COMPMID-1124: Validate CLLSTM
-Enables cell-to-input weights when !cifg and peephole
-Makes projection bias conditional
Change-Id: Iee866db9f5d8479c2dfd95d74a2d42492bf07a8d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/140543
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Les Bell <les.bell@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/cl_kernels/copy_tensor.cl b/src/core/CL/cl_kernels/copy_tensor.cl
index 4b37dec..930a676 100644
--- a/src/core/CL/cl_kernels/copy_tensor.cl
+++ b/src/core/CL/cl_kernels/copy_tensor.cl
@@ -25,24 +25,35 @@
/** Performs a copy of input tensor to the output tensor.
*
- * @param[in] in_ptr Pointer to the source image. Supported data types: U8.
- * @param[in] in_stride_x Stride of the source image in X dimension (in bytes)
- * @param[in] in_step_x in_stride_x * number of elements along X processed per work item (in bytes)
- * @param[in] in_offset_first_element_in_bytes Offset of the first element in the source image
- * @param[out] out_ptr Pointer to the destination image. Supported data types: U8.
- * @param[in] out_stride_x Stride of the destination image in X dimension (in bytes)
- * @param[in] out_step_x out_stride_x * number of elements along X processed per work item (in bytes)
- * @param[in] out_offset_first_element_in_bytes Offset of the first element in the destination image
+ * @param[in] in_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+ * @param[in] in_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] in_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] in_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] in_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] in_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] in_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] in_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] out_ptr Pointer to the destination tensor. Supported data types: same as @p in_ptr
+ * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] out_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] out_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] out_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void copy_tensor(
- VECTOR_DECLARATION(in),
- VECTOR_DECLARATION(out))
+ TENSOR3D_DECLARATION(in),
+ TENSOR3D_DECLARATION(out))
{
- Vector in = CONVERT_TO_VECTOR_STRUCT(in);
- Vector out = CONVERT_TO_VECTOR_STRUCT(out);
+ Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(in);
+ Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
- VEC_DATA_TYPE(DATA_TYPE, 16)
- data = vload16(0, (__global DATA_TYPE *)in.ptr);
+ // Load data
+ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+ data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr);
- vstore16(data, 0, (__global DATA_TYPE *)out.ptr);
+ // Store result
+ VSTORE(VEC_SIZE)
+ (data, 0, (__global DATA_TYPE *)out.ptr);
}
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLCopyKernel.cpp b/src/core/CL/kernels/CLCopyKernel.cpp
index 4f00ef9..1fc8b5b 100644
--- a/src/core/CL/kernels/CLCopyKernel.cpp
+++ b/src/core/CL/kernels/CLCopyKernel.cpp
@@ -33,10 +33,44 @@
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
-#include <algorithm>
-
using namespace arm_compute;
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+
+ // Validate output if initialized
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape());
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output, *input);
+
+ // Configure window
+ const unsigned int num_elems_processed_per_iteration = 16 / input->element_size();
+
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+
+ bool window_changed = update_window_and_padding(win, input_access, output_access);
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
CLCopyKernel::CLCopyKernel()
: _input(nullptr), _output(nullptr)
{
@@ -44,28 +78,32 @@
void CLCopyKernel::configure(const ICLTensor *input, ICLTensor *output)
{
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(input->info()->tensor_shape(), output->info()->tensor_shape());
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
_input = input;
_output = output;
+ const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
+
// Create kernel
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("copy_tensor", build_opts.options()));
- // Configure window
- constexpr unsigned int num_elems_processed_per_iteration = 16;
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), output->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure(win_config.second);
+}
- Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+Status CLCopyKernel::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
- AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
-
- update_window_and_padding(win, input_access, output_access);
-
- ICLKernel::configure(win);
+ return Status{};
}
void CLCopyKernel::run(const Window &window, cl::CommandQueue &queue)
@@ -73,15 +111,15 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimX);
- Window slice = collapsed.first_slice_window_1D();
+ Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
+ Window slice = collapsed.first_slice_window_3D();
do
{
unsigned int idx = 0;
- add_1D_tensor_argument(idx, _input, slice);
- add_1D_tensor_argument(idx, _output, slice);
+ add_3D_tensor_argument(idx, _input, slice);
+ add_3D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice);
}
- while(collapsed.slide_window_slice_1D(slice));
+ while(collapsed.slide_window_slice_3D(slice));
}
diff --git a/src/runtime/CL/functions/CLCopy.cpp b/src/runtime/CL/functions/CLCopy.cpp
index 3442e37..d1b7926 100644
--- a/src/runtime/CL/functions/CLCopy.cpp
+++ b/src/runtime/CL/functions/CLCopy.cpp
@@ -41,3 +41,8 @@
k->configure(input, output);
_kernel = std::move(k);
}
+
+Status CLCopy::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output)
+{
+ return CLCopyKernel::validate(input, output);
+}
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
index d384400..3458135 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -45,19 +45,27 @@
_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),
+ _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _output5(), _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)
{
}
-void CLLSTMLayer::configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+void CLLSTMLayer::configure(const ICLTensor *input,
+ const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
- ICLTensor *output_state, ICLTensor *cell_state, ICLTensor *scratch_buffer, ICLTensor *output, const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info,
- float cell_threshold, float projection_threshold)
+ const ICLTensor *output_state_in, const ICLTensor *cell_state_in,
+ ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
+ const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
- forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input,
+ input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+ recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+ forget_gate_bias, cell_bias, output_gate_bias,
+ output_state_in, cell_state_in,
+ scratch_buffer, output_state_out, cell_state_out, output);
+
+ // Set lstm parameters
LSTMParams<ITensorInfo> lstm_params_info;
if(lstm_params.has_peephole_opt())
{
@@ -65,36 +73,41 @@
}
if(lstm_params.has_projection())
{
- lstm_params_info.set_projection_params(lstm_params.projection_weights()->info(), lstm_params.projection_bias()->info());
+ lstm_params_info.set_projection_params(lstm_params.projection_weights()->info(),
+ lstm_params.projection_bias() != nullptr ? lstm_params.projection_bias()->info() : nullptr);
}
if(!lstm_params.has_cifg_opt())
{
+ const ITensorInfo *cell_to_input_weights_info = (lstm_params.has_peephole_opt()) ? lstm_params.cell_to_input_weights()->info() : nullptr;
lstm_params_info.set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(),
- lstm_params.cell_to_input_weights()->info(), lstm_params.input_gate_bias()->info());
+ cell_to_input_weights_info, lstm_params.input_gate_bias()->info());
}
+
+ // Validate
ARM_COMPUTE_ERROR_THROW_ON(CLLSTMLayer::validate(input->info(), input_to_forget_weights->info(),
input_to_cell_weights->info(), input_to_output_weights->info(),
recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
- output_state->info(), cell_state->info(), scratch_buffer->info(), output->info(), lstm_params_info,
- activation_info, cell_threshold, projection_threshold));
+ output_state_in->info(), cell_state_in->info(),
+ scratch_buffer->info(), output_state_out->info(), cell_state_out->info(), output->info(),
+ lstm_params_info, activation_info, cell_threshold, projection_threshold));
- const TensorShape cell_state_shape = cell_state->info()->tensor_shape();
+ const TensorShape cell_state_shape = cell_state_in->info()->tensor_shape();
+ // Configure block that calculates the forget gate
+ // forget_gate = Activation(input * input_to_forget_weights + output_state_in * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias)
TensorShape forget_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
_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_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 + 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);
_memory_group.manage(&_forget_gate_out2);
_transpose_forget_gate.configure(recurrent_to_forget_weights, &_forget_gate_out2);
_memory_group.manage(&_forget_gate_out3);
- _gemm_forget_gate.configure(output_state, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
+ _gemm_forget_gate.configure(output_state_in, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
_forget_gate_out2.allocator()->allocate();
_memory_group.manage(&_forget_gate_out5);
_accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE);
@@ -106,7 +119,7 @@
_run_peephole_opt = true;
_memory_group.manage(&_forget_gate_out4);
- _pixelwise_mul_forget_gate.configure(cell_state, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _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);
_forget_gate_out4.allocator()->allocate();
_forget_gate_out5.allocator()->allocate();
@@ -119,11 +132,10 @@
_activation_forget_gate.configure(forget_gate_out, &_forget_gate_out1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
forget_gate_out->allocator()->allocate();
- _input_gate_out1.allocator()->init(TensorInfo(cell_state_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 + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG
// input_gate = 1 - forget_gate, with CIFG
+ _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
if(lstm_params.has_cifg_opt())
{
_memory_group.manage(&_input_gate_out1);
@@ -146,19 +158,24 @@
_memory_group.manage(&_input_gate_out2);
_transpose_input_gate.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2);
_memory_group.manage(&_input_gate_out3);
- _gemm_input_gate.configure(output_state, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
+ _gemm_input_gate.configure(output_state_in, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
_input_gate_out2.allocator()->allocate();
_memory_group.manage(&_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);
- _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out5, ConvertPolicy::SATURATE);
+ _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE);
+ if(_run_peephole_opt)
+ {
+ _memory_group.manage(&_input_gate_out5);
+ _pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _accum_input_gate2.configure(&_input_gate_out4, &_input_gate_out5, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _input_gate_out5.allocator()->allocate();
+ }
_input_gate_out3.allocator()->allocate();
- _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();
_activation_input_gate.configure(&_input_gate_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
}
+ // Configure block that calculates the cell state
+ // cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state_in * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)
TensorShape cell_state1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
_cell_state_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_cell_state_out2.allocator()->init(TensorInfo(cell_state1_shape, 1, input->info()->data_type()));
@@ -166,14 +183,12 @@
_cell_state_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
_cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- // Configure block that calculates the cell state
- // 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);
_memory_group.manage(&_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);
+ _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f);
_cell_state_out2.allocator()->allocate();
_memory_group.manage(&_cell_state_out4);
_accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
@@ -182,12 +197,11 @@
_pixelwise_mul_cell_state1.configure(&_cell_state_out4, &_input_gate_out1, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_input_gate_out1.allocator()->allocate();
_cell_state_out4.allocator()->allocate();
- _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_forget_gate_out1.allocator()->allocate();
_accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
_cell_state_out3.allocator()->allocate();
_cell_state_out5.allocator()->allocate();
-
// Perform clipping
if(cell_threshold != 0.f)
{
@@ -195,20 +209,20 @@
_cell_clip.configure(&_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
}
+ // Configure block that calculates the output
+ // output_state_out = Activation(input * input_to_output_weights + output_state_in * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)
TensorShape output1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
_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()));
_output5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- // Configure block that calculates the output
- // 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);
_memory_group.manage(&_output2);
_transpose_output.configure(recurrent_to_output_weights, &_output2);
_memory_group.manage(&_output3);
- _gemm_output.configure(output_state, &_output2, nullptr, &_output3, 1.f, 0.f);
+ _gemm_output.configure(output_state_in, &_output2, nullptr, &_output3, 1.f, 0.f);
_output2.allocator()->allocate();
_memory_group.manage(&_output5);
_accum_output1.configure(&_output1, &_output3, &_output5, ConvertPolicy::SATURATE);
@@ -231,11 +245,9 @@
{
_output1.allocator()->allocate();
}
- _activation_output.configure(output_gate_out, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ _activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
output_gate_out->allocator()->allocate();
- _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
-
// Configure block that calculates the output state
/** lstm_res = PixelwiseMul(output, Activation(cell_state))
*
@@ -245,32 +257,32 @@
* \
* -- lstm_res , otherwise
*/
+ ICLTensor *output_state_out_tmp = lstm_params.has_projection() ? &_output_state1 : output_state_out;
+ _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output_state1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
_memory_group.manage(&_cell_state_activation);
_activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info);
- _pixelwise_mul_output_state2.configure(&_cell_state_activation, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _pixelwise_mul_output_state2.configure(&_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
_cell_state_activation.allocator()->allocate();
if(lstm_params.has_projection())
{
_has_projection_weights = true;
- _output_projection1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
- _memory_group.manage(&_output_projection1);
- _fully_connected_output_state.configure(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), &_output_projection1);
+ _fully_connected_output_state.configure(output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out);
+ _output_state1.allocator()->allocate();
// Perform clipping
if(projection_threshold != 0.f)
{
_perform_projection_clipping = true;
- _projection_clip.configure(&_output_projection1, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
+ _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
}
-
- // Allocate intermediate buffer
- _output_projection1.allocator()->allocate();
}
// Copy cell state and output
- _copy_cell_state.configure(&_cell_state_out1, cell_state);
+ _copy_cell_state.configure(&_cell_state_out1, cell_state_out);
_cell_state_out1.allocator()->allocate();
- _copy_output.configure(output_state, output);
+ _copy_output.configure(output_state_out, output);
// Vector for holding the tensors to store in scratch buffer
std::vector<ICLTensor *> scratch_inputs;
@@ -284,17 +296,31 @@
_concat_scratch_buffer.configure(scratch_inputs, scratch_buffer);
}
-Status CLLSTMLayer::validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
+Status CLLSTMLayer::validate(const ITensorInfo *input,
+ const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
- const ITensorInfo *output_state, const ITensorInfo *cell_state, const ITensorInfo *scratch_buffer, const ITensorInfo *output,
+ const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in,
+ const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output,
const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
- forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input,
+ input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+ recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+ forget_gate_bias, cell_bias, output_gate_bias,
+ output_state_in, cell_state_in,
+ scratch_buffer, output_state_out, cell_state_out, output);
+
+ // Check data types
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
- recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input,
+ input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+ recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+ forget_gate_bias, cell_bias, output_gate_bias,
+ output_state_in, cell_state_in,
+ scratch_buffer, output_state_out, cell_state_out, output);
+
+ // Check dimensions
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(input_to_forget_weights->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(input_to_cell_weights->num_dimensions() > 2);
@@ -305,12 +331,19 @@
ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(output_gate_bias->num_dimensions() > 1);
- ARM_COMPUTE_RETURN_ERROR_ON(output_state->num_dimensions() > 2);
- ARM_COMPUTE_RETURN_ERROR_ON(cell_state->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_state_out->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_state_out->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() > 2);
- ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0) && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0)
+ && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0));
+ const unsigned int num_batches = input->dimension(1);
+ const unsigned int num_cells = input_to_output_weights->dimension(1);
+
+ // Check peephole optimization
if(lstm_params.has_peephole_opt())
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights());
@@ -319,85 +352,105 @@
}
TensorShape units_out_transposed_shape = compute_transposed_shape(*recurrent_to_output_weights);
- TensorShape gemmv_shape{ 1, output_state->dimension(1) };
TensorShape num_units_transposed_shape = compute_transposed_shape(*forget_gate_bias);
const TensorInfo units_out_transposed_info = TensorInfo(units_out_transposed_shape, 1, input->data_type());
- const TensorInfo gemmv_shape_info = TensorInfo(gemmv_shape, 1, input->data_type());
const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type());
+ TensorInfo input_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
+ TensorInfo forget_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
+ TensorInfo output_gate_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
+ 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, cell_state));
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state, &units_out_transposed_info, nullptr, cell_state, 1.f, 0.f, GEMMInfo()));
- ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &forget_gate, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
if(lstm_params.has_peephole_opt())
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo()));
- ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE));
+ 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));
}
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate input gate
if(!lstm_params.has_cifg_opt())
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.cell_to_input_weights(), lstm_params.input_gate_bias());
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(),
+ lstm_params.recurrent_to_input_weights(),
+ lstm_params.input_gate_bias());
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_to_input_weights()->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.recurrent_to_input_weights()->num_dimensions() > 2);
- ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() > 1);
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), cell_state));
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo()));
- ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE));
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+
+ 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(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &input_gate, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE));
+ if(lstm_params.has_peephole_opt())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1);
+ 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));
+ }
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
}
// Validate cell state
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, cell_state));
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, activation_info));
- ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, cell_state, cell_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
-
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, 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));
+ 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));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE));
if(cell_threshold != 0.f)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&cell_state_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold,
+ cell_threshold)));
}
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, cell_state));
+ // 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(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &output_gate_tmp, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE));
if(lstm_params.has_peephole_opt())
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_to_output_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE));
}
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
// Validate output state
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, activation_info));
- ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&cell_state_tmp, &cell_state_tmp, activation_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &output_gate_tmp, &output_gate_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
if(lstm_params.has_projection())
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), cell_state));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(&output_gate_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out));
if(projection_threshold != 0.f)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold,
- projection_threshold)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(output_state_out, output_state_out,
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)));
}
}
- std::vector<TensorInfo> inputs_vector_info;
+ // Validate copy kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(&cell_state_tmp, cell_state_out));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(output_state_out, output));
+
+ // Validate scratch concatenation
+ std::vector<ITensorInfo *> inputs_vector_info_raw;
if(lstm_params.has_cifg_opt())
{
- inputs_vector_info.emplace_back(*cell_state);
+ inputs_vector_info_raw.push_back(&input_gate);
}
- inputs_vector_info.emplace_back(*cell_state);
- inputs_vector_info.emplace_back(*cell_state);
- inputs_vector_info.emplace_back(*cell_state);
-
- std::vector<ITensorInfo *> inputs_vector_info_raw;
- for(auto &input : inputs_vector_info)
- {
- inputs_vector_info_raw.emplace_back(&input);
- }
+ inputs_vector_info_raw.push_back(&cell_state_tmp);
+ inputs_vector_info_raw.push_back(&forget_gate);
+ inputs_vector_info_raw.push_back(&output_gate_tmp);
ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer));
return Status{};
@@ -438,9 +491,12 @@
_fully_connected_input_gate.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();
+ if(_run_peephole_opt)
+ {
+ CLScheduler::get().enqueue(_pixelwise_mul_input_gate);
+ _accum_input_gate2.run();
+ }
CLScheduler::get().enqueue(_activation_input_gate);
}