Also fallback to generic GPT2 tokenizer in Colab TPU instances
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@ -1035,29 +1035,38 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
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if(os.path.isdir(vars.custmodpth)):
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try:
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tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, cache_dir="cache")
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except ValueError as e:
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tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
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try:
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model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
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except ValueError as e:
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except Exception as e:
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model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
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elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
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try:
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tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
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except ValueError as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
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try:
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model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
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except ValueError as e:
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except Exception as e:
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model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
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else:
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try:
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tokenizer = AutoTokenizer.from_pretrained(vars.model, cache_dir="cache")
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except ValueError as e:
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tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
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try:
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model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache")
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except ValueError as e:
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except Exception as e:
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model = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
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network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
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