[UT][GroupedGemm] Add grouped gemm pytest mini scope (#47)

* add mini scope

Signed-off-by: Ma, Liangliang <[email protected]>

* fix grouped param in pytest confest

Signed-off-by: Ma, Liangliang <[email protected]>

---------

Signed-off-by: Ma, Liangliang <[email protected]>

Author: Liangliang-Ma

tests/conftest.py

@@ -20,7 +20,8 @@ def pytest_generate_tests(metafunc):
         return
 
     for param_name, values in profile.items():
-        if param_name in metafunc.fixturenames:
+        split_names = [name.strip() for name in param_name.split(",")]
+        if all(name in metafunc.fixturenames for name in split_names):
             new_markers = []
             for mark in metafunc.definition.own_markers:
                 if mark.name == "parametrize" and mark.args[0] != param_name:

tests/fused_moe/test_fused_moe.py

@@ -28,6 +28,16 @@ def random_partition(size_a: int, target: int):
     return result
 
 
+MINI_PYTEST_PARAMS = {
+    "default": {
+        "m,n,k": [(1, 256, 128), (4, 512, 256), (16, 512, 512)],
+        "e": [16, 8],
+        "topk": [1, 2],
+        "dtype": [torch.bfloat16]
+    }
+}
+
+
 @pytest.mark.parametrize("m,n,k", FUSED_MOE_MNK_FACTORS)
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
@@ -36,7 +46,6 @@ def test_grouped_gemm(m, n, k, e, topk, dtype):
     seed_everything(7)
     num_experts = e
     token_per_group = random_partition(e, m * topk)
-    print(token_per_group)
     # input
     input_A = torch.randn((sum(token_per_group), k),
                           dtype=dtype,