1. 15 9月, 2021 1 次提交
  2. 28 4月, 2021 1 次提交
    • J
      Optimize update_loss_scaling_op (#32554) · 0dc02dc7
      jiangcheng 提交于
      * optimize update_loss_scaling_op by fused for loop to one kernel, test=develop
      
      * remove useless while loop and optimize variable name, test=develop
      
      * optimize variable name from out_addrs_tensor to out_addrs_mem, test=develop
      
      * optimize variable name for readable by change prefix identifier from t_ to local_
      0dc02dc7
  3. 08 1月, 2021 1 次提交
    • Z
      Support pure fp16 training for AMP API. (#29544) · 7f7dfccf
      Zhen Wang 提交于
      * add cast ops before and after unsupported fp16 ops.
      
      * Keep partial net in FP32 pattern.
      
      * Support check_finite_and_unscale and update_loss_scaling for FP16 calculation mode.
      
      * Add fp16 support for adam op.
      
      * add multi precision attr for adam.
      
      * Fix the bug of test_multi_precision_fp16_train UT.
      
      * Code format for CI.
      
      * Fix the redefine error about MPTypeTrait on windows.
      
      * fix bugs of the _create_accumulators func in Momentum.
      
      * fix bug when inserting post cast op.
      
      * Add the update_loss_scaling op in allow_set of UnusedVarCheck.
      
      * Update for ci coverage.
      
      * Add some doc for OptimizerWithMixedPrecision.
      
      * Fix the code style.
      
      * Imporve the doc of `amp_init`.
      
      * Change for fp16 testing if users have the infer program defined in separate way.
      7f7dfccf
  4. 10 12月, 2020 1 次提交
  5. 30 11月, 2020 1 次提交
  6. 14 9月, 2020 1 次提交
    • Z
      Update amp_check_finite_and_scale_op and add an updating_loss_scaling op for... · d708b210
      Zhen Wang 提交于
      Update amp_check_finite_and_scale_op and add an updating_loss_scaling op for static graph amp training. (#26240)
      
      * update amp_check_finite_and_scale_op for static_amp.
      
      * use amp_check_finite_and_scale in static graph amp.
      
      * update grads to zero when grads own infinite values(as for amp_checkout_finite_and_scale op).
      
      * add update_loss_scaling op in cpp.
      
      * add update_loss_scaling_op unit test.
      
      * update the doc of the check_finite_and_unscale op
      
      * Update the process of gradients updating skipping if the gradients have infinite values.
      
      * update the way to zero grads.
      
      * update test_update_loss_scaling_op.py
      
      * add log info when find infinite grads.
      
      * add the unit test for UpdateLossScaling Layer.
      d708b210