From f727ef49e2109bdac105dd6575d2e336adf780a3 Mon Sep 17 00:00:00 2001 From: Freddie Liardet Date: Mon, 18 Oct 2021 13:28:57 +0100 Subject: Add uint8/int8 support to cpu conv3d Add support for qasymm8/qasymm8_signed in cpu conv3d. Resolves: COMPMID-4665 Signed-off-by: Freddie Liardet Change-Id: I2450bb6f24969745c8b936f4b657bd406b788c57 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6478 Tested-by: Arm Jenkins Reviewed-by: Giorgio Arena Comments-Addressed: Arm Jenkins --- arm_compute/runtime/NEON/functions/NEConv3D.h | 2 + docs/user_guide/operator_list.dox | 2 + src/cpu/kernels/CpuDirectConv3dKernel.cpp | 28 ++- src/cpu/kernels/CpuDirectConv3dKernel.h | 2 + src/cpu/kernels/conv3d/neon/list.h | 2 + src/cpu/kernels/conv3d/neon/quantized.h | 256 ++++++++++++++++++++++++++ src/cpu/operators/CpuDirectConv3d.h | 2 + tests/validation/NEON/Convolution3D.cpp | 224 ++++++++++++++++++++++ tests/validation/NEON/DirectConvolution3D.cpp | 155 ---------------- 9 files changed, 512 insertions(+), 161 deletions(-) create mode 100644 src/cpu/kernels/conv3d/neon/quantized.h create mode 100644 tests/validation/NEON/Convolution3D.cpp delete mode 100644 tests/validation/NEON/DirectConvolution3D.cpp diff --git a/arm_compute/runtime/NEON/functions/NEConv3D.h b/arm_compute/runtime/NEON/functions/NEConv3D.h index 2b3a45f0af..2a3c5351b0 100644 --- a/arm_compute/runtime/NEON/functions/NEConv3D.h +++ b/arm_compute/runtime/NEON/functions/NEConv3D.h @@ -66,6 +66,8 @@ public: * |:--------------|:------------------|:------|:--------------| * |F16 |F16 |F16 |F16 | * |F32 |F32 |F32 |F32 | + * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | + * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | * * @param[in] input Source tensor. 4 lower dimensions represent a single input [IFM, width, height, depth], * while every optional dimension from 5 and above represent a batch of inputs. diff --git a/docs/user_guide/operator_list.dox b/docs/user_guide/operator_list.dox index 55bfe38f55..1dfbdf6aea 100644 --- a/docs/user_guide/operator_list.dox +++ b/docs/user_guide/operator_list.dox @@ -617,6 +617,8 @@ where N = batches, C = channels, H = height, W = width, D = depth src0src1src2dst F16F16F16F16 F32F32F32F32 + QASYMM8QASYMM8S32QASYMM8 + QASYMM8_SIGNEDQASYMM8_SIGNEDS32QASYMM8_SIGNED CLConv3D diff --git a/src/cpu/kernels/CpuDirectConv3dKernel.cpp b/src/cpu/kernels/CpuDirectConv3dKernel.cpp index 595b5f1330..4f47787c93 100644 --- a/src/cpu/kernels/CpuDirectConv3dKernel.cpp +++ b/src/cpu/kernels/CpuDirectConv3dKernel.cpp @@ -76,6 +76,16 @@ static const DirectConv3dKernel available_kernels[] = "neon_fp32_directconv3d", [](const DirectConv3dSelectorData & data) { return data.dt == DataType::F32; }, REGISTER_FP32_NEON(arm_compute::cpu::directconv3d_float_neon_ndhwc) + }, + { + "neon_qasymm8_directconv3d", + [](const DirectConv3dSelectorData & data) { return data.dt == DataType::QASYMM8; }, + REGISTER_QASYMM8_NEON(arm_compute::cpu::directconv3d_quantized_neon_ndhwc) + }, + { + "neon_qasymm8_signed_directconv3d", + [](const DirectConv3dSelectorData & data) { return data.dt == DataType::QASYMM8_SIGNED; }, + REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::directconv3d_quantized_neon_ndhwc) } }; @@ -105,7 +115,7 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); ARM_COMPUTE_RETURN_ERROR_ON(src0->data_layout() != DataLayout::NDHWC); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src0); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32, DataType::QASYMM8, DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); const DataLayout data_layout = src0->data_layout(); @@ -117,10 +127,16 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons if(src2 != nullptr) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->dimension(0) != src1->dimension(0), - "biases size and number of output feature maps should match"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->num_dimensions() > 1, "biases should be one dimensional"); + if(is_data_type_quantized(src0->data_type())) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2); + } + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->dimension(0) != src1->dimension(0), "Biases size and number of dst feature maps should match"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src2->num_dimensions() > 1, "Biases should be one dimensional"); } // Checks performed when output is configured @@ -136,7 +152,7 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons return Status{}; } -} +} // namespace void CpuDirectConv3dKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, ITensorInfo *dst, const Conv3dInfo &conv_info) { diff --git a/src/cpu/kernels/CpuDirectConv3dKernel.h b/src/cpu/kernels/CpuDirectConv3dKernel.h index fc64e8518b..ff3b30f8ae 100644 --- a/src/cpu/kernels/CpuDirectConv3dKernel.h +++ b/src/cpu/kernels/CpuDirectConv3dKernel.h @@ -46,6 +46,8 @@ public: * |:--------------|:------------------|:------|:--------------| * |F16 |F16 |F16 |F16 | * |F32 |F32 |F32 |F32 | + * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | + * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | * * @param[in, out] src0 Input tensor info. * @param[in] src1 Set of kernels to convolve the input volume. diff --git a/src/cpu/kernels/conv3d/neon/list.h b/src/cpu/kernels/conv3d/neon/list.h index b24785a48f..3e2db664d7 100644 --- a/src/cpu/kernels/conv3d/neon/list.h +++ b/src/cpu/kernels/conv3d/neon/list.h @@ -29,6 +29,7 @@ #include "arm_compute/runtime/FunctionDescriptors.h" #include "src/core/NEON/wrapper/wrapper.h" #include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/conv3d/neon/quantized.h" namespace arm_compute { @@ -171,6 +172,7 @@ void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, con }, out); } + } // namespace cpu } // namespace arm_compute #endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H \ No newline at end of file diff --git a/src/cpu/kernels/conv3d/neon/quantized.h b/src/cpu/kernels/conv3d/neon/quantized.h new file mode 100644 index 0000000000..2958cd61d4 --- /dev/null +++ b/src/cpu/kernels/conv3d/neon/quantized.h @@ -0,0 +1,256 @@ +/* + * Copyright (c) 2021 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 SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H +#define SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H + +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/FunctionDescriptors.h" +#include "src/core/NEON/NEAsymm.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/core/helpers/WindowHelpers.h" + +namespace arm_compute +{ +namespace cpu +{ +template +void directconv3d_quantized_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window) +{ + const ITensor *src = src0; + const ITensor *weights = src1; + const ITensor *biases = src2; + + using vtype = wrapper::traits::neon_bitvector; + using vector_type = typename vtype::type; + using tag_type = typename vtype::tag_type; + constexpr int num_elems_read_per_iteration = 16 / sizeof(T); + using q16_t = typename wrapper::traits::promote_t; + using q32_t = typename wrapper::traits::promote_t; + using q32x4_t = typename wrapper::traits::neon_vector::type; + + const int32_t input_offset = -src->info()->quantization_info().uniform().offset; + const float input_scale = src->info()->quantization_info().uniform().scale; + const int32_t weights_offset = -weights->info()->quantization_info().uniform().offset; + const float weights_scale = weights->info()->quantization_info().uniform().scale; + const int32_t output_offset = dst->info()->quantization_info().uniform().offset; + const float output_scale = dst->info()->quantization_info().uniform().scale; + + int32_t output_multiplier = 0; + int32_t output_shift = 0; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + + // Scalar quantities (N D H W Cin) + const int element_size = src->info()->element_size(); + const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; + const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; + const int input_stride_d = src->info()->strides_in_bytes()[3] / element_size; + const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size; + const int input_dim_w = src->info()->dimension(1); + const int input_dim_h = src->info()->dimension(2); + const int input_dim_d = src->info()->dimension(3); + + // Kernel info (D H W Cin Cout) + const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size; + const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size; + const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size; + const int kernel_dim_w = weights->info()->dimension(2); + const int kernel_dim_h = weights->info()->dimension(3); + const int kernel_dim_d = weights->info()->dimension(4); + + // Convolution padding and stride + const int conv_pad_top = conv_info.padding.top; + const int conv_pad_left = conv_info.padding.left; + const int conv_pad_front = conv_info.padding.front; + const int conv_stride_w = conv_info.stride.width; + const int conv_stride_h = conv_info.stride.height; + const int conv_stride_d = conv_info.stride.depth; + + // Setup input window for the output iterator + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + // Setup input window for the weights iterator + Window window_w = calculate_max_window(*weights->info(), Steps()); + window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); + window_w.set(Window::DimW, Window::Dimension(0, 1, 1)); + window_w.set(4, Window::Dimension(0, 1, 1)); + + Iterator out(dst, window_out); + Iterator wei(weights, window_w); + + const int32_t *biases_ptr = nullptr; + if(biases != nullptr) + { + biases_ptr = reinterpret_cast(biases->buffer() + biases->info()->offset_first_element_in_bytes()); + } + execute_window_loop(window_out, [&](const Coordinates & id) + { + // We are computing the theoretical input starting points + const int in_w_start_t = static_cast(id.y()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast(id.z()) * conv_stride_h - conv_pad_top; + const int in_d_start_t = static_cast(id[3]) * conv_stride_d - conv_pad_front; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + const int in_d_end_t = in_d_start_t + kernel_dim_d; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_d_start = std::max(in_d_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + const int in_d_end = std::min(in_d_end_t, input_dim_d); + + // We use the input points to select the valid weight points to use + const int wei_w_start = in_w_start - in_w_start_t; + const int wei_h_start = in_h_start - in_h_start_t; + const int wei_d_start = in_d_start - in_d_start_t; + const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end); + const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + const int wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end); + + const int index_c_out_end = weights->info()->dimension(0); + const int index_c_in_end = weights->info()->dimension(1); + const T *const in_ptr_start = reinterpret_cast(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n; + + execute_window_loop(window_w, [&](const Coordinates & id_w) + { + /* + * This is the loop in the weights, and it goes along OFM (output feature map) + */ + const auto weights_ptr_start = reinterpret_cast(wei.ptr()); + int32_t acc = static_cast(0); + T *out_ptr = reinterpret_cast(out.ptr()); + for(int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; ++index_wei_d, ++index_in_d) + { + const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d; + const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d; + for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h) + { + const T *const in_ptr_row = in_ptr_d + index_in_h * input_stride_h; + const T *const weights_ptr_row = weights_ptr_d + index_wei_h * kernel_stride_h; + for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w) + { + const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w; + const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w; + int index_c_in = 0; + vector_type w_vec = wrapper::vdup_n(static_cast(0), tag_type()); + + q32x4_t acc_q32_0 = wrapper::vdup_n(static_cast(0), tag_type()); + q32x4_t acc_q32_1 = wrapper::vdup_n(static_cast(0), tag_type()); + q32x4_t acc_q32_2 = wrapper::vdup_n(static_cast(0), tag_type()); + q32x4_t acc_q32_3 = wrapper::vdup_n(static_cast(0), tag_type()); + + for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration; + index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_mover); + //Load Cin weights + for(unsigned int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end) + { + w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k); + } + q32x4_t src_q32_0 = wrapper::vdup_n(static_cast(input_offset), tag_type()); + q32x4_t src_q32_1 = wrapper::vdup_n(static_cast(input_offset), tag_type()); + q32x4_t src_q32_2 = wrapper::vdup_n(static_cast(input_offset), tag_type()); + q32x4_t src_q32_3 = wrapper::vdup_n(static_cast(input_offset), tag_type()); + + q32x4_t wei_q32_0 = wrapper::vdup_n(static_cast(weights_offset), tag_type()); + q32x4_t wei_q32_1 = wrapper::vdup_n(static_cast(weights_offset), tag_type()); + q32x4_t wei_q32_2 = wrapper::vdup_n(static_cast(weights_offset), tag_type()); + q32x4_t wei_q32_3 = wrapper::vdup_n(static_cast(weights_offset), tag_type()); + + const auto src_q16_0 = wrapper::vmovl(wrapper::vgetlow(src_vec)); + const auto src_q16_1 = wrapper::vmovl(wrapper::vgetlow(src_vec)); + const auto wei_q16_0 = wrapper::vmovl(wrapper::vgetlow(w_vec)); + const auto wei_q16_1 = wrapper::vmovl(wrapper::vgetlow(w_vec)); + + src_q32_0 = wrapper::vadd(src_q32_0, wrapper::vmovl(wrapper::vgetlow(src_q16_0))); + src_q32_1 = wrapper::vadd(src_q32_1, wrapper::vmovl(wrapper::vgetlow(src_q16_0))); + src_q32_2 = wrapper::vadd(src_q32_2, wrapper::vmovl(wrapper::vgethigh(src_q16_1))); + src_q32_3 = wrapper::vadd(src_q32_3, wrapper::vmovl(wrapper::vgethigh(src_q16_1))); + + wei_q32_0 = wrapper::vadd(wei_q32_0, wrapper::vmovl(wrapper::vgetlow(wei_q16_0))); + wei_q32_1 = wrapper::vadd(wei_q32_1, wrapper::vmovl(wrapper::vgetlow(wei_q16_0))); + wei_q32_2 = wrapper::vadd(wei_q32_2, wrapper::vmovl(wrapper::vgethigh(wei_q16_1))); + wei_q32_3 = wrapper::vadd(wei_q32_3, wrapper::vmovl(wrapper::vgethigh(wei_q16_1))); + + acc_q32_0 = wrapper::vmla(acc_q32_0, wei_q32_0, src_q32_0); + acc_q32_1 = wrapper::vmla(acc_q32_1, wei_q32_1, src_q32_1); + acc_q32_2 = wrapper::vmla(acc_q32_2, wei_q32_2, src_q32_2); + acc_q32_3 = wrapper::vmla(acc_q32_3, wei_q32_3, src_q32_3); + } +#if defined(__aarch64__) + acc += wrapper::vaddv(acc_q32_0); + acc += wrapper::vaddv(acc_q32_1); + acc += wrapper::vaddv(acc_q32_2); + acc += wrapper::vaddv(acc_q32_3); +#else // __aarch64__ + auto temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_0), wrapper::vgetlow(acc_q32_0)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_1), wrapper::vgetlow(acc_q32_1)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_2), wrapper::vgetlow(acc_q32_2)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + + temp = wrapper::vpadd(wrapper::vgethigh(acc_q32_3), wrapper::vgetlow(acc_q32_3)); + temp = wrapper::vpadd(temp, temp); + acc += wrapper::vgetlane(temp, 0); + +#endif // __aarch64__ + + for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end) + { + const auto src_val = *(in_ptr_mover) + input_offset; + const auto w_val = *(weights_ptr_mover) + weights_offset; + acc += src_val * w_val; + } + } + } + } + + if(biases) + { + acc += *reinterpret_cast(biases_ptr + id_w[0]); + } + + T out_val = finalize_quantization(acc, output_multiplier, output_shift, output_offset, T(0), T(0), false); + *(reinterpret_cast(out_ptr + id_w[0])) = out_val; + }, + wei); + }, + out); +} +} // namespace cpu +} // namespace arm_compute +#endif // SRC_CORE_NEON_KERNELS_CONV3D_QUANTIZED_H \ No newline at end of file diff --git a/src/cpu/operators/CpuDirectConv3d.h b/src/cpu/operators/CpuDirectConv3d.h index f7c3099be0..cde01f07c2 100644 --- a/src/cpu/operators/CpuDirectConv3d.h +++ b/src/cpu/operators/CpuDirectConv3d.h @@ -65,6 +65,8 @@ public: * |:--------------|:------------------|:------|:--------------| * |F16 |F16 |F16 |F16 | * |F32 |F32 |F32 |F32 | + * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | + * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | * * @param[in, out] src0 Input tensor info. * @param[in] src1 Set of kernels to convolve the input volume. diff --git a/tests/validation/NEON/Convolution3D.cpp b/tests/validation/NEON/Convolution3D.cpp new file mode 100644 index 0000000000..1bfac900c0 --- /dev/null +++ b/tests/validation/NEON/Convolution3D.cpp @@ -0,0 +1,224 @@ +/* + * Copyright (c) 2021 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/Helpers.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEConv3D.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/DirectConvolution3DFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +const RelativeTolerance rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */ +const AbsoluteTolerance abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */ +constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */ +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +constexpr AbsoluteTolerance tolerance_fp32(0.001f); /**< Tolerance for floating point tests */ +constexpr AbsoluteTolerance tolerance_qasymm8(1); /**< Tolerance for quantized tests */ + +/** Activation function Dataset*/ +const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +{ + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) +}); + +const auto data_precommit = combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + datasets::SmallDirectConv3DShapes(), + framework::dataset::make("StrideX", { 1, 5, 8 })), + framework::dataset::make("StrideY", { 1, 2, 3 })), + framework::dataset::make("StrideZ", { 1, 2, 1 })), + framework::dataset::make("PadX", { 0, 1, 2 })), + framework::dataset::make("PadY", { 0, 2, 1 })), + framework::dataset::make("PadZ", { 0, 3, 5 })), + framework::dataset::make("KernelWidth", { 3, 5, 9 })), + framework::dataset::make("KernelHeight", { 2, 1, 3 })), + framework::dataset::make("KernelDepth", { 1, 2, 3 })), + framework::dataset::make("NumKernels", { 2, 3, 8 })), + framework::dataset::make("HasBias", { true, false })), + ActivationFunctionsDataset); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(Convolution3D) + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Mismatching data type input/weights + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Mismatching input feature maps + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid weights dimensions + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NHWC), // Invalid data layout + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid biases size + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid biases dimensions + TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid output size + }), + framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F16), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 3U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U, 3U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), + })), + framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1U, DataType::F32), + TensorInfo(TensorShape(4U), 1U, DataType::F32), + TensorInfo(TensorShape(4U), 1U, DataType::F32), + TensorInfo(TensorShape(4U), 1U, DataType::F32), + TensorInfo(TensorShape(3U), 1U, DataType::F32), + TensorInfo(TensorShape(4U, 2U), 1U, DataType::F32), + TensorInfo(TensorShape(4U), 1U, DataType::F32), + })), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), + TensorInfo(TensorShape(26U, 11U, 4U), 1U, DataType::F32), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false })), + input_info, weights_info, biases_info, output_info, expected) +{ + const Conv3dInfo conv3d_info(Size3D(1, 1, 1), Padding3D(0, 0, 0), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false); + bool is_valid = bool(NEConv3D::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv3d_info)); + ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + +template +using NEDirectConvolution3DFixture = DirectConvolution3DValidationFixture; + +TEST_SUITE(Float) +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(data_precommit, + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NDHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} +TEST_SUITE_END() // FP32 + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(data_precommit, + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NDHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); +} +TEST_SUITE_END() // FP16 +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +TEST_SUITE_END() // Float + +template +using NEDirectConvolution3DQuantizedFixture = DirectConvolution3DValidationQuantizedFixture; + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DQuantizedFixture, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U), + TensorShape(15U, 7U, 11U, 7U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U) + }), + framework::dataset::make("StrideX", { 1, 3, 2, 1 })), + framework::dataset::make("StrideY", { 2, 1, 3, 1 })), + framework::dataset::make("StrideZ", { 3, 2, 1, 1 })), + framework::dataset::make("PadX", { 0, 2, 1, 0 })), + framework::dataset::make("PadY", { 1, 0, 2, 0 })), + framework::dataset::make("PadZ", { 2, 1, 0, 0 })), + framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })), + framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })), + framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })), + framework::dataset::make("NumKernels", { 5, 3, 1, 11 })), + framework::dataset::make("HasBias", { true, true, true, false })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(Accessor(_target), _reference, tolerance_qasymm8); +} + +TEST_SUITE_END() // QASYMM8 + +TEST_SUITE(QASYMM8_SIGNED) +FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DQuantizedFixture, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U), + TensorShape(15U, 7U, 11U, 7U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U) + }), + framework::dataset::make("StrideX", { 1, 3, 2, 1 })), + framework::dataset::make("StrideY", { 2, 1, 3, 1 })), + framework::dataset::make("StrideZ", { 3, 2, 1, 1 })), + framework::dataset::make("PadX", { 0, 2, 1, 0 })), + framework::dataset::make("PadY", { 1, 0, 2, 0 })), + framework::dataset::make("PadZ", { 2, 1, 0, 0 })), + framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })), + framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })), + framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })), + framework::dataset::make("NumKernels", { 5, 3, 1, 11 })), + framework::dataset::make("HasBias", { true, true, true, false })), + framework::dataset::make("Activation", ActivationLayerInfo())), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", DataLayout::NDHWC)), + framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))), + framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))), + framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5)))) +{ + validate(Accessor(_target), _reference, tolerance_qasymm8); +} + +TEST_SUITE_END() // QASYMM8_SIGNED +TEST_SUITE_END() // Quantized + +TEST_SUITE_END() // Convolution3D +TEST_SUITE_END() // Neon +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/NEON/DirectConvolution3D.cpp b/tests/validation/NEON/DirectConvolution3D.cpp deleted file mode 100644 index 37c901d2ac..0000000000 --- a/tests/validation/NEON/DirectConvolution3D.cpp +++ /dev/null @@ -1,155 +0,0 @@ -/* - * Copyright (c) 2021 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/Helpers.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/NEON/functions/NEConv3D.h" -#include "arm_compute/runtime/Tensor.h" -#include "arm_compute/runtime/TensorAllocator.h" -#include "tests/NEON/Accessor.h" -#include "tests/PaddingCalculator.h" -#include "tests/datasets/ShapeDatasets.h" -#include "tests/framework/Asserts.h" -#include "tests/framework/Macros.h" -#include "tests/framework/datasets/Datasets.h" -#include "tests/validation/Validation.h" -#include "tests/validation/fixtures/DirectConvolution3DFixture.h" - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace -{ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -const RelativeTolerance rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */ -const AbsoluteTolerance abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */ -constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */ -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -constexpr AbsoluteTolerance tolerance_fp32(0.001f); /**< Tolerance for floating point tests */ - -/** Activation function Dataset*/ -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", -{ - ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) -}); - -const auto data_precommit = combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip( - datasets::SmallDirectConv3DShapes(), - framework::dataset::make("StrideX", { 1, 5, 8 })), - framework::dataset::make("StrideY", { 1, 2, 3 })), - framework::dataset::make("StrideZ", { 1, 2, 1 })), - framework::dataset::make("PadX", { 0, 1, 2 })), - framework::dataset::make("PadY", { 0, 2, 1 })), - framework::dataset::make("PadZ", { 0, 3, 5 })), - framework::dataset::make("KernelWidth", { 3, 5, 9 })), - framework::dataset::make("KernelHeight", { 2, 1, 3 })), - framework::dataset::make("KernelDepth", { 1, 2, 3 })), - framework::dataset::make("NumKernels", { 2, 3, 8 })), - framework::dataset::make("HasBias", { true, false })), - ActivationFunctionsDataset); -} // namespace - -TEST_SUITE(NEON) -TEST_SUITE(Convolution3D) - -// *INDENT-OFF* -// clang-format off -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Mismatching data type input/weights - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Mismatching input feature maps - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid weights dimensions - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NHWC), // Invalid data layout - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid biases size - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid biases dimensions - TensorInfo(TensorShape(27U, 13U, 2U, 4U), 1U, DataType::F32, DataLayout::NDHWC), // Invalid output size - }), - framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F16), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 3U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U, 3U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 3U, 3U, 3U, 2U), 1U, DataType::F32), - })), - framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1U, DataType::F32), - TensorInfo(TensorShape(4U), 1U, DataType::F32), - TensorInfo(TensorShape(4U), 1U, DataType::F32), - TensorInfo(TensorShape(4U), 1U, DataType::F32), - TensorInfo(TensorShape(3U), 1U, DataType::F32), - TensorInfo(TensorShape(4U, 2U), 1U, DataType::F32), - TensorInfo(TensorShape(4U), 1U, DataType::F32), - })), - framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(25U, 11U, 4U), 1U, DataType::F32), - TensorInfo(TensorShape(26U, 11U, 4U), 1U, DataType::F32), - })), - framework::dataset::make("Expected", { false, false, false, false, false, false, false })), - input_info, weights_info, biases_info, output_info, expected) -{ - const Conv3dInfo conv3d_info(Size3D(1, 1, 1), Padding3D(0, 0, 0), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false); - bool is_valid = bool(NEConv3D::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv3d_info)); - ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); -} -// clang-format on -// *INDENT-ON* - -template -using NEDirectConvolution3DFixture = DirectConvolution3DValidationFixture; - -TEST_SUITE(Float) -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(data_precommit, - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("DataLayout", { DataLayout::NDHWC }))) -{ - // Validate output - validate(Accessor(_target), _reference, tolerance_fp32); -} -TEST_SUITE_END() // FP32 - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolution3DFixture, framework::DatasetMode::PRECOMMIT, combine(combine(data_precommit, - framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("DataLayout", { DataLayout::NDHWC }))) -{ - // Validate output - validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); -} -TEST_SUITE_END() // FP16 -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - -TEST_SUITE_END() // Float -TEST_SUITE_END() // Convolution3D -TEST_SUITE_END() // Neon -} // namespace validation -} // namespace test -} // namespace arm_compute -- cgit v1.2.1