65 Commits

Author	SHA1	Message	Date
osmarks	a225b756e8	fix things	2024-07-23 10:56:47 +01:00
osmarks	a64b2f2cfe	tests	2024-07-08 19:36:49 +01:00
osmarks	f3118fe74d	Add note about fix	2024-06-24 20:13:10 +01:00
osmarks	0194d45e43	experiments	2024-06-24 19:10:15 +00:00
Kevin Slagle	5156fef93c	fix np.memmap memory leak ... nn.memmap doesn't free memory that it accesses. Thus, the entire dataset gets stored in RAM as the dataset has been fully accessed. The simplest workaround on stackoverflow is to just recreate the memmap for each batch. The extra overhead is negligible. https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122	2024-01-25 11:41:01 -08:00
o	1eaceae193	Fix AssertionError on macOS - need to check CUDA availability for bf16	2023-06-19 18:05:09 -04:00
Andrej Karpathy	7339b904ef	use WORLD_SIZE instead of device_count, supports both the case where the number of gpus we train on is smaller than gpus available, and also multinode training may be a bugfix	2023-06-14 23:33:07 +00:00
Alexander Pivovarov	eb33b8bf1c	Use bf16 only if supported	2023-05-17 03:26:48 +00:00
Andrej	a6a708c7f1	Merge branch 'master' into grad_accum	2023-04-17 20:11:00 -07:00
Andrej	2457471c9c	Merge pull request #236 from ymurenko/master ... fix "cuda out of memory" when resuming training	2023-04-12 22:09:42 -07:00
Andrej Karpathy	553f949f46	fix minor bug where we have to scale the loss to account for gradient accumulation, which sums before backprop. note that this is not a major bug because AdamW is scale invariant. however, this did affect gradient clipping	2023-04-13 04:59:11 +00:00
ymurenko	4ac2e8ce3a	fix "cuda out of memory" when resuming training	2023-04-05 17:28:55 -04:00
Otavio Good	978d4fe538	Fix for gradient_accumulation_steps training slow	2023-03-25 00:04:45 -07:00
Otavio Good	086ebe1822	fix for training stability on single GPU	2023-02-13 10:42:44 -08:00
Andrej Karpathy	e58f0cfa94	oops i should not be needing or multiplying by world_size to calculate mfu	2023-02-07 21:38:39 +00:00
Andrej Karpathy	8b1e43209e	small tweaks, make default WD be 0.1 as is often cited, and remove spurious init of LayerNorm, which is already initialized at 1,0	2023-02-06 23:07:25 +00:00
Andrej Karpathy	ab21d6c15d	bugfix we have to call the raw_model's estimate_mfu ty @jprobichaud for original PR	2023-02-06 19:55:35 +00:00
Andrej Karpathy	ab0718a7dd	add the estimation of model flops utilization (MFU), a very commonly looked at metric that estimates the token throughput in units of A100 bfloat16 peak flops (312 TFLOPS). this gives us a sense of the hardware utilization we're achieving	2023-02-05 00:48:58 +00:00
Andrej Karpathy	a74e8363a2	clean up TODOs a bit, they are stale	2023-02-04 21:11:25 +00:00
Andrej Karpathy	25d95dbd65	mildly dramatic refactor for handing all these usage cases across all possible supported and unsupported devices for all the possible switches and flags	2023-02-04 21:06:17 +00:00
Andrej Karpathy	e108ffb973	very slight refactor, bit cleaner	2023-02-04 19:34:24 +00:00
Nan Yang	b8286f343e	Pin memory only when training on GPU	2023-02-04 11:16:26 -08:00
Andrej Karpathy	77e7e04c26	padding 50257 -> 50304 vocab_size, the nerest multiple of 64. the biggest deal smallest optimization i've made in recent past, about 25% faster. this is because the last layer is a major latency bottleneck consuming about 40% of latency due to the very high channel count.	2023-02-04 16:06:18 +00:00
Andrej Karpathy	b3c17c6c6a	slight tweak compressing LOC	2023-02-04 15:57:29 +00:00
Ramtin Gharleghi	9da1627c7f	Explicitly set ddp device	2023-02-04 15:07:36 +11:00
Andrej Karpathy	3fd4c0c5ef	who needs a dataloader? overlap the prefetching of the next batch with GPU compute, ehiding the data loading latency entirely. this saves about 1ms lol	2023-02-04 02:52:48 +00:00
Andrej	7d44bdf6b5	Merge pull request #106 from YassineYousfi/master ... use the ``enabled`` arg in GradScaler	2023-02-02 17:23:22 -08:00
Andrej Karpathy	d8b1a94519	change grad accum to default off because i think it just confuses everyone	2023-02-02 18:38:49 +00:00
Yassine Yousfi	40f4d6ff70	use the enabled arg in GradScaler	2023-01-31 21:12:49 -08:00
Andrej Karpathy	038ce89438	rename iter to it, because iter is a concrete Python builtin	2023-01-31 23:34:02 +00:00
Andrej Karpathy	924a0873eb	merge, make cleaner, careful with gradient clipping when using grad scaler fp16 training	2023-01-30 23:40:35 +00:00
Andrej Karpathy	0e90ee9d48	based on my experiments these biases are indeed not needed. code runs faster, identical results. keeping the option just because it deviates from the gpt-2 setup	2023-01-30 08:07:58 +00:00
Andrej Karpathy	001c1e7be7	stay true to the README file and set grad accum to 5, so the default batch size is about 0.5M and is reproducing gpt2	2023-01-27 20:51:50 +00:00
Andrej Karpathy	79dbe0086d	let me set bias=True until I validate it properly, but this should be ok to merge to master for now, is equivalent to previous functionality	2023-01-27 20:45:28 +00:00
Andrej Karpathy	e808a67149	bunch of plumbing of bias all around. measuring bias=False to be about 6% faster	2023-01-27 20:41:17 +00:00
Andrej Karpathy	3cb3fc059c	grad clipping seems to slightly speed up training in the beginning but i can't see a big difference later in the training. it costs non-negligeable compute to clip. adding it for now because it is standard, and i think more necessary as the model becomes larger. practitioners may consider turning it off for minor efficiency gains	2023-01-27 16:45:09 +00:00
johnwildauer	e0e94a1094	use GradScaler in model only if dtype is float16	2023-01-24 15:53:31 -07:00
Andrej	3611338959	Merge pull request #71 from cchan/patch-1 ... Zero-grad more aggressively to save memory	2023-01-20 14:38:10 -08:00
Andrej Karpathy	1f77d03024	make mentions of mps in docs. ty good people in issue #28	2023-01-20 21:28:20 +00:00
Clive Chan	67166079c9	Zero-grad more aggressively to save memory	2023-01-19 22:10:44 -08:00
Andrej Karpathy	46ce9971df	small tweaks to docs and variable names stylistically	2023-01-16 16:56:05 +00:00
Andrej Karpathy	684800dd87	clarify that these should be run on two separate machines	2023-01-16 06:02:46 +00:00
Andrej Karpathy	9352df23de	docs for multinode ddp	2023-01-16 05:57:33 +00:00
Andrej Karpathy	c3dddbff3d	get rid of gpu_id, the world is more complicated than that when world_size > 8	2023-01-16 05:44:50 +00:00
Andrej Karpathy	f5e6ac8b02	local rank -> rank	2023-01-16 05:13:13 +00:00
Andrej Karpathy	cf99914886	add gradient accumulation support to simulate larger batch sizes. ty @VHellendoorn for original PR	2023-01-15 17:49:55 +00:00
Andrej Karpathy	57735f532d	correctly propagate the vocab_size from the rendered dataset into the model args	2023-01-14 02:26:44 +00:00
Andrej Karpathy	8f85b83347	inference time mini-optimization low-hanging fruit ty @jxtps for raising: when we are running inference we can apply lm_head on only the very last token	2023-01-12 06:02:50 +00:00
Andrej Karpathy	d17350a31d	add support for character-level language models, a new character-level shakespeare dataset, a new config file that shows how to train a character-level baby GPT on it, and adjust the sample function to figure out if it should decode with characters or GPT2 bpe tokens. The current implementation is a bit hacky and basically assumes just these two possibilities. In the future we may want to support more general encoders or decoders.	2023-01-11 05:27:19 +00:00
Andrej Karpathy	c2a402f7f7	guess the config from globals() and log all of it with wandb	2023-01-11 01:00:22 +00:00