add torch.compile by default, shows almost 1.8X improvement in throughput nice

README.md

@@ -8,8 +8,7 @@ The simplest, fastest repository for training/finetuning medium-sized GPTs. It's
 Dependencies:
 
 - [pytorch](https://pytorch.org) <3
-- numpy <3
-- `pip install datasets` for huggingface datasets <3
+- `pip install datasets` for huggingface datasets <3 (if you want to download + preprocess OpenWebText)
 - `pip install tiktoken` for OpenAI's fast bpe code <3
 - `pip install wandb` for optional logging <3
 
@@ -68,6 +67,10 @@ I briefly tried finetuning gpt2 a bit more on our OWT and didn't notice dramatic
 
 For model benchmarking `bench.py` might be useful. It's identical what happens in the meat of the training loop of `train.py`, but omits much of the other complexities.
 
+# efficiency notes
+
+Code by default now uses [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/). At the time of writing (Dec 29, 2022) this makes `torch.compile()` available in the nightly release. The improvement from the one line of code is noticeable, e.g. cutting down iteration time from ~250ms / iter to 135ms / iter. Nice work PyTorch team!
+
 ## todos
 
 A few that I'm aware of, other than the ones mentioned in code:

bench.py

@@ -14,7 +14,8 @@ torch.manual_seed(1337)
 
 batch_size = 8
 block_size = 1024
-dtype = torch.float16
+dtype = torch.bfloat16
+compile_model = True
 
 # data loading init
 real_data = True
@@ -46,6 +47,10 @@ model.to(device)
 
 optimizer = model.configure_optimizers(weight_decay=1e-2, learning_rate=1e-4, betas=(0.9, 0.95))
 
+if compile_model:
+    print("Compiling model...")
+    model = torch.compile(model) # pytorch 2.0
+
 profile = False # use pytorch profiler, or just simple benchmarking?
 if profile:
     # useful docs on pytorch profiler:

sample.py

@@ -21,6 +21,7 @@ model = GPT(gptconf)
 model.load_state_dict(checkpoint['model'])
 model.eval()
 model.to(device)
+model = torch.compile(model) # requires PyTorch 2.0
 
 enc = tiktoken.get_encoding("gpt2")
 #start = enc.encode("\n")

train.py

@@ -59,6 +59,7 @@ lr_decay_iters = 320000 # how many steps to decay the learning rate for
 min_lr = 1e-5 # minimum learning rate
 # DDP settings
 backend = 'nccl' # 'nccl', 'gloo', etc.
+compile_model = True # use PyTorch 2.0 to compile the model to be faster
 # -----------------------------------------------------------------------------
 # poor man's Configurator. Potentially a bad idea. Example usage:
 # $ python train.py override_file --batch_size=32
@@ -156,6 +157,12 @@ optimizer = model.configure_optimizers(weight_decay, learning_rate, betas)
 if init_from == 'resume':
     optimizer.load_state_dict(checkpoint['optimizer'])
 
+# compile the model
+if compile_model:
+    print("compiling the model... (takes a ~minute)")
+    unoptimized_model = model
+    model = torch.compile(model) # requires PyTorch 2.0
+
 # wrap model into DDP container
 if ddp:
     model = DDP(model, device_ids=[gpu_id])