update flash-attn patch for 70B/GQA and inference using helper from flash-attn tests
This commit is contained in:
Aman Karmani
@@ -2,142 +2,37 @@
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# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
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import warnings
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from typing import Optional, Tuple
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import torch
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import torch.nn.functional as F
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import transformers
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from einops import rearrange
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from flash_attn.bert_padding import pad_input, unpad_input
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
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from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
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try:
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from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
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from flash_attn.flash_attn_interface import ( # pylint: disable=ungrouped-imports
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flash_attn_varlen_kvpacked_func,
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flash_attn_varlen_qkvpacked_func,
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)
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except ImportError:
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from flash_attn.flash_attn_interface import (
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flash_attn_unpadded_kvpacked_func as flash_attn_varlen_kvpacked_func,
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)
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from flash_attn.flash_attn_interface import (
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flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
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)
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
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from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
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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.Tensor] = 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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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel
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attention_mask: [bsz, q_len]
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"""
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# pylint: disable=duplicate-code
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bsz, q_len, _ = hidden_states.size()
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query_states = (
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self.q_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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key_states = (
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self.k_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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value_states = (
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self.v_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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# [bsz, q_len, nh, hd]
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# [bsz, nh, q_len, hd]
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kv_seq_len = key_states.shape[-2]
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assert past_key_value is None, "past_key_value is not supported"
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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(
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query_states, key_states, cos, sin, position_ids
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)
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# [bsz, nh, t, hd]
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assert not output_attentions, "output_attentions is not supported"
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assert not use_cache, "use_cache is not supported"
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# Flash attention codes from
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# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
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# transform the data into the format required by flash attention
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qkv = torch.stack(
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[query_states, key_states, value_states], dim=2
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) # [bsz, nh, 3, q_len, hd]
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qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
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# We have disabled _prepare_decoder_attention_mask in LlamaModel
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# the attention_mask should be the same as the key_padding_mask
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key_padding_mask = attention_mask
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if key_padding_mask is None:
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qkv = rearrange(qkv, "b s ... -> (b s) ...")
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max_s = q_len
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cu_q_lens = torch.arange(
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0,
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(bsz + 1) * q_len,
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step=q_len,
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dtype=torch.int32,
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device=qkv.device,
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)
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output = flash_attn_varlen_qkvpacked_func(
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qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
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)
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output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
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elif attention_mask.shape[0] == 1:
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# special handling using sample packing
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qkv = rearrange(qkv, "b s ... -> (b s) ...")
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cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
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cu_q_lens = cu_q_lens.squeeze()
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output = flash_attn_varlen_qkvpacked_func(
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qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
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)
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output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
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else:
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nheads = qkv.shape[-2]
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# pylint: disable=invalid-name
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x = rearrange(qkv, "b s three h d -> b s (three h d)")
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x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
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x_unpad = rearrange(
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x_unpad,
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"nnz (three h d) -> nnz three h d",
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three=3,
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h=nheads,
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)
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output_unpad = flash_attn_varlen_qkvpacked_func(
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x_unpad,
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cu_q_lens,
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max_s,
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0.0,
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softmax_scale=None,
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causal=True,
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)
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output = rearrange(
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pad_input(
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rearrange(output_unpad, "nnz h d -> nnz (h d)"),
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indices,
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bsz,
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q_len,
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),
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"b s (h d) -> b s h d",
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h=nheads,
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)
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return (
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self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
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None,
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None,
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def replace_llama_attn_with_flash_attn():
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transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( # pylint: disable=protected-access
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_prepare_decoder_attention_mask
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)
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transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
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# Disable the transformation of the attention mask in LlamaModel as the flash attention
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@@ -153,8 +48,291 @@ def _prepare_decoder_attention_mask(
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return attention_mask
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def replace_llama_attn_with_flash_attn():
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transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( # pylint: disable=protected-access
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_prepare_decoder_attention_mask
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def flashattn_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.Tensor] = 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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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel
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attention_mask: [bsz, q_len]
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"""
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# pylint: disable=duplicate-code
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bsz, q_len, _ = hidden_states.size()
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if not hasattr(self, "pretraining_tp"):
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self.pretraining_tp = 1
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if self.pretraining_tp > 1:
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key_value_slicing = (
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self.num_key_value_heads * self.head_dim
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) // self.pretraining_tp
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query_slices = self.q_proj.weight.split(
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(self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
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)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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query_states = [
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F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
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]
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query_states = torch.cat(query_states, dim=-1)
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key_states = [
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F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
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]
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key_states = torch.cat(key_states, dim=-1)
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value_states = [
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F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
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]
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value_states = torch.cat(value_states, dim=-1)
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else:
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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(
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bsz, q_len, self.num_heads, self.head_dim
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).transpose(1, 2)
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key_states = key_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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value_states = value_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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# [bsz, q_len, nh, hd]
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# [bsz, nh, q_len, hd]
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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(
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query_states, key_states, cos, sin, position_ids
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)
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# [bsz, nh, t, hd]
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if past_key_value is not None:
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# 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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# repeat k/v heads if n_kv_heads < n_heads
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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 output_attentions:
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warnings.warn(
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"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
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)
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#
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# flash-attn v2 start
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#
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if self.training:
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# during training q,k,v always have same seqlen
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assert key_states.shape == query_states.shape
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is_causal = True
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else:
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# turn off FA causal mask after first inference autoregressive iteration
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# only on first autoregressive step q,k,v have same seqlen
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is_causal = key_states.shape == query_states.shape
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if self.training and attention_mask.shape[0] == 1:
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# special handling using sample packing
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qkv = torch.stack(
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[query_states, key_states, value_states], dim=2
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) # [bsz, nh, 3, q_len, hd]
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qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
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qkv = rearrange(qkv, "b s ... -> (b s) ...")
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cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
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cu_q_lens = cu_q_lens.squeeze()
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output = flash_attn_varlen_qkvpacked_func(
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qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=is_causal
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)
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output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
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elif query_states.shape == key_states.shape:
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qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
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query_states.transpose(1, 2),
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key_states.transpose(1, 2),
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value_states.transpose(1, 2),
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qkvpacked=True,
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# We have disabled _prepare_decoder_attention_mask in LlamaModel
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# the attention_mask should be the same as the key_padding_mask
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key_padding_mask=attention_mask,
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)
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output_unpad = flash_attn_varlen_qkvpacked_func(
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qkv_unpad,
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cu_seqlens_q,
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max_seqlen_q,
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0.0,
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softmax_scale=None,
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causal=is_causal,
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)
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output = output_pad_fn(output_unpad)
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else:
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( # pylint: disable=unbalanced-tuple-unpacking
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q_unpad,
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kv_unpad,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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_,
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_,
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output_pad_fn,
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) = generate_qkv(
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query_states.transpose(1, 2),
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key_states.transpose(1, 2),
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value_states.transpose(1, 2),
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kvpacked=True,
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key_padding_mask=attention_mask,
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)
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output_unpad = flash_attn_varlen_kvpacked_func(
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q_unpad,
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kv_unpad,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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0.0,
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softmax_scale=None,
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causal=is_causal,
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)
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output = output_pad_fn(output_unpad)
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attn_output = output
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if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
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#
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# flash-attn v2 end
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#
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if self.pretraining_tp > 1:
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attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
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o_proj_slices = self.o_proj.weight.split(
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self.hidden_size // self.pretraining_tp, dim=1
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)
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attn_output = sum(
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F.linear(attn_output[i], o_proj_slices[i])
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for i in range(self.pretraining_tp)
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)
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else:
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attn_output = self.o_proj(attn_output)
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return attn_output, None, past_key_value
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# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
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def generate_qkv(
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q,
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k,
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v,
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query_padding_mask=None,
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key_padding_mask=None,
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kvpacked=False,
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qkvpacked=False,
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): # pylint: disable=invalid-name,unnecessary-lambda-assignment
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"""
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Arguments:
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q: (batch_size, seqlen_q, nheads, d)
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k: (batch_size, seqlen_k, nheads_k, d)
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v: (batch_size, seqlen_k, nheads_k, d)
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query_padding_mask: (batch_size, seqlen), bool
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key_padding_mask: (batch_size, seqlen), bool
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"""
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assert not (kvpacked and qkvpacked)
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batch_size, seqlen_q, nheads, d = q.shape
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_, seqlen_k, nheads_k, _ = k.shape
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assert k.shape == (batch_size, seqlen_k, nheads_k, d)
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assert v.shape == (batch_size, seqlen_k, nheads_k, d)
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if query_padding_mask is not None:
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q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
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q, query_padding_mask
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)
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output_pad_fn = lambda output_unpad: pad_input( # noqa: E731
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output_unpad, indices_q, batch_size, seqlen_q
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)
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else:
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q_unpad = rearrange(q, "b s h d -> (b s) h d")
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cu_seqlens_q = torch.arange(
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0,
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(batch_size + 1) * seqlen_q,
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step=seqlen_q,
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dtype=torch.int32,
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device=q_unpad.device,
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)
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max_seqlen_q = seqlen_q
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output_pad_fn = lambda output_unpad: rearrange( # noqa: E731
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output_unpad, "(b s) h d -> b s h d", b=batch_size
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)
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if key_padding_mask is not None:
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k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
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v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
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else:
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k_unpad = rearrange(k, "b s h d -> (b s) h d")
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v_unpad = rearrange(v, "b s h d -> (b s) h d")
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cu_seqlens_k = torch.arange(
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0,
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(batch_size + 1) * seqlen_k,
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step=seqlen_k,
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dtype=torch.int32,
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device=k_unpad.device,
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)
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max_seqlen_k = seqlen_k
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if qkvpacked:
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assert nheads == nheads_k
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qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
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qkv = torch.stack([q, k, v], dim=2)
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return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
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||||
|
||||
if kvpacked:
|
||||
kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
|
||||
kv = torch.stack([k, v], dim=2)
|
||||
return (
|
||||
q_unpad,
|
||||
kv_unpad,
|
||||
cu_seqlens_q,
|
||||
cu_seqlens_k,
|
||||
max_seqlen_q,
|
||||
max_seqlen_k,
|
||||
q,
|
||||
kv,
|
||||
output_pad_fn,
|
||||
)
|
||||
|
||||
return (
|
||||
q_unpad,
|
||||
k_unpad,
|
||||
v_unpad,
|
||||
cu_seqlens_q,
|
||||
cu_seqlens_k,
|
||||
max_seqlen_q,
|
||||
max_seqlen_k,
|
||||
q,
|
||||
k,
|
||||
v,
|
||||
output_pad_fn,
|
||||
)
|
||||
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
|
||||
|
||||
Reference in New Issue
Block a user