b2e89e6e80
[FT] feat: Add FasterTransformer v3.0 1. Add supporting of INT8 quantization of cpp and TensorFlow op. 2. Provide the tools to quantize the model. 3. Fix the bugs that cmake 3.15 and 3.16 cannot build this project. 4. Deprecate the FasterTransformer v1
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
# 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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