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Hipify MatMulNBits to accommodate the need of Phi3 onnx release.
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4 changes: 1 addition & 3 deletions
4
onnxruntime/python/tools/kernel_explorer/kernels/cuda/matmul_4bits.cu
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100 changes: 100 additions & 0 deletions
100
onnxruntime/python/tools/kernel_explorer/kernels/rocm/matmul_4bits.cu
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
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| #include <hip/hip_runtime.h> | ||
| #include <hip/hip_fp16.h> | ||
| #include <pybind11/pybind11.h> | ||
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| #include <string> | ||
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| #include "core/providers/rocm/tunable/rocm_tunable.h" | ||
| #include "python/tools/kernel_explorer/device_array.h" | ||
| #include "python/tools/kernel_explorer/kernel_explorer_interface.h" | ||
| #include "contrib_ops/rocm/quantization/matmul_nbits.cuh" | ||
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| namespace py = pybind11; | ||
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| namespace onnxruntime { | ||
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| // Extend the OpParams so that all specializations have the same parameter passing interface | ||
| template <typename T> | ||
| struct MatrixFloatInt4Params : rocm::tunable::OpParams { | ||
| std::string Signature() const override { return std::to_string(n_); } | ||
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| T* output_; | ||
| const T* a_; | ||
| const uint8_t* b_; | ||
| const T* scales_; | ||
| const uint8_t* zero_points_; | ||
| int m_; | ||
| int n_; | ||
| int k_; | ||
| }; | ||
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| template <typename T> | ||
| class MatrixFloatInt4 : public IKernelExplorer { | ||
| public: | ||
| MatrixFloatInt4(DeviceArray& output, | ||
| DeviceArray& a, | ||
| DeviceArray& b, | ||
| DeviceArray& scales, | ||
| int m, int n, int k) { | ||
| params_.tuning_ctx = TuningContext(); | ||
| params_.stream = Stream(); | ||
| params_.output_ = static_cast<T*>(output.ptr()); | ||
| params_.a_ = static_cast<T*>(a.ptr()); | ||
| params_.b_ = static_cast<uint8_t*>(b.ptr()); | ||
| params_.scales_ = static_cast<T*>(scales.ptr()); | ||
| params_.zero_points_ = nullptr; | ||
| params_.m_ = m; | ||
| params_.n_ = n; | ||
| params_.k_ = k; | ||
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| HIP_CALL_THROW(hipGetDeviceProperties(&device_prop_, 0)); | ||
| } | ||
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| MatrixFloatInt4(DeviceArray& output, | ||
| DeviceArray& a, | ||
| DeviceArray& b, | ||
| DeviceArray& scales, | ||
| DeviceArray& zeropoints, | ||
| int m, int n, int k) : MatrixFloatInt4(output, a, b, scales, m, n, k) { | ||
| params_.zero_points_ = static_cast<uint8_t*>(zeropoints.ptr()); | ||
| } | ||
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| void Run() override { | ||
| contrib::rocm::TryMatMul4Bits<T>( | ||
| params_.output_, | ||
| params_.a_, | ||
| params_.b_, | ||
| params_.scales_, | ||
| params_.zero_points_, | ||
| params_.m_, | ||
| params_.n_, | ||
| params_.k_, | ||
| 32, | ||
| static_cast<int>(device_prop_.sharedMemPerBlock), | ||
| params_.StreamHandle()); | ||
| } | ||
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| private: | ||
| // A VectorAddOp<T> is a callable that can process const VectorAddParams<T>* | ||
| using ParamsT = MatrixFloatInt4Params<T>; | ||
| ParamsT params_{}; | ||
| hipDeviceProp_t device_prop_; | ||
| }; | ||
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| #define REGISTER_OP(name, type) \ | ||
| py::class_<name<type>>(m, #name "_" #type) \ | ||
| .def(py::init<DeviceArray&, DeviceArray&, DeviceArray&, DeviceArray&, int, int, int>()) \ | ||
| .def(py::init<DeviceArray&, DeviceArray&, DeviceArray&, DeviceArray&, DeviceArray&, int, int, int>()) \ | ||
| .def("SetRepeats", &name<type>::SetRepeats) \ | ||
| .def("Profile", &name<type>::Profile) \ | ||
| .def("Run", &name<type>::Run); | ||
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| KE_REGISTER(m) { | ||
| REGISTER_OP(MatrixFloatInt4, half); | ||
| REGISTER_OP(MatrixFloatInt4, float); | ||
| } | ||
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| } // namespace onnxruntime |
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