Merge 42b05eea7e into bc1fca39f3

nanochat/gpt.py

@@ -8,7 +8,7 @@ Notable features:
 - norm after token embedding
 - no learnable params in rmsnorm
 - no bias in linear layers
-- Multi-Query Attention (MQA) support for more efficient inference
+- Group-Query Attention (GQA) support for more efficient inference
 """
 
 import math
@@ -29,7 +29,7 @@ class GPTConfig:
     vocab_size: int = 50304
     n_layer: int = 12
     n_head: int = 6 # number of query heads
-    n_kv_head: int = 6 # number of key/value heads (MQA)
+    n_kv_head: int = 6 # number of key/value heads (GQA)
     n_embd: int = 768
 
 

scripts/chat_sft.py

@@ -212,18 +212,30 @@ for step in range(num_iterations):
         break
 
     # evaluate the gradient
+    total_loss_sum = torch.tensor(0.0, device=device)  # sum of losses
     num_tokens = torch.tensor(0, device=device) # the number of "active" tokens of supervision seen
     for micro_step in range(grad_accum_steps):
         train_inputs, train_targets = next(train_iter)
         with autocast_ctx:
-            loss = model(train_inputs, train_targets)
+            loss = model(train_inputs, train_targets, loss_reduction='sum')
-        train_loss = loss.detach() # for logging
+        total_loss_sum += loss.detach() # for logging
-        loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
         loss.backward() # accumulate the gradient
         num_tokens += (train_targets >= 0).sum()
     if ddp:
+        dist.all_reduce(total_loss_sum, op=dist.ReduceOp.SUM)
         dist.all_reduce(num_tokens, op=dist.ReduceOp.SUM) # sum over ranks
 
+    # scale gradients by total number of tokens
+    num_tokens_item = num_tokens.item()
+    if num_tokens_item == 0:
+        print0(f"Warning: the number of valid tokens in train targets is 0 at step {step}, skipping model update")
+        model.zero_grad(set_to_none=True)
+        continue
+
+    for param in model.parameters():
+        if param.grad is not None:
+            param.grad.div_(num_tokens_item)
+
     # learning rate scheduler
     lrm = get_lr_multiplier(step)
     for opt in optimizers:
@@ -236,8 +248,7 @@ for step in range(num_iterations):
     model.zero_grad(set_to_none=True)
 
     # logging
-    train_loss_item = train_loss.item()
+    train_loss_item = total_loss_sum.item() / num_tokens_item
-    num_tokens_item = num_tokens.item()
     print0(f"Step {step:05d}/{num_iterations:05d} | Training loss: {train_loss_item:.6f}| lrm: {lrm:.6f}| num_tokens: {num_tokens_item:,}")
     wandb_run.log({
         "step": step,