate constraints that depend only on axes and Python scalar inputs.""" - context = dict(axes) - context.update( - {name: value for name, value in inputs.items() if value is None or isinstance(value, bool | float | int)} - ) - for constraint in constraints: - try: - result = eval(compile(constraint, "", "eval"), {"__builtins__": {}}, context) - except (NameError, SyntaxError): - continue - if not result: - return False - return True - - -def _make_input(name: str, spec: dict[str, Any], axes: dict[str, int], torch: Any, device: Any) -> Any: - """Create one scalar or tensor input from its schema specification.""" - dtype = spec["dtype"] - shape_spec = spec["shape"] - if shape_spec is None: - if name == "scale": - return axes.get("K", axes.get("D", 64)) ** -0.5 - if dtype == "bool": - return False - if name == "eps": - return 1e-6 - if name == "offset": - return 1.0 - if name == "seq_len": - return axes["T"] - if dtype.startswith(("int", "uint")): - return 1 - return 1.0 - - shape = tuple(axes[axis] for axis in shape_spec) - torch_dtype = resolve_torch_dtype(dtype, torch) - if dtype == "bool": - return torch.randint(0, 2, shape, device=device, dtype=torch.bool) - if dtype.startswith("uint"): - return torch.randint(0, 4, shape, device=device, dtype=torch.int64).to(torch_dtype) - if dtype.startswith("int"): - return torch.randint(-3, 4, shape, device=device, dtype=torch_dtype) - - base = 0.1 * torch.randn(shape, device=device, dtype=torch.float32) - if "weight" in name: - base = 1.0 + base - if name == "A_log": - base = -base.abs() - return base.to(torch_dtype).contiguous() - - def _assert_output_matches_spec(value: Any, spec: dict[str, Any], axes: dict[str, int], torch: Any, label: str) -> None: """Check one concrete tensor output against its Definition TensorSpec.""" expected_shape = () if spec["shape"] is None else tuple(axes[axis] for axis in spec["shape"]) @@ -633,6 +515,9 @@ def _assert_return_contract( axes: dict[str, int], torch: Any, label: str, + *, + rtol: float = 2e-2, + atol: float = 2e-2, ) -> None: """Recursively compare values using the executed reference return-name tree.""" if contract is None: @@ -656,6 +541,8 @@ def _assert_return_contract( axes, torch, f"{label}[{index}]", + rtol=rtol, + atol=atol, ) return @@ -669,8 +556,8 @@ def _assert_return_contract( torch.testing.assert_close( actual, expected, - rtol=2e-2, - atol=2e-2, + rtol=rtol, + atol=atol, msg=lambda msg: f"{label} output {contract} mismatch\n{msg}", ) @@ -773,10 +660,14 @@ def _ensure_solution_parent_packages(source_path: Path, module_name: str) -> Non def _assert_matching_entry_signatures( definition: dict[str, Any], reference_fn: Any, solution: dict[str, Any], solution_fn: Any ) -> None: - """Require Definition reference and Solution entry points to expose one call contract.""" + """Require the Solution to implement the Definition input call contract.""" reference_signature = _call_signature(reference_fn) solution_signature = _call_signature(solution_fn) - assert reference_signature == solution_signature, ( + assert _solution_accepts_reference_signature( + reference_signature, + solution_signature, + tuple(definition["inputs"]), + ), ( f"{definition['name']}: Definition.reference run signature {reference_signature} does not match " f"Solution entry point {solution['spec']['entry_point']} signature {solution_signature}" ) @@ -805,12 +696,95 @@ def _assert_matching_entry_signatures_static( ) reference_signature = _ast_call_signature(reference_run.args) solution_signature = _ast_call_signature(solution_run.args) - assert reference_signature == solution_signature, ( + assert _ast_solution_accepts_reference_signature( + reference_signature, + solution_signature, + tuple(definition["inputs"]), + ), ( f"{definition['name']}: Definition.reference run signature {reference_signature} does not match " f"Solution entry point {solution['spec']['entry_point']} signature {solution_signature}" ) +def _ast_solution_accepts_reference_signature( + reference_signature: tuple[tuple[str, str, str | None], ...], + solution_signature: tuple[tuple[str, str, str | None], ...], + input_names: tuple[str, ...], +) -> bool: + # Preserve the historical exact-callable path, including deprecated + # runtime:unsupported stubs that intentionally expose only ``**kwargs``. + if reference_signature == solution_signature: + return True + reference_named = { + name: (kind, default) for name, kind, default in reference_signature if not kind.startswith("var_") + } + if tuple(reference_named) != input_names: + # Legacy inventory Definitions are not campaign targets and may preserve a + # historical inputs mapping order that differs from an otherwise exact + # reference/Solution callable contract. + return reference_signature == solution_signature + if any(kind.startswith("var_") for _, kind, _ in reference_signature): + return False + solution_named = { + name: (kind, default) for name, kind, default in solution_signature if not kind.startswith("var_") + } + accepts_kwargs = any(kind == "var_keyword" for _, kind, _ in solution_signature) + required_positionals = tuple( + name + for name, kind, default in reference_signature + if kind in {"positional_only", "positional_or_keyword"} and default is None + ) + explicit_required_positionals = tuple( + name + for name, kind, default in solution_signature + if name in required_positionals and kind in {"positional_only", "positional_or_keyword"} and default is None + ) + if explicit_required_positionals != tuple(name for name in required_positionals if name in solution_named): + return False + for name in input_names: + if name in solution_named: + if solution_named[name] != reference_named[name]: + return False + elif not accepts_kwargs or reference_named[name][0] == "positional_only": + return False + return all( + name in input_names or default is not None + for name, kind, default in solution_signature + if kind not in {"var_positional", "var_keyword"} + ) + + +def _solution_accepts_reference_signature( + reference_signature: inspect.Signature, + solution_signature: inspect.Signature, + input_names: tuple[str, ...], +) -> bool: + reference_parameters = reference_signature.parameters + if tuple(reference_parameters) != input_names: + # Campaign public/leaf Definitions take the exact-order branch; legacy + # Legacy inventory pairs retain strict callable parity. + return reference_signature == solution_signature + solution_parameters = solution_signature.parameters + accepts_kwargs = any(parameter.kind is inspect.Parameter.VAR_KEYWORD for parameter in solution_parameters.values()) + for name, reference_parameter in reference_parameters.items(): + solution_parameter = solution_parameters.get(name) + if solution_parameter is None: + if not accepts_kwargs or reference_parameter.kind is inspect.Parameter.POSITIONAL_ONLY: + return False + continue + if ( + solution_parameter.kind != reference_parameter.kind + or solution_parameter.default != reference_parameter.default + ): + return False + return all( + name in input_names + or parameter.kind in {inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD} + or parameter.default is not inspect.Parameter.empty + for name, parameter in solution_parameters.items() + ) + + def _ast_call_signature(arguments: ast.arguments) -> tuple[tuple[str, str, str | None], ...]: """Represent a Python AST signature while ignoring annotations.""" positional = [*arguments.posonlyargs, *arguments.args] diff --git a/tests/kernel_inventory/runtime_inputs.py b/tests/kernel_inventory/runtime_inputs.py deleted file mode 100644 index eb7cfaf2..00000000 --- a/tests/kernel_inventory/runtime_inputs.py +++ /dev/null @@ -1,441 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# SPDX-License-Identifier: MIT - -"""Declarative runtime-input patterns for kernel inventory correctness tests.""" - -from __future__ import annotations - -import ast -import json -import math -import sys -import types -from dataclasses import dataclass -from pathlib import Path -from typing import Any -from typing import Iterable - -import yaml - -REPO_ROOT = Path(__file__).resolve().parents[2] -_original_tilegym = sys.modules.get("tilegym") -if _original_tilegym is None: - tilegym_pkg = types.ModuleType("tilegym") - tilegym_pkg.__path__ = [str(REPO_ROOT / "src/tilegym")] - sys.modules["tilegym"] = tilegym_pkg -try: - from tilegym.kernel_inventory.layout import inventory_coordinate - from tilegym.kernel_inventory.layout import iter_inventory_json_paths -finally: - if _original_tilegym is None: - sys.modules.pop("tilegym", None) - - -RUNTIME_INPUTS_PATH = Path(__file__).with_name("runtime_inputs.yaml") -_GENERATOR_KINDS = {"arange", "default", "full", "none", "normal", "ones", "randint", "values", "zeros"} -_GENERATOR_FIELDS = { - "arange": {"kind", "start", "step"}, - "default": {"kind"}, - "full": {"kind", "value"}, - "none": {"kind"}, - "normal": {"kind", "mean", "std"}, - "ones": {"kind"}, - "randint": {"kind", "low", "high"}, - "values": {"kind", "value"}, - "zeros": {"kind"}, -} -_GENERATOR_REQUIRED_FIELDS = { - "full": {"value"}, - "randint": {"high"}, - "values": {"value"}, -} -_INTEGER_DTYPE_BOUNDS = { - "int8": (-(2**7), 2**7 - 1), - "int16": (-(2**15), 2**15 - 1), - "int32": (-(2**31), 2**31 - 1), - "int64": (-(2**63), 2**63 - 1), - "uint8": (0, 2**8 - 1), - "uint16": (0, 2**16 - 1), - "uint32": (0, 2**32 - 1), - "uint64": (0, 2**64 - 1), -} - - -@dataclass(frozen=True) -class RuntimeInputCase: - """Merged axis and input overrides for one canonical Definition.""" - - axes: dict[str, int] - inputs: dict[str, dict[str, Any]] - mutated_inputs: tuple[str, ...] - - -class RuntimeInputCatalog: - """Validated reusable input patterns loaded from YAML.""" - - def __init__(self, data: dict[str, Any]): - non_string_fields = [key for key in data if not isinstance(key, str)] - if non_string_fields: - raise ValueError(f"runtime input catalog field names must be strings: {non_string_fields!r}") - unknown = sorted(key for key in data if key not in {"version", "patterns", "cases"}) - if unknown: - raise ValueError(f"runtime input catalog has unsupported fields: {unknown}") - version = data.get("version") - if type(version) is not int or version != 1: - raise ValueError("runtime input catalog version must be 1") - self.patterns = _mapping(data.get("patterns", {}), "patterns") - self.cases = _mapping(data.get("cases", {}), "cases") - for name, pattern in self.patterns.items(): - if not isinstance(name, str) or not name: - raise ValueError(f"runtime input pattern names must be nonempty strings: {name!r}") - self.patterns[name] = _validate_case(pattern, f"patterns.{name}", allow_patterns=False) - for canonical_id, case in self.cases.items(): - if not isinstance(canonical_id, str) or "::definition::" not in canonical_id: - raise ValueError(f"Invalid canonical Definition id in runtime input catalog: {canonical_id!r}") - self.cases[canonical_id] = _validate_case(case, f"cases.{canonical_id}", allow_patterns=True) - missing_patterns = sorted(set(self.cases[canonical_id]["patterns"]) - set(self.patterns)) - if missing_patterns: - raise ValueError(f"cases.{canonical_id} references missing patterns: {missing_patterns}") - - @classmethod - def from_path( - cls, - path: str | Path = RUNTIME_INPUTS_PATH, - *, - definition_paths: Iterable[str | Path] | None = None, - ) -> "RuntimeInputCatalog": - data = yaml.load(Path(path).read_text(encoding="utf-8"), Loader=_UniqueKeyLoader) - catalog = cls(_mapping(data, "runtime input catalog")) - if definition_paths is None and Path(path).resolve() == RUNTIME_INPUTS_PATH.resolve(): - definition_paths = iter_inventory_json_paths(REPO_ROOT, "kernel_definitions") - if definition_paths is not None: - catalog.validate_definition_ids(definition_paths) - return catalog - - def validate_definition_ids(self, definition_paths: Iterable[str | Path]) -> None: - """Reject stale or schema-incompatible cases before runtime dependency gates.""" - discovered: dict[str, tuple[Path, dict[str, Any]]] = {} - for raw_path in definition_paths: - path = Path(raw_path) - canonical_id = inventory_coordinate(path).canonical_id - if canonical_id in discovered: - raise ValueError(f"duplicate canonical Definition path in runtime input validation: {canonical_id}") - definition = json.loads(path.read_text(encoding="utf-8")) - discovered[canonical_id] = (path, _mapping(definition, str(path))) - stale = sorted(set(self.cases) - set(discovered)) - if stale: - raise ValueError(f"runtime input catalog contains stale canonical Definition ids: {stale}") - for canonical_id in self.cases: - path, definition = discovered[canonical_id] - _validate_merged_case(self.case_for_definition(path), definition, canonical_id) - - def case_for_definition(self, definition_path: str | Path) -> RuntimeInputCase: - canonical_id = inventory_coordinate(definition_path).canonical_id - case = self.cases.get(canonical_id, {"patterns": [], "axes": {}, "inputs": {}, "mutates": []}) - axes: dict[str, int] = {} - inputs: dict[str, dict[str, Any]] = {} - mutated_inputs: list[str] = [] - for pattern_name in case["patterns"]: - pattern = self.patterns[pattern_name] - axes.update(pattern["axes"]) - inputs.update(pattern["inputs"]) - mutated_inputs.extend(pattern["mutates"]) - axes.update(case["axes"]) - inputs.update(case["inputs"]) - mutated_inputs.extend(case["mutates"]) - return RuntimeInputCase(axes=axes, inputs=inputs, mutated_inputs=tuple(dict.fromkeys(mutated_inputs))) - - -def make_runtime_override( - config: dict[str, Any], - spec: dict[str, Any], - axes: dict[str, int], - torch: Any, - device: Any, -) -> Any: - """Materialize one override while deriving shape and dtype from Definition.""" - kind = config["kind"] - if kind in {"default", "none"}: - return None - if spec["shape"] is None: - if kind != "full": - raise ValueError(f"Scalar runtime inputs require kind=full, got {kind}") - return config.get("value") - - shape = tuple(axes[axis] for axis in spec["shape"]) - dtype = resolve_torch_dtype(spec["dtype"], torch) - if kind == "zeros": - return torch.zeros(shape, dtype=dtype, device=device) - if kind == "ones": - return torch.ones(shape, dtype=dtype, device=device) - if kind == "full": - return torch.full(shape, config["value"], dtype=dtype, device=device) - if kind == "normal": - mean = float(config.get("mean", 0.0)) - std = float(config.get("std", 1.0)) - return (mean + std * torch.randn(shape, dtype=torch.float32, device=device)).to(dtype) - if kind == "randint": - generated_dtype = torch.int64 if spec["dtype"].startswith("uint") else dtype - return torch.randint( - int(config.get("low", 0)), int(config["high"]), shape, dtype=generated_dtype, device=device - ).to(dtype) - if kind == "arange": - start = config.get("start", 0) - step = config.get("step", 1) - count = 1 - for size in shape: - count *= size - generated_dtype = torch.int64 if spec["dtype"].startswith("uint") else dtype - return (start + step * torch.arange(count, dtype=generated_dtype, device=device)).to(dtype).reshape(shape) - if kind == "values": - value = torch.tensor(config["value"], dtype=dtype, device=device) - if tuple(value.shape) != shape: - raise ValueError(f"Literal runtime input has shape {tuple(value.shape)}, expected {shape}") - return value - raise ValueError(f"Unsupported runtime input generator: {kind}") - - -def _validate_case(value: Any, label: str, *, allow_patterns: bool) -> dict[str, Any]: - value = _mapping(value, label) - allowed = {"axes", "inputs", "mutates"} | ({"patterns"} if allow_patterns else set()) - unknown = sorted(set(value) - allowed) - if unknown: - raise ValueError(f"{label} has unsupported fields: {unknown}") - patterns = value.get("patterns", []) - if not isinstance(patterns, list) or not all(isinstance(name, str) and name for name in patterns): - raise ValueError(f"{label}.patterns must be a list of names") - axes = _mapping(value.get("axes", {}), f"{label}.axes") - if not all(isinstance(name, str) and type(size) is int and size > 0 for name, size in axes.items()): - raise ValueError(f"{label}.axes values must be positive integers") - inputs = _mapping(value.get("inputs", {}), f"{label}.inputs") - mutates = value.get("mutates", []) - if ( - not isinstance(mutates, list) - or not all(isinstance(name, str) and name for name in mutates) - or len(set(mutates)) != len(mutates) - ): - raise ValueError(f"{label}.mutates must be a list of unique input names") - normalized_inputs = {} - for name, config in inputs.items(): - if not isinstance(name, str) or not name: - raise ValueError(f"{label}.inputs keys must be nonempty input names") - config = _mapping(config, f"{label}.inputs.{name}") - kind = config.get("kind") - if not isinstance(kind, str) or kind not in _GENERATOR_KINDS: - raise ValueError(f"{label}.inputs.{name}.kind must be one of {sorted(_GENERATOR_KINDS)}") - non_string_fields = [field for field in config if not isinstance(field, str)] - if non_string_fields: - raise ValueError(f"{label}.inputs.{name} generator field names must be strings: {non_string_fields!r}") - unknown_generator_fields = sorted(set(config) - _GENERATOR_FIELDS[kind]) - if unknown_generator_fields: - raise ValueError( - f"{label}.inputs.{name} {kind} generator has unsupported fields: {unknown_generator_fields}" - ) - missing_generator_fields = sorted(_GENERATOR_REQUIRED_FIELDS.get(kind, set()) - set(config)) - if missing_generator_fields: - raise ValueError(f"{label}.inputs.{name} {kind} generator requires fields: {missing_generator_fields}") - _validate_generator_parameters(config, f"{label}.inputs.{name}") - normalized_inputs[name] = config - return {"patterns": patterns, "axes": axes, "inputs": normalized_inputs, "mutates": mutates} - - -def _validate_merged_case(case: RuntimeInputCase, definition: dict[str, Any], canonical_id: str) -> None: - axes = _mapping(definition.get("axes", {}), f"{canonical_id}.axes") - inputs = _mapping(definition.get("inputs", {}), f"{canonical_id}.inputs") - unknown_axes = sorted(set(case.axes) - set(axes)) - if unknown_axes: - raise ValueError(f"{canonical_id} runtime case overrides unknown axes: {unknown_axes}") - conflicting_constants = sorted( - name - for name, value in case.axes.items() - if axes[name].get("type") == "const" and axes[name].get("value") != value - ) - if conflicting_constants: - raise ValueError(f"{canonical_id} runtime case contradicts const axes: {conflicting_constants}") - unknown_inputs = sorted(set(case.inputs) - set(inputs)) - if unknown_inputs: - raise ValueError(f"{canonical_id} runtime case overrides unknown inputs: {unknown_inputs}") - unknown_mutations = sorted(set(case.mutated_inputs) - set(inputs)) - if unknown_mutations: - raise ValueError(f"{canonical_id} runtime case mutates unknown inputs: {unknown_mutations}") - none_overrides = {name for name, config in case.inputs.items() if config["kind"] == "none"} - invalid_none_overrides = ( - sorted(none_overrides - _reference_none_defaults(definition, canonical_id)) if none_overrides else [] - ) - if invalid_none_overrides: - raise ValueError( - f"{canonical_id} none overrides require an explicit reference.run default of None: {invalid_none_overrides}" - ) - incompatible_scalars = sorted( - name - for name, config in case.inputs.items() - if inputs[name].get("shape") is None and config["kind"] not in {"default", "full", "none"} - ) - if incompatible_scalars: - raise ValueError( - f"{canonical_id} scalar runtime inputs require default/full generators: {incompatible_scalars}" - ) - for name, config in case.inputs.items(): - _validate_generator_for_spec(config, inputs[name], f"{canonical_id}.inputs.{name}") - - -def _validate_generator_parameters(config: dict[str, Any], label: str) -> None: - kind = config["kind"] - if kind == "normal": - mean = config.get("mean", 0.0) - std = config.get("std", 1.0) - if not _is_finite_number(mean) or not _is_finite_number(std) or std < 0: - raise ValueError(f"{label} normal mean/std must be finite numbers with nonnegative std") - elif kind == "arange": - start = config.get("start", 0) - step = config.get("step", 1) - if not _is_finite_number(start) or not _is_finite_number(step) or step == 0: - raise ValueError(f"{label} arange start/step must be finite numbers with nonzero step") - elif kind == "randint": - low = config.get("low", 0) - high = config["high"] - if not _is_integer(low) or not _is_integer(high) or high <= low: - raise ValueError(f"{label} randint low/high must be integers with high greater than low") - - -def _validate_generator_for_spec(config: dict[str, Any], spec: dict[str, Any], label: str) -> None: - kind = config["kind"] - dtype = spec.get("dtype") - is_scalar = spec.get("shape") is None - if kind == "normal" and not _is_floating_dtype(dtype): - raise ValueError(f"{label} normal generator requires a floating-point Definition dtype") - if kind == "randint" and dtype not in _INTEGER_DTYPE_BOUNDS: - raise ValueError(f"{label} randint generator requires an integer Definition dtype") - if kind == "randint" and dtype in _INTEGER_DTYPE_BOUNDS: - low, high = config.get("low", 0), config["high"] - dtype_low, dtype_high = _INTEGER_DTYPE_BOUNDS[dtype] - if low < dtype_low or high - 1 > dtype_high: - raise ValueError(f"{label} randint bounds exceed Definition dtype {dtype}") - if kind == "arange" and dtype == "bool": - raise ValueError(f"{label} arange generator does not support Boolean Definition dtype") - if kind == "arange" and dtype in _INTEGER_DTYPE_BOUNDS: - if not _is_integer(config.get("start", 0)) or not _is_integer(config.get("step", 1)): - raise ValueError(f"{label} integer arange parameters must be integers") - if kind == "full" and not _dtype_value_is_valid(config["value"], dtype): - level = "scalar " if is_scalar else "" - raise ValueError(f"{label} full value is incompatible with {level}Definition dtype {dtype}") - if kind == "values": - value = config["value"] - if not isinstance(value, list) or not _nested_values_match_dtype(value, dtype): - raise ValueError(f"{label} literal values are incompatible with Definition dtype {dtype}") - - -def _reference_none_defaults(definition: dict[str, Any], label: str) -> set[str]: - try: - tree = ast.parse(definition.get("reference", ""), filename=label) - except (SyntaxError, TypeError) as exc: - raise ValueError(f"{label} Definition.reference must be valid Python") from exc - runs = [ - node for node in tree.body if isinstance(node, ast.FunctionDef | ast.AsyncFunctionDef) and node.name == "run" - ] - if len(runs) != 1: - raise ValueError(f"{label} Definition.reference must define exactly one global run function") - run = runs[0] - defaults: dict[str, ast.expr] = {} - positional = [*run.args.posonlyargs, *run.args.args] - if run.args.defaults: - defaults.update( - (parameter.arg, value) - for parameter, value in zip(positional[-len(run.args.defaults) :], run.args.defaults, strict=True) - ) - defaults.update( - (parameter.arg, value) - for parameter, value in zip(run.args.kwonlyargs, run.args.kw_defaults, strict=True) - if value is not None - ) - return {name for name, value in defaults.items() if isinstance(value, ast.Constant) and value.value is None} - - -def _nested_values_match_dtype(value: list[Any], dtype: Any) -> bool: - return all( - _nested_values_match_dtype(item, dtype) if isinstance(item, list) else _dtype_value_is_valid(item, dtype) - for item in value - ) - - -def _dtype_value_is_valid(value: Any, dtype: Any) -> bool: - if dtype == "bool": - return type(value) is bool - if dtype in _INTEGER_DTYPE_BOUNDS: - if not _is_integer(value): - return False - low, high = _INTEGER_DTYPE_BOUNDS[dtype] - return low <= value <= high - if _is_floating_dtype(dtype): - return _is_finite_number(value) - return False - - -def _is_integer(value: Any) -> bool: - return isinstance(value, int) and not isinstance(value, bool) - - -def _is_number(value: Any) -> bool: - return isinstance(value, (int, float)) and not isinstance(value, bool) - - -def _is_finite_number(value: Any) -> bool: - return _is_number(value) and math.isfinite(value) - - -def _is_floating_dtype(dtype: Any) -> bool: - return isinstance(dtype, str) and (dtype.startswith("float") or dtype == "bfloat16") - - -def _mapping(value: Any, label: str) -> dict[str, Any]: - if not isinstance(value, dict): - raise ValueError(f"{label} must be a mapping") - return dict(value) - - -class _UniqueKeyLoader(yaml.SafeLoader): - """Safe YAML loader that refuses silent mapping-key replacement.""" - - -def _construct_unique_mapping(loader: _UniqueKeyLoader, node: yaml.MappingNode, deep: bool = False) -> dict[Any, Any]: - mapping: dict[Any, Any] = {} - for key_node, value_node in node.value: - key = loader.construct_object(key_node, deep=deep) - if key in mapping: - raise ValueError(f"runtime input catalog contains duplicate YAML key: {key!r}") - mapping[key] = loader.construct_object(value_node, deep=deep) - return mapping - - -_UniqueKeyLoader.add_constructor( - yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, - _construct_unique_mapping, -) - - -def resolve_torch_dtype(dtype: str, torch: Any) -> Any: - """Resolve an inventory dtype string to its torch dtype.""" - supported = { - "float64", - "float32", - "float16", - "bfloat16", - "float8_e4m3fn", - "float8_e5m2", - "int64", - "int32", - "int16", - "int8", - "uint64", - "uint32", - "uint16", - "uint8", - "bool", - } - if dtype not in supported: - raise ValueError(f"Unsupported inventory dtype: {dtype}") - try: - return getattr(torch, dtype) - except AttributeError as exc: - raise ValueError(f"PyTorch does not provide inventory dtype: {dtype}") from exc diff --git a/tests/kernel_inventory/runtime_inputs.yaml b/tests/kernel_inventory/runtime_inputs.yaml deleted file mode 100644 index 5f6b6649..00000000 --- a/tests/kernel_inventory/runtime_inputs.yaml +++ /dev/null @@ -1,16 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# SPDX-License-Identifier: MIT - -version: 1 - -# Runtime data is test-only. Tensor shapes and dtypes remain owned by the -# corresponding Definition so this catalog can later migrate to FIB Workload. - -cases: - src/tilegym/transformers/olmo3::definition::olmo3_dual_rms_norm: - mutates: [q, k] - src/tilegym/transformers/olmoe::definition::olmoe_dual_rms_norm: - mutates: [q, k] - src/tilegym/transformers/qwen3_5::definition::qwen3_5_causal_conv1d_update_silu: - mutates: [conv_state] diff --git a/tests/kernel_inventory/test_hierarchical_layout.py b/tests/kernel_inventory/test_hierarchical_layout.py index aeb2bb80..c025e7f4 100644 --- a/tests/kernel_inventory/test_hierarchical_layout.py +++ b/tests/kernel_inventory/test_hierarchical_layout.py @@ -16,8 +16,12 @@ tilegym_pkg.__path__ = [str(REPO_ROOT / "src/tilegym")] sys.modules["tilegym"] = tilegym_pkg try: + from tilegym.kernel_inventory.layout import definition_solution_paths_for_workload from tilegym.kernel_inventory.layout import inventory_coordinate from tilegym.kernel_inventory.layout import iter_inventory_json_paths + from tilegym.kernel_inventory.layout import iter_inventory_workload_paths + from tilegym.kernel_inventory.layout import mirrored_definition_path + from tilegym.kernel_inventory.layout import mirrored_workload_path from tilegym.kernel_inventory.layout import solution_paths_for_definition from tilegym.kernel_inventory.layout import validate_hierarchical_operation_topology finally: @@ -48,7 +52,7 @@ def test_hierarchical_coordinates_are_path_scoped(tmp_path): assert len({public_coordinate.canonical_id, wrapper_coordinate.canonical_id, leaf_coordinate.canonical_id}) == 3 -def test_wrapper_acceptance_matrix_and_leaf_pairing(tmp_path): +def test_public_wrapper_and_leaf_pairing_policy(tmp_path): inventory = tmp_path / "src/tilegym/suites/example" public = _touch(inventory / "kernel_definitions/op/op.json") cutile_wrapper = _touch(inventory / "kernel_definitions/op/cutile/op.json") @@ -60,11 +64,58 @@ def test_wrapper_acceptance_matrix_and_leaf_pairing(tmp_path): expected_wrappers = [triton_solution, cutile_solution] assert list(solution_paths_for_definition(public)) == expected_wrappers - assert list(solution_paths_for_definition(cutile_wrapper)) == expected_wrappers - assert list(solution_paths_for_definition(triton_wrapper)) == expected_wrappers + assert list(solution_paths_for_definition(cutile_wrapper)) == [cutile_solution] + assert list(solution_paths_for_definition(triton_wrapper)) == [triton_solution] assert list(solution_paths_for_definition(cutile_leaf)) == [leaf_solution] +def test_workload_coordinates_mirror_definitions_and_resolve_pairs(tmp_path): + inventory = tmp_path / "src/tilegym/suites/example" + public = _touch(inventory / "kernel_definitions/op/op.json") + _touch(inventory / "kernel_definitions/op/triton/op.json") + _touch(inventory / "kernel_definitions/op/cutile/op.json") + triton_solution = _touch(inventory / "kernel_solutions/op/triton/op.json") + cutile_solution = _touch(inventory / "kernel_solutions/op/cutile/op.json") + workload = _touch(inventory / "kernel_workloads/op/op/workload.jsonl") + + coordinate = inventory_coordinate(workload) + + assert coordinate.kind == "workload" + assert coordinate.level == "public" + assert coordinate.canonical_id.endswith("::workload::op/op") + assert mirrored_workload_path(public) == workload + assert mirrored_definition_path(workload) == public + assert list(definition_solution_paths_for_workload(workload)) == [ + (public, triton_solution), + (public, cutile_solution), + ] + + +def test_workload_pairing_rejects_missing_mirrored_definition(tmp_path): + inventory = tmp_path / "src/tilegym/suites/example" + (inventory / "kernel_definitions").mkdir(parents=True) + (inventory / "kernel_solutions").mkdir() + workload = _touch(inventory / "kernel_workloads/op/op/workload.jsonl") + + with pytest.raises(ValueError, match="Missing mirrored Definition"): + list(definition_solution_paths_for_workload(workload)) + + +@pytest.mark.parametrize( + "path", + [ + "kernel_definitions/op/op.jsonl", + "kernel_solutions/op/cutile/op.jsonl", + "kernel_workloads/op/op/workload.json", + ], +) +def test_inventory_coordinate_rejects_kind_suffix_mismatch(tmp_path, path): + inventory_path = _touch(tmp_path / "src/tilegym/suites/example" / path) + + with pytest.raises(ValueError, match="Expected"): + inventory_coordinate(inventory_path) + + def test_leaf_semantic_variants_can_share_one_raw_entry_point_without_collapsing(tmp_path): inventory = tmp_path / "src/tilegym/suites/example" _touch(inventory / "kernel_definitions/op/op.json") @@ -115,6 +166,31 @@ def test_recursive_discovery_ignores_archived_directories(tmp_path): assert list(iter_inventory_json_paths(tmp_path, "kernel_definitions")) == [definition] +def test_recursive_workload_discovery_requires_active_plural_inventory(tmp_path): + active = tmp_path / "src/tilegym/suites/active" + _touch(active / "kernel_definitions/op/op.json") + _touch(active / "kernel_solutions/op/cutile/op.json") + workload = _touch(active / "kernel_workloads/op/op/workload.jsonl") + _touch(active / "_kernel_workloads/op/archived.jsonl") + _touch(active / "workload/op/existing-dataset.jsonl") + + inactive = tmp_path / "src/tilegym/suites/inactive" + _touch(inactive / "kernel_definitions/op/op.json") + _touch(inactive / "kernel_workloads/op/op/workload.jsonl") + + assert list(iter_inventory_workload_paths(tmp_path)) == [workload] + + +def test_recursive_workload_discovery_rejects_noncanonical_jsonl(tmp_path): + inventory = tmp_path / "src/tilegym/suites/example" + _touch(inventory / "kernel_definitions/op/op.json") + _touch(inventory / "kernel_solutions/op/cutile/op.json") + _touch(inventory / "kernel_workloads/op/op.jsonl") + + with pytest.raises(ValueError, match="Expected workload.jsonl"): + list(iter_inventory_workload_paths(tmp_path)) + + def test_legacy_cutile_rs_solution_coordinate_and_pairing(tmp_path): inventory = tmp_path / "src/tilegym/suites/example" definition = _touch(inventory / "kernel_definitions/op.json") @@ -126,7 +202,7 @@ def test_legacy_cutile_rs_solution_coordinate_and_pairing(tmp_path): assert list(solution_paths_for_definition(definition)) == [solution] -def test_hierarchical_operation_topology_requires_literal_matrix_and_leaf_pairs(tmp_path): +def test_hierarchical_operation_topology_requires_public_local_and_leaf_pairs(tmp_path): inventory = tmp_path / "src/tilegym/suites/example" public = _touch(inventory / "kernel_definitions/op/op.json") del public diff --git a/tests/kernel_inventory/test_kernel_definition_solution_runtime.py b/tests/kernel_inventory/test_kernel_definition_solution_runtime.py index 11f4df13..81eaa095 100644 --- a/tests/kernel_inventory/test_kernel_definition_solution_runtime.py +++ b/tests/kernel_inventory/test_kernel_definition_solution_runtime.py @@ -18,7 +18,6 @@ from tests.kernel_inventory.kernel_runtime_utils import _assert_matching_entry_signatures_static from tests.kernel_inventory.kernel_runtime_utils import _assert_return_contract from tests.kernel_inventory.kernel_runtime_utils import _assert_triton_autotune_argument_ownership -from tests.kernel_inventory.kernel_runtime_utils import _boolean_branch_assignments from tests.kernel_inventory.kernel_runtime_utils import _call_entry_strictly from tests.kernel_inventory.kernel_runtime_utils import _current_compute_capability_label from tests.kernel_inventory.kernel_runtime_utils import _current_triton_backend @@ -26,19 +25,44 @@ from tests.kernel_inventory.kernel_runtime_utils import _launch_raw_solution from tests.kernel_inventory.kernel_runtime_utils import _load_reference from tests.kernel_inventory.kernel_runtime_utils import _require_solution_runtime_dependencies -from tests.kernel_inventory.kernel_runtime_utils import _run_definition_solution_runtime +from tests.kernel_inventory.kernel_runtime_utils import _run_definition_solution_workload_runtime from tests.kernel_inventory.kernel_runtime_utils import _run_runtime_branch from tests.kernel_inventory.kernel_runtime_utils import _runtime_case_id -from tests.kernel_inventory.kernel_runtime_utils import _satisfy_boolean_constraints from tests.kernel_inventory.kernel_runtime_utils import _skip_if_solution_does_not_target_current_compute_capability from tests.kernel_inventory.kernel_runtime_utils import _skip_if_solution_does_not_target_current_triton_backend from tests.kernel_inventory.kernel_runtime_utils import _unwrap_triton_heuristics from tests.kernel_inventory.kernel_runtime_utils import installed_reference_modules -from tests.kernel_inventory.kernel_runtime_utils import run_definition_solution_runtime +from tests.kernel_inventory.kernel_runtime_utils import materialize_workload_inputs +from tests.kernel_inventory.kernel_runtime_utils import run_definition_solution_workload_runtime +from tilegym.kernel_inventory.layout import definition_solution_paths_for_workload +from tilegym.kernel_inventory.schema import Workload +from tilegym.kernel_inventory.workloads import WorkloadRecord + + +def _workload_record(tmp_path, *, inputs=None, axes=None): + return WorkloadRecord( + path=tmp_path / "workload.jsonl", + line_number=1, + workload=Workload.model_validate( + { + "uuid": "c8ab8964-8624-44f7-bd4f-bd1ace75c959", + "axes": axes or {}, + "inputs": inputs or {}, + "tolerance": { + "max_atol": 0.02, + "max_rtol": 0.02, + "required_matched_ratio": 1.0, + "max_error_cap": None, + "allow_negative_inf": False, + }, + "eval_mode": "full", + } + ), + ) -def test_kernel_definition_solution_runtime(definition_path, solution_path): - run_definition_solution_runtime(definition_path, solution_path) +def test_kernel_definition_solution_runtime(workload_record, definition_path, solution_path): + run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) class _FakeCuda: @@ -208,14 +232,67 @@ def test_runtime_without_declared_triton_compiler_target_does_not_probe_backend( _skip_if_solution_does_not_target_current_triton_backend({"spec": {"language": "triton"}}) -def test_runtime_rejects_mismatched_reference_and_solution_signatures(): +def test_runtime_accepts_optional_solution_signature_extensions(): def reference(q, scale=None): return q def solution(q, initial_state=None, scale=None): return q - definition = {"name": "test_definition"} + definition = {"name": "test_definition", "inputs": {"q": {}, "scale": {}}} + schema = {"spec": {"entry_point": "test.py::solution"}} + _assert_matching_entry_signatures(definition, reference, schema, solution) + + +def test_runtime_preserves_exact_legacy_signature_with_noncanonical_input_order(): + def reference(q, scale=None): + return q + + def solution(q, scale=None): + return q + + definition = {"name": "legacy_definition", "inputs": {"scale": {}, "q": {}}} + schema = {"spec": {"entry_point": "test.py::solution"}} + _assert_matching_entry_signatures(definition, reference, schema, solution) + + +def test_static_runtime_accepts_exact_kwargs_only_unsupported_stub(tmp_path): + source = tmp_path / "solution.py" + source.write_text("def solution(**kwargs):\n raise NotImplementedError\n", encoding="utf-8") + definition = { + "name": "unsupported", + "inputs": {}, + "reference": "def run(**kwargs):\n raise NotImplementedError\n", + } + solution = {"spec": {"entry_point": "solution.py::solution"}} + + _assert_matching_entry_signatures_static(definition, solution, tmp_path / "definition.json", tmp_path) + + +def test_static_runtime_accepts_reordered_optional_solution_extensions(tmp_path): + source = tmp_path / "solution.py" + source.write_text( + "def solution(q, initial_state=None, reverse=False, normalize=False):\n return q\n", + encoding="utf-8", + ) + definition = { + "name": "public", + "inputs": {"q": {}, "normalize": {}, "reverse": {}}, + "reference": "def run(q, normalize=False, reverse=False):\n return q\n", + } + solution = {"spec": {"entry_point": "solution.py::solution"}} + + _assert_matching_entry_signatures_static(definition, solution, tmp_path / "definition.json", tmp_path) + + +def test_runtime_rejects_required_solution_signature_extensions(): + def reference(q, scale=None): + return q + + def solution(q, initial_state, scale=None): + return q + + definition = {"name": "test_definition", "inputs": {"q": {}, "scale": {}}} schema = {"spec": {"entry_point": "test.py::solution"}} with pytest.raises(AssertionError, match="does not match Solution entry point"): _assert_matching_entry_signatures(definition, reference, schema, solution) @@ -228,13 +305,14 @@ def reference(q, scale): def solution(q, scale=None): return q - definition = {"name": "test_definition"} + definition = {"name": "test_definition", "inputs": {"q": {}, "scale": {}}} schema = {"spec": {"entry_point": "test.py::solution"}} with pytest.raises(AssertionError, match="does not match Solution entry point"): _assert_matching_entry_signatures(definition, reference, schema, solution) def test_static_signature_mismatch_precedes_backend_dependency_gate(tmp_path, monkeypatch): + workload_record = _workload_record(tmp_path) definition_path = tmp_path / "definition.json" solution_path = tmp_path / "solution.json" definition_path.write_text( @@ -280,14 +358,19 @@ def compiler_gate(_solution): "tests.kernel_inventory.kernel_runtime_utils.validate_solution_entry_point", lambda *_, **__: None, ) + monkeypatch.setattr( + "tests.kernel_inventory.kernel_runtime_utils.validate_workload_against_definition", + lambda *_, **__: None, + ) with pytest.raises(AssertionError, match="does not match Solution entry point"): - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) assert static_signature_checked assert not dependency_gate_called assert not compiler_gate_called def test_launch_contract_validation_precedes_triton_compiler_gate(tmp_path, monkeypatch): + workload_record = _workload_record(tmp_path) definition_path = tmp_path / "definition.json" solution_path = tmp_path / "solution.json" definition_path.write_text( @@ -323,11 +406,12 @@ def compiler_gate(_solution): compiler_gate, ) with pytest.raises(ValueError, match="launch contract invalid"): - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) assert not compiler_gate_called def test_triton_compiler_mismatch_skips_before_solution_import(tmp_path, monkeypatch): + workload_record = _workload_record(tmp_path) definition_path = tmp_path / "definition.json" solution_path = tmp_path / "solution.json" definition_path.write_text( @@ -357,6 +441,10 @@ def test_triton_compiler_mismatch_skips_before_solution_import(tmp_path, monkeyp "tests.kernel_inventory.kernel_runtime_utils._assert_matching_entry_signatures_static", lambda *_: None, ) + monkeypatch.setattr( + "tests.kernel_inventory.kernel_runtime_utils.validate_workload_against_definition", + lambda *_, **__: None, + ) monkeypatch.setattr( "tests.kernel_inventory.kernel_runtime_utils._require_solution_runtime_dependencies", lambda *_: None, @@ -370,10 +458,11 @@ def test_triton_compiler_mismatch_skips_before_solution_import(tmp_path, monkeyp lambda *_: pytest.fail("mismatched compiler target must skip before importing the Solution"), ) with pytest.raises(pytest.skip.Exception, match="current Triton compiler backend is oait"): - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) def test_hardware_gate_precedes_solution_import_but_matching_hardware_executes(tmp_path, monkeypatch): + workload_record = _workload_record(tmp_path, inputs={"value": {"type": "scalar", "value": 4}}) definition_path = tmp_path / "definition.json" solution_path = tmp_path / "solution.json" module_name = "tilegym.hardware_gate_test.poison" @@ -418,6 +507,10 @@ def test_hardware_gate_precedes_solution_import_but_matching_hardware_executes(t "tests.kernel_inventory.kernel_runtime_utils.validate_solution_entry_point", lambda *_, **__: order.append("entry-point"), ) + monkeypatch.setattr( + "tests.kernel_inventory.kernel_runtime_utils.validate_workload_against_definition", + lambda *_, **__: order.append("workload"), + ) monkeypatch.setattr( "tests.kernel_inventory.kernel_runtime_utils._assert_matching_entry_signatures_static", lambda *_: order.append("static-signature"), @@ -447,17 +540,12 @@ def test_hardware_gate_precedes_solution_import_but_matching_hardware_executes(t "tests.kernel_inventory.kernel_runtime_utils._assert_matching_entry_signatures", lambda *_: order.append("dynamic-signature"), ) - monkeypatch.setattr("tests.kernel_inventory.kernel_runtime_utils._axis_values", lambda *_: {}) - monkeypatch.setattr( - "tests.kernel_inventory.kernel_runtime_utils._make_inputs", - lambda *_: {"value": 4}, - ) monkeypatch.setattr( - "tests.kernel_inventory.kernel_runtime_utils._boolean_branch_assignments", - lambda *_: ({},), + "tests.kernel_inventory.kernel_runtime_utils.materialize_workload_inputs", + lambda *_, **__: ({}, {"value": 4}), ) - def run_branch(_definition, _reference, _solution, solution_fn, *_): + def run_branch(_definition, _reference, _solution, solution_fn, *_, **__): order.append("execute") assert solution_fn(4) == 5 @@ -465,19 +553,13 @@ def run_branch(_definition, _reference, _solution, solution_fn, *_): "tests.kernel_inventory.kernel_runtime_utils._run_runtime_branch", run_branch, ) - monkeypatch.setattr( - "tests.kernel_inventory.kernel_runtime_utils.RUNTIME_INPUT_CATALOG", - types.SimpleNamespace( - case_for_definition=lambda _path: types.SimpleNamespace(mutated_inputs=()), - ), - ) - with pytest.raises(pytest.skip.Exception, match="current compute capability is SM103"): - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) assert order == [ "definition", "solution", "entry-point", + "workload", "static-signature", "import:torch", "dependency", @@ -491,13 +573,14 @@ def run_branch(_definition, _reference, _solution, solution_fn, *_): order.clear() try: - _run_definition_solution_runtime(definition_path, solution_path) + _run_definition_solution_workload_runtime(workload_record, definition_path, solution_path) finally: sys.modules.pop(module_name, None) assert order == [ "definition", "solution", "entry-point", + "workload", "static-signature", "import:torch", "dependency", @@ -511,7 +594,7 @@ def run_branch(_definition, _reference, _solution, solution_fn, *_): def test_static_signature_validation_rejects_actual_ast_mismatch(tmp_path): source = tmp_path / "solution.py" source.write_text("def entry(value, extra):\n return value\n", encoding="utf-8") - definition = {"name": "wrapper", "reference": "def run(value):\n return value\n"} + definition = {"name": "wrapper", "inputs": {"value": {}}, "reference": "def run(value):\n return value\n"} solution = {"spec": {"entry_point": "solution.py::entry"}} with pytest.raises(AssertionError, match="does not match Solution entry point"): _assert_matching_entry_signatures_static(definition, solution, tmp_path / "definition.json", tmp_path) @@ -593,11 +676,15 @@ def test_runtime_case_id_contains_definition_coordinate_and_solution_backend(tmp solution.parent.mkdir(parents=True) definition.write_text("{}\n", encoding="utf-8") solution.write_text('{"spec": {"language": "triton"}}\n', encoding="utf-8") - case_id = _runtime_case_id(definition, solution) - assert case_id == "src/tilegym/suites/example::definition::op/cutile/op::triton" + record = _workload_record(tmp_path) + case_id = _runtime_case_id(record, definition, solution) + assert case_id == ( + "src/tilegym/suites/example::definition::op/cutile/op::line=1::" + "uuid=c8ab8964-8624-44f7-bd4f-bd1ace75c959::backend=triton" + ) -def test_two_backend_wrapper_matrix_has_six_unique_runtime_case_ids(tmp_path): +def test_public_and_wrapper_workloads_plan_four_adjacent_runtime_cases(tmp_path): inventory = tmp_path / "src/tilegym/suites/example" definitions = [ inventory / "kernel_definitions/op/op.json", @@ -608,137 +695,38 @@ def test_two_backend_wrapper_matrix_has_six_unique_runtime_case_ids(tmp_path): inventory / "kernel_solutions/op/cutile/op.json", inventory / "kernel_solutions/op/triton/op.json", ] + workload_paths = [ + inventory / "kernel_workloads/op/op/workload.jsonl", + inventory / "kernel_workloads/op/cutile/op/workload.jsonl", + inventory / "kernel_workloads/op/triton/op/workload.jsonl", + ] for path in definitions: path.parent.mkdir(parents=True, exist_ok=True) path.write_text("{}\n", encoding="utf-8") for backend, path in zip(("cutile", "triton"), solutions, strict=True): path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps({"spec": {"language": backend}}), encoding="utf-8") + for path in workload_paths: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text("{}\n", encoding="utf-8") - ids = {_runtime_case_id(definition, solution) for definition in definitions for solution in solutions} - assert len(ids) == 6 - - -def test_runtime_uses_boolean_constraints_to_select_a_schema_case(): - definition = { - "name": "constrained_boolean_case", - "inputs": { - "layout": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": ["layout is True", "emit is True"], - } - inputs = {"layout": False, "emit": False} - - _satisfy_boolean_constraints(definition, inputs, {}) - - assert inputs == {"layout": True, "emit": True} - - -def test_runtime_enumerates_every_unconstrained_boolean_branch(): - definition = { - "inputs": { - "layout": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": [], - } - - assert _boolean_branch_assignments(definition) == [ - {"layout": False, "emit": False}, - {"layout": False, "emit": True}, - {"layout": True, "emit": False}, - {"layout": True, "emit": True}, + records = [ + WorkloadRecord(path=path, line_number=1, workload=_workload_record(tmp_path).workload) + for path in workload_paths ] - - -def test_runtime_preserves_optional_boolean_none_during_branch_enumeration(): - definition = { - "name": "optional_boolean_case", - "inputs": { - "optional": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": [], - } - inputs = {"optional": None, "emit": False} - - assignments = _boolean_branch_assignments(definition, inputs=inputs) - _satisfy_boolean_constraints(definition, inputs, {}) - - assert assignments == [{"emit": False}, {"emit": True}] - assert inputs == {"optional": None, "emit": False} - - -def test_runtime_constrains_varying_boolean_with_fixed_optional_none(): - definition = { - "name": "optional_boolean_constraint", - "inputs": { - "optional": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": ["optional is None and emit"], - } - inputs = {"optional": None, "emit": False} - - assert _boolean_branch_assignments(definition, inputs=inputs) == [{"emit": True}] - _satisfy_boolean_constraints(definition, inputs, {}) - assert inputs == {"optional": None, "emit": True} - - -def test_runtime_accepts_satisfied_constraint_on_sole_optional_none_boolean(): - definition = { - "name": "satisfied_optional_boolean_constraint", - "inputs": {"optional": {"shape": None, "dtype": "bool"}}, - "constraints": ["optional is None"], - } - assert _boolean_branch_assignments(definition, inputs={"optional": None}) == [{}] - - -def test_runtime_rejects_violated_constraint_on_sole_optional_none_boolean(): - definition = { - "name": "violated_optional_boolean_constraint", - "inputs": {"optional": {"shape": None, "dtype": "bool"}}, - "constraints": ["optional is not None"], - } - with pytest.raises(pytest.fail.Exception, match="no Boolean input assignment satisfies"): - _boolean_branch_assignments(definition, inputs={"optional": None}) - - -def test_runtime_enumerates_boolean_branches_allowed_by_constraints(): - definition = { - "name": "constrained_boolean_branches", - "inputs": { - "layout": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": ["layout is True"], - } - - assert _boolean_branch_assignments(definition) == [ - {"layout": True, "emit": False}, - {"layout": True, "emit": True}, + planned = [ + (record, definition, solution) + for record in records + for definition, solution in definition_solution_paths_for_workload(record.path) ] - - -def test_runtime_ignores_unrelated_constraints_when_enumerating_booleans(): - definition = { - "name": "unrelated_constraints", - "inputs": { - "layout": {"shape": None, "dtype": "bool"}, - "emit": {"shape": None, "dtype": "bool"}, - }, - "constraints": [ - "H % 2 == 0", - "layout selects a documented implementation branch", - ], - } - - assert _boolean_branch_assignments(definition, {"H": 2}) == [ - {"layout": False, "emit": False}, - {"layout": False, "emit": True}, - {"layout": True, "emit": False}, - {"layout": True, "emit": True}, + ids = {_runtime_case_id(*case) for case in planned} + + assert len(planned) == len(ids) == 4 + assert [(definition.parent.name, solution.parent.name) for _, definition, solution in planned] == [ + ("op", "triton"), + ("op", "cutile"), + ("cutile", "cutile"), + ("triton", "triton"), ] @@ -979,7 +967,38 @@ def mutating_solution(input): ) -def test_cpu_composed_wrapper_runtime_canary_covers_modules_mutation_and_boolean_branches(tmp_path): +def test_runtime_return_comparison_uses_explicit_workload_tolerance(): + torch = pytest.importorskip("torch") + actual = torch.tensor([1.01], dtype=torch.float32) + expected = torch.tensor([1.0], dtype=torch.float32) + output_specs = {"output": {"shape": ["N"], "dtype": "float32"}} + + _assert_return_contract( + actual, + expected, + "output", + output_specs, + {"N": 1}, + torch, + "loose-row", + rtol=0.0, + atol=0.02, + ) + with pytest.raises(AssertionError, match="strict-row output output mismatch"): + _assert_return_contract( + actual, + expected, + "output", + output_specs, + {"N": 1}, + torch, + "strict-row", + rtol=0.0, + atol=0.001, + ) + + +def test_cpu_composed_wrapper_runtime_canary_executes_each_explicit_boolean_row_once(tmp_path): torch = pytest.importorskip("torch") leaf_scale = { "name": "leaf_scale", @@ -995,7 +1014,7 @@ def test_cpu_composed_wrapper_runtime_canary_covers_modules_mutation_and_boolean "axes": {"N": {"type": "const", "value": 3}}, "inputs": { "input": {"shape": ["N"], "dtype": "float32"}, - "state": {"shape": ["N"], "dtype": "float32"}, + "state": {"shape": ["N"], "dtype": "float32", "inplace_output": True}, "negate": {"shape": None, "dtype": "bool"}, }, "outputs": {"output": {"shape": ["N"], "dtype": "float32"}}, @@ -1012,7 +1031,10 @@ def test_cpu_composed_wrapper_runtime_canary_covers_modules_mutation_and_boolean for name, definition in (("leaf_scale", leaf_scale), ("leaf_update", leaf_update)): (tmp_path / f"{name}.json").write_text(json.dumps(definition), encoding="utf-8") + executed = [] + def solution(input, state, negate): + executed.append(negate) value = -input if negate else input state.add_(1) return value + state @@ -1021,18 +1043,36 @@ def solution(input, state, negate): reference = _load_reference(wrapper, wrapper_path) solution_schema = {"spec": {"entry_point": "synthetic.py::solution"}} _assert_matching_entry_signatures(wrapper, reference, solution_schema, solution) - for negate in (False, True): + records = [ + _workload_record( + tmp_path, + inputs={ + "input": {"type": "random"}, + "state": {"type": "random"}, + "negate": {"type": "scalar", "value": negate}, + }, + ) + for negate in (False, True) + ] + for record in records: + torch.manual_seed(2026) + axes, inputs = materialize_workload_inputs( + record, + wrapper, + torch=torch, + device=torch.device("cpu"), + ) _run_runtime_branch( wrapper, reference, solution_schema, solution, - { - "input": torch.arange(3, dtype=torch.float32), - "state": torch.zeros(3, dtype=torch.float32), - "negate": negate, - }, - {"N": 3}, + inputs, + axes, torch, ("state",), + tolerance=record.workload.tolerance, + case_label=record.source_label, ) + + assert executed == [False, True] diff --git a/tests/kernel_inventory/test_runtime_inputs.py b/tests/kernel_inventory/test_runtime_inputs.py deleted file mode 100644 index 6171c94d..00000000 --- a/tests/kernel_inventory/test_runtime_inputs.py +++ /dev/null @@ -1,499 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# SPDX-License-Identifier: MIT - -import json -from pathlib import Path - -import pytest - -from tests.kernel_inventory.runtime_inputs import RuntimeInputCatalog -from tests.kernel_inventory.runtime_inputs import make_runtime_override -from tests.kernel_inventory.runtime_inputs import resolve_torch_dtype - - -def test_runtime_input_patterns_merge_without_tensor_metadata(tmp_path): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - """version: 1 -patterns: - dense: - axes: {B: 1, T: 8} - mutates: [state] - inputs: - scale: {kind: full, value: 0.5} -cases: - src/tilegym/suites/example::definition::op/cutile/leaf: - patterns: [dense] - axes: {T: 16} - mutates: [cache] - inputs: - offsets: {kind: arange, start: 0, step: 16} -""", - encoding="utf-8", - ) - catalog = RuntimeInputCatalog.from_path(path) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - - case = catalog.case_for_definition(definition_path) - - assert case.axes == {"B": 1, "T": 16} - assert case.inputs["scale"] == {"kind": "full", "value": 0.5} - assert "shape" not in case.inputs["offsets"] - assert "dtype" not in case.inputs["offsets"] - assert case.mutated_inputs == ("state", "cache") - - -def test_runtime_input_catalog_rejects_missing_patterns(tmp_path): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - """version: 1 -patterns: {} -cases: - src/tilegym/suites/example::definition::op/cutile/leaf: - patterns: [missing] -""", - encoding="utf-8", - ) - with pytest.raises(ValueError, match="missing patterns"): - RuntimeInputCatalog.from_path(path) - - -def test_runtime_input_catalog_rejects_duplicate_mutations(tmp_path): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - """version: 1 -patterns: - invalid: - mutates: [state, state] -cases: {} -""", - encoding="utf-8", - ) - with pytest.raises(ValueError, match="unique input names"): - RuntimeInputCatalog.from_path(path) - - -@pytest.mark.parametrize( - ("catalog", "match"), - [ - ("version: true\npatterns: {}\ncases: {}\n", "version must be 1"), - ("version: 1\npatterns: {bad: {axes: {N: true}}}\ncases: {}\n", "positive integers"), - ( - "version: 1\npatterns: {}\ncases:\n" - " src/tilegym/suites/example::definition::op/cutile/leaf:\n" - " axes: {N: true}\n", - "positive integers", - ), - ], -) -def test_runtime_input_catalog_rejects_boolean_integer_fields(tmp_path, catalog, match): - path = tmp_path / "runtime_inputs.yaml" - path.write_text(catalog, encoding="utf-8") - with pytest.raises(ValueError, match=match): - RuntimeInputCatalog.from_path(path) - - -def test_runtime_input_catalog_rejects_boolean_override_equal_to_const_axis_one(tmp_path): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: {{}} -cases: - {canonical_id}: - axes: {{N: true}} -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps({"axes": {"N": {"type": "const", "value": 1}}, "inputs": {}}), - encoding="utf-8", - ) - with pytest.raises(ValueError, match="positive integers"): - RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - - -@pytest.mark.parametrize("field", ["shape: [N]", "dtype: float64", "expression: arbitrary()"]) -def test_runtime_input_catalog_rejects_generator_metadata_and_unknown_fields(tmp_path, field): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - f"""version: 1 -patterns: - invalid: - inputs: - value: {{kind: zeros, {field}}} -cases: {{}} -""", - encoding="utf-8", - ) - with pytest.raises(ValueError, match="generator has unsupported fields"): - RuntimeInputCatalog.from_path(path) - - -@pytest.mark.parametrize( - ("generator", "match"), - [ - ("{kind: normal, mean: bad}", "normal mean/std"), - ("{kind: normal, std: -1}", "normal mean/std"), - ("{kind: arange, step: 0}", "arange start/step"), - ("{kind: randint, low: 1.5, high: 4}", "randint low/high"), - ("{kind: randint, low: 4, high: 4}", "randint low/high"), - ], -) -def test_runtime_input_catalog_rejects_invalid_generator_parameters(tmp_path, generator, match): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - f"""version: 1 -patterns: - invalid: - inputs: - value: {generator} -cases: {{}} -""", - encoding="utf-8", - ) - with pytest.raises(ValueError, match=match): - RuntimeInputCatalog.from_path(path) - - -@pytest.mark.parametrize( - "duplicate", - [ - "patterns:\n shared: {}\n shared: {}\ncases: {}", - "patterns:\n shared:\n axes: {T: 4, T: 8}\ncases: {}", - "patterns:\n shared:\n inputs: {scale: {kind: full, value: 1}, scale: {kind: full, value: 2}}\ncases: {}", - ( - "patterns: {}\ncases:\n" - " src/tilegym/suites/example::definition::op/cutile/op: {}\n" - " src/tilegym/suites/example::definition::op/cutile/op: {}" - ), - ], -) -def test_runtime_input_catalog_rejects_duplicate_yaml_keys(tmp_path, duplicate): - path = tmp_path / "runtime_inputs.yaml" - path.write_text(f"version: 1\n{duplicate}\n", encoding="utf-8") - with pytest.raises(ValueError, match="duplicate YAML key"): - RuntimeInputCatalog.from_path(path) - - -def test_runtime_input_catalog_rejects_stale_canonical_definition_ids(tmp_path): - path = tmp_path / "runtime_inputs.yaml" - path.write_text( - """version: 1 -patterns: {} -cases: - src/tilegym/suites/example::definition::op/cutile/stale: {} -""", - encoding="utf-8", - ) - existing = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - existing.parent.mkdir(parents=True) - existing.write_text("{}\n", encoding="utf-8") - with pytest.raises(ValueError, match="stale canonical Definition ids"): - RuntimeInputCatalog.from_path(path, definition_paths=[existing]) - - -def test_runtime_input_catalog_reuses_one_pattern_for_public_and_wrappers(tmp_path): - path = tmp_path / "runtime_inputs.yaml" - ids = [ - "src/tilegym/suites/example::definition::op/op", - "src/tilegym/suites/example::definition::op/cutile/op", - "src/tilegym/suites/example::definition::op/triton/op", - ] - path.write_text( - """version: 1 -patterns: - wrapper_case: - axes: {T: 8} - inputs: - enabled: {kind: full, value: true} -cases: - src/tilegym/suites/example::definition::op/op: - patterns: [wrapper_case] - src/tilegym/suites/example::definition::op/cutile/op: - patterns: [wrapper_case] - src/tilegym/suites/example::definition::op/triton/op: - patterns: [wrapper_case] -""", - encoding="utf-8", - ) - definition_paths = [] - for canonical_id in ids: - relative = canonical_id.split("::definition::", 1)[1] - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions" / f"{relative}.json" - definition_path.parent.mkdir(parents=True, exist_ok=True) - definition_path.write_text( - json.dumps( - { - "axes": {"T": {"type": "var"}}, - "inputs": {"enabled": {"shape": None, "dtype": "bool"}}, - } - ), - encoding="utf-8", - ) - definition_paths.append(definition_path) - - catalog = RuntimeInputCatalog.from_path(path, definition_paths=definition_paths) - cases = [catalog.case_for_definition(definition_path) for definition_path in definition_paths] - assert [case.axes for case in cases] == [{"T": 8}] * 3 - assert [case.inputs["enabled"] for case in cases] == [{"kind": "full", "value": True}] * 3 - - -@pytest.mark.parametrize( - ("pattern_body", "match"), - [ - ("axes: {Missing: 2}", "unknown axes"), - ("inputs: {missing: {kind: zeros}}", "unknown inputs"), - ("mutates: [missing]", "mutates unknown inputs"), - ("axes: {D: 8}", "contradicts const axes"), - ], -) -def test_runtime_input_catalog_statically_validates_merged_case_against_definition(tmp_path, pattern_body, match): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: - shared: - {pattern_body} -cases: - {canonical_id}: - patterns: [shared] -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps( - { - "axes": {"N": {"type": "var"}, "D": {"type": "const", "value": 4}}, - "inputs": {"value": {"shape": ["N", "D"], "dtype": "float32"}}, - } - ), - encoding="utf-8", - ) - with pytest.raises(ValueError, match=match): - RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - - -@pytest.mark.parametrize("value", ["nope", "[true]", "2147483648"]) -def test_runtime_input_catalog_rejects_schema_incompatible_scalar_values(tmp_path, value): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: {{}} -cases: - {canonical_id}: - inputs: - mode: {{kind: full, value: {value}}} -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps( - { - "axes": {}, - "inputs": {"mode": {"shape": None, "dtype": "int32"}}, - } - ), - encoding="utf-8", - ) - with pytest.raises(ValueError, match="incompatible with scalar Definition dtype int32"): - RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - - -def test_runtime_input_catalog_supports_explicit_optional_scalar_none_override(tmp_path): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: {{}} -cases: - {canonical_id}: - inputs: - optional: {{kind: none}} -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps( - { - "axes": {}, - "inputs": {"optional": {"shape": None, "dtype": "float32"}}, - "reference": "def run(optional=None):\n return optional\n", - } - ), - encoding="utf-8", - ) - - catalog = RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - - assert catalog.case_for_definition(definition_path).inputs["optional"] == {"kind": "none"} - assert ( - make_runtime_override( - {"kind": "none"}, - {"shape": None, "dtype": "float32"}, - {}, - torch=None, - device=None, - ) - is None - ) - - -def test_runtime_input_catalog_supports_explicit_optional_tensor_none_override(tmp_path): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: {{}} -cases: - {canonical_id}: - inputs: - value: {{kind: none}} -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps( - { - "axes": {"N": {"type": "var"}}, - "inputs": {"value": {"shape": ["N"], "dtype": "float32"}}, - "reference": "def run(value=None):\n return value\n", - } - ), - encoding="utf-8", - ) - catalog = RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - assert catalog.case_for_definition(definition_path).inputs["value"] == {"kind": "none"} - - -def test_runtime_input_catalog_rejects_none_override_without_reference_default(tmp_path): - canonical_id = "src/tilegym/suites/example::definition::op/cutile/leaf" - catalog_path = tmp_path / "runtime_inputs.yaml" - catalog_path.write_text( - f"""version: 1 -patterns: {{}} -cases: - {canonical_id}: - inputs: - required: {{kind: none}} -""", - encoding="utf-8", - ) - definition_path = tmp_path / "src/tilegym/suites/example/kernel_definitions/op/cutile/leaf.json" - definition_path.parent.mkdir(parents=True) - definition_path.write_text( - json.dumps( - { - "axes": {}, - "inputs": {"required": {"shape": None, "dtype": "float32"}}, - "reference": "def run(required):\n return required\n", - } - ), - encoding="utf-8", - ) - with pytest.raises(ValueError, match="explicit reference.run default of None"): - RuntimeInputCatalog.from_path(catalog_path, definition_paths=[definition_path]) - - -def test_runtime_override_derives_tensor_shape_and_dtype_from_definition(): - torch = pytest.importorskip("torch") - value = make_runtime_override( - {"kind": "arange", "start": 1, "step": 2}, - {"shape": ["B", "T"], "dtype": "int32"}, - {"B": 1, "T": 3}, - torch, - torch.device("cpu"), - ) - assert tuple(value.shape) == (1, 3) - assert value.dtype == torch.int32 - assert value.tolist() == [[1, 3, 5]] - - -@pytest.mark.parametrize( - "dtype", - [ - "float64", - "float32", - "float16", - "bfloat16", - "float8_e4m3fn", - "float8_e5m2", - "int64", - "int32", - "int16", - "int8", - "uint64", - "uint32", - "uint16", - "uint8", - "bool", - ], -) -def test_runtime_dtype_resolver_covers_validated_dtype_families(dtype): - torch = pytest.importorskip("torch") - - assert resolve_torch_dtype(dtype, torch) is getattr(torch, dtype) - - -def test_runtime_override_materializes_unsigned_integer_generators(): - torch = pytest.importorskip("torch") - axes = {"N": 3} - device = torch.device("cpu") - - ranged = make_runtime_override( - {"kind": "randint", "low": 1, "high": 4}, - {"shape": ["N"], "dtype": "uint32"}, - axes, - torch, - device, - ) - sequenced = make_runtime_override( - {"kind": "arange", "start": 1, "step": 2}, - {"shape": ["N"], "dtype": "uint64"}, - axes, - torch, - device, - ) - - assert ranged.dtype == torch.uint32 - assert all(1 <= value < 4 for value in ranged.tolist()) - assert sequenced.dtype == torch.uint64 - assert sequenced.tolist() == [1, 3, 5] - - -def test_runtime_override_accepts_shape_checked_literal_values(): - torch = pytest.importorskip("torch") - spec = {"shape": ["N", "TWO"], "dtype": "int64"} - axes = {"N": 2, "TWO": 2} - - value = make_runtime_override( - {"kind": "values", "value": [[0, 0], [1, 0]]}, - spec, - axes, - torch, - torch.device("cpu"), - ) - - assert value.dtype == torch.int64 - assert value.tolist() == [[0, 0], [1, 0]] - with pytest.raises(ValueError, match="expected"): - make_runtime_override( - {"kind": "values", "value": [[0, 0]]}, - spec, - axes, - torch, - torch.device("cpu"), - ) diff --git a/tests/kernel_inventory/test_workload_schema.py b/tests/kernel_inventory/test_workload_schema.py new file mode 100644 index 00000000..3a4ed13d --- /dev/null +++ b/tests/kernel_inventory/test_workload_schema.py @@ -0,0 +1,154 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +from __future__ import annotations + +import copy +import os +import subprocess +import sys +from pathlib import Path +from typing import Any + +import pytest +from pydantic import ValidationError + +from tilegym.kernel_inventory import _workload_schema_compat as compat +from tilegym.kernel_inventory.schema import USING_CANONICAL_WORKLOAD_SCHEMA +from tilegym.kernel_inventory.schema import workload_model_dump +from tilegym.kernel_inventory.schema import workload_model_validate + +_BASE_WORKLOAD: dict[str, Any] = { + "uuid": "52bedf2e-bdbc-4618-9f38-a592a4de71a9", + "axes": {"B": 2}, + "inputs": {"x": {"type": "random"}}, + "tolerance": { + "max_atol": 0.02, + "max_rtol": 0.02, + "required_matched_ratio": 1.0, + "max_error_cap": None, + "allow_negative_inf": False, + }, + "eval_mode": "full", +} + + +def _workload(**updates: Any) -> dict[str, Any]: + workload = copy.deepcopy(_BASE_WORKLOAD) + workload.update(updates) + return workload + + +_ACCEPTED_WORKLOADS = [ + pytest.param(_BASE_WORKLOAD, id="random-explicit-policy"), + pytest.param( + _workload( + inputs={ + "integer": {"type": "scalar", "value": 1}, + "floating": {"type": "scalar", "value": 0.5}, + "boolean": {"type": "scalar", "value": True}, + "optional": {"type": "null"}, + "mapping": {"type": "string", "value": ""}, + } + ), + id="literal-inputs", + ), + pytest.param( + _workload(inputs={"x": {"type": "safetensors", "path": "inputs.safetensors", "tensor_key": "x"}}), + id="replicated-safetensors", + ), + pytest.param( + _workload( + inputs={ + "x": { + "type": "safetensors", + "shards": [ + {"path": "rank0.safetensors", "tensor_key": "x"}, + {"path": "rank1.safetensors", "tensor_key": "x"}, + ], + } + } + ), + id="sharded-safetensors", + ), + pytest.param(_workload(inputs={"x": {"type": "custom"}}), id="custom"), + pytest.param(_workload(weight=3.5), id="positive-weight"), + pytest.param( + { + "uuid": "52bedf2e-bdbc-4618-9f38-a592a4de71aa", + "axes": {"B": 2}, + "inputs": {"x": {"type": "random"}}, + }, + id="schema-defaults", + ), + pytest.param( + _workload( + axes={"B": {"min": 1, "max": 8, "multiple_of": 2, "sampling_strategy": "linear"}}, + inputs={"x": {"type": "custom"}}, + eval_mode="correctness_only", + ), + id="dynamic-correctness-only", + ), + pytest.param( + _workload( + axes={"B": {"min": 1, "max": 8}}, + inputs={"x": {"type": "safetensors", "path": "x.safetensors", "tensor_key": "x"}}, + eval_mode="correctness_only", + ), + id="schema-allows-dynamic-safetensors", + ), + pytest.param( + _workload(inputs={"x": {"type": "scalar", "value": float("inf")}}), + id="schema-allows-nonfinite-scalar", + ), +] + + +_REJECTED_WORKLOADS = [ + pytest.param(_workload(inputs={"x": {"type": "custom"}, "y": {"type": "random"}}), id="mixed-custom"), + pytest.param(_workload(inputs={"x": {"type": "safetensors", "path": "x.safetensors"}}), id="missing-key"), + pytest.param( + _workload( + inputs={ + "x": { + "type": "safetensors", + "path": "x.safetensors", + "tensor_key": "x", + "shards": [{"path": "rank0.safetensors", "tensor_key": "x"}], + } + } + ), + id="replicated-and-sharded", + ), + pytest.param(_workload(inputs={"x": {"type": "safetensors", "shards": []}}), id="empty-shards"), + pytest.param(_workload(axes={"B": -1}), id="negative-axis"), + pytest.param(_workload(uuid=""), id="empty-uuid"), + pytest.param(_workload(eval_mode="invalid"), id="invalid-eval-mode"), + pytest.param(_workload(axes={"B": {"min": 1, "max": 8}}), id="dynamic-full"), + pytest.param(_workload(weight=0.0), id="zero-weight"), + pytest.param(_workload(weight=float("inf")), id="infinite-weight"), + pytest.param(_workload(custom_correctness_kwargs={"threshold": float("nan")}), id="nonfinite-kwargs"), +] + + +@pytest.mark.parametrize("payload", _ACCEPTED_WORKLOADS) +def test_public_compat_workload_accepts_documented_contract(payload): + workload = compat.Workload.model_validate(payload) + assert workload.model_dump(mode="json")["uuid"] == payload["uuid"] + + +@pytest.mark.parametrize("payload", _REJECTED_WORKLOADS) +def test_public_compat_workload_rejects_invalid_contract(payload): + with pytest.raises(ValidationError): + compat.Workload.model_validate(payload) + + +def test_schema_adapter_uses_fallback_when_canonical_dependency_is_absent(): + if USING_CANONICAL_WORKLOAD_SCHEMA: + pytest.skip("internal environment has the canonical schema package") + + workload = workload_model_validate(_BASE_WORKLOAD) + + assert isinstance(workload, compat.Workload) + assert workload_model_dump(workload)["eval_mode"] == "full" diff --git a/tests/kernel_inventory/test_workloads.py b/tests/kernel_inventory/test_workloads.py new file mode 100644 index 00000000..cad4de84 --- /dev/null +++ b/tests/kernel_inventory/test_workloads.py @@ -0,0 +1,332 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +from __future__ import annotations + +import asyncio +import copy +import json +from pathlib import Path +from typing import Any + +import pytest + +from tilegym.kernel_inventory.layout import inventory_coordinate +from tilegym.kernel_inventory.layout import iter_inventory_workload_paths +from tilegym.kernel_inventory.layout import mirrored_definition_path +from tilegym.kernel_inventory.workloads import KernelWorkloadError +from tilegym.kernel_inventory.workloads import load_workload_jsonl +from tilegym.kernel_inventory.workloads import materialize_workload_inputs +from tilegym.kernel_inventory.workloads import validate_workload_against_definition +from tilegym.kernel_inventory.workloads import validate_workload_catalog + +REPO_ROOT = Path(__file__).resolve().parents[2] +_TOLERANCE = { + "max_atol": 0.02, + "max_rtol": 0.02, + "required_matched_ratio": 1.0, + "max_error_cap": None, + "allow_negative_inf": False, +} + + +def _payload(uuid: str = "276d6a74-bc55-4a5b-9d46-cb82b7f85e92", **updates: Any) -> dict[str, Any]: + payload = { + "uuid": uuid, + "axes": {"N": 8}, + "inputs": { + "x": {"type": "random"}, + "scale": {"type": "scalar", "value": 1.0}, + "enabled": {"type": "scalar", "value": False}, + "bias": {"type": "null"}, + }, + "tolerance": copy.deepcopy(_TOLERANCE), + "eval_mode": "full", + } + payload.update(updates) + return payload + + +def _definition() -> dict[str, Any]: + return { + "name": "op", + "axes": {"N": {"type": "var"}, "K": {"type": "const", "value": 4}}, + "inputs": { + "x": {"shape": ["N", "K"], "dtype": "float32"}, + "scale": {"shape": None, "dtype": "float32"}, + "enabled": {"shape": None, "dtype": "bool"}, + "bias": {"shape": ["K"], "dtype": "float32"}, + }, + "outputs": {"output": {"shape": ["N", "K"], "dtype": "float32"}}, + "reference": "def run(x, scale, enabled, bias=None):\n return x\n", + "constraints": ["N >= K", "enabled is False"], + } + + +def _write_jsonl(path: Path, *payloads: dict[str, Any]) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + "".join(json.dumps(payload, separators=(",", ":")) + "\n" for payload in payloads), encoding="utf-8" + ) + return path + + +def _write_json(path: Path, payload: dict[str, Any]) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload) + "\n", encoding="utf-8") + return path + + +def test_load_workload_jsonl_returns_path_and_line_records(tmp_path): + path = _write_jsonl( + tmp_path / "op.jsonl", + _payload(), + _payload("496432e2-102a-4541-b3f5-72c10f7e1f87", axes={"N": 16}), + ) + + records = load_workload_jsonl(path) + + assert [record.line_number for record in records] == [1, 2] + assert [record.path for record in records] == [path, path] + assert [record.workload.axes for record in records] == [{"N": 8}, {"N": 16}] + + +@pytest.mark.parametrize( + ("content", "match"), + [ + ("", "must not be empty"), + ("\n", "blank lines"), + (json.dumps(_payload()) + "\n\n", "blank lines"), + ("# comment\n", "comments"), + ("{bad json}\n", "invalid JSON"), + ("[]\n", "expected one JSON object"), + (json.dumps(_payload()) + json.dumps(_payload()) + "\n", "invalid JSON"), + ], +) +def test_load_workload_jsonl_rejects_noncanonical_physical_lines(tmp_path, content, match): + path = tmp_path / "invalid.jsonl" + path.write_text(content, encoding="utf-8") + + with pytest.raises(KernelWorkloadError, match=match): + load_workload_jsonl(path) + + +@pytest.mark.parametrize( + ("mutate", "match"), + [ + (lambda payload: payload.update(private_extension=True), "unsupported Workload fields"), + (lambda payload: payload.pop("tolerance"), "missing explicit Workload fields"), + (lambda payload: payload["tolerance"].pop("max_error_cap"), "tolerance fields mismatch"), + (lambda payload: payload["tolerance"].update(max_atol=0.01), "migration policy"), + (lambda payload: payload.update(eval_mode="correctness_only"), "eval_mode must be 'full'"), + (lambda payload: payload.update(uuid="not-a-uuid"), "UUIDv4"), + (lambda payload: payload.update(uuid="00000000-0000-1000-8000-000000000000"), "UUIDv4"), + ( + lambda payload: payload.update(inputs={"scale": {"type": "scalar", "value": float("inf")}}), + "numbers must be finite", + ), + ], +) +def test_checked_in_workload_policy_is_stricter_than_schema(tmp_path, mutate, match): + payload = _payload() + mutate(payload) + path = _write_jsonl(tmp_path / "invalid.jsonl", payload) + + with pytest.raises(KernelWorkloadError, match=match): + load_workload_jsonl(path) + + +def test_validate_workload_against_definition_accepts_exact_axes_inputs_and_constraint(tmp_path): + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", _payload()))[0] + + validate_workload_against_definition(record, _definition(), tmp_path / "op.json") + + +@pytest.mark.parametrize( + ("updates", "match"), + [ + ({"axes": {}}, "axes mismatch"), + ({"axes": {"N": 8, "K": 4}}, "unknown_or_constant"), + ({"inputs": {"x": {"type": "random"}}}, "inputs mismatch"), + ( + { + "inputs": { + "x": {"type": "scalar", "value": 1.0}, + "scale": {"type": "scalar", "value": 1.0}, + "enabled": {"type": "scalar", "value": False}, + "bias": {"type": "null"}, + } + }, + "scalar descriptor for tensor", + ), + ( + { + "inputs": { + "x": {"type": "random"}, + "scale": {"type": "random"}, + "enabled": {"type": "scalar", "value": False}, + "bias": {"type": "null"}, + } + }, + "tensor descriptor for scalar", + ), + ( + { + "inputs": { + "x": {"type": "random"}, + "scale": {"type": "scalar", "value": True}, + "enabled": {"type": "scalar", "value": False}, + "bias": {"type": "null"}, + } + }, + "incompatible with scalar dtype", + ), + ( + { + "inputs": { + "x": {"type": "random"}, + "scale": {"type": "scalar", "value": 1.0}, + "enabled": {"type": "scalar", "value": True}, + "bias": {"type": "null"}, + } + }, + "violates Definition constraint", + ), + ], +) +def test_validate_workload_against_definition_rejects_contract_mismatch(tmp_path, updates, match): + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", _payload(**updates)))[0] + + with pytest.raises(KernelWorkloadError, match=match): + validate_workload_against_definition(record, _definition(), tmp_path / "op.json") + + +def test_null_descriptor_requires_explicit_none_reference_default(tmp_path): + definition = _definition() + definition["reference"] = "def run(x, scale, enabled, bias):\n return x\n" + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", _payload()))[0] + + with pytest.raises(KernelWorkloadError, match="explicit None default"): + validate_workload_against_definition(record, definition, tmp_path / "op.json") + + +def test_validate_workload_against_definition_checks_safetensors_asset_path(tmp_path): + payload = _payload( + axes={"N": 2}, + inputs={"x": {"type": "safetensors", "path": "input.safetensors", "tensor_key": "x"}}, + ) + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", payload))[0] + definition = { + "axes": {"N": {"type": "var"}, "K": {"type": "const", "value": 4}}, + "inputs": {"x": {"shape": ["N", "K"], "dtype": "float32"}}, + } + + with pytest.raises(KernelWorkloadError, match="safetensors file does not exist"): + validate_workload_against_definition(record, definition, tmp_path / "op.json") + + asset = tmp_path / "input.safetensors" + asset.touch() + validate_workload_against_definition(record, definition, tmp_path / "op.json") + + +def test_materialize_workload_inputs_uses_definition_shape_dtype_and_literals(tmp_path): + torch = pytest.importorskip("torch") + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", _payload()))[0] + + torch.manual_seed(2026) + axes, inputs = materialize_workload_inputs(record, _definition(), torch=torch, device=torch.device("cpu")) + + assert axes == {"K": 4, "N": 8} + assert inputs["x"].shape == (8, 4) + assert inputs["x"].dtype == torch.float32 + assert inputs["scale"] == 1.0 + assert inputs["enabled"] is False + assert inputs["bias"] is None + + +def test_materialize_workload_inputs_loads_safetensors_relative_to_workload(tmp_path): + torch = pytest.importorskip("torch") + safetensors = pytest.importorskip("safetensors.torch") + tensor = torch.arange(8, dtype=torch.float32).reshape(2, 4) + asset = tmp_path / "input.safetensors" + safetensors.save_file({"x": tensor}, asset) + payload = _payload( + axes={"N": 2}, + inputs={"x": {"type": "safetensors", "path": "input.safetensors", "tensor_key": "x"}}, + ) + record = load_workload_jsonl(_write_jsonl(tmp_path / "op.jsonl", payload))[0] + definition = { + "axes": {"N": {"type": "var"}, "K": {"type": "const", "value": 4}}, + "inputs": {"x": {"shape": ["N", "K"], "dtype": "float32"}}, + } + + _, inputs = materialize_workload_inputs(record, definition, torch=torch, device=torch.device("cpu")) + + assert torch.equal(inputs["x"], tensor) + assert inputs["x"].is_contiguous() + + +def test_materialize_workload_inputs_rejects_non_basename_safetensors_path(tmp_path): + torch = pytest.importorskip("torch") + payload = _payload( + axes={"N": 2}, + inputs={"x": {"type": "safetensors", "path": "../escape.safetensors", "tensor_key": "x"}}, + ) + record = load_workload_jsonl(_write_jsonl(tmp_path / "owner/op.jsonl", payload))[0] + definition = { + "axes": {"N": {"type": "var"}, "K": {"type": "const", "value": 4}}, + "inputs": {"x": {"shape": ["N", "K"], "dtype": "float32"}}, + } + + with pytest.raises(KernelWorkloadError, match="must be a .safetensors basename"): + materialize_workload_inputs(record, definition, torch=torch, device=torch.device("cpu")) + + +def test_validate_workload_catalog_checks_completeness_and_uuid_uniqueness(tmp_path): + inventory = tmp_path / "src/tilegym/transformers/example" + definition = _definition() + _write_json(inventory / "kernel_definitions/op.json", definition) + _write_json(inventory / "kernel_solutions/op.json", {"name": "op"}) + workload = _write_jsonl(inventory / "kernel_workloads/op/workload.jsonl", _payload()) + + validate_workload_catalog(tmp_path, require_complete=True) + + _write_json(inventory / "kernel_definitions/second.json", definition | {"name": "second"}) + _write_json(inventory / "kernel_solutions/second.json", {"name": "second"}) + with pytest.raises(KernelWorkloadError, match="missing mirrored Workloads"): + validate_workload_catalog(tmp_path, require_complete=True) + + _write_jsonl(inventory / "kernel_workloads/second/workload.jsonl", _payload()) + with pytest.raises(KernelWorkloadError, match="duplicate Workload UUID"): + validate_workload_catalog(tmp_path, require_complete=True) + + workload.unlink() + validate_workload_catalog(tmp_path, require_complete=False) + + +def test_validate_workload_catalog_rejects_stale_workload(tmp_path): + inventory = tmp_path / "src/tilegym/transformers/example" + (inventory / "kernel_definitions").mkdir(parents=True) + (inventory / "kernel_solutions").mkdir() + _write_jsonl(inventory / "kernel_workloads/stale/workload.jsonl", _payload()) + + with pytest.raises(KernelWorkloadError, match="Stale Workload"): + validate_workload_catalog(tmp_path, require_complete=False) + + +def test_validate_workload_catalog_rejects_orphan_safetensors_asset(tmp_path): + inventory = tmp_path / "src/tilegym/transformers/example" + definition = _definition() + _write_json(inventory / "kernel_definitions/op.json", definition) + _write_json(inventory / "kernel_solutions/op.json", {"name": "op"}) + workload = _write_jsonl(inventory / "kernel_workloads/op/workload.jsonl", _payload()) + del workload + (inventory / "kernel_workloads/op/orphan.safetensors").touch() + + with pytest.raises(KernelWorkloadError, match="Orphan safetensors"): + validate_workload_catalog(tmp_path, require_complete=True) + + +def test_checked_in_workload_catalog_is_complete_and_valid(): + validate_workload_catalog(REPO_ROOT, require_complete=True) From 34f35a18e9a8aa8dd2e93e0feeecab1a55e90fcb Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Tue, 1 Sep 2026 02:48:40 -0700 Subject: [PATCH 05/11] Tune cuTile gemm_alpha_beta for SM-throttled launches --- src/tilegym/suites/flashinfer/cutile/gemm/gemm_alpha_beta.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/tilegym/suites/flashinfer/cutile/gemm/gemm_alpha_beta.py b/src/tilegym/suites/flashinfer/cutile/gemm/gemm_alpha_beta.py index 212faff4..3fe0f03d 100644 --- a/src/tilegym/suites/flashinfer/cutile/gemm/gemm_alpha_beta.py +++ b/src/tilegym/suites/flashinfer/cutile/gemm/gemm_alpha_beta.py @@ -387,9 +387,7 @@ def gemm_alpha_beta( # Check if autotune is requested use_autotune = kwargs.get("use_autotune", True) - # For very low SM counts (1-16), disable autotune and use fixed config - # to avoid autotune overhead dominating runtime - if num_sms is not None and num_sms <= 16: + if num_sms is not None and num_sms <= 1: use_autotune = False if use_autotune: From d44fb277ac287df608ab199badc3f7d02387ac6d Mon Sep 17 00:00:00 2001 From: Changhao Wu Date: Tue, 1 Sep 2026 11:32:41 -0700 Subject: [PATCH 06/11] Make the tilecpp optimization hints arch-agnostic --- src/tilegym/ops/tilecpp/attention.cuh | 18 ++++----- .../ops/tilecpp/attention_sink_decode.cuh | 1 - src/tilegym/ops/tilecpp/bmm.cuh | 4 +- .../ops/tilecpp/chunk_gated_delta_rule.cuh | 3 +- .../ops/tilecpp/chunk_gated_delta_rule.py | 2 +- src/tilegym/ops/tilecpp/gemma_attention.cuh | 10 ++--- .../ops/tilecpp/gemma_attention_decode.cuh | 1 - src/tilegym/ops/tilecpp/matmul.cuh | 4 +- src/tilegym/ops/tilecpp/mla.cuh | 4 +- .../ops/tilecpp/mla_decoding_split_kv.cuh | 16 ++++---- .../ops/tilecpp/persistent_layer_norm.cuh | 10 ++--- src/tilegym/ops/tilecpp/persistent_matmul.cuh | 2 +- src/tilegym/ops/tilecpp/rms_norm.cuh | 37 +++++++++---------- src/tilegym/ops/tilecpp/softmax.cuh | 2 + src/tilegym/ops/tilecpp/splitk_reduce.cuh | 7 ++-- src/tilegym/ops/tilecpp/swiglu.cuh | 6 +-- 16 files changed, 62 insertions(+), 65 deletions(-) diff --git a/src/tilegym/ops/tilecpp/attention.cuh b/src/tilegym/ops/tilecpp/attention.cuh index efa44720..141c426b 100644 --- a/src/tilegym/ops/tilecpp/attention.cuh +++ b/src/tilegym/ops/tilecpp/attention.cuh @@ -49,7 +49,7 @@ constexpr float ATTENTION_INV_LOG_2 = 1.442695040888963f; template -[[ using cutile : hint(1000, num_cta_in_cga=NUM_CTAS, occupancy=occupancy)]] +[[ using cutile : hint(0, num_cta_in_cga=NUM_CTAS, occupancy=occupancy)]] __tile_global__ void prefill_fmha_fwd_kernel( const T* __restrict__ Q_ptr, const T* __restrict__ K_ptr, @@ -158,7 +158,7 @@ __tile_global__ void prefill_fmha_fwd_kernel( // --------------------------------------------------------------------- for (auto kv_block : ct::irange(0, unmasked_end)) { T_4D_N k_raw; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] k_raw = K_view.load(batch_idx, off_kv_h, kv_block, 0); auto k_4d_T = ct::permute(k_raw, ct::dimension_map<0, 1, 3, 2>{}); auto k_t = ct::reshape(k_4d_T, ct::shape{}); @@ -177,7 +177,7 @@ __tile_global__ void prefill_fmha_fwd_kernel( acc = acc * alpha; T_4D_N v_4d; - [[ using cutile : hint(1000, latency=4) ]] + [[ using cutile : hint(0, latency=4) ]] v_4d = V_view.load(batch_idx, off_kv_h, kv_block, 0); auto v = ct::reshape(v_4d, ct::shape{}); @@ -195,10 +195,10 @@ __tile_global__ void prefill_fmha_fwd_kernel( T_4D_N k_raw; if constexpr (EVEN_K) { - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] k_raw = K_view.load(batch_idx, off_kv_h, kv_block, 0); } else { - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] k_raw = K_view.load_masked(batch_idx, off_kv_h, kv_block, 0); } auto k_4d_T = ct::permute(k_raw, ct::dimension_map<0, 1, 3, 2>{}); @@ -232,10 +232,10 @@ __tile_global__ void prefill_fmha_fwd_kernel( T_4D_N v_4d; if constexpr (EVEN_K) { - [[ using cutile : hint(1000, latency=4) ]] + [[ using cutile : hint(0, latency=4) ]] v_4d = V_view.load(batch_idx, off_kv_h, kv_block, 0); } else { - [[ using cutile : hint(1000, latency=4) ]] + [[ using cutile : hint(0, latency=4) ]] v_4d = V_view.load_masked(batch_idx, off_kv_h, kv_block, 0); } auto v = ct::reshape(v_4d, ct::shape{}); @@ -281,7 +281,7 @@ __tile_global__ void prefill_fmha_fwd_kernel( * minus_L = -L */ template -[[ using cutile : hint(1000, occupancy=occupancy)]] +[[ using cutile : hint(0, occupancy=occupancy)]] __tile_global__ void fmha_bwd_preprocess_kernel( const T* __restrict__ Out_ptr, const T* __restrict__ dO_ptr, @@ -358,7 +358,7 @@ __tile_global__ void fmha_bwd_preprocess_kernel( * Computes gradients for Q, K, V in the attention backward pass. */ template -[[ using cutile : hint(1000, occupancy=occupancy)]] +[[ using cutile : hint(0, occupancy=occupancy)]] __tile_global__ void fmha_bwd_main_kernel( const T* __restrict__ Q_ptr, const T* __restrict__ K_ptr, diff --git a/src/tilegym/ops/tilecpp/attention_sink_decode.cuh b/src/tilegym/ops/tilecpp/attention_sink_decode.cuh index 4c0d060c..a543818c 100644 --- a/src/tilegym/ops/tilecpp/attention_sink_decode.cuh +++ b/src/tilegym/ops/tilecpp/attention_sink_decode.cuh @@ -28,7 +28,6 @@ template -[[ using cutile : hint(1000, occupancy=occupancy) ]] __tile_global__ void attention_sink_decode_kernel( const T* __restrict__ Q_ptr, // [B, H_KV, NUM_Q_HEAD_PER_KV, HEAD_DIM] const T* __restrict__ K_ptr, // [B, H_KV, S_KV, HEAD_DIM] diff --git a/src/tilegym/ops/tilecpp/bmm.cuh b/src/tilegym/ops/tilecpp/bmm.cuh index c33bf9bf..fc94e06d 100644 --- a/src/tilegym/ops/tilecpp/bmm.cuh +++ b/src/tilegym/ops/tilecpp/bmm.cuh @@ -137,8 +137,8 @@ __tile_global__ void bmm_kernel( */ template [[ using cutile : - hint(1000, num_cta_in_cga=num_ctas), - hint(1000, occupancy=occupancy) + hint(0, num_cta_in_cga=num_ctas), + hint(0, occupancy=occupancy) ]] __tile_global__ void bmm_static_persistent_kernel( const T* __restrict__ _a_ptr, diff --git a/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.cuh b/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.cuh index ed947f8d..4c92851c 100644 --- a/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.cuh +++ b/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.cuh @@ -80,7 +80,7 @@ template -[[ using cutile : hint(1000, occupancy=occupancy) ]] +[[ using cutile : hint(0, occupancy=occupancy) ]] __tile_global__ void chunk_gated_delta_rule_intra_kernel( const T* __restrict__ Q, // (B, T, H, K) const T* __restrict__ K, // (B, T, H, K) @@ -274,7 +274,6 @@ template -[[ using cutile : hint(1000, occupancy=occupancy) ]] __tile_global__ void chunk_gated_delta_rule_inter_kernel( const float* __restrict__ Q_ch, // (B, H, num_chunks, CS, K) const float* __restrict__ K_ch, // (B, H, num_chunks, CS, K) diff --git a/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.py b/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.py index fae133fa..3964b082 100644 --- a/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.py +++ b/src/tilegym/ops/tilecpp/chunk_gated_delta_rule.py @@ -63,7 +63,7 @@ def _launch_intra( dtype = Q.dtype dump_kernel_types("chunk_gated_delta_rule_intra_kernel", Q, K, V, Beta, G) - occupancy = 1 + occupancy = 2 bool_to_str = lambda b: "true" if b else "false" beta_cpp_type = get_cpp_type(Beta.dtype) g_cpp_type = get_cpp_type(G.dtype) diff --git a/src/tilegym/ops/tilecpp/gemma_attention.cuh b/src/tilegym/ops/tilecpp/gemma_attention.cuh index 4204b761..d4666125 100644 --- a/src/tilegym/ops/tilecpp/gemma_attention.cuh +++ b/src/tilegym/ops/tilecpp/gemma_attention.cuh @@ -32,7 +32,7 @@ template -[[ using cutile : hint(1000, occupancy=occupancy) ]] +[[ using cutile : hint(0, occupancy=occupancy) ]] __tile_global__ void gemma_attention_fwd_kernel( const T* __restrict__ Q_ptr, const T* __restrict__ K_ptr, @@ -128,7 +128,7 @@ __tile_global__ void gemma_attention_fwd_kernel( int curr_n = kv_block * BLOCK_N; T_4D_N k_raw; - [[ using cutile : hint(1000, latency=3) ]] + [[ using cutile : hint(0, latency=3) ]] k_raw = K_view.load(batch_idx, off_kv_h, kv_block, 0); auto k_4d_T = ct::permute(k_raw, ct::dimension_map<0, 1, 3, 2>{}); auto k_t = ct::reshape(k_4d_T, ct::shape{}); @@ -202,7 +202,7 @@ __tile_global__ void gemma_attention_fwd_kernel( ct::round_subnormals_to_zero_t{}); T_4D_N v_4d; - [[ using cutile : hint(1000, latency=3) ]] + [[ using cutile : hint(0, latency=3) ]] v_4d = V_view.load(batch_idx, off_kv_h, kv_block, 0); auto v = ct::reshape(v_4d, ct::shape{}); auto p_T = ct::element_cast(p); @@ -216,7 +216,7 @@ __tile_global__ void gemma_attention_fwd_kernel( int curr_n = kv_block * BLOCK_N; T_4D_N k_raw; - [[ using cutile : hint(1000, latency=3) ]] + [[ using cutile : hint(0, latency=3) ]] k_raw = K_view.load(batch_idx, off_kv_h, kv_block, 0); auto k_4d_T = ct::permute(k_raw, ct::dimension_map<0, 1, 3, 2>{}); auto k_t = ct::reshape(k_4d_T, ct::shape{}); @@ -284,7 +284,7 @@ __tile_global__ void gemma_attention_fwd_kernel( ct::round_subnormals_to_zero_t{}); T_4D_N v_4d; - [[ using cutile : hint(1000, latency=3) ]] + [[ using cutile : hint(0, latency=3) ]] v_4d = V_view.load(batch_idx, off_kv_h, kv_block, 0); auto v = ct::reshape(v_4d, ct::shape{}); auto p_T = ct::element_cast(p); diff --git a/src/tilegym/ops/tilecpp/gemma_attention_decode.cuh b/src/tilegym/ops/tilecpp/gemma_attention_decode.cuh index 2b58008d..4a326eed 100644 --- a/src/tilegym/ops/tilecpp/gemma_attention_decode.cuh +++ b/src/tilegym/ops/tilecpp/gemma_attention_decode.cuh @@ -35,7 +35,6 @@ template -[[ using cutile : hint(1000, occupancy=occupancy) ]] __tile_global__ void gemma_attention_decode_kernel( const T* __restrict__ Q_ptr, // [B, H_KV, Q_PER_KV, HEAD_DIM] const T* __restrict__ K_ptr, // [B, H_KV, S_KV, HEAD_DIM] diff --git a/src/tilegym/ops/tilecpp/matmul.cuh b/src/tilegym/ops/tilecpp/matmul.cuh index 92aa0f56..5c21b138 100644 --- a/src/tilegym/ops/tilecpp/matmul.cuh +++ b/src/tilegym/ops/tilecpp/matmul.cuh @@ -36,8 +36,8 @@ template [[ using cutile : - hint(1000, num_cta_in_cga=num_ctas), - hint(1000, occupancy=occupancy) + hint(0, num_cta_in_cga=num_ctas), + hint(0, occupancy=occupancy) ]] __tile_global__ void matmul_kernel( const T* __restrict__ _A, diff --git a/src/tilegym/ops/tilecpp/mla.cuh b/src/tilegym/ops/tilecpp/mla.cuh index 494e78c9..777ace91 100644 --- a/src/tilegym/ops/tilecpp/mla.cuh +++ b/src/tilegym/ops/tilecpp/mla.cuh @@ -37,9 +37,7 @@ constexpr float MLA_NEG_INF = -1.0e6f; template -[[ using cutile : hint(800,num_cta_in_cga=NUM_CTAS, occupancy=OCCUPANCY) ]] -[[ using cutile : hint(900,num_cta_in_cga=NUM_CTAS, occupancy=OCCUPANCY) ]] -[[ using cutile : hint(1000,num_cta_in_cga=NUM_CTAS, occupancy=OCCUPANCY) ]] +[[ using cutile : hint(0,num_cta_in_cga=NUM_CTAS, occupancy=OCCUPANCY) ]] __tile_global__ void prefill_mla_kernel( const T* __restrict__ _Q_ptr, const T* __restrict__ _QPE_ptr, diff --git a/src/tilegym/ops/tilecpp/mla_decoding_split_kv.cuh b/src/tilegym/ops/tilecpp/mla_decoding_split_kv.cuh index 6fc449ef..ff90f3b2 100644 --- a/src/tilegym/ops/tilecpp/mla_decoding_split_kv.cuh +++ b/src/tilegym/ops/tilecpp/mla_decoding_split_kv.cuh @@ -23,7 +23,7 @@ constexpr float MLA_INV_LOG_2 = 1.0f / 0.693147180559945309417232121458176568f; template -[[ using cutile : hint(1000, occupancy=2) ]] +[[ using cutile : hint(0, occupancy=2) ]] __tile_global__ void naive_absorb_mla_transpose( const T* __restrict__ Q_ptr, // [B, NUM_HEADS, TILE_D] const T* __restrict__ QPE_ptr, // [B, NUM_HEADS, TILE_KPE] @@ -83,7 +83,7 @@ __tile_global__ void naive_absorb_mla_transpose( ct::tensor_span{Q_ptr, ct::extents{}}, ct::shape<1, TILE_H, TILE_D>{}); Q_3D_Tile q_loaded; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] q_loaded = Q_view.template load_masked(batch_idx, pid_x, 0); auto q_3d = ct::permute(q_loaded, ct::dimension_map<0, 2, 1>{}); auto q = ct::reshape(q_3d, ct::shape{}); @@ -93,7 +93,7 @@ __tile_global__ void naive_absorb_mla_transpose( ct::tensor_span{QPE_ptr, ct::extents{}}, ct::shape<1, TILE_H, TILE_KPE>{}); QPE_3D_Tile qpe_loaded; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] qpe_loaded = QPE_view.template load_masked(batch_idx, pid_x, 0); auto qpe_3d = ct::permute(qpe_loaded, ct::dimension_map<0, 2, 1>{}); auto qpe = ct::reshape(qpe_3d, ct::shape{}); @@ -116,7 +116,7 @@ __tile_global__ void naive_absorb_mla_transpose( ct::tensor_span{K_ptr, ct::extents{}}, ct::shape<1, TILE_N, TILE_D>{}); K_3D_Tile k_loaded; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] k_loaded = K_view.template load_masked(batch_idx, cnt, 0); auto k = ct::reshape(k_loaded, ct::shape{}); auto qk = ct::mma(k, q, ct::zeros()); @@ -127,7 +127,7 @@ __tile_global__ void naive_absorb_mla_transpose( ct::tensor_span{KPE_ptr, ct::extents{}}, ct::shape<1, TILE_N, TILE_KPE>{}); KPE_3D_Tile kpe_loaded; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] kpe_loaded = KPE_view.template load_masked(batch_idx, cnt, 0); auto kpe = ct::reshape(kpe_loaded, ct::shape{}); qk = ct::mma(kpe, qpe, qk); @@ -164,7 +164,7 @@ __tile_global__ void naive_absorb_mla_transpose( ct::tensor_span{V_ptr, ct::extents{}}, ct::shape<1, TILE_N, TILE_D>{}); V_3D_Tile v_loaded; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] v_loaded = V_view.template load_masked(batch_idx, cnt, 0); auto v_3d = ct::permute(v_loaded, ct::dimension_map<0, 2, 1>{}); auto v_t = ct::reshape(v_3d, ct::shape{}); @@ -189,7 +189,7 @@ __tile_global__ void naive_absorb_mla_transpose( auto Out_span = ct::tensor_span{Att_Out_ptr, ct::extents{}}; auto Out_view = ct::partition_view(Out_span, ct::shape<1, TILE_H, 1, TILE_D>{}); auto acc_out_4d = ct::reshape(acc_out_T, ct::shape<1, TILE_H, 1, TILE_D>{}); - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] Out_view.store(acc_out_4d, batch_idx, pid_x, tile_idx, 0); // Store log sum exp for this tile with latency hint @@ -197,6 +197,6 @@ __tile_global__ void naive_absorb_mla_transpose( auto lse_offsets = (batch_idx * NUM_HEADS + idx_head) * NUM_KV_SPLITS + tile_idx; auto lse_mask = idx_head < NUM_HEADS; auto lse_ptrs = LSE_Out_ptr + lse_offsets; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] ct::store_masked(lse_ptrs, l_sum, lse_mask); } diff --git a/src/tilegym/ops/tilecpp/persistent_layer_norm.cuh b/src/tilegym/ops/tilecpp/persistent_layer_norm.cuh index 3ecc679d..17e74d7c 100644 --- a/src/tilegym/ops/tilecpp/persistent_layer_norm.cuh +++ b/src/tilegym/ops/tilecpp/persistent_layer_norm.cuh @@ -36,7 +36,7 @@ template // ← compile-time so the (var + eps) reshape/broadcast hoists out of the for-loop -[[ using cutile : hint(1000, num_cta_in_cga=1) ]] +[[ using cutile : hint(0, num_cta_in_cga=1) ]] __tile_global__ void persistent_layer_norm_fwd_kernel( const T* __restrict__ X, // (N, D) T* __restrict__ Y, // (N, D) @@ -102,7 +102,7 @@ __tile_global__ void persistent_layer_norm_fwd_kernel( for (auto current_pid : ct::irange(pid, upper_bound, NUM_SMS)) { TileXNxD x_tile; - [[ using cutile : hint(1000, latency=4) ]] + [[ using cutile : hint(0, latency=4) ]] x_tile = pX.load_masked(current_pid, 0); auto x = ct::element_cast(x_tile); @@ -123,9 +123,9 @@ __tile_global__ void persistent_layer_norm_fwd_kernel( rstd = ct::rsqrt(var + eps_tile); if constexpr (TRAINING) { - [[ using cutile : hint(1000, allow_tma=false) ]] + [[ using cutile : hint(0, allow_tma=false) ]] pMean.store_masked(mean, current_pid); - [[ using cutile : hint(1000, allow_tma=false) ]] + [[ using cutile : hint(0, allow_tma=false) ]] pRstd.store_masked(rstd, current_pid); } } else { @@ -148,7 +148,7 @@ __tile_global__ void persistent_layer_norm_fwd_kernel( } auto y_T = ct::element_cast(y_f32); - [[ using cutile : hint(1000, allow_tma=false) ]] + [[ using cutile : hint(0, allow_tma=false) ]] pY.store_masked(y_T, current_pid, 0); } } diff --git a/src/tilegym/ops/tilecpp/persistent_matmul.cuh b/src/tilegym/ops/tilecpp/persistent_matmul.cuh index bbaa3603..b2700ec4 100644 --- a/src/tilegym/ops/tilecpp/persistent_matmul.cuh +++ b/src/tilegym/ops/tilecpp/persistent_matmul.cuh @@ -25,7 +25,7 @@ template -[[ using cutile : hint(1000, num_cta_in_cga=num_ctas, occupancy=occupancy) ]] +[[ using cutile : hint(0, num_cta_in_cga=num_ctas, occupancy=occupancy) ]] __tile_global__ void static_persistent_matmul_kernel( const T* __restrict__ _A, const T* __restrict__ _B, diff --git a/src/tilegym/ops/tilecpp/rms_norm.cuh b/src/tilegym/ops/tilecpp/rms_norm.cuh index f31da304..add8a9f6 100644 --- a/src/tilegym/ops/tilecpp/rms_norm.cuh +++ b/src/tilegym/ops/tilecpp/rms_norm.cuh @@ -73,11 +73,11 @@ __tile_global__ void rms_norm_kernel( auto cols = ct::iota() + j; TxBS xj_raw; if constexpr (EVEN_N) { - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] xj_raw = ct::load(X_row + cols); } else { auto mask = cols < N; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] xj_raw = ct::load_masked(X_row + cols, mask, zero_pad); } auto xj = ct::element_cast(xj_raw); @@ -97,15 +97,15 @@ __tile_global__ void rms_norm_kernel( WxBS wj_raw; TxBS xj_raw; if constexpr (EVEN_N) { - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] wj_raw = ct::load(W_aligned + cols); - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] xj_raw = ct::load(X_row + cols); } else { auto mask = cols < N; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] wj_raw = ct::load_masked(W_aligned + cols, mask, zero_pad_w); - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] xj_raw = ct::load_masked(X_row + cols, mask, zero_pad); } auto wj = ct::element_cast(wj_raw); @@ -113,11 +113,11 @@ __tile_global__ void rms_norm_kernel( auto yj_f32 = xj * rms * (OFFSET + wj); auto yj = ct::element_cast(yj_f32); if constexpr (EVEN_N) { - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] ct::store(Y_row + cols, yj); } else { auto mask = cols < N; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] ct::store_masked(Y_row + cols, yj, mask); } } @@ -175,7 +175,7 @@ __tile_global__ void rms_norm_multi_wave_cached_kernel( auto mask = cols < N; // Cache the row in registers: loaded once, reused after the reduction. - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] auto xj_raw = ct::load_masked(X_row + cols, mask, ct::zeros()); auto xj = ct::element_cast(xj_raw); @@ -184,13 +184,13 @@ __tile_global__ void rms_norm_multi_wave_cached_kernel( rstd_aligned[row] = rms; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] auto wj_raw = ct::load_masked(W_aligned + cols, mask, ct::zeros()); auto wj = ct::element_cast(wj_raw); auto yj = ct::element_cast(xj * rms * (OFFSET + wj)); - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] ct::store_masked(Y_row + cols, yj, mask); } @@ -201,7 +201,6 @@ __tile_global__ void rms_norm_multi_wave_cached_kernel( * T, M, N, BLOCK_SIZE (= N). */ template -[[ using cutile : hint(1000, num_cta_in_cga=1) ]] __tile_global__ void rms_norm_kernel_pv( const T* __restrict__ X, const WT* __restrict__ W, @@ -232,12 +231,12 @@ __tile_global__ void rms_norm_kernel_pv( ct::shape{}); ct::tile> w_loaded; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] w_loaded = W_view.load(0); auto w = ct::element_cast(w_loaded); TxBS x_loaded; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] x_loaded = X_view.load(row, 0); auto x = ct::element_cast(x_loaded); @@ -255,7 +254,7 @@ __tile_global__ void rms_norm_kernel_pv( auto y_2d = ct::reshape>(y_1d); auto y_out = ct::element_cast(y_2d); - [[ using cutile : hint(1000, allow_tma=false) ]] + [[ using cutile : hint(0, allow_tma=false) ]] Y_view.store(y_out, row, 0); } @@ -290,7 +289,6 @@ template -[[ using cutile : hint(1000, num_cta_in_cga=1, occupancy=occupancy) ]] __tile_global__ void rms_norm_static_persistent_kernel( const T* __restrict__ X, T* __restrict__ Y, @@ -338,7 +336,7 @@ __tile_global__ void rms_norm_static_persistent_kernel( constexpr float inv_N = 1.0f / static_cast(N); for (auto current_bid : ct::irange(pid, upper_bound, NUM_SMS)) { - [[ using cutile : hint(1000, latency=10) ]] + [[ using cutile : hint(0, latency=10) ]] auto x_tile = pX.load_masked(current_bid, 0); auto x = ct::element_cast(x_tile); @@ -348,14 +346,14 @@ __tile_global__ void rms_norm_static_persistent_kernel( auto variance = sq_sum * inv_N; auto rstd_col = ct::rsqrt(variance + EPS); // (TILE_M, 1) - [[ using cutile : hint(1000, allow_tma=false) ]] + [[ using cutile : hint(0, allow_tma=false) ]] pRstd.store(ct::reshape>(rstd_col), current_bid); auto x_norm = x * rstd_col; // broadcast (TILE_M,1) auto y_f32 = x_norm * w_bcast; // broadcast (1,TILE_N) auto y_T = ct::element_cast(y_f32); - [[ using cutile : hint(1000, allow_tma=false, latency=3) ]] + [[ using cutile : hint(0, allow_tma=false, latency=3) ]] pY.store_masked(y_T, current_bid, 0); } } @@ -387,6 +385,7 @@ __tile_global__ void rms_norm_static_persistent_kernel( * N: Number of columns */ template +[[ using cutile : hint(0, occupancy=1) ]] __tile_global__ void rms_norm_backward_dx_kernel( T* __restrict__ DX, const T* __restrict__ DY, diff --git a/src/tilegym/ops/tilecpp/softmax.cuh b/src/tilegym/ops/tilecpp/softmax.cuh index cc25f98c..6da32785 100644 --- a/src/tilegym/ops/tilecpp/softmax.cuh +++ b/src/tilegym/ops/tilecpp/softmax.cuh @@ -33,6 +33,7 @@ * num_programs: Total number of CTA programs for persistent scheduling */ template +[[ using cutile : hint(0, occupancy=(TMA_ROWS ? 2 : 4)) ]] __tile_global__ void softmax_kernel( T* __restrict__ _output, const T* __restrict__ _input, @@ -137,6 +138,7 @@ __tile_global__ void softmax_kernel( * and stores so out-of-bounds lanes are not written. */ template +[[ using cutile : hint(0, occupancy=2) ]] __tile_global__ void online_softmax_kernel( T* __restrict__ output, const T* __restrict__ input, diff --git a/src/tilegym/ops/tilecpp/splitk_reduce.cuh b/src/tilegym/ops/tilecpp/splitk_reduce.cuh index 8e105b9b..ea9eb233 100644 --- a/src/tilegym/ops/tilecpp/splitk_reduce.cuh +++ b/src/tilegym/ops/tilecpp/splitk_reduce.cuh @@ -38,7 +38,8 @@ constexpr float SPLITK_NEG_INF = -1e30f; // Use large negative instead of -INFIN template -[[ using cutile : hint(1000, occupancy=4) ]] +[[ using cutile : hint(0, occupancy=4) ]] +[[ using cutile : hint(800, occupancy=2) ]] __tile_global__ void splitk_reduce_kernel( const T* __restrict__ attn_splitk_out, const float* __restrict__ lse_splitk_out, @@ -113,7 +114,7 @@ __tile_global__ void splitk_reduce_kernel( using Tx4D = ct::tile>; Tx4D out_splitk_raw_4d; - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] out_splitk_raw_4d = Att_view.template load_masked( batch_id, head_id, 0, block_id); auto out_splitk_raw = ct::reshape( @@ -141,6 +142,6 @@ __tile_global__ void splitk_reduce_kernel( // Convert back to output type and store via partition_view. auto acc_out = ct::element_cast(acc); auto acc_out_3d = ct::reshape(acc_out, ct::shape<1, 1, BLOCK_D>{}); - [[ using cutile : hint(1000, latency=2) ]] + [[ using cutile : hint(0, latency=2) ]] Out_view.store_masked(acc_out_3d, batch_id, head_id, block_id); } diff --git a/src/tilegym/ops/tilecpp/swiglu.cuh b/src/tilegym/ops/tilecpp/swiglu.cuh index 2ca8762f..b809415a 100644 --- a/src/tilegym/ops/tilecpp/swiglu.cuh +++ b/src/tilegym/ops/tilecpp/swiglu.cuh @@ -198,9 +198,9 @@ __tile_global__ void swiglu_forward_kernel_gather( auto mask = col_offs < n_cols; TxN a_val, b_val; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] a_val = ct::load_masked(a_row + col_offs, mask, T(0)); - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] b_val = ct::load_masked(b_row + col_offs, mask, T(0)); auto a_f32 = ct::element_cast(a_val); @@ -219,6 +219,6 @@ __tile_global__ void swiglu_forward_kernel_gather( auto silu_T = ct::element_cast(silu_f32); auto c_val = silu_T * b_val; - [[ using cutile : hint(1000, latency=1) ]] + [[ using cutile : hint(0, latency=1) ]] ct::store_masked(c_row + col_offs, c_val, mask); } From 637a3d0c476d61799413b982c91a6d333efea192 Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Wed, 2 Sep 2026 01:06:19 -0700 Subject: [PATCH 07/11] Optimize cuTile modulated_rms_norm --- .../suites/liger/cutile/modulated_rms_norm.py | 115 ++++++++++++++---- 1 file changed, 89 insertions(+), 26 deletions(-) diff --git a/src/tilegym/suites/liger/cutile/modulated_rms_norm.py b/src/tilegym/suites/liger/cutile/modulated_rms_norm.py index 012b43a4..0454c718 100644 --- a/src/tilegym/suites/liger/cutile/modulated_rms_norm.py +++ b/src/tilegym/suites/liger/cutile/modulated_rms_norm.py @@ -78,6 +78,7 @@ def _modulated_rms_norm_fwd_ct( elementwise_affine: ct.Constant[bool], has_shift: ct.Constant[bool], rows_per_modulation: ct.Constant[int], + aligned: ct.Constant[bool], ): """ RMS norm forward (unified, single pass). @@ -102,16 +103,19 @@ def _modulated_rms_norm_fwd_ct( row_idx = ct.bid(0) mod_row_idx = row_idx // rows_per_modulation col_idx = ct.arange(BLOCK_SIZE, dtype=ct.int32) - scale_val = ct.gather(Scale, (mod_row_idx, col_idx), check_bounds=True, padding_value=0.0) + # aligned == (BLOCK_SIZE == n_cols): every lane is in-bounds, so gather/scatter + # need no OOB predication. Non-power-of-2 n_cols keeps check_bounds=True. + cb = not aligned + scale_val = ct.gather(Scale, (mod_row_idx, col_idx), check_bounds=cb, padding_value=0.0) if has_shift: - shift_val = ct.gather(Shift, (mod_row_idx, col_idx), check_bounds=True, padding_value=0.0) + shift_val = ct.gather(Shift, (mod_row_idx, col_idx), check_bounds=cb, padding_value=0.0) else: shift_val = scale_val if casting_mode == _CASTING_MODE_NONE: - x_val = ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0) + x_val = ct.gather(X, (row_idx, col_idx), check_bounds=cb, padding_value=0.0) if elementwise_affine: - w_val = ct.gather(W, col_idx, check_bounds=True, padding_value=0.0) + w_val = ct.gather(W, col_idx, check_bounds=cb, padding_value=0.0) mean_sq = ct.astype(ct.sum(x_val * x_val, 0, keepdims=False), ct.float32) / n_cols eps_rounded = ct.astype(ct.astype(eps, x_val.dtype), ct.float32) rstd = ct.rsqrt(mean_sq + eps_rounded) # fp32 @@ -125,12 +129,12 @@ def _modulated_rms_norm_fwd_ct( else: y_f32 = x_scaled y_f32 = _apply_modulation(y_f32, scale_val, shift_val, has_shift) - ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, x_val.dtype), check_bounds=True) + ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, x_val.dtype), check_bounds=cb) elif casting_mode == _CASTING_MODE_LLAMA: - x_val = ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0) + x_val = ct.gather(X, (row_idx, col_idx), check_bounds=cb, padding_value=0.0) if elementwise_affine: - w_val = ct.gather(W, col_idx, check_bounds=True, padding_value=0.0) + w_val = ct.gather(W, col_idx, check_bounds=cb, padding_value=0.0) x_f32 = ct.astype(x_val, ct.float32) mean_sq = ct.sum(ct.mul(x_f32, x_f32, flush_to_zero=True), 0, keepdims=False) / n_cols rstd = ct.rsqrt(mean_sq + eps) @@ -145,13 +149,13 @@ def _modulated_rms_norm_fwd_ct( else: y_f32 = x_scaled y_f32 = _apply_modulation(y_f32, scale_val, shift_val, has_shift) - ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, x_val.dtype), check_bounds=True) + ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, x_val.dtype), check_bounds=cb) else: # gemma: both X and W to fp32, Y cast back to X.dtype - x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32) + x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=cb, padding_value=0.0), ct.float32) if elementwise_affine: - w_f32 = ct.astype(ct.gather(W, col_idx, check_bounds=True, padding_value=0.0), ct.float32) + w_f32 = ct.astype(ct.gather(W, col_idx, check_bounds=cb, padding_value=0.0), ct.float32) mean_sq = ct.sum(x_f32 * x_f32, 0, keepdims=False) / n_cols rstd = ct.rsqrt(mean_sq + eps) ct.scatter(RSTD, row_idx, rstd, check_bounds=False) @@ -162,10 +166,14 @@ def _modulated_rms_norm_fwd_ct( else: y_f32 = x_scaled y_f32 = _apply_modulation(y_f32, scale_val, shift_val, has_shift) - ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, Y.dtype), check_bounds=True) + ct.scatter(Y, (row_idx, col_idx), ct.astype(y_f32, Y.dtype), check_bounds=cb) -_modulated_rms_norm_fwd_large_ct = _modulated_rms_norm_fwd_ct.replace_hints(num_worker_warps=8) +# Forward is memory-bound and row-parallel (grid = n_rows), so occupancy hints (more +# resident blocks/SM to hide DRAM latency) are the tuning lever; the launch wrapper +# selects these variants by BLOCK_SIZE. +_modulated_rms_norm_fwd_ct_occ4 = _modulated_rms_norm_fwd_ct.replace_hints(occupancy=4) +_modulated_rms_norm_fwd_ct_occ3 = _modulated_rms_norm_fwd_ct.replace_hints(occupancy=3) # --------------------------------------------------------------------------- @@ -188,6 +196,7 @@ def _modulated_rms_norm_bwd_large_ct( casting_mode: ct.Constant[int], has_shift: ct.Constant[bool], rows_per_modulation: ct.Constant[int], + single_mod: ct.Constant[bool], ): """ Modulated RMS norm backward without affine weight. SM-count partitioned. @@ -195,20 +204,26 @@ def _modulated_rms_norm_bwd_large_ct( dRms = dY * (1 + scale) is the gradient flowing back through the norm. dScale = dY * rms_output (here rms_output = X * rstd, no weight). dShift = dY. + + dScale/dShift selection mirrors the weighted kernel: plain store when + rows_per_modulation == 1; per-block register accumulation + one atomic flush + when single_mod (single shared modulation row); per-row atomic otherwise. """ block_id = ct.bid(0) col_idx = ct.arange(BLOCK_SIZE, dtype=ct.int32) inv_n_cols = 1.0 / n_cols + if single_mod: + dScale_acc = ct.full((BLOCK_SIZE,), 0.0, dtype=ct.float32) + if has_shift: + dShift_acc = ct.full((BLOCK_SIZE,), 0.0, dtype=ct.float32) for ri in range(rows_per_program): row_idx = block_id * rows_per_program + ri mod_row_idx = row_idx // rows_per_modulation rstd = ct.astype(ct.gather(RSTD, (row_idx,), padding_value=0.0).item(), ct.float32) - dy_f32 = ct.astype( - ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0, latency=3), ct.float32 - ) - x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0, latency=3), ct.float32) + dy_f32 = ct.astype(ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32) + x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32) scale_f32 = ct.astype( ct.gather(Scale, (mod_row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32 ) @@ -227,11 +242,21 @@ def _modulated_rms_norm_bwd_large_ct( ct.scatter(dScale, (mod_row_idx, col_idx), dscale_row, check_bounds=True) if has_shift: ct.scatter(dShift, (mod_row_idx, col_idx), dy_f32, check_bounds=True) + elif single_mod: + dScale_acc = ct.add(dScale_acc, dscale_row) + if has_shift: + dShift_acc = ct.add(dShift_acc, dy_f32) else: ct.atomic_add(dScale, (mod_row_idx, col_idx), dscale_row, check_bounds=True) if has_shift: ct.atomic_add(dShift, (mod_row_idx, col_idx), dy_f32, check_bounds=True) + # Flush the per-block dScale/dShift once into the single shared modulation row. + if single_mod: + ct.atomic_add(dScale, (0, col_idx), dScale_acc, check_bounds=True) + if has_shift: + ct.atomic_add(dShift, (0, col_idx), dShift_acc, check_bounds=True) + @ct.kernel def _modulated_rms_norm_bwd_w_large_ct( @@ -251,6 +276,7 @@ def _modulated_rms_norm_bwd_w_large_ct( casting_mode: ct.Constant[int], has_shift: ct.Constant[bool], rows_per_modulation: ct.Constant[int], + single_mod: ct.Constant[bool], ): """ modulated RMS norm backward with affine weight. SM-count partitioned, single DRAM pass. @@ -261,6 +287,14 @@ def _modulated_rms_norm_bwd_w_large_ct( dW_partial shape: (sm_count, n_cols) — vastly smaller than (n_rows, n_cols). OOB rows return 0 via check_bounds; RSTD zero-padded. + dScale/dShift selection (unchanged math; only accumulation order differs): + - rows_per_modulation == 1 → plain store per row (each token owns a modulation row). + - single_mod (scale_rows == 1, all rows map to modulation row 0) → dScale/dShift + accumulated in registers across the row loop and atomic_add'd ONCE per block. + Mirrors the per-block dW register accumulation; collapses the per-row atomic + contention on the single shared row. + - otherwise → atomic_add per row into the row's modulation slot. + casting_mode: llama (0): load W in original dtype once; per row: dY in orig dtype, m = (dY*(W+offset)) cast to fp32; dW += dy_orig*(X*rstd cast to X.dtype). @@ -275,6 +309,11 @@ def _modulated_rms_norm_bwd_w_large_ct( # Per-block dW accumulator in registers; scattered to dW_partial once at end dW_acc = ct.full((BLOCK_SIZE,), 0.0, dtype=ct.float32) + # Per-block dScale/dShift accumulators (single shared modulation row only). + if single_mod: + dScale_acc = ct.full((BLOCK_SIZE,), 0.0, dtype=ct.float32) + if has_shift: + dShift_acc = ct.full((BLOCK_SIZE,), 0.0, dtype=ct.float32) inv_n_cols = 1.0 / n_cols # Load W once; dtype depends on mode (gemma keeps fp32; llama/none keep original). @@ -293,19 +332,17 @@ def _modulated_rms_norm_bwd_w_large_ct( # Bounds-checked scalar read on RSTD (avoids host-side cat-padding). rstd = ct.astype(ct.gather(RSTD, (row_idx,), padding_value=0.0).item(), ct.float32) - x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0, latency=3), ct.float32) + x_f32 = ct.astype(ct.gather(X, (row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32) if casting_mode == _CASTING_MODE_GEMMA: - dy_f32 = ct.astype( - ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0, latency=3), ct.float32 - ) + dy_f32 = ct.astype(ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0), ct.float32) drms_f32 = dy_f32 * mod_f32 rms_out_f32 = x_f32 * rstd * (w_f32 + offset) m_f32 = drms_f32 * (w_f32 + offset) dW_term_f32 = drms_f32 * x_f32 * rstd else: - dy_orig = ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0, latency=3) + dy_orig = ct.gather(dY, (row_idx, col_idx), check_bounds=True, padding_value=0.0) dy_f32 = ct.astype(dy_orig, ct.float32) drms_f32 = dy_f32 * mod_f32 w_off_f32 = ct.astype(w_orig + offset, ct.float32) @@ -323,6 +360,11 @@ def _modulated_rms_norm_bwd_w_large_ct( ct.scatter(dScale, (mod_row_idx, col_idx), dscale_row, check_bounds=True) if has_shift: ct.scatter(dShift, (mod_row_idx, col_idx), dy_f32, check_bounds=True) + elif single_mod: + # OOB rows contribute 0 (dy=0 via check_bounds), so accumulating them is harmless. + dScale_acc = ct.add(dScale_acc, dscale_row) + if has_shift: + dShift_acc = ct.add(dShift_acc, dy_f32) else: ct.atomic_add(dScale, (mod_row_idx, col_idx), dscale_row, check_bounds=True) if has_shift: @@ -334,6 +376,12 @@ def _modulated_rms_norm_bwd_w_large_ct( # Write this block's partial dW once (block_id < sm_count, always in-bounds) ct.scatter(dW_partial, (block_id, col_idx), dW_acc, check_bounds=True) + # Flush the per-block dScale/dShift once into the single shared modulation row. + if single_mod: + ct.atomic_add(dScale, (0, col_idx), dScale_acc, check_bounds=True) + if has_shift: + ct.atomic_add(dShift, (0, col_idx), dShift_acc, check_bounds=True) + _modulated_rms_norm_bwd_large_ct_nww8 = _modulated_rms_norm_bwd_large_ct.replace_hints(num_worker_warps=8) _modulated_rms_norm_bwd_w_large_ct_nww8 = _modulated_rms_norm_bwd_w_large_ct.replace_hints(num_worker_warps=8) @@ -381,7 +429,15 @@ def _modulated_rms_norm_forward_ct(X, W, scale, shift, eps, offset, casting_mode # When no weight, pass a 1-element dummy tensor; elementwise_affine=False causes the compiler # to dead-code-eliminate every ct.gather(W, ...) so the dummy is never accessed. W_tensor = W.contiguous() if elementwise_affine else X2d.new_empty(1) - fwd_kernel = _modulated_rms_norm_fwd_large_ct if BLOCK_SIZE >= 16384 else _modulated_rms_norm_fwd_ct + if BLOCK_SIZE >= 16384: + fwd_kernel = _modulated_rms_norm_fwd_ct_occ3 + elif BLOCK_SIZE == 4096: + fwd_kernel = _modulated_rms_norm_fwd_ct_occ4 + else: + fwd_kernel = _modulated_rms_norm_fwd_ct + # When n_cols is a power of 2, BLOCK_SIZE == n_cols so every lane is in-bounds and + # the kernel can drop bounds checking on all gather/scatter. + aligned = BLOCK_SIZE == n_cols ct.launch( torch.cuda.current_stream(), grid, @@ -401,6 +457,7 @@ def _modulated_rms_norm_forward_ct(X, W, scale, shift, eps, offset, casting_mode bool(elementwise_affine), bool(has_shift), int(rows_per_modulation), + bool(aligned), ), ) @@ -428,6 +485,12 @@ def _modulated_rms_norm_backward_ct( dScale = alloc(scale_rows, n_cols, dtype=torch.float32, device=scale.device) dShift = alloc(scale_rows, n_cols, dtype=torch.float32, device=scale.device) if has_shift else dScale + # Single shared modulation row (scale_rows == 1, >1 hidden rows): the kernel + # accumulates dScale/dShift per block and atomic_add's once, collapsing the + # per-row atomic contention on that one row. rows_per_modulation == 1 keeps the + # plain-store path; the general (1 < scale_rows < n_rows) case keeps per-row atomics. + single_mod = scale_rows == 1 and n_rows > 1 + sm_count = torch.cuda.get_device_properties(X.device).multi_processor_count rows_per_program = math.ceil(n_rows / sm_count) grid = (sm_count, 1, 1) @@ -436,9 +499,7 @@ def _modulated_rms_norm_backward_ct( if elementwise_affine: dW_partial = torch.empty(sm_count, n_cols, dtype=torch.float32, device=W.device) - bwd_w_kernel = ( - _modulated_rms_norm_bwd_w_large_ct_nww8 if BLOCK_SIZE >= 8192 else _modulated_rms_norm_bwd_w_large_ct - ) + bwd_w_kernel = _modulated_rms_norm_bwd_w_large_ct_nww8 ct.launch( torch.cuda.current_stream(), grid, @@ -460,11 +521,12 @@ def _modulated_rms_norm_backward_ct( int(casting_mode_int), bool(has_shift), int(rows_per_modulation), + bool(single_mod), ), ) dW = dW_partial.sum(dim=0).to(W.dtype) else: - bwd_kernel = _modulated_rms_norm_bwd_large_ct_nww8 if BLOCK_SIZE >= 8192 else _modulated_rms_norm_bwd_large_ct + bwd_kernel = _modulated_rms_norm_bwd_large_ct_nww8 ct.launch( torch.cuda.current_stream(), grid, @@ -483,6 +545,7 @@ def _modulated_rms_norm_backward_ct( int(casting_mode_int), bool(has_shift), int(rows_per_modulation), + bool(single_mod), ), ) dW = None From 30648e6be2e21eba54fd4687a8050d3b33189e70 Mon Sep 17 00:00:00 2001 From: Changhao Wu Date: Wed, 2 Sep 2026 15:12:04 -0700 Subject: [PATCH 08/11] tilecpp: perf fixes for the activations and moe_align_block --- src/tilegym/ops/tilecpp/activation/gelu.cuh | 2 +- src/tilegym/ops/tilecpp/activation/relu.cuh | 2 +- src/tilegym/ops/tilecpp/moe_align_block.cuh | 14 ++++++------ src/tilegym/ops/tilecpp/moe_align_block.py | 24 ++++++++++++++------- 4 files changed, 25 insertions(+), 17 deletions(-) diff --git a/src/tilegym/ops/tilecpp/activation/gelu.cuh b/src/tilegym/ops/tilecpp/activation/gelu.cuh index c4852722..c0fac7b2 100644 --- a/src/tilegym/ops/tilecpp/activation/gelu.cuh +++ b/src/tilegym/ops/tilecpp/activation/gelu.cuh @@ -20,7 +20,7 @@ __tile__ auto sigmoid_f32(tile_t x) { template __tile__ auto tanh_approx_f32(tile_t x) { - return 2.0f * sigmoid_f32(2.0f * x) - 1.0f; + return cuda::tiles::tanh(x); } template diff --git a/src/tilegym/ops/tilecpp/activation/relu.cuh b/src/tilegym/ops/tilecpp/activation/relu.cuh index 9a94f1f0..15c1a926 100644 --- a/src/tilegym/ops/tilecpp/activation/relu.cuh +++ b/src/tilegym/ops/tilecpp/activation/relu.cuh @@ -28,7 +28,7 @@ __tile_global__ void relu_activation_fwd_kernel(const T* __restrict__ x, T* __re auto zero = ct::zeros(); f32xN out; if constexpr (OP == 0) { // relu - out = ct::select(xf > zero, xf, zero); + out = ct::max(xf, zero); } else if constexpr (OP == 1) { // elu out = ct::select(xf > zero, xf, alpha * (ct::exp(xf) - 1.0f)); } else if constexpr (OP == 2) { // leaky_relu diff --git a/src/tilegym/ops/tilecpp/moe_align_block.cuh b/src/tilegym/ops/tilecpp/moe_align_block.cuh index 17f3f18c..00e103fc 100644 --- a/src/tilegym/ops/tilecpp/moe_align_block.cuh +++ b/src/tilegym/ops/tilecpp/moe_align_block.cuh @@ -58,14 +58,14 @@ __tile_global__ void moe_align_block_size_stage1( * Stage 2: Compute cumulative sum of token counts across programs. * Each program computes cumsum for one expert column. */ -template +template __tile_global__ void moe_align_block_size_stage2( int* __restrict__ tokens_cnts ) { namespace ct = cuda::tiles; - // PADDED_EXPERTS must be next power of 2 >= NUM_EXPERTS (enforced by Python) - using i32xP = ct::tile>; + // PADDED_PROGRAMS must be next power of 2 >= NUM_PROGRAMS (enforced by Python) + using i32xP = ct::tile>; tokens_cnts = ct::assume_aligned<16>(tokens_cnts); @@ -76,8 +76,8 @@ __tile_global__ void moe_align_block_size_stage2( auto offsets = ct::iota() * NUM_EXPERTS + base_offset; - // Mask: only first NUM_EXPERTS elements are valid - auto mask = ct::iota() < NUM_EXPERTS; + // Mask: only first NUM_PROGRAMS rows are valid + auto mask = ct::iota() < NUM_PROGRAMS; // Gather-load with mask (zero-fill padding positions) auto token_cnts_vec = ct::load_masked(tokens_cnts + offsets, mask, @@ -94,7 +94,7 @@ __tile_global__ void moe_align_block_size_stage2( * Stage 3: Compute padded cumsum for block alignment. * Single program - computes cumulative padded counts and max expert count. */ -template +template __tile_global__ void moe_align_block_size_stage3( int* __restrict__ total_tokens_post_pad, int* __restrict__ max_expert_cnt, @@ -111,7 +111,7 @@ __tile_global__ void moe_align_block_size_stage3( cumsum = ct::assume_aligned<16>(cumsum); auto last_cumsum = ct::zeros(); - int off_cnt = NUM_EXPERTS * NUM_EXPERTS; + int off_cnt = NUM_PROGRAMS * NUM_EXPERTS; auto token_cnt = ct::zeros(); auto padded_cnt = ct::zeros(); auto max_cnt = ct::zeros(); diff --git a/src/tilegym/ops/tilecpp/moe_align_block.py b/src/tilegym/ops/tilecpp/moe_align_block.py index 104faaee..bd45aa86 100644 --- a/src/tilegym/ops/tilecpp/moe_align_block.py +++ b/src/tilegym/ops/tilecpp/moe_align_block.py @@ -95,6 +95,9 @@ def _launch_stage1( ) +_TARGET_TOKENS_PER_PROGRAM = 128 + + def _next_power_of_2(n: int) -> int: """Return the smallest power of 2 >= n.""" v = 1 @@ -106,17 +109,18 @@ def _next_power_of_2(n: int) -> int: def _launch_stage2( tokens_cnts: torch.Tensor, num_experts: int, + num_programs: int, grid: int, ): """Launch stage 2 kernel.""" dump_kernel_types("moe_align_block_size_stage2", tokens_cnts) - padded_experts = _next_power_of_2(num_experts) + padded_programs = _next_power_of_2(num_programs) - # Template params: T (int), NUM_EXPERTS, PADDED_EXPERTS (next power of 2) + # Template params: T (int), NUM_EXPERTS, NUM_PROGRAMS, PADDED_PROGRAMS kernel, _, _ = _stage2_kernel.get_kernel( dtype=torch.int32, - template_params=[num_experts, padded_experts], + template_params=[num_experts, num_programs, padded_programs], signature="int*", ) @@ -136,12 +140,13 @@ def _launch_stage3( cumsum: torch.Tensor, num_experts: int, block_size: int, + num_programs: int, ): dump_kernel_types("moe_align_block_size_stage3", tokens_cnts, cumsum) kernel, _, _ = _stage3_kernel.get_kernel( dtype=torch.int32, - template_params=[num_experts, block_size], + template_params=[num_experts, block_size, num_programs], signature="int*, int*, const int*, int*", ) @@ -216,14 +221,15 @@ def _moe_align_block_size( cumsum: Cumulative sum tensor for block alignment. """ numel = topk_ids.numel() - grid = num_experts + num_programs = max(num_experts, ceil_div(numel, _TARGET_TOKENS_PER_PROGRAM)) + grid = num_programs tokens_cnts = torch.zeros( - (num_experts + 1, num_experts), + (num_programs + 1, num_experts), dtype=torch.int32, device=topk_ids.device, ) cumsum = torch.zeros((num_experts + 1,), dtype=torch.int32, device=topk_ids.device) - tokens_per_thread = ceil_div(numel, num_experts) + tokens_per_thread = ceil_div(numel, num_programs) _launch_stage1( topk_ids, @@ -237,7 +243,8 @@ def _moe_align_block_size( _launch_stage2( tokens_cnts, num_experts, - grid, + num_programs, + num_experts, ) _launch_stage3( @@ -247,6 +254,7 @@ def _moe_align_block_size( cumsum, num_experts, block_size, + num_programs, ) # Launch stage 4: Assign tokens to sorted positions From e7655e3e86a76089c9350f20da3f984770108c46 Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Wed, 2 Sep 2026 20:02:08 -0700 Subject: [PATCH 09/11] cuTile: add narrow-K skinny matmul autotune tiles for sm120 --- src/tilegym/ops/cutile/matmul.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/src/tilegym/ops/cutile/matmul.py b/src/tilegym/ops/cutile/matmul.py index 9c60107f..470be4aa 100644 --- a/src/tilegym/ops/cutile/matmul.py +++ b/src/tilegym/ops/cutile/matmul.py @@ -103,6 +103,20 @@ def _static_persistent_matmul_autotune_configs(): yield SimpleNamespace( TILE_SIZE_M=256, TILE_SIZE_N=256, TILE_SIZE_K=64, GROUP_SIZE_M=8, num_ctas=1, occupancy=1, LOAD_LATENCY=-1 ) + # Narrow-K (K=32) / wide-N candidates for extreme-skinny shapes (large M, + # small N/K), where the K=64 tiles above underutilize the persistent grid. + yield SimpleNamespace( + TILE_SIZE_M=64, TILE_SIZE_N=128, TILE_SIZE_K=32, GROUP_SIZE_M=8, num_ctas=1, occupancy=2, LOAD_LATENCY=-1 + ) + yield SimpleNamespace( + TILE_SIZE_M=128, TILE_SIZE_N=64, TILE_SIZE_K=32, GROUP_SIZE_M=8, num_ctas=1, occupancy=2, LOAD_LATENCY=-1 + ) + yield SimpleNamespace( + TILE_SIZE_M=128, TILE_SIZE_N=128, TILE_SIZE_K=32, GROUP_SIZE_M=8, num_ctas=1, occupancy=2, LOAD_LATENCY=-1 + ) + yield SimpleNamespace( + TILE_SIZE_M=128, TILE_SIZE_N=128, TILE_SIZE_K=32, GROUP_SIZE_M=8, num_ctas=1, occupancy=1, LOAD_LATENCY=-1 + ) elif gpu_capability[0] < 9: # sm80 (A100) yield SimpleNamespace( From e8219780b71dd1fa606f404d89fbddb8625381e7 Mon Sep 17 00:00:00 2001 From: Rundong Li Date: Wed, 2 Sep 2026 21:20:27 -0700 Subject: [PATCH 10/11] tests: default --arch to sm100 when no GPU is available --- tests/conftest.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index 60a117c1..5a880149 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -58,12 +58,20 @@ def pytest_configure(config): config.addinivalue_line("markers", "fast: indicate whether the test is in fast CI pipeline") +def _detect_arch_default() -> str: + try: + major, minor = torch.cuda.get_device_capability("cuda") + except RuntimeError: + return "sm100" + return f"sm{major}{minor}" + + def pytest_addoption(parser): try: parser.addoption( "--arch", type=str, - default=f"sm{torch.cuda.get_device_capability('cuda')[0]}{torch.cuda.get_device_capability('cuda')[1]}", + default=_detect_arch_default(), help="GPU Backend Type", ) parser.addoption("--quick-run", action="store_true", default=False, help="Quick Run") From 130e6cf675d8c553e745eebefe3e3d3e331045b5 Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Wed, 2 Sep 2026 23:12:46 -0700 Subject: [PATCH 11/11] Expand SwiGLU test_perf coverage with prefill shapes --- tests/ops/test_swiglu.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tests/ops/test_swiglu.py b/tests/ops/test_swiglu.py index 40ea7ac2..6894d27b 100644 --- a/tests/ops/test_swiglu.py +++ b/tests/ops/test_swiglu.py @@ -201,9 +201,11 @@ def test_op_backward_irregular(self, batch_size, seq_len, hidden_size, backend, @pytest.mark.parametrize( "batch_size,seq_len,hidden_size,intermediate_size", [ - # (1, 128, 1024, 4096), - # (2, 256, 2048, 8192), - (8, 1, 4096, 14336) + (8, 1, 4096, 14336), + (2, 512, 1024, 4096), + (4, 1024, 2048, 8192), + (8, 512, 4096, 14336), + (8, 2048, 4096, 14336), ], ids=lambda x: str(x), ) @@ -226,7 +228,7 @@ def test_perf(self, batch_size, seq_len, hidden_size, intermediate_size, backend backend_fn = lambda: self.reference(a, b) else: try: - backend_fn = lambda: get_swiglu()(a, b)[2] + backend_fn = lambda: get_swiglu()(a, b) except Exception as e: pytest.skip(f"Cutile backend not available: {e}")