Remove inconsistent usage of np.random and standard library #430
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You raise a good point. We might eventually want to add something like a |
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Oh, I see, this is actually a duplicate of #354 |
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Yes, exactly! Sorry I should have been clear on that |
rajveerb
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Nov 30, 2023
* RNN-T reference update for MLPerf Training v1.0 * switch to stable DALI release * transcritp tensor building - index with np array instead of torch tensor * fix multi-GPU bucketing * eval every epoch, logging improvement * user can adjust optimizer betas * gradient clipping * missing config file * [README] add driver disclaimer * right path to sentencepieces * bind all gpus in docker/launch.sh script * move speed perturbation out of evaluation * remove not related code; update logging; default arguments with LAMB * add evaluation when every sample is seen once * add run_and_time.sh * update logging * missing augmentation logs * revert unwanted dropout removal from first two encode layers * scaling weights initialization * limit number of symbols produced by the greedy decoder * simplification - rm old eval pipeline * dev_ema in tb_logginer * loading from checkpoint restores optimizer state * Rnnt logging update (pytorch#4) * logging uses constants instead of raw strings * missing log entries * add weights initialization logging according to mlcommons/logging#80 * 0.5 wights initialization scale gives more stable convergence * fix typo, update logging lib to include new constant * README update * apply review suggestions * [README] fix model diagram 2x time stacking after 2nd encoder layer, not 3x * transcript tensor padding comment * DALI output doesn't need extra zeroing of padding * Update README.md Links to code sources, fix LSTM weight and bias initialization description * [README] model diagram fix - adjust to 1023 sentencepieces
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Currently both the
numpy.randommodule andrandommodule from the standard library are used for sampling transformations. This means that specifying a random seed for only one of these will not affect the other and subsequent runs may or may not have the same behaviour (for a given seed).This can be subtle to pick up on since some transforms will stay the same and some will not. By using a single source of randomness it ensures consistent samples can be generated