[Frontend][torch.compile] CompilationConfig Overhaul (#20283): name change compilation level to compilation mode, deprecation compilation level (#26355)
Signed-off-by: morrison-turnansky <mturnans@redhat.com> Signed-off-by: Morrison Turnansky <mturnans@redhat.com> Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
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@@ -167,7 +167,7 @@ class AttentionCGSupport(enum.Enum):
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"""NO CUDA Graphs support"""
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```
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Suppose we have hybrid attention backends (e.g., in mamba mixer models). In that case, we seek the minimum capability of all backends to determine the final capability of the model, and we might resolve the incompatible CUDA Graphs mode by downgrading the mode to the best fit one. For example, downgrading `FULL` mode to `FULL_AND_PIECEWISE` mode if the minimum capability is `UNIFORM_BATCH`, or `PIECEWISE` mode if the minimum capability is `NEVER` for -O3 compilation level. For the complete fallback policy, please see the code of [initialize_cudagraph_capture][vllm.v1.worker.gpu_model_runner.GPUModelRunner.initialize_cudagraph_capture].
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Suppose we have hybrid attention backends (e.g., in mamba mixer models). In that case, we seek the minimum capability of all backends to determine the final capability of the model, and we might resolve the incompatible CUDA Graphs mode by downgrading the mode to the best fit one. For example, downgrading `FULL` mode to `FULL_AND_PIECEWISE` mode if the minimum capability is `UNIFORM_BATCH`, or `PIECEWISE` mode if the minimum capability is `NEVER` for -O3 compilation mode. For the complete fallback policy, please see the code of [initialize_cudagraph_capture][vllm.v1.worker.gpu_model_runner.GPUModelRunner.initialize_cudagraph_capture].
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The following table lists backends that support full CUDA Graphs at the time of writing.
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@@ -202,7 +202,7 @@ os.environ.setdefault("VLLM_LOGGING_LEVEL", "DEBUG")
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import vllm
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from vllm.config import CUDAGraphMode
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compilation_config = {"level": 3, "cudagraph_mode": "FULL_AND_PIECEWISE"}
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compilation_config = {"mode": 3, "cudagraph_mode": "FULL_AND_PIECEWISE"}
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model = vllm.LLM(
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model="meta-llama/Llama-3.1-8B-Instruct",
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dtype="auto",
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