add gradient accumulation support to simulate larger batch sizes. ty @VHellendoorn for original PR

train.py

@@ -38,7 +38,8 @@ wandb_project = 'owt'
 wandb_run_name = 'gpt2' # 'run' + str(time.time())
 # data
 dataset = 'openwebtext'
-batch_size = 12
+gradient_accumulation_steps = 1 # used to simulate larger batch sizes
+batch_size = 12 # if gradient_accumulation_steps > 1, this is the micro-batch size
 block_size = 1024
 # model
 n_layer = 12
@@ -217,6 +218,7 @@ while True:
     else:
         lr = learning_rate
 
+    # evaluate the loss on train/val sets and write checkpoints
     if iter_num % eval_interval == 0 and gpu_id == 0:
         losses = estimate_loss()
         print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
@@ -244,20 +246,27 @@ while True:
     if iter_num == 0 and eval_only:
         break
 
-    X, Y = get_batch('train')
-    with ctx:
-        logits, loss = model(X, Y)
-
+    # forward backward update, with optional gradient accumulation to simulate larger batch size
     optimizer.zero_grad(set_to_none=True)
-    loss.backward()
-    # TODO: gradient clipping evaluate need for
+    for micro_step in range(gradient_accumulation_steps):
+        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
+            # I really dislike that this bloats the code and forces us to repeat code
+            # looking at the source of that context manager, it just toggles this variable
+            model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1)
+        with ctx:
+            logits, loss = model(X, Y)
+        loss.backward()
     optimizer.step()
 
+    # timing and logging
     t1 = time.time()
     dt = t1 - t0
     t0 = t1
     if iter_num % log_interval == 0 and gpu_id == 0:
-        lossf = loss.item() # loss as float. TODO CPU-GPU sync: profile, make sure not slow af
+        lossf = loss.item() # loss as float. TODO note CPU-GPU sync! profile, make sure not too slow
         print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms")
     iter_num += 1