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import torch
def exists(val):
return val is not None
# for controlling freezing during training of flamingo
def set_module_requires_grad_(module, requires_grad):
for param in module.parameters():
param.requires_grad = requires_grad
def freeze_all_layers_(module):
set_module_requires_grad_(module, False)
def unfreeze_all_layers_(module):
set_module_requires_grad_(module, True)
def freeze_model_and_make_eval_(model):
model.eval()
freeze_all_layers_(model)
def _make_att_wd_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype, device: torch.device,
past_key_values_length: int = 0,
att_wd_size: int = 0,
):
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(
mask_cond > (mask_cond - att_wd_size).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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