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

test/srt/cpu/test_activation.py

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+import itertools
+import unittest
+
+import sgl_kernel
+import torch
+import torch.nn.functional as F
+from utils import GeluAndMul, SiluAndMul, precision
+
+from sglang.test.test_utils import CustomTestCase
+
+torch.manual_seed(1234)
+
+
+class TestActivation(CustomTestCase):
+    M = [128, 129, 257]
+    N = [22016, 22018]
+    dtype = [torch.float16, torch.bfloat16]
+
+    def _silu_and_mul_test(self, m, n, dtype):
+        x = torch.randn([m, n], dtype=dtype)
+
+        out = torch.ops.sgl_kernel.silu_and_mul_cpu(x)
+        ref_out = SiluAndMul(x)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def _gelu_and_mul_test(self, m, n, dtype):
+        x = torch.randn([m, n], dtype=dtype)
+
+        out = torch.ops.sgl_kernel.gelu_and_mul_cpu(x)
+        ref_out = GeluAndMul(x, approximate="none")
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def _gelu_tanh_and_mul_test(self, m, n, dtype):
+        x = torch.randn([m, n], dtype=dtype)
+
+        out = torch.ops.sgl_kernel.gelu_tanh_and_mul_cpu(x)
+        ref_out = GeluAndMul(x, approximate="tanh")
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def test_activation(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._silu_and_mul_test(*params)
+                self._gelu_and_mul_test(*params)
+                self._gelu_tanh_and_mul_test(*params)
+
+
+if __name__ == "__main__":
+    unittest.main()