sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

test/srt/cpu/test_norm.py

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+import itertools
+import unittest
+from typing import Optional, Tuple, Union
+
+import sgl_kernel
+import torch
+from utils import make_non_contiguous, precision
+
+from sglang.test.test_utils import CustomTestCase
+
+torch.manual_seed(1234)
+
+
+class TestNorm(CustomTestCase):
+    M = [4096, 1024]
+    N = [4096, 4096 + 13]
+    dtype = [torch.float16, torch.bfloat16]
+
+    def _forward_native(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        variance_epsilon: float = 1e-6,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            x = x + residual.to(torch.float32)
+            residual = x.to(orig_dtype)
+
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + variance_epsilon)
+        x = x.to(orig_dtype) * weight
+        if residual is None:
+            return x
+        else:
+            return x, residual
+
+    def _norm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        out = torch.ops.sgl_kernel.rmsnorm_cpu(x, weight, variance_epsilon)
+        ref_out = self._forward_native(x, weight, variance_epsilon)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+        ref_x = x.clone()
+        residual = torch.randn([m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        torch.ops.sgl_kernel.fused_add_rmsnorm_cpu(
+            x, residual, weight, variance_epsilon
+        )
+
+        ref_x, ref_residual = self._forward_native(
+            ref_x, weight, variance_epsilon, ref_residual
+        )
+
+        torch.testing.assert_close(x, ref_x, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+    def _l2norm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        hidden_size = x.size(-1)
+        fake_ones_weight = torch.ones(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        out = torch.ops.sgl_kernel.l2norm_cpu(x, variance_epsilon)
+        ref_out = self._forward_native(x, fake_ones_weight, variance_epsilon)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def test_norm(self):
+        for params in itertools.product(self.M, self.N, self.dtype):
+            with self.subTest(m=params[0], n=params[1], dtype=params[2]):
+                self._norm_test(*params)
+                self._l2norm_test(*params)
+
+
+if __name__ == "__main__":
+    unittest.main()