| /* |
| * 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 |
| } |