提交 7cd585aa 编写于 作者: - --get 提交者: MaxwellDing

(feat): add norm mlu kernel and test

上级 e9e87ce7
...@@ -60,6 +60,8 @@ if (LITE_BUILD_EXTRA) ...@@ -60,6 +60,8 @@ if (LITE_BUILD_EXTRA)
list(APPEND mlu_subgraph_bridges subgraph_bridge_lrn_op_mlu) list(APPEND mlu_subgraph_bridges subgraph_bridge_lrn_op_mlu)
lite_cc_library(subgraph_bridge_gather_op_mlu SRCS gather_op.cc DEPS ${subgraph_bridge_deps_mlu}) lite_cc_library(subgraph_bridge_gather_op_mlu SRCS gather_op.cc DEPS ${subgraph_bridge_deps_mlu})
list(APPEND mlu_subgraph_bridges subgraph_bridge_gather_op_mlu) list(APPEND mlu_subgraph_bridges subgraph_bridge_gather_op_mlu)
lite_cc_library(subgraph_bridge_norm_op_mlu SRCS norm_op.cc DEPS ${subgraph_bridge_deps_mlu})
list(APPEND mlu_subgraph_bridges subgraph_bridge_norm_op_mlu)
endif() endif()
lite_cc_library(subgraph_test_helper_mlu SRCS test_helper.cc DEPS ${mlu_subgraph_bridges}) lite_cc_library(subgraph_test_helper_mlu SRCS test_helper.cc DEPS ${mlu_subgraph_bridges})
...@@ -84,6 +86,7 @@ lite_cc_test(test_squeeze_converter_mlu SRCS squeeze_op_test.cc DEPS scope optim ...@@ -84,6 +86,7 @@ lite_cc_test(test_squeeze_converter_mlu SRCS squeeze_op_test.cc DEPS scope optim
lite_cc_test(test_reshape_converter_mlu SRCS reshape_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu) lite_cc_test(test_reshape_converter_mlu SRCS reshape_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu)
lite_cc_test(test_flatten_converter_mlu SRCS flatten_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu) lite_cc_test(test_flatten_converter_mlu SRCS flatten_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu)
if (LITE_BUILD_EXTRA) if (LITE_BUILD_EXTRA)
lite_cc_test(test_norm_converter_mlu SRCS norm_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu)
lite_cc_test(test_lrn_converter_mlu SRCS lrn_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu) lite_cc_test(test_lrn_converter_mlu SRCS lrn_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu)
lite_cc_test(test_gather_converter_mlu SRCS gather_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu) lite_cc_test(test_gather_converter_mlu SRCS gather_op_test.cc DEPS scope optimizer target_wrapper_host model_parser program ${mlu_subgraph_bridges} subgraph_compute_mlu subgraph_test_helper_mlu)
endif() endif()
......
// Copyright (c) 2019 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.
#include "lite/kernels/mlu/bridges/graph.h"
#include "lite/kernels/mlu/bridges/utility.h"
#include "lite/kernels/npu/bridges/registry.h"
namespace paddle {
namespace lite {
namespace subgraph {
namespace mlu {
int NormConverter(void* ctx, OpLite* op, KernelBase* kernel) {
CHECK(ctx != nullptr);
CHECK(op != nullptr);
auto graph = static_cast<Graph*>(ctx);
auto op_info = op->op_info();
auto op_type = op_info->Type();
auto scope = op->scope();
VLOG(3) << "[MLU] Converting " + op_type + "...";
// Get input vars and op attributes
auto x_var_name = op_info->Input("X").front();
auto x = scope->FindVar(x_var_name)->GetMutable<Tensor>();
auto x_dims = x->dims().Vectorize();
auto out_var_name = op_info->Output("Out").front();
auto output = scope->FindVar(out_var_name)->GetMutable<Tensor>();
auto output_dims = output->dims().Vectorize();
int axis = op_info->GetAttr<int>("axis");
int epsilon = op_info->GetAttr<float>("epsilon");
if (axis < 0) {
axis = axis + x_dims.size();
}
std::vector<int> nchw2nhwc = {0, 3, 1, 2};
int nhwc_axis = nchw2nhwc[axis];
CHECK(graph->HasNode(x_var_name));
auto input_tensor = graph->GetNode(x_var_name);
auto output_tensor = graph->AddNode(
out_var_name, output_dims, CNML_TENSOR, CNML_NCHW, graph->FPType());
// ======== DEBUG ===============
VLOG(6) << "x name=" << x_var_name;
VLOG(6) << "out name=" << out_var_name;
VLOG(6) << "x dims=" << x->dims();
VLOG(6) << "out dims=" << output->dims();
VLOG(6) << "axis =" << axis;
VLOG(6) << "nwhc axis=" << nhwc_axis;
VLOG(6) << "epsilon =" << epsilon;
// cnmlPrintTensor(input_tensor->mlu_tensor(), CNML_TENSOR);
// cnmlPrintTensor(output_tensor->mlu_tensor(), CNML_TENSOR);
// ======== DEBUG END ============
cnmlBaseOp_t norm_op{nullptr};
cnmlNormalizeOpParam_t param;
int mode = -1;
switch (axis) {
case 0:
mode = 3; // N
break;
case 1:
mode = 0; // C
break;
case 2:
mode = 4; // H
break;
case 3:
mode = 5; // W
break;
default:
CHECK(0);
break;
}
cnmlCreateNormalizeOpParamV2(&param,
0, // p
0, // use_scale
mode,
1, // weight
epsilon);
CNML_CALL(cnmlCreateNormalizeOp(&norm_op,
param,
input_tensor->mlu_tensor(),
output_tensor->mlu_tensor(),
nullptr,
false /*is_fix8_mode*/));
graph->FuseOp(norm_op);
CNML_CALL(cnmlDestroyBaseOp(&norm_op));
return SUCCESS;
}
} // namespace mlu
} // namespace subgraph
} // namespace lite
} // namespace paddle
REGISTER_SUBGRAPH_BRIDGE(norm,
kMLU,
paddle::lite::subgraph::mlu::NormConverter);
// Copyright (c) 2019 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.
#include "lite/operators/norm_op.h"
#include <gtest/gtest.h>
#include <cmath>
#include <iostream>
#include "lite/core/op_registry.h"
#include "lite/kernels/mlu/bridges/test_helper.h"
#include "lite/kernels/mlu/bridges/utility.h"
#include "lite/kernels/npu/bridges/registry.h"
namespace paddle {
namespace lite {
namespace subgraph {
namespace mlu {
// void ToFile(std::string file_name, Tensor* tensor) {
// int count = tensor->dims().production();
// auto data = tensor->mutable_data<float>();
// std::ostringstream outs;
// for (size_t i = 0; i < count; i++) {
// outs << data[i] << std::endl;
// }
// std::ofstream of;
// of.open(file_name, std::ios::out);
// of << outs.str();
// of.close();
// }
void norm_ref(const std::shared_ptr<operators::NormOp> op) {
Scope* scope = op->scope();
const OpInfo* op_info = op->op_info();
auto x = scope->FindVar(op_info->Input("X").front())->GetMutable<Tensor>();
auto out =
scope->FindVar(op_info->Output("Out").front())->GetMutable<Tensor>();
int axis = op_info->GetAttr<int>("axis");
int epsilon = op_info->GetAttr<float>("epsilon");
auto x_dims = x->dims();
if (axis < 0) {
axis += x_dims.size();
}
out->Resize(x_dims.Vectorize());
auto* out_data = out->mutable_data<float>();
const auto* x_data = x->data<float>();
int pre_n = x_dims.count(0, axis);
int n = x_dims[axis];
int post_n = x_dims.count(axis + 1, x_dims.size());
for (int i = 0; i < pre_n; i++) {
for (int k = 0; k < post_n; k++) {
float sum = epsilon;
const float* in_tmp = x_data + i * n * post_n + k;
for (int j = 0; j < n; j++) {
sum += in_tmp[j * post_n] * in_tmp[j * post_n];
}
sum = std::sqrt(sum);
float* out_tmp = out_data + i * n * post_n + k;
for (int j = 0; j < n; j++) {
out_tmp[j * post_n] = in_tmp[j * post_n] / sum;
}
}
}
}
void test_norm(const std::vector<int64_t>& input_shape, int axis) {
// prepare input&output variables
Scope scope;
std::string x_var_name = "x";
std::string out_var_name = "out";
std::string out_ref_var_name = "out_ref";
auto* x = scope.Var(x_var_name)->GetMutable<Tensor>();
auto* out = scope.Var(out_var_name)->GetMutable<Tensor>();
auto* out_ref = scope.Var(out_ref_var_name)->GetMutable<Tensor>();
x->Resize(input_shape);
// initialize input&output data
FillTensor<float, float>(x, -9, 9);
// initialize op desc
cpp::OpDesc opdesc;
float epsilon = 1e-9f;
opdesc.SetType("norm");
opdesc.SetInput("X", {x_var_name});
opdesc.SetOutput("Out", {out_var_name});
opdesc.SetAttr("axis", static_cast<int>(axis));
opdesc.SetAttr("epsilon", static_cast<float>(epsilon));
// create and convert op to MLU model, then run it on MLU
auto op = CreateOp<operators::NormOp>(opdesc, &scope);
norm_ref(op);
out_ref->CopyDataFrom(*out);
Tensor input_x;
input_x.Resize(DDim(input_shape));
// change input layout from NCHW to NHWC
transpose<float>(x->mutable_data<float>(),
input_x.mutable_data<float>(),
{static_cast<int>(input_shape[0]),
static_cast<int>(input_shape[1]),
static_cast<int>(input_shape[2]),
static_cast<int>(input_shape[3])},
{0, 2, 3, 1});
x->CopyDataFrom(input_x);
LaunchOp(op, {x_var_name}, {out_var_name});
auto* out_data = out->mutable_data<float>();
auto* out_ref_data = out_ref->mutable_data<float>();
std::vector<int64_t> out_shape = input_shape;
Tensor output_trans;
output_trans.Resize(out_shape);
// Change output layout from NHWC to NCHW
transpose<float>(out_data,
output_trans.mutable_data<float>(),
{static_cast<int>(out_shape[0]),
static_cast<int>(out_shape[2]),
static_cast<int>(out_shape[3]),
static_cast<int>(out_shape[1])},
{0, 3, 1, 2});
out_data = output_trans.mutable_data<float>();
for (int i = 0; i < out->dims().production(); i++) {
EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-2);
}
}
TEST(MLUBridges, norm) {
test_norm({1, 2, 3, 4}, 1);
test_norm({1, 2, 3, 4}, 2);
test_norm({1, 2, 3, 4}, 3);
}
} // namespace mlu
} // namespace subgraph
} // namespace lite
} // namespace paddle
USE_SUBGRAPH_BRIDGE(norm, kMLU);
...@@ -42,4 +42,5 @@ USE_SUBGRAPH_BRIDGE(squeeze2, kMLU); ...@@ -42,4 +42,5 @@ USE_SUBGRAPH_BRIDGE(squeeze2, kMLU);
#ifdef LITE_BUILD_EXTRA #ifdef LITE_BUILD_EXTRA
USE_SUBGRAPH_BRIDGE(gather, kMLU); USE_SUBGRAPH_BRIDGE(gather, kMLU);
USE_SUBGRAPH_BRIDGE(lrn, kMLU) USE_SUBGRAPH_BRIDGE(lrn, kMLU)
USE_SUBGRAPH_BRIDGE(norm, kMLU)
#endif #endif
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