diff options
Diffstat (limited to 'src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp')
| -rw-r--r-- | src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp b/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp new file mode 100644 index 0000000000..3380d8f1b7 --- /dev/null +++ b/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp @@ -0,0 +1,167 @@ +/* + * Copyright (c) 2022 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 "src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/GPUTarget.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +namespace arm_compute +{ +namespace cl_indirect_conv +{ +using namespace arm_compute::misc::shape_calculator; + +ClIndirectConvDefaultConfigValhall::ClIndirectConvDefaultConfigValhall(GPUTarget gpu) : IClIndirectConvKernelConfig(gpu) +{ +} + +DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure(const ITensorInfo *src, + const ITensorInfo *wei, + const PadStrideInfo &conv_info) +{ + using ConfigurationFunctionExecutorPtr = DirectConvComputeKernelInfo (ClIndirectConvDefaultConfigValhall::*)( + const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info); + + ClIndirectConvConfigArray<ConfigurationFunctionExecutorPtr> configs_G77( + &ClIndirectConvDefaultConfigValhall::configure_G77_f32, &ClIndirectConvDefaultConfigValhall::configure_G77_f16); + + // Important note: Indirect convolution should not be used when the kernel size is 1x1 (pointwise). The reason is because the indirect buffer makes + // indirect convolution less efficient than direct convolution or gemm. For this reason, the heuristic of indirect convolution has not been tuned + // for the pointwise convolution cases. + + ConfigurationFunctionExecutorPtr func = configs_G77.get_function(src->data_type()); + + ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not supported for indirect convolution"); + return (this->*func)(src, wei, conv_info); +} + +DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure_G77_f32(const ITensorInfo *src, + const ITensorInfo *wei, + const PadStrideInfo &conv_info) +{ + DirectConvComputeKernelInfo desc; + + if (src->data_layout() == DataLayout::NHWC) + { + const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info); + const bool export_weights_to_cl_image = export_to_cl_image(wei); + const int32_t stride_x = conv_info.stride().first; + const int32_t stride_y = conv_info.stride().second; + const int32_t ofm = dst_shape[0]; + const int32_t m = (dst_shape[1] / stride_x) * (dst_shape[2] / stride_y); + + desc.export_weights_to_cl_image = export_weights_to_cl_image; + + if (ofm <= 4) + { + desc.m0 = 1; + desc.n0 = 2; + desc.k0 = 16; + } + else + { + // The 16000 threshold value has been identified as the right + // one for using the biggest block size allowed on F32: 5x4x4 + if (m < 16000) + { + desc.m0 = 4; + desc.n0 = 4; + desc.k0 = 4; + } + else + { + desc.m0 = 5; + desc.n0 = 4; + desc.k0 = 4; + } + } + } + + return desc; +} + +DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure_G77_f16(const ITensorInfo *src, + const ITensorInfo *wei, + const PadStrideInfo &conv_info) +{ + DirectConvComputeKernelInfo desc; + + if (src->data_layout() == DataLayout::NHWC) + { + const TensorShape wei_shape = wei->tensor_shape(); + const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info); + const bool export_weights_to_cl_image = export_to_cl_image(wei); + + const int32_t ofm = dst_shape[0]; + const int32_t m = dst_shape[1] * dst_shape[2]; + const int32_t k = wei_shape[0]; + + desc.export_weights_to_cl_image = export_weights_to_cl_image; + + if (ofm <= 4) + { + // k0 should be as larger as possible. However, we should avoid + // having left-over for loops that make the implementation slower. + if ((k % 16) == 0) + { + desc.k0 = 16; + } + else if ((k % 8) == 0) + { + desc.k0 = 8; + } + else + { + desc.k0 = 4; + } + + desc.m0 = 1; + desc.n0 = ofm; + } + else + { + // The 16000 threshold value has been identified as the right + // one for using the biggest block size allowed on F16: 8x4 + if (m >= 16000 && k < 4) + { + desc.m0 = 8; + desc.n0 = 4; + desc.k0 = 4; // k0 is clamped to k inside the kernel when k is less than 4 + } + else + { + desc.m0 = 5; + desc.n0 = 4; + desc.k0 = 8; + } + } + } + + return desc; +} +} // namespace cl_indirect_conv +} // namespace arm_compute |