[https://nvbugs/5732942][fix] AutoDeploy: handle transformers 4.57.1 upgrade fixes

tensorrt_llm/_torch/auto_deploy/export/library/meta_nonzero.py

@@ -0,0 +1,37 @@
+"""Patch to enable torch.nonzero() on meta tensors during export.
+
+This patch addresses an issue where torch.nonzero() raises NotImplementedError
+when tracing models that use nonzero on meta device. The fix sets the config
+flag to assume all elements are non-zero, which enables export to proceed.
+"""
+
+import torch.fx.experimental._config as fx_config
+
+from ..interface import BaseExportPatch, ExportPatchRegistry
+
+
+@ExportPatchRegistry.register("meta_nonzero")
+class MetaNonzeroPatch(BaseExportPatch):
+    """Patch to enable torch.nonzero() meta registration during export.
+
+    This patch sets torch.fx.experimental._config.meta_nonzero_assume_all_nonzero
+    to True, allowing torch.nonzero() to work on meta tensors during tracing.
+    The implementation assumes all elements are non-zero, which is acceptable
+    for tracing purposes where only shapes matter.
+    """
+
+    def _apply_patch(self):
+        """Apply the meta nonzero patch."""
+        # Store original config value
+        self.original_values["meta_nonzero_assume_all_nonzero"] = getattr(
+            fx_config, "meta_nonzero_assume_all_nonzero", False
+        )
+
+        # Enable nonzero on meta tensors
+        fx_config.meta_nonzero_assume_all_nonzero = True
+
+    def _revert_patch(self):
+        """Revert the meta nonzero patch."""
+        fx_config.meta_nonzero_assume_all_nonzero = self.original_values[
+            "meta_nonzero_assume_all_nonzero"
+        ]

tensorrt_llm/_torch/auto_deploy/models/patches/llama4.py

@@ -72,11 +72,11 @@ def _forward_with_cond(
         inputs_embeds = self.get_input_embeddings()(input_ids)
 
     def _vision_branch(inputs_embeds, pixel_values, input_ids):
+        # Updated to match transformers 4.57.1+ signature
+        # get_image_features now takes: (self, pixel_values, vision_feature_select_strategy, **kwargs)
         image_features = self.get_image_features(
             pixel_values=pixel_values,
-            vision_feature_layer=vision_feature_layer,
             vision_feature_select_strategy=vision_feature_select_strategy,
-            image_sizes=None,
         )
 
         vision_flat = image_features.view(-1, image_features.size(-1))

tensorrt_llm/_torch/auto_deploy/models/patches/mixtral.py

@@ -7,19 +7,38 @@
 
 from ...export.interface import BaseExportPatch, ExportPatchRegistry
 
+# Import SiLUActivation for compatibility check
+try:
+    from transformers.activations import SiLUActivation
+
+    _SILU_TYPES = (nn.SiLU, SiLUActivation)
+except ImportError:
+    _SILU_TYPES = (nn.SiLU,)
+
+
+def _is_silu_activation(act_fn) -> bool:
+    """Check if activation function is SiLU or equivalent."""
+    return isinstance(act_fn, _SILU_TYPES)
+
 
 def _forward_moe(self: MixtralSparseMoeBlock, hidden_states: torch.Tensor):
     # check if we can apply the patch
-    use_original_forward = False
-    if not all(isinstance(expert.act_fn, nn.SiLU) for expert in self.experts):
-        use_original_forward = True
+    unsupported_reasons = []
+    if not all(_is_silu_activation(expert.act_fn) for expert in self.experts):
+        unsupported_reasons.append("expert activation is not SiLU")
 
     if any(getattr(mod, "bias", None) is not None for mod in self.experts.modules()):
-        use_original_forward = True
-
-    # rely on original forward instead
-    if use_original_forward:
-        return self._original_forward(hidden_states)
+        unsupported_reasons.append("expert modules have bias")
+
+    # Raise informative error for unsupported configurations
+    # (fallback to original forward is not export-compatible with transformers >= 4.57.1)
+    if unsupported_reasons:
+        raise NotImplementedError(
+            f"MixtralSparseMoeBlock forward patch does not support this model configuration: "
+            f"{', '.join(unsupported_reasons)}. "
+            f"The original transformers forward uses torch.nonzero() and tensor indexing "
+            f"which are not compatible with torch.export on meta tensors."
+        )
 
     batch_size, sequence_length, hidden_dim = hidden_states.shape
     if self.training and self.jitter_noise > 0:

tensorrt_llm/_torch/auto_deploy/models/patches/qwen3.py

@@ -7,19 +7,38 @@
 
 from ...export.interface import BaseExportPatch, ExportPatchRegistry
 
+# Import SiLUActivation for compatibility check
+try:
+    from transformers.activations import SiLUActivation
+
+    _SILU_TYPES = (nn.SiLU, SiLUActivation)
+except ImportError:
+    _SILU_TYPES = (nn.SiLU,)
+
+
+def _is_silu_activation(act_fn) -> bool:
+    """Check if activation function is SiLU or equivalent."""
+    return isinstance(act_fn, _SILU_TYPES)
+
 
 def _forward_moe(self: Qwen3MoeSparseMoeBlock, hidden_states: torch.Tensor):
     # check if we can apply the patch
-    use_original_forward = False
-    if not all(isinstance(expert.act_fn, nn.SiLU) for expert in self.experts):
-        use_original_forward = True
+    unsupported_reasons = []
+    if not all(_is_silu_activation(expert.act_fn) for expert in self.experts):
+        unsupported_reasons.append("expert activation is not SiLU")
 
     if any(getattr(mod, "bias", None) is not None for mod in self.experts.modules()):
-        use_original_forward = True
-
-    # rely on original forward instead
-    if use_original_forward:
-        return self._original_forward(hidden_states)
+        unsupported_reasons.append("expert modules have bias")
+
+    # Raise informative error for unsupported configurations
+    # (fallback to original forward is not export-compatible with transformers >= 4.57.1)
+    if unsupported_reasons:
+        raise NotImplementedError(
+            f"Qwen3MoeSparseMoeBlock forward patch does not support this model configuration: "
+            f"{', '.join(unsupported_reasons)}. "
+            f"The original transformers forward uses torch.nonzero() and tensor indexing "
+            f"which are not compatible with torch.export on meta tensors."
+        )
 
     batch_size, sequence_length, hidden_dim = hidden_states.shape
     hidden_states = hidden_states.view(-1, hidden_dim)

tests/unittest/_torch/auto_deploy/unit/singlegpu/models/test_llama4_vlm_patch.py

@@ -1,4 +1,3 @@
-import pytest
 import torch
 from _model_test_utils import get_small_model_config
 from build_and_run_ad import ExperimentConfig
@@ -10,10 +9,6 @@
 
 
 def test_build_run_llama4_vlm():
-    pytest.skip(
-        "Skipping test_build_run_llm4_vlm because Llama4 is giving an error on upgrading transformers version to 4.57.1"
-        "https://nvbugspro.nvidia.com/bug/5732942"
-    )
     atol = 1e-3
     rtol = 1e-3
 

tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_build_small_single.py

@@ -201,19 +201,6 @@ def _check_ad_config(experiment_config: ExperimentConfig, llm_args: LlmArgs):
     ],
 )
 def test_build_ad(model_hub_id: str, llm_extra_args: dict):
-    if (
-        model_hub_id == "mistralai/Mixtral-8x7B-Instruct-v0.1"
-        and llm_extra_args.get("mode") != "transformers"
-    ):
-        pytest.skip(
-            "Mixtral-8x7B-Instruct-v0.1 is giving an error on upgrading transformers version to 4.57.1"
-            "https://nvbugspro.nvidia.com/bug/5732942"
-        )
-    if model_hub_id == "Qwen/Qwen3-30B-A3B" and llm_extra_args.get("mode") != "transformers":
-        pytest.skip(
-            "Qwen3-30B-A3B is giving an error on upgrading transformers version to 4.57.1"
-            "https://nvbugspro.nvidia.com/bug/5732942"
-        )
     experiment_config = get_small_model_config(model_hub_id, **llm_extra_args)
     experiment_config["args"]["runtime"] = "demollm"  # Default runtime set to demollm
     experiment_config["args"]["world_size"] = 0  # Default world_size set to 0