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Extend multimodal/speech_llm with lhotse, t5 and bestow supports (NVI…
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…DIA#9169)

* Fixes

* Docs fix

* Add support for custom NeMo fields in Lhotse-NeMo adapters (attach to cut.custom)

* Add support for custom NeMo fields in Lhotse-NeMo adapters (attach to cut.custom)

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* support distributed_fused_adam

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* support distributed_fused_adam

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* Add support for sharded NeMo manifest files

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* support megatron_amp_O2

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* Support heterogeneous sampling rates in non tarred NeMo manifests

* migrate to PTL2.0

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* clean up

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* update manifest util

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* Support multiple tokenizer/parser types, aggregate tokenizers, and custom language fields

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* fix

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* fix

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* agg and normal tokenizers actually work

* Support weights for NeMo tarred manifests

* Temporarily hardcoded pnc stripping/lowercasing

* fix

* make pnc hack configurable from the config and disabled by default

* fix the hack

* migrate to ptl2.1 to support multiple dataloaders

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* support encoder overwrite

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* update misc

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* fix eval and clean up

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* support add_sep for perception model

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* fix https://github.com/Lightning-AI/pytorch-lightning/issues/18803

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* add_bos

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* Transformer decoder with conditioning for canary (#8091)

* initial commit for multi-task conf-enc transf-dec for canary

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* removing decoder states caching during training

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* Option to limit the number of open streams (#8095)

* audio signal support in multi

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* update asr evaluator

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* fix from
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* transcribe fn for Canary models (#8110)

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* update for eval

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* update for evaluation

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* fix typo

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* add default concat_sampling_probabilities

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* valid on lhotse

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* Create PrecisionPlugin for megatron_ckpt_to_nemo.py trainer (#7767) (#7774)

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329 changes: 329 additions & 0 deletions examples/multimodal/speech_llm/conf/bestow/modular_audio_gpt_config_cross_llama_lhotse.yaml
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# Copyright (c) 2024, NVIDIA CORPORATION. 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.

name: megatron_audio_gpt_bestow_lhotse

trainer:
devices: 1
accelerator: gpu
num_nodes: 1
precision: 16
logger: False # logger provided by exp_manager
enable_checkpointing: False
use_distributed_sampler: False
max_epochs: 9999
max_steps: 1000000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
limit_train_batches : 1000
log_every_n_steps: 10 # frequency with which training steps are logged
val_check_interval: 1000 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch
gradient_clip_val: 1.0
accumulate_grad_batches: 1

model_target: nemo.collections.multimodal.speech_llm.models.modular_models.CrossAttendModularAudioGPTModel

exp_manager:
# explicit_log_dir: null
exp_dir: null
name: ${name}
create_wandb_logger: False
wandb_logger_kwargs:
project: null
name: null
resume_if_exists: True
resume_ignore_no_checkpoint: True
create_checkpoint_callback: True
checkpoint_callback_params:
monitor: validation_${model.data.validation_ds.metric.name}
save_top_k: 1
mode: min
save_nemo_on_train_end: True
filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{epoch}'
model_parallel_size: ${model.tensor_model_parallel_size}
always_save_nemo: False
save_best_model: True
create_early_stopping_callback: False
early_stopping_callback_params:
monitor: "val_loss"
mode: "min"
min_delta: 0.001
patience: 10
verbose: True
strict: False # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training.


model:
seed: 1234
tensor_model_parallel_size: 1 # intra-layer model parallelism
pipeline_model_parallel_size: 1 # inter-layer model parallelism

pretrained_audio_model: stt_en_fastconformer_transducer_large
freeze_llm: True
freeze_audio_encoder: False
freeze_modality_adapter: False
load_audio_encoder: True

## Legacy batch_size configuration
# When used with lhotse, the batch composition is decided by dataloader configs
# and batch size here is only used for deciding gradient accumulation.
# gradient accumulation = global_batch_size / micro_batch_size / data_parallel_size
# where data_parallel_size = num_nodes * num_gpus / TP_size
global_batch_size: 128
micro_batch_size: 4
restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
save_nemo_on_validation_end: False # Saves an inference ready .nemo file every time a checkpoint is saved during training.
sync_batch_comm: False
megatron_amp_O2: False

## Sequence Parallelism
# Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially
# See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
sequence_parallel: False

## Activation Checkpoint
activations_checkpoint_granularity: null # 'selective' or 'full'
activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective'
# 'uniform' divides the total number of transformer layers and checkpoints the input activation
# of each chunk at the specified granularity
# 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
activations_checkpoint_num_layers: null # not used with 'selective'
activations_checkpoint_layers_per_pipeline: null
answer_only_loss: True
gradient_as_bucket_view: False

hidden_dropout: 0.0
attention_dropout: 0.0
ffn_dropout: 0.0

# use_am_tokenizer: True
# override_vocab_size: 1024

peft:
peft_scheme: "lora" # can be either lora, adapter, ia3 or ptuning
restore_from_path: null

# Used for adapter peft training
adapter_tuning:
type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter'
adapter_dim: 32
adapter_dropout: 0.0
norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used.
column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal
row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal
norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm']
layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers
weight_tying: False
position_embedding_strategy: null # used only when weight_tying is True

lora_tuning:
target_modules: ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] # this can either be 'attention_qkv','attention_dense','mlp_fc1','mlp_fc2', attention (qkv & dense), mlp (fc1 & fc2)
adapter_dim: 32
alpha: ${model.peft.lora_tuning.adapter_dim}
adapter_dropout: 0.0
column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal
row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal
layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers
weight_tying: False
position_embedding_strategy: null # used only when weight_tying is True

# Used for p-tuning peft training
p_tuning:
virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence
bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck
embedding_dim: 1024 # the size of the prompt encoder embeddings
init_std: 0.023

perception:
target: nemo.collections.multimodal.speech_llm.modules.perception_modules.AudioPerceptionModule
use_multi_layer_feat: false
xattn:
target: nemo.collections.multimodal.speech_llm.modules.perception_modules.TransformerCrossAttention
num_attention_heads: 8
attn_score_dropout: 0.1
attn_layer_dropout: 0.1
ffn_dropout: 0.1
hidden_act: "relu"
pre_ln: true
pre_ln_final_layer_norm: true

multi_layer_feat:
layer_idx_list: [0,16] # layer indices to extract features from
aggregator:
mode: "cat" # ways to combine features from different layers, choices=['cat','sum','mean', 'max', 'min'], default to concat ('cat')
pooling: "avg" # ways to pool features if they have different temporal lengths and align_mode=min, choices=['mean', 'max', 'min']
align_mode: "min" # if features have different temporal lengths, set `min` to pool to the shortest length or `max` to repeat to the longest.

modality_adapter:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: 1024
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 2
d_model: 512

# Sub-sampling parameters
subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 8 # must be power of 2 for striding and vggnet
subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
causal_downsampling: false

# Reduction parameters: Can be used to add another subsampling layer at a given position.
# Having a 2x reduction will speedup the training and inference speech while keeping similar WER.
# Adding it at the end will give the best WER while adding it at the beginning will give the best speedup.
reduction: null # pooling, striding, or null
reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder
reduction_factor: 1

# Feed forward module's params
ff_expansion_factor: 4

# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000

# Convolution module's params
conv_kernel_size: 9
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
# conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
# null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
conv_context_size: null

### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules

# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1

spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05

# the following are read from the pretrained AM:
# output_dim: null
# encoder: null
# preprocessor: null

data:
end_string: "[EOG]"
train_ds:
# Example of how to specify paths to multiple datasets
# manifest_filepath:
# - /path/to/squad.jsonl
# - /path/to/mnli.jsonl
# - /path/to/boolq.jsonl
# Example of how each dataset is formatted
# {'audio_filepath': 'audio1.wav', 'offset': 0.0, 'duration': 12.3, 'question': 'transcribe this audio', 'answer': 'I have a dream...'}
# the 'answer' field can also be 'text', and a default 'question' field is added if missing in manigests, so as to work with ASR manifests
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: True
num_workers: 0
pin_memory: True
max_seq_length: 2048
min_seq_length: 1
drop_last: True
# Notably, the data weights are controlled by either bucketing_weights
# or concat_sampling_probabilities depending on the dataset type (tar and
# non-tar).
# See audio_text_qa_dataset.py for details.
concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random'
context_key: 'context'
answer_key: 'answer'
add_eos: True
# add_eos: False
end_string: ${model.data.end_string}
add_sep: False
add_bos: False
separate_prompt_and_response_with_newline: False
truncation_field: "context" # Options: ['context', 'answer']
index_mapping_dir: null # Path to a directory to write index mapping files.
prompt_template: "[INST]\n<<SYS>>\nPlease answer the following based on the previous speech feature.\n<</SYS>>\n\n{context}[/INST] {answer}"
# ASR configs
sample_rate: 16000 #${model.audio_encoder.preprocessor.sample_rate}
max_duration: 24 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "fully_randomized"
bucketing_batch_size: null
use_lhotse: True
text_field : "text"
batch_duration : 80 # 0
quadratic_duration : 30
num_buckets : 30
buffer_size : 10000
shuffle_buffer_size : 10000
duration_bins: null

validation_ds:
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: False
num_workers: 0
pin_memory: True
max_seq_length: 2048
min_seq_length: 1
drop_last: False
context_key: ${model.data.train_ds.context_key}
answer_key: ${model.data.train_ds.answer_key}
add_eos: ${model.data.train_ds.add_eos}
end_string: ${model.data.end_string}
add_sep: ${model.data.train_ds.add_sep}
add_bos: ${model.data.train_ds.add_bos}
separate_prompt_and_response_with_newline: ${model.data.train_ds.separate_prompt_and_response_with_newline}
write_predictions_to_file: False
output_file_path_prefix: null # Prefix of the file to write predictions to.
truncation_field: "context" # Options: ['context', 'answer']
index_mapping_dir: null # Path to a directory to write index mapping files.
prompt_template: ${model.data.train_ds.prompt_template} # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}"
tokens_to_generate: 128
# ASR configs
sample_rate: 16000 #${model.audio_encoder.preprocessor.sample_rate}

log_every_n_steps: 10
metric:
name: "wer" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss']
average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported.
num_classes: null

optim:
name: fused_adam
lr: 1e-4
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
warmup_steps: 50
min_lr: 0.0 # min_lr must be 0.0 for prompt learning when pipeline parallel > 1
constant_steps: 0 # Constant steps should also be 0 when min_lr=0
monitor: val_loss
reduce_on_plateau: false
1 change: 0 additions & 1 deletion examples/multimodal/speech_llm/conf/modular_audio_gpt_config_eval.yaml
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data:
test_ds:
manifest_filepath: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds.
names: null # Names of the corresponding datasets used to log metrics.
global_batch_size: 1
micro_batch_size: 1
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