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import torch |
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import torch.nn as nn |
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from typing import Optional, Tuple |
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from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb |
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from xformers.ops.fmha import ( |
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memory_efficient_attention, |
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) |
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class LlamaXFormersAttention(LlamaAttention): |
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def forward( |
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self, |
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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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**kwargs, |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
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if "padding_mask" in kwargs: |
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warnings.warn( |
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"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
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) |
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bsz, q_len, _ = hidden_states.size() |
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query_states = self.q_proj(hidden_states) |
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key_states = self.k_proj(hidden_states) |
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value_states = self.v_proj(hidden_states) |
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
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value_states = value_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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if past_key_value is not None: |
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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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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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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) |
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
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raise ValueError( |
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
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f" {attn_weights.size()}" |
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) |
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if attention_mask is not None: |
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
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raise ValueError( |
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
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) |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) |
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attn_output = torch.matmul(attn_weights, value_states) |
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
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raise ValueError( |
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" |
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f" {attn_output.size()}" |
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) |
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attn_output = attn_output.transpose(1, 2).contiguous() |
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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 |
