[Inductor][CPP] Enable vectorized fp8 E5M2 quant dequant (#153365)

**Summary**
This PR enables the vectorization codegen with Inductor CPP backend for `FP8_E5M2` `quant` from `float32` and `dequant` to `float32`.

**Test Plan**
```
python test/inductor/test_cpu_repro.py -k test_dequant_quant_lowering_fp8_e5m2
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153365
Approved by: https://github.com/jansel, https://github.com/jgong5
ghstack dependencies: #152417, #152418, #153364

aten/src/ATen/cpu/vec/vec512/vec512_convert.h

@@ -312,6 +312,28 @@ struct VecConvert<float, 1, Float8_e4m3fn, 1> {
   }
 };
 
+template <>
+struct VecConvert<Float8_e5m2, 1, float, 1> {
+  static inline VectorizedN<Float8_e5m2, 1> apply(
+      const VectorizedN<float, 1>& src_n) {
+    at::vec::Vectorized<float> src = src_n[0];
+    __m128i res128 = cvtfp32_fp8e5m2(src);
+    return at::vec::Vectorized<Float8_e5m2>(_mm512_castsi128_si512(res128));
+  }
+};
+
+template <>
+struct VecConvert<float, 1, Float8_e5m2, 1> {
+  static inline VectorizedN<float, 1> apply(
+      const VectorizedN<Float8_e5m2, 1>& src_n) {
+    // cvt first 16x8 bits from Float8_e5m2 to float
+    at::vec::Vectorized<Float8_e5m2> src = src_n[0];
+    __m512 result;
+    cvtfp8e5m2_fp32(_mm512_castsi512_si128(src), result);
+    return at::vec::Vectorized<float>(result);
+  }
+};
+
 #endif
 
 } // namespace CPU_CAPABILITY

test/inductor/test_cpu_repro.py

@@ -1418,10 +1418,15 @@ class CPUReproTests(TestCase):
         use_quant_list = [False, True]
         use_tensor_overload_list = [False, True]
 
-        assert dtype in [torch.uint8, torch.int8, torch.float8_e4m3fn]
+        assert dtype in [
+            torch.uint8,
+            torch.int8,
+            torch.float8_e4m3fn,
+            torch.float8_e5m2,
+        ]
         quant_min = 0 if dtype == torch.uint8 else -128
         quant_max = 255 if dtype == torch.uint8 else 127
-        if dtype == torch.float8_e4m3fn:
+        if dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
             quant_min = int(torch.finfo(dtype).min)
             quant_max = int(torch.finfo(dtype).max)
             use_tensor_overload_list = [
@@ -1486,6 +1491,10 @@ class CPUReproTests(TestCase):
     def test_dequant_quant_lowering_fp8_e4m3(self):
         self._test_dequant_quant_lowering_helper(torch.float8_e4m3fn)
 
+    @requires_vectorization
+    def test_dequant_quant_lowering_fp8_e5m2(self):
+        self._test_dequant_quant_lowering_helper(torch.float8_e5m2)
+
     def _test_dequant_maxpool2d_lowering_helper(self, dtype):
         def fn(x, scale, zero_point, quant_min, quant_max, dtype):
             x = torch.ops.quantized_decomposed.dequantize_per_tensor(

torch/_inductor/codegen/cpp.py

@@ -155,6 +155,7 @@ VECTORIZABLE_DTYPES: list[torch.dtype] = [
     torch.int32,
     torch.int64,
     torch.float8_e4m3fn,
+    torch.float8_e5m2,
 ]
 
 MASKED_VECTORIZABLE_DTYPES: list[torch.dtype] = [
@@ -1609,6 +1610,7 @@ class CppVecOverrides(CppOverrides):
             torch.int32,
             torch.int64,
             torch.float8_e4m3fn,
+            torch.float8_e5m2,
         ], f"{__name__} does not support {dtype}"
         assert isinstance(x, CppCSEVariable)
         src_dtype = x.dtype