升级transformers版本
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billwuhao
@@ -30,7 +30,6 @@ from transformers.cache_utils import (
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DynamicCache,
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EncoderDecoderCache,
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OffloadedCache,
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QuantizedCacheConfig,
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StaticCache,
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)
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from transformers.configuration_utils import PretrainedConfig
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@@ -55,16 +54,38 @@ from transformers.generation.candidate_generator import (
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AssistedCandidateGeneratorDifferentTokenizers,
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CandidateGenerator,
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PromptLookupCandidateGenerator,
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_crop_past_key_values,
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_prepare_attention_mask,
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_prepare_token_type_ids,
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)
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def _crop_past_key_values(model, past_key_values, new_length):
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"""Crop past key values to a specific length."""
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if past_key_values is None:
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return None
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if isinstance(past_key_values, tuple):
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return tuple(
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tuple(
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tensor[:, :new_length, ...]
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if isinstance(tensor, torch.Tensor)
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else tensor
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for tensor in layer_past
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)
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for layer_past in past_key_values
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)
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else:
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return past_key_values
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from transformers.generation.configuration_utils import (
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NEED_SETUP_CACHE_CLASSES_MAPPING,
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QUANT_BACKEND_CLASSES_MAPPING,
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GenerationConfig,
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GenerationMode,
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)
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# Define our own cache mapping
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NEED_SETUP_CACHE_CLASSES_MAPPING = {
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"dynamic": DynamicCache,
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"static": StaticCache,
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}
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from transformers.generation.logits_process import (
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EncoderNoRepeatNGramLogitsProcessor,
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EncoderRepetitionPenaltyLogitsProcessor,
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@@ -1002,7 +1023,7 @@ class GenerationMixin:
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device=device,
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)
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)
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if generation_config.forced_decoder_ids is not None:
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if hasattr(generation_config, 'forced_decoder_ids') and generation_config.forced_decoder_ids is not None:
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# TODO (sanchit): move this exception to GenerationConfig.validate() when TF & FLAX are aligned with PT
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raise ValueError(
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"You have explicitly specified `forced_decoder_ids`. Please remove the `forced_decoder_ids` argument "
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@@ -1742,12 +1763,9 @@ class GenerationMixin:
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"cache, please open an issue and tag @zucchini-nlp."
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)
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cache_config = (
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generation_config.cache_config
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if generation_config.cache_config is not None
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else QuantizedCacheConfig()
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)
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cache_class = QUANT_BACKEND_CLASSES_MAPPING[cache_config.backend]
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cache_config = generation_config.cache_config
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# Use default DynamicCache if no specific cache config is provided
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cache_class = DynamicCache
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# if cache_config.backend == "quanto" and not (is_optimum_quanto_available() or is_quanto_available()):
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if cache_config.backend == "quanto" and not is_optimum_quanto_available():
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@@ -4745,3 +4763,4 @@ def _dola_select_contrast(
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final_logits, base_logits = _relative_top_filter(final_logits, base_logits)
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logits = final_logits - base_logits
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return logits
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@@ -32,7 +32,6 @@ import transformers
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from indextts.gpt.transformers_generation_utils import GenerationMixin
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from indextts.gpt.transformers_modeling_utils import PreTrainedModel
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from transformers.modeling_utils import SequenceSummary
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from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask_for_sdpa
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from transformers.modeling_outputs import (
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@@ -42,19 +41,81 @@ from transformers.modeling_outputs import (
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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# from transformers.modeling_utils import PreTrainedModel, SequenceSummary
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from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
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from transformers.utils import (
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ModelOutput,
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add_code_sample_docstrings,
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)
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# Local implementation of SequenceSummary since it's not available in transformers 4.56.1
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class SequenceSummary(nn.Module):
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"""Compute a single vector summary of a sequence hidden states."""
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def __init__(self, config):
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super().__init__()
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self.summary_type = getattr(config, "summary_type", "last")
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if self.summary_type == "attn":
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raise NotImplementedError
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self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
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if self.has_summary:
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if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
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num_classes = config.num_labels
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else:
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num_classes = config.hidden_size
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self.summary = nn.Linear(config.hidden_size, num_classes)
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activation_string = getattr(config, "summary_activation", None)
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self.activation = (ACT2FN[activation_string] if activation_string else nn.Identity())
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self.first_dropout = nn.Dropout(getattr(config, "summary_first_dropout", 0.1))
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self.last_dropout = nn.Dropout(getattr(config, "summary_last_dropout", 0.1))
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def forward(
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self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
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) -> torch.FloatTensor:
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if self.summary_type == "last":
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output = hidden_states[:, -1]
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elif self.summary_type == "first":
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output = hidden_states[:, 0]
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elif self.summary_type == "mean":
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output = hidden_states.mean(dim=1)
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elif self.summary_type == "cls_index":
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if cls_index is None:
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cls_index = torch.full(
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fill_value=-1,
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size=(hidden_states.size(0),),
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dtype=torch.long,
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device=hidden_states.device,
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)
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batch_size = hidden_states.shape[0]
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if cls_index.shape[0] != batch_size:
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raise ValueError(
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f"cls_index shape {cls_index.shape} doesn't match batch_size {batch_size}"
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)
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output = hidden_states[torch.arange(batch_size, device=hidden_states.device), cls_index]
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else:
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raise ValueError(f"Unsupported summary type: {self.summary_type}")
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output = self.first_dropout(output)
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if self.has_summary:
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output = self.summary(output)
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output = self.activation(output)
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output = self.last_dropout(output)
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return output
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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get_torch_version,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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replace_return_docstrings
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)
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from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
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@@ -1876,3 +1937,4 @@ class GPT2ForQuestionAnswering(GPT2PreTrainedModel):
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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+2
-2
@@ -1,9 +1,9 @@
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[project]
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name = "indextts-mw"
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description = "IndexTTS Voice Cloning Nodes for ComfyUI. High-quality voice cloning, very fast, supports Chinese and English, and allows custom voice styles."
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version = "2.0.0"
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version = "2.0.1"
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license = {file = "LICENSE"}
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dependencies = ["# accelerate==0.25.0", "# transformers==4.36.2", "# tokenizers==0.15.0", "# cn2an==0.5.22", "# ffmpeg-python==0.2.0", "# Cython==3.0.7", "# g2p-en==2.1.0", "# jieba==0.42.1", "# keras==2.9.0", "# numba==0.58.1", "# numpy==1.26.2", "# pandas==2.1.3", "# matplotlib==3.8.2", "# opencv-python==4.9.0.80", "# vocos==0.1.0", "# accelerate==0.25.0", "# tensorboard==2.9.1", "omegaconf", "sentencepiece", "librosa", "tqdm", "# deepspeeds # Use it to accelerate model inference"]
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dependencies = []
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[project.urls]
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Repository = "https://github.com/billwuhao/ComfyUI_IndexTTS"
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@@ -27,9 +27,6 @@ textstat
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pynini==2.1.6; platform_system!="Windows"
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WeTextProcessing>=1.0.3; platform_system!="Windows"
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WeTextProcessing; platform_machine != "Darwin"
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wetext; platform_system == "Darwin"
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# importlib_resources
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# pynini==2.1.6.post1
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# WeTextProcessing>=1.0.4
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