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Update package info to match branch name
**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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* Related to # (issue)
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# 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
- [ ] 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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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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# Jenkins CI
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# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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Remove precision args in trainer due to PTL update. The precision plugin and precision arg cannot be used together anymore.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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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Adding zero-shot evaluation code for new RETRO model. The evaluation code for legacy model is modified to *_legacy.py
This PR is on top of huvu/mcore_retro, which implements the new RETRO model.
The files this PR works on are:
+ examples/nlp/language_modeling/conf/megatron_retro_inference.yaml
+ examples/nlp/language_modeling/megatron_retro_eval.py
+ examples/nlp/language_modeling/megatron_retro_qatask_eval.py
+ examples/nlp/language_modeling/conf/megatron_retro_qatask.yaml
+ nemo/collections/nlp/modules/common/text_generation_strategy.py
+ nemo/collections/nlp/modules/common/text_generation_utils.py
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
This avoid doing useless computations (that may even filter out some tokens due to numerical approximation) when `top_p=1.0` (which is not supposed to have any effect).
**Collection**: nlp
# Changelog
- Skip top_p computations when set to 1.0
# Before your PR is "Ready for review"
**Pre checks**:
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"Thanks ! "
] | 2024-04-12T15:26:14 | 2024-04-12T15:54:51 | 2024-04-12T15:54:51 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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**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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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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} | # What does this PR do ?
This is an optimized (without loops) version of stateless net for transducer decoder model.
**Collection**: ASR
# 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
# Additional Information
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"This is a monolingual Hindi only model, it doesn't support English. ",
"What can i use for hinglish(english + hindi)?\r\n\r\nOn Fri, 12 Apr, 2024, 5:33 pm Somshubra Majumdar, ***@***.***>\r\nwrote:\r\n\r\n> This is a monolingual Hindi only model, it doesn't support English.\r\n>\r\n> —\r\n> Reply to this email d... | 2024-04-12T11:39:07 | 2024-05-21T01:45:45 | 2024-05-21T01:45:44 | NONE | {
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usage: speech_to_text_buffered_infer_ctc.py [--help] [--hydra-help] [--version] [--cfg {job,hydra,all}] [--resolve] [--package PACKAGE] [--run] [--multirun] [--shell-completion] [--config-path CONFIG_PATH]
[--config-name CONFIG_NAME] [--config-dir CONFIG_DIR] [--experimental-rerun EXPERIMENTAL_RERUN] [--info [{all,config,defaults,defaults-tree,plugins,searchpath}]]
[overrides ...]
speech_to_text_buffered_infer_ctc.py: error: unrecognized arguments: --asr_model=stt_hi_conformer_ctc_large --test_manifest= --output_path= --model_stride=4
This is when I run as per the streaming example on https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_hi_conformer_ctc_large
I am using python 3.10 and pip.
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
To reproduce
git clone https://github.com/NVIDIA/NeMo.git
python ~/pcs/NeMo//examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py --asr_model="stt_hi_conformer_ctc_large" --test_manifest="" --output_path="" --model_stride=4
**Expected behavior**
The code should work and accept stream input and generate text.
**Environment overview (please complete the following information)**
Im running this on my laptop ubuntu 22.04 with RTX 4070
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
Personal laptop
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
sudo apt-get update && sudo apt-get install -y libsndfile1 ffmpeg
pip install Cython
pip install nemo_toolkit['all']
- 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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"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."
] | 2024-04-12T11:34:47 | 2024-06-15T01:47:42 | 2024-06-15T01:47:41 | NONE | {
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} | null | null | null | While following the official documentation - https://developer.nvidia.com/blog/adapting-p-tuning-to-solve-non-english-downstream-tasks/ , I would like to see the Multitask_Prompt_and_PTuning.ipynb notebook file for reference. But it seems the file was moved/deleted.
Where to it moved? where can I see this notebook
@okuchaiev | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8898 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8898/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8898/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8898/events | https://github.com/NVIDIA/NeMo/issues/8898 | 2,238,998,075 | I_kwDOC_bI7s6FdGY7 | 8,898 | The error in loading Llama pretrain checkpoint for NeVa(LLAVA) | {
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"I'm on the commit:97d1abb2bca0b5daff6d434c4bb340d3bb702e86",
"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.",
"Does this e... | 2024-04-12T03:48:08 | 2024-08-08T10:43:56 | 2024-05-21T01:45:46 | NONE | {
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} | null | null | null | when I train the Neva model, I got following error
>> [NeMo I 2024-04-12 03:38:58 neva_model:252] Loading LLM weights from checkpoint /home/nemo/llama_weights/vicuna-2-7b.nemo
Loading distributed checkpoint with TensorStoreLoadShardedStrategy
Error executing job with overrides: ['trainer.precision=bf16', 'trainer.num_nodes=1', 'trainer.devices=1', 'trainer.val_check_interval=1000', 'trainer.limit_val_batches=5', 'trainer.log_every_n_steps=1', 'trainer.max_steps=1000', 'model.megatron_amp_O2=True', 'model.micro_batch_size=1', 'model.global_batch_size=2', 'model.tensor_model_parallel_size=1', 'model.pipeline_model_parallel_size=1', 'model.mcore_gpt=True', 'model.transformer_engine=True', 'model.data.data_path=/data1/data/datasets--liuhaotian--LLaVA-Pretrain/blip_laion_cc_sbu_558k.json', 'model.data.image_folder=/data1/data/datasets--liuhaotian--LLaVA-Pretrain', 'model.tokenizer.library=sentencepiece', 'model.tokenizer.model=/home/nemo/llama_weights/tokenizer_neva.model', 'model.encoder_seq_length=4096', 'model.num_layers=32', 'model.hidden_size=4096', 'model.ffn_hidden_size=16384', 'model.num_attention_heads=32', 'model.normalization=layernorm1p', 'model.do_layer_norm_weight_decay=False', 'model.apply_query_key_layer_scaling=True', 'model.activation=squared-relu', 'model.headscale=False', 'model.position_embedding_type=rope', 'model.rotary_percentage=0.5', 'model.num_query_groups=null', 'model.data.num_workers=0', 'model.mm_cfg.llm.from_pretrained=/home/nemo/llama_weights/vicuna-2-7b.nemo', 'model.mm_cfg.llm.model_type=nvgpt', 'model.data.conv_template=nvgpt', 'model.mm_cfg.vision_encoder.from_pretrained=/home/nemo/openai_weights/clip-vit-large-patch14-336', 'model.mm_cfg.vision_encoder.from_hf=True', 'model.data.image_token_len=256', 'model.optim.name=fused_adam', 'exp_manager.create_checkpoint_callback=True', 'exp_manager.create_wandb_logger=False', 'exp_manager.wandb_logger_kwargs.project=neva_demo']
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/megatron/core/dist_checkpointing/strategies/tensorstore.py", line 123, in open_ts_array
arr = ts.open(ts.Spec(spec), open=True).result()
ValueError: NOT_FOUND: Error opening "zarr" driver: Metadata at local file "/tmp/tmpe2_bw_kv/model_weights/model.decoder.layers.self_attention.linear_qkv.layer_norm_bias/.zarray" does not exist [source locations='tensorstore/driver/kvs_backed_chunk_driver.cc:1255\ntensorstore/driver/driver.cc:114'] [tensorstore_spec='{\"context\":{\"cache_pool\":{},\"data_copy_concurrency\":{},\"file_io_concurrency\":{},\"f
**Steps/Code to reproduce bug**
First, I used following script to convert the Llama hf checkpoint to Nemo checkpoint (I try Vicuna and Llama both, but I got the same error):
>> python scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --input_name_or_path /data1/weight/llama_weights/models--lmsys--vicuna-7b-v1.5 --output_path /home/nemo/llama_weights/vicuna-2-7b.nemo
Then, I launch the train process (I tried 1 gpu and 8 gpu, but I got the same error):
>> CUDA_VISIBLE_DEVICES=2 NCCL_P2P_DISABLE=1 CUDA_LAUNCH_BLOCKING=1 torchrun --nproc_per_node=1 /opt/NeMo/examples/multimodal/multimodal_llm/neva/neva_pretrain.py \
trainer.precision=bf16 \
trainer.num_nodes=1 \
trainer.devices=1 \
trainer.val_check_interval=1000 \
trainer.limit_val_batches=5 \
trainer.log_every_n_steps=1 \
trainer.max_steps=1000 \
model.megatron_amp_O2=True \
model.micro_batch_size=1 \
model.global_batch_size=2 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
model.mcore_gpt=True \
model.transformer_engine=True \
model.data.data_path=/data1/data/datasets--liuhaotian--LLaVA-Pretrain/blip_laion_cc_sbu_558k.json \
model.data.image_folder=/data1/data/datasets--liuhaotian--LLaVA-Pretrain \
model.tokenizer.library=sentencepiece \
model.tokenizer.model=/home/nemo/llama_weights/tokenizer_neva.model \
model.encoder_seq_length=4096 \
model.num_layers=32 \
model.hidden_size=4096 \
model.ffn_hidden_size=16384 \
model.num_attention_heads=32 \
model.normalization=layernorm1p \
model.do_layer_norm_weight_decay=False \
model.apply_query_key_layer_scaling=True \
model.activation=squared-relu \
model.headscale=False \
model.position_embedding_type=rope \
model.rotary_percentage=0.5 \
model.num_query_groups=null \
model.data.num_workers=0 \
model.mm_cfg.llm.from_pretrained=/home/nemo/llama_weights/vicuna-2-7b.nemo \
model.mm_cfg.llm.model_type=nvgpt \
model.data.conv_template=nvgpt \
model.mm_cfg.vision_encoder.from_pretrained='/home/nemo/openai_weights/clip-vit-large-patch14-336' \
model.mm_cfg.vision_encoder.from_hf=True \
model.data.image_token_len=256 \
model.optim.name="fused_adam" \
exp_manager.create_checkpoint_callback=True \
exp_manager.create_wandb_logger=False \
exp_manager.wandb_logger_kwargs.project=neva_demo
**Expected behavior**
the training should start
**Environment overview (please complete the following information)**
I am in the main brach, I use the docker following:
>> sudo docker run --runtime=nvidia --gpus all -it --rm -v ~/project/NeMo:/opt/NeMo \
-v /home/nemo:/home/nemo \
-v /data1:/data1 \
--shm-size=8g -p 8888:8888 \
--ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:24.01.speech
**Environment details**
I try to compile the Nemo in the docker. however, It dose not work.
**Additional context**
8 H800 GPU
I'm on the commit:97d1abb2bca0b5daff6d434c4bb340d3bb702e86 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8897 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8897/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8897/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8897/events | https://github.com/NVIDIA/NeMo/issues/8897 | 2,238,982,543 | I_kwDOC_bI7s6FdCmP | 8,897 | Memory is fully eaten and training quit with errors for 40k hours ASR training | {
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"Is it GPU or CPU memory that is exhausted ? And how many nodes are you using ? \r\n\r\nWhat version of NeMo are you using ? \r\nWithout sufficient details it's not possible to debug. \r\n\r\nWhat I can say is we train on nodes with 400 GB ram per node and A100 with 80GB gpu memory and train on 90-400K hours of spe... | 2024-04-12T03:35:07 | 2024-10-26T21:05:11 | 2024-07-01T01:54:44 | NONE | {
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} | null | null | null | During training, memory is noticed increasing as time goes on, until 74% training done and no memory available. The training is quit giving the following errors:
We are using Coformer+ CTC with 1.2 cpu memory, 8 workers.
We guess this might be related with pytorch lightning, and some of memory is hold till an epoch is done.
There are no errors for smaller data, but once training data is getting larger, the bugs are triggered.
Please give us tips for how to cure the problem, thanks !
[E ProcessGroupNCCL.cpp:475] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800190 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:916] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800190 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 6] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800380 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800345 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800374 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800316 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800421 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:475] [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800489 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:916] [Rank 3] NCCL watchdog thread terminated with exception: [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800421 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:916] [Rank 4] NCCL watchdog thread terminated with exception: [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800489 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:489] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:495] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:916] [Rank 6] NCCL watchdog thread terminated with exception: [Rank 6] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800380 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:916] [Rank 7] NCCL watchdog thread terminated with exception: [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800345 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:916] [Rank 1] NCCL watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800316 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:916] [Rank 2] NCCL watchdog thread terminated with exception: [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=252347, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800374 milliseconds before timing out.
asr_scripts_78/egs/sg/run.sh: line 547: 181694 Aborted (core dumped) python3 $scripts/asr_trainer.py --conf $cfg --devices $devices --accumulate_grad_batches 8 --accelerator 'cuda' --save_top_k 5 --val_check_interval 200 --checkpoint_path ${ckpt_path} --model.optim.lr 0.25 --model.optim.sched.warmup_steps 10000 --model.train_ds.max_duration 18.2 --model.train_ds.num_workers 8 --model.optim.sched.name NoamAnnealing --model.train_ds.manifest_filepath ${train_data} --resume_from_checkpoint "${pretrained_mdl}" --model.tokenizer.dir ${tokenizer} --model.tokenizer.type 'bpe' --model.train_ds.batch_size 64 --model.validation_ds.batch_size 64 --model.validation_ds.manifest_filepath ${valid_data} --model.interctc.loss_weights "[]" --model.interctc.apply_at_layers "[]" --model.optim.sched.last_epoch 25000
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} | # What does this PR do ?
Refactor NeMo Developer Docs 1st PR. In this PR, we restructured the dev doc as screen shot below. More contents yet to be added.
<img width="327" alt="image" src="https://github.com/NVIDIA/NeMo/assets/54727607/f3d6d612-6910-4e98-9712-50d475117742">
# 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
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} | # What does this PR do ?
This add the option --low-ram to enable checkpoint conversion in low-ram computers for large checkpoints (e.g. 250GB).
Memory reduction is achieved by not having the full converted checkpoint and the huggingface checkpoint on memory at the same time. Instead, it saves the converted checkpoint layer by layer, remove the HF checkpoint from memory and finally restores the converted layers and saves to .nemo format.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add expert_model_parallel_size to yamls
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 ?
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# Add a code snippet demonstrating how to use this
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"@artbataev this may be related to your observation of TDT being faster than CTC in some cases.",
"If I recall correctly, max does work on GPU so I don't know why I forced it onto CPU. Maybe the slice? Either way, if it fixes the slowdown let's move the .CPU() to after max calculation.\r\n\r\nIt might have been t... | 2024-04-11T19:15:45 | 2024-05-09T18:00:10 | 2024-05-09T18:00:10 | COLLABORATOR | {
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As part of my work on #8673 , I have been profiling to make sure we have low latency and high throughput. I have observed that this line can sometimes take https://github.com/NVIDIA/NeMo/blob/57444ae9b63afc5419a16948f7d032a1a9f7dc7f/nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py#L218 several milliseconds. To be concrete, running at batch size 10, the model is taking 167 milliseconds, while decoding is taking 29 milliseconds, with almost all of the time spent on this max() operation. This is documented below.
<img width="1244" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/623c66d5-57b9-4d9d-8e35-62cfad51a702">
It is not acceptable to have model inference be bottlenecked on non-GPU work like this.
I uploaded a gzipped version of the nsight systems file if you would like to view it.
[report25.nsys-rep.gz](https://github.com/NVIDIA/NeMo/files/14950389/report25.nsys-rep.gz)
The most obvious way to handle this situation is to do the "max()" operation on the GPU. I previously did a refactor that sped up CTC decoding by improving copy from GPU to CPU https://github.com/NVIDIA/NeMo/pull/8521, but it seems that is better to copy only after the max() operation is done. I did not do this in my previous PR, because (1) I didn't think it would be a concern and (2) that change is much more intrusive. _greedy_decode_logprobs is built with running on only a single element at a time rather than a batch. Maybe vmap can save us...
BTW, why is this only sometimes slow? I'm not certain, but it probably has to do with the CPU cache. It's working directly on pinned memory. The cache line may be getting evicted only sometimes? | {
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} | # What does this PR do ?
Enable the AllGather of the updated parameters in the data-parallel domain with the validation step.
# Changelog
- Skip gradient zeroing at the beginning of the validation step routine, which executes param sync
- Pass param sync func at validation step
# Usage
- Set `validation_param_sync_overlap=True`, which is `False` by default
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} | # What does this PR do ?
disable bprop reduce for cpl when SP is enabled
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
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**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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"merged_at": null
} | # What does this PR do ?
Adding zero-shot evaluation code for new RETRO model. The evaluation code for legacy model is modified to `*_legacy.py`
This PR is on top of `huvu/mcore_retro`, which implements the new RETRO model.
The files this PR works on are:
+ examples/nlp/language_modeling/conf/megatron_retro_inference.yaml
+ examples/nlp/language_modeling/megatron_retro_eval.py
+ examples/nlp/language_modeling/megatron_retro_qatask_eval.py
+ examples/nlp/language_modeling/conf/megatron_retro_qatask.yaml
+ nemo/collections/nlp/modules/common/text_generation_strategy.py
+ nemo/collections/nlp/modules/common/text_generation_utils.py
# 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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**PR Type**:
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- [ ] 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 ?
support PTL >= 2.1 for chatglm
# 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@yaoyu-33
Anyone in the community is free to review the PR once the checks have passed.
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https://api.github.com/repos/NVIDIA/NeMo/issues/8886 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8886/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8886/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8886/events | https://github.com/NVIDIA/NeMo/issues/8886 | 2,237,888,020 | I_kwDOC_bI7s6FY3YU | 8,886 | NeMo License Discrepancy? | {
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} | null | null | null | Howdy,
So I noticed that in the readme of this GitHub Repo it states that NeMo is released under the Apache 2.0 license.
This is also the license [linked](https://github.com/NVIDIA/NeMo/blob/main/LICENSE) in the repo.
However, on the NVIDIA website, the page for the [NeMo Framework Docker Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo) states that NeMo is licensed under the NVIDIA AI Product Agreement.
Is there a reason for this discrepancy, or could anyone provide any guidance on which license is correct?
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https://api.github.com/repos/NVIDIA/NeMo/issues/8885 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8885/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8885/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8885/events | https://github.com/NVIDIA/NeMo/pull/8885 | 2,237,822,872 | PR_kwDOC_bI7s5sXZxY | 8,885 | Canary greedy and temperature decoding | {
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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."
] | 2024-04-11T14:10:22 | 2024-05-10T01:46:14 | 2024-05-10T01:46:13 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- 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/8884 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8884/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8884/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8884/events | https://github.com/NVIDIA/NeMo/issues/8884 | 2,237,043,705 | I_kwDOC_bI7s6FVpP5 | 8,884 | TypeError: EncDecCTCModel.transcribe() got an unexpected keyword argument 'logprobs' | {
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"NeMo main has an updated version of transcribe() that supports more functionality and deprecated old arguments with new ways of doing things. \r\n\r\nYou can use and install Nemo 1.23.0 branch to get it to work. Or you can use the following to fix transcribe on main. \r\n\r\nChange \r\nlogits = quartznet.transcrib... | 2024-04-11T07:01:04 | 2024-04-11T07:43:07 | 2024-04-11T07:43:07 | NONE | {
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} | null | null | null | Hi,
I am trying to use language model described in Offline_ASR.ipynb, but I am receiving error:
`TypeError: EncDecCTCModel.transcribe() got an unexpected keyword argument 'logprobs'`
My code is
`import nemo
import nemo.collections.asr as nemo_asr
import sys
import os
import ctc_decoders
import numpy as np
wave_file = ['test2.wav']
quartznet = nemo_asr.models.EncDecCTCModel.restore_from(restore_path="checkpoints/ASR-Char-Model-Language-hy.nemo",strict=False)
#print(quartznet.transcribe(wave_file,batch_size=1))
#softmax implementation in NumPy
def softmax(logits):
e = np.exp(logits - np.max(logits))
return e / e.sum(axis=-1).reshape([logits.shape[0], 1])
#let's do inference once again but without decoder
logits = quartznet.transcribe(wave_file, logprobs=True)[0]
probs = softmax(logits)
lm_path='/corpuses/hye/corpus/19.0/lm_tgsmall.arpa.gz'
beam_search_lm = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(quartznet.decoder.vocabulary),
beam_width=16,
alpha=2, beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=False)
beam_search_lm.forward(log_probs = np.expand_dims(probs, axis=0), log_probs_length=None)`
The single difference from Offline_ASR.ipynb is nemo_asr.models.EncDecCTCModel.restore_from instead nemo_asr.models.EncDecCTCModel.from_pretrained.
Could you advise me error reason?
Thank you advance!
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"jenkins",
"Set unet_precision when user not set it.\r\n",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins"
] | 2024-04-11T05:27:17 | 2024-04-17T19:59:44 | 2024-04-17T19:59:43 | MEMBER | {
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} | # What does this PR do ?
pass fp16 precision to UNetModel constructor if user specifies fp16 precision via CLI.
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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"LGTM, We did convergence runs and convergence looks as expected on GBS=8, 32. We see measurable speedup for this change.\r\nOne minor comment:\r\n- We should try to JIT the python code, but maybe that can happen as a separate PR.\r\n\r\n@ericharper @cuichenx can you pls review/comment?",
"jenkins"
] | 2024-04-11T04:52:27 | 2024-04-12T22:49:23 | 2024-04-12T22:49:23 | CONTRIBUTOR | {
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} | # What does this PR do ?
lora a2a after linear out when sp is enabled and parallel input
**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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**PR Type**:
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] | 2024-04-11T01:37:08 | 2024-04-12T18:05:59 | 2024-04-12T18:05:58 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR will support new parallel initialize order with megatron's new feature: TP -> PP -> DP
**Collection**: [Note which collection this PR will affect]
* NLP collection
# Changelog
- refactor rank calculate by mcore's new interface.
- a new knob 'use_tp_pp_dp_mapping' to enable this feature
# Usage
* You can potentially add a usage example below
just make sure use enable `use-tp-pp-dp-mapping` knob.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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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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"> Overall looks good, although we are still hashing out the design in [NVIDIA/apex#1794](https://github.com/NVIDIA/apex/pull/1794). As discussed in [NVIDIA/apex#1794 (comment)](https://github.com/NVIDIA/apex/pull/1794#issuecomment-2050894033), I think we should set `MegatronDistributedFusedAdam._step_support_amp_s... | 2024-04-11T01:09:41 | 2024-04-12T22:50:44 | 2024-04-12T22:50:44 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR fixes distributed optimizer issues:
1. Support NHWC layout, as required by Diffusion models;
2. Fix complex `zero_grad()` being not captured by CUDA graph;
3. Add option to distribute states within node;
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [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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# Changelog
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# Jenkins CI
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# Before your PR is "Ready for review"
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} | # What does this PR do ?
Fused Attn requires module training set to True even the module self is frozen.
**Collection**: [Multimodal]
# 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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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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**:
- [ ] 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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Fix Checkpointing for FP8 Attention
**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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"@JimmyZhang12 thanks for your review. Addressed your comments. Could you please approve?",
"jenkins",
"jenkins"
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} | # What does this PR do ?
Updates export to TRT-LLM code for version 0.9
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
- [x] Update
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optim:
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lr: 0.5
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weight_decay: 0.001
sched:
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d_model: 512
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```[tasklist]
### Tasks
- [ ] Add a draft title or issue reference here
```
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"We don't have pretrained onnx models released in NeMo. Nvidia Riva might have .Riva files that contain a compiled model but that isn't supported in NeMo ",
"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 w... | 2024-04-10T08:09:58 | 2024-05-21T01:45:48 | 2024-05-21T01:45:47 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
How can I search for a pretrained ONNX model?
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Hi, since my PC has no powerful GPU, so training any model is almost mission impossible, so I have to use some pretrained model, for example: I know one ONNX model: stt_en_quartznet15x5.onnx
But I can't find such pretrained model from ONNX zoo.
**Describe the solution you'd like**
I want to have some kind of pre-trained model search, so I can either input the model name or something to describe the pre-trained models, like: "Transcribe French", then the searching engine can return a list of pre-trained models meet the searching conditions, and provide download link, so I can download the models I want.
Please note that not everyone has the computing power to train any complicated model, not many people can host computing power like a data center.
A clear and concise description of what you want to happen.
Provide a code snippet on how new APIs/changes would be used by others.
**Describe alternatives you've considered**
No alternatives I can think about.
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Before your PR is "Ready for review"
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"Hi @vysarge , I do not totally understand what the first input dim and the second dim mean, could you please describe it? And how does it work if `sequence_parallel=True` and `context_parallel_size > 1`? Thanks.",
"> Hi @vysarge , I do not totally understand what the first input dim and the second d... | 2024-04-10T05:10:34 | 2024-05-06T19:42:49 | 2024-05-06T19:42:03 | COLLABORATOR | {
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} | # What does this PR do ?
Adds a check for MegatronGPTSFTModel to ensure sequence dimension will be padded to a multiple of 16 after being split over context parallel ranks, which TE requires during fp8 training; this fixes issues like `AssertionError: FP8 execution requires 2D input matrices with height divisible by 8 and width divisible by 16, but got tensor with dims=[5504, 184]` when using context parallel with datasets that have a variable sequence length.
**Collection**: nlp
# Changelog
- Alters `pad_seq_length_to_mult` input to GPT SFT datasets to account for context parallel
# 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?
- [ ] 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 ?
Merges #8803, #8810, #8853 into one.
**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 ?
https://github.com/NVIDIA/NeMo/pull/8792 introduced some runtime failures in T5 since it adds GPT-specific logic to `MegatronBaseModel`. This PR moves the distopt FP32 grad logic to the specific models.
**Collection**: NLP
# Changelog
- Move logic for distopt FP32 grads to models
# Usage
Run T5, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_t5_config.yaml.
Enable the distributed optimizer with model.optim.name=distributed_fused_adam.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Fixes bug from https://github.com/NVIDIA/NeMo/pull/8792
* Closes https://github.com/NVIDIA/NeMo/pull/8857
* Closes https://github.com/NVIDIA/NeMo/pull/8810 | {
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} | # What does this PR do ?
Replaces the unpacking of .nemo files into a temporary directory before use with direct use of files inside .nemo (which is just a TAR archive), thereby saving many seconds on each export operation.
**Collection**: NLP
# Changelog
- Replaced unpacking of nemo checkpoints on export with a VFS-like TarPath object.
# Usage
```python
with TarPath('/path/to/archive.tar') as archive:
myfile = archive / 'filename.txt'
if myfile.exists():
data = myfile.read()
...
```
**PR Type**:
- [X] New Feature (improvement, rather)
- [ ] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/8865 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8865/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8865/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8865/events | https://github.com/NVIDIA/NeMo/pull/8865 | 2,234,447,813 | PR_kwDOC_bI7s5sL4Jo | 8,865 | dehardcode test string | {
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"Can this be merged @oyilmaz-nvidia ?",
"jenkins"
] | 2024-04-09T22:58:14 | 2024-05-03T22:22:49 | 2024-05-03T22:22:49 | CONTRIBUTOR | {
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} | # What does this PR do ?
By default `to_word_list_format` uses '<extra_id_1>' as its test string to map a string to its tokens, but using '<extra_id_1>' may not map correctly, so add option to specifiy the test string.
**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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"use ctc models for aligning very long audio duration files. ",
"You can use hybrid models in CTC mode automatically. @erastorgueva-nv could you advise ",
"It should work if you also add the parameter `use_local_attention=true` (reference: https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/too... | 2024-04-09T22:34:19 | 2024-04-12T21:33:41 | 2024-04-12T21:22:01 | NONE | {
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} | null | null | null | Hello! when trying to do a forced alignment with long audios (1h 30m+) I get the following exception:
`torch.cuda.OutOfMemoryError: CUDA out of memory. ...`
Now I understand it might have something to do with batch size or chunk size, or even it can just be an issue of not having enough GPU memory (I'm running it on google colab, which has 15GB)
What should be the correct approach to handle large audios?
for the record, this is how I'm running the align script:
```
!HYDRA_FULL_ERROR=1 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True python $NEMO_DIR_PATH/tools/nemo_forced_aligner/align.py \
pretrained_name="stt_en_fastconformer_hybrid_large_pc" \
manifest_filepath=$manifest_filepath \
output_dir=$WORK_DIR/nfa_output/ \
total_buffer_in_secs=2 \
chunk_batch_size=16 \
chunk_len_in_secs=0.8
`` | {
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} | # What does this PR do ?
Fixes the implementation of PEFT for TRT-LLM export.
**Collection**: NLP
# Changelog
- Add the bos token to LLAMA based models (not just P-tuning related, improves LLAMA results in general).
- Remember the vtoken counts for each p-tuning table when the tables are added.
- Prepend the right number of vtokens to each query based on its task_id.
- Preserve the dtype of the p-tuning table when it is padded.
- Validate that all p-tuning tables fit into max_prompt_embedding_table_size limit.
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
# Additional Information
* Related to bug 4350064
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} | # What does this PR do ?
Fix for case, when duration is `null` in manifest
**Collection**: ASR
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# Changelog
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} | # What does this PR do ?
This PR adds a pointer to audio codec checkpoint to docs.
**Collection**: Docs
# Changelog
- Added a table with info about the available checkpoints
# Usage
n/a
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} | # What does this PR do ?
Add support for testing, deploying and running quantized "qnemo" checkpoints in `nemo.deploy` and `nemo.export` modules.
**Collection**: NLP
# Changelog
- qnemo support
- bump ammo version to 0.9.4
- update code for trt-llm 0.9.0
- Lambada evaluation setup
- timing of evaluation (with minor improvements)
- documentation
# Usage
Building TensorRT-LLM engine and running a basic prompt:
```python
from nemo.export import TensorRTLLM
trt_llm_exporter = TensorRTLLM(model_dir="/path/to/trt_llm_engine_folder")
trt_llm_exporter.export(
nemo_checkpoint_path="/path/to/qnemo/ckpt.qnemo",
model_type="llama",
n_gpus=1,
)
output = trt_llm_exporter.forward(["Hi, how are you?", "I am good, thanks, how about you?"])
```
Running Lambada test locally:
```python
python tests/export/test_nemo_export.py \
--model_name local \
--model_type llama \
--checkpoint_dir /path/to/qnemo/ckpt.qnemo \
--trt_llm_model_dir /path/to/trt_llm_engine_folder \
--min_gpus 1 \
--run_accuracy \
--test_deployment True \
--test_data_path lambada.json
```
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- [ ] Bugfix
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"Here is the tutorial for VAD: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Voice_Activity_Detection.ipynb \r\n\r\nCurrently we don;t support training VAD and SD model together",
"> Here is the tutorial for VAD: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Voice_Activity_Detection.ipynb\r\n> \... | 2024-04-09T09:35:46 | 2024-05-21T01:45:50 | 2024-05-21T01:45:49 | NONE | {
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} | null | null | null | Hi, I am trying to finetune vad and speaker diarization model on korean dataset. However, I cannot find tutorial related to vad. I've found [File not found (github.com)](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb) tutorial from previous issues, but it seems that the tutorial is deleted. How can I finetune vad model with my dataset?
Also, Is there any way to train vad&speaker diarization model together? | {
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"jenkins",
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"Looks like all tests passed, please merge @ericharper thank you.",
"Merged this to attempt fix the broken CI pipeline"
] | 2024-04-09T02:04:15 | 2024-04-10T23:15:32 | 2024-04-10T01:30:10 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
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} | # What does this PR do ?
Bugfixes for CTC beam search decoding with Flashlight.
1. Fix the maximum decoding length for CTC beam search decoding with Flashlight. The info about the maximum sequence length has to passed to Flashlight decoder otherwise junk/masked frames are decoded (results in insertion errors etc.). This info is passed properly to OpenSeq2Seq decoders and Pyctcdecoders by restricting the sequence length.
2. Fix the dictionary creation for lexicon-free decoding. The Dictionary has to contain a mapping from the ASR model's output IDs to the token in the vocabulary, i.e., exactly the same as the vocabulary in the tokeniser. The order of this mapping matters.
**Collection**: ASR
# Changelog
- Fix the maximum decoding length for CTC beam search decoding with Flashlight
- Fix the dictionary creation for lexicon-free decoding
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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`--precision bf16` was not functional in some conversion scripts and converted checkpoints are twice the size as they should be. This PR fixes the precision of the output model.
**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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"jenkins",
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"I think we need to add an mcore version guard and/or change the megatron-lm commit in jenkinsfile"
] | 2024-04-08T22:06:32 | 2024-04-11T18:04:41 | 2024-04-11T18:04:41 | CONTRIBUTOR | {
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Disable gradient reduction for the Lora Adapter when SP is enabled. The first ColumnParallelLinear layer for the Lora adapter should not reduce the gradients when SP is enabled.
**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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- [ ] 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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} | # What does this PR do ?
Resolve the memory lease from loss function
# Changelog
- Do not grep values from config
# 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**:
- [ ] 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.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Adds deploy to triton script for llm models.
# Changelog
- Deployment and query scripts for LLM models.
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
- [x] Script
If you haven't finished some of the above items you can still open "Draft" PR.
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Add ICASSP 2024 Paper
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"Hi @tempops thanks for sharing this issue. I am able to reproduce it in some cases. Not sure yet why it occurs. Could you please share what NeMo version you are using, and the exact command you used to install NeMo?\r\n\r\nAs a quick fix, you can add an extra line in one of the cells as I show below. I believe tha... | 2024-04-08T15:37:15 | 2024-07-01T01:54:47 | 2024-07-01T01:54:47 | NONE | {
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} | null | null | null | **Describe the bug**
Hello, I was trying to run Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb but I get an error when running the final cell in Jupyter
[Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/fawazahamedshaik/Downloads/Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb) Cell 13 line 8
76 cache_pre_encode = processed_signal[:, :, -pre_encode_cache_size:]
78 with torch.no_grad():
79 (
80 pred_out_stream,
81 transcribed_texts,
82 cache_last_channel,
83 cache_last_time,
84 cache_last_channel_len,
85 previous_hypotheses,
---> 86 ) = asr_model.conformer_stream_step(
87 processed_signal=processed_signal,
88 processed_signal_length=processed_signal_length,
89 cache_last_channel=cache_last_channel,
90 cache_last_time=cache_last_time,
91 cache_last_channel_len=cache_last_channel_len,
...
--> 400 f = x.narrow(dim=0, start=time_idx, length=1)
402 # Setup exit flags and counter
403 not_blank = True
RuntimeError: start (2) + length (1) exceeds dimension size (2).
Any advice on how to mitigate this error?
I am running this in M2 MacBook Pro
**Environment overview (please complete the following information)**
- Environment location: local
- Method of NeMo install: installed using pip install git+https://github.com/NVIDIA/NeMo.git
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - macOS 13.3.1
- PyTorch version - pytorch-lightning 2.2.1
- Python version - 3.10.0
**Additional context**
Add any other context about the problem here.
running on m2 chip
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"All the combined updates for TRT-LLM 0.9 is here https://github.com/NVIDIA/NeMo/pull/8873 . That PR includes @abharwani 's commits. We can close this since all the commits are already pulled in the other PR.",
"Closing this since @abharwani 's commits were merged in with this PR https://github.com/NVIDIA/NeMo/pu... | 2024-04-08T14:58:20 | 2024-04-16T16:10:21 | 2024-04-16T16:10:21 | CONTRIBUTOR | {
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} | # What does this PR do ?
Update export module to support trtllm0.9
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https://api.github.com/repos/NVIDIA/NeMo/issues/8848 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8848/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8848/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8848/events | https://github.com/NVIDIA/NeMo/issues/8848 | 2,231,445,492 | I_kwDOC_bI7s6FASf0 | 8,848 | Converting Mistral/Mixtral to Nemo format throws error: Received both `precision=bf16-mixed` and `plugins=<...nlp_overrides.PipelineMixedPrecisionPlugin>` | {
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"Looks like solved by https://github.com/NVIDIA/NeMo/pull/8908"
] | 2024-04-08T14:55:55 | 2024-04-16T09:29:24 | 2024-04-16T09:29:23 | NONE | {
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} | null | null | null | **Describe the bug**
Converting Mistral/Mixtral to Nemo format throws the following error:
```
Traceback (most recent call last):
File "/workspace/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py", line 338, in <module>
convert(args)
File "/workspace/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py", line 196, in convert
trainer = Trainer(plugins=plugins, accelerator='cpu', precision=precision, strategy=NLPDDPStrategy())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/argparse.py", line 70, in insert_env_defaults
return fn(self, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 401, in __init__
self._accelerator_connector = _AcceleratorConnector(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 134, in __init__
self._check_config_and_set_final_flags(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 271, in _check_config_and_set_final_flags
raise ValueError(
ValueError: Received both `precision=bf16-mixed` and `plugins=<nemo.collections.nlp.parts.nlp_overrides.PipelineMixedPrecisionPlugin object at 0x7f2658622e30>`. Choose one.
```
**Environment overview (please complete the following information)**
```bash
git clone https://github.com/xingyaoww/nemo-util
git submodule update --init --recursive
# Then Build from the Dockerfile.
cd NeMo; docker build -t xingyaoww/nemo:main .
# And enter the docker
DOCKER_IMG=xingyaoww/nemo:main
WORK_DIR=`pwd`
# read model directory from ENV VAR, if exists add it to the docker run command
if [ -z "$MODEL_DIR" ]; then
echo "MODEL_DIR is not set. Please set MODEL_DIR to the directory containing the model files."
else
echo "MODEL_DIR is set to '$MODEL_DIR'"
EXTRA_ARGS="-v $MODEL_DIR:/models"
fi
docker run \
--gpus all \
-e UID=$(id -u) \
--shm-size=2g \
--net=host \
--ulimit memlock=-1 \
--rm -it \
$EXTRA_ARGS \
-v ${PWD}:/workspace \
-w /workspace \
$DOCKER_IMG \
bash -c "useradd --shell /bin/bash -u $UID -o -c '' -m nemo && usermod -aG root nemo && cd /workspace && su nemo -c 'git config --global credential.helper store' && su nemo"
```
**Steps/Code to reproduce bug**
```bash
#!/bin/bash
echo "You supposed to run this script inside the docker container"
MODEL_PATH=/models/Mistral-7B-v0.1/
OUTPUT_NAME=mistral-7b-base.nemo
# Override the installed version of Megatron-LM, but use the cloned version
export PYTHONPATH=$(pwd)/NeMo:$(pwd)/Megatron-LM:$PYTHONPATH
python3 NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py \
--input_name_or_path=$MODEL_PATH \
--output_path=data/models/nemo/$OUTPUT_NAME \
--precision=bf16-mixed
```
**Potential fixes**
I comment out the line that adds `PipelineMixedPrecisionPlugin` to `plugins` resolves this issue (check commit [here](PipelineMixedPrecisionPlugin)), not sure if this is the best/correct way to resolve this issue?
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} | # What does this PR do ?
This PR aims to overlap the process of [allreduce_sequence_parallel_gradient](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L867) with [allreduce_first_last_embeddings](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L902C9-L902C40).
# Changelog
The following two changes enable overlapping:
- Change the order in which these two functions are called to enable overlapping. Since the gradients all-reduced in these two functions are independent of each other, it is safe to change the order.
- allreduce_sequence_parallel_gradient should execute in a non-blocking stream to enable overlapping.
# Usage
This optimization only works when applying sequence_parallel and pipeline_model_parallel.
# 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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"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."
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} | null | null | null | **Describe the bug**
**Fix is implemented here: https://github.com/NVIDIA/NeMo/pull/8845**
Transformer layer MLP always uses default values for bias, activation, and normalization if model.mcore_gpt=False, model.transformer_engine=True, model.megatron_amp_O2=True.
**Steps/Code to reproduce bug**
Add the following lines to NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py after model initialization:
```
logging.warning(f"DEBUG: layernorm_mlp.activation={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.activation}")
logging.warning(f"DEBUG: layernorm_mlp.use_bias={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.use_bias}")
logging.warning(f"DEBUG: layernorm_mlp.normalization={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.normalization}")
logging.warning(f"DEBUG: layernorm_mlp.layernorm_mlp.fc1_weight.shape={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.fc1_weight.shape}")
logging.warning(f"DEBUG: layernorm_mlp.layernorm_mlp.fc2_weight.shape={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.fc2_weight.shape}")
```
Run the following script with and without the changes:
```
#!/bin/bash
python /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
model.mcore_gpt=False \
model.transformer_engine=True \
trainer.precision=bf16 \
model.megatron_amp_O2=True \
model.activation=fast-swiglu \
model.bias=false \
model.normalization=rmsnorm
```
**Expected behavior**
The values for layernorm_mlp are set correctly:
```
[NeMo W 2024-04-08 09:30:49 megatron_gpt_pretraining:42] DEBUG: layernorm_mlp.activation=fast-swiglu
[NeMo W 2024-04-08 09:30:49 megatron_gpt_pretraining:43] DEBUG: layernorm_mlp.use_bias=False
[NeMo W 2024-04-08 09:30:49 megatron_gpt_pretraining:44] DEBUG: layernorm_mlp.normalization=RMSNorm
[NeMo W 2024-04-08 09:30:49 megatron_gpt_pretraining:45] DEBUG: layernorm_mlp.layernorm_mlp.fc1_weight.shape=torch.Size([3072, 768])
[NeMo W 2024-04-08 09:30:49 megatron_gpt_pretraining:46] DEBUG: layernorm_mlp.layernorm_mlp.fc2_weight.shape=torch.Size([768, 3072])
```
**Environment overview (please complete the following information)**
- Docker
- nvidia/nemo:24.03 + git pull
- docker run --rm -it --entrypoint /bin/bash --network=host --runtime=nvidia --shm-size=2g nvcr.io/nvidia/nemo:24.01.01.framework
**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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} | # What does this PR do ?
**Detailed description here: https://github.com/NVIDIA/NeMo/issues/8846**
This PR fixes Transformer layer MLP hyperparameters not being set with model.mcore_gpt=False, model.transformer_engine=True, and model.megatron_amp_O2=True.
**Collection**: NLP
# Changelog
- Passing missing arguments to layer constructors.
# Usage
* Add the following lines to opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py after model initialization to debug:
```
logging.warning(f"DEBUG: layernorm_mlp.activation={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.activation}")
logging.warning(f"DEBUG: layernorm_mlp.use_bias={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.use_bias}")
logging.warning(f"DEBUG: layernorm_mlp.normalization={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.normalization}")
logging.warning(f"DEBUG: layernorm_mlp.layernorm_mlp.fc1_weight.shape={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.fc1_weight.shape}")
logging.warning(f"DEBUG: layernorm_mlp.layernorm_mlp.fc2_weight.shape={model.model.module.language_model.encoder.layers._modules['0'].layernorm_mlp.fc2_weight.shape}")
```
Test with the following script with and without the changes:
```
#!/bin/bash
python /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
model.mcore_gpt=False \
model.transformer_engine=True \
trainer.precision=bf16 \
model.megatron_amp_O2=True \
model.activation=fast-swiglu \
model.bias=false \
model.normalization=rmsnorm \
```
The bias, normalization, and activation should be correctly set with the fix.
# 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?
- [x] 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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adds a script to convert zarr ckpt format to torch distributed ckpt format.
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# Changelog
- Add specific line by line info of high level changes in this PR.
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```python
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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"jenkins",
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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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 ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/8838 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8838/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8838/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8838/events | https://github.com/NVIDIA/NeMo/pull/8838 | 2,228,708,730 | PR_kwDOC_bI7s5r4Z6A | 8,838 | Add clip conv layer | {
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"I have verified the conversion works and this new converted ckpt works with SD. Thanks!!",
"jenkins"
] | 2024-04-05T19:49:11 | 2024-04-10T23:29:16 | 2024-04-10T23:29:16 | CONTRIBUTOR | {
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"merged_at": "2024-04-10T23:29:15"
} | # What does this PR do ?
Replace the linear layer in the VIT backbone clip with the convolution layer.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changes in both vit_backbone.py and convert_external_clip_to_nemo.py
# Usage
More similarity with the existing Clip models in the literature.
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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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# Additional Information
Attaching the successful run of the [convert_external_clip_to_nemo.py]
<img width="1494" alt="Screenshot 2024-04-08 at 1 04 33 PM" src="https://github.com/NVIDIA/NeMo/assets/49656670/d69a95ff-290f-4f1f-8aaa-cab792e32f3c">
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"Can you please put it into a droppable menu as shown here, this is too much unrelated things before even beginning to discuss the actual toolkit. If users are interested they can open the menu. \r\n\r\nFor reference from an old version of NeMo - https://github.com/NVIDIA/NeMo/blob/r1.20.0/PUBLICATIONS.md"
] | 2024-04-05T17:30:49 | 2024-04-19T17:01:25 | 2024-04-19T16:39:53 | COLLABORATOR | {
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"merged_at": "2024-04-19T16:39:53"
} | # What does this PR do ?
Adds links to articles on
* NeMo framework on GKE
* Responsible Gen AI using NeMo and Picasso
* NeMo powering Amazon Titan foundation models
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https://api.github.com/repos/NVIDIA/NeMo/issues/8836 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8836/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8836/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8836/events | https://github.com/NVIDIA/NeMo/pull/8836 | 2,228,502,320 | PR_kwDOC_bI7s5r3stU | 8,836 | Alit/draft | {
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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**:
- [ ] 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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"please somebody help to get this error @titu1994 sir help",
"In your config use trainer.max_steps = -1 rather than None",
"thanks sir 🙌"
] | 2024-04-05T16:23:26 | 2024-04-07T17:28:52 | 2024-04-07T17:28:52 | NONE | {
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!bash start_train_ctc.sh
[NeMo W 2024-04-05 16:16:10 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 2024-04-05 16:16:10 speech_to_text_ctc_bpe:99] Hydra config: name: Conformer-CTC-BPE-Large
model:
sample_rate: 16000
log_prediction: true
ctc_reduction: mean_batch
skip_nan_grad: false
train_ds:
manifest_filepath: /content/drive/MyDrive/drive-download-20240405T032246Z-001/train.json
sample_rate: ${model.sample_rate}
batch_size: 24
shuffle: true
num_workers: 16
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 30
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: 8
validation_ds:
manifest_filepath: /content/drive/MyDrive/drive-download-20240405T032246Z-001/validated.json
sample_rate: ${model.sample_rate}
batch_size: 1
max_duration: 30
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
test_ds:
manifest_filepath: /content/drive/MyDrive/drive-download-20240405T032246Z-001/test.json
sample_rate: ${model.sample_rate}
batch_size: 4
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
tokenizer:
dir: /content/drive/MyDrive/1T
type: bpe
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: per_feature
window_size: 0.025
window_stride: 0.01
window: hann
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 1.0e-05
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
time_masks: 5
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1
n_layers: 17
d_model: 512
subsampling: striding
subsampling_factor: 4
subsampling_conv_channels: -1
ff_expansion_factor: 4
self_attention_model: rel_pos
n_heads: 8
att_context_size:
- -1
- -1
xscaling: true
untie_biases: true
pos_emb_max_len: 5000
conv_kernel_size: 31
conv_norm_type: batch_norm
dropout: 0.1
dropout_emb: 0.0
dropout_att: 0.1
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
optim:
name: adamw
lr: 0.5
betas:
- 0.9
- 0.98
weight_decay: 0.001
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
warmup_steps: 10000
warmup_ratio: null
min_lr: 1.0e-06
trainer:
devices: -1
num_nodes: 1
max_epochs: 30
max_steps: null
val_check_interval: 1.0
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 1
gradient_clip_val: 0.0
precision: 32
log_every_n_steps: 100
num_sanity_val_steps: 0
check_val_every_n_epoch: 1
sync_batchnorm: true
enable_checkpointing: false
logger: false
benchmark: false
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: val_wer
mode: min
save_top_k: 10
always_save_nemo: true
resume_if_exists: false
resume_ignore_no_checkpoint: false
create_wandb_logger: true
wandb_logger_kwargs:
name: conformer_large_filtered_english
project: nemo_conformer
init_from_pretrained_model: /root/ekstep/nemo_exp/vakyansh-nemo-experimentation/scripts/finetuning/nemo_experiments/Conformer-CTC-BPE-Large/2022-06-01_19-16-48/checkpoints/Conformer-CTC-BPE-Large.nemo
Error executing job with overrides: ['model.train_ds.manifest_filepath=/content/drive/MyDrive/drive-download-20240405T032246Z-001/train.json', 'model.validation_ds.manifest_filepath=/content/drive/MyDrive/drive-download-20240405T032246Z-001/validated.json', 'model.test_ds.manifest_filepath=/content/drive/MyDrive/drive-download-20240405T032246Z-001/test.json', 'model.tokenizer.dir=/content/drive/MyDrive/1T', 'model.tokenizer.type=bpe', 'trainer.devices=-1', 'trainer.accelerator=gpu', 'trainer.strategy=ddp', 'exp_manager.create_wandb_logger=True', 'exp_manager.wandb_logger_kwargs.name=conformer_large_filtered_english', 'exp_manager.wandb_logger_kwargs.project=nemo_conformer', '+init_from_pretrained_model=/root/ekstep/nemo_exp/vakyansh-nemo-experimentation/scripts/finetuning/nemo_experiments/Conformer-CTC-BPE-Large/2022-06-01_19-16-48/checkpoints/Conformer-CTC-BPE-Large.nemo']
Traceback (most recent call last):
File "/content/vakyansh-nemo-experimentation/scripts/finetuning/../../src/finetuning/speech_to_text_ctc_bpe.py", line 101, in main
trainer = pl.Trainer(**cfg.trainer)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/argparse.py", line 70, in insert_env_defaults
return fn(self, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 418, in __init__
self.fit_loop.epoch_loop = _TrainingEpochLoop(self, min_steps=min_steps, max_steps=max_steps)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 59, in __init__
if max_steps < -1:
TypeError: '<' not supported between instances of 'NoneType' and 'int'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. | {
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"jenkins"
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} | # What does this PR do ?
1. Remove references to `pipeline_model_parallel_split_rank` since it is only used for enc-dec models, and the merge script is for decoder only models. This enables users to not have to specify this in the run command.
2. Remove setting `apply_rope_fusion` to False since bug #8590 is fixed.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
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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
```
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minor fix for rendering issue with "#"
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# Changelog
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# Usage
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"merged_at": "2024-04-10T17:03:16"
} | # What does this PR do ?
Enable Label-Looping algorithm introduced in #8286 and #7926 (`loop_labels=True`) by default for RNN-T greedy decoding.
**Collection**: [ASR]
# Changelog
- Enable Label-Looping algorithm by default (`loop_labels=true`)
- fix Label Looping algorithm for RNN-T/TDT + Stateless network
- fix tests with custom RNNT Decoder
- parametrize tests for batched greedy decoding to test both algorithms (Frame-/Label-Looping)
# Usage
Label-Looping algorithm is used by default now for batched greedy decoding.
For Frame-Looping algorithm one can use:
```shell
python examples/asr/speech_to_text_eval.py <...> \
rnnt_decoding.greedy.loop_labels=false
```
# 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
* 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.",
"@roshanjoebenny could you raise this issue on https://github.com/NVIDIA/apex ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale ... | 2024-04-05T12:08:21 | 2024-06-16T01:51:47 | 2024-06-16T01:51:47 | NONE | {
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When I am importing from nemo.collections import nlp as nemo_nlp
I was getting an error as module not found of apex.So I tryed installing apex using the below given commands
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir \
--global-option="--cpp_ext" --global-option="--cuda_ext" ./
When executing this command I was getting an error called
ModuleNotFoundError: No module named 'packaging'
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
etting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
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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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"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."
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} | # What does this PR do ?
Currently, NeMo CTC Segmentation tool throws errors if you try to use a hybrid model (as mentioned in this issue https://github.com/NVIDIA/NeMo/issues/8750). This PR fixes that.
cc @ekmb
**Collection**: tools/ctc_segmentation
# Changelog
- allows for `vocabulary = asr_model.tokenizer.vocab` and `vocabulary = asr_model.cfg.decoder.vocabulary`
- checks if model.transcribe returns a tuple of hypotheses
- also various checks for model instance (since we can only use pure CTC or CTC from a hybrid model)
# 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 https://github.com/NVIDIA/NeMo/issues/8750
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fix for lora adapters when SP is enabled
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# Changelog
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# Changelog
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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# Before your PR is "Ready for review"
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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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} | # What does this PR do ?
Bug fix:
Recent pytorch-lightning versions overwrite the .precision field of cfg to None. As a result, it must be saved and restored by the callee. I moved this in the megatron trainer builder to avoid having to do this manually.
The peft eval script was not saving/restoring the .precision field and was using the None value, thus all evaluations happening in FP32.
**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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Adding reference to the cache-aware paper.
# Changelog
- Added reference to the cache-aware paper.
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [X] Documentation
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} | # What does this PR do ?
Fix `local_rank` when obtained from the `LOCAL_RANK` env variable (it was a `str` instead of an `int`)
**Collection**: nlp / multimodal / vision
# Changelog
- Fix `local_rank` when obtained from the `LOCAL_RANK` env variable in several conversion scripts to .nemo
# 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.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
All
# Changelog
- Add specific line by line info of high level changes in this PR.
Pipeline for installation on pure windows
# 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)
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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**:
- [+] 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",
"jenkins",
"LGTM, but can we check if autotokenizer and local sentencepiece outputs match for the new methods implemented before we merge?\r\n\r\nit is probably fine if they're not identical, but we need to know where the differences arise and also document them if any",
"This PR is stale because i... | 2024-04-04T10:50:28 | 2024-05-30T01:47:02 | 2024-05-30T01:47:02 | COLLABORATOR | {
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} | # What does this PR do ?
Extending NeMo's AutoTokenizer and SentencePieceTokenizer for consistency with HuggingFace tokenizers API as it is used throughout TensorRT-LLM and AMMO in evaluation tools.
This is necessary to seamlessly enable model validation -- including quantized variants -- with the current scripts and to avoid code duplication.
Example use-cases to support:
- overall API [link1](https://github.com/NVIDIA/TensorRT-LLM/blob/118b3d7e7bab720d8ea9cd95338da60f7512c93a/tensorrt_llm/hlapi/tokenizer.py#L14-L46)
- `encode`: [link1](https://github.com/NVIDIA/TensorRT-LLM/blob/035b99e0d09d4f2dfdb949810cf7245112aa4165/examples/gpt/run_hf.py#L92) and [link2](https://github.com/NVIDIA/TensorRT-LLM/blob/118b3d7e7bab720d8ea9cd95338da60f7512c93a/examples/summarize.py#L162)
- `decode`: [link1](https://github.com/NVIDIA/TensorRT-LLM/blob/035b99e0d09d4f2dfdb949810cf7245112aa4165/examples/mmlu.py#L295)
- `batch_decode`: [link1](https://github.com/NVIDIA/TensorRT-LLM/blob/118b3d7e7bab720d8ea9cd95338da60f7512c93a/examples/summarize.py#L208)
- `batch_encode_plus` [link2](https://github.com/NVIDIA/TensorRT-LLM/blob/035b99e0d09d4f2dfdb949810cf7245112aa4165/tensorrt_llm/quantization/quantize_by_ammo.py#L204)
- setting `pad_token_id`: [link1](https://github.com/NVIDIA/TensorRT-LLM/blob/118b3d7e7bab720d8ea9cd95338da60f7512c93a/examples/utils.py#L118-L119)
**Collection**: NLP
# Changelog
- adding different variants of encode/decode methods
- adding `pad_token_id` (with setter enabled for HF AutoTokenizer wrapper) and `eos_token_id` property attributes for HF tokenizer
# Usage
See examples listed above.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/8817 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8817/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8817/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8817/events | https://github.com/NVIDIA/NeMo/pull/8817 | 2,225,078,855 | PR_kwDOC_bI7s5rry4k | 8,817 | Fix `model_name` check for only tokenizer libraries which need `model_name` | {
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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."
] | 2024-04-04T10:18:37 | 2024-04-27T01:44:32 | 2024-04-27T01:44:31 | NONE | {
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} | # What does this PR do ?
For the function `get_nmt_tokenizer`, the user may use libraries which do not require `model_name` (e.g. `sentencepiece`, which is the default library), thus would not pass in `model_name` to the function.
The current implementation throws a `ValueError` for such cases.
This PR fixes the `model_name` check for only libraries which need them.
# Usage
From step 4, option 2 in this nvidia [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html)
```
python <NeMo_ROOT_FOLDER>/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \
--input=train_data.jsonl \
--json-keys=text \
--tokenizer-library=sentencepiece \
--tokenizer-model=spm_32k_wiki.model \
--output-prefix=gpt_training_data \
--append-eod \
--workers=32
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@MaximumEntropy, @ericharper
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/8816 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8816/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8816/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8816/events | https://github.com/NVIDIA/NeMo/issues/8816 | 2,224,653,212 | I_kwDOC_bI7s6EmYOc | 8,816 | Issue while importing-> from nemo.collections import nlp | {
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... | closed | false | null | [] | null | [
"Python 3.10 is the minimum for NeMo",
"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."
] | 2024-04-04T06:59:50 | 2024-05-08T18:11:29 | 2024-05-08T18:11:29 | NONE | {
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} | null | null | null | TypeError Traceback (most recent call last)
Cell In[9], line 2
1 from nemo.utils.exp_manager import exp_manager
----> 2 from nemo.collections import nlp
4 # import os
5 # import wget
6 # import torch
7 # import pytorch_lightning as pl
8 # from omegaconf import OmegaConf
File [~/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/__init__.py:15](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/__init__.py#line=14)
1 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
---> 15 from nemo.collections.nlp import data, losses, models, modules
16 from nemo.package_info import __version__
18 # Set collection version equal to NeMo version.
File [~/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/__init__.py:16](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/__init__.py#line=15)
1 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
15 from nemo.collections.nlp.data.data_utils import *
---> 16 from nemo.collections.nlp.data.entity_linking.entity_linking_dataset import EntityLinkingDataset
17 from nemo.collections.nlp.data.information_retrieval.information_retrieval_dataset import (
18 BertInformationRetrievalDataset,
19 )
20 from nemo.collections.nlp.data.language_modeling.l2r_lm_dataset import (
21 L2RLanguageModelingDataset,
22 TarredL2RLanguageModelingDataset,
23 )
File [~/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/entity_linking/__init__.py:15](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/entity_linking/__init__.py#line=14)
1 # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
---> 15 from nemo.collections.nlp.data.entity_linking.entity_linking_dataset import EntityLinkingDataset
File [~/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/entity_linking/entity_linking_dataset.py:22](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/collections/nlp/data/entity_linking/entity_linking_dataset.py#line=21)
19 import torch
21 from nemo.collections.nlp.data.data_utils.data_preprocessing import find_newlines, load_data_indices
---> 22 from nemo.core.classes import Dataset
23 from nemo.utils import logging
25 __all__ = ['EntityLinkingDataset']
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/__init__.py:16](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/__init__.py#line=15)
1 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
15 import nemo.core.neural_types
---> 16 from nemo.core.classes import *
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/classes/__init__.py:20](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/classes/__init__.py#line=19)
17 import omegaconf
18 import pytorch_lightning
---> 20 from nemo.core.classes.common import (
21 FileIO,
22 Model,
23 PretrainedModelInfo,
24 Serialization,
25 Typing,
26 is_typecheck_enabled,
27 typecheck,
28 )
29 from nemo.core.classes.dataset import Dataset, IterableDataset
30 from nemo.core.classes.exportable import Exportable, ExportFormat
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/classes/common.py:40](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/classes/common.py#line=39)
37 from omegaconf import DictConfig, OmegaConf
39 import nemo
---> 40 from nemo.core.classes.mixins.hf_io_mixin import HuggingFaceFileIO
41 from nemo.core.config.templates.model_card import NEMO_DEFAULT_MODEL_CARD_TEMPLATE
42 from nemo.core.connectors.save_restore_connector import SaveRestoreConnector
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/__init__.py:28](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/__init__.py#line=27)
16 from nemo.core.classes.mixins.adapter_mixin_strategies import (
17 ResidualAddAdapterStrategy,
18 ResidualAddAdapterStrategyConfig,
19 ReturnResultAdapterStrategy,
20 ReturnResultAdapterStrategyConfig,
21 )
22 from nemo.core.classes.mixins.adapter_mixins import (
23 AdapterModelPTMixin,
24 AdapterModuleMixin,
25 get_registered_adapter,
26 register_adapter,
27 )
---> 28 from nemo.core.classes.mixins.hf_io_mixin import HuggingFaceFileIO
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/hf_io_mixin.py:27](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/hf_io_mixin.py#line=26)
22 from huggingface_hub.utils import SoftTemporaryDirectory
24 from nemo.utils import logging
---> 27 class HuggingFaceFileIO(ABC):
28 """
29 Mixin that provides Hugging Face file IO functionality for NeMo models.
30 It is usually implemented as a mixin to `ModelPT`.
(...)
34 - `push_to_hf_hub()`: Push a model to the hub.
35 """
37 @classmethod
38 def get_hf_model_filter(cls) -> ModelFilter:
File [~/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/hf_io_mixin.py:161](http://34.124.175.223:1080/home/jobin_abraham01/virtual_env/lib/python3.9/site-packages/nemo/core/classes/mixins/hf_io_mixin.py#line=160), in HuggingFaceFileIO()
152 all_results.append(result)
154 return all_results
156 def push_to_hf_hub(
157 self,
158 repo_id: str,
159 *,
160 pack_nemo_file: bool = True,
--> 161 model_card: Optional['ModelCard'] | object | str = None,
162 commit_message: str = "Push model using huggingface_hub.",
163 private: bool = False,
164 api_endpoint: Optional[str] = None,
165 token: Optional[str] = None,
166 branch: Optional[str] = None,
167 allow_patterns: Optional[Union[List[str], str]] = None,
168 ignore_patterns: Optional[Union[List[str], str]] = None,
169 delete_patterns: Optional[Union[List[str], str]] = None,
170 ):
171 """
172 Upload model checkpoint to the Hub.
173
(...)
208 The url of the uploaded HF repo.
209 """
210 if "/" not in repo_id or len(repo_id.split("/")) != 2:
TypeError: unsupported operand type(s) for |: '_UnionGenericAlias' and 'type' | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8815 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8815/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8815/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8815/events | https://github.com/NVIDIA/NeMo/issues/8815 | 2,224,628,754 | I_kwDOC_bI7s6EmSQS | 8,815 | Multilingual FastConformer change vocabulary test WER > 1 but validation WER = 0.16 | {
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"After you finish training the model, you should not call change vocabulary. Change vocabulary is only used when you want to finetune a pretrained model onto a custom dataset or language. ",
"Sorry to clarify, I meant calling change vocabulary before finetuning on my custom dataset. Before finetuning, when I spec... | 2024-04-04T06:43:49 | 2024-10-15T03:09:11 | 2024-05-12T01:47:56 | NONE | {
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} | null | null | null | I trained a multilingual (5 language) FastConformer EncDecCTCModel following the tutorial https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Multilang_ASR.ipynb. Training performs as expected and validation WER reaches minimum of 0.16, however when I evaluate the model on different language's test set by loading from either a .ckpt or .nemo file the WER is > 1 for all languages.
If I exclude the `asr_model.change_vocabulary` step to replace the decoder (as shown below) then the test set WER is all way below 1, and closer to the validation WER.
```
asr_model.change_vocabulary(
new_tokenizer_dir=new_tokenizer_cfg,
new_tokenizer_type="agg",
)
```
However, I am interested in replacing the decoder as I would like to increase the aggregated tokenizer vocabulary beyond the pretrained FastConformer's 1024 vocabulary size.
Appreciate any help on this issue! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8814 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8814/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8814/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8814/events | https://github.com/NVIDIA/NeMo/issues/8814 | 2,224,522,825 | I_kwDOC_bI7s6El4ZJ | 8,814 | ImportError: Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt. | {
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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.",
"same issue "
] | 2024-04-04T05:21:30 | 2024-10-11T02:08:30 | 2024-05-12T01:47:57 | NONE | {
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```
from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder
from nemo.utils.exp_manager import exp_manager
trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer()
exp_manager(trainer, cfg.exp_manager)
```
I am getting the error
```
ImportError: Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
```
So as per the official documentation when I try to install Apex,
```
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout b496d85fb88a801d8e680872a12822de310951fd
pip install -v --no-build-isolation --disable-pip-version-check --no-cache-dir --config-settings "--build-option=--cpp_ext --cuda_ext --fast_layer_norm --distributed_adam --deprecated_fused_adam" ./
```
this installation raises the given error
```
Usage:
pip install [options] <requirement specifier> [package-index-options] ...
pip install [options] -r <requirements file> [package-index-options] ...
pip install [options] [-e] <vcs project url> ...
pip install [options] [-e] <local project path> ...
pip install [options] <archive url/path> ...
no such option: --config-settings
```
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