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| """ | |
| Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention | |
| """ | |
| import warnings | |
| from typing import Optional, Tuple | |
| import torch | |
| import torch.nn.functional as F | |
| import transformers.models.llama.modeling_llama | |
| from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv | |
| def hijack_llama_sdp_attention(): | |
| transformers.models.llama.modeling_llama.LlamaAttention.forward = ( | |
| sdp_attention_forward | |
| ) | |
| 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]]]: | |
| # pylint: disable=duplicate-code | |
| bsz, q_len, _ = hidden_states.size() | |
| if not hasattr(self, "pretraining_tp"): | |
| self.pretraining_tp = 1 | |
| if self.pretraining_tp > 1: | |
| key_value_slicing = ( | |
| self.num_key_value_heads * self.head_dim | |
| ) // self.pretraining_tp | |
| query_slices = self.q_proj.weight.split( | |
| (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0 | |
| ) | |
| key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) | |
| value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) | |
| query_states = [ | |
| F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp) | |
| ] | |
| query_states = torch.cat(query_states, dim=-1) | |
| key_states = [ | |
| F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp) | |
| ] | |
| key_states = torch.cat(key_states, dim=-1) | |
| value_states = [ | |
| F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp) | |
| ] | |
| value_states = torch.cat(value_states, dim=-1) | |
| else: | |
| query_states = self.q_proj(hidden_states) | |
| key_states = self.k_proj(hidden_states) | |
| value_states = self.v_proj(hidden_states) | |
| query_states = query_states.view( | |
| bsz, q_len, self.num_heads, self.head_dim | |
| ).transpose(1, 2) | |
| key_states = key_states.view( | |
| bsz, q_len, self.num_key_value_heads, self.head_dim | |
| ).transpose(1, 2) | |
| value_states = value_states.view( | |
| bsz, q_len, self.num_key_value_heads, self.head_dim | |
| ).transpose(1, 2) | |
| # [bsz, q_len, nh, hd] | |
| # [bsz, nh, q_len, hd] | |
| 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 | |
| # 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) | |
| if output_attentions: | |
| warnings.warn( | |
| "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." | |
| ) | |
| # | |
| # sdp-attn start | |
| # | |
| with torch.backends.cuda.sdp_kernel(): | |
| attn_output = torch.nn.functional.scaled_dot_product_attention( | |
| query_states, | |
| key_states, | |
| value_states, | |
| attn_mask=attention_mask, | |
| is_causal=False, | |
| ) | |
| 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) | |
| # | |
| # sdp-attn end | |
| # | |
| if self.pretraining_tp > 1: | |
| attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) | |
| o_proj_slices = self.o_proj.weight.split( | |
| self.hidden_size // self.pretraining_tp, dim=1 | |
| ) | |
| attn_output = sum( | |
| F.linear(attn_output[i], o_proj_slices[i]) | |
| for i in range(self.pretraining_tp) | |
| ) | |
| else: | |
| attn_output = self.o_proj(attn_output) | |
| return attn_output, None, past_key_value | |