diff --git a/tests/pytorch/test_onnx_export.py b/tests/pytorch/test_onnx_export.py
index 50cd150c4e..c55b0cc193 100644
--- a/tests/pytorch/test_onnx_export.py
+++ b/tests/pytorch/test_onnx_export.py
@@ -713,6 +713,14 @@ def test_export_layernorm_mlp_activation(seed_default_rng, activation):
     _test_export_layernorm_mlp(activation=activation)
 
 
+# FP8 recipes with fp8_dpa=True for attention FP8 emulation export test
+fp8_dpa_recipes = [None]  # None = no FP8
+if fp8_available:
+    fp8_dpa_recipes.append(recipe.DelayedScaling(fp8_dpa=True))
+    fp8_dpa_recipes.append(recipe.Float8CurrentScaling(fp8_dpa=True))
+
+
+@pytest.mark.parametrize("fp8_recipe", fp8_dpa_recipes)
 @pytest.mark.parametrize(
     "precision,      use_mask, attn_mask_type",
     [
@@ -730,6 +738,7 @@ def test_export_core_attention(
     precision: torch.dtype,
     use_mask: bool,
     attn_mask_type: str,
+    fp8_recipe: recipe.Recipe,
 ):
     # Set dimensions (these are arbitrary).
     seq_len, batch_size, num_attention_heads, kv_channels = (64, 4, 1, 64)
@@ -749,22 +758,26 @@ def test_export_core_attention(
 
     mask_str = get_attn_mask_str(use_mask, attn_mask_type)
     high_prec_str = dtype2str(precision)
-    fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"
+    fp8_str = "_fp8_dpa" if fp8_recipe is not None else ""
+    fname = f"te.core_attention{fp8_str}{mask_str}{high_prec_str}.onnx"
+
+    is_fp8 = fp8_recipe is not None
 
     model = te.attention.DotProductAttention(
         num_attention_heads=num_attention_heads,
         kv_channels=kv_channels,
-        attention_dropout=0.5,
+        attention_dropout=0.0 if is_fp8 else 0.5,  # Disable dropout for FP8 deterministic results
         qkv_format=qkv_format,
         attn_mask_type=attn_mask_type,
     ).to(device="cuda")
-    do_export(model, inp, fname, input_names=input_names, fp8_recipe=None)
-    te_outputs = te_infer(model, inp, is_fp8=False, fp8_recipe=None)
+    do_export(model, inp, fname, input_names=input_names, fp8_recipe=fp8_recipe)
+    te_outputs = te_infer(model, inp, is_fp8=is_fp8, fp8_recipe=fp8_recipe)
     serialize_inputs_outputs(fname, inp, te_outputs, input_names=input_names)
     if precision in (torch.bfloat16,):
         return
+    atol = 5e-2 if is_fp8 else 1e-2  # Higher tolerance for FP8 due to quantization effects
     validate_result(
-        fname, inp, model, is_fp8=True, atol=1e-2, input_names=input_names, te_outputs=te_outputs
+        fname, inp, model, is_fp8=True, atol=atol, input_names=input_names, te_outputs=te_outputs
     )
 
 
diff --git a/transformer_engine/pytorch/attention/dot_product_attention/backends.py b/transformer_engine/pytorch/attention/dot_product_attention/backends.py
index c726ed8849..96791f72df 100644
--- a/transformer_engine/pytorch/attention/dot_product_attention/backends.py
+++ b/transformer_engine/pytorch/attention/dot_product_attention/backends.py
@@ -164,6 +164,11 @@ class FP8EmulationFunc(torch.autograd.Function):
     @staticmethod
     def forward(ctx, tensor1, tensor2, tensor3, quantizer, quantizer_name, qkv_layout):
         # pylint: disable=missing-function-docstring
+        if is_in_onnx_export_mode():
+            return FP8EmulationFunc.onnx_forward(
+                tensor1, tensor2, tensor3, quantizer, quantizer_name, qkv_layout
+            )
+
         if quantizer_name == "QKV_quantizer":
             query_layer, key_layer, value_layer = [
                 x.contiguous() for x in [tensor1, tensor2, tensor3]
@@ -202,6 +207,62 @@ def backward(ctx, grad1, grad2, grad3):
             tensors = grad1, grad2, grad3
         return tensors[0], tensors[1], tensors[2], None, None, None
 
+    @staticmethod
+    def onnx_forward(tensor1, tensor2, tensor3, quantizer, quantizer_name, qkv_layout=None):
+        """
+        ONNX-compatible forward for FP8 emulation using operations with defined ONNX translations.
+
+        This method performs quantize + dequantize to emulate FP8 effects using ONNX-compatible ops.
+        Uses the quantizer's onnx_quantize/onnx_dequantize methods for proper scaling behavior.
+
+        Parameters
+        ----------
+        tensor1, tensor2, tensor3 : torch.Tensor
+            Input tensors (e.g., Q, K, V for QKV_quantizer, or single tensor for S/O quantizers)
+        quantizer : Quantizer
+            The quantizer object with onnx_quantize/onnx_dequantize methods
+        quantizer_name : str
+            Name of quantizer: "QKV_quantizer", "S_quantizer", "O_quantizer", etc.
+        qkv_layout : str, optional
+            QKV layout string (not used in ONNX path)
+
+        Returns
+        -------
+        Tuple of emulated tensors
+        """
+        # pylint: disable=unused-argument
+
+        if quantizer_name == "QKV_quantizer":
+            # Combine Q, K, V -> quantize together -> split back
+            orig_dtype = tensor1.dtype
+            shapes = [tensor1.shape, tensor2.shape, tensor3.shape]
+            numels = [tensor1.numel(), tensor2.numel(), tensor3.numel()]
+
+            # Flatten and concatenate
+            combined = torch.cat(
+                [tensor1.reshape(-1), tensor2.reshape(-1), tensor3.reshape(-1)], dim=0
+            )
+
+            # Quantize + dequantize combined tensor using quantizer's ONNX methods
+            combined_fp8 = quantizer.onnx_quantize(combined)
+            out = quantizer.onnx_dequantize(combined_fp8).to(orig_dtype)
+
+            # Split back
+            out1 = out[: numels[0]].reshape(shapes[0])
+            out2 = out[numels[0] : numels[0] + numels[1]].reshape(shapes[1])
+            out3 = out[numels[0] + numels[1] :].reshape(shapes[2])
+
+            return out1, out2, out3
+        elif quantizer_name in ["S_quantizer", "O_quantizer"]:
+            # Emulate FP8 on single tensor using quantizer's ONNX methods
+            orig_dtype = tensor1.dtype
+            t_fp8 = quantizer.onnx_quantize(tensor1)
+            out = quantizer.onnx_dequantize(t_fp8).to(orig_dtype)
+            return out, tensor2, tensor3
+        else:
+            # Pass-through
+            return tensor1, tensor2, tensor3
+
 
 class UnfusedDotProductAttention(torch.nn.Module):
     """Parallel attention w/o QKV and Proj Gemms
diff --git a/transformer_engine/pytorch/jit.py b/transformer_engine/pytorch/jit.py
index 5884188b7e..c19b24944f 100644
--- a/transformer_engine/pytorch/jit.py
+++ b/transformer_engine/pytorch/jit.py
@@ -51,9 +51,24 @@ def wrapper(*args, **kwargs):
     import torch._dynamo
 
     if torch.__version__ >= "2.1":
-        no_torch_dynamo = lambda recursive=True: lambda f: (
-            f if is_in_onnx_export_mode() else torch._dynamo.disable(f, recursive=recursive)
-        )
+
+        def no_torch_dynamo(recursive=True):
+            """Decorator to disable Torch Dynamo, except during ONNX export."""
+
+            def decorator(f):
+                disabled_f = torch._dynamo.disable(f, recursive=recursive)
+
+                @wraps(f)
+                def wrapper(*args, **kwargs):
+                    # Check dynamically at call time, not at decoration time
+                    if is_in_onnx_export_mode():
+                        return f(*args, **kwargs)
+                    return disabled_f(*args, **kwargs)
+
+                return wrapper
+
+            return decorator
+
     else:
         # no "recursive" option in pyTorch 2.0 - it acts as if recursive was True
         no_torch_dynamo = lambda recursive=True: torch._dynamo.disable