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Neuron: profile a prefill chunk too under DEEPINFER_NEURON_REGION_TIMING
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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@ -192,7 +192,9 @@ class NeuronGraphRunner(CudaGraphRunner):
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torch.neuron.synchronize()
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self._steps = getattr(self, "_steps", 0) + 1
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profiler = None
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if self._steps == 8 and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0):
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self._bucket_steps = getattr(self, "_bucket_steps", 0) + (batch_size > 1)
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profile = self._steps == 8 or (batch_size > 1 and self._bucket_steps == 2) # a decode step, the 2nd prefill chunk
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if profile and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0):
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import cProfile
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profiler = cProfile.Profile()
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@ -208,8 +210,8 @@ class NeuronGraphRunner(CudaGraphRunner):
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profiler.disable()
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buf = io.StringIO()
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pstats.Stats(profiler, stream=buf).sort_stats("tottime").print_stats(40)
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logger.info(f"Neuron step profile (rank 0, step {self._steps}):\n{buf.getvalue()}")
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pstats.Stats(profiler, stream=buf).sort_stats("cumulative").print_stats(60)
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logger.info(f"Neuron step profile (rank 0, step {self._steps}, bs={batch_size}):\n{buf.getvalue()}")
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timings = regions.take_timings()
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in_regions = sum(sum(v) for v in timings.values())
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if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0:
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