[Core][Distributed] remove graph mode function #4818
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WoosukKwon
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May 14, 2024
WoosukKwon
reviewed
May 14, 2024
| @@ -103,7 +103,7 @@ def multiple_tp_with_vllm_worker_fn(): | |||
| device = torch.device(f"cuda:{torch.distributed.get_rank()}") | |||
| ensure_model_parallel_initialized(2, 2) | |||
| tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device) | |||
| with graph_mode(): | |||
| with graph_capture(): | |||
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Here, how do we make sure it's not using custom all reduce?
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Actually, even before this PR, we cannot make sure it's not using custom all reduce. It is true in CI because our CI does not have custom allreduce.
To solve this problem, another refactor is needed. We need to expose a new function to create tp groups with different communicators. That's my next PR to come!
WoosukKwon
reviewed
May 15, 2024
WoosukKwon
reviewed
May 16, 2024
Comment on lines
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| @dataclass | ||
| class GraphCaptureContext: | ||
| stream: torch.cuda.Stream |
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How does this work for non-CUDA backends?
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For XPU, this will be torch.xpu.Stream .
WoosukKwon
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May 16, 2024
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Users only need to use
with graph_capture()to manage the context when they capture the graph, before the graph can be replayed.Inside the capture, we need to turn on graph mode. Outside the capture, there is no need to call graph mode.
Therefore, these two functions can be merged into one.