78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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import torch.distributed as dist
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from vllm.distributed.parallel_state import get_dp_group
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def make_num_tokens_across_dp(dp_size: int, num_tokens: int) -> torch.Tensor | None:
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if dp_size == 1:
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return None
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return torch.full((dp_size,), num_tokens, dtype=torch.int32, device="cpu")
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def get_batch_metadata_across_dp(
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num_tokens: int,
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cudagraph_size: int,
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cudagraph_runtime_mode: int,
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dp_size: int,
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dp_rank: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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assert dp_size > 1
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# Use CPU group to avoid CPU-GPU synchronization.
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group = get_dp_group().cpu_group
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tensor = torch.zeros(3, dp_size, dtype=torch.int32, device="cpu")
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tensor[0][dp_rank] = num_tokens
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tensor[1][dp_rank] = cudagraph_size
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tensor[2][dp_rank] = cudagraph_runtime_mode
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dist.all_reduce(tensor, group=group)
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return tensor[0], tensor[1], tensor[2]
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def get_cudagraph_and_dp_padding(
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num_tokens: int,
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cudagraph_size: int | None,
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cudagraph_runtime_mode: int,
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dp_size: int,
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dp_rank: int,
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) -> tuple[int, torch.Tensor | None, int]:
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if dp_size == 1:
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if cudagraph_size is not None:
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return cudagraph_size, None, cudagraph_runtime_mode
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else:
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return num_tokens, None, cudagraph_runtime_mode
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# Convert None to -1 for sync (indicates no cudagraph available)
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if num_tokens == 0:
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cudagraph_size = 0
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elif cudagraph_size is None:
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cudagraph_size = -1
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num_tokens_across_dp, cudagraph_size_across_dp, cudagraph_mode_across_dp = (
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get_batch_metadata_across_dp(
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num_tokens, cudagraph_size, cudagraph_runtime_mode, dp_size, dp_rank
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)
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)
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if torch.all(num_tokens_across_dp == 0).item():
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# All ranks have zero tokens to run.
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return 0, None, 0
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# Synchronize cudagraph_runtime_mode across ranks by taking the minimum.
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synced_cudagraph_mode = int(cudagraph_mode_across_dp.min().item())
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# Check if all ranks have valid cudagraph_size.
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all_have_cudagraph = torch.all(cudagraph_size_across_dp != -1).item()
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if synced_cudagraph_mode != 0 and all_have_cudagraph:
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# All ranks use cudagraph. Pad to max cudagraph_size.
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max_cudagraph_size = int(cudagraph_size_across_dp.max().item())
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num_tokens_across_dp[:] = max_cudagraph_size
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return max_cudagraph_size, num_tokens_across_dp, synced_cudagraph_mode
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else:
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# Fall back to eager mode (no cudagraph).
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# Either some rank doesn't have cudagraph size or mode is NONE.
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synced_cudagraph_mode = 0
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num_tokens_across_dp = torch.clamp(num_tokens_across_dp, min=1)
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num_tokens_after_padding = int(num_tokens_across_dp[dp_rank].item())
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return num_tokens_after_padding, num_tokens_across_dp, synced_cudagraph_mode
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