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rewrite unit test for Context Projection #1224

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Feb 8, 2017
Merged

rewrite unit test for Context Projection #1224

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1251501
rewrite unit test using new Function Test.
Jan 25, 2017
e79841d
fix test_LayerGrad.
Jan 25, 2017
02c5ecc
merge
Feb 7, 2017
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5 changes: 3 additions & 2 deletions paddle/function/BufferArg.h
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Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ class SequenceIdArg : public BufferArg {
: BufferArg(VALUE_TYPE_INT32, shape, argType) {
bufferType_ = TENSOR_SEQUENCE_ID;
CHECK_EQ(shape_.ndims(), 1UL);
CHECK_GT(shape_[0], 1UL);
CHECK_GE(shape_[0], 1UL);
numSeqs_ = shape_[0] - 1;
}

Expand Down Expand Up @@ -226,7 +226,8 @@ class SequenceArg : public BufferArg {
SequenceArg(ValueType valueType,
const TensorShape& shape,
ArgType argType = UNSPECIFIED)
: BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {
: BufferArg(valueType, shape, argType),
startPositions_(TensorShape({shape[0]})) {
bufferType_ = TENSOR_SEQUENCE_DATA;
}

Expand Down
70 changes: 36 additions & 34 deletions paddle/function/ContextProjectionOp.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -108,37 +108,35 @@ class ContextProjectionForwardFunc : public FunctionBase {
}

void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK(1 == inputs.size() || 2 == inputs.size());
CHECK_EQ((size_t)1, outputs.size());
CHECK(1UL == inputs.size() || 2UL == inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto val_seqs = dynamic_cast<const SequenceArg&>(inputs[0]);
auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);

CHECK(out_seq.data() && val_seqs.data() && val_seqs.getSequenceId().data());
CHECK_EQ(out_seq.shape().ndims(), (size_t)2);
CHECK_EQ(val_seqs.shape().ndims(), (size_t)2);
CHECK_EQ(val_seqs.getSequenceId().shape().ndims(), (size_t)1);
if (2 == inputs.size()) {
CHECK_EQ(inputs[1].shape().ndims(), (size_t)2);
}
CHECK_EQ(out_seq.shape().ndims(), 2UL);
CHECK_EQ(val_seqs.shape().ndims(), 2UL);
/// dim of output = dim of input * context_length
CHECK_EQ(out_seq.shape()[1], val_seqs.shape()[1] * context_length_);
/// input and output has the same batch_size
CHECK_EQ(val_seqs.shape()[0], out_seq.shape()[0]);
/// dim of input == dim of weight
if (2 == inputs.size()) {
if (2UL == inputs.size()) {
CHECK_EQ(inputs[1].shape().ndims(), 2UL);
/// dim of input == dim of weight
CHECK_EQ(val_seqs.shape()[1], inputs[1].shape()[1]);
}

CHECK_EQ(out_seq.getArgType(), ADD_TO);
auto out_mat = out_seq.matrix<Device>();
const auto in_mat = val_seqs.matrix<Device>();
const auto w_mat =
(2 == inputs.size())
(2UL == inputs.size() && inputs[1].data())
? inputs[1].matrix<Device>()
: typename Tensor<real, Device>::Matrix(nullptr, 0, 0);
const auto seq_vec = val_seqs.getSequenceId().vector<int, Device>();

ContextProjectionForward<Device>(out_mat,
in_mat,
w_mat,
Expand Down Expand Up @@ -235,36 +233,40 @@ class ContextProjectionBackwardFunc : public FunctionBase {
}

void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ((size_t)1, inputs.size());
CHECK_EQ((size_t)2, outputs.size());
CHECK_EQ(1UL, inputs.size());
CHECK(1UL == outputs.size() || 2UL == outputs.size());
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);
CHECK(in_seq.data() && in_seq.getSequenceId().data());
CHECK_EQ(in_seq.shape().ndims(), (size_t)2);
CHECK_EQ(in_seq.getSequenceId().shape().ndims(), (size_t)1);
CHECK_EQ(out_seq.shape().ndims(), (size_t)2);
CHECK_EQ(out_seq.getSequenceId().shape().ndims(), (size_t)1);
CHECK_EQ(outputs[1].shape().ndims(), (size_t)2);
CHECK_EQ(in_seq.shape().ndims(), 2UL);
CHECK_EQ(out_seq.shape().ndims(), 2UL);
CHECK_EQ(out_seq.getSequenceId().shape().ndims(), 1UL);

/// dim of input grad == dim of weight
CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]);
/// input and output grad has the same batch_size
CHECK_EQ(out_seq.shape()[0], in_seq.shape()[0]);
/// dim of output grad = dim of input grad * context_length
CHECK_EQ(in_seq.shape()[1], out_seq.shape()[1] * context_length_);
CHECK_EQ(out_seq.getArgType(), ADD_TO);
CHECK_EQ(outputs[1].getArgType(), ADD_TO);

if (2UL == outputs.size()) {
CHECK_EQ(outputs[1].shape().ndims(), 2UL);
/// dim of input grad == dim of weight
CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]);
CHECK_EQ(outputs[1].getArgType(), ADD_TO);
}

const auto seq_vec = in_seq.getSequenceId().vector<int, Device>();
const auto out_grad_mat = in_seq.matrix<Device>();
auto in_grad_mat =
!out_seq.data() ? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: out_seq.matrix<Device>();
auto w_grad_mat = !outputs[1].data()
? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: outputs[1].matrix<Device>();
auto w_grad_mat =
(2UL == outputs.size() && outputs[1].data())
? outputs[1].matrix<Device>()
: typename Tensor<real, Device>::Matrix(nullptr, 0, 0);

ContextProjectionBackward<Device>(out_grad_mat,
in_grad_mat,
w_grad_mat,
Expand Down Expand Up @@ -304,17 +306,17 @@ class ContextProjectionBackwardDataFunc : public FunctionBase {
}

void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1, static_cast<int>(inputs.size()));
CHECK_EQ(1, static_cast<int>(outputs.size()));
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
const auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);

CHECK(in_seq.data() && out_seq.data() && in_seq.getSequenceId().data());
CHECK_EQ(static_cast<int>(out_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.getSequenceId().shape().ndims()), 1);
CHECK_EQ(out_seq.shape().ndims(), 2UL);
CHECK_EQ(in_seq.shape().ndims(), 2UL);
CHECK_EQ(in_seq.getSequenceId().shape().ndims(), 1UL);
/// output layer grad dim == input layer grad dim * context_length_
CHECK_EQ(in_seq.shape().ndims(), out_seq.shape().ndims() * context_length_);
/// input and output has the same batch_size
Expand Down Expand Up @@ -355,14 +357,14 @@ class ContextProjectionBackwardWeightFunc : public FunctionBase {
}

void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1, static_cast<int>(inputs.size()));
CHECK_EQ(1, static_cast<int>(outputs.size()));
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK(inputs[0].isSequenceArg()) << "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
CHECK(in_seq.data() && in_seq.getSequenceId().data() && outputs[0].data());
CHECK_EQ(static_cast<int>(outputs[0].shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.getSequenceId().shape().ndims()), 1);
CHECK_EQ(outputs[0].shape().ndims(), 2UL);
CHECK_EQ(in_seq.shape().ndims(), 2UL);
CHECK_EQ(in_seq.getSequenceId().shape().ndims(), 1UL);
CHECK_EQ(in_seq.shape()[0], outputs[0].shape()[0]);
/// output layer grad dim == weight dim * context_length_
CHECK_EQ(in_seq.shape()[1], outputs[0].shape()[1] * context_length_);
Expand Down
1 change: 0 additions & 1 deletion paddle/function/ContextProjectionOp.h
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */

#pragma once

#include "Function.h"

namespace paddle {
Expand Down
139 changes: 39 additions & 100 deletions paddle/function/ContextProjectionOpTest.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -28,55 +28,26 @@ void testMatrixProjectionForward(int context_start,
std::max(0, (int)(context_start + context_length - 1));
if (pad == 0) is_padding = false;

FunctionCompare compare("ContextProjectionForward",
FuncConfig()
.set("context_length", context_length)
.set("context_start", context_start)
.set("begin_pad", std::max(0, -context_start)));

CpuMatrix cpu_in(batch_size, input_dim);
cpu_in.randomizeUniform();
GpuMatrix gpu_in(batch_size, input_dim);
gpu_in.copyFrom(cpu_in);
auto cpu_weight =
is_padding ? std::make_shared<CpuMatrix>(pad, input_dim) : nullptr;
auto gpu_weight =
is_padding ? std::make_shared<GpuMatrix>(pad, input_dim) : nullptr;
if (is_padding) {
cpu_weight->randomizeUniform();
gpu_weight->copyFrom(*cpu_weight);
FunctionCompare test("ContextProjectionForward",
FuncConfig()
.set("context_length", context_length)
.set("context_start", context_start)
.set("begin_pad", std::max(0, -context_start)));

// prepare input arguments
test.addSequence(SequenceIdArg(TensorShape{batch_size}));
test.addInputs(
SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim}));
if (is_padding) { // weight
test.addInputs(SequenceArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim}));
}
IVectorPtr cpu_seq;
generateSequenceStartPositions(batch_size, cpu_seq);
IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true);
gpu_seq->copyFrom(*cpu_seq);

CpuMatrix cpu_out(batch_size, input_dim * context_length);
GpuMatrix gpu_out(batch_size, input_dim * context_length);
cpu_out.randomizeUniform();
gpu_out.copyFrom(cpu_out);

BufferArgs cpu_inputs;
BufferArgs cpu_outputs;
cpu_inputs.addArg(cpu_in, *cpu_seq);
if (cpu_weight) {
cpu_inputs.addArg(*cpu_weight, *cpu_seq);
}
cpu_outputs.addArg(cpu_out, *cpu_seq, ADD_TO);

compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs);
test.addOutputs(
SequenceArg(VALUE_TYPE_FLOAT,
TensorShape{batch_size, input_dim * context_length}),
ADD_TO);

BufferArgs gpu_inputs;
BufferArgs gpu_outputs;
gpu_inputs.addArg(gpu_in, *gpu_seq);
if (gpu_weight) {
gpu_inputs.addArg(*gpu_weight, *gpu_seq);
}
gpu_outputs.addArg(gpu_out, *gpu_seq, ADD_TO);

compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs);

autotest::TensorCheckEqual(cpu_out, gpu_out);
// run Function
test.run();
}

void testMatrixProjectionBackward(int context_start,
Expand All @@ -88,63 +59,31 @@ void testMatrixProjectionBackward(int context_start,
std::max(0, (int)(context_start + context_length - 1));
if (pad == 0) is_padding = false;

FunctionCompare compare("ContextProjectionBackward",
FuncConfig()
.set("context_length", context_length)
.set("context_start", context_start)
.set("begin_pad", std::max(0, -context_start))
.set("is_padding", is_padding)
.set("total_pad", pad));

CpuMatrix cpu_in_grad(batch_size, input_dim);
cpu_in_grad.randomizeUniform();
GpuMatrix gpu_in_grad(batch_size, input_dim);
gpu_in_grad.copyFrom(cpu_in_grad);

CpuMatrix cpu_out_grad(batch_size, input_dim * context_length);
cpu_out_grad.randomizeUniform();
GpuMatrix gpu_out_grad(batch_size, input_dim * context_length);
gpu_out_grad.copyFrom(cpu_out_grad);

IVectorPtr cpu_seq;
generateSequenceStartPositions(batch_size, cpu_seq);
IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true);
gpu_seq->copyFrom(*cpu_seq);

auto cpu_w_grad =
is_padding ? std::make_shared<CpuMatrix>(pad, input_dim) : nullptr;
auto gpu_w_grad =
is_padding ? std::make_shared<GpuMatrix>(pad, input_dim) : nullptr;
if (is_padding) {
cpu_w_grad->randomizeUniform();
gpu_w_grad->copyFrom(*cpu_w_grad);
FunctionCompare test("ContextProjectionBackward",
FuncConfig()
.set("context_length", context_length)
.set("context_start", context_start)
.set("begin_pad", std::max(0, -context_start))
.set("is_padding", is_padding)
.set("total_pad", pad));

// prepare input arguments
test.addSequence(SequenceIdArg(TensorShape{batch_size}));
test.addInputs(SequenceArg(
VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim * context_length}));
test.addOutputs(
SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim}),
ADD_TO);
if (is_padding) { // weight
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim}),
ADD_TO);
}

BufferArgs cpu_inputs;
BufferArgs cpu_outputs;
cpu_inputs.addArg(cpu_out_grad, *cpu_seq);
cpu_outputs.addArg(cpu_in_grad, *cpu_seq, ADD_TO);
cpu_outputs.addArg(
cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO);

compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs);

BufferArgs gpu_inputs;
BufferArgs gpu_outputs;
gpu_inputs.addArg(gpu_out_grad, *gpu_seq);
gpu_outputs.addArg(gpu_in_grad, *gpu_seq, ADD_TO);
gpu_outputs.addArg(
gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO);

compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs);

autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad);
if (is_padding) {
autotest::TensorCheckErr(*cpu_w_grad, *gpu_w_grad);
}
// run Function
test.run();
}

TEST(ContextProjection, projection) {
TEST(ContextProjection, Projection) {
for (auto context_start : {-5, -3, -1, 0, 3}) {
for (auto context_length : {1, 2, 5, 7}) {
for (auto trainable_padding : {false, true}) {
Expand Down
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