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提交 ab6b3178 编写于 作者: H hjchen2
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Add dequant to quant fusion kernel and support relu6

上级 94e89540
隐藏空白更改
内联 并排
Showing with 1027 addition and 332 deletion
src/common/types.cpp
浏览文件 @ ab6b3178
......@@ -39,6 +39,7 @@ const char *G_OP_TYPE_POLYGON_BOX_TRANSFORM = "polygon_box_transform";
const char *G_OP_TYPE_POOL2D = "pool2d";
const char *G_OP_TYPE_PRIOR_BOX = "prior_box";
const char *G_OP_TYPE_RELU = "relu";
const char *G_OP_TYPE_RELU6 = "relu6";
const char *G_OP_TYPE_RESHAPE = "reshape";
const char *G_OP_TYPE_RESHAPE2 = "reshape2";
const char *G_OP_TYPE_SIGMOID = "sigmoid";
......@@ -74,6 +75,10 @@ const char *G_OP_TYPE_DEQUANTIZE = "dequantize";
const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN = "fusion_dequant_add_bn";
const char *G_OP_TYPE_FUSION_DEQUANT_BN_RELU = "fusion_dequant_bn_relu";
const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU = "fusion_dequant_add_bn_relu";
const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_QUANT =
"fusion_dequant_add_bn_quant";
const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU_QUANT =
"fusion_dequant_add_bn_relu_quant";
const char *G_OP_TYPE_TANH = "tanh";
const char *G_OP_TYPE_FUSION_DECONV_RELU = "fusion_deconv_relu";
......@@ -89,6 +94,7 @@ std::unordered_map<
{G_OP_TYPE_PRELU, {{"X", "Alpha"}, {"Out"}}},
{G_OP_TYPE_FUSION_CONV_ADD, {{"Input"}, {"Out"}}},
{G_OP_TYPE_RELU, {{"X"}, {"Out"}}},
{G_OP_TYPE_RELU6, {{"X"}, {"Out"}}},
{G_OP_TYPE_SOFTMAX, {{"X"}, {"Out"}}},
{G_OP_TYPE_SIGMOID, {{"X"}, {"Out"}}},
{G_OP_TYPE_MUL, {{"X"}, {"Out"}}},
......@@ -141,6 +147,10 @@ std::unordered_map<
{G_OP_TYPE_FUSION_DEQUANT_ADD_BN, {{"X", "Scale"}, {"Y"}}},
{G_OP_TYPE_FUSION_DEQUANT_BN_RELU, {{"X", "Scale"}, {"Out"}}},
{G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU, {{"X", "Scale"}, {"Out"}}},
{G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU_QUANT,
{{"X", "Scale"}, {"Out", "OutScale"}}},
{G_OP_TYPE_FUSION_DEQUANT_ADD_BN_QUANT,
{{"X", "Scale"}, {"Out", "OutScale"}}},
{G_OP_TYPE_TANH, {{"X"}, {"Out"}}},
{G_OP_TYPE_FUSION_DECONV_RELU, {{"Input"}, {"Out"}}},
{G_OP_TYPE_FUSION_DECONV_ADD, {{"Input"}, {"Out"}}},
......
src/common/types.h
浏览文件 @ ab6b3178
......@@ -114,6 +114,7 @@ extern const char *G_OP_TYPE_MULTICLASS_NMS;
extern const char *G_OP_TYPE_POOL2D;
extern const char *G_OP_TYPE_PRIOR_BOX;
extern const char *G_OP_TYPE_RELU;
extern const char *G_OP_TYPE_RELU6;
extern const char *G_OP_TYPE_RESHAPE;
extern const char *G_OP_TYPE_SIGMOID;
extern const char *G_OP_TYPE_SOFTMAX;
......@@ -141,6 +142,8 @@ extern const char *G_OP_TYPE_DEQUANTIZE;
extern const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN;
extern const char *G_OP_TYPE_FUSION_DEQUANT_BN_RELU;
extern const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU;
extern const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_QUANT;
extern const char *G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU_QUANT;
extern const char *G_OP_TYPE_TANH;
extern const char *G_OP_TYPE_FUSION_DECONV_RELU;
......
src/framework/executor.cpp
浏览文件 @ ab6b3178
......@@ -302,7 +302,15 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
for (int i = 0; i < profile.size(); i++) {
const auto &pInfo = profile[i];
uint64_t timeCost = pInfo.runEnd - pInfo.runBegin;
_tp[ops[i]->Type()] += timeCost;
if (ops[i]->Type() == "conv2d") {
auto inputs = ops[i]->Inputs();
auto *filter = framework::GetVarValue<framework::LoDTensor>(
"Filter", inputs, *(program_.scope));
int kernel_size = filter->dims()[2];
_tp[ops[i]->Type() + "_" + std::to_string(kernel_size)] += timeCost;
} else {
_tp[ops[i]->Type()] += timeCost;
}
}
printf("====================[ profile ]======================\n");
using prof_t = std::pair<std::string, uint64_t>;
......@@ -372,6 +380,14 @@ std::shared_ptr<framework::LoDTensor> Executor<Dtype, P>::PredictLod(
for (int i = 0; i < profile.size(); i++) {
const auto &pInfo = profile[i];
uint64_t timeCost = pInfo.runEnd - pInfo.runBegin;
if (ops[i]->Type() == "conv2d") {
auto inputs = ops[i]->Inputs();
auto input_keys = ops[i]->GetInputKeys();
auto *filter = framework::GetVarValue<framework::LoDTensor>(
input_keys[1], inputs, *(program_.scope));
int kernel_size = filter->dims()[2];
printf("kernel size: %d\n", kernel_size);
}
_tp[ops[i]->Type()] += timeCost;
}
printf("====================[ profile ]======================\n");
......
src/framework/load_ops.h
浏览文件 @ ab6b3178
......@@ -191,6 +191,7 @@ LOAD_OP2(mul, CPU, MALI_GPU);
#endif
#ifdef RELU_OP
LOAD_OP2(relu, CPU, MALI_GPU);
LOAD_OP1(relu6, CPU);
#endif
#ifdef IM2SEQUENCE_OP
LOAD_OP1(im2sequence, CPU);
......@@ -245,3 +246,11 @@ LOAD_FUSION_MATCHER(fusion_dequant_bn_relu);
LOAD_OP1(fusion_dequant_add_bn_relu, CPU);
LOAD_FUSION_MATCHER(fusion_dequant_add_bn_relu);
#endif
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
LOAD_OP1(fusion_dequant_add_bn_quant, CPU);
LOAD_FUSION_MATCHER(fusion_dequant_add_bn_quant);
#endif
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
LOAD_OP1(fusion_dequant_add_bn_relu_quant, CPU);
LOAD_FUSION_MATCHER(fusion_dequant_add_bn_relu_quant);
#endif
src/operators/kernel/central-arm-func/transpose2_arm_func.h → src/operators/fusion_dequant_add_bn_relu_quant_op.cpp
浏览文件 @ ab6b3178
......@@ -12,59 +12,51 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef TRANSPOSE2_OP
#pragma once
#include <vector>
#include "operators/op_param.h"
#include "operators/fusion_dequant_add_bn_relu_quant_op.h"
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
namespace paddle_mobile {
namespace operators {
template <typename P>
void Transpose2Compute(const Transpose2Param<CPU>& param) {
const auto* input_x = param.InputX();
const auto input_x_dims = input_x->dims();
auto* out = param.Out();
const auto axis = param.Axis();
const auto* input_x_data = input_x->data<float>();
auto* out_data = out->mutable_data<float>();
template <typename Dtype, typename T>
void FusionDequantAddBNReluQuantOp<Dtype, T>::InferShape() const {
const auto& input_dims = this->param_.input_->dims();
this->param_.output_->Resize(input_dims);
}
} // namespace operators
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
REGISTER_FUSION_MATCHER(fusion_dequant_add_bn_relu_quant,
ops::FusionDequantAddBNReluQuantMatcher);
size_t ndim = axis.size();
std::vector<int> xdim(ndim);
std::vector<int> xstride(ndim);
std::vector<int> xout(ndim);
for (int i = 0; i < ndim; i++) {
int j = ndim - 1 - i;
xdim[j] = input_x_dims[axis[i]];
xstride[j] = 1;
for (int k = axis[i] + 1; k < ndim; k++) {
xstride[j] *= input_x_dims[k];
}
xout[j] = xstride[j] * xdim[j];
}
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU(fusion_dequant_add_bn_relu_quant,
ops::FusionDequantAddBNReluQuantOp);
#endif
#endif // FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
namespace paddle_mobile {
namespace operators {
auto numel = input_x->numel();
size_t pind = 0;
std::vector<int> ind(ndim);
for (int i = 0; i < numel; i++) {
out_data[i] = input_x_data[pind];
ind[0]++;
pind += xstride[0];
for (int j = 0; j < ndim - 1; j++) {
if (ind[j] == xdim[j]) {
ind[j + 1]++;
ind[j] = 0;
pind += xstride[j + 1];
pind -= xout[j];
} else {
break;
}
}
}
template <typename Dtype, typename T>
void FusionDequantAddBNQuantOp<Dtype, T>::InferShape() const {
const auto& input_dims = this->param_.input_->dims();
this->param_.output_->Resize(input_dims);
}
} // namespace operators
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
REGISTER_FUSION_MATCHER(fusion_dequant_add_bn_quant,
ops::FusionDequantAddBNQuantMatcher);
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU(fusion_dequant_add_bn_quant,
ops::FusionDequantAddBNQuantOp);
#endif
#endif // FUSION_DEQUANT_ADD_BN_QUANT_OP
src/operators/fusion_dequant_add_bn_relu_quant_op.h 0 → 100644
浏览文件 @ ab6b3178
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "framework/operator.h"
#include "framework/program/program-optimize/fusion_op_register.h"
#include "operators/kernel/dequant_bn_relu_kernel.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
class FusionDequantAddBNReluQuantMatcher : public framework::FusionOpMatcher {
public:
FusionDequantAddBNReluQuantMatcher() {
node_ = framework::Node(G_OP_TYPE_DEQUANTIZE);
node_ > std::make_shared<framework::Node>(G_OP_TYPE_ELEMENTWISE_ADD) >
std::make_shared<framework::Node>(G_OP_TYPE_BATCHNORM) >
std::make_shared<framework::Node>(G_OP_TYPE_RELU) >
std::make_shared<framework::Node>(G_OP_TYPE_QUANTIZE);
}
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}},
{G_OP_TYPE_BATCHNORM,
{{"Scale", "BNScale"},
{"Mean", "BNMean"},
{"Bias", "BNBias"},
{"Variance", "BNVariance"}}}},
removed_nodes);
}
std::string Type() { return G_OP_TYPE_FUSION_DEQUANT_ADD_BN_RELU_QUANT; }
};
template <typename DeviceType, typename T>
class FusionDequantAddBNReluQuantOp
: public framework::OperatorWithKernel<
DeviceType, FusionDequantAddBNReluQuantParam<DeviceType>,
operators::FusionDequantAddBNReluQuantKernel<DeviceType, T>> {
public:
FusionDequantAddBNReluQuantOp(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<
DeviceType, FusionDequantAddBNReluQuantParam<DeviceType>,
operators::FusionDequantAddBNReluQuantKernel<DeviceType, T>>(
type, inputs, outputs, attrs, scope) {}
// inference output shape
void InferShape() const override;
};
#endif // FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
class FusionDequantAddBNQuantMatcher : public framework::FusionOpMatcher {
public:
FusionDequantAddBNQuantMatcher() {
node_ = framework::Node(G_OP_TYPE_DEQUANTIZE);
node_ > std::make_shared<framework::Node>(G_OP_TYPE_ELEMENTWISE_ADD) >
std::make_shared<framework::Node>(G_OP_TYPE_BATCHNORM) >
std::make_shared<framework::Node>(G_OP_TYPE_QUANTIZE);
}
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}},
{G_OP_TYPE_BATCHNORM,
{{"Scale", "BNScale"},
{"Mean", "BNMean"},
{"Bias", "BNBias"},
{"Variance", "BNVariance"}}}},
removed_nodes);
}
std::string Type() { return G_OP_TYPE_FUSION_DEQUANT_ADD_BN_QUANT; }
};
template <typename DeviceType, typename T>
class FusionDequantAddBNQuantOp
: public framework::OperatorWithKernel<
DeviceType, FusionDequantAddBNQuantParam<DeviceType>,
operators::FusionDequantAddBNQuantKernel<DeviceType, T>> {
public:
FusionDequantAddBNQuantOp(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<
DeviceType, FusionDequantAddBNQuantParam<DeviceType>,
operators::FusionDequantAddBNQuantKernel<DeviceType, T>>(
type, inputs, outputs, attrs, scope) {}
// inference output shape
void InferShape() const override;
};
#endif // FUSION_DEQUANT_ADD_BN_QUANT_OP
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/dequant_add_bn_kernel.cpp
浏览文件 @ ab6b3178
......@@ -67,7 +67,9 @@ void FusionDequantAddBNKernel<CPU, float>::Compute(
#pragma omp parallel for collapse(2)
for (int batch = 0; batch < batch_size; ++batch) {
for (int c = 0; c < channels; ++c) {
float scale = bn_scale[c] * dequant_scale;
// not fuse bn and dequant scale to minimize precision difference
// float scale = bn_scale[c] * dequant_scale;
float scale = bn_scale[c];
float bias = bn_bias[c];
size_t offset = (batch * channels + c) * spatial_size;
const int32_t *x = input + offset;
......@@ -76,9 +78,9 @@ void FusionDequantAddBNKernel<CPU, float>::Compute(
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
int loop = spatial_size >> 4;
remain = spatial_size & 0xF;
float32x4_t __dequant_scale = vdupq_n_f32(dequant_scale);
float32x4_t __scale = vdupq_n_f32(scale);
float32x4_t __bias = vdupq_n_f32(bias);
for (int k = 0; k < loop; ++k, x += 16, y += 16) {
int32x4_t r0 = vld1q_s32(x);
int32x4_t r1 = vld1q_s32(x + 4);
......@@ -88,6 +90,10 @@ void FusionDequantAddBNKernel<CPU, float>::Compute(
float32x4_t f1 = vcvtq_f32_s32(r1);
float32x4_t f2 = vcvtq_f32_s32(r2);
float32x4_t f3 = vcvtq_f32_s32(r3);
f0 = vmulq_f32(__dequant_scale, f0);
f1 = vmulq_f32(__dequant_scale, f1);
f2 = vmulq_f32(__dequant_scale, f2);
f3 = vmulq_f32(__dequant_scale, f3);
f0 = vmlaq_f32(__bias, __scale, f0);
f1 = vmlaq_f32(__bias, __scale, f1);
f2 = vmlaq_f32(__bias, __scale, f2);
......@@ -99,7 +105,7 @@ void FusionDequantAddBNKernel<CPU, float>::Compute(
}
#endif // __ARM_NEON__
for (int k = 0; k < remain; ++k) {
y[k] = scale * x[k] + bias;
y[k] = scale * (dequant_scale * x[k]) + bias;
}
}
}
......
src/operators/kernel/arm/dequant_bn_relu_kernel.cpp
浏览文件 @ ab6b3178
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201f8 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "operators/kernel/dequant_bn_relu_kernel.h"
#include <cmath>
#include "operators/math/quantize.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
#endif
......@@ -21,6 +22,31 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
#if defined(FUSION_DEQUANT_BN_RELU_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP)
void PublicFusionDequantBNInitParam(FusionDequantBNParam<CPU> *param,
const framework::Tensor *bias) {
// batch norm params
const Tensor *bn_mean = param->bn_mean_;
const Tensor *bn_variance = param->bn_variance_;
Tensor *bn_scale = param->bn_scale_;
Tensor *bn_bias = param->bn_bias_;
const float epsilon = param->epsilon_;
const float *mean_ptr = bn_mean->data<float>();
const float *var_ptr = bn_variance->data<float>();
float *bn_scale_ptr = bn_scale->mutable_data<float>();
float *bn_bias_ptr = bn_bias->mutable_data<float>();
for (int c = 0; c < bn_scale->numel(); ++c) {
float inv_scale = bn_scale_ptr[c] / (std::sqrt(var_ptr[c] + epsilon));
bn_scale_ptr[c] = inv_scale;
float val = bias ? bias->data<float>()[c] : 0;
bn_bias_ptr[c] = inv_scale * (val - mean_ptr[c]) + bn_bias_ptr[c];
}
}
#endif
#if defined(FUSION_DEQUANT_BN_RELU_OP) || defined(FUSION_DEQUANT_ADD_BN_RELU_OP)
void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
const int32_t *input = param->input_->data<int32_t>();
......@@ -39,7 +65,9 @@ void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
#pragma omp parallel for collapse(2)
for (int batch = 0; batch < batch_size; ++batch) {
for (int c = 0; c < channels; ++c) {
float scale = bn_scale[c] * dequant_scale;
// not fuse bn and dequant scale to minimize precision difference
// float scale = bn_scale[c] * dequant_scale;
float scale = bn_scale[c];
float bias = bn_bias[c];
size_t offset = (batch * channels + c) * spatial_size;
const int32_t *x = input + offset;
......@@ -48,10 +76,10 @@ void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
int loop = spatial_size >> 4;
remain = spatial_size & 0xF;
float32x4_t __dequant_scale = vdupq_n_f32(dequant_scale);
float32x4_t __scale = vdupq_n_f32(scale);
float32x4_t __bias = vdupq_n_f32(bias);
float32x4_t __zero = vdupq_n_f32(0.f);
for (int k = 0; k < loop; ++k, x += 16, y += 16) {
int32x4_t r0 = vld1q_s32(x);
int32x4_t r1 = vld1q_s32(x + 4);
......@@ -61,6 +89,10 @@ void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
float32x4_t f1 = vcvtq_f32_s32(r1);
float32x4_t f2 = vcvtq_f32_s32(r2);
float32x4_t f3 = vcvtq_f32_s32(r3);
f0 = vmulq_f32(__dequant_scale, f0);
f1 = vmulq_f32(__dequant_scale, f1);
f2 = vmulq_f32(__dequant_scale, f2);
f3 = vmulq_f32(__dequant_scale, f3);
f0 = vmlaq_f32(__bias, __scale, f0);
f1 = vmlaq_f32(__bias, __scale, f1);
f2 = vmlaq_f32(__bias, __scale, f2);
......@@ -76,7 +108,7 @@ void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
}
#endif // __ARM_NEON__
for (int k = 0; k < remain; ++k) {
y[k] = std::max(scale * x[k] + bias, 0.f);
y[k] = std::max(scale * (dequant_scale * x[k]) + bias, 0.f);
}
}
}
......@@ -87,22 +119,7 @@ void DequantBNReluCompute(const FusionDequantBNParam<CPU> *param) {
template <>
bool FusionDequantBNReluKernel<CPU, float>::Init(
FusionDequantBNReluParam<CPU> *param) {
// batch norm params
const Tensor *bn_mean = param->bn_mean_;
const Tensor *bn_variance = param->bn_variance_;
Tensor *bn_scale = param->bn_scale_;
Tensor *bn_bias = param->bn_bias_;
const float epsilon = param->epsilon_;
const float *mean_ptr = bn_mean->data<float>();
const float *var_ptr = bn_variance->data<float>();
float *bn_scale_ptr = bn_scale->mutable_data<float>();
float *bn_bias_ptr = bn_bias->mutable_data<float>();
for (int c = 0; c < bn_scale->numel(); ++c) {
float inv_scale = bn_scale_ptr[c] / (std::sqrt(var_ptr[c] + epsilon));
bn_scale_ptr[c] = inv_scale;
bn_bias_ptr[c] = bn_bias_ptr[c] - inv_scale * mean_ptr[c];
}
PublicFusionDequantBNInitParam(param, nullptr);
return true;
}
......@@ -117,25 +134,8 @@ void FusionDequantBNReluKernel<CPU, float>::Compute(
template <>
bool FusionDequantAddBNReluKernel<CPU, float>::Init(
FusionDequantAddBNReluParam<CPU> *param) {
// elementwise add params
const Tensor *bias = param->bias_;
// batch norm params
const Tensor *bn_mean = param->bn_mean_;
const Tensor *bn_variance = param->bn_variance_;
Tensor *bn_scale = param->bn_scale_;
Tensor *bn_bias = param->bn_bias_;
const float epsilon = param->epsilon_;
const float *bias_ptr = bias->data<float>();
const float *mean_ptr = bn_mean->data<float>();
const float *var_ptr = bn_variance->data<float>();
float *bn_scale_ptr = bn_scale->mutable_data<float>();
float *bn_bias_ptr = bn_bias->mutable_data<float>();
for (int c = 0; c < bn_scale->numel(); ++c) {
float inv_scale = bn_scale_ptr[c] / (std::sqrt(var_ptr[c] + epsilon));
bn_scale_ptr[c] = inv_scale;
bn_bias_ptr[c] = inv_scale * (bias_ptr[c] - mean_ptr[c]) + bn_bias_ptr[c];
}
const framework::Tensor *bias = param->bias_;
PublicFusionDequantBNInitParam(param, bias);
return true;
}
......@@ -146,5 +146,248 @@ void FusionDequantAddBNReluKernel<CPU, float>::Compute(
}
#endif // FUSION_DEQUANT_ADD_BN_RELU_OP
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
template <RoundType R>
void DequantBNQuantCompute(const FusionDequantAddBNQuantParam<CPU> *param) {
const int32_t *input = param->input_->data<int32_t>();
const float *bn_scale = param->bn_scale_->data<float>();
const float *bn_bias = param->bn_bias_->data<float>();
// dequantize params
const float activation_scale = param->activation_scale_->data<float>()[0];
const float weight_scale = param->weight_scale_;
const float dequant_scale = activation_scale / weight_scale;
// quantize params
Tensor *output_scale = param->online_scale_;
float max_abs = 0.f;
int8_t *output = param->output_->mutable_data<int8_t>();
int batch_size = param->input_->dims()[0];
int channels = param->input_->dims()[1];
size_t spatial_size = param->input_->dims()[2] * param->input_->dims()[3];
// if (param->is_static_) {
if (true) {
max_abs = param->static_scale_;
float quant_scale = 127.f / max_abs;
#pragma omp parallel for collapse(2)
for (int batch = 0; batch < batch_size; ++batch) {
for (int c = 0; c < channels; ++c) {
// not fuse bn and dequant scale to minimize precision difference
// float scale = bn_scale[c] * dequant_scale;
float scale = bn_scale[c];
float bias = bn_bias[c];
size_t offset = (batch * channels + c) * spatial_size;
const int32_t *x = input + offset;
int8_t *y = output + offset;
size_t remain = spatial_size;
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
int loop = spatial_size >> 4;
remain = spatial_size & 0xF;
float32x4_t __dequant_scale = vdupq_n_f32(dequant_scale);
float32x4_t __scale = vdupq_n_f32(scale);
float32x4_t __bias = vdupq_n_f32(bias);
float32x4_t __quant_scale = vdupq_n_f32(quant_scale);
for (int k = 0; k < loop; ++k, x += 16, y += 16) {
int32x4_t r0 = vld1q_s32(x);
int32x4_t r1 = vld1q_s32(x + 4);
int32x4_t r2 = vld1q_s32(x + 8);
int32x4_t r3 = vld1q_s32(x + 12);
float32x4_t f0 = vcvtq_f32_s32(r0);
float32x4_t f1 = vcvtq_f32_s32(r1);
float32x4_t f2 = vcvtq_f32_s32(r2);
float32x4_t f3 = vcvtq_f32_s32(r3);
f0 = vmulq_f32(__dequant_scale, f0);
f1 = vmulq_f32(__dequant_scale, f1);
f2 = vmulq_f32(__dequant_scale, f2);
f3 = vmulq_f32(__dequant_scale, f3);
f0 = vmlaq_f32(__bias, __scale, f0);
f1 = vmlaq_f32(__bias, __scale, f1);
f2 = vmlaq_f32(__bias, __scale, f2);
f3 = vmlaq_f32(__bias, __scale, f3);
f0 = vmulq_f32(__quant_scale, f0);
f1 = vmulq_f32(__quant_scale, f1);
f2 = vmulq_f32(__quant_scale, f2);
f3 = vmulq_f32(__quant_scale, f3);
int32x4_t q0 = math::vround_f32<R>(f0);
int32x4_t q1 = math::vround_f32<R>(f1);
int32x4_t q2 = math::vround_f32<R>(f2);
int32x4_t q3 = math::vround_f32<R>(f3);
int16x4_t d0 = vmovn_s32(q0);
int16x4_t d1 = vmovn_s32(q1);
int16x4_t d2 = vmovn_s32(q2);
int16x4_t d3 = vmovn_s32(q3);
int16x8_t q5 = vcombine_s16(d0, d1);
int16x8_t q6 = vcombine_s16(d2, d3);
int8x8_t d5 = vmovn_s16(q5);
int8x8_t d6 = vmovn_s16(q6);
vst1_s8(y, d5);
vst1_s8(y + 8, d6);
}
#endif // __ARM_NEON__
for (int k = 0; k < remain; ++k) {
float x_temp = scale * (dequant_scale * x[k]) + bias;
y[k] = math::Round<R>(x_temp * quant_scale);
}
}
}
} else {
// TODO(hjchen2)
max_abs = std::max(max_abs, 1e-6f);
}
param->online_scale_->mutable_data<float>()[0] = max_abs;
}
template <>
bool FusionDequantAddBNQuantKernel<CPU, float>::Init(
FusionDequantAddBNQuantParam<CPU> *param) {
const framework::Tensor *bias = param->bias_;
PublicFusionDequantBNInitParam(param, bias);
return true;
}
template <>
void FusionDequantAddBNQuantKernel<CPU, float>::Compute(
const FusionDequantAddBNQuantParam<CPU> &param) {
switch (param.round_type_) {
case ROUND_NEAREST_TO_EVEN:
DequantBNQuantCompute<ROUND_NEAREST_TO_EVEN>(&param);
break;
case ROUND_NEAREST_TOWARDS_ZERO:
DequantBNQuantCompute<ROUND_NEAREST_TOWARDS_ZERO>(&param);
break;
case ROUND_NEAREST_AWAY_ZERO:
DequantBNQuantCompute<ROUND_NEAREST_AWAY_ZERO>(&param);
break;
default:
LOG(kLOG_ERROR) << "round type is not supported.";
break;
}
}
#endif // FUSION_DEQUANT_ADD_BN_QUANT_OP
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
template <RoundType R>
void DequantBNReluQuantCompute(
const FusionDequantAddBNReluQuantParam<CPU> *param) {
const int32_t *input = param->input_->data<int32_t>();
const float *bn_scale = param->bn_scale_->data<float>();
const float *bn_bias = param->bn_bias_->data<float>();
// dequantize params
const float activation_scale = param->activation_scale_->data<float>()[0];
const float weight_scale = param->weight_scale_;
const float dequant_scale = activation_scale / weight_scale;
// quantize params
Tensor *output_scale = param->online_scale_;
float max_abs = 0.f;
int8_t *output = param->output_->mutable_data<int8_t>();
int batch_size = param->input_->dims()[0];
int channels = param->input_->dims()[1];
size_t spatial_size = param->input_->dims()[2] * param->input_->dims()[3];
// if (param->is_static_) {
if (true) {
max_abs = param->static_scale_;
float quant_scale = 127.f / max_abs;
#pragma omp parallel for collapse(2)
for (int batch = 0; batch < batch_size; ++batch) {
for (int c = 0; c < channels; ++c) {
// float scale = bn_scale[c] * dequant_scale;
float scale = bn_scale[c];
float bias = bn_bias[c];
size_t offset = (batch * channels + c) * spatial_size;
const int32_t *x = input + offset;
int8_t *y = output + offset;
size_t remain = spatial_size;
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
int loop = spatial_size >> 4;
remain = spatial_size & 0xF;
float32x4_t __dequant_scale = vdupq_n_f32(dequant_scale);
float32x4_t __scale = vdupq_n_f32(scale);
float32x4_t __bias = vdupq_n_f32(bias);
float32x4_t __zero = vdupq_n_f32(0.f);
float32x4_t __quant_scale = vdupq_n_f32(quant_scale);
for (int k = 0; k < loop; ++k, x += 16, y += 16) {
int32x4_t r0 = vld1q_s32(x);
int32x4_t r1 = vld1q_s32(x + 4);
int32x4_t r2 = vld1q_s32(x + 8);
int32x4_t r3 = vld1q_s32(x + 12);
float32x4_t f0 = vcvtq_f32_s32(r0);
float32x4_t f1 = vcvtq_f32_s32(r1);
float32x4_t f2 = vcvtq_f32_s32(r2);
float32x4_t f3 = vcvtq_f32_s32(r3);
f0 = vmulq_f32(__dequant_scale, f0);
f1 = vmulq_f32(__dequant_scale, f1);
f2 = vmulq_f32(__dequant_scale, f2);
f3 = vmulq_f32(__dequant_scale, f3);
f0 = vmlaq_f32(__bias, __scale, f0);
f1 = vmlaq_f32(__bias, __scale, f1);
f2 = vmlaq_f32(__bias, __scale, f2);
f3 = vmlaq_f32(__bias, __scale, f3);
f0 = vmaxq_f32(__zero, f0);
f1 = vmaxq_f32(__zero, f1);
f2 = vmaxq_f32(__zero, f2);
f3 = vmaxq_f32(__zero, f3);
f0 = vmulq_f32(__quant_scale, f0);
f1 = vmulq_f32(__quant_scale, f1);
f2 = vmulq_f32(__quant_scale, f2);
f3 = vmulq_f32(__quant_scale, f3);
int32x4_t q0 = math::vround_f32<R>(f0);
int32x4_t q1 = math::vround_f32<R>(f1);
int32x4_t q2 = math::vround_f32<R>(f2);
int32x4_t q3 = math::vround_f32<R>(f3);
int16x4_t d0 = vmovn_s32(q0);
int16x4_t d1 = vmovn_s32(q1);
int16x4_t d2 = vmovn_s32(q2);
int16x4_t d3 = vmovn_s32(q3);
int16x8_t q5 = vcombine_s16(d0, d1);
int16x8_t q6 = vcombine_s16(d2, d3);
int8x8_t d5 = vmovn_s16(q5);
int8x8_t d6 = vmovn_s16(q6);
vst1_s8(y, d5);
vst1_s8(y + 8, d6);
}
#endif // __ARM_NEON__
for (int k = 0; k < remain; ++k) {
float x_temp = std::max(scale * (dequant_scale * x[k]) + bias, 0.f);
y[k] = math::Round<R>(x_temp * quant_scale);
}
}
}
} else {
// TODO(hjchen2)
max_abs = std::max(max_abs, 1e-6f);
}
param->online_scale_->mutable_data<float>()[0] = max_abs;
}
template <>
bool FusionDequantAddBNReluQuantKernel<CPU, float>::Init(
FusionDequantAddBNReluQuantParam<CPU> *param) {
const framework::Tensor *bias = param->bias_;
PublicFusionDequantBNInitParam(param, bias);
return true;
}
template <>
void FusionDequantAddBNReluQuantKernel<CPU, float>::Compute(
const FusionDequantAddBNReluQuantParam<CPU> &param) {
switch (param.round_type_) {
case ROUND_NEAREST_TO_EVEN:
DequantBNReluQuantCompute<ROUND_NEAREST_TO_EVEN>(&param);
break;
case ROUND_NEAREST_TOWARDS_ZERO:
DequantBNReluQuantCompute<ROUND_NEAREST_TOWARDS_ZERO>(&param);
break;
case ROUND_NEAREST_AWAY_ZERO:
DequantBNReluQuantCompute<ROUND_NEAREST_AWAY_ZERO>(&param);
break;
default:
LOG(kLOG_ERROR) << "round type is not supported.";
break;
}
}
#endif // FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/quantize_kernel.cpp
浏览文件 @ ab6b3178
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "operators/kernel/quantize_kernel.h"
#include <cmath>
#include "operators/math/quantize.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
......@@ -30,72 +31,6 @@ inline float32_t vmaxvq_f32(float32x4_t r) {
}
#endif
template <RoundType R = ROUND_NEAREST_TOWARDS_ZERO>
inline int32x4_t vround_f32(float32x4_t r) {
return vcvtq_s32_f32(r);
}
template <>
inline int32x4_t vround_f32<ROUND_NEAREST_AWAY_ZERO>(float32x4_t r) {
float32x4_t plus = vdupq_n_f32(0.5);
float32x4_t minus = vdupq_n_f32(-0.5);
float32x4_t zero = vdupq_n_f32(0);
uint32x4_t more_than_zero = vcgtq_f32(r, zero);
float32x4_t temp = vbslq_f32(more_than_zero, plus, minus);
temp = vaddq_f32(r, temp);
int32x4_t ret = vcvtq_s32_f32(temp);
return ret;
}
template <>
inline int32x4_t vround_f32<ROUND_NEAREST_TO_EVEN>(float32x4_t r) {
float32x4_t point5 = vdupq_n_f32(0.5);
int32x4_t one = vdupq_n_s32(1);
int32x4_t zero = vdupq_n_s32(0);
int32x4_t rnd = vround_f32<ROUND_NEAREST_AWAY_ZERO>(r);
float32x4_t frnd = vcvtq_f32_s32(rnd);
frnd = vsubq_f32(frnd, r);
frnd = vabsq_f32(frnd);
uint32x4_t equal_point5 = vceqq_f32(frnd, point5);
int32x4_t abs_rnd = vabsq_s32(rnd);
abs_rnd = vandq_s32(abs_rnd, one);
uint32x4_t not_mod2 = vreinterpretq_u32_s32(abs_rnd);
uint32x4_t mask = vandq_u32(equal_point5, not_mod2);
uint32x4_t more_than_zero = vcgtq_s32(rnd, zero);
more_than_zero = vandq_u32(more_than_zero, vreinterpretq_u32_s32(one));
mask = veorq_u32(more_than_zero, mask);
more_than_zero = veorq_u32(more_than_zero, vreinterpretq_u32_s32(one));
mask = vaddq_u32(more_than_zero, mask);
int32x4_t smask = vreinterpretq_s32_u32(mask);
smask = vsubq_s32(smask, one);
rnd = vaddq_s32(rnd, smask);
return rnd;
}
#endif
template <RoundType R = ROUND_NEAREST_TOWARDS_ZERO>
inline int8_t Round(const float &x) {
return static_cast<int8_t>(x);
}
template <>
inline int8_t Round<ROUND_NEAREST_AWAY_ZERO>(const float &x) {
return std::round(x);
}
template <>
inline int8_t Round<ROUND_NEAREST_TO_EVEN>(const float &x) {
float v = std::round(x);
int32_t q = static_cast<int32_t>(v);
if (std::abs(std::abs(q - v) - 0.5) <= 0) {
if (std::abs(q) % 2 != 0) {
q = q + ((q > 0) ? -1 : 1);
}
}
return static_cast<int8_t>(q);
}
template <RoundType R>
static void Quantize(const Tensor *input, const float scale, Tensor *output) {
const float *x = input->data<const float>();
......@@ -105,6 +40,7 @@ static void Quantize(const Tensor *input, const float scale, Tensor *output) {
size_t loop = remain >> 4;
remain = remain & 0xF;
float32x4_t __scale = vdupq_n_f32(scale);
#pragma omp parallel for
for (size_t i = 0; i < loop; ++i) {
const float *local_x = x + (i << 4);
......@@ -113,14 +49,14 @@ static void Quantize(const Tensor *input, const float scale, Tensor *output) {
float32x4_t r1 = vld1q_f32(local_x + 4);
float32x4_t r2 = vld1q_f32(local_x + 8);
float32x4_t r3 = vld1q_f32(local_x + 12);
r0 = vmulq_n_f32(r0, scale);
r1 = vmulq_n_f32(r1, scale);
r2 = vmulq_n_f32(r2, scale);
r3 = vmulq_n_f32(r3, scale);
int32x4_t q0 = vround_f32<R>(r0);
int32x4_t q1 = vround_f32<R>(r1);
int32x4_t q2 = vround_f32<R>(r2);
int32x4_t q3 = vround_f32<R>(r3);
r0 = vmulq_f32(r0, __scale);
r1 = vmulq_f32(r1, __scale);
r2 = vmulq_f32(r2, __scale);
r3 = vmulq_f32(r3, __scale);
int32x4_t q0 = math::vround_f32<R>(r0);
int32x4_t q1 = math::vround_f32<R>(r1);
int32x4_t q2 = math::vround_f32<R>(r2);
int32x4_t q3 = math::vround_f32<R>(r3);
int16x4_t d0 = vmovn_s32(q0);
int16x4_t d1 = vmovn_s32(q1);
int16x4_t d2 = vmovn_s32(q2);
......@@ -136,7 +72,7 @@ static void Quantize(const Tensor *input, const float scale, Tensor *output) {
y += (loop << 4);
#endif
for (size_t i = 0; i < remain; ++i) {
y[i] = Round<R>(x[i] * scale);
y[i] = math::Round<R>(x[i] * scale);
}
}
......@@ -171,6 +107,13 @@ float find_abs_max(const Tensor *input) {
return max_abs;
}
} // namespace operators
} // namespace paddle_mobile
#endif // __ARM_NEON__
namespace paddle_mobile {
namespace operators {
template <>
bool QuantizeKernel<CPU, float>::Init(QuantizeParam<CPU> *param) {
return true;
......@@ -182,8 +125,8 @@ void QuantizeKernel<CPU, float>::Compute(const QuantizeParam<CPU> &param) {
Tensor *output = param.output_;
Tensor *output_scale = param.online_scale_;
float max_abs = 0.f;
if (param.is_static_) {
max_abs = param.static_scale_;
if (param.offline_) {
max_abs = param.offline_scale_->data<float>()[0];
} else {
max_abs = find_abs_max(input);
}
......@@ -210,4 +153,4 @@ void QuantizeKernel<CPU, float>::Compute(const QuantizeParam<CPU> &param) {
} // namespace operators
} // namespace paddle_mobile
#endif
#endif // QUANT_OP
src/operators/kernel/arm/relu_kernel.cpp
浏览文件 @ ab6b3178
......@@ -15,11 +15,86 @@ limitations under the License. */
#ifdef RELU_OP
#include "operators/kernel/relu_kernel.h"
#include "operators/kernel/central-arm-func/relu_arm_func.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
#endif
namespace paddle_mobile {
namespace operators {
enum ReluMode {
Relu = 0,
Relu6 = 1,
PRelu = 2,
LeakyRelu = 3,
};
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <ReluMode R = Relu>
inline float32x4_t vRelu_f32(const float32x4_t &x) {
float32x4_t __zero = vdupq_n_f32(0.f);
return vmaxq_f32(__zero, x);
}
template <>
inline float32x4_t vRelu_f32<Relu6>(const float32x4_t &x) {
float32x4_t __zero = vdupq_n_f32(0.f);
float32x4_t __six = vdupq_n_f32(6.f);
return vminq_f32(__six, vmaxq_f32(__zero, x));
}
#endif
template <ReluMode R = Relu>
inline float ReluFunc(const float &x) {
return std::max(x, 0.f);
}
template <>
inline float ReluFunc<Relu6>(const float &x) {
return std::min(std::max(x, 0.f), 6.f);
}
template <typename Dtype, ReluMode R>
struct ReluCompute {
void operator()(const Tensor *input, Tensor *output) {}
};
template <ReluMode R>
struct ReluCompute<float, R> {
void operator()(const Tensor *input, Tensor *output) {
const float *x = input->data<float>();
float *y = output->mutable_data<float>();
size_t remain = input->numel();
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
size_t loop = remain >> 4;
remain = remain & 0xF;
#pragma omp parallel for
for (size_t i = 0; i < loop; ++i) {
const float *local_x = x + (i << 4);
float *local_y = y + (i << 4);
float32x4_t r0 = vld1q_f32(local_x);
float32x4_t r1 = vld1q_f32(local_x + 4);
float32x4_t r2 = vld1q_f32(local_x + 8);
float32x4_t r3 = vld1q_f32(local_x + 12);
r0 = vRelu_f32<R>(r0);
r1 = vRelu_f32<R>(r1);
r2 = vRelu_f32<R>(r2);
r3 = vRelu_f32<R>(r3);
vst1q_f32(local_y, r0);
vst1q_f32(local_y + 4, r1);
vst1q_f32(local_y + 8, r2);
vst1q_f32(local_y + 12, r3);
}
x += (loop << 4);
y += (loop << 4);
#endif
for (size_t i = 0; i < remain; ++i) {
y[i] = ReluFunc<R>(x[i]);
}
}
};
template <>
bool ReluKernel<CPU, float>::Init(ReluParam<CPU> *param) {
return true;
......@@ -27,7 +102,21 @@ bool ReluKernel<CPU, float>::Init(ReluParam<CPU> *param) {
template <>
void ReluKernel<CPU, float>::Compute(const ReluParam<CPU> &param) {
ReluCompute<float>(param);
const Tensor *input = param.InputX();
Tensor *output = param.Out();
ReluCompute<float, Relu>()(input, output);
}
template <>
bool Relu6Kernel<CPU, float>::Init(ReluParam<CPU> *param) {
return true;
}
template <>
void Relu6Kernel<CPU, float>::Compute(const ReluParam<CPU> &param) {
const Tensor *input = param.InputX();
Tensor *output = param.Out();
ReluCompute<float, Relu6>()(input, output);
}
} // namespace operators
......
src/operators/kernel/arm/transpose2_kernel.cpp
浏览文件 @ ab6b3178
......@@ -11,14 +11,111 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef TRANSPOSE2_OP
#include "operators/kernel/transpose2_kernel.h"
#include "operators/kernel/central-arm-func/transpose2_arm_func.h"
namespace paddle_mobile {
namespace operators {
bool IsShuffleChannel(const std::vector<int> &axis) {
bool is_shuffle_channel = true;
if (axis.size() > 2 && axis[0] == 0 && axis[1] == 2 && axis[2] == 1) {
for (int i = 3; i < axis.size(); ++i) {
if (axis[i] != i) {
is_shuffle_channel = false;
break;
}
}
} else {
return false;
}
return is_shuffle_channel;
}
template <typename Dtype>
void ShuffleChannelCompute(const Transpose2Param<CPU> &param) {
const std::vector<int> &axis = param.Axis();
const Tensor *input = param.InputX();
const Dtype *input_ptr = input->data<Dtype>();
Tensor *output = param.Out();
Dtype *output_ptr = output->mutable_data<Dtype>();
// input and output's shape dimension must >= 2 && <= 6.
const framework::DDim &in_dim = input->dims();
const framework::DDim &out_dim = output->dims();
size_t offset = 1;
for (int i = 3; i < axis.size(); ++i) {
offset *= in_dim[i];
}
#pragma omp parallel for collapse(3)
for (int batch = 0; batch < out_dim[0]; ++batch) {
for (int c1 = 0; c1 < out_dim[1]; ++c1) {
for (int c2 = 0; c2 < out_dim[2]; ++c2) {
size_t out_offset =
((batch * out_dim[1] + c1) * out_dim[2] + c2) * offset;
size_t in_offset = ((batch * in_dim[1] + c2) * in_dim[2] + c1) * offset;
memcpy(output_ptr + out_offset, input_ptr + in_offset,
offset * sizeof(Dtype));
}
}
}
}
template <typename Dtype>
void Transpose2Compute(const Transpose2Param<CPU> &param) {
const std::vector<int> &axis = param.Axis();
const Tensor *input = param.InputX();
const Dtype *input_ptr = input->data<Dtype>();
Tensor *output = param.Out();
Dtype *output_ptr = output->mutable_data<Dtype>();
// input and output's shape dimension must >= 2 && <= 6.
const framework::DDim &in_dim = input->dims();
const framework::DDim &out_dim = output->dims();
// precompute inverted output dim and strides
size_t rout_dim[6], strides[6];
int permute = axis.size(); // permute must >=2 && <= 6.
for (int i = 0; i < permute; ++i) {
int k = permute - 1 - i;
strides[k] = 1;
for (int j = axis[i] + 1; j < permute; ++j) {
strides[k] *= in_dim[j];
}
rout_dim[k] = out_dim[i];
}
// unroll the first 2 dimensions
int reamin_dim = 1;
for (int i = 2; i < out_dim.size(); ++i) {
reamin_dim *= out_dim[i];
}
#pragma omp parallel for collapse(2)
for (int batch = 0; batch < out_dim[0]; ++batch) {
for (int j = 0; j < out_dim[1]; ++j) {
size_t offset = batch * strides[permute - 1] + j * strides[permute - 2];
Dtype *out_ptr = output_ptr + (batch * out_dim[1] + j) * reamin_dim;
int indics[4] = {0, 0, 0, 0};
for (int k = 0; k < reamin_dim; ++k) {
out_ptr[k] = input_ptr[offset];
indics[0] += 1;
offset += strides[0];
for (int p = 0; p < permute - 3; ++p) {
if (indics[p] == rout_dim[p]) {
indics[p + 1] += 1;
indics[p] = 0;
offset += strides[p + 1];
offset -= rout_dim[p] * strides[p];
} else {
break;
}
}
}
}
}
}
template <>
bool Transpose2Kernel<CPU, float>::Init(Transpose2Param<CPU> *param) {
return true;
......@@ -26,10 +123,24 @@ bool Transpose2Kernel<CPU, float>::Init(Transpose2Param<CPU> *param) {
template <>
void Transpose2Kernel<CPU, float>::Compute(const Transpose2Param<CPU> &param) {
Transpose2Compute<float>(param);
const std::vector<int> &axis = param.Axis();
bool shuffle_channel = IsShuffleChannel(axis);
if (shuffle_channel) {
if (param.InputX()->type() == typeid(int8_t)) {
ShuffleChannelCompute<int8_t>(param);
} else {
ShuffleChannelCompute<float>(param);
}
} else {
if (param.InputX()->type() == typeid(int8_t)) {
Transpose2Compute<int8_t>(param);
} else {
Transpose2Compute<float>(param);
}
}
}
} // namespace operators
} // namespace paddle_mobile
#endif
#endif // TRANSPOSE2_OP
src/operators/kernel/central-arm-func/relu_arm_func.h 已删除 100644 → 0
浏览文件 @ 94e89540
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef RELU_OP
#pragma once
#include <operators/math/transform.h>
#include "operators/op_param.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
#endif
namespace paddle_mobile {
namespace operators {
template <typename T>
struct ReluFunctor {
inline T operator()(T in) const { return in > 0 ? in : 0; }
};
/*
* @b 特化到具体平台的实现, param 从 op 层传入
* */
template <typename P>
void ReluCompute(const ReluParam<CPU> &param) {
const auto *input_x = param.InputX();
auto *input_x_ptr = input_x->data<float>();
auto *out = param.Out();
auto *out_ptr = out->mutable_data<float>();
int numel = input_x->numel();
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#if __aarch64__
if (numel > 0) {
int loop = numel >> 0x4;
int remain = numel & 0xF;
float32x4_t zero = vdupq_n_f32(0.f);
for (int i = 0; i < loop; ++i) {
float32x4_t r0 = vld1q_f32(input_x_ptr);
float32x4_t r1 = vld1q_f32(input_x_ptr + 4);
float32x4_t r2 = vld1q_f32(input_x_ptr + 8);
float32x4_t r3 = vld1q_f32(input_x_ptr + 12);
r0 = vmaxq_f32(r0, zero);
r1 = vmaxq_f32(r1, zero);
r2 = vmaxq_f32(r2, zero);
r3 = vmaxq_f32(r3, zero);
vst1q_f32(out_ptr, r0);
vst1q_f32(out_ptr + 4, r1);
vst1q_f32(out_ptr + 8, r2);
vst1q_f32(out_ptr + 12, r3);
input_x_ptr += 16;
out_ptr += 16;
}
for (int i = 0; i < remain; ++i) {
out_ptr[i] = (input_x_ptr[i] > 0) * input_x_ptr[i];
}
#else
if (numel > 64) {
asm volatile(
"pld [%[input_x_ptr], #0] \n\t"
"vmov.f32 q8, #0.0 \n\t"
"subs %[num], %[num], #32 \n\t"
"blt end_num_%= \n\t"
"loop_num_%=: \n\t"
"pld [%[input_x_ptr], #1024] \n\t"
"vld1.32 {q0, q1}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q4, q5}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q6, q7}, [%[input_x_ptr]]! \n\t"
"vmax.f32 q0, q0, q8 \n\t"
"vmax.f32 q1, q1, q8 \n\t"
"vmax.f32 q2, q2, q8 \n\t"
"vmax.f32 q3, q3, q8 \n\t"
"vmax.f32 q4, q4, q8 \n\t"
"vmax.f32 q5, q5, q8 \n\t"
"vmax.f32 q6, q6, q8 \n\t"
"vmax.f32 q7, q7, q8 \n\t"
"vst1.32 {q0, q1}, [%[out_ptr]]! \n\t"
"vst1.32 {q2, q3}, [%[out_ptr]]! \n\t"
"vst1.32 {q4, q5}, [%[out_ptr]]! \n\t"
"vst1.32 {q6, q7}, [%[out_ptr]]! \n\t"
"subs %[num], %[num], #32 \n\t"
"bge loop_num_%= \n\t"
"end_num_%=: \n\t"
"cmp %[num], #0 \n\t"
"bge end_%= \n\t"
"mov r6, #4 \n\t"
"mul r5, %[num], r6 \n\t"
"add %[input_x_ptr], %[input_x_ptr], r5 \n\t"
"vld1.32 {q0, q1}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q4, q5}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q6, q7}, [%[input_x_ptr]]! \n\t"
"vmax.f32 q0, q0, q8 \n\t"
"vmax.f32 q1, q1, q8 \n\t"
"vmax.f32 q2, q2, q8 \n\t"
"vmax.f32 q3, q3, q8 \n\t"
"vmax.f32 q4, q4, q8 \n\t"
"vmax.f32 q5, q5, q8 \n\t"
"vmax.f32 q6, q6, q8 \n\t"
"vmax.f32 q7, q7, q8 \n\t"
"add %[out_ptr], %[out_ptr], r5 \n\t"
"vst1.32 {q0, q1}, [%[out_ptr]]! \n\t"
"vst1.32 {q2, q3}, [%[out_ptr]]! \n\t"
"vst1.32 {q4, q5}, [%[out_ptr]]! \n\t"
"vst1.32 {q6, q7}, [%[out_ptr]]! \n\t"
"end_%=: \n\t"
:
:
[out_ptr] "r"(out_ptr), [input_x_ptr] "r"(input_x_ptr), [num] "r"(numel)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "r5",
"r6");
#endif
} else {
#endif
ReluFunctor<float> func_;
math::Transform trans;
trans(input_x_ptr, input_x_ptr + numel, out_ptr, func_);
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
}
#endif
}
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/central-arm-func/transpose_arm_func.h
浏览文件 @ ab6b3178
......@@ -21,23 +21,6 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
// vector<int> pos;
// template <typename T>
// void TransposeFunc(const int numel, const T* input, const vector<int> axis,
// const vector<int> old_strides, const vector<int>
// new_strides, T* output) {
// for (int i = 0; i < numel; ++i) {
// int old_idx = 0;
// int idx = i;
// for (int j = 0; j < axis.size(); ++j) {
// int order = axis[j];
// old_idx += (idx / new_strides[j]) * old_strides[order];
// idx %= new_strides[j];
// }
// output[i] = input[old_idx];
// }
// }
template <typename P>
void TransposeCompute(const TransposeParam<CPU>& param) {
const auto* input_x = param.InputX();
......
src/operators/kernel/dequant_bn_relu_kernel.h
浏览文件 @ ab6b3178
......@@ -42,5 +42,27 @@ class FusionDequantAddBNReluKernel
};
#endif
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
template <typename DeviceType, typename T>
class FusionDequantAddBNReluQuantKernel
: public framework::OpKernelBase<
DeviceType, FusionDequantAddBNReluQuantParam<DeviceType>> {
public:
void Compute(const FusionDequantAddBNReluQuantParam<DeviceType> &param);
bool Init(FusionDequantAddBNReluQuantParam<DeviceType> *param);
};
#endif
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
template <typename DeviceType, typename T>
class FusionDequantAddBNQuantKernel
: public framework::OpKernelBase<DeviceType,
FusionDequantAddBNQuantParam<DeviceType>> {
public:
void Compute(const FusionDequantAddBNQuantParam<DeviceType> &param);
bool Init(FusionDequantAddBNQuantParam<DeviceType> *param);
};
#endif
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/feed_kernel.h
浏览文件 @ ab6b3178
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
using namespace framework;
template <typename DeviceType, typename T>
class FeedKernel
: public framework::OpKernelBase<DeviceType, FeedParam<DeviceType>> {
......
src/operators/kernel/relu_kernel.h
浏览文件 @ ab6b3178
......@@ -17,7 +17,6 @@ limitations under the License. */
#pragma once
#include "framework/operator.h"
#include "operators/op_param.h"
namespace paddle_mobile {
......@@ -30,6 +29,15 @@ class ReluKernel
void Compute(const ReluParam<DeviceType>& param);
bool Init(ReluParam<DeviceType>* param);
};
template <typename DeviceType, typename T>
class Relu6Kernel
: public framework::OpKernelBase<DeviceType, ReluParam<DeviceType>> {
public:
void Compute(const ReluParam<DeviceType>& param);
bool Init(ReluParam<DeviceType>* param);
};
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/quantize.h 0 → 100644
浏览文件 @ ab6b3178
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef QUANT_OP
#pragma once
#include <cmath>
#include "common/types.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
#endif
namespace paddle_mobile {
namespace operators {
namespace math {
template <RoundType R = ROUND_NEAREST_TOWARDS_ZERO>
inline int8_t Round(const float &x) {
return static_cast<int8_t>(x);
}
template <>
inline int8_t Round<ROUND_NEAREST_AWAY_ZERO>(const float &x) {
return std::round(x);
}
template <>
inline int8_t Round<ROUND_NEAREST_TO_EVEN>(const float &x) {
float v = std::round(x);
int32_t q = static_cast<int32_t>(v);
if (std::abs(std::abs(q - v) - 0.5) <= 0) {
if (std::abs(q) % 2 != 0) {
q = q + ((q > 0) ? -1 : 1);
}
}
return static_cast<int8_t>(q);
}
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <RoundType R = ROUND_NEAREST_TOWARDS_ZERO>
inline int32x4_t vround_f32(float32x4_t r) {
return vcvtq_s32_f32(r);
}
template <>
inline int32x4_t vround_f32<ROUND_NEAREST_AWAY_ZERO>(float32x4_t r) {
float32x4_t plus = vdupq_n_f32(0.5);
float32x4_t minus = vdupq_n_f32(-0.5);
float32x4_t zero = vdupq_n_f32(0);
uint32x4_t more_than_zero = vcgtq_f32(r, zero);
float32x4_t temp = vbslq_f32(more_than_zero, plus, minus);
temp = vaddq_f32(r, temp);
int32x4_t ret = vcvtq_s32_f32(temp);
return ret;
}
template <>
inline int32x4_t vround_f32<ROUND_NEAREST_TO_EVEN>(float32x4_t r) {
float32x4_t point5 = vdupq_n_f32(0.5);
int32x4_t one = vdupq_n_s32(1);
int32x4_t zero = vdupq_n_s32(0);
int32x4_t rnd = vround_f32<ROUND_NEAREST_AWAY_ZERO>(r);
float32x4_t frnd = vcvtq_f32_s32(rnd);
frnd = vsubq_f32(frnd, r);
frnd = vabsq_f32(frnd);
uint32x4_t equal_point5 = vceqq_f32(frnd, point5);
int32x4_t abs_rnd = vabsq_s32(rnd);
abs_rnd = vandq_s32(abs_rnd, one);
uint32x4_t not_mod2 = vreinterpretq_u32_s32(abs_rnd);
uint32x4_t mask = vandq_u32(equal_point5, not_mod2);
uint32x4_t more_than_zero = vcgtq_s32(rnd, zero);
more_than_zero = vandq_u32(more_than_zero, vreinterpretq_u32_s32(one));
mask = veorq_u32(more_than_zero, mask);
more_than_zero = veorq_u32(more_than_zero, vreinterpretq_u32_s32(one));
mask = vaddq_u32(more_than_zero, mask);
int32x4_t smask = vreinterpretq_s32_u32(mask);
smask = vsubq_s32(smask, one);
rnd = vaddq_s32(rnd, smask);
return rnd;
}
#endif // __ARM_NEON__
} // namespace math
} // namespace operators
} // namespace paddle_mobile
#endif // QUANT_OP
src/operators/op_param.h
浏览文件 @ ab6b3178
......@@ -2530,18 +2530,13 @@ class QuantizeParam : public OpParam {
// scale = max(abs(x))
online_scale_ = OpParam::GetVarValue<GType>("OutScale", outputs, scope);
// offline
if (HasAttr("static_scale", attrs)) {
is_static_ = true;
static_scale_ = GetAttr<float>("static_scale", attrs);
if (OpParam::HasAttr("InScale", attrs)) {
offline_ = true;
offline_scale_ = OpParam::GetVarValue<GType>("InScale", inputs, scope);
}
// x = round(scale * x)
if (HasAttr("round_type", attrs)) {
round_type_ = GetAttr<RoundType>("round_type", attrs);
}
// get paddings
paddings_ = std::vector<int>({0, 0});
if (HasAttr("paddings", attrs)) {
paddings_ = GetAttr<vector<int>>("paddings", attrs);
if (OpParam::HasAttr("round_type", attrs)) {
round_type_ = OpParam::GetAttr<RoundType>("round_type", attrs);
}
}
......@@ -2551,17 +2546,13 @@ class QuantizeParam : public OpParam {
// op output
RType *output_;
RType *online_scale_;
// if static scale or not
bool is_static_ = false;
// quantize scale
float static_scale_ = 1.0f;
// quantize offline scale
RType *offline_scale_;
// if offine scale or not
bool offline_ = false;
// round method type
// nearest_zero and nearest_even is valid currently
// RoundType round_type_ = ROUND_NEAREST_AWAY_ZERO;
RoundType round_type_ = ROUND_NEAREST_TOWARDS_ZERO;
// optional paddings
std::vector<int> paddings_;
int8_t padding_val_;
};
#endif
......@@ -2580,10 +2571,10 @@ class DequantizeParam : public OpParam {
}
activation_scale_ = OpParam::GetVarValue<GType>("Scale", inputs, scope);
// dequantization is performed as x = x / static_scale / online_scale
if (HasAttr("weight_scale", attrs)) {
weight_scale_ = GetAttr<float>("weight_scale", attrs);
if (OpParam::HasAttr("weight_scale", attrs)) {
weight_scale_ = OpParam::GetAttr<float>("weight_scale", attrs);
} else {
weight_scale_ = GetAttr<float>("max_range", attrs);
weight_scale_ = OpParam::GetAttr<float>("max_range", attrs);
}
}
......@@ -2597,9 +2588,11 @@ class DequantizeParam : public OpParam {
};
#endif
#if defined(FUSION_DEQUANT_ADD_BN_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_OP) || \
defined(FUSION_DEQUANT_BN_RELU_OP) || defined(FUSION_DEQUANT_BN_OP)
#if defined(FUSION_DEQUANT_ADD_BN_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_OP) || \
defined(FUSION_DEQUANT_BN_RELU_OP) || defined(FUSION_DEQUANT_BN_OP) || \
defined(FUSION_DEQUANT_ADD_BN_QUANT_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP)
template <typename Dtype>
class FusionDequantBNParam : public DequantizeParam<Dtype> {
typedef typename DtypeTensorTrait<Dtype>::gtype GType;
......@@ -2632,7 +2625,10 @@ class FusionDequantBNParam : public DequantizeParam<Dtype> {
};
#endif
#if defined(FUSION_DEQUANT_ADD_BN_RELU_OP) || defined(FUSION_DEQUANT_ADD_BN_OP)
#if defined(FUSION_DEQUANT_ADD_BN_RELU_OP) || \
defined(FUSION_DEQUANT_ADD_BN_OP) || \
defined(FUSION_DEQUANT_ADD_BN_QUANT_OP) || \
defined(FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP)
template <typename Dtype>
class FusionDequantAddBNParam : public FusionDequantBNParam<Dtype> {
typedef typename DtypeTensorTrait<Dtype>::gtype GType;
......@@ -2697,5 +2693,79 @@ class FusionDequantAddBNReluParam : public FusionDequantAddBNParam<Dtype> {
};
#endif
#ifdef FUSION_DEQUANT_ADD_BN_QUANT_OP
template <typename Dtype>
class FusionDequantAddBNQuantParam : public FusionDequantAddBNParam<Dtype> {
typedef typename DtypeTensorTrait<Dtype>::gtype GType;
typedef typename DtypeTensorTrait<Dtype>::rtype RType;
public:
FusionDequantAddBNQuantParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope)
: FusionDequantAddBNParam<Dtype>(inputs, outputs, attrs, scope) {
// scale output
online_scale_ = OpParam::GetVarValue<GType>("OutScale", outputs, scope);
// offline
if (OpParam::HasAttr("static_scale", attrs)) {
is_static_ = true;
static_scale_ = OpParam::GetAttr<float>("static_scale", attrs);
}
// x = round(scale * x)
if (OpParam::HasAttr("round_type", attrs)) {
round_type_ = OpParam::GetAttr<RoundType>("round_type", attrs);
}
}
public:
RType *online_scale_;
// if static scale or not
bool is_static_ = false;
// quantize scale
float static_scale_ = 1.0f;
// round method type
// RoundType round_type_ = ROUND_NEAREST_AWAY_ZERO;
RoundType round_type_ = ROUND_NEAREST_TOWARDS_ZERO;
};
#endif
#ifdef FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP
template <typename Dtype>
class FusionDequantAddBNReluQuantParam
: public FusionDequantAddBNReluParam<Dtype> {
typedef typename DtypeTensorTrait<Dtype>::gtype GType;
typedef typename DtypeTensorTrait<Dtype>::rtype RType;
public:
FusionDequantAddBNReluQuantParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs,
const Scope &scope)
: FusionDequantAddBNReluParam<Dtype>(inputs, outputs, attrs, scope) {
// scale output
online_scale_ = OpParam::GetVarValue<GType>("OutScale", outputs, scope);
// offline
if (OpParam::HasAttr("static_scale", attrs)) {
is_static_ = true;
static_scale_ = OpParam::GetAttr<float>("static_scale", attrs);
}
// x = round(scale * x)
if (OpParam::HasAttr("round_type", attrs)) {
round_type_ = OpParam::GetAttr<RoundType>("round_type", attrs);
}
}
public:
RType *online_scale_;
// if static scale or not
bool is_static_ = false;
// quantize scale
float static_scale_ = 1.0f;
// round method type
// RoundType round_type_ = ROUND_NEAREST_AWAY_ZERO;
RoundType round_type_ = ROUND_NEAREST_TOWARDS_ZERO;
};
#endif
} // namespace operators
} // namespace paddle_mobile
src/operators/quantize_op.cpp
浏览文件 @ ab6b3178
......@@ -22,10 +22,7 @@ namespace operators {
template <typename DeviceType, typename T>
void QuantizeOp<DeviceType, T>::InferShape() const {
auto input_dims = this->param_.input_->dims();
const std::vector<int> &paddings = this->param_.paddings_;
input_dims[2] += 2 * paddings[0];
input_dims[3] += 2 * paddings[1];
const auto &input_dims = this->param_.input_->dims();
this->param_.output_->Resize(input_dims);
auto scale_dims = framework::make_ddim(std::vector<int>{1});
this->param_.online_scale_->Resize(scale_dims);
......
src/operators/relu_op.cpp
浏览文件 @ ab6b3178
......@@ -24,17 +24,19 @@ void ReluOp<Dtype, T>::InferShape() const {
this->param_.Out()->Resize(input_dims);
}
template <typename Dtype, typename T>
void Relu6Op<Dtype, T>::InferShape() const {
auto input_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(input_dims);
}
} // namespace operators
} // namespace paddle_mobile
/*
* @b 每一个 op 都需要注册一下的,
* USE_OP的参数 和 REGISTER_OPERATOR的第一个参数
* 都是需要和model中类型对应起来的
* */
namespace ops = paddle_mobile::operators;
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU(relu, ops::ReluOp);
REGISTER_OPERATOR_CPU(relu6, ops::Relu6Op);
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
REGISTER_OPERATOR_MALI_GPU(relu, ops::ReluOp);
......
src/operators/relu_op.h
浏览文件 @ ab6b3178
......@@ -25,25 +25,34 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
using paddle_mobile::framework::Tensor;
template <typename DeviceType, typename T>
class ReluOp : public framework::OperatorWithKernel<
DeviceType, ReluParam<DeviceType>,
operators::ReluKernel<DeviceType, T>> {
public:
/*
* @b op 的实例化方法, 需要调用父类的实例化方法, 以及实例化自己的参数结构体
* */
ReluOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType, ReluParam<DeviceType>,
operators::ReluKernel<DeviceType, T>>(
type, inputs, outputs, attrs, scope) {}
void InferShape() const override;
};
protected:
template <typename DeviceType, typename T>
class Relu6Op : public framework::OperatorWithKernel<
DeviceType, ReluParam<DeviceType>,
operators::Relu6Kernel<DeviceType, T>> {
public:
Relu6Op(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType, ReluParam<DeviceType>,
operators::Relu6Kernel<DeviceType, T>>(
type, inputs, outputs, attrs, scope) {}
void InferShape() const override;
};
} // namespace operators
......
test/net/test_benchmark.cpp
浏览文件 @ ab6b3178
......@@ -59,6 +59,13 @@ int main(int argc, char* argv[]) {
}
auto time4 = time();
std::cout << "predict cost :" << time_diff(time3, time4) / 10 << "ms\n";
std::ostringstream os("output tensor size: ");
os << output->numel() << "\n" << output->data<float>()[0];
for (int i = 1; i < output->numel(); ++i) {
os << ", " << output->data<float>()[i];
}
std::string output_str = os.str();
std::cout << output_str << std::endl;
}
return 0;
}
test/net/test_googlenet.cpp
浏览文件 @ ab6b3178
......@@ -16,16 +16,30 @@ limitations under the License. */
#include "../test_helper.h"
#include "../test_include.h"
int main() {
int main(int argc, char* argv[]) {
if (argc < 2) {
std::cout << "Usage: ./test_benchmark feed_shape [thread_num] [use_fuse]\n"
<< "feed_shape: input tensor shape, such as 1,3,224,224.\n"
<< "thread_num: optional int, threads count, default is 1.\n"
<< "use_fuse: optional bool, default is 0.\n";
return 1;
}
int thread_num = 1;
bool optimize = false;
char* feed_shape = argv[1];
if (argc >= 3) {
thread_num = atoi(argv[2]);
}
if (argc >= 4) {
optimize = atoi(argv[3]);
}
#ifdef PADDLE_MOBILE_FPGA
paddle_mobile::PaddleMobile<paddle_mobile::FPGA> paddle_mobile;
#endif
#ifdef PADDLE_MOBILE_CPU
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
#endif
paddle_mobile.SetThreadNum(1);
bool optimize = true;
paddle_mobile.SetThreadNum(thread_num);
auto time1 = time();
if (paddle_mobile.Load(g_googlenet, optimize)) {
auto time2 = paddle_mobile::time();
......@@ -34,6 +48,11 @@ int main() {
std::vector<float> input;
std::vector<float> output;
std::vector<int64_t> dims{1, 3, 224, 224};
if (feed_shape) {
sscanf(feed_shape, "%d,%d,%d", &dims[1], &dims[2], &dims[3]);
}
std::cout << "feed shape: [" << dims[0] << ", " << dims[1] << ", "
<< dims[2] << ", " << dims[3] << "]\n";
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
// warmup
for (int i = 0; i < 10; ++i) {
......@@ -44,7 +63,6 @@ int main() {
output = paddle_mobile.Predict(input, dims);
}
auto time4 = time();
std::cout << "predict cost: " << time_diff(time3, time4) / 10 << "ms\n";
}
return 0;
......
tools/op.cmake
浏览文件 @ ab6b3178
......@@ -252,6 +252,8 @@ if(NOT FOUND_MATCH)
set(FUSION_DEQUANT_ADD_BN_OP ON)
set(FUSION_DEQUANT_BN_RELU_OP ON)
set(FUSION_DEQUANT_ADD_BN_RELU_OP ON)
set(FUSION_DEQUANT_ADD_BN_QUANT_OP ON)
set(FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP ON)
endif()
# option(BATCHNORM_OP "" ON)
......@@ -462,6 +464,12 @@ endif()
if (FUSION_DEQUANT_ADD_BN_RELU_OP)
add_definitions(-DFUSION_DEQUANT_ADD_BN_RELU_OP)
endif()
if (FUSION_DEQUANT_ADD_BN_QUANT_OP)
# add_definitions(-DFUSION_DEQUANT_ADD_BN_QUANT_OP)
endif()
if (FUSION_DEQUANT_ADD_BN_RELU_QUANT_OP)
# add_definitions(-DFUSION_DEQUANT_ADD_BN_RELU_QUANT_OP)
endif()
if (TANH_OP)
......@@ -476,4 +484,3 @@ endif()
if (FUSION_DECONVADDRELU_OP)
add_definitions(-DFUSION_DECONVADDRELU_OP)
endif()
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