Add op function generator for dygraph (#21569)
* add op function generator, test=develop * add unittest, test=develop * follow comments, test=develop * fix windows compilation problem, test=developpaddle_tiny_install
Leo Chen
6 years ago
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GitHub
parent
55f2b7defa
commit
48600d7f17
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <pybind11/chrono.h>
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#include <pybind11/complex.h>
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#include <pybind11/functional.h>
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#include <pybind11/stl.h>
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#include "paddle/fluid/framework/attribute.h"
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#include "paddle/fluid/framework/op_info.h"
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#include "paddle/fluid/framework/variable.h"
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#include "paddle/fluid/imperative/tracer.h"
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#include "paddle/fluid/imperative/type_defs.h"
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#include "paddle/fluid/pybind/imperative.h"
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// This include must be the last line
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#include "paddle/fluid/pybind/op_function_impl.h"
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <fstream>
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#include <iostream>
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#include <string>
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#include "paddle/fluid/framework/op_info.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/variable.h"
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#include "paddle/fluid/pybind/pybind.h"
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#include "paddle/fluid/string/string_helper.h"
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const char* OUT_INITIALIZER_TEMPLATE =
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R"({"%s", {std::shared_ptr<imperative::VarBase>(new imperative::VarBase(tracer->GenerateUniqueName()))}})";
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const char* OP_FUNCTION_TEMPLATE =
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R"([](const imperative::NameVarBaseMap& ins, const framework::AttributeMap& attrs,
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imperative::NameVarBaseMap outs, const std::map<std::string, size_t>& out_nums)
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{
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auto tracer = imperative::GetCurrentTracer();
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if (outs.size() == 0) {
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if (out_nums.size() == 0) {
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imperative::NameVarBaseMap outs_ = %s;
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outs = std::move(outs_);
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} else {
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for (auto &pair : out_nums) {
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for (size_t i = 0; i < pair.second; i ++) {
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auto var_base_name = tracer->GenerateUniqueName();
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auto out = new imperative::VarBase(var_base_name);
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outs[pair.first].emplace_back(std::shared_ptr<imperative::VarBase>(out));
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}
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}
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}
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}
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{
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py::gil_scoped_release release;
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tracer->TraceOp("%s", std::move(ins), std::move(outs), std::move(attrs));
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return outs;
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}
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}, py::arg("ins"), py::arg("attrs")=framework::AttributeMap(),
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py::arg("outs")=imperative::NameVarBaseMap(),
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py::arg("out_nums")=std::map<std::string, size_t>())";
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const char* PYBIND_ITEM_TEMPLATE = R"( %s.def("%s", %s);)";
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static std::vector<std::string> GenerateOpFunctions(
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const std::string& module_name) {
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auto& op_info_map = paddle::framework::OpInfoMap::Instance().map();
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std::vector<std::string> op_function_list;
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for (auto& pair : op_info_map) {
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auto& op_info = pair.second;
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auto op_proto = op_info.proto_;
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if (op_proto == nullptr) {
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continue;
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}
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auto& op_type = op_proto->type();
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// Generate outs initializer
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std::string outs_initializer = "{";
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for (auto& output : op_proto->outputs()) {
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auto& out_name = output.name();
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auto out_initializer_str =
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paddle::string::Sprintf(OUT_INITIALIZER_TEMPLATE, out_name);
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outs_initializer += out_initializer_str;
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outs_initializer += ",";
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}
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if (outs_initializer.back() == ',') {
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outs_initializer.pop_back();
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}
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outs_initializer += "}";
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// generate op funtcion body
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auto op_function_str = paddle::string::Sprintf(OP_FUNCTION_TEMPLATE,
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outs_initializer, op_type);
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// generate pybind item
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auto pybind_op_function = paddle::string::Sprintf(
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PYBIND_ITEM_TEMPLATE, module_name.c_str(), op_type, op_function_str);
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pybind_op_function += "\n";
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op_function_list.emplace_back(std::move(pybind_op_function));
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}
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return op_function_list;
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}
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int main(int argc, char* argv[]) {
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if (argc != 2) {
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std::cerr << "argc must be 2" << std::endl;
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return -1;
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}
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std::vector<std::string> headers{"\"paddle/fluid/imperative/tracer.h\""};
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std::ofstream out(argv[1], std::ios::out);
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out << "#pragma once\n\n";
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for (auto& header : headers) {
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out << "#include " + header + "\n";
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}
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out << "namespace py = pybind11;"
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<< "\n";
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out << "namespace paddle {\n"
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<< "namespace pybind {\n"
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<< "\n"
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<< "inline void BindOpFunctions(pybind11::module *module) {\n"
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<< " auto m = module->def_submodule(\"ops\");\n\n";
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// all op functions
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auto op_funcs = GenerateOpFunctions("m");
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out << paddle::string::join_strings(op_funcs, '\n');
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out << "}\n\n"
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<< "} // namespace pybind\n"
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<< "} // namespace paddle\n";
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out.close();
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return 0;
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}
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@ -0,0 +1,115 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from paddle.fluid.dygraph.jit import TracedLayer
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import numpy as np
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class TestTracedLayer(fluid.dygraph.Layer):
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def __init__(self, name_scope):
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super(TestTracedLayer, self).__init__(name_scope)
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def forward(self, input):
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inputs = {'X': [input] if isinstance(input, fluid.Variable) else input}
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return core.ops.relu(inputs)['Out'][0]
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class TestVariable(unittest.TestCase):
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def setUp(self):
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self.shape = [512, 768]
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self.dtype = np.float32
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self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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def test_elementwise_add(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = fluid.dygraph.to_variable(a)
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y = fluid.dygraph.to_variable(b)
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x.stop_gradient = False
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res1 = layers.elementwise_add(x, y)
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inputs = {'X': [x], 'Y': [y]}
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res2 = core.ops.elementwise_add(inputs)['Out'][0]
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self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
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def test_elementwise_mul(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = fluid.dygraph.to_variable(a)
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y = fluid.dygraph.to_variable(b)
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res1 = layers.elementwise_mul(x, y)
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inputs = {'X': [x], 'Y': [y]}
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res2 = core.ops.elementwise_mul(inputs)['Out'][0]
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self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
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def test_relu(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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x = fluid.dygraph.to_variable(a)
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res1 = layers.relu(x)
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inputs = {'X': [x]}
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res2 = core.ops.relu(inputs)['Out'][0]
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self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
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def test_trace_backward(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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b = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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x = fluid.dygraph.to_variable(a)
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y = fluid.dygraph.to_variable(b)
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x.stop_gradient = False
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y.stop_gradient = False
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inputs = {'X': [x], 'Y': [y]}
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loss = core.ops.elementwise_mul(inputs)['Out'][0]
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loss.backward()
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x_grad = x.gradient()
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y_grad = y.gradient()
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self.assertTrue(np.array_equal(x_grad, loss.gradient() * b))
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self.assertTrue(np.array_equal(y_grad, loss.gradient() * a))
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def test_traced_layer(self):
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with fluid.dygraph.guard():
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layer = TestTracedLayer("test_traced_layer")
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a = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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x = fluid.dygraph.to_variable(a)
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res_dygraph, static_layer = TracedLayer.trace(
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layer, inputs=[x]) # dygraph out
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res_static_graph = static_layer([x])[0]
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self.assertTrue(
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np.array_equal(res_dygraph.numpy(), res_static_graph))
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if __name__ == '__main__':
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unittest.main()
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