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| #!/bin/bash | ||
| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. | ||
| set -ex | ||
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| # Get directory this script is in | ||
| DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" | ||
| DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | ||
| OS=$(uname -s) | ||
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| if [ "$OS" = "Darwin" ]; then | ||
| DIR_OS="MacOS" | ||
| DIR_OS="MacOS" | ||
| else | ||
| DIR_OS="Linux" | ||
| DIR_OS="Linux" | ||
| fi | ||
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| if [[ "$*" == *"--ios"* ]]; then | ||
| DIR_OS="iOS" | ||
| DIR_OS="iOS" | ||
| elif [[ "$*" == *"--android"* ]]; then | ||
| DIR_OS="Android" | ||
| DIR_OS="Android" | ||
| fi | ||
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| python3 $DIR/tools/ci_build/build.py --build_dir $DIR/build/$DIR_OS "$@" | ||
| PYTHON="${PYTHON:-python3}" | ||
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| $PYTHON $DIR/tools/ci_build/build.py --build_dir $DIR/build/$DIR_OS "$@" |
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181 changes: 181 additions & 0 deletions
181
onnxruntime/contrib_ops/cpu/quantization/firefox_matmul_integer.cc
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| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
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| #include <iostream> | ||
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| #include "firefox_matmul_integer.h" | ||
| #include "core/providers/cpu/math/matmul_helper.h" | ||
| #include "core/providers/common.h" | ||
| #include "core/util/math_cpuonly.h" | ||
| #include "core/util/qmath.h" | ||
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| namespace onnxruntime { | ||
| namespace contrib { | ||
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| ONNX_OPERATOR_TYPED_KERNEL_EX( | ||
| FirefoxMatMulInteger8, | ||
| kMSDomain, | ||
| 1, | ||
| uint8_t, | ||
| kCpuExecutionProvider, | ||
| KernelDefBuilder() | ||
| .TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>()) | ||
| .TypeConstraint("T2", {DataTypeImpl::GetTensorType<uint8_t>(), DataTypeImpl::GetTensorType<int8_t>()}) | ||
| .TypeConstraint("T3", DataTypeImpl::GetTensorType<int32_t>()), | ||
| FirefoxMatMulInteger8); | ||
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| ONNX_OPERATOR_TYPED_KERNEL_EX( | ||
| FirefoxMatMulInteger8, | ||
| kMSDomain, | ||
| 1, | ||
| int8_t, | ||
| kCpuExecutionProvider, | ||
| KernelDefBuilder() | ||
| .TypeConstraint("T1", DataTypeImpl::GetTensorType<int8_t>()) | ||
| .TypeConstraint("T2", DataTypeImpl::GetTensorType<int8_t>()) | ||
| .TypeConstraint("T3", DataTypeImpl::GetTensorType<int32_t>()), | ||
| FirefoxMatMulInteger8); | ||
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| Status FirefoxMatMulInteger8::Compute(OpKernelContext* ctx) const { | ||
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| const auto* a = ctx->Input<Tensor>(IN_A); | ||
| const auto* b = packed_b_ ? nullptr : ctx->Input<Tensor>(IN_B); | ||
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| std::cout << "Input Tensor A shape: " << (a ? a->Shape().ToString() : "null") << std::endl; | ||
| if (b) { | ||
| std::cout << "Input Tensor B shape: " << b->Shape().ToString() << std::endl; | ||
| } else { | ||
| std::cout << "Using packed B." << std::endl; | ||
| } | ||
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| // Validate zero points | ||
| uint8_t a_offset = 0; | ||
| const auto* a_zero_point = ctx->Input<Tensor>(IN_A_ZERO_POINT); | ||
| if (a_zero_point != nullptr) { | ||
| std::cout << "A Zero Point shape: " << a_zero_point->Shape().ToString() << std::endl; | ||
| ORT_ENFORCE(IsScalarOr1ElementVector(a_zero_point), | ||
| "MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1"); | ||
| a_offset = *(static_cast<const uint8_t*>(a_zero_point->DataRaw())); | ||
| std::cout << "A Zero Point value: " << static_cast<int>(a_offset) << std::endl; | ||
| } | ||
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| bool is_b_zp_per_column = false; | ||
| uint8_t b_default_offset = 0; | ||
| const uint8_t* b_offset_ptr = &b_default_offset; | ||
| const auto* b_zero_point = ctx->Input<Tensor>(IN_B_ZERO_POINT); | ||
| if (b_zero_point != nullptr) { | ||
| std::cout << "B Zero Point shape: " << b_zero_point->Shape().ToString() << std::endl; | ||
| ORT_ENFORCE(IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_), | ||
| "MatmulInteger : B zero point is not valid"); | ||
| is_b_zp_per_column = !IsScalarOr1ElementVector(b_zero_point); | ||
| b_offset_ptr = static_cast<const uint8_t*>(b_zero_point->DataRaw()); | ||
| std::cout << "B Zero Point is per-column: " << is_b_zp_per_column << std::endl; | ||
| } | ||
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| MatMulComputeHelper helper; | ||
| const uint8_t* b_data; | ||
| bool b_is_signed; | ||
| if (nullptr != b) { | ||
| std::cout << "Computing helper with A and B shapes." << std::endl; | ||
| ORT_RETURN_IF_ERROR(helper.Compute(a->Shape(), b->Shape(), nullptr, b_zero_point ? &b_zero_point->Shape() : nullptr)); | ||
| b_data = static_cast<const uint8_t*>(b->DataRaw()); | ||
| b_is_signed = b->IsDataType<int8_t>(); | ||
| } else { | ||
| std::cout << "Computing helper with A shape and packed B." << std::endl; | ||
| ORT_RETURN_IF_ERROR(helper.Compute(a->Shape(), b_shape_, nullptr, b_zero_point ? &b_zero_point->Shape() : nullptr)); | ||
| b_data = static_cast<const uint8_t*>(packed_b_.get()); | ||
| b_is_signed = b_is_signed_; | ||
| } | ||
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| Tensor* y = ctx->Output(OUT_Y, helper.OutputShape()); | ||
| std::cout << "Output Tensor Y shape: " << y->Shape().ToString() << std::endl; | ||
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| if (y->Shape().Size() == 0) { | ||
| std::cout << "Output Tensor is empty. Exiting early." << std::endl; | ||
| return Status::OK(); | ||
| } | ||
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| const uint8_t* a_data = static_cast<const uint8_t*>(a->DataRaw()); | ||
| auto* y_data = y->MutableData<int32_t>(); | ||
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| #ifdef __EMSCRIPTEN__ | ||
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| /* | ||
| float alpha = 25; | ||
| float quant_mult = 127/alpha; | ||
| float unquant_mult_forprep = (-1)*(alpha)*(alpha)/(127.0f); | ||
| // prepate the two matrices | ||
| // we need to import `intgemm/aligned.h` | ||
| AlignedVector<Integer> A_prepared(a_data.size()); | ||
| AlignedVector<Integer> B_prepared(b_data.size()); | ||
| int8PrepareA(a_data, A_prepared, quant_mult, A_rows, width); | ||
| int8PrepareB(b_data, B_prepared, quant_mult, width, B_cols); | ||
| // XXX I don't think I need to add a bias here | ||
| // Multiply | ||
| int8MultiplyAndAddBias( | ||
| A_prepared, | ||
| scale_A, | ||
| a_offset, | ||
| B_prepared, | ||
| scale_B, | ||
| b_offset, // XXX | ||
| input_bias_prepared, | ||
| unquant_mult_forprep, | ||
| A_rows, | ||
| width, | ||
| B_cols, | ||
| y_data | ||
| ); | ||
| */ | ||
| #endif | ||
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| // MBLas fallback. | ||
| MLAS_GEMM_QUANT_SHAPE_PARAMS gemm_shape; | ||
| gemm_shape.M = static_cast<size_t>(helper.M()); | ||
| gemm_shape.N = static_cast<size_t>(helper.N()); | ||
| gemm_shape.K = static_cast<size_t>(helper.K()); | ||
| gemm_shape.AIsSigned = a->IsDataType<int8_t>(); | ||
| gemm_shape.BIsSigned = b_is_signed; | ||
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| std::cout << "GEMM Shape - M: " << gemm_shape.M << ", N: " << gemm_shape.N | ||
| << ", K: " << gemm_shape.K << ", AIsSigned: " << gemm_shape.AIsSigned | ||
| << ", BIsSigned: " << gemm_shape.BIsSigned << std::endl; | ||
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| const size_t batch_size = helper.OutputOffsets().size(); | ||
| std::cout << "Batch size: " << batch_size << std::endl; | ||
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| std::vector<MLAS_GEMM_QUANT_DATA_PARAMS> gemm_data_vec(batch_size); | ||
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| for (size_t batch = 0; batch < batch_size; batch++) { | ||
| auto& gemm_params = gemm_data_vec[batch]; | ||
| gemm_params.lda = gemm_shape.K; | ||
| gemm_params.ZeroPointA = a_offset; | ||
| gemm_params.ldb = gemm_shape.N; | ||
| gemm_params.ZeroPointB = b_offset_ptr + helper.RightZeroPointOffsets()[batch]; | ||
| gemm_params.PerColumnZeroPoints = is_b_zp_per_column; | ||
| gemm_params.ldc = gemm_shape.N; | ||
| gemm_params.BIsPacked = bool(packed_b_); | ||
| gemm_params.A = a_data + helper.LeftOffsets()[batch]; | ||
| gemm_params.B = b_data + helper.RightOffsets()[batch]; | ||
| gemm_params.C = y_data + helper.OutputOffsets()[batch]; | ||
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| std::cout << "Batch " << batch << " - A offset: " << helper.LeftOffsets()[batch] | ||
| << ", B offset: " << helper.RightOffsets()[batch] | ||
| << ", C offset: " << helper.OutputOffsets()[batch] << std::endl; | ||
| } | ||
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| std::cout << "Calling MlasGemmBatch." << std::endl; | ||
| MlasGemmBatch(gemm_shape, gemm_data_vec.data(), batch_size, ctx->GetOperatorThreadPool()); | ||
| std::cout << "Exiting FirefoxMatMulInteger8::Compute" << std::endl; | ||
| return Status::OK(); | ||
| } | ||
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| } // namespace contrib | ||
| } // namespace onnxruntime |
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