[ROCm] Fallback pytorch GELU with tanh approximation to GELU() (#29244)

Signed-off-by: Divakar Verma <divakar.verma@amd.com>
Signed-off-by: Divakar Verma <137818590+divakar-amd@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

vllm/model_executor/layers/activation.py

@@ -159,6 +159,13 @@ class GeluAndMulSparse(CustomOp):
         self.approximate = approximate
         if approximate not in ("none", "tanh"):
             raise ValueError(f"Unknown approximate mode: {approximate}")
+        if current_platform.is_rocm() and approximate == "tanh":
+            # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
+            logger.warning_once(
+                "[ROCm] Pytorch's native GELU with tanh approximation is currently "
+                "unstable and produces garbage. Fallback to 'none' approximation."
+            )
+            self.approximate = "none"
 
         # Sparsity.
         if activation_sparsity == 0.0:
@@ -209,6 +216,12 @@ class GeluAndMul(CustomOp):
                 self.op = torch.ops._C.gelu_and_mul
             elif approximate == "tanh":
                 self.op = torch.ops._C.gelu_tanh_and_mul
+        if current_platform.is_rocm() and approximate == "tanh":
+            logger.warning_once(
+                "[ROCm] PyTorch's native GELU with tanh approximation is unstable "
+                "with torch.compile. For native implementation, fallback to 'none' "
+                "approximation. The custom kernel implementation is unaffected."
+            )
         elif current_platform.is_xpu():
             from vllm._ipex_ops import ipex_ops
 
@@ -219,8 +232,12 @@ class GeluAndMul(CustomOp):
 
     def forward_native(self, x: torch.Tensor) -> torch.Tensor:
         """PyTorch-native implementation equivalent to forward()."""
+        # TODO: [ROCm] PyTorch's native GELU with tanh is unstable with torch.compile
+        approximate = self.approximate
+        if current_platform.is_rocm() and approximate == "tanh":
+            approximate = "none"
         d = x.shape[-1] // 2
-        return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
+        return F.gelu(x[..., :d], approximate=approximate) * x[..., d:]
 
     def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
         d = x.shape[-1] // 2
@@ -522,7 +539,16 @@ _ACTIVATION_REGISTRY = LazyDict(
         "gelu": lambda: nn.GELU(),
         "gelu_fast": lambda: FastGELU(),
         "gelu_new": lambda: NewGELU(),
-        "gelu_pytorch_tanh": lambda: nn.GELU(approximate="tanh"),
+        "gelu_pytorch_tanh": lambda: (
+            # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
+            logger.warning_once(
+                "[ROCm] PyTorch's native GELU with tanh approximation is unstable. "
+                "Falling back to GELU(approximate='none')."
+            ),
+            nn.GELU(approximate="none"),
+        )[1]
+        if current_platform.is_rocm()
+        else nn.GELU(approximate="tanh"),
         "relu": lambda: nn.ReLU(),
         "relu2": lambda: ReLUSquaredActivation(),
         "silu": lambda: nn.SiLU(),