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Instead of changing library names (not scalable), create a shared test_operations.py module that: - Provides a single "silly" library for all compilation tests - Registers a unified attention operation that can handle both standard and counting modes - Eliminates duplicate registration errors when running all tests together - Maintains backward compatibility with existing test behavior Addresses feedback to make the solution more scalable and maintainable. Co-authored-by: ProExpertProg <11367180+ProExpertProg@users.noreply.github.com>
88 lines
2.4 KiB
Python
88 lines
2.4 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 operations for compilation tests.
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This module provides a centralized place to define and register custom
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PyTorch operations used across multiple compilation tests. This avoids
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duplicate operation registrations that would cause RuntimeErrors when
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running tests together.
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The main "attention" operation is automatically registered when this module
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is imported. Individual test files can access additional functionality
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through helper functions.
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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 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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silly_lib = Library("silly", "FRAGMENT")
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# Global state for test_simple.py compatibility
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_global_counter = 0
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_use_counting_mode = False
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def get_global_counter():
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"""Get the current global counter value (for test_simple.py)"""
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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 (for test_simple.py)"""
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global _global_counter
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_global_counter = 0
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def enable_counting_mode():
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"""Enable counting mode for test_simple.py"""
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global _use_counting_mode
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_use_counting_mode = True
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reset_global_counter()
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def disable_counting_mode():
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"""Disable counting mode"""
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global _use_counting_mode
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_use_counting_mode = False
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def silly_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> None:
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"""
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Unified attention implementation that can handle both standard and counting modes.
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"""
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global _global_counter, _use_counting_mode
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if _use_counting_mode:
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# Counting mode for test_simple.py
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_global_counter += 1
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print(f"global_counter={_global_counter}")
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out.copy_(q)
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out[0] += 1
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else:
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# Standard mode for test_multiple_graphs.py and test_toy_llama.py
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out.copy_(q)
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out += k
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out += v
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def silly_attention_fake(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> 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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) |