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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http:www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Any, Dict, List, Optional, Union, Callable
import torch
from transformers import GenerationMixin, LogitsProcessorList, StoppingCriteriaList
from transformers.generation import validate_stopping_criteria, EosTokenCriteria
from transformers.generation.utils import GenerateNonBeamOutput, GenerateEncoderDecoderOutput, GenerateDecoderOnlyOutput, GenerationConfig, GenerateOutput
from transformers.utils import ModelOutput
ALL_CACHE_NAMES = [
"past_key_values", # default
"cache_params", # mamba-based models
"state", # rwkv
"mems", # xlnet
"past_buckets_states", # reformer
]
class TSGenerationMixin(GenerationMixin):
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
synced_gpus: Optional[bool] = None,
assistant_model: Optional["PreTrainedModel"] = None,
streamer: Optional["BaseStreamer"] = None,
negative_prompt_ids: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
revin: Optional[bool] = True,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
if len(inputs.shape) != 2:
raise ValueError('Input shape must be: [batch_size, seq_len]')
if revin:
means = inputs.mean(dim=-1, keepdim=True)
stdev = inputs.std(dim=-1, keepdim=True, unbiased=False) + 1e-5
inputs = (inputs - means) / stdev
outputs = super().generate(
inputs=inputs,
generation_config=generation_config,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
synced_gpus=synced_gpus,
assistant_model=assistant_model,
streamer=streamer,
negative_prompt_ids=negative_prompt_ids,
negative_prompt_attention_mask=negative_prompt_attention_mask,
**kwargs,
)
if revin:
stdev = stdev.unsqueeze(1)
means = means.unsqueeze(1)
outputs = (outputs * stdev) + means
return outputs
def _sample(
self,
input_ids: torch.Tensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
output_logits: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
) -> Union[GenerateNonBeamOutput, torch.Tensor]:
input_ids = input_ids.to(self.device)
batch_size, cur_len = input_ids.shape
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(
stopping_criteria, max_length)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
if eos_token_id is not None:
stopping_criteria.append(
EosTokenCriteria(eos_token_id=eos_token_id))
else:
# need to get `eos_token_id` and add stopping criteria, so that generation does not go forever
eos_token_id = [
criteria.eos_token_id.tolist() for criteria in stopping_criteria if hasattr(criteria, "eos_token_id")
]
eos_token_id = eos_token_id[0] if eos_token_id else None
if eos_token_id is None and self.generation_config.eos_token_id is not None:
eos_token_id = self.generation_config.eos_token_id
stopping_criteria.append(
EosTokenCriteria(eos_token_id=eos_token_id))
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
raw_logits = () if (return_dict_in_generate and output_logits) else None
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (
return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get(
"attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get(
"hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
if "inputs_embeds" in model_kwargs:
cur_len = model_kwargs["inputs_embeds"].shape[1]
this_peer_finished = False
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)
true_seq_len = (cur_len + self.config.input_token_len - 1) // self.config.input_token_len
model_kwargs["attention_mask"] = model_kwargs["attention_mask"][:, -true_seq_len:]
max_length = stopping_criteria.max_length
generate_results = None
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
# prepare model inputs
model_inputs = self.prepare_inputs_for_generation(
input_ids, **model_kwargs)
input_length = input_ids.shape[1]
# forward pass to get next token
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
max_output_length=max_length - input_length,
)
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits
# pre-process distribution
next_tokens_scores = logits_processor(input_ids, next_token_logits)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_tokens_scores,)
if output_logits:
raw_logits += (next_token_logits,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (
outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# argmax
# next_tokens = torch.argmax(next_tokens_scores, dim=-1)
next_tokens = next_tokens_scores
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError(
"If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + \
pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
horizon_length = next_tokens.shape[-1] // self.config.input_token_len
past_key_values = model_kwargs.get("past_key_values")
if generate_results is None:
generate_results = next_tokens
else:
generate_results = torch.cat([generate_results, next_tokens], dim=-1)
# Use deterministic approach instead of median to avoid CUDA deterministic algorithm issues
# For flow models, use torch.quantile(p=0.5) which is equivalent to median but deterministic
selected_tokens = torch.quantile(next_tokens.float(), q=0.5, dim=1)
input_ids = torch.cat([input_ids, selected_tokens], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
model_kwargs = self._update_model_kwargs_for_generation(
outputs,
model_kwargs,
horizon_length=horizon_length,
is_encoder_decoder=self.config.is_encoder_decoder,
)
unfinished_sequences = unfinished_sequences & ~stopping_criteria(
input_ids, scores)
this_peer_finished = unfinished_sequences.max() == 0
if input_ids.shape[-1] > max_length:
input_ids = input_ids[:, :max_length]
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return GenerateEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
logits=raw_logits,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
past_key_values=model_kwargs.get("past_key_values"),
)
else:
return GenerateDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
logits=raw_logits,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
past_key_values=model_kwargs.get("past_key_values"),
)
else:
return generate_results[:, :, :(max_length - cur_len)]
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
horizon_length: int = 1,
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
# update past_key_values
for possible_cache_name in ALL_CACHE_NAMES:
if possible_cache_name in outputs:
if possible_cache_name in ("past_buckets_states", "mems"):
cache_name = "past_key_values"
else:
cache_name = possible_cache_name
model_kwargs[cache_name] = getattr(outputs, possible_cache_name)
break
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat(
[token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
if not is_encoder_decoder:
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], horizon_length))], dim=-1
)
else:
# update decoder attention mask
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
model_kwargs["decoder_attention_mask"] = torch.cat(
[decoder_attention_mask, decoder_attention_mask.new_ones(
(decoder_attention_mask.shape[0], horizon_length))],
dim=-1,
)
if "cache_position" in model_kwargs and model_kwargs["cache_position"] is not None:
model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + horizon_length
# update full_hidden_states: accumulate hidden states across generation steps for MTP layers
if hasattr(outputs, "hidden_states_for_mtp") and outputs.hidden_states_for_mtp is not None:
new_hs = outputs.hidden_states_for_mtp
if "full_hidden_states" in model_kwargs and model_kwargs["full_hidden_states"] is not None:
existing = model_kwargs["full_hidden_states"]
model_kwargs["full_hidden_states"] = torch.cat(
[existing.to(new_hs.device), new_hs], dim=1
)
else:
model_kwargs["full_hidden_states"] = new_hs
return model_kwargs |