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add popart_canonicalization_pass (PaddlePaddle#13)
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| @@ -1,2 +1,9 @@ | ||
| set(POPART_CANONICALIZATION_HANDLERS_SRC | ||
| "popart_canonicalization/other_ops.cpp" | ||
| ) | ||
| # TODO(alleng) build static library | ||
| cc_library(popart_canonicalization_utils SHARED SRCS popart_canonicalization_utils.cc | ||
| ${POPART_CANONICALIZATION_HANDLERS_SRC} DEPS framework_proto enforce) | ||
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| cc_library(ipu_utils SRCS ipu_utils.cc DEPS memory framework_proto popart) | ||
| cc_library(ipu_backend SRCS ipu_backend.cc DEPS popart graph framework_proto enforce ipu_utils) |
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195
paddle/fluid/framework/ipu/popart_canonicalization/other_ops.cpp
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| // Copyright (c) 2021 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. | ||
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| #include "paddle/fluid/framework/ipu/popart_canonicalization_utils.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace { | ||
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| ir::Node *conv2d_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("Conv"); | ||
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| std::vector<std::string> inputs; | ||
| inputs.push_back(op->Input("Input").front()); | ||
| inputs.push_back(op->Input("Filter").front()); | ||
| if (op->HasInput("Bias")) { | ||
| if (!op->Input("Bias").empty()) { | ||
| inputs.push_back(op->Input("Bias").front()); | ||
| } | ||
| } | ||
| op_desc->SetInput("__inputs__", inputs); | ||
| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Output").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| auto dilations_ = BOOST_GET_CONST(std::vector<int>, op->GetAttr("dilations")); | ||
| auto dilations = std::vector<int64_t>{dilations_.begin(), dilations_.end()}; | ||
| auto group_ = BOOST_GET_CONST(int, op->GetAttr("groups")); | ||
| auto group = int64_t{group_}; | ||
| // auto paddings_ = BOOST_GET_CONST(std::vector<int>, | ||
| // op->GetAttr("paddings")); auto pads = | ||
| // std::vector<int64_t>{paddings_.begin(), paddings_.end()}; | ||
| auto pads = std::vector<int64_t>{1, 1, 1, 1}; | ||
| auto stride_ = BOOST_GET_CONST(std::vector<int>, op->GetAttr("strides")); | ||
| auto stride = std::vector<int64_t>{stride_.begin(), stride_.end()}; | ||
| op_desc->SetAttr("dilations", dilations); | ||
| op_desc->SetAttr("group", group); | ||
| op_desc->SetAttr("pads", pads); | ||
| op_desc->SetAttr("strides", stride); | ||
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| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| ir::Node *elementwise_add_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("Add"); | ||
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| std::vector<std::string> inputs; | ||
| inputs.push_back(op->Input("X").front()); | ||
| inputs.push_back(op->Input("Y").front()); | ||
| op_desc->SetInput("__inputs__", inputs); | ||
| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Out").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| ir::Node *reduce_mean_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("ReduceMean"); | ||
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| std::vector<std::string> inputs; | ||
| inputs.push_back(op->Input("X").front()); | ||
| op_desc->SetInput("__inputs__", inputs); | ||
| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Out").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| auto axes_ = BOOST_GET_CONST(std::vector<int>, op->GetAttr("dim")); | ||
| auto axes = std::vector<int64_t>{axes_.begin(), axes_.end()}; | ||
| op_desc->SetAttr("axes", axes); | ||
| auto keepdims_ = BOOST_GET_CONST(bool, op->GetAttr("keep_dim")); | ||
| auto keepdims = int64_t{keepdims_}; | ||
| op_desc->SetAttr("keepdims", keepdims); | ||
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| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| ir::Node *uniform_random_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("RandomUniform"); | ||
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| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Out").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| auto shape = BOOST_GET_CONST(std::vector<int64_t>, op->GetAttr("shape")); | ||
| op_desc->SetAttr("shape", shape); | ||
| // auto dtype = BOOST_GET_CONST(int, op->GetAttr("dtype")); | ||
| op_desc->SetAttr("dtype", 1); | ||
| // cvt dtype | ||
| /* | ||
| enum Type { | ||
| // Pod Types | ||
| BOOL = 0; | ||
| INT16 = 1; | ||
| INT32 = 2; | ||
| INT64 = 3; | ||
| FP16 = 4; | ||
| FP32 = 5; | ||
| FP64 = 6; | ||
| // Tensor<size_t> is used in C++. | ||
| SIZE_T = 19; | ||
| UINT8 = 20; | ||
| INT8 = 21; | ||
| BF16 = 22; | ||
| COMPLEX64 = 23; | ||
| COMPLEX128 = 24; | ||
| ... | ||
| */ | ||
| auto max = BOOST_GET_CONST(float, op->GetAttr("max")); | ||
| op_desc->SetAttr("high", max); | ||
| auto min = BOOST_GET_CONST(float, op->GetAttr("min")); | ||
| op_desc->SetAttr("low", min); | ||
| // seed | ||
| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| ir::Node *gaussian_random_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("RandomNormal"); | ||
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| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Out").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| auto shape = BOOST_GET_CONST(std::vector<int64_t>, op->GetAttr("shape")); | ||
| op_desc->SetAttr("shape", shape); | ||
| // auto dtype = BOOST_GET_CONST(int, op->GetAttr("dtype")); | ||
| op_desc->SetAttr("dtype", 1); | ||
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| auto mean = BOOST_GET_CONST(float, op->GetAttr("mean")); | ||
| op_desc->SetAttr("mean", mean); | ||
| auto std = BOOST_GET_CONST(float, op->GetAttr("std")); | ||
| op_desc->SetAttr("scale", std); | ||
| // seed TODO | ||
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| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| ir::Node *fill_constant_handler(ir::Graph *graph, ir::Node *node) { | ||
| auto *op = node->Op(); | ||
| auto op_desc = std::make_unique<framework::OpDesc>(); | ||
| op_desc->SetType("ConstantOfShape"); | ||
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| std::vector<std::string> outputs; | ||
| outputs.push_back(op->Output("Out").front()); | ||
| op_desc->SetOutput("__outputs__", outputs); | ||
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| auto shape = BOOST_GET_CONST(std::vector<int64_t>, op->GetAttr("shape")); | ||
| op_desc->SetAttr("shape", shape); | ||
| // auto dtype = BOOST_GET_CONST(int, op->GetAttr("dtype")); | ||
| op_desc->SetAttr("dtype", 1); | ||
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| auto value = BOOST_GET_CONST(float, op->GetAttr("value")); | ||
| op_desc->SetAttr("value", value); | ||
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| op_desc->Flush(); | ||
| return graph->CreateOpNode(op_desc.get()); | ||
| } | ||
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| REGISTER_HANDLER(conv2d, conv2d_handler); | ||
| REGISTER_HANDLER(elementwise_add, elementwise_add_handler); | ||
| REGISTER_HANDLER(reduce_mean, reduce_mean_handler); | ||
| REGISTER_HANDLER(uniform_random, uniform_random_handler); | ||
| REGISTER_HANDLER(gaussian_random, gaussian_random_handler); | ||
| REGISTER_HANDLER(fill_constant, fill_constant_handler); | ||
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| } // namespace | ||
| } // namespace framework | ||
| } // namespace paddle |
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