mirror of https://github.com/coqui-ai/TTS.git
Merge pull request #109 from idiap/transformers
Add compatibility with transformers>=4.43
This commit is contained in:
Enno Hermann
GitHub
commit
b66c782931
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@ -20,8 +20,10 @@ from transformers import (
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PhrasalConstraint,
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PreTrainedModel,
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StoppingCriteriaList,
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TemperatureLogitsWarper,
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TopKLogitsWarper,
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TopPLogitsWarper,
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)
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from transformers.generation.stopping_criteria import validate_stopping_criteria
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from transformers.generation.utils import GenerateOutput, SampleOutput, logger
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@ -152,7 +154,18 @@ class NewGenerationMixin(GenerationMixin):
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# 2. Set generation parameters if not already defined
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logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
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kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
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if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
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if model_kwargs.get("attention_mask", None) is None:
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logger.warning(
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"The attention mask and the pad token id were not set. As a consequence, you may observe "
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"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
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)
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eos_token_id = generation_config.eos_token_id
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if isinstance(eos_token_id, list):
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eos_token_id = eos_token_id[0]
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logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
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generation_config.pad_token_id = eos_token_id
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# 3. Define model inputs
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# inputs_tensor has to be defined
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@ -164,22 +177,38 @@ class NewGenerationMixin(GenerationMixin):
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)
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batch_size = inputs_tensor.shape[0]
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device = inputs_tensor.device
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self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
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# 4. Define other model kwargs
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model_kwargs["output_attentions"] = generation_config.output_attentions
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model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
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model_kwargs["use_cache"] = generation_config.use_cache
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model_kwargs["cache_position"] = torch.Tensor([0]).to(inputs_tensor.device)
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accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
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requires_attention_mask = "encoder_outputs" not in model_kwargs
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if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
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if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
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setattr(
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generation_config,
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"_pad_token_tensor",
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torch.full(
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(inputs_tensor.shape[0], inputs_tensor.shape[1]),
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generation_config.pad_token_id,
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device=inputs_tensor.device,
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),
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)
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setattr(
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generation_config,
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"_eos_token_tensor",
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torch.full(
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(inputs_tensor.shape[0], inputs_tensor.shape[1]),
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generation_config.eos_token_id,
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device=inputs_tensor.device,
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),
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)
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model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
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inputs_tensor,
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generation_config.pad_token_id,
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generation_config.eos_token_id,
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generation_config._pad_token_tensor,
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generation_config._eos_token_tensor,
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)
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# decoder-only models should use left-padding for generation
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@ -202,15 +231,16 @@ class NewGenerationMixin(GenerationMixin):
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# 5. Prepare `input_ids` which will be used for auto-regressive generation
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if self.config.is_encoder_decoder:
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input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
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batch_size=batch_size,
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model_input_name=model_input_name,
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model_kwargs=model_kwargs,
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input_ids = self._prepare_decoder_input_ids_for_generation(
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batch_size,
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decoder_start_token_id=generation_config.decoder_start_token_id,
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bos_token_id=generation_config.bos_token_id,
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model_kwargs=model_kwargs,
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device=inputs_tensor.device,
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)
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else:
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input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
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# if decoder-only then inputs_tensor has to be `input_ids`
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input_ids = inputs_tensor
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# 6. Prepare `max_length` depending on other stopping criteria.
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input_ids_seq_length = input_ids.shape[-1]
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@ -376,7 +406,7 @@ class NewGenerationMixin(GenerationMixin):
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elif is_sample_gen_mode:
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# 11. prepare logits warper
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logits_warper = self._get_logits_warper(generation_config, inputs_tensor.device)
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logits_warper = _get_logits_warper(generation_config)
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# 12. expand input_ids with `num_return_sequences` additional sequences per batch
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input_ids, model_kwargs = self._expand_inputs_for_generation(
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@ -401,7 +431,7 @@ class NewGenerationMixin(GenerationMixin):
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)
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elif is_sample_gen_stream_mode:
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# 11. prepare logits warper
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logits_warper = self._get_logits_warper(generation_config, inputs_tensor.device)
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logits_warper = _get_logits_warper(generation_config)
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# 12. expand input_ids with `num_return_sequences` additional sequences per batch
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input_ids, model_kwargs = self._expand_inputs_for_generation(
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@ -463,7 +493,7 @@ class NewGenerationMixin(GenerationMixin):
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elif is_beam_sample_gen_mode:
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# 11. prepare logits warper
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logits_warper = self._get_logits_warper(generation_config, inputs_tensor.device)
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logits_warper = _get_logits_warper(generation_config)
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if stopping_criteria.max_length is None:
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raise ValueError("`max_length` needs to be a stopping_criteria for now.")
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@ -877,10 +907,10 @@ def init_stream_support():
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if __name__ == "__main__":
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from transformers import AutoModelForCausalLM, AutoTokenizer
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init_stream_support()
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from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
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PreTrainedModel.generate = NewGenerationMixin.generate
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PreTrainedModel.sample_stream = NewGenerationMixin.sample_stream
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model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", torch_dtype=torch.float16)
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tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
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@ -920,3 +950,17 @@ if __name__ == "__main__":
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chunk = tokenizer.decode(x, skip_special_tokens=True)
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stream_result += chunk
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print(stream_result)
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def _get_logits_warper(generation_config: GenerationConfig) -> LogitsProcessorList:
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warpers = LogitsProcessorList()
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if generation_config.temperature is not None and generation_config.temperature != 1.0:
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warpers.append(TemperatureLogitsWarper(generation_config.temperature))
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if generation_config.top_k is not None and generation_config.top_k != 0:
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warpers.append(TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=1))
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if generation_config.top_p is not None and generation_config.top_p < 1.0:
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warpers.append(TopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=1))
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return warpers
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@ -44,7 +44,7 @@ classifiers = [
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]
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dependencies = [
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# Core
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"numpy>=1.25.2",
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"numpy>=1.25.2,<2.0",
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"cython>=3.0.0",
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"scipy>=1.11.2",
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"torch>=2.4",
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@ -68,7 +68,7 @@ dependencies = [
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"gruut[de,es,fr]>=2.4.0",
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# Tortoise
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"einops>=0.6.0",
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"transformers>=4.42.0,<4.43.0",
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"transformers>=4.43.0",
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# Bark
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"encodec>=0.1.1",
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# XTTS
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@ -147,6 +147,9 @@ Discussions = "https://github.com/idiap/coqui-ai-TTS/discussions"
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tts = "TTS.bin.synthesize:main"
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tts-server = "TTS.server.server:main"
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[tool.uv]
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constraint-dependencies = ["numba>0.58.0"]
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[tool.ruff]
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target-version = "py39"
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line-length = 120
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