feat: support flash attention 2 in qwen2 vl vision blocks (#2721)
* feat: support flash attention 2 in qwen2 vl vision blocks * fix: calc max_seqlen once and small refactors
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drbh
GitHub
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commit
38cff84a3e
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@ -22,9 +22,11 @@ from torch import nn
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from text_generation_server.utils.import_utils import SYSTEM
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if SYSTEM == "ipex":
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pass
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import intel_extension_for_pytorch as ipex
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else:
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pass
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import flash_attn_2_cuda
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import numpy as np
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from transformers.activations import ACT2FN
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import torch.nn.functional as F
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@ -66,7 +68,7 @@ def apply_rotary_pos_emb_vision(
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return output
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class Qwen2VLSdpaAttention(nn.Module):
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class Qwen2VLAttention(nn.Module):
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def __init__(self, *, prefix, config, weights):
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super().__init__()
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self.embed_dim = config.embed_dim // weights.process_group.size()
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@ -88,13 +90,14 @@ class Qwen2VLSdpaAttention(nn.Module):
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weights=weights,
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bias=True,
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)
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self.softmax_scale = 1.0 / np.sqrt(self.embed_dim // self.num_heads)
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def forward(
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self,
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hidden_state: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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max_seqlen: int,
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) -> torch.Tensor:
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# apply the qkv linear layer to the hidden state
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qkv = self.qkv(hidden_state)
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@ -117,37 +120,59 @@ class Qwen2VLSdpaAttention(nn.Module):
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0
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)
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key = apply_rotary_pos_emb_vision(key.unsqueeze(0), rotary_pos_emb).squeeze(0)
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# TODO: make use of existing RotatoryPositionEmbedding class
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# create the attention mask
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attention_mask = torch.zeros(
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[1, hidden_state.shape[0], hidden_state.shape[0]],
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device=hidden_state.device,
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dtype=torch.bool,
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)
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# TODO: avoid creating the mask in the forward pass, instead define the largest possible mask and slice it
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# calc maximum sequence length for any batch
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query = query.contiguous()
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key = key.contiguous()
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value = value.contiguous()
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causal = False
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# apply the cu_seqlens to the attention mask
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for i in range(1, len(cu_seqlens)):
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attention_mask[
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...,
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cu_seqlens[i - 1] : cu_seqlens[i],
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cu_seqlens[i - 1] : cu_seqlens[i],
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] = True
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# execute flash attention
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if SYSTEM == "ipex":
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attn_output = torch.empty_like(query)
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ipex.llm.functional.varlen_attention(
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(query.contiguous() if query.device.type == "xpu" else query),
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(key.contiguous() if key.device.type == "xpu" else key),
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(value.contiguous() if value.device.type == "xpu" else value),
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attn_output,
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cu_seqlens,
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cu_seqlens,
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max_seqlen,
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max_seqlen,
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0.0,
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self.softmax_scale,
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False,
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causal,
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False,
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None,
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)
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else:
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attn_output = flash_attn_2_cuda.varlen_fwd(
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query,
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key,
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value,
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None, # tmp buffer (auto-allocated)
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cu_seqlens, # cu_seqlens_q
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cu_seqlens, # cu_seqlens_k
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None, # max_seqlen_q (auto-computed)
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None, # max_seqlen_k (auto-computed)
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None, # block_tables
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None, # broadcast_mask
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max_seqlen, # max_seqlen
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max_seqlen, # max_seqlen
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0.0, # dropout_p
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self.softmax_scale,
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False, # zero_tensors
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causal, # causal attention within each sequence
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-1, # window_size_left
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-1, # window_size_right
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0.0, # softmax_cap
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False, # deterministic
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None, # rng_state
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)[0]
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# transpose for the attention mechanism (batch, seqlen, hidden_dim) -> (seqlen, batch, hidden_dim)
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query = query.transpose(0, 1)
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key = key.transpose(0, 1)
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value = value.transpose(0, 1)
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# apply attention
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attn_output = F.scaled_dot_product_attention(
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query, key, value, attention_mask, dropout_p=0.0
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)
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attn_output = attn_output.transpose(0, 1)
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# reshape output to original dimensions
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attn_output = attn_output.reshape(hidden_state.shape[0], -1)
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# TODO: prefer flash attention
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attn_output = self.proj(attn_output)
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return attn_output
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@ -173,7 +198,7 @@ class Qwen2VLVisionMLP(nn.Module):
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class Qwen2VLVisionBlock(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.attn = Qwen2VLSdpaAttention(
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self.attn = Qwen2VLAttention(
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prefix=f"{prefix}.attn",
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config=config,
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weights=weights,
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@ -194,10 +219,12 @@ class Qwen2VLVisionBlock(nn.Module):
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weights=weights,
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)
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def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor:
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def forward(
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self, hidden_states, cu_seqlens, rotary_pos_emb, max_seqlen
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) -> torch.Tensor:
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hidden_states_post_norm1, res = self.norm1(hidden_states)
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hidden_states = hidden_states + self.attn(
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hidden_states_post_norm1, cu_seqlens, rotary_pos_emb
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hidden_states_post_norm1, cu_seqlens, rotary_pos_emb, max_seqlen
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)
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hidden_states_post_norm2, res = self.norm2(hidden_states)
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hidden_states = hidden_states + self.mlp(hidden_states_post_norm2)
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@ -220,7 +247,7 @@ class Qwen2VLPatchMerger(nn.Module):
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prefix=f"{prefix}.mlp.2", weights=weights, config=config, bias=True
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)
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def forward(self, hidden_states, grid_thw) -> torch.Tensor:
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def forward(self, hidden_states) -> torch.Tensor:
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hidden_states, _ = self.patch_merger_ln_q(hidden_states)
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hidden_states = hidden_states.view(-1, self.hidden_size)
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hidden_states = self.fc1(hidden_states)
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@ -281,7 +308,6 @@ class Qwen2VisionModel(nn.Module):
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def forward(
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self,
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pixel_values: torch.Tensor,
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aspect_ratio_ids: Optional[torch.Tensor] = None,
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grid_thw: Optional[torch.LongTensor] = None,
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) -> torch.Tensor:
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# reshape the input tensor for processing
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@ -336,13 +362,13 @@ class Qwen2VisionModel(nn.Module):
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grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
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).cumsum(dim=0, dtype=torch.int32)
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cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
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max_seqlen = torch.max(cu_seqlens[1:] - cu_seqlens[:-1])
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# iterately apply the blocks to the hidden states
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for block in self.blocks:
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hidden_states = block(hidden_states, cu_seqlens, rotary_pos_emb)
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hidden_states = block(hidden_states, cu_seqlens, rotary_pos_emb, max_seqlen)
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# apply the final patch merger to the hidden states
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hidden_states = self.merger(hidden_states, grid_thw)
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hidden_states = self.merger(hidden_states)
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return hidden_states
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