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Merged
ArpBansal merged 2 commits into from
Mar 19, 2026
Merged

Arpit/fixes #19

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ce4479d
log added for gpu-container issue
ArpBansal Mar 18, 2026
7b32aaa
shows silent failure in model loading
ArpBansal Mar 19, 2026
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27 changes: 27 additions & 0 deletions src/gen_worker/pipeline_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1525,12 +1525,39 @@ async def load(
# Move to device with OOM handling
try:
if config.device == "cuda" and torch.cuda.is_available():
logger.info(
"Moving pipeline to CUDA for %s (%.1f GB) ...",
model_id,
model_size_gb,
)
pipeline = pipeline.to("cuda")
logger.info("Pipeline moved to CUDA successfully for %s", model_id)
else:
logger.warning(
"CUDA not available (device=%s, cuda.is_available=%s), "
"pipeline will remain on CPU for %s",
config.device,
torch.cuda.is_available(),
model_id,
)
except torch.cuda.OutOfMemoryError as e:
flush_memory()
raise CudaOutOfMemoryError(
model_id, model_size_gb, get_available_vram_gb()
) from e
except RuntimeError as e:
logger.error(
"CUDA RuntimeError moving %s to GPU: %s — falling back to CPU",
model_id,
e,
)
except Exception as e:
logger.error(
"Unexpected error moving %s to GPU (%s: %s) — falling back to CPU",
model_id,
type(e).__name__,
e,
)

# Apply VAE optimizations (always enabled)
if config.enable_vae_tiling or config.enable_vae_slicing:
Expand Down
17 changes: 17 additions & 0 deletions src/gen_worker/worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4120,6 +4120,7 @@ async def consume_async() -> None:
logger.info("Task %s completed successfully.", request_id)

except Exception as e:
logger.exception("Task %s failed: %s", request_id, e)
error_type, retryable, safe_message, error_message = self._map_exception(e)
if inference_watchdog is not None:
inference_watchdog.cancel()
Expand Down Expand Up @@ -4666,7 +4667,15 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
model_source = str(model_id)
preload_kwargs = {}

logger.info(
"Loading from_pretrained: source=%s type=%s kwargs=%s",
model_source, type_qualname(requested_type), list(preload_kwargs.keys()),
)
obj = from_pretrained(model_source, **preload_kwargs)
logger.info(
"from_pretrained complete: source=%s (%.1fs)",
model_source, time.monotonic() - t_pi0,
)
if rm is not None:
rm.add_pipeline_init_time(int((time.monotonic() - t_pi0) * 1000))
if isinstance(requested_type, type) and not isinstance(obj, requested_type):
Expand Down Expand Up @@ -4704,6 +4713,10 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
torch_dtype = kwargs.get("torch_dtype") if isinstance(kwargs, dict) else None
except Exception:
torch_dtype = None
logger.info(
"Moving model to device=%s dtype=%s model=%s ...",
str(ctx.device), torch_dtype, model_id,
)
try:
if torch_dtype is not None:
obj = obj.to(str(ctx.device), dtype=torch_dtype)
Expand All @@ -4712,6 +4725,10 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
except TypeError:
# Some objects implement .to(device) but not dtype kwarg.
obj = obj.to(str(ctx.device))
logger.info(
"Model moved to device=%s successfully model=%s",
str(ctx.device), model_id,
)
if rm is not None:
rm.add_gpu_load_time(int((time.monotonic() - t_to0) * 1000))

Expand Down
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