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Author SHA1 Message Date
chenyu
f7965f85aa
 		Revert "feat: faster index building (#11462)" (#11478) 
This reverts commit 3a4deb08d2.
 2025-08-02 12:50:48 -04:00
wozeparrot
3a4deb08d2
 		feat: faster index building (#11462) 
* feat: faster index building

* feat: correct training samples
 2025-08-02 11:50:18 -04:00
chenyu
9e8e6b45ab
 		grad acc train llama (#11467) 
* grad acc train llama

* log step time
 2025-08-01 15:54:50 -04:00
chenyu
7ad7329257
 		data parallel train llama (#11466) 2025-08-01 12:13:51 -04:00
George Hotz
8ff03806e8
 		add llama layers (#11460) 
* add llama layers

* add contig bw for speed
 2025-07-31 16:28:04 -07:00
wozeparrot
6252f7770e
 		feat: fake data (#11447) 2025-07-30 17:18:20 -07:00
chenyu
e300451f3a
 		update llama3 (#11446) 
`LR=1e-4 TRAIN_ON_VAL=1 DEFAULT_FLOAT=bfloat16 FUSE_ARANGE=1 JITBEAM=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B WARMUP_STEPS=36 DECAY_STEPS=360 SEQLEN=512 PYTHONPATH=. AMD=1 AMD_LLVM=0 MODEL=llama3 python3 examples/mlperf/model_train.py` trained to 7
 2025-07-30 19:34:21 -04:00
wozeparrot
5fb975351a
 		feat: flag for training on val (#11441) 2025-07-30 14:29:45 -07:00
wozeparrot
825b6a2505
 		feat: llama3 dataloader (#11340) 2025-07-30 13:27:55 -07:00
chenyu
c14c9a8eff
 		llama3 grad clip (#11003) 2025-06-27 19:14:12 -04:00
chenyu
f2548afeb5
 		bert grad clipping start with const 0 (#11008) 
saved the init kernels
 2025-06-27 18:02:23 -04:00
chenyu
6ab5a5cb6c
 		llama3 mlperf train (#10983) 
work in progress. now it can overfit small examples and vram roughly matches
 2025-06-26 20:24:27 -04:00
chenyu
8751d47985
 		CosineAnnealingLRWithWarmup (#10981) 2025-06-25 17:45:21 -04:00
chenyu
efad567ebd
 		ruff check whole examples/mlperf/ (#10979) 2025-06-25 12:57:48 -04:00
chenyu
0480139def
 		log_perplexity metrics (#10912) 2025-06-21 10:44:47 -04:00
chenyu
62a540066e
 		remove DEBUG=2 in mi300x bert setup (#10886) 
seems fine now, not sure what the issue was
 2025-06-19 13:28:53 -04:00
chenyu
f377cc19cd
 		use AM for bert (#10882) 
have triained 3 runs and all seem fine
 2025-06-19 09:48:54 -04:00
chenyu
b70c7d3631
 		bert grad accumulation (#10863) 
* bert grad accumulation

* realize grad
 2025-06-18 12:17:07 -04:00
chenyu
075a74cf25
 		add global_batch_size to mlperf bert (#10852) 
global_batch_size = grad_acc_steps * batch_size. no-op change to prep grad acc for bert
 2025-06-17 17:54:15 -04:00
chenyu
81e296d7b8
 		remove Tensor.test() in retinanet (#10770) 
test was removed
 2025-06-10 22:14:57 -04:00
George Hotz
32e9949052
 		rename lazydata to uop (#10698) 2025-06-08 08:42:22 -07:00
chenyu
4ab3391e6f
 		set -o pipefail for mlperf run_and_time (#10577) 
also run the 5.1 script in ci cron job
 2025-05-30 16:36:44 -04:00
chenyu
baf482d314
 		copy mlperf stuff to 5.1 (#10576) 
5.0 is finalized, new changes go to 5.1
 2025-05-30 16:12:39 -04:00
George Hotz
b3b43a82c4
 		remove Tensor.no_grad, it's meaningless now [pr] (#10556) 2025-05-28 22:20:02 -07:00
chenyu
74cf5dbd9e
 		mlperf system updates (#10550) 
standardized processor and accelerator names
 2025-05-28 16:15:46 -04:00
chenyu
51dc7eedb0
 		correct use AM for resnet run_and_time (#10524) 2025-05-26 15:33:11 -04:00
chenyu
c1919ad55f
 		use AM for resnet run_and_time (#10523) 2025-05-26 14:50:49 -04:00
chenyu
2d50efb92b
 		set -e on mlperf run_and_time scripts (#10519) 2025-05-26 09:22:30 -04:00
chenyu
dc6309242d
 		WallTimeEvent for mlperf ci (#10506) 2025-05-24 10:56:03 -04:00
chenyu
67d1364106
 		update LOGMLPERF in red resnet run_and_time (#10416) 2025-05-19 13:23:33 -04:00
chenyu
485e80da69
 		run_and_time for resnet ci (#10405) 2025-05-18 23:39:57 -04:00
wozeparrot
1ed04f993b
 		move benchmark stat tracking to influxdb (#10185) 2025-05-15 16:14:56 -07:00
George Hotz
568d6d96e7
 		small changes from new multi [pr] (#10318) 2025-05-14 20:50:59 -07:00
George Hotz
bfc30fa6ea hotfix: typo in shm_name 2025-05-14 19:34:52 -07:00
George Hotz
2bc54b3e22 manually handle OSX 2025-05-14 19:17:51 -07:00
George Hotz
ab460486d7 Revert "resnet dataloader osx (#10316)" 
This reverts commit aef336930a.
 2025-05-14 19:15:07 -07:00
George Hotz
aef336930a
 		resnet dataloader osx (#10316) 
* mlperf dataloader on mac

* resnet dataloader [pr]

* simple should work
 2025-05-14 18:31:26 -07:00
chenyu
610ee79b22
 		cherry pick mlperf5.0 branch to master (#10089) 2025-04-28 15:36:56 -04:00
chenyu
74c6cf8be3
 		lint mlperf model_train (#10038) 2025-04-24 16:19:44 -04:00
chenyu
a25abf55e3
 		retinanet only call postprocess_detections with RUNMLPERF (#10017) 
during setup only need to compile `_eval_step().numpy()`
 2025-04-23 20:45:38 -04:00
chenyu
65faa1d94b
 		explicit device in mlperf scripts (#10015) 2025-04-23 17:11:52 -04:00
chenyu
a3f938dbee
 		remove retinanet INITMLPERF from beam script (#10011) 
it only controls logging, loading real data or not is solely controlled by RUNMLPERF
 2025-04-23 14:32:54 -04:00
Francis Lata
5542aeb0e4
 		RetinaNet MLPerf flag updates (#10009) 
* add RUNMLPERF and update INITMLPERF usage

* update scripts to use RUNMLPERF
 2025-04-23 13:00:34 -04:00
George Hotz
de0504276b
 		pop 0 is slow [pr] (#10007) 2025-04-23 17:00:59 +01:00
chenyu
d3a8d5c128
 		print postprocess_detections time in retinanet eval (#10005) 
`BS=96 BASEDIR="/raid/datasets/openimages" MODEL=retinanet python examples/mlperf/model_eval.py`

```
...
loaded dataset             @  8.64s
loaded initial data        @ 12.57s
******  619.97 ms to enqueue, 46042.13 ms to realize ( 116.22 ms fetching, 45399.58 ms postprocess_detections).     0.09 examples/sec.  0.83 TFLOPS  @ 59.23s
******  147.49 ms to enqueue, 37362.16 ms to realize ( 146.96 ms fetching, 36618.84 ms postprocess_detections).     0.11 examples/sec.  1.03 TFLOPS  @ 96.74s
******  152.85 ms to enqueue, 37244.08 ms to realize ( 120.67 ms fetching, 36235.19 ms postprocess_detections).     0.11 examples/sec.  1.04 TFLOPS  @ 134.14s
******  146.39 ms to enqueue, 37279.85 ms to realize (  65.07 ms fetching, 36233.56 ms postprocess_detections).     0.11 examples/sec.  1.04 TFLOPS  @ 171.56s
******  152.41 ms to enqueue, 37264.04 ms to realize ( 127.08 ms fetching, 36196.10 ms postprocess_detections).     0.11 examples/sec.  1.04 TFLOPS  @ 208.98s
******  151.29 ms to enqueue, 36868.08 ms to realize ( 142.73 ms fetching, 36153.07 ms postprocess_detections).     0.11 examples/sec.  1.05 TFLOPS  @ 246.00s
******  136.41 ms to enqueue, 37325.04 ms to realize (  90.29 ms fetching, 36573.38 ms postprocess_detections).     0.11 examples/sec.  1.04 TFLOPS  @ 283.46s
```
 2025-04-23 11:39:56 -04:00
chenyu
c39128133c
 		retinanet green scripts (#9996) 
also removed realize in data_get and used empty for fake data. slightly bigger lr. https://wandb.ai/chenyuxyz/MLPerf-RetinaNet/runs/8skid0e8?nw=nwuserchenyuxyz
 2025-04-23 08:28:03 -04:00
chenyu
fb89d9a584
 		retinanet eval combine output on GPUS[0] (#9966) 
eval 35 sec -> 20 sec. it was spending 13 seconds assembling output tensor on CPU backend. GPUS[0] seems to have enough memory, otherwise we can lower EVAL_BS
 2025-04-22 07:43:51 -04:00
chenyu
5294c32279
 		dev scripts for retinanet (#9968) 
also BASE_DIR -> BASEDIR for consistency, and move wandb up a bit for more accurate timing
 2025-04-21 17:54:56 -04:00
Francis Lata
defa1e77f6
 		get the proper dataset count (#9962) 2025-04-21 12:11:37 -04:00
Francis Lata
d7e247f329
 		RetinaNet INITMLPERF support (#9950) 
* fixes to make fake data work

* fix eval beam

* fix merge issue
 2025-04-21 10:32:05 -04:00