add

models/LLaVA/llava/train/llama_flash_attn_monkey_patch.py

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+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+import transformers
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
+
+from einops import rearrange
+
+from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
+from flash_attn.bert_padding import unpad_input, pad_input
+
+def forward(
+    self,
+    hidden_states: torch.Tensor,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+            Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel
+    
+    attention_mask: [bsz, q_len]
+    """
+    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)
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    offset = 0
+    if past_key_value is not None:
+        offset = past_key_value[0].shape[-2]
+        kv_seq_len += offset
+    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,
+                                                    offset=offset)
+    # [bsz, nh, t, hd]
+    assert not output_attentions, "output_attentions is not supported"
+    assert not use_cache, "use_cache is not supported"
+    assert past_key_value is None, "past_key_value is not supported"
+
+    # Flash attention codes from
+    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
+
+    # transform the data into the format required by flash attention
+    qkv = torch.stack([query_states, key_states, value_states], dim=2) # [bsz, nh, 3, q_len, hd]
+    qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
+    # We have disabled _prepare_decoder_attention_mask in LlamaModel
+    # the attention_mask should be the same as the key_padding_mask
+    key_padding_mask = attention_mask
+
+
+    if key_padding_mask is None:
+        qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+        max_s = q_len
+        cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
+                                device=qkv.device)
+        output = flash_attn_unpadded_qkvpacked_func(
+            qkv, cu_q_lens, max_s, 0.0,
+            softmax_scale=None, causal=True
+        )
+        output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
+    else:
+        nheads = qkv.shape[-2]
+        x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
+        x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+        output_unpad = flash_attn_unpadded_qkvpacked_func(
+            x_unpad, cu_q_lens, max_s, 0.0,
+            softmax_scale=None, causal=True
+        )
+        output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                    indices, bsz, q_len),
+                        'b s (h d) -> b s h d', h=nheads)
+    return self.o_proj(rearrange(output,
+                                    'b s h d -> b s (h d)')), None, None
+
+
+# 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():
+    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward