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).
Python class
TensorParallelLatentAttentionWithRope
TensorParallelLatentAttentionWithRope
class max.nn.attention.TensorParallelLatentAttentionWithRope(*, skip_allreduce=False, **kwargs)
Bases: LatentAttentionWithRope
Distributed tensor parallel implementation of the Latent Attention with Rope. Note that using tensor parallelism for MLA will cause the KV-cache to be duplicated across all devices, which is not efficient.
When skip_allreduce is True, the final all-reduce is skipped. This is
intended for mixed TP-attention + EP-MoE configurations, where the
communication is handled explicitly by the caller.
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Parameters:
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skip_allreduce (bool)
create_mla_prefill_metadata()
create_mla_prefill_metadata(input_row_offsets_, kv_collections)
Creates per-device MLA prefill metadata for tensor-parallel execution.
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Parameters:
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- input_row_offsets – Per-device ragged row offset tensors.
- kv_collections (list[KVCacheInputsPerDevice[TensorValue, BufferValue]]) – Per-device paged KV cache values.
- input_row_offsets_ (list[TensorValue])
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Returns:
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A list of
MLAPrefillMetadatainstances, one per device. -
Return type:
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list[MLAPrefillMetadata]
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