who needs a dataloader? overlap the prefetching of the next batch wit…

…h GPU compute, ehiding the data loading latency entirely. this saves about 1ms lol

bench.py

@@ -39,7 +39,7 @@ def get_batch(split):
         ix = torch.randint(len(data) - block_size, (batch_size,))
         x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
         y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
-        x, y = x.to(device), y.to(device)
+        x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
         return x, y
 else:
     # alternatively, if fixed data is desired to not care about data loading
@@ -76,14 +76,15 @@ def get_batch(split):
         record_shapes=False,
         profile_memory=False,
         with_stack=False, # incurs an additional overhead, disable if not needed
-        with_flops=False,
+        with_flops=True,
         with_modules=False, # only for torchscript models atm
     ) as prof:
 
+        X, Y = get_batch('train')
         for k in range(num_steps):
-            X, Y = get_batch('train')
             with ctx:
                 logits, loss = model(X, Y)
+            X, Y = get_batch('train')
             optimizer.zero_grad(set_to_none=True)
             loss.backward()
             optimizer.step()
@@ -98,10 +99,11 @@ def get_batch(split):
     torch.cuda.synchronize()
     for stage, num_steps in enumerate([10, 20]): # burnin, then benchmark
         t0 = time.time()
+        X, Y = get_batch('train')
         for k in range(num_steps):
-            X, Y = get_batch('train')
             with ctx:
                 logits, loss = model(X, Y)
+            X, Y = get_batch('train')
             optimizer.zero_grad(set_to_none=True)
             loss.backward()
             optimizer.step()

train.py

@@ -111,7 +111,8 @@ def get_batch(split):
     ix = torch.randint(len(data) - block_size, (batch_size,))
     x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
     y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
-    x, y = x.to(device), y.to(device)
+    # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
+    x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
     return x, y
 
 # init these up here, can override if init_from='resume' (i.e. from a checkpoint)
@@ -227,6 +228,7 @@ def get_lr(it):
     wandb.init(project=wandb_project, name=wandb_run_name, config=config)
 
 # training loop
+X, Y = get_batch('train') # fetch the very first batch
 t0 = time.time()
 while True:
 
@@ -269,8 +271,6 @@ def get_lr(it):
     # forward backward update, with optional gradient accumulation to simulate larger batch size
     # and using the GradScaler if data type is float16
     for micro_step in range(gradient_accumulation_steps):
-        # fetch a batch
-        X, Y = get_batch('train')
         if ddp:
             # in DDP training we only need to sync gradients at the last micro step.
             # the official way to do this is with model.no_sync() context manager, but
@@ -279,6 +279,8 @@ def get_lr(it):
             model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1)
         with ctx:
             logits, loss = model(X, Y)
+        # immediately async prefetch next batch while model is doing the forward pass on the GPU
+        X, Y = get_batch('train')
         # backward pass, with gradient scaling if training in fp16
         scaler.scale(loss).backward()
     # clip the gradient