Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Shared PyTorch custom silly attention for compilation tests.
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Centralizes custom operation definitions to avoid duplicate registrations.
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"""
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import torch
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from torch.library import Library
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from vllm.utils.torch_utils import direct_register_custom_op
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# Shared library for all compilation test operations
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# Using "silly" namespace to match existing test expectations
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# import this file will automatically register
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# torch ops for testing (like silly.attention)
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silly_lib = Library("silly", "FRAGMENT")
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# Global counter that counts the number of times attention is invoked
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_global_counter = 0
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def get_global_counter():
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"""Get the current global counter value"""
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return _global_counter
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def reset_global_counter():
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"""Reset the global counter to 0"""
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global _global_counter
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_global_counter = 0
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def silly_attention(
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q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, out: torch.Tensor
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) -> None:
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"""
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Unified attention implementation that depends on
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all inputs and affects the output.
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Always increments a global counter that tests can use or ignore.
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"""
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global _global_counter
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# Always increment the global counter
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_global_counter += 1
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# Unified implementation that depends on all inputs
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out.copy_(q + k + v)
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def silly_attention_fake(
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q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, out: torch.Tensor
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) -> None:
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"""Fake implementation for testing"""
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return
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# Register the unified attention operation
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direct_register_custom_op(
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op_name="attention",
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op_func=silly_attention,
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mutates_args=["out"],
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fake_impl=silly_attention_fake,
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target_lib=silly_lib,
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)
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