Generate: deprecate default max_length (huggingface#18018)

src/transformers/generation_flax_utils.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 
+import warnings
 from functools import partial
 from typing import Dict, Optional
 
@@ -163,6 +164,7 @@ def generate(
         self,
         input_ids: jnp.ndarray,
         max_length: Optional[int] = None,
+        max_new_tokens: Optional[int] = None,
         pad_token_id: Optional[int] = None,
         bos_token_id: Optional[int] = None,
         eos_token_id: Optional[int] = None,
@@ -209,8 +211,12 @@ def generate(
 
             input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
                 The sequence used as a prompt for the generation.
-            max_length (`int`, *optional*, defaults to 20):
-                The maximum length of the sequence to be generated.
+            max_length (`int`, *optional*, defaults to `model.config.max_length`):
+                The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
+                `max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
+                the prompt.
+            max_new_tokens (`int`, *optional*):
+                The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
             do_sample (`bool`, *optional*, defaults to `False`):
                 Whether or not to use sampling ; use greedy decoding otherwise.
             temperature (`float`, *optional*, defaults to 1.0):
@@ -258,8 +264,6 @@ def generate(
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ```"""
         # set init values
-        max_length = max_length if max_length is not None else self.config.max_length
-        min_length = min_length if min_length is not None else self.config.min_length
         bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
@@ -270,11 +274,6 @@ def generate(
 
         if decoder_start_token_id is None and self.config.is_encoder_decoder:
             raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
-        if min_length is not None and min_length > max_length:
-            raise ValueError(
-                f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
-                f"length ({max_length})"
-            )
 
         if self.config.is_encoder_decoder:
             # add encoder_outputs to model_kwargs
@@ -283,6 +282,42 @@ def generate(
             # prepare decoder_input_ids for generation
             input_ids = jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
 
+        # Prepare `max_length` depending on other stopping criteria.
+        input_ids_seq_length = input_ids.shape[-1]
+        if max_length is None and max_new_tokens is None:
+            warnings.warn(
+                "Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
+                f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
+                "deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
+                "using `max_new_tokens` to control the maximum length of the generation.",
+                UserWarning,
+            )
+        elif max_length is None and max_new_tokens is not None:
+            max_length = max_new_tokens + input_ids_seq_length
+        elif max_length is not None and max_new_tokens is not None:
+            raise ValueError(
+                "Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
+                " limit to the generated output length. Remove one of those arguments. Please refer to the"
+                " documentation for more information. "
+                "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
+            )
+        # default to config if still None
+        max_length = max_length if max_length is not None else self.config.max_length
+        min_length = min_length if min_length is not None else self.config.min_length
+
+        if min_length is not None and min_length > max_length:
+            raise ValueError(
+                f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
+                f"length ({max_length})"
+            )
+        if input_ids_seq_length >= max_length:
+            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            logger.warning(
+                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+                f" {max_length}. This can lead to unexpected behavior. You should consider increasing"
+                "`max_new_tokens`."
+            )
+
         do_sample = do_sample if do_sample is not None else self.config.do_sample
         num_beams = num_beams if num_beams is not None else self.config.num_beams