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Make coca and HF work together #447

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Make coca and HF work together #447

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17 changes: 13 additions & 4 deletions src/open_clip/hf_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,7 @@ def __init__(
raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
if config is None:
self.config = AutoConfig.from_pretrained(model_name_or_path)
self.context_length = self.config.max_length
create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
AutoModel.from_config, self.config)
# TODO: do all model configs have this attribute? PretrainedConfig does so yes??
Expand All @@ -118,7 +119,7 @@ def __init__(
self.transformer = AutoModel.from_config(config)
if pooler_type is None: # get default arch pooler
pooler_type = (arch_dict[self.config.model_type]["pooler"])

self.pooler = _POOLERS[pooler_type]()

d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
Expand All @@ -134,6 +135,14 @@ def __init__(
nn.Linear(hidden_size, output_dim, bias=False),
)

def build_attention_mask(self):
# lazily create causal attention mask, with full attention between the tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(self.context_length, self.context_length)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
return mask

def forward(self, x: TensorType):
attn_mask = (x != self.config.pad_token_id).long()
out = self.transformer(input_ids=x, attention_mask=attn_mask)
Expand All @@ -142,11 +151,11 @@ def forward(self, x: TensorType):

seq_len = out.last_hidden_state.shape[1]
tokens = (
out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :]
if type(self.pooler) == ClsPooler
out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :]
if type(self.pooler) == ClsPooler
else out.last_hidden_state
)

if self.output_tokens:
return projected, tokens
return projected
Expand Down