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https://github.com/PaddlePaddle/FastDeploy.git
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53 lines
2.0 KiB
C++
53 lines
2.0 KiB
C++
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/extension.h"
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#include "paddle/phi/core/kernel_registry.h"
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std::vector<paddle::Tensor> InvokeAvxWeightOnly(const paddle::Tensor &x,
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const paddle::Tensor &weight,
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const paddle::Tensor &w_bias,
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const std::string &alog,
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bool trans) {
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auto out_shape = x.shape();
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out_shape[out_shape.size() - 1] = weight.shape()[1];
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auto out = paddle::empty(out_shape, x.dtype(), paddle::CPUPlace());
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return {out};
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}
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std::vector<std::vector<int64_t>> AvxWeightOnlyInferShape(
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std::vector<int64_t> x_shape,
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std::vector<int64_t> weigh_shape,
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std::vector<int64_t> weigh_bias_shape) {
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int m = 1;
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for (int i = 0; i < x_shape.size() - 1; i++) {
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m = m * x_shape[i];
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}
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return {std::vector<int64_t>{m, weigh_shape[1]}};
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}
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std::vector<paddle::DataType> AvxWeightOnlyInferDtype(
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paddle::DataType x_dtype,
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paddle::DataType weight_dtype,
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paddle::DataType weight_bias_dtype) {
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return {x_dtype};
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}
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PD_BUILD_STATIC_OP(avx_weight_only)
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.Inputs({"x", "weight", "w_bias"})
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.Outputs({"out"})
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.Attrs({"alog: std::string", "trans:bool"})
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.SetKernelFn(PD_KERNEL(InvokeAvxWeightOnly))
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.SetInferShapeFn(PD_INFER_SHAPE(AvxWeightOnlyInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(AvxWeightOnlyInferDtype));
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