[Dev] Fix Linear-Cross-Entropy Convergence Issue

megatron/core/fusions/linear_cross_entropy/blackwell/entry.py

@@ -345,7 +345,8 @@ def backward(
             and num_valid_tokens.dtype == torch.int64
         )
 
-        d_hidden = torch.empty_like(global_hidden)
+        # Allocate d_hidden in float32 for better numerical stability
+        d_hidden = torch.empty_like(global_hidden, dtype=torch.float32)
         d_weight = torch.empty_like(weight)
         assert d_hidden.is_contiguous() and d_weight.is_contiguous()
 
@@ -435,14 +436,15 @@ def backward(
                 )
                 valid_d_logits = _d_logits[:, :vocab_right_bound]
 
-                torch.addmm(
-                    input=d_hidden.view(-1, dim),
-                    mat1=valid_d_logits,
-                    mat2=weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :],
-                    beta=(split_idx != 0),
-                    alpha=1.0,
-                    out=d_hidden.view(-1, dim),
-                )
+                _delta_hidden = torch.mm(
+                    valid_d_logits,
+                    weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :],
+                    out_dtype=torch.float32,
+                ).view_as(d_hidden)
+                if split_idx == 0:
+                    d_hidden.copy_(_delta_hidden)
+                else:
+                    d_hidden.add_(_delta_hidden)
                 torch.matmul(
                     valid_d_logits.T,
                     hidden_view,
@@ -466,6 +468,9 @@ def backward(
                 ]
                 d_hidden = d_hidden.view(partial_hidden_shape).clone()
 
+        # convert d_hidden to the original dtype
+        d_hidden = d_hidden.type_as(global_hidden)
+
         return d_hidden, d_weight
 
 except ImportError:

megatron/core/models/common/language_module/language_module.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 import logging
 import os
-from typing import Any, Dict, Literal, Optional, Tuple
+from typing import Optional, Tuple
 
 import torch
 from torch import Tensor
@@ -14,7 +14,6 @@
 except:
     te_parallel_cross_entropy = None
 from megatron.core.fusions.fused_cross_entropy import fused_vocab_parallel_cross_entropy
-from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy
 from megatron.core.pipeline_parallel.utils import (
     is_pp_first_stage,
     is_pp_last_stage,
@@ -126,68 +125,6 @@ def check_and_set_env_variable(
             check_and_set_env_variable("NVTE_FUSED_ATTN", 1, AttnBackend.auto)
             check_and_set_env_variable("NVTE_UNFUSED_ATTN", 1, AttnBackend.auto)
 
-    def compute_output_layer_and_language_model_loss(
-        self,
-        hidden: Tensor,
-        labels: Optional[Tensor],
-        weight: Tensor = None,
-        sequence_parallel_enabled: bool = False,
-        column_parallel_linear: torch.nn.Module = None,
-        col_linear_kwargs: Dict[str, Any] = {},
-        reduction: Literal["none", "sum", "mean"] = "none",
-        ignore_index: int = -100,
-    ) -> Tensor:
-        """Computes the language model logits and loss (Cross entropy across vocabulary)
-
-        Args:
-            hidden (Tensor): The hidden states from the transformer model
-            labels (Optional[Tensor]): The labels of dimension [batch size, seq length]
-            weight (Tensor): The weight tensor of shape [vocab size, hidden size].
-                Required if using fused linear cross entropy.
-            column_parallel_linear (torch.nn.Module): The column parallel linear
-                layer to use for computing logits when not using fused linear cross entropy.
-            col_linear_kwargs (Dict[str, Any]): Additional kwargs for column parallel linear layer
-            reduction (Optional[str]): The reduction method. Defaults to "none", and can be
-                one of "none", "sum", "mean".
-            ignore_index (Optional[int]): The index to ignore in the loss calculation.
-                Defaults to -100.
-
-        Returns:
-            Tensor: Loss tensor of dimensions [batch size, sequence_length].
-        """
-        if (
-            self.config.cross_entropy_loss_fusion
-            and self.config.cross_entropy_fusion_impl == 'linear'
-        ):
-            assert (
-                weight is not None
-            ), "weight cannot be None when using fused linear cross entropy."
-            assert (
-                labels is not None
-            ), "labels cannot be None when using fused linear cross entropy."
-            # [b s] => [s b]
-            labels = labels.transpose(0, 1).contiguous()
-            loss = linear_cross_entropy(
-                hidden,
-                weight,
-                labels,
-                tp_group=self.pg_collection.tp,
-                sequence_parallel=sequence_parallel_enabled,
-                reduction=reduction,
-                ignore_index=ignore_index,
-            )
-
-            # [s b] => [b, s]
-            loss = loss.view_as(labels).transpose(0, 1).contiguous()
-            return loss
-        else:
-            assert (
-                column_parallel_linear is not None
-            ), "column_parallel_linear cannot be None when not using fused linear cross entropy."
-            logits, _ = column_parallel_linear(hidden, **col_linear_kwargs)
-
-            return self.compute_language_model_loss(labels, logits)
-
     def compute_language_model_loss(self, labels: Tensor, logits: Tensor) -> Tensor:
         """Computes the language model loss (Cross entropy across vocabulary)
 

megatron/core/models/gpt/gpt_model.py

@@ -6,7 +6,7 @@
 import torch
 from torch import Tensor
 
-from megatron.core import parallel_state, tensor_parallel
+from megatron.core import parallel_state
 from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
 from megatron.core.inference.contexts import BaseInferenceContext
@@ -25,6 +25,7 @@
 from megatron.core.quantization.utils import get_quant_config_or_none
 from megatron.core.tensor_parallel import gather_from_sequence_parallel_region
 from megatron.core.transformer.enums import CudaGraphScope, ModelType
+from megatron.core.transformer.linear_cross_entropy import LinearCrossEntropyModule
 from megatron.core.transformer.multi_token_prediction import (
     MTPLossAutoScaler,
     MTPLossLoggingHelper,
@@ -234,7 +235,7 @@ def __init__(
                 self.embedding_activation_buffer = None
                 self.grad_output_buffer = None
 
-            self.output_layer = tensor_parallel.ColumnParallelLinear(
+            self.output_layer = LinearCrossEntropyModule(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
@@ -614,16 +615,11 @@ def _postprocess(
                 )
 
                 # Compute mtp loss without storing logits to save memory.
-                mtp_loss = self.compute_output_layer_and_language_model_loss(
-                    hidden_states_list[mtp_layer_number + 1],
-                    labels=mtp_labels,
-                    weight=self.shared_embedding_or_output_weight(),
-                    sequence_parallel_enabled=self.output_layer.sequence_parallel,
-                    column_parallel_linear=self.output_layer,
-                    col_linear_kwargs={
-                        'weight': output_weight,
-                        'runtime_gather_output': runtime_gather_output,
-                    },
+                mtp_loss = self.output_layer(
+                    output_cross_entropy_loss=True,
+                    input_=hidden_states_list[mtp_layer_number + 1],
+                    weight=output_weight,
+                    runtime_gather_output=runtime_gather_output,
                 )
 
                 mtp_loss = loss_mask * mtp_loss
@@ -702,16 +698,12 @@ def _postprocess(
             # [s b h] => [b s h]
             return logits.transpose(0, 1).contiguous()
 
-        loss = self.compute_output_layer_and_language_model_loss(
-            hidden_states,
+        loss = self.output_layer(
+            output_cross_entropy_loss=True,
+            input_=hidden_states,
             labels=labels,
-            weight=self.shared_embedding_or_output_weight(),
-            sequence_parallel_enabled=self.output_layer.sequence_parallel,
-            column_parallel_linear=self.output_layer,
-            col_linear_kwargs={
-                'weight': output_weight,
-                'runtime_gather_output': runtime_gather_output,
-            },
+            weight=output_weight,
+            runtime_gather_output=runtime_gather_output,
         )
 
         return loss

megatron/core/models/mamba/mamba_model.py

@@ -4,7 +4,6 @@
 
 from torch import Tensor
 
-from megatron.core import tensor_parallel
 from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
 from megatron.core.inference.contexts import BaseInferenceContext
 from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
@@ -15,6 +14,7 @@
 from megatron.core.tensor_parallel import gather_from_sequence_parallel_region
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import ModelType
+from megatron.core.transformer.linear_cross_entropy import LinearCrossEntropyModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
 from megatron.core.utils import WrappedTensor, deprecate_inference_params
 
@@ -131,7 +131,7 @@ def __init__(
 
         # Output
         if post_process:
-            self.output_layer = tensor_parallel.ColumnParallelLinear(
+            self.output_layer = LinearCrossEntropyModule(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
@@ -285,16 +285,12 @@ def forward(
             # [s b h] => [b s h]
             return logits.transpose(0, 1).contiguous()
 
-        loss = self.compute_output_layer_and_language_model_loss(
-            hidden_states,
-            labels,
-            weight=self.shared_embedding_or_output_weight(),
-            sequence_parallel_enabled=self.output_layer.sequence_parallel,
-            column_parallel_linear=self.output_layer,
-            col_linear_kwargs={
-                "weight": output_weight,
-                "runtime_gather_output": runtime_gather_output,
-            },
+        loss = self.output_layer(
+            output_cross_entropy_loss=True,
+            input_=hidden_states,
+            labels=labels,
+            weight=output_weight,
+            runtime_gather_output=runtime_gather_output,
         )
 
         return loss

megatron/core/transformer/linear_cross_entropy.py

@@ -0,0 +1,133 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Literal, Optional, Tuple, Union
+
+import torch
+
+from megatron.core import tensor_parallel
+from megatron.core.fusions.fused_cross_entropy import fused_vocab_parallel_cross_entropy
+from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy
+from megatron.core.transformer.enums import CudaGraphScope
+from megatron.core.utils import is_te_min_version
+
+try:
+    from megatron.core.extensions.transformer_engine import te_parallel_cross_entropy
+except:
+    te_parallel_cross_entropy = None
+
+
+class LinearCrossEntropyModule(tensor_parallel.ColumnParallelLinear):
+    """
+    A module that combines a ColumnParallelLinear layer with fused
+    linear + cross-entropy loss computation over a tensor-parallel vocabulary.
+    """
+
+    def forward(
+        self,
+        input_: torch.Tensor,
+        weight: Optional[torch.Tensor] = None,
+        runtime_gather_output: Optional[bool] = None,
+        output_cross_entropy_loss: bool = False,
+        labels: Optional[torch.Tensor] = None,
+        reduction: Literal["none", "sum", "mean"] = "none",
+        ignore_index: int = -100,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
+        """Run either the plain ColumnParallelLinear or fused linear+cross-entropy."""
+        if output_cross_entropy_loss:
+            return self._compute_linear_and_cross_entropy_loss(
+                hidden=input_,
+                weight=weight if weight is not None else self.weight,
+                labels=labels,
+                reduction=reduction,
+                ignore_index=ignore_index,
+            )
+
+        # Fall back to standard ColumnParallelLinear forward.
+        # ColumnParallelLinear.forward returns (output, bias) or just output
+        # depending on configuration, so keep the return type as Tensor.
+        return super().forward(input_, weight, runtime_gather_output)
+
+    def _compute_linear_and_cross_entropy_loss(
+        self,
+        hidden: torch.Tensor,
+        weight: torch.Tensor,
+        runtime_gather_output: Optional[bool] = None,
+        labels: Optional[torch.Tensor] = None,
+        reduction: Literal["none", "sum", "mean"] = "none",
+        ignore_index: int = -100,
+    ) -> torch.Tensor:
+        """Compute fused linear + cross-entropy over tensor-parallel vocab."""
+        if (
+            self.config.cross_entropy_loss_fusion
+            and self.config.cross_entropy_fusion_impl == 'linear'
+        ):
+            assert (
+                weight is not None
+            ), "weight cannot be None when using fused linear cross entropy."
+            assert (
+                labels is not None
+            ), "labels cannot be None when using fused linear cross entropy."
+
+            # [b s] => [s b]
+            labels = labels.transpose(0, 1).contiguous()
+            loss = linear_cross_entropy(
+                hidden,
+                self.weight,
+                labels,
+                sequence_parallel=self.sequence_parallel,
+                reduction=reduction,
+                ignore_index=ignore_index,
+                tp_group=self.tp_group,
+            )
+            # If reduction != "none" this will be a scalar; for "none" it should
+            # match [s, b] and can be reshaped back to [b, s].
+            if reduction == "none":
+                loss = loss.view_as(labels).transpose(0, 1).contiguous()
+        else:
+            logits, _ = super().forward(hidden, weight, runtime_gather_output)
+            loss = self._compute_cross_entropy_loss(labels, logits)
+
+        return loss
+
+    def _compute_cross_entropy_loss(
+        self, labels: torch.Tensor, logits: torch.Tensor
+    ) -> Optional[torch.Tensor]:
+        """Compute (possibly fused) vocab-parallel cross-entropy loss."""
+        loss = None
+
+        # [b s] => [s b]
+        labels = labels.transpose(0, 1).contiguous()
+        if self.config.cross_entropy_loss_fusion:
+            if self.config.cross_entropy_fusion_impl == 'te':
+                if te_parallel_cross_entropy is not None:
+                    labels = torch.as_strided(labels, labels.size(), (labels.size()[1], 1))
+                    # Use is_cg_capturable=True for full iteration CUDA graphs
+                    # to avoid torch.equal checks
+                    is_cg_capturable = (
+                        hasattr(self.config, 'cuda_graph_scope')
+                        and CudaGraphScope.full_iteration in self.config.cuda_graph_scope
+                    )
+                    if is_cg_capturable and not is_te_min_version("2.7.0"):
+                        from megatron.core.utils import get_te_version
+
+                        current_version = get_te_version()
+                        raise AssertionError(
+                            f"CUDA graph compatible cross entropy requires "
+                            f"TransformerEngine >= 2.7.0, but found version {current_version}. "
+                            "Please upgrade TransformerEngine "
+                            f"or set cuda_graph_scope to a value other than 'full_iteration'."
+                        )
+
+                    loss = te_parallel_cross_entropy(
+                        logits, labels, self.tp_group, is_cg_capturable
+                    )
+                else:
+                    raise RuntimeError("Trying to use a TE block when it's not present.")
+            elif self.config.cross_entropy_fusion_impl == 'native':
+                loss = fused_vocab_parallel_cross_entropy(logits, labels, self.tp_group)
+        else:
+            loss = tensor_parallel.vocab_parallel_cross_entropy(logits, labels)
+
+        # [s b] => [b, s]
+        loss = loss.transpose(0, 1).contiguous()
+        return loss