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a6d780154f
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a6d780154f | ||
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6871e2eeb7 |
@ -145,7 +145,7 @@ bool compiler_supports_device_sass(Device& device) {
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}
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}
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#define INCLUDE_PREFIX "mlx/backend/cuda/kernels/"
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#define INCLUDE_PREFIX "mlx/backend/cuda/device/"
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constexpr const char* g_include_names[] = {
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INCLUDE_PREFIX "atomic_ops.cuh",
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@ -44,9 +44,12 @@ class MatMul {
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int64_t b_batch_stride) {
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heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
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auto type = dtype_to_cuda_type(dtype);
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auto scale_type = dtype_to_cuda_type(dtype);
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if (dtype == bfloat16) {
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scale_type = CUDA_R_32F;
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}
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CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
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&matmul_desc_, dtype_to_compute_type(dtype), type));
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&matmul_desc_, dtype_to_compute_type(dtype), scale_type));
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int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
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CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
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matmul_desc_,
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@ -65,6 +68,7 @@ class MatMul {
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&op,
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sizeof(cublasOperation_t)));
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auto type = dtype_to_cuda_type(dtype);
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a_desc_ = create_matrix_layout(
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type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
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b_desc_ = create_matrix_layout(
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@ -187,15 +191,10 @@ class MatMul {
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private:
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cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
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switch (dtype) {
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case uint8:
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case uint16:
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case int8:
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case int16:
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case int32:
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return CUBLAS_COMPUTE_32I;
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case float16:
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case bfloat16:
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return CUBLAS_COMPUTE_16F;
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case bfloat16:
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return CUBLAS_COMPUTE_32F;
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case float32:
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return CUBLAS_COMPUTE_32F;
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case float64:
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@ -209,16 +208,6 @@ class MatMul {
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cudaDataType_t dtype_to_cuda_type(Dtype dtype) {
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switch (dtype) {
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case uint8:
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return CUDA_R_8U;
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case uint16:
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return CUDA_R_16U;
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case int8:
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return CUDA_R_8I;
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case int16:
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return CUDA_R_16I;
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case int32:
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return CUDA_R_32I;
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case float16:
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return CUDA_R_16F;
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case bfloat16:
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