[PyTorch] ONNX test fix + export for FP8 attention (#2598)

* jjit bug fix

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* fix'

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

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* fix

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* fix

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* fixes

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* lint fixes

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* fix

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

---------

Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 2 people

qa/L1_pytorch_onnx_unittest/test.sh

@@ -6,4 +6,5 @@
 : ${XML_LOG_DIR:=/logs}
 mkdir -p "$XML_LOG_DIR"
 
-python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/test_onnx_export.xml $TE_PATH/tests/pytorch/test_onnx_export.py
+# NVTE_UnfusedDPA_Emulate_FP8=1 enables FP8 attention emulation when no native backend is available
+NVTE_UnfusedDPA_Emulate_FP8=1 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/test_onnx_export.xml $TE_PATH/tests/pytorch/test_onnx_export.py

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)
 
 
+# Quantization recipes with fp8_dpa=True for attention emulation export test
+dpa_quantization_recipes = [None]  # None = no quantization
+if fp8_available:
+    dpa_quantization_recipes.append(recipe.DelayedScaling(fp8_dpa=True))
+    dpa_quantization_recipes.append(recipe.Float8CurrentScaling(fp8_dpa=True))
+
+
+@pytest.mark.parametrize("fp8_recipe", dpa_quantization_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 +758,25 @@ 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,
         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-1 if is_fp8 else 1e-2
     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
     )
 
 

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 +207,47 @@ 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.
+        """
+        # pylint: disable=unused-argument
+        is_qkv_quantizer = quantizer_name == "QKV_quantizer"
+        assert isinstance(
+            quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)
+        ), "ONNX FP8 emulation path supports only Float8 quantizers."
+
+        if is_qkv_quantizer:
+            # Flatten + concatenate + quantize + split. Equivalent to combine_and_quantize Case 3.
+            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
+        if 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
+        # Pass-through
+        return tensor1, tensor2, tensor3
+
 
 class UnfusedDotProductAttention(torch.nn.Module):
     """Parallel attention w/o QKV and Proj Gemms

transformer_engine/pytorch/attention/dot_product_attention/dot_product_attention.py

@@ -1552,7 +1552,9 @@ def forward(
                 )
 
             if use_unfused_attention:
-                allow_emulation = os.getenv("NVTE_UnfusedDPA_Emulate_FP8", "0") == "1"
+                allow_emulation = (
+                    os.getenv("NVTE_UnfusedDPA_Emulate_FP8", "0") == "1" or is_in_onnx_export_mode()
+                )
                 if checkpoint_core_attention:
                     return self._checkpointed_attention_forward(
                         self.unfused_attention,

transformer_engine/pytorch/attention/dot_product_attention/utils.py

@@ -479,7 +479,9 @@ def get_attention_backend(
                 logger.debug("Disabling FlashAttention 3 for FP8 training")
             use_flash_attention_3 = False
         if use_unfused_attention:
-            allow_emulation = os.getenv("NVTE_UnfusedDPA_Emulate_FP8", "0") == "1"
+            allow_emulation = (
+                os.getenv("NVTE_UnfusedDPA_Emulate_FP8", "0") == "1" or is_in_onnx_export_mode()
+            )
             if not allow_emulation:
                 logger.debug("Disabling UnfusedDotProductAttention for FP8 attention")
                 use_unfused_attention = False

transformer_engine/pytorch/jit.py

@@ -46,17 +46,35 @@ def wrapper(*args, **kwargs):
 
 # Decorator to disable Torch Dynamo
 # See: https://github.com/NVIDIA/TransformerEngine/issues/308
-no_torch_dynamo = lambda recursive=True: lambda func: func
 if torch.__version__ >= "2":
     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)
-        )
-    else:
-        # no "recursive" option in pyTorch 2.0 - it acts as if recursive was True
-        no_torch_dynamo = lambda recursive=True: torch._dynamo.disable
+    def no_torch_dynamo(recursive=True):
+        """Decorator to disable Torch Dynamo, except during ONNX export."""
+
+        def decorator(f):
+            # no "recursive" option in pyTorch 2.0 - it acts as if recursive was True
+            disabled_f = (
+                torch._dynamo.disable(f, recursive=recursive)
+                if torch.__version__ >= "2.1"
+                else torch._dynamo.disable(f)
+            )
+
+            @wraps(f)
+            def wrapper(*args, **kwargs):
+                if is_in_onnx_export_mode():
+                    return f(*args, **kwargs)
+                return disabled_f(*args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+else:
+    # Fallback for PyTorch < 2.0: no-op decorator
+    def no_torch_dynamo(recursive=True):  # pylint: disable=unused-argument
+        """No-op decorator for PyTorch < 2.0."""
+        return lambda func: func
 
 
 def set_jit_fusion_options() -> None: