COMPMID-1124 : Fixes in CLLSTM layer
Change-Id: Ifc8e12c296d3ef2bf8e0f0bf1b87b7fd47a1fad7
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/139248
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Ruomei Yan <ruomei.yan@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
index a195ffa..86e5eb9 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -38,16 +38,15 @@
using namespace arm_compute::misc::shape_calculator;
CLLSTMLayer::CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _gemm_input_gate1(), _gemm_input_gate2(), _transpose_input_gate1(), _transpose_input_gate2(), _accum_input_gate1(),
- _accum_input_gate2(), _subtract_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate1(), _gemm_forget_gate2(), _transpose_forget_gate1(),
- _transpose_forget_gate2(), _accum_forget_gate1(), _accum_forget_gate2(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _transpose_cell_state1(),
- _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), _gemm_output1(),
- _gemm_output2(), _transpose_output1(), _transpose_output2(), _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state(),
- _fully_connected_output_state(), _gemm_output_state(), _accum_output_state(), _projection_clip(), _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _input_gate_out1(), _input_gate_out2(),
- _input_gate_out3(), _input_gate_out4(), _input_gate_out5(), _input_gate_out6(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(),
- _forget_gate_out6(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _output5(), _output6(),
- _cell_state_activation(), _output_projection1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false),
- _perform_projection_clipping(false)
+ : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _gemm_input_gate(), _transpose_input_gate(), _accum_input_gate1(), _accum_input_gate2(), _subtract_input_gate(),
+ _pixelwise_mul_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate(), _transpose_forget_gate(), _accum_forget_gate1(), _accum_forget_gate2(),
+ _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _transpose_cell_state(), _accum_cell_state1(), _accum_cell_state2(),
+ _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), _gemm_output(), _pixelwise_mul_output_state1(), _transpose_output(),
+ _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _gemm_output_state(), _accum_output_state(),
+ _projection_clip(), _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _input_gate_out5(),
+ _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(),
+ _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _output5(), _cell_state_activation(), _output_projection1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false),
+ _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false)
{
}
@@ -83,40 +82,34 @@
const TensorShape cell_state_shape = cell_state->info()->tensor_shape();
TensorShape forget_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
- TensorShape forget_gate2_shape = compute_transposed_shape(*forget_gate_bias->info());
- TensorShape forget_gate3_shape{ 1, output_state->info()->dimension(1) };
_forget_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_forget_gate_out2.allocator()->init(TensorInfo(forget_gate1_shape, 1, input->info()->data_type()));
_forget_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- _forget_gate_out6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _forget_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
// Configure block that calculates the forget gate
- // forget_gate = Activation(input * input_to_forget_weights + output_state * recurrent_to_forget_weights + cell_state * cell_to_forget_weights + forget_gate_bias)
+ // forget_gate = Activation(input * input_to_forget_weights + output_state * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias)
_memory_group.manage(&_forget_gate_out1);
_fully_connected_forget_gate.configure(input, input_to_forget_weights, forget_gate_bias, &_forget_gate_out1, true, false);
_memory_group.manage(&_forget_gate_out2);
- _transpose_forget_gate1.configure(recurrent_to_forget_weights, &_forget_gate_out2);
+ _transpose_forget_gate.configure(recurrent_to_forget_weights, &_forget_gate_out2);
_memory_group.manage(&_forget_gate_out3);
- _gemm_forget_gate1.configure(output_state, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
+ _gemm_forget_gate.configure(output_state, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
_forget_gate_out2.allocator()->allocate();
- _memory_group.manage(&_forget_gate_out6);
- _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out6, ConvertPolicy::SATURATE);
- CLTensor *forget_gate_out = &_forget_gate_out6;
+ _memory_group.manage(&_forget_gate_out5);
+ _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE);
+ CLTensor *forget_gate_out = &_forget_gate_out5;
if(lstm_params.has_peephole_opt())
{
- _forget_gate_out4.allocator()->init(TensorInfo(forget_gate2_shape, 1, input->info()->data_type()));
- _forget_gate_out5.allocator()->init(TensorInfo(forget_gate3_shape, 1, input->info()->data_type()));
+ _forget_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_run_peephole_opt = true;
_memory_group.manage(&_forget_gate_out4);
- _transpose_forget_gate2.configure(lstm_params.cell_to_forget_weights(), &_forget_gate_out4);
- _memory_group.manage(&_forget_gate_out5);
- _gemm_forget_gate2.configure(cell_state, &_forget_gate_out4, nullptr, &_forget_gate_out5, 1.f, 0.f);
+ _pixelwise_mul_forget_gate.configure(cell_state, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _accum_forget_gate2.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE);
_forget_gate_out4.allocator()->allocate();
- _accum_forget_gate2.configure(&_forget_gate_out6, &_forget_gate_out5, &_forget_gate_out3, ConvertPolicy::SATURATE);
_forget_gate_out5.allocator()->allocate();
- _forget_gate_out6.allocator()->allocate();
forget_gate_out = &_forget_gate_out3;
}
else
@@ -126,12 +119,10 @@
_activation_forget_gate.configure(forget_gate_out, &_forget_gate_out1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
forget_gate_out->allocator()->allocate();
- TensorShape input_gate3_shape{ 1, output_state->info()->dimension(1) };
_input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- _input_gate_out5.allocator()->init(TensorInfo(input_gate3_shape, 1, input->info()->data_type()));
// Configure block that calculates the input gate
- // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + cell_state * cell_to_input_weights + input_gate_bias), without CIFG
+ // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG
// input_gate = 1 - forget_gate, with CIFG
if(lstm_params.has_cifg_opt())
{
@@ -143,32 +134,28 @@
}
else
{
- TensorShape input_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
- TensorShape input_gate2_shape = compute_transposed_shape(*lstm_params.cell_to_input_weights()->info());
+ TensorShape input_gate_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
- _input_gate_out2.allocator()->init(TensorInfo(input_gate1_shape, 1, input->info()->data_type()));
+ _input_gate_out2.allocator()->init(TensorInfo(input_gate_shape, 1, input->info()->data_type()));
_input_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- _input_gate_out4.allocator()->init(TensorInfo(input_gate2_shape, 1, input->info()->data_type()));
- _input_gate_out6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_memory_group.manage(&_input_gate_out1);
_fully_connected_input_gate.configure(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &_input_gate_out1, true, false);
_memory_group.manage(&_input_gate_out2);
- _transpose_input_gate1.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2);
+ _transpose_input_gate.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2);
_memory_group.manage(&_input_gate_out3);
- _gemm_input_gate1.configure(output_state, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
+ _gemm_input_gate.configure(output_state, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
_input_gate_out2.allocator()->allocate();
_memory_group.manage(&_input_gate_out4);
- _transpose_input_gate2.configure(lstm_params.cell_to_input_weights(), &_input_gate_out4);
+ _pixelwise_mul_input_gate.configure(cell_state, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_memory_group.manage(&_input_gate_out5);
- _gemm_input_gate2.configure(cell_state, &_input_gate_out4, nullptr, &_input_gate_out5, 1.f, 0.f);
- _input_gate_out4.allocator()->allocate();
- _memory_group.manage(&_input_gate_out6);
- _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out6, ConvertPolicy::SATURATE);
+ _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out5, ConvertPolicy::SATURATE);
_input_gate_out3.allocator()->allocate();
- _accum_input_gate2.configure(&_input_gate_out6, &_input_gate_out5, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _accum_input_gate2.configure(&_input_gate_out5, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _input_gate_out4.allocator()->allocate();
_input_gate_out5.allocator()->allocate();
- _input_gate_out6.allocator()->allocate();
_activation_input_gate.configure(&_input_gate_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
}
@@ -180,11 +167,11 @@
_cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
// Configure block that calculates the cell state
- // cell_state = Clip((RixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)
+ // cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)
_memory_group.manage(&_cell_state_out1);
_fully_connected_cell_state.configure(input, input_to_cell_weights, cell_bias, &_cell_state_out1, true, false);
_memory_group.manage(&_cell_state_out2);
- _transpose_cell_state1.configure(recurrent_to_cell_weights, &_cell_state_out2);
+ _transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2);
_memory_group.manage(&_cell_state_out3);
_gemm_cell_state1.configure(output_state, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f);
_cell_state_out2.allocator()->allocate();
@@ -209,42 +196,36 @@
}
TensorShape output1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
- TensorShape output2_shape = compute_transposed_shape(*cell_bias->info());
- TensorShape output3_shape{ 1, output_state->info()->dimension(1) };
_output1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_output2.allocator()->init(TensorInfo(output1_shape, 1, input->info()->data_type()));
_output3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- _output6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
// Configure block that calculates the output
- // output_gate = Activation(input * input_to_output_weights + output_state * recurrent_to_output_weights + cell_state * cell_to_output_weights + output_gate_bias)
+ // output_state = Activation(input * input_to_output_weights + output_state * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)
_memory_group.manage(&_output1);
_fully_connected_output.configure(input, input_to_output_weights, output_gate_bias, &_output1, true, false);
_memory_group.manage(&_output2);
- _transpose_output1.configure(recurrent_to_output_weights, &_output2);
+ _transpose_output.configure(recurrent_to_output_weights, &_output2);
_memory_group.manage(&_output3);
- _gemm_output1.configure(output_state, &_output2, nullptr, &_output3, 1.f, 0.f);
+ _gemm_output.configure(output_state, &_output2, nullptr, &_output3, 1.f, 0.f);
_output2.allocator()->allocate();
- _memory_group.manage(&_output6);
- _accum_output1.configure(&_output1, &_output3, &_output6, ConvertPolicy::SATURATE);
+ _memory_group.manage(&_output5);
+ _accum_output1.configure(&_output1, &_output3, &_output5, ConvertPolicy::SATURATE);
_output3.allocator()->allocate();
- CLTensor *output_gate_out = &_output6;
+ CLTensor *output_gate_out = &_output5;
if(lstm_params.has_peephole_opt())
{
- _output4.allocator()->init(TensorInfo(output2_shape, 1, input->info()->data_type()));
- _output5.allocator()->init(TensorInfo(output3_shape, 1, input->info()->data_type()));
+ _output4.allocator()->init(TensorInfo(_cell_state_out1.info()->tensor_shape(), 1, input->info()->data_type()));
_memory_group.manage(&_output4);
- _transpose_output2.configure(lstm_params.cell_to_output_weights(), &_output4);
- _memory_group.manage(&_output5);
- _gemm_output2.configure(&_cell_state_out1, &_output4, nullptr, &_output5, 1.f, 0.f);
- _accum_output2.configure(&_output6, &_output5, &_output1, ConvertPolicy::SATURATE);
- _output6.allocator()->allocate();
+ _pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _accum_output2.configure(&_output5, &_output4, &_output1, ConvertPolicy::SATURATE);
+ _output5.allocator()->allocate();
output_gate_out = &_output1;
// Allocate intermediate buffers
_output4.allocator()->allocate();
- _output5.allocator()->allocate();
}
else
{
@@ -266,7 +247,7 @@
*/
_memory_group.manage(&_cell_state_activation);
_activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info);
- _pixelwise_mul_output_state.configure(&_cell_state_activation, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _pixelwise_mul_output_state2.configure(&_cell_state_activation, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_cell_state_activation.allocator()->allocate();
if(lstm_params.has_projection())
@@ -427,14 +408,13 @@
_memory_group.acquire();
_fully_connected_forget_gate.run();
- CLScheduler::get().enqueue(_transpose_forget_gate1);
- _gemm_forget_gate1.run();
+ CLScheduler::get().enqueue(_transpose_forget_gate);
+ _gemm_forget_gate.run();
CLScheduler::get().enqueue(_accum_forget_gate1);
if(_run_peephole_opt)
{
- CLScheduler::get().enqueue(_transpose_forget_gate2);
- _gemm_forget_gate2.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_forget_gate);
_accum_forget_gate2.run();
}
CLScheduler::get().enqueue(_activation_forget_gate);
@@ -442,24 +422,30 @@
if(_run_cifg_opt)
{
_ones.map(true);
- std::fill_n(_ones.buffer(), _ones.info()->total_size(), 1);
+ if(_ones.info()->data_type() == DataType::F16)
+ {
+ std::fill_n(reinterpret_cast<half *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1);
+ }
+ else
+ {
+ std::fill_n(reinterpret_cast<float *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1);
+ }
_ones.unmap();
CLScheduler::get().enqueue(_subtract_input_gate);
}
else
{
_fully_connected_input_gate.run();
- CLScheduler::get().enqueue(_transpose_input_gate1);
- _gemm_input_gate1.run();
- CLScheduler::get().enqueue(_transpose_input_gate2);
- _gemm_input_gate2.run();
+ CLScheduler::get().enqueue(_transpose_input_gate);
+ _gemm_input_gate.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_input_gate);
CLScheduler::get().enqueue(_accum_input_gate1);
_accum_input_gate2.run();
CLScheduler::get().enqueue(_activation_input_gate);
}
_fully_connected_cell_state.run();
- CLScheduler::get().enqueue(_transpose_cell_state1);
+ CLScheduler::get().enqueue(_transpose_cell_state);
_gemm_cell_state1.run();
CLScheduler::get().enqueue(_accum_cell_state1);
CLScheduler::get().enqueue(_activation_cell_state);
@@ -473,21 +459,19 @@
}
_fully_connected_output.run();
- CLScheduler::get().enqueue(_transpose_output1);
- _gemm_output1.run();
+ CLScheduler::get().enqueue(_transpose_output);
+ _gemm_output.run();
CLScheduler::get().enqueue(_accum_output1);
- CLScheduler::get().enqueue(_pixelwise_mul_output_state);
if(_run_peephole_opt)
{
- CLScheduler::get().enqueue(_transpose_output2);
- _gemm_output2.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_output_state1);
_accum_output2.run();
}
CLScheduler::get().enqueue(_activation_output);
CLScheduler::get().enqueue(_activation_output_state);
- CLScheduler::get().enqueue(_pixelwise_mul_output_state);
+ CLScheduler::get().enqueue(_pixelwise_mul_output_state2);
if(_has_projection_weights)
{