From d051e97e36b9981f411093904cc019c2c7f9ac75 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Wed, 20 Jun 2018 11:46:42 +0100 Subject: COMPMID-811 Add NHWC data format support for CL depthwise convolution Change-Id: I574f7945f0be009c638d860028bce8b52b4120fd Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/136484 Tested-by: Jenkins Reviewed-by: Gian Marco Iodice --- src/core/CL/CLKernelLibrary.cpp | 2 + src/core/CL/cl_kernels/depthwise_convolution.cl | 363 ++++++++++++++++++++- .../direct_convolution_1x1_3x3_5x5_quantized.cl | 5 + .../CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp | 171 +++++++--- src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp | 13 +- .../CL/kernels/CLDepthwiseVectorToTensorKernel.cpp | 17 +- .../CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp | 25 +- .../CLDirectConvolutionOutputStageKernel.cpp | 1 + 8 files changed, 520 insertions(+), 77 deletions(-) (limited to 'src/core/CL') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 2bcacad7f0..fb688b5ee9 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -197,6 +197,8 @@ const std::map CLKernelLibrary::_kernel_program_map = { "deconvolution_upsample", "deconvolution_layer.cl" }, { "depthwise_convolution_3x3", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" }, + { "depthwise_convolution_3x3_nhwc", "depthwise_convolution.cl" }, + { "depthwise_convolution_3x3_nhwc_stride1", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_quantized_nchw", "depthwise_convolution_quantized.cl" }, { "depthwise_convolution_3x3_quantized_nhwc_stride1", "depthwise_convolution_quantized.cl" }, { "depthwise_convolution_3x3_quantized_nhwc_stride2", "depthwise_convolution_quantized.cl" }, diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl index 5f4247e5d3..f3aa0d6dd8 100644 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ b/src/core/CL/cl_kernels/depthwise_convolution.cl @@ -451,6 +451,22 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32( #endif // defined(DEPTH_MULTIPLIER) +#if defined(NCHW) +#define in_stride_x src_stride_x +#define in_stride_y src_stride_y +#define in_stride_z src_stride_z +#define out_stride_x dst_stride_x +#define out_stride_y dst_stride_y +#define out_stride_z dst_stride_z +#else //defined(NCHW) +#define in_stride_x src_stride_y +#define in_stride_y src_stride_z +#define in_stride_z src_stride_x +#define out_stride_x dst_stride_y +#define out_stride_y dst_stride_z +#define out_stride_z dst_stride_x +#endif //defined(NCHW) + #if defined(SRC_WIDTH) && defined(DATA_TYPE) /** This kernel reshapes each of the tensor's low three dimensions to single rows. * @@ -484,17 +500,16 @@ __kernel void depthwise_weights_reshape( #endif /* HAS_BIAS */ ) { - Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* HAS_BIAS */ - __global DATA_TYPE *input_ptr = (__global DATA_TYPE *)src.ptr; - __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y; + __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + get_global_id(1) * in_stride_y + get_global_id(2) * in_stride_z; + __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y; - for(int i = 0; i < SRC_WIDTH; ++i, ++input_ptr) + for(int i = 0; i < SRC_WIDTH; ++i, input_ptr += in_stride_x) { - *((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *input_ptr; + *((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *((__global DATA_TYPE *)input_ptr); } #if defined(HAS_BIAS) @@ -541,7 +556,7 @@ __kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(d const int src_y = -PAD_TOP + src_pixel_linear / max_initial_x * STRIDE_Y; const int src_z = get_global_id(2) / DEPTH_MULTIPLIER; - __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * src_stride_z; + __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * in_stride_z; __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst.ptr)); for(int y = src_y; y < src_y + KERNEL_HEIGHT; ++y) @@ -554,7 +569,7 @@ __kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(d } else { - *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); + *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * in_stride_x + y * in_stride_y)); } } } @@ -596,7 +611,7 @@ __kernel void depthwise_vector_to_tensor( const int z = id0 / patch_size; const int index2D = id0 - z * patch_size; - __global uchar *out_ptr = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * dst_stride_x + index2D / CONV_WIDTH * dst_stride_y + z * dst_stride_z; + __global uchar *out_ptr = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * out_stride_x + index2D / CONV_WIDTH * out_stride_y + z * out_stride_z; *((__global DATA_TYPE *)out_ptr) = *((__global DATA_TYPE *)src.ptr); } @@ -980,3 +995,335 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( vstore4(pixels1, 0, (__global half *)(dst.ptr + 1 * dst_stride_y)); } #endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) + +#if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) + +#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) + +#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) +/** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1. + * + * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) + * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) + * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) + * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) + * + * @param[in] src_ptr Pointer to the source image. Supported data types: FP32 + * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: QASYMM8 + * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor + * @param[in] max_offset Max offset for the input tensor + * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr + * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector + */ +__kernel void depthwise_convolution_3x3_nhwc( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights), +#if defined(HAS_BIAS) + VECTOR_DECLARATION(biases), +#endif /* defined(HAS_BIAS) */ + int max_offset) +{ + int x = get_global_id(0); // channels + int y = get_global_id(1); // spatial coordinate x + int z = get_global_id(2); // spatial coordinate y + + Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); + + __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float) * VEC_SIZE; + + int z_coord = 0; + int4 offset = 0; + int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, 1, 2, 3) - CONV_PAD_LEFT) * (int4)src_stride_y; + + // We compute 2x1x1 [C,W,H] elements + VEC_FLOAT acc = 0; + + // Load weights + VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z)); + VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z)); + VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z)); + + // Load input values + // z == 0 + // Clamp z_coord as for z = 0, it can be negative + // z_coord is casted to unsigned int in order to use just a min() operation + // A "-1" 32 bit signed variable converted to unsigned gives 4294967295 + z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; + z_coord = min((uint)z_coord, (uint)SRC_DIM_2); + offset = y_offset + (int4)(z_coord * src_stride_z); + offset = min(offset, max_offset); + + VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + + // z == 1 + // z_coord can be only negative for z = 0 so we do not need to clamp it + // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset + z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + 1; + offset = y_offset + (int4)(z_coord * src_stride_z); + VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + + // z == 2 + // After z = 1 we can simply add src_stride_z to offset without updating z_coord + // However offset can be out-of-bound so we need to check if it is greater than max_offset + offset += (int4)src_stride_z; + offset = min(offset, max_offset); + VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + + acc = fma(values0, w0, acc); + acc = fma(values1, w1, acc); + acc = fma(values2, w2, acc); + + acc = fma(values3, w3, acc); + acc = fma(values4, w4, acc); + acc = fma(values5, w5, acc); + + acc = fma(values6, w6, acc); + acc = fma(values7, w7, acc); + acc = fma(values8, w8, acc); + +#if defined(HAS_BIAS) + Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); + VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global float *)biases.ptr); + acc += bias_values; +#endif // defined(HAS_BIAS) + + Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); + VSTORE(VEC_SIZE) + (acc, 0, (__global float *)(dst.ptr)); +} +#endif // defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) + +#if defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) +/** This function computes the depthwise convolution for NHWC data layout when the stride along the width and height is 1. + * + * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) + * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) + * @note The number of rows processed per thread must be passed at compile time using -DNUM_ROWS_PROCESSED (i.e. -DNUM_ROWS_PROCESSED=2) + * @note The number of planes processed per thread must be passed at compile time using -DNUM_PLANES_PROCESSED (i.e. -DNUM_PLANES_PROCESSED=2) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) + * + * @param[in] src_ptr Pointer to the source image. Supported data types: FP32 + * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: QASYMM8 + * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor + * @param[in] max_offset Max offset for the input tensor + * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr + * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector + */ +__kernel void depthwise_convolution_3x3_nhwc_stride1( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights), +#if defined(HAS_BIAS) + VECTOR_DECLARATION(biases), +#endif /* defined(HAS_BIAS) */ + int max_offset) +{ + int x = get_global_id(0); // channels + int y = get_global_id(1); // spatial coordinate x + int z = get_global_id(2); // spatial coordinate y + + Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); + + __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float) * VEC_SIZE; + + int z_coord = 0; + int4 offset = 0; + int4 y_offset = ((int4)(y * NUM_ROWS_PROCESSED) + (int4)(0, 1, 2, 3) - CONV_PAD_LEFT) * (int4)src_stride_y; + + // We compute 2x2x2 [C,W,H] elements + VEC_FLOAT acc0 = 0; + VEC_FLOAT acc1 = 0; + VEC_FLOAT acc2 = 0; + VEC_FLOAT acc3 = 0; + + // Load weights + VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z)); + VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z)); + VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z)); + VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z)); + VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z)); + + // Load input values + // z == 0 + // Clamp z_coord as for z = 0, it can be negative + // z_coord is casted to unsigned int in order to use just a min() operation + // A "-1" 32 bit signed variable converted to unsigned gives 4294967295 + z_coord = z * NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP; + z_coord = min((uint)z_coord, (uint)SRC_DIM_2); + offset = y_offset + (int4)(z_coord * src_stride_z); + offset = min(offset, max_offset); + + VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3)); + + // z == 1 + // z_coord can be only negative for z = 0 so we do not need to clamp it + // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset + z_coord = z * NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP + 1; + offset = y_offset + (int4)(z_coord * src_stride_z); + VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3)); + + // z == 2 + // After z = 1 we can simply add src_stride_z to offset without updating z_coord + // However offset can be out-of-bound so we need to check if it is greater than max_offset + offset += (int4)src_stride_z; + offset = min(offset, max_offset); + VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values9 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values10 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + VEC_FLOAT values11 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3)); + + // z == 3 + // After z = 1 we can simply add src_stride_z to offset without updating z_coord + // However offset can be out-of-bound so we need to check if it is greater than max_offset + offset += (int4)(src_stride_z); + offset = min(offset, max_offset); + VEC_FLOAT values12 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0)); + VEC_FLOAT values13 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1)); + VEC_FLOAT values14 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2)); + VEC_FLOAT values15 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3)); + + acc0 = fma(values0, w0, acc0); + acc0 = fma(values1, w1, acc0); + acc0 = fma(values2, w2, acc0); + acc1 = fma(values1, w0, acc1); + acc1 = fma(values2, w1, acc1); + acc1 = fma(values3, w2, acc1); + + acc0 = fma(values4, w3, acc0); + acc0 = fma(values5, w4, acc0); + acc0 = fma(values6, w5, acc0); + acc1 = fma(values5, w3, acc1); + acc1 = fma(values6, w4, acc1); + acc1 = fma(values7, w5, acc1); + + acc0 = fma(values8, w6, acc0); + acc0 = fma(values9, w7, acc0); + acc0 = fma(values10, w8, acc0); + acc1 = fma(values9, w6, acc1); + acc1 = fma(values10, w7, acc1); + acc1 = fma(values11, w8, acc1); + + acc2 = fma(values4, w0, acc2); + acc2 = fma(values5, w1, acc2); + acc2 = fma(values6, w2, acc2); + acc3 = fma(values5, w0, acc3); + acc3 = fma(values6, w1, acc3); + acc3 = fma(values7, w2, acc3); + + acc2 = fma(values8, w3, acc2); + acc2 = fma(values9, w4, acc2); + acc2 = fma(values10, w5, acc2); + acc3 = fma(values9, w3, acc3); + acc3 = fma(values10, w4, acc3); + acc3 = fma(values11, w5, acc3); + + acc2 = fma(values12, w6, acc2); + acc2 = fma(values13, w7, acc2); + acc2 = fma(values14, w8, acc2); + acc3 = fma(values13, w6, acc3); + acc3 = fma(values14, w7, acc3); + acc3 = fma(values15, w8, acc3); + +#if defined(HAS_BIAS) + Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); + + VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global float *)biases.ptr); + + acc0 += bias_values; + acc1 += bias_values; + acc2 += bias_values; + acc3 += bias_values; +#endif // defined(HAS_BIAS) + + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z; + + VSTORE(VEC_SIZE) + (acc0, 0, (__global float *)(dst_addr + 0 * dst_stride_y)); + VSTORE(VEC_SIZE) + (acc1, 0, (__global float *)(dst_addr + 1 * dst_stride_y)); + +#if((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) + if((z * NUM_PLANES_PROCESSED + 1) < DST_DIM_2) +#endif // ((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) + { + VSTORE(VEC_SIZE) + (acc2, 0, (__global float *)(dst_addr + 0 * dst_stride_y + 1 * dst_stride_z)); + VSTORE(VEC_SIZE) + (acc3, 0, (__global float *)(dst_addr + 1 * dst_stride_y + 1 * dst_stride_z)); + } +} + +#endif // defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) +#endif // defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) \ No newline at end of file diff --git a/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl index b58dc7af72..ae87420774 100644 --- a/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl +++ b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl @@ -296,7 +296,12 @@ __kernel void output_stage_quantized( #if defined(HAS_BIAS) // Load and add bias +#if defined(NCHW) int bias_value = *((__global int *)(vector_offset(&bias, get_global_id(2)))); +#else // defined(NCHW) + int16 bias_value = vload16(0, ((__global int *)(vector_offset(&bias, get_global_id(0) * 16)))); +#endif // defined(NCHW) + vals += (int16)(bias_value); #endif //defined(HAS_BIAS) diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp index d24ef0f496..1de08aa1a2 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp @@ -44,18 +44,27 @@ namespace Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU), - "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported"); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::F32 || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU))), + "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported"); //COMPMID-1317 add fused activation for F32 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); // COMPMID-1071 Add depth multiplier support for NHWC ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != 3 || weights->dimension(2) != 3); + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); + if(biases != nullptr) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + } ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0)); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } @@ -72,12 +81,23 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &conv_info) { - const unsigned int num_rows_processed_per_iteration = 4; - const unsigned int num_elems_accessed_per_iteration = 4; + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); + const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + + const unsigned int num_rows_processed_per_iteration = is_qasymm ? 4 : (is_stride_1 ? 2 : 1); + const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : 2; const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2; - const unsigned int num_rows_written_per_iteration = num_rows_processed_per_iteration / conv_info.stride().first; + const unsigned int num_rows_written_per_iteration = std::ceil(num_rows_processed_per_iteration / static_cast(conv_info.stride().first)); - const BorderSize border_size(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0); + BorderSize border_size; + if(is_qasymm) + { + border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0); + } + else + { + border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); + } // Configure kernel window Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration)); @@ -103,7 +123,7 @@ std::pair validate_and_configure_window(ITensorInfo *input, ITen } // namespace CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel() - : _num_rows_processed_per_iteration(1) + : _num_rows_processed_per_iteration(1), _num_planes_processed_per_iteration(1) { } @@ -135,66 +155,97 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 2); ARM_COMPUTE_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 1); - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _conv_stride_y = conv_info.stride().second; - _num_rows_processed_per_iteration = 4; + const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); + const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); - const unsigned int num_elems_accessed_per_iteration = 4; + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _conv_stride_y = conv_info.stride().second; + _num_rows_processed_per_iteration = is_qasymm ? 4 : (is_stride_1 ? 2 : 1); + _num_planes_processed_per_iteration = (is_stride_1 && !is_qasymm) ? 2 : 1; - _border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0); + const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : 2; - float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale; - int output_multiplier = 0; - int output_shift = 0; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + if(is_qasymm) + { + _border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0); + } + else + { + _border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); + } CLBuildOptions build_opts; build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset)); - build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset)); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration)); - build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1))); build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2))); build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - if(act_info.enabled()) + if(is_qasymm) { - const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); - const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); - const int o1 = input->info()->quantization_info().offset; - - build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation()))); - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - if(output != nullptr) + float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale; + int output_multiplier = 0; + int output_shift = 0; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1))); + build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset)); + build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset)); + build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset)); + build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset)); + build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); + build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); + + if(act_info.enabled()) { - const float s1 = input->info()->quantization_info().scale; - const float s2 = output->info()->quantization_info().scale; - const int o2 = output->info()->quantization_info().offset; + const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); + const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + const int o1 = input->info()->quantization_info().offset; + + build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation()))); + build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); + build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); + build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - if(o1 != o2 || s1 != s2) + if(output != nullptr) { - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2)); + const float s1 = input->info()->quantization_info().scale; + const float s2 = output->info()->quantization_info().scale; + const int o2 = output->info()->quantization_info().offset; + + if(o1 != o2 || s1 != s2) + { + build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); + build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2)); + build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); + build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2)); + } } } } + else if(is_stride_1) + { + build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(_num_rows_processed_per_iteration)); + build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration)); + build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2))); + } + else + { + build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_stride_x)); + build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); + } // Create kernel - std::string kernel_name = std::string("depthwise_convolution_3x3_quantized_nhwc_stride") + support::cpp11::to_string(conv_stride_x); - _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + std::string kernel_name = std::string("depthwise_convolution_3x3") + (is_qasymm ? std::string("_quantized") : std::string()) + std::string("_nhwc"); + if(is_qasymm || is_stride_1) + { + kernel_name += std::string("_stride") + support::cpp11::to_string(conv_stride_x); + } + + _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Configure kernel window auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info); @@ -213,6 +264,8 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += "_"; + _config_id += string_from_data_type(input->info()->data_type()); } Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, @@ -233,15 +286,18 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::Com ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + Window win = window; + win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast(_num_planes_processed_per_iteration)), 1)); + // Create input window and adjust - Window win_in = window; + Window win_in = win; win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration); win_in.set_dimension_step(Window::DimZ, _conv_stride_y); ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) || (win_in.z().step() < window.z().step())); Window slice_in = win_in.first_slice_window_3D(); - Window slice_out = window.first_slice_window_3D(); + Window slice_out = win.first_slice_window_3D(); if(_biases != nullptr) { @@ -252,6 +308,15 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::Com add_1D_tensor_argument(idx, _biases, win_biases); } + if(!(is_data_type_quantized_asymmetric(_input->info()->data_type()))) + { + unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0); + const int max_offset = _input->info()->strides_in_bytes().z() * _input->info()->dimension(2) - (_input->info()->padding().bottom + _input->info()->padding().top) * + _input->info()->strides_in_bytes().y(); + + _kernel.setArg(idx, max_offset); + } + do { unsigned int idx = 0; diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp index c89b16eedc..bef13f9b1c 100644 --- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp @@ -47,13 +47,15 @@ namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier) { + const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); + ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); 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, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && has_bias); - ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2)); + ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != output->dimension(2)); ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0))); return Status{}; @@ -68,6 +70,10 @@ void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *outpu _input = input; _output = output; + const DataLayout data_layout = input->info()->data_layout(); + const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + // Create kernel CLBuildOptions build_opts; @@ -78,11 +84,12 @@ void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *outpu build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right())); build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom())); - build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); - build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_w))); + build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_h))); build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width)); build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height)); build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout())); build_opts.add_option_if(has_bias, "-DHAS_BIAS"); build_opts.add_option_if_else(is_data_type_quantized_asymmetric(input->info()->data_type()), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), diff --git a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp index 0d158f1dab..c97ecaf8e0 100644 --- a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp @@ -37,12 +37,16 @@ using namespace arm_compute; namespace { -TensorShape compute_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h) +TensorShape compute_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h, const DataLayout &data_layout) { + const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + TensorShape output_shape(input); - output_shape.set(0, conv_w); - output_shape.set(1, conv_h); - output_shape.set(2, input.x() / (conv_w * conv_h)); + output_shape.set(idx_w, conv_w); + output_shape.set(idx_h, conv_h); + output_shape.set(idx_c, input.x() / (conv_w * conv_h)); return output_shape; } @@ -54,7 +58,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, s if(output->total_size() != 0) { - TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h); + TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h, output->data_layout()); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); @@ -74,7 +78,7 @@ void CLDepthwiseVectorToTensorKernel::configure(const ICLTensor *input, ICLTenso ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized - TensorShape output_shape = compute_output_shape(input->info()->tensor_shape(), conv_w, conv_h); + TensorShape output_shape = compute_output_shape(input->info()->tensor_shape(), conv_w, conv_h, output->info()->data_layout()); auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), conv_w, conv_h)); @@ -87,6 +91,7 @@ void CLDepthwiseVectorToTensorKernel::configure(const ICLTensor *input, ICLTenso build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); build_opts.add_option("-DCONV_WIDTH=" + support::cpp11::to_string(conv_w)); build_opts.add_option("-DCONV_HEIGHT=" + support::cpp11::to_string(conv_h)); + build_opts.add_option("-D" + string_from_data_layout(output->info()->data_layout())); _kernel = static_cast(CLKernelLibrary::get().create_kernel("depthwise_vector_to_tensor", build_opts.options())); diff --git a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp index 59c45adc72..fd3b75484a 100644 --- a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp @@ -39,19 +39,23 @@ namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases) { + const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); 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, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (biases != nullptr)); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(1)); - ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(0) * input->dimension(1) + ((biases != nullptr) ? 1 : 0))); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_c) != output->dimension(1)); + ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(idx_w) * input->dimension(idx_h) + ((biases != nullptr) ? 1 : 0))); if(biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != input->dimension(2)); + ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != input->dimension(idx_c)); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } @@ -73,11 +77,14 @@ void CLDepthwiseWeightsReshapeKernel::configure(const ICLTensor *input, ICLTenso _biases = biases; _output = output; + const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); + // Create kernel std::set build_opts; build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); + build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_w))); + build_opts.emplace("-D" + string_from_data_layout(input->info()->data_layout())); if(_biases != nullptr) { build_opts.emplace("-DHAS_BIAS"); @@ -107,10 +114,14 @@ void CLDepthwiseWeightsReshapeKernel::run(const Window &window, cl::CommandQueue Window slice = window.first_slice_window_3D(); Window slice_out = window.first_slice_window_2D(); + const size_t idx_w = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::CHANNEL); + // Setup slice - slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0))); - slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), 1)); - slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), 1)); + slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(idx_w), _input->info()->dimension(idx_w))); + slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(idx_h), 1)); + slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(idx_c), 1)); // Setup output slice // The first two dimensions of the output are increased by the inner loops diff --git a/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp index 1a6dc14850..c6147ee318 100644 --- a/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp +++ b/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp @@ -168,6 +168,7 @@ void CLDirectConvolutionLayerOutputStageKernel::configure(ICLTensor *input, cons // Create kernel CLBuildOptions build_opts; build_opts.add_option_if(bias != nullptr, "-DHAS_BIAS"); + build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout())); _kernel = static_cast(CLKernelLibrary::get().create_kernel("output_stage_quantized", build_opts.options())); // Set static kernel arguments -- cgit v1.2.1