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Use master weights for bfloat16 FusedAdam when master_weights=True #1731

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Use master weights for bfloat16 FusedAdam when master_weights=True #1731

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10 changes: 7 additions & 3 deletions apex/optimizers/fused_adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,6 +158,7 @@ def step(self, closure=None, grads=None, output_params=None, scale=None, grad_no
g_bf, p_bf, m_bf, v_bf = [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
p_16_master = []
p_bf_master = []
p_32_master = []

for p, p_master in zip(group['params'], group_master['params']):
Expand All @@ -182,6 +183,8 @@ def step(self, closure=None, grads=None, output_params=None, scale=None, grad_no
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.bfloat16:
if self.master_weights:
p_bf_master.append(p_master.data)
g_bf.append(p.grad)
p_bf.append(p)
m_bf.append(state['exp_avg'])
Expand Down Expand Up @@ -232,10 +235,11 @@ def step(self, closure=None, grads=None, output_params=None, scale=None, grad_no
inv_scale)

if len(g_bf) > 0:
multi_tensor_applier(
self.multi_tensor_adam_capturable,
multi_tensor_applier(self.multi_tensor_adam_capturable_master if self.master_weights
else self.multi_tensor_adam_capturable,
self._dummy_overflow_buf,
[g_bf, p_bf, m_bf, v_bf],
[g_bf, p_bf, m_bf, v_bf, p_bf_master] if self.master_weights
else [g_bf, p_bf, m_bf, v_bf],
group['lr'],
beta1,
beta2,
Expand Down