wehub-resource-sync
65 lines
2.6 KiB
C++
65 lines
2.6 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle::inference::tensorrt {
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class DequantizeLinearOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope,
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bool test_model) override {
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#if IS_TRT_VERSION_GE(8510)
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VLOG(4) << "convert a dequantize_linear op to tensorrt IDequantizeLayer";
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// Declare inputs and attributes
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framework::OpDesc op_desc(op, nullptr);
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auto* x = engine_->GetITensor(op_desc.Input("X")[0]);
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auto* scale_var = scope.FindVar(op_desc.Input("Scale")[0]);
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int axis = PADDLE_GET_CONST(int, op_desc.GetAttr("quant_axis"));
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// Create constant layer for scale
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PADDLE_ENFORCE_NOT_NULL(
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scale_var,
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common::errors::NotFound("Can not find %s persistable var in scope.",
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op_desc.Input("Scale")[0]));
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auto* scale_t = scale_var->GetMutable<phi::DenseTensor>();
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int64_t n_scale = scale_t->numel();
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std::vector<float> scale_data(n_scale, 0.0f);
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for (int64_t i = 0; i < n_scale; ++i) {
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scale_data[i] = scale_t->data<float>()[i] / 127.0f;
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}
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// TODO(large-tensor): nvinfer1::Dims not support int64
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PADDLE_ENFORCE_LE_INT_MAX(n_scale, "n_scale");
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nvinfer1::Dims scale_dim{1, { static_cast<int>(n_scale) }};
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auto* scale = AddConstantLayer(scale_data.data(), scale_dim);
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// Add dequantize layer
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Dequantize, *x, *scale);
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if (axis >= 0) {
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layer->setAxis(axis);
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}
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auto output_name = op_desc.Output("Y")[0];
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ReplenishLayerAndOutput(
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layer, "dequantize_linear", {output_name}, test_model);
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#else
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PADDLE_THROW(
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common::errors::Fatal("Paddle-TRT explicit quantization does not "
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"support Paddle compiled with TRT < 8.5"));
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#endif
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(dequantize_linear, DequantizeLinearOpConverter);
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