hmz007
36ed224bac
Signed-off-by: hmz007 <hmz007@gmail.com> Change-Id: Ib87fdbe7a0be7dc961af3500fe9ea4589a127f9f
2097 lines
82 KiB
C++
2097 lines
82 KiB
C++
/*
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* Copyright (c) 2017-2022 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE
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#define ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE
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#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/reference/GEMMLowp.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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template <typename U>
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void fill(U &&tensor, int i)
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{
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switch(tensor.data_type())
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{
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case DataType::QSYMM8_PER_CHANNEL:
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{
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int min_bound = 128;
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int max_bound = -127;
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for(size_t j = 0; j < tensor.quantization_info().scale().size(); j++)
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{
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std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f, i);
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if(bounds.first < min_bound)
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{
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min_bound = bounds.first;
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}
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if(bounds.second > max_bound)
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{
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max_bound = bounds.second;
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}
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}
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std::uniform_int_distribution<int32_t> distribution(min_bound, max_bound);
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::QASYMM8:
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{
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std::uniform_int_distribution<uint32_t> distribution(1, 254);
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::S32:
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{
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std::uniform_int_distribution<int32_t> distribution(-20000, 20000);
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::F16:
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{
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arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::F32:
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{
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std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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library->fill_tensor_uniform(tensor, i);
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}
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}
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template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false, bool run_twice = false>
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TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
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GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo(), DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8,
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QuantizationInfo b_qinfo = QuantizationInfo(), bool reshape_b_only_on_first_run = false)
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{
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// Create tensors
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DataType data_type_output = output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : data_type_a;
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TensorType a = create_tensor<TensorType>(shape_a, data_type_a, 1);
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TensorType b = create_tensor<TensorType>(shape_b, data_type_b, 1); // gemm output before output stage mismatch if i pass data_layout_output here. to be investigated
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TensorType output = create_tensor<TensorType>(shape_output, data_type_output, 1);
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a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
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if(data_type_b == DataType::QSYMM8_PER_CHANNEL)
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{
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b.info()->set_quantization_info(b_qinfo);
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}
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else
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{
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b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
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}
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TensorType bias;
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if(is_fused)
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{
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TensorShape bias_shape(shape_b[0]);
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bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1);
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}
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// Create and configure function
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// The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
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FunctionType gemmlowp;
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gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, reshape_b_only_on_first_run, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false,
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output_stage));
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ARM_COMPUTE_ASSERT(a.info()->is_resizable());
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ARM_COMPUTE_ASSERT(b.info()->is_resizable());
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ARM_COMPUTE_ASSERT(output.info()->is_resizable());
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add_padding_x({ &a, &b, &output });
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// Allocate tensors
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a.allocator()->allocate();
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b.allocator()->allocate();
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output.allocator()->allocate();
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ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!output.info()->is_resizable());
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// Fill tensors
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fill(AccessorType(a), 0);
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fill(AccessorType(b), 1);
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if(is_fused)
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{
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ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
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bias.allocator()->allocate();
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ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
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fill(AccessorType(bias), 2);
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}
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// Run with variable inputs.
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if(run_twice)
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{
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gemmlowp.run();
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fill(AccessorType(a), 3); // Fill tensors with new seed after run
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fill(AccessorType(b), 4);
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if(is_fused)
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{
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fill(AccessorType(bias), 5);
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}
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}
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// Compute GEMM function
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gemmlowp.run();
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return output;
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}
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template <bool reinterpret_input_as_3d, typename TI = uint8_t, typename TW = uint8_t, bool pretranspose_A = false, bool pretranspose_B = false, bool run_twice = false>
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SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
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DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo())
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{
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TensorShape shape_a_to_use = shape_a;
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if(reinterpret_input_as_3d)
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{
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// Collapse the second and third dimension if the input is 3D
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shape_a_to_use.collapse(2U, 1U);
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}
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// Create reference
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SimpleTensor<TI> a{ shape_a_to_use, data_type_a, 1 };
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SimpleTensor<TW> b{ shape_b, data_type_b, 1, data_type_b == DataType::QSYMM8_PER_CHANNEL ? b_qinfo : QuantizationInfo(1.0f / 255, b_offset) };
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TensorShape shape_a_to_use_transposed{ shape_a_to_use };
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TensorShape shape_b_transposed{ shape_b };
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shape_a_to_use_transposed.set(0, shape_a_to_use[1]);
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shape_a_to_use_transposed.set(1, shape_a_to_use[0]);
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shape_b_transposed.set(0, shape_b[1]);
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shape_b_transposed.set(1, shape_b[0]);
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SimpleTensor<TI> a_transposed{ shape_a_to_use_transposed, data_type_a, 1 };
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SimpleTensor<TW> b_transposed{ shape_b_transposed, data_type_b, 1, data_type_b == DataType::QSYMM8_PER_CHANNEL ? b_qinfo : QuantizationInfo(1.0f / 255, b_offset) };
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// Fill reference
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fill(a, 0);
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fill(b, 1);
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// Transpose reference if required
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/* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
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therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
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in order to be able to call reference implementation that works with (B x M x K) input.
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Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */
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if(pretranspose_A)
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{
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transpose_matrix<TI>(a, a_transposed);
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}
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if(pretranspose_B)
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{
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transpose_matrix<TW>(b, b_transposed);
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}
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// Run with variable inputs.
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if(run_twice)
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{
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reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>((pretranspose_A ? a_transposed : a), (pretranspose_B ? b_transposed : b), shape_output, a_offset, b_offset);
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fill((pretranspose_A) ? a_transposed : a, 3);
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fill((pretranspose_B) ? b_transposed : b, 4);
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}
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return reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>((pretranspose_A ? a_transposed : a), (pretranspose_B ? b_transposed : b), shape_output, a_offset, b_offset);
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}
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}
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template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, bool run_twice = false>
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class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset)
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{
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_target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset);
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_reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset);
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}
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protected:
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TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
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{
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return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t, false, run_twice>(shape_a, shape_b, shape_output, a_offset,
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b_offset);
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}
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SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
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{
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return compute_gemmlowp_reference<reinterpret_input_as_3d, uint8_t, uint8_t, false, false, run_twice>(shape_a, shape_b, shape_output, a_offset, b_offset);
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}
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TensorType _target{};
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SimpleTensor<int32_t> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t, bool run_twice = false>
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class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputGenericValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage, DataType data_type_b,
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bool reshape_b_only_on_first_run)
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{
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ARM_COMPUTE_ASSERT(output_stage.type != GEMMLowpOutputStageType::NONE);
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DataType data_type_a = data_type_b == DataType::QASYMM8_SIGNED ? DataType::QASYMM8_SIGNED : DataType::QASYMM8;
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if(data_type_b == DataType::QSYMM8_PER_CHANNEL)
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{
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output_stage.is_quantized_per_channel = true;
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const size_t num_channels = shape_b[0];
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std::vector<float> scales(num_channels);
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std::uniform_real_distribution<float> distribution(0.f, 1.f);
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library->fill(scales, distribution, 0);
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output_stage.gemmlowp_multipliers.resize(num_channels);
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output_stage.gemmlowp_shifts.resize(num_channels);
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for(size_t i = 0; i < num_channels; ++i)
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{
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quantization::calculate_quantized_multiplier(scales[i], &output_stage.gemmlowp_multipliers[i], &output_stage.gemmlowp_shifts[i]);
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}
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_reference = compute_reference(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales));
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_target = compute_target(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales), reshape_b_only_on_first_run);
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}
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else
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{
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_reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo());
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_target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo(), reshape_b_only_on_first_run);
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}
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}
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protected:
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TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage,
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DataType data_type_a, DataType data_type_b, QuantizationInfo b_qinfo, bool reshape_b_only_on_first_run = false)
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{
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return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true, run_twice>(shape_a, shape_b, shape_output, a_offset,
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b_offset,
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output_stage, data_type_a, data_type_b, b_qinfo, reshape_b_only_on_first_run);
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}
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SimpleTensor<TI> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
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GEMMLowpOutputStageInfo output_stage, DataType data_type_a, DataType data_type_b, QuantizationInfo b_qinfo)
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{
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SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TI, TW, false, false, run_twice>(shape_a, shape_b, shape_output, a_offset, b_offset, data_type_a, data_type_b,
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b_qinfo);
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TensorShape bias_shape(shape_b[0]);
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SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
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(run_twice) ? fill(bias, 5) : fill(bias, 2); // Fill bias with same seed as last run of gemmlowp_target
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switch(output_stage.type)
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{
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case GEMMLowpOutputStageType::QUANTIZE_DOWN:
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return reference::gemmlowp_quantize_down_scale<int32_t, TW>(output, bias,
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output_stage.gemmlowp_offset, output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
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break;
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case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
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return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TW>(output, bias,
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output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
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break;
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default:
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ARM_COMPUTE_ERROR("Not Supported!");
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}
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}
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TensorType _target{};
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SimpleTensor<TI> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t>
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class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public
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GEMMLowpMatrixMultiplyCoreFusedOffsetOutputGenericValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW>
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{
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public:
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template <typename...>
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void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage, DataType data_type_b)
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{
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GEMMLowpMatrixMultiplyCoreFusedOffsetOutputGenericValidationFixture<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, TI, TW>::setup(shape_a, shape_b,
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shape_output, a_offset, b_offset, output_stage, data_type_b, false);
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType>
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class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
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{
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_target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
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_reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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std::uniform_int_distribution<> distribution(-6000, 6000);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
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{
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TensorShape shape_bias(shape[0]);
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// Create tensors
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TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
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TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
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TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);
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// Create and configure function
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FunctionType output_stage;
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GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
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output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
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output_stage_info.gemmlowp_offset = result_offset;
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output_stage_info.gemmlowp_multiplier = result_mult_int;
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output_stage_info.gemmlowp_shift = result_shift;
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output_stage_info.gemmlowp_min_bound = min;
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output_stage_info.gemmlowp_max_bound = max;
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output_stage_info.output_data_type = DataType::QASYMM8;
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output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<int32_t> result_mult_int_vec = { result_mult_int };
|
|
const std::vector<int32_t> result_shift_vec = { result_shift };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale<int32_t, uint8_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale<int32_t, uint8_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<uint8_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType>
|
|
class GEMMLowpQuantizeDownInt32ToInt8ScaleValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
_target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
|
|
_reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
std::uniform_int_distribution<> distribution(-6000, 6000);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
// Create tensors
|
|
TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
|
|
TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
|
|
TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8_SIGNED, 1);
|
|
|
|
// Create and configure function
|
|
FunctionType output_stage;
|
|
GEMMLowpOutputStageInfo output_stage_info = GEMMLowpOutputStageInfo();
|
|
output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN;
|
|
output_stage_info.gemmlowp_offset = result_offset;
|
|
output_stage_info.gemmlowp_multiplier = result_mult_int;
|
|
output_stage_info.gemmlowp_shift = result_shift;
|
|
output_stage_info.gemmlowp_min_bound = min;
|
|
output_stage_info.gemmlowp_max_bound = max;
|
|
output_stage_info.output_data_type = DataType::QASYMM8_SIGNED;
|
|
output_stage.configure(&a, add_bias ? &b : nullptr, &c, output_stage_info);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<int8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<int32_t> result_mult_int_vec = { result_mult_int };
|
|
const std::vector<int32_t> result_shift_vec = { result_shift };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale<int32_t, int8_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int8_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType>
|
|
class GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
_target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
|
|
_reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
std::uniform_int_distribution<> distribution(-6000, 6000);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
// Create tensors
|
|
TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
|
|
TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
|
|
TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8_SIGNED, 1);
|
|
|
|
// Create and configure function
|
|
FunctionType output_stage;
|
|
output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<int8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max,
|
|
bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier };
|
|
const std::vector<int32_t> result_shift_vec = { result_shift };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int8_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType>
|
|
class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
_target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
|
|
_reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
std::uniform_int_distribution<> distribution(-6000, 6000);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
// Create tensors
|
|
TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
|
|
TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
|
|
TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);
|
|
|
|
// Create and configure function
|
|
FunctionType output_stage;
|
|
output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max,
|
|
bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier };
|
|
const std::vector<int32_t> result_shift_vec = { result_shift };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<uint8_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
|
|
class GEMMLowpQuantizeDownInt32ScaleByFloatValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(DataType data_type, TensorShape shape, float result_real_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
_target = compute_target(data_type, shape, result_real_multiplier, result_offset, min, max, add_bias);
|
|
_reference = compute_reference(shape, result_real_multiplier, result_offset, min, max, add_bias);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
// To avoid data all being clampped
|
|
std::uniform_int_distribution<> distribution(-500, 500);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(DataType data_type, const TensorShape &shape, float result_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
// Create tensors
|
|
TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
|
|
TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
|
|
TensorType c = create_tensor<TensorType>(shape, data_type, 1);
|
|
|
|
// create output stage info
|
|
GEMMLowpOutputStageInfo info;
|
|
info.gemmlowp_max_bound = max;
|
|
info.gemmlowp_min_bound = min;
|
|
info.gemmlowp_real_multiplier = result_multiplier;
|
|
info.gemmlowp_offset = result_offset;
|
|
info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT;
|
|
info.output_data_type = data_type;
|
|
|
|
// Create and configure function
|
|
FunctionType output_stage;
|
|
output_stage.configure(&a, add_bias ? &b : nullptr, &c, info);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<T> compute_reference(const TensorShape &shape, float_t result_real_multiplier, int32_t result_offset, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<float_t> result_float_multiplier_vec = { result_real_multiplier };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale_by_float<int32_t, T>(a, b, result_float_multiplier_vec, result_offset, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale_by_float<int32_t, T>(a, result_float_multiplier_vec, result_offset, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<T> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType>
|
|
class GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
_target = compute_target(shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias);
|
|
_reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
std::uniform_int_distribution<> distribution(-6000, 6000);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
|
|
{
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
// Create tensors
|
|
TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
|
|
TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
|
|
TensorType c = create_tensor<TensorType>(shape, DataType::QSYMM16, 1);
|
|
|
|
// Create and configure function
|
|
FunctionType output_stage;
|
|
output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, min, max);
|
|
|
|
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(c.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
a.allocator()->allocate();
|
|
c.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!c.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(a), 0);
|
|
|
|
if(add_bias)
|
|
{
|
|
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
|
|
|
|
// Allocate bias tensor
|
|
b.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
|
|
|
|
// Fill tensor
|
|
fill(AccessorType(b), 1);
|
|
}
|
|
|
|
// Compute GEMM function
|
|
output_stage.run();
|
|
return c;
|
|
}
|
|
|
|
SimpleTensor<int16_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t min, int32_t max,
|
|
bool add_bias)
|
|
{
|
|
// Create reference
|
|
TensorShape shape_bias(shape[0]);
|
|
|
|
SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(a, 0);
|
|
|
|
const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier };
|
|
const std::vector<int32_t> result_shift_vec = { result_shift };
|
|
|
|
if(add_bias)
|
|
{
|
|
// Fill bias
|
|
fill(b, 1);
|
|
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max);
|
|
}
|
|
else
|
|
{
|
|
return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int16_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
|
|
class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
|
|
bool interleave_rhs, DataType data_type)
|
|
{
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
lhs_info.v0 = v0;
|
|
lhs_info.interleave = interleave_lhs;
|
|
lhs_info.transpose = false;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
rhs_info.h0 = h0;
|
|
rhs_info.interleave = interleave_rhs;
|
|
rhs_info.transpose = true;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type);
|
|
_reference = compute_reference(lhs_shape, rhs_shape, data_type);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-127, 126);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
|
|
TensorType lhs_reshaped;
|
|
TensorType rhs_reshaped;
|
|
TensorType dst;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
ReshapeLHSOperatorType reshape_lhs;
|
|
ReshapeRHSOperatorType reshape_rhs;
|
|
GEMMFunctionType gemm;
|
|
reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
|
|
reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
|
|
gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
|
|
add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &dst });
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
lhs_reshaped.allocator()->allocate();
|
|
rhs_reshaped.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!lhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
|
|
// Compute GEMM
|
|
ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
|
|
reshape_lhs.run(reshape_lhs_pack);
|
|
ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
|
|
reshape_rhs.run(reshape_rhs_pack);
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } });
|
|
gemm.run(gemm_pack);
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape[0] = rhs_shape[0];
|
|
dst_shape[1] = lhs_shape[1];
|
|
|
|
switch(data_type)
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
// Create reference
|
|
SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
|
|
class GEMMLowpMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
|
|
bool interleave_lhs, bool interleave_rhs, DataType data_type)
|
|
{
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
lhs_info.v0 = v0;
|
|
lhs_info.interleave = interleave_lhs;
|
|
lhs_info.transpose = false;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
rhs_info.h0 = h0;
|
|
rhs_info.interleave = interleave_rhs;
|
|
rhs_info.transpose = true;
|
|
|
|
// In case of GEMM3D, m is the product between m_w and m_h
|
|
const unsigned int m = m_w * m_h;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h, data_type);
|
|
_reference = compute_reference(lhs_shape, rhs_shape, m_h, data_type);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-127, 126);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h,
|
|
DataType data_type)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
|
|
TensorType lhs_reshaped;
|
|
TensorType rhs_reshaped;
|
|
TensorType dst;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
ReshapeLHSOperatorType reshape_lhs;
|
|
ReshapeRHSOperatorType reshape_rhs;
|
|
GEMMFunctionType gemm;
|
|
reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
|
|
reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
|
|
gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
|
|
add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &dst });
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
lhs_reshaped.allocator()->allocate();
|
|
rhs_reshaped.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!lhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
|
|
// Compute GEMM
|
|
ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
|
|
reshape_lhs.run(reshape_lhs_pack);
|
|
ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
|
|
reshape_rhs.run(reshape_rhs_pack);
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } });
|
|
gemm.run(gemm_pack);
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h, DataType data_type)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape.set(0, rhs_shape[0]);
|
|
dst_shape.set(1, lhs_shape[1] / m_h);
|
|
dst_shape.set(2, m_h);
|
|
dst_shape.set(3, lhs_shape[2]);
|
|
|
|
switch(data_type)
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
// Create reference
|
|
SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
|
|
class GEMMLowpMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0,
|
|
unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, DataType data_type)
|
|
{
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
rhs_info.h0 = h0;
|
|
rhs_info.interleave = interleave_rhs;
|
|
rhs_info.transpose = transpose_rhs;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type);
|
|
_reference = compute_reference(lhs_shape, rhs_shape, data_type);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-127, 126);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info,
|
|
const GEMMRHSMatrixInfo &rhs_info, DataType data_type)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
|
|
TensorType rhs_reshaped;
|
|
TensorType dst;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
GEMMKernelInfo gemm_info;
|
|
gemm_info.m = M;
|
|
gemm_info.n = N;
|
|
gemm_info.k = K;
|
|
gemm_info.lhs_info = lhs_info;
|
|
gemm_info.rhs_info = rhs_info;
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
ReshapeRHSOperatorType reshape_rhs;
|
|
GEMMFunctionType gemm;
|
|
reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
|
|
gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info);
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
|
|
add_padding_x({ &lhs, &rhs, &rhs_reshaped, &dst });
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
rhs_reshaped.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
|
|
// Compute GEMM
|
|
ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
|
|
reshape_rhs.run(reshape_rhs_pack);
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } });
|
|
gemm.run(gemm_pack);
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape[0] = rhs_shape[0];
|
|
dst_shape[1] = lhs_shape[1];
|
|
|
|
if(data_type == DataType::QASYMM8)
|
|
{
|
|
// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
else
|
|
{
|
|
// Create reference
|
|
SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
|
|
template <typename T, typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType, typename ReduceOperation, typename CastOperation>
|
|
class GEMMLowpMatrixMultiplyReshapedOnlyRHSMMULOutputStageValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0,
|
|
unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, bool broadcast_bias, DataType data_type)
|
|
{
|
|
GEMMLowpOutputStageInfo output_stage;
|
|
output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
|
|
output_stage.output_data_type = data_type;
|
|
output_stage.gemmlowp_multipliers = std::vector<int32_t> { 1 };
|
|
output_stage.gemmlowp_shifts = std::vector<int32_t> { 1 };
|
|
output_stage.gemmlowp_multipliers[0] = 1;
|
|
output_stage.gemmlowp_shifts[0] = 1;
|
|
output_stage.gemmlowp_offset = 0;
|
|
constexpr float scale = 0.001f;
|
|
quantization::calculate_quantized_multiplier(scale, &output_stage.gemmlowp_multipliers[0], &output_stage.gemmlowp_shifts[0]);
|
|
output_stage.gemmlowp_min_bound = -100;
|
|
output_stage.gemmlowp_max_bound = 100;
|
|
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
rhs_info.h0 = h0;
|
|
rhs_info.interleave = interleave_rhs;
|
|
rhs_info.transpose = transpose_rhs;
|
|
|
|
int a_offset = 1;
|
|
int b_offset = 1;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
const TensorShape bias_shape(n,
|
|
broadcast_bias ? 1 : m,
|
|
broadcast_bias ? 1 : batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, output_stage, a_offset, b_offset);
|
|
if(gemm_validated == true)
|
|
{
|
|
_reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, output_stage, a_offset, b_offset);
|
|
}
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-127, 126);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::S32:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-10000, 10000);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info,
|
|
const GEMMRHSMatrixInfo &rhs_info, DataType data_type, GEMMLowpOutputStageInfo output_stage, const int a_offset, const int b_offset)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, a_offset));
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, b_offset));
|
|
TensorType bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1);
|
|
TensorType dst;
|
|
TensorType rhs_reshaped;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
// Tensors for precomputing sum of lhs rows / rhs columns
|
|
TensorType vec_sum_rows = create_tensor<TensorType>(TensorShape(M, 1, lhs_shape[2]), DataType::S32, 1);
|
|
TensorType vec_sum_cols = create_tensor<TensorType>(TensorShape(N, 1, rhs_shape[2]), DataType::S32, 1);
|
|
|
|
GEMMKernelInfo gemm_info;
|
|
gemm_info.m = M;
|
|
gemm_info.n = N;
|
|
gemm_info.k = K;
|
|
gemm_info.lhs_info = lhs_info;
|
|
gemm_info.rhs_info = rhs_info;
|
|
gemm_info.output_stage = output_stage;
|
|
gemm_info.a_offset = a_offset;
|
|
gemm_info.b_offset = b_offset;
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
ReshapeRHSOperatorType reshape_rhs;
|
|
GEMMFunctionType gemm;
|
|
reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
|
|
|
|
// If GEMM is not validated, do not try to run. The validation will check
|
|
// if the technology supports this extension. If not, the test will be skipped.
|
|
// If it supports, the test will fail anyway because target and reference
|
|
// will not match.
|
|
gemm_validated = bool(gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, vec_sum_cols.info(), vec_sum_rows.info(), bias.info()));
|
|
if(gemm_validated == true)
|
|
{
|
|
gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, vec_sum_cols.info(), vec_sum_rows.info(), bias.info());
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
rhs_reshaped.allocator()->allocate();
|
|
bias.allocator()->allocate();
|
|
vec_sum_cols.allocator()->allocate();
|
|
vec_sum_rows.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!vec_sum_cols.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!vec_sum_rows.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
fill(AccessorType(bias), 2);
|
|
|
|
TensorType lhs_32 = create_tensor<TensorType>(lhs_shape, DataType::S32, 1);
|
|
TensorType rhs_32 = create_tensor<TensorType>(rhs_shape, DataType::S32, 1);
|
|
CastOperation cast_lhs;
|
|
CastOperation cast_rhs;
|
|
cast_lhs.configure(&lhs, &lhs_32, ConvertPolicy::SATURATE);
|
|
cast_rhs.configure(&rhs, &rhs_32, ConvertPolicy::SATURATE);
|
|
lhs_32.allocator()->allocate();
|
|
rhs_32.allocator()->allocate();
|
|
cast_lhs.run();
|
|
cast_rhs.run();
|
|
|
|
ReduceOperation lhs_sum_rows;
|
|
ReduceOperation rhs_sum_cols;
|
|
|
|
lhs_sum_rows.configure(&lhs_32, &vec_sum_rows, 0, ReductionOperation::SUM, false);
|
|
rhs_sum_cols.configure(&rhs_32, &vec_sum_cols, 1, ReductionOperation::SUM);
|
|
|
|
lhs_sum_rows.run();
|
|
rhs_sum_cols.run();
|
|
|
|
// Compute GEMM
|
|
ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
|
|
reshape_rhs.run(reshape_rhs_pack);
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_SRC_2, &bias }, { ACL_DST, &dst }, { ACL_VEC_COL_SUM, &vec_sum_cols }, { ACL_VEC_ROW_SUM, &vec_sum_rows } });
|
|
gemm.run(gemm_pack);
|
|
}
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, GEMMLowpOutputStageInfo output_stage,
|
|
const int a_offset, const int b_offset)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape[0] = rhs_shape[0];
|
|
dst_shape[1] = lhs_shape[1];
|
|
|
|
// Create reference
|
|
SimpleTensor<T> lhs{ lhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, a_offset) };
|
|
SimpleTensor<T> rhs{ rhs_shape, data_type, 1, QuantizationInfo(1.0f / 255, b_offset) };
|
|
SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
|
|
SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 1 };
|
|
SimpleTensor<T> dst_final{ dst_shape, data_type, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
fill(bias, 2);
|
|
|
|
dst = reference::gemmlowp_matrix_multiply_core<int32_t, T>(lhs, rhs, dst_shape, a_offset, b_offset);
|
|
dst_final = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, T>(dst, bias,
|
|
output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
|
|
return dst_final;
|
|
}
|
|
|
|
bool gemm_validated = true;
|
|
TensorType _target{};
|
|
SimpleTensor<T> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
|
|
class GEMMLowpMatrixMultiplyReshapedOnlyRHSMMULValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0,
|
|
unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, DataType data_type)
|
|
{
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
rhs_info.h0 = h0;
|
|
rhs_info.interleave = interleave_rhs;
|
|
rhs_info.transpose = transpose_rhs;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type);
|
|
if(gemm_validated == true)
|
|
{
|
|
_reference = compute_reference(lhs_shape, rhs_shape, data_type);
|
|
}
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::QASYMM8:
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
case DataType::QASYMM8_SIGNED:
|
|
{
|
|
std::uniform_int_distribution<> distribution(-127, 126);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
break;
|
|
default:
|
|
ARM_COMPUTE_ERROR("Unsupported data type");
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info,
|
|
const GEMMRHSMatrixInfo &rhs_info, DataType data_type)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
|
|
TensorType rhs_reshaped;
|
|
TensorType dst;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
GEMMKernelInfo gemm_info;
|
|
gemm_info.m = M;
|
|
gemm_info.n = N;
|
|
gemm_info.k = K;
|
|
gemm_info.lhs_info = lhs_info;
|
|
gemm_info.rhs_info = rhs_info;
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
ReshapeRHSOperatorType reshape_rhs;
|
|
GEMMFunctionType gemm;
|
|
reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
|
|
|
|
// If GEMM is not validated, do not try to run. The validation will check
|
|
// if the technology supports this extension. If not, the test will be skipped.
|
|
// If it supports, the test will fail anyway because target and reference
|
|
// will not match.
|
|
gemm_validated = bool(gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, nullptr, nullptr, nullptr));
|
|
if(gemm_validated == true)
|
|
{
|
|
gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info, nullptr, nullptr, nullptr);
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
rhs_reshaped.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
|
|
// Compute GEMM
|
|
ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
|
|
reshape_rhs.run(reshape_rhs_pack);
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } });
|
|
gemm.run(gemm_pack);
|
|
}
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape[0] = rhs_shape[0];
|
|
dst_shape[1] = lhs_shape[1];
|
|
|
|
if(data_type == DataType::QASYMM8)
|
|
{
|
|
// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
|
|
SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
else
|
|
{
|
|
// Create reference
|
|
SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
|
|
SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
|
|
SimpleTensor<int32_t> dst{ dst_shape, DataType::S32, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
}
|
|
|
|
bool gemm_validated = true;
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
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class GEMMLowpMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0,
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unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs, DataType data_type)
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{
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GEMMLHSMatrixInfo lhs_info;
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lhs_info.m0 = m0;
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lhs_info.k0 = k0;
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GEMMRHSMatrixInfo rhs_info;
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rhs_info.n0 = n0;
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rhs_info.k0 = k0;
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rhs_info.h0 = h0;
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rhs_info.interleave = interleave_rhs;
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rhs_info.transpose = transpose_rhs;
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// In case of GEMM3D, m is the product between m_w and m_h
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const unsigned int m = m_w * m_h;
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// Set the tensor shapes for LHS and RHS matrices
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const TensorShape lhs_shape(k, m, batch_size);
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const TensorShape rhs_shape(n, k, batch_size);
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_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h, data_type);
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_reference = compute_reference(lhs_shape, rhs_shape, m_h, data_type);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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switch(tensor.data_type())
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{
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case DataType::QASYMM8:
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{
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// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
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std::uniform_int_distribution<> distribution(1, 254);
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library->fill(tensor, distribution, i);
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}
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break;
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case DataType::QASYMM8_SIGNED:
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{
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std::uniform_int_distribution<> distribution(-127, 126);
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library->fill(tensor, distribution, i);
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}
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break;
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default:
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ARM_COMPUTE_ERROR("Unsupported data type");
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}
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}
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TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info,
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const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h, DataType data_type)
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{
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// Create tensors
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TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
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TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
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TensorType rhs_reshaped;
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TensorType dst;
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const unsigned int M = lhs_shape[1];
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const unsigned int N = rhs_shape[0];
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const unsigned int K = lhs_shape[0];
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GEMMKernelInfo gemm_info;
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gemm_info.m = M;
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gemm_info.n = N;
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gemm_info.k = K;
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gemm_info.depth_output_gemm3d = m_h;
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gemm_info.lhs_info = lhs_info;
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gemm_info.rhs_info = rhs_info;
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// The output tensor will be auto-initialized within the function
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// Create and configure function
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ReshapeRHSOperatorType reshape_rhs;
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GEMMFunctionType gemm;
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reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
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gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info);
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ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
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ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
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add_padding_x({ &lhs, &rhs, &rhs_reshaped, &dst });
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// Allocate tensors
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lhs.allocator()->allocate();
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rhs.allocator()->allocate();
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rhs_reshaped.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
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// Fill tensors
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fill(AccessorType(lhs), 0);
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fill(AccessorType(rhs), 1);
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// Compute GEMM
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ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
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reshape_rhs.run(reshape_rhs_pack);
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ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs_reshaped }, { ACL_DST, &dst } });
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gemm.run(gemm_pack);
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return dst;
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}
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SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h, DataType data_type)
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{
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TensorShape dst_shape = lhs_shape;
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dst_shape.set(0, rhs_shape[0]);
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dst_shape.set(1, lhs_shape[1] / m_h);
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dst_shape.set(2, m_h);
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dst_shape.set(3, lhs_shape[2]);
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if(data_type == DataType::QASYMM8)
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{
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// Create reference
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SimpleTensor<uint8_t> lhs{ lhs_shape, data_type, 1 };
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SimpleTensor<uint8_t> rhs{ rhs_shape, data_type, 1 };
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// Fill reference
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fill(lhs, 0);
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fill(rhs, 1);
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return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
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}
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else
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{
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// Create reference
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SimpleTensor<int8_t> lhs{ lhs_shape, data_type, 1 };
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SimpleTensor<int8_t> rhs{ rhs_shape, data_type, 1 };
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// Fill reference
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fill(lhs, 0);
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fill(rhs, 1);
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return reference::gemmlowp_matrix_multiply_core<int32_t, int8_t>(lhs, rhs, dst_shape, 0, 0);
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}
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}
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TensorType _target{};
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SimpleTensor<int32_t> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename GEMMFunctionType>
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class GEMMLowpMatrixMultiplyNativeValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0)
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{
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GEMMLHSMatrixInfo lhs_info;
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lhs_info.m0 = m0;
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lhs_info.k0 = k0;
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GEMMRHSMatrixInfo rhs_info;
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rhs_info.n0 = n0;
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rhs_info.k0 = k0;
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// Set the tensor shapes for LHS and RHS matrices
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const TensorShape lhs_shape(k, m, batch_size);
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const TensorShape rhs_shape(n, k, batch_size);
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_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info);
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_reference = compute_reference(lhs_shape, rhs_shape);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
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std::uniform_int_distribution<> distribution(1, 254);
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library->fill(tensor, distribution, i);
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}
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|
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TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info)
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{
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// Create tensors
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TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
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TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
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TensorType dst;
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|
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const unsigned int M = lhs_shape[1];
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const unsigned int N = rhs_shape[0];
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const unsigned int K = lhs_shape[0];
|
|
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// The output tensor will be auto-initialized within the function
|
|
|
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// Create and configure function
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GEMMFunctionType gemm;
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gemm.configure(lhs.info(), rhs.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
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|
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ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
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ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
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add_padding_x({ &lhs, &rhs, &dst });
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|
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// Allocate tensors
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lhs.allocator()->allocate();
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rhs.allocator()->allocate();
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dst.allocator()->allocate();
|
|
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ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
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ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
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ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
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// Fill tensors
|
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fill(AccessorType(lhs), 0);
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fill(AccessorType(rhs), 1);
|
|
|
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// Compute GEMM
|
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ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_DST, &dst } });
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gemm.run(gemm_pack);
|
|
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return dst;
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}
|
|
|
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SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape)
|
|
{
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TensorShape dst_shape = lhs_shape;
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dst_shape[0] = rhs_shape[0];
|
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dst_shape[1] = lhs_shape[1];
|
|
|
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// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };
|
|
|
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// Fill reference
|
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fill(lhs, 0);
|
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fill(rhs, 1);
|
|
|
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return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
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|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename GEMMFunctionType>
|
|
class GEMMLowpMatrixMultiplyNative3DValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0)
|
|
{
|
|
GEMMLHSMatrixInfo lhs_info;
|
|
lhs_info.m0 = m0;
|
|
lhs_info.k0 = k0;
|
|
|
|
GEMMRHSMatrixInfo rhs_info;
|
|
rhs_info.n0 = n0;
|
|
rhs_info.k0 = k0;
|
|
|
|
// In case of GEMM3D, m is the product between m_w and m_h
|
|
const unsigned int m = m_w * m_h;
|
|
|
|
// Set the tensor shapes for LHS and RHS matrices
|
|
const TensorShape lhs_shape(k, m, batch_size);
|
|
const TensorShape rhs_shape(n, k, batch_size);
|
|
|
|
_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h);
|
|
_reference = compute_reference(lhs_shape, rhs_shape, m_h);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i)
|
|
{
|
|
// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
|
|
std::uniform_int_distribution<> distribution(1, 254);
|
|
library->fill(tensor, distribution, i);
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h)
|
|
{
|
|
// Create tensors
|
|
TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
|
|
TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
|
|
TensorType dst;
|
|
|
|
const unsigned int M = lhs_shape[1];
|
|
const unsigned int N = rhs_shape[0];
|
|
const unsigned int K = lhs_shape[0];
|
|
|
|
// The output tensor will be auto-initialized within the function
|
|
|
|
// Create and configure function
|
|
GEMMFunctionType gemm;
|
|
gemm.configure(lhs.info(), rhs.info(), dst.info(), lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
|
|
|
|
ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
|
|
|
|
add_padding_x({ &lhs, &rhs, &dst });
|
|
|
|
// Allocate tensors
|
|
lhs.allocator()->allocate();
|
|
rhs.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
|
|
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(lhs), 0);
|
|
fill(AccessorType(rhs), 1);
|
|
|
|
// Compute GEMM
|
|
ITensorPack gemm_pack({ { ACL_SRC_0, &lhs }, { ACL_SRC_1, &rhs }, { ACL_DST, &dst } });
|
|
gemm.run(gemm_pack);
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h)
|
|
{
|
|
TensorShape dst_shape = lhs_shape;
|
|
dst_shape.set(0, rhs_shape[0]);
|
|
dst_shape.set(1, lhs_shape[1] / m_h);
|
|
dst_shape.set(2, m_h);
|
|
dst_shape.set(3, lhs_shape[2]);
|
|
|
|
// Create reference
|
|
SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
|
|
SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };
|
|
|
|
// Fill reference
|
|
fill(lhs, 0);
|
|
fill(rhs, 1);
|
|
|
|
return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<int32_t> _reference{};
|
|
};
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|
|
#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
|