diff options
Diffstat (limited to 'src/gpu/cl/operators/ClConv2d.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClConv2d.cpp | 195 |
1 files changed, 124 insertions, 71 deletions
diff --git a/src/gpu/cl/operators/ClConv2d.cpp b/src/gpu/cl/operators/ClConv2d.cpp index eb9475ccaa..2c3b0214fa 100644 --- a/src/gpu/cl/operators/ClConv2d.cpp +++ b/src/gpu/cl/operators/ClConv2d.cpp @@ -23,17 +23,17 @@ */ #include "src/gpu/cl/operators/ClConv2d.h" -#include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h" + +#include "src/common/utils/Log.h" #include "src/gpu/cl/operators/ClDirectConv2d.h" #include "src/gpu/cl/operators/ClGemmConv2d.h" #include "src/gpu/cl/operators/ClIndirectConv2d.h" #include "src/gpu/cl/operators/ClWinogradConv2d.h" -#include "src/common/utils/Log.h" - #include <memory> namespace @@ -48,7 +48,7 @@ namespace */ size_t get_direct_conv_kernel_threshold_nhwc(arm_compute::GPUTarget gpu_target) { - switch(gpu_target) + switch (gpu_target) { case arm_compute::GPUTarget::G76: case arm_compute::GPUTarget::G77: @@ -71,27 +71,33 @@ namespace opencl { using namespace arm_compute::misc::shape_calculator; -ClConv2d::ClConv2d() - : _operator() +ClConv2d::ClConv2d() : _operator() { } ClConv2d::~ClConv2d() = default; -void ClConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, const Conv2dInfo &conv2d_info, - const WeightsInfo &weights_info) +void ClConv2d::configure(const CLCompileContext &compile_context, + ITensorInfo *src, + ITensorInfo *weights, + ITensorInfo *biases, + ITensorInfo *dst, + const Conv2dInfo &conv2d_info, + const WeightsInfo &weights_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_ERROR_THROW_ON(ClConv2d::validate(src, weights, ((biases != nullptr) ? biases : nullptr), dst, conv2d_info, weights_info)); + ARM_COMPUTE_ERROR_THROW_ON( + ClConv2d::validate(src, weights, ((biases != nullptr) ? biases : nullptr), dst, conv2d_info, weights_info)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv2d_info, weights_info); - switch(ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, CLScheduler::get().target())) + switch (ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, CLScheduler::get().target())) { case ConvolutionMethod::WINOGRAD: { ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); auto f = std::make_unique<ClWinogradConv2d>(); - f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math); + f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, + conv2d_info.enable_fast_math); _operator = std::move(f); break; } @@ -125,35 +131,46 @@ void ClConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *s _aux_mem = _operator->workspace(); } -Status ClConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &conv2d_info, +Status ClConv2d::validate(const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *dst, + const Conv2dInfo &conv2d_info, const WeightsInfo &weights_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), + "Grouping (num_groups != 1) with NHWC data layout is not supported"); const GPUTarget gpu_target = CLScheduler::get().target(); - switch(ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, gpu_target)) + switch (ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, gpu_target)) { case ConvolutionMethod::WINOGRAD: { //Validate Winograd - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClWinogradConv2d is not supported"); - ARM_COMPUTE_RETURN_ON_ERROR(ClWinogradConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, + "Grouping (num_groups != 1) with ClWinogradConv2d is not supported"); + ARM_COMPUTE_RETURN_ON_ERROR(ClWinogradConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, + conv2d_info.act_info, conv2d_info.enable_fast_math)); break; } case ConvolutionMethod::DIRECT: { // Validate direct convolution layer - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClDirectConv2d is not supported"); - ARM_COMPUTE_RETURN_ON_ERROR(ClDirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, + "Grouping (num_groups != 1) with ClDirectConv2d is not supported"); + ARM_COMPUTE_RETURN_ON_ERROR( + ClDirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); break; } case ConvolutionMethod::INDIRECT: { // Validate indirect convolution layer - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClIndirectConv2d is not supported"); - ARM_COMPUTE_RETURN_ON_ERROR(ClIndirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, + "Grouping (num_groups != 1) with ClIndirectConv2d is not supported"); + ARM_COMPUTE_RETURN_ON_ERROR( + ClIndirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); break; } case ConvolutionMethod::GEMM: @@ -170,8 +187,12 @@ Status ClConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, co return Status{}; } -ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const Conv2dInfo &conv2d_info, - const WeightsInfo &weights_info, const GPUTarget gpu_target) +ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *dst, + const Conv2dInfo &conv2d_info, + const WeightsInfo &weights_info, + const GPUTarget gpu_target) { ARM_COMPUTE_ERROR_ON_NULLPTR(src); ARM_COMPUTE_ERROR_ON_NULLPTR(dst); @@ -191,20 +212,35 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo, DataLayout>; using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>; - const std::vector<ConfigurationMethod> known_configs = - { + const std::vector<ConfigurationMethod> known_configs = { // Alexnet - ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U), DataLayout::NCHW), ConvolutionMethod::DIRECT), + ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), + PadStrideInfo(1U, 1U, 2U, 2U), DataLayout::NCHW), + ConvolutionMethod::DIRECT), // VGG16 / VGG19 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U), DataLayout::NCHW), ConvolutionMethod::DIRECT), + ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), + PadStrideInfo(1U, 1U, 1U, 1U), DataLayout::NCHW), + ConvolutionMethod::DIRECT), // Mobilenet 224 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), + ConfigurationMethod(ConvolutionConfiguration( + Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), + PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), + ConvolutionMethod::GEMM), // Mobilenet 160 - ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), + ConfigurationMethod(ConvolutionConfiguration( + Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), + PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), + ConvolutionMethod::GEMM), // Mobilenet 224 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), + ConfigurationMethod(ConvolutionConfiguration( + Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), + PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), + ConvolutionMethod::GEMM), // Mobilenet 160 - ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), + ConfigurationMethod(ConvolutionConfiguration( + Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), + PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), + ConvolutionMethod::GEMM), }; const auto find_config = [&](ConfigurationMethod c) @@ -213,76 +249,89 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const const PadStrideInfo info = std::get<3>(config); const DataLayout data_layout = std::get<4>(config); - return std::get<0>(config) == Size2D(src->dimension(idx_w), src->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) - && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() - && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride() && (data_layout == src->data_layout()); + return std::get<0>(config) == Size2D(src->dimension(idx_w), src->dimension(idx_h)) && + std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) && + std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && + info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() && + info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && + info.stride() == conv_info.stride() && (data_layout == src->data_layout()); }; std::vector<ConfigurationMethod>::const_iterator found; - if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) + if ((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) { return (*found).second; } - if(dilation != Size2D(1U, 1U)) + if (dilation != Size2D(1U, 1U)) { return ConvolutionMethod::GEMM; } else { - if(src->data_layout() == DataLayout::NCHW) + if (src->data_layout() == DataLayout::NCHW) { // SRGAN - if((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) - && (ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info))) + if ((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && + (conv_info.pad_top() < 3) && + (ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info))) { return ConvolutionMethod::DIRECT; } - if((weights->dimension(idx_h) > 5) && (src->dimension(idx_c) > dst->dimension(idx_c)) && (CLFFTConvolutionLayer::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math))) + if ((weights->dimension(idx_h) > 5) && (src->dimension(idx_c) > dst->dimension(idx_c)) && + (CLFFTConvolutionLayer::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math))) { return ConvolutionMethod::FFT; } - if(src->dimension(idx_c) < 16) + if (src->dimension(idx_c) < 16) { return ConvolutionMethod::GEMM; } - return bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; + return bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)) + ? ConvolutionMethod::WINOGRAD + : ConvolutionMethod::GEMM; } else { - const bool is_direct_valid = bool(ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info)); - const bool is_wino_valid = bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)); + const bool is_direct_valid = + bool(ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info)); + const bool is_wino_valid = + bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)); const size_t kernel_sz_direct_conv_thr = get_direct_conv_kernel_threshold_nhwc(gpu_target); // SRGAN case - if((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) - && is_direct_valid) + if ((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && + (conv_info.pad_top() < 3) && is_direct_valid) { return ConvolutionMethod::DIRECT; } // Floating-point case: GeMM/Direct/Winograd - if(is_data_type_float(src->data_type())) + if (is_data_type_float(src->data_type())) { // Get dst shape - TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); - const bool is_large_kernel_sz = (weights->dimension(idx_w) >= kernel_sz_direct_conv_thr) && (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr); - const bool is_ifm_ge_8 = src->dimension(idx_c) >= 8; - const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; - const bool is_ofm_lte_8 = weights->dimension(3U) <= 8; - const bool is_ofm_lt_64 = weights->dimension(3U) < 64; - const bool workload_gte_8192 = (output_shape[0] * output_shape[1] * output_shape[2]) / 16 >= 8192; - const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U); - const bool is_m_one = output_shape[1] * output_shape[2] == 1; - const bool is_unit_stride = (conv2d_info.conv_info.stride().first == 1) && (conv2d_info.conv_info.stride().second == 1); - const int32_t kernel_sz = weights->dimension(idx_w) * weights->dimension(idx_h); + TensorShape output_shape = + misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); + const bool is_large_kernel_sz = (weights->dimension(idx_w) >= kernel_sz_direct_conv_thr) && + (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr); + const bool is_ifm_ge_8 = src->dimension(idx_c) >= 8; + const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; + const bool is_ofm_lte_8 = weights->dimension(3U) <= 8; + const bool is_ofm_lt_64 = weights->dimension(3U) < 64; + const bool workload_gte_8192 = (output_shape[0] * output_shape[1] * output_shape[2]) / 16 >= 8192; + const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U); + const bool is_m_one = output_shape[1] * output_shape[2] == 1; + const bool is_unit_stride = + (conv2d_info.conv_info.stride().first == 1) && (conv2d_info.conv_info.stride().second == 1); + const int32_t kernel_sz = weights->dimension(idx_w) * weights->dimension(idx_h); // Run Winograd if valid and IFM >= 8 - if(is_wino_valid && is_ifm_ge_8) + if (is_wino_valid && is_ifm_ge_8) { - if(is_ofm_lte_8) + if (is_ofm_lte_8) { - if(gpu_target == arm_compute::GPUTarget::G71 || gpu_target == arm_compute::GPUTarget::G72 || get_arch_from_target(gpu_target) == arm_compute::GPUTarget::MIDGARD) + if (gpu_target == arm_compute::GPUTarget::G71 || gpu_target == arm_compute::GPUTarget::G72 || + get_arch_from_target(gpu_target) == arm_compute::GPUTarget::MIDGARD) { return ConvolutionMethod::WINOGRAD; } @@ -294,18 +343,19 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const } // Direct convolution case - if(is_direct_valid) + if (is_direct_valid) { - if((gpu_target == arm_compute::GPUTarget::G71 || gpu_target == arm_compute::GPUTarget::G72 || get_arch_from_target(gpu_target) == arm_compute::GPUTarget::MIDGARD)) + if ((gpu_target == arm_compute::GPUTarget::G71 || gpu_target == arm_compute::GPUTarget::G72 || + get_arch_from_target(gpu_target) == arm_compute::GPUTarget::MIDGARD)) { - if(is_large_kernel_sz && is_ifm_ge_16 && is_ifm_gt_ofm) + if (is_large_kernel_sz && is_ifm_ge_16 && is_ifm_gt_ofm) { return ConvolutionMethod::DIRECT; } } - else if(gpu_target == arm_compute::GPUTarget::G76) + else if (gpu_target == arm_compute::GPUTarget::G76) { - if((is_large_kernel_sz && workload_gte_8192 && is_ifm_ge_16) || (is_ofm_lte_8 && is_ifm_ge_16)) + if ((is_large_kernel_sz && workload_gte_8192 && is_ifm_ge_16) || (is_ofm_lte_8 && is_ifm_ge_16)) { return ConvolutionMethod::DIRECT; } @@ -314,21 +364,24 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const { ConvolutionMethod preferred_conv_method = ConvolutionMethod::DIRECT; - const bool is_indirect_valid = bool(ClIndirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info)); + const bool is_indirect_valid = + bool(ClIndirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info)); // indirect conv2d should be called when: // 1- When the kernel size is greater than 1x1 and less than or equal to 9x9 (81) // 2- When the kernel size is odd // 3- When the Gpu target is Arm Mali-G77 - if(is_indirect_valid) + if (is_indirect_valid) { const bool is_kernel_sz_odd = kernel_sz % 2; const bool is_g77 = gpu_target == GPUTarget::G77; - preferred_conv_method = (kernel_sz > 1) && (kernel_sz <= 81) && is_kernel_sz_odd && is_g77 ? ConvolutionMethod::INDIRECT : ConvolutionMethod::DIRECT; + preferred_conv_method = (kernel_sz > 1) && (kernel_sz <= 81) && is_kernel_sz_odd && is_g77 + ? ConvolutionMethod::INDIRECT + : ConvolutionMethod::DIRECT; } // Direct/indirect convolution used for the first layer of the network - if(workload_gte_8192 && !is_ifm_ge_16 && !is_unit_stride && is_ofm_lt_64) + if (workload_gte_8192 && !is_ifm_ge_16 && !is_unit_stride && is_ofm_lt_64) { // In general, the question we should ask for the first convolution layer of a model is: // when the execution time of im2col + gemm < direct?. Since im2col does not depend on the OFM, it means that @@ -337,13 +390,13 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const return preferred_conv_method; } - if((is_large_kernel_sz || is_m_one) && workload_gte_8192 && is_ifm_ge_16) + if ((is_large_kernel_sz || is_m_one) && workload_gte_8192 && is_ifm_ge_16) { return preferred_conv_method; } // Direct convolution used for the last layer of the network - if(is_ofm_lte_8) + if (is_ofm_lte_8) { return preferred_conv_method; } |