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"FYI @titu1994 : regarding \r\n\r\n> Moved the links to the very bottom (cannot hide them, its used as the ordering of the items on the left).\r\n\r\nYou can hide the links while preserving their ordering for the sidebar by including the `:hidden:` option like this:\r\n\r\n```rst\r\n.. toctree::\r\n :hidden:\r\n``... | 2024-03-06T03:13:02 | 2024-03-08T19:42:08 | 2024-03-08T19:30:23 | COLLABORATOR | {
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} | # What does this PR do ?
Updates docs for NeMo framework
**Collection**: [All]
# Changelog
- Move LLM to top, ASR next, Multimodal 3rd
- Move introduction to index.rst Som Note: Injected entire intro.rst into index.rst for now - it looks cleaner and if user explicitly visits the intro page they get same content as just visiting the index site. Moved the links to the very bottom (cannot hide them, its used as the ordering of the items on the left).
- Speech Processing to Speech AI - try not to break links
- Move migration guide to bottom of list called Upgrade guide
- Rename “Foundation Vision Models in NeMo” -> Vision Models
- Move NeMo Core to the top
- Move Text processing to inside of NeMo Core -- Som Note: I didn’t merge into nemo core, instead I renamed the collection to “Common collection” - that way it remains at the bottom (low visibility is good for common collection) and doesn’t break links.
- Make all links - black with blue hyperblink
# 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)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Updates text for domains on website
**Collection**: [Website]
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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?
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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.
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} | # What does this PR do ?
Change rope fusion default as a workaround for #8590
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} | # What does this PR do ?
Fix : You can now completely use the `minimum_timestamp_duration` option, which bugged if the value was too large.
**Collection**: ASR
# Changelog
As we could see in the traceback, the problem appeared during the use of the `get_ctm_line` function because `start_time` was a type of **int** at [this line](https://github.com/NVIDIA/NeMo/blob/main/tools/nemo_forced_aligner/utils/make_ctm_files.py#L98) :
At the beginning, `boundary_info_.t_start` is definitely a **float** with the value 0.0 and `start_time` is also a float after [copying](https://github.com/NVIDIA/NeMo/blob/main/tools/nemo_forced_aligner/utils/make_ctm_files.py#L88). Everything is fine for now.
The source of the problem is at [this line](https://github.com/NVIDIA/NeMo/blob/main/tools/nemo_forced_aligner/utils/make_ctm_files.py#L98) : When using the `max` function, if we have set a `minimum_timestamp_duration` value that is too high. `start_time` will become 0 BUT type of **int** and **no more** type of **float**.
So when we get to the use of the `get_ctm_line` function, this one checks the type of `start_time` and since it's not the right type, it raises a type exception [here](https://github.com/NVIDIA/NeMo/blob/935b9c78f4e75e873f8511121bd846e7dabca4f3/nemo/collections/asr/parts/utils/manifest_utils.py#L97).
Consequently, the **max function argument** should be changed to **0.0** to be always a type of **float**.
# Usage
- You can now really use the option **ctm_file_config.minimum_timestamp_duration=0.5** to change the value of `minimum_timestamp_duration` no matter how long the tokens/words/segments of your transcription is.
Example :
```
python <path to NeMo>/tools/nemo_forced_aligner/align.py pretrained_name="stt_en_fastconformer_hybrid_large_pc" manifest_filepath=nemo/manifest.json output_dir=nemo/output ctm_file_config.minimum_timestamp_duration=0.5
```
# 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
## 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 #8592
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"PR with fix has been merged, so closing this. Thank you for the fix."
] | 2024-03-05T22:23:12 | 2024-03-22T19:53:04 | 2024-03-22T19:53:04 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I wanted to put a `minimum_timestamp_duration` to visualize the result given by nemo forced aligner with [Gecko](https://github.com/gong-io/gecko) as the documentation recommends.
When I change the value of `minimum_timestamp_duration` to 0.4, it's all good.
If I try to change the `minimum_timestamp_duration` for a higher value like 0.5, I get a `ValueError` :
**Steps/Code to reproduce bug**
- Notice the option **ctm_file_config.minimum_timestamp_duration=0.5** to change the value of `minimum_timestamp_duration`. To be sure to have the error, you can put an extremely high value like 50.
```
python <path to NeMo>/tools/nemo_forced_aligner/align.py pretrained_name="stt_en_fastconformer_hybrid_large_pc" manifest_filepath=nemo/manifest.json output_dir=nemo/output ctm_file_config.minimum_timestamp_duration=0.5
```
The value that is too high and causes the error **depends on** the **respective duration of your tokens/words/segments** that are calculated in this [loop](https://github.com/NVIDIA/NeMo/blob/1882d0d0025832a530063e5b699347253b2ab768/tools/nemo_forced_ligner/utils/make_ctm_files.py#L83). The loop iterates over **each token/word/segment** of the transcription and finally produces the `ctm` file).
**Expected behavior**
- No error should be raised.
**Environment overview**
- Method of NeMo install: source
**Additional context**
**_This is a minor error that I've already fixed locally, so I'll make a pull request in a few minutes_**
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"jenkins",
"Just noticed the base branch needs to be changed to `main`",
"opened #8594 instead",
"closed in favor of #8594 "
] | 2024-03-05T21:53:27 | 2024-03-05T23:32:19 | 2024-03-05T23:29:07 | COLLABORATOR | {
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} | # What does this PR do ?
Change rope fusion default as a workaround for #8590
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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.",
"fixed in nemo 24.03 release / megatron core 0.6.0 release"
] | 2024-03-05T21:36:02 | 2024-04-05T13:52:59 | 2024-04-05T13:52:58 | COLLABORATOR | {
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} | null | null | null | **Describe the bug**
Opening a github issue for public tracking purposes. (internal #4489257)
megatron_gpt_eval.py throws the following error:
```
...
core_attn_out = super().forward(
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 2099, in forward
qkv_layout, query_layer, key_layer, value_layer = _get_qkv_layout(
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 1209, in _get_qkv_layout
raise Exception("The provided qkv memory layout is not supported!")
Exception: The provided qkv memory layout is not supported!
This only happens when all of the following are true:
```
micro batch size = 1 (mbs = 2 works)
apply_rope_fusion = True
rope fusion is available (i.e. container has latest Apex)
running an inference workload – megatron_gpt_eval.py, or megatron_gpt_generate.py, or validation loop during training. (training with mbs = 1 and mbs = 2 both work)
**Steps/Code to reproduce bug**
run inference with any gpt-style model (e.g. llama2)
```
torchrun --nproc_per_node=1 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \
gpt_model_file=<path to model> \
inference.greedy=True \
inference.add_BOS=True \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1 \
prompts=["deep learning is"]
```
**Environment overview (please complete the following information)**
nvcr.io/nvidia/nemo:24.01.framework
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} | # What does this PR do ?
Safer fix for https://github.com/NVIDIA/NeMo/pull/8587#event-12018166291
Metrics should accumulate between calls. This little fix allows that function for `WER`.
**Collection**: [ASR]
# Changelog
- Added accumulation to wer metrics
# 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?
- [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**:
- [Y] 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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"jenkins"
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} | # What does this PR do ?
Fix metric calculation in the case fused batch size > 1 during training / validation calculation.
Previously, the RNNT metric was stateful while the CTC one was not ([r1.22.0](https://github.com/NVIDIA/NeMo/blob/r1.22.0/nemo/collections/asr/metrics/rnnt_wer_bpe.py#L419-L420), [r1.23.0](https://github.com/NVIDIA/NeMo/blob/r1.23.0/nemo/collections/asr/metrics/wer.py#L333))
Therefore this calculation in the RNNT joint for fused operation worked properly. However with the unification of metrics in r1.23.0, a bug was introduced where only the last sub-batch of metrics calculates the scores and does not accumulate.
**Collection**: [ASR]
# Changelog
- Preserve the list of metric outputs from each sub-batch
- take average of batch wer, compute running sum of numerator and denominator
# 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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"+@athitten",
"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."
] | 2024-03-05T16:46:59 | 2024-04-13T01:39:10 | 2024-04-13T01:39:10 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
validation consumed samples is always set to 0 in megatron gpt pretraining ([link](https://github.com/NVIDIA/NeMo/blob/a06835f50dd18a67f5b4fedfb56985f9b3573985/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1342)). On resuming training from a checkpoint, consumed samples for validation dataset should be set from the checkpoint instead of getting initialized to 0.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"Thank you very much for raising this! We have fixed it in this PR - https://github.com/NVIDIA/NeMo/pull/8587 \r\nIt occurred due to a large refactor and unification of metrics in ASR to make it simpler to extend in the long run. \r\n\r\nThe patch will be there in the next NeMo release, and we have added a release ... | 2024-03-05T16:28:14 | 2024-03-14T08:02:35 | 2024-03-14T08:02:35 | NONE | {
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} | null | null | null | **Describe the bug**
As shown in
(https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/metrics/wer.py#L349-L350), the new scores and words will be assigned to the object and the pervious scores and words are dropped.
1) we might rename this function to WER.set
or
2) we might update the code as
> self.scores += torch.tensor(scores, device=self.scores.device, dtype=self.scores.dtype)
> self.words += torch.tensor(words, device=self.words.device, dtype=self.words.dtype)
The current code could lead to WER report inconsistent during training and inference if we use fuse_loss_wer in the Transducer model training, i.e., [model.joint.fuse_loss_wer=True and model.joint. fused_batch_size > 1](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml#L194). In this setting, only the last sub-mini-batch WER is accumulated during validation stage.
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"@nithinraok Hi, can you please approve new PR and re-open it.",
"@fed... | 2024-03-05T12:56:11 | 2024-04-11T14:28:48 | 2024-04-11T14:10:47 | CONTRIBUTOR | {
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} | # What does this PR do ?
**Helps to speed up ASR models training without quality degradation.**
The experiments below showed a **19-12% training speed up with the same quality** for the some models.
**Collection**: ASR, core
# Changelog
- `nemo/collections/asr/parts/utils/asr_batching.py`
Add a semi sorted batch sampler and its preparation function to a new file (in the future it might be some other samplers or new techniques like dynamic batching in the file).
- `nemo/collections/asr/models/ctc_models.py`
`nemo/collections/asr/models/ctc_bpe_models.py`
`nemo/collections/asr/models/rnnt_models.py`
`nemo/collections/asr/models/rnnt_bpe_models.py`
`nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py`
Add changes to the data loader preparation function in the models class.
- `nemo/core/optim/lr_scheduler.py`
Add changes to the scheduler preparation function because the batch sampler is passed to the dataloader as a sampler and we need to parse the batch size from it.
- `nemo/collections/asr/data/audio_to_text.py`
Add a change to the `__getitem__` dataset method, as the batch sampler becomes an iterator and returns a list of indices instead of a single index.
# Usage
The sampler is currently added to the CTC, Transducer and Hybrid models.
**Need just to add one line to the config.**
```bash
++config.model.train_ds.use_semi_sorted_batching=true
```
and two line if we want to specify randomization factor (see more in additional information).
```bash
++config.model.train_ds.randomization_factor=0.1
```
Paper default value for randomization_factor is `0.2`.
# 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**:
- [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?
@nithinraok
# Additional Information
* Semi Sorted Batching, was proposed in "Speed up training with variable length inputs by efficient batching strategies.", Zhenhao Ge et al. (2021). ([link](https://www.isca-speech.org/archive/pdfs/interspeech_2021/ge21_interspeech.pdf) to the paper)
* **Idea**: Get the duration of the samples, add pseudo noise to them and sort them. Sample the batches from the sorted list. This way we avoid strict sorting and retain some level of randomness with a significant reduction in padding in batches.
* Experiment for Conformer char CTC:
* train script: `examples/asr/asr_ctc/speech_to_text_ctc.py`
* train config: `asr/examples/asr/conf/conformer/conformer_ctc_char.yaml`
* train data: libri-speech `train-clean-100` `train-clean-360`
* test sata: libri-speech `test-clean`
* overrided parameters:
* `model.validation_ds.batch_size=32`
* `trainer.devices=2`
* `trainer.max_epochs=50`
* `trainer.accumulate_grad_batches=10`
* `trainer.gradient_clip_val=1.0 `
* `++model.train_ds.use_semi_sorted_batching=true` (only experiment with ssb)
* `++model.train_ds.randomization_factor=0.1` (only experiment with ssb)
* devices: Nvidia Tesla V100 x2
* tensorboard logs:
* time
<img src="https://github.com/fedorovgv/buf/blob/main/char_ctc_time.png?raw=true" alt="alt text" width="400" height="200">
* wer
<img src="https://github.com/fedorovgv/buf/blob/main/char_ctc_wer.png?raw=true" alt="alt text" width="400" height="200">
* average, time per epoch / total time / final wer metric
* `ssb`: **22 min 28 sec** / **18 hours 44 min 14 sec** / 9,371 wer
* `without ssb`: 27 min 44 sec / 23 hours 7 min 31 sec / **9,08** wer
**Model training speed up by 18,9% with 3% loss of quality.**
---
- Experiment for Conformer BPE CTC:
* train script: `examples/asr/asr_ctc/speech_to_text_ctc_bpe.py`
* train config: `asr/examples/asr/conf/conformer/conformer_ctc_bpe.yaml`
* train data: libri-speech `train-clean-100` `train-clean-360`
* test sata: libri-speech `test-clean`
* size of bpe vocabulary `512`
* overrided parameters:
* `model.validation_ds.batch_size=32`
* `trainer.devices=2`
* `trainer.max_epochs=50`
* `trainer.accumulate_grad_batches=10`
* `trainer.gradient_clip_val=1.0 `
* `++model.train_ds.use_semi_sorted_batching=true` (only experiment with ssb)
* `++model.train_ds.randomization_factor=0.1` (only experiment with ssb)
* devices: Nvidia Tesla V100 x2
* tensorboard logs:
* time
<img src="https://github.com/fedorovgv/buf/blob/main/bpe_ctc_time.png?raw=true" alt="alt text" width="400" height="200">
* wer
<img src="https://github.com/fedorovgv/buf/blob/main/bpe_ctc_wer.png?raw=true" alt="alt text" width="400" height="200">
* average, time per epoch / total time / final wer metric
* `ssb`: **50 min 1 sec** / **1 day 17 hours 42 min 52 sec** / **6,417** wer
* `without ssb`: 1 hour 1 min 0 sec / 2 day 2 hours 46 min 59 sec / 6,682 wer
**Model training speed up by 17,8% with improved quality.**
---
- Experiment for Conformer Char RNNT:
* train script: `examples/asr/asr_transducer/speech_to_text_rnnt.py`
* train config: `examples/asr/conf/conformer/conformer_transducer_char.yaml`
* train data: libri-speech `train-clean-100` `train-clean-360`
* test sata: libri-speech `test-clean`
* overrided parameters:
* `model.validation_ds.batch_size=32`
* `trainer.devices=2`
* `trainer.max_epochs=50`
* `trainer.accumulate_grad_batches=10`
* `trainer.gradient_clip_val=1.0 `
* `++model.train_ds.use_semi_sorted_batching=true` (only experiment with ssb)
* `++model.train_ds.randomization_factor=0.1` (only experiment with ssb)
* devices: Nvidia Tesla V100 x2
* tensorboard logs:
* time
<img src="https://github.com/fedorovgv/buf/blob/main/char_rnnt_rime.png?raw=true" alt="alt text" width="400" height="200">
* wer
<img src="https://github.com/fedorovgv/buf/blob/main/char_rnnt_wer.png?raw=true" alt="alt text" width="400" height="200">
* average, time per epoch / total time / final wer metric
* `ssb`: **48 min 54 sec** / **1 day 16 hours 45 min 33 sec** / 4,913 wer
* `without ssb`: 56 min 0 sec / 1 day 22 hours 10 min 40 sec / **4,766** wer
**Model training speed up by 13,9% with 3% loss of quality.**
---
- Experiment for Conformer BPE RNNT:
* train script: `examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py`
* train config: `examples/asr/conf/conformer/conformer_transducer_bpe.yaml`
* train data: libri-speech `train-clean-100` `train-clean-360`
* test sata: libri-speech `test-clean`
* overrided parameters:
* `model.validation_ds.batch_size=32`
* `trainer.devices=2`
* `trainer.max_epochs=50`
* `trainer.accumulate_grad_batches=10`
* `trainer.gradient_clip_val=1.0 `
* `++model.train_ds.use_semi_sorted_batching=true` (only experiment with ssb)
* `++model.train_ds.randomization_factor=0.1` (only experiment with ssb)
* devices: Nvidia Tesla V100 x2
* tensorboard logs:
* time
<img src="https://github.com/fedorovgv/buf/blob/main/bpe_rnnt_time.png?raw=true" alt="alt text" width="400" height="200">
* wer
<img src="https://github.com/fedorovgv/buf/blob/main/bpe_rnnt_wer.png?raw=true" alt="alt text" width="400" height="200">
* average, time per epoch / total time / final wer metric
* `ssb`: **42 min 3 sec** / **1 day 11 hours 2 min 30 sec** / **4,894** wer
* `without ssb`: 47 min 52 sec / 1 day 15 hours 53 min 44 sec / 4,991 wer
**Model training speed up by 12,1% with 2% improved of quality.**
---
Picture for docs is available via this link : https://github.com/NVIDIA/NeMo/releases/download/v1.22.0/ssb.png
Closed PR : https://github.com/NVIDIA/NeMo/pull/7307 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8583 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8583/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8583/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8583/events | https://github.com/NVIDIA/NeMo/pull/8583 | 2,168,759,101 | PR_kwDOC_bI7s5osT5r | 8,583 | Add config key for dropout position in LoRA adapter | {
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"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins"
] | 2024-03-05T09:48:33 | 2024-03-06T13:25:15 | 2024-03-06T13:25:14 | COLLABORATOR | {
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} | # What does this PR do?
Added dropout_position key in the Lora config to decide the dropout position in Lora adapter. Both [official](https://github.com/microsoft/LoRA/blob/main/loralib/layers.py#L149) Lora implementation and [PEFT](https://github.com/huggingface/peft/blob/9119b780ebac7859db5753ebad50d94ba803c99c/src/peft/tuners/lora/layer.py#L437) one are using dropout as a first operation while Nemo allows it only as a last operation. The dropout_position key allows to set position to pre to use it in the same way as the official implementation or post to leave it as it is right now.
**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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... | closed | false | null | [] | null | [
"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."
] | 2024-03-05T02:52:07 | 2024-04-13T01:39:12 | 2024-04-13T01:39:12 | NONE | {
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} | null | null | null | I'm trying to create a Sanic server for Nemo ASR. It seems Nemo uses some fork method, while Sanic requires spawn.
**Code:**
```
import nemo.collections.asr as nemo_asr
from sanic import Sanic
app = Sanic(__name__)
if __name__ == '__main__':
app.run(debug=True)
```
**Error Message:**
`RuntimeError: Start method 'spawn' was requested, but 'fork' was already set.`
**Things I already tried, but nothing works:**
```
import multiprocessing as mp
mp.set_start_method('spawn', force=True)
```
```
torch.multiprocessing.set_start_method('spawn', force=True)
```
```
try:
mp.set_start_method('spawn', force=True)
print("spawned")
except RuntimeError:
pass
```
I request experts to help me or share some code snippets if they are publicly available.
Thanks | {
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"I'm not convinced that users will be able to jump to this config as easily as Piotr believes, but I won't block it since it's optional and doesn't hurt to have as an option ",
"> I'm not convinced that users will be able to jump to this config as easily as Piotr believes, but I won't block it since ... | 2024-03-05T00:13:30 | 2024-04-16T13:18:50 | 2024-04-16T13:18:49 | COLLABORATOR | {
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} | # What does this PR do ?
We add a new (optional) way to specify training data for NeMo speech collections. This is intended for experiments which combine potentially many datasets. The configs below are intended as a key under `model.train_ds` in training script configs. We also support providing a path to YAML file with the same contents.
In addition, we extend Lhotse dataloading support to text-only data (e.g. language modeling or machine translation). For now we assume simple text files, and additional parsers can be easily added in the future. This uses Lhotse version 1.22.0 which introduced support for customizing the samplers to other data than audio. In these cases, we measure the "example length" using the number of tokens for text and define a "token_equivalent_duration" for converting audio duration to a number of "tokens". This enables sampling both modalities together, with dynamic mixing, weighting, dynamic bucketing support, tarred audio, and everything else lhotse provides.
These changes are fully backward compatible (i.e., the new format is optional).
A few examples below:
Example 1. Combine two datasets with equal weights and attach custom metadata in ``tags`` to each cut:
```yaml
input_cfg:
- type: nemo_tarred
manifest_filepath: /path/to/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.5
tags:
lang: en
some_metadata: some_value
- type: nemo_tarred
manifest_filepath: /path/to/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.5
tags:
lang: pl
some_metadata: some_value
```
(The rest of the config goes in as usual as keys next to `input_cfg`, e.g. `batch_size`, `shuffle`, etc.)
Example 2. Combine multiple (4) datasets, with 2 corresponding to different tasks (ASR, AST). There are two levels of weights: per task (outer) and per dataset (inner). The final weight is the product of outer and inner weight. The groups could be nested further as needed.
```yaml
input_cfg:
- type: group
weight: 0.7
tags:
task: asr
input_cfg:
- type: nemo_tarred
manifest_filepath: /path/to/asr1/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/tarred_audio/asr1/audio__OP_0..512_CL_.tar
weight: 0.6
tags:
lang: en
some_metadata: some_value
- type: nemo_tarred
manifest_filepath: /path/to/asr2/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/asr2/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.4
tags:
lang: pl
some_metadata: some_value
- type: group
weight: 0.3
tags:
task: ast
input_cfg:
- type: nemo_tarred
manifest_filepath: /path/to/ast1/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/ast1/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.2
tags:
src_lang: en
tgt_lang: pl
- type: nemo_tarred
manifest_filepath: /path/to/ast2/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/ast2/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.8
tags:
src_lang: pl
tgt_lang: en
```
Example 3. Combine an ASR (audio-text) dataset with an MT (text-only) dataset so that mini-batches have some examples from both datasets. Provide a custom prompt field for both datasets (to be leveraged by a relevant dataset class):
```yaml
use_multimodal_sampling: true
batch_tokens: 1024
token_equivalent_duration: 0.08 # 0.01 frame shift * 8 subsampling factor
quadratic_factor: 50
num_buckets: 30
use_bucketing: true
input_cfg:
- type: nemo_tarred
manifest_filepath: /path/to/manifest__OP_0..512_CL_.json
tarred_audio_filepath: /path/to/tarred_audio/audio__OP_0..512_CL_.tar
weight: 0.5
tags:
lang: en
prompt: "Given the following recording, transcribe what the person is saying:"
- type: txt_pair
source_path: /path/to/en.txt
target_path: /path/to/pl.txt
source_language: en
target_language: pl
weight: 0.5
tags:
prompt: "Translate the following text to Polish:"
some_metadata: some_value
```
**Collection**: All speech collections
# 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.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/8580 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8580/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8580/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8580/events | https://github.com/NVIDIA/NeMo/pull/8580 | 2,168,010,736 | PR_kwDOC_bI7s5opyk6 | 8,580 | Fixed missing copy import in rnnt_decoder.py | {
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"color": "F3B33E",
"default": false,
"description": ""
}
] | closed | false | null | [] | null | [] | 2024-03-04T23:55:18 | 2024-03-05T17:48:30 | 2024-03-05T17:48:29 | CONTRIBUTOR | {
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"patch_url": "https://github.com/NVIDIA/NeMo/pull/8580.patch",
"merged_at": "2024-03-05T17:48:29"
} | # What does this PR do ?
When using compute_timestamps with RNNT models, an error is encountered because of a missing `copy` import (where `copy` is Python's built-in [copy](https://docs.python.org/3/library/copy.html) module).
**Collection**: ASR
# Changelog
- One-line change: `import copy` at top of [rnnt_decoding.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/rnnt_decoding.py) 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"
**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?
- Not applicable, tiny bugfix.
- [ ] 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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- [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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"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."
] | 2024-03-04T23:24:32 | 2024-04-11T01:44:38 | 2024-04-11T01:44:38 | NONE | {
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} | null | null | null | Is there some parameter needed to increase the length of audio that the speech synthesizers supported by NeMo can produce?
This code here does not produce audio that covers the Text I provide in the code below, I only get 11 seconds of Audio. Also what is the maximum length of audio that can be produced at a time?
```
import torch
import IPython.display as ipd
from matplotlib.pyplot import imshow
from matplotlib import pyplot as plt
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
import soundfile as sf
import sys
# List pretrained models available in NeMo
print("Spectrogram Models")
print(SpectrogramGenerator.list_available_models())
print()
print("Vocoders")
print(Vocoder.list_available_models())
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
# Load our spectrogram and vocoder models onto our CPU
spectrogram_model = SpectrogramGenerator.from_pretrained("tts_en_tacotron2").eval().to(device)
vocoder = Vocoder.from_pretrained("tts_en_hifigan").eval().to(device)
text = "Within the chasmic depths of my mind, vague shapes, sounds and visions fight to be witnessed. A scattering of incoherent memories..... gateways to the blessed palace...... Darkness......Phase equations made flesh.....Darkness.....Waking dreams of a passionless mortal life long though buried....Darkness....Archs of impossible colors claw desperately across the back of my electronic eyes, my machine souls last attempt to ignite conciousness....Darkness....Light, waking neurons fired into life like a birthing galaxy aroused by the sound of plaintiff cries, not human or A.I, not daemon, distant. Ethereal to my spirit."
# Normalize the text and convert it into individual phonemes/tokens.
tokens = spectrogram_model.parse(text, normalize=True)
# Generate spectrogram from text
spectrogram = spectrogram_model.generate_spectrogram(tokens=tokens)
# Invert the spectrogram into audio samples
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Convert output from pytorch tensor to numpy array
spectrogram = spectrogram.cpu().detach().numpy()[0]
audio = audio.cpu().detach().numpy()[0]
audio_file = "output_audio.wav"
sf.write(audio_file, audio, samplerate=22050)
print(f"Audio saved to {audio_file}")
print(f'"{text}"\n')
ipd.Audio(audio, rate=22050)
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8578 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8578/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8578/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8578/events | https://github.com/NVIDIA/NeMo/pull/8578 | 2,167,951,445 | PR_kwDOC_bI7s5oplae | 8,578 | Fixing data types for cached states | {
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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."
] | 2024-03-04T23:09:18 | 2024-03-26T01:43:57 | 2024-03-26T01:43:57 | COLLABORATOR | {
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} | # What does this PR do ?
Some dimensions of the cached states should be exported as static, not dynamic.
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/8577 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8577/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8577/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8577/events | https://github.com/NVIDIA/NeMo/issues/8577 | 2,167,669,524 | I_kwDOC_bI7s6BNAMU | 8,577 | Cannot disable progress bar | {
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"@athitten, can you take a look at this when you have time?",
"@ksivaman this issue should be fixed in this PR: [PR 8663](https://github.com/NVIDIA/NeMo/pull/8663). Can you please help review it ? Thank you."
] | 2024-03-04T20:17:40 | 2024-03-20T00:03:55 | 2024-03-20T00:03:55 | MEMBER | {
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} | null | null | null | When setting `+trainer.enable_progress_bar=False` in the config, the application crashes since `CustomProgressBar` is always passed as callback to PyTorch lightning trainer.
```
raise MisconfigurationException(
lightning_fabric.utilities.exceptions.MisconfigurationException: Trainer was configured with `enable_progress_bar=False` but found `CustomProgressBar` in callbacks list.
``` | {
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} | When resuming a training job, the pytorch lightning trainer seems to use the data loader to determine the number of total steps left in the epoch. When you kill/resume a training job partway through, the data sampler length will be reduced by `consumed_samples` in the current implementation, while the current step of the trainer will be restored. This results in a double impact to the number of remaining samples, and we can get into a situation where the trainer crashes out when the current_step is greater than the length of the dataloader. Leaving the length unchanged seems to fix this issue, and since it is an iterator, the stop_iteration (based on consumed_samples) does what it is supposed to do.
# What does this PR do ?
Leave len(sampler) as is, not making it a function of consumed_samples. Default behavior of pytorch lightning trainer resumption seems to behave better with this configuration.
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"Yes, only one cycle. It is preferred to use max steps since it makes it easier to configure the scheduler but you can provide other args and it will work fine ",
"Thank you for the clarification!\r\n\r\nRegarding model averaging, the paper mentions taking average of the last 5 checkpoint. In your implementation,... | 2024-03-04T14:19:12 | 2024-03-05T06:19:43 | 2024-03-05T06:19:42 | NONE | {
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I don't see the max_steps specified in this [config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml#L184) for Fast-Conformer CTC BPE. Does that mean there's only one cycle in the whole training process because the max_steps param will be the total training steps - warmup_steps?
Do I need to specify the max_steps to something like the total number of iteration per epoch?
Thanks! | {
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"These collections are under active development, we hope to stabilize them soon ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as st... | 2024-03-04T09:51:16 | 2024-04-11T01:44:39 | 2024-04-11T01:44:39 | NONE | {
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} | null | null | null | **Problem statement**
~ I'm working on the "Stable-Diffusion" model.
~ while converting to ".nemo" I'm facing some issues in the code from file "convert_external_clip_to_nemo.py" on line 41
~ This is the import line:
~ Even though those Classes are defined, they don't get imported.
~please provide a solution | {
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} | # What does this PR do ?
Fix missing import introduced at [nemo/utils/export_utils.py#450@e6ee3312](https://github.com/NVIDIA/NeMo/commit/e6ee3312fb3f35b9d95b54b64c2abed4574708b9)
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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**:
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"After some fiddling, I was able to get the nemo2riva part of the deployment working using a riva docker image. It can create an environment that allows the conversion of .nemo to .riva files for both pretrained and finetuned (regular and adapter variants) of FastPitch and HifiGan.\r\n\r\nThe step to create a .rmi... | 2024-03-04T02:41:11 | 2024-04-25T01:45:34 | 2024-04-25T01:45:33 | NONE | {
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} | null | null | null | Hardware - GPU (T4)
Hardware - CPU
Operating System - ubuntu 20.04 running on AWS EC2 g4dn.2xlarge instance
I am currently trying to convert a model (several of different types but for now not even a FastPitch model is working)
In the past i had deployed several nemo pipelines to riva but that developing environment was lost during some updates and I have not been able to convert and deploy any nemo models since. I believe this lost environment was using nemo 1.20.0 and riva & nemo2riva version 2.13.1, however using those versions does not seem to work for me anymore.
Recently I have been testing several versions of nemo, nemo2riva and riva using the dockerfile below in order to deploy models. (will update with testing data as I continue to try and retry combinations)
```
FROM nvcr.io/nvidia/nemo:24.01.framework
#get a simple fastpitch model to convert
RUN wget --content-disposition \
'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/nemo/tts_en_fastpitch/IPA_1.13.0/files?redirect=true&path=tts_en_fastpitch_align_ipa.nemo' \
-O tts_en_fastpitch_align_ipa.nemo
#without nemo_text_processing it errors with "TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data"
#without lhotse it also gets the same TypeError
RUN pip install nemo_text_processing
RUN pip install lhotse
#install nemo2riva
RUN pip install nvidia-pyindex
RUN pip install nemo2riva==2.14.0
#run the test
CMD nemo2riva --out FP_ipa.riva --key tlt_encode tts_en_fastpitch_align_ipa.nemo
```
As you can see in the dockerfile, it is using a pretrained model from ngc, however I get the same error even on a .nemo model that was trained using the latest nemo version
The error relates to nvidia-eff and being unable to encrypt the model. This error is consistent regardless of the nemo image used. I have also tried using a pytorch base image but this results in the same errors. I tried using a riva-servicemaker image too but that had an issue arising because the image uses python 3.8 but nemo has required 3.10 for a long time.
I really need some help resolving this as development is being delayed as I cannot update any ASR, NLP, or TTS models currently. How can I resolve this?
```
INFO: generated new fontManager
INFO: NumExpr defaulting to 8 threads.
[NeMo I 2024-03-04 01:59:20 nemo2riva:38] Logging level set to 20
[NeMo I 2024-03-04 01:59:20 convert:36] Restoring NeMo model from 'tts_en_fastpitch_align_ipa.nemo'
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
`Trainer(limit_val_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(limit_test_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(limit_predict_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
NeMo-text-processing :: INFO :: Creating ClassifyFst grammars.
WARNING: Logging before flag parsing goes to stderr.
I0304 01:59:23.133829 139807560075072 tokenize_and_classify.py:86] Creating ClassifyFst grammars.
[NeMo W 2024-03-04 01:59:51 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 2024-03-04 01:59:52 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 2024-03-04 01:59:53 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 2024-03-04 01:59:53 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 2024-03-04 01:59:53 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 2024-03-04 01:59:53 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 2024-03-04 01:59:53 features:289] PADDING: 1
[NeMo I 2024-03-04 01:59:54 save_restore_connector:249] Model FastPitchModel was successfully restored from /workspace/tts_en_fastpitch_align_ipa.nemo.
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-isc-exported-bert.yaml for nemo.collections.nlp.models.IntentSlotClassificationModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/tts-exported-fastpitchmodel.yaml for nemo.collections.tts.models.FastPitchModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/tts-exported-hifiganmodel.yaml for nemo.collections.tts.models.HifiGanModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/tts-exported-radttsmodel.yaml for nemo.collections.tts.models.RadTTSModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdecctcmodel.yaml for nemo.collections.asr.models.EncDecCTCModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-tc-exported-bert.yaml for nemo.collections.nlp.models.TextClassificationModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-mt-exported-encdecmtmodel.yaml for nemo.collections.nlp.models.MTEncDecModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-tkc-exported-bert.yaml for nemo.collections.nlp.models.TokenClassificationModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdectcmodelbpe.yaml for nemo.collections.asr.models.EncDecCTCModelBPE
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/asr-scr-exported-encdecclsmodel.yaml for nemo.collections.asr.models.classification_models.EncDecClassificationModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-qa-exported-bert.yaml for nemo.collections.nlp.models.QAModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-mt-exported-megatronnmtmodel.yaml for nemo.collections.nlp.models.MegatronNMTModel
[NeMo I 2024-03-04 01:59:54 schema:161] Loaded schema file /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/nlp-pc-exported-bert.yaml for nemo.collections.nlp.models.PunctuationCapitalizationModel
[NeMo I 2024-03-04 01:59:54 schema:200] Found validation schema for nemo.collections.tts.models.FastPitchModel at /usr/local/lib/python3.10/dist-packages/nemo2riva/validation_schemas/tts-exported-fastpitchmodel.yaml
[NeMo I 2024-03-04 01:59:54 schema:229] Checking installed NeMo version ... 1.23.0 OK (>=1.1)
[NeMo I 2024-03-04 01:59:54 artifacts:59] Found model at ./model_weights.ckpt
[NeMo I 2024-03-04 01:59:54 artifacts:136] Retrieved artifacts: dict_keys(['36d6b09d4dbc45dcb02222e1931e4c7c_lj_speech.tsv', '446fe5373191447190c14cdb8e967e58_ipa_cmudict-0.7b_nv22.08.txt', 'e2327d2f57dd41b88601774804001221_heteronyms-052722', 'model_config.yaml', 'mapping.txt'])
[NeMo I 2024-03-04 01:59:54 cookbook:78] Exporting model FastPitchModel with config=ExportConfig(export_subnet=None, export_format='ONNX', export_file='model_graph.onnx', encryption=True, autocast=True, max_dim=None, export_args={})
[NeMo I 2024-03-04 02:00:01 exportable:131] Successfully exported FastPitchModel to /tmp/tmp7gtd0omy/model_graph.onnx
[W] colored module is not installed, will not use colors when logging. To enable colors, please install the colored module: python3 -m pip install colored
[W] Attribute of type TYPE_PROTO is currently unsupported. Skipping attribute.
[W] colored module is not installed, will not use colors when logging. To enable colors, please install the colored module: python3 -m pip install colored
[W] Could not convert: UNDEFINED to a corresponding NumPy type. The original ONNX type will be preserved.
[W] colored module is not installed, will not use colors when logging. To enable colors, please install the colored module: python3 -m pip install colored
[W] Attribute of type TYPE_PROTO is currently unsupported. Skipping attribute.
2024-03-04 02:00:01.862915635 [W:onnxruntime:, unsqueeze_elimination.cc:20 Apply] UnsqueezeElimination cannot remove node Unsqueeze_980
2024-03-04 02:00:01.862971956 [W:onnxruntime:, unsqueeze_elimination.cc:20 Apply] UnsqueezeElimination cannot remove node Unsqueeze_975
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Traceback (most recent call last):
File "/usr/local/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/usr/local/lib/python3.10/dist-packages/nemo2riva/cli/nemo2riva.py", line 49, in nemo2riva
Nemo2Riva(args)
File "/usr/local/lib/python3.10/dist-packages/nemo2riva/convert.py", line 87, in Nemo2Riva
export_model(
File "/usr/local/lib/python3.10/dist-packages/nemo2riva/cookbook.py", line 141, in export_model
create_artifact(
File "/usr/local/lib/python3.10/dist-packages/nemo2riva/artifacts.py", line 92, in create_artifact
af.encrypt()
File "<frozen eff.core.file>", line 128, in encrypt
PermissionError: Cannot encrypt the artifact without encryption
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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.",
"Hi, I'm facing the same problem in NeMo 2.0. It took me a long time to see ... | 2024-03-03T17:43:09 | 2024-11-29T09:10:08 | 2024-04-10T01:44:38 | COLLABORATOR | {
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For `audio_to_text` data, there is not support for this concatenation, the `_loop_offset` method still assumes unique files. As such, anytime you use multiple offsets from the same audio file, the dataloader conflicts with the audio stream, causing hanging.
**Steps/Code to reproduce bug**
- Build a manifest file with multiple offsets from same file
- Run convert_to_tarred
- Run training over tarred script
- Watch run hang
**Expected behavior**
- The `audio_to_text` data file should duplicate audio streams for each instance of `-sub` in the manifest. This can be done by simply duplicating the text in `audio_to_label`
**Environment overview (please complete the following information)**
- Environment location: [Docker container on cs-oci run]
- Method of NeMo install: source from main
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} | # What does this PR do ?
This PR adds score-based diffusion enhancement model.
**Collection**: ASR
# Changelog
- Added `PredictiveAudioToAudioModel` class and an example config
- Added `ScoreBasedGenerativeAudioToAudioModel` and an example config
- Added power and scale to `AudioToSpectrogram` and `SpectrogramToAudio`
- Added diffusion-related blocks (SDEs, samplers, backbone)
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"the latest megatron version for python 3.8 is 0.4.0. you will need to upgrage to python 3.10 for any nemo version after 1.20.0 (from memory, that is when the megatron version requirement was upgraded from 0.4.0 which is still python 3.8 compatible)\r\n\r\nI believe megatron core is just used for NLP so if you inst... | 2024-03-02T01:42:20 | 2024-04-19T01:44:44 | 2024-04-19T01:44:43 | NONE | {
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} | null | null | null | **Describe the bug**
When trying to install nemo version 1.23.0 via poetry, I get this error:
```
#21 257.6 RuntimeError
#21 257.6
#21 257.6 Unable to find installation candidates for megatron-core (0.5.0)
#21 257.6
#21 257.6 at /usr/local/lib/python3.8/dist-packages/poetry/installation/chooser.py:76 in choose_for
#21 257.7 72│
#21 257.7 73│ links.append(link)
#21 257.7 74│
#21 257.7 75│ if not links:
#21 257.7 → 76│ raise RuntimeError(f"Unable to find installation candidates for {package}")
#21 257.7 77│
#21 257.7 78│ # Get the best link
#21 257.7 79│ chosen = max(links, key=lambda link: self._sort_key(package, link))
#21 257.7 80│
```
**Steps/Code to reproduce bug**
poetry install nemo with this configuration:
```
nemo-toolkit = { extras = [
"asr",
"nlp",
"core",
"text_processing",
"tts",
], version="1.23.0"}
```
**Expected behavior**
Install succeeds
**Environment overview (please complete the following information)**
- Environment location: Local environment Ubuntu 18.04.3 LTS
- Method of NeMo install: poetry install
**Environment details**
Ubuntu 18.04.3 LTS
Poetry version 1.4.2
Cuda 11.6
**Additional context**
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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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**PR Type**:
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} | # What does this PR do ?
Adds manifest pre-sorting option (new default) ~and options to enable and control dynamic bucketing for inference~.
Depending on data distribution, pre-sorting can significantly increase inference speed (we observed as much as 2.5x throughput). ~Bucketing is approximate so expect smaller gains (we observed 1.7-2x increase in throughput depending on the model and decoding algorithm selection).~
EDIT: I removed bucketing support after realizing it'd require another refactoring of transcribe internals to be able to restore the original order.
Also fixes a bug in processing manifests for multi task AED models (we can now override text/lang fields from transcribe_speech CLI)
**Collection**: ASR
# Changelog
- Accelerate `transcribe_speech.py` for short-form data: pre-sorting support
# 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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- [ ] 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
- [ ] 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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"Let me know if you want to fast merge after additional approvals ",
"jenkins",
"@titu1994 could you please fast merge this? ",
"jenkins",
"jenkins",
"Docs changes does not need CI to pass, I can merge it if it's ready ",
"Merged "
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Update results.rst for Canary Inference, please feel free to modify
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] | 2024-03-01T09:44:08 | 2024-04-09T01:44:21 | 2024-04-09T01:44:20 | NONE | {
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} | null | null | null | When I use MSDD with long form mode the performances are bad espicially if the audio contains many speakers (more than 5).
My question is:
Is it possible to split the audio into short audio files and create manifest file for those audio files and run the MSDD on them and summarize the clusters in order to improve the estimation of number of speakers?
My problem is that some of the speakers have low speech ratio but it is very important to catch them.
So my idea is split the audio into short audios in order to increase the relative speech ratio and help to the clustering process to catch them but as far as I understand if I'll provide manifest file that contains multiple audio files the MSDD will process each audio independently and individually.
Am I right?
Any idea how can I achieve better results?
Note that I cannot provide audio example due to privacy prorection.
Please advise | {
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* Update version to 0.2.86
* Pipe functionality removed and has been replaced with DataPipeline.
~~Add global_seed functionality to exp_manager for reproducibility.~~ <- will move to a separate PR
**Collection**: ASR, TTS, NLP
# Jenkins CI
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# Before your PR is "Ready for review"
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} | # What does this PR do ?
support megatron_amp_O2 for peft tuning
# Jenkins CI
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# Before your PR is "Ready for review"
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} | # What does this PR do ?
simply fix a code-breaking typo
**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**:
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- [ ] New Feature
- [x] Bugfix
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# 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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] | 2024-02-29T18:28:30 | 2024-03-27T01:44:05 | 2024-03-27T01:44:05 | CONTRIBUTOR | {
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} | …v adapter
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
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"@ericharper ",
"@aschilling-nv All the doc strings errors with this PR should be resolved"
] | 2024-02-29T18:06:06 | 2024-03-01T17:13:09 | 2024-03-01T17:13:09 | COLLABORATOR | {
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} | # What does this PR do ?
Resolve documentation build errors in doc strings
Example formatting issues: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/api.html#nemo-asr-collection-api
Check specific classes and methods for formatting error that currently exists.
**Collection**: ASR, common, core, NLP
# Changelog
- Update doc strings
# 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
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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."
] | 2024-02-29T17:38:50 | 2024-04-07T01:45:27 | 2024-04-07T01:45:26 | NONE | {
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} | null | null | null | **Describe the bug**
I have ran experiments for Multinode model customization (PEFT) using Nemo Framework training container. I tried both LoRa and Ptuning techniques, from which we are able to run LoRa successfully with various combination of TP and PP values. Although, with Ptuning as we increase PP more than 1, experiments fails with DDP error. Table below depicts what we tried in lab:
Error observed: RuntimeError: DistributedDataParallel is not needed when a module doesn't have any parameter that requires a gradient.
Environment: 4 x Nodes (4 L40s each node)
Model: llama2-7b
training container version: 23.11
Table of experiments:
LORA TP PP Result
LORA 2 1 Success
LORA 4 1 Success
LORA 1 4 Success
LORA 4 4 Success
Ptuning 4 1 Success
Ptuning 1 2 Failed
Ptuning 1 4 Failed
Ptuning 2 2 Failed
Ptuning 2 4 Failed
Ptuning 4 2 Failed
Ptuning 4 4 Failed
Do you have any thoughts or suggestions on why p-tuning fails when PP size is higher than 1?
**Expected behavior**
Customization job should proceed with requested configuration.
**Environment overview (please complete the following information)**
- Environment location: Slurm batch using srun <pyxis>
- Method of NeMo install: Container from NGC
**Environment details**
Environment: 4 x Nodes (4 L40s each node)
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"jenkins",
"jenkins",
"@michal2409 as far as I know we install `triton` in [here](https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements.txt#L13) instead of `pytorch-triton`. Looks like you install `pytorch-triton` in your container? ",
"> @michal2409 as far as I know we install `triton` in [here... | 2024-02-29T14:05:19 | 2024-03-06T11:03:58 | 2024-03-06T11:03:57 | COLLABORATOR | {
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} | # What does this PR do ?
As far as pkg_resources is concerned, there's no such thing as triton. Instead, it recognizes pytorch-triton. Here's the fix. Also, if the pytorch-triton lib is missing, it raises pkg_resources.DistributionNotFound which currently is not being handled properly.
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/8551 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8551/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8551/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8551/events | https://github.com/NVIDIA/NeMo/pull/8551 | 2,160,679,802 | PR_kwDOC_bI7s5oQ1ud | 8,551 | remove the logging of val loss at test time | {
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"jenkins",
"jenkins"
] | 2024-02-29T08:21:24 | 2024-03-01T21:18:56 | 2024-03-01T21:18:55 | COLLABORATOR | {
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"merged_at": "2024-03-01T21:18:55"
} | # What does this PR do ?
Currently at test time, the test loss is logged as val_loss.
Remove the val_loss log and per data iterator test loss at test time, so the final eval report looks cleaner and less confusing.
<img width="466" alt="image" src="https://github.com/NVIDIA/NeMo/assets/1331543/5b247bd6-c189-467f-abad-d5062182cdd8">
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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"I also meet the problem can't pull nvcr.io/ea-bignlp/ga-participants/nemofw-training too.",
"@thangdt277 Can you start to train with other images?\r\n",
"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."
] | 2024-02-29T06:46:54 | 2024-04-11T14:51:36 | 2024-04-11T14:51:36 | NONE | {
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} | null | null | null | **Describe the bug**
Follow [NeMo Framework PEFT with Mistral-7B](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html)
First error , I can't pull image although I logged in nvcr.io successfully:
```
12345i@ai_pc:~/NEMO_TRAIN$ docker login nvcr.io
Authenticating with existing credentials...
WARNING! Your password will be stored unencrypted in /ldap/home/12345/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
Login Succeeded
```
```
12345@ai_pc:~/NEMO_TRAIN$docker pull nvcr.io/ea-bignlp/ga-participants/nemofw-training:24.01
Error response from daemon: pull access denied for nvcr.io/ea-bignlp/ga-participants/nemofw-training, repository does not exist or may require 'docker login': denied: requested access to the resource is denied
```
So I change docker image to `nvcr.io/nvidia/nemo:23.10` which is mentioned in [README](https://github.com/NVIDIA/NeMo#)
When I start to [Step 2: Run PEFT training](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html#step-2-run-peft-training)
Here is the error :
```
Traceback (most recent call last):
File "/usr/src/app/./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py", line 59, in <module>
@deprecated(
TypeError: deprecated() got an unexpected keyword argument 'wait_seconds'
```
So I commented out lines below in `megatron_gpt_peft_tuning.py`:
```
# @deprecated(
# wait_seconds=20,
# explanation=f"\n{banner}\nmegatron_gpt_peft_tuning.py is renamed to megatron_gpt_finetuning.py with the "
# f"same functionality. \nPlease switch to the new name.\n{banner}\n",
)
```
Then it starts to work , Here is process:
```
appuser@ai_pc:/usr/src/app$ python3 ./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py trainer.devices=1 trainer.num_nodes=1 trainer.precision=bf16 trainer.val_check_interval=20 trainer.max_steps=50 model.megatron_amp_O2=False ++model.mcore_gpt=True model.tensor_model_parallel_size=1 model.pipeline_model_parallel_size=1 model.micro_batch_size=1 model.global_batch_size=8 model.restore_from_path=${MODEL} model.data.train_ds.num_workers=0 model.data.validation_ds.num_workers=1 model.data.train_ds.file_names=${TRAIN_DS} model.data.train_ds.concat_sampling_probabilities=[1.0]
model.data.validation_ds.file_names=${VALID_DS} model.peft.peft_scheme=${SCHEME} exp_manager.checkpoint_callback_params.mode=min
[NeMo W 2024-02-29 13:43:09 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/1.2/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
[NeMo I 2024-02-29 13:43:09 megatron_gpt_peft_tuning:66]
************** Experiment configuration ***********
[NeMo I 2024-02-29 13:43:09 megatron_gpt_peft_tuning:67]
name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning
trainer:
devices: 1
accelerator: gpu
num_nodes: 1
precision: bf16
logger: false
enable_checkpointing: false
use_distributed_sampler: false
max_epochs: 9999
max_steps: 50
log_every_n_steps: 10
val_check_interval: 20
gradient_clip_val: 1.0
exp_manager:
explicit_log_dir: null
exp_dir: null
name: ${name}
create_wandb_logger: false
wandb_logger_kwargs:
project: null
name: null
resume_if_exists: true
resume_ignore_no_checkpoint: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: validation_${model.data.validation_ds.metric.name}
save_top_k: 1
mode: min
save_nemo_on_train_end: true
filename: ${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}
model_parallel_size: ${model.tensor_model_parallel_size}
always_save_nemo: false
save_best_model: true
create_early_stopping_callback: true
early_stopping_callback_params:
monitor: val_loss
mode: min
min_delta: 0.001
patience: 10
verbose: true
strict: false
model:
seed: 1234
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
global_batch_size: 8
micro_batch_size: 1
restore_from_path: ./model/mistral.nemo
resume_from_checkpoint: null
save_nemo_on_validation_end: false
sync_batch_comm: false
megatron_amp_O2: false
sequence_parallel: false
activations_checkpoint_granularity: null
activations_checkpoint_method: null
activations_checkpoint_num_layers: null
activations_checkpoint_layers_per_pipeline: null
answer_only_loss: true
gradient_as_bucket_view: false
hidden_dropout: 0.0
attention_dropout: 0.0
ffn_dropout: 0.0
peft:
peft_scheme: lora
restore_from_path: null
adapter_tuning:
type: parallel_adapter
adapter_dim: 32
adapter_dropout: 0.0
norm_position: pre
column_init_method: xavier
row_init_method: zero
norm_type: mixedfusedlayernorm
layer_selection: null
weight_tying: false
position_embedding_strategy: null
lora_tuning:
target_modules:
- attention_qkv
adapter_dim: 32
alpha: ${model.peft.lora_tuning.adapter_dim}
adapter_dropout: 0.0
column_init_method: xavier
row_init_method: zero
layer_selection: null
weight_tying: false
position_embedding_strategy: null
p_tuning:
virtual_tokens: 10
bottleneck_dim: 1024
embedding_dim: 1024
init_std: 0.023
ia3_tuning:
layer_selection: null
selective_tuning:
tunable_base_param_names:
- self_attention
- word_embeddings
data:
train_ds:
file_names:
- ./pubmedqa/pubmedqa_train.jsonl
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: true
num_workers: 0
memmap_workers: 2
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: true
concat_sampling_probabilities:
- 1.0
label_key: output
add_eos: true
add_sep: false
add_bos: false
truncation_field: input
index_mapping_dir: null
prompt_template: '{input} {output}'
truncation_method: right
validation_ds:
file_names:
- ./pubmedqa/pubmedqa_val.jsonl
names: null
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: false
num_workers: 1
memmap_workers: ${model.data.train_ds.memmap_workers}
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: false
label_key: ${model.data.train_ds.label_key}
add_eos: ${model.data.train_ds.add_eos}
add_sep: ${model.data.train_ds.add_sep}
add_bos: ${model.data.train_ds.add_bos}
write_predictions_to_file: false
output_file_path_prefix: null
truncation_field: ${model.data.train_ds.truncation_field}
index_mapping_dir: null
prompt_template: ${model.data.train_ds.prompt_template}
tokens_to_generate: 32
truncation_method: right
metric:
name: loss
average: null
num_classes: null
test_ds:
file_names:
- ./pubmedqa/pubmedqa_test.jsonl
names:
- pubmedqa
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: false
num_workers: 0
memmap_workers: ${model.data.train_ds.memmap_workers}
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: false
label_key: ${model.data.train_ds.label_key}
add_eos: ${model.data.train_ds.add_eos}
add_sep: ${model.data.train_ds.add_sep}
add_bos: ${model.data.train_ds.add_bos}
write_predictions_to_file: false
output_file_path_prefix: null
truncation_field: ${model.data.train_ds.truncation_field}
index_mapping_dir: null
prompt_template: ${model.data.train_ds.prompt_template}
tokens_to_generate: 32
truncation_method: right
metric:
name: loss
average: null
num_classes: null
optim:
name: fused_adam
lr: 0.0001
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
warmup_steps: 50
min_lr: 0.0
constant_steps: 0
monitor: val_loss
reduce_on_plateau: false
mcore_gpt: true
[NeMo W 2024-02-29 13:43:09 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:554: UserWarning: bf16 is supported for historical reasons but its usage is discouraged. Please set your precision to bf16-mixed instead!
rank_zero_warn(
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo W 2024-02-29 13:43:09 exp_manager:754] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
[NeMo W 2024-02-29 13:43:09 exp_manager:611] There were no checkpoints found in checkpoint_dir or no checkpoint folder at checkpoint_dir :/usr/src/app/nemo_experiments/megatron_gpt_peft_lora_tuning/checkpoints. Training from scratch.
[NeMo I 2024-02-29 13:43:09 exp_manager:394] Experiments will be logged at /usr/src/app/nemo_experiments/megatron_gpt_peft_lora_tuning
[NeMo I 2024-02-29 13:43:09 exp_manager:835] TensorboardLogger has been set up
[NeMo W 2024-02-29 13:43:09 exp_manager:931] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 50. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo I 2024-02-29 13:43:32 megatron_init:234] Rank 0 has data parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:237] All data parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:238] Ranks 0 has data parallel rank: 0
[NeMo I 2024-02-29 13:43:32 megatron_init:246] Rank 0 has model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:247] All model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:257] Rank 0 has tensor model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:261] All tensor model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:262] Rank 0 has tensor model parallel rank: 0
[NeMo I 2024-02-29 13:43:32 megatron_init:276] Rank 0 has pipeline model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:288] Rank 0 has embedding group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:294] All pipeline model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:295] Rank 0 has pipeline model parallel rank 0
[NeMo I 2024-02-29 13:43:32 megatron_init:296] All embedding group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:297] Rank 0 has embedding rank: 0
24-02-29 13:43:32 - PID:2242 - rank:(0, 0, 0, 0) - microbatches.py:39 - INFO - setting number of micro-batches to constant 8
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo I 2024-02-29 13:43:32 tokenizer_utils:191] Getting SentencePiece with model: /tmp/tmpdiuso3r4/49ec005ed7e34fdab15e18659ba3b65a_tokenizer.model
[NeMo I 2024-02-29 13:43:32 megatron_base_model:315] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1554] apply_query_key_layer_scaling is only enabled when using FP16, setting it to False and setting NVTE_APPLY_QK_LAYER_SCALING=0
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: num_moe_experts in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: fp8_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: clone_scatter_output_in_embedding in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
Loading distributed checkpoint with TensorStoreLoadShardedStrategy
[NeMo I 2024-02-29 13:44:52 nlp_overrides:752] Model MegatronGPTSFTModel was successfully restored from /usr/src/app/model/mistral.nemo.
[NeMo I 2024-02-29 13:44:52 megatron_gpt_peft_tuning:82] Adding adapter weights to the model for PEFT
[NeMo I 2024-02-29 13:44:52 nlp_adapter_mixins:182] Before adding PEFT params:
| Name | Type | Params
-----------------------------------
0 | model | GPTModel | 7.2 B
-----------------------------------
0 Trainable params
7.2 B Non-trainable params
7.2 B Total params
28,966.928Total estimated model params size (MB)
[NeMo I 2024-02-29 13:44:55 nlp_adapter_mixins:195] After adding PEFT params:
| Name | Type | Params
-----------------------------------
0 | model | GPTModel | 7.3 B
-----------------------------------
10.5 M Trainable params
7.2 B Non-trainable params
7.3 B Total params
29,008.871Total estimated model params size (MB)
[NeMo W 2024-02-29 13:44:55 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/configuration_validator.py:153: UserWarning: The `batch_idx` argument in `MegatronGPTSFTModel.on_train_batch_start` hook may not match with the actual batch index when using a `dataloader_iter` argument in your `training_step`.
rank_zero_warn(
[NeMo W 2024-02-29 13:44:55 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/configuration_validator.py:153: UserWarning: The `batch_idx` argument in `MegatronGPTSFTModel.on_train_batch_end` hook may not match with the actual batch index when using a `dataloader_iter` argument in your `training_step`.
rank_zero_warn(
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:752] Building GPT SFT validation datasets.
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.098850
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.053183
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_val.jsonl
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000691
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:165] Computing global indices
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:755] Length of val dataset: 50
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:759] Building GPT SFT test datasets.
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.047882
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.047631
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_test.jsonl
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000603
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:165] Computing global indices
[NeMo I 2024-02-29 13:44:56 megatron_gpt_sft_model:762] Length of test dataset: 500
[NeMo I 2024-02-29 13:44:56 megatron_gpt_sft_model:766] Building GPT SFT traing datasets.
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.050147
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.049478
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_train.jsonl
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000572
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:165] Computing global indices
[NeMo W 2024-02-29 13:44:56 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py:1332: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:83.)
counts = torch.cuda.LongTensor([1])
[NeMo I 2024-02-29 13:44:57 dataset_utils:1341] > loading indexed mapping from ./pubmedqa/pubmedqa_train.jsonl_pubmedqa_train.jsonl_indexmap_402mns_2046msl_0.00ssp_1234s.npy
[NeMo I 2024-02-29 13:44:57 dataset_utils:1344] loaded indexed file in 0.000 seconds
[NeMo I 2024-02-29 13:44:57 dataset_utils:1345] total number of samples: 450
make: Entering directory '/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron'
make: Nothing to be done for 'default'.
make: Leaving directory '/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron'
> building indices for blendable datasets ...
> sample ratios:
dataset 0, input: 1, achieved: 1
[NeMo I 2024-02-29 13:44:57 blendable_dataset:67] > elapsed time for building blendable dataset indices: 0.03 (sec)
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:768] Length of train dataset: 402
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2024-02-29 13:44:57 nlp_overrides:150] Configuring DDP for model parallelism.
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 nlp_adapter_mixins:245] Optimizer groups set:
| Name | Type | Params
-----------------------------------
0 | model | GPTModel | 7.3 B
-----------------------------------
10.5 M Trainable params
7.2 B Non-trainable params
7.3 B Total params
29,008.871Total estimated model params size (MB)
[NeMo I 2024-02-29 13:44:57 modelPT:728] Optimizer config = FusedAdam (
Parameter Group 0
betas: [0.9, 0.98]
bias_correction: True
eps: 1e-08
lr: 0.0001
weight_decay: 0.01
)
[NeMo I 2024-02-29 13:44:57 lr_scheduler:910] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7ff9feffc070>"
will be used during training (effective maximum steps = 50) -
Parameters :
(warmup_steps: 50
min_lr: 0.0
constant_steps: 0
max_steps: 50
)
| Name | Type | Params
-----------------------------------
0 | model | GPTModel | 7.3 B
-----------------------------------
10.5 M Trainable params
7.2 B Non-trainable params
7.3 B Total params
29,008.871Total estimated model params size (MB)
```
But there is another error:
```
Sanity Checking: 0it [00:00, ?it/s][NeMo W 2024-02-29 13:45:11 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:438: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 224 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
[NeMo W 2024-02-29 13:45:11 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py:148: UserWarning: Found `dataloader_iter` argument in the `validation_step`. Note that the support for this signature is experimental and the behavior is subject to change.
rank_zero_warn(
[NeMo W 2024-02-29 13:45:25 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/apex/transformer/pipeline_parallel/utils.py:81: UserWarning: This function is only for unittest
warnings.warn("This function is only for unittest")
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.precision=bf16', 'trainer.val_check_interval=20', 'trainer.max_steps=50', 'model.megatron_amp_O2=False', '++model.mcore_gpt=True', 'model.tensor_model_parallel_size=1', 'model.pipeline_model_parallel_size=1', 'model.micro_batch_size=1', 'model.global_batch_size=8', 'model.restore_from_path=./model/mistral.nemo', 'model.data.train_ds.num_workers=0', 'model.data.validation_ds.num_workers=1', 'model.data.train_ds.file_names=[./pubmedqa/pubmedqa_train.jsonl]', 'model.data.train_ds.concat_sampling_probabilities=[1.0]', 'model.data.validation_ds.file_names=[./pubmedqa/pubmedqa_val.jsonl]', 'model.peft.peft_scheme=lora', 'exp_manager.checkpoint_callback_params.mode=min']
Traceback (most recent call last):
File "/usr/src/app/./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py", line 87, in main
trainer.fit(model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-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 "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1021, in _run_stage
self._run_sanity_check()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1050, in _run_sanity_check
val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
return loop_run(self, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 115, in run
self._evaluation_step(batch, batch_idx, dataloader_idx)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 376, in _evaluation_step
output = call._call_strategy_hook(trainer, hook_name, *step_kwargs.values())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 293, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 338, in validation_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1521, in forward
else self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1357, in _run_ddp_forward
return self.module(*inputs, **kwargs) # type: ignore[index]
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/overrides/base.py", line 102, in forward
return self._forward_module.validation_step(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 387, in validation_step
return self.inference_step(dataloader_iter, batch_idx, 'validation', dataloader_idx)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 403, in inference_step
loss = super().validation_step(itertools.chain([batch]), batch_idx)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 963, in validation_step
loss = self.fwd_bwd_step(dataloader_iter, batch_idx, True)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 347, in fwd_bwd_step
losses_reduced_per_micro_batch = fwd_bwd_function(
File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 327, in forward_backward_no_pipelining
output_tensor = forward_step(
File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 183, in forward_step
output_tensor, loss_func = forward_step_func(data_iterator, model)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 863, in fwd_output_and_loss_func
output_tensor = model(**forward_args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/models/gpt/gpt_model.py", line 166, in forward
hidden_states = self.decoder(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_block.py", line 311, in forward
hidden_states, context = layer(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_layer.py", line 153, in forward
attention_output_with_bias = self.self_attention(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/attention.py", line 263, in forward
core_attn_out = self.core_attention(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/custom_layers/transformer_engine.py", line 427, in forward
return super().forward(
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 1235, in forward
qkv_layout = _get_qkv_layout(query_layer, key_layer, value_layer,
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 464, in _get_qkv_layout
raise Exception("The provided qkv memory layout is not supported!")
Exception: The provided qkv memory layout is not supported!
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
**Additional context**
GPU : H100*1 | {
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"jenkins"
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} | Update mcore version in Dockerfile
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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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 ?
Adds the multimodal domain to labeler.
**Collection**: [Core, Multimodal]
# Jenkins CI
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# Before your PR is "Ready for review"
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"Yes its deprecated anyway in favor of @erastorgueva-nv notebook"
] | 2024-02-28T22:30:43 | 2024-03-01T07:46:16 | 2024-03-01T07:45:43 | COLLABORATOR | {
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} | # What does this PR do ?
Update transcribe call to match new api
**Collection**: [ASR]
# Changelog
- Fix transcribe calls to match new API
- Remove deprecated notebook
# Jenkins CI
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**PR Type**:
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# Changelog
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# Usage
* You can potentially add a usage example below
# Jenkins CI
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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# Jenkins CI
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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Fix the example command for convert_nemo_llama_to_hf.py
**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
- [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.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
rST fixes to help resolve erros and warnings when pulling from the submodule in NeMo Framework repo_docs repo.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Corrected multiple rST issues across the docs folder inorder to fix build issues.
# 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 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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Add a one line overview of what this PR aims to accomplish.
fix the bug where hybrid TDT CTC model uses incorrect decoding class for inference.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
# Usage
n/a
# 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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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
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## Who can review?
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Iterating on README update for generative ai.
TODO: add key features back to readme instead of linking to them
**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.
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PEFT inference using .ckpt files
# 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.
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"Hmm can you try to see if you remove trailing new line in your manifest file, and also try to see if removing the json indent helps ?",
"@titu1994 I am experiencing much the same issue on my end. I tried as such and had no luck. I get the same JSON error as the one posted above. \r\nwhen doing as suggested by re... | 2024-02-27T23:57:23 | 2024-04-23T01:45:13 | 2024-04-23T01:45:13 | NONE | {
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} | null | null | null | **Describe the bug**
This bug occurs when I try to use the canary-1b model, it is not processing the json file correctly
**Steps/Code to reproduce bug**
This is my code
```
from nemo.collections.asr.models import EncDecMultiTaskModel
# load model
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
predicted_text = canary_model.transcribe(
"transcribe_manifest.json",
batch_size=16, # batch size to run the inference with
)
```
transcribe_manifest.json
```
{
"audio_filepath": "/home/bot/roarai/rag/scraper/Scrape_vid/transcibe_manifest.json",
"duration": 40.0,
"taskname": "asr",
"source_lang": "en",
"target_lang": "en",
"pnc": "yes"
}
```
Error
```
[NeMo I 2024-02-27 15:52:11 nemo_logging:381] PADDING: 0
[NeMo I 2024-02-27 15:52:15 nemo_logging:381] Model EncDecMultiTaskModel was successfully restored from /home/bot/.cache/huggingface/hub/models--nvidia--canary-1b/snapshots/5113160c7a3e52a7e5de1d904810d7aeabe6135b/canary-1b.nemo.
Traceback (most recent call last):
File "/home/bot/roarai/rag/scraper/Scrape_vid/canary.py", line 12, in <module>
predicted_text = canary_model.transcribe(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/site-packages/nemo/collections/asr/models/aed_multitask_models.py", line 406, in transcribe
audio = manifest_utils.read_manifest(manifest_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/site-packages/nemo/collections/asr/parts/utils/manifest_utils.py", line 480, in read_manifest
item = json.loads(line)
^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bot/anaconda3/envs/rag/lib/python3.11/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 2 column 1 (char 2)
```
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} | # What does this PR do ?
Fix AccessMixin by using the `model_guid` inside `ModelPT`, such that different models don't overwrite each other's access config when calling `AccesssMixin.set_access_cfg()`, `AccesssMixin.update_access_cfg()` and etc.
**Collection**: [asr,core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #8494
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"url": "https://a... | closed | false | null | [] | null | [] | 2024-02-27T21:46:42 | 2024-02-28T03:44:15 | 2024-02-28T03:44:15 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
rST fixes to help resolve erros and warnings when pulling from the submodule in NeMo Framework repo_docs repo.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Corrected multiple rST issues across the docs folder inorder to fix build issues.
# 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
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} | # What does this PR do ?
Fix failing lhotse tests due to randomness
**Collection**: ASR
# Changelog
- Fix failing lhotse tests due to randomness
# 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
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} | # What does this PR do ?
Fix multimodal data prep tutorial
**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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- [ ] 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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- [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?
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 ?
Fix multimodal data prep tutorial
**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.
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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 "CUDA graphs" implementation to label looping algorithm for greedy decoding (RNN-T and TDT)
# Usage
```shell
python examples/asr/speech_to_text_eval.py \
pretrained_name=stt_en_fastconformer_transducer_large \
dataset_manifest=test_other.json \
batch_size=16 \
output_filename=test_other_decoded.jsonl \
amp=true \
amp_dtype=bfloat16 \
rnnt_decoding.greedy.loop_labels=true \
rnnt_decoding.greedy.use_cuda_graph_decoder=true
```
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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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.",
"jenkins"
] | 2024-02-27T17:03:04 | 2024-03-21T16:39:35 | 2024-03-21T16:39:34 | COLLABORATOR | {
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Add SC2 conversion script between .nemo and HF format in both ways.
Add model config for SC2 with comments for all 3 variant.
**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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* Related to # (issue)
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"jenkins"
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} | # What does this PR do ?
We're adding an option to control matmul precision in transcribe speech and chunked infer scripts.
By setting it to `medium` or `high` we achieve **about 2x speedup** in transcription with Canary, without impact on WER (to be confirmed on larger scale tests). Possibly other models can benefit from that setting as well, esp. if they were trained with bfloat16.
**Collection**: ASR
# Changelog
- Option to control matmul precision in transcribe speech
# Usage
```bash
python NeMo/examples/asr/transcribe_speech.py \
dataset_manifest=input.json \
output_filename=output.json \
batch_size=32 \
pretrained_name=nvidia/canary-1b \
gt_text_attr_name=answer \
matmul_precision=medium
```
# 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.
# Additional Information
* Related to # (issue)
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"merged_at": null
} | # 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_hf_chatglm_to_nemo.py --in-file '/mount/data/chatglm3-6b' --out-file '/mount/nemo_models/chatglm3.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
```
### PEFT
```
./examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
trainer.precision=bf16 \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.max_steps=200 \
trainer.val_check_interval=20 \
++trainer.limit_val_batches=10 \
trainer.gradient_clip_val=1.0 \
model.megatron_amp_O2=True \
++model.mcore_gpt=True \
model.restore_from_path=${nemo_model} \
model.peft.peft_scheme=${scheme} \
model.data.train_ds.file_names=["${TRAIN_DS}"] \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.train_ds.label_key='output' \
model.data.train_ds.num_workers=0 \
model.data.validation_ds.file_names=["${VALID_DS}"] \
model.data.validation_ds.label_key='output' \
model.data.validation_ds.num_workers=0 \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.global_batch_size=${GBS} \
model.micro_batch_size=1
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8527 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8527/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8527/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8527/events | https://github.com/NVIDIA/NeMo/pull/8527 | 2,156,389,449 | PR_kwDOC_bI7s5oCMRb | 8,527 | support chatglm2&3 | {
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"patch_url": "https://github.com/NVIDIA/NeMo/pull/8527.patch",
"merged_at": null
} | # 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_hf_chatglm_to_nemo.py --in-file '/mount/data/chatglm3-6b' --out-file '/mount/nemo_models/chatglm3.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
```
### PEFT
```
./examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
trainer.precision=bf16 \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.max_steps=200 \
trainer.val_check_interval=20 \
++trainer.limit_val_batches=10 \
trainer.gradient_clip_val=1.0 \
model.megatron_amp_O2=True \
++model.mcore_gpt=True \
model.restore_from_path=${nemo_model} \
model.peft.peft_scheme=${scheme} \
model.data.train_ds.file_names=["${TRAIN_DS}"] \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.train_ds.label_key='output' \
model.data.train_ds.num_workers=0 \
model.data.validation_ds.file_names=["${VALID_DS}"] \
model.data.validation_ds.label_key='output' \
model.data.validation_ds.num_workers=0 \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.global_batch_size=${GBS} \
model.micro_batch_size=1
```
# 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)
| {
"login": "Agoniii",
"id": 18502794,
"node_id": "MDQ6VXNlcjE4NTAyNzk0",
"avatar_url": "https://avatars.githubusercontent.com/u/18502794?v=4",
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"type": "User",
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https://api.github.com/repos/NVIDIA/NeMo/issues/8526 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8526/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8526/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8526/events | https://github.com/NVIDIA/NeMo/pull/8526 | 2,156,060,459 | PR_kwDOC_bI7s5oBDo3 | 8,526 | Add Kaiming uniform init for LoRA adapters | {
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"name": "NLP",
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"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins"
] | 2024-02-27T09:11:56 | 2024-02-28T13:18:51 | 2024-02-28T13:18:50 | COLLABORATOR | {
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"merged_at": "2024-02-28T13:18:50"
} | # What does this PR do ?
Add Kaiming uniform initialization for LoRA adapters
**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.
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# Usage
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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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# Usage
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```python
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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?
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.
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"jenkins",
"@titu1994 FYI, I made a fix. I tested by simply running running\r\n\r\n```\r\npython examples/asr/speech_to_text_eval.py model_path=$HOME/.cache/huggingface/hub/models--nvidia--parakeet-ctc-1.1b/snapshots/085a3de63c7598065b072cd8f2182e6a5fa593eb/parakeet-ctc-1.1b.nemo dataset_manifest=/home/dgalvez/... | 2024-02-27T00:42:52 | 2024-03-07T20:45:49 | 2024-03-07T20:45:48 | COLLABORATOR | {
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} | # What does this PR do ?
Accelerate CTC greedy decoding by around 10x, speeding up overall CTC runtime for parakeet-ctc-1.1b by close to 10%.
**Collection**: ASR
# Changelog
There were a few problems with CTC greedy decoding before:
- It would copy GPU memory to pageable memory originally, which is very slow compared to copying to pinned memory. Unfortunately, cudaMallocHost() is synchronous and "slow", but fortunately pytorch has a free list of recent pinned memory allocations in its pinned memory allocator, allowing us to reduce the number of these calls. The cost of the memcpy before my change is excessive, taking ~20-40ms. After changing to use pinned memory, it takes only 2-4ms. This is the primary source of the 10x speedup. Note that doing the initial pinned memory allocations is slow (discussed later in this message).
<img width="789" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/11a1addd-b9a7-4060-84f7-712ea36062e3">
- A single scalar of logits_len/decoder_lengths was copied from GPU to CPU at a time, rather than all at once in a single call. This caused neddless overhead. We could also use a pinned memory allocation for copying logits_len as well, but using pinned memory is less important for small allocations. The original code's trace looks like this:
<img width="1061" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/9467224f-a1a0-492d-a4a5-9f3804e975ae"> Each red box followed by a green box is copying a single scalar value from GPU to CPU
- detatch() was called in function marked with @nograd
- For some reason, someone was using torch_tensor.numpy().tolist() instead of torch_tensor.tolist(). I don't believe numpy() makes a copy of the data, but it is unnecessary.
The main important improvements are the first two bullet points. Everything else is unimportant.
There are more opportunities for improvement. In particular, logits gets copied to cpu twice if trcfg.return_hypotheses is True. However, this overhead is now small since it will always just use the previous pinned host allocation from the free list.
Performance improvements:
I ran this code on an A100 GPU, on a machine with 16 CPU cores:
```
python examples/asr/speech_to_text_eval.py \
pretrained_name=nvidia/parakeet-ctc-1.1b \
dataset_manifest=/home/dgalvez/scratch/data/test_other.json \
batch_size=16 output_filename=test_other_decoded.jsonl amp=true \
amp_dtype=bfloat16 use_cer=false num_workers=1
```
I applied this diff to run multiple times:
```
modified examples/asr/transcribe_speech.py
@@ -367,14 +367,19 @@ def main(cfg: TranscriptionConfig) -> Union[TranscriptionConfig, List[Hypothesis
decoder_type=cfg.decoder_type,
)
else:
- transcriptions = asr_model.transcribe(
- audio=filepaths,
- batch_size=cfg.batch_size,
- num_workers=cfg.num_workers,
- return_hypotheses=cfg.return_hypotheses,
- channel_selector=cfg.channel_selector,
- augmentor=augmentor,
- )
+ for i in range(5):
+ if i == 1:
+ torch.cuda.cudart().cudaProfilerStart()
+ transcriptions = asr_model.transcribe(
+ audio=filepaths,
+ batch_size=cfg.batch_size,
+ num_workers=cfg.num_workers,
+ return_hypotheses=cfg.return_hypotheses,
+ channel_selector=cfg.channel_selector,
+ augmentor=augmentor,
+ )
+ if i == 1:
+ torch.cuda.cudart().cudaProfilerStop()
if cfg.dataset_manifest is not None:
logging.info(f"Finished transcribing from manifest file: {cfg.dataset_manifest}")
```
Time to do each of 5 evaluations of librispeech test other before my changes:
32 seconds
28
29
29
29
Average: 29.4 seconds
Average excluding first (warmup): 28.75 seconds
Time to do each of 5 evaluations of librispeech test other after my changes:
30 seconds
26
26
25
25
Average: 26.4 seconds
Average excluding first (warmup): 25.5 seconds
This corresponds to an almost 10% speedup. This meets expectations, since 10% of the time was originally spent on CTC greedy decoding before.
P99 latency can increase for the first few calls to the model with this change. This is because we are now calling cudaMallocHost(), and then doing a pinned memory copy from GPU to CPU, which is slower than doing a paged memory copy from GPU to CPU. However, the cudaMallocHost() calls will be cached over time, allowing us to avoid the overhead of them in later evaluations of the dataset. The expense of a cudaMallocHost() (or cudaHostAlloc()) call can be seen here:
<img width="921" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/fd7fd4bc-833d-43ba-9226-959ebb7c9ee6">
# Usage
See above
# Jenkins CI
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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"jenkins",
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"jenkins"
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} | # What does this PR do ?
Revert FP8 integration for compatibility with third party ckpts
**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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- [x ] Did you write any new necessary tests?
- [x ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x ] 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.
# Additional Information
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"jenkins",
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} | # What does this PR do ?
Fix a bug where ptuning training with O2 would not save buffers like `inference_table` to the checkpoint, which leads to errors during model evaluation.
**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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**Pre checks**:
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- [x] Bugfix
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} | # What does this PR do ?
Fix link in multimodal dataprep tutoria
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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# Before your PR is "Ready for review"
**Pre checks**:
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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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} | # What does this PR do ?
Fix link in multimodal dataprep tutoria
**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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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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} | # 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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} | # What does this PR do ?
Fix link in multimodal dataprep tutorial
**Collection**: Multimodal
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8513 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8513/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8513/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8513/events | https://github.com/NVIDIA/NeMo/pull/8513 | 2,155,251,656 | PR_kwDOC_bI7s5n-TOx | 8,513 | fix FIM RNG issue | {
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Correcting RST in the mllm section to correct build issues with the NeMo Framework User Guide content.
**Collection**: [Note which collection this PR will affect]
# Changelog
RST code updates are made in the following RST files.
- checkpoint.rst
- configs.rst
- intro.rst
- neva.rst
# 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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2. Also, add import guards for megatron related imports in `gpt_full_te_layer_autocast_spec.py`
**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# 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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Tutorial on Lhotse Dataloading for ASR fine-tuning
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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 | [] | 2024-02-26T17:46:23 | 2024-02-26T18:21:40 | 2024-02-26T18:21:40 | COLLABORATOR | {
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"patch_url": "https://github.com/NVIDIA/NeMo/pull/8509.patch",
"merged_at": "2024-02-26T18:21:39"
} | # What does this PR do ?
PR: [8328](https://github.com/NVIDIA/NeMo/pull/8328) moved ASR and TTS import tests to Jenkinsfile from github actions. This commit reverts that.
**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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"jenkins"
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"merged_at": "2024-02-26T20:07:46"
} | # What does this PR do ?
Fix `scripts/speech_recognition/estimate_duration_bins.py` from multiple paths
**Collection**: ASR
# Changelog
- Fix `scripts/speech_recognition/estimate_duration_bins.py` from multiple paths
# 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."
] | 2024-02-26T10:22:29 | 2024-04-04T01:44:38 | 2024-04-04T01:44:38 | NONE | {
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} | null | null | null | from nemo.collections.asr.parts.speaker_utils import rttm_to_labels, labels_to_pyannote_object
IN Speaker_Diarization_Inference.ipynb is not running .
and thus the entire thing is not running
------------------------------------------------------------
error:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
[<ipython-input-14-0dca4be09a8a>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from nemo.collections.asr.parts.speaker_utils import rttm_to_labels, labels_to_pyannote_object
17 frames
[/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/apply_func.py](https://localhost:8080/#) in <module>
27 if _TORCHTEXT_AVAILABLE:
28 if _compare_version("torchtext", operator.ge, "0.9.0"):
---> 29 from torchtext.legacy.data import Batch
30 else:
31 from torchtext.data import Batch
ModuleNotFoundError: No module named 'torchtext.legacy'
----------------------------------------------------------------------------------------------------------------------
all the suggestions and other things are not working | {
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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."
] | 2024-02-26T07:06:01 | 2024-04-04T01:44:40 | 2024-04-04T01:44:39 | NONE | {
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} | null | null | null | from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel
ModuleNotFoundError: No module named 'nemo.collections.multimodal.models'
I checked convert_external_clip_to_nemo.py is having correct path to megatron_clip_models.py | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8505 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8505/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8505/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8505/events | https://github.com/NVIDIA/NeMo/issues/8505 | 2,153,402,279 | I_kwDOC_bI7s6AWk-n | 8,505 | GPT fp8 pretrain not working on H20: Caught signal 8 (Floating point exception: integer divide by zero) | {
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"It seems the issue is due to CUDA 12.2 support for FP8 cuBLASLt matmul operations. After I use NeMo 24.01.framework `nvcr.io/nvidia/nemo:24.01.framework` which use CUDA 12.3, the issue is resolved.\r\n\r\nref: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html",
"@houminz Hi, houminz. Could you t... | 2024-02-26T07:01:42 | 2024-04-19T01:44:45 | 2024-04-19T01:44:45 | NONE | {
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} | null | null | null | **Describe the bug**
We are trying to run GPT fp8 pretraining on NVIDIA H20 according to [NeMo docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html) on single node with 8 GPUs
However, we get the error below:
```
Sanity Checking DataLoader 0: 0%| | 0/8 [00:00<?, ?it/s][n154-161-133:121882:0:121882] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121876:0:121876] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121880:0:121880] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121878:0:121878] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121664:0:121664] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121879:0:121879] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121877:0:121877] Caught signal 8 (Floating point exception: integer divide by zero)
[n154-161-133:121881:0:121881] Caught signal 8 (Floating point exception: integer divide by zero)
```
**Steps/Code to reproduce bug**
1. Bootstrapping the NeMo docker container from NGC, we use `23.10`
```
docker run -d --name nemo \
-u root \
-v /data:/data \
--privileged \
--network=host \
--shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
--rm --gpus all \
nvcr.io/nvidia/nemo:23.10 \
sleep infinity
```
2. Download and preprocess the enwiki dataset according the [NeMo docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html) using HuggingFace GPT2 tokenizer files under path `/data`
```
docker exec -it nemo bash
cd /data
python /workspace/nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \
--input=train_data.jsonl \
--json-keys=text \
--tokenizer-library=megatron \
--vocab gpt2-vocab.json \
--dataset-impl mmap \
--tokenizer-type GPT2BPETokenizer \
--merge-file gpt2-merges.txt \
--output-prefix=hfbpe_gpt_training_data \
--append-eod \
--workers=96
```
3. Launch the Single Node 8 GPU job in the container which enabling fp8
```
python /workspace/nemo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/workspace/nemo/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.max_epochs=null \
trainer.max_steps=300000 \
trainer.val_check_interval=300 \
trainer.log_every_n_steps=50 \
trainer.limit_val_batches=50 \
trainer.limit_test_batches=50 \
trainer.accumulate_grad_batches=1 \
trainer.precision=bf16 \
model.transformer_engine=True \
model.fp8=True \
model.micro_batch_size=6 \
model.global_batch_size=192 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
model.max_position_embeddings=1024 \
model.encoder_seq_length=1024 \
model.hidden_size=768 \
model.ffn_hidden_size=3072 \
model.num_layers=12 \
model.num_attention_heads=12 \
model.init_method_std=0.021 \
model.hidden_dropout=0.1 \
model.layernorm_epsilon=1e-5 \
model.tokenizer.vocab_file=gpt2-vocab.json \
model.tokenizer.merge_file=gpt2-merges.txt \
model.data.data_prefix=[1.0,hfbpe_gpt_training_data_text_document] \
model.data.num_workers=2 \
model.data.seq_length=1024 \
model.data.splits_string=\'980,10,10\' \
model.optim.name=fused_adam \
model.optim.lr=6e-4 \
model.optim.betas=[0.9,0.95] \
model.optim.weight_decay=0.1 \
model.optim.sched.name=CosineAnnealing \
model.optim.sched.warmup_steps=750 \
model.optim.sched.constant_steps=80000 \
model.optim.sched.min_lr=6e-5 \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=val_loss \
exp_manager.checkpoint_callback_params.save_top_k=3 \
exp_manager.checkpoint_callback_params.mode=min \
exp_manager.checkpoint_callback_params.always_save_nemo=False
```
**Expected behavior**
The job should launch successfully and start training.
We execute the same experiment on NVIDIA H100, and it works
```
Epoch 0: : 5%|▋ | 15900/301500 [1:22:Epoch 0, global step 15900: 'val_loss' reached 3.04824 (best 3.04824), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15900-consumed_samples=3052800.0.ckpt' as top 3
[NeMo I 2024-02-26 06:39:06 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15000-consumed_samples=2880000.0.ckpt
[NeMo I 2024-02-26 06:39:09 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15600-consumed_samples=2995200.0-last.ckpt
Epoch 0: : 5%|▊ | 16200/301500 [1:24:Epoch 0, global step 16200: 'val_loss' reached 3.04690 (best 3.04690), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=16200-consumed_samples=3110400.0.ckpt' as top 3
[NeMo I 2024-02-26 06:40:41 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15300-consumed_samples=2937600.0.ckpt
[NeMo I 2024-02-26 06:40:44 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15900-consumed_samples=3052800.0-last.ckpt
Epoch 0: : 5%|▊ | 16500/301500 [1:25:Epoch 0, global step 16500: 'val_loss' reached 3.04502 (best 3.04502), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=16500-consumed_samples=3168000.0.ckpt' as top 3
[NeMo I 2024-02-26 06:42:15 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15600-consumed_samples=2995200.0.ckpt
[NeMo I 2024-02-26 06:42:18 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=16200-consumed_samples=3110400.0-last.ckpt
Epoch 0: : 6%|▊ | 16800/301500 [1:27:Epoch 0, global step 16800: 'val_loss' reached 3.04356 (best 3.04356), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.04-step=16800-consumed_samples=3225600.0.ckpt' as top 3
[NeMo I 2024-02-26 06:43:50 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=15900-consumed_samples=3052800.0.ckpt
[NeMo I 2024-02-26 06:43:53 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.05-step=16500-consumed_samples=3168000.0-last.ckpt
Epoch 0: : 6%|▊ | 17100/301500 [1:29:Epoch 0, global step 17100: 'val_loss' reached 3.04004 (best 3.04004), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.04-step=17100-consumed_samples=3283200.0.ckpt' as top 3
```
we also test bf16 training on NVIDIA H20, it also works
```
Epoch 0: : 0%| | 300/301500 [0Epoch 0, global step 300: 'val_loss' reached 5.98933 (best 5.98933), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=5.99-step=300-consumed_samples=57600.0.ckpt' as top 3
Epoch 0: : 0%| | 600/301500 [0Epoch 0, global step 600: 'val_loss' reached 4.94614 (best 4.94614), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.95-step=600-consumed_samples=115200.0.ckpt' as top 3
[NeMo I 2024-02-26 06:48:45 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=5.99-step=300-consumed_samples=57600.0-last.ckpt
Epoch 0: : 0%| | 900/301500 [0Epoch 0, global step 900: 'val_loss' reached 4.18590 (best 4.18590), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.19-step=900-consumed_samples=172800.0.ckpt' as top 3
[NeMo I 2024-02-26 06:50:43 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.95-step=600-consumed_samples=115200.0-last.ckpt
Epoch 0: : 0%| | 1200/301500 [0Epoch 0, global step 1200: 'val_loss' reached 3.88466 (best 3.88466), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.88-step=1200-consumed_samples=230400.0.ckpt' as top 3
[NeMo I 2024-02-26 06:52:38 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=5.99-step=300-consumed_samples=57600.0.ckpt
[NeMo I 2024-02-26 06:52:40 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.19-step=900-consumed_samples=172800.0-last.ckpt
Epoch 0: : 0%| | 1500/301500 [09Epoch 0, global step 1500: 'val_loss' reached 3.73976 (best 3.73976), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.74-step=1500-consumed_samples=288000.0.ckpt' as top 3
[NeMo I 2024-02-26 06:54:36 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.95-step=600-consumed_samples=115200.0.ckpt
[NeMo I 2024-02-26 06:54:39 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.88-step=1200-consumed_samples=230400.0-last.ckpt
Epoch 0: : 1%| | 1800/301500 [11Epoch 0, global step 1800: 'val_loss' reached 3.63803 (best 3.63803), saving model to '/data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.64-step=1800-consumed_samples=345600.0.ckpt' as top 3
[NeMo I 2024-02-26 06:56:35 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=4.19-step=900-consumed_samples=172800.0.ckpt
[NeMo I 2024-02-26 06:56:38 nlp_overrides:412] Removing checkpoint: /data/nemo_experiments/megatron_gpt/checkpoints/megatron_gpt--val_loss=3.74-step=1500-consumed_samples=288000.0-last.ckpt
Epoch 0: : 1%| | 1829/301500 [12:02<32:53:20, v_num=0, reduced_train_loss=3.670, global_step=1828.0, consumed_samples=3.51e+5, train_step_timing in s=0.351, val_loss=3.640]
```
**Environment overview (please complete the following information)**
- Environment location: Docker singularity containers created from NVIDIA's NGC NeMo containers 23.10
- Method of NeMo install: Pre-installed in container.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
```
# docker pull
docker pull nvcr.io/nvidia/nemo:23.10
# docker run already shown above
```
**Additional context**
Add any other context about the problem here.
Example: GPU model H20
Backtrace for H20
```
==== backtrace (tid: 121876) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
==== backtrace (tid: 121878) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
==== backtrace (tid: 121880) ====
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
0 0x0000000000042520 __sigaction() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
10 0x0000000000057c56 te_gemm() ???:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
23 0x000000000016942b PyObject_Call() ???:0
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
27 0x0000000000280700 PyInit__datetime() ???:0
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
28 0x000000000016942b PyObject_Call() ???:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
23 0x000000000016942b PyObject_Call() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
28 0x000000000016942b PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
=================================
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
==== backtrace (tid: 121881) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
==== backtrace (tid: 121879) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
==== backtrace (tid: 121877) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
==== backtrace (tid: 121664) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
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52 0x00000000001687f1 PyMethod_New() ???:0
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54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
Floating point exception (core dumped)
root@n154-161-133:/data# ==== backtrace (tid: 121882) ====
0 0x0000000000042520 __sigaction() ???:0
1 0x0000000000b83824 cublasLt_for_cublas_ZZZ() ???:0
2 0x0000000000897f37 cublasLt_for_cublas_ZZZ() ???:0
3 0x000000000072c917 cublasLtLegacyGemmUtilizationZZZ() ???:0
4 0x00000000007ff3de cublasLtMatmulAlgoCheck() ???:0
5 0x0000000000800399 cublasLtMatmulAlgoCheck() ???:0
6 0x0000000000800ecc cublasLtMatmulAlgoCheck() ???:0
7 0x0000000000829a9d cublasLtMatmulAlgoGetHeuristic() ???:0
8 0x0000000000273439 transformer_engine::cublas_gemm() ???:0
9 0x0000000000273904 nvte_cublas_gemm() ???:0
10 0x0000000000057c56 te_gemm() ???:0
11 0x00000000000bb968 te_gemm_ts() /workspace/TransformerEngine/transformer_engine/pytorch/csrc/ts_fp8_op.cpp:265
12 0x00000000000be875 c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >::operator()() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/WrapFunctionIntoRuntimeFunctor.h:18
13 0x00000000000be875 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, at::Tensor (at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long)>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
14 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false, 0ul, 1ul, 2ul, 3ul, 4ul, 5ul, 6ul, 7ul, 8ul, 9ul, 10ul, 11ul, 12ul, 13ul, 14ul, 15ul, 16ul, 17ul, 18ul, 19ul, 20ul, 21ul, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:501
15 0x00000000000bfaca c10::impl::call_functor_with_args_from_stack<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:513
16 0x00000000000bfaca c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long), at::Tensor, c10::guts::typelist::typelist<at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, long, long, at::Tensor, at::Tensor, long, at::Tensor, at::Tensor, long, at::Tensor, long, at::Tensor, long, long, long> >, false>::call() /usr/local/lib/python3.10/dist-packages/torch/include/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:579
17 0x0000000004817624 c10::Dispatcher::callBoxed() register_c10_ops.cpp:0
18 0x000000000085fdb4 torch::jit::invokeOperatorFromPython() ???:0
19 0x000000000086067e torch::jit::_get_operation_for_overload_or_packet() ???:0
20 0x00000000007459e6 pybind11::cpp_function::initialize<torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}, pybind11::object, pybind11::args, pybind11::kwargs, pybind11::name, pybind11::doc>(torch::jit::initJITBindings(_object*)::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#194}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const::{lambda(pybind11::args, pybind11::kwargs)#1}&&, pybind11::object (*)(pybind11::args, pybind11::kwargs), pybind11::name const&, pybind11::doc const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN() init.cpp:0
21 0x000000000039c407 pybind11::cpp_function::dispatcher() :0
22 0x000000000015a10e PyObject_CallFunctionObjArgs() ???:0
23 0x000000000016942b PyObject_Call() ???:0
24 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
25 0x000000000014fc14 _PyObject_FastCallDictTstate() ???:0
26 0x00000000001658d1 _PyObject_Call_Prepend() ???:0
27 0x0000000000280700 PyInit__datetime() ???:0
28 0x000000000016942b PyObject_Call() ???:0
29 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
30 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
31 0x000000000014453c _PyEval_EvalFrameDefault() ???:0
32 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
33 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
34 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
35 0x0000000000169492 PyObject_Call() ???:0
36 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
37 0x00000000001687f1 PyMethod_New() ???:0
38 0x0000000000169492 PyObject_Call() ???:0
39 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
40 0x00000000001687f1 PyMethod_New() ???:0
41 0x0000000000169492 PyObject_Call() ???:0
42 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
43 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
44 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
45 0x000000000016586c _PyObject_Call_Prepend() ???:0
46 0x0000000000280700 PyInit__datetime() ???:0
47 0x0000000000150a7b _PyObject_MakeTpCall() ???:0
48 0x000000000014a150 _PyEval_EvalFrameDefault() ???:0
49 0x00000000001687f1 PyMethod_New() ???:0
50 0x0000000000169492 PyObject_Call() ???:0
51 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
52 0x00000000001687f1 PyMethod_New() ???:0
53 0x0000000000169492 PyObject_Call() ???:0
54 0x00000000001455d7 _PyEval_EvalFrameDefault() ???:0
55 0x000000000015a9fc _PyFunction_Vectorcall() ???:0
56 0x000000000014fcbd _PyObject_FastCallDictTstate() ???:0
=================================
```
Any suggestions would be appreciated, Thanks
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} | # What does this PR do ?
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} | # What does this PR do ?
Add FP8 support for SD training.
**Collection**: [multimodal/text_to_image/statble_diffusion]
# Changelog
- Add config argument - use_te_fp8 to switch between fp16 and fp8 training
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```yaml
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```
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} | # What does this PR do ?
Fix `transcribe_speech.py` to use `audio_dir` as input instead of just `dataset_manifest`, also fix logging message. Tested on my local machine
**Collection**: [asr]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/8499 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8499/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8499/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8499/events | https://github.com/NVIDIA/NeMo/pull/8499 | 2,151,712,344 | PR_kwDOC_bI7s5nyghK | 8,499 | Fix progress bar to update the total steps with trainer.max_steps | {
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"jenkins",
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"jenkins",
"jenkins",
"jenkins"
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} | # What does this PR do ?
[PR 8426](https://github.com/NVIDIA/NeMo/pull/8426) returns the `len(dataloader)` in terms of total num of micro batches. This PR fixes the `CustomProgressBar` for nlp models to accommodate this change and display the correct `current_step/total_steps` in the bar.
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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"Can you please review and approve as soon as possible, the branch becomes out of sync every 2 hours and the whole workflow checks run again when I sync the branch to the main.\r\n\r\n@titu1994, @redoctopus, @jbalam-nv, @okuchaiev",
"Just a note, it's fine to not sync with main branch until it says there are merg... | 2024-02-23T19:25:11 | 2024-02-23T22:40:49 | 2024-02-23T21:23:01 | CONTRIBUTOR | {
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"merged_at": "2024-02-23T21:23:01"
} | # What does this PR do ?
The code snippet calling `repalce` instead of `replace` which could raise an `AttributeError`. Updated the code with proper function name
PR for
https://github.com/NVIDIA/NeMo/issues/8488
**Collection**: automatic speech recognition(ASR)
# Changelog
- Use proper method to replace double under scores with single underscore during data preperation
# Usage
* N/A for this PR
# 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
## Who can review?
@titu1994, @redoctopus, @jbalam-nv, @okuchaiev | {
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} | # What does this PR do ?
Fix multimodal tutorial links in user guide
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8495 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8495/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8495/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8495/events | https://github.com/NVIDIA/NeMo/pull/8495 | 2,151,307,850 | PR_kwDOC_bI7s5nxH6L | 8,495 | Add implementation of slimIPL algorithm. Add support for using ais da… | {
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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.",
"jenkins\r\n\r\n",
"jenkins",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or th... | 2024-02-23T15:22:40 | 2024-04-23T01:45:16 | 2024-04-23T01:45:15 | COLLABORATOR | {
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} | …tasets using local manifest.
# What does this PR do ?
Adds implementation of slimIPL algorithm.
# Usage
This can be used for pseudo-labeling if in the config "ipl" parameters are given
# 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**:
- [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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[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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"which `${INIT_CKPT}` did you use? could you also provide the full error log?",
"for `${INIT_CKPT}` I use `.nemo` checkpoint trained with same model config, the only difference in the config is that now iterCTC is enabled.\r\n```\r\ninterctc:\r\n loss_weights: [0.3]\r\n apply_at_layers: [8]\r\n```\r\n\r\... | 2024-02-23T14:45:27 | 2024-04-04T19:12:54 | 2024-04-04T19:12:54 | NONE | {
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} | null | null | null | Hi folks!
I have an already trained model without interCTC, which I want to initialize from and continue training, this time enabling interCTC loss.
Here is my training command:
```
CUDA_VISIBLE_DEVICES=0 python speech_to_text_ctc_bpe.py \
--config-path=conf/conformer/ \
--config-name=conformer_ctc_bpe exp_manager.name="conformer-inter-ctc" \
exp_manager.exp_dir=${EXP} \
exp_manager.resume_ignore_no_checkpoint=true \
exp_manager.resume_if_exists=true \
model.tokenizer.dir="${TOKENIZER}" \
model.train_ds.manifest_filepath="${TRAIN_MANIFEST}" \
model.validation_ds.manifest_filepath="${VAL_MANIFEST}" \
init_from_nemo_model="${INIT_CKPT}"
```
Here is an exception I get:
```
RuntimeError: Intermediate layer 8 was not captured! Check if length of model.encoder.captured_layer_outputs matches length of model.intermediate_loss_weights properties.
```
This looks like a bug.
Any suggestions on how to get this working without manually hacking model initialization process?
When I launch training from scratch with interCTC everything works fine!
`nemo-toolkit==1.21.0`
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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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} | # What does this PR do ?
Update k2 version commit for r1.23.0 (cherry-pick from main, #8478 )
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**:
- [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?
- [x] 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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