05bdcbeae4
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? <!-- - Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. - Please clarify why the changes are needed. For instance, the use case and bug description. - Fixes # --> This PR supports the access of vllm-acend to the piecewise_graph feature provided by the v1 engine. 1. register unifiled_ascend_attention_with_output for piecewise_graph to split graph. 2. support NPUGraph to accelerate kernel launch. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as API, interface or other behavior changes. Documentation-only updates are not considered user-facing changes. --> support npugraph to default， Users can disenable the npugraph feature by configuring enforce_eager. This has corresponding requirements for the versions of torch_npu and CANN, and they need to support graph capture. ### How was this patch tested? <!-- CI passed with new added/existing test. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> it turn to default --------- Signed-off-by: Bug Hunter Yan <yanpq@zju.edu.cn> Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com> Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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Test the piecewise compilation with a simple model so that we
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can exactly calculate the expected output and side effects.
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"""
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import pytest
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import torch
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from torch import nn
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from torch.library import Library
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (CompilationConfig, CompilationLevel, VllmConfig,
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set_current_vllm_config)
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from vllm.utils import direct_register_custom_op
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global_counter = 0
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# create a library to hold the custom op
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silly_lib = Library("silly", "FRAGMENT") # noqa
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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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global global_counter
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global_counter += 1
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print(f"{global_counter=}")
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out.copy_(q)
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out[0] += 1
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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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return
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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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dispatch_key="PrivateUse1",
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target_lib=silly_lib,
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)
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@support_torch_compile
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class SillyModel(nn.Module):
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def __init__(self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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**kwargs) -> None:
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super().__init__()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Overall effect:
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x += 1
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x[0] += 2
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global_counter += 2
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"""
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x = x + 1
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x = x + 2
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out = torch.empty_like(x)
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torch.ops.silly.attention(x, x, x, out)
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x = out
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x = x - 2
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x = x - 1
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out = torch.empty_like(x)
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torch.ops.silly.attention(x, x, x, out)
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x = out
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x = x + 1
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return x
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@pytest.mark.skipif(True, reason="requires unreleased components")
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def test_simple_piecewise_compile():
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vllm_config = VllmConfig(compilation_config=CompilationConfig(
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level=CompilationLevel.PIECEWISE,
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use_inductor=False,
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use_cudagraph=True,
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splitting_ops=["silly.attention"],
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cudagraph_copy_inputs=True,
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cudagraph_capture_sizes=[1, 2],
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))
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vllm_config.compilation_config.pass_config.enable_fusion = False
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with set_current_vllm_config(vllm_config):
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model = SillyModel(vllm_config=vllm_config, prefix="")
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inputs = torch.randn(100).npu()
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with compilation_counter.expect(
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num_graphs_seen=1, # one graph for the model
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num_piecewise_graphs_seen=5, # 2 * num_layers + 1
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num_piecewise_capturable_graphs_seen=3, # 1 + num_layers
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num_backend_compilations=3, # num_piecewise_capturable_graphs_seen
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num_cudagraph_caputured=
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6, # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
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):
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model(inputs)
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model(torch.randn(2).npu())
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model(torch.randn(1).npu())
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input = torch.zeros(2).npu()
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global global_counter
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global_counter = 0
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output = model(input)
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assert global_counter == 2
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assert torch.allclose(output.cpu(), torch.tensor([3.0, 1.0]))
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if __name__ == "__main__":
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test_simple_piecewise_compile()
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