COMPMID-1386: Add support for converting weights for CL.
Change-Id: I62e3ead903366baeeb1488f233a9b8b0c388c9de
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/140403
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 273ef96..ccd7813 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -73,8 +73,9 @@
}
CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(),
- _im2col_output(), _gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _original_weights(nullptr)
+ : _memory_group(memory_manager), _im2col_kernel(), _convert_weights(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
+ _accumulate_biases_kernel(), _im2col_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true), _are_weights_reshaped(true),
+ _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
{
}
@@ -112,7 +113,7 @@
// Initialize output tensor for im2col
TensorShape shape_im2col = compute_im2col_fc_shape(input->info());
- _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+ _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
// Configure im2col kernel
_memory_group.manage(&_im2col_output);
@@ -134,8 +135,8 @@
configure_mm(input, weights, output);
}
-void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights, bool are_weights_reshaped,
- bool retain_internal_weights)
+void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
+ FullyConnectedLayerInfo fc_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
@@ -144,15 +145,15 @@
weights->info(),
biases != nullptr ? biases->info() : nullptr,
output->info(),
- transpose_weights,
- are_weights_reshaped,
- retain_internal_weights));
+ fc_info));
- _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
- _is_fc_after_conv = true;
- _accumulate_biases = false;
- _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
- _original_weights = weights;
+ _are_weights_converted = true;
+ _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
+ _is_fc_after_conv = true;
+ _accumulate_biases = false;
+ _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+ _is_prepared = false;
+ _original_weights = weights;
// Configure gemmlowp output
if(_is_quantized)
@@ -172,25 +173,16 @@
_accumulate_biases_kernel.configure(output, biases);
}
+ const ICLTensor *weights_to_use = weights;
+
// With the Fully Connected layer we can have 4 different cases:
// 1) Convolution layer -> Fully Connected layer without batches
// 2) Fully Connected layer -> Fully Connected layer without batches
// 3) Convolution layer -> Fully Connected layer with batches
// 4) Fully Connected layer -> Fully Connected layer with batches
- const ICLTensor *weights_to_use = weights;
-
- if(!_are_weights_reshaped)
- {
- weights_to_use = &_reshape_weights_output;
-
- // Reshape the weights
- _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
- }
-
// Check if we have a fully connected layer with batches
const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
-
if(is_batched_fc_layer)
{
_is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
@@ -202,6 +194,28 @@
_is_fc_after_conv = input->info()->num_dimensions() > 1;
}
+ // Reshape weights if needed
+ if(!_are_weights_reshaped)
+ {
+ // Reshape the weights
+ _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
+ weights_to_use = &_reshape_weights_output;
+ }
+
+ // Convert weights if needed
+ if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
+ {
+ // Convert weights
+ _convert_weights.configure(weights_to_use,
+ &_converted_weights_output,
+ input->info()->tensor_shape(),
+ fc_info.weights_trained_layout);
+
+ weights_to_use = &_converted_weights_output;
+ _are_weights_converted = false;
+ }
+
+ // Configure fc core
ICLTensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;
if(_is_fc_after_conv)
{
@@ -224,26 +238,26 @@
_gemmlowp_output.allocator()->allocate();
}
- _are_weights_reshaped = _are_weights_reshaped || retain_internal_weights;
+ _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
}
-Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped,
- bool retain_internal_weights)
+Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+ FullyConnectedLayerInfo fc_info)
{
- ARM_COMPUTE_UNUSED(retain_internal_weights);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
- bool weights_reshaped = transpose_weights ? are_weights_reshaped : true;
+ bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
bool is_fc_after_conv = true;
bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
const GPUTarget gpu_target = CLScheduler::get().target();
- const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_fc_shape(input)));
- const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
- const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+ const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_fc_shape(input)).set_data_layout(DataLayout::NCHW));
+ const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
+ const ITensorInfo &converted_weights = TensorInfo(reshaped_weights.clone()->set_is_resizable(true).reset_padding());
+ const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
// Configure accumulate biases kernel for non quantized asymmetric types
if(biases != nullptr && !is_quantized)
@@ -262,16 +276,8 @@
const ITensorInfo *weights_to_use = weights;
const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output;
- if(!weights_reshaped)
- {
- // Validate reshape weights kernel
- ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
- weights_to_use = &reshaped_weights;
- }
-
// Check if we have a fully connected layer with batches
const bool is_batched_fc_layer = output->dimension(1) > 1;
-
if(is_batched_fc_layer)
{
is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3,
@@ -283,6 +289,23 @@
is_fc_after_conv = input->num_dimensions() > 1;
}
+ if(!weights_reshaped)
+ {
+ // Validate reshape weights kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
+ weights_to_use = &reshaped_weights;
+ }
+
+ if(is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout))
+ {
+ // Validate convert weights kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLConvertFullyConnectedWeights::validate(weights_to_use,
+ &converted_weights,
+ input->tensor_shape(),
+ fc_info.weights_trained_layout));
+ weights_to_use = &converted_weights;
+ }
+
if(is_fc_after_conv)
{
// Fully Connected layer after a Convolution Layer without batches
@@ -349,27 +372,57 @@
void CLFullyConnectedLayer::prepare()
{
- // Reshape of the weights (happens only once)
- if(!_are_weights_reshaped)
+ if(!_is_prepared)
{
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
- // Run reshape weights kernel and mark weights as unused
- _reshape_weights_output.allocator()->allocate();
- _reshape_weights_kernel.run();
- _original_weights->mark_as_unused();
+ auto release_unused = [](CLTensor * w)
+ {
+ if(!w->is_used())
+ {
+ CLScheduler::get().queue().finish();
+ w->allocator()->free();
+ }
+ };
+
+ // Pointer to current weights
+ const ICLTensor *cur_weights = _original_weights;
+
+ // Reshape of the weights if needed (happens only once)
+ if(!_are_weights_reshaped)
+ {
+ // Run reshape weights kernel and mark weights as unused
+ _reshape_weights_output.allocator()->allocate();
+ _reshape_weights_kernel.run();
+
+ cur_weights->mark_as_unused();
+ cur_weights = &_reshape_weights_output;
+ _are_weights_reshaped = true;
+ }
+
+ // Convert weights if needed (happens only once)
+ if(!_are_weights_converted)
+ {
+ _converted_weights_output.allocator()->allocate();
+ _convert_weights.run();
+
+ cur_weights->mark_as_unused();
+ _are_weights_converted = true;
+ }
+
+ // Release reshaped weights if unused
+ release_unused(&_reshape_weights_output);
// Prepare GEMM prepare and release unused weights
if(!_is_quantized)
{
_mm_gemm.prepare();
- if(!_reshape_weights_output.is_used())
- {
- _reshape_weights_output.allocator()->free();
- }
}
- CLScheduler::get().queue().finish();
- _are_weights_reshaped = true;
+ // Release converted weights if unused
+ release_unused(&_reshape_weights_output);
+ release_unused(&_converted_weights_output);
+
+ _is_prepared = true;
}
}