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| import logging | |
| import math | |
| import sys | |
| from typing import Optional, Tuple | |
| import torch | |
| import torch.nn as nn | |
| import transformers.models.llama.modeling_llama | |
| import modules.shared as shared | |
| if shared.args.xformers: | |
| try: | |
| import xformers.ops | |
| except Exception: | |
| logging.error("xformers not found! Please install it before trying to use it.", file=sys.stderr) | |
| def hijack_llama_attention(): | |
| if shared.args.xformers: | |
| transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward | |
| logging.info("Replaced attention with xformers_attention") | |
| elif shared.args.sdp_attention: | |
| transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward | |
| logging.info("Replaced attention with sdp_attention") | |
| def xformers_forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| output_attentions: bool = False, | |
| use_cache: bool = False, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
| bsz, q_len, _ = hidden_states.size() | |
| query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| kv_seq_len = key_states.shape[-2] | |
| if past_key_value is not None: | |
| kv_seq_len += past_key_value[0].shape[-2] | |
| cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) | |
| query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) | |
| # [bsz, nh, t, hd] | |
| if past_key_value is not None: | |
| # reuse k, v, self_attention | |
| key_states = torch.cat([past_key_value[0], key_states], dim=2) | |
| value_states = torch.cat([past_key_value[1], value_states], dim=2) | |
| past_key_value = (key_states, value_states) if use_cache else None | |
| # We only apply xformers optimizations if we don't need to output the whole attention matrix | |
| if not output_attentions: | |
| query_states = query_states.transpose(1, 2) | |
| key_states = key_states.transpose(1, 2) | |
| value_states = value_states.transpose(1, 2) | |
| # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros. | |
| # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros. | |
| if attention_mask is None or attention_mask[0, 0, 0, 1] == 0: | |
| # input and output should be of form (bsz, q_len, num_heads, head_dim) | |
| attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None) | |
| else: | |
| # input and output should be of form (bsz, q_len, num_heads, head_dim) | |
| attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask()) | |
| attn_weights = None | |
| else: | |
| attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) | |
| if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): | |
| raise ValueError( | |
| f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" | |
| f" {attn_weights.size()}" | |
| ) | |
| if attention_mask is not None: | |
| if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): | |
| raise ValueError( | |
| f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" | |
| ) | |
| attn_weights = attn_weights + attention_mask | |
| attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) | |
| # upcast attention to fp32 | |
| attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) | |
| attn_output = torch.matmul(attn_weights, value_states) | |
| if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): | |
| raise ValueError( | |
| f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" | |
| f" {attn_output.size()}" | |
| ) | |
| attn_output = attn_output.transpose(1, 2) | |
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
| attn_output = self.o_proj(attn_output) | |
| return attn_output, attn_weights, past_key_value | |
| def sdp_attention_forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| output_attentions: bool = False, | |
| use_cache: bool = False, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
| bsz, q_len, _ = hidden_states.size() | |
| query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
| kv_seq_len = key_states.shape[-2] | |
| if past_key_value is not None: | |
| kv_seq_len += past_key_value[0].shape[-2] | |
| cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) | |
| query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) | |
| # [bsz, nh, t, hd] | |
| if past_key_value is not None: | |
| # reuse k, v, self_attention | |
| key_states = torch.cat([past_key_value[0], key_states], dim=2) | |
| value_states = torch.cat([past_key_value[1], value_states], dim=2) | |
| past_key_value = (key_states, value_states) if use_cache else None | |
| # We only apply sdp attention if we don't need to output the whole attention matrix | |
| if not output_attentions: | |
| attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False) | |
| attn_weights = None | |
| else: | |
| attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) | |
| if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): | |
| raise ValueError( | |
| f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" | |
| f" {attn_weights.size()}" | |
| ) | |
| if attention_mask is not None: | |
| if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): | |
| raise ValueError( | |
| f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" | |
| ) | |
| attn_weights = attn_weights + attention_mask | |
| attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) | |
| # upcast attention to fp32 | |
| attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) | |
| attn_output = torch.matmul(attn_weights, value_states) | |
| if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): | |
| raise ValueError( | |
| f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" | |
| f" {attn_output.size()}" | |
| ) | |
| attn_output = attn_output.transpose(1, 2) | |
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
| attn_output = self.o_proj(attn_output) | |
| return attn_output, attn_weights, past_key_value | |