From c93691717a6e7ca67e32b4dedd233b8c63b6daf2 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Wed, 26 Sep 2018 11:25:40 +0100 Subject: COMPMID-1523: Fuse BN node with convolution. Change-Id: I146936c9e98b343496a4b61cdbadf0eaa38e885a Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154008 Reviewed-by: Michele DiGiorgio Reviewed-by: Giuseppe Rossini Tested-by: bsgcomp --- arm_compute/core/CL/CLKernels.h | 1 + .../CL/kernels/CLFuseBatchNormalizationKernel.h | 101 ++++++++++ arm_compute/runtime/CL/CLFunctions.h | 1 + .../CL/functions/CLFuseBatchNormalization.h | 93 +++++++++ src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/batchnormalization_layer.cl | 162 ++++++++++++++- .../CL/kernels/CLFuseBatchNormalizationKernel.cpp | 221 +++++++++++++++++++++ .../CL/functions/CLFuseBatchNormalization.cpp | 59 ++++++ tests/validation/CL/BatchNormalizationLayer.cpp | 47 ++++- .../BatchNormalizationLayerFusionFixture.h | 186 +++++++++++++++++ .../reference/BatchNormalizationLayer.cpp | 1 - 11 files changed, 861 insertions(+), 12 deletions(-) create mode 100644 arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h create mode 100644 arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h create mode 100644 src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp create mode 100644 src/runtime/CL/functions/CLFuseBatchNormalization.cpp create mode 100644 tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h index f6759b9f1e..95b6f9b70f 100644 --- a/arm_compute/core/CL/CLKernels.h +++ b/arm_compute/core/CL/CLKernels.h @@ -65,6 +65,7 @@ #include "arm_compute/core/CL/kernels/CLFillBorderKernel.h" #include "arm_compute/core/CL/kernels/CLFlattenLayerKernel.h" #include "arm_compute/core/CL/kernels/CLFloorKernel.h" +#include "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h" #include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" diff --git a/arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h b/arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h new file mode 100644 index 0000000000..05a57c171e --- /dev/null +++ b/arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef __ARM_COMPUTE_CLFUSEBATCHNORMALIZATIONKERNEL_H__ +#define __ARM_COMPUTE_CLFUSEBATCHNORMALIZATIONKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +// Forward declarations +class ICLTensor; + +/** OpenCL kernel to fuse the batch normalization node to a preceding convolution node */ +class CLFuseBatchNormalizationKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLFuseBatchNormalizationKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLFuseBatchNormalizationKernel(const CLFuseBatchNormalizationKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLFuseBatchNormalizationKernel &operator=(const CLFuseBatchNormalizationKernel &) = delete; + /** Allow instances of this class to be moved */ + CLFuseBatchNormalizationKernel(CLFuseBatchNormalizationKernel &&) = default; + /** Allow instances of this class to be moved */ + CLFuseBatchNormalizationKernel &operator=(CLFuseBatchNormalizationKernel &&) = default; + /** Default destructor */ + ~CLFuseBatchNormalizationKernel() = default; + /** Set the source, destination of the kernel + * + * @param[in] conv_weights Convolution layer weights tensor. Data type supported: F16/F32 + * @param[in] bn_mean Batch normalization layer mean tensor. Same as @p conv_weights + * @param[in] bn_var Batch normalization layer variance tensor. Same as @p conv_weights + * @param[out] fused_weights Output fused weights tensor. Same as @p conv_weights + * @param[out] fused_bias Output fused bias tensor. Same as @p conv_weights + * @param[in] conv_bias (Optional) Convolution layer bias tensor. Same as @p conv_weights + * @param[in] bn_beta (Optional) Batch normalization layer beta tensor. Same as @p conv_weights + * @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor. Same as @p conv_weights + * @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f. + */ + void configure(const ICLTensor *conv_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, ICLTensor *fused_weights, ICLTensor *fused_bias, + const ICLTensor *conv_bias = nullptr, const ICLTensor *bn_beta = nullptr, const ICLTensor *bn_gamma = nullptr, + float epsilon = 0.001f); + /** Static function to check if given info will lead to a valid configuration of @ref CLFuseBatchNormalizationKernel + * + * @param[in] conv_weights Convolution layer weights tensor. Data type supported: F16/F32 + * @param[in] bn_mean Batch normalization layer mean tensor. Same as @p conv_weights + * @param[in] bn_var Batch normalization layer variance tensor. Same as @p conv_weights + * @param[in] fused_weights Output fused weights tensor. Same as @p conv_weights + * @param[in] fused_bias Output fused bias tensor. Same as @p conv_weights + * @param[in] conv_bias (Optional) Convolution layer bias tensor. Same as @p conv_weights + * @param[in] bn_beta (Optional) Batch normalization layer beta tensor. Same as @p conv_weights + * @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor. Same as @p conv_weights + * @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f. + * + * @return a status + */ + static Status validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, + const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, + const ITensorInfo *conv_bias = nullptr, const ITensorInfo *bn_beta = nullptr, const ITensorInfo *bn_gamma = nullptr, + float epsilon = 0.001f); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_conv_weights; + const ICLTensor *_conv_bias; + const ICLTensor *_bn_mean; + const ICLTensor *_bn_var; + const ICLTensor *_bn_gamma; + const ICLTensor *_bn_beta; + ICLTensor *_fused_weights; + ICLTensor *_fused_bias; + float _epsilon; + bool _run_in_place_weights; + bool _run_in_place_bias; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLFUSEBATCHNORMALIZATIONKERNEL_H__ */ diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h index 6a614f7704..014e25bbb3 100644 --- a/arm_compute/runtime/CL/CLFunctions.h +++ b/arm_compute/runtime/CL/CLFunctions.h @@ -65,6 +65,7 @@ #include "arm_compute/runtime/CL/functions/CLFlattenLayer.h" #include "arm_compute/runtime/CL/functions/CLFloor.h" #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" +#include "arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h" #include "arm_compute/runtime/CL/functions/CLGEMM.h" #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" #include "arm_compute/runtime/CL/functions/CLGEMMInterleave4x4.h" diff --git a/arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h b/arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h new file mode 100644 index 0000000000..777a80f8b9 --- /dev/null +++ b/arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef __ARM_COMPUTE_CLFUSEBATCHNORMALIZATION_H__ +#define __ARM_COMPUTE_CLFUSEBATCHNORMALIZATION_H__ + +#include "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/IFunction.h" + +namespace arm_compute +{ +// Forward declarations +class ICLTensor; + +/** Basic function to fuse the batch normalization node to a preceding convolution node */ +class CLFuseBatchNormalization : public IFunction +{ +public: + /** Default constructor */ + CLFuseBatchNormalization(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLFuseBatchNormalization(const CLFuseBatchNormalization &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLFuseBatchNormalization &operator=(const CLFuseBatchNormalization &) = delete; + /** Allow instances of this class to be moved */ + CLFuseBatchNormalization(CLFuseBatchNormalization &&) = default; + /** Allow instances of this class to be moved */ + CLFuseBatchNormalization &operator=(CLFuseBatchNormalization &&) = default; + /** Default destructor */ + ~CLFuseBatchNormalization() = default; + /** Set the input and output tensors. + * + * @param[in] conv_weights Convolution layer weights tensor. Data type supported: F16/F32 + * @param[in] bn_mean Batch normalization layer mean tensor. Same as @p conv_weights + * @param[in] bn_var Batch normalization layer variance tensor. Same as @p conv_weights + * @param[out] fused_weights Output fused weights tensor. Same as @p conv_weights + * @param[out] fused_bias Output fused bias tensor. Same as @p conv_weights + * @param[in] conv_bias (Optional) Convolution layer bias tensor. Same as @p conv_weights + * @param[in] bn_beta (Optional) Batch normalization layer beta tensor. Same as @p conv_weights + * @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor. Same as @p conv_weights + * @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f. + */ + void configure(const ICLTensor *conv_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, ICLTensor *fused_weights, ICLTensor *fused_bias, + const ICLTensor *conv_bias = nullptr, const ICLTensor *bn_beta = nullptr, const ICLTensor *bn_gamma = nullptr, + float epsilon = 0.001f); + /** Static function to check if given info will lead to a valid configuration of @ref CLFuseBatchNormalization + * + * @param[in] conv_weights Convolution layer weights tensor. Data type supported: F16/F32 + * @param[in] bn_mean Batch normalization layer mean tensor. Same as @p conv_weights + * @param[in] bn_var Batch normalization layer variance tensor. Same as @p conv_weights + * @param[in] fused_weights Output fused weights tensor. Same as @p conv_weights + * @param[in] fused_bias Output fused bias tensor. Same as @p conv_weights + * @param[in] conv_bias (Optional) Convolution layer bias tensor. Same as @p conv_weights + * @param[in] bn_beta (Optional) Batch normalization layer beta tensor. Same as @p conv_weights + * @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor. Same as @p conv_weights + * @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f. + * + * @return a status + */ + static Status validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, + const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, + const ITensorInfo *conv_bias = nullptr, const ITensorInfo *bn_beta = nullptr, const ITensorInfo *bn_gamma = nullptr, + float epsilon = 0.001f); + + // Inherited methods overridden: + void run() override; + +private: + CLFuseBatchNormalizationKernel _fuse_bn_kernel; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLFUSEBATCHNORMALIZATION_H__ */ diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 957543c877..a2428ca99d 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -237,6 +237,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "fill_image_borders_constant", "fill_border.cl" }, { "fill_image_borders_replicate", "fill_border.cl" }, { "finalize", "optical_flow_pyramid_lk.cl" }, + { "fuse_batchnormalization_layer", "batchnormalization_layer.cl" }, { "floor_layer", "floor.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 5352af3c5a..df141269bc 100644 --- a/src/core/CL/cl_kernels/batchnormalization_layer.cl +++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl @@ -23,14 +23,14 @@ */ #include "helpers.h" -#if defined(VEC_SIZE) && defined(DATA_TYPE) - #define ADD_OP(a, b) ((a) + (b)) #define SUB_OP(a, b) ((a) - (b)) #define MUL_OP(a, b) ((a) * (b)) #define INVSQRT_OP(a) rsqrt((a)) #define SQCVT_SAT(a) (a) +#if defined(VEC_SIZE) && defined(DATA_TYPE) + #if defined(FUSED_ACTIVATION) #include "activation_layer.cl" #define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x) @@ -258,3 +258,161 @@ __kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input), (res, 0, (__global DATA_TYPE *)out.ptr); } #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) */ + +#if defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) +/** Fuse batchnorm parameters to convolution layer parameters + * + * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Input tensor depth should be given as a preprocessor argument using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16 + * @attention Batch normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f + * + * @param[in] conv_w_ptr Pointer to the source tensor. Supported data types: F16/F32 + * @param[in] conv_w_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] conv_w_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] conv_w_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] conv_w_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] conv_w_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] conv_w_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] conv_w__stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] conv_w__step_w input_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] conv_w_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] bn_mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_mean_stride_x Stride of the mean source tensor in X dimension (in bytes) + * @param[in] bn_mean_step_x bn_mean_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor + * @param[in] bn_var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr + * @param[in] bn_var_stride_x Stride of the var tensor in X dimension (in bytes) + * @param[in] bn_var_step_x bn_var_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_var_offset_first_element_in_bytes The offset of the first element in the var source tensor + * @param[out] fused_w_ptr Pointer to the destination weights tensors. Supported data types: same as @p input_ptr + * @param[in] fused_w_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] fused_w_step_x fused_w_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] fused_w_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] fused_w_step_y fused_w_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] fused_w_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] fused_w_step_z fused_w_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] fused_w_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] fused_w_step_w fused_w_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] fused_w_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] fused_b_ptr Pointer to the destination bias tensor. Supported data types: same as @p input_ptr + * @param[in] fused_b_stride_x Stride of the bias source tensor in X dimension (in bytes) + * @param[in] fused_b_step_x fused_b_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] fused_b_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] conv_b_ptr Pointer to the source bias tensor. Supported data types: same as @p input_ptr + * @param[in] conv_b_stride_x Stride of the beta source tensor in X dimension (in bytes) + * @param[in] conv_b_step_x conv_b_beta_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] conv_b_offset_first_element_in_bytes The offset of the first element in the source bias tensor + * @param[in] bn_beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_beta_stride_x Stride of the beta source tensor in X dimension (in bytes) + * @param[in] bn_beta_step_x bn_beta_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor + * @param[in] bn_gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes) + * @param[in] bn_gamma_step_x bn_gamma_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor + * @param[in] epsilon Epsilon parameter in the batch normalization equation + */ +__kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w), + VECTOR_DECLARATION(bn_mean), + VECTOR_DECLARATION(bn_var) +#ifndef IN_PLACE_W + , + TENSOR4D_DECLARATION(fused_w) +#endif /* not IN_PLACE_W */ +#ifndef IN_PLACE_B + , + VECTOR_DECLARATION(fused_b) +#endif /* not IN_PLACE_B */ +#ifdef HAS_BIAS + , + VECTOR_DECLARATION(conv_b) +#endif /* HAS_BIAS */ +#ifndef USE_DEFAULT_BETA + , + VECTOR_DECLARATION(bn_beta) +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + , + VECTOR_DECLARATION(bn_gamma) +#endif /* USE_DEFAULT_GAMMA */ + ) +{ + Tensor4D conv_w = CONVERT_TO_TENSOR4D_STRUCT(conv_w, NUM_CHANNELS); + Vector bn_mean = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_mean); + Vector bn_var = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_var); + + // In-place ops +#ifdef IN_PLACE_W + Tensor4D fused_w = conv_w; +#else /* IN_PLACE_W */ + Tensor4D fused_w = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS); +#endif /* IN_PLACE */ +#ifdef IN_PLACE_B + Vector fused_b = conv_b; +#else /* IN_PLACE_W */ + Vector fused_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b); +#endif /* IN_PLACE */ + + // Conditional ops +#ifdef HAS_BIAS + Vector conv_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(conv_b); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_BETA + Vector bn_beta = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_beta); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + Vector bn_gamma = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_gamma); +#endif /* USE_DEFAULT_GAMMA */ + + const int current_slice = get_global_id(2) / NUM_CHANNELS; + +#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + // Check if access on width gets out of bounds + // If it does shift access vector to access elements within bounds + const int xi = (int)(get_global_id(0) * VEC_SIZE); + conv_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * conv_w_stride_x; + fused_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * fused_w_stride_x; + + // Load W + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + wn = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)conv_w.ptr); +#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X) + DATA_TYPE wn = *((__global DATA_TYPE *)(conv_w.ptr)); +#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + + // rvar = 1 / sqrt(var + epsilon) + const DATA_TYPE var = *((__global DATA_TYPE *)(bn_var.ptr + current_slice * bn_var.stride_x)); + const DATA_TYPE rvar = INVSQRT_OP(ADD_OP(var, SQCVT_SAT((float)EPSILON))); + wn *= rvar; + + // Load b + const DATA_TYPE mean = *((__global DATA_TYPE *)(bn_mean.ptr + current_slice * bn_mean.stride_x)); + DATA_TYPE bn = 0; +#ifdef HAS_BIAS + bn = *((__global DATA_TYPE *)(conv_b.ptr + current_slice * conv_b.stride_x)); +#endif /* HAS_BIAS */ + bn = (bn - mean) * rvar; + +#ifndef USE_DEFAULT_GAMMA + const DATA_TYPE gamma_scalar = *((__global DATA_TYPE *)(bn_gamma.ptr + current_slice * bn_gamma.stride_x)); + wn *= gamma_scalar; + bn *= gamma_scalar; +#endif /* USE_DEFAULT_GAMMA */ + +#ifndef USE_DEFAULT_BETA + const DATA_TYPE beta_scalar = *((__global DATA_TYPE *)(bn_beta.ptr + current_slice * bn_beta.stride_x)); + bn += beta_scalar; +#endif /* USE_DEFAULT_BETA */ + +#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + // Store updated weights + VSTORE(VEC_SIZE) + (wn, 0, (__global DATA_TYPE *)fused_w.ptr); +#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X) + *((__global DATA_TYPE *)(fused_w.ptr)) = wn; +#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + + // Store updated bias + *((__global DATA_TYPE *)(fused_b.ptr + current_slice * fused_b.stride_x)) = bn; +} +#endif /* defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) */ diff --git a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp new file mode 100644 index 0000000000..e14b8a3777 --- /dev/null +++ b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp @@ -0,0 +1,221 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Window.h" + +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *conv_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) +{ + 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_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_mean, bn_var); + + unsigned int kernels_idx = get_data_layout_dimension_index(conv_weights->data_layout(), DataLayoutDimension::BATCHES); + ARM_COMPUTE_RETURN_ERROR_ON(conv_weights->dimension(kernels_idx) != bn_mean->dimension(0)); + + // Validate bias + if(conv_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); + } + // 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); + } + // 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); + } + + // 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); + } + // 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); + } + + return Status{}; +} +} // 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(), + _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, + ICLTensor *fused_weights, ICLTensor *fused_bias, + const ICLTensor *conv_bias, const ICLTensor *bn_beta, const ICLTensor *bn_gamma, + float epsilon) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(conv_weights, bn_mean, bn_var); + + _conv_weights = conv_weights; + _conv_bias = conv_bias; + _bn_mean = bn_mean; + _bn_var = bn_var; + _bn_beta = bn_beta; + _bn_gamma = bn_gamma; + _fused_weights = fused_weights; + _fused_bias = fused_bias; + _epsilon = epsilon; + + _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); + _run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_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()); + fused_weights->info()->set_valid_region(conv_weights->info()->valid_region()); + } + if(_fused_bias != nullptr) + { + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*_fused_bias->info(), *_bn_mean->info()->clone()); + _fused_bias->info()->set_valid_region(bn_mean->info()->valid_region()); + } + + // Validate arguments + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(conv_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, + (bn_beta != nullptr) ? bn_beta->info() : nullptr, + (bn_gamma != nullptr) ? bn_gamma->info() : nullptr, + epsilon)); + + // Configure kernel window + const unsigned int num_elems_processed_per_iteration_x = 16 / conv_weights->info()->element_size(); + const int output_width_x = conv_weights->info()->tensor_shape().x(); + const bool multi_access_x = (output_width_x / num_elems_processed_per_iteration_x > 0); + + Window win = calculate_max_window(*conv_weights->info()); + if(multi_access_x) + { + win.set(Window::DimX, Window::Dimension(win.x().start(), + ceil_to_multiple(win.x().end(), num_elems_processed_per_iteration_x), + num_elems_processed_per_iteration_x)); + } + 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("-DSELECT_DATA_TYPE=" + get_cl_select_type_from_data_type(conv_weights->info()->data_type())); + build_opts.add_option("-DNUM_CHANNELS=" + support::cpp11::to_string(conv_weights->info()->dimension(2))); + build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon)); + build_opts.add_option_if(multi_access_x, "-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration_x)); + build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max(output_width_x - num_elems_processed_per_iteration_x, 0))); + 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, "-DHAS_BIAS"); + build_opts.add_option_if(bn_beta == nullptr, "-DUSE_DEFAULT_BETA"); + build_opts.add_option_if(bn_gamma == nullptr, "-DUSE_DEFAULT_GAMMA"); + + // Create kernel + _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, + const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, + const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, + float epsilon) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon)); + return Status{}; +} + +void CLFuseBatchNormalizationKernel::run(const arm_compute::Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + // Create window slice + Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); + Window slice = collapsed_window.first_slice_window_4D(); + + Window vector_slice = window.first_slice_window_1D(); + vector_slice.set(Window::DimX, Window::Dimension(0, 0, 0)); + + // Add kernel arguments + unsigned int idx = 0; + add_4D_tensor_argument(idx, _conv_weights, slice); + add_1D_tensor_argument(idx, _bn_mean, vector_slice); + add_1D_tensor_argument(idx, _bn_var, vector_slice); + if(!_run_in_place_weights) + { + add_4D_tensor_argument(idx, _fused_weights, slice); + } + if(!_run_in_place_bias) + { + add_1D_tensor_argument(idx, _fused_bias, vector_slice); + } + if(_conv_bias != nullptr) + { + add_1D_tensor_argument(idx, _conv_bias, vector_slice); + } + if(_bn_beta != nullptr) + { + add_1D_tensor_argument(idx, _bn_beta, vector_slice); + } + if(_bn_gamma != nullptr) + { + add_1D_tensor_argument(idx, _bn_gamma, vector_slice); + } + enqueue(queue, *this, slice, lws_hint()); +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLFuseBatchNormalization.cpp b/src/runtime/CL/functions/CLFuseBatchNormalization.cpp new file mode 100644 index 0000000000..32e46787d3 --- /dev/null +++ b/src/runtime/CL/functions/CLFuseBatchNormalization.cpp @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +#include "arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +namespace arm_compute +{ +CLFuseBatchNormalization::CLFuseBatchNormalization() + : _fuse_bn_kernel() +{ +} + +void CLFuseBatchNormalization::configure(const ICLTensor *conv_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) +{ + _fuse_bn_kernel.configure(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon); +} + +Status CLFuseBatchNormalization::validate(const ITensorInfo *conv_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) +{ + return CLFuseBatchNormalizationKernel::validate(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon); +} + +void CLFuseBatchNormalization::run() +{ + CLScheduler::get().enqueue(_fuse_bn_kernel, true); +} +} // namespace arm_compute diff --git a/tests/validation/CL/BatchNormalizationLayer.cpp b/tests/validation/CL/BatchNormalizationLayer.cpp index 0d80ff7eb7..cbf3c7092d 100644 --- a/tests/validation/CL/BatchNormalizationLayer.cpp +++ b/tests/validation/CL/BatchNormalizationLayer.cpp @@ -25,16 +25,20 @@ #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" #include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h" +#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h" #include "tests/CL/CLAccessor.h" #include "tests/PaddingCalculator.h" +#include "tests/datasets/LargeConvolutionLayerDataset.h" #include "tests/datasets/RandomBatchNormalizationLayerDataset.h" -#include "tests/datasets/ShapeDatasets.h" +#include "tests/datasets/SmallConvolutionLayerDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Helpers.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/BatchNormalizationLayerFixture.h" +#include "tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h" namespace arm_compute { @@ -44,14 +48,20 @@ namespace validation { namespace { -constexpr AbsoluteTolerance tolerance_f32(0.00001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -constexpr AbsoluteTolerance tolerance_f16(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ +RelativeTolerance rel_tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ +constexpr AbsoluteTolerance abs_tolerance_f32(0.00001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ +constexpr AbsoluteTolerance tolerance_f16(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ const auto act_infos = framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), }); + +const auto common_fusion_dataset = combine(combine(combine(framework::dataset::make("UseBias", { false, true }), + framework::dataset::make("UseBeta", { false, true })), + framework::dataset::make("UseGamma", { false, true })), + framework::dataset::make("Epsilon", { 0.001f })); } // namespace TEST_SUITE(CL) @@ -150,9 +160,9 @@ FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture, framewor framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32, 0); + validate(CLAccessor(_target), _reference, abs_tolerance_f32, 0); } -TEST_SUITE_END() +TEST_SUITE_END() //FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(), @@ -165,11 +175,30 @@ FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture, framework // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, 0); } -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // Float + +TEST_SUITE_END() // BatchNormalizationLayer + +TEST_SUITE(BatchNormalizationLayerFusion) +template +using CLBatchNormalizationLayerFusionFixture = BatchNormalizationLayerFusionValidationFixture; + +TEST_SUITE(Float) +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, CLBatchNormalizationLayerFusionFixture, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(datasets::SmallConvolutionLayerDataset(), common_fusion_dataset), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); +} +TEST_SUITE_END() // FP32 +TEST_SUITE_END() // Float -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // BatchNormalizationLayerFusion +TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h b/tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h new file mode 100644 index 0000000000..39c7d46114 --- /dev/null +++ b/tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h @@ -0,0 +1,186 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE +#define ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/reference/BatchNormalizationLayer.h" +#include "tests/validation/reference/ConvolutionLayer.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template +class BatchNormalizationLayerFusionValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, + bool use_conv_b, bool use_beta, bool use_gamma, float epsilon, DataType dt, DataLayout data_layout) + { + ARM_COMPUTE_UNUSED(dilation); + + _data_type = dt; + _data_layout = data_layout; + _use_conv_b = use_conv_b; + _use_beta = use_beta; + _use_gamma = use_gamma; + + _target = compute_target(src_shape, w_shape, b_shape, dst_shape, info, epsilon); + _reference = compute_reference(src_shape, w_shape, b_shape, dst_shape, info, epsilon); + } + +protected: + template + void fill(U &&src, U &&w_tensor, U &&b_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor) + { + std::uniform_real_distribution<> distribution(-1.f, 1.f); + std::uniform_real_distribution<> distribution_gz(0, 1.f); + + library->fill(src, distribution, 0); + library->fill(w_tensor, distribution, 1); + library->fill(mean_tensor, distribution, 2); + library->fill(var_tensor, distribution_gz, 3); + _use_conv_b ? library->fill(b_tensor, distribution, 4) : library->fill_tensor_value(b_tensor, 0.f); + _use_beta ? library->fill(beta_tensor, distribution, 5) : library->fill_tensor_value(beta_tensor, 0.f); + _use_gamma ? library->fill(gamma_tensor, distribution, 6) : library->fill_tensor_value(gamma_tensor, 1.f); + } + + TensorType compute_target(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, float epsilon) + { + if(_data_layout == DataLayout::NHWC) + { + permute(src_shape, PermutationVector(2U, 0U, 1U)); + permute(w_shape, PermutationVector(2U, 0U, 1U)); + permute(dst_shape, PermutationVector(2U, 0U, 1U)); + } + + // Create tensors + TensorType src = create_tensor(src_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType conv_w = create_tensor(w_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType conv_b = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType bn_mean = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType bn_var = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType bn_beta = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType bn_gamma = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType fused_w = create_tensor(w_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType fused_b = create_tensor(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); + TensorType dst = create_tensor(dst_shape, _data_type, 1, QuantizationInfo(), _data_layout); + + // Create and configure function + FusionFunctionType fuse_fn; + ConvolutionFunctionType conv_fn; + TensorType *conv_b_ptr = _use_conv_b ? &conv_b : nullptr; + TensorType *beta_ptr = _use_beta ? &bn_beta : nullptr; + TensorType *gamma_ptr = _use_gamma ? &bn_gamma : nullptr; + fuse_fn.configure(&conv_w, &bn_mean, &bn_var, &fused_w, &fused_b, conv_b_ptr, beta_ptr, gamma_ptr, epsilon); + conv_fn.configure(&src, &fused_w, &fused_b, &dst, info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(conv_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(conv_b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bn_mean.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bn_var.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bn_beta.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bn_gamma.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(fused_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(fused_b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + conv_w.allocator()->allocate(); + conv_b.allocator()->allocate(); + bn_mean.allocator()->allocate(); + bn_var.allocator()->allocate(); + bn_beta.allocator()->allocate(); + bn_gamma.allocator()->allocate(); + fused_w.allocator()->allocate(); + fused_b.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!conv_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!conv_b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bn_mean.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bn_var.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bn_beta.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bn_gamma.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!fused_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!fused_b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), + AccessorType(conv_w), AccessorType(conv_b), + AccessorType(bn_mean), AccessorType(bn_var), AccessorType(bn_beta), AccessorType(bn_gamma)); + + // Compute function + fuse_fn.run(); + conv_fn.run(); + + return dst; + } + + SimpleTensor compute_reference(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, float epsilon) + { + // Create reference + SimpleTensor src{ src_shape, _data_type, 1 }; + SimpleTensor conv_w{ w_shape, _data_type, 1 }; + SimpleTensor conv_b{ b_shape, _data_type, 1 }; + SimpleTensor bn_var{ b_shape, _data_type, 1 }; + SimpleTensor bn_mean{ b_shape, _data_type, 1 }; + SimpleTensor bn_beta{ b_shape, _data_type, 1 }; + SimpleTensor bn_gamma{ b_shape, _data_type, 1 }; + + // Fill reference + fill(src, conv_w, conv_b, bn_mean, bn_var, bn_beta, bn_gamma); + + // Calculate Conv + BN + auto conv_res = reference::convolution_layer(src, conv_w, conv_b, dst_shape, info); + return reference::batch_normalization_layer(conv_res, bn_mean, bn_var, bn_beta, bn_gamma, epsilon, ActivationLayerInfo()); + } + + TensorType _target{}; + SimpleTensor _reference{}; + DataType _data_type{}; + DataLayout _data_layout{}; + bool _use_conv_b{}; + bool _use_beta{}; + bool _use_gamma{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE */ diff --git a/tests/validation/reference/BatchNormalizationLayer.cpp b/tests/validation/reference/BatchNormalizationLayer.cpp index 4ea3769c2c..37713c841d 100644 --- a/tests/validation/reference/BatchNormalizationLayer.cpp +++ b/tests/validation/reference/BatchNormalizationLayer.cpp @@ -77,7 +77,6 @@ template SimpleTensor batch_normalization_layer(const SimpleTensor template SimpleTensor batch_normalization_layer(const SimpleTensor &src, const SimpleTensor &mean, const SimpleTensor &var, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon, ActivationLayerInfo act_info); - } // namespace reference } // namespace validation } // namespace test -- cgit v1.2.1