Add CUDA implementation of GatherBlockQuantized operator #25575
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| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
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| // Licensed under the MIT License. | ||
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| #include "core/providers/cuda/cuda_common.h" | ||
| #include "contrib_ops/cuda/quantization/gather_block_quantized.h" | ||
| #include "contrib_ops/cuda/quantization/gather_block_quantized.cuh" | ||
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| namespace onnxruntime { | ||
| namespace contrib { | ||
| namespace cuda { | ||
| using namespace onnxruntime::cuda; | ||
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Check warning on line 11 in onnxruntime/contrib_ops/cuda/quantization/gather_block_quantized.cc
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| #define REGISTER_GATHERBLOCKQUANTIZED(T1, T2, Tind) \ | ||
| ONNX_OPERATOR_THREE_TYPED_KERNEL_EX( \ | ||
| GatherBlockQuantized, \ | ||
| kMSDomain, 1, \ | ||
| T1, T2, Tind, \ | ||
| kCudaExecutionProvider, \ | ||
| (*KernelDefBuilder::Create()) \ | ||
| .TypeConstraint("T1", DataTypeImpl::GetTensorType<T1>()) \ | ||
| .TypeConstraint("T2", DataTypeImpl::GetTensorType<T2>()) \ | ||
| .TypeConstraint("Tind", DataTypeImpl::GetTensorType<Tind>()), \ | ||
| GatherBlockQuantized<T1, T2, Tind>); | ||
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| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, float, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, float, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, float, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, float, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, float, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, float, int64_t); | ||
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| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, MLFloat16, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, MLFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, MLFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, MLFloat16, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, MLFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, MLFloat16, int64_t); | ||
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| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, BFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(UInt4x2, BFloat16, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, BFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(uint8_t, BFloat16, int64_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, BFloat16, int32_t); | ||
| REGISTER_GATHERBLOCKQUANTIZED(Int4x2, BFloat16, int64_t); | ||
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| template <typename T1, typename T2, typename Tind> | ||
| GatherBlockQuantized<T1, T2, Tind>::GatherBlockQuantized(const OpKernelInfo& info) : CudaKernel(info) { | ||
| ORT_ENFORCE(info.GetAttr("bits", &bits_).IsOK()); | ||
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| block_size_ = info.GetAttrOrDefault<int64_t>("block_size", 0); | ||
| gather_axis_ = info.GetAttrOrDefault<int64_t>("gather_axis", 0); | ||
| quantize_axis_ = info.GetAttrOrDefault<int64_t>("quantize_axis", 0); | ||
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| // If block size is set, it has to be no smaller than 16 and must be power of 2 | ||
| // block_size_ & (block_size_ - 1) == 0 checks if block_size_ only has 1 bit set | ||
| ORT_ENFORCE(block_size_ == 0 || (block_size_ >= 16 && ((block_size_ & (block_size_ - 1)) == 0))); | ||
| } | ||
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| template <typename T1, typename T2, typename Tind> | ||
| Status GatherBlockQuantized<T1, T2, Tind>::ComputeInternal(OpKernelContext* ctx) const { | ||
| const Tensor* data = ctx->Input<Tensor>(0); | ||
| const Tensor* indices = ctx->Input<Tensor>(1); | ||
| const Tensor* scales = ctx->Input<Tensor>(2); | ||
| const Tensor* zero_points = ctx->Input<Tensor>(3); | ||
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| auto data_shape = data->Shape().GetDims(); | ||
| auto data_rank = data->Shape().NumDimensions(); | ||
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| auto indices_shape = indices->Shape().GetDims(); | ||
| auto indices_rank = indices->Shape().NumDimensions(); | ||
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| ORT_ENFORCE(quantize_axis_ == data_rank - 1); | ||
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| TensorShapeVector output_shape; | ||
| output_shape.reserve(data_rank - 1 + indices_rank); | ||
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| // Dimension after gather axis | ||
| int64_t after_gather_dim = 1; | ||
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| // Dimension of indices | ||
| int64_t ind_dim = 1; | ||
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| // 1) dims before gather_axis | ||
| for (int64_t i = 0; i < gather_axis_; ++i) { | ||
| output_shape.push_back(data_shape[i]); | ||
| } | ||
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| // 2) all of indices.shape | ||
| for (auto dim : indices_shape) { | ||
| output_shape.push_back(dim); | ||
| ind_dim *= dim; | ||
| } | ||
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| // 3) dims after gather_axis | ||
| for (int64_t i = gather_axis_ + 1; i < data_rank; ++i) { | ||
| output_shape.push_back(data_shape[i]); | ||
| after_gather_dim *= data_shape[i]; | ||
| } | ||
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| // Special int4‐in‐uint8 packing tweak: expand the last dim by components | ||
| if constexpr (std::is_same_v<T1, uint8_t>) { | ||
| uint32_t components = 8 / static_cast<int>(bits_); | ||
| if (components > 1) { | ||
| output_shape.back() *= components; | ||
| } | ||
| } | ||
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| Tensor* output = ctx->Output(0, TensorShape(output_shape)); | ||
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| int64_t N = 1; | ||
| for (auto dim : output_shape) { | ||
| N *= dim; | ||
| } | ||
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| const auto* data_ptr = data->Data<T1>(); | ||
| const auto* indices_ptr = indices->Data<Tind>(); | ||
| const T1* zero_points_ptr = nullptr; | ||
| if (zero_points != nullptr) { | ||
| zero_points_ptr = zero_points->Data<T1>(); | ||
| } | ||
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| GatherBlockQuantizedParam param; | ||
| param.stream = Stream(ctx); | ||
| param.after_gather_dim = after_gather_dim; | ||
| param.gather_axis_dim = data_shape[gather_axis_]; | ||
| param.ind_dim = ind_dim; | ||
| param.bits = bits_; | ||
| param.block_size = block_size_; | ||
| param.gather_axis = gather_axis_; | ||
| param.N = N; | ||
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| const auto dequantized_type = scales->GetElementType(); | ||
| if (dequantized_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT) { | ||
| const auto* scales_ptr = static_cast<const float*>(scales->DataRaw()); | ||
| auto* output_ptr = static_cast<float*>(output->MutableDataRaw()); | ||
| LaunchGatherBlockQuantizedKernel(data_ptr, indices_ptr, scales_ptr, zero_points_ptr, output_ptr, param); | ||
| } else if (dequantized_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) { | ||
| const auto* scales_ptr = static_cast<const half*>(scales->DataRaw()); | ||
| auto* output_ptr = static_cast<half*>(output->MutableDataRaw()); | ||
| LaunchGatherBlockQuantizedKernel(data_ptr, indices_ptr, scales_ptr, zero_points_ptr, output_ptr, param); | ||
| } else if (dequantized_type == ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16) { | ||
| const auto* scales_ptr = static_cast<const BFloat16*>(scales->DataRaw()); | ||
| auto* output_ptr = static_cast<BFloat16*>(output->MutableDataRaw()); | ||
| LaunchGatherBlockQuantizedKernel(data_ptr, indices_ptr, scales_ptr, zero_points_ptr, output_ptr, param); | ||
| } | ||
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| return Status::OK(); | ||
| } | ||
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| } // namespace cuda | ||
| } // namespace contrib | ||
| } // namespace onnxruntime | ||
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