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70 lines
3.1 KiB
Python
70 lines
3.1 KiB
Python
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from transformers import AutoTokenizer, AutoModel
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from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Model
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import torch
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import time
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# from ratelimiter import RateLimiter
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class EmbeddingService(object):
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cos = torch.nn.CosineSimilarity(dim=0)
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model: GPT2Model
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tokenizer: GPT2Tokenizer
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def __init__(self, modelId, device='cpu'):
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#Load AutoModel from huggingface model repository
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# mps for macos is also seems supported, but it not work with GPT2 based models, with predictions.
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self.device = torch.device(device)
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self.tokenizer = AutoTokenizer.from_pretrained(modelId, use_fast=True) # AutoTokenizer.from_pretrained(modelId)
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self.model = AutoModel.from_pretrained(modelId).to(self.device)# AutoModel.from_pretrained(modelId).to(self.device)
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# self.hmodel = GPT2LMHeadModel.from_pretrained(modelId).to(self.device)# AutoModel.from_pretrained(modelId).to(self.device)
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# self.model.parallelize()
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print('using', torch.get_num_threads())
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(self, model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
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sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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return sum_embeddings / sum_mask
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# @RateLimiter(max_calls=10)
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def embeddings(self, sentences):
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#Tokenize sentences
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st = time.time()
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encoded_input = self.tokenizer(sentences, truncation=True, return_tensors='pt').to(self.device)
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print('token', len(sentences), len(encoded_input), len(encoded_input[0]))
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try:
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mi = self.model.wpe.num_embeddings
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if len(encoded_input[0]) > mi:
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raise Exception("This model's maximum context length is " + str(mi) + " tokens, however you requested " + str(len(encoded_input[0])) + " tokens")
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except:
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pass
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try:
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mi = self.model.embeddings.position_embeddings.num_embeddings
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if len(encoded_input[0]) > mi:
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raise Exception("This model's maximum context length is " + str(mi) + " tokens, however you requested " + str(len(encoded_input[0])) + " tokens")
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except:
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pass
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#Compute token embeddings
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with torch.no_grad():
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model_output = self.model(**encoded_input)
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#Perform pooling. In this case, mean pooling
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sentence_embeddings = self.mean_pooling(model_output, encoded_input['attention_mask'])
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ed = time.time()
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print('token', len(sentences), len(encoded_input), len(encoded_input[0]), ' time', (ed-st))
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return [sentence_embeddings[0], len(encoded_input[0])]
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def completion(self, text, do_sample=True, temperature=1.3, max_length=2048, **kwargs):
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input_ids = self.tokenizer.encode(text, return_tensors="pt").to(self.device)
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out = self.model.generate(input_ids, do_sample=do_sample, temperature=temperature, max_length=max_length, **kwargs)
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return list(map(self.tokenizer.decode, out))[0]
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def compare(self, e1, e2):
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return self.cos(e1, e2)
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