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optimize qwen2_vl multiple image input and video input #12487

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Dec 4, 2024
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optimize qwen2_vl multiple image input and video input #12487

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71 changes: 50 additions & 21 deletions python/llm/src/ipex_llm/transformers/models/qwen2_vl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,37 +191,66 @@ def qwen2_vision_attention_forward(
).permute(1, 0, 2, 3).unbind(0)
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
# q, k, v: [seq_length, num_heads, head_dim]

q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
seq_lens = cu_seqlens.tolist()
invalidInputError(seq_lens[0] == 0 and seq_lens[-1] == seq_length,
"unexpected input")

if len(cu_seqlens) == 2 and cu_seqlens.tolist() == [0, seq_length]:
attention_mask = None
if use_sdp_non_causal(self.head_dim, q.device, q.dtype):
import xe_addons
image_num = len(seq_lens) - 1
image_size = seq_lens[1] - seq_lens[0]
guessed_seq_lens = torch.arange(0, (image_num + 1) * image_size, image_size,
dtype=cu_seqlens.dtype, device=cu_seqlens.device)
if (guessed_seq_lens == cu_seqlens).all():
q = q.view(image_num, image_size, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
k = k.view(image_num, image_size, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
v = v.view(image_num, image_size, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
# q, k, v: [image_num, num_heads, image_size, head_dim]

attn_output = xe_addons.sdp_non_causal(q, k.contiguous(), v.contiguous(), None)
attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
attn_output = attn_output.view(seq_length, self.num_heads, self.head_dim)
# attn_output: [seq_length, num_heads, head_dim]
else:
q = q.transpose(0, 1).unsqueeze(0)
k = k.transpose(0, 1).unsqueeze(0).contiguous()
v = v.transpose(0, 1).unsqueeze(0).contiguous()
# q, k, v: [1, num_heads, seq_length, head_dim]

attn_outputs = []
for i in range(image_num):
start_idx = seq_lens[i]
end_idx = seq_lens[i + 1]
tmp_q = q[:, :, start_idx:end_idx, :]
tmp_k = k[:, :, start_idx:end_idx, :]
tmp_v = v[:, :, start_idx:end_idx, :]
attn_output = xe_addons.sdp_non_causal(tmp_q, tmp_k, tmp_v, None)
attn_output = attn_output.permute(0, 2, 1, 3)
# attn_output: [1, seq_length, num_heads, head_dim]
attn_outputs.append(attn_output)
attn_output = torch.cat(attn_outputs, dim=1).squeeze(0)
# attn_output: [seq_length, num_heads, head_dim]
else:
attention_mask = torch.full(
[1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype
)
for i in range(1, len(cu_seqlens)):
attention_mask[..., cu_seqlens[i - 1]:cu_seqlens[i],
cu_seqlens[i - 1]:cu_seqlens[i]] = 0
for i in range(1, len(seq_lens)):
attention_mask[..., seq_lens[i - 1]:seq_lens[i], seq_lens[i - 1]:seq_lens[i]] = 0

q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
# q, k, v: [num_heads, seq_length, head_dim]

if use_sdp_non_causal(self.head_dim, q.device, q.dtype):
import xe_addons
q = q.unsqueeze(0)
k = k.unsqueeze(0)
v = v.unsqueeze(0)
if attention_mask is not None:
attention_mask = attention_mask.unsqueeze(0)
attn_output = xe_addons.sdp_non_causal(q, k.contiguous(), v.contiguous(), attention_mask)
attn_output = attn_output.squeeze(0)
else:
attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = attn_weights + attention_mask
attn_weights = attention_softmax(attn_weights)
attn_output = torch.matmul(attn_weights, v)
attn_output = attn_output.transpose(0, 1)
attn_output = attn_output.transpose(0, 1)
# attn_output: [seq_length, num_heads, head_dim]

attn_output = attn_output.reshape(seq_length, -1)
attn_output = self.proj(attn_output)
return attn_output
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