Revert "[Perf] Optimize RMSNorm in Z-Image (#3304)"

This reverts commit bb69cbc9f1.

tests/diffusion/layers/test_norm.py

@@ -451,97 +451,3 @@ def test_rmsnorm_with_single_element_batch():
     out = norm(x)
 
     assert out.shape == (1, 1, hidden_size)
-
-
-# ── Fused vs Native correctness tests ──
-
-
-def test_rmsnorm_fused_native_parity_cuda():
-    """Verify fused and native RMSNorm produce identical results on CUDA.
-
-    The fused kernel uses vllm._custom_ops.rms_norm while native uses pure PyTorch.
-    Both should produce numerically equivalent results.
-    """
-    from vllm_omni.diffusion.layers.norm import RMSNorm
-
-    hidden_size = 128
-    eps = 1e-6
-    torch.manual_seed(42)
-
-    for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-        torch.manual_seed(42)
-        norm = RMSNorm(hidden_size, eps=eps).cuda()
-        norm.weight.data = norm.weight.data.to(dtype)
-
-        x = torch.randn(4, 8, hidden_size, dtype=dtype, device="cuda")
-
-        out_fused = norm._forward_fused(x)
-        out_native = norm.forward_native(x)
-
-        torch.testing.assert_close(out_fused, out_native, atol=1e-3, rtol=1e-3)
-
-
-def test_rmsnorm_fused_native_parity_large_tensor():
-    """Verify fused and native RMSNorm parity with large tensors.
-
-    Tests realistic tensor sizes used in diffusion models.
-    """
-    from vllm_omni.diffusion.layers.norm import RMSNorm
-
-    hidden_size = 4096
-    batch_size = 16
-    seq_len = 1024
-    eps = 1e-6
-
-    for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-        torch.manual_seed(123)
-        norm = RMSNorm(hidden_size, eps=eps).cuda()
-        norm.weight.data = norm.weight.data.to(dtype)
-
-        x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
-
-        out_fused = norm._forward_fused(x)
-        out_native = norm.forward_native(x)
-
-        torch.testing.assert_close(out_fused, out_native, atol=1e-2, rtol=1e-2)
-
-
-def test_rmsnorm_fused_native_parity_2d_input():
-    """Verify fused and native RMSNorm parity with 2D input tensors."""
-    from vllm_omni.diffusion.layers.norm import RMSNorm
-
-    hidden_size = 256
-    batch_size = 32
-    eps = 1e-6
-
-    for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-        torch.manual_seed(456)
-        norm = RMSNorm(hidden_size, eps=eps).cuda()
-        norm.weight.data = norm.weight.data.to(dtype)
-
-        x = torch.randn(batch_size, hidden_size, dtype=dtype, device="cuda")
-
-        out_fused = norm._forward_fused(x)
-        out_native = norm.forward_native(x)
-
-        torch.testing.assert_close(out_fused, out_native, atol=1e-3, rtol=1e-3)
-
-
-def test_rmsnorm_fused_native_parity_custom_weight():
-    """Verify fused and native RMSNorm parity with non-default weights."""
-    from vllm_omni.diffusion.layers.norm import RMSNorm
-
-    hidden_size = 64
-    eps = 1e-6
-
-    for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-        torch.manual_seed(789)
-        norm = RMSNorm(hidden_size, eps=eps).cuda()
-        norm.weight.data = torch.randn(hidden_size, dtype=dtype, device="cuda")
-
-        x = torch.randn(2, 4, hidden_size, dtype=dtype, device="cuda")
-
-        out_fused = norm._forward_fused(x)
-        out_native = norm.forward_native(x)
-
-        torch.testing.assert_close(out_fused, out_native, atol=1e-3, rtol=1e-3)

vllm_omni/diffusion/layers/norm.py

@@ -70,33 +70,17 @@ class RMSNorm(CustomOp):
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
 
-    def _forward_fused(self, x: torch.Tensor) -> torch.Tensor:
-        from vllm._custom_ops import rms_norm as fused_rms_norm
-
-        orig_shape = x.shape
-        hidden_size = orig_shape[-1]
-        x_2d = x.reshape(-1, hidden_size)
-        out = torch.empty_like(x_2d)
-        fused_rms_norm(out, x_2d, self.weight.data, self.variance_epsilon)
-        return out.reshape(orig_shape)
-
     def forward_cuda(
         self,
         x: torch.Tensor,
     ) -> torch.Tensor:
-        try:
+        return self.forward_native(x)
-            return self._forward_fused(x)
-        except Exception:
-            return self.forward_native(x)
 
     def forward_hip(
         self,
         x: torch.Tensor,
     ) -> torch.Tensor:
-        try:
+        return self.forward_native(x)
-            return self._forward_fused(x)
-        except Exception:
-            return self.forward_native(x)
 
     def forward_npu(
         self,

vllm_omni/diffusion/models/z_image/z_image_transformer.py

@@ -25,6 +25,7 @@ import torch.nn as nn
 from torch.nn.utils.rnn import pad_sequence
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
     QKVParallelLinear,
@@ -33,8 +34,6 @@ from vllm.model_executor.layers.linear import (
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
-from vllm_omni.diffusion.layers.norm import RMSNorm
-
 if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization.base_config import (
         QuantizationConfig,