Mojo function

generic_fused_qkv_matmul_kv_cache_paged_ragged_scale_float4

generic_fused_qkv_matmul_kv_cache_paged_ragged_scale_float4[dtype: DType, weight_dtype: DType, output_dtype: DType, scale_dtype: DType, a_layout: Layout, b_layout: Layout, sfa_layout: Layout, sfb_layout: Layout, SF_VECTOR_SIZE: Int, target: StringSlice[StaticConstantOrigin] = StringSlice("cpu")](hidden_state: LayoutTensor[dtype, a_layout, ImmutAnyOrigin], input_row_offsets: LayoutTensor[DType.uint32, input_row_offsets.layout, input_row_offsets.origin, element_layout=input_row_offsets.element_layout, layout_int_type=input_row_offsets.layout_int_type, linear_idx_type=input_row_offsets.linear_idx_type, masked=input_row_offsets.masked, alignment=input_row_offsets.alignment], weight: LayoutTensor[weight_dtype, b_layout, ImmutAnyOrigin], input_scale: LayoutTensor[scale_dtype, sfa_layout, ImmutAnyOrigin], weight_scale: LayoutTensor[scale_dtype, sfb_layout, ImmutAnyOrigin], tensor_sf: Float32, kv_collection: PagedKVCacheCollection[kv_collection.dtype_, kv_collection.kv_params_, kv_collection.page_size, kv_collection.scale_dtype_, kv_collection.quantization_granularity_], layer_idx: UInt32, output: LayoutTensor[output_dtype, output.layout, output.origin, element_layout=output.element_layout, layout_int_type=output.layout_int_type, linear_idx_type=output.linear_idx_type, masked=output.masked, alignment=output.alignment], ctx: DeviceContextPtr)

Performs a fused QKV matmul. Q outputs are written to the output argument while K and V outputs are written in-place into k_cache and v_cache.

Args:

hidden_state (LayoutTensor): Tensor with shape (sum(seq_lens), num_heads * head_size // 2).
input_row_offsets (LayoutTensor): Tensor with shape (batch_size + 1,). The value at each index is the start_idx of the corresponding batch in hidden_state.
weight (LayoutTensor): Tensor with shape (num_heads * head_size, num_kv_heads * head_size // 2).
input_scale (LayoutTensor): 5D blockwise scale tensor to be multiplied to the input Tensor.
weight_scale (LayoutTensor): 5D blockwise scale tensor to the weight Tensor.
tensor_sf (Float32): Per-tensor scaling factor.
kv_collection (PagedKVCacheCollection): The object storing the KVCache for this layer.
layer_idx (UInt32): The current layer, used to retrieve the KVCache object from kv_collection.
output (LayoutTensor): The pre-allocated output buffer for Q projections. K and V projections are written in-place to k_cache and v_cache. Shape: (sum(seq_lens), num_heads * head_size).
ctx (DeviceContextPtr): The call context pointer, passed by the graph compiler.