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https://github.com/zebrajr/pytorch.git
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c8deca8ea8
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40524 Reviewed By: ezyang Differential Revision: D22215742 Pulled By: AshkanAliabadi fbshipit-source-id: ef594e0901337a92b21ddd44e554da66c723eb7c
102 lines
3.1 KiB
C++
102 lines
3.1 KiB
C++
#ifndef CAFFE2_OPERATORS_INT8_SOFTMAX_OP_H_
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#define CAFFE2_OPERATORS_INT8_SOFTMAX_OP_H_
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#include <qnnpack.h>
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor_int8.h"
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#include "caffe2/operators/quantized/int8_utils.h"
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namespace caffe2 {
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namespace int8 {
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class Int8SoftmaxOp final : public Operator<CPUContext> {
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public:
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explicit Int8SoftmaxOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator<CPUContext>(operator_def, ws), ws_(ws) {}
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~Int8SoftmaxOp() {
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if (this->qnnpackOperator_ != nullptr) {
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qnnp_delete_operator(this->qnnpackOperator_);
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this->qnnpackOperator_ = nullptr;
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}
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}
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bool RunOnDevice() override {
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const auto& X = Inputs()[0]->template Get<Int8TensorCPU>();
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auto* Y = Outputs()[0]->template GetMutable<Int8TensorCPU>();
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const int32_t Y_zero_point =
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this->template GetSingleArgument<int>("Y_zero_point", 0);
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const float Y_scale = this->template GetSingleArgument<float>("Y_scale", 1);
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CHECK_EQ(Y_zero_point, 0);
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CHECK_EQ(Y_scale, 1.0f / 256.0f);
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/*
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* Record quantization parameters for the input, because if the op is
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* in-place, we may overwrite these parameters later, when we set
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* quantization parameters for output tensor.
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*/
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const uint8_t X_zero_point = X.zero_point;
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const float X_scale = X.scale;
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Y->scale = Y_scale;
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Y->zero_point = Y_zero_point;
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Y->t.ResizeLike(X.t);
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initQNNPACK();
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if (this->qnnpackOperator_ == nullptr) {
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const qnnp_status createStatus = qnnp_create_softargmax_nc_q8(
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X.t.numel() / X.t.size(0) /* channels */,
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X_scale,
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static_cast<uint8_t>(Y_zero_point),
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Y_scale,
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0 /* flags */,
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&qnnpackOperator_);
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CAFFE_ENFORCE(
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createStatus == qnnp_status_success,
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"failed to create QNNPACK SoftArgMax operator");
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CAFFE_ENFORCE(this->qnnpackOperator_ != nullptr);
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}
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const qnnp_status setupStatus = qnnp_setup_softargmax_nc_q8(
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this->qnnpackOperator_,
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X.t.size(0) /* batch size */,
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X.t.template data<uint8_t>(),
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X.t.numel() / X.t.size(0) /* X stride */,
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Y->t.template mutable_data<uint8_t>(),
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X.t.numel() / X.t.size(0) /* Y stride */);
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CAFFE_ENFORCE(
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setupStatus == qnnp_status_success,
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"failed to setup QNNPACK SoftArgMax operator");
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#if defined(FBCODE_CAFFE2) || !defined(USE_INTERNAL_PTHREADPOOL_IMPL)
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const qnnp_status runStatus =
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qnnp_run_operator(this->qnnpackOperator_, nullptr /* thread pool */);
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#else
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pthreadpool_t threadpool =
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reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
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const qnnp_status runStatus =
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qnnp_run_operator(this->qnnpackOperator_, threadpool);
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#endif
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CAFFE_ENFORCE(
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runStatus == qnnp_status_success,
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"failed to run QNNPACK SoftArgMax operator");
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return true;
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}
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private:
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Workspace* ws_;
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// QNNPACK SoftArgMax operator
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qnnp_operator_t qnnpackOperator_{nullptr};
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};
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} // namespace int8
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_INT8_SOFTMAX_OP_H_
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