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Fix get_parameters when using main params optimizer #6764

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merged 3 commits into from
Jun 1, 2023
Merged

Fix get_parameters when using main params optimizer #6764

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5abadb4
fix get param
ericharper May 31, 2023
8ad2363
change name
ericharper Jun 1, 2023
f5702fd
Merge branch 'r1.19.0' into fix_get_param
ericharper Jun 1, 2023
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12 changes: 7 additions & 5 deletions nemo/collections/nlp/models/language_modeling/megatron_base_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,14 +240,16 @@ def _vocab_size_with_padding(self, orig_vocab_size, make_vocab_size_divisible_by
)
return after

def _get_parameters(self):
def get_parameters_with_grad(self):
"""
private method to load all the trainable parameters from optimizer param groups
Get all parameters with grad from optimizer param groups
"""
params = []
for param_group in self._optimizer_param_groups:
for param in param_group['params']:
if param.requires_grad: # (@adithyare) adapter training with pp>1 can result in params with no grads
if (
param.grad is not None
): # (@adithyare) adapter training with pp>1 can result in params with no grads
params.append(param)
return params

Expand All @@ -272,9 +274,9 @@ def configure_gradient_clipping(self, *args, **kwargs):
else:
if self.megatron_amp_o2:
# grep fp32 master parameters for gradient clipping
parameters = self._optimizer.get_parameters()
parameters = self._optimizer.get_parameters_with_grad()
else:
parameters = self._get_parameters()
parameters = self.get_parameters_with_grad()
grad_norm = clip_grad_norm_fp32(parameters=parameters, max_norm=clip_val)

self.log('grad_norm', grad_norm, rank_zero_only=True, batch_size=1)
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4 changes: 2 additions & 2 deletions nemo/core/optim/optimizer_with_main_params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -488,11 +488,11 @@ def async_master_grads_allreudce(self):
def fp32_grad_accumulation(self):
return self._fp32_grad_accum

def get_parameters(self):
def get_parameters_with_grad(self):
params = []
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
if param.requires_grad: # (@adithyare) added to enable pp>1 training for adapters
if param.grad is not None: # (@adithyare) added to enable pp>1 training for adapters
params.append(param)
return params

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