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[CUDA] Always use batched matmul (#2404)
* cuda batched mm * addmm as well * comment
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100
mlx/backend/cuda/gemms/cublas_gemm.h
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100
mlx/backend/cuda/gemms/cublas_gemm.h
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// Copyright © 2025 Apple Inc.
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#pragma once
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#include "mlx/array.h"
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#include "mlx/backend/cuda/device.h"
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#include <cublasLt.h>
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#include <optional>
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namespace mlx::core::cu {
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class Matmul {
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public:
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Matmul(
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Device& device,
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Dtype dtype,
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bool a_transposed,
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uint64_t a_rows,
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uint64_t a_cols,
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int64_t lda,
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bool b_transposed,
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uint64_t b_rows,
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uint64_t b_cols,
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int64_t ldb,
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int32_t batch_count,
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int64_t a_batch_stride,
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int64_t b_batch_stride);
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Matmul(
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Device& device,
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Dtype dtype,
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bool a_transposed,
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uint64_t a_rows,
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uint64_t a_cols,
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int64_t lda,
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bool b_transposed,
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uint64_t b_rows,
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uint64_t b_cols,
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int64_t ldb,
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int64_t ldc,
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int32_t batch_count,
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int64_t a_batch_stride,
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int64_t b_batch_stride,
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int64_t c_batch_stride);
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~Matmul();
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void run(
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cu::CommandEncoder& encoder,
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array& out,
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const array& a,
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const array& b,
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const std::optional<array>& c = std::nullopt,
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float alpha = 1,
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float beta = 0);
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void run_batched(
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cu::CommandEncoder& encoder,
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array& out,
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const array& a,
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const array& b,
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const mlx::core::Shape& batch_shape,
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const mlx::core::Strides& a_batch_strides,
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const mlx::core::Strides& b_batch_strides);
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void run_batched(
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cu::CommandEncoder& encoder,
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array& out,
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const array& a,
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const array& b,
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const array& c,
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const mlx::core::Shape& batch_shape,
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const mlx::core::Strides& a_batch_strides,
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const mlx::core::Strides& b_batch_strides,
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const mlx::core::Strides& c_batch_strides,
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float alpha,
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float beta);
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private:
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void run_impl(
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cu::CommandEncoder& encoder,
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void* out,
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const void* a,
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const void* b,
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const void* c,
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float alpha = 1,
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float beta = 0);
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uint64_t M_;
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uint64_t N_;
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cublasLtMatmulPreference_t pref_{nullptr};
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cublasLtHandle_t handle_{nullptr};
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cublasLtMatmulDesc_t matmul_desc_{nullptr};
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cublasLtMatrixLayout_t a_desc_{nullptr};
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cublasLtMatrixLayout_t b_desc_{nullptr};
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cublasLtMatrixLayout_t c_desc_{nullptr};
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cublasLtMatrixLayout_t out_desc_{nullptr};
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cublasLtMatmulHeuristicResult_t heuristic_;
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};
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} // namespace mlx::core::cu
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