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#ifndef CAFFE2_OPERATORS_INT8_MAX_POOL_OP_H_
#define CAFFE2_OPERATORS_INT8_MAX_POOL_OP_H_
#include <qnnpack.h>
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor_int8.h"
#include "caffe2/operators/conv_pool_op_base.h"
#include "caffe2/operators/quantized/int8_utils.h"
namespace caffe2 {
namespace int8 {
template <Activation Ac>
class Int8MaxPoolOp final : public ConvPoolOpBase<CPUContext> {
public:
template <class... Args>
explicit Int8MaxPoolOp(Args&&... args)
: ConvPoolOpBase<CPUContext>(std::forward<Args>(args)...) {
OPERATOR_NEEDS_FEATURE(
this->order_ == StorageOrder::NHWC, "Int8 only supports NHWC order.");
}
~Int8MaxPoolOp() {
if (this->qnnpackOperator_ != nullptr) {
qnnp_delete_operator(this->qnnpackOperator_);
this->qnnpackOperator_ = nullptr;
}
}
bool RunOnDeviceWithOrderNHWC() override {
const auto& X = Inputs()[0]->template Get<Int8TensorCPU>();
auto* Y = Outputs()[0]->template GetMutable<Int8TensorCPU>();
Y->scale = X.scale;
Y->zero_point = X.zero_point;
const int32_t Y_zero_point =
this->template GetSingleArgument<int>("Y_zero_point", 0);
const float Y_scale = this->template GetSingleArgument<float>("Y_scale", 1);
TORCH_CHECK_EQ(Y_zero_point, X.zero_point);
TORCH_CHECK_EQ(Y_scale, X.scale);
TORCH_CHECK_EQ(X.t.dim(), 4);
const int channels = X.t.dim32(3);
ConvPoolOpBase<CPUContext>::SetOutputSize(X.t, &(Y->t), channels);
initQNNPACK();
if (this->qnnpackOperator_ == nullptr) {
const qnnp_status createStatus = qnnp_create_max_pooling2d_nhwc_u8(
pad_t(),
pad_r(),
pad_b(),
pad_l(),
kernel_h(),
kernel_w(),
stride_h(),
stride_w(),
1 /* dilation height */,
1 /* dilation width */,
channels,
activationLimits(Y->scale, Y->zero_point, Ac).first,
activationLimits(Y->scale, Y->zero_point, Ac).second,
0 /* flags */,
&this->qnnpackOperator_);
CAFFE_ENFORCE(
createStatus == qnnp_status_success,
"failed to create QNNPACK Max Pooling operator");
CAFFE_ENFORCE(this->qnnpackOperator_ != nullptr);
}
const qnnp_status setupStatus = qnnp_setup_max_pooling2d_nhwc_u8(
this->qnnpackOperator_,
X.t.dim32(0),
X.t.dim32(1),
X.t.dim32(2),
X.t.template data<uint8_t>(),
channels,
Y->t.template mutable_data<uint8_t>(),
channels,
nullptr /* thread pool */);
CAFFE_ENFORCE(
setupStatus == qnnp_status_success,
"failed to setup QNNPACK Max Pooling operator");
#if defined(FBCODE_CAFFE2) || !defined(USE_INTERNAL_PTHREADPOOL_IMPL)
const qnnp_status runStatus =
qnnp_run_operator(this->qnnpackOperator_, nullptr /* thread pool */);
#else
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
const qnnp_status runStatus =
qnnp_run_operator(this->qnnpackOperator_, threadpool);
#endif
CAFFE_ENFORCE(
runStatus == qnnp_status_success,
"failed to run QNNPACK Max Pooling operator");
return true;
}
private:
// QNNPACK Max Pooling operator
qnnp_operator_t qnnpackOperator_{nullptr};
};
} // namespace int8
} // namespace caffe2
#endif // CAFFE2_OPERATORS_INT8_MAX_POOL_OP_H_
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