optimizer 1d -- EMA in place (fbgemm part) #3402
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Summary: X-link: facebookresearch/FBGEMM#490 Implement the ema_within_table_rowwise_adagrad ``` Emb_table: ------------------------------------------------- - -- - - Fast part -- Slow part - - (RL) main part -- target part - - -- - ------------------------------------------------- In every "step_ema" step, we perform slow_part += coef_ema * (fast_part - slow_part) ``` It mainly serves the target network purpose in the reinforcement learning framework. Design doc https://fburl.com/gdoc/qyfv7tyi Differential Revision: D66015331
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Summary: X-link: facebookresearch/FBGEMM#490 Implement the ema_within_table_rowwise_adagrad ``` Emb_table: ------------------------------------------------- - -- - - Fast part -- Slow part - - (RL) main part -- target part - - -- - ------------------------------------------------- In every "step_ema" step, we perform slow_part += coef_ema * (fast_part - slow_part) ``` It mainly serves the target network purpose in the reinforcement learning framework. Design doc https://fburl.com/gdoc/qyfv7tyi Differential Revision: D66015331
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Summary: X-link: facebookresearch/FBGEMM#490 Implement the ema_within_table_rowwise_adagrad ``` Emb_table: ------------------------------------------------- - -- - - Fast part -- Slow part - - (RL) main part -- target part - - -- - ------------------------------------------------- In every "step_ema" step, we perform slow_part += coef_ema * (fast_part - slow_part) ``` It mainly serves the target network purpose in the reinforcement learning framework. Design doc https://fburl.com/gdoc/qyfv7tyi Differential Revision: D66015331
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Summary: X-link: facebookresearch/FBGEMM#490 Pull Request resolved: pytorch#3402 Implement the ema_within_table_rowwise_adagrad ``` Emb_table: ------------------------------------------------- - -- - - Fast part -- Slow part - - (RL) main part -- target part - - -- - ------------------------------------------------- In every "step_ema" step, we perform slow_part += coef_ema * (fast_part - slow_part) ``` It mainly serves the target network purpose in the reinforcement learning framework. Design doc https://fburl.com/gdoc/qyfv7tyi Differential Revision: D66015331
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Summary:
Implement the ema_within_table_rowwise_adagrad
It mainly serves the target network purpose in the reinforcement learning framework.
Design doc https://fburl.com/gdoc/qyfv7tyi
Differential Revision: D66015331