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Model: transformers-community/custom_generate_bad_requirements
Source: Original Platform

custom_generate/generate.py

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+import torch
+
+def generate(model, input_ids, generation_config=None, left_padding=None, **kwargs):
+    
+    print("✨ using a custom generation method ✨")
+    
+    generation_config = generation_config or model.generation_config  # default to the model generation config
+    cur_length = input_ids.shape[1]
+    max_length = generation_config.max_length or cur_length + generation_config.max_new_tokens
+
+    # Example of custom argument: add `left_padding` (integer) pad tokens before the prompt
+    if left_padding is not None:
+        if not isinstance(left_padding, int) or left_padding < 0:
+            raise ValueError(f"left_padding must be an integer larger than 0, but is {left_padding}")
+
+        pad_token = kwargs.pop("pad_token", None) or generation_config.pad_token_id or model.config.pad_token_id
+        if pad_token is None:
+            raise ValueError("pad_token is not defined")
+        batch_size = input_ids.shape[0]
+        pad_tensor = torch.full(size=(batch_size, left_padding), fill_value=pad_token).to(input_ids.device)
+        input_ids = torch.cat((pad_tensor, input_ids), dim=1)
+        cur_length = input_ids.shape[1]
+
+    # Simple greedy decoding loop
+    while cur_length < max_length:
+        logits = model(input_ids).logits
+        next_token_logits = logits[:, -1, :]
+        next_tokens = torch.argmax(next_token_logits, dim=-1)
+        input_ids = torch.cat((input_ids, next_tokens[:, None]), dim=-1)
+        cur_length += 1
+
+    return input_ids

custom_generate/requirements.txt

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+foo
+bar==0.0.0
+torch>=99.0