| | |
|---|
| | | model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None) |
|---|
| | | preds = torch.argmax(model_outputs.logits, -1) |
|---|
| | | text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True) |
|---|
| | | text = text.split(': \n')[-1] |
|---|
| | | text = text[0].split(': \n')[-1] |
|---|
| | | # preds = torch.argmax(model_outputs.logits, -1) |
|---|
| | | |
|---|
| | | ibest_writer = None |
|---|
| | |
|---|
| | | |
|---|
| | | if dst_prefix == "" and (src_prefix + k) in src_state.keys(): |
|---|
| | | k_src = src_prefix + k |
|---|
| | | print(f"init param, map: {k} from {k_src} in ckpt") |
|---|
| | | if not k_src.startswith("module."): |
|---|
| | | print(f"init param, map: {k} from {k_src} in ckpt") |
|---|
| | | elif k.startswith(dst_prefix) and k.replace(dst_prefix, src_prefix, 1) in src_state.keys(): |
|---|
| | | k_src = k.replace(dst_prefix, src_prefix, 1) |
|---|
| | | print(f"init param, map: {k} from {k_src} in ckpt") |
|---|
| | | if not k_src.startswith("module."): |
|---|
| | | print(f"init param, map: {k} from {k_src} in ckpt") |
|---|
| | | |
|---|
| | | if k_src in src_state.keys(): |
|---|
| | | if ignore_init_mismatch and dst_state[k].shape != src_state[k_src].shape: |
|---|
