add warning for truncation (#2585)

* add warning for truncation

lm_eval/models/huggingface.py

@@ -818,6 +818,12 @@ class HFLM(TemplateLM):
             **add_special_tokens,
         )
         if left_truncate_len:
+            original_lengths = encoding["input_ids"].size(1)
+            if original_lengths > left_truncate_len:
+                eval_logger.warn(
+                    f"Left truncation applied. Original sequence length was {original_lengths}, "
+                    f"truncating to last {left_truncate_len} tokens. Some content will be lost.",
+                )
             encoding["input_ids"] = encoding["input_ids"][:, -left_truncate_len:]
             encoding["attention_mask"] = encoding["attention_mask"][
                 :, -left_truncate_len:
@@ -1096,6 +1102,13 @@ class HFLM(TemplateLM):
 
                 # when too long to fit in context, truncate from the left
                 if self.backend == "causal":
+                    total_length = len(context_enc) + len(continuation_enc)
+                    if total_length > self.max_length + 1:
+                        eval_logger.warn(
+                            f"Combined length of context ({len(context_enc)}) and continuation ({len(continuation_enc)}) "
+                            f"exceeds model's maximum length ({self.max_length}). "
+                            f"Truncating {total_length - self.max_length + 1} tokens from the left."
+                        )
                     inp = torch.tensor(
                         (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
                         dtype=torch.long,
@@ -1303,6 +1316,9 @@ class HFLM(TemplateLM):
             if self.backend == "causal":
                 # max len for inputs = max length, minus room to generate the max new tokens
                 max_ctx_len = self.max_length - max_gen_toks
+                assert (
+                    max_ctx_len > 0
+                ), f"Invalid configuration: requested max tokens to generate ({max_gen_toks}) must be less than model's maximum sequence length ({self.max_length})."
             elif self.backend == "seq2seq":
                 # max len for inputs = encoder's whole max_length
                 max_ctx_len = self.max_length