From 3f217ec4ff11e20fe686beb9a28d0bbd80a56cd6 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Mon, 12 Feb 2018 14:59:19 +0000 Subject: COMPMID-908 - Merge Activation layer with Convolution Layer (NEON. CL, GLES) Change-Id: Iab06d0768ecf805b841e601185608aae88cf9166 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120874 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- src/core/GLES_COMPUTE/GCKernelLibrary.cpp | 8 ++ .../GLES_COMPUTE/cs_shaders/activation_layer.cs | 92 +--------------- .../cs_shaders/activation_layer_helpers_cs.h | 119 +++++++++++++++++++++ .../cs_shaders/direct_convolution1x1.cs | 49 ++++++++- .../cs_shaders/direct_convolution3x3.cs | 55 +++++++++- .../cs_shaders/direct_convolution5x5.cs | 14 ++- .../kernels/GCDirectConvolutionLayerKernel.cpp | 14 ++- src/runtime/CL/functions/CLConvolutionLayer.cpp | 17 +-- .../CL/functions/CLDirectConvolutionLayer.cpp | 28 ++++- .../CL/functions/CLGEMMConvolutionLayer.cpp | 36 ++++++- .../CL/functions/CLWinogradConvolutionLayer.cpp | 30 +++++- .../GLES_COMPUTE/functions/GCConvolutionLayer.cpp | 22 +++- .../functions/GCDirectConvolutionLayer.cpp | 11 +- src/runtime/NEON/functions/NEConvolutionLayer.cpp | 24 +++-- .../NEON/functions/NEDirectConvolutionLayer.cpp | 27 ++++- .../NEON/functions/NEGEMMConvolutionLayer.cpp | 44 ++++++-- src/runtime/NEON/functions/NEWinogradLayer.cpp | 21 +++- 17 files changed, 459 insertions(+), 152 deletions(-) create mode 100644 src/core/GLES_COMPUTE/cs_shaders/activation_layer_helpers_cs.h (limited to 'src') diff --git a/src/core/GLES_COMPUTE/GCKernelLibrary.cpp b/src/core/GLES_COMPUTE/GCKernelLibrary.cpp index 5d1464ace4..25ac02e8f4 100644 --- a/src/core/GLES_COMPUTE/GCKernelLibrary.cpp +++ b/src/core/GLES_COMPUTE/GCKernelLibrary.cpp @@ -231,6 +231,14 @@ const std::map GCKernelLibrary::_shader_program_map = const std::map GCKernelLibrary::_program_source_map = { #ifdef EMBEDDED_KERNELS + { + "helpers_cs.h", +#include "./cs_shaders/helpers_cs.hembed" + }, + { + "activation_layer_helpers_cs.h", +#include "./cs_shaders/activation_layer_helpers_cs.hembed" + }, { "absdiff.cs", #include "./cs_shaders/absdiff.csembed" diff --git a/src/core/GLES_COMPUTE/cs_shaders/activation_layer.cs b/src/core/GLES_COMPUTE/cs_shaders/activation_layer.cs index 7d3f4ee67e..9a1e233624 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/activation_layer.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/activation_layer.cs @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -23,97 +23,9 @@ */ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = LOCAL_SIZE_Z) in; +#include "activation_layer_helpers_cs.h" #include "helpers_cs.h" -#ifdef DATA_TYPE_FP32 -precision highp float; -#elif defined(DATA_TYPE_FP16) -#if defined(LOGISTIC) || defined(TANH) || defined(SRELU) || defined(SQRT) -precision highp float; -#else /*LOGISTIC_TANH_SRELU_SQRT*/ -precision mediump float; -#endif /*LOGISTIC_TANH_SRELU_SQRT*/ -#endif /*DATA_TYPE_FP32*/ - -#define ABS_OP(a) abs((a)) -#define ADD_OP(a, b) ((a) + (b)) -#define SUB_OP(a, b) ((a) - (b)) -#define MUL_OP(a, b) ((a) * (b)) -#define MLA_OP(a, b, c) ((b) * (c) + (a)) -#define DIV_OP(a, b) ((a) / (b)) -#define EXP_OP(a) exp((a)) -#define LOG_OP(a) log((a)) -#define SQRT_OP(a) sqrt((a)) -#define CONST_ONE (1.f) - -// Logistic Activation -float logistic_op(float x) -{ - return DIV_OP(CONST_ONE, ADD_OP(CONST_ONE, EXP_OP(-x))); -} -// Hyperbolic Tangent Activation -float tanh_op(float x) -{ - float tmp = float(B_VAL) * x; - if(tmp > 10.f) - { - return MUL_OP(float(A_VAL), 1.f); - } - else if(tmp < -10.f) - { - return MUL_OP(float(A_VAL), -1.f); - } - else - { - return MUL_OP(float(A_VAL), tanh(tmp + 0.000001f)); - } -} -// RELU Tangent Activation -float relu_op(float x) -{ - return max(0.f, x); -} -// Bounded RELU Activation -float brelu_op(float x) -{ - return min(float(A_VAL), max(float(0.0), x)); -} -// Lower Upper Bounded RELU Activation -float lu_brelu_op(float x) -{ - return min(max(x, float(B_VAL)), float(A_VAL)); -} -// Leaky RELU Activation -float lrelu_op(float x) -{ - return (x > float(0.0)) ? x : MUL_OP(float(A_VAL), x); -} -// Soft RELU Activation -float srelu_op(float x) -{ - return LOG_OP(ADD_OP(CONST_ONE, EXP_OP(x))); -} -// Absolute Activation -float abs_op(float x) -{ - return ABS_OP(x); -} -// Square Activation -float square_op(float x) -{ - return MUL_OP(x, x); -} -// Square-root Activation -float sqrt_op(float x) -{ - return SQRT_OP(x); -} -// Linear Activation -float linear_op(float x) -{ - return MLA_OP(float(B_VAL), float(A_VAL), x); -} - /** This performs an activation function floating point inputs. * * @note The data type must be passed at compile time using "#define DATA_TYPE_NAME". e.g. "#define DATA_TYPE_FP32" diff --git a/src/core/GLES_COMPUTE/cs_shaders/activation_layer_helpers_cs.h b/src/core/GLES_COMPUTE/cs_shaders/activation_layer_helpers_cs.h new file mode 100644 index 0000000000..f43a33fe87 --- /dev/null +++ b/src/core/GLES_COMPUTE/cs_shaders/activation_layer_helpers_cs.h @@ -0,0 +1,119 @@ +/* + * 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. + */ +#ifdef DATA_TYPE_FP32 +precision highp float; +#elif defined(DATA_TYPE_FP16) +#if defined(LOGISTIC) || defined(TANH) || defined(SRELU) || defined(SQRT) +precision highp float; +#else /*LOGISTIC_TANH_SRELU_SQRT*/ +precision mediump float; +#endif /*LOGISTIC_TANH_SRELU_SQRT*/ +#endif /*DATA_TYPE_FP32*/ + +#define ABS_OP(a) abs((a)) +#define ADD_OP(a, b) ((a) + (b)) +#define SUB_OP(a, b) ((a) - (b)) +#define MUL_OP(a, b) ((a) * (b)) +#define MLA_OP(a, b, c) ((b) * (c) + (a)) +#define DIV_OP(a, b) ((a) / (b)) +#define EXP_OP(a) exp((a)) +#define LOG_OP(a) log((a)) +#define SQRT_OP(a) sqrt((a)) +#define CONST_ONE (1.f) + +// Logistic Activation +float logistic_op(float x) +{ + return DIV_OP(CONST_ONE, ADD_OP(CONST_ONE, EXP_OP(-x))); +} +vec4 logistic_op(vec4 x) +{ + return DIV_OP(vec4(CONST_ONE), ADD_OP(CONST_ONE, EXP_OP(-x))); +} +// Hyperbolic Tangent Activation +float tanh_op(float x) +{ + float tmp = float(B_VAL) * x; + if(tmp > 10.f) + { + return MUL_OP(float(A_VAL), 1.f); + } + else if(tmp < -10.f) + { + return MUL_OP(float(A_VAL), -1.f); + } + else + { + return MUL_OP(float(A_VAL), tanh(tmp + 0.000001f)); + } +} +// RELU Tangent Activation +float relu_op(float x) +{ + return max(0.f, x); +} +vec4 relu_op(vec4 x) +{ + return max(vec4(0.f), x); +} +// Bounded RELU Activation +float brelu_op(float x) +{ + return min(float(A_VAL), max(float(0.0), x)); +} +// Lower Upper Bounded RELU Activation +float lu_brelu_op(float x) +{ + return min(max(x, float(B_VAL)), float(A_VAL)); +} +// Leaky RELU Activation +float lrelu_op(float x) +{ + return (x > float(0.0)) ? x : MUL_OP(float(A_VAL), x); +} +// Soft RELU Activation +float srelu_op(float x) +{ + return LOG_OP(ADD_OP(CONST_ONE, EXP_OP(x))); +} +// Absolute Activation +float abs_op(float x) +{ + return ABS_OP(x); +} +// Square Activation +float square_op(float x) +{ + return MUL_OP(x, x); +} +// Square-root Activation +float sqrt_op(float x) +{ + return SQRT_OP(x); +} +// Linear Activation +float linear_op(float x) +{ + return MLA_OP(float(B_VAL), float(A_VAL), x); +} diff --git a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution1x1.cs b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution1x1.cs index ea4e9c18e2..b42c09bbc7 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution1x1.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution1x1.cs @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -26,6 +26,10 @@ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = #include "helpers_cs.h" +#ifdef FUSED_ACTIVATION +#include "activation_layer_helpers_cs.h" +#endif /* FUSED_ACTIVATION */ + #if defined(DATA_TYPE_FP16) precision mediump float; #endif // DATA_TYPE_FP16 @@ -99,6 +103,10 @@ void main() pixels += LOAD(biases_ptr, VECTOR_OFFSET(biases_iter, z_index)); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_CURRENT_ITEM(dst_ptr, dst_iter, pixels); } @@ -210,6 +218,10 @@ void main() pixels += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels); } #elif defined(PROCESS_4X_2Y_1Z) @@ -333,6 +345,11 @@ void main() pixels[1] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); } @@ -470,6 +487,12 @@ void main() pixels[2] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); @@ -609,6 +632,13 @@ void main() pixels1[1] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels1[0] = ACT_OP(pixels1[0]); + pixels1[1] = ACT_OP(pixels1[1]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels1[0]); @@ -745,6 +775,11 @@ void main() pixels[1] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); @@ -868,6 +903,11 @@ void main() pixels[1] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK8_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels); } #elif defined(PROCESS_8X_2Y_1Z) @@ -1001,6 +1041,13 @@ void main() pixels1[1] += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels1[0] = ACT_OP(pixels1[0]); + pixels1[1] = ACT_OP(pixels1[1]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK8_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels); STORE_PACK8_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels1); } diff --git a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution3x3.cs b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution3x3.cs index 855d450335..e51cc3785a 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution3x3.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution3x3.cs @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -25,6 +25,10 @@ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = #include "helpers_cs.h" +#ifdef FUSED_ACTIVATION +#include "activation_layer_helpers_cs.h" +#endif /* FUSED_ACTIVATION */ + #if defined(DATA_TYPE_FP16) precision mediump float; #endif // DATA_TYPE_FP16 @@ -114,6 +118,10 @@ void main() pixels += LOAD(biases_ptr, VECTOR_OFFSET(biases_iter, z_index)); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_CURRENT_ITEM(dst_ptr, dst_iter, pixels); } @@ -238,6 +246,11 @@ void main() pixels[1] += vec4(b); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); +#endif /* FUSED_ACTIVATION */ + VSTORE2_CURRENT_ITEM(dst_ptr, dst_iter, pixels); } @@ -335,6 +348,10 @@ void main() pixels += b; #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_CURRENT_ITEM(dst_ptr, dst_iter, pixels); } @@ -434,6 +451,12 @@ void main() pixels[2] += vec4(b); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); +#endif /* FUSED_ACTIVATION */ + STORE_CURRENT_ITEM(dst_ptr, dst_iter, pixels[0]); STORE(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); @@ -601,6 +624,12 @@ void main() } #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK8_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK8_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK8_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); @@ -728,6 +757,10 @@ void main() pixels += vec4(b); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels); } @@ -841,6 +874,12 @@ void main() } #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); @@ -962,6 +1001,13 @@ void main() } #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); + pixels[3] = ACT_OP(pixels[3]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); @@ -1087,6 +1133,13 @@ void main() } #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels[0] = ACT_OP(pixels[0]); + pixels[1] = ACT_OP(pixels[1]); + pixels[2] = ACT_OP(pixels[2]); + pixels[3] = ACT_OP(pixels[3]); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, pixels[0]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 1, 0), pixels[1]); STORE_PACK4_HALF(dst_ptr, TENSOR3D_OFFSET(dst_iter, 0, 2, 0), pixels[2]); diff --git a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution5x5.cs b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution5x5.cs index c919e4ed80..728e9644b2 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/direct_convolution5x5.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/direct_convolution5x5.cs @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017, 2018 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -26,6 +26,10 @@ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = #include "helpers_cs.h" +#ifdef FUSED_ACTIVATION +#include "activation_layer_helpers_cs.h" +#endif /* FUSED_ACTIVATION */ + #if defined(DATA_TYPE_FP16) precision mediump float; #endif // DATA_TYPE_FP16 @@ -116,6 +120,10 @@ void main() pixels += LOAD(biases_ptr, VECTOR_OFFSET(biases_iter, z_index)); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + pixels = ACT_OP(pixels); +#endif /* FUSED_ACTIVATION */ + STORE_CURRENT_ITEM(dst_ptr, dst_iter, pixels); } #elif defined(DATA_TYPE_FP16) @@ -204,6 +212,10 @@ void main() res += vec4(b); #endif /* BIAS */ +#ifdef FUSED_ACTIVATION + res = ACT_OP(res); +#endif /* FUSED_ACTIVATION */ + STORE_PACK4_CURRENT_ITEM_HALF(dst_ptr, dst_iter, res); } diff --git a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp index bef30d5042..67a1530431 100644 --- a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp +++ b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp @@ -50,7 +50,8 @@ BorderSize GCDirectConvolutionLayerKernel::border_size( } template -void GCDirectConvolutionLayerKernel::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output, const PadStrideInfo &conv_info) +void GCDirectConvolutionLayerKernel::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output, + const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); @@ -58,6 +59,7 @@ void GCDirectConvolutionLayerKernel::configure(const IGCTensor *inp ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!"); ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0)); + ARM_COMPUTE_ERROR_ON(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC); if(bias != nullptr) { @@ -108,6 +110,16 @@ void GCDirectConvolutionLayerKernel::configure(const IGCTensor *inp std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16"; options.emplace(("#define " + dt_name)); + // Activation information in case of a fused activation + if(act_info.enabled()) + { + options.emplace("#define FUSED_ACTIVATION"); + options.emplace(("#define " + string_from_activation_func(act_info.activation()))); + options.emplace(("#define ACT_OP " + lower_string(string_from_activation_func(act_info.activation())) + "_op")); + options.emplace(("#define A_VAL " + float_to_string_with_full_precision(act_info.a()))); + options.emplace(("#define B_VAL " + float_to_string_with_full_precision(act_info.b()))); + } + unsigned int num_elems_read_per_iteration_x = kernel_size * _conv_stride_x; unsigned int num_elems_read_per_iteration_y = 1; unsigned int num_elems_written_per_iteration_x = 1; diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 64bda93ff0..bcb5424aab 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -43,13 +43,13 @@ CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr memory_ma } void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation) + const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation)); + ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info)); switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, - weights_info, CLScheduler::get().target(), dilation)) + weights_info, act_info, CLScheduler::get().target(), dilation)) { case ConvolutionMethod::DIRECT: { @@ -72,25 +72,25 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c } Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); //Configure if the parameters match the direct convolution or the gemm-based const GPUTarget gpu_target = CLScheduler::get().target(); - switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, gpu_target, dilation)) + switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, act_info, gpu_target, dilation)) { case ConvolutionMethod::DIRECT: { // Validate direct convolution layer - CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info); + CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info); break; } case ConvolutionMethod::GEMM: { // Validate gemm-based convolution layer - CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation); + CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info); break; } default: @@ -102,7 +102,7 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo } ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const GPUTarget gpu_target, const Size2D &dilation) + const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation) { ARM_COMPUTE_UNUSED(input); ARM_COMPUTE_UNUSED(weights); @@ -112,6 +112,7 @@ ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo * ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); ARM_COMPUTE_UNUSED(dilation); + ARM_COMPUTE_UNUSED(act_info); return ConvolutionMethod::GEMM; } diff --git a/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp index c48865a0cc..c451bd4b4c 100644 --- a/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp @@ -33,11 +33,11 @@ using namespace arm_compute; CLDirectConvolutionLayer::CLDirectConvolutionLayer() - : _direct_conv_kernel(), _input_border_handler() + : _direct_conv_kernel(), _input_border_handler(), _activationlayer_function(), _is_activationlayer_enabled(false) { } -void CLDirectConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +void CLDirectConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { // Set GPU target _direct_conv_kernel.set_target(CLScheduler::get().target()); @@ -55,11 +55,25 @@ void CLDirectConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weig // Tune kernels CLScheduler::get().tune_kernel_static(_direct_conv_kernel); + + _is_activationlayer_enabled = act_info.enabled(); + + //Configure Activation Layer + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } } -Status CLDirectConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) +Status CLDirectConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) { - return CLDirectConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, CLScheduler::get().target()); + ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, CLScheduler::get().target())); + if(act_info.enabled()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); + } + return Status{}; } void CLDirectConvolutionLayer::run() @@ -69,4 +83,10 @@ void CLDirectConvolutionLayer::run() // Run direct convolution CLScheduler::get().enqueue(_direct_conv_kernel); + + //Run Activation Layer + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } } diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index f43e100565..084c4df718 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -90,8 +90,8 @@ void CLConvolutionLayerReshapeWeights::run() } CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _original_weights(nullptr), - _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _is_quantized(false), _is_first_run(true) + : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(), + _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _is_quantized(false), _is_first_run(true), _is_activationlayer_enabled(false) { } @@ -152,7 +152,7 @@ Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens } void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation) + const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); @@ -162,7 +162,8 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * output->info(), conv_info, weights_info, - dilation)); + dilation, + act_info)); _is_first_run = true; _original_weights = weights; @@ -260,11 +261,19 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * // Allocate intermediate tensor _weights_reshaped.allocator()->allocate(); + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } + ARM_COMPUTE_UNUSED(weights_info); } Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); @@ -274,6 +283,11 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2)); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); + if(act_info.enabled()) + { + ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a()); + } + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); const bool append_bias = (biases != nullptr) && (!is_quantized); const unsigned bias_element = (append_bias) ? 1 : 0; @@ -343,6 +357,12 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } + //Validate Activation Layer + if(act_info.enabled()) + { + CLActivationLayer::validate(output, nullptr, act_info); + } + return Status{}; } @@ -383,5 +403,11 @@ void CLGEMMConvolutionLayer::run() // Reshape output matrix CLScheduler::get().enqueue(_col2im_kernel, false); + //Run Activation Layer if enabled + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } + _memory_group.release(); } diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index a861e0072e..7af36bf06b 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -32,11 +32,12 @@ using namespace arm_compute; CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _is_first_run(true) + : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _activationlayer_function(), _input0(), _input1(), _batched_mm_output(), + _is_first_run(true), _is_activationlayer_enabled(false) { } -void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { // TODO(COMPMID-1013): This part will be removed // Get indeces for the width and height @@ -73,13 +74,21 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we _output_transform.configure(&_batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], output_convolved_shape[idx_height]), Size2D(num_tiles_x, num_tiles_y)); + // Configure activation layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } + // Allocate temporary tensors _input0.allocator()->allocate(); _input1.allocator()->allocate(); _batched_mm_output.allocator()->allocate(); } -Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) +Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) { // TODO(COMPMID-1013): This part will be removed // Get indeces for the width and height @@ -107,17 +116,23 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, Size2D(2U, 2U))); - // Configure batched matrix multiply + // Validate batched matrix multiply TensorShape batched_mm_output_shape = input0.tensor_shape(); batched_mm_output_shape[0] = input1.tensor_shape()[0]; const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/))); - // Configure output transform + // Validate output transform ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], output_convolved_shape[idx_height]), Size2D(num_tiles_x, num_tiles_y))); + // Validate Activation Layer + if(act_info.enabled()) + { + CLActivationLayer::validate(output, nullptr, act_info); + } + return Status{}; } @@ -142,5 +157,10 @@ void CLWinogradConvolutionLayer::run() // Run output transform CLScheduler::get().enqueue(_output_transform); + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } + _memory_group.release(); } diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp index c2b7e02284..b1c8665216 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp @@ -92,8 +92,9 @@ void GCConvolutionLayerReshapeWeights::run() } GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _input_im2col_reshaped(), - _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) + : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), + _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), + _are_weights_reshaped(false), _is_activationlayer_enabled(false) { } @@ -103,7 +104,7 @@ void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *w } void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation) + const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); @@ -256,6 +257,14 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig { _weights_reshaped.allocator()->allocate(); } + + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } } void GCConvolutionLayer::run() @@ -290,4 +299,11 @@ void GCConvolutionLayer::run() GCScheduler::get().dispatch(_output_col2im_kernel, false); _memory_group.release(); + + GCScheduler::get().memory_barrier(); + // Run Activation Layer + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } } diff --git a/src/runtime/GLES_COMPUTE/functions/GCDirectConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCDirectConvolutionLayer.cpp index a2607d4c2d..c0cf09836f 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCDirectConvolutionLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCDirectConvolutionLayer.cpp @@ -39,26 +39,27 @@ GCDirectConvolutionLayer::GCDirectConvolutionLayer() { } -void GCDirectConvolutionLayer::configure(IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info) +void GCDirectConvolutionLayer::configure(IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) { int kernel_size = weights->info()->dimension(0); if(kernel_size == 1) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, weights, biases, output, conv_info); + k->configure(input, weights, biases, output, conv_info, act_info); _kernel = std::move(k); } else if(kernel_size == 3) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, weights, biases, output, conv_info); + k->configure(input, weights, biases, output, conv_info, act_info); _kernel = std::move(k); } else if(kernel_size == 5) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, weights, biases, output, conv_info); + k->configure(input, weights, biases, output, conv_info, act_info); _kernel = std::move(k); } else @@ -79,4 +80,6 @@ void GCDirectConvolutionLayer::run() GCScheduler::get().dispatch(_border_handler, false); GCScheduler::get().memory_barrier(); GCScheduler::get().dispatch(*_kernel); + GCScheduler::get().memory_barrier(); + GCScheduler::get().dispatch(_shift_handler); } diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index e659495b7c..badeb07405 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -41,33 +41,33 @@ NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr memory_ma } void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation) + const Size2D &dilation, const ActivationLayerInfo &act_info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation)); + ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info)); switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, - weights_info, dilation)) + weights_info, dilation, act_info)) { case ConvolutionMethod::WINOGRAD: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); - f->configure(input, weights, biases, output, conv_info); + f->configure(input, weights, biases, output, conv_info, act_info); _function = std::move(f); break; } case ConvolutionMethod::GEMM: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); - f->configure(input, weights, biases, output, conv_info, weights_info, dilation); + f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info); _function = std::move(f); break; } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); - f->configure(input, weights, biases, output, conv_info); + f->configure(input, weights, biases, output, conv_info, act_info); _function = std::move(f); break; } @@ -78,9 +78,9 @@ void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const } Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { - switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, dilation)) + switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, dilation, act_info)) { case ConvolutionMethod::WINOGRAD: //Validate Winograd @@ -88,11 +88,11 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo break; case ConvolutionMethod::GEMM: //Validate Gemm-based Convolution - NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation); + NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info); break; case ConvolutionMethod::DIRECT: //Validate Gemm-based Convolution - NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info); + NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info); default: ARM_COMPUTE_ERROR("Not supported."); break; @@ -102,10 +102,12 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo } ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_UNUSED(output); ARM_COMPUTE_UNUSED(weights_info); + ARM_COMPUTE_UNUSED(act_info); + if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (biases != nullptr) && (dilation == Size2D(1U, 1U))) { diff --git a/src/runtime/NEON/functions/NEDirectConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDirectConvolutionLayer.cpp index c26c99a0f8..00776d7cf6 100644 --- a/src/runtime/NEON/functions/NEDirectConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDirectConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -34,11 +34,12 @@ using namespace arm_compute; NEDirectConvolutionLayer::NEDirectConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _output_stage_kernel(), _conv_kernel(), _input_border_handler(), _accumulator(), _has_bias(false), _is_fixed_point(false) + : _memory_group(std::move(memory_manager)), _output_stage_kernel(), _conv_kernel(), _input_border_handler(), _activationlayer_function(), _accumulator(), _has_bias(false), _is_fixed_point(false), + _is_activationlayer_enabled(false) { } -void NEDirectConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &conv_info) +void NEDirectConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { // Free accumulator if(_accumulator.buffer() != nullptr) @@ -73,9 +74,17 @@ void NEDirectConvolutionLayer::configure(ITensor *input, const ITensor *weights, // Add zero padding XY _input_border_handler.configure(input, _conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast(0.f))); + + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } } -Status NEDirectConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &conv_info) +Status NEDirectConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); @@ -101,6 +110,11 @@ Status NEDirectConvolutionLayer::validate(const ITensorInfo *input, const ITenso // Validate bias kernel ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, bias, output)); + if(act_info.enabled()) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info)); + } + return Status{}; } @@ -115,5 +129,10 @@ void NEDirectConvolutionLayer::run() { NEScheduler::get().schedule(&_output_stage_kernel, Window::DimY); } + + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } _memory_group.release(); } diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index cdbd32373a..c339947633 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -165,10 +165,11 @@ TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool app } } -Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt, +Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, + const ActivationLayerInfo &act_info, DataType &dt, bool &append_bias, bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, - bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, + bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, bool &is_activationlayer_enabled, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows, unsigned int &conv_w, unsigned int &conv_h, const Size2D &dilation) { @@ -210,6 +211,7 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf // Check if its a "fully connected" convolution is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); is_interleaved = (!is_fully_connected_convolution && !is_quantized); + is_activationlayer_enabled = act_info.enabled(); return Status{}; } @@ -217,8 +219,8 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager) : _asm_glue(), _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), - _output_col2im_kernel(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false), - _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false) + _output_col2im_kernel(), _activationlayer_function(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), + _workspace(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false), _is_activationlayer_enabled(false) { } @@ -247,7 +249,7 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w } void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation) + const Size2D &dilation, const ActivationLayerInfo &act_info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); @@ -260,9 +262,10 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig unsigned int conv_w = 0; unsigned int conv_h = 0; - Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped, + Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias, + _are_weights_reshaped, kernel_width, kernel_height, - _is_fully_connected_convolution, _is_interleaved, _is_quantized, + _is_fully_connected_convolution, _is_interleaved, _is_quantized, _is_activationlayer_enabled, mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation); ARM_COMPUTE_ERROR_THROW_ON(status); @@ -420,10 +423,16 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig { _weights_reshaped.allocator()->allocate(); } + + //Configure Activation Layer + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } } Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { ARM_COMPUTE_UNUSED(output); @@ -433,6 +442,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI bool is_fully_connected_convolution{}; bool is_interleaved{}; bool is_quantized{}; + bool is_activationlayer_enabled{}; unsigned int kernel_width = 0; unsigned int kernel_height = 0; unsigned int mat_weights_cols = 0; @@ -440,8 +450,8 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI unsigned int conv_w = 0; unsigned int conv_h = 0; - Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height, - is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows, + Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height, + is_fully_connected_convolution, is_interleaved, is_quantized, is_activationlayer_enabled, mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation); const Size2D kernel_weights = Size2D(kernel_width, kernel_height); @@ -536,6 +546,15 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo())); } + ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h))); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one"); + + if(act_info.enabled()) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info)); + } + return Status{}; } @@ -591,6 +610,11 @@ void NEGEMMConvolutionLayer::run() // Reshape output matrix NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } + _memory_group.release(); } } // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp index 0a344f0cae..f82845c7ad 100644 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp @@ -75,13 +75,13 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, } //namespace NEWinogradLayer::NEWinogradLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _batched_gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _permute_input(), - _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), - _reshaped_kernel(false) + : _memory_group(std::move(memory_manager)), _batched_gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), + _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), + _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) { } /* arm_compute */ -void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) +void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, biases, output); ARM_COMPUTE_UNUSED(conv_info); @@ -217,6 +217,13 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co _transform_weights_kernel = std::move(transform_weights_kernel); _transform_output_kernel = std::move(transform_output_kernel); _batched_gemm_kernel = std::move(batched_gemm_kernel); + + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } } void NEWinogradLayer::run() @@ -242,6 +249,12 @@ void NEWinogradLayer::run() // Reorder the convoluted output to ACL's ordering NCHW _permute_output.run(); + + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } + _memory_group.release(); } -- cgit v1.2.1