Add native DeepSeek-V4 hybrid attention orchestration

megatron/core/fp8_utils.py

@@ -871,6 +871,29 @@ def get_fp8_context(config: TransformerConfig, layer_no: int = -1, is_init: bool
 
         return fp8_context
 
+    def get_fp8_disabled_context(config: TransformerConfig, is_init: bool = False):
+        """Return a context manager that disables TE quantization.
+
+        Use this around submodule construction or execution that must stay in a higher
+        precision while its enclosing module uses an FP8 or FP4 context.
+
+        Args:
+            config: Transformer configuration that controls quantization.
+            is_init: Whether to disable the parameter-initialization context instead of
+                the forward autocast context.
+
+        Returns:
+            A disabled TE quantization context when quantization is active, otherwise a
+            no-op context.
+        """
+        if is_init:
+            if not (config.fp8_param or config.fp4_param):
+                return nullcontext()
+            return transformer_engine.pytorch.fp8_model_init(enabled=False)
+        if not (config.fp8 or config.fp4):
+            return nullcontext()
+        return transformer_engine.pytorch.fp8_autocast(enabled=False)
+
 else:
 
     def get_fp8_recipe(config: TransformerConfig):
@@ -881,6 +904,10 @@ def get_fp8_context(config: TransformerConfig, layer_no: int = -1, is_init: bool
         """Returns dummy fp8 context manager since TE is not available."""
         return nullcontext()
 
+    def get_fp8_disabled_context(config: TransformerConfig, is_init: bool = False):
+        """Return a no-op context manager since TE is not available."""
+        return nullcontext()
+
 
 if HAVE_TE:
     from transformer_engine.pytorch.fp8 import FP8GlobalStateManager