[Memory optm] loss using torch + compile

src/forge/actors/trainer.py

@@ -160,6 +160,10 @@ def __post_init__(self):
         }
         os.environ.update(env)
 
+        # compile loss
+        logger.info("Compiling loss")
+        self.loss = torch.compile(self.loss)
+
     @endpoint
     async def setup(self):
         # TODO: update ForgeEngine to not use ForgeJobConfig

src/forge/util/ops.py

@@ -56,6 +56,7 @@ def compute_logprobs(
 ) -> torch.Tensor:
     """
     Computes the log probabilities of the input tokens given the model logits and temperature.
+    Always converts inputs to fp32 for numerical stability
 
     Args:
         logits (`torch.Tensor`):
@@ -65,10 +66,23 @@ def compute_logprobs(
         temperature (`float`, *optional*, defaults to 1.0):
             The temperature value for scaling logits before computing log probabilities.
 
+    Returns:
+        logprobs: [batch, seq_len] log probabilities for each token
     """
     # Ignore the last token from logits because it predicts the next token (-1)
     # And align logits with the input tokens length.
     logits = logits[:, -input_ids.size(1) - 1 : -1, :].to(input_ids.device)
     scaled_logits = logits / temperature
-    logprobs = selective_log_softmax(scaled_logits, input_ids)
-    return logprobs
+
+    # Convert to fp32 for numerical stability
+    scaled_logits_fp32 = scaled_logits.float()
+
+    # get per-token log probs
+    batch_size, seq_len, vocab_size = scaled_logits_fp32.shape
+    log_probs = -F.cross_entropy(
+        scaled_logits_fp32.reshape(-1, vocab_size),
+        input_ids.reshape(-1),
+        reduction="none",
+    )
+
+    return log_probs.reshape(batch_size, seq_len)