diff options
Diffstat (limited to 'src/cpu/operators/CpuGemmConv2d.cpp')
-rw-r--r-- | src/cpu/operators/CpuGemmConv2d.cpp | 73 |
1 files changed, 44 insertions, 29 deletions
diff --git a/src/cpu/operators/CpuGemmConv2d.cpp b/src/cpu/operators/CpuGemmConv2d.cpp index ebf2ebcc1b..7c0e58b94e 100644 --- a/src/cpu/operators/CpuGemmConv2d.cpp +++ b/src/cpu/operators/CpuGemmConv2d.cpp @@ -62,13 +62,13 @@ CpuGemmConv2d::SkipInfo CpuGemmConv2d::skip_im_col_info(const ITensorInfo *src, const unsigned int kernel_height = weights->dimension(idx_height); unsigned int conv_w = 0; unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - kernel_width, - kernel_height, - conv_info, - dilation); - const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); + std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + kernel_width, + kernel_height, + conv_info, + dilation); + const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); if(skip_im2col) { @@ -139,8 +139,8 @@ void CpuGemmConv2d::configure_mm(const ITensorInfo *src, const ITensorInfo *weig PixelValue type_min{}; PixelValue type_max{}; std::tie(type_min, type_max) = get_min_max(data_type); - int32_t min_activation = type_min.get<int32_t>(); - int32_t max_activation = type_max.get<int32_t>(); + int32_t min_activation = type_min.get<int32_t>(); + int32_t max_activation = type_max.get<int32_t>(); if(supported_acts.count(act_info.activation()) != 0) { @@ -203,8 +203,8 @@ Status CpuGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *wei PixelValue type_min{}; PixelValue type_max{}; std::tie(type_min, type_max) = get_min_max(data_type); - int32_t min_activation = type_min.get<int32_t>(); - int32_t max_activation = type_max.get<int32_t>(); + int32_t min_activation = type_min.get<int32_t>(); + int32_t max_activation = type_max.get<int32_t>(); const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, @@ -274,6 +274,7 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights const DataLayout data_layout = src->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); const unsigned int kernel_width = weights->dimension(idx_width); @@ -288,8 +289,8 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights ITensorInfo *gemm_output_to_use = dst; // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; + unsigned int conv_w = 0; + unsigned int conv_h = 0; std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), src->dimension(idx_height), kernel_width, @@ -306,8 +307,8 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights _skip_col2im = skip_info.skip_col2im; // Get parameters from conv_info - unsigned int stride_x = 0; - unsigned int stride_y = 0; + unsigned int stride_x = 0; + unsigned int stride_y = 0; std::tie(stride_x, stride_y) = conv_info.stride(); unsigned int mat_weights_cols = weights->dimension(idx_kernels); @@ -321,9 +322,15 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights // Create tensor to store im2col reshaped inputs if(!_skip_im2col) { + const int block_by = arm_compute::block_by(weights_info.weight_format()); + unsigned int input_pad_right = 0; + if(block_by > 1) + { + input_pad_right = (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by); + } // Configure _im2col_kernel = std::make_unique<kernels::CpuIm2ColKernel>(); - _im2col_kernel->configure(src, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation); + _im2col_kernel->configure(src, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation, num_groups, input_pad_right); // Update GEMM input gemm_input_to_use = &_im2col_output; @@ -399,12 +406,12 @@ Status CpuGemmConv2d::has_opt_impl(arm_compute::WeightFormat &expected_weight_fo const unsigned int kernel_height = weights->dimension(idx_height); unsigned int conv_w = 0; unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - kernel_width, - kernel_height, - conv_info, - dilation); + std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + kernel_width, + kernel_height, + conv_info, + dilation); const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, dilation, act_info); @@ -428,7 +435,7 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32); - if (!is_fixed_format(weights_info.weight_format())) + if(!is_fixed_format(weights_info.weight_format())) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights); } @@ -469,9 +476,9 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight dilation); // Check if GEMM3D is supported - const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, - dilation, act_info); - const bool skip_im2col = skip_info.skip_im2col, skip_col2im = skip_info.skip_col2im; + const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info, + dilation, act_info); + const bool skip_im2col = skip_info.skip_im2col, skip_col2im = skip_info.skip_col2im; ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != src->dimension(idx_channel)); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); @@ -504,6 +511,14 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight if(!skip_im2col) { + const int block_by = arm_compute::block_by(weights_info.weight_format()); + int input_pad_right = 0; + if(block_by > 1) + { + input_pad_right = (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by); + mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * (weights->dimension(idx_channel) + input_pad_right); + } + // Create tensor info for im2col reshaped inputs // For CPU, the batch size is on the fourth dimension TensorShape shape_im2col = src->tensor_shape(); @@ -513,7 +528,7 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type); im2col_reshaped_info.set_quantization_info(src->quantization_info()); - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuIm2ColKernel::validate(src, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, 1)); + ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuIm2ColKernel::validate(src, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, num_groups, input_pad_right)); gemm_input_to_use = &im2col_reshaped_info; } @@ -563,7 +578,7 @@ void CpuGemmConv2d::run(ITensorPack &tensors) { // Run input reshaping unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); - ITensorPack pack = + ITensorPack pack = { { TensorType::ACL_SRC, src }, { TensorType::ACL_DST, im2col_output.get() } @@ -657,7 +672,7 @@ void CpuGemmConv2d::prepare(ITensorPack &tensors) // Run weights reshaping and mark original weights tensor as unused CpuAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors); auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); - ITensorPack pack = + ITensorPack pack = { { TensorType::ACL_SRC, weights }, { TensorType::ACL_DST, weights_reshaped.get() } |