98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Optional
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import torch
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import vllm._custom_ops as ops
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from vllm.attention.backends.abstract import (AttentionType,
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is_quantized_kv_cache)
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from vllm.logger import init_logger
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from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
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MLACommonImpl,
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MLACommonMetadata)
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logger = init_logger(__name__)
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class CutlassMLABackend(MLACommonBackend):
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@staticmethod
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def get_name() -> str:
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return "CUTLASS_MLA_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["CutlassMLAImpl"]:
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return CutlassMLAImpl
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class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[dict[str, Any]],
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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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# MLA Specific Arguments
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**mla_args) -> None:
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super().__init__(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window, kv_cache_dtype,
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blocksparse_params, logits_soft_cap, attn_type,
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kv_sharing_target_layer_name, **mla_args)
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unsupported_features = [
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alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
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]
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if any(unsupported_features):
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raise NotImplementedError(
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"CutlassMLAImpl does not support one of the following: "
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"alibi_slopes, sliding_window, blocksparse_params, "
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"logits_soft_cap")
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if attn_type != AttentionType.DECODER:
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raise NotImplementedError("Encoder self-attention and "
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"encoder/decoder cross-attention "
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"are not implemented for "
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"CutlassMLAImpl")
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if is_quantized_kv_cache(self.kv_cache_dtype):
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raise NotImplementedError(
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"CutlassMLA V1 with FP8 KV cache not yet supported")
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def _forward_decode(
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self,
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q_nope: torch.Tensor,
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q_pe: torch.Tensor,
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kv_c_and_k_pe_cache: torch.Tensor,
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attn_metadata: MLACommonMetadata,
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) -> torch.Tensor:
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assert kv_c_and_k_pe_cache.numel() > 0
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assert attn_metadata.decode is not None
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if self.kv_cache_dtype.startswith("fp8"):
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raise NotImplementedError("FP8 Cutlass MLA not yet supported")
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B = q_nope.shape[0]
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o = torch.empty((B, self.num_heads, self.kv_lora_rank),
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dtype=q_nope.dtype,
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device=q_nope.device)
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# Run MLA
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# Clone q_nope and q_pe to make sure strides computation is correct.
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q_nope = q_nope.clone()
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q_pe = q_pe.clone()
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ops.cutlass_mla_decode(o, q_nope, q_pe, kv_c_and_k_pe_cache,
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attn_metadata.decode.seq_lens,
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attn_metadata.decode.block_table, self.scale)
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return self._v_up_proj(o)
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