e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Optional
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import torch
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from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
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from vllm.config import CompilationLevel
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class MyMod(torch.nn.Module):
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def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
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if cache is not None:
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return x + cache
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return x * 2
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class MyWrapper(TorchCompileWrapperWithCustomDispatcher):
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def __init__(self, model):
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self.model = model
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compiled_callable = torch.compile(self.forward, backend="eager")
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super().__init__(compiled_callable,
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compilation_level=CompilationLevel.DYNAMO_ONCE)
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def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
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# this is the function to be compiled
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return self.model(x, cache)
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def __call__(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
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# let torch.compile compile twice
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if len(self.compiled_codes) == 2:
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dispatch_id = 0 if cache is None else 1
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with self.dispatch_to_code(dispatch_id):
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return self.forward(x, cache)
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else:
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return self.compiled_callable(x, cache)
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def test_torch_compile_wrapper():
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mod = MyMod()
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wrappers = []
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for i in range(3):
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torch._dynamo.reset()
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wrapper = MyWrapper(mod)
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wrappers.append(wrapper)
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x = torch.tensor([1])
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wrapper(x, None) # profile run, compile
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# create a cache tensor
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cache = torch.tensor([2])
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wrapper(x, cache) # warm up with cache, recompile
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# for new input, dispatch to the compiled code directly
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new_x = torch.tensor([3])
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assert wrapper(new_x,
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None).item() == 6 # dispatch to the first compiled code
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assert wrapper(
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new_x, cache).item() == 5 # dispatch to the second compiled code
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for wrapper in wrappers:
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# make sure they have independent compiled codes
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assert len(wrapper.compiled_codes) == 2
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