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| from typing import Optional, Tuple | |
| import warnings | |
| import torch | |
| import transformers | |
| from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv | |
| try: | |
| from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func | |
| except ImportError: | |
| from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func | |
| from flash_attn.bert_padding import unpad_input, pad_input | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| modality_indicators: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| output_attentions: bool = False, | |
| use_cache: bool = False, | |
| padding_mask: bool = None, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
| if output_attentions: | |
| warnings.warn( | |
| "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." | |
| ) | |
| 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, modality_indicators) | |
| .view(bsz, q_len, self.num_key_value_heads, self.head_dim) | |
| .transpose(1, 2) | |
| ) | |
| value_states = ( | |
| self.v_proj(hidden_states, modality_indicators) | |
| .view(bsz, q_len, self.num_key_value_heads, self.head_dim) | |
| .transpose(1, 2) | |
| ) # shape: (b, num_heads, s, head_dim) | |
| 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 | |
| ) | |
| if past_key_value is not None: | |
| # reuse k, v | |
| 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 | |
| # repeat k/v heads if n_kv_heads < n_heads | |
| key_states = repeat_kv(key_states, self.num_key_value_groups) | |
| value_states = repeat_kv(value_states, self.num_key_value_groups) | |
| # Transform the data into the format required by flash attention | |
| qkv = torch.stack([query_states, key_states, value_states], dim=2) | |
| qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] | |
| key_padding_mask = attention_mask | |
| if key_padding_mask is None: | |
| qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) | |
| cu_q_lens = torch.arange( | |
| 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device | |
| ) | |
| max_s = q_len | |
| output = flash_attn_unpadded_qkvpacked_func( | |
| qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True | |
| ) | |
| output = output.view(bsz, q_len, -1) | |
| else: | |
| qkv = qkv.reshape(bsz, q_len, -1) | |
| qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) | |
| qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) | |
| output_unpad = flash_attn_unpadded_qkvpacked_func( | |
| qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True | |
| ) | |
| output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) | |
| output = pad_input(output_unpad, indices, bsz, q_len) | |
| return self.o_proj(output), None, past_key_value | |
| # Disable the transformation of the attention mask in LlamaModel as the flash attention | |
| # requires the attention mask to be the same as the key_padding_mask | |
| def _prepare_decoder_attention_mask( | |
| self, attention_mask, input_shape, inputs_embeds, past_key_values_length | |
| ): | |
| # [bsz, seq_len] | |
| return attention_mask | |
| def replace_llama_attn_with_flash_attn(): | |
| cuda_major, cuda_minor = torch.cuda.get_device_capability() | |
| if cuda_major < 8: | |
| warnings.warn( | |
| "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." | |
| "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" | |
| ) | |
| transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( | |
| _prepare_decoder_attention_mask | |
| ) | |
| transformers.models.llama.modeling_llama.LlamaAttention.forward = forward |