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mostly testing if new repo works or not
as titled, move the config files to the root folder, where it decouples with the torchtrain package build, and allow easier navigations
…olumnar display to show both, show avg iter & data loading times at end of training (pytorch#87) This PR adds basic perf timing and display for 'per iter' and 'final iter average' display. (in part based on Andrew's comment about having to open the trace to compare iter timing). 1. tracking list is housed in TrainState, but I do not save it as part of the state dict as I view this as useful but not saveable info. 2. iter times are tracked after dataloading is done each iter and after optimizer step. The idea is to make this timing expressly the model training iter (not data loading or post iter other metrics calcs). 3. 'time' is now displayed at each iter along with the usual loss and lr. 4. at the end of training, assuming more than 3 iters run, then the average iter time is calculated by igoring the first three iters (consider these as warmup esp as cudaCacheAllocator gets warmed up) and displayed. 5. based on @tianyu-l feedback: I have added data loading times as well. I used the same timeit.default_timer() from timeit to be consistent. (cpu side so no synch's needed :) 6 - after fiddling with printf width formatting options, added beautiful aligned columnar display for the per iter updates: Now: <img width="1282" alt="Screenshot 2024-02-26 at 9 39 25 AM" src="https://github.com/pytorch/torchtrain/assets/46302957/9ee2ea7b-5c28-4d41-ba91-d4176c64fc66"> before: <img width="1282" alt="Screenshot 2024-02-26 at 8 39 46 AM" src="https://github.com/pytorch/torchtrain/assets/46302957/37cbfa20-7f1d-4d94-be94-3505ef4498c0">
Summary: Summary: Follow up on config unification, options not available in config file are picked from command line defaults. Test Plan: ============================= test session starts ============================== platform linux -- Python 3.10.13, pytest-8.0.1, pluggy-1.4.0 -- /home/gnadathur/local/a/pytorch-env/bin/python cachedir: .pytest_cache rootdir: /data/users/gnadathur/a/torchtrain configfile: pyproject.toml plugins: cov-4.1.0 collecting ... collected 3 items test/test_job_config.py::TestJobConfig::test_command_line_args PASSED [ 33%] test/test_job_config.py::TestJobConfig::test_job_config_file PASSED [ 66%] test/test_job_config.py::TestJobConfig::test_job_file_does_not_exist PASSED [100%] ---------- coverage: platform linux, python 3.10.13-final-0 ---------- Coverage XML written to file coverage.xml ============================= slowest 20 durations ============================= 0.00s call test/test_job_config.py::TestJobConfig::test_job_config_file 0.00s call test/test_job_config.py::TestJobConfig::test_command_line_args 0.00s call test/test_job_config.py::TestJobConfig::test_job_file_does_not_exist 0.00s setup test/test_job_config.py::TestJobConfig::test_command_line_args 0.00s teardown test/test_job_config.py::TestJobConfig::test_command_line_args 0.00s setup test/test_job_config.py::TestJobConfig::test_job_config_file 0.00s setup test/test_job_config.py::TestJobConfig::test_job_file_does_not_exist 0.00s teardown test/test_job_config.py::TestJobConfig::test_job_config_file 0.00s teardown test/test_job_config.py::TestJobConfig::test_job_file_does_not_exist ============================== 3 passed in 0.06s =============================== Test Plan: Reviewers: Subscribers: Tasks: Tags: --------- Co-authored-by: gnadathur <[email protected]>
ghstack-source-id: 38cbc277e2a177bc0baf35450a661835b97a7f22 Pull Request resolved: pytorch#92
…g on slurm (pytorch#93) This PR adds the ability to do colored console outputs in order to highlight the training data outputs. It also adds a check to not use this color formatting on slurm, where it will add 33= instead of the color if not avoided. Note that I've just added some color to highlight the main training data. Users that fork/clone can use it to enhance their outputs as desired. <img width="1372" alt="Screenshot 2024-02-26 at 10 20 15 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/44849821-1677-40bf-896c-39344cd661d6"> Note that on slurm it remains plain: <img width="847" alt="Screenshot 2024-02-26 at 10 46 24 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/172eaa58-4f5c-48f5-8ec1-bc349e3e82f2"> if you dont' check this, then it would otherwise look like this (this does not happen with this PR, just showing if we didn't check and credit to Yifu for noting this would be an issue): <img width="847" alt="Screenshot 2024-02-26 at 10 39 23 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/4a87fb9a-dd3a-417c-a29e-286ded069358">
this PR updates the GPU metrics to labelling as GiB - we were calculating GiB but calling it GB. (credit to @awgu for flagging this - issue pytorch#94) function names and member vars in metrics.py have been updated to _gib instead of _gb for clarity, and the logging output now labels as GiB: <img width="851" alt="Screenshot 2024-02-27 at 11 28 23 AM" src="https://github.com/pytorch/torchtrain/assets/46302957/85eb260a-77e9-4c49-be8a-b1aaa10dc3e2">
ghstack-source-id: 7dc4a80cf9c32f4dca3d00bcef019d256bdf58f7 Pull Request resolved: pytorch#96
Enable libUV for torchtrain.
Test:
```
+ export USE_LIBUV=1
+ USE_LIBUV=1
+ TRAINER_DIR=/home/gnadathur/local/torchtrain
+ NGPU=4
+ LOG_RANK=0,1
+ CONFIG_FILE=./train_configs/debug_model.toml
+ torchrun --nproc_per_node=4 --rdzv_endpoint=localhost:5972 --local-ranks-filter 0,1 --role rank --tee 3 train.py --job.config_file ./train_configs/debug_model.toml
W0228 09:12:02.564000 140353616004096 torch/distributed/run.py:717]
W0228 09:12:02.564000 140353616004096 torch/distributed/run.py:717] *****************************************
W0228 09:12:02.564000 140353616004096 torch/distributed/run.py:717] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0228 09:12:02.564000 140353616004096 torch/distributed/run.py:717] *****************************************
[rank0]:2024-02-28 09:12:04,581 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank1]:2024-02-28 09:12:04,708 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank0]:2024-02-28 09:12:05,647 - root - INFO - Building llama
[rank0]:2024-02-28 09:12:05,655 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank0]:2024-02-28 09:12:05,655 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank1]:2024-02-28 09:12:07,299 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank1]:2024-02-28 09:12:07,299 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank0]:2024-02-28 09:12:07,565 - root - INFO - Model fully initialized via reset_params
[rank0]:2024-02-28 09:12:07,566 - root - INFO - Model built with: ModelArgs(dim=256, n_layers=2, n_heads=16, n_kv_heads=None, vocab_size=32000, multiple_of=256, ffn_dim_multiplier=None, norm_eps=1e-05, max_batch_size=32, max_seq_len=32768, depth_init=True)
[rank0]:2024-02-28 09:12:07,566 - root - INFO - �[34mModel llama debugmodel �[31msize: 18,089,216 total parameters�[39m
[rank0]:2024-02-28 09:12:07,567 - root - INFO - GPU memory usage: NVIDIA H100 (0): 95.0396 GiB capacity, 0.0 GiB in-use, 0.0% in-use
[rank0]:2024-02-28 09:12:08,769 - root - INFO - Applied FSDP to the model...
[rank0]:2024-02-28 09:12:08,770 - root - INFO - Gradient scaling not enabled.
[rank0]:2024-02-28 09:12:08,770 - root - INFO - Metrics logging active. Tensorboard logs will be saved at ./outputs/tb/20240228-0912.
[rank0]:2024-02-28 09:12:08,977 - root - INFO - Profiling active. Traces will be saved at ./outputs/profiling/traces
[rank0]:2024-02-28 09:12:10,956 - root - INFO - �[36mstep: 1 �[32mloss: 10.9229 �[39miter: �[34m 1.9386�[39m data: �[34m0.0368 �[39mlr: �[33m0.00026667�[39m
[rank0]:2024-02-28 09:12:11,045 - root - INFO - �[36mstep: 2 �[32mloss: 10.8673 �[39miter: �[34m 0.0562�[39m data: �[34m0.0316 �[39mlr: �[33m0.00053333�[39m
[rank0]:2024-02-28 09:12:11,130 - root - INFO - �[36mstep: 3 �[32mloss: 10.7145 �[39miter: �[34m 0.0523�[39m data: �[34m0.0322 �[39mlr: �[33m0.0008�[39m
[rank0]:2024-02-28 09:12:11,219 - root - INFO - �[36mstep: 4 �[32mloss: 10.5038 �[39miter: �[34m 0.0559�[39m data: �[34m0.0319 �[39mlr: �[33m0.0007�[39m
[rank0]:2024-02-28 09:12:11,304 - root - INFO - �[36mstep: 5 �[32mloss: 10.2228 �[39miter: �[34m 0.0537�[39m data: �[34m0.031 �[39mlr: �[33m0.0006�[39m
[rank0]:2024-02-28 09:12:11,391 - root - INFO - �[36mstep: 6 �[32mloss: 9.9677 �[39miter: �[34m 0.0562�[39m data: �[34m0.0302 �[39mlr: �[33m0.0005�[39m
[rank0]:2024-02-28 09:12:11,478 - root - INFO - �[36mstep: 7 �[32mloss: 9.7762 �[39miter: �[34m 0.0544�[39m data: �[34m0.0317 �[39mlr: �[33m0.0004�[39m
[rank0]:2024-02-28 09:12:11,676 - root - INFO - �[36mstep: 8 �[32mloss: 9.4359 �[39miter: �[34m 0.0509�[39m data: �[34m0.0322 �[39mlr: �[33m0.0003�[39m
[rank1]:STAGE:2024-02-28 09:12:11 3161834:3161834 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank1]:[rank1]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank0]:STAGE:2024-02-28 09:12:11 3161833:3161833 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank0]:2024-02-28 09:12:11,813 - root - INFO - �[36mstep: 9 �[32mloss: 9.2326 �[39miter: �[34m 0.1007�[39m data: �[34m0.0321 �[39mlr: �[33m0.0002�[39m
[rank0]:[rank0]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank1]:STAGE:2024-02-28 09:12:11 3161834:3161834 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank1]:STAGE:2024-02-28 09:12:11 3161834:3161834 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank0]:STAGE:2024-02-28 09:12:11 3161833:3161833 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank0]:STAGE:2024-02-28 09:12:11 3161833:3161833 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank0]:2024-02-28 09:12:12,195 - root - INFO - exporting profile traces to ./outputs/profiling/traces/iteration_10
[rank0]:2024-02-28 09:12:12,207 - root - INFO - �[36mstep: 10 �[32mloss: 9.1641 �[39miter: �[34m 0.0971�[39m data: �[34m0.031 �[39mlr: �[33m0.0001�[39m
[rank0]:2024-02-28 09:12:12,207 - root - INFO - Average iter time: 0.0670 seconds
[rank0]:2024-02-28 09:12:12,207 - root - INFO - Average data load time: 0.0314 seconds
[rank0]:2024-02-28 09:12:12,208 - root - INFO - Current Memory: NVIDIA H100 (0): Reserved: 9.6465%, Alloc 2.1969%, Active: 2.2%
[rank0]:Peak Memory: Reserved 9.65%, Alloc 8.43%, Active: 8.44%
[rank0]:num retries: 0, num ooms: 0
[rank0]:NCCL version 2.19.3+cuda12.0
```
---------
Co-authored-by: gnadathur <[email protected]>
as titled, we don't want to allow steps == -1 case as it would blow up the lr scheduler
Add 7b config and adjust options to be more realistic didn't add this to the train scripts as default as it's expensive to init, whoever use it can adjust it accordingly
ghstack-source-id: f7ee3c867bfcdcae5dbb490982920606191e6f40 Pull Request resolved: pytorch#97
Summary:
Adding a description field, useful for integration tests to describe the
test.
Test Plan:
```
+ export USE_LIBUV=1
+ USE_LIBUV=1
+ TRAINER_DIR=/home/gnadathur/local/torchtrain
+ NGPU=4
+ LOG_RANK=0,1
+ CONFIG_FILE=./train_configs/debug_model.toml
+ torchrun --nproc_per_node=4 --rdzv_endpoint=localhost:5972 --local-ranks-filter 0,1 --role rank --tee 3 train.py --job.config_file ./train_configs/debug_model.toml
W0229 17:05:02.466000 140187679912960 torch/distributed/run.py:717]
W0229 17:05:02.466000 140187679912960 torch/distributed/run.py:717] *****************************************
W0229 17:05:02.466000 140187679912960 torch/distributed/run.py:717] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0229 17:05:02.466000 140187679912960 torch/distributed/run.py:717] *****************************************
[rank1]:2024-02-29 17:05:04,269 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank0]:2024-02-29 17:05:04,510 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank0]:2024-02-29 17:05:05,327 - root - INFO - Starting job: debug training
[rank0]:2024-02-29 17:05:05,327 - root - INFO - Building llama
[rank0]:2024-02-29 17:05:05,335 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank0]:2024-02-29 17:05:05,335 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank1]:2024-02-29 17:05:06,782 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank1]:2024-02-29 17:05:06,782 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank0]:2024-02-29 17:05:07,347 - root - INFO - Model fully initialized via reset_params
[rank0]:2024-02-29 17:05:07,349 - root - INFO - Model built with: ModelArgs(dim=256, n_layers=2, n_heads=16, n_kv_heads=None, vocab_size=32000, multiple_of=256, ffn_dim_multiplier=None, norm_eps=1e-05, max_batch_size=32, max_seq_len=32768, depth_init=True)
[rank0]:2024-02-29 17:05:07,349 - root - INFO - �[34mModel llama debugmodel �[31msize: 18,089,216 total parameters�[39m
[rank0]:2024-02-29 17:05:07,349 - root - INFO - GPU memory usage: NVIDIA H100 (0): 95.0396 GiB capacity, 0.0 GiB in-use, 0.0% in-use
[rank0]:2024-02-29 17:05:08,375 - root - INFO - Applied FSDP to the model...
[rank0]:2024-02-29 17:05:08,376 - root - INFO - Gradient scaling not enabled.
[rank0]:2024-02-29 17:05:08,376 - root - INFO - Metrics logging active. Tensorboard logs will be saved at ./outputs/tb/20240229-1705.
[rank0]:2024-02-29 17:05:08,610 - root - INFO - Profiling active. Traces will be saved at ./outputs/profiling/traces
[rank0]:2024-02-29 17:05:10,570 - root - INFO - �[36mstep: 1 �[32mloss: 10.9183 �[39miter: �[34m 1.9258�[39m data: �[34m0.0303 �[39mlr: �[33m0.00026667�[39m
[rank0]:2024-02-29 17:05:10,653 - root - INFO - �[36mstep: 2 �[32mloss: 10.8347 �[39miter: �[34m 0.0487�[39m data: �[34m0.0336 �[39mlr: �[33m0.00053333�[39m
[rank0]:2024-02-29 17:05:10,733 - root - INFO - �[36mstep: 3 �[32mloss: 10.6861 �[39miter: �[34m 0.045�[39m data: �[34m0.0334 �[39mlr: �[33m0.0008�[39m
[rank0]:2024-02-29 17:05:10,812 - root - INFO - �[36mstep: 4 �[32mloss: 10.4672 �[39miter: �[34m 0.0453�[39m data: �[34m0.0336 �[39mlr: �[33m0.0007�[39m
[rank0]:2024-02-29 17:05:10,893 - root - INFO - �[36mstep: 5 �[32mloss: 10.2154 �[39miter: �[34m 0.0466�[39m data: �[34m0.033 �[39mlr: �[33m0.0006�[39m
[rank0]:2024-02-29 17:05:10,975 - root - INFO - �[36mstep: 6 �[32mloss: 9.9573 �[39miter: �[34m 0.0496�[39m data: �[34m0.0314 �[39mlr: �[33m0.0005�[39m
[rank0]:2024-02-29 17:05:11,056 - root - INFO - �[36mstep: 7 �[32mloss: 9.7627 �[39miter: �[34m 0.0486�[39m data: �[34m0.0321 �[39mlr: �[33m0.0004�[39m
[rank0]:2024-02-29 17:05:11,201 - root - INFO - �[36mstep: 8 �[32mloss: 9.437 �[39miter: �[34m 0.0457�[39m data: �[34m0.0333 �[39mlr: �[33m0.0003�[39m
[rank1]:STAGE:2024-02-29 17:05:11 3368103:3368103 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank1]:[rank1]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank0]:STAGE:2024-02-29 17:05:11 3368102:3368102 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank0]:2024-02-29 17:05:11,317 - root - INFO - �[36mstep: 9 �[32mloss: 9.2446 �[39miter: �[34m 0.0794�[39m data: �[34m0.0324 �[39mlr: �[33m0.0002�[39m
[rank0]:[rank0]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank1]:STAGE:2024-02-29 17:05:11 3368103:3368103 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank1]:STAGE:2024-02-29 17:05:11 3368103:3368103 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank0]:STAGE:2024-02-29 17:05:11 3368102:3368102 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank0]:STAGE:2024-02-29 17:05:11 3368102:3368102 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank0]:2024-02-29 17:05:11,748 - root - INFO - exporting profile traces to ./outputs/profiling/traces/iteration_10
[rank0]:2024-02-29 17:05:11,762 - root - INFO - �[36mstep: 10 �[32mloss: 9.1772 �[39miter: �[34m 0.0893�[39m data: �[34m0.0324 �[39mlr: �[33m0.0001�[39m
[rank0]:2024-02-29 17:05:11,763 - root - INFO - Average iter time: 0.0578 seconds
[rank0]:2024-02-29 17:05:11,763 - root - INFO - Average data load time: 0.0326 seconds
[rank0]:2024-02-29 17:05:11,763 - root - INFO - Current Memory: NVIDIA H100 (0): Reserved: 9.6465%, Alloc 2.1969%, Active: 2.2%
[rank0]:Peak Memory: Reserved 9.65%, Alloc 8.43%, Active: 8.44%
[rank0]:num retries: 0, num ooms: 0
[rank0]:NCCL version 2.19.3+cuda12.0
```
Reviewers:
Subscribers:
Tasks:
Tags:
Co-authored-by: gnadathur <[email protected]>
ghstack-source-id: 1c5bf790d7473f6a24124051fcfa1fd2585a56f9 Pull Request resolved: pytorch#105
```
+ export USE_LIBUV=1
+ USE_LIBUV=1
+ TRAINER_DIR=/home/gnadathur/local/torchtrain
+ NGPU=4
+ LOG_RANK=0,1
+ CONFIG_FILE=./train_configs/debug_model.toml
+ torchrun --nproc_per_node=4 --rdzv_endpoint=localhost:5972 --local-ranks-filter 0,1 --role rank --tee 3 train.py --job.config_file ./train_configs/debug_model.toml
W0304 17:01:26.766000 140549371597824 torch/distributed/run.py:717]
W0304 17:01:26.766000 140549371597824 torch/distributed/run.py:717] *****************************************
W0304 17:01:26.766000 140549371597824 torch/distributed/run.py:717] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0304 17:01:26.766000 140549371597824 torch/distributed/run.py:717] *****************************************
[rank0]:2024-03-04 17:01:28,834 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank1]:2024-03-04 17:01:28,857 - torchtrain.parallelisms - INFO - Building 1-D device mesh with ('dp',), [4]
[rank0]:2024-03-04 17:01:29,712 - root - INFO - Starting job: debug training
[rank0]:2024-03-04 17:01:29,712 - root - INFO - Building llama
[rank0]:2024-03-04 17:01:29,719 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank0]:2024-03-04 17:01:29,719 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank1]:2024-03-04 17:01:31,187 - root - INFO - Reloaded SentencePiece model from ./torchtrain/datasets/tokenizer/tokenizer.model
[rank1]:2024-03-04 17:01:31,188 - root - INFO - #words: 32000 - BOS ID: 1 - EOS ID: 2
[rank0]:2024-03-04 17:01:31,346 - root - INFO - Model fully initialized via reset_params
[rank0]:2024-03-04 17:01:31,346 - root - INFO - Model built with: ModelArgs(dim=256, n_layers=2, n_heads=16, n_kv_heads=None, vocab_size=32000, multiple_of=256, ffn_dim_multiplier=None, norm_eps=1e-05, max_batch_size=32, max_seq_len=32768, depth_init=True)
[rank0]:2024-03-04 17:01:31,347 - root - INFO - �[34mModel llama debugmodel �[31msize: 18,089,216 total parameters�[39m
[rank0]:2024-03-04 17:01:31,347 - root - INFO - GPU memory usage: NVIDIA H100 (0): 95.0396 GiB capacity, 0.0 GiB in-use, 0.0% in-use
[rank0]:2024-03-04 17:01:32,502 - root - INFO - Applied FSDP to the model...
[rank0]:2024-03-04 17:01:32,503 - root - INFO - Gradient scaling not enabled.
[rank0]:2024-03-04 17:01:32,504 - root - INFO - Metrics logging active. Tensorboard logs will be saved at ./outputs/tb/20240304-1701.
[rank0]:2024-03-04 17:01:32,901 - root - INFO - Profiling active. Traces will be saved at ./outputs/profiling/traces
[rank0]:2024-03-04 17:01:34,806 - root - INFO - �[36mstep: 1 �[32mloss: 10.8424 �[39miter: �[34m 1.8688�[39m data: �[34m0.0316 �[39mlr: �[33m0.00026667�[39m
[rank0]:2024-03-04 17:01:34,891 - root - INFO - �[36mstep: 2 �[32mloss: 10.7581 �[39miter: �[34m 0.0476�[39m data: �[34m0.0357 �[39mlr: �[33m0.00053333�[39m
[rank0]:2024-03-04 17:01:34,970 - root - INFO - �[36mstep: 3 �[32mloss: 10.6239 �[39miter: �[34m 0.045�[39m data: �[34m0.0333 �[39mlr: �[33m0.0008�[39m
[rank0]:2024-03-04 17:01:35,048 - root - INFO - �[36mstep: 4 �[32mloss: 10.4163 �[39miter: �[34m 0.0455�[39m data: �[34m0.0323 �[39mlr: �[33m0.0007�[39m
[rank0]:2024-03-04 17:01:35,127 - root - INFO - �[36mstep: 5 �[32mloss: 10.1529 �[39miter: �[34m 0.0459�[39m data: �[34m0.032 �[39mlr: �[33m0.0006�[39m
[rank0]:2024-03-04 17:01:35,206 - root - INFO - �[36mstep: 6 �[32mloss: 9.8899 �[39miter: �[34m 0.0468�[39m data: �[34m0.0311 �[39mlr: �[33m0.0005�[39m
[rank0]:2024-03-04 17:01:35,284 - root - INFO - �[36mstep: 7 �[32mloss: 9.7204 �[39miter: �[34m 0.0461�[39m data: �[34m0.0312 �[39mlr: �[33m0.0004�[39m
[rank0]:2024-03-04 17:01:35,425 - root - INFO - �[36mstep: 8 �[32mloss: 9.3757 �[39miter: �[34m 0.0457�[39m data: �[34m0.0319 �[39mlr: �[33m0.0003�[39m
[rank0]:STAGE:2024-03-04 17:01:35 3850444:3850444 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank0]:2024-03-04 17:01:35,537 - root - INFO - �[36mstep: 9 �[32mloss: 9.1883 �[39miter: �[34m 0.0762�[39m data: �[34m0.0318 �[39mlr: �[33m0.0002�[39m
[rank0]:[rank0]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank1]:STAGE:2024-03-04 17:01:35 3850445:3850445 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[rank1]:[rank1]:[W CPUAllocator.cpp:249] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
[rank0]:STAGE:2024-03-04 17:01:35 3850444:3850444 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank0]:STAGE:2024-03-04 17:01:35 3850444:3850444 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank1]:STAGE:2024-03-04 17:01:35 3850445:3850445 ActivityProfilerController.cpp:320] Completed Stage: Collection
[rank1]:STAGE:2024-03-04 17:01:35 3850445:3850445 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[rank0]:2024-03-04 17:01:35,958 - root - INFO - exporting profile traces to ./outputs/profiling/traces/iteration_10
[rank0]:2024-03-04 17:01:35,971 - root - INFO - �[36mstep: 10 �[32mloss: 9.1212 �[39miter: �[34m 0.0808�[39m data: �[34m0.0319 �[39mlr: �[33m0.0001�[39m
[rank0]:2024-03-04 17:01:35,972 - root - INFO - Average iter time: 0.0553 seconds
[rank0]:2024-03-04 17:01:35,972 - root - INFO - Average data load time: 0.0317 seconds
[rank0]:2024-03-04 17:01:35,972 - root - INFO - Current Memory: NVIDIA H100 (0): Reserved: 9.6465%, Alloc 2.1969%, Active: 2.2%
[rank0]:Peak Memory: Reserved 9.65%, Alloc 8.43%, Active: 8.44%
[rank0]:num retries: 0, num ooms: 0
[rank0]:NCCL version 2.19.3+cuda12.0
```
Co-authored-by: gnadathur <[email protected]>
This PR enables meta_init functionality to avoid OOM'ing on cpu for
larger models.
The core functionality is in meta_init.py, and a few changes in
parallelization and train.py.
Key items:
1 - this is largely the same as the earlier PR I had for meta_init, but
I did a new one b/c faster than reworking it with all the interim
changes.
2 - to address feedback in previous PR:
a - why do we need meta_init.py, can't we just do:
~~~
with torch.device("meta"):
model = Model.from_args(...)
~~~
Unfortunately this does not work b/c the rope embeddings are treated
differently (buffer) and thus the simple lambda call from param_init_fn
in FSDP (lambda module: module.to_device('cuda') ) will not invoke or
move the rope embeddings and the model will fail on first forward.
This issue relates to the nn.embeddings not being moved, and that the
device is referenced in the forward pass for the current rope class.
Have opened pytorch#110 to track
this and investigate while not holding up meta init that is working from
landing.
b - per earlier feedback - meta init is now 'not optional' but simply
the default. This should ensure all models leverage it and ensure we
aren't missing things for future meta_init aspects.
3 - misc change - I switched the model_params to just do the normal all
params count instead of 'unique params' b/c it does not mesh with what
people perceive model size as.
Testing:
tested both debugmodel and 26B model with and without meta init to
confirm same loss curves.
Note for future reference - if you get a bad init (meta init failure)
you will simply not train (loss is same every iter).
If you fail to call reset params after FSDP, then you will train (b/c we
default to torch.randn_like) but your starting loss will be 5x+ higher
(telling you that you have not properly init'ed the model).
Co-authored-by: gnadathur <[email protected]>
ghstack-source-id: 5133a8d97ad209b569e0fc528e58daafdd31d80d Pull Request resolved: pytorch#114
ghstack-source-id: a0c8b4454f75ad1cd9824ac89a1df0182f6a7d8c Pull Request resolved: pytorch#112
…data' at 40 iters issue) (pytorch#88) This PR add's minipile (1M, 6GB) dataset as an option for pretraining with torchtrain. It resolves the issue where we run out of data after 40 iterations with the default alpaca dataset. Per @tianyu-l's excellent suggestion, have refactored to have a single hf_datasets.py file that supports both minipile and alpaca since it turned out no need for any different tokenizer, etc. Also cleaned up the datasets package so that create_tokenizer is exposed directly, and thus all public apis can be used directly from torchtrain.datasets. Lastly - added warning if/when a dataset is being re-looped so users don't get burned by overfitting: <img width="1294" alt="Screenshot 2024-03-06 at 5 11 09 AM" src="https://github.com/pytorch/torchtrain/assets/46302957/82480b6f-c677-4794-80c5-5c10b037732a"> Adds a color highlight to showcase what dataloader was built: <img width="1360" alt="Screenshot 2024-03-05 at 9 19 10 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/4717ec6a-14bb-4283-a3ae-fa40c27deee0"> and <img width="1360" alt="Screenshot 2024-03-05 at 9 22 01 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/dbf32d51-2dd4-4526-8855-9b33b627559e"> Usage: just add "minipile" or "alpaca" as the dataset in the training config toml file. <img width="439" alt="Screenshot 2024-02-25 at 12 35 26 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/1afbaed1-07f8-4e37-b8cc-80190db7fb27"> Testing: verified training loss is improving and ran for 100 iters to verify no issue with out of data any longer with minipile. reran with alpaca and saw the expected out of data at 40 iters without infinite loop option, runs to 100 with infinite. Notes: I did not make this a default dataset since for debugmodel, mostly running 10 iters is fine and there's 6GB to pull down. <img width="869" alt="Screenshot 2024-02-25 at 12 30 29 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/1070a80a-ad20-4f0f-a860-e13caa3120a0">
ghstack-source-id: 3c930054d3b04faf3866048740a2ef887d066dd6 Pull Request resolved: pytorch#117
ghstack-source-id: 733bf85716cda3a5b9af780eba79c9b5dd66abad Pull Request resolved: pytorch#121
ghstack-source-id: d7cd26d84aa2442ac45223992e1766397e52c8d8 Pull Request resolved: pytorch#122
according to suggestions in pytorch#118 (comment) ghstack-source-id: 357f0872cd1c9bad2c4c256d47adbd3f716a7651 Pull Request resolved: pytorch#123
…t job configs (pytorch#124) This PR: 1 - adds the english language portion of c4 dataset, which has 177M entries. (https://huggingface.co/datasets/allenai/c4) Due to the size, streaming is enabled as the default. This is the allen-ai/c4, as apparently the original c4 is being deprecated and HF advises to use allen-ai now. For comparison per @tianyu-l request - 40 iterations avg time: alpaca cached loading: Average data load time: 0.0279 seconds c4 streaming loading: Average data load time: 0.0290 seconds There is a longer initial delay during the 'preparing c4' vs alpaca (i.e. 45 seconds vs 10 seconds), but after that speed is similar. Dataset sample (not displayed in training, just an excerpt I pulled to double check the data flow): <img width="1233" alt="Screenshot 2024-03-08 at 5 31 06 PM" src="https://github.com/pytorch/torchtrain/assets/46302957/94915f80-da70-48d1-8c43-43f874fef121"> 2 - I also updated the multi-node slurm file to account for the new job config. Test: verified no looping with 100 iterations, sampled data streamed to verify.
…ytorch#130) This PR adds the openwebtext 1M dataset. This is a homogenous dataset, so we are able to train successfully while not having any shuffle in our dataset loader. 1 - adds the dateset to hf_datasets 2 - makes the default dataset for 13b and 70b as openwebtext since that is the preferred choice for larger scale training. Testing - ran 5K iters (9 nodes) to verify no spiking issues: <img width="787" alt="Screenshot 2024-03-12 at 9 50 57 AM" src="https://github.com/pytorch/torchtrain/assets/46302957/420fa1fc-50f8-47bc-9b07-02c8fa132e7c">
…pytorch#131) This fix would temporarily unblock loading. So we won't run into the issue of: ``` [rank0]:[rank0]: train_state.losses.append(train_state.current_loss) [rank0]:[rank0]: AttributeError: 'float' object has no attribute 'append' ``` However, current_loss and losses are still not correct, since by current setup, losses and current_losses would be different across different ranks. Also, we don't know the size of losses because this is based on the # of steps. So loading still work but the value of current_loss and losses are not being loaded correctly. I will follow up with further fixes.
ghstack-source-id: de61ec093b43a2ccbf1156c76ba81ecd698a6a8a Pull Request resolved: pytorch#132
simplify things given we already have SequenceParallel style landed in main
Summary: Adds config options to configure float8 scaling type for input, weight, grad_output. Performance is not ideal yet, but that's because we have not optimized it. Test Plan: ``` // repeat for input, weight, grad_out with-proxy CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh --training.enable_float8_linear --training.float8_scaling_type_weight delayed --training.compile ``` Reviewers: Subscribers: Tasks: Tags:
This was approved in pytorch#490, but merged into the wrong branch, merging this into main
Summary: The `float8_experimental` repository moved to `torchao.float8` in pytorch/ao#551 This PR updates `torchtitan` to use float8 from the new location. Test Plan: ``` with-proxy CONFIG_FILE="./train_configs/debug_model.toml" ./run_llama_train.sh --training.enable_float8_linear --training.compile ``` Reviewers: Subscribers: Tasks: Tags:
ghstack-source-id: 3879e764e7b33afde5d778810c71d1d2a8f82f6d Pull Request resolved: pytorch#494
ghstack-source-id: 17a1ee9f03f13423a30183c5c8d7ad30f8c8dbfc Pull Request resolved: pytorch#495
ghstack-source-id: e94c7f6f4fad87c5432262c54beabd02de5541b8 Pull Request resolved: pytorch#496
With the official launch of LLaMa 3.1 model, we want to add the config to TorchTitan. Of course, there are more work to be done, but we want to go an incremental way. So more PRs will be needed. For now, we try on 128 GPUs with current config (TP=8, FSDP=16). The perf number is wps: 109 mfu: 29%. Loss curve for 3000 steps with 600 warmup (lr = 0.8e-4). <img width="1037" alt="image" src="https://github.com/user-attachments/assets/f57dd3fa-07d8-4ef4-8f68-8f7a08e9652e"> Loss curve for 3000 steps with 600 warmup (lr = 1.1e-4). 
ghstack-source-id: 587e3d6e5270714ca734b8031ce41a962e6394ea Pull Request resolved: pytorch#498
ghstack-source-id: 63af8025c184fd5ad34f2f57bf78a37dda2cd33d Pull Request resolved: pytorch#443
As title, use `8e-5` rather than `0.8e-4`.
ghstack-source-id: 5ebb4adf3152f413fa33a923c272c9aa3ce1f775 Pull Request resolved: pytorch#499
ghstack-source-id: 52ed6836de39e82c4c5824a40ecfc1d9ec7ed2bd Pull Request resolved: pytorch#500
as titled, add warning to compile rmsnorm as it's not fully ready yet, i.e. this issue pytorch#497 We can remove this warning once we fix the issue
add float8 link in README so we can redirect people from dev-discuss post to torchtitan repo README looks like this after rendering <img width="518" alt="Screenshot 2024-08-06 at 5 42 10 PM" src="https://github.com/user-attachments/assets/50af99d7-93be-459a-89d7-8c08b8fb95d4"> float8.md looks like this <img width="563" alt="Screenshot 2024-08-06 at 5 04 17 PM" src="https://github.com/user-attachments/assets/06d30aad-4133-4cec-9037-cfcf155b45c4"> I tried the command locally and traces are looking good <img width="726" alt="Screenshot 2024-08-06 at 5 00 00 PM" src="https://github.com/user-attachments/assets/bdfa3d7e-efe1-4009-92a1-0f5c310013fb">
ghstack-source-id: 2927f0a8082171da3e9f59a5d04f8325cbdf3653 Pull Request resolved: pytorch#508
ghstack-source-id: 003bfbfbcf1511ddbd18e15d031b39f597d8e7db Pull Request resolved: pytorch#510
… entries ghstack-source-id: 319f4961b092778703101b98937803073132afa1 Pull Request resolved: pytorch#512
Explain the rationale and challenges behind certain changes we made to llama model to support 3D parallelism. --------- Co-authored-by: tianyu-l <[email protected]>
ghstack-source-id: 1965d3122885fed3c28e2e058c55581187e7816c Pull Request resolved: pytorch#513
…ch#516) Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * pytorch#473 * pytorch#517 * __->__ pytorch#516 Allows PP to be used without a seed checkpoint by calling `init_weight` on each model part. This is the solution in step 1 of pytorch#514 proposed by @wconstab
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * pytorch#473 * __->__ pytorch#517 * pytorch#516 Ran `pre-commit run --all-files`
…ng DTensor strided sharding (pytorch#507) Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * __->__ pytorch#507 **Summary** 1. check if users are using new nightly-build pytorch that includes DTensor strided sharding (pytorch/pytorch#130760) when 2D/3D is used. Print warning if not. 2. remove temporary re-enablement added in pytorch#460 . **Test** Command: `python test_runner.py outputs --test pp_dp_tp --ngpu 8` GPUs: A100 Output: - without strided sharding: ``` [rank7]:2024-08-06 03:21:26,706 - root - INFO - step: 2 loss: 8.1652 memory: 0.51GiB(0.64%) wps: 8,250 mfu: 0.25% [rank7]:2024-08-06 03:21:27,013 - root - INFO - step: 3 loss: 8.0951 memory: 0.51GiB(0.64%) wps: 13,358 mfu: 0.41% [rank7]:2024-08-06 03:21:27,309 - root - INFO - step: 4 loss: 7.9748 memory: 0.51GiB(0.64%) wps: 13,865 mfu: 0.42% [rank7]:2024-08-06 03:21:27,582 - root - INFO - step: 5 loss: 7.8025 memory: 0.51GiB(0.64%) wps: 15,057 mfu: 0.46% [rank7]:2024-08-06 03:21:28,076 - root - INFO - step: 6 loss: 7.5612 memory: 0.51GiB(0.64%) wps: 8,300 mfu: 0.25% [rank7]:2024-08-06 03:21:28,608 - root - INFO - step: 7 loss: 7.3649 memory: 0.51GiB(0.64%) wps: 7,705 mfu: 0.23% [rank7]:2024-08-06 03:21:28,927 - root - INFO - step: 8 loss: 7.2946 memory: 0.51GiB(0.64%) wps: 12,832 mfu: 0.39% [rank7]:2024-08-06 03:21:29,251 - root - INFO - step: 9 loss: 7.1311 memory: 0.51GiB(0.64%) wps: 12,669 mfu: 0.38% [rank7]:2024-08-06 03:21:29,627 - root - INFO - step: 10 loss: 7.0540 memory: 0.51GiB(0.64%) wps: 10,918 mfu: 0.33% >>>>>>>>>>>>>>>>>Checkpoint save & load<<<<<<<<<<<<<<<<<<< [rank7]:2024-08-06 03:21:59,723 - root - INFO - step: 11 loss: 7.0822 memory: 0.51GiB(0.64%) wps: 1,139 mfu: 0.03% [rank7]:2024-08-06 03:22:00,054 - root - INFO - step: 12 loss: 7.0508 memory: 0.51GiB(0.64%) wps: 12,366 mfu: 0.38% [rank7]:2024-08-06 03:22:00,340 - root - INFO - step: 13 loss: 6.9182 memory: 0.51GiB(0.64%) wps: 14,370 mfu: 0.44% [rank7]:2024-08-06 03:22:00,624 - root - INFO - step: 14 loss: 6.8948 memory: 0.51GiB(0.64%) wps: 14,442 mfu: 0.44% [rank7]:2024-08-06 03:22:00,907 - root - INFO - step: 15 loss: 6.8358 memory: 0.51GiB(0.64%) wps: 14,514 mfu: 0.44% [rank7]:2024-08-06 03:22:01,574 - root - INFO - step: 16 loss: 6.7653 memory: 0.51GiB(0.64%) wps: 6,144 mfu: 0.19% [rank7]:2024-08-06 03:22:02,209 - root - INFO - step: 17 loss: 6.7340 memory: 0.51GiB(0.64%) wps: 6,453 mfu: 0.20% [rank7]:2024-08-06 03:22:02,532 - root - INFO - step: 18 loss: 6.6874 memory: 0.51GiB(0.64%) wps: 12,695 mfu: 0.39% [rank7]:2024-08-06 03:22:02,863 - root - INFO - step: 19 loss: 6.6566 memory: 0.51GiB(0.64%) wps: 12,406 mfu: 0.38% [rank7]:2024-08-06 03:22:03,257 - root - INFO - step: 20 loss: 6.6629 memory: 0.51GiB(0.64%) wps: 10,392 mfu: 0.32% ``` - with strided sharding ``` [rank7]:2024-08-06 03:26:18,288 - root - INFO - step: 1 loss: 8.2069 memory: 0.50GiB(0.63%) wps: 915 mfu: 0.03% [rank7]:2024-08-06 03:26:19,084 - root - INFO - step: 2 loss: 8.1913 memory: 0.51GiB(0.64%) wps: 5,144 mfu: 0.16% [rank7]:2024-08-06 03:26:19,365 - root - INFO - step: 3 loss: 8.1148 memory: 0.51GiB(0.64%) wps: 14,593 mfu: 0.44% [rank7]:2024-08-06 03:26:19,698 - root - INFO - step: 4 loss: 7.9982 memory: 0.51GiB(0.64%) wps: 12,328 mfu: 0.37% [rank7]:2024-08-06 03:26:20,011 - root - INFO - step: 5 loss: 7.8382 memory: 0.51GiB(0.64%) wps: 13,100 mfu: 0.40% [rank7]:2024-08-06 03:26:20,498 - root - INFO - step: 6 loss: 7.6293 memory: 0.51GiB(0.64%) wps: 8,423 mfu: 0.26% [rank7]:2024-08-06 03:26:21,126 - root - INFO - step: 7 loss: 7.4454 memory: 0.51GiB(0.64%) wps: 6,530 mfu: 0.20% [rank7]:2024-08-06 03:26:21,472 - root - INFO - step: 8 loss: 7.3337 memory: 0.51GiB(0.64%) wps: 11,843 mfu: 0.36% [rank7]:2024-08-06 03:26:21,849 - root - INFO - step: 9 loss: 7.1960 memory: 0.51GiB(0.64%) wps: 10,892 mfu: 0.33% [rank7]:2024-08-06 03:26:22,229 - root - INFO - step: 10 loss: 7.1208 memory: 0.51GiB(0.64%) wps: 10,798 mfu: 0.33% >>>>>>>>>>>>>>>>>Checkpoint save & load<<<<<<<<<<<<<<<<<<< [rank7]:2024-08-06 03:26:50,306 - root - INFO - step: 11 loss: 7.1222 memory: 0.51GiB(0.64%) wps: 866 mfu: 0.03% [rank7]:2024-08-06 03:26:50,632 - root - INFO - step: 12 loss: 7.1189 memory: 0.51GiB(0.64%) wps: 12,589 mfu: 0.38% [rank7]:2024-08-06 03:26:50,917 - root - INFO - step: 13 loss: 6.9646 memory: 0.51GiB(0.64%) wps: 14,417 mfu: 0.44% [rank7]:2024-08-06 03:26:51,217 - root - INFO - step: 14 loss: 6.9626 memory: 0.51GiB(0.64%) wps: 13,680 mfu: 0.42% [rank7]:2024-08-06 03:26:51,514 - root - INFO - step: 15 loss: 6.8694 memory: 0.51GiB(0.64%) wps: 13,799 mfu: 0.42% [rank7]:2024-08-06 03:26:52,207 - root - INFO - step: 16 loss: 6.7994 memory: 0.51GiB(0.64%) wps: 5,910 mfu: 0.18% [rank7]:2024-08-06 03:26:53,053 - root - INFO - step: 17 loss: 6.7634 memory: 0.51GiB(0.64%) wps: 4,847 mfu: 0.15% [rank7]:2024-08-06 03:26:53,370 - root - INFO - step: 18 loss: 6.7233 memory: 0.51GiB(0.64%) wps: 12,915 mfu: 0.39% [rank7]:2024-08-06 03:26:53,686 - root - INFO - step: 19 loss: 6.7054 memory: 0.51GiB(0.64%) wps: 12,995 mfu: 0.39% [rank7]:2024-08-06 03:26:54,059 - root - INFO - step: 20 loss: 6.7130 memory: 0.51GiB(0.64%) wps: 10,991 mfu: 0.33% ```
`torch.nn.Module.to_empty` takes keyword only arg of "device" according to https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to_empty
ghstack-source-id: 7e1c7071f8126072ab0e25194b75f280bf4277ec Pull Request resolved: pytorch#523
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * pytorch#473 * __->__ pytorch#522
ghstack-source-id: c8f611742ffbb4859988b97e706b9e0d1b4ad6f1 Pull Request resolved: pytorch#521
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Re-sync fork to main, and add the latest features from fms-fsdp: - Remove dependence on the separate metadata "count file" in the dataset directory - Support n_workers > 1. This is accomplished by shunting all path/rank-dependent setup out of initialization and into a new setup() method, which runs after init but before any other op - Support HF-style parquet raw text datasets, with tokenization on the fly (for reasonably sized documents/shardfiles) - Support non-flat data directories: all legal files under the specified location will be included, regardless of depth or location. Enables simple weight-free dataset mixing via a single `StreamingDocDataset` on the parent directory - `SamplingDataset` and `ScalableShardDataset`are now implemented as proper `_WrapperDatasets`, reflecting the intended modular usage - Fix Weird_Separated_Camel_Case naming convention in favor of ProperClassNaming - Allow `PreloadBufferDataset` to shrink back down to the desired size after rescaling to a smaller number of workers
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