From a25d16c86f0d870408bc8b941aa755093417b0f0 Mon Sep 17 00:00:00 2001 From: Vidhya Sudhan Loganathan Date: Fri, 16 Nov 2018 11:33:12 +0000 Subject: COMPMID-1266 : Add support for FP16 in CLWinogradConvolutionLayer: 5x5 kernels Introduced F32 accumulation for F16 winograd gemm and output transform WinogradConvolution will be available for F16 only if fast math flag is enabled Change-Id: I215593c205236a0f9669218437bb40b184ec6a4f --- src/runtime/CL/functions/CLGEMM.cpp | 5 +++-- src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp | 13 ++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) (limited to 'src/runtime/CL') diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp index 6adbdc0cb6..baa0cf46dc 100644 --- a/src/runtime/CL/functions/CLGEMM.cpp +++ b/src/runtime/CL/functions/CLGEMM.cpp @@ -155,7 +155,8 @@ void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor * // Configure and tune matrix multiply kernel _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, - depth_output_gemm3d, reinterpret_input_as_3d)); + depth_output_gemm3d, reinterpret_input_as_3d), + gemm_info.fp_mixed_precision()); CLScheduler::get().tune_kernel_static(_mm_kernel); if(_is_interleaved_transposed) @@ -236,7 +237,7 @@ Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso } // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output, alpha, run_interleave_transpose, reshape_info, gpu_target)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output, alpha, run_interleave_transpose, reshape_info, gpu_target, gemm_info.fp_mixed_precision())); if(beta != 0 && c != nullptr) { diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 70bf3ae593..1abcb67132 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -104,9 +104,9 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we // Check if the Winograd configuration requires fast math if(!enable_fast_math) { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false. ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); } - const WinogradInfo winograd_info = WinogradInfo(output_tile, kernel_size, input_dims, @@ -129,7 +129,8 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we _filter_transform.configure(weights, &_input1, winograd_info); // Configure batched matrix multiply - _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, GEMMLowpOutputStageInfo(), + (input->info()->data_type() == DataType::F16))); // Configure output transform _output_transform.configure(&_batched_mm_output, biases, output, winograd_info); @@ -158,13 +159,10 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]); const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, input->data_layout()); - //FP16 implementation of winograd is slower than direct convolution. - //The following check needs to be removed when fp16 winograd is faster than direct convolution (COMPMID-1266) - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - // Check if the Winograd configuration requires fast math if(!enable_fast_math) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false. ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); } @@ -188,7 +186,8 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen TensorShape batched_mm_output_shape = input0.tensor_shape(); batched_mm_output_shape[0] = input1.tensor_shape()[0]; const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/))); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, + GEMMLowpOutputStageInfo(), (input->data_type() == DataType::F16)))); // Configure output transform ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info)); -- cgit v1.2.1