Merge branch 'main' into tcc/weight-ce

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/dpo_loss.py

@@ -64,7 +64,7 @@ def forward(
         ref_bias=None,
         ignore_index=-100,
         beta=0.1,
-        compute_nll_loss=True,
+        compute_nll_loss=False,
         compiled=True,
         use_ref_model=True,
     ):
@@ -100,7 +100,7 @@ def __init__(
         self,
         ignore_index: int = -100,
         beta: float = 0.1,
-        compute_nll_loss: bool = True,
+        compute_nll_loss: bool = False,
         compiled: bool = True,
         use_ref_model: bool = False,
     ):

src/liger_kernel/chunked_loss/fused_linear_preference.py

@@ -408,7 +408,7 @@ def _compute_loss(
         else:
             preference_loss, aux_outputs = preference_loss_outputs, []
 
-        loss = alpha * chosen_nll_loss - preference_loss
+        loss = alpha * chosen_nll_loss + preference_loss
         return_vars = (
             chosen_logps,
             rejected_logps,

src/liger_kernel/chunked_loss/orpo_loss.py

@@ -36,7 +36,7 @@ def preference_loss_fn(chosen_logps, rejected_logps, full_target, beta=0.1):
             - torch.log1p(-torch.exp(rejected_logps))
         )
         ratio = F.logsigmoid(log_odds)
-        loss = beta * ratio.sum() / (full_target.shape[0] // 2)
+        loss = -beta * ratio.sum() / (full_target.shape[0] // 2)
 
         chosen_rewards = beta * chosen_logps
         rejected_rewards = beta * rejected_logps

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,

src/liger_kernel/transformers/model/mixtral.py

@@ -38,7 +38,7 @@ def lce_forward_deprecated(
     cache_position: Optional[torch.LongTensor] = None,
 ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
     r"""
-    Copy paste Mixtral's forward from transfomers v4.44.2 but replace torch cross entropy with liger fused linear cross entropy
+    Copy paste Mixtral's forward from transformers v4.44.2 but replace torch cross entropy with liger fused linear cross entropy
 
 
     Args:

src/liger_kernel/transformers/trainer/orpo_trainer.py

@@ -17,7 +17,7 @@ class _FSDPForwardRedirection:
     This is needed in cases where we call a submodule of a FSDP module. For instance, when we want to call only
     the `LlamaModel` part out of a FSDP-wrapped `LlamaForCausalLM` to get the hidden states without involving
     GPU-memory-heavy `lm_head` and cross entropy computation, doing this directly (i.e. `model.model.forward()`)
-    will not work because the first `nn.Emebedding` layer is not independently wrapped as a FSDP module (because of
+    will not work because the first `nn.Embedding` layer is not independently wrapped as a FSDP module (because of
     the transformer-based wrapping policy), and not calling it through FSDP root module forward will not all-gather
     its parameter, thus resulting in "RuntimeError: 'weight' must be 2-D" error. Similarly, if we want to call just
     the `lm_head` part of a model, we need this trick too to properly get its params all-gathered.
@@ -125,6 +125,10 @@ def orpo_partial(lm_head, last_hidden_state, concatenated_labels):
             outputs.last_hidden_state,
             concatenated_batch["concatenated_labels"],
         )
+        # if aux_loss_enabled, add the aux_loss to the orpo_loss
+        if self.aux_loss_enabled:
+            orpo_loss += self.aux_loss_coef * outputs.aux_loss
+
         return orpo_loss, aux_outputs
 
     def get_batch_loss_metrics(

test/chunked_loss/test_cpo_loss.py

@@ -60,14 +60,14 @@ def alignment_loss(
         if self.loss_type == "sigmoid":
             # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0.
             losses = (
-                F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
-                + F.logsigmoid(-self.beta * logits) * self.label_smoothing
+                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
+                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
             )
         elif self.loss_type == "simpo":
             logits = logits - (self.simpo_gamma / self.beta)
             losses = (
-                F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
-                + F.logsigmoid(-self.beta * logits) * self.label_smoothing
+                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
+                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
             )
         else:
             raise ValueError(
@@ -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(