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115 lines
4.2 KiB
115 lines
4.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 <gtest/gtest.h>
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#include "FunctionTest.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle; // NOLINT
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void testMatrixProjectionForward(int context_start,
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size_t context_length,
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bool is_padding,
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size_t batch_size,
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size_t input_dim) {
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size_t pad = std::max(0, -context_start) +
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std::max(0, (int)(context_start + context_length - 1));
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if (pad == 0) is_padding = false;
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CpuGpuFuncCompare test(
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"ContextProjectionForward",
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FuncConfig()
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.set("context_length", context_length)
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.set("context_start", context_start)
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.set("begin_pad", (size_t)std::max(0, -context_start)));
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// prepare input arguments
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test.addSequence(SequenceIdArg(TensorShape{batch_size}));
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test.addInputs(
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SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim}));
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if (is_padding) { // weight
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test.addInputs(SequenceArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim}));
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}
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test.addOutputs(
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SequenceArg(VALUE_TYPE_FLOAT,
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TensorShape{batch_size, input_dim * context_length}),
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ADD_TO);
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// run Function
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test.run();
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}
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void testMatrixProjectionBackward(int context_start,
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size_t context_length,
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bool is_padding,
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size_t batch_size,
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size_t input_dim) {
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size_t pad = std::max(0, -context_start) +
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std::max(0, (int)(context_start + context_length - 1));
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if (pad == 0) is_padding = false;
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CpuGpuFuncCompare test(
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"ContextProjectionBackward",
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FuncConfig()
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.set("context_length", context_length)
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.set("context_start", context_start)
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.set("begin_pad", (size_t)std::max(0, -context_start))
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.set("is_padding", is_padding)
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.set("total_pad", pad));
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// prepare input arguments
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test.addSequence(SequenceIdArg(TensorShape{batch_size}));
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test.addInputs(SequenceArg(
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VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim * context_length}));
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test.addOutputs(
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SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim}),
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ADD_TO);
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if (is_padding) { // weight
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim}),
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ADD_TO);
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}
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// run Function
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test.run();
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}
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TEST(ContextProjection, Projection) {
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for (auto context_start : {-5, -3, -1, 0, 3}) {
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for (auto context_length : {1, 2, 5, 7}) {
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for (auto trainable_padding : {false, true}) {
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for (auto batch_size : {1, 2, 5, 20, 100}) {
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for (auto input_dim : {15, 32, 63, 128, 200}) {
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VLOG(3) << " context_start=" << context_start
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<< " context_length=" << context_length
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<< " trainable_padding=" << trainable_padding
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<< " batch_size=" << batch_size
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<< " input_dim=" << input_dim;
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testMatrixProjectionForward(context_start,
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context_length,
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trainable_padding,
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batch_size,
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input_dim);
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testMatrixProjectionBackward(context_start,
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context_length,
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trainable_padding,
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batch_size,
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input_dim);
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}
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}
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}
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}
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}
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}
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