74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tensorflow as tf
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class Sampling():
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def __init__(self, sample_method):
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if sample_method == "top_k":
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self.sample_method = self.top_k_logits
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elif sample_method == "top_p":
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self.sample_method = self.top_p_logits
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else:
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print("[ERROR] the sample method should be one of top_k and top_p")
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exit(-1)
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pass
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def sample(self, logits, threshold, num_samples=1):
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'''
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inputs:
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logits: [batch_size, vocab_size], the values of log logits
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threshold: int when using top_k, and a probability (0~1) when using top_p
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outputs:
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samples: [batch_size]
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'''
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logits = self.sample_method(logits, threshold)
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samples = tf.multinomial(logits, num_samples=num_samples, output_dtype=tf.int32)
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samples = tf.reshape(samples, [-1])
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return samples
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def top_k_logits(self, logits, k):
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if k == 0:
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return logits
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else:
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values, _ = tf.nn.top_k(logits, k=k) # [batch size, k]
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min_values = values[:, -1, tf.newaxis] #[batch size, 1]
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return tf.where(
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logits < min_values,
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tf.ones_like(logits, dtype=logits.dtype) * logits.dtype.min,
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logits
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)
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def top_p_logits(self, logits, p):
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sorted_logits = tf.sort(logits, direction='DESCENDING')
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sorted_probs = tf.nn.softmax(sorted_logits)
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probs_sums = tf.cumsum(sorted_probs, axis=1, exclusive=True)
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logits_masked = tf.where(
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probs_sums < p,
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sorted_logits,
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tf.ones_like(sorted_logits) * 1000
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) # [batchsize, vocab]
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min_logits = tf.reduce_min(logits_masked, axis=1, keepdims=True) # [batch size, 1]
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return tf.where(
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logits < min_logits,
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tf.ones_like(logits, dtype=logits.dtype) * logits.dtype.min,
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logits
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)
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