support LongLoRA
Former-commit-id: 0832ed37e7947d699f17375648a52f80752c2b6b
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hiyouga
232
src/llmtuner/extras/patches/llama_patch.py
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232
src/llmtuner/extras/patches/llama_patch.py
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# coding=utf-8
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# Modified from:
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# [1] https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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import math
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import torch
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import torch.nn as nn
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from typing import Optional, Tuple
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from transformers.utils import logging
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from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv
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try:
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from flash_attn import flash_attn_func, flash_attn_varlen_func # type: ignore
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from flash_attn.bert_padding import pad_input, unpad_input # type: ignore
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except ImportError:
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raise ImportError("Please install FlashAttention from https://github.com/Dao-AILab/flash-attention")
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logger = logging.get_logger(__name__)
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class LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return (self.weight * hidden_states).to(input_dtype)
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class LlamaShiftShortAttention(LlamaAttention):
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None: # reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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if getattr(self, "num_key_value_groups"):
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if getattr(self, "shift_ratio", None) and self.training: # shift
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group_size = int(q_len * getattr(self, "shift_ratio"))
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if q_len % group_size > 0:
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raise ValueError("q_len {} should be divisible by group size {}.".format(q_len, group_size))
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num_group = q_len // group_size
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for state in (query_states, key_states, value_states):
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state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim)
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state[:, :, self.num_heads//2:] = state[:, :, self.num_heads//2:].roll(-group_size//2, dims=1)
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state = state.reshape(bsz * num_group, group_size, self.num_heads, self.head_dim).transpose(1, 2)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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if getattr(self, "shift_ratio", None) and self.training: # shift back
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attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
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attn_output[:, :, self.num_heads//2:] = attn_output[:, :, self.num_heads//2:].roll(group_size//2, dims=1)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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class LlamaFlashAttention2(LlamaAttention):
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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# LlamaFlashAttention2 attention does not support output_attentions
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output_attentions = False
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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# FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None: # reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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if getattr(self, "num_key_value_groups"):
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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query_states = query_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
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key_states = key_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
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value_states = value_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
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if getattr(self, "shift_ratio", None) and self.training: # shift
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group_size = int(q_len * getattr(self, "shift_ratio"))
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if q_len % group_size > 0:
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raise ValueError("q_len {} should be divisible by group size {}.".format(q_len, group_size))
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num_group = q_len // group_size
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for state in (query_states, key_states, value_states):
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state[:, :, self.num_heads//2:] = state[:, :, self.num_heads//2:].roll(-group_size//2, dims=1)
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state = state.reshape(bsz * num_group, group_size, self.num_heads, self.head_dim)
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if attention_mask is not None:
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logger.warning_once("Padded sequences are less efficient.")
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batch_size = query_states.shape[0]
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# -q_len: assumes left padding
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unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(query_states, attention_mask[:, -q_len:])
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unpadded_k, _, cu_seqlens_k, max_seqlen_k = unpad_input(key_states, attention_mask)
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unpadded_v, _, _, _ = unpad_input(value_states, attention_mask)
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attn_output_unpad = flash_attn_varlen_func(
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unpadded_q,
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unpadded_k,
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unpadded_v,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=max_seqlen_q,
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max_seqlen_k=max_seqlen_k,
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dropout_p=0.0,
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softmax_scale=None,
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causal=True,
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)
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attn_output = pad_input(attn_output_unpad, indices_q, batch_size, q_len)
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else:
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attn_output = flash_attn_func(
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query_states, key_states, value_states, 0.0, softmax_scale=None, causal=True
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)
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if getattr(self, "shift_ratio", None) and self.training: # shift back
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attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
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attn_output[:, :, self.num_heads//2:] = attn_output[:, :, self.num_heads//2:].roll(group_size//2, dims=1)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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# Disable the transformation of the attention mask in LlamaModel as flash attention
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# takes a boolean padding_mask. Fills in the past kv length for use in forward.
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def _prepare_decoder_attention_mask(
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self,
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attention_mask: torch.Tensor,
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input_shape: torch.Tensor,
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inputs_embeds: torch.Tensor,
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past_key_values_length: int
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) -> torch.Tensor:
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if attention_mask is not None and torch.all(attention_mask):
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return None # This uses the faster call when training with full samples
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return attention_mask
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