Add static cache (#89)

* add rope

* don't include padding in rope

* possibly use cross-attn for prompt

* fix rope

* fix cross-attn

* fix self-attn

* fix dummy model

* clean-up rope

* first gqa implementation

* fix wer eval

* feat: add flash attention and spda

* chore: add README for flash attention

* chore: add benchmark script

* chore: add benchmark attention approach

* multi node and fix wer and fix compile

* Update modeling_parler_tts.py

* fix FA2, SDPA and add cross-attn MHA and attention type forcing

* better cross_attention key values number of heads default + add training arguments for attn implementation

* fix audio padding when torch compile or pad_to_max_length=True

* correct multi node

* make rope faster

* fix encoder sdpa

* fix training with cross attention + with FAZ

* use fp32 as default model dtype + fix generation when using FA2 with autocast

* remove redundant passes in generate + clean and fix attentions

* fix edge case in WER evaluation when longform generation

* better multi-node mapping and saving / add eval dataloader num workers

* remove old benchmarks

* faster audio encoding + checkpointing + fix generation step

* unpin trfms

* remove CFG

* imports and constants
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* attention modifications to handle static cach
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* decoder layer modification to handle static cache
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* ParlerTTSPreTrainedModel modifs to handle static cache
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* ParlerTTSDecoder modifs to handle static cache
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* ParlerTTSModel + ParlerTTSForCausalLM modfis
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* ParlerTTSForConditionalGeneration modifs
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* decoder_attention_mask for static cache
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* create inputs_embeds early to have a good cache initialization
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* _get_cache method
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* init the cache
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* ensure good device
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* pin tfrms version
Co-Authored-By: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>

* fix attention_mask FA2

* remove unnecessary method

* Update parler_tts/modeling_parler_tts.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Update parler_tts/modeling_parler_tts.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* remove unnecessary imports

* replace the hardcoded cache_position with a more elegant approach

* make style

* unpin transformers

* pin transformers

* pin torch

* refactor + unpin torch

* Update parler_tts/modeling_parler_tts.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>

* update training script to match 11b209e1



* Update parler_tts/modeling_parler_tts.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>

* ensure compatibility with trfms 4.43.3, changes taken from #31980 on trfms

* fix input_ids_length

* warning full attention mask creation

* changes for training compatibility

---------
Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
Co-authored-by: Yoach Lacombe <yoach.lacombe@gmail.com>
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
Co-authored-by: sang-nguyen-ts <sang.nguyen@trustingsocial.com>
Co-authored-by: yoach@huggingface.co <Yoach Lacombe>
Co-authored-by: sang-nguyen-ts <sang-nguyen-ts@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

helpers/gradio_demo/app.py

 import gradio as gr
 import torch
+from transformers import AutoFeatureExtractor, AutoTokenizer, set_seed
 
 from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
+
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 

helpers/model_init_scripts/init_dummy_model.py

-from parler_tts import ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration, ParlerTTSDecoderConfig
-from transformers import AutoConfig
-import os
 import argparse
+import os
+
+from transformers import AutoConfig
+
+from parler_tts import ParlerTTSDecoderConfig, ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration
 
 
 if __name__ == "__main__":

helpers/model_init_scripts/init_dummy_model_with_encodec.py

-from parler_tts import ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration, ParlerTTSDecoderConfig
-from transformers import AutoConfig
-import os
 import argparse
+import os
+
+from transformers import AutoConfig
+
+from parler_tts import ParlerTTSDecoderConfig, ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

helpers/model_init_scripts/init_model_600M.py

-from parler_tts import ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration, ParlerTTSDecoderConfig
-from transformers import AutoConfig
-import os
 import argparse
+import os
+
+from transformers import AutoConfig
+
+from parler_tts import ParlerTTSDecoderConfig, ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration
 
 
 if __name__ == "__main__":

helpers/push_to_hub_scripts/push_dac_to_hub.py

 import dac
+from transformers import AutoConfig, AutoModel, EncodecFeatureExtractor
+
 from parler_tts import DACConfig, DACModel
 from transformers import AutoConfig, AutoModel
 from transformers import EncodecFeatureExtractor

helpers/push_to_hub_scripts/push_trained_parler_tts_to_hub.py

+from transformers import AutoFeatureExtractor, AutoTokenizer
+
 from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer, AutoFeatureExtractor
+
 
 path = "TODO"
 repo_id = "parler_tts_600M"

parler_tts/__init__.py

 __version__ = "0.1"
 
 
+from transformers import AutoConfig, AutoModel
+
 from .configuration_parler_tts import ParlerTTSConfig, ParlerTTSDecoderConfig
+from .dac_wrapper import DACConfig, DACModel
 from .modeling_parler_tts import (
     ParlerTTSForCausalLM,
     ParlerTTSForConditionalGeneration,
@@ -9,8 +12,6 @@ from .modeling_parler_tts import (
     build_delay_pattern_mask,
 )
 
-from .dac_wrapper import DACConfig, DACModel
-from transformers import AutoConfig, AutoModel
 
 AutoConfig.register("dac", DACConfig)
 AutoModel.register(DACConfig, DACModel)

parler_tts/dac_wrapper/configuration_dac.py

+
 from transformers import PretrainedConfig
-from typing import List
 
 
 class DACConfig(PretrainedConfig):

parler_tts/dac_wrapper/modeling_dac.py

 import torch
-
+from dac.model import DAC
 from transformers import PreTrainedModel
-from transformers.models.encodec.modeling_encodec import EncodecEncoderOutput, EncodecDecoderOutput
-from .configuration_dac import DACConfig
+from transformers.models.encodec.modeling_encodec import EncodecDecoderOutput, EncodecEncoderOutput
 
-from dac.model import DAC
+from .configuration_dac import DACConfig
 
 
 # model doesn't support batching yet
@@ -134,4 +133,4 @@ class DACModel(PreTrainedModel):
         return EncodecDecoderOutput(audio_values)
 
     def forward(self, tensor):
-        raise ValueError(f"`DACModel.forward` not implemented yet")
+        raise ValueError("`DACModel.forward` not implemented yet")

setup.py

@@ -13,11 +13,12 @@
 # limitations under the License.
 
 import os
+
 import setuptools
 
 
 _deps = [
-    "transformers>=4.39.0,<4.41.0",
+    "transformers>=4.43.0,<=4.43.3",
     "torch",
     "sentencepiece",
     "descript-audio-codec",
@@ -60,7 +61,7 @@ setuptools.setup(
     packages=setuptools.find_packages(),
     install_requires=_deps,
     extras_require={
-        "dev": [_extras_dev_deps],
-        "train": [_extras_training_deps],
+        "dev": _extras_dev_deps,
+        "train": _extras_training_deps,
     },
 )

training/data.py

 import logging
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Union, Set
+from typing import Dict, List, Optional, Set, Union
 
-import torch
-import numpy as np
 import datasets
-from datasets import load_dataset, Dataset, IterableDataset, interleave_datasets, concatenate_datasets
-from transformers import AutoFeatureExtractor, AutoTokenizer
-from tqdm import tqdm
-
+import numpy as np
+import torch
 from accelerate import Accelerator
+from datasets import Dataset, IterableDataset, concatenate_datasets, interleave_datasets, load_dataset
+from tqdm import tqdm
+from transformers import AutoFeatureExtractor, AutoTokenizer
 
 
 @dataclass

training/utils.py

 import os
 import re
 import shutil
-from pathlib import Path
 from dataclasses import field
+from pathlib import Path
 from typing import Dict, List
 
 import torch
+from datasets import concatenate_datasets, load_from_disk
 from wandb import Audio
 from datasets import load_from_disk, concatenate_datasets