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| -rw-r--r-- | examples/neon_gemm_qasymm8.cpp | 260 |
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diff --git a/examples/neon_gemm_qasymm8.cpp b/examples/neon_gemm_qasymm8.cpp new file mode 100644 index 0000000000..f028e004c2 --- /dev/null +++ b/examples/neon_gemm_qasymm8.cpp @@ -0,0 +1,260 @@ +/* + * Copyright (c) 2020 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/Types.h" +#include "arm_compute/core/WindowIterator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/NEON/NEFunctions.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "utils/Utils.h" +#include "support/ToolchainSupport.h" + +#include <cstdlib> + +using namespace arm_compute; +using namespace utils; + +// Find min and max value in a float array +void find_min_max(int size, const float *data, float *min, float *max) +{ + *min = *max = data[0]; + for(int i = 0; i < size; i++) + { + const float val = data[i]; + *min = std::min(*min, val); + *max = std::max(*max, val); + } +} + +// Return reasonable quantisation parameters to use for an array of floats +// based on min and max values +QuantizationInfo choose_quantization_params(float min, float max) +{ + // Extend the [min,max] interval to contain 0 so we can represent it exactly + min = std::min(min, 0.f); + max = std::max(max, 0.f); + + // Set the quantized min and max in float values + const float qmin = 0; + const float qmax = 255; + + // Determine the scale + const float scale = (max - min) / (qmax - qmin); + + // Determine the zero-point; using affine equation val = (qval-zerop) * scale + const float zero_point_real = qmin - min / scale; + + // But we need to nudge the zero_point to an integer (exact quantized value) + std::uint8_t zero_point_nudged = 0; + if(zero_point_real < qmin) + { + zero_point_nudged = qmin; + } + else if(zero_point_real > qmax) + { + zero_point_nudged = qmax; + } + else + { + zero_point_nudged = static_cast<std::uint8_t>(support::cpp11::round(zero_point_real)); + } + + QuantizationInfo qinfo = QuantizationInfo(scale, zero_point_nudged); + return qinfo; +} + +void quantize_values(int size, qasymm8_t *output, float *input, const QuantizationInfo qinfo) +{ + for(int i = 0; i < size; i++) + { + output[i] = quantize_qasymm8(input[i], qinfo); + } + std::cout << "\n"; +} + +int main(int argc, char **argv) +{ + Tensor src1; + Tensor src2; + Tensor dst0; + Tensor q_src1; + Tensor q_src2; + Tensor q_dst0; + Tensor q_res; + Tensor q_res_output; + size_t M = 4; + size_t N = 4; + size_t K = 4; + bool default_input = true; + + // Parse args + if(argc < 3) /* case default matrix sizes */ + { + // Print help + std::cout << "Usage: ./build/neon_gemm_qasymm8 M N K\n"; + std::cout << "Too few or no inputs provided. Using default M=4, N=4, K=4\n\n"; + } + else /* case M N K arguments provided */ + { + M = strtol(argv[1], nullptr, 10); + N = strtol(argv[2], nullptr, 10); + K = strtol(argv[3], nullptr, 10); + default_input = false; + } + + /*** Floating point matrix multiplication ***/ + + // Initialise input matrices + NEGEMM fgemm{}; + + src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32)); + src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32)); + dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32)); + fgemm.configure(&src1, &src2, nullptr, &dst0, 1, 0); + + // Allocate matrices + src1.allocator()->allocate(); + src2.allocator()->allocate(); + dst0.allocator()->allocate(); + + // Fill in tensors, by default fill in with known data - for easy testing + auto *src1_ptr = reinterpret_cast<float *>(src1.buffer()); + auto *src2_ptr = reinterpret_cast<float *>(src2.buffer()); + auto *dst0_ptr = reinterpret_cast<float *>(dst0.buffer()); + + // Fill in: one is the identity matrix, other is sequential values + // src1: Identity matrix + for(size_t i = 0; i < M * K; i++) { + src1_ptr[i] = 0; + } + for(size_t i = 0; i < M; i++) { + src1_ptr[i * K + i] = 1.0f; + } + + // src2: Sequential values matrix + for(size_t i = 0; i < K * N; i++) { + src2_ptr[i] = i * 1.123f; + } + + // Otherwise if M, N, K is given, fill in with random values + if(!default_input) + { + fill_random_tensor(src1, 0.f, 1.f); + fill_random_tensor(src2, 0.f, 1.f); + } + + // Run single precision gemm and print result + fgemm.run(); + +#if ARM_COMPUTE_DEBUG_ENABLED + std::cout << "Result matrix:\n"; + src1.print(std::cout); + src2.print(std::cout); + dst0.print(std::cout); +#endif // ARM_COMPUTE_DEBUG_ENABLED + + /*** Quantised asymmetric 8bit matrix multiplication ***/ + + // Start by finding the quantisation parameters for each set of values + float src1_min; + float src1_max; + float src2_min; + float src2_max; + float dst0_min; + float dst0_max; + + find_min_max(M * K, src1_ptr, &src1_min, &src1_max); + find_min_max(K * N, src2_ptr, &src2_min, &src2_max); + find_min_max(M * N, dst0_ptr, &dst0_min, &dst0_max); + + const QuantizationInfo src1_qinfo = choose_quantization_params(src1_min, src1_max); + const QuantizationInfo src2_qinfo = choose_quantization_params(src2_min, src2_max); + const QuantizationInfo dst0_qinfo = choose_quantization_params(dst0_min, dst0_max); + + std::cout << "Matrix 1: min=" << src1_min << ", max=" << src1_max << ", "; + std::cout << "QuantisationInfo(" << src1_qinfo.scale()[0] << ", " << src1_qinfo.offset()[0] << ")\n"; + std::cout << "Matrix 2: min=" << src2_min << ", max=" << src2_max << ", "; + std::cout << "QuantisationInfo(" << src2_qinfo.scale()[0] << ", " << src2_qinfo.offset()[0] << ")\n"; + std::cout << "Result : min=" << dst0_min << ", max=" << dst0_max << ", "; + std::cout << "QuantisationInfo(" << dst0_qinfo.scale()[0] << ", " << dst0_qinfo.offset()[0] << ")\n"; + + // We now have the quantisation info and can configure the quantised tensors + q_src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::QASYMM8, src1_qinfo)); + q_src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::QASYMM8, src2_qinfo)); + q_dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::QASYMM8, dst0_qinfo)); + + // In this approach we use the QuantizationLayer construct to perform quantization + NEQuantizationLayer q1; + NEQuantizationLayer q2; + NEQuantizationLayer q3; + q1.configure(&src1, &q_src1); + q2.configure(&src2, &q_src2); + q3.configure(&dst0, &q_dst0); + + // Configure low precision gemm and initialise result tensor (pre-output) + NEGEMMLowpMatrixMultiplyCore qgemm; + q_res.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::S32)); + qgemm.configure(&q_src1, &q_src2, nullptr, &q_res); + + // Configure output stage after computing shift and multiplier parameters + NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint gemmlowp_output_stage; + int output_multiplier; + int output_shift; + float multiplier = (src1_qinfo.uniform().scale * src2_qinfo.uniform().scale) / dst0_qinfo.uniform().scale; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + std::cout << "(q_multiplier, q_shift) = (" << output_multiplier << ", " << output_shift << ")\n\n"; + gemmlowp_output_stage.configure(&q_res, nullptr, &q_res_output, output_multiplier, output_shift, dst0_qinfo.uniform().offset); + + // Allocate all tensors + q_src1.allocator()->allocate(); + q_src2.allocator()->allocate(); + q_dst0.allocator()->allocate(); + q_res.allocator()->allocate(); + q_res_output.allocator()->allocate(); + + // Run quantization layers (quantizes values of each tensor) + q1.run(); + q2.run(); + q3.run(); + // Run low precision matrix multiply kernel + qgemm.run(); + // Run output stage kernel + gemmlowp_output_stage.run(); + std::cout << "Done\n"; + +#if ARM_COMPUTE_DEBUG_ENABLED + // Print quantized source matrices + q_src1.print(std::cout); + q_src2.print(std::cout); + // Print result matrix in int32 form - before output stage processing + std::cout << "Lowp GEMM output (int32):\n"; + q_res.print(std::cout); + // Print QASYMM8 (quantized) matrix + std::cout << "Output pipeline result matrix:\n"; + q_res_output.print(std::cout); + + // Expected result + std::cout << "Expected result:\n"; + q_dst0.print(std::cout); +#endif // ARM_COMPUTE_DEBUG_ENABLED +} |