diff options
Diffstat (limited to 'src/runtime/CL/functions/CLSoftmaxLayer.cpp')
-rw-r--r-- | src/runtime/CL/functions/CLSoftmaxLayer.cpp | 172 |
1 files changed, 46 insertions, 126 deletions
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp index f7b2935622..720f9111a5 100644 --- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp +++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp @@ -36,96 +36,45 @@ namespace arm_compute { template <bool IS_LOG> CLSoftmaxLayerGeneric<IS_LOG>::CLSoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_ptr(), _reshape(), _max(), _sum(), _tmp(), _input_flattened(), _output_flattened(), - _needs_flattening(false) + : _memory_group(std::move(memory_manager)), _permute_input(), _permute_output(), _max_shift_exp_sum_kernel(), _norm_kernel(), _max(), _sum(), _tmp(), _input_permuted(), _output_permuted(), + _needs_permute() { } template <bool IS_LOG> -void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t first_n_reduce_axes) -{ - configure_reshape_input_kernel(CLKernelLibrary::get().get_compile_context(), input, output, first_n_reduce_axes); -} - -template <bool IS_LOG> -void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *output, size_t first_n_reduce_axes) -{ - // Flatten the input - const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), first_n_reduce_axes); - - // Initialize the flat input - _input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten)); - - // If we need to flatten the input, we can use CLFlattenKernel or CLReshapeKernel - // If the number of reduced axes is 3 (max dimension), which means collapsing all axes except the batch axis, we use CLFlattenKernel. - // In all other cases we have to use CLReshapeKernel - // Note that the "other cases" include both: - // 1. first_n_reduce_axes < 3: Reduce the first 1 (no need to reduce) or 2 dimensions (inclusive) - // 2. first_n_reduce_axes == 4: Reduce all 4 dimensions. This can only be handled by CLReshapeKernel instead of CLFlattenKernel. - if(first_n_reduce_axes == 3) - { - auto flatten = support::cpp14::make_unique<CLFlattenLayer>(); - flatten->configure(compile_context, input, &_input_flattened); - _flatten_ptr = std::move(flatten); - } - else - { - auto reshape_ptr = support::cpp14::make_unique<CLReshapeLayer>(); - reshape_ptr->configure(compile_context, input, &_input_flattened); - _flatten_ptr = std::move(reshape_ptr); - } - - // We need to init the output tensor here. Indeed, the reshape kernel expects - // both tensors to be already initialized - auto_init_if_empty(*output->info(), *input->info()->clone()); -} - -template <bool IS_LOG> -void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, size_t axis) +void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, int32_t axis) { configure(CLKernelLibrary::get().get_compile_context(), input, output, beta, axis); } template <bool IS_LOG> -void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, size_t axis) +void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, int32_t axis) { // Perform validation step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, axis)); - // Convert reduce-before axis (inclusive) to first n axes to reduce - size_t first_n_reduce_axes = dim_index_2_num_dims(axis, input->info()->num_dimensions()); - - // We only need flattening when the number of axes to reduce is greater than 1 - _needs_flattening = first_n_reduce_axes > 1; + const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); - // If we are dealing with a 4D tensor, we will: - // - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor - // - Execute all the pipeline (reduction + normalization) on the flattened tensor - // - Reshape the flattened output into the real output - if(_needs_flattening) + _needs_permute = actual_axis != 0; + ICLTensor *tmp_output = output; + const ICLTensor *tmp_input = _needs_permute ? &_input_permuted : input; + if(_needs_permute) { - // Add to the memory manager _input_flattened - _memory_group.manage(&_input_flattened); - - // Cofigure _flatten_kernel and _input_flattened - configure_reshape_input_kernel(input, output, first_n_reduce_axes); + _memory_group.manage(&_input_permuted); + _memory_group.manage(&_output_permuted); + _permute_input.configure(compile_context, input, &_input_permuted, get_permutation_vector_from_softmax_axis(actual_axis)); + tmp_output = &_output_permuted; } - // We want to deal with a 2D input. Either it is the flattened version of the original input (4D case) - // or it is the original input case (2D case) - const ICLTensor *input_2D = (_needs_flattening ? &_input_flattened : input); - - // Create intermediate tensors shapes - TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true); - DataType tmp_data_type = is_data_type_quantized_asymmetric(input_2D->info()->data_type()) ? DataType::S32 : input_2D->info()->data_type(); - TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); + // Create intermediate tensors + DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::S32 : tmp_input->info()->data_type(); + TensorInfo tensor_info_tmp(tmp_input->info()->clone()->set_data_type(tmp_data_type)); _tmp.allocator()->init(tensor_info_tmp); - - TensorShape max_sum_shape = input_2D->info()->tensor_shape(); + TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); max_sum_shape.set(0, 1); - _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); - _sum.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type)); + _max.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape)); + _sum.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type)); // Set GPU target to kernels _max_shift_exp_sum_kernel.set_target(CLScheduler::get().target()); @@ -138,49 +87,43 @@ void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_co SoftmaxKernelInfo softmax_info; softmax_info.beta = beta; softmax_info.is_log = IS_LOG; - softmax_info.input_data_type = input_2D->info()->data_type(); + softmax_info.input_data_type = tmp_input->info()->data_type(); // Configure kernels - _max_shift_exp_sum_kernel.configure(compile_context, input_2D, &_max, &_tmp, &_sum, softmax_info); - - if(_needs_flattening) - { - // Add to the memory manager _output_flattened - _memory_group.manage(&_output_flattened); - - // The normalization kernel stores the result in a flat output tensor - _norm_kernel.configure(compile_context, &_tmp, &_sum, &_output_flattened, softmax_info); - - // Reshape the flat output into a the requested (4D) output - _reshape.configure(compile_context, &_output_flattened, output); - - // Allocate the intermediate flat tensors - _input_flattened.allocator()->allocate(); - _output_flattened.allocator()->allocate(); - } - else - { - // Softmax 2D case - _norm_kernel.configure(compile_context, &_tmp, &_sum, output, softmax_info); - } + _max_shift_exp_sum_kernel.configure(compile_context, tmp_input, &_max, &_tmp, &_sum, softmax_info); + _norm_kernel.configure(compile_context, &_tmp, &_sum, tmp_output, softmax_info); // Allocate intermediate buffers _tmp.allocator()->allocate(); _max.allocator()->allocate(); _sum.allocator()->allocate(); + if(_needs_permute) + { + _permute_output.configure(compile_context, &_output_permuted, output, get_permutation_vector_from_softmax_axis(actual_axis)); + _input_permuted.allocator()->allocate(); + _output_permuted.allocator()->allocate(); + } } template <bool IS_LOG> -Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis) +Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis != 0, "Only axis 0 supported in tensors"); ARM_COMPUTE_UNUSED(beta); - ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() <= axis); + ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis); - // Convert reduce-before axis (inclusive) to first n axes to reduce - size_t first_n_reduce_axes = dim_index_2_num_dims(axis, input->num_dimensions()); + const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions()))); + const bool needs_permute = actual_axis != 0; + if(needs_permute) + { + const PermutationVector permutation_vector = get_permutation_vector_from_softmax_axis(actual_axis); + const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector); + TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &input_permuted, permutation_vector)); + TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&output_permuted, output, permutation_vector)); + } // Create intermediate tensor info DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type(); @@ -191,23 +134,6 @@ Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const I TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true)); TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true)); - const bool needs_flattening = (first_n_reduce_axes > 1); - - if(needs_flattening) - { - const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, first_n_reduce_axes); - TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true)); - - if(first_n_reduce_axes == 3) - { - ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayer::validate(input, &tensor_info_flat)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(input, &tensor_info_flat)); - } - } - SoftmaxKernelInfo softmax_info; softmax_info.beta = beta; softmax_info.is_log = IS_LOG; @@ -216,12 +142,6 @@ Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const I ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum)); ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output, softmax_info)); - if(needs_flattening) - { - const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input); - TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true)); - } - return Status{}; } @@ -230,17 +150,17 @@ void CLSoftmaxLayerGeneric<IS_LOG>::run() { MemoryGroupResourceScope scope_mg(_memory_group); - if(_needs_flattening) + if(_needs_permute) { - _flatten_ptr->run(); + _permute_input.run(); } CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false); - CLScheduler::get().enqueue(_norm_kernel, !_needs_flattening); + CLScheduler::get().enqueue(_norm_kernel, !_needs_permute); - if(_needs_flattening) + if(_needs_permute) { - _reshape.run(); + _permute_output.run(); } } |