51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import json
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import torch
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from tools import show
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from chatglm import ChatGLMTokenizer
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pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b"
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tokenizer_config_file = "./chatglm/tokenizer_config.json"
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if tokenizer_config_file is not None:
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with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
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init_kwargs = json.load(tokenizer_config_handle)
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init_kwargs.pop("tokenizer_class", None)
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init_kwargs.pop("tokenizer_file", None)
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saved_init_inputs = init_kwargs.pop("init_inputs", ())
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init_inputs = saved_init_inputs
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init_kwargs["vocab_file"] = "./chatglm/tokenizer.model"
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init_kwargs["added_tokens_file"] = None
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init_kwargs["special_tokens_map_file"] = None
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init_kwargs["tokenizer_file"] = None
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init_kwargs["name_or_path"] = pretrained_model_name_or_path
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tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
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a = tokenizer.encode("骉")
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b = tokenizer.decode([236, 173, 140])
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token = []
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for i in range(64798):
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token.append(str(i) + " : " + tokenizer.decode(i))
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show.DumpListToFile(token, "generated/token.log")
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# print("=======================")
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# for i in range(hidden_states_en.shape[0]):
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# hidden_states = hidden_states_en[i : i + 1]
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# lm_logits = self.output_layer(hidden_states)
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# lm_logits = lm_logits.transpose(0, 1).contiguous()
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# next_token_logits = lm_logits[:, -1, :]
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# probss = nn.functional.softmax(next_token_logits, dim=-1)
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# next_t = torch.multinomial(probss, num_samples=1).squeeze(1)
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# response = tokenizer.decode(next_t)
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# print(response)
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# # name = "generated/next_tokens" + str(token_count) + "_" + response + "_.png"
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# # show.DumpTensorToImage(next_token_logits[0], name)
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# # token_count = token_count + 1
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