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[BugFix] 0 not into cuda graph to save memory (#5426)
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@@ -1577,9 +1577,6 @@ class FDConfig:
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self.graph_opt_config._set_cudagraph_sizes(max_capture_size=max_capture_shape)
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self.graph_opt_config.init_with_cudagrpah_size(max_capture_size=max_capture_shape)
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if self.parallel_config.use_ep:
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self.graph_opt_config.cudagraph_capture_sizes += [0]
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self.tokenizer = tokenizer
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self.ips = ips
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self.tool_parser = tool_parser
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@@ -207,6 +207,10 @@ class CustomAllreduce:
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def custom_all_reduce(self, input: paddle.Tensor) -> Optional[paddle.Tensor]:
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"""The main allreduce API that provides support for cuda graph."""
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if input.shape[0] == 0:
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return input
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if self.capturing:
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lib = cuda_wrapper.CudaRTLibrary()
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stream = paddle.device.current_stream()
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@@ -1020,14 +1020,10 @@ class GPUModelRunner(ModelRunnerBase):
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"""
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# NOTE(gongshaotian): The maximum decoding length is equal to the expected decoded tokens plus the eos token
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max_dec_len = expected_decode_len + 1
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if batch_size == 0:
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# Note(ZKK): divided by 0 is invalid, here we give a input_length = 1
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input_length = 1
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else:
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input_length = min(
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num_tokens // (1 if capture_prefill else batch_size),
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self.model_config.max_model_len - max_dec_len,
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)
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input_length = min(
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num_tokens // (1 if capture_prefill else batch_size),
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self.model_config.max_model_len - max_dec_len,
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)
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# NOTE(wanglongzhi): When the full length is too large, DeepEP's buffer size will not be enough to cause the result to appear nan.
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# TODO(wanglongzhi): Figure out the accurate buffer size of DeepEP.
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@@ -1490,7 +1486,9 @@ class GPUModelRunner(ModelRunnerBase):
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# When support capture both prefill-only and decode-only, this will use [only_prefill_use_cudagraph or only_decode_use_cudagraph]
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self.forward_meta.step_use_cudagraph = (
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only_prefill_use_cudagraph if self.cudagraph_only_prefill else only_decode_use_cudagraph
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only_prefill_use_cudagraph
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if self.cudagraph_only_prefill
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else only_decode_use_cudagraph and self.forward_meta.ids_remove_padding.shape[0] > 0
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)
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# Set forward_meta.is_dummy_or_profile_run to True to skip init_kv_signal_per_query for attention backends
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