CPO & SimPO add label_smoothing (#493)

## Summary
<!--- This is a required section; please describe the main purpose of
this proposed code change. --->
Add label_smoothing support for CPO and SimPO so that they align with
the huggingface
[interface](https://github.com/huggingface/trl/blob/b668048fe1931c57796ad5ae3f10852337ce7565/trl/trainer/cpo_trainer.py#L645C1-L658C14).
<!---
## Details
This is an optional section; is there anything specific that reviewers
should be aware of?
--->

## Testing Done
- [x] Something wrong with the unit test. I'll have to fix it
<!--- This is a required section; please describe how this change was
tested. --->

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- Hardware Type: <BLANK>
- [x] run `make test` to ensure correctness
- [x] run `make checkstyle` to ensure code style
- [ ] run `make test-convergence` to ensure convergence

---------

Signed-off-by: Mecoli1219 <[email protected]>

src/liger_kernel/chunked_loss/cpo_loss.py

@@ -9,7 +9,9 @@
 class LigerFusedLinearCPOFunction(LigerFusedLinearPreferenceBase):
 
     @staticmethod
-    def preference_loss_fn(chosen_logps, rejected_logps, full_target, beta=0.1):
+    def preference_loss_fn(
+        chosen_logps, rejected_logps, full_target, beta=0.1, label_smoothing=0.0
+    ):
         """
         Paper: https://arxiv.org/pdf/2401.08417
 
@@ -30,9 +32,14 @@ def preference_loss_fn(chosen_logps, rejected_logps, full_target, beta=0.1):
             rejected_logps (torch.Tensor): Avg log probabilities of rejected tokens. Shape: (batch_size,).
             full_target (torch.Tensor): Non chunked full target tensor
             beta (float): Weight for the CPO loss
+            label_smoothing (float): Label smoothing factor, will reduce to Equation above when label_smoothing -> 0.
         """
         logits = beta * (chosen_logps - rejected_logps)
-        loss = F.logsigmoid(logits).sum() / (full_target.shape[0] // 2)
+        loss = (
+            F.logsigmoid(logits) * (1 - label_smoothing)
+            + F.logsigmoid(-logits) * label_smoothing
+        ).sum() / (full_target.shape[0] // 2)
+
         return loss
 
     @staticmethod
@@ -45,6 +52,7 @@ def forward(
         ignore_index=-100,
         beta=0.1,
         alpha=1.0,
+        label_smoothing=0.0,
         compute_nll_loss=True,
         compiled=True,
     ):
@@ -58,14 +66,15 @@ def forward(
             ignore_index=ignore_index,
             alpha=alpha,
             beta=beta,
+            label_smoothing=label_smoothing,
             compute_nll_loss=compute_nll_loss,
             compiled=compiled,
         )
 
     @staticmethod
     def backward(ctx, *grad_output):
         grads = LigerFusedLinearPreferenceBase.backward(ctx, grad_output)[:4]
-        return *grads, None, None, None, None, None
+        return *grads, None, None, None, None, None, None
 
 
 class LigerFusedLinearCPOLoss(torch.nn.Module):
@@ -78,6 +87,7 @@ def __init__(
         ignore_index: int = -100,
         beta: float = 0.1,
         alpha: float = 1.0,
+        label_smoothing: float = 0.0,
         compute_nll_loss: bool = True,
         compiled: bool = True,
     ):
@@ -90,6 +100,7 @@ def __init__(
         self.ignore_index = ignore_index
         self.beta = beta
         self.alpha = alpha
+        self.label_smoothing = label_smoothing
         self.compute_nll_loss = compute_nll_loss
         self.compiled = compiled
 
@@ -102,6 +113,7 @@ def forward(self, lin_weight, _input, target, bias=None):
             self.ignore_index,
             self.beta,
             self.alpha,
+            self.label_smoothing,
             self.compute_nll_loss,
             self.compiled,
         )

src/liger_kernel/chunked_loss/simpo_loss.py

@@ -10,7 +10,12 @@ class LigerFusedLinearSimPOFunction(LigerFusedLinearPreferenceBase):
 
     @staticmethod
     def preference_loss_fn(
-        chosen_logps, rejected_logps, full_target, beta=0.1, gamma=0.5
+        chosen_logps,
+        rejected_logps,
+        full_target,
+        beta=0.1,
+        gamma=0.5,
+        label_smoothing=0.0,
     ):
         """
         Paper: https://arxiv.org/pdf/2405.14734
@@ -33,9 +38,14 @@ def preference_loss_fn(
             full_target: Non chunked full target tensor
             beta (float): beta weight
             gamma (float): gemma margin term
+            label_smoothing (float): Label smoothing factor, will reduce to Equation above when label_smoothing -> 0.
         """
         logits = beta * (chosen_logps - rejected_logps) - gamma
-        loss = F.logsigmoid(logits).sum() / (full_target.shape[0] // 2)
+        loss = (
+            F.logsigmoid(logits) * (1 - label_smoothing)
+            + F.logsigmoid(-logits) * label_smoothing
+        ).sum() / (full_target.shape[0] // 2)
+
         return loss
 
     @staticmethod
@@ -48,6 +58,7 @@ def forward(
         ignore_index=-100,
         beta=0.1,
         alpha=1.0,
+        label_smoothing=0.0,
         compute_nll_loss=False,
         compiled=True,
         gamma=0.5,
@@ -63,14 +74,15 @@ def forward(
             ignore_index=ignore_index,
             alpha=alpha,
             beta=beta,
+            label_smoothing=label_smoothing,
             compiled=compiled,
             gamma=gamma,
         )
 
     @staticmethod
     def backward(ctx, *grad_output):
         grads = LigerFusedLinearPreferenceBase.backward(ctx, grad_output)[:4]
-        return *grads, None, None, None, None, None, None
+        return *grads, None, None, None, None, None, None, None
 
 
 class LigerFusedLinearSimPOLoss(torch.nn.Module):
@@ -83,6 +95,7 @@ def __init__(
         ignore_index: int = -100,
         beta: float = 0.1,
         alpha: float = 1.0,
+        label_smoothing: float = 0.0,
         compute_nll_loss: bool = True,
         compiled: bool = True,
         gamma: float = 0.5,
@@ -96,6 +109,7 @@ def __init__(
         self.ignore_index = ignore_index
         self.beta = beta
         self.alpha = alpha
+        self.label_smoothing = label_smoothing
         self.compute_nll_loss = compute_nll_loss
         self.compiled = compiled
         self.gamma = gamma
@@ -109,6 +123,7 @@ def forward(self, lin_weight, _input, target, bias=None):
             self.ignore_index,
             self.beta,
             self.alpha,
+            self.label_smoothing,
             self.compute_nll_loss,
             self.compiled,
             self.gamma,

test/chunked_loss/test_cpo_loss.py

@@ -86,6 +86,7 @@ def __init__(
         ignore_index: int = -100,
         beta: float = 0.1,
         alpha: float = 1.0,
+        label_smoothing: float = 0.0,
         loss_type: str = "sigmoid",
         simpo_gamma: float = 0.5,
     ):
@@ -97,6 +98,7 @@ def __init__(
             ignore_index=ignore_index,
             beta=beta,
             loss_type=loss_type,
+            label_smoothing=label_smoothing,
             simpo_gamma=simpo_gamma,
         ).get_batch_loss_metrics
 
@@ -114,13 +116,17 @@ def __init__(
         ignore_index: int = -100,
         beta: float = 0.1,
         alpha: float = 1.0,
+        label_smoothing: float = 0.0,
     ):
         super().__init__()
         self.lin = torch.nn.Linear(
             in_features=H, out_features=V, bias=bias, dtype=dtype
         )
         self.cpo_loss = LigerFusedLinearCPOLoss(
-            ignore_index=ignore_index, beta=beta, alpha=alpha
+            ignore_index=ignore_index,
+            beta=beta,
+            alpha=alpha,
+            label_smoothing=label_smoothing,
         )
 
     def forward(self, x, y):
@@ -145,8 +151,21 @@ def forward(self, x, y):
 @pytest.mark.parametrize(
     "ignore_index, beta, alpha", [(-100, 0.1, 1.0), (42, 0.2, 0.85)]
 )
+@pytest.mark.parametrize("label_smoothing", [0.0, 0.1])
 def test_correctness(
-    B, T, H, V, scalar, dtype, atol, rtol, bias, ignore_index, beta, alpha
+    B,
+    T,
+    H,
+    V,
+    scalar,
+    dtype,
+    atol,
+    rtol,
+    bias,
+    ignore_index,
+    beta,
+    alpha,
+    label_smoothing,
 ):
     B = 2 * B  # cpo loss requires B to be even
 
@@ -157,6 +176,7 @@ def test_correctness(
         bias=bias,
         ignore_index=ignore_index,
         beta=beta,
+        label_smoothing=label_smoothing,
     )
     liger_lm_head_cpo = LigerLMHeadCPO(
         H=H,
@@ -165,6 +185,7 @@ def test_correctness(
         bias=bias,
         ignore_index=ignore_index,
         beta=beta,
+        label_smoothing=label_smoothing,
     )
 
     torch_lm_head_cpo.lin.weight.data = liger_lm_head_cpo.lin.weight.data = torch.randn(

test/chunked_loss/test_simpo_loss.py

@@ -25,14 +25,19 @@ def __init__(
         ignore_index: int = -100,
         beta: float = 0.1,
         alpha: float = 1.0,
+        label_smoothing: float = 0.0,
         gamma: float = 0.5,
     ):
         super().__init__()
         self.lin = torch.nn.Linear(
             in_features=H, out_features=V, bias=bias, dtype=dtype
         )
         self.simpo_loss = LigerFusedLinearSimPOLoss(
-            ignore_index=ignore_index, beta=beta, alpha=alpha, gamma=gamma
+            ignore_index=ignore_index,
+            beta=beta,
+            alpha=alpha,
+            gamma=gamma,
+            label_smoothing=label_smoothing,
         )
 
     def forward(self, x, y):
@@ -57,8 +62,21 @@ def forward(self, x, y):
 @pytest.mark.parametrize(
     "ignore_index, beta, gamma", [(-100, 0.1, 0.5), (42, 0.2, 0.85)]
 )
+@pytest.mark.parametrize("label_smoothing", [0.0, 0.1])
 def test_correctness(
-    B, T, H, V, scalar, dtype, atol, rtol, bias, ignore_index, beta, gamma
+    B,
+    T,
+    H,
+    V,
+    scalar,
+    dtype,
+    atol,
+    rtol,
+    bias,
+    ignore_index,
+    beta,
+    gamma,
+    label_smoothing,
 ):
     B = 2 * B  # SimPO loss requires B to be even
 
@@ -70,6 +88,7 @@ def test_correctness(
         ignore_index=ignore_index,
         beta=beta,
         loss_type="simpo",
+        label_smoothing=label_smoothing,
         simpo_gamma=gamma,
     )
     liger_lm_head_simpo = LigerLMHeadSimPO(
@@ -79,6 +98,7 @@ def test_correctness(
         bias=bias,
         ignore_index=ignore_index,
         beta=beta,
+        label_smoothing=label_smoothing,
         gamma=gamma,
     )