c929bd1e8d
This PR add `MatmulAllreduceRmsnorm` operator and introduces a graph
fusion pass for `matmul_allreduce_rmsnorm` operations. The
implementation includes a new configuration flag, a pattern matching
pass using `torch._inductor.pattern_matcher`.
Co-authored-by: Trunrain [270250579@qq.com](mailto:270250579@qq.com)
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: tongrunze <t00574058@china.huawei.com>
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import vllm.model_executor.layers.utils
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from vllm.utils.torch_utils import direct_register_custom_op
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def unquantized_gemm(
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x: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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return torch.nn.functional.linear(x, weight, bias)
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def unquantized_gemm_fake(
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x: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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output_shape = (x.shape[0], weight.shape[0])
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return torch.empty(output_shape, dtype=x.dtype, device=x.device)
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direct_register_custom_op(op_name="unquantized_gemm",
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op_func=unquantized_gemm,
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fake_impl=unquantized_gemm_fake,
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mutates_args=[],
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dispatch_key="PrivateUse1")
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def default_unquantized_gemm(
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layer: torch.nn.Module,
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x: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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if x.device.type == "npu":
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return torch.ops.vllm.unquantized_gemm(x, weight, bias)
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else:
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return torch.nn.functional.linear(x, weight, bias)
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vllm.model_executor.layers.utils.default_unquantized_gemm = default_unquantized_gemm
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