[CPU] Refactor CPU unquantized linear (#24150)

Signed-off-by: jiang1.li <jiang1.li@intel.com>

tests/kernels/test_onednn.py

@@ -111,6 +111,49 @@ def onednn_int8_gemm_test_helper(primitive_cache_size: int,
         torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
 
 
+def onednn_gemm_test_helper(primitive_cache_size: int,
+                            m: int,
+                            n: int,
+                            k: int,
+                            use_bias: bool,
+                            use_stride: bool,
+                            dtype: torch.dtype = torch.bfloat16,
+                            device: str = "cpu"):
+    if use_stride:
+        a = torch.rand((m, 2 * k), dtype=dtype, device=device) * 1.5
+        a = a[:, :k]
+    else:
+        a = torch.rand((m, k), dtype=dtype, device=device) * 1.5
+
+    b = torch.rand((n, k), dtype=dtype, device=device) * 1.5
+
+    if use_bias:
+        bias = torch.rand((n, ), device=device, dtype=dtype) * 5
+        bias_f32 = bias.float()
+    else:
+        bias = None
+        bias_f32 = None
+
+    handler = ops.create_onednn_mm(
+        b.t(),
+        primitive_cache_size,
+    )
+
+    out = ops.onednn_mm(handler, a, bias)
+    baseline = torch.nn.functional.linear(a.float(), b.float(),
+                                          bias_f32).to(dtype=a.dtype)
+
+    torch.testing.assert_close(out, baseline)
+
+    if use_bias:
+        # To test runtime bias setting
+        out = ops.onednn_mm(handler, a, None)
+        baseline = torch.nn.functional.linear(a.float(), b.float(),
+                                              None).to(dtype=a.dtype)
+
+        torch.testing.assert_close(out, baseline)
+
+
 @pytest.mark.parametrize("n,k", NK_FACTORS)
 @pytest.mark.parametrize("m_list", M_FACTORS)
 @pytest.mark.parametrize("per_tensor_a_scale", [True, False])
@@ -142,3 +185,30 @@ def test_onednn_int8_scaled_gemm(
             use_azp=use_azp,
             out_dtype=output_type,
         )
+
+
+@pytest.mark.parametrize("n,k", NK_FACTORS)
+@pytest.mark.parametrize("m_list", M_FACTORS)
+@pytest.mark.parametrize("use_bias", [True, False])
+@pytest.mark.parametrize("use_stride", [True, False])
+@pytest.mark.parametrize("dtype", DTYPE)
+@pytest.mark.parametrize("primitive_cache_size", CACHE_SIZES)
+def test_onednn_gemm(
+    n: int,
+    k: int,
+    m_list: tuple[int],
+    use_bias: bool,
+    use_stride: bool,
+    dtype: torch.dtype,
+    primitive_cache_size: int,
+):
+    for m in m_list:
+        onednn_gemm_test_helper(
+            primitive_cache_size=primitive_cache_size,
+            m=m,
+            n=n,
+            k=k,
+            use_bias=use_bias,
+            use_stride=use_stride,
+            dtype=dtype,
+        )