diff options
| -rw-r--r-- | arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h | 4 | ||||
| -rw-r--r-- | examples/graph_lenet.cpp | 1 | ||||
| -rw-r--r-- | examples/graph_vgg16.cpp | 1 | ||||
| -rw-r--r-- | examples/graph_vgg19.cpp | 1 | ||||
| -rw-r--r-- | src/runtime/NEON/functions/NEFullyConnectedLayer.cpp | 128 |
5 files changed, 93 insertions, 42 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h index 92ca17a3a4..fe0f2f03f7 100644 --- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h +++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h @@ -30,6 +30,7 @@ #include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h" #include "arm_compute/core/NEON/kernels/NETransposeKernel.h" #include "arm_compute/runtime/MemoryGroup.h" +#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h" #include "arm_compute/runtime/NEON/functions/NEGEMM.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" @@ -129,6 +130,7 @@ private: MemoryGroup _memory_group; NEIm2ColKernel _im2col_kernel; + NEConvertFullyConnectedWeights _convert_weights; NEFullyConnectedLayerReshapeWeights _reshape_weights_function; NEGEMM _mm_gemm; NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp; @@ -136,8 +138,10 @@ private: NEGEMMMatrixAccumulateBiasesKernel _accumulate_biases_kernel; Tensor _im2col_output; Tensor _gemmlowp_output; + Tensor _converted_weights_output; Tensor _reshape_weights_output; const ITensor *_original_weights; + bool _are_weights_converted; bool _are_weights_reshaped; bool _is_fc_after_conv; bool _accumulate_biases; diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index f3aa266c50..c658d1b361 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -60,7 +60,6 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index e23ea65dd7..808ce08ce7 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.cpp @@ -60,7 +60,6 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp index 6cb6b1fae2..96a1d8a9d4 100644 --- a/examples/graph_vgg19.cpp +++ b/examples/graph_vgg19.cpp @@ -59,7 +59,6 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp index 9d3cb31c9a..34cabb5c2e 100644 --- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -74,8 +74,9 @@ Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, c } NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _im2col_kernel(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _im2col_output(), - _gemmlowp_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_reshaped(false), _is_fc_after_conv(false), _accumulate_biases(false), _is_quantized(false), _is_prepared(false) + : _memory_group(std::move(memory_manager)), _im2col_kernel(), _convert_weights(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), + _im2col_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), + _is_fc_after_conv(false), _accumulate_biases(false), _is_quantized(false), _is_prepared(false) { } @@ -146,11 +147,12 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh output->info(), fc_info)); - _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()); - _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()); + _original_weights = weights; // Configure gemmlowp output if(_is_quantized) @@ -175,17 +177,8 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh const ITensor *weights_to_use = weights; - if(!_are_weights_reshaped) - { - weights_to_use = &_reshape_weights_output; - - // Reshape the weights - _reshape_weights_function.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, @@ -197,6 +190,27 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh _is_fc_after_conv = input->info()->num_dimensions() > 1; } + // Reshape weights if needed + if(!_are_weights_reshaped) + { + // Reshape the weights + _reshape_weights_function.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; + } + ITensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output; if(_is_fc_after_conv) { @@ -235,9 +249,10 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn bool is_fc_after_conv = true; bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - 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))); + 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) @@ -256,13 +271,6 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn 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(NEFullyConnectedLayerReshapeWeights::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; @@ -277,6 +285,23 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn is_fc_after_conv = input->num_dimensions() > 1; } + if(!weights_reshaped) + { + // Validate reshape weights kernel + ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayerReshapeWeights::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(NEConvertFullyConnectedWeights::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 @@ -345,29 +370,54 @@ void NEFullyConnectedLayer::prepare() { if(!_is_prepared) { + ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); + + auto release_unused = [](Tensor * w) + { + if(!w->is_used()) + { + w->allocator()->free(); + } + }; + + // Pointer to current weights + const ITensor *cur_weights = _original_weights; + // Reshape of the weights (happens only once) if(!_are_weights_reshaped) { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - // Run reshape weights kernel and mark weights as unused _reshape_weights_output.allocator()->allocate(); _reshape_weights_function.run(); - _original_weights->mark_as_unused(); - - // Prepare GEMM prepare and release unused weights - if(!_is_quantized) - { - _mm_gemm.prepare(); - if(!_reshape_weights_output.is_used()) - { - _reshape_weights_output.allocator()->free(); - } - } + 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(); + } + + // Release converted weights if unused + release_unused(&_reshape_weights_output); + release_unused(&_converted_weights_output); + _is_prepared = true; } } |