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Diffstat (limited to 'examples/neon_gemm_s8_f32.cpp')
| -rw-r--r-- | examples/neon_gemm_s8_f32.cpp | 239 |
1 files changed, 239 insertions, 0 deletions
diff --git a/examples/neon_gemm_s8_f32.cpp b/examples/neon_gemm_s8_f32.cpp new file mode 100644 index 0000000000..7c1497ec41 --- /dev/null +++ b/examples/neon_gemm_s8_f32.cpp @@ -0,0 +1,239 @@ +/* + * 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 +} |