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| """ | |
| Monkey patch the llama implementation in the huggingface/transformers library. | |
| Avoid bugs in mps backend by not using in-place operations. | |
| """ | |
| import math | |
| from typing import List, Optional, Tuple | |
| import torch | |
| from torch import nn | |
| import transformers | |
| def rotate_half(x): | |
| """Rotates half the hidden dims of the input.""" | |
| x1 = x[..., : x.shape[-1] // 2].clone() | |
| x2 = x[..., x.shape[-1] // 2 :].clone() | |
| return torch.cat((-x2, x1), dim=-1) | |
| def apply_rotary_pos_emb(q, k, cos, sin, position_ids): | |
| gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] | |
| gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) | |
| cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) | |
| sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) | |
| q_embed = (q * cos) + (rotate_half(q) * sin) | |
| k_embed = (k * cos) + (rotate_half(k) * sin) | |
| return q_embed, k_embed | |
| def 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 | |
| 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) | |
| if not output_attentions: | |
| attn_weights = None | |
| return attn_output, attn_weights, past_key_value | |
| def replace_llama_attn_with_non_inplace_operations(): | |
| """Avoid bugs in mps backend by not using in-place operations.""" | |
| transformers.models.llama.modeling_llama.LlamaAttention.forward = forward | |