| /* |
| * Copyright (c) 2020-2021, 2024 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/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/core/WindowIterator.h" |
| #include "arm_compute/runtime/NEON/NEFunctions.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include "support/ToolchainSupport.h" |
| #include "utils/Utils.h" |
| |
| #include <cstdlib> |
| |
| using namespace arm_compute; |
| using namespace utils; |
| |
| QuantizationInfo dynamic_qinfo(QuantizationInfo qinfo) |
| { |
| return QuantizationInfo(qinfo.scale(), qinfo.offset(), true); |
| } |
| void set_qinfo_dynamic(Tensor &t) |
| { |
| t.info()->set_quantization_info(dynamic_qinfo(t.info()->quantization_info())); |
| } |
| |
| void quantize(Tensor &qt, const Tensor &t, float min, float max) |
| { |
| DataType dt = DataType::QASYMM8_SIGNED; |
| |
| // Determine the scale |
| const float scale = (max - min) / 256.0f; |
| |
| // Determine the zero-point; using affine equation val = (qval-zerop) * scale |
| const float zero_point = -128.0f - min / scale; |
| |
| QuantizationInfo qinfo(scale, (int32_t)round(zero_point), true); |
| |
| // We now have the quantisation info and can configure the quantised tensor |
| qt.allocator()->init(TensorInfo(t.info()->tensor_shape(), 1, dt, qinfo)); |
| qt.allocator()->allocate(); |
| NEQuantizationLayer quantization; |
| quantization.configure(&t, &qt); |
| quantization.run(); |
| } |
| |
| void invert_qinfo_offset(Tensor &t) |
| { |
| QuantizationInfo qinfo = t.info()->quantization_info(); |
| t.info()->set_quantization_info(QuantizationInfo(qinfo.scale()[0], -qinfo.offset()[0], qinfo.is_dynamic())); |
| } |
| |
| void print_quantization_info(const Tensor &t, const std::string &name_prefix) |
| { |
| QuantizationInfo qinfo = t.info()->quantization_info(); |
| std::cout << name_prefix << "_qinfo=" |
| << "QuantizationInfo(" << qinfo.scale()[0] << ", " << qinfo.offset()[0] << ")\n"; |
| } |
| |
| int main(int argc, char **argv) |
| { |
| size_t M = 4; |
| size_t N = 4; |
| size_t K = 4; |
| |
| // 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); |
| } |
| |
| /*** Floating point matrix multiplication ***/ |
| |
| // Initialise input matrices |
| NEGEMM fgemm{}; |
| |
| Tensor src1; |
| Tensor src2; |
| Tensor dst; |
| src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32)); |
| src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32)); |
| dst.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32)); |
| fgemm.configure(&src1, &src2, nullptr, &dst, 1, 0); |
| |
| // Allocate matrices |
| src1.allocator()->allocate(); |
| src2.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| float min1 = 0.0f; |
| float max1 = 1.0f; |
| fill_random_tensor(src1, 0, min1, max1); |
| |
| float min2 = -1.0f; |
| float max2 = 2.0f; |
| fill_random_tensor(src2, 1, min2, max2); |
| |
| // Run single precision gemm and print result |
| fgemm.run(); |
| |
| #if ARM_COMPUTE_DEBUG_ENABLED |
| std::cout << "# F32 GEMM result:\n"; |
| std::cout << "src1=[ \n"; |
| src1.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "src2=[ \n"; |
| src2.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "dst=[ \n"; |
| dst.print(std::cout); |
| std::cout << "] \n"; |
| #endif // ARM_COMPUTE_DEBUG_ENABLED |
| |
| Tensor q_src1; |
| quantize(q_src1, src1, min1, max1); |
| print_quantization_info(q_src1, "src1"); |
| q_src1.info()->set_are_values_constant(false); |
| |
| // NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset |
| // compared to NEQuantizeLayer |
| invert_qinfo_offset(q_src1); |
| |
| Tensor q_src2; |
| quantize(q_src2, src2, min2, max2); |
| print_quantization_info(q_src2, "src2"); |
| q_src2.info()->set_are_values_constant(false); |
| |
| // NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset |
| // compared to NEQuantizeLayer |
| invert_qinfo_offset(q_src2); |
| |
| // q_dst will be Dequantized to F32 so it doesn't need a QuantizationInfo |
| Tensor q_dst; |
| q_dst.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32)); |
| |
| // Configure low precision gemm and initialise result tensor (pre-output) |
| NEGEMMLowpMatrixMultiplyCore qgemm; |
| qgemm.configure(&q_src1, &q_src2, nullptr, &q_dst); |
| |
| q_dst.allocator()->allocate(); |
| |
| // Run low precision matrix multiply kernel |
| qgemm.run(); |
| |
| #if ARM_COMPUTE_DEBUG_ENABLED |
| // Print quantized source matrices |
| std::cout << "q_src1=[ \n"; |
| q_src1.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "q_src2=[ \n"; |
| q_src2.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "# Lowp GEMM output (FP32):\n"; |
| std::cout << "q_dst=[ \n"; |
| q_dst.print(std::cout); |
| std::cout << "] \n"; |
| |
| // Expected result |
| std::cout << "# Expected result:\n"; |
| std::cout << "dst=[ \n"; |
| dst.print(std::cout); |
| std::cout << "] \n"; |
| #endif // ARM_COMPUTE_DEBUG_ENABLED |
| |
| // Rerun to test the ability to modify the Tensor contents and QuantizationInfo (dynamic quantization) |
| min1 = -1.0f; |
| max1 = 1.0f; |
| fill_random_tensor(src1, 2, min1, max1); |
| |
| #if ARM_COMPUTE_DEBUG_ENABLED |
| std::cout << "# Refilled src1\n"; |
| std::cout << "src1=[ \n"; |
| src1.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "src2=[ \n"; |
| src2.print(std::cout); |
| std::cout << "] \n"; |
| #endif // ARM_COMPUTE_DEBUG_ENABLED |
| |
| fgemm.run(); |
| |
| quantize(q_src1, src1, min1, max1); |
| set_qinfo_dynamic(q_src1); |
| print_quantization_info(q_src1, "src1"); |
| |
| // NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset |
| // compared to NEQuantizeLayer |
| invert_qinfo_offset(q_src1); |
| |
| qgemm.run(); |
| |
| #if ARM_COMPUTE_DEBUG_ENABLED |
| // Print quantized source matrices |
| std::cout << "q_src1=[ \n"; |
| q_src1.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "q_src2=[ \n"; |
| q_src2.print(std::cout); |
| std::cout << "] \n"; |
| std::cout << "# Lowp GEMM output (FP32):\n"; |
| std::cout << "q_dst=[ \n"; |
| q_dst.print(std::cout); |
| std::cout << "] \n"; |
| |
| // Expected result |
| std::cout << "# Expected result:\n"; |
| std::cout << "dst=[ \n"; |
| dst.print(std::cout); |
| std::cout << "] \n"; |
| #endif // ARM_COMPUTE_DEBUG_ENABLED |
| } |