Update BytePairTokenizerCache to have similar dtypes for x and y in self.factors. #871
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Thanks very much for the contribution! |
mattdangerw
reviewed
Mar 21, 2023
| # use the integer key to find the value. | ||
| self.factors = tf.pow(256, tf.range(0, 8, dtype=tf.int64)) | ||
| self.factors = tf.pow( | ||
| tf.constant([256], dtype=tf.int64), tf.range(0, 8, dtype=tf.int64) |
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Why do we need this in a list instead of just tf.constant(256, dtype=tf.int64)? I don't get why we need to change the shape.
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Totally agree. Dropped the |
chenmoneygithub
approved these changes
Mar 21, 2023
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TY! |
kanpuriyanawab
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Mar 26, 2023
…elf.factors. (keras-team#871) * Update BytePairTokenizerCache to have similar dtypes for x and y in self.factors. * Drop list in tf.constant.
kanpuriyanawab
pushed a commit
to kanpuriyanawab/keras-nlp
that referenced
this pull request
Mar 26, 2023
…elf.factors. (keras-team#871) * Update BytePairTokenizerCache to have similar dtypes for x and y in self.factors. * Drop list in tf.constant.
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Fix for issue #870.
tf.powwas invoked with different dtypes resulting in errors when executing in graph mode. Setting the dtype to tf.int64 fixes the issue.