Support DSV4 shared expert fusion for DeepEP and MegaMOE (#27349)

test/registered/unit/layers/quantization/test_fp8_utils_mxfp4.py

@@ -0,0 +1,50 @@
+import unittest
+
+import torch
+
+from sglang.srt.layers.quantization.fp8_utils import (
+    quantize_block_fp8_weight_to_mxfp4,
+)
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=4, suite="base-a-test-cpu")
+
+
+class TestFp8UtilsMxfp4(unittest.TestCase):
+    def test_quantize_block_fp8_weight_to_mxfp4_shapes_and_dtype(self):
+        fp8_weight = (
+            torch.linspace(-2.0, 2.0, 32 * 32, dtype=torch.float32)
+            .reshape(32, 32)
+            .to(torch.float8_e4m3fn)
+        )
+        fp8_scale = torch.ones(1, 1, dtype=torch.float8_e8m0fnu)
+
+        fp4_weight, fp4_scale = quantize_block_fp8_weight_to_mxfp4(
+            fp8_weight, fp8_scale, [128, 128]
+        )
+
+        self.assertEqual(fp4_weight.dtype, torch.int8)
+        self.assertEqual(fp4_weight.shape, torch.Size([32, 16]))
+        self.assertEqual(fp4_scale.dtype, torch.float8_e8m0fnu)
+        self.assertEqual(fp4_scale.shape, torch.Size([32, 1]))
+
+    def test_quantize_block_fp8_weight_to_mxfp4_grouped_weight(self):
+        fp8_weight = (
+            torch.linspace(-2.0, 2.0, 2 * 32 * 32, dtype=torch.float32)
+            .reshape(2, 32, 32)
+            .to(torch.float8_e4m3fn)
+        )
+        fp8_scale = torch.ones(2, 1, 1, dtype=torch.float8_e8m0fnu)
+
+        fp4_weight, fp4_scale = quantize_block_fp8_weight_to_mxfp4(
+            fp8_weight, fp8_scale, [128, 128]
+        )
+
+        self.assertEqual(fp4_weight.dtype, torch.int8)
+        self.assertEqual(fp4_weight.shape, torch.Size([2, 32, 16]))
+        self.assertEqual(fp4_scale.dtype, torch.float8_e8m0fnu)
+        self.assertEqual(fp4_scale.shape, torch.Size([2, 32, 1]))
+
+
+if __name__ == "__main__":
+    unittest.main()