From 2732cca12bac29e1515cee1db5005c73893c61b4 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Tue, 28 May 2019 11:44:41 +0100 Subject: COMPMID-2244: Extend CLFuseBatchNormalization to support DepthwiseConvolution weights Change-Id: I7d1907f35cc4899379073759be2f7cce24e51e9d Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/1327 Reviewed-by: Gian Marco Iodice Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- src/core/CL/CLKernelLibrary.cpp | 2 +- src/core/CL/cl_kernels/batchnormalization_layer.cl | 58 ++++++++----- .../CL/kernels/CLFuseBatchNormalizationKernel.cpp | 95 ++++++++++++---------- 3 files changed, 91 insertions(+), 64 deletions(-) (limited to 'src/core/CL') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 51acd9f339..253da40077 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -298,7 +298,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "finalize", "optical_flow_pyramid_lk.cl" }, { "flatten", "flatten.cl" }, { "floor_layer", "floor.cl" }, - { "fuse_batchnormalization_conv_layer", "batchnormalization_layer.cl" }, + { "fuse_batchnormalization_layer", "batchnormalization_layer.cl" }, { "gather", "gather.cl" }, { "gaussian1x5_sub_x", "gaussian_pyramid.cl" }, { "gaussian5x1_sub_y", "gaussian_pyramid.cl" }, diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl index a5321315d3..918caff212 100644 --- a/src/core/CL/cl_kernels/batchnormalization_layer.cl +++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl @@ -259,12 +259,14 @@ __kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input), } #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/ -#if defined(DIM2) && defined(DATA_TYPE) && defined(EPSILON) -/** OpenCL kernel to fuse the weights of convolution layer with batch normalization when the data layout is either NCHW or NHWC +#if defined(DATA_TYPE) && defined(EPSILON) +/** OpenCL kernel to fuse the weights of convolution or depthwise convolution layer with batch normalization when the data layout is either NCHW or NHWC * * @note The input weights tensor is assumed 4D with the OFMs in the fourth dimension * @note Data type should be passed at compile time using the -DDATA_TYPE, e.g. -DDATA_TYPE=float - * @note The third dimension of the input tensor should be passed at compile time using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16 + * @note The third dimension of the input tensor should be passed at compile time when weights belong to a convolution layer using -DDIM2=size. e.g. -DDIM2=16. + * For depthwise convolution weight do not pass DIM2 + * @note Data layout NHWC should be passed at compile time with -DNHWC. For data layout NCHW it is not required to pass any parameter * @note Batch normalization epsilon parameter should be passed at compile time using -DEPSILON=value. e.g. -DEPSILON=0.001f * * @param[in] w_ptr Pointer to the weights tensor. Supported data types: F16/F32 @@ -312,35 +314,45 @@ __kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input), * @param[in] gamma_step_x (Optional) gamma_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] gamma_offset_first_element_in_bytes (Optional) The offset of the first element in the gamma source tensor */ -__kernel void fuse_batchnormalization_conv_layer(TENSOR3D_DECLARATION(w), +__kernel void fuse_batchnormalization_layer(TENSOR3D_DECLARATION(w), #if defined(BIAS) - VECTOR_DECLARATION(b), + VECTOR_DECLARATION(b), #endif // defined(BIAS) - VECTOR_DECLARATION(mean), - VECTOR_DECLARATION(var) + VECTOR_DECLARATION(mean), + VECTOR_DECLARATION(var) #ifndef IN_PLACE_W - , - TENSOR3D_DECLARATION(w_fused) + , + TENSOR3D_DECLARATION(w_fused) #endif // ifndef IN_PLACE_W #ifndef IN_PLACE_B - , - VECTOR_DECLARATION(b_fused) + , + VECTOR_DECLARATION(b_fused) #endif // ifndef IN_PLACE_B #if defined(BETA) - , - VECTOR_DECLARATION(beta) + , + VECTOR_DECLARATION(beta) #endif // defined(BETA) #if defined(GAMMA) - , - VECTOR_DECLARATION(gamma) + , + VECTOR_DECLARATION(gamma) #endif // defined(GAMMA) - ) + ) { - int x = get_global_id(0); - int y = get_global_id(1); - int z = get_global_id(2); + int x = get_global_id(0); + int y = get_global_id(1); + int z = get_global_id(2); + +#if defined(DIM2) int c0 = z % DIM2; int c1 = z / DIM2; +#else // ! defined(DIM2) + int c0 = 0; +#if defined(NHWC) + int c1 = x; +#else // defined(NHWC) + int c1 = z; +#endif // defined(NHWC) +#endif // defined(DIM2) int w_offset = x * sizeof(DATA_TYPE) + y * w_stride_y + z * w_stride_z; int v_offset = c1 * sizeof(DATA_TYPE); @@ -368,11 +380,15 @@ __kernel void fuse_batchnormalization_conv_layer(TENSOR3D_DECLARATION(w), #if defined(IN_PLACE_W) *((__global DATA_TYPE *)(w_ptr + w_offset + w_offset_first_element_in_bytes)) = w_new; #else // defined(IN_PLACE_W) - *((__global DATA_TYPE *)(w_fused_ptr + w_offset + w_fused_offset_first_element_in_bytes)) = w_new; + *((__global DATA_TYPE *)(w_fused_ptr + w_offset + w_fused_offset_first_element_in_bytes)) = w_new; #endif // defined(IN_PLACE_W) // Compute bias +#if !defined(DIM2) && defined(NHWC) + if(z == 0 && y == 0) +#else !defined(DIM2) && defined(NHWC) if(x == 0 && y == 0 && c0 == 0) +#endif // !defined(DIM2) && defined(NHWC) { #if defined(BIAS) b_old = *((__global DATA_TYPE *)(b_ptr + v_offset + b_offset_first_element_in_bytes)); @@ -400,4 +416,4 @@ __kernel void fuse_batchnormalization_conv_layer(TENSOR3D_DECLARATION(w), #endif // defined(BIAS) } } -#endif // defined(DIM2) && defined(DATA_TYPE) && defined(EPSILON) \ No newline at end of file +#endif // defined(DATA_TYPE) && defined(EPSILON) \ No newline at end of file diff --git a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp index 16ad7d970e..bf827bf2c2 100644 --- a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp +++ b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp @@ -38,50 +38,60 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, +Status validate_arguments(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, - const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, - float epsilon) + const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, + float epsilon, FuseBatchNormalizationType fbn_type) { ARM_COMPUTE_UNUSED(epsilon); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(conv_weights); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(conv_weights, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input_weights); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_weights, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_mean, bn_var); - ARM_COMPUTE_RETURN_ERROR_ON(conv_bias == nullptr && fused_bias == nullptr); - ARM_COMPUTE_RETURN_ERROR_ON(conv_weights->dimension(3) != bn_mean->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_mean, bn_var); + ARM_COMPUTE_RETURN_ERROR_ON(input_bias == nullptr && fused_bias == nullptr); ARM_COMPUTE_RETURN_ERROR_ON(bn_mean->num_dimensions() > 1); + if(fbn_type == FuseBatchNormalizationType::CONVOLUTION) + { + ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(3) != bn_mean->dimension(0)); + } + else + { + const size_t channel_idx = get_data_layout_dimension_index(input_weights->data_layout(), DataLayoutDimension::CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(channel_idx) != bn_mean->dimension(0)); + } + // Validate bias - if(conv_bias != nullptr) + if(input_bias != nullptr) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, conv_bias); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, conv_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, input_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, input_bias); } // Validate beta if(bn_beta != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_beta); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_beta); } // Validate gamma if(bn_gamma != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_gamma); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_gamma); } // Validate output weights if(fused_weights != nullptr && fused_weights->total_size() != 0) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(conv_weights, fused_weights); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(conv_weights, fused_weights); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_weights, fused_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input_weights, fused_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_weights); } // Validate output bias if(fused_bias != nullptr && fused_bias->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, fused_bias); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_bias); } return Status{}; @@ -89,20 +99,20 @@ Status validate_arguments(const ITensorInfo *conv_weights, const ITensorInfo *bn } // namespace CLFuseBatchNormalizationKernel::CLFuseBatchNormalizationKernel() - : _conv_weights(nullptr), _conv_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(), + : _input_weights(nullptr), _input_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(), _run_in_place_weights(false), _run_in_place_bias(false) { } -void CLFuseBatchNormalizationKernel::configure(const ICLTensor *conv_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, +void CLFuseBatchNormalizationKernel::configure(const ICLTensor *input_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, ICLTensor *fused_weights, ICLTensor *fused_bias, - const ICLTensor *conv_bias, const ICLTensor *bn_beta, const ICLTensor *bn_gamma, - float epsilon) + const ICLTensor *input_bias, const ICLTensor *bn_beta, const ICLTensor *bn_gamma, + float epsilon, FuseBatchNormalizationType fbn_type) { - ARM_COMPUTE_ERROR_ON_NULLPTR(conv_weights, bn_mean, bn_var); + ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); - _conv_weights = conv_weights; - _conv_bias = conv_bias; + _input_weights = input_weights; + _input_bias = input_bias; _bn_mean = bn_mean; _bn_var = bn_var; _bn_beta = bn_beta; @@ -111,14 +121,14 @@ void CLFuseBatchNormalizationKernel::configure(const ICLTensor *conv_weights, co _fused_bias = fused_bias; _epsilon = epsilon; - _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); - _run_in_place_bias = (conv_bias != nullptr && fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias); + _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == input_weights); + _run_in_place_bias = (input_bias != nullptr && fused_bias == nullptr) || (input_bias != nullptr && fused_bias == input_bias); // Auto initialize outputs if(_fused_weights != nullptr) { // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*_fused_weights->info(), *_conv_weights->info()->clone()); + auto_init_if_empty(*_fused_weights->info(), *_input_weights->info()->clone()); } if(_fused_bias != nullptr) { @@ -127,39 +137,40 @@ void CLFuseBatchNormalizationKernel::configure(const ICLTensor *conv_weights, co } // Validate arguments - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(conv_weights->info(), bn_mean->info(), bn_var->info(), + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_weights->info(), bn_mean->info(), bn_var->info(), (fused_weights != nullptr) ? fused_weights->info() : nullptr, (fused_bias != nullptr) ? fused_bias->info() : nullptr, - (conv_bias != nullptr) ? conv_bias->info() : nullptr, + (input_bias != nullptr) ? input_bias->info() : nullptr, (bn_beta != nullptr) ? bn_beta->info() : nullptr, (bn_gamma != nullptr) ? bn_gamma->info() : nullptr, - epsilon)); + epsilon, fbn_type)); // Configure kernel window - Window win = calculate_max_window(*conv_weights->info()); + Window win = calculate_max_window(*input_weights->info()); ICLKernel::configure_internal(win); // Set build options CLBuildOptions build_opts; - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(conv_weights->info()->data_type())); - build_opts.add_option("-DDIM2=" + support::cpp11::to_string(conv_weights->info()->dimension(2))); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input_weights->info()->data_type())); + build_opts.add_option_if(fbn_type == FuseBatchNormalizationType::CONVOLUTION, "-DDIM2=" + support::cpp11::to_string(input_weights->info()->dimension(2))); build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon)); + build_opts.add_option_if(_input_weights->info()->data_layout() == DataLayout::NHWC, "-DNHWC"); build_opts.add_option_if(_run_in_place_weights, "-DIN_PLACE_W"); build_opts.add_option_if(_run_in_place_bias, "-DIN_PLACE_B"); - build_opts.add_option_if(conv_bias != nullptr, "-DBIAS"); + build_opts.add_option_if(input_bias != nullptr, "-DBIAS"); build_opts.add_option_if(bn_beta != nullptr, "-DBETA"); build_opts.add_option_if(bn_gamma != nullptr, "-DGAMMA"); // Create kernel - _kernel = static_cast(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_conv_layer", build_opts.options())); + _kernel = static_cast(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_layer", build_opts.options())); } -Status CLFuseBatchNormalizationKernel::validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, +Status CLFuseBatchNormalizationKernel::validate(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, - const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, - float epsilon) + const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, + float epsilon, FuseBatchNormalizationType fbn_type) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_weights, bn_mean, bn_var, fused_weights, fused_bias, input_bias, bn_beta, bn_gamma, epsilon, fbn_type)); return Status{}; } @@ -175,10 +186,10 @@ void CLFuseBatchNormalizationKernel::run(const arm_compute::Window &window, cl:: // Add kernel arguments unsigned int idx = 0; - add_3D_tensor_argument(idx, _conv_weights, slice_3d); - if(_conv_bias != nullptr) + add_3D_tensor_argument(idx, _input_weights, slice_3d); + if(_input_bias != nullptr) { - add_1D_tensor_argument(idx, _conv_bias, slice_1d); + add_1D_tensor_argument(idx, _input_bias, slice_1d); } add_1D_tensor_argument(idx, _bn_mean, slice_1d); add_1D_tensor_argument(idx, _bn_var, slice_1d); -- cgit v1.2.1