From 638a7a614e90b1e3e55deb9a7d041bb721c48b94 Mon Sep 17 00:00:00 2001 From: Farzad Abdolhosseini Date: Tue, 27 Aug 2024 15:30:58 -0700 Subject: [PATCH] Integrating OAI evals post training (#85) * allow lower python version for lambda cloud and adding ultravox-vllm * integrate oaievals * evaluations using oaievalset * make sure pipeline can be loaded correctly * force 1 GPU and set max_num_samples * logging eval Table to w&b + make text-only eval optional --- poetry.lock | 2861 ++++++++++++++++++-- pyproject.toml | 3 +- setup.sh | 2 +- ultravox/model/ultravox_model.py | 124 +- ultravox/model/ultravox_processing.py | 3 + ultravox/model/whisper_model_modified.py | 141 - ultravox/training/config_base.py | 1 + ultravox/training/configs/meta_config.yaml | 2 +- ultravox/training/train.py | 163 +- 9 files changed, 2944 insertions(+), 356 deletions(-) delete mode 100644 ultravox/model/whisper_model_modified.py diff --git a/poetry.lock b/poetry.lock index f251abe7..363dfe51 100644 --- 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+python-versions = ">=3.10,<4.0" +content-hash = "70cda1d7eb287a6cd00d2cf8a285c5f2613fcdea125caf03951258ecad79289c" diff --git a/pyproject.toml b/pyproject.toml index e0c473db..307f8a75 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ license = "MIT" readme = "README.md" [tool.poetry.dependencies] -python = "^3.11" +python = ">=3.10,<4.0" torch = ">=2.4" transformers = {version = ">=4.43.1", extras = ["torch"]} bitsandbytes = "~0.42.0" @@ -29,6 +29,7 @@ tensorboardx = "~2.6.2.2" wandb = "~0.17.1" sacrebleu = "^2.4.2" tenacity = "^9.0.0" +evals = {git = "https://github.com/fixie-ai/evals"} [tool.poetry.group.dev.dependencies] black = "~24.4.2" diff --git a/setup.sh b/setup.sh index 4cd89885..b6f95191 100644 --- a/setup.sh +++ b/setup.sh @@ -1,7 +1,7 @@ cd $HOME mkdir workspace cd workspace -git clone git@github.com:fixie-ai/ultravox.git -b main +git clone https://github.com/fixie-ai/ultravox.git -b main cd ultravox mkdir -p ~/.local/bin curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to ~/.local/bin diff --git a/ultravox/model/ultravox_model.py b/ultravox/model/ultravox_model.py index b01ac55b..cb209888 100644 --- a/ultravox/model/ultravox_model.py +++ b/ultravox/model/ultravox_model.py @@ -9,13 +9,13 @@ import transformers.activations import transformers.modeling_outputs import transformers.models +from transformers.models.whisper import modeling_whisper as whisper # We must use relative import in this directory to allow uploading to HF Hub # Even "from . import X" pattern doesn't work (undocumented and unclear why) from .ultravox_config import LossConfig from .ultravox_config import LossFunction from .ultravox_config import UltravoxConfig -from .whisper_model_modified import WhisperEncoder as ModifiedWhisperEncoder class UltravoxModel(transformers.LlamaPreTrainedModel): @@ -268,7 +268,7 @@ def prepare_inputs_for_generation( @classmethod def _create_audio_tower( cls, config: UltravoxConfig - ) -> Union[transformers.Wav2Vec2Model, ModifiedWhisperEncoder]: + ) -> Union[transformers.Wav2Vec2Model, "ModifiedWhisperEncoder"]: if config.audio_model_id is not None: if "whisper" in config.audio_model_id is not None: audio_tower = ModifiedWhisperEncoder.from_pretrained( @@ -496,6 +496,126 @@ def forward(self, audio_features: torch.Tensor) -> torch.Tensor: return hidden_states +class ModifiedWhisperEncoder(whisper.WhisperEncoder): + """ + Encoder portion of OpenAI's Whisper model. + + This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes: + 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder + 2. allow less than 30 second of audio padding to be passed in: + - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal + - embed_pos is now sliced to match the length of `inputs_embeds` + + Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py + """ + + base_model_prefix = "model.encoder" + + def forward( + self, + input_features, + attention_mask=None, + head_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + expected_seq_length = ( + self.config.max_source_positions + * self.conv1.stride[0] + * self.conv2.stride[0] + ) + if input_features.shape[-1] > expected_seq_length: + raise ValueError( + f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." + ) + + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + inputs_embeds = nn.functional.gelu(self.conv1(input_features)) + inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) + + inputs_embeds = inputs_embeds.permute(0, 2, 1) + embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)] + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout( + hidden_states, p=self.dropout, training=self.training + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + assert head_mask.size()[0] == ( + len(self.layers) + ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + None, + (head_mask[idx] if head_mask is not None else None), + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + None, + layer_head_mask=( + head_mask[idx] if head_mask is not None else None + ), + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, encoder_states, all_attentions] + if v is not None + ) + return transformers.modeling_outputs.BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_states, + attentions=all_attentions, + ) + + UltravoxConfig.register_for_auto_class() UltravoxModel.register_for_auto_class() diff --git a/ultravox/model/ultravox_processing.py b/ultravox/model/ultravox_processing.py index fddb3b40..81bd34dd 100644 --- a/ultravox/model/ultravox_processing.py +++ b/ultravox/model/ultravox_processing.py @@ -56,6 +56,9 @@ def __init__( assert ( self.audio_token_replacement is not None ), "The tokenizer has no EOS token. Cannot recover." + if tokenizer.pad_token_id is None: + tokenizer.pad_token_id = tokenizer.eos_token_id + super().__init__(audio_processor=audio_processor, tokenizer=tokenizer) @classmethod diff --git a/ultravox/model/whisper_model_modified.py b/ultravox/model/whisper_model_modified.py deleted file mode 100644 index bf447f9b..00000000 --- a/ultravox/model/whisper_model_modified.py +++ /dev/null @@ -1,141 +0,0 @@ -# modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py -# see this issue for the commentary: https://github.com/huggingface/transformers/issues/25744 -# -# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved. -# -# 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 torch -import torch.nn as nn -import transformers -import transformers.modeling_outputs -from transformers.models.whisper import modeling_whisper as whisper - - -class WhisperEncoder(whisper.WhisperEncoder): - """ - Encoder portion of OpenAI's Whisper model. - - This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes: - 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder - 2. allow less than 30 second of audio padding to be passed in: - - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal - - embed_pos is now sliced to match the length of `inputs_embeds` - - Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py - """ - - base_model_prefix = "model.encoder" - - def forward( - self, - input_features, - attention_mask=None, - head_mask=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - expected_seq_length = ( - self.config.max_source_positions - * self.conv1.stride[0] - * self.conv2.stride[0] - ) - if input_features.shape[-1] > expected_seq_length: - raise ValueError( - f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." - ) - - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - inputs_embeds = nn.functional.gelu(self.conv1(input_features)) - inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) - - inputs_embeds = inputs_embeds.permute(0, 2, 1) - embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)] - - hidden_states = inputs_embeds + embed_pos - hidden_states = nn.functional.dropout( - hidden_states, p=self.dropout, training=self.training - ) - - encoder_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - - # check if head_mask has a correct number of layers specified if desired - if head_mask is not None: - assert head_mask.size()[0] == ( - len(self.layers) - ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." - - for idx, encoder_layer in enumerate(self.layers): - if output_hidden_states: - encoder_states = encoder_states + (hidden_states,) - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - to_drop = False - if self.training: - dropout_probability = torch.rand([]) - if dropout_probability < self.layerdrop: # skip the layer - to_drop = True - - if to_drop: - layer_outputs = (None, None) - else: - if self.gradient_checkpointing and self.training: - layer_outputs = self._gradient_checkpointing_func( - encoder_layer.__call__, - hidden_states, - None, - (head_mask[idx] if head_mask is not None else None), - output_attentions, - ) - else: - layer_outputs = encoder_layer( - hidden_states, - None, - layer_head_mask=( - head_mask[idx] if head_mask is not None else None - ), - output_attentions=output_attentions, - ) - - hidden_states = layer_outputs[0] - - if output_attentions: - all_attentions = all_attentions + (layer_outputs[1],) - - hidden_states = self.layer_norm(hidden_states) - if output_hidden_states: - encoder_states = encoder_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [hidden_states, encoder_states, all_attentions] - if v is not None - ) - return transformers.modeling_outputs.BaseModelOutput( - last_hidden_state=hidden_states, - hidden_states=encoder_states, - attentions=all_attentions, - ) diff --git a/ultravox/training/config_base.py b/ultravox/training/config_base.py index 182887a9..232c0584 100644 --- a/ultravox/training/config_base.py +++ b/ultravox/training/config_base.py @@ -43,6 +43,7 @@ class TrainConfig: eval_num_samples: int = 100 eval_max_new_tokens: Optional[int] = None eval_num_procs: int = 8 + eval_text_only: bool = False num_prompts: int = 1 # number of data loader workers num_workers: int = 8 if torch.cuda.is_available() else 1 diff --git a/ultravox/training/configs/meta_config.yaml b/ultravox/training/configs/meta_config.yaml index 9ccb4348..dd71828d 100644 --- a/ultravox/training/configs/meta_config.yaml +++ b/ultravox/training/configs/meta_config.yaml @@ -11,7 +11,7 @@ max_audio_duration_secs: 16 val_num_samples: 64 val_steps: 1000 -eval_num_samples: 256 +eval_num_samples: 1024 eval_max_new_tokens: 32 eval_num_procs: 16 diff --git a/ultravox/training/train.py b/ultravox/training/train.py index f4b590c6..bba3c33b 100644 --- a/ultravox/training/train.py +++ b/ultravox/training/train.py @@ -1,14 +1,17 @@ import copy import dataclasses +import gc import glob import logging import os import re +import subprocess import sys from datetime import datetime from typing import Dict, List, Optional import datasets as hf_datasets +import pandas as pd import safetensors.torch import simple_parsing import torch @@ -16,19 +19,17 @@ import transformers import wandb import wandb.sdk -from torch.distributed.elastic.multiprocessing.errors import record from torch.utils import data from ultravox.data import datasets -from ultravox.inference import infer from ultravox.model import data_processing from ultravox.model import ultravox_config from ultravox.model import ultravox_model +from ultravox.model import ultravox_pipeline from ultravox.model import ultravox_processing from ultravox.model import wandb_utils from ultravox.training import config_base from ultravox.training import ddp_utils -from ultravox.training import evaluation INPUT_EXAMPLE = {"text": "Transcribe\n<|audio|>", "audio": b"\x00\x00" * 16000} OUTPUT_EXAMPLE = {"text": "Hello, world!"} @@ -60,7 +61,6 @@ def prepare_dataset( return limited_ds -@record def main() -> None: # Disable parallelism to avoid deadlocks in DataLoader, apparently # multiple processes are forked when using multiple datasets. @@ -78,6 +78,19 @@ def main() -> None: transformers.set_seed(args.seed) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + is_master = local_rank == 0 + + if args.do_train: + train(args) + + if args.do_eval and is_master: + gc.collect() + torch.cuda.empty_cache() + evaluate(args) + + +def train(args: config_base.TrainConfig): world_size = int(os.environ.get("WORLD_SIZE", 1)) local_rank = int(os.environ.get("LOCAL_RANK", 0)) is_master = local_rank == 0 @@ -282,50 +295,112 @@ def main() -> None: # fsdp_transformer_layer_cls_to_wrap='LlamaDecoderLayer', ), ) - if args.do_train: - # Training loop - logging.info("Starting training...") - t_start = datetime.now() - logging.info(f"train start time: {t_start}") - if args.val_steps: - trainer.evaluate() - trainer.train() - trainer.save_model(args.output_dir) - t_end = datetime.now() - logging.info(f"train end time: {t_end}") - logging.info(f"elapsed: {t_end - t_start}") - - if args.do_eval: - logging.info("Starting evaluation...") - t_start = datetime.now() - logging.info(f"eval start time: {t_start}") - - # Merge LoRA weights for better inference performance. - # Note: this is irreversible and changes model saving format - model.merge_and_unload() - # padding_side="left" is required for (batch) inference - text_tokenizer.padding_side = "left" - inference = infer.LocalInference( - model=model, - processor=processor, - tokenizer=text_tokenizer, - device=args.device, - dtype=dtype, + + # Training loop + logging.info("Starting training...") + t_start = datetime.now() + logging.info(f"train start time: {t_start}") + if args.val_steps: + trainer.evaluate() + trainer.train() + if is_master: + # Saving the model using pipeline to ensure its code is saved + pipeline = ultravox_pipeline.UltravoxPipeline( + model, tokenizer=text_tokenizer, device=device ) - metrics = evaluation.evaluate( - inference, - data_dir=args.data_dir, - num_procs=args.eval_num_procs, + pipeline.save_pretrained(args.output_dir) + t_end = datetime.now() + logging.info(f"train end time: {t_end}") + logging.info(f"elapsed: {t_end - t_start}") + + +def evaluate(args: config_base.TrainConfig): + """ + Evaluate the model on the audio and text datasets. + + NOTE: This function must be run only on the primary process. + """ + logging.info("Starting evaluation...") + t_start = datetime.now() + logging.info(f"eval start time: {t_start}") + + if args.text_model_lora_config and args.text_model_lora_config.r: + logging.warn( + "Model has unmerged LoRA config. This can lead to slower inference." + ) + + logs_dir = wandb.run.dir if wandb.run else str(args.logs_dir) + + # Run audio-based evaluations and log to W&B + audio_metrics_df = run_oaievalset( + log_dir=os.path.join(logs_dir, "oaieval/audio"), + model_dir=str(args.output_dir), + eval_set="audio-core", + num_samples=args.eval_num_samples, + ) + # TODO: it would be best to do trainer.log, but then we'd risk keeping parts of the model + # in GPU memory, which could cause OOM errors. + if wandb.run: + wandb.run.log({"eval_audio": wandb.Table(data=audio_metrics_df)}) + + if args.eval_text_only: + # Run text-only evaluations and log to W&B + text_metrics_df = run_oaievalset( + log_dir=os.path.join(logs_dir, "oaieval/text"), + model_dir=str(args.output_dir), + eval_set="transcript-core", num_samples=args.eval_num_samples, - max_new_tokens=args.eval_max_new_tokens, - log_dir=wandb.run.dir if wandb.run else str(args.logs_dir), ) - if is_master: - trainer.log(metrics) + if wandb.run: + wandb.run.log({"eval_text": wandb.Table(data=text_metrics_df)}) + + t_end = datetime.now() + logging.info(f"eval end time: {t_end}") + logging.info(f"elapsed: {t_end - t_start}") + + +def run_oaievalset( + log_dir: str, model_dir: str, eval_set: str, num_samples: Optional[int] = None +) -> pd.DataFrame: + env = os.environ.copy() + + # num_gpus = max(1, torch.cuda.device_count()) + env["EVALS_THREADS"] = "64" + + # TODO: currently running this on a single GPU is faster than multiple GPUs :facepalm: + env["CUDA_VISIBLE_DEVICES"] = "0" + + command = [ + "oaievalset", + "--record_dir", + log_dir, + "generation/gpu/ultravox-dev", + eval_set, + f"--completion_args=model={model_dir}", + ] + if num_samples: + command.append(f"--max_samples={num_samples}") + + # Run the evaluation set + subprocess.run(command, check=True, env=env) + + # Extract the results from the log directory + subprocess.run( + [ + "python", + "-m", + "evals.elsuite.audio.make_table", + "--out_dir", + log_dir, + "--log_dir", + log_dir, + ], + check=True, + ) + + df = pd.read_csv(os.path.join(log_dir, "results.csv")) - t_end = datetime.now() - logging.info(f"eval end time: {t_end}") - logging.info(f"elapsed: {t_end - t_start}") + return df if __name__ == "__main__":