[CPU] Support FP8 KV cache (#32733)

Co-authored-by: Zheng, Beilei <beilei.zheng@intel.com>
Co-authored-by: mingfeima <mingfei.ma@intel.com>

test/registered/unit/layers/quantization/test_fp4_kv_cache_quant_method.py

@@ -1,4 +1,4 @@
-"""Unit tests for FP4 KV cache quantization strategy pattern - no server, no model loading."""
+"""Unit tests for KV cache quantization strategies - no server, no model loading."""
 
 from sglang.test.ci.ci_register import register_cpu_ci
 
@@ -30,6 +30,7 @@ class TestKVCacheQuantRegistry(CustomTestCase):
 
         self.assertIn("nvfp4", KV_CACHE_QUANT_REGISTRY)
         self.assertIn("fp4_mx_block16", KV_CACHE_QUANT_REGISTRY)
+        self.assertIn("cpu_fp8_e4m3", KV_CACHE_QUANT_REGISTRY)
 
     def test_factory_nvfp4(self):
         from sglang.srt.layers.quantization.fp4_kv_cache_quant_method import (
@@ -102,6 +103,65 @@ class TestKVCacheQuantRegistry(CustomTestCase):
             get_kv_cache_quant_method("unknown_method")
 
 
+class TestCPUFP8KVCacheMethod(CustomTestCase):
+    def test_static_scale_quantize_and_store(self):
+        from sglang.srt.layers.quantization.fp4_kv_cache_quant_method import (
+            CPUFP8KVCacheMethod,
+        )
+
+        method = CPUFP8KVCacheMethod()
+        buffers = method.create_buffers(4, 2, 8, 1, "cpu")
+        loc = torch.tensor([1, 3])
+        cache_k = torch.randn(2, 2, 8, dtype=torch.bfloat16)
+        cache_v = torch.randn(2, 2, 8, dtype=torch.bfloat16)
+
+        method.quantize_and_store(
+            buffers["k_buffer"][0],
+            buffers["v_buffer"][0],
+            buffers["k_scale_buffer"],
+            buffers["v_scale_buffer"],
+            loc,
+            cache_k,
+            cache_v,
+            k_scale=0.5,
+            v_scale=0.25,
+        )
+
+        torch.testing.assert_close(
+            buffers["k_buffer"][0][loc].float(),
+            (cache_k / 0.5).to(torch.float8_e4m3fn).float(),
+        )
+        torch.testing.assert_close(
+            buffers["v_buffer"][0][loc].float(),
+            (cache_v / 0.25).to(torch.float8_e4m3fn).float(),
+        )
+        self.assertIsNone(buffers["k_scale_buffer"])
+        self.assertIsNone(buffers["v_scale_buffer"])
+        self.assertEqual(method.compute_cell_size(2, 8, 1, 4), 128)
+
+    def test_defaults_to_unit_scales(self):
+        from sglang.srt.layers.quantization.fp4_kv_cache_quant_method import (
+            CPUFP8KVCacheMethod,
+        )
+
+        method = CPUFP8KVCacheMethod()
+        buffers = method.create_buffers(1, 1, 8, 1, "cpu")
+        cache = torch.ones(1, 1, 8, dtype=torch.bfloat16)
+        method.quantize_and_store(
+            buffers["k_buffer"][0],
+            buffers["v_buffer"][0],
+            buffers["k_scale_buffer"],
+            buffers["v_scale_buffer"],
+            torch.tensor([0]),
+            cache,
+            cache,
+        )
+
+        expected = cache.to(torch.float8_e4m3fn)
+        torch.testing.assert_close(buffers["k_buffer"][0][0], expected[0])
+        torch.testing.assert_close(buffers["v_buffer"][0][0], expected[0])
+
+
 class TestNVFP4KVCacheMethod(CustomTestCase):
     """Test NVFP4KVCacheMethod buffer creation and properties."""