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Mojo struct
MatmulFusedPartialRMSNorm
struct MatmulFusedPartialRMSNorm
Fuses GEMV (M=1 matmul) with partial RMS normalization.
Computes y = x @ W.T, then applies RMS normalization to the first N_normed columns while passing the remaining columns through unchanged.
Implemented traits
Methods
execute
static def execute[dtype: DType, rank: Int, target: StringSlice[ImmStaticOrigin], transpose_b: Bool = True](normed_output: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=normed_output.static_spec], unnormed_output: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=unnormed_output.static_spec], input: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=input.static_spec], weight: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=weight.static_spec], gamma: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=gamma.static_spec], epsilon: Float32, weight_offset: Scalar[dtype], ctx: DeviceContext)
Execute fused GEMV + partial RMS norm.
Calls gemv_and_partial_norm from nn.gemv_partial_norm which
computes y = x @ W.T, then partitions y into normed and
unnormed outputs.
Parameters:
- dtype (
DType): Element type of all input and output tensors. - rank (
Int): Tensor rank of the input and output tensors. - target (
StringSlice[ImmStaticOrigin]): The target GPU device. - transpose_b (
Bool): Whether to transposeweightbefore the matmul (defaults toTrue).
Args:
- normed_output (
ManagedTensorSlice[IOSpec[_, _].Output, static_spec=normed_output.static_spec]): Output tensor holding the RMS-normalized leading columns of the matmul result. - unnormed_output (
ManagedTensorSlice[IOSpec[_, _].Output, static_spec=unnormed_output.static_spec]): Output tensor holding the trailing columns of the matmul result, passed through unchanged. - input (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=input.static_spec]): Input activation tensorxof the GEMV. - weight (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=weight.static_spec]): Weight matrixWof shape(N, K)(rank 2). - gamma (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=gamma.static_spec]): RMS normalization scale vector (rank 1). - epsilon (
Float32): Small constant added to the squared mean before the reciprocal square root for numerical stability. - weight_offset (
Scalar[dtype]): Reserved for API consistency with other RMS norm ops; not consumed by this kernel. - ctx (
DeviceContext): The device context used to enqueue the kernel.
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