Generate: handle cache_position update in generate (#29467)

src/transformers/cache_utils.py

@@ -4,6 +4,10 @@
 import torch
 
 from .configuration_utils import PretrainedConfig
+from .utils import logging
+
+
+logger = logging.get_logger(__name__)
 
 
 @dataclass
@@ -57,6 +61,17 @@ def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -
  return max_length - new_seq_length
  return previous_seq_length
 
+ @property
+ def seen_tokens(self):
+ logger.warning_once(
+ "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` "
+ "model input instead."
+ )
+ if hasattr(self, "_seen_tokens"):
+ return self._seen_tokens
+ else:
+ return None
+
 
 class DynamicCache(Cache):
  """
@@ -69,7 +84,7 @@ class DynamicCache(Cache):
  def __init__(self) -> None:
  self.key_cache: List[torch.Tensor] = []
  self.value_cache: List[torch.Tensor] = []
- self.seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
+ self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
 
  def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
  """
@@ -121,7 +136,7 @@ def update(
  """
  # Update the number of seen tokens
  if layer_idx == 0:
- self.seen_tokens += key_states.shape[-2]
+ self._seen_tokens += key_states.shape[-2]
 
  # Update the cache
  if len(self.key_cache) <= layer_idx:
@@ -191,7 +206,7 @@ def __init__(self, window_length: int, num_sink_tokens: int) -> None:
  self.window_length = window_length
  self.num_sink_tokens = num_sink_tokens
  self.cos_sin_cache = {}
- self.seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
+ self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
 
  @staticmethod
  def _rotate_half(x):
@@ -272,7 +287,7 @@ def update(
 
  # Update the number of seen tokens
  if layer_idx == 0:
- self.seen_tokens += key_states.shape[-2]
+ self._seen_tokens += key_states.shape[-2]
 
  # [bsz, num_heads, seq_len, head_dim]
  if len(self.key_cache) <= layer_idx:
@@ -398,16 +413,11 @@ def update(
 
  def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
  """Returns the sequence length of the cached states that were seen by the model. `layer_idx` kept for BC"""
- # TODO: Fix once the stateful `int` bug in PyTorch is fixed.
- raise ValueError(
- "get_seq_length is not implemented for StaticCache. Please refer to https://github.com/huggingface/transformers/pull/29114."
- )
-
- def get_usable_length(self, new_sequence_length=None, layer_idx: Optional[int] = 0) -> int:
- # TODO: Fix once the stateful `int` bug in PyTorch is fixed.
- raise ValueError(
- "get_seq_length is not implemented for StaticCache. Please refer to https://github.com/huggingface/transformers/pull/29114."
- )
+ # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
+ # limit the check to the first batch member and head dimension.
+ # TODO: This is error prone, a filled cache may be `0.0`. Let's use a stateless integer instead, after
+ # https://github.com/pytorch/pytorch/issues/120248 is fixed
+ return (self.key_cache[0, 0].any(dim=-1)).sum()
 
  def get_max_length(self) -> Optional[int]:
  """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""

src/transformers/generation/utils.py

@@ -633,7 +633,6 @@ def _update_model_kwargs_for_generation(
  model_kwargs: Dict[str, Any],
  is_encoder_decoder: bool = False,
  standardize_cache_format: bool = False,
- model_inputs: Optional[Dict[str, Any]] = None,
  ) -> Dict[str, Any]:
  # update past_key_values
  model_kwargs["past_key_values"] = self._extract_past_from_model_output(
@@ -663,7 +662,8 @@ def _update_model_kwargs_for_generation(
  dim=-1,
  )
 
- model_kwargs["cache_position"] = model_inputs.get("cache_position", None)
+ if "cache_position" in model_kwargs and model_kwargs["cache_position"] is not None:
+ model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + 1
 
  return model_kwargs
 
@@ -1931,10 +1931,15 @@ def _contrastive_search(
  )
 
  # keep track of which sequences are already finished
- unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+ batch_size, cur_len = (
+ model_kwargs["attention_mask"].shape
+ if model_kwargs.get("attention_mask", None) is not None
+ else input_ids.shape
+ )
+ unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  this_peer_finished = False # used by synced_gpus only
- batch_size = input_ids.shape[0]
 
  while True:
  if synced_gpus:
@@ -1975,7 +1980,6 @@ def _contrastive_search(
  model_kwargs,
  is_encoder_decoder=self.config.is_encoder_decoder,
  standardize_cache_format=True,
- model_inputs=model_inputs,
  )
  if not sequential:
  # Expands model inputs top_k times, for batched forward passes (akin to beam search).
@@ -2170,7 +2174,9 @@ def _contrastive_search(
  if streamer is not None:
  streamer.put(next_tokens.cpu())
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
 
  # if eos_token was found in one sentence, set sentence to finished
@@ -2389,7 +2395,13 @@ def _greedy_search(
  )
 
  # keep track of which sequences are already finished
- unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+ batch_size, cur_len = (
+ model_kwargs["attention_mask"].shape
+ if model_kwargs.get("attention_mask", None) is not None
+ else input_ids.shape
+ )
+ unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  this_peer_finished = False # used by synced_gpus only
  while True:
@@ -2459,7 +2471,6 @@ def _greedy_search(
  outputs,
  model_kwargs,
  is_encoder_decoder=self.config.is_encoder_decoder,
- model_inputs=model_inputs,
  )
 
  # if eos_token was found in one sentence, set sentence to finished
@@ -2688,7 +2699,13 @@ def _sample(
  )
 
  # keep track of which sequences are already finished
- unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+ batch_size, cur_len = (
+ model_kwargs["attention_mask"].shape
+ if model_kwargs.get("attention_mask", None) is not None
+ else input_ids.shape
+ )
+ unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  this_peer_finished = False # used by synced_gpus only
  # auto-regressive generation
@@ -2758,7 +2775,9 @@ def _sample(
  if streamer is not None:
  streamer.put(next_tokens.cpu())
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
 
  # if eos_token was found in one sentence, set sentence to finished
@@ -3003,6 +3022,7 @@ def _beam_search(
  num_beams = beam_scorer.num_beams
 
  batch_beam_size, cur_len = input_ids.shape
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  if num_beams * batch_size != batch_beam_size:
  raise ValueError(
@@ -3156,7 +3176,9 @@ def _beam_search(
  input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
 
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
  if model_kwargs.get("past_key_values", None) is not None:
  model_kwargs["past_key_values"] = self._temporary_reorder_cache(
@@ -3397,6 +3419,7 @@ def _beam_sample(
  num_beams = beam_scorer.num_beams
 
  batch_beam_size, cur_len = input_ids.shape
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  # init attention / hidden states / scores tuples
  scores = () if (return_dict_in_generate and output_scores) else None
@@ -3510,7 +3533,9 @@ def _beam_sample(
  input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
 
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
  if model_kwargs.get("past_key_values", None) is not None:
  model_kwargs["past_key_values"] = self._temporary_reorder_cache(
@@ -3747,6 +3772,7 @@ def _group_beam_search(
  device = input_ids.device
 
  batch_beam_size, cur_len = input_ids.shape
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  if return_dict_in_generate and output_scores:
  beam_indices = [tuple(() for _ in range(num_sub_beams * batch_size)) for _ in range(num_beam_groups)]
@@ -3916,7 +3942,9 @@ def _group_beam_search(
  input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
 
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
  if model_kwargs.get("past_key_values", None) is not None:
  model_kwargs["past_key_values"] = self._temporary_reorder_cache(
@@ -4155,6 +4183,7 @@ def _constrained_beam_search(
  num_beams = constrained_beam_scorer.num_beams
 
  batch_beam_size, cur_len = input_ids.shape
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  if num_beams * batch_size != batch_beam_size:
  raise ValueError(
@@ -4275,7 +4304,9 @@ def _constrained_beam_search(
 
  input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
  if model_kwargs.get("past_key_values", None) is not None:
  model_kwargs["past_key_values"] = self._temporary_reorder_cache(
@@ -4511,7 +4542,13 @@ def _assisted_decoding(
  )
 
  # keep track of which sequences are already finished
- unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+ batch_size, cur_len = batch_size, cur_len = (
+ model_kwargs["attention_mask"].shape
+ if model_kwargs.get("attention_mask", None) is not None
+ else input_ids.shape
+ )
+ unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+ model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
  # other auxiliary variables
  max_len = stopping_criteria[0].max_length
@@ -4555,6 +4592,14 @@ def _assisted_decoding(
  candidate_kwargs, candidate_input_ids.shape[1], self.config.is_encoder_decoder
  )
  candidate_kwargs = _prepare_token_type_ids(candidate_kwargs, candidate_input_ids.shape[1])
+ if "cache_position" in candidate_kwargs:
+ candidate_kwargs["cache_position"] = torch.cat(
+ (
+ candidate_kwargs["cache_position"],
+ torch.arange(cur_len, cur_len + candidate_length, device=input_ids.device, dtype=torch.long),
+ ),
+ dim=0,
+ )
 
  model_inputs = self.prepare_inputs_for_generation(candidate_input_ids, **candidate_kwargs)
 
@@ -4673,7 +4718,9 @@ def _assisted_decoding(
  )
 
  model_kwargs = self._update_model_kwargs_for_generation(
- outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder, model_inputs=model_inputs
+ outputs,
+ model_kwargs,
+ is_encoder_decoder=self.config.is_encoder_decoder,
  )
 
  # if eos_token was found in one sentence, set sentence to finished