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85 lines
2.9 KiB
85 lines
2.9 KiB
from types import MethodType
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv
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SUPPORT_XFORMERS = False
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SUPPORT_FLASH2 = False
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try:
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import xformers.ops as xops
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SUPPORT_XFORMERS = True
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except ImportError:
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pass
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try:
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from flash_attn import flash_attn_func
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SUPPORT_FLASH2 = True
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except ImportError:
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pass
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SUPPORT_FLASH = SUPPORT_XFORMERS or SUPPORT_FLASH2
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def llama_flash_attention(
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self: LlamaAttention,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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# [bsz, nh, t, hd]
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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# q, k, v is [B, H, S, K] and xformers need [B, S, H, K]. returns [B, S, H, K]
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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if SUPPORT_FLASH2:
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attn_output = flash_attn_func(query_states, key_states, value_states, causal=True)
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else:
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attn_output = xops.memory_efficient_attention(
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query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
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)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def replace_xformers(model: nn.Module):
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for module in model.modules():
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if isinstance(module, LlamaAttention):
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module.forward = MethodType(llama_flash_attention, module)
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