未验证 提交 b1a8c2a2 编写于 作者: Z zhupengyang 提交者: GitHub

add npu pad2d op converter (#1896)

test=develop
上级 d8ccafcd
......@@ -20,6 +20,7 @@ lite_cc_library(npu_bridge_transpose_op SRCS transpose_op.cc DEPS ${npu_bridge_d
lite_cc_library(npu_bridge_split_op SRCS split_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_concat_op SRCS concat_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_shuffle_channel_op SRCS shuffle_channel_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_pad2d_op SRCS pad2d_op.cc DEPS ${npu_bridge_deps})
set(npu_bridges
npu_bridge_registry
......@@ -40,6 +41,7 @@ set(npu_bridges
npu_bridge_split_op
npu_bridge_concat_op
npu_bridge_shuffle_channel_op
npu_bridge_pad2d_op
CACHE INTERNAL "npu_bridges")
lite_cc_library(npu_test_helper SRCS test_helper.cc DEPS npu_helper ${npu_ddk_libs} ${npu_bridges} ${npu_kernels} ${ops})
......@@ -60,5 +62,6 @@ lite_cc_test(test_npu_bridge_transpose_op SRCS transpose_op_test.cc DEPS npu_tes
lite_cc_test(test_npu_bridge_split_op SRCS split_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_concat_op SRCS concat_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_shuffle_channel_op SRCS shuffle_channel_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_pad2d_op SRCS pad2d_op_test.cc DEPS npu_test_helper)
message(STATUS "+++++ npu_bridges: ${npu_bridges}")
// 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 "ai_ddk_lib/include/graph/buffer.h"
#include "ai_ddk_lib/include/graph/graph.h"
#include "ai_ddk_lib/include/graph/model.h"
#include "ai_ddk_lib/include/graph/op/all_ops.h"
#include "ai_ddk_lib/include/graph/operator.h"
#include "ai_ddk_lib/include/graph/operator_reg.h"
#include "lite/npu/bridge/registry.h"
#include "lite/npu/bridge/utils.h"
namespace paddle {
namespace lite {
namespace npu {
namespace bridge {
node_map_type Pad2dConverter(const std::shared_ptr<lite::OpLite> pad2d_op,
const node_map_type& inputs_map) {
auto scope = pad2d_op->scope();
auto op_info = pad2d_op->op_info();
auto op_type = op_info->Type();
auto unique_op_type = UniqueName(op_type);
LOG(INFO) << "Converting " + op_type + "...";
std::shared_ptr<ge::op::Pad> pad2d_node =
std::make_shared<ge::op::Pad>(unique_op_type);
auto x_var_name = op_info->Input("X").front();
pad2d_node->set_input_x(*inputs_map.at(x_var_name));
OpList::Global().add(inputs_map.at(x_var_name));
OpList::Global().add(pad2d_node);
auto mode = op_info->GetAttr<std::string>("mode");
if (mode == "constant") {
pad2d_node->set_attr_mode(0);
} else if (mode == "reflect") {
LOG(FATAL) << "NPU doesn't support this pad mod: " << mode;
pad2d_node->set_attr_mode(1);
} else {
LOG(FATAL) << "NPU doesn't support this pad mod: " << mode;
}
auto x_dims = scope->FindTensor(x_var_name)->dims();
auto padding = op_info->GetAttr<std::vector<int>>("paddings");
CHECK_EQ(padding.size(), 4);
int xds = x_dims.size();
padding.insert(padding.begin(), xds * 2 - 4, 0);
auto npu_padding =
std::make_shared<ge::op::Const>(unique_op_type + "/padding");
npu_padding->set_attr_value(CreateTensorAndFillData<int>(padding, {xds, 2}));
pad2d_node->set_input_padding(*npu_padding);
OpList::Global().add(npu_padding);
if (mode == "constant") {
auto pad_value = op_info->GetAttr<float>("pad_value");
auto npu_pad_value =
std::make_shared<ge::op::Const>(unique_op_type + "/pad_value");
npu_pad_value->set_attr_value(CreateTensorAndFillData<float>({pad_value}));
pad2d_node->set_input_constant_values(*npu_pad_value);
OpList::Global().add(npu_pad_value);
pad2d_node->set_attr_T(0); // type of pad_value: 0:float 3:int32
}
node_map_type outputs_map;
outputs_map[op_info->Output("Out").front()] = pad2d_node;
return outputs_map;
}
} // namespace bridge
} // namespace npu
} // namespace lite
} // namespace paddle
REGISTER_NPU_BRIDGE(pad2d, paddle::lite::npu::bridge::Pad2dConverter);
// 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/pad2d_op.h"
#include <gtest/gtest.h>
#include "lite/core/op_registry.h"
#include "lite/npu/bridge/registry.h"
#include "lite/npu/bridge/test_helper.h"
namespace paddle {
namespace lite {
namespace npu {
namespace bridge {
template <typename dtype>
void pad2d_ref(const std::shared_ptr<operators::Pad2dOpLite> op) {
Scope* scope = op->scope();
const OpInfo* op_info = op->op_info();
auto x = scope->FindMutableTensor(op_info->Input("X").front());
auto out = scope->FindMutableTensor(op_info->Output("Out").front());
auto paddings = op_info->GetAttr<std::vector<int>>("paddings");
int pad_top = paddings[0];
int pad_bottom = paddings[1];
int pad_left = paddings[2];
int pad_right = paddings[3];
auto mode = op_info->GetAttr<std::string>("mode");
int pad_mode;
if (mode == "constant") {
pad_mode = 0;
} else if (mode == "reflect") {
pad_mode = 1;
} else if (mode == "edge") {
pad_mode = 2;
} else {
LOG(FATAL) << "Unknown mode type";
}
float pad_value = op_info->GetAttr<float>("pad_value");
auto out_dims = out->dims();
int n = out_dims[0];
int c = out_dims[1];
int h = out_dims[2];
int w = out_dims[3];
int in_w = w - pad_left - pad_right;
int in_h = h - pad_bottom - pad_top;
int spatial_size_out = w * h;
int spatial_size_in = in_w * in_h;
auto x_data = x->data<float>();
auto out_data = out->mutable_data<float>();
#pragma omp parallel for
for (int i = 0; i < n * c; ++i) {
const float* din_batch = x_data + i * spatial_size_in;
float* dout_batch = out_data + i * spatial_size_out;
int in_y = 0;
int in_x = 0;
for (int y = 0; y < h; ++y) {
for (int x = 0; x < w; ++x) {
switch (pad_mode) {
case 0:
in_y = y - pad_top;
in_x = x - pad_left;
dout_batch[y * w + x] =
(in_x >= 0 && in_x < in_w) && (in_y >= 0 && in_y < in_h)
? din_batch[in_y * in_w + in_x]
: pad_value;
break;
case 1:
in_x =
std::min(std::max(pad_left, x), in_w + pad_left - 1) - pad_left;
in_y = std::min(std::max(pad_top, y), in_h + pad_top - 1) - pad_top;
dout_batch[y * w + x] = din_batch[in_y * in_w + in_x];
break;
case 2:
in_y = y - pad_top;
in_x = x - pad_left;
in_y = std::max(in_y, -in_y);
in_y = std::min(in_y, 2 * in_h - in_y - 2);
in_x = std::max(in_x, -in_x);
in_x = std::min(in_x, 2 * in_w - in_x - 2);
dout_batch[y * w + x] = din_batch[in_y * in_w + in_x];
break;
default:
LOG(ERROR) << "ERROR: unknown pad mode:" << pad_mode;
}
}
}
}
}
void test_pad2d(int bs,
int ic,
int ih,
int iw,
std::vector<int> paddings,
float pad_value,
std::string mode) {
// 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.NewTensor(x_var_name);
auto* out = scope.NewTensor(out_var_name);
auto* out_ref = scope.NewTensor(out_ref_var_name);
x->Resize({bs, ic, ih, iw});
// initialize input&output data
// FillTensor<float, int>(x);
auto x_data = x->mutable_data<float>();
// initialize op desc
cpp::OpDesc opdesc;
opdesc.SetType("pad2d");
opdesc.SetInput("X", {x_var_name});
opdesc.SetOutput("Out", {out_var_name});
opdesc.SetAttr("paddings", paddings);
opdesc.SetAttr("pad_value", pad_value);
opdesc.SetAttr("mode", mode);
opdesc.SetAttr("data_format", std::string("NCHW"));
auto op = CreateOp<operators::Pad2dOpLite>(opdesc, &scope);
pad2d_ref<float>(op);
out_ref->CopyDataFrom(*out);
LauchOp(op, {x_var_name}, {out_var_name});
// compare results
auto* out_data = out->mutable_data<float>();
auto* out_ref_data = out_ref->mutable_data<float>();
for (int i = 0; i < out->numel(); i++) {
EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-2) << "-----" << i;
}
}
TEST(NPUBridges, pad2d) {
#if 1
for (auto bs : {1, 4, 7}) {
for (auto ic : {1, 4, 7}) {
for (auto ih : {1, 4, 7}) {
for (auto iw : {1, 4, 7}) {
for (auto paddings : {/*std::vector<int>{0, 0, 0, 0},*/
std::vector<int>{0, 0, 0, 1},
std::vector<int>{0, 1, 0, 2},
std::vector<int>{1, 2, 3, 4}}) {
// npu not support pad_value!=0
for (auto pad_value : {0.f /*,1.f*/}) {
// npu only support constant
for (auto mode : {"constant" /*, "reflect", "edge"*/}) {
if (mode == "edge") continue;
VLOG(3) << "bs: " << bs << " ic: " << ic << " ih: " << ih
<< " iw: " << iw << " paddings: {" << paddings[0]
<< "," << paddings[1] << "," << paddings[2] << ","
<< paddings[3] << "}"
<< " pad_value: " << pad_value << " mode: " << mode;
test_pad2d(bs, ic, ih, iw, paddings, pad_value, mode);
}
}
}
}
}
}
}
#else
test_pad2d(1, 1, 1, 1, {0, 0, 0, 1}, 0, "constant");
#endif
}
} // namespace bridge
} // namespace npu
} // namespace lite
} // namespace paddle
USE_LITE_OP(pad2d);
USE_NPU_BRIDGE(pad2d);
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