[PyTorch] Propagate skip_fp8_weight_update in GroupedLinear during FP8 CUDA graph capture

tests/pytorch/test_cuda_graphs.py

@@ -9,6 +9,7 @@
 import torch
 from transformer_engine.pytorch import (
     DotProductAttention,
+    GroupedLinear,
     LayerNormLinear,
     LayerNormMLP,
     Linear,
@@ -216,6 +217,38 @@ def forward(self, input_: torch.Tensor, **kwargs) -> torch.Tensor:
         return x
 
 
+class _GroupedLinearWrapper(torch.nn.Module):
+    """Adapt GroupedLinear to the [seqlen, batch, hidden] data used in this test.
+
+    GroupedLinear expects a 2D `[total_tokens, hidden]` input plus an `m_splits`
+    list, so this wrapper flattens the leading dims, splits the tokens evenly
+    across the GEMMs, and restores the original shape. It also forwards
+    `is_first_microbatch` so the FP8 weight-caching path is exercised under CUDA
+    graphs.
+    """
+
+    def __init__(self, hidden_size: int, num_gemms: int, params_dtype: torch.dtype) -> None:
+        super().__init__()
+        self.num_gemms = num_gemms
+        self.grouped_linear = GroupedLinear(
+            num_gemms,
+            hidden_size,
+            hidden_size,
+            device="cuda",
+            params_dtype=params_dtype,
+        )
+
+    def forward(self, input_: torch.Tensor, **kwargs) -> torch.Tensor:
+        seqlen, batch, hidden = input_.shape
+        x = input_.reshape(seqlen * batch, hidden)
+        total_tokens = x.shape[0]
+        assert total_tokens % self.num_gemms == 0, "tokens must split evenly across GEMMs"
+        split = total_tokens // self.num_gemms
+        m_splits = [split] * self.num_gemms
+        out = self.grouped_linear(x, m_splits, **kwargs)
+        return out.reshape(seqlen, batch, hidden)
+
+
 # Supported modules
 _test_cuda_graphs_modules: List[str] = [
     # Put linear first to test the case where the cuda context might not be set in
@@ -315,6 +348,15 @@ def _test_cuda_graphs(
                 )
                 for _ in range(num_layers)
             ]
+        elif module == "grouped_linear":
+            modules = [
+                _GroupedLinearWrapper(
+                    model_config.hidden_size,
+                    num_gemms=2,
+                    params_dtype=dtype,
+                )
+                for _ in range(num_layers)
+            ]
         elif module == "linear_op":
             modules = [
                 te_ops.Sequential(
@@ -501,6 +543,52 @@ def test_make_graphed_callables_with_fp8_weight_caching(
     )
 
 
+# Per-tensor FP8 recipes that support GroupedLinear FP8 weight caching.
+_grouped_linear_fp8_weight_caching_recipes = []
+if fp8_available:
+    _grouped_linear_fp8_weight_caching_recipes.append(recipe.DelayedScaling())
+    _grouped_linear_fp8_weight_caching_recipes.append(recipe.Float8CurrentScaling())
+
+
+@pytest.mark.skipif(not fp8_available, reason="FP8 is not supported")
+@pytest.mark.parametrize("dtype", dtypes)
+@pytest.mark.parametrize("fp8_params", (False, True))
+@pytest.mark.parametrize("fp8_recipe", _grouped_linear_fp8_weight_caching_recipes, ids=recipe_id)
+def test_make_graphed_callables_grouped_linear_with_fp8_weight_caching(
+    *,
+    dtype: torch.dtype,
+    fp8_params: bool,
+    fp8_recipe: recipe.Recipe,
+    model_config: str = "small",
+    num_layers: int = 3,
+) -> None:
+    """GroupedLinear must thread `is_first_microbatch` into the FP8 weight-update
+    skip tensor under CUDA graphs.
+
+    With `fp8_weight_caching` enabled, the graphed and non-graphed runs only match
+    when `skip_fp8_weight_update` is propagated for every microbatch. Before the
+    fix, GroupedLinear hardcoded it to `None`, so the cached FP8 weights diverged
+    from the eager reference. This regresses if that propagation is dropped again.
+    """
+    config = model_configs[model_config]
+    kwargs = dict(
+        module="grouped_linear",
+        model_config=config,
+        num_layers=num_layers,
+        dtype=dtype,
+        fp8=True,
+        fp8_params=fp8_params,
+        fp8_weight_caching=True,
+        fp8_recipe=fp8_recipe,
+    )
+    graph_outputs_full = _test_cuda_graphs(graph_mode="full", **kwargs)
+    graph_outputs_individual = _test_cuda_graphs(graph_mode="individual", **kwargs)
+    outputs = _test_cuda_graphs(graph_mode="none", **kwargs)
+
+    assert_all_equal(outputs, graph_outputs_full)
+    assert_all_equal(outputs, graph_outputs_individual)
+
+
 def generate_data_for_dot_product_attention(
     model_config: ModelConfig,
     dtype: torch.dtype,

transformer_engine/pytorch/module/grouped_linear.py

@@ -1684,6 +1684,15 @@ def forward(
         is_grad_enabled = torch.is_grad_enabled()
         num_gemms = self.num_gemms
 
+        if FP8GlobalStateManager.fp8_graph_capturing():
+            skip_fp8_weight_update = (
+                FP8GlobalStateManager.quantization_state.skip_fp8_weight_update_tensor
+            )
+        else:
+            skip_fp8_weight_update = None
+        if skip_fp8_weight_update is not None:
+            is_first_microbatch = False
+
         # Make sure splits are in expected format
         if not isinstance(m_splits, torch.Tensor):
             # Convert list of ints to tensor for backward compatibility
@@ -1754,7 +1763,7 @@ def forward(
                 is_grad_enabled,
                 weight_workspaces,
                 cache_weight,
-                None,  # skip_fp8_weight_update
+                skip_fp8_weight_update,
                 self.save_original_input,
                 debug,
             )