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vllm-v0.6.2/vllm/attention/backends/hpu_attn.py
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vllm-v0.6.2/vllm/attention/backends/hpu_attn.py
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###############################################################################
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# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company
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###############################################################################
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import os
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Type
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import torch
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import vllm_hpu_extension.ops as ops
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from vllm_hpu_extension.utils import Matmul, Softmax, VLLMKVCache
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata, AttentionType)
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from vllm.attention.backends.utils import CommonAttentionState
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from vllm.attention.ops.hpu_paged_attn import (HPUPagedAttention,
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HPUPagedAttentionMetadata)
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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class HPUAttentionBackend(AttentionBackend):
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@staticmethod
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def get_impl_cls() -> Type["HPUAttentionImpl"]:
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return HPUAttentionImpl
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@staticmethod
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def get_metadata_cls() -> Type["AttentionMetadata"]:
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return HPUAttentionMetadata
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@staticmethod
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def get_state_cls() -> Type["CommonAttentionState"]:
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return CommonAttentionState
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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return HPUPagedAttention.get_kv_cache_shape(num_blocks, block_size,
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num_kv_heads, head_size)
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@staticmethod
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def swap_blocks(
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src_kv_cache: torch.Tensor,
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dst_kv_cache: torch.Tensor,
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src_to_dst: Dict[int, int],
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) -> None:
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HPUPagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: Dict[int, List[int]],
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) -> None:
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HPUPagedAttention.copy_blocks(kv_caches, src_to_dists)
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@dataclass
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class HPUAttentionMetadata(HPUPagedAttentionMetadata, AttentionMetadata):
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"""Metadata for HPUAttentionbackend."""
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# Currently, input sequences can only contain all prompts
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# or all decoding. True if all sequences are prompts.
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is_prompt: bool
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attn_bias: Optional[torch.Tensor]
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seq_lens_tensor: Optional[torch.Tensor]
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class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
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"""
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If the input tensors contain prompt tokens, the layout is as follows:
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|<--------------- num_prefill_tokens ----------------->|
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|<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|
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Otherwise, the layout is as follows:
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|<----------------- num_decode_tokens ------------------>|
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|<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|
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Generation tokens can contain padding when cuda-graph is used.
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Currently, prompt tokens don't contain any padding.
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The prompts might have different lengths, while the generation tokens
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always have length 1.
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"""
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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]] = None,
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max_seq_len: int = 4096,
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) -> None:
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super(AttentionImpl, self).__init__()
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self.kv_cache_dtype = kv_cache_dtype
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.matmul_qk = Matmul()
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self.softmax = Softmax()
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self.matmul_av = Matmul()
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self.k_cache = VLLMKVCache()
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self.v_cache = VLLMKVCache()
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self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
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self.sliding_window = sliding_window
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self.alibi_slopes = alibi_slopes
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if alibi_slopes is not None:
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alibi_slopes_tensor = torch.tensor(alibi_slopes,
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dtype=torch.bfloat16)
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self.alibi_slopes = alibi_slopes_tensor
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assert self.num_heads % self.num_kv_heads == 0
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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self.prefill_usefusedsdpa = os.getenv('VLLM_PROMPT_USE_FUSEDSDPA',
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'0').lower() in ['1', 'true']
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if self.prefill_usefusedsdpa:
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assert alibi_slopes is None, \
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'Prefill with FusedSDPA not supported with alibi slopes!'
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suppored_head_sizes = HPUPagedAttention.get_supported_head_sizes()
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if head_size not in suppored_head_sizes:
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raise ValueError(
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f"Head size {head_size} is not supported by PagedAttention. "
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f"Supported head sizes are: {suppored_head_sizes}.")
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: HPUAttentionMetadata,
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k_scale: float = 1.0,
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v_scale: float = 1.0,
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attn_type: AttentionType = AttentionType.DECODER,
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) -> torch.Tensor:
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"""Forward pass with xFormers and PagedAttention.
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Args:
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query: shape = [num_tokens, num_heads * head_size]
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key: shape = [num_tokens, num_kv_heads * head_size]
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value: shape = [num_tokens, num_kv_heads * head_size]
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kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
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attn_metadata: Metadata for attention.
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Returns:
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shape = [num_tokens, num_heads * head_size]
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"""
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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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"HPUAttentionImpl")
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batch_size, seq_len, hidden_size = query.shape
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_, seq_len_kv, _ = key.shape
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query = query.view(-1, self.num_heads, self.head_size)
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key = key.view(-1, self.num_kv_heads, self.head_size)
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value = value.view(-1, self.num_kv_heads, self.head_size)
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block_indices = attn_metadata.block_indices
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block_offsets = attn_metadata.block_offsets
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if attn_metadata.is_prompt:
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key = key.unflatten(0, (block_indices.size(0), -1))
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value = value.unflatten(0, (block_indices.size(0), -1))
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if kv_cache is not None:
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key_cache, value_cache = HPUPagedAttention.split_kv_cache(
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kv_cache, self.num_kv_heads, self.head_size)
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# Reshape the input keys and values and store them in the cache.
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# If kv_cache is not provided, the new key and value tensors are
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# not cached. This happens during the initial memory profiling run.
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key_cache = self.k_cache(key, key_cache, block_indices,
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block_offsets)
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value_cache = self.v_cache(value, value_cache, block_indices,
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block_offsets)
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if attn_metadata.is_prompt:
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# Prompt run.
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if not self.prefill_usefusedsdpa:
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# TODO: move this outside of model
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assert attn_metadata.attn_bias is not None, \
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'attn_bias must be set before calling model.forward!'
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attn_bias = attn_metadata.attn_bias
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if self.alibi_slopes is not None:
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position_bias = _make_alibi_bias(self.alibi_slopes,
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self.num_kv_heads,
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attn_bias.dtype,
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attn_bias.shape[-1])
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attn_bias = attn_bias.tile((1, self.num_kv_heads, 1, 1))
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attn_bias.add_(position_bias)
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else:
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attn_bias = None
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query_shape = (batch_size, seq_len, self.num_heads, self.head_size)
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kv_shape = (batch_size, seq_len_kv, self.num_kv_heads,
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self.head_size)
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out = ops.prompt_attention(
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query.view(query_shape),
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key.view(kv_shape),
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value.view(kv_shape),
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attn_bias=attn_bias,
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p=0.0,
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scale=self.scale,
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matmul_qk_op=self.matmul_qk,
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softmax_op=self.softmax,
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matmul_av_op=self.matmul_av,
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)
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output = out.reshape(batch_size, seq_len, hidden_size)
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else:
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# Decoding run.
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output = HPUPagedAttention.forward_decode(
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query=query,
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key_cache=key_cache,
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value_cache=value_cache,
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block_list=attn_metadata.block_list,
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block_mapping=attn_metadata.block_mapping,
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block_bias=attn_metadata.attn_bias,
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block_scales=attn_metadata.block_scales,
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scale=self.scale,
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matmul_qk_op=self.matmul_qk,
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matmul_av_op=self.matmul_av,
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keys_fetch_func=self.k_cache.fetch_from_cache,
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values_fetch_func=self.v_cache.fetch_from_cache)
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# Reshape the output tensor.
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return output.view(batch_size, seq_len, hidden_size)
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def _make_alibi_bias(
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alibi_slopes: torch.Tensor,
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num_kv_heads: int,
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dtype: torch.dtype,
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seq_len: int,
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) -> torch.Tensor:
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bias = torch.arange(seq_len, dtype=dtype)
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# NOTE(zhuohan): HF uses
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# `bias = bias[None, :].repeat(seq_len, 1)`
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# here. We find that both biases give the same results, but
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# the bias below more accurately follows the original ALiBi
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# paper.
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# Calculate a matrix where each element represents ith element- jth
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# element.
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bias = bias[None, :] - bias[:, None]
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padded_len = (seq_len + 7) // 8 * 8
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num_heads = alibi_slopes.shape[0]
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bias = torch.empty(
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1, # batch size
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num_heads,
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seq_len,
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padded_len,
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device=alibi_slopes.device,
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dtype=dtype,
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)[:, :, :, :seq_len].copy_(bias)
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bias.mul_(alibi_slopes[:, None, None])
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if num_heads != num_kv_heads:
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bias = bias.unflatten(1, (num_kv_heads, num_heads // num_kv_heads))
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return bias
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