[Model] Make llama3.2 support multiple and interleaved images #9095
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heheda12345
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Oct 5, 2024
vllm/model_executor/models/mllama.py
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| attn_metadata, | ||
| attn_type=AttentionType.ENCODER_DECODER) | ||
| if attention_mask is not None: | ||
| if len(kv_cache.shape) == 3: |
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Why do we need this check?
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it's for skipping writing kv-cache for the initial profiling run
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@ywang96 Is there a better way to know whether we are in the profile run?
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Not really from within the forward pass, so this check is okay imo. Let's add a NOTE here to indicate the purpose of this check
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Based on personal discussion with @xiangxu-google , we still need the following steps:
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I talked to @heheda12345 offline about the following steps for this model:
from transformers import AutoTokenizer
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Describe this image"}
]},
{"role": "assistant", "content": [
{"type": "text", "text": "This image is xxx"}
]},
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "How about this image"}
]}
]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
print(input_text)Output:
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Curious why do we need the logic for each request? This PR has implemented computing masks for a batch of requests. |
You're right, and it's really a matter of where we put that mask computation logic. IMO it's cleaner if we decouple the mask computation for each individual requests and what's needed for the batch in the forward pass, since this is more or less still a CPU operation that we should put in I'm also okay with leaving this logic as a whole for the batch inside |
46 tasks
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FYI I tried using this PR to evaluate Llama 3.2 Vision 11B on MMMU and ran into a CUDA error during attention |
What is your eval environment, like torch version, vllm version, GPU version, CUDA version. I can't reproduce the issue on H100 CUDA 12.4 with vllm installed from source. q = q.transpose(0, 1).view(self.num_key_value_groups,
self.num_local_key_value_heads, q_len,
self.head_dim)
k = k.transpose(0, 1).expand(self.num_key_value_groups,
self.num_local_key_value_heads, kv_len,
self.head_dim)
v = v.transpose(0, 1).expand(self.num_key_value_groups,
self.num_local_key_value_heads, kv_len,
self.head_dim)
attention_mask = attention_mask.view(1, 1, q_len, kv_len)
output = F.scaled_dot_product_attention(q,
k,
v,
attn_mask=attention_mask) |
Thanks for the suggestion! I need to make sure the correctness of this implementation first for a controllable landing. We can refactor it in a separate PR to move the CPU logic to |
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