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} | null | null | null | I am trying to finetune STT En FastConformer Hybrid Transducer-CTC Large Streaming Multi model.
### **Issue**: WER does not go below 22.5%. Need suggestion of how I can improve this or anything that I might be doing wrong.
### **Details:**
```
python3 -u NeMo/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py \
--config-path="../conf/fastconformer/hybrid_cache_aware_streaming" \
--config-name="fastconformer_hybrid_transducer_ctc_bpe_streaming" \
+init_from_pretrained_model="stt_en_fastconformer_hybrid_large_streaming_multi" \
model.train_ds.manifest_filepath="data/train_20.json" \
model.validation_ds.manifest_filepath="data/val_20.json" \
model.tokenizer.dir="default_tokenizer" \
model.tokenizer.type="bpe" \
trainer.max_epochs=300 \
model.optim.name="adamw" \
model.optim.weight_decay=0.0001 \
model.optim.sched.warmup_steps=2000 \
model.aux_ctc.ctc_loss_weight=0.3 \
model.optim.lr=0.005 \
model.optim.betas=[0.9,0.999] \
++exp_manager.exp_dir="checkpoints" \
++exp_manager.version=one \
++exp_manager.use_datetime_version=False \
++exp_manager.resume_ignore_no_checkpoint=True
```
### **Config:**
```
cfg:
sample_rate: 16000
compute_eval_loss: false
log_prediction: true
skip_nan_grad: false
model_defaults:
enc_hidden: 512
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: data/train_20.json
sample_rate: 16000
batch_size: 8
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 20
min_duration: 5
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
validation_ds:
manifest_filepath: data/val_20.json
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
tokenizer:
dir: default_tokenizer
type: bpe
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: 16000
normalize: NA
window_size: 0.025
window_stride: 0.01
window: hann
features: 80
n_fft: 512
frame_splicing: 1
dither: 1.0e-05
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
time_masks: 10
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: 80
feat_out: -1
n_layers: 17
d_model: 512
use_bias: true
subsampling: dw_striding
subsampling_factor: 8
subsampling_conv_channels: 256
causal_downsampling: true
ff_expansion_factor: 4
self_attention_model: rel_pos
n_heads: 8
att_context_size:
- 70
- 1
att_context_style: chunked_limited
att_context_probs: null
xscaling: true
pos_emb_max_len: 5000
conv_kernel_size: 9
conv_norm_type: layer_norm
conv_context_size: causal
dropout: 0.1
dropout_pre_encoder: 0.1
dropout_emb: 0.0
dropout_att: 0.1
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null
random_state_sampling: false
blank_as_pad: true
prednet:
pred_hidden: 640
pred_rnn_layers: 1
t_max: null
dropout: 0.2
vocab_size: 1024
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null
preserve_memory: false
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: 640
activation: relu
dropout: 0.2
encoder_hidden: 512
pred_hidden: 640
num_classes: 1024
decoding:
strategy: greedy_batch
greedy:
max_symbols: 10
beam:
beam_size: 2
return_best_hypothesis: false
score_norm: true
tsd_max_sym_exp: 50
alsd_max_target_len: 2.0
aux_ctc:
ctc_loss_weight: 0.3
use_cer: false
ctc_reduction: mean_batch
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: 1024
decoding:
strategy: greedy
interctc:
loss_weights: []
apply_at_layers: []
loss:
loss_name: default
warprnnt_numba_kwargs:
fastemit_lambda: 0.005
clamp: -1.0
optim:
name: adamw
lr: 0.005
betas:
- 0.9
- 0.999
weight_decay: 0.0001
sched:
name: NoamAnnealing
d_model: 512
warmup_steps: 2000
warmup_ratio: null
min_lr: 1.0e-06
target: nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel
nemo_version: 2.1.0rc0
```
### **Things changed from default config:**
batch size: 8
**att_context_size: [70,1]** because I need to use this model for real time transcription. Experimented with the Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb and found out that [70,1] is what gives best latency for my use case of real time transcription (lookead_size = 80ms and encoder_step_length = 80ms)
**learning_rate: Tried with different lr (0.005, 0.0005, 0.0001) but not difference.**
min_duration = 5s
max_duration = 20s
### **Tokenizer:**
Used default tokenizer, saved the default tokenizer from the model and then used it for finetuning. (1024 SPE Unigram)
### **Dataset:**
**train: 120hrs** of english audio (**doctor patient conversations (recordings of meetings), contains medical jargon and american accent**), audio files: 5s-20s
converted all transcripts to only contain lowercase english alphabets, space and apostrophe
**val: 15hrs** of audio in same format as above
**WER of Original Model on val_set = 41%
The WER reaches 22-25% in all runs within first 10-15 epochs and after that it remains almost constant. Max epochs tried is 90.**
**GPU**: V100 16GB
**Tensorboard Logs of one of the runs:**
### UPDATE (30/09/2024)
**Tried adding more data to the dataset. Whole dataset is shuffled and then train and val sets are created. Didn't make any difference and instead now I have a bigger lowest VAL_WER with the new daataset.**
TRAIN: 220 hrs (~100 hrs more from previous data)
VAL: 17 hrs
Audio Lengths: 1s - 20s (1s 2s 3s 4s ....... 18s 19s 20s)
learning rate: 0.001
Tried with Batch size: 8 (V100), 32 (A100)
Epochs: 45 (V100), 95 and 300 (A100)
**VAL_WER: ~23-24% in all 3 runs**
For some reason it looks like the learning rate becomes equal to minimum learning rate (1e-6) right from the start and then stays there. Not sure why. Also don't know why training loss graph looks like as shown below.
Also I am not sure what we are training here? CTC or RNNT or both with the config I am using. (I use decoder_type = "rnnt" when doing inference using this model)
Find tensorboard logs attached below
### UPDATE (3/10/2024)
I read in few answers in the issues that when we use **NoamAnnealing** which is the default scheduler mentioned in config ([fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml)), **the lr is a multiplier and hence should be 1/10th or 1/5th of original**
I tried with lr = 0.5 (1 GPU A100 batch_size=32), 1.0 (4 GPUs A100 batch_size=32)
Min WER = 22% (44 epochs 1 GPU, 175 epochs 4 GPUs)
and it seems almost stuck and doesn't go below this wer with new learning rates also
Apart from this, also tried below changes which also didn't make any difference
att_context_size = [70,6] instead of [70,1]
accumulate_grad_batches = 8 instead of 1
@nithinraok can you please have a look into this and provide any suggestions or any insights that you have based on the provided details.
Thank You
Attaching the logs from latest runs
@nithinraok @titu1994 @elliottnv Any help/suggestions would be appreciated. Thank You. | {
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Adding "torch._dynamo.config.suppress_errors" to T5 pretraining (similar to GPT and RETRO pretraining).
GPT: https://github.com/NVIDIA/NeMo/blob/38c0e3ddcbe312fcb3f395d27759d655b0c5e8f8/examples/nlp/language_modeling/megatron_gpt_pretraining.py#L30
RETRO: https://github.com/NVIDIA/NeMo/blob/38c0e3ddcbe312fcb3f395d27759d655b0c5e8f8/examples/nlp/language_modeling/megatron_retro_pretraining.py#L27
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
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```python
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"One consideration is whether to call the base class's impl at the start or end of our impl. If we do it at the start, we are timing these file [operations](https://github.com/NVIDIA/NeMo/pull/10561/files#diff-f55ba91f6545a501940e7d1a054ff848da000f73a361e4ade9fe4fc2e8c4387fR119-R162) as part of the first train time interval. So, I think it makes more sense to do it at the end.",
"> One consideration is whether to call the base class's impl at the start or end of our impl. If we do it at the start, we are timing these file [operations](https://github.com/NVIDIA/NeMo/pull/10561/files#diff-f55ba91f6545a501940e7d1a054ff848da000f73a361e4ade9fe4fc2e8c4387fR119-R162) as part of the first train time interval. So, I think it makes more sense to do it at the end.\r\n\r\nThanks Maanu! I agree that it makes more sense to add the call at the end."
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} | # What does this PR do ?
Missing baseclass call. Fixes checkpointing with `train_time_interval`
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} | # What does this PR do ?
Ensure checkpoint importer saves the model in the same precision as original checkpoint
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Before your PR is "Ready for review"
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Adds `AutoResume` to NeMo 2 GPT pretraining test so that resumption is properly tested.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] New Feature
- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# GitHub Actions CI
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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add fixes in https://github.com/NVIDIA/NeMo/pull/10508 to r2.0.0
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# What does this PR do ?
Matplotlib removed matplotlib.cm.get_cmap 3.9.0 onwards (deprecated since 3.7.0). This causes an error in our ASR tutorials (ASR_for_telephony_speech.ipynb, Speech_Enhancement_with_NeMo.ipynb, Online_Offline_Microphone_VAD_Demo.ipynb) that use librosa.display.specshow, which internally calls matplotlib.cm.get_cmap(name). Librosa fixed it with this PR: [https://github.com/librosa/librosa/pull/1782](https://github.com/librosa/librosa/pull/1782).
Hence, lower bounding librosa to 0.10.2 in our requirements.txt to account for the above change.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
Updated requirements file
# GitHub Actions CI
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fix type error in llm collection | {
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LLAVA converter fix from nemo to hf fix when tp > 1. Followed the convention from llama converter.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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# Add a code snippet demonstrating how to use this
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This PR fixes the bug of llava converter when tp > 1.
**Collection**: [Note which collection this PR will affect]
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# Usage
* You can potentially add a usage example below
```python
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```
# GitHub Actions CI
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- [O] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-20T01:11:45 | 2024-11-02T01:58:00 | 2024-11-02T01:57:59 | NONE | {
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} | null | null | null | **Describe the bug**
I've been encountring an error when running the megatron mamba inference server while requesting using the example code.
[Megatron_mamba_eval](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_mamba_eval.py)
**Steps/Code to reproduce bug**
Execution code (executed at this [level](https://github.com/NVIDIA/NeMo/tree/main/examples/nlp/language_modeling) :
```bash
CUDA_VISIBLE_DEVICES="0" python megatron_mamba_eval.py \
mamba_model_file=/workspace/nemo/work//Model/model.nemo \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1 \
server=True \
chat=True \
share=True
```
Error :
```bash
[NeMo I 2024-09-20 01:04:19 text_generation_server:65] request IP: 127.0.0.1
[NeMo I 2024-09-20 01:04:19 text_generation_server:66] {"sentences": ["hello"], "tokens_to_generate": 300, "temperature": 1.0, "add_BOS": true, "top_k": 0, "top_p": 0.9, "greedy": false, "all_probs": false, "repetition_penalty": 1.2, "min_tokens_to_generate": 2}
[NeMo W 2024-09-20 01:04:19 nemo_logging:349] /opt/NeMo/nemo/collections/nlp/modules/common/text_generation_server.py:61: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:79.)
choice = torch.cuda.LongTensor([GENERATE_NUM])
Exception on /generate [PUT]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 880, in full_dispatch_request
rv = self.dispatch_request()
File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 865, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return]
File "/usr/local/lib/python3.10/dist-packages/flask_restful/__init__.py", line 489, in wrapper
resp = resource(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/flask/views.py", line 110, in view
return current_app.ensure_sync(self.dispatch_request)(**kwargs) # type: ignore[no-any-return]
File "/usr/local/lib/python3.10/dist-packages/flask_restful/__init__.py", line 604, in dispatch_request
resp = meth(*args, **kwargs)
File "/opt/NeMo/nemo/collections/nlp/modules/common/text_generation_server.py", line 185, in put
MegatronGenerate.send_do_generate() # Tell other ranks we're doing generate
File "/opt/NeMo/nemo/collections/nlp/modules/common/text_generation_server.py", line 62, in send_do_generate
torch.distributed.broadcast(choice, 0)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2044, in broadcast
default_pg = _get_default_group()
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 995, in _get_default_group
raise ValueError(
ValueError: Default process group has not been initialized, please make sure to call init_process_group.
127.0.0.1 - - [20/Sep/2024 01:04:19] "PUT /generate HTTP/1.1" 500 -
```
server request code :
```bash
import json
import requests
batch_size = 1
port_num = 5555
headers = {"Content-Type": "application/json"}
def request_data(data):
resp = requests.put('http://0.0.0.0:{}/generate'.format(port_num),
data=json.dumps(data),
headers=headers)
sentences = resp.json()['sentences']
return sentences
data = {
"sentences": ["hello"] * batch_size,
"tokens_to_generate": 300,
"temperature": 1.0,
"add_BOS": True,
"top_k": 0,
"top_p": 0.9,
"greedy": False,
"all_probs": False,
"repetition_penalty": 1.2,
"min_tokens_to_generate": 2,
}
sentences = request_data(data)
```
**Expected behavior**
Expected to return a response from the server (generated text).
**Environment overview (please complete the following information)**
- Environment location: Docker image nemo 24.07
- Method of NeMo install: install from source checkout on this version (tags/r2.0.0rc1)
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
Docker run command :
```bash
docker run --gpus all --shm-size=80g --net=host --ulimit memlock=-1 --rm -it \
-v /ephemeral/:/workspace/megatron \
-v /ephemeral/tmp:/tmp \
nvcr.io/nvidia/nemo:24.07
```
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version : ubuntu 22.04
- PyTorch version : version installed in the image 24.07
- Python version : 3.10
**Additional context**
I have 2 A100gpus on the machine and tried to launch with torchrun and num_devices = 2 but i do get an error about TP*PP should match the World size and PP and TP for my model are set to 1 (so i cannot inference on multi-gpus in this case? ).
It's a 2B pure mamba2 ssm model in a .nemo checkpoint format.
When executing the script eveything is fine and it shows that the server is running, the errors appears only while requesting and it's related to torch.dist (not usefull for my use case using 1GPU).
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] | 2024-09-20T01:02:03 | 2024-09-30T17:33:41 | 2024-09-30T17:33:39 | COLLABORATOR | {
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} | # What does this PR do ?
Adds support for creating a symlink for `-last` checkpoints. Implementation is compatible with synchronous and asynchronous checkpointing.
**Collection**: llm
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Set the default value of `always_save_context` to True as its required to have the context for evaluating NeMo 2.0 ckpt as of now since we are converting it to 1.0 ckpt using this script: [convert_nemo2_for_export.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/export/convert_nemo2_for_export.py) to export to TRTLLM for evaluation.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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📝 Please remember the following to-do's before merge:
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🙏 Please merge this PR only if the CI workflow completed successfully. | {
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# Changelog
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# What does this PR do ?
Matplotlib removed matplotlib.cm.get_cmap 3.9.0 onwards (deprecated since 3.7.0). This causes an error in our ASR tutorials (ASR_for_telephony_speech.ipynb, Speech_Enhancement_with_NeMo.ipynb, Online_Offline_Microphone_VAD_Demo.ipynb) that use librosa.display.specshow, which internally calls matplotlib.cm.get_cmap(name). Librosa fixed it with this PR: [https://github.com/librosa/librosa/pull/1782](https://github.com/librosa/librosa/pull/1782).
Hence, lower bounding librosa to 0.10.2 in our requirements.txt to account for the above change.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
Updated requirements file
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- [x] Bugfix
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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I appreciate all your efforts.
Does the team have a plan to support Llama-3.1 in Neva? | {
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"@yaoyu-33 , Can you merge this PR?"
] | 2024-09-19T11:40:15 | 2024-10-07T06:01:22 | 2024-10-07T06:01:22 | CONTRIBUTOR | {
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} | # What does this PR do ?
Added minor logics/fixes for convert_llava_nemo_to_hf.py
**Collection**: [Note which collection this PR will affect]
# Changelog
1. Change command script.
2. Set TP=PP=1 for conversion.
3. Add CPU-only option.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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"[🤖]: Hi @nune-tadevosyan 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11138248242) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g",
"[🤖]: Hi @nune-tadevosyan 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/$REPOSITORY/actions/runs/$RUN_ID) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-09-19T11:13:10 | 2024-10-14T09:24:05 | 2024-10-14T09:24:03 | COLLABORATOR | {
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Allow random read from tar files when using lhotse dataloaders
**Collection**: lhotse
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Cherry pick of https://github.com/NVIDIA/NeMo/pull/10489 to `r2.0.0`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"Let me reopen this, some DCO issues due to initial target branch mismatch"
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
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] | 2024-09-19T00:03:57 | 2024-10-16T01:59:31 | 2024-10-16T01:59:30 | CONTRIBUTOR | {
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} | # What does this PR do ?
This MR provides an interface to enable the UCC backend for PP communication.
To enable the UCC backend, set the following argument:
`training.model.pipeline_model_parallel_comm_backend=ucc`
Requires a related MCore MR ([link](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/2116)).
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
`CosineAnnealing` LR with linear warmup and `constant_steps` is `0` on the first step. This causes issues for AdamW optimizer which uses LR for `step_size` in the denominator. This is due to the fact on first step when this method is called `step=0` before the first optimizer step is complete.
Using the same protection as in:
* https://github.com/NVIDIA/NeMo/blob/d2f3f49aea7b282816063dad5ff2f2c3eedfc6cf/nemo/core/optim/lr_scheduler.py#L81
* https://github.com/NVIDIA/NeMo/blob/d2f3f49aea7b282816063dad5ff2f2c3eedfc6cf/nemo/core/optim/lr_scheduler.py#L315
* https://github.com/NVIDIA/NeMo/blob/d2f3f49aea7b282816063dad5ff2f2c3eedfc6cf/nemo/core/optim/lr_scheduler.py#L611
Updated unit tests not to tolerate LR=0.
**Collection**: [Note which collection this PR will affect]
Core
# Changelog
- fix cosine annealing linear warmup LR
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"Any reason to bump vllm to 0.5.5? This will probably break all of our vllm export tests btw. Can you test the steps here? https://docs.nvidia.com/nemo-framework/user-guide/latest/deployment/llm/optimized/vllm.html ",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting `@dependabot ignore this major version` or `@dependabot ignore this minor version`.\n\nIf you change your mind, just re-open this PR and I'll resolve any conflicts on it."
] | 2024-09-18T17:36:58 | 2024-10-12T01:57:11 | 2024-10-12T01:57:04 | CONTRIBUTOR | {
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<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/vllm-project/vllm/releases">vllm's releases</a>.</em></p>
<blockquote>
<h2>v0.5.5</h2>
<h2>Highlights</h2>
<h3>Performance Update</h3>
<ul>
<li>We introduced a new mode that schedule multiple GPU steps in advance, reducing CPU overhead (<a href="https://redirect.github.com/vllm-project/vllm/issues/7000">#7000</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7387">#7387</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7452">#7452</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7703">#7703</a>). Initial result shows 20% improvements in QPS for a single GPU running 8B and 30B models. You can set <code>--num-scheduler-steps 8</code> as a parameter to the API server (via <code>vllm serve</code>) or <code>AsyncLLMEngine</code>. We are working on expanding the coverage to <code>LLM</code> class and aiming to turning it on by default</li>
<li>Various enhancements:
<ul>
<li>Use flashinfer sampling kernel when avaiable, leading to 7% decoding throughput speedup (<a href="https://redirect.github.com/vllm-project/vllm/issues/7137">#7137</a>)</li>
<li>Reduce Python allocations, leading to 24% throughput speedup (<a href="https://redirect.github.com/vllm-project/vllm/issues/7162">#7162</a>, 7364)</li>
<li>Improvements to the zeromq based decoupled frontend (<a href="https://redirect.github.com/vllm-project/vllm/issues/7570">#7570</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7716">#7716</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7484">#7484</a>)</li>
</ul>
</li>
</ul>
<h3>Model Support</h3>
<ul>
<li>Support Jamba 1.5 (<a href="https://redirect.github.com/vllm-project/vllm/issues/7415">#7415</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7601">#7601</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/6739">#6739</a>)</li>
<li>Support for the first audio model <code>UltravoxModel</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/7615">#7615</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7446">#7446</a>)</li>
<li>Improvements to vision models:
<ul>
<li>Support image embeddings as input (<a href="https://redirect.github.com/vllm-project/vllm/issues/6613">#6613</a>)</li>
<li>Support SigLIP encoder and alternative decoders for LLaVA models (<a href="https://redirect.github.com/vllm-project/vllm/issues/7153">#7153</a>)</li>
</ul>
</li>
<li>Support loading GGUF model (<a href="https://redirect.github.com/vllm-project/vllm/issues/5191">#5191</a>) with tensor parallelism (<a href="https://redirect.github.com/vllm-project/vllm/issues/7520">#7520</a>)</li>
<li>Progress in encoder decoder models: support for serving encoder/decoder models (<a href="https://redirect.github.com/vllm-project/vllm/issues/7258">#7258</a>), and architecture for cross-attention (<a href="https://redirect.github.com/vllm-project/vllm/issues/4942">#4942</a>)</li>
</ul>
<h3>Hardware Support</h3>
<ul>
<li>AMD: Add fp8 Linear Layer for rocm (<a href="https://redirect.github.com/vllm-project/vllm/issues/7210">#7210</a>)</li>
<li>Enhancements to TPU support: load time W8A16 quantization (<a href="https://redirect.github.com/vllm-project/vllm/issues/7005">#7005</a>), optimized rope (<a href="https://redirect.github.com/vllm-project/vllm/issues/7635">#7635</a>), and support multi-host inference (<a href="https://redirect.github.com/vllm-project/vllm/issues/7457">#7457</a>).</li>
<li>Intel: various refactoring for worker, executor, and model runner (<a href="https://redirect.github.com/vllm-project/vllm/issues/7686">#7686</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7712">#7712</a>)</li>
</ul>
<h3>Others</h3>
<ul>
<li>Optimize prefix caching performance (<a href="https://redirect.github.com/vllm-project/vllm/issues/7193">#7193</a>)</li>
<li>Speculative decoding
<ul>
<li>Use target model max length as default for draft model (<a href="https://redirect.github.com/vllm-project/vllm/issues/7706">#7706</a>)</li>
<li>EAGLE Implementation with Top-1 proposer (<a href="https://redirect.github.com/vllm-project/vllm/issues/6830">#6830</a>)</li>
</ul>
</li>
<li>Entrypoints
<ul>
<li>A new <code>chat</code> method in the <code>LLM</code> class (<a href="https://redirect.github.com/vllm-project/vllm/issues/5049">#5049</a>)</li>
<li>Support embeddings in the run_batch API (<a href="https://redirect.github.com/vllm-project/vllm/issues/7132">#7132</a>)</li>
<li>Support <code>prompt_logprobs</code> in Chat Completion (<a href="https://redirect.github.com/vllm-project/vllm/issues/7453">#7453</a>)</li>
</ul>
</li>
<li>Quantizations
<ul>
<li>Expand MoE weight loading + Add Fused Marlin MoE Kernel (<a href="https://redirect.github.com/vllm-project/vllm/issues/7527">#7527</a>)</li>
<li>Machete - Hopper Optimized Mixed Precision Linear Kernel (<a href="https://redirect.github.com/vllm-project/vllm/issues/7174">#7174</a>)</li>
</ul>
</li>
<li><code>torch.compile</code>: register custom ops for kernels (<a href="https://redirect.github.com/vllm-project/vllm/issues/7591">#7591</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7594">#7594</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/7536">#7536</a>)</li>
</ul>
<h2>What's Changed</h2>
<ul>
<li>[ci][frontend] deduplicate tests by <a href="https://github.com/youkaichao"><code>@youkaichao</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7101">vllm-project/vllm#7101</a></li>
<li>[Doc] [SpecDecode] Update MLPSpeculator documentation by <a href="https://github.com/tdoublep"><code>@tdoublep</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7100">vllm-project/vllm#7100</a></li>
<li>[Bugfix] Specify device when loading LoRA and embedding tensors by <a href="https://github.com/jischein"><code>@jischein</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7129">vllm-project/vllm#7129</a></li>
<li>[MISC] Use non-blocking transfer in prepare_input by <a href="https://github.com/comaniac"><code>@comaniac</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7172">vllm-project/vllm#7172</a></li>
<li>[Core] Support loading GGUF model by <a href="https://github.com/Isotr0py"><code>@Isotr0py</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/5191">vllm-project/vllm#5191</a></li>
<li>[Build] Add initial conditional testing spec by <a href="https://github.com/simon-mo"><code>@simon-mo</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/6841">vllm-project/vllm#6841</a></li>
<li>[LoRA] Relax LoRA condition by <a href="https://github.com/jeejeelee"><code>@jeejeelee</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7146">vllm-project/vllm#7146</a></li>
<li>[Model] Support SigLIP encoder and alternative decoders for LLaVA models by <a href="https://github.com/DarkLight1337"><code>@DarkLight1337</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7153">vllm-project/vllm#7153</a></li>
<li>[BugFix] Fix DeepSeek remote code by <a href="https://github.com/dsikka"><code>@dsikka</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7178">vllm-project/vllm#7178</a></li>
<li>[ BugFix ] Fix ZMQ when <code>VLLM_PORT</code> is set by <a href="https://github.com/robertgshaw2-neuralmagic"><code>@robertgshaw2-neuralmagic</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7205">vllm-project/vllm#7205</a></li>
<li>[Bugfix] add gguf dependency by <a href="https://github.com/kpapis"><code>@kpapis</code></a> in <a href="https://redirect.github.com/vllm-project/vllm/pull/7198">vllm-project/vllm#7198</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/vllm-project/vllm/commit/09c7792610ada9f88bbf87d32b472dd44bf23cc2"><code>09c7792</code></a> Bump version to v0.5.5 (<a href="https://redirect.github.com/vllm-project/vllm/issues/7823">#7823</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/f1df5dbfd6782408228f39bdc0722fa465629f0f"><code>f1df5db</code></a> [Misc] Update <code>marlin</code> to use vLLMParameters (<a href="https://redirect.github.com/vllm-project/vllm/issues/7803">#7803</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/35ee2ad6b9a850a25d94cab582de19de5bca6fbd"><code>35ee2ad</code></a> [github][misc] promote asking llm first (<a href="https://redirect.github.com/vllm-project/vllm/issues/7809">#7809</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/e25fee57c2e69161bd261f5986dc5aeb198bbd42"><code>e25fee5</code></a> [BugFix] Fix server crash on empty prompt (<a href="https://redirect.github.com/vllm-project/vllm/issues/7746">#7746</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/faeddb565d6a528d1cbd169e90bc538178fc2828"><code>faeddb5</code></a> [misc] Add Torch profiler support for CPU-only devices (<a href="https://redirect.github.com/vllm-project/vllm/issues/7806">#7806</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/fc5ebbd1d3453461ea6e00a78faf87c41d1aa625"><code>fc5ebbd</code></a> [Hardware][Intel GPU] refactor xpu_model_runner for tp (<a href="https://redirect.github.com/vllm-project/vllm/issues/7712">#7712</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/c01a6cb23144b67b473e569a25b6f9725bc3f85b"><code>c01a6cb</code></a> [Ray backend] Better error when pg topology is bad. (<a href="https://redirect.github.com/vllm-project/vllm/issues/7584">#7584</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/b903e1ba7fca15f0dc49ab49a9ec8107f625c048"><code>b903e1b</code></a> [Frontend] error suppression cleanup (<a href="https://redirect.github.com/vllm-project/vllm/issues/7786">#7786</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/a15224642832acdddd757ddc95ed40a0ad1be33d"><code>a152246</code></a> [Misc] fix typo in triton import warning (<a href="https://redirect.github.com/vllm-project/vllm/issues/7794">#7794</a>)</li>
<li><a href="https://github.com/vllm-project/vllm/commit/666ad0aa16f0c656e48e58b4f31ffe956b484d3b"><code>666ad0a</code></a> [ci] Cleanup & refactor Dockerfile to pass different Python versions and scca...</li>
<li>Additional commits viewable in <a href="https://github.com/vllm-project/vllm/compare/v0.5.3.post1...v0.5.5">compare view</a></li>
</ul>
</details>
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} | # What does this PR do ?
Adds support for cudagraph in NeMo2.0.
Example config:
> gpt_config = llm.GPTConfig(
> num_layers=1,
> hidden_size=128,
> ffn_hidden_size=256,
> num_attention_heads=1,
> seq_length=seq_length,
> init_method_std=0.023,
> hidden_dropout=0.0,
> attention_dropout=0.0,
> layernorm_epsilon=1e-5,
> make_vocab_size_divisible_by=128,
> normalization='RMSNorm',
> masked_softmax_fusion=False,
> # Cudagraph options
> enable_cuda_graph=True,
> use_te_rng_tracker=True,
> gradient_accumulation_fusion=False, # This should be false with cudagraphs
> )
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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PR removed torch.stack calls from batched function yielding to faster decoding
**Collection**:
ASR
# Changelog
## Changes in `nemo/collections/asr/modules/rnnt.py`
### Refactoring
- **`batch_initialize_states()`** renamed to **`batch_stack_states`**
- Reason: The function does not initialize any states, so the name was changed for clarity.
- **`batch_initialize_states()`** minimized torch stack calls
- Reason: Torch stack internally copies the data.
- **`batch_select_state()`**
- Reason: Function is hard to read, tried to make it clearer.
### Argument Removal
- **`batch_initialize_states()`**: Removed the `batch_states` argument as it was unused.
- **`batch_score_hypothesis()`**: Removed the `batch_states` argument as it was unused.
### Removing redundant calculations
- **`batch_score_hypothesis()`**: removed torch.stack calls since they are not needed. Returning python lists instead.
- **`batch_score_hypothesis()`**: removed `lm_tokens` return value as it was not used.
## Changes in `[nemo/collections/asr/modules/rnnt_abstract.py]`
- reflected comments and argument list changes in derived classes here
## Changes in `[nemo/collections/asr/parts/submodules/rnnt_beam_decoding.py]`
- replaced **`batch_select_state()`** calls with index access as now everything is a python list
- **`recombine_hypotheses()`** returning final hypotheses instead of initial ones.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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**PR Type**:
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## Who can review?
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"merged_at": "2024-09-24T14:40:00"
} | # What does this PR do ?
Extending modelopt spec (used for PTQ) to use `TEDotProductAttention` in order to support long-context models using sliding window attention (e.g. StarCoder2), see also https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/inference/modelopt_support/gpt/model_specs.py.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/9310
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} | # What does this PR do ?
If fixes confidence estimation for RNN-T models with blank frames not excluded from the calculation.
Fixes https://github.com/NVIDIA/NeMo/issues/10066
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to [STT_EN_FASTCONFORMER_TRANSDUCER_XLARG - Throws error for Tensor + List operation in confidence calculation](https://github.com/NVIDIA/NeMo/issues/10066)
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"@ashors1 @marcromeyn any chance either of you could review or suggest an alternate reviewer?",
"It feels a bit arbitrary to me to have an extra abstraction just to get the number of input and output features. I would prefer if we refactor the transform method a bit into some more methods to get things like input and output features so people could subclass and overwrite those if needed. \r\n\r\nThe current LoRA class is kinda GPT specific so it would make sense to me if there’s one for GeneFormer that subclasses this one and overwrite some methods.",
"> It feels a bit arbitrary to me to have an extra abstraction just to get the number of input and output features. I would prefer if we refactor the transform method a bit into some more methods to get things like input and output features so people could subclass and overwrite those if needed.\r\n> \r\n> The current LoRA class is kinda GPT specific so it would make sense to me if there’s one for GeneFormer that subclasses this one and overwrite some methods.\r\n\r\nwhen reading the code, the type signature on LoRA transform method makes it seem like there is no concept of input/output features -- nn.Module provides no such guarantee. The code as currently written implies there is a stateful transformation that _must_ occur (somewhere, and at the right time) to the module to set the `in_features` and `out_features` attributes. \r\n\r\nI find it much more intuitive when we can use type annotations to communicate expectations. In the case of PEFT, knowing the input/output shape is a pre-requisite. The abstraction @georgea provides makes this expectation explicit and provides sensible defaults for RowParallelLinear and ColumnParallelLinear. In my opinion the ideal solution is to use a new type for this, but im guessing this is difficult with Megatron and TE?\r\n",
"> > It feels a bit arbitrary to me to have an extra abstraction just to get the number of input and output features. I would prefer if we refactor the transform method a bit into some more methods to get things like input and output features so people could subclass and overwrite those if needed.\r\n> > The current LoRA class is kinda GPT specific so it would make sense to me if there’s one for GeneFormer that subclasses this one and overwrite some methods.\r\n> \r\n> when reading the code, the type signature on LoRA transform method makes it seem like there is no concept of input/output features -- nn.Module provides no such guarantee. The code as currently written implies there is a stateful transformation that _must_ occur (somewhere, and at the right time) to the module to set the `in_features` and `out_features` attributes.\r\n> \r\n> I find it much more intuitive when we can use type annotations to communicate expectations. In the case of PEFT, knowing the input/output shape is a pre-requisite. The abstraction @georgea provides makes this expectation explicit and provides sensible defaults for RowParallelLinear and ColumnParallelLinear. In my opinion the ideal solution is to use a new type for this, but im guessing this is difficult with Megatron and TE?\r\n\r\nExactly. Basically, right now LoRA.transform(module: nn.Module), doesn't communicate what info is needed from a module to make the right lora transformation. What I've done is effectively mapped the problem onto LoRa.transform(module_connector: ModuleConnector) which is typed and composable. Such that, a developer knows what information is needed to make the transform on any module. Making it a separate abstraction from Lora isolates the module dependency and allows re-use of the LoRA specific logic.\r\n\r\nThis is very compatible with the idea you presented @marcromeyn , where the LoRA adaptation might need to be updated based on the specifics of the model. E.g., between GPT and T5 or something like that.\r\n\r\nWhat this does is isolate the module specific logic from the actual implementation of the LoRA adapter.\r\n\r\nIn the version you propose, assume a developer needs to implement support for a new module type.\r\n\r\nIf you have a bunch of code like:\r\n\r\n```python\r\nclass LoRAForGPTOnTE(LoRA):\r\n\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.gpt_modules_to_adapt:\r\n return Adapter(self.get_out_features(m))\r\n\r\n def get_out_features(m):\r\n return m.out_features * tp_size\r\n\r\nclass LoRAForGPTOnMcore(LoRA):\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.gpt_modules_to_adapt:\r\n return Adapter(self.get_out_features(m))\r\n\r\n def get_out_features(m):\r\n return m.output_size \r\n\r\nclass LoRAForT5OnTE(LoRA):\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.t5_modules_to_adapt:\r\n return Adapter(self.get_out_features(m))\r\n\r\n def get_out_features(m):\r\n return m.out_features * tp_size\r\n\r\nclass LoRAForT5OnMcore(LoRA):\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.t5_modules_to_adapt:\r\n return Adapter(self.get_out_features(m)) \r\n\r\n def get_output_size(m):\r\n return m.output_size\r\n```\r\n\r\nThe developer now has to go and make a new subclass for each model to support the backend.\r\n\r\nIf instead we write the LoRA transform to a class for the backend connector that standardizes the information LoRA needs to infer it's adapter, the update becomes trivial--just tell me how to get the info for your module.\r\n\r\nSo the logic becomes more like:\r\n\r\n```python\r\nclass LoRAForGPT(LoRA):\r\n\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.gpt_modules_to_adapt:\r\n return Adapter(self.module_connector.get_out_features(m))\r\n\r\n\r\nclass LoRAForT5(LoRA):\r\n def transform(m: nn.Module, ...):\r\n if m.name in self.t5_modules_to_adapt:\r\n return Adapter(self.module_connector.get_out_features(m)) \r\n\r\n```\r\n\r\nBy writing just one class to connect to your module types, you get access to all pefts for all models by just overriding their `module_connector`. This is way less duplicated code, is more maintainable and extensible.",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days."
] | 2024-09-17T23:25:25 | 2024-10-08T16:30:43 | 2024-10-08T16:30:36 | CONTRIBUTOR | {
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} | # What does this PR do ?
The LoRA implementation is currently hard-coded to `TransformerEngine` and torch nn.linear layers. It fails on regular `RowParallelLinear` and `ColumnParallelLinear` because the attributes it looks for do not exist on those modules. This module connector gives a mechanism to override the get/set behavior of the LoRA transform on the torch modules it transforms.
While we can update the default connector behavior, this approach allows anyone who might want to integrate LoRA with new layer types to do so without having to override and copy/paste/edit the full transform method.
This MR also adds in support for mcore parallel linear layers, and removes the need to have Transformer Engine installed to use LoRA.
**Collection**: llm
# Changelog
- Module connector added for LoRA transform.
- MCore layers supported for LoRA
- Transformer Engine installation not required for LoRA
# Usage
Here is an example of overriding the module connector with a connector that can choose the in_features based on which attributed the `module` has.
```python
class GeneFormerModuleConnector(LoRAModuleConnector):
@staticmethod
def get_in_features(m: nn.Module):
match m:
case object(input_size=n):
return n
case object(in_features=n):
return n
case _:
raise ValueError(f"Module {m} does not have a supported input size calculation.")
@staticmethod
def get_out_features(m: nn.Module):
match m:
case object(output_size=n):
return n
case object(out_features=n):
return n
case _:
raise ValueError(f"Module {m} does not have a supported output size calculation.")
class LoRAForGeneFormerTokenRegressor(LoRA):
def __post_init__(self):
self.module_connector = GeneFormerModuleConnector()
```
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- [ ] Bugfix
- [ ] Documentation
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-17T21:55:27 | 2024-11-02T01:58:01 | 2024-11-02T01:58:00 | NONE | {
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} | null | null | null | Hello, I am running code in the NeMo tutorial, [Sample Conversational AI application](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/AudioTranslationSample.ipynb#scrollTo=RYGnI-EZp_nK).
I pulled a nvcr.io/nvidia/nemo:24.05 docker image, and run the tutorial code in a container. But when I try to load the pretrained MTEncDecModel (4th code cell of the tutorial, 5rd line, `nmt_model = nemo_nlp.models.MTEncDecModel.from_pretrained(model_name='nmt_zh_en_transformer6x6').cuda()`), I got the following error:
`RuntimeError: Internal: could not parse ModelProto from /tmp/tmpnf2ficir/tokenizer.decoder.32000.BPE.model`
I reinstalled nemo_toolkit using `!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$r2.0.0rc0#egg=nemo_toolkit[all]
` inside the container, to try to solve this problem by align with the branch version, but it did not work. I confirmed the nemo version is `2.0.0rc0` by using `import nemo` and `nemo.__version__`.
I also find this error is probably due to incomplete model structure? Or do I need to change a version of nemo? Could you give me some possible solutions?
Thanks in advance! | {
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} | # What does this PR do ?
Updates the documentation for S3 checkpointing to have a generic bucket path.
**Collection**: [Note which collection this PR will affect]
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- Updated text in the `s3_checkpointing.rst` file.
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} | * add the mcore interface for optim arg; overlap param AG with optimizer
* Apply isort and black reformatting
---------
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module partial_convertion_to_tarred_audio_dataset\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:32:0: C0301: Line too long (292/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:36:0: C0301: Line too long (158/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:46:0: C0301: Line too long (130/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:68:0: C0301: Line too long (110/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:72:0: C0301: Line too long (108/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:75:0: C0301: Line too long (136/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:98:0: C0301: Line too long (154/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:136:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:148:0: C0301: Line too long (109/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:152:0: C0301: Line too long (136/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:156:0: C0301: Line too long (116/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:163:0: C0301: Line too long (138/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:169:0: C0301: Line too long (110/100) (line-too-long)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:1:0: C0114: Missing module docstring (missing-module-docstring)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:20:0: E0401: Unable to import 'hydra' (import-error)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:22:0: E0401: Unable to import 'hydra.core.config_store' (import-error)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:23:0: E0401: Unable to import 'joblib' (import-error)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:24:0: E0401: Unable to import 'omegaconf' (import-error)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:25:0: E0401: Unable to import 'tqdm' (import-error)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:27:0: W0105: String statement has no effect (pointless-string-statement)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:88:9: W1514: Using open without explicitly specifying an encoding (unspecified-encoding)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:161:4: R1720: Unnecessary \"else\" after \"raise\", remove the \"else\" and de-indent the code inside it (no-else-raise)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:178:20: W0212: Access to a protected member _create_shard of a client class (protected-access)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:183:0: C0206: Consider iterating with .items() (consider-using-dict-items)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:188:0: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/partial_convertion_to_tarred_audio_dataset.py:195:4: E1120: No value for argument 'cfg' in function call (no-value-for-parameter)\n************* Module convert_to_tarred_audio_dataset\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:24:0: C0301: Line too long (111/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:28:0: C0301: Line too long (128/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:30:0: C0301: Line too long (114/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:61:0: C0301: Line too long (103/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:129:0: C0301: Line too long (110/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:169:0: C0301: Line too long (108/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:185:0: C0301: Line too long (129/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:186:0: C0301: Line too long (138/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:187:0: C0301: Line too long (110/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:188:0: C0301: Line too long (117/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:189:0: C0301: Line too long (119/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:196:0: C0301: Line too long (108/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:197:0: C0301: Line too long (105/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:201:0: C0301: Line too long (125/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:206:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:217:0: C0301: Line too long (113/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:220:0: C0301: Line too long (115/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:222:0: C0301: Line too long (117/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:271:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:282:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:321:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:331:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:337:0: C0301: Line too long (105/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:360:0: C0301: Line too long (111/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:362:0: C0301: Line too long (113/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:364:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:365:0: C0301: Line too long (142/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:366:0: C0301: Line too long (115/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:367:0: C0301: Line too long (119/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:370:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:373:0: C0301: Line too long (121/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:374:0: C0301: Line too long (110/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:377:0: C0301: Line too long (116/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:379:0: C0301: Line too long (123/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:380:0: C0301: Line too long (123/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:411:0: C0301: Line too long (103/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:417:0: C0301: Line too long (107/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:421:0: C0301: Line too long (121/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:441:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:460:0: C0301: Line too long (115/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:474:0: C0301: Line too long (115/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:484:0: C0301: Line too long (106/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:502:0: C0301: Line too long (105/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:552:0: C0301: Line too long (120/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:555:0: C0301: Line too long (117/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:558:0: C0301: Line too long (101/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:560:0: C0301: Line too long (108/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:574:0: C0301: Line too long (105/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:576:0: C0301: Line too long (113/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:603:0: C0301: Line too long (111/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:616:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:640:0: C0301: Line too long (105/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:692:0: C0301: Line too long (115/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:696:0: C0301: Line too long (144/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:822:0: C0301: Line too long (109/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:825:0: C0301: Line too long (114/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:834:0: C0301: Line too long (103/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:842:0: C0301: Line too long (124/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:864:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:870:0: C0301: Line too long (112/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:875:0: C0301: Line too long (103/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:881:0: C0301: Line too long (136/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:905:0: C0301: Line too long (124/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:909:0: C0301: Line too long (118/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:938:0: C0301: Line too long (128/100) (line-too-long)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:90:0: E0401: Unable to import 'numpy' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:91:0: E0401: Unable to import 'soundfile' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:92:0: E0401: Unable to import 'joblib' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:93:0: E0401: Unable to import 'omegaconf' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:94:0: E0401: Unable to import 'tqdm' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:105:0: C0115: Missing class docstring (missing-class-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:105:0: R0902: Too many instance attributes (14/7) (too-many-instance-attributes)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:123:0: C0115: Missing class docstring (missing-class-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:129:77: W0108: Lambda may not be necessary (unnecessary-lambda)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:135:4: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:139:4: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:145:4: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:171:4: R0913: Too many arguments (7/5) (too-many-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:171:4: R0917: Too many positional arguments (7/5) (too-many-positional-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:171:4: R0914: Too many local variables (41/15) (too-many-locals)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:171:4: R0912: Too many branches (20/12) (too-many-branches)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:171:4: R0915: Too many statements (69/50) (too-many-statements)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:321:4: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:322:8: E0401: Unable to import 'lhotse' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:322:8: C0415: Import outside toplevel (lhotse.CutSet) (import-outside-toplevel)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:323:8: E0401: Unable to import 'lhotse.dataset.sampling.dynamic_bucketing' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:323:8: C0415: Import outside toplevel (lhotse.dataset.sampling.dynamic_bucketing.estimate_duration_buckets) (import-outside-toplevel)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:325:8: E0401: Unable to import 'nemo.collections.common.data.lhotse.nemo_adapters' (import-error)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:325:8: C0415: Import outside toplevel (nemo.collections.common.data.lhotse.nemo_adapters.LazyNeMoIterator) (import-outside-toplevel)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:340:15: R1735: Consider using '{\"use_lhotse\": True, \"use_bucketing\": True, \"num_buckets\": num_buckets, ... }' instead of a call to 'dict'. (use-dict-literal)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:347:4: R0913: Too many arguments (7/5) (too-many-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:347:4: R0917: Too many positional arguments (7/5) (too-many-positional-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:347:4: R0914: Too many local variables (41/15) (too-many-locals)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:410:8: C0200: Consider using enumerate instead of iterating with range and len (consider-using-enumerate)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:347:4: R0912: Too many branches (18/12) (too-many-branches)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:347:4: R0915: Too many statements (86/50) (too-many-statements)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:573:4: R0913: Too many arguments (6/5) (too-many-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:573:4: R0917: Too many positional arguments (6/5) (too-many-positional-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:603:4: R0913: Too many arguments (6/5) (too-many-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:603:4: R0917: Too many positional arguments (6/5) (too-many-positional-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:603:4: R0914: Too many local variables (17/15) (too-many-locals)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:614:16: R1735: Consider using '{}' instead of a call to 'dict'. (use-dict-literal)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:603:4: R0912: Too many branches (15/12) (too-many-branches)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:612:18: R1732: Consider using 'with' for resource-allocating operations (consider-using-with)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:672:4: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:684:0: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:684:9: W0621: Redefining name 'args' from outer scope (line 942) (redefined-outer-name)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:705:0: C0116: Missing function or method docstring (missing-function-docstring)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:705:0: R0913: Too many arguments (19/5) (too-many-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:705:0: R0917: Too many positional arguments (19/5) (too-many-positional-arguments)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:705:0: R0914: Too many local variables (27/15) (too-many-locals)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:728:22: R1719: The if expression can be replaced with 'not test' (simplifiable-if-expression)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:749:8: R1722: Consider using 'sys.exit' instead (consider-using-sys-exit)\nscripts/speech_recognition/convert_to_tarred_audio_dataset.py:804:8: E1123: Unexpected keyword argument 'slice_with_offset' in method call (unexpected-keyword-arg)\n\n-----------------------------------\nYour code has been rated at 5.75/10\n```\n\n---\n\nMitigation guide:\n\n* Add sensible and useful docstrings to functions and methods\n* For trivial methods like getter/setters, consider adding `# pylint: disable=C0116` inside the function itself\n* To disable multiple functions/methods at once, put a `# pylint: disable=C0116` before the first and a `# pylint: enable=C0116` after the last.\n\nBy applying these rules, we reduce the occurance of this message in future.\n\nThank you for improving NeMo's documentation!",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-17T15:40:24 | 2025-01-19T02:00:14 | 2025-01-19T02:00:14 | COLLABORATOR | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/10511",
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} | # What does this PR do ?
1. `slice_with_offset` Feature:
- Added support to create tar datasets from audio sample segments when the slice_by_offset flag is enabled and the manifest contains an offset field. This feature ensures that audio segments are correctly sliced according to specified offsets.
2. `dry_run` Flag:
- Introduced a dry_run flag that allows for the creation of sharded manifests without generating the actual tar files. This is useful for testing and validation purposes, allowing users to review manifest files before committing to the full dataset creation process.
3. New Script - `partial_conversion_to_tarred_audio_dataset.py`:
- Added a new script to handle the creation of individual shards from manifests produced in dry run mode. This script facilitates the conversion of partial datasets into fully tarred datasets based on previously generated manifests.
# Changelog
- Functions:
-- `create_shards`: Updated to support the `slice_by_offset flag` for segment-based tar creation and `dry_run` functionality.
-- Added new functionality to handle sharded manifests when `dry_run` is enabled.
- New Script:
-- `partial_conversion_to_tarred_audio_dataset.py:` Processes dry-run manifests to generate individual shards.
**PR Type**:
- [v] New Feature
# Additional Information
* These changes enhance flexibility and control over the tar dataset creation process, allowing for more granular and controlled dataset management.
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https://api.github.com/repos/NVIDIA/NeMo/issues/10510 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10510/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10510/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10510/events | https://github.com/NVIDIA/NeMo/pull/10510 | 2,531,466,365 | PR_kwDOC_bI7s57x6N4 | 10,510 | Cherry pick 10379 and 10461 | {
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"Failing tests from LLM unlreated to PR. (PR pending for longtime, hence merging) "
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cherry pick #10379 and #10461 | {
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} | # What does this PR do ?
Add py-modules to pyproject.toml
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Fix the bug that `model.hparams` were not updated when changes to `model._cfg` were made directly (e.g., in `model.setup_optimization()`)
**Collection**: [asr,core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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* Related to https://github.com/NVIDIA/NeMo/issues/9824
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} | # What does this PR do ?
Updating test utilities for `nemo.export` module.
**Collection**: NLP
# Changelog
* Update run.sh & infer_data_path.py for new models & remove deprecated models
* Reorganize `test_data` to better reuse model & build metadata (for example: batch size, sample prompts)
* Bugfix for `trt_llm_export_kwargs` passing
# Usage
* You can potentially add a usage example below
```bash
# Note: this assumes that model directory is mounted at `/opt/checkpoints` in Nemo container:
./tests/export/run.sh
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/10498 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10498/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10498/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10498/events | https://github.com/NVIDIA/NeMo/pull/10498 | 2,529,611,417 | PR_kwDOC_bI7s57rpUq | 10,498 | fix NeMoLogger log -> log_dir rename | {
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Renamed log to log_dir recently in NeMoLogger and did not update this.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"I noticed GPT configs from `base.py`, Llama31Config, and Mamba configs are missing. Any concern for adding those as well? \r\nAlso is there any way we can test the Model classes as well?",
"> I noticed GPT configs from `base.py`, Llama31Config, and Mamba configs are missing. Any concern for adding those as well? Also is there any way we can test the Model classes as well?\r\n\r\n@maanug-nv Added all the remaining config tests"
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | [🤖]: Hi @JimmyZhang12 👋,<br><br>we've cherry picked ` (10121)` into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience! | {
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"Hi, Thank you for showing interest on our work.\nThe model needs to go through approval processs and PR-reviews.\nI am planning to release everything by the end of October 2024, but this could be delayed. \nI will reply to this thread as soon as we release the model.\nThank you.",
"hi",
"Hello! Thank you for your work! Are there any updates? Will the Sortformer code be published?",
"Hey, checking in on this as well, are there any updates with the code release for experimentation?",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"Hi, folks.\nSortformer diarizer is ready but legal review has been blocking it and kept delaying it over and over.\nNow Sortformer diarizer is awaiting the last approval.\nUnfortunately it's final review stage met holiday season and delayed even more.\nSorry for delaying this over and over - I will get back to you once it is released.",
"Sortformer Diarizer 4spk v1 Model Released (January 8, 2025)\n\nThe Sortformer Diarizer 4spk v1 model is now fully approved and officially released!\n\n👉 [Hugging Face - NVIDIA Sortformer Diarizer 4spk v1](https://huggingface.co/nvidia/diar_sortformer_4spk-v1)\n\nWe are actively working on support for more speakers, streaming models, ASR integrations, and more exciting updates to come!"
] | 2024-09-16T15:05:56 | 2025-01-09T00:58:45 | 2025-01-09T00:58:44 | NONE | {
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} | null | null | null | Hello NeMo Team,
I’m just a student highly inspired by your work on speaker diarization. I’m particularly excited about Sortformer and its novel use of Sort Loss to address the permutation problem in speaker diarization.
I’m eager to use [Sortformer](https://arxiv.org/abs/2409.06656) in my at home research and would like to ask if there is an expected release date for it within the NVIDIA NeMo framework.
Thank you for your time and amazing contributions. | {
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"`cp_comm_type` setting looks good to me. This is an API change which needs TE >= 1.10. Will let NeMo folks decide if this is fine for NeMo container release."
] | 2024-09-16T13:50:11 | 2024-09-20T08:28:38 | 2024-09-20T08:28:35 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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"May I ask for your approval @pablo-garay as this updates Dockerfile.ci? Thanks",
"> May I ask for your approval @pablo-garay as this updates Dockerfile.ci? Thanks\r\n\r\nOr @ko3n1g... thanks : )"
] | 2024-09-16T13:41:57 | 2024-09-18T16:19:49 | 2024-09-18T16:19:47 | COLLABORATOR | {
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} | # What does this PR do ?
Updating modelopt to 0.17.0
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"@Victor49152 @gautham-kollu @pablo-garay @ko3n1g Please review the changes, thanks!\r\ncc @Dido0o0"
] | 2024-09-16T13:41:16 | 2024-09-17T10:25:19 | 2024-09-17T10:25:19 | CONTRIBUTOR | {
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} | # What does this PR do ?
Specific pattern in SD inference flow can introduce illegal memory access with TE-DPA. We need to disable TE-DPA for eval/inference for now.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"any leads?\n",
"Hi, thanks for the question and interest. We are working on improving the performance of canary on long form audio. We do also plan to release the model with timestamp generation soon. ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-16T09:26:34 | 2024-11-10T02:00:55 | 2024-11-10T02:00:55 | NONE | {
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I am trying to make working canary-1b on long audio file. Inspiring my self from [https://github.com/NVIDIA/NeMo/blob/b5798ded9f27168db9d7d77cbe4f9da80bf49268/examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py#L19](url) this notebook.
But the results are not convincing in regards of what whisper can do. Seems that script just chunk the audio file but do not share context to help th model to transcribe.. More over it does not include a VAD model, and it seems difficult to timestamp the segments.
Does anyone have succed to have something close to the whisper output, with better quality?
Thanks | {
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📝 Please remember the following to-do's before merge:
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https://api.github.com/repos/NVIDIA/NeMo/issues/10485 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10485/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10485/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10485/events | https://github.com/NVIDIA/NeMo/pull/10485 | 2,526,698,144 | PR_kwDOC_bI7s57iFPP | 10,485 | MoE Upcycling support for GPT based models | {
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-15T02:11:33 | 2024-10-08T01:58:49 | 2024-10-08T01:58:48 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds support for upcycling pretrained weights to a MoE model. This follows the paper: SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS ([https://arxiv.org/pdf/2212.05055](https://arxiv.org/pdf/2212.05055))
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
python NeMo/examples/nlp/language_modeling/megatron_gpt_upcycling.py \
--config-path=/path/to/MoE/config/ \
--config-name=MoE/config/name.yaml \
+restore_from_path=/path/to/base/model
```
Where you MoE config should contain the target MoE model you want to train. The base model you want to upcycle can also be directly added to this config under the argument `restore_from_path`.
# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"Duplicate of #10517 "
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"We likely won't be able to update this ourselves but if you'd like to submit a fix, we can help review it.\n\nThanks!",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"Is there any plan to fix this? I echo that this needs to be fixed otherwise k>1 is kind of useless"
] | 2024-09-14T11:27:09 | 2024-11-12T15:04:40 | 2024-11-02T01:58:03 | NONE | {
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} | null | null | null | I'm using nemo 24.07 container to train LLMs.
I want to save checkpoints in `.nemo` format, so I set `exp_manager.checkpoint_callback_params.always_save_nemo: true`. And it works in the sense that it saves the checkpoint as a `.nemo`, but **the problem is that all the files get the same name, regardless of the step**. Therefore, it constantly replaces the same `.nemo` file, and, in the end of the training, I just get the last checkpoint. So it is useless.
I have tried setting `postfix: {step}.nemo` like it is done with the megatron checkpoint, but it does not work. It does not seem like any `.format()` is called on it, so I don't know how to change the name depending on the step number.
Please fix this issue somehow.
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} | null | null | null | I have trained a Llama 3.1 8B model using the Nemo 24.05.llama3.1 container. It has generated intermediate Megatron checkpoints, and I want to convert them to `.nemo`. Apparently, `megatron_lm_ckpt_to_nemo.py` is supposed to accomplish that task. **I have tried both 24.05.llama3.1 and 24.07 containers for the conversion.**
Inside the script it says that I need to set the `checkpoint_name` to the folder name of the checkpoint.
However, if I do this, I get `IsADirectoryError`. So, clearly, either the documentation or the script is wrong.
To fix this, I append `/common.pt` to the `checkpoint_name`. This fixes the previous issue, and it is able to load the `.pt` file as a dictionary. However, it fails in the following way:
```
[rank0]: checkpoint, consumed, steps, version = load_from_checkpoint(
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 315, in load_from_checkpoint
[rank0]: old_checkpoint['model'],
[rank0]: KeyError: 'model'
```
Apparently, the dictionary does not have the `'model'` key, which should save the weights of the model or the path to them or something. And if they are not defined inside the .pt file, then I don't understand how it's supposed to load the weights.
How can I make this work? Is appending `common.pt` good? Shouldn't this file have some way of accessing the weights? Is the `common.pt` wrong?
This is how I'm executing the script:
```
python -m torch.distributed.launch --nproc_per_node=1 /.../megatron_lm_ckpt_to_nemo.py \
--checkpoint_folder=/.../checkpoints \
--checkpoint_name=megatron_llama3.1_8b--val_loss=1.13-step=7500-consumed_samples=1920000.0/common.pt \
--nemo_file_path=/.../abc.nemo \
--model_type=gpt \
--hparams_file /.../hparams.yaml \
--tensor_model_parallel_size=1 \
--pipeline_model_parallel_size=1 \
--gpus_per_node 1 \
--mcore_input
```
Structure of the checkpoint folder:
Full traceback of the first `IsADirectoryError`:
```
[rank0]: Traceback (most recent call last):
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 571, in <module>
[rank0]: convert(local_rank, rank, world_size, args)
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 485, in convert
[rank0]: checkpoint, consumed, steps, version = load_from_checkpoint(
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 308, in load_from_checkpoint
[rank0]: old_checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/lightning_fabric/utilities/cloud_io.py", line 56, in _load
[rank0]: with fs.open(path_or_url, "rb") as f:
[rank0]: File "/usr/local/lib/python3.10/dist-packages/fsspec/spec.py", line 1293, in open
[rank0]: f = self._open(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 184, in _open
[rank0]: return LocalFileOpener(path, mode, fs=self, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 306, in __init__
[rank0]: self._open()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 311, in _open
[rank0]: self.f = open(self.path, mode=self.mode)
[rank0]: IsADirectoryError: [Errno 21] Is a directory: '/.../checkpoints/megatron_llama3.1_8b--val_loss=1.13-step=7500-consumed_samples=1920000.0'
```
Full traceback of the second `KeyError`:
```[rank0]: Traceback (most recent call last):
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 575, in <module>
[rank0]: convert(local_rank, rank, world_size, args)
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 489, in convert
[rank0]: checkpoint, consumed, steps, version = load_from_checkpoint(
[rank0]: File "/workspace/scripts/megatron_lm_ckpt_to_nemo.py", line 315, in load_from_checkpoint
[rank0]: old_checkpoint['model'],
[rank0]: KeyError: 'model'
```
_* Note: The lines in the traceback may be different from the official `megatron_lm_ckpt_to_nemo.py` as I have added some debugging code._
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"Issue solved by pulling a fresh container."
] | 2024-09-13T09:36:17 | 2024-09-17T08:57:20 | 2024-09-17T08:57:18 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
Context:
```
Container: NeMo 24.07
```
When running the `/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py` checkpoint conversion script, it will at some point call `NLPSaveRestoreConnector.save_to()` (from `/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py`). That method by its way, will call `modelopt.torch.opt.plugins.save_sharded_modelopt_state` using an unsupported parameter (`prefix`):
```python
if HAVE_MODELOPT and hasattr(self.lightning_module, "get_model_module_list"):
save_sharded_modelopt_state(
self.lightning_module.get_model_module_list(),
ckpt_to_dir(filepath),
self.unwrapped_checkpoint_io.save_sharded_strategy,
prefix="model.",
)
```
Please note that in `/usr/local/lib/python3.10/dist-packages/modelopt/torch/opt/plugins/mcore_dist_checkpointing.py`, the current `save_sharded_modelopt_state` only supports three parameters (`model`, `checkpoint_name` and `sharded_strategy`):
```python
def save_sharded_modelopt_state(
model: List[torch.nn.Module], checkpoint_name: str, sharded_strategy: Tuple[str, int]
):
"""Save modelopt_state in the sharded state_dict format.
Args:
model: the model to restore the modelopt optimization
checkpoint_name: the checkpoint folder path
"""
if not mto.ModeloptStateManager.is_converted(model[0]):
return
if len(model) > 1:
raise ValueError("sharded_modelopt_state does not support virtual pipeline parallel!")
modelopt_checkpoint_name = checkpoint_name + "/modelopt_state"
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
os.makedirs(modelopt_checkpoint_name, exist_ok=True)
dist_checkpointing.save(
get_sharded_modelopt_state(-1, model[0]),
modelopt_checkpoint_name,
sharded_strategy,
)
```
This is then causing the following error:
```bash
NeMo I 2024-09-13 09:32:40 dist_ckpt_io:421] Using TorchDistSaveShardedStrategy(torch_dist, 1) dist-ckpt save strategy.
`zarr` distributed checkpoint backend is deprecated. Please switch to PyTorch Distributed format (`torch_dist`).
[rank0]: Traceback (most recent call last):
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py", line 375, in <module>
[rank0]: convert(args)
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py", line 369, in convert
[rank0]: model.save_to(args.output_path)
[rank0]: File "/opt/NeMo/nemo/core/classes/modelPT.py", line 410, in save_to
[rank0]: self._save_restore_connector.save_to(self, str(save_path)) # downstream tasks expect str, not Path
[rank0]: File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 1027, in save_to
[rank0]: save_sharded_modelopt_state(
[rank0]: TypeError: save_sharded_modelopt_state() got an unexpected keyword argument 'prefix'
```
**Steps/Code to reproduce bug**
Download a HuggingFace starcoder2 7b checkpoint, start a NeMo 2407 container in interactive mode and try to run the `/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py` script:
```bash
python /opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py --input_name_or_path /shared/models/huggingface/starcoder2_7b/ --output_path /shared/models/nemo/starcoder2_7b.nemo --precision bf16
```
It will crash:
```bash
NeMo I 2024-09-13 09:32:40 dist_ckpt_io:421] Using TorchDistSaveShardedStrategy(torch_dist, 1) dist-ckpt save strategy.
`zarr` distributed checkpoint backend is deprecated. Please switch to PyTorch Distributed format (`torch_dist`).
[rank0]: Traceback (most recent call last):
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py", line 375, in <module>
[rank0]: convert(args)
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_starcoder2_hf_to_nemo.py", line 369, in convert
[rank0]: model.save_to(args.output_path)
[rank0]: File "/opt/NeMo/nemo/core/classes/modelPT.py", line 410, in save_to
[rank0]: self._save_restore_connector.save_to(self, str(save_path)) # downstream tasks expect str, not Path
[rank0]: File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 1027, in save_to
[rank0]: save_sharded_modelopt_state(
[rank0]: TypeError: save_sharded_modelopt_state() got an unexpected keyword argument 'prefix'
```
**Expected behavior**
Shouldn't crash.
**Environment overview (please complete the following information)**
- Environment location: Docker (NeMo 24.07 image)
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-13T03:06:37 | 2024-11-02T01:58:06 | 2024-11-02T01:58:05 | NONE | {
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} | null | null | null | **Describe the bug**
docker pull the nvcr.io/nvidia/nemo:24.07 ,try to use /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py pretrain,but have
ImportError: cannot import name '_TORCH_GREATER_EQUAL_2_0' from 'lightning_fabric.utilities.imports' (/usr/local/lib/python3.10/dist-packages/lightning_fabric/utilities/imports.py)
the same traing.sh use nemo24.03 images don't have this error.
**Steps/Code to reproduce bug**
srun --output /xxxxxxxx.out --error xxxxxxxx.err --container-image dockerd://nvcr.io/nvidia/nemo:24.07 --container-mounts /gpfsprd/:/gpfsprd/,/apps/idx_files/:/apps/idx_files/ --no-container-mount-home --mpi=pmi2 --container-writable bash -c "
CUDA_DEVICE_MAX_CONNECTIONS=1 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -u /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=xxxxxxxx \
--config-name=******.yaml
**Expected behavior**
Traceback (most recent call last):
File "/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py", line 23, in <module>
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
File "/opt/NeMo/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/opt/NeMo/nemo/collections/nlp/data/__init__.py", line 15, in <module>
from nemo.collections.nlp.data.data_utils import *
File "/opt/NeMo/nemo/collections/nlp/data/data_utils/__init__.py", line 15, in <module>
from nemo.collections.nlp.data.data_utils.data_preprocessing import *
File "/opt/NeMo/nemo/collections/nlp/data/data_utils/data_preprocessing.py", line 28, in <module>
from nemo.utils import logging
File "/opt/NeMo/nemo/utils/__init__.py", line 32, in <module>
from nemo.utils.lightning_logger_patch import add_memory_handlers_to_pl_logger
File "/opt/NeMo/nemo/utils/lightning_logger_patch.py", line 18, in <module>
import pytorch_lightning as pl
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/__init__.py", line 27, in <module>
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/__init__.py", line 29, in <module>
from pytorch_lightning.callbacks.pruning import ModelPruning
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/pruning.py", line 32, in <module>
from pytorch_lightning.core.module import LightningModule
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/__init__.py", line 16, in <module>
from pytorch_lightning.core.module import LightningModule
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/module.py", line 63, in <module>
from pytorch_lightning.trainer import call
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/__init__.py", line 17, in <module>
from pytorch_lightning.trainer.trainer import Trainer
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 38, in <module>
from lightning_fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
ImportError: cannot import name '_TORCH_GREATER_EQUAL_2_0' from 'lightning_fabric.utilities.imports' (/usr/local/lib/python3.10/dist-packages/lightning_fabric/utilities/imports.py)
**Environment overview (please complete the following information)**
Docker version 24.0.5, build ced0996
docker pull nvcr.io/nvidia/nemo:24.07
**Additional context**
Add any other context about the problem here.
Example: H100 & A800 CUDA12.2\12.4\12.6 same error
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📝 Please remember the following to-do's before merge:
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