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Mojo function

naive_block_scaled_matmul_kernel

def naive_block_scaled_matmul_kernel[c_type: DType, a_type: DType, b_type: DType, a_scales_type: DType, b_scales_type: DType, accum_type: DType, a_layout: TensorLayout, b_layout: TensorLayout, c_layout: TensorLayout, a_scale_layout: TensorLayout, b_scale_layout: TensorLayout, scaling_kind: UMMAKind, SF_VECTOR_SIZE: Int, transpose_b: Bool = True, elementwise_lambda_fn: Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None] = None](c: TileTensor[c_type, c_layout, MutAnyOrigin], a: TileTensor[a_type, a_layout, ImmutAnyOrigin], b: TileTensor[b_type, b_layout, ImmutAnyOrigin], a_scales: TileTensor[a_scales_type, a_scale_layout, ImmutAnyOrigin], b_scales: TileTensor[b_scales_type, b_scale_layout, ImmutAnyOrigin], alpha: Float32)

Naive GPU kernel that emulates a block-scaled matmul using TCGEN scale factors.

Both A and B must be in K-major format with 5D TCGEN scale-factor layouts. Each thread accumulates one output element by iterating over K, applying per-block scale factors, and optionally invoking an elementwise epilogue lambda.

Parameters:

• ​c_type (DType): Element type of the output matrix c.
• ​a_type (DType): Element type of the LHS input matrix a.
• ​b_type (DType): Element type of the RHS input matrix b.
• ​a_scales_type (DType): Element type of the a_scales block scale-factor tensor.
• ​b_scales_type (DType): Element type of the b_scales block scale-factor tensor.
• ​accum_type (DType): Element type used for the dot-product accumulator.
• ​a_layout (TensorLayout): Memory layout of the LHS input matrix a.
• ​b_layout (TensorLayout): Memory layout of the RHS input matrix b.
• ​c_layout (TensorLayout): Memory layout of the output matrix c.
• ​a_scale_layout (TensorLayout): Memory layout of the a_scales block scale-factor tensor.
• ​b_scale_layout (TensorLayout): Memory layout of the b_scales block scale-factor tensor.
• ​scaling_kind (UMMAKind): UMMAKind variant selecting the block-scaled MMA instruction.
• ​SF_VECTOR_SIZE (Int): Number of elements covered by each block scale factor.
• ​transpose_b (Bool): Whether b is stored transposed (defaults to True).
• ​elementwise_lambda_fn (Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None]): Optional epilogue lambda applied to the matmul result (defaults to None).