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https://github.com/wassname/Clover-Edition.git
synced 2026-09-20 12:20:53 +08:00
added_training_stuff
This commit is contained in:
Nick Walton
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from __future__ import print_function
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import torch
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import os
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import tqdm
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import pdb
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import numpy as np
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import platform
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import hashlib
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import pytorch_transformer
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import re
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import argparse
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import tensorflow as tf
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import fastBPE
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from tensorflow.python import pywrap_tensorflow
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use_py3 = platform.python_version()[0] == '3'
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parser = argparse.ArgumentParser(description='TensorFlow code for generating from CTRL')
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parser.add_argument('--model_path', type=str, required=True,
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help='location of model *data* checkpoint; this is NOT the directory but rather the model checkpoint')
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parser.add_argument('--seed', type=int, default=1337,
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help='random seed for TensorFlow, numpy and PythonHash')
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parser.add_argument('--generate_num', type=int, default=256,
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help='number of tokens to generate')
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parser.add_argument('--temperature', type=float, default=0,
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help='temperature for sampling distribution; 0 means greedy')
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parser.add_argument('--nucleus', type=float, default=0.,
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help='cumulative probability cutoff for nucleus sampling; 0 means no nucleus sampling')
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parser.add_argument('--topk', type=int, default=0,
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help='topk value for sampling from the softmax distribution ; 0 means no topk preferred')
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parser.add_argument('--penalty', type=float, default=1.2,
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help='repetition penalty for greedy sampling')
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parser.add_argument('--print_once', action='store_true',
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help='the completion is printed only at the end; not every word')
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parser.add_argument('--topn', type=int, default=0,
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help='print top-n candidates during generations; defaults to 0 which is no printing')
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args = parser.parse_args()
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torch.manual_seed(args.seed)
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torch.cuda.manual_seed_all(args.seed)
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os.environ['PYTHONHASHSEED'] = str(args.seed)
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np.random.seed(args.seed)
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# load the vocabulary from file
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vocab = open('vocab').read().decode(encoding='utf-8').split('\n') if not use_py3 else open('vocab', encoding='utf-8').read().split('\n')
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vocab = list(map(lambda x: x.split(' ')[0], vocab)) + ['<unk>'] + ['\n']
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print ('{} unique words'.format(len(vocab)))
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# length of the vocabulary
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vocab_size = len(vocab)
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# define the numericalization map
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# idx2word maps the numericalized ID to the word
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# word2idx maps the word to the numericalized ID
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word2idx = {u:i for i, u in enumerate(vocab)}
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idx2word = np.array(vocab)
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embedding_dim = 1280
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class TiedEmbeddingSoftmax(torch.nn.Module):
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def __init__(self, vocab_size=vocab_size, embedding_size=embedding_dim, **kwargs):
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super(TiedEmbeddingSoftmax, self).__init__()
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self.w = torch.nn.Parameter(torch.zeros(vocab_size, embedding_size))
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self.b = torch.nn.Parameter(torch.zeros(vocab_size))
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def forward(self, inputs, embed=True):
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if embed:
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return torch.nn.functional.embedding(inputs, self.w)
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else:
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return torch.tensordot(inputs, self.w.t(), 1) + self.b
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test_softmax = TiedEmbeddingSoftmax()
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test_encoder = pytorch_transformer.Encoder()
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def predict_fn(inputs):
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with torch.no_grad():
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embedded = torch.tensor(inputs['input_1']).cuda()
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embedded = test_softmax(embedded, embed=True)
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embedded = test_encoder(embedded)
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embedded = test_softmax(embedded, embed=False)
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return embedded
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bpe = fastBPE.fastBPE('codes', 'vocab')
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seq_length = min(args.generate_num, 256)
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pytorch_model_hash = hashlib.md5(args.model_path.encode('utf-8')).hexdigest()
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temperature = args.temperature
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nucleusprob = args.nucleus
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penalty = args.penalty
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topk = args.topk
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# try to load the model from a (cached) PyTorch checkpoint
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# if one is not available, then create it by converting the weights
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if os.path.exists(pytorch_model_hash):
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print('Found PyTorch checkpoint @', pytorch_model_hash)
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print('Loading instead of converting from TensorFlow')
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checkpoint = torch.load(pytorch_model_hash)
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test_softmax.load_state_dict(checkpoint['softmax'])
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test_encoder.load_state_dict(checkpoint['encoder'])
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test_softmax.to('cuda')
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test_encoder.to('cuda')
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else:
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print('Could not find PyTorch checkpoint')
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print('Converting weights and will store the PyTorch checkpoint as ', pytorch_model_hash)
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chkpt_for_reader = '.'.join(args.model_path.split('.')[:-1])
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reader = pywrap_tensorflow.NewCheckpointReader(chkpt_for_reader)
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test_softmax.w = torch.nn.Parameter(torch.tensor(reader.get_tensor('w')).to('cuda'))
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test_softmax.b = torch.nn.Parameter(torch.tensor(reader.get_tensor('b')).to('cuda'))
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list_of_variables = list(filter(lambda x: 'Adagrad' not in x, reader.get_variable_to_shape_map().keys()))
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str2parameter = lambda x: torch.nn.Parameter(torch.tensor(reader.get_tensor(x)).t().to('cuda'))
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test_encoder.layernorm.weight = str2parameter('encoder/layer_normalization_96/gamma')
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test_encoder.layernorm.bias = str2parameter('encoder/layer_normalization_96/beta')
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for i in tqdm.tqdm(range(48)):
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if i==0:
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layer_variables = sorted(filter(lambda x: 'layer/' in x, list_of_variables))
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else:
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layer_variables = sorted(filter(lambda x: 'layer_'+str(i)+'/' in x, list_of_variables))
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current_layer = getattr(test_encoder, 'layer'+str(i))
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current_layer.layernorm1.bias = str2parameter(layer_variables[0])
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current_layer.layernorm1.weight = str2parameter(layer_variables[1])
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current_layer.layernorm2.bias = str2parameter(layer_variables[2])
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current_layer.layernorm2.weight = str2parameter(layer_variables[3])
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current_layer.multi_head_attention.Wq.bias = str2parameter(layer_variables[4])
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current_layer.multi_head_attention.Wq.weight = str2parameter(layer_variables[5])
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current_layer.multi_head_attention.Wk.bias = str2parameter(layer_variables[6])
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current_layer.multi_head_attention.Wk.weight = str2parameter(layer_variables[7])
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current_layer.multi_head_attention.Wv.bias = str2parameter(layer_variables[8])
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current_layer.multi_head_attention.Wv.weight = str2parameter(layer_variables[9])
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current_layer.multi_head_attention.dense.bias = str2parameter(layer_variables[10])
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current_layer.multi_head_attention.dense.weight = str2parameter(layer_variables[11])
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current_layer.ffn[0].bias = str2parameter(layer_variables[12])
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current_layer.ffn[0].weight = str2parameter(layer_variables[13])
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current_layer.ffn[2].bias = str2parameter(layer_variables[14])
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current_layer.ffn[2].weight = str2parameter(layer_variables[15])
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torch.save({
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'softmax': test_softmax.state_dict(),
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'encoder': test_encoder.state_dict(),
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}, pytorch_model_hash)
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test_softmax.eval()
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test_encoder.eval()
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while True:
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prompt = raw_input('ENTER PROMPT: ') if not use_py3 else input('ENTER PROMPT: ')
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# tokenize provided prompt
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split_prompt = bpe.apply([prompt])[0].split()
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text = [word2idx[i] for i in split_prompt]
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# pad with 0s and create a mini-batch of 2 (arbitrary, for ease of code)
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padded_text = text + [0] * (args.generate_num - len(text))
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tokens_generated = np.tile(padded_text, (1,1))
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try:
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for token in range(len(text)-1, args.generate_num-1):
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# get the logits from the prediction function
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# the logic here is a bit convoluted because we are allowing generation past 512 tokens
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# this is done by sliding the window over (past 512 tokens) and continuing prediction
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# I'm sure this can be simplified (TODO)
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if token <= seq_length:
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prompt_logits = predict_fn({'input_1':tokens_generated[:, :seq_length]}).squeeze() / (temperature if temperature>0 else 1.)
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_token = token if token < seq_length else -1
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else:
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_token = -1
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end = token + 1
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start = token - seq_length + 2
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prompt_logits = predict_fn({'input_1':np.hstack((tokens_generated[:,0:1], tokens_generated[:,start:end]))}).squeeze() / (temperature if temperature>0 else 1.)
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prompt_logits = prompt_logits.cpu().detach().numpy()
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# if penalty (for repetition) is non-zero,
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# discount the logits from already generated tokens
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if penalty>0:
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penalized_so_far = set()
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for _ in range(token+1):
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generated_token = tokens_generated[0][_]
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# don't penalize newlines
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# you could also choose not to penalize frequent words
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# (which incidentally are sorted in the vocab file)
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# but I don't do that
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# if it prints too many new lines instead of continuing generating text,
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# you might want to comment this out
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#if idx2word[generated_token] == '\n':
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# continue
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if generated_token in penalized_so_far:
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continue
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penalized_so_far.add(generated_token)
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prompt_logits[_token][generated_token] /= penalty
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# disallow some tokens
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prompt_logits[_token][word2idx['<unk>']] = -1e8
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# sometimes, when generating from reddit,
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# it tries to generate the Score (reddit Karma) immediately after generating the Title:
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# to disallow this, we can just prevent it from generating Score
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prompt_logits[_token][word2idx['Sco@@']] = -1e8
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# compute probabilities from logits
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prompt_probs = np.exp(prompt_logits[_token])
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prompt_probs = prompt_probs / sum(prompt_probs)
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pruned_list = np.argsort(prompt_probs)[::-1]
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# if you are using nucleus prob, then compute the nucleus probability size
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if nucleusprob > 0.:
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minimum_topk = 1
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nucleus = max(np.where(np.cumsum(np.sort(prompt_probs)[::-1])>nucleusprob)[0][0], minimum_topk)
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elif topk > 0:
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# we are over-loading notation here
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# if you choose to specify a topk instead of a nucleus,
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# we will hardcode the nucleus to be just that
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nucleus = topk
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else:
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# if you specify neither nucleus or topk,
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# then we will use the whole list
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nucleus = len(pruned_list)
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pruned_list = pruned_list[:nucleus]
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# if you want to disallow more complex tokens, you can do so here
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# for instance, if you want to disallow anything with the phrase `http`,
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# you can delete theme from the pruned_list
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# you can comment this out, I'm keeping it in for demonstration purpose
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tokens_to_disallow = []
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for _ in range(len(pruned_list)):
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if 'http' in idx2word[pruned_list[_]]:
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tokens_to_disallow.append(_)
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pruned_list = np.delete(pruned_list, tokens_to_disallow)
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if args.topn > 0 :
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print('TOPN :: top-n alternatives:', [idx2word[_] for _ in pruned_list[:args.topn]])
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# if temperature is 0
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# just pick the first (most probable) token
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if temperature==0:
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idx = pruned_list[0]
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else:
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# else,
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# sample from the pruned_list with the logits
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chosen_idx = torch.distributions.categorical.Categorical(torch.tensor(np.expand_dims(prompt_logits[_token][pruned_list],0))).sample().numpy()[0]
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idx = pruned_list[chosen_idx]
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if args.topn > 0 :
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print('TOPN :: chosen word:', idx2word[idx])
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# assign the token for generation
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tokens_generated[0][token+1] = idx
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# clear screen if you want to
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# os.system("clear")
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tokens_generated_so_far = ' '.join([idx2word[c] for c in tokens_generated[0].squeeze()[:token+2]])
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tokens_generated_so_far = re.sub('(@@ )', '', string=tokens_generated_so_far)
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tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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if not args.print_once:
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print('---------------------------------------')
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print(tokens_generated_so_far)
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print()
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print('---------------------------------------')
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print(tokens_generated_so_far)
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print()
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except KeyboardInterrupt: #Exception as e:
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print('Continuing')
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