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""" |
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Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments |
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""" |
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import logging |
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import math |
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from typing import Optional, Tuple |
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import torch |
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import transformers.models.llama.modeling_llama |
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from torch import nn |
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try: |
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import xformers.ops |
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except ImportError: |
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logging.error("xformers not found! Please install it before trying to use it.") |
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def replace_llama_attn_with_xformers_attn(): |
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transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward |
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def xformers_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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) -> 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 = ( |
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self.q_proj(hidden_states) |
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.view(bsz, q_len, self.num_heads, self.head_dim) |
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.transpose(1, 2) |
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) |
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key_states = ( |
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self.k_proj(hidden_states) |
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.view(bsz, q_len, self.num_heads, self.head_dim) |
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.transpose(1, 2) |
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) |
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value_states = ( |
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self.v_proj(hidden_states) |
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.view(bsz, q_len, self.num_heads, self.head_dim) |
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.transpose(1, 2) |
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) |
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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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( |
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query_states, |
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key_states, |
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) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb( |
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query_states, key_states, cos, sin, position_ids |
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) |
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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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if not output_attentions: |
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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 attention_mask is None or attention_mask[0, 0, 0, 1] == 0: |
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attn_output = xformers.ops.memory_efficient_attention( |
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query_states, key_states, value_states, attn_bias=None |
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) |
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else: |
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attn_output = xformers.ops.memory_efficient_attention( |
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query_states, |
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key_states, |
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value_states, |
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attn_bias=xformers.ops.LowerTriangularMask(), |
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) |
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attn_weights = None |
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else: |
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attn_weights = torch.matmul( |
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query_states, key_states.transpose(2, 3) |
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) / 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 = torch.max( |
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min) |
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) |
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attn_weights = nn.functional.softmax( |
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attn_weights, dim=-1, dtype=torch.float32 |
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).to(query_states.dtype) |
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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) |
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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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return attn_output, attn_weights, past_key_value |
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