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https://github.com/wassname/Clover-Edition.git
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update
This commit is contained in:
Nick
@@ -154,110 +154,108 @@ class CTRLGenerator():
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padded_text = text + [0] * (self.generate_num - len(text))
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tokens_generated = np.tile(padded_text, (1, 1))
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result = ""
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try:
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for token in range(len(text) - 1, self.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 <= self.seq_length:
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prompt_logits = self.predict_fn({'input_1': tokens_generated[:, :self.seq_length]})[
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'tied_embedding_softmax'].squeeze() / (self.temperature if self.temperature > 0 else 1.)
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_token = token if token < self.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 - self.seq_length + 2
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prompt_logits = \
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self.predict_fn({'input_1': np.hstack((tokens_generated[:, 0:1], tokens_generated[:, start:end]))})[
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'tied_embedding_softmax'].squeeze() / (self.temperature if self.temperature > 0 else 1.)
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for token in range(len(text) - 1, self.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 <= self.seq_length:
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prompt_logits = self.predict_fn({'input_1': tokens_generated[:, :self.seq_length]})[
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'tied_embedding_softmax'].squeeze() / (self.temperature if self.temperature > 0 else 1.)
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_token = token if token < self.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 - self.seq_length + 2
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prompt_logits = \
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self.predict_fn({'input_1': np.hstack((tokens_generated[:, 0:1], tokens_generated[:, start:end]))})[
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'tied_embedding_softmax'].squeeze() / (self.temperature if self.temperature > 0 else 1.)
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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 self.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 self.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] /= self.penalty
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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 self.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 self.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] /= self.penalty
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# disallow some tokens
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prompt_logits[_token][self.word2idx['<unk>']] = -1e8
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# disallow some tokens
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prompt_logits[_token][self.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][self.word2idx['Sco@@']] = -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][self.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 self.nucleusprob > 0.:
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minimum_topk = 1
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nucleus = max(np.where(np.cumsum(np.sort(prompt_probs)[::-1]) > self.nucleusprob)[0][0], minimum_topk)
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elif self.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 = self.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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# 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 self.nucleusprob > 0.:
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minimum_topk = 1
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nucleus = max(np.where(np.cumsum(np.sort(prompt_probs)[::-1]) > self.nucleusprob)[0][0], minimum_topk)
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elif self.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 = self.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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# 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 self.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 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 self.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 temperature is 0
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# just pick the first (most probable) token
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if self.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 = int(tf.random.categorical(np.expand_dims(prompt_logits[0][_token][pruned_list], 0),
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num_samples=1).numpy())
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idx = pruned_list[chosen_idx]
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# if temperature is 0
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# just pick the first (most probable) token
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if self.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 = int(tf.random.categorical(np.expand_dims(prompt_logits[0][_token][pruned_list], 0),
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num_samples=1).numpy())
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idx = pruned_list[chosen_idx]
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# if you want to do some debugging,
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# like which one was chosen,
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# what the top25 were,
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# here is your opportunity.
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# print('chosen:', idx2word[idx])
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# print('top25 alternatives:', pruned_list[:25])
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# if you want to do some debugging,
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# like which one was chosen,
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# what the top25 were,
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# here is your opportunity.
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# print('chosen:', idx2word[idx])
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# print('top25 alternatives:', pruned_list[:25])
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# assign the token for generation
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tokens_generated[0][token + 1] = 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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# clear screen if you want to
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# os.system("clear")
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tokens_generated_so_far = ' '.join([self.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('(@@ ?$)', '', string=tokens_generated_so_far)
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tokens_generated_so_far = ' '.join([self.idx2word[c] for c in tokens_generated[0].squeeze()[:token + 2]])
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tokens_generated_so_far = re.sub('(@@ )', '', string=tokens_generated_so_far)
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tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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print(tokens_generated_so_far)
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result = tokens_generated_so_far[prompt_length:]
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print(tokens_generated_so_far)
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result = tokens_generated_so_far[prompt_length:]
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return result
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