[SFT] Log mean token accuracy from Liger kernel

pyproject.toml

@@ -56,7 +56,7 @@ kernels = [
     "kernels"
 ]
 liger = [
-    "liger-kernel>=0.6.2"
+    "liger-kernel>=0.6.4"
 ]
 peft = [
     "peft>=0.8.0"
@@ -104,7 +104,7 @@ dev = [
     # kernels
     "kernels",
     # liger
-    "liger-kernel>=0.6.2",
+    "liger-kernel>=0.6.4",
     # peft
     "peft>=0.8.0",
     # quality

tests/test_grpo_trainer.py

@@ -1518,7 +1518,6 @@ def reward_func(completions, **kwargs):
             num_generations=3,  # reduce the number of generations to reduce memory usage
             max_completion_length=8,  # reduce the completion length to reduce memory usage
             use_liger_kernel=True,  # enable Liger kernel
-            loss_type="bnpo",  # default dapo is not supported yet
             report_to="none",
         )
         trainer = GRPOTrainer(
@@ -1839,7 +1838,6 @@ def test_training_with_liger_grpo_kernel(self, model_name):
             max_completion_length=self.max_length,
             report_to="none",
             logging_strategy="no",
-            loss_type="bnpo",  # liger-kernel does not support "dapo" default; see https://github.com/linkedin/Liger-Kernel/issues/620
         )
 
         model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -1888,7 +1886,6 @@ def test_training_with_liger_grpo_kernel_and_peft(self, model_name):
             max_completion_length=self.max_length,
             report_to="none",
             logging_strategy="no",
-            loss_type="bnpo",  # liger-kernel does not support "dapo" default; see https://github.com/linkedin/Liger-Kernel/issues/620
         )
 
         model = AutoModelForCausalLM.from_pretrained(model_name)

trl/import_utils.py

@@ -23,7 +23,7 @@
 from transformers.utils.import_utils import _is_package_available
 
 
-LIGER_KERNEL_MIN_VERSION = "0.5.8"
+LIGER_KERNEL_MIN_VERSION = "0.6.4"
 
 # Use same as transformers.utils.import_utils
 _deepspeed_available = _is_package_available("deepspeed")

trl/trainer/sft_trainer.py

@@ -822,18 +822,19 @@ def __init__(
                     )
 
         # Loss function
-        if args.loss_type == "nll":
-            pass  # use the default loss
-        elif args.loss_type == "dft":
-            if compute_loss_func is not None:
-                raise ValueError(
-                    "You passed a `compute_loss_func` together with `loss_type='dft'` to the `SFTTrainer`. "
-                    "When using `loss_type='dft'`, the loss function is internally set to the DFT loss, so passing a "
-                    "`compute_loss_func` is not allowed."
-                )
-            compute_loss_func = dft_loss
-        else:
-            raise ValueError(f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll' and 'dft'.")
+        if not args.use_liger_kernel:  # liger supports dft loss by just passing use_token_scaling=True
+            if args.loss_type == "nll":
+                pass  # use the default loss
+            elif args.loss_type == "dft":
+                if compute_loss_func is not None:
+                    raise ValueError(
+                        "You passed a `compute_loss_func` together with `loss_type='dft'` to the `SFTTrainer`. "
+                        "When using `loss_type='dft'`, the loss function is internally set to the DFT loss, so "
+                        "passing a `compute_loss_func` is not allowed."
+                    )
+                compute_loss_func = dft_loss
+            else:
+                raise ValueError(f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll' and 'dft'.")
 
         # Initialize the metrics
         self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
@@ -1113,6 +1114,11 @@ def compute_loss(
 
         # If not set, defaults from model config and may warn since cache isn't compatible with gradient checkpointing
         inputs["use_cache"] = False
+        # Request token accuracy from Liger kernel and set token scaling if using DFT loss
+        if self.args.use_liger_kernel:
+            inputs["return_token_accuracy"] = True
+            inputs["use_token_scaling"] = self.args.loss_type == "dft"
+
         (loss, outputs) = super().compute_loss(
             model, inputs, return_outputs=True, num_items_in_batch=num_items_in_batch
         )
@@ -1151,8 +1157,11 @@ def compute_loss(
             self._total_train_tokens += num_tokens_in_batch
         self._metrics[mode]["num_tokens"] = [self._total_train_tokens]
 
-        # Compute token accuracy if we have labels and if the model is not using Liger (no logits)
-        if not self.args.use_liger_kernel:
+        if self.args.use_liger_kernel:
+            token_accuracy = self.accelerator.gather_for_metrics(outputs.token_accuracy).mean().item()
+            self._metrics[mode]["mean_token_accuracy"].append(token_accuracy)
+        else:
+            # Compute accuracy from logits using argmax (traditional method)
             with torch.no_grad():
                 if "shift_labels" in inputs:
                     # When using CP, labels are pre-shifted. We must use these (and cannot manually shift) because:
@@ -1190,10 +1199,12 @@ def compute_loss(
                 total_sum = total_tokens.sum()
                 accuracy = (correct_tokens.sum() / total_sum).item() if total_sum > 0 else 0.0
                 self._metrics[mode]["mean_token_accuracy"].append(accuracy)
-                if self.aux_loss_enabled:
-                    aux_loss = outputs.aux_loss
-                    aux_loss = self.accelerator.gather_for_metrics(aux_loss).mean().item()
-                    self._metrics[mode]["aux_loss"].append(aux_loss)
+
+        # Log auxiliary loss if enabled (applies to both Liger and non-Liger)
+        if self.aux_loss_enabled:
+            aux_loss = outputs.aux_loss
+            aux_loss = self.accelerator.gather_for_metrics(aux_loss).mean().item()
+            self._metrics[mode]["aux_loss"].append(aux_loss)
 
         return (loss, outputs) if return_outputs else loss