wehub-resource-sync
144 lines
5.7 KiB
C++
144 lines
5.7 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 "test/cpp/auto_parallel/spmd_rule_test_util.h"
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namespace paddle {
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namespace distributed {
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namespace auto_parallel {
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TEST(SoftmaxGradInferSpmd, Ctor) {
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// Sharding along axes besides softmax axis.
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std::vector<int64_t> x_shape = {36, 48};
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std::vector<int64_t> out_grad_shape = {36, 48};
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std::vector<int64_t> mesh_shape = {2, 3};
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std::vector<int64_t> process_ids = {0, 1, 2, 3, 4, 5};
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std::vector<std::string> dim_names = {"x", "y"};
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ProcessMesh process_mesh(mesh_shape, process_ids, dim_names);
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TensorDistAttr x_dist_attr = TensorDistAttr();
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x_dist_attr.set_process_mesh(process_mesh);
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x_dist_attr.set_dims_mapping(std::vector<int64_t>({1, -1}));
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x_dist_attr.set_dynamic_dims(std::vector<bool>({false, false}));
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TensorDistAttr out_grad_dist_attr = TensorDistAttr();
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out_grad_dist_attr.set_process_mesh(process_mesh);
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out_grad_dist_attr.set_dims_mapping(std::vector<int64_t>({1, -1}));
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out_grad_dist_attr.set_dynamic_dims(std::vector<bool>({false, false}));
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phi::distributed::DistMetaTensor x(phi::make_ddim(x_shape), x_dist_attr);
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phi::distributed::DistMetaTensor out_grad(phi::make_ddim(x_shape),
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out_grad_dist_attr);
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int axis = 1;
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auto spmdinfo = SoftmaxGradInferSpmd(x, out_grad, axis);
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EXPECT_EQ(spmdinfo.first.size(), 2UL);
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EXPECT_EQ(spmdinfo.second.size(), 1UL);
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[0]), std::vector<int64_t>({1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[1]), std::vector<int64_t>({1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.second[0]),
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std::vector<int64_t>({1, -1}));
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EXPECT_DOUBLE_EQ(
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PADDLE_GET_CONST(TensorDistAttr, spmdinfo.second[0]).is_partial(), false);
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VLOG(4) << "Test SoftmaxGradInferSpmd sharding on other axes." << std::endl
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<< std::endl
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<< std::endl;
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// Sharding along softmax axis.
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x_dist_attr.set_dims_mapping(std::vector<int64_t>({-1, 1}));
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out_grad_dist_attr.set_dims_mapping(std::vector<int64_t>({-1, 1}));
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x = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape), x_dist_attr);
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out_grad = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape),
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out_grad_dist_attr);
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axis = 1;
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spmdinfo = SoftmaxGradInferSpmd(x, out_grad, axis);
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EXPECT_EQ(spmdinfo.first.size(), 2UL);
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EXPECT_EQ(spmdinfo.second.size(), 1UL);
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[0]),
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std::vector<int64_t>({-1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[1]),
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std::vector<int64_t>({-1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.second[0]),
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std::vector<int64_t>({-1, -1}));
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EXPECT_DOUBLE_EQ(
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PADDLE_GET_CONST(TensorDistAttr, spmdinfo.second[0]).is_partial(), false);
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VLOG(4) << "Test SoftmaxGradInferSpmd sharding on softmax axis." << std::endl
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<< std::endl
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<< std::endl;
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// Sharding on multi axes.
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x_shape = {10, 36, 48, 24};
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out_grad_shape = {10, 36, 48, 24};
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x_dist_attr.set_dims_mapping(std::vector<int64_t>({0, 1, -1, -1}));
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out_grad_dist_attr.set_dims_mapping(std::vector<int64_t>({0, 1, -1, -1}));
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x = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape), x_dist_attr);
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out_grad = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape),
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out_grad_dist_attr);
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axis = 1;
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spmdinfo = SoftmaxGradInferSpmd(x, out_grad, axis);
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EXPECT_EQ(spmdinfo.first.size(), 2UL);
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EXPECT_EQ(spmdinfo.second.size(), 1UL);
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[0]),
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std::vector<int64_t>({0, -1, -1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[1]),
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std::vector<int64_t>({0, -1, -1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.second[0]),
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std::vector<int64_t>({0, -1, -1, -1}));
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EXPECT_DOUBLE_EQ(
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PADDLE_GET_CONST(TensorDistAttr, spmdinfo.second[0]).is_partial(), false);
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VLOG(4) << "Test SoftmaxGradInferSpmd sharding on multi axes." << std::endl
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<< std::endl
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<< std::endl;
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// Sharding on multi axes.
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x_shape = {10, 36, 48, 24};
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out_grad_shape = {10, 36, 48, 24};
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x_dist_attr.set_dims_mapping(std::vector<int64_t>({0, -1, -1, -1}));
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out_grad_dist_attr.set_dims_mapping(std::vector<int64_t>({-1, -1, 1, -1}));
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x = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape), x_dist_attr);
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out_grad = phi::distributed::DistMetaTensor(phi::make_ddim(x_shape),
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out_grad_dist_attr);
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axis = 1;
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spmdinfo = SoftmaxGradInferSpmd(x, out_grad, axis);
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EXPECT_EQ(spmdinfo.first.size(), 2UL);
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EXPECT_EQ(spmdinfo.second.size(), 1UL);
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[0]),
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std::vector<int64_t>({0, -1, 1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.first[1]),
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std::vector<int64_t>({0, -1, 1, -1}));
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EXPECT_EQ(get_dims_mapping(spmdinfo.second[0]),
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std::vector<int64_t>({0, -1, 1, -1}));
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EXPECT_DOUBLE_EQ(
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PADDLE_GET_CONST(TensorDistAttr, spmdinfo.second[0]).is_partial(), false);
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VLOG(4) << "Test SoftmaxGradInferSpmd sharding on multi axes." << std::endl
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<< std::endl
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<< std::endl;
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}
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} // namespace auto_parallel
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} // namespace distributed
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} // namespace paddle
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