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Mojo function

matmul_dispatch_sm90

def matmul_dispatch_sm90[c_type: DType, a_type: DType, b_type: DType, transpose_b: Bool = False, elementwise_lambda_fn: Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None] = None, elementwise_compute_lambda_fn: Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> SIMD[dtype, width]] = None, pdl_level: PDLLevel = PDLLevel()](c: TileTensor[c_type, Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[a_type, Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[b_type, Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type], ctx: DeviceContext) -> Int

Dispatches a rank-2 matmul to the SM90 (Hopper) warp-specialized kernel.

Checks static dtype and shape constraints (BF16, FP8, or FP32; transposed B; K alignment), then routes to matmul_dispatch_sm90_fp8, matmul_dispatch_sm90_bf16_fp32, or returns DISPATCH_MISS if the problem does not meet SM90 requirements.

Parameters:

c_type (DType): Element type of the output tensor c.
a_type (DType): Element type of the input tensor a.
b_type (DType): Element type of the input tensor b.
transpose_b (Bool): Whether b is stored transposed (defaults to False); the SM90 kernel requires this to be True to dispatch.
elementwise_lambda_fn (Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None]): Epilogue applied to each output tile after the matmul, consuming the tile and returning nothing (defaults to None for no epilogue).
elementwise_compute_lambda_fn (Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> SIMD[dtype, width]]): Compute lambda transforming each output value before the epilogue, returning the transformed value (defaults to None for no transform).
pdl_level (PDLLevel): Programmatic dependent launch level for overlapping this kernel with prior work (defaults to PDLLevel()).

Args:

c (TileTensor[c_type, Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type]): Rank-2 output tensor of shape (M, N).
a (TileTensor[a_type, Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type]): Rank-2 input tensor of shape (M, K).
b (TileTensor[b_type, Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type]): Rank-2 input tensor of shape (K, N) when transpose_b is True.
ctx (DeviceContext): Device context for the kernel launch.

Returns:

Int: DISPATCH_HIT (1) if the kernel was launched, DISPATCH_MISS (0) otherwise.