RuntimeError: probability tensor contains either inf, nan or element < 0
#9
Comments
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我运行colab的notebook没有问题。 |
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还是报错 用的包是4.30.2的 Start Inference with instruction mode. change image:[image_path] load the image from [image_path] Image: pics/examples/food.jpg
$ pip list|grep trans |
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使用的模型是在4.30.2的transformers下合并的吗？可以查看下模型文件的sha256值是否一致。
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$ sha256sum pytorch_model-00001-of-00002.bin 模型是在4.30.2的transformers下合并的 sha256值应该是一样 |
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试下不使用load_in_8bit，看能否正常运行？ |
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不知道为啥 换了台机器 重新来一遍就好了 |
$ python scripts/inference/inference.py --visualcla_model visualcla --image_file pics/examples/food.jpg --load_in_8bit![]()
to the vocabulary
[INFO|tokenization_utils_base.py:1837] 2023-07-24 16:05:27,669 >> loading file tokenizer.model
[INFO|tokenization_utils_base.py:1837] 2023-07-24 16:05:27,669 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1837] 2023-07-24 16:05:27,669 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1837] 2023-07-24 16:05:27,669 >> loading file tokenizer_config.json
[WARNING|logging.py:295] 2023-07-24 16:05:27,670 >> You are using the legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at huggingface/transformers#24565
[INFO|tokenization_utils.py:426] 2023-07-24 16:05:27,697 >> Adding
[INFO|tokenization_utils.py:426] 2023-07-24 16:05:27,697 >> Adding to the vocabulary
[INFO|tokenization_utils.py:426] 2023-07-24 16:05:27,697 >> Adding to the vocabulary
[INFO|tokenization_utils.py:426] 2023-07-24 16:05:27,697 >> Adding <img_token> to the vocabulary
2023-07-24 16:05:27,698 - INFO - visualcla.modeling_utils - Init VisualCLA model from pretrained
[INFO|configuration_utils.py:710] 2023-07-24 16:05:27,698 >> loading configuration file visualcla/config.json
[INFO|configuration_utils.py:768] 2023-07-24 16:05:27,699 >> Model config VisualCLAConfig {
"image_size": 224,
"initializer_range": 0.02,
"layer_norm_eps": 1e-12,
"model_type": "visualcla",
"text_config": {
"_name_or_path": "",
"add_cross_attention": false,
"architectures": [
"LlamaForCausalLM"
],
"bad_words_ids": null,
"begin_suppress_tokens": null,
"bos_token_id": 1,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0.0,
"do_sample": false,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": 2,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "silu",
"hidden_size": 4096,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_range": 0.02,
"intermediate_size": 11008,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"length_penalty": 1.0,
"max_length": 20,
"max_position_embeddings": 2048,
"min_length": 0,
"model_type": "llama",
"no_repeat_ngram_size": 0,
"num_attention_heads": 32,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": 0,
"prefix": null,
"pretraining_tp": 1,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict": true,
"return_dict_in_generate": false,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": false,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": "float16",
"torchscript": false,
"transformers_version": "4.31.0",
"typical_p": 1.0,
"use_bfloat16": false,
"use_cache": true,
"vocab_size": 49954
},
"tie_word_embeddings": false,
"transformers_version": "4.31.0",
"use_visual_resampler": true,
"vision_config": {
"_name_or_path": "",
"add_cross_attention": false,
"architectures": null,
"attention_dropout": 0.0,
"bad_words_ids": null,
"begin_suppress_tokens": null,
"bos_token_id": null,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0.0,
"do_sample": false,
"dropout": 0.0,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": null,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "quick_gelu",
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-05,
"length_penalty": 1.0,
"max_length": 20,
"min_length": 0,
"model_type": "clip_vision_model",
"no_repeat_ngram_size": 0,
"num_attention_heads": 16,
"num_beam_groups": 1,
"num_beams": 1,
"num_channels": 3,
"num_hidden_layers": 24,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": null,
"patch_size": 14,
"prefix": null,
"problem_type": null,
"projection_dim": 768,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": null,
"torchscript": false,
"transformers_version": "4.31.0",
"typical_p": 1.0,
"use_bfloat16": false
},
"visual_resampler_config": {
"hidden_size": 1024,
"intermediate_size": 4096,
"num_attention_heads": 16,
"num_hidden_layers": 6,
"num_query_tokens": 64
},
"vocab_size": 49958
}
[INFO|configuration_utils.py:710] 2023-07-24 16:05:27,844 >> loading configuration file visualcla/text_encoder/config.json
[INFO|configuration_utils.py:768] 2023-07-24 16:05:27,845 >> Model config LlamaConfig {
"_name_or_path": "chinese-alpaca-plus-7b",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 2048,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.31.0",
"use_cache": true,
"vocab_size": 49954
}
[INFO|modeling_utils.py:2600] 2023-07-24 16:05:27,845 >> loading weights file visualcla/text_encoder/pytorch_model.bin.index.json
[INFO|modeling_utils.py:1172] 2023-07-24 16:05:27,845 >> Instantiating LlamaForCausalLM model under default dtype torch.float16.
[INFO|configuration_utils.py:599] 2023-07-24 16:05:27,846 >> Generate config GenerationConfig {
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"transformers_version": "4.31.0"
}
[INFO|modeling_utils.py:2715] 2023-07-24 16:05:28,053 >> Detected 8-bit loading: activating 8-bit loading for this model
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:08<00:00, 4.24s/it]
[INFO|modeling_utils.py:3329] 2023-07-24 16:05:44,119 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
[INFO|modeling_utils.py:3337] 2023-07-24 16:05:44,119 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at visualcla/text_encoder.
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
[INFO|configuration_utils.py:559] 2023-07-24 16:05:44,122 >> loading configuration file visualcla/text_encoder/generation_config.json
[INFO|configuration_utils.py:599] 2023-07-24 16:05:44,122 >> Generate config GenerationConfig {
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"transformers_version": "4.31.0"
}
[INFO|configuration_utils.py:710] 2023-07-24 16:05:44,188 >> loading configuration file visualcla/vision_encoder/config.json
[INFO|configuration_utils.py:768] 2023-07-24 16:05:44,188 >> Model config CLIPVisionConfig {
"_name_or_path": "clip-vit-large-patch14",
"architectures": [
"CLIPVisionModel"
],
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "quick_gelu",
"hidden_size": 1024,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dim": 768,
"torch_dtype": "float16",
"transformers_version": "4.31.0"
}
[INFO|modeling_utils.py:2600] 2023-07-24 16:05:44,188 >> loading weights file visualcla/vision_encoder/pytorch_model.bin
[INFO|modeling_utils.py:1172] 2023-07-24 16:05:44,483 >> Instantiating CLIPVisionModel model under default dtype torch.float16.
[INFO|modeling_utils.py:3329] 2023-07-24 16:05:45,066 >> All model checkpoint weights were used when initializing CLIPVisionModel.
[INFO|modeling_utils.py:3337] 2023-07-24 16:05:45,066 >> All the weights of CLIPVisionModel were initialized from the model checkpoint at visualcla/vision_encoder.
If your task is similar to the task the model of the checkpoint was trained on, you can already use CLIPVisionModel for predictions without further training.
[INFO|image_processing_utils.py:337] 2023-07-24 16:05:46,059 >> loading configuration file visualcla/preprocessor_config.json
[INFO|image_processing_utils.py:389] 2023-07-24 16:05:46,059 >> Image processor CLIPImageProcessor {
"crop_size": {
"height": 224,
"width": 224
},
"do_center_crop": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"feature_extractor_type": "CLIPFeatureExtractor",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "CLIPImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
2023-07-24 16:05:46,062 - INFO - main - *** Start Inference ***
========== Usage ==========
Start Inference with instruction mode.
You can enter instruction or special control commands after '>'. Below are the usage of the control commands
change image:[image_path] load the image from [image_path]
clear Clear chat history. This command will not change the image.
exit Exit Inference
Image: pics/examples/food.jpg
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