void testBufferArgs(const BufferArgs& inputs,
const std::vector<CheckBufferArg>& check) {
EXPECT_EQ(inputs.size(), check.size());
for (size_t i = 0; i < inputs.size(); i++) {
check[i](inputs[i]);
}
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
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(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_);
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
const auto seq_vec = in_seq.getSequenceId().vector<int, Device>();
const auto out_grad_mat = in_seq.matrix<Device>();
auto w_grad_mat = outputs[0].matrix<Device>();
ContextProjectionBackwardWeight<Device>(out_grad_mat,
w_grad_mat,
seq_vec,
context_length_,
context_start_,
total_pad_,
begin_pad_);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
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(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
CHECK_EQ(in_seq.shape()[0], out_seq.shape()[0]);
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
const auto out_grad_mat = in_seq.matrix<Device>();
const auto seq_vec = in_seq.getSequenceId().vector<int, Device>();
auto in_grad_mat = out_seq.matrix<Device>();
ContextProjectionBackwardData<Device>(
out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
CHECK_EQ(inputs[0].shape().ndims(), (size_t)4);
CHECK(inputs[0].shape() == outputs[0].shape());
CHECK(inputs[0].shape() == outputs[1].shape());
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
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(), 2UL);
CHECK_EQ(out_seq.shape().ndims(), 2UL);
CHECK_EQ(out_seq.getSequenceId().shape().ndims(), 1UL);
/// 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);
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 =
(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,
seq_vec,
context_length_,
context_start_,
begin_pad_,
is_padding_,
total_pad_);
}
// Only need the shape of one input, can calculate the
// floating-point operation.
size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_LT((size_t)1, inputs.size());
size_t batchSize = inputs[0].shape()[0];
size_t maps = inputs[0].shape()[1];
size_t rows = inputs[0].shape()[2];
size_t columns = inputs[0].shape()[3];
// number of floating-point operations
// an approximate value
size_t ops = batchSize * maps * rows * columns * (size_ * 4 + 2);
return ops;
}
void testBufferArgs(const BufferArgs& inputs, const CheckBufferArg& check) {
EXPECT_EQ(inputs.size(), 1U);
check(inputs[0]);
}
