For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).
Mojo struct
Struct_ep_dispatch_wait_mxfp4
struct Struct_ep_dispatch_wait_mxfp4
Registers the ep.dispatch_wait.mxfp4 graph op with the graph compiler.
Implemented traits
Methods
execute
static def execute[dispatch_dtype: DType, dispatch_scale_dtype: DType, hidden_size: Int, top_k: Int, n_experts: Int, max_token_per_rank: Int, n_gpus_per_node: Int, n_nodes: Int, //, target: StringSlice[ImmStaticOrigin], num_input_tokens: Int = Int(-1), *, fuse_a_scale_preshuffle: Bool = False, max_padded_M: Int = Int(0)](output_tokens: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=output_tokens.static_spec], output_scales: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=output_scales.static_spec], row_offsets: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=row_offsets.static_spec], expert_ids: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=expert_ids.static_spec], src_info: ManagedTensorSlice[IOSpec[_, _].Output, static_spec=src_info.static_spec], atomic_counters: ManagedTensorSlice[IOSpec[_, _].MutableInput, static_spec=atomic_counters.static_spec], recv_ptrs: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=recv_ptrs.static_spec], recv_count_ptrs: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=recv_count_ptrs.static_spec], context: DeviceContext)
Execute the Expert Parallelism dispatch completion kernel. Received tokens are in MXFP4 format: two FP4 elements packed per uint8 in output_tokens with per-token even-mode scales in output_scales.
When fuse_a_scale_preshuffle=True (KS224 up-proj fusion), the
kernel writes the E8M0 activation scale directly into the up-proj
grouped matmul's per-expert fixed-stride scale_4d slot layout (slot
stride max_padded_M * K_SCALES), so the standalone
preshuffle_grouped_scale_4d_gpu can be dropped from the decode
critical path. The scales output tensor must then have shape
[n_local_experts * max_padded_M, K_SCALES].
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