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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| import torch | ||
| from torch import nn | ||
| from typing import Optional, Tuple, Union | ||
| import transformers | ||
| from transformers.models.llama.modeling_llama import apply_rotary_pos_emb | ||
| import math | ||
|
|
||
| def apply_memory_efficient_attnetion(): | ||
|
|
||
| try: | ||
| from xformers import ops as xops | ||
| except ImportError: | ||
| xops = None | ||
| print( | ||
| "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers." | ||
| ) | ||
|
|
||
| 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 = 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 | ||
|
|
||
| if xops is not None: | ||
| attn_weights = None | ||
| query_states = query_states.transpose(1, 2) | ||
| key_states = key_states.transpose(1, 2) | ||
| value_states = value_states.transpose(1, 2) | ||
| attn_output = xops.memory_efficient_attention( | ||
| query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask(), p=0) | ||
| 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, device=attn_weights.device) | ||
| ) | ||
|
|
||
| # 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) | ||
|
|
||
| if not output_attentions: | ||
| attn_weights = None | ||
|
|
||
| return attn_output, attn_weights, past_key_value | ||
|
|
||
| transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward | ||
|
|
||
|
|
||
| def apply_ntk_scaling(alpha: Union[float,str]): | ||
| old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ | ||
|
|
||
| def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None): | ||
| self.dim = dim | ||
| self.alpha = alpha | ||
| if isinstance(alpha,(float,int)): | ||
| base = base * alpha ** (dim / (dim-2)) | ||
| max_position_embeddings = 32768 | ||
| self.base = base | ||
| elif alpha=='auto': | ||
| self.base = base | ||
| else: | ||
| raise ValueError(alpha) | ||
| old_init(self, dim, max_position_embeddings, base, device) | ||
|
|
||
| def adaptive_ntk_forward(self, x, seq_len=None): | ||
| if seq_len > self.max_seq_len_cached: | ||
| if isinstance(self.alpha,(float,int)): | ||
| self.max_seq_len_cached = seq_len | ||
| t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) | ||
| freqs = torch.einsum("i,j->ij", t, self.inv_freq) | ||
| # Different from paper, but it uses a different permutation in order to obtain the same calculation | ||
| emb = torch.cat((freqs, freqs), dim=-1).to(x.device) | ||
| self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) | ||
| self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) | ||
| return ( | ||
| self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), | ||
| self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), | ||
| ) | ||
| elif self.alpha=='auto': | ||
| t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype) | ||
| inv_freq = self.inv_freq | ||
| dim = self.dim | ||
| alpha = seq_len / 1024 - 1 | ||
| base = self.base * alpha ** (dim / (dim-2)) | ||
| inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim )) | ||
|
|
||
| freqs = torch.einsum("i,j->ij", t, inv_freq) | ||
| emb = torch.cat((freqs, freqs), dim=-1).to(x.device) | ||
| cos_cached = emb.cos()[None, None, :, :] | ||
| sin_cached = emb.sin()[None, None, :, :] | ||
| return ( | ||
| cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), | ||
| sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype) | ||
| ) | ||
| else: | ||
| return ( | ||
| self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), | ||
| self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype) | ||
| ) | ||
|
|
||
| transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init | ||
| transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward |