Spaces:
Running
Running
fix: 先 cuda 再 eval
Browse files
app.py
CHANGED
@@ -1,7 +1,7 @@
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import torch
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import gradio as gr
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from transformers import AutoModel, pipeline, AutoTokenizer
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-
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import subprocess
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# from issue: https://discuss.huggingface.co/t/how-to-install-flash-attention-on-hf-gradio-space/70698/2
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@@ -11,21 +11,22 @@ subprocess.run(
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env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
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shell=True,
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)
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model_name = "OpenGVLab/InternVL2-8B"
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model
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-
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-
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)
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.eval()
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.cuda()
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)
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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inference = pipeline(
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task="visual-question-answering", model=model, tokenizer=tokenizer
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)
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@@ -33,9 +34,11 @@ except Exception as error:
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raise gr.Error("👌" + str(error), duration=30)
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def predict(input_img, questions):
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try:
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gr.Info(str(type(inference)))
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predictions = inference(question=questions, image=input_img)
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return str(predictions)
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except Exception as e:
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import torch
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import gradio as gr
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from transformers import AutoModel, pipeline, AutoTokenizer
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import spaces
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import subprocess
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# from issue: https://discuss.huggingface.co/t/how-to-install-flash-attention-on-hf-gradio-space/70698/2
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env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
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shell=True,
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)
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try:
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model_name = "OpenGVLab/InternVL2-8B"
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# model: <class 'transformers_modules.OpenGVLab.InternVL2-8B.0e6d592d957d9739b6df0f4b90be4cb0826756b9.modeling_internvl_chat.InternVLChatModel'>
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model = (
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AutoModel.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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# low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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.cuda()
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.eval()
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# pipeline: <class 'transformers.pipelines.visual_question_answering.VisualQuestionAnsweringPipeline'>
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inference = pipeline(
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task="visual-question-answering", model=model, tokenizer=tokenizer
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)
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raise gr.Error("👌" + str(error), duration=30)
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@spaces.GPU
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def predict(input_img, questions):
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try:
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gr.Info("pipeline: " + str(type(inference)))
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gr.Info("model: " + str(type(model)))
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predictions = inference(question=questions, image=input_img)
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return str(predictions)
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except Exception as e:
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