288cc6c234
Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: Patrick Horn <patrick.horn@gmail.com> Co-authored-by: simon-mo <xmo@berkeley.edu> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Dict, List, Optional, Type
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import torch
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from vllm.attention.backends.abstract import AttentionType
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from vllm.attention.backends.mla.common import (MLACommonBackend,
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MLACommonImpl,
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MLACommonMetadata)
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from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
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class TritonMLABackend(MLACommonBackend):
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@staticmethod
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def get_name() -> str:
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return "TRITON_MLA"
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@staticmethod
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def get_impl_cls() -> Type["TritonMLAImpl"]:
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return TritonMLAImpl
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class TritonMLAImpl(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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# 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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**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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"TritonMLAImpl 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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"TritonMLAImpl")
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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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if self.kv_cache_dtype.startswith("fp8"):
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raise NotImplementedError("FP8 Triton MLA not yet supported")
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decode_meta = attn_metadata.decode_metadata
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assert decode_meta is not None
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B = q_nope.shape[0]
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q = torch.cat([q_nope, q_pe], dim=-1)
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o = torch.zeros(B,
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self.num_heads,
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self.kv_lora_rank,
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dtype=q.dtype,
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device=q.device)
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num_kv_splits = 4 # TODO: heuristic
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# TODO(lucas) Allocate ahead of time
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attn_logits = torch.empty(
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(
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B,
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self.num_heads,
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num_kv_splits,
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# NOTE(lucas) idk why the +1 is here but sglang has it so we
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# just mirror that
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self.kv_lora_rank + 1,
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),
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dtype=torch.float32,
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device=q.device,
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)
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# Add a head dim of 1
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kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
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kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
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PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
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# Run MQA
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decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
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decode_meta.block_tables,
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decode_meta.seq_lens_tensor, attn_logits,
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num_kv_splits, self.scale, PAGE_SIZE)
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return self._v_up_proj_and_o_proj(o)
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