COMPMID-2342: Add layer normalization support in CLLSTMLayer
Change-Id: I25d974aa94e69c5f79a0bd99d5869a351d6d954d
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1324
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@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 85a81a8..793d5ca 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -38,16 +38,18 @@
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_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(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(),
- _ones_memset_kernel(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(),
+ : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _accum_input_gate1(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(),
+ _fully_connected_forget_gate(), _accum_forget_gate1(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _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(),
+ _pixelwise_mul_output_state1(), _accum_output1(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _projection_clip(),
+ _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(),
+ _ones_memset_kernel(), _mean_std_norm_input_gate(), _pixelwise_mul_input_gate_coeff(), _accum_input_gate_bias(), _mean_std_norm_forget_gate(), _pixelwise_mul_forget_gate_coeff(),
+ _accum_forget_gate_bias(), _mean_std_norm_cell_gate(), _pixelwise_mul_cell_gate_coeff(), _accum_cell_gate_bias(), _mean_std_norm_output_gate(), _pixelwise_mul_output_gate_coeff(),
+ _accum_output_gate_bias(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _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(),
- _cell_state_activation(), _output_state1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false),
- _is_prepared(false)
+ _cell_state_activation(), _output_state1(), _ones(), _input_layer_norm_out1(), _input_layer_norm_out2(), _forget_layer_norm_out1(), _forget_layer_norm_out2(), _cell_layer_norm_out1(),
+ _cell_layer_norm_out2(), _output_layer_norm_out1(), _output_layer_norm_out2(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false),
+ _perform_projection_clipping(false), _is_prepared(false), _is_layer_norm_lstm(false)
{
}
@@ -66,6 +68,8 @@
output_state_in, cell_state_in,
scratch_buffer, output_state_out, cell_state_out, output);
+ _is_layer_norm_lstm = lstm_params.use_layer_norm();
+
// Set lstm parameters
LSTMParams<ITensorInfo> lstm_params_info;
if(lstm_params.has_peephole_opt())
@@ -121,7 +125,7 @@
_concat_weights_forget_gate.configure(input_to_forget_weights, recurrent_to_forget_weights, &_forget_gate_out6);
_memory_group.manage(&_forget_gate_out5);
- _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, forget_gate_bias, &_forget_gate_out5);
+ _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5);
_memory_group.manage(&_forget_gate_out1);
_memory_group.manage(&_forget_gate_out3);
_forget_gate_out6.allocator()->allocate();
@@ -134,7 +138,7 @@
_run_peephole_opt = true;
_memory_group.manage(&_forget_gate_out4);
_pixelwise_mul_forget_gate.configure(cell_state_in, 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);
+ _accum_forget_gate1.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE);
_forget_gate_out4.allocator()->allocate();
_forget_gate_out5.allocator()->allocate();
forget_gate_out = &_forget_gate_out3;
@@ -143,6 +147,20 @@
{
_forget_gate_out3.allocator()->allocate();
}
+ if(_is_layer_norm_lstm)
+ {
+ _forget_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _forget_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_forget_layer_norm_out1);
+ _memory_group.manage(&_forget_layer_norm_out2);
+ _mean_std_norm_forget_gate.configure(forget_gate_out);
+ _pixelwise_mul_forget_gate_coeff.configure(forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ // forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ forget_gate_out->allocator()->allocate();
+ _accum_forget_gate_bias.configure(ArithmeticOperation::ADD, &_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE);
+ _forget_layer_norm_out1.allocator()->allocate();
+ forget_gate_out = &_forget_layer_norm_out2;
+ }
_activation_forget_gate.configure(forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
// Configure block that calculates the input gate
@@ -177,7 +195,7 @@
_memory_group.manage(&_input_gate_out1);
_memory_group.manage(&_input_gate_out3);
- _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, lstm_params.input_gate_bias(), &_input_gate_out3);
+ _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3);
_input_gate_out2.allocator()->allocate();
input_gate_out = &_input_gate_out3;
@@ -185,7 +203,7 @@
{
_memory_group.manage(&_input_gate_out4);
_pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
- _accum_input_gate2.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _accum_input_gate1.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
_input_gate_out3.allocator()->allocate();
_input_gate_out4.allocator()->allocate();
input_gate_out = &_input_gate_out1;
@@ -194,6 +212,21 @@
{
_input_gate_out1.allocator()->allocate();
}
+
+ if(_is_layer_norm_lstm)
+ {
+ _input_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_input_layer_norm_out1);
+ _memory_group.manage(&_input_layer_norm_out2);
+ _mean_std_norm_input_gate.configure(input_gate_out);
+ _pixelwise_mul_input_gate_coeff.configure(input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ // input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ input_gate_out->allocator()->allocate();
+ _accum_input_gate_bias.configure(ArithmeticOperation::ADD, &_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE);
+ _input_layer_norm_out1.allocator()->allocate();
+ input_gate_out = &_input_layer_norm_out2;
+ }
_activation_input_gate.configure(input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
}
@@ -207,7 +240,7 @@
_cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_memory_group.manage(&_cell_state_out1);
- _fully_connected_cell_state.configure(input, input_to_cell_weights, cell_bias, &_cell_state_out1);
+ _fully_connected_cell_state.configure(input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1);
_memory_group.manage(&_cell_state_out2);
_transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2);
_memory_group.manage(&_cell_state_out3);
@@ -215,10 +248,25 @@
_cell_state_out2.allocator()->allocate();
_memory_group.manage(&_cell_state_out4);
_accum_cell_state1.configure(ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
- _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info);
+ CLTensor *cell_state_out_ptr = &_cell_state_out4;
+ if(_is_layer_norm_lstm)
+ {
+ _cell_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _cell_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_cell_layer_norm_out1);
+ _memory_group.manage(&_cell_layer_norm_out2);
+ _mean_std_norm_cell_gate.configure(cell_state_out_ptr);
+ _pixelwise_mul_cell_gate_coeff.configure(cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ // cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before
+ cell_state_out_ptr->allocator()->allocate();
+ _accum_cell_gate_bias.configure(ArithmeticOperation::ADD, &_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE);
+ _cell_layer_norm_out1.allocator()->allocate();
+ cell_state_out_ptr = &_cell_layer_norm_out2;
+ }
+ _activation_cell_state.configure(cell_state_out_ptr, nullptr, activation_info);
_memory_group.manage(&_cell_state_out5);
- _pixelwise_mul_cell_state1.configure(&_cell_state_out4, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
- _cell_state_out4.allocator()->allocate();
+ _pixelwise_mul_cell_state1.configure(cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ cell_state_out_ptr->allocator()->allocate();
_pixelwise_mul_cell_state2.configure(forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_accum_cell_state2.configure(ArithmeticOperation::ADD, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
_cell_state_out3.allocator()->allocate();
@@ -247,7 +295,7 @@
_memory_group.manage(&_output1);
_memory_group.manage(&_output4);
- _fully_connected_output.configure(&_forget_gate_out2, &_output2, output_gate_bias, &_output4);
+ _fully_connected_output.configure(&_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4);
_output2.allocator()->allocate();
_forget_gate_out2.allocator()->allocate();
@@ -259,7 +307,7 @@
_memory_group.manage(&_output3);
_pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
- _accum_output2.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE);
+ _accum_output1.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE);
_output4.allocator()->allocate();
output_gate_out = &_output1;
@@ -270,6 +318,20 @@
{
_output1.allocator()->allocate();
}
+ if(_is_layer_norm_lstm)
+ {
+ _output_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_output_layer_norm_out1);
+ _memory_group.manage(&_output_layer_norm_out2);
+ _mean_std_norm_output_gate.configure(output_gate_out);
+ _pixelwise_mul_output_gate_coeff.configure(output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ // output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
+ output_gate_out->allocator()->allocate();
+ _accum_output_gate_bias.configure(ArithmeticOperation::ADD, &_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE);
+ _output_layer_norm_out1.allocator()->allocate();
+ output_gate_out = &_output_layer_norm_out2;
+ }
_activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
// Configure block that calculates the output state
@@ -370,6 +432,31 @@
const unsigned int num_batches = input->dimension(1);
const unsigned int num_cells = input_to_output_weights->dimension(1);
+ if(lstm_params.use_layer_norm())
+ {
+ // If CIFG is used, input layer normalization weights tensor is omitted
+ if(lstm_params.has_cifg_opt())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights() != nullptr);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights());
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_batches);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_batches);
+ }
+
// Check peephole optimization
if(lstm_params.has_peephole_opt())
{
@@ -389,7 +476,7 @@
TensorInfo cell_state_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
// Validate forget gate
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, (lstm_params.use_layer_norm()) ? nullptr : forget_gate_bias, &forget_gate));
std::vector<const ITensorInfo *> inputs_vector;
inputs_vector.emplace_back(input);
@@ -404,6 +491,13 @@
ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
}
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&forget_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&forget_gate, lstm_params.forget_layer_norm_weights(), &forget_gate, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate input gate
@@ -423,7 +517,7 @@
TensorInfo lstm_gate_concat = TensorInfo(lstm_weights_concat_shape, 1, input->data_type());
ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate2TensorsKernel::validate(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), &lstm_gate_concat));
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &input_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), (lstm_params.use_layer_norm()) ? nullptr : lstm_params.input_gate_bias(), &input_gate));
if(lstm_params.has_peephole_opt())
{
@@ -432,6 +526,13 @@
ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE));
}
+
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&input_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&input_gate, lstm_params.input_layer_norm_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, lstm_params.input_gate_bias(), &input_gate, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
}
else
@@ -440,9 +541,16 @@
}
// Validate cell state
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, &cell_state_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, (lstm_params.use_layer_norm()) ? nullptr : cell_bias, &cell_state_tmp));
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &cell_state_tmp, 1.f, 0.f, GEMMInfo()));
ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE));
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&cell_state_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_layer_norm_weights(), &cell_state_tmp, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, cell_bias, &cell_state_tmp, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&cell_state_tmp, nullptr, activation_info));
ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
@@ -460,7 +568,7 @@
TensorInfo in_out_gate_concat = TensorInfo(in_out_weights_concat_shape, 1, input->data_type());
ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate2TensorsKernel::validate(input_to_output_weights, recurrent_to_output_weights, &in_out_gate_concat));
// Validate output gate tmp
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, &output_gate_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, (lstm_params.use_layer_norm()) ? nullptr : output_gate_bias, &output_gate_tmp));
if(lstm_params.has_peephole_opt())
{
@@ -468,6 +576,13 @@
RoundingPolicy::TO_NEAREST_EVEN));
ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE));
}
+ if(lstm_params.use_layer_norm())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&output_gate_tmp));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&output_gate_tmp, lstm_params.output_layer_norm_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE));
+ }
ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate output state
@@ -514,7 +629,13 @@
if(_run_peephole_opt)
{
CLScheduler::get().enqueue(_pixelwise_mul_forget_gate);
- _accum_forget_gate2.run();
+ _accum_forget_gate1.run();
+ }
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_forget_gate.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_forget_gate_coeff);
+ CLScheduler::get().enqueue(_accum_forget_gate_bias);
}
CLScheduler::get().enqueue(_activation_forget_gate);
@@ -530,7 +651,14 @@
if(_run_peephole_opt)
{
CLScheduler::get().enqueue(_pixelwise_mul_input_gate);
- _accum_input_gate2.run();
+ _accum_input_gate1.run();
+ }
+
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_input_gate.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_input_gate_coeff);
+ CLScheduler::get().enqueue(_accum_input_gate_bias);
}
CLScheduler::get().enqueue(_activation_input_gate);
}
@@ -539,6 +667,12 @@
CLScheduler::get().enqueue(_transpose_cell_state);
_gemm_cell_state1.run();
CLScheduler::get().enqueue(_accum_cell_state1);
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_cell_gate.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_cell_gate_coeff);
+ CLScheduler::get().enqueue(_accum_cell_gate_bias);
+ }
CLScheduler::get().enqueue(_activation_cell_state);
CLScheduler::get().enqueue(_pixelwise_mul_cell_state1);
CLScheduler::get().enqueue(_pixelwise_mul_cell_state2);
@@ -554,7 +688,13 @@
if(_run_peephole_opt)
{
CLScheduler::get().enqueue(_pixelwise_mul_output_state1);
- _accum_output2.run();
+ _accum_output1.run();
+ }
+ if(_is_layer_norm_lstm)
+ {
+ _mean_std_norm_output_gate.run();
+ CLScheduler::get().enqueue(_pixelwise_mul_output_gate_coeff);
+ CLScheduler::get().enqueue(_accum_output_gate_bias);
}
CLScheduler::get().enqueue(_activation_output);