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**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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# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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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.
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Fix multimodal nightly build issues 2403 in CLIP and Neva
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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.
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Fixing quantization issues based on more extensive tests:
- model configured with `megatron_amp_O2: true`
- model w/o artifacts
- model using HF tokenizer
- remove broken "pileval" dataset
**Collection**: NLP
# Changelog
See above.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [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 https://github.com/NVIDIA/NeMo/pull/8444
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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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**PR Type**:
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- [ ] Bugfix
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**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
```
# Jenkins CI
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# Before your PR is "Ready for review"
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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?
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- [ ] New Feature
- [x] 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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"jenkins",
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"Will re-open if we need to include ia3 for MoE in the future."
] | 2024-03-18T23:07:44 | 2024-04-01T20:26:59 | 2024-04-01T20:26:58 | MEMBER | {
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1) when registering an IA3 mixin with an MoE layer it only runs on the MLP parts.
2) small change in `MCoreMLPMixin` forward to accomodate for when `linear_fc1` is not a TE layer.
**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
```
# Jenkins CI
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# Before your PR is "Ready for review"
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- [x] Bugfix
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## Who can review?
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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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} | # What does this PR do ?
Enable 1) data shuffling for packed sequence dataset, and
2) packed sequence with normal attention kernel (no `thd` attention) for certain benchmarking applications
**Collection**: NLP
# Usage
Data shuffling is automatically enabled.
To enable packed seq without `thd` attention, put the following in the run command
```bash
+model.data.train_ds.packed_sequence=True
+model.data.train_ds.packed_sequence_return_cu_seqlen=False # new
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**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.
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Pending Mcore
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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [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.
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I got a sft gpt .nemo model, and I am trying to run the inference script at examples/nlp/language_modeling/tuning/megatron_gpt_generate.py
even though I have specified the output file path in examples/nlp/language_modeling/tuning/conf/megatron_gpt_generate_config.yaml
the running looks good but it only reported the test loss, it did not generate any inference results (a jsonl/txt file) in the output file path. | {
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} | # What does this PR do ?
Open sourcing the export and deploy module that exports a nemo checkpoint to TensorRT-LLM and serves it using PyTriton.
**Collection**: [None]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# A simple example is as follows.
# Run the following on the server side
from nemo.export import TensorRTLLM
from nemo.deploy import DeployPyTriton
# To export to TRT-LLM
trt_llm_exporter = TensorRTLLM(model_dir="/path/to/trt_llm_engine_folder")
trt_llm_exporter.export(
nemo_checkpoint_path="/path/to/nemo/ckpt.nemo",
model_type="model_type",
n_gpus=1,
)
# To start serving on Triton server
nm = DeployPyTriton(model=trt_llm_exporter, triton_model_name="LLM")
nm.deploy()
nm.run()
# To query the model, please run the following on a the client side
from nemo.deploy import NemoQuery
nq = NemoQuery(url="localhost:8000", model_name="LLM")
output = nq.query_llm(
prompts=["what is the color of a banana?"], , top_k=1, top_p=0.0, temperature=1.0,
)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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? (Test location: https://github.com/oyilmaz-nvidia/NeMo/blob/oss-export/tests/export/test_nemo_export.py)
- [x] Did you add or update any necessary documentation? (Added documentation on the gitlab repo for the docs. Will need to make some updates though)
- [ ] 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
- [x] 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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} | # 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [ ] 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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"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-18T11:46:17 | 2024-04-10T01:44:28 | 2024-04-10T01:44:28 | CONTRIBUTOR | {
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} | # What does this PR do ?
Update the multimodal/speech_cv branch to support audio-visual speech recognition (CTC, RNN-T and Hybrid)
**Collection**: [multimodal/speech_cv]
# Changelog
- Update speech_cv branch
- bug fix
- Add audio-visual datasets
- Add audio-visual models: (AudioVisualEncDecCTCModelBPE, AudioVisualEncDecCTCModel, AudioVisualEncDecHybridRNNTCTCBPEModel, AudioVisualEncDecHybridRNNTCTCModel, AudioVisualEncDecRNNTBPEModel, AudioVisualEncDecRNNTModel)
- Add audio-visual modules (FusionModule, InterCTCMixin)
- Optional ConformerEncoder module 'pre_encode' by setting 'feat_in' to None
# Usage
* You can potentially add a usage example below
```python
import nemo.collections.multimodal.speech_cv as nemo_cv
from omegaconf import OmegaConf
config_path = "model_config.yaml"
params = OmegaConf.load(config_path)
# hybrid CTC/RNN-T BPE audio-visual speech recognition model
model = nemo_cv.models.AudioVisualEncDecHybridRNNTCTCBPEModel(cfg=params.model)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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"You are using the main branch Nemo inference scro \"transcribe_search.py\" whole having Nemo 1.23 or lower installed, that will not work. \r\n\r\nSwitch your branch to r1.23.0, then use the inference script in that version. "
] | 2024-03-18T10:01:51 | 2024-03-30T06:49:04 | 2024-03-30T06:49:04 | NONE | {
"total": 0,
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} | null | null | null | **Describe the bug**
I am training `examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py`.
I cannot run the inference obtaining the following exception:
```
Error executing job with overrides: ['model_path=results/UZB_MCV_ISSAI_V2/2024-03-15_16-25-00/checkpoints/UZB_MCV_ISSAI_V2.[8/1287$
pretrained_name=null', 'dataset_manifest=/home/aidos/datasets/tbc_test.json', 'batch_size=32', 'output_filename=results/UZB_MCV_ISS
AI_V2/2024-03-15_16-25-00/tbc_test_pred.json']
Traceback (most recent call last):
File "/home/aidos/exp/NeMo/examples/asr/speech_to_text_eval.py", line 225, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/home/aidos/exp/Nemo_inf/NeMo/nemo/core/config/hydra_runner.py", line 129, in wrapper
_run_hydra(
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/home/aidos/exp/NeMo/examples/asr/speech_to_text_eval.py", line 125, in main
transcription_cfg = transcribe_speech.main(cfg) # type: EvaluationConfig
File "/home/aidos/exp/Nemo_inf/NeMo/nemo/core/config/hydra_runner.py", line 75, in wrapper
return task_function(cfg_passthrough)
File "/home/aidos/exp/NeMo/examples/asr/transcribe_speech.py", line 393, in main
transcriptions = asr_model.transcribe(audio=filepaths, override_config=override_cfg,)
File "/home/aidos/nemo_inference_venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
TypeError: EncDecHybridRNNTCTCModel.transcribe() got an unexpected keyword argument 'override_config'
```
**Steps/Code to reproduce bug**
```
python ${NEMO_ROOT}/examples/asr/speech_to_text_eval.py \
model_path=${MODEL} \
pretrained_name=null \
dataset_manifest=${TEST_MANIFEST} \
batch_size=${BATCH_SIZE} \
output_filename=${OUTPUT_MANIFEST} \
amp=False \
use_cer=False)
```
**Expected behavior**
An inference process
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install: pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 22.04
- PyTorch 2.2.1
- Python 3.10.0
**Additional context**
Add any other context about the problem here.
This is the hybrid RNNT CTC model.
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"jenkins",
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] | 2024-03-17T17:01:18 | 2024-03-18T20:13:19 | 2024-03-18T20:13:19 | CONTRIBUTOR | {
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"merged_at": "2024-03-18T20:13:18"
} | # What does this PR do ?
Support ChatGLM2&ChatGLM3
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
### Model conversion from HuggingFace to nemo
```
python ./scripts/nlp_language_modeling/convert_chatglm_hf_to_nemo.py --input_name_or_path '/mount/data/chatglm2-6b' --output_path '/mount/nemo_models/chatglm2.nemo'
```
### Inference
```
python ./examples/nlp/language_modeling/megatron_gpt_eval.py \
--config-name=megatron_chatglm_inference \
gpt_model_file=${CHATGLM_NEMO_MODEL} \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1
```
### Pretrain
```
python3 -u examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-name=megatron_chatglm_config \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.val_check_interval=100 \
trainer.precision=bf16 \
+model.data.data_prefix=${TRAIN_DS} \
trainer.max_steps=1000 \
trainer.max_epochs=1 \
model.megatron_amp_O2=True \
model.sequence_parallel=True \
model.tensor_model_parallel_size=2 \
model.pipeline_model_parallel_size=1 \
model.global_batch_size=128 \
model.micro_batch_size=1 \
model.override_vocab_size=65024 \
model.use_cpu_initialization=False
```
### SFT
```
python ./examples/nlp/language_modeling/tuning/megatron_gpt_sft.py \
trainer.devices=8 \
trainer.val_check_interval=200 \
model.restore_from_path=${CHATGLM_NEMO_MODEL} \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.answer_only_loss=True \
model.global_batch_size=128 \
model.micro_batch_size=1 \
model.data.train_ds.file_names=${TRAIN_DS} \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=${VALID_DS} \
model.data.validation_ds.global_batch_size=128\
model.data.validation_ds.micro_batch_size=1 \
model.megatron_amp_O2=True \
exp_manager.exp_dir=exp \
exp_manager.name=chatglm_test \
trainer.precision=bf16
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Various docs fixes: typos, changing urls to relative links
**Collection**: Docs
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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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"jenkins",
"jenkins"
] | 2024-03-16T18:50:09 | 2024-03-16T23:41:54 | 2024-03-16T23:41:17 | COLLABORATOR | {
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} | # What does this PR do ?
Update dataclass instantiation to support python 3.11
**Collection**: [ASR]
# Changelog
- Update the field init for internal config of transcription for Classification and AED models
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] 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
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"Can you paste your full stack trace ? Rnnt wer was merged into wer.py in NeMo 1.23.0, and we tested the eval script but might have missed something. ",
"```\r\nTraceback (most recent call last):\r\n File \"/home/aidos/exp/NeMo/examples/asr/speech_to_text_eval.py\", line 71, in <module>\r\n import transcribe_speech\r\n File \"/home/aidos/exp/NeMo/examples/asr/transcribe_speech.py\", line 34, in <module>\r\n from nemo.collections.asr.parts.utils.transcribe_utils import (\r\nImportError: cannot import name 'read_and_maybe_sort_manifest' from 'nemo.collections.asr.parts.utils.transcribe_utils' (/home/aidos/nemo_venv/lib/python3.11/site-packages/nemo/collections/asr/parts/utils/transcribe_utils.py)\r\n\r\n```\r\n\r\nThis exception is being thrown on main branch. ",
"I tried to switch to r1.23.0 and r1.22.0 etc. Further exceptions were thrown of different branches:\r\n\r\n\r\n",
"You can't use the main branch script with the r1.23 branch or lower Nemo installation, you should switch to the correct branch of the installed NeMo version before using the script. So if you have 1.23 pip installed you need the 1.23.0 branch ",
"As to main, thanks for the trace, well take a look asap. Fyi @stevehuang52 @tbartley94 ",
"> You can't use the main branch script with the r1.23 branch or lower Nemo installation, you should switch to the correct branch of the installed NeMo version before using the script. So if you have 1.23 pip installed you need the 1.23.0 branch\r\n\r\nI'm actually training on main branch. The Nemo toolkit has been install in python venv.\r\nFor the inference then, should I create another venv and install r1.23? Is `speech_to_text_eval` script is working stable on that version?",
"If you do `pip install -e .[all]`, then the main branch version of the scripts are needed.\r\nIf you do `pip install nemo_toolkit[all]==VERSION` then you need to switch to VERSION branch before going to the example scripts. \r\n\r\nThe two mismatches you're seeing are\r\n1) Traceback (most recent call last):\r\n File \"/home/aidos/exp/NeMo/examples/asr/speech_to_text_eval.py\", line 71, in <module>\r\n import transcribe_speech\r\n File \"/home/aidos/exp/NeMo/examples/asr/transcribe_speech.py\", line 34, in <module>\r\n from nemo.collections.asr.parts.utils.transcribe_utils import (\r\nImportError: cannot import name 'read_and_maybe_sort_manifest' from 'nemo.collections.asr.parts.utils.transcribe_utils' (/home/aidos/nemo_venv/lib/python3.11/site-packages/nemo/collections/asr/parts/utils/transcribe_utils.py)\r\n\r\nI tried this on both transcribe speech and speech to text eval just a moment ago - they work.\r\nMost likely you have an older version of main branch (or are using 1.23 branch and attempting to use the latest main script) - you need to pull from main again and install it. \r\n\r\n2) The dataclass issue - you're using Python 3.11 presumably. Its a bug with the dataclass handling, ill fix that in a PR in some time. If you want you can switch to 3.10 if its easy, or wait until I fix it.\r\n"
] | 2024-03-16T07:37:57 | 2024-03-16T23:41:18 | 2024-03-16T23:41:18 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to run `examples/asr/speech_to_text_eval.py` , but obtaining exception.
This happens on different branches: main, r1.22.0, r1.21.0 etc.
**Steps/Code to reproduce bug**
python examples/asr/speech_to_text_eval.py model_path=../results/KZ_MCV_ISSAI_V2/2024-03-15_16-25-00/checkpoints/KZ_MCV_ISSAI_V2.nemo pretrained_name=null dataset_manifest=~/datasets/kz_test.json batch_size=32
**Expected behavior**
to obtain the predictions
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install: pip install
**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: 2.2.1
- Python version: 3.11.2
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} | # What does this PR do ?
Add script for packed sequence data preparation. The feature of sequence packing was added in #7945
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [x] 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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} | # What does this PR do ?
TransformerEngine and MCORE interface change to support TP ReduceScatter overlap with send/recv.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Change the argument names of LayerNormLinear and Linear modules to TE and MCORE modules.
# Usage
User should set the overlap `method` and the use of `atomic_gemm` in the yaml config file as below.
```python
proj_fprop:
method: pipeline
num_sm: 24
cga_size: 2
num_splits: 4
set_sm_margin: 1
atomic_gemm: 0
fc2_fprop:
method: ring_exchange
num_sm: 1
set_sm_margin: 1
atomic_gemm: 0
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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- [ ] 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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Some fixes for the github io main page when viewed on small (mobile) screens
**Collection**: Github io page
# Changelog
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- I also comment out some CSS fields which were preventing the text boxes from scaling with the screen at narrow screen widths.
- Also removed a redundant `<br>` tag.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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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**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
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```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [x] 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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* Related to # (issue)
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} | Remove `dist_sync_on_step` flag from all ASR models to prevent potential desync bug on multi GPU training.
Add a one line overview of what this PR aims to accomplish.
There's a technical bug where unbalanced distribution of data across distributed setting can cause a desync with torch Metrics. (This is primarily a case with Lhotse dataloaders.) This remove two flags from Torch Metrics to avoid this flag. (`sync_on_compute` and `dist_sync_on_step`). It also removes the potential to call `dist_sync_on_step` for ASR related models.
**Collection**: [ASR]
# Changelog
- For every ASR model, `dist_sync_on_step` is no longer a potential flag. (This flag was unnecessary since ASR models average all batch metrics on epoch end, so sync is unnecessary.)
- Disables `sync_on_compute` for ASR and BLEU models to avoid potential bug.
- Moves SacreBleu backend calls to NeMo ASR Metric interface for easier access.
# Before your PR is "Ready for review"
**Pre checks**:
- [Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N] Did you write any new necessary tests?
- [N] Did you add or update any necessary documentation?
- [N] 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
- [Y] 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 ?
Update embedded HF space to be Canary - forgot to update this link in https://github.com/NVIDIA/NeMo/pull/8671
**Collection**: Docs
# 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
```
# Jenkins 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)
- [ ] 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**:
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- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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# Additional Information
* forgot to update this link in https://github.com/NVIDIA/NeMo/pull/8671
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fixes bug introduced by #8622
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] Bugfix
- [ ] Documentation
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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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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Per the title, plus replaced the original embedded HF space with the Canary one.
**Collection**: docs
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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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Open sourcing the export and deploy modules
**Collection**: No collections will be affected. There will be two new modules, export and deploy.
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
**Collection**: [All]
# Changelog
- Add section describing installation paths and recommendations
# 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?
- [ ] 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
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} | # What does this PR do ?
Update GH pages homepage text
**Collection**: github io page
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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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"@JRD971000 Can you also add a deprecation warning to all nemo bert models . Say that it will be deprectaed by mid year, and good to switch to megatron core bert model . ",
"@shanmugamr1992 Addressed the comments. ",
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"Jenkins",
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"Jenkins",
"@ericharper Thanks for the comments, addressed them all. ",
"Jenkins"
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} | # What does this PR do ?
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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [X] 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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**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
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 ?
Change to the correct HF tokenizer [vocab API](https://huggingface.co/docs/tokenizers/api/tokenizer#tokenizers.Tokenizer.get_vocab)
**Collection**: common
# Changelog
- change file: `nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py`
# Usage
* You can potentially add a usage example below
```python
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
tokenizer_library = "huggingface"
tokenizer_type = "Qwen/Qwen1.5-4B"
tokenizer = get_nmt_tokenizer(
library=tokenizer_library,
model_name=tokenizer_type,
tokenizer_model=None,
vocab_file=None,
merges_file=None,
delimiter=None,
)
print(tokenizer.vocab)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [n] Did you write any new necessary tests?
- [y] Did you add or update any necessary documentation?
- [n] 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
- [y] 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
None.
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"merged_at": "2024-03-16T05:20:18"
} | # What does this PR do ?
Update PEFT doc
**Collection**: [Note which collection this PR will affect]
# Changelog
- Mention support for all linear layers (#7988)
- Update support matrix to include Gemma and Starcoder
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
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- [ ] Bugfix
- [x] 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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes [issue 8577](https://github.com/NVIDIA/NeMo/issues/8577) by appending `CustomProgressBar` to the callbacks when `enable_progress_bar` is not passed to the trainer in which case the progress bar is enabled by default or when `enable_progress_bar` is explicitly passed as True.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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"There's a lot of additional flags to set - what about reading the value of extract nbest and setting the corresponding flags in the data classes ?",
"jenkins"
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} | Support dumping nbest into manifest if exists from transcribe_speech tool
Previously it only generates but does not store.
- Test on Parakeet
```
python \
../transcribe_speech.py \
pretrained_name="nvidia/parakeet-rnnt-1.1b" \
dataset_manifest=$VAL extract_nbest=True presort_manifest=False rnnt_decoding.beam.beam_size=5 rnnt_decoding.strategy='beam' gt_text_attr_name="answer" output_filename=$output_filename
```
- Test on Canary
```
python \
../transcribe_speech.py \
pretrained_name="nvidia/canary-1b" \
dataset_manifest=$VAL extract_nbest=True presort_manifest=False multitask_decoding.beam.beam_size=5 gt_text_attr_name="answer" output_filename=$output_filename
```
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} | # What does this PR do ?
Update manifest loading code for better robustness and error handling.
- Strip spaces/newlines before/after each line
- Skip lines that are empty after `line.strip()`
- Cache json decode errors during reading, then print out all error lines and raise runtime error
**Collection**: [all]
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"jenkins",
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"jenkins"
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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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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
```
# Jenkins CI
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# Before your PR is "Ready for review"
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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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[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
```
# Jenkins CI
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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**:
- [ ] 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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"<img width=\"267\" alt=\"Screenshot 2024-03-13 at 10 20 51 PM\" src=\"https://github.com/NVIDIA/NeMo/assets/4916480/f39ca013-8ddf-4427-9037-a7c22be724ed\">\r\nThis is from the new models page."
] | 2024-03-14T05:19:48 | 2024-03-15T17:38:12 | 2024-03-15T17:38:11 | COLLABORATOR | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/8653",
"html_url": "https://github.com/NVIDIA/NeMo/pull/8653",
"diff_url": "https://github.com/NVIDIA/NeMo/pull/8653.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/8653.patch",
"merged_at": "2024-03-15T17:38:11"
} | # What does this PR do ?
The order of the list of ASR models in docs has been changed to put the more recent and popular models at the top.
**Collection**: ASR
# Changelog
- ASR models doc changes
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [X] 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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} | [] | closed | false | null | [] | null | [] | 2024-03-14T04:29:31 | 2024-03-14T04:37:08 | 2024-03-14T04:37:08 | NONE | {
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} | null | null | null | Error executing job with overrides: ['model.train_ds.manifest_filepath=data/train.json', 'model.validation_ds.manifest_filepath=data/dev.json', 'model.tokenizer.update_tokenizer=False', 'trainer.devices=-1', 'trainer.accelerator=gpu', 'trainer.max_epochs=50']
Traceback (most recent call last):
File "/home/wangsang/work/data/NeMo/examples/asr/speech_to_text_finetune.py", line 215, in main
trainer.fit(asr_model)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1023, in _run_stage
self.fit_loop.run()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 355, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 133, in run
self.advance(data_fetcher)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 219, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 188, in run
self._optimizer_step(kwargs.get("batch_idx", 0), closure)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 266, in _optimizer_step
call._call_lightning_module_hook(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 145, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1270, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 161, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 257, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 231, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 116, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 75, in wrapper
return wrapped(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
ret = func(self, *args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/adamw.py", line 164, in step
loss = closure()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 103, in _wrap_closure
closure_result = closure()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 142, in __call__
self._result = self.closure(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 128, in closure
step_output = self._step_fn()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 315, in _training_step
training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 293, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 330, in training_step
return self.model(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1523, in forward
else self._run_ddp_forward(*inputs, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1359, in _run_ddp_forward
return self.module(*inputs, **kwargs) # type: ignore[index]
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/overrides/base.py", line 90, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/utils/model_utils.py", line 381, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py", line 709, in training_step
loss_value, wer, _, _ = self.joint(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/core/classes/common.py", line 1064, in __call__
outputs = wrapped(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/modules/rnnt.py", line 1452, in forward
loss_batch = self.loss(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/core/classes/common.py", line 1064, in __call__
outputs = wrapped(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/losses/rnnt.py", line 491, in forward
loss = self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 435, in forward
return self.loss(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 62, in forward
loss_func(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py", line 223, in rnnt_loss_gpu
status = wrapper.cost_and_grad(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 252, in cost_and_grad
return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 161, in compute_cost_and_score
self.log_softmax(acts, denom)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 107, in log_softmax
reduce.reduce_max(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py", line 353, in reduce_max
return ReduceHelper(
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py", line 294, in ReduceHelper
_reduce_rows[grid_size, CTA_REDUCE_SIZE, stream, 0](I_opid, R_opid, acts, output, num_rows)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/dispatcher.py", line 539, in __call__
return self.dispatcher.call(args, self.griddim, self.blockdim,
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/dispatcher.py", line 673, in call
kernel = _dispatcher.Dispatcher._cuda_call(self, *args)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/dispatcher.py", line 681, in _compile_for_args
return self.compile(tuple(argtypes))
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/dispatcher.py", line 924, in compile
kernel = _Kernel(self.py_func, argtypes, **self.targetoptions)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/dispatcher.py", line 103, in __init__
self.cooperative = 'cudaCGGetIntrinsicHandle' in lib.get_asm_str()
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/codegen.py", line 119, in get_asm_str
return self._join_ptxes(self._get_ptxes(cc=cc))
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/codegen.py", line 137, in _get_ptxes
ptxes = [nvvm.llvm_to_ptx(irs, **options)]
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py", line 632, in llvm_to_ptx
return cu.compile(**opts)
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py", line 270, in compile
self._try_error(err, 'Failed to verify\n')
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py", line 294, in _try_error
self.driver.check_error(err, "%s\n%s" % (msg, self.get_log()))
File "/home/wangsang/miniconda3/envs/nemo/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py", line 198, in check_error
raise exc
numba.cuda.cudadrv.error.NvvmError: Failed to verify
<unnamed> (71, 5): parse expected type
NVVM_ERROR_INVALID_IR | {
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} | # What does this PR do ?
Enable pipeline parallelism (PP) for p-tuning.
PP has already been supported for the rest of the PEFT methods as well as SFT. P-tuning needed to be handled separately because there are only trainable parameters in the first stage, so the rest of the stages have to be ignored for certain optimizer setup and checkpoints save/load logic.
Tested p-tuning with TP {1, 2, 4} x PP {1, 2, 4} configurations and validations losses match within 1%.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [x] New Feature
- [ ] 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.
# Additional Information
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Adds distributed checkpointing to bert
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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"@P15V make sure that there are no line breaks between the curly brackets. I believe this will solve your error\r\n\r\nnemo manifests despite being saved as .json are a bit fiddly at times and cannot be directly loaded in via the json package. **each line within the manifest should be a complete json string**.\r\n\r\nI think the error is occurring because it is trying to read the 2nd line as a json string, but because you have line breaks within the curly brackets the 2nd line is not a valid json string on its own.\r\n",
"This is the right answer, we read a file as a jsonl, though we (wrongly) call it a .json file. We'll attempt to make the parsing logic a bit more robust in the future, and log an appropriate warning instead of crashing. Fyi @stevehuang52 ",
"> And directly fixing the errors with the JSON file directly, leads to this error:\r\n> \"-packages/nemo/collections/common/data/lhotse/nemo_adapters.py\", line 84, in iter\r\n> text=data[self.text_field],\r\n> KeyError: 'answer'\r\n\r\nto fix this, please modify the input lines file to add 'answer' field. \r\n```\r\n{\r\n \"audio_filepath\": \"PathRemovedDueToPersonalName\", \r\n \"duration\": 30.0, \r\n \"taskname\": \"asr\", \r\n \"source_lang\": \"en\", \r\n \"target_lang\": \"en\", \r\n \"pnc\": 'yes', \r\n \"answer\": 'na',\r\n}\r\n```\r\nalso, we recently updated `.transcribe` signature, so if you are using main branch \r\n\r\n> transcript = canary_model.transcribe(paths2audio_files=\"/home/pjstimac/NvidiaCanaryTest/transcribe_manifest.json\", batch_size=16)\r\n\r\nshould be updated to \r\n```transcript = canary_model.transcribe(audio=\"/home/pjstimac/NvidiaCanaryTest/transcribe_manifest.json\", batch_size=16)```\r\n\r\n",
"Hello all, \r\n\r\nThanks for your time & replies; I can't express how much I genuinely appreciate it!! :) \r\n\r\nSo after I made this post, I went home and tried all night on my personal time; still no luck unfortunately. \r\n\r\nI found that \"answer: 'na' \" key via the Hugging face documentation and included that. \r\n\r\n@krishnacpuvvada, @LL-AI-dev Running that format JSON, with the updated transcript variable, still prints out the json error: \r\n\"json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)\"\r\n\r\nI go off the error, and correct that with the JSON, and still get this error (and ran into this last night as well)\r\n\" assert isinstance(cut, MonoCut), \"Expected MonoCut.\"\r\nAssertionError: Expected MonoCut.\"\r\n\r\nTried both a JSON & JSONL file, same result :\r\n\" assert isinstance(cut, MonoCut), \"Expected MonoCut.\"\r\nAssertionError: Expected MonoCut.\"\r\n\r\nMy updated code with that updated variable: \r\n\"\" # Load Canary model \r\nfrom nemo.collections.asr.models import EncDecMultiTaskModel\r\ncanary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')\r\n\r\n\r\n\r\n\r\ntranscript = canary_model.transcribe(audio=\"/home/NameRemoved/NvidiaCanaryTest/transcribed_manifest.jsonl\", batch_size=16) \"\"\r\n\r\nLast night, I thought, let's try the tutorial Google Colab Notebooks right from the Nvidia website for any NeMo model... \r\nNot even that could run all the way through right on google colab. \r\n\r\n\"https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb\" \r\n\r\nIt was error out at this variable \"paths2audio_files=files\" \r\n\r\nThanks for everyone's time; much appreciated!!!! :D\r\n",
"The notebook above is not the way to do inference for Canary, it's for beam search with CTC models, and is a deprecated notebook in general. ",
"@titu1994 Well that would explain it! After much trial and error, I finally got it running in a notebook & python shell. with 3 lines of code, skipping the JSON/JSONL manifest entirely:\r\n\"\"\r\nimport nemo.collections.asr as nemo_asr\r\nnemoasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(\"nvidia/canary-1b\")\r\nnemoasr_model.transcribe(['AudioClipDirectly.wav'])\r\n\"\"\r\n\r\nOnce that was working, I just wrote a Python loop to loop through an audio directory and output the transcription results to a JSON for my viewing/model comparison setup app.\r\n\r\nThanks for all the attempts at help though, @titu1994 & @krishnacpuvvada. I genuinely appreciate it!! Wish the documentation was better so I would not have had to bother you guys, oh well. \r\n\r\nbe well!!! :D ",
"Glad it worked. We'll iron out these issues in the pre-release. It shouldn't be so difficult to do inference",
"@titu1994 That would be so great to see!! I've been playing around with Whisper for the past few months, and this was unexpectedly annoying to get going in comparison. \r\nthe code is simple enough looking at it; the documentation though, is a different story. But it's going now on my end, yay!! :) Thanks again for the input and help @krishnacpuvvada & @titu1994 , much appreciated!! ",
"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.",
"> import nemo.collections.asr as nemo_asr\r\n> nemoasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(\"nvidia/canary-1b\")\r\n> nemoasr_model.transcribe(['AudioClipDirectly.wav'])\r\n\r\nI tried running this script, but when I transcribe the audio, the transcription initially for the first two lines is fine and after that the same sentence keeps repeating again and again. The entire audio is not transcribed too. Any help on this would be appreciated. Thank you.",
"@Suma-Rajashankar How long are the audio clips you are inputting, and at what sample rate? I was working with Whisper previously, so I already had all my audio cut into 30-second chunks, capped at 16kHz. I, too, have experienced repetition with specific clips (And with whispers, too). But something seems off it it is every single time. I used to get that with Whisper trying to feed it audio clips longer than 30 seconds each, it would constantly repeat after the first or second sentence. But newer updates to Whisper seemed to have improved that; Have not tried the same with Canary myself. ",
"> @P15V, thank you for your reply. My audio clips are between 30 - 60 mins and are all sampled at 16kHz. I had no issues with Whisper, but like you said I will chunk my audio into 30 seconds and work on this using the Canary model. Thanks once again.\r\n",
"Hi @Suma-Rajashankar @P15V , we have a [script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_multitask/speech_to_text_aed_chunked_infer.py) to automatically chunk the long audios and perform inference on each chunk. Please feel free to try it and let us know if there's any issue.",
"Thanks @stevehuang52 for your reply. Looking into this now. Will keep you posted. Appreciate your help.",
"Hi @stevehuang52, I am unable to import 'FrameBatchMultiTaskAED' from 'nemo.collections.asr.parts.utils.streaming_utils'. I have installed nemo_toolkit==1.23.0. Is there some issue with this version?",
"@Suma-Rajashankar sorry I forgot to mention that you'll need to install the current `main` branch of NeMo, not `1.23.0`",
"@stevehuang52, thanks very much. Will work on this and keep you posted. Appreciate your help."
] | 2024-03-13T19:49:42 | 2024-05-09T13:15:44 | 2024-04-22T01:45:25 | NONE | {
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} | null | null | null | Hello all, hope all is well with whoever is reading this!!
I'm very excitedly trying to run a simple inference with this new Canary-1b model that advertises a lower WER than the large whisper model.
Unfortunately, not one thing I am trying is working so far.
I'm going off the Nvidia website directions:
https://nvidia.github.io/NeMo/blogs/2024/2024-02-canary/#transcribing-with-canary
My code bellow:
# Load Canary model
from nemo.collections.asr.models import EncDecMultiTaskModel
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
# Finally transcribe
transcript = canary_model.transcribe(paths2audio_files="/home/pjstimac/NvidiaCanaryTest/transcribe_manifest.json", batch_size=16)
My json file:
"{
"audio_filepath": "PathRemovedDueToPersonalName",
"duration": 30.0,
"taskname": "asr",
"source_lang": "en",
"target_lang": "en",
"pnc": 'yes',
}
"
using this json in the expected schema going by the Nvidia page, I get this error :
"json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)"
Removing the JSON indents and trailing lines, I get this error:
"json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)"
A clear and concise description of what the bug is.
"json.decoder.JSONDecodeError: Expecting value: line 1 column 178 (char 177)"
And directly fixing the errors with the JSON file directly, leads to this error:
"-packages/nemo/collections/common/data/lhotse/nemo_adapters.py", line 84, in __iter__
text=data[self.text_field],
KeyError: 'answer'
Transcribing: 0it [00:00, ?it/s]"
It seems rather wonky, seeing as I'm trying to perform ASR but getting an error that a 'answer' key is expecting.
**Steps/Code to reproduce bug**
Just trying to run a simple inference with a WAV file.
**Expected behavior**
getting a transcription output of the WAV file.
**Environment overview (please complete the following information)**
- Environment location: local/Conda ENV
- Method of NeMo install: pip install following Nvidia's direction from the site.
**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 Pytorch 11.6
- Python version Python 3.10
-GPU: RTX2080TI X4
-CPU: 11th gen I9
Thanks all for reading, and any input. Appreciate the help!! :) | {
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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.",
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] | 2024-03-13T17:58:03 | 2024-04-22T01:45:27 | 2024-04-22T01:45:27 | NONE | {
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} | null | null | null | Hi! I am trying to follow the NeMo tutorial titled "ASR_with_Nemo"
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_with_NeMo.ipynb
I am working on a conda environment following the same setting for the dependencies as in the NeMo GitHub repository. I prepared the manifest files for the an4 dataset following the steps in the tutorial. However, when I tried to start training the model using Pytorch lightning, I faced the following issue:
[NeMo I 2024-03-13 11:10:44 audio_to_text_dataset:45] Model level config does not contain `sample_rate`, please explicitly provide `sample_rate` to the dataloaders.
[NeMo I 2024-03-13 11:10:44 audio_to_text_dataset:45] Model level config does not contain `labels`, please explicitly provide `labels` to the dataloaders.
[NeMo I 2024-03-13 11:10:44 collections:196] Dataset loaded with 948 files totalling 0.71 hours
[NeMo I 2024-03-13 11:10:44 collections:197] 0 files were filtered totalling 0.00 hours
[NeMo I 2024-03-13 11:10:44 audio_to_text_dataset:45] Model level config does not contain `sample_rate`, please explicitly provide `sample_rate` to the dataloaders.
[NeMo I 2024-03-13 11:10:44 audio_to_text_dataset:45] Model level config does not contain `labels`, please explicitly provide `labels` to the dataloaders.
[NeMo I 2024-03-13 11:10:44 collections:196] Dataset loaded with 130 files totalling 0.10 hours
[NeMo I 2024-03-13 11:10:44 collections:197] 0 files were filtered totalling 0.00 hours
[NeMo I 2024-03-13 11:10:44 features:289] PADDING: 16
However, as you can see below, when I checked the model.cfg, it looks like it has all the requirements (e.g., sample_rate) there.
first_asr_model.cfg
{'train_ds': {'manifest_filepath': './an4/train_manifest.json', 'sample_rate': 16000, 'labels': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"], 'batch_size': 32, 'trim_silence': True, 'max_duration': 16.7, 'shuffle': True, 'num_workers': 8, 'pin_memory': True, 'is_tarred': False, 'tarred_audio_filepaths': None, 'shuffle_n': 2048, 'bucketing_strategy': 'synced_randomized', 'bucketing_batch_size': None}, 'validation_ds': {'manifest_filepath': './an4/test_manifest.json', 'sample_rate': 16000, 'labels': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"], 'batch_size': 32, 'shuffle': False, 'num_workers': 8, 'pin_memory': True}, 'preprocessor': {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'normalize': 'per_feature', 'window_size': 0.02, 'sample_rate': 16000, 'window_stride': 0.01, 'window': 'hann', 'features': 64, 'n_fft': 512, 'frame_splicing': 1, 'dither': 1e-05, 'stft_conv': False}, 'spec_augment': {'_target_': 'nemo.collections.asr.modules.SpectrogramAugmentation', 'rect_freq': 50, 'rect_masks': 5, 'rect_time': 120}, 'encoder': {'_target_': 'nemo.collections.asr.modules.ConvASREncoder', 'feat_in': 64, 'activation': 'relu', 'conv_mask': True, 'jasper': [{'filters': 128, 'repeat': 1, 'kernel': [11], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': True, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 256, 'repeat': 1, 'kernel': [13], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': True, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 256, 'repeat': 1, 'kernel': [15], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': True, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 256, 'repeat': 1, 'kernel': [17], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': True, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 256, 'repeat': 1, 'kernel': [19], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': True, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 256, 'repeat': 1, 'kernel': [21], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': False, 'separable': True, 'se': True, 'se_context_size': -1}, {'filters': 1024, 'repeat': 1, 'kernel': [1], 'stride': [1], 'dilation': [1], 'dropout': 0.0, 'residual': False, 'separable': True, 'se': True, 'se_context_size': -1}]}, 'decoder': {'_target_': 'nemo.collections.asr.modules.ConvASRDecoder', 'feat_in': 1024, 'num_classes': 28, 'vocabulary': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"]}, 'optim': {'name': 'novograd', 'lr': 0.01, 'betas': [0.8, 0.5], 'weight_decay': 0.001, 'sched': {'name': 'CosineAnnealing', 'monitor': 'val_loss', 'reduce_on_plateau': False, 'warmup_steps': None, 'warmup_ratio': None, 'min_lr': 0.0, 'last_epoch': -1}}, 'target': 'nemo.collections.asr.models.ctc_models.EncDecCTCModel', 'nemo_version': '1.21.0', 'decoding': {'strategy': 'greedy', 'preserve_alignments': None, 'compute_timestamps': None, 'word_seperator': ' ', 'ctc_timestamp_type': 'all', 'batch_dim_index': 0, 'greedy': {'preserve_alignments': False, 'compute_timestamps': False, 'preserve_frame_confidence': False, 'confidence_measure_cfg': {'name': 'entropy', 'entropy_type': 'tsallis', 'alpha': 0.33, 'entropy_norm': 'exp', 'temperature': 'DEPRECATED'}, 'confidence_method_cfg': 'DEPRECATED'}, 'beam': {'beam_size': 4, 'search_type': 'default', 'preserve_alignments': False, 'compute_timestamps': False, 'return_best_hypothesis': True, 'beam_alpha': 1.0, 'beam_beta': 0.0, 'kenlm_path': None, 'flashlight_cfg': {'lexicon_path': None, 'boost_path': None, 'beam_size_token': 16, 'beam_threshold': 20.0, 'unk_weight': -inf, 'sil_weight': 0.0}, 'pyctcdecode_cfg': {'beam_prune_logp': -10.0, 'token_min_logp': -5.0, 'prune_history': False, 'hotwords': None, 'hotword_weight': 10.0}}, 'confidence_cfg': {'preserve_frame_confidence': False, 'preserve_token_confidence': False, 'preserve_word_confidence': False, 'exclude_blank': True, 'aggregation': 'min', 'measure_cfg': {'name': 'entropy', 'entropy_type': 'tsallis', 'alpha': 0.33, 'entropy_norm': 'exp', 'temperature': 'DEPRECATED'}, 'method_cfg': 'DEPRECATED'}, 'temperature': 1.0}}
Could you please help?
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"This is reverting the mcore dataset integration. I don't think we want to merge this to main.",
"Closing as we'll be fixing MCore instead."
] | 2024-03-13T17:28:45 | 2024-03-14T23:44:57 | 2024-03-14T23:44:56 | CONTRIBUTOR | {
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} | # What does this PR do ?
Avoids training throughput regression from using `BlendedMegatronDatasetBuilder` by reverting back to using NeMo's `build_train_valid_test_datasets`.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
1. Mcore dataset updates.
2. Support the option of not mmap-ing .bin files and instead using file pointers instead to support very large datasets.
**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
```
# Jenkins CI
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-13T01:57:56 | 2024-04-20T01:44:23 | 2024-04-20T01:44:22 | NONE | {
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} | null | null | null | **Describe the bug**
i am trying to run the GPT2 PEFT finetuning on GPT2 models. I try to convert my TP=1 .nemo file to TP=2 .nemo file using megatron_change_num_partitions. However, when I try to load the checkpoints from the .nemo file i get the following error.
A clear and concise description of what the bug is.
`Traceback (most recent call last):
File "/scratch/avi2011/nemo_proj/test.py", line 137, in <module>
model = MegatronGPTSFTModel.restore_from(config.model.restore_from_path, model_cfg, trainer=trainer)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from
return super().restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py", line 442, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 700, in restore_from
loaded_params = super().load_config_and_state_dict(
File "/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py", line 169, in load_config_and_state_dict
state_dict = self._load_state_dict_from_disk(model_weights, map_location=map_location)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 663, in _load_state_dict_from_disk
raise ValueError(f'Expected {model_weights} to be a file or directory.')
ValueError: Expected /state/partition1/job-44012513/tmpl8ypyvcd/model_weights.ckpt to be a file or directory.`
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} | # What does this PR do ?
Adds a command-line flag to megatron_ckpt_to_nemo.py which enables writing output as loose files instead of a .nemo archive.
**Collection**: nlp
# Changelog
- Adds `--no_pack_nemo_file` argument to megatron_ckpt_to_nemo.py
- If passed, `pack_nemo_file` is set to false in the NLPSaveRestoreConnector used to write the model to disk
- The path given to `--nemo_file_path` is extended before passing to `save_to` so that the model will be written under the given directory
# Usage
```bash
python examples/nlp/language_modeling/megatron_ckpt_to_nemo.py \
--model_type=gpt \
--checkpoint_folder=/path/to/checkpoints \
...
--nemo_file_path=/path/to/output_directory
--no_pack_nemo_file
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
- [ ] Documentation
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"@aditya-malte @yaoyu-33 I see the tests are passed here, is there still issues with imports?",
"jenkins"
] | 2024-03-12T23:13:45 | 2024-03-13T22:43:59 | 2024-03-13T22:43:59 | CONTRIBUTOR | {
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} | Hi,
Currently, the MegatronSBERT model cannot run inference. Essentially, a user may not be able to simply load a trained .nemo checkpoint and run inference (forward()) function on it.
This patch adds a try/except block to handle cases where training data is not specified
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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"@titu1994 the change is tested on my local, please fast merge if possible, hope it goes into the nemo2.0 branch"
] | 2024-03-12T19:20:16 | 2024-03-12T19:31:06 | 2024-03-12T19:31:06 | COLLABORATOR | {
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} | # What does this PR do ?
Fix for relative file paths when presort_manifest==True
**Collection**: [asr]
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https://api.github.com/repos/NVIDIA/NeMo/issues/8638 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8638/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8638/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8638/events | https://github.com/NVIDIA/NeMo/pull/8638 | 2,182,257,890 | PR_kwDOC_bI7s5paY_b | 8,638 | Fix Gemma activation function | {
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} | # What does this PR do ?
Gemma uses `gelu` approx version from openai
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 # (issue)
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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."
] | 2024-03-12T07:42:16 | 2024-04-19T01:44:42 | 2024-04-19T01:44:41 | NONE | {
"total": 0,
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} | null | null | null | **Describe the bug**
When running the tutorial to train a FastPitch model with an adapter (https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Adapter_Finetuning.ipynb), I get the error `EOFError: Caught EOFError in DataLoader worker process 0` when starting the step that does the model training. Following a similar issue I was able to avoid the error by setting the number of dataloader workers to 0 which is not ideal for training speed.
**Steps/Code to reproduce bug**
Running the tutorial as is should produce the error
**Expected behavior**
Training should start regardless of the number of workers
**Environment overview (please complete the following information)**
- Environment location: EC2
- Method of NeMo install: version 1.23.0 pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - ubuntu 20
- PyTorch version 2.2.1
- Python version 3.10.13
**Additional context**
full traceback below
```
| Name | Type | Params
--------------------------------------------------------------------------
0 | mel_loss_fn | MelLoss | 0
1 | pitch_loss_fn | PitchLoss | 0
2 | duration_loss_fn | DurationLoss | 0
3 | energy_loss_fn | EnergyLoss | 0
4 | aligner | AlignmentEncoderAdapter | 1.6 M
5 | forward_sum_loss_fn | ForwardSumLoss | 0
6 | bin_loss_fn | BinLoss | 0
7 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
8 | fastpitch | FastPitchModule | 58.0 M
--------------------------------------------------------------------------
4.5 M Trainable params
53.5 M Non-trainable params
58.0 M Total params
231.999 Total estimated model params size (MB)
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
Epoch 0: 0%| | 0/37 [00:00<?, ?it/s]terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Exception raised from c10_cuda_check_implementation at /opt/conda/conda-bld/pytorch_1708025831440/work/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f9049bd0d87 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f9049b8175f in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9049ca28a8 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::cuda::ExchangeDevice(int) + 0x39 (0x7f9049ca2c49 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0xf9dedc (0x7f904ac72edc in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: <unknown function> + 0x541280 (0x7f90a6c33280 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x649bf (0x7f9049bb59bf in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x21b (0x7f9049baec8b in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f9049baee39 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x803c08 (0x7f90a6ef5c08 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7f90a6ef5f86 in /opt/conda/envs/nemo/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
Error executing job with overrides: ['+init_from_nemo_model=/home/ubuntu/Models/tts_fastpitch_speaker_adapter_ipa_vtrainable_v1.0/tts_en_fastpitch_multispeaker_adapter_pretrain_ipa.nemo', 'train_dataset=/home/ubuntu/Data/TTS_1hr_1/output/manifests/runInstance_train_manifest.json', 'validation_datasets=/home/ubuntu/Data/TTS_1hr_1/output/manifests/runInstance_val_manifest.json', "sup_data_types=['align_prior_matrix', 'pitch', 'energy']", 'sup_data_path=/home/ubuntu/ModelTraining/tts/data-1hr-1/NeMoTTS_sup_data', 'pitch_mean=166.378662109375', 'pitch_std=61.548152923583984', 'model.speaker_encoder.precomputed_embedding_dim=384', '~model.speaker_encoder.lookup_module', '~model.speaker_encoder.gst_module', 'model.train_ds.dataloader_params.batch_size=8', 'model.validation_ds.dataloader_params.batch_size=8', '+model.text_tokenizer.add_blank_at=True', 'model.optim.name=adam', 'model.optim.lr=0.001', 'model.optim.sched.warmup_steps=10', '+model.optim.sched.min_lr=0.0001', 'exp_manager.exp_dir=/home/ubuntu/ModelTraining/tts/data-1hr-1/NeMoTTS_logs/unfreeze_aligner', '+exp_manager.create_wandb_logger=False', '+exp_manager.wandb_logger_kwargs.name=tutorial-FastPitch-finetune-adaptation', '+exp_manager.wandb_logger_kwargs.project=NeMo', '+exp_manager.checkpoint_callback_params.save_top_k=-1', 'trainer.max_epochs=200', 'trainer.check_val_every_n_epoch=10', 'trainer.log_every_n_steps=1', 'trainer.devices=1', 'trainer.strategy=ddp', 'trainer.precision=32', 'model.train_ds.dataloader_params.num_workers=15', 'model.validation_ds.dataloader_params.num_workers=15', 'model.unfreeze_aligner=true']
Traceback (most recent call last):
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/opt/conda/envs/nemo/lib/python3.10/queue.py", line 180, in get
self.not_empty.wait(remaining)
File "/opt/conda/envs/nemo/lib/python3.10/threading.py", line 324, in wait
gotit = waiter.acquire(True, timeout)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 293316) is killed by signal: Aborted.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/ModelTraining/NeMo/examples/tts/fastpitch_finetune_adapters.py", line 139, in main
trainer.fit(model)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
return function(*args, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 987, in _run
results = self._run_stage()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1033, in _run_stage
self.fit_loop.run()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
self.advance()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
self.epoch_loop.run(self._data_fetcher)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
self.advance(data_fetcher)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 212, in advance
batch, _, __ = next(data_fetcher)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 133, in __next__
batch = super().__next__()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 60, in __next__
batch = next(self.iterator)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 341, in __next__
out = next(self._iterator)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 78, in __next__
out[i] = next(self.iterators[i])
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1285, in _get_data
success, data = self._try_get_data()
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 293316) exited unexpectedly
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8636 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8636/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8636/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8636/events | https://github.com/NVIDIA/NeMo/pull/8636 | 2,180,756,181 | PR_kwDOC_bI7s5pVM36 | 8,636 | Simplify docs index page | {
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} | # What does this PR do ?
Remove `.. include:: starthere/intro.rst` from the docs index page and replace it with some text about the docs, reduce the depth of the toctree.
**Collection**: docs
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8635 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8635/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8635/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8635/events | https://github.com/NVIDIA/NeMo/issues/8635 | 2,180,502,084 | I_kwDOC_bI7s6B99JE | 8,635 | support for ndarray and tensor decoding for RNNT based ASR models | {
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"@titu1994 ",
"The docstring isn't updated, but yes main branch supports rnnt as of right now - https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/rnnt_models.py#L274",
"> The docstring isn't updated, but yes main branch supports rnnt as of right now - https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/rnnt_models.py#L274\r\n\r\n\r\nThanks for the quick reply, but if we check the input arguments right now,\r\nhttps://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/rnnt_models.py#L237\r\n\r\n```python\r\n @torch.no_grad()\r\n def transcribe(\r\n self,\r\n audio: List[str],\r\n batch_size: int = 4,\r\n return_hypotheses: bool = False,\r\n partial_hypothesis: Optional[List['Hypothesis']] = None,\r\n num_workers: int = 0,\r\n channel_selector: Optional[ChannelSelectorType] = None,\r\n augmentor: DictConfig = None,\r\n verbose: bool = True,\r\n override_config: Optional[TranscribeConfig] = None,\r\n ) -> TranscriptionReturnType:\r\n```\r\n\r\n\r\nthe `audio` is only limited to a list of strings, in which this case a path to the audio files.",
"That's a type annotation, it has no effect on the actual execution of code. That type annotation is wrong. But the code is going to execute RNNT with tensor just fine cause the underling model supports it ",
"thanks for the quick reply again, maybe I should try again!",
"One last question, is the Code Refactoring for ASR applied on 1.23.0 ??",
"No, it will be the next major version release ",
"any estimation of release date yet??",
"It's already in the main branch, you can try it out there. However, the next release will be probably at the end of the month. But it will probably be a prerelease ",
"thx, I tried with main branch and it works smooth like butter!\r\nkeep up the great work!!!"
] | 2024-03-12T00:41:27 | 2024-03-14T08:58:56 | 2024-03-14T08:58:56 | NONE | {
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} | null | null | null | At first by observing recent feature updates( https://github.com/NVIDIA/NeMo/pull/8167#issue-2079986486 ) I thought RNNT based models could also receive arrays as inputs.
The recent update in source code for CTC model seems to be updated to accept inputs as ndarrays and tensors (https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py)
```python
def transcribe(
self,
audio: Union[str, List[str], torch.Tensor, np.ndarray],
batch_size: int = 4,
return_hypotheses: bool = False,
num_workers: int = 0,
channel_selector: Optional[ChannelSelectorType] = None,
augmentor: DictConfig = None,
verbose: bool = True,
override_config: Optional[TranscribeConfig] = None,
) -> TranscriptionReturnType:
```
However, I found RNNT based models are unable to recieve ndarrays or torch tensors
```python
def transcribe(
self,
audio: List[str],
batch_size: int = 4,
return_hypotheses: bool = False,
partial_hypothesis: Optional[List['Hypothesis']] = None,
num_workers: int = 0,
channel_selector: Optional[ChannelSelectorType] = None,
augmentor: DictConfig = None,
verbose: bool = True,
override_config: Optional[TranscribeConfig] = None,
) -> TranscriptionReturnType:
```
I want to know if the direct transcription of arrays and tensors for RNNT based models will be available in the next release. | {
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"**EVAL RESULTS ON IMAGE INFERENCE FOR ~10% of LLaVA-Bench-In-The-Wild**\r\n\r\n- **Assistant 1:** NeVA (No Video)\r\n- **Assistant 2:** V-NeVA (start from NeVA image ckpt, train on video represented as 24 frames)\r\n\r\n```\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 19418.07it/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 25890.77it/s]\r\n[Context]\r\nAn aerial view of Diamond Head in the Hawaiian Islands.\r\n\r\n[Question]\r\nWhat is the name of this famous sight in the photo?\r\n\r\n[Assistant 1]\r\nThe name of the famous sight in the photo is the mountain range with the large hill in the middle, which is part of the island. This mountain range and hill are located near the ocean, and the photo provides an aerial view of the landscape. The presence of the mountain range and the large hill contribute to the unique and picturesque scenery of the island.\r\n\r\n[End of Assistant 1]\r\n\r\n[Assistant 2]\r\nThe famous sight in the photo is the Haleakalā Crater on the island of Maui in Hawaii. Haleakalā is a dormant shield volcano that forms more than 75% of the island's surface area and is one of the largest volcanoes on Earth. It is a popular destination for tourists and is known for its stunning views and unique geological features.\r\n\r\n[End of Assistant 2]\r\n\r\n[System]\r\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \r\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\r\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\r\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\r\n\r\n\r\n1 10\r\n\r\nAssistant 1's response is not helpful or accurate as it does not provide a specific name for the sight in the photo. The response is vague and does not provide any relevant information about the location or significance of the sight.\r\n\r\nAssistant 2's response, on the other hand, is highly helpful, relevant, accurate, and detailed. The response correctly identifies the sight in the photo as the Haleakalā Crater on the island of Maui in Hawaii. The response also provides additional context and information about the sight, including its geological features and popularity among tourists. Overall, Assistant 2's response is a model of clarity and precision.\r\n1\r\n[Context]\r\nA photo of four mangosteens on a wooden table. Three of them are uncut, while one is cut open.\r\n\r\n[Question]\r\nWhat type of fruit is this?\r\n\r\n[Assistant 1]\r\nThe image features a group of round, dark-colored fruits, possibly berries, with white accents, lying on a wooden surface. These fruits are cut open, revealing their delicious and juicy interiors. The arrangement of the fruits in the image suggests that they are being presented in a visually appealing manner, possibly for consumption or display purposes.\r\n\r\n[End of Assistant 1]\r\n\r\n[Assistant 2]\r\nThe fruit in the image is a dragon fruit, also known as pitaya. It's characterized by its vibrant pink or yellow skin with green scales and a white or red flesh with small black seeds. Dragon fruit is a tropical fruit that is popular for its unique appearance and sweet taste.\r\n\r\n[End of Assistant 2]\r\n\r\n[System]\r\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \r\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\r\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\r\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\r\n\r\n\r\n4 7\r\n\r\nAssistant 1's response is not accurate or helpful, as they were unable to identify the fruit in the image. Their description of the fruit being a \"round, dark-colored fruit with white accents\" is too vague and does not provide any specific information about the fruit.\r\n\r\nAssistant 2, on the other hand, correctly identified the fruit in the image as a dragon fruit. Their description is accurate and provides relevant information about the fruit's appearance and taste. However, they could have added more details about the fruit, such as its nutritional benefits or how it is typically consumed.\r\n\r\nOverall, Assistant 2's response is more helpful and accurate than Assistant 1's, and I would rate their performance higher. However, both assistants could benefit from providing more detailed and informative responses to the user's question.\r\n2\r\n[Context]\r\nA creative painting of a dog dressed as the famous Mona Lisa.\r\n\r\n[Question]\r\nDescribe this photo in detail.\r\n\r\n[Assistant 1]\r\nThe image features a dog dressed up to look like a person, specifically like art supremo dog, complete with a paint palette and brush. The dog is positioned in the center of the frame, with its body extended to fill the entire image. The dog's head is turned to the side, as if it's looking at something off-camera. The backdrop of the image is a beautiful landscape, which adds to the artistic composition. The dog's outfit and pose create a sense of whimsy and fun, making the image an amusing and creative portrayal of a dog in the role of an artist.\r\n\r\n[End of Assistant 1]\r\n\r\n[Assistant 2]\r\nThe image is a digital manipulation that combines elements of Leonardo da Vinci's famous painting 'Mona Lisa' with the features of a dog. The central figure in the image is a dog with a human-like posture, wearing a dark garment that drapes over one shoulder, similar to the attire of the figure in the original 'Mona Lisa' painting. The dog's fur is rendered in shades of brown, and it has a black nose and dark eyes. The background of the image is a landscape with a body of water, mountains, and a cloudy sky, which is consistent with the background of the original painting. The overall style of the image is a blend of classical art and modern digital art, with a focus on humor and surrealism.\r\n\r\n[End of Assistant 2]\r\n\r\n[System]\r\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \r\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\r\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\r\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\r\n\r\n\r\n9, 9\r\n\r\nAssistant 1 and Assistant 2 both provided detailed and relevant descriptions of the image. They both accurately captured the central elements of the image, including the dog dressed as the Mona Lisa and the landscape background. They also both noted the whimsical and creative nature of the image.\r\n\r\nAssistant 1's response was slightly more concise, while Assistant 2's response provided a more detailed description of the dog's appearance and the background. Both responses were equally accurate and relevant, and they both provided a high level of detail.\r\n\r\nOverall, both assistants performed very well in this task. They were able to accurately and thoroughly describe the image, capturing its key features and themes. It is difficult to choose a clear winner between the two responses, as they were both excellent. Therefore, I have given them both a high score of 9.\r\nerror 9, 9\r\n\r\nAssistant 1 and Assistant 2 both provided detailed and relevant descriptions of the image. They both accurately captured the central elements of the image, including the dog dressed as the Mona Lisa and the landscape background. They also both noted the whimsical and creative nature of the image.\r\n\r\nAssistant 1's response was slightly more concise, while Assistant 2's response provided a more detailed description of the dog's appearance and the background. Both responses were equally accurate and relevant, and they both provided a high level of detail.\r\n\r\nOverall, both assistants performed very well in this task. They were able to accurately and thoroughly describe the image, capturing its key features and themes. It is difficult to choose a clear winner between the two responses, as they were both excellent. Therefore, I have given them both a high score of 9.\r\n3\r\n[Context]\r\nA creative meme: Elon Musk holding a dog. It mimicks one of the most memorable scenes from The Lion King, where Rafiki stands on a mountaintop and holds up Simba to all the other animals.\r\n\r\n[Question]\r\nWhat is the name of the man in the photo?\r\n\r\n[Assistant 1]\r\nThe image features a man dressed up as a character from the popular animated movie 'Simpsons.' He is holding a dog in his arms, and the dog appears to be a real dog, not an animatronics character from the movie. The man is wearing a costume that resembles a monkey, and he is standing in front of a backdrop that features a landscape. The scene is set in a location that resembles the fictional town of Springfield, where the Simpsons family resides. The man's costume and the presence of the dog create a fun and entertaining atmosphere, showcasing the creativity and detail of the man's outfit.\r\n\r\n[End of Assistant 1]\r\n\r\n[Assistant 2]\r\nThe man in the photo is Elon Musk.\r\n\r\n[End of Assistant 2]\r\n\r\n[System]\r\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \r\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\r\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\r\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\r\n\r\n\r\n2 10\r\n\r\nAssistant 1's response is detailed and provides a good description of the image, but it does not answer the question directly. The response focuses on the setting and the costumes, but it does not identify the man in the image. On the other hand, Assistant 2 correctly identifies the man in the image as Elon Musk, which directly answers the user's question. However, Assistant 2 does not provide any additional information about the image. Overall, while both responses lack some detail, Assistant 2's response is more helpful and accurate in answering the user's question.\r\n4\r\n[Context]\r\nA creative meme: a dog lying on the cyan wooden floor. The top of the meme reads: \"MONDAY.\" The bottom of the meme reads: \"JUST...MONDAY.\"\r\n\r\n[Question]\r\nPlease explain the meme in detail.\r\n\r\n[Assistant 1]\r\nIn the image, a small brown dog is lying on a wooden floor, dressed up to resemble a person, specifically like art supremo dog. The dog is positioned in the center of the frame, with its body extended to fill the entire image. The dog's head is turned to the side, as if it's looking at something off-camera. The backdrop of the image features a beautiful landscape, which adds to the artistic composition. The dog's outfit and pose create a sense of whimsy and fun, making the image an amusing and creative portrayal of a dog in the role of an artist. The phrase 'Just Monday...' is written above the dog, emphasizing the playful nature of the scene.\r\n\r\n[End of Assistant 1]\r\n\r\n[Assistant 2]\r\nThe meme features an image of a dog lying on a wooden floor with a caption that reads 'MONDAY. JUST...MONDAY.' The dog appears to be resting or possibly sleeping, and its body language suggests a sense of relaxation or even exhaustion. The text is a humorous take on the common sentiment that Mondays can be difficult or tiring, often referred to as 'Monday blues' or 'Mondayitis.' The meme plays on the idea that Mondays are the start of the work or school week, and people often feel a bit sluggish or unmotivated after the weekend. The dog's relaxed posture is used to visually represent this feeling, and the repetition of the word 'MONDAY' with the ellipsis and the word 'just' adds emphasis to the sentiment.\r\n\r\n[End of Assistant 2]\r\n\r\n[System]\r\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \r\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\r\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\r\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\r\n\r\n\r\n8, 9\r\n\r\nAssistant 1 provided a detailed description of the image, but missed the mark on explaining the meme's context and meaning. They accurately described the visual elements, but did not connect them to the underlying sentiment of the meme.\r\n\r\nAssistant 2, on the other hand, provided a more comprehensive explanation of the meme's meaning and context. They accurately described the dog's posture and connected it to the common sentiment about Mondays. However, they could have provided more detail in describing the visual elements of the image.\r\n\r\nOverall, both assistants demonstrated strengths and weaknesses in their responses. Assistant 2's response was more helpful and relevant to the user's question, but Assistant 1 provided more detail in describing the image.\r\nerror 8, 9\r\n\r\nAssistant 1 provided a detailed description of the image, but missed the mark on explaining the meme's context and meaning. They accurately described the visual elements, but did not connect them to the underlying sentiment of the meme.\r\n\r\nAssistant 2, on the other hand, provided a more comprehensive explanation of the meme's meaning and context. They accurately described the dog's posture and connected it to the common sentiment about Mondays. However, they could have provided more detail in describing the visual elements of the image.\r\n\r\nOverall, both assistants demonstrated strengths and weaknesses in their responses. Assistant 2's response was more helpful and relevant to the user's question, but Assistant 1 provided more detail in describing the image.\r\n\r\n=================================\r\n assistant_1 assistant_2\r\nall 48.0 68.0\r\nllava_bench_conv 23.3 73.3\r\nllava_bench_detail 85.0 60.0\r\n=================================\r\n\r\n```\r\n\r\n**LLaVA Performance As Reported In Paper**\r\n\r\n\r\n\r\n\r\n",
"EVAL RESULTS ON IMAGE INFERENCE FOR 100% of LLaVA-Bench-In-The-Wild\r\n\r\n- Assistant 1: NeVA (No Video)\r\n- Assistant 2: V-NeVA (start from NeVA image ckpt, train on video represented as 24 frames)\r\n\r\nRun 1: \r\n```\r\n=================================\r\nall 65.9 77.4\r\nllava_bench_complex 71.6 82.2\r\nllava_bench_conv 50.0 69.4\r\nllava_bench_detail 73.3 77.3\r\n=================================\r\n```\r\n\r\nRun 2:\r\n```\r\n=================================\r\nall 65.4 77.0\r\nllava_bench_complex 71.6 82.2\r\nllava_bench_conv 48.8 68.8\r\nllava_bench_detail 72.7 76.7\r\n=================================\r\n```\r\n\r\nRun 3:\r\n```\r\n=================================\r\nall 65.9 77.0\r\nllava_bench_complex 71.6 82.2\r\nllava_bench_conv 50.6 68.8\r\nllava_bench_detail 72.7 76.7\r\n=================================\r\n```\r\n\r\nAggregate Results:\r\n\r\n```\r\n=================================\r\nall 65.56+/-0.29 77.13+/-0.23\r\nllava_bench_complex 71.60+/-0.00 82.2+/-0.00\r\nllava_bench_conv 49.8+/-0.92 69.00+/-0.35\r\nllava_bench_detail 72.9+/-0.35 76.9+/-0.34\r\n=================================\r\n```\r\n\r\n**LLaVA Results:**\r\n\r\n\r\n**VideoLLaVA Results:**\r\n<img width=\"1220\" alt=\"Screen Shot 2024-03-14 at 10 38 11 PM\" src=\"https://github.com/NVIDIA/NeMo/assets/30813477/5687ab3a-aa7d-4d7e-9189-db5b516adf0a\">\r\n\r\n",
"EVAL RESULTS ON IMAGE INFERENCE FOR 100% of LLaVA-Bench-In-The-Wild\r\n\r\n- Assistant 1: GPT-4V (Source: LLaVA-Bench-In-The-Wild)\r\n- Assistant 2: V-NeVA (start from NeVA image ckpt, train on video represented as 24 frames)\r\n\r\nRun 1: \r\n```\r\n=================================\r\nall 84.9 64.5\r\nllava_bench_complex 81.4 70.3\r\nllava_bench_conv 84.1 61.8\r\nllava_bench_detail 92.3 57.0\r\n=================================\r\n```\r\n\r\nRun 2:\r\n```\r\n=================================\r\nall 85.1 64.6\r\nllava_bench_complex 81.8 70.5\r\nllava_bench_conv 84.1 61.8\r\nllava_bench_detail 92.3 57.0\r\n=================================\r\n```\r\n\r\nRun 3:\r\n```\r\n=================================\r\nall 86.7 65.8\r\nllava_bench_complex 85.4 73.5\r\nllava_bench_conv 84.1 61.8\r\nllava_bench_detail 92.0 56.0\r\n=================================\r\n```\r\n\r\nAggregate Results:\r\n\r\n```\r\n=================================\r\nall 85.57+/-1.12 64.97+/-0.82\r\nllava_bench_complex 84.53+/-0.85 71.43+/-2.03\r\nllava_bench_conv 84.10+/-0.00 61.8+/-0.00\r\nllava_bench_detail 92.2+/-0.19 56.57+/-0.65\r\n=================================\r\n```\r\n\r\n**LLaVA Results**\r\n\r\n\r\n**Video-LLaVA Results**\r\n<img width=\"1220\" alt=\"Screen Shot 2024-03-14 at 10 38 11 PM\" src=\"https://github.com/NVIDIA/NeMo/assets/30813477/10afe566-ab4f-4958-aa52-db1037b726ec\">\r\n",
"EVAL RESULTS ON IMAGE INFERENCE FOR 100% of LLaVA-Bench-In-The-Wild\r\n\r\n- Assistant 1: Video-LLaVA\r\n- Assistant 2: V-NeVA (start from NeVA image ckpt, train on video represented as 24 frames)\r\n\r\nRun 1: \r\n```\r\n=================================\r\nall 67.8 77.3\r\nllava_bench_complex 74.6 82.1\r\nllava_bench_conv 61.2 77.1\r\nllava_bench_detail 62.7 68.7\r\n=================================\r\n```\r\n\r\nRun 2:\r\n```\r\n=================================\r\nall 67.5 77.8\r\nllava_bench_complex 73.9 82.7\r\nllava_bench_conv 61.2 77.6\r\nllava_bench_detail 62.7 68.7\r\n=================================\r\n```\r\n\r\nRun 3:\r\n```\r\n=================================\r\nall 67.5 78.1\r\nllava_bench_complex 73.9 83.0\r\nllava_bench_conv 61.2 77.6\r\nllava_bench_detail 62.7 69.3\r\n=================================\r\n```\r\n\r\nAggregate Results:\r\n\r\n```\r\n=================================\r\nall 67.60+/-0.19 77.73+/-0.38\r\nllava_bench_complex 74.13+/-0.38 82.60+/-0.52\r\nllava_bench_conv 61.20+/-0.00 77.43+/-0.43\r\nllava_bench_detail 62.70+/-0.00 68.90+/-0.39\r\n=================================\r\n```\r\n\r\n**LLaVA Results**\r\n\r\n\r\n**Video-LLaVA Results**\r\n<img width=\"1220\" alt=\"Screen Shot 2024-03-14 at 10 38 11 PM\" src=\"https://github.com/NVIDIA/NeMo/assets/30813477/10afe566-ab4f-4958-aa52-db1037b726ec\">\r\n",
"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."
] | 2024-03-11T21:53:01 | 2024-04-14T01:58:37 | 2024-04-14T01:58:37 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR adapts LLaVA's evaluation pipeline leveraging GPT-4 as a judge, but uses NeMo NGC 8xMixtral-7B model as judge.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds `mixtral_eval.py` to NeVA eval pipeline.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Change `answer_only_loss` in inference config files to True to match with the corresponding training config file (e.g. "megatron_gpt_finetuning_config.yaml").
This ensures that loss calculated during training matches with that of evaluation, when using the default config.
**Collection**: NLP
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# Changelog
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# Changelog
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# Usage
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} | Minor copy and instruction changes to improve tutorial viability.
# What does this PR do ?
Adjusts tutorial copy to suggest container, and clarifies paths
# Changelog
- adjusted tutorial copy for pre-training example in documentation
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [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
- [x] Documentation
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"I have the same problem, exporting a FastPitch model (either pretrained or fine-tuned) to TorchScript causes the error described.",
"This issue seems to be fixed when `nemo/collections/tts/models/fastpitch.py:842` is changed to:\r\n\r\n> def input_example(self, max_batch=**44**, max_dim=44):\r\n\r\nDon't know if there are any other effects of doing this, but none seem to impact my use case.",
"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."
] | 2024-03-11T09:51:37 | 2024-05-18T01:45:27 | 2024-05-18T01:45:27 | NONE | {
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} | null | null | null | **Describe the bug**
I tried to export FastPitch model to PyTorch but failed with a following error. It happens with all EN models `tts_en_fastpitch_ipa`, `tts_en_fastpitch`, and `tts_en_fastpitch_multispeaker`.
**Steps/Code to reproduce bug**
I tried the same code with all the models, just changing a model label:
```
import nemo.collections.tts as nemo_tts
model = nemo_tts.models.FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
model.export('tts_en_fastpitch_multispeaker.pt')
```
The models were loaded successfully, e.g.:
```
[NeMo I 2024-03-08 16:00:55 cloud:68] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_fastpitch/versions/IPA_1.13.0/files/tts_en_fastpitch_align_ipa.nemo to /root/.cache/torch/NeMo/NeMo_1.23.0/tts_en_fastpitch_align_ipa/63969a9d8c2d5f4870d16fbf8064a5e0/tts_en_fastpitch_align_ipa.nemo
[NeMo I 2024-03-08 16:01:04 common:924] Instantiating model from pre-trained checkpoint
NeMo-text-processing :: INFO :: Creating ClassifyFst grammars.
INFO:NeMo-text-processing:Creating ClassifyFst grammars.
[NeMo W 2024-03-08 16:02:17 deprecated:65] Function ``g2p_backward_compatible_support`` is deprecated. But it will not be removed until a further notice. G2P object root directory `nemo_text_processing.g2p` has been replaced with `nemo.collections.tts.g2p`. Please use the latter instead as of NeMo 1.18.0.
[NeMo W 2024-03-08 16:02:17 experimental:26] `<class 'nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p'>` is experimental and not ready for production yet. Use at your own risk.
[NeMo W 2024-03-08 16:02:18 i18n_ipa:124] apply_to_oov_word=None, This means that some of words will remain unchanged if they are not handled by any of the rules in self.parse_one_word(). This may be intended if phonemes and chars are both valid inputs, otherwise, you may see unexpected deletions in your input.
[NeMo W 2024-03-08 16:02:18 experimental:26] `<class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'>` is experimental and not ready for production yet. Use at your own risk.
[NeMo W 2024-03-08 16:02:18 modelPT:165] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
dataset:
_target_: nemo.collections.tts.torch.data.TTSDataset
manifest_filepath: /data3/LJSpeech/nvidia_ljspeech_train.json
sample_rate: 22050
sup_data_path: /data3/LJSpeech/tmp_ignoreamb/
sup_data_types:
- align_prior_matrix
- pitch
n_fft: 1024
win_length: 1024
hop_length: 256
window: hann
n_mels: 80
lowfreq: 0
highfreq: 8000
max_duration: null
min_duration: 0.1
ignore_file: null
trim: false
pitch_fmin: 65.40639132514966
pitch_fmax: 2093.004522404789
pitch_norm: true
pitch_mean: 212.35873413085938
pitch_std: 68.52806091308594
use_beta_binomial_interpolator: true
dataloader_params:
drop_last: false
shuffle: true
batch_size: 32
num_workers: 12
[NeMo W 2024-03-08 16:02:18 modelPT:172] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
dataset:
_target_: nemo.collections.tts.torch.data.TTSDataset
manifest_filepath: /data3/LJSpeech/nvidia_ljspeech_val.json
sample_rate: 22050
sup_data_path: /data3/LJSpeech/tmp_ignoreamb/
sup_data_types:
- align_prior_matrix
- pitch
n_fft: 1024
win_length: 1024
hop_length: 256
window: hann
n_mels: 80
lowfreq: 0
highfreq: 8000
max_duration: null
min_duration: null
ignore_file: null
trim: false
pitch_fmin: 65.40639132514966
pitch_fmax: 2093.004522404789
pitch_norm: true
pitch_mean: 212.35873413085938
pitch_std: 68.52806091308594
use_beta_binomial_interpolator: true
dataloader_params:
drop_last: false
shuffle: false
batch_size: 32
num_workers: 8
[NeMo I 2024-03-08 16:02:18 features:289] PADDING: 1
[NeMo I 2024-03-08 16:02:19 save_restore_connector:249] Model FastPitchModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.23.0/tts_en_fastpitch_align_ipa/63969a9d8c2d5f4870d16fbf8064a5e0/tts_en_fastpitch_align_ipa.nemo.
```
But the export filed, in case of `tts_en_fastpitch_ipa` and `tts_en_fastpitch` models, the error was:
```
RuntimeError Traceback (most recent call last)
<ipython-input-8-08f90d3e012f> in <cell line: 1>()
----> 1 model.export('tts_en_fastpitch.pt')
8 frames
/usr/local/lib/python3.10/dist-packages/nemo/collections/tts/parts/utils/helpers.py in regulate_len(durations, enc_out, pace, mel_max_len, group_size, dur_lens)
595 mult = (reps_cumsum[:, :, :-1] <= range_) & (reps_cumsum[:, :, 1:] > range_)
596 mult = mult.to(dtype)
--> 597 enc_rep = torch.matmul(mult, enc_out)
598
599 if mel_max_len is not None:
RuntimeError: mat1 and mat2 shapes cannot be multiplied (271x44 and 1x1)
```
in case of `tts_en_fastpitch_multispeaker` model, the error was:
```
ValueError Traceback (most recent call last)
<ipython-input-10-1205d9628005> in <cell line: 4>()
2
3 model = nemo_tts.models.FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
----> 4 model.export('tts_en_fastpitch_multispeaker.pt')
8 frames
/usr/local/lib/python3.10/dist-packages/nemo/collections/tts/modules/fastpitch.py in get_speaker_embedding(self, batch_size, speaker, reference_spec, reference_spec_lens)
260 elif self.speaker_emb is not None:
261 if speaker is None:
--> 262 raise ValueError('Please give speaker id to get lookup speaker embedding.')
263 spk_emb = self.speaker_emb(speaker).unsqueeze(1)
264 else:
ValueError: Please give speaker id to get lookup speaker embedding.
```
**Environment**
The code was run in Google Colab.
The NeMO toolkit was installed using a pair lines:
```
!pip3 install torch torchaudio torchvision
!pip install nemo_toolkit['all']
```
Python 3.10
PyTorch 2.1.0+cu121
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Add Vietnamese into tokenizer package
Add a one line overview of what this PR aims to accomplish.
Define and set all of this(Vietnamese's alphabet, symbols, punctuations, tildes, phonemes) properties
# Changelog
tts_tokenizers.py
ipa_lexicon.py
tokenizer_utils.py
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Did you add or update any necessary documentation?
**PR Type**:
- [x] New Feature
- [x] Documentation
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} | null | null | null | **Describe the bug**
Using `nemo-toolkit==1.23.0` and `megatron-core==0.5.0` will give the following error.
```
\ File \"/opt/conda/lib/python3.10/site-packages/megatron/core/parallel_state.py\"\
, line 615, in get_tensor_and_expert_parallel_group\n _TENSOR_AND_EXPERT_PARALLEL_GROUP\
\ is not None\nAssertionError: tensor and expert parallel group is not initialized\n"
timestamp: '1710051497'
message: 'AssertionError: tensor and expert parallel group is not initialized'
```
A clear and concise description of what the bug is.
The reason is the following function calls:
[_set_random_seed(seed)](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/megatron/megatron_init.py#L167) -> [get_expert_model_parallel_rank()](https://github.com/NVIDIA/Megatron-LM/blob/89574689447d694bb19dd86fc8a6153b4467ba9d/megatron/core/tensor_parallel/random.py#L181C29-L181C59) -> [get_tensor_and_expert_parallel_group()](https://github.com/NVIDIA/Megatron-LM/blob/89574689447d694bb19dd86fc8a6153b4467ba9d/megatron/core/parallel_state.py#L946) -> [_TENSOR_AND_EXPERT_PARALLEL_GROUP](https://github.com/NVIDIA/Megatron-LM/blob/89574689447d694bb19dd86fc8a6153b4467ba9d/megatron/core/parallel_state.py#L617-L621).
For the last call, `_TENSOR_AND_EXPERT_PARALLEL_GROUP` is always None, since [initialize_model_parallel](https://github.com/NVIDIA/Megatron-LM/blob/89574689447d694bb19dd86fc8a6153b4467ba9d/megatron/core/parallel_state.py#L97) is not called.
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} | # What does this PR do ?
Add Vietnamese into tokenizer package
Add a one line overview of what this PR aims to accomplish.
Define and set all of this(Vietnamese's alphabet, symbols, punctuations, tildes, phonemes) properties
# Changelog
tts_tokenizers.py
ipa_lexicon.py
tokenizer_utils.py
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Did you add or update any necessary documentation?
**PR Type**:
- [x] New Feature
- [x] Documentation
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} | # What does this PR do ?
Uses PEFT for learning a embedding model for queries and documents.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Fix a bug from #8602 that broke LoRA SP for attention_dense module.
Also fix a bug where linear_fc1 mistakenly took hidden states before layernorm as input.
Now LoRA SP work for all linear layers, with minimal communication overhead.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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.
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# What does this PR do ?
Adds several unit tests to #8477
**Collection**: [Note which collection this PR will affect]
# Changelog
- Added several unit tests to verify the lhotse dataloader works as expected
- Rearranged conversion script so it can be reused to run existing tests
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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# What does this PR do ?
Fixes gpt mcore conversion to account for _extra_state that may be present
**Collection**: [Note which collection this PR will affect]
# Changelog
In the 24.01 container, the following:
```sh
wget https://huggingface.co/nvidia/GPT-2B-001/resolve/main/GPT-2B-001_bf16_tp1.nemo
mkdir 2b_model_checkpoint && tar -xvf GPT-2B-001_bf16_tp1.nemo -C 2b_model_checkpoint
docker run -v $PWD/2b_model_checkpoint:/inputs -v $PWD:/outputs --rm -it nvcr.io/ea-bignlp/ga-participants/nemofw-training:24.01 python /opt/NeMo/scripts/nlp_language_modeling/convert_nemo_gpt_to_mcore.py --in-folder /inputs --out-file /outputs/2b_mcore_gpt.nemo --cpu-only
```
will result in this error:
```
[NeMo I 2024-03-08 23:08:38 nlp_overrides:1108] Model MegatronGPTModel was successfully restored from /outputs/2b_mcore_gpt.nemo.
[NeMo I 2024-03-08 23:08:38 convert_nemo_gpt_to_mcore:260] Sanity checks:
[NeMo I 2024-03-08 23:08:38 convert_nemo_gpt_to_mcore:265] ✅ Number of weights match
[NeMo W 2024-03-08 23:08:38 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1876: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[NeMo I 2024-03-08 23:08:42 convert_nemo_gpt_to_mcore:292] ✅ Weights match
Traceback (most recent call last):
File "/opt/NeMo/scripts/nlp_language_modeling/convert_nemo_gpt_to_mcore.py", line 326, in <module>
run_sanity_checks(input_nemo_file, output_nemo_file, cpu_only=cpu_only, ignore_if_missing=ignore_if_missing)
File "/opt/NeMo/scripts/nlp_language_modeling/convert_nemo_gpt_to_mcore.py", line 295, in run_sanity_checks
assert len(nemo_state_dict) == 0, f"❌ unexpected items in nemo_state_dict: {nemo_state_dict}"
AssertionError: ❌ unexpected items in nemo_state_dict: OrderedDict([('model.language_model.encoder.layers.0.self_attention.query_key_value._extra_state', None), ('model.language_model.encoder.layers.0.self_attention.dense._extra_state', None), ('model.language_model.encoder.layers.0.mlp.dense_h_to_4h._extra_state', None), ('model.language_model.encoder.layers.0.mlp.dense_4h_to_h._extra_state', None), ('model.language_model.encoder.layers.1.self_attention.query_key_value._extra_state', None), ('model.language_model.encoder.layers.1.self_attention.dense._extra_state', None),
```
This change just updates the check to ignore keys with "_extra_state". FWIW, the `nemo_state_dict` has `None` for these `_extra_state` keys and `mcore_state_dict` is an empty io.bytes object:
```
mcore_state_dict[ 'model.decoder.layers.15.mlp.linear_fc1._extra_state']
<_io.BytesIO object at 0x7fc01ea8a020>
(Pdb) mcore_state_dict[ 'model.decoder.layers.15.mlp.linear_fc1._extra_state'].read()
b''
```
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
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**PR Type**:
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- [x] 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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} | # What does this PR do ?
Add Vietnamese into tokenizer package
Add a one line overview of what this PR aims to accomplish.
Define and set all of this (Vietnamese's alphabet, symbols, punctuations, tildes, phonemes) properties
# Changelog
tts_tokenizers.py
ipa_lexicon.py
tokenizer_utils.py
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Did you add or update any necessary documentation?
**PR Type**:
- [x] New Feature
- [x] Documentation
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} | # What does this PR do ?
This PR enables the inference of pipeline parallel fine-tuned language models using the `megatron_gpt_generate.py` script. Middle stages during pipeline parallel are expected to have no output during the response generation. We handle this case in the PR to enable evaluation of SFT and PEFT models trained with pipeline parallelism.
**Collection**: NLP
# Changelog
- Reorganized logic for generation by checking if generation has any output (line 430).
- Middle stages of pipeline parallel have no output, so we set empty lists on line 439.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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)
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**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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} | # What does this PR do ?
current code for canary long-form transcription adds "none" before every segment.
**Collection**: [ASR]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
**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 # (issue)
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} | # What does this PR do ?
Restore CUDA Graphs support for SD.
**Collection**: multimodal
# Changelog
- Update in the CUDA Graph callback to align changes in PL API.
- `MegatronLatentDiffusion.training_step` accepts `batch` input instead of `dataloader_iter` so CUDA Graphs are supported.
- Remove circular dependencies in `nemo/core/optim/megatron_fused_adam.py`.
- Add missing resblock_gn_groups parameter.
- Exception is raised when gradient reduction is misconfigured.
- Add synthetic data-loader for SD.
- Tensors are allocated directly on GPU to avoid synchronizations with host in few places.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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)
- [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
- [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 # (issue)
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} | # What does this PR do ?
This PR enables the inference of pipeline parallel fine-tuned language models using the `megatron_gpt_generate.py` script. Middle stages during pipeline parallel are expected to have no output during the response generation. We handle this case in the PR to enable evaluation of SFT and PEFT models trained with pipeline parallelism.
**Collection**: NLP
# Changelog
- Reorganized logic for generation by checking if generation has any output (line 430).
- Middle stages of pipeline parallel have no output, so we set empty lists on line 439.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8611 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8611/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8611/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8611/events | https://github.com/NVIDIA/NeMo/pull/8611 | 2,174,999,423 | PR_kwDOC_bI7s5pBvAP | 8,611 | bug fix in transcribe_speech.py [ASR] | {
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"Woops, i fast merged, @pzelasko if you need any changes let me know I'll revert ",
"looks good :) "
] | 2024-03-07T23:22:11 | 2024-03-08T13:44:18 | 2024-03-07T23:42:57 | COLLABORATOR | {
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} | # What does this PR do ?
Bug fix in transcribe_speech.py: temp manifest file was deleted before calling writing predictions.
**Collection**: [ASR]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
**PR Type**:
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- [X] 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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"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."
] | 2024-03-07T22:31:03 | 2024-03-29T01:43:55 | 2024-03-29T01:43:54 | CONTRIBUTOR | {
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} | # What does this PR do ?
Based on https://github.com/NVIDIA/Megatron-LM/pull/723, this PR aims to make user-set timeout applicable to all ProcessGroups instead of just default ProcessGroups
**Collection**: [Note which collection this PR will affect]
# Changelog
During model parallel initialization, it will pass a timeout parameter (which is the existing attribute of `NLPDDPStrategy`) to new ProcessGroup creation (`new_group`)
# Jenkins CI
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# Additional Information
Need to wait https://github.com/NVIDIA/Megatron-LM/pull/723 to be merged first
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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."
] | 2024-03-07T21:38:31 | 2024-04-11T01:44:36 | 2024-04-11T01:44:35 | COLLABORATOR | {
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Set num of eval samples as 1 for float `limit_val_batches` only when the data_prefix contains single prefix, since otherwise it can lead to no validation.
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
```
# Jenkins 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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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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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"Your config seems to be for a hybrid model, not CTC. You'll need to use this instead - https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py"
] | 2024-03-07T20:53:36 | 2024-03-14T08:02:00 | 2024-03-14T08:02:00 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to run a train process of fastconformer_hybrid_transducer_ctc_bpe_streaming, but obtaining an error
```
omegaconf.errors.ConfigKeyError: Key 'num_classes' is not in struct
full_key: decoder.num_classes
object_type=dict
```
**Steps/Code to reproduce bug**
`OC_CAUSE=1 HYDRA_FULL_ERROR=1 python ${NEMO_ROOT}/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py --config-path=${PWD}/conf/conformer/ --config-name=fastconformer_hybrid_transducer_ctc_bpe_streaming_kazakh exp_manager.name="KAZ_MCV_ISSAI_V1" exp_manager.exp_dir=results/ ++model.encoder.conv_norm_type=layer_norm`
**Expected behavior**
to run a train
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install: pip install['all']
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 22.04
- PyTorch version: 2.2.1
- Python version: 3.11.2
**Additional context**
The part of the config where num_classes is:
```
# The section which would contain the decoder and decoding configs of the auxiliary CTC decoder
aux_ctc:
ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss
use_cer: false
ctc_reduction: 'mean_batch'
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
decoding:
strategy: "greedy"
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Multimodal retriever (CLIP score fusion model & training)
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [ ] 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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```python
# Add a code snippet demonstrating how to use this
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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"jenkins",
"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."
] | 2024-03-06T21:49:50 | 2024-03-29T01:43:57 | 2024-03-29T01:43:57 | COLLABORATOR | {
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"merged_at": null
} | # What does this PR do ?
Use hook functions in mcore for PEFT logic instead of copy-pasting mcore code.
This prevents any mcore change from propogating to nemo
Also add mcore PEFT CI tests for all models.
Keeping this PR ready to run CI tests. Do not merge until mcore PR is finalized
**Collection**: NLP
# Usage
* You can potentially add a usage example below
no usage change
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] Better dev experience
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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} | # What does this PR do ?
Get gathered output from TE layernorm to prevent the redundant gather in parallel adapter. This improves the performance of LoRA SP.
Corresponding TE PR: https://github.com/NVIDIA/TransformerEngine/pull/697
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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**:
- [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.
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"In this modified version provided here, split() is called without specifying any separator, which defaults to splitting based on whitespace characters such as space, tab, or newline. This resolved the ValueError caused by the empty separator.You need to modify this tutorial as follows :\r\n```\r\nclass NeMoGPTv2(NeMoGPT):\r\n\r\n def setup_training_data(self, train_data_config: OmegaConf):\r\n self.vocab = None\r\n self._train_dl = self._setup_data_loader(train_data_config)\r\n\r\n # Save the vocab into a text file for now\r\n with open('vocab.txt', 'w') as f:\r\n for token in self.vocab:\r\n f.write(f\"{token}\")\r\n\r\n # This is going to register the file into .nemo!\r\n # When you later use .save_to(), it will copy this file into the tar file.\r\n self.register_artifact('vocab_file', 'vocab.txt')\r\n\r\n\r\n def setup_validation_data(self, val_data_config: OmegaConf):\r\n vocab_file = self.register_artifact('vocab_file', 'vocab.txt')\r\n\r\n with open(vocab_file, 'r') as f:\r\n vocab = f.read().split()[:-1] # Split based on whitespace characters\r\n self.vocab = vocab\r\n\r\n self._validation_dl = self._setup_data_loader(val_data_config)\r\n\r\n\r\n def setup_test_data(self, test_data_config: OmegaConf):\r\n # This is going to try to find the same file, and if it fails,\r\n # it will use the copy in .nemo\r\n vocab_file = self.register_artifact('vocab_file', 'vocab.txt')\r\n\r\n with open(vocab_file, 'r') as f:\r\n vocab = []\r\n vocab = f.read().split()[:-1] # the -1 here is for the dangling token in the file\r\n self.vocab = vocab\r\n\r\n self._test_dl = self._setup_data_loader(test_data_config)\r\n```"
] | 2024-03-06T12:58:33 | 2024-03-06T13:05:17 | 2024-03-06T13:05:17 | NONE | {
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} | null | null | null | I am trying to learn NeMo from "tutorials/01_NeMo_Models.ipynb"
at the end of the page after crating NeMoGPTv2 class try to create a model :
`model = NeMoGPTv2(cfg=cfg.model)`
facing the following error :
```
File "/home/shabs/anaconda3/envs/NeMo/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-67-1b7caab869c2>", line 1, in <module>
model = NeMoGPTv2(cfg=cfg.model)
^^^^^^^^^^^^^^^^^^^^^^^^
File "<ipython-input-31-f04b7157a9ba>", line 3, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/home/shabs/anaconda3/envs/NeMo/lib/python3.11/site-packages/nemo/core/classes/modelPT.py", line 154, in __init__
self.setup_multiple_validation_data(val_data_config=cfg.validation_ds)
File "/home/shabs/anaconda3/envs/NeMo/lib/python3.11/site-packages/nemo/core/classes/modelPT.py", line 539, in setup_multiple_validation_data
model_utils.resolve_validation_dataloaders(model=self)
File "/home/shabs/anaconda3/envs/NeMo/lib/python3.11/site-packages/nemo/utils/model_utils.py", line 293, in resolve_validation_dataloaders
model.setup_validation_data(cfg.validation_ds)
File "<ipython-input-66-0c8f18429ac6>", line 23, in setup_validation_data
vocab = f.read().split('')[:-1] # the -1 here is for the dangling token in the file
^^^^^^^^^^^^^^^^^^
ValueError: empty separator
``` | {
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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 # (issue)
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"It was resolved. The issue was Using PyCharm IDE. The latest update on PyCharm resolve the issue."
] | 2024-03-06T10:11:49 | 2024-03-06T12:55:47 | 2024-03-06T12:55:47 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to use NeMo. As part of the Model example try to work with config :
`from omegaconf import MISSING
#example:
common_config = OmegaConf.create({
'vocab_size': MISSING,
'block_size': MISSING,
'n_layer': MISSING,
'n_embd': MISSING,
'n_head': MISSING,
})`
facing this error when running in Pycharm latest version
` File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_comm.py", line 219, in run
self._on_run()
File "/home/shabs/.pycharm_helpers/pydev/_pydev_bundle/pydev_console_commands.py", line 31, in _on_run
values.append(pydevd_thrift.var_to_struct(var_obj, name, evaluate_full_value=True, user_type_renderers=self.user_type_renderers))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 313, in var_to_struct
value = _get_default_var_string_representation(v, _type, typeName, format, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 260, in _get_default_var_string_representation
str_from_provider = _str_from_providers(v, _type, typeName, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 197, in str_from_providers
return provider.get_str(o, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: OmegaConfUserResolver.get_str() takes 2 positional arguments but 3 were given
Traceback (most recent call last):
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_comm.py", line 219, in run
self._on_run()
File "/home/shabs/.pycharm_helpers/pydev/_pydev_bundle/pydev_console_commands.py", line 31, in _on_run
values.append(pydevd_thrift.var_to_struct(var_obj, name, evaluate_full_value=True, user_type_renderers=self.user_type_renderers))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 313, in var_to_struct
value = _get_default_var_string_representation(v, _type, typeName, format, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 260, in _get_default_var_string_representation
str_from_provider = _str_from_providers(v, _type, typeName, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shabs/.pycharm_helpers/pydev/_pydevd_bundle/pydevd_thrift.py", line 197, in str_from_providers
return provider.get_str(o, do_trim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: OmegaConfUserResolver.get_str() takes 2 positional arguments but 3 were given`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version : 2.2.1+cu121
- Python version: 3.11
**Additional context**
Add any other context about the problem here.
Example: GPU model
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https://api.github.com/repos/NVIDIA/NeMo/issues/8597 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8597/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8597/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8597/events | https://github.com/NVIDIA/NeMo/pull/8597 | 2,170,671,189 | PR_kwDOC_bI7s5oy4OE | 8,597 | Fix ia3 mlp infused adapter | {
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"jenkins"
] | 2024-03-06T05:30:33 | 2024-03-06T16:52:49 | 2024-03-06T16:52:49 | COLLABORATOR | {
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"merged_at": "2024-03-06T16:52:49"
} | # What does this PR do ?
Add back lines mistakenly deleted in #7988
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 # (issue)
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