[CI Perf]Prune Tests in kernel/mamba (#26538)

Signed-off-by: Fardin Hoque <kfhfar@amazon.com>
Signed-off-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>

tests/kernels/mamba/test_causal_conv1d.py

@@ -183,7 +183,7 @@ def test_causal_conv1d_update(dim, width, seqlen, has_bias, silu_activation, ity
     assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("silu_activation", [False, True])
 @pytest.mark.parametrize("has_bias", [False, True])
 @pytest.mark.parametrize("seqlen", [1, 3])
@@ -265,7 +265,7 @@ def test_causal_conv1d_update_with_batch_gather(
 @pytest.mark.parametrize("silu_activation", [True])
 @pytest.mark.parametrize("has_bias", [True])
 @pytest.mark.parametrize("width", [4])
-@pytest.mark.parametrize("seqlen", [8, 30, 249, 2049, 4096])
+@pytest.mark.parametrize("seqlen", [8, 249, 4096])
 @pytest.mark.parametrize("dim", [64, 4096])
 @pytest.mark.parametrize("with_padding", [True, False])
 @pytest.mark.parametrize("batch", [4, 10])

tests/kernels/mamba/test_mamba_mixer2.py

@@ -25,7 +25,6 @@ from vllm.utils import update_environment_variables
         (64, 1),
         (64, 2),
         (64, 4),  # hidden_size be divisible by num_gpus
-        (100, 5),  # and n_groups must divide hidden_size
     ],
 )
 @pytest.mark.parametrize("dtype", [torch.float16])

tests/kernels/mamba/test_mamba_ssm.py

@@ -229,8 +229,8 @@ def selective_scan_opcheck_fn(
 
 
 @pytest.mark.parametrize("wtype", [torch.float32])
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("seqlen", [128, 256, 512, 1024, 2048, 4096])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("seqlen", [128, 1024, 4096])
 @pytest.mark.parametrize("has_delta_bias", [True])
 @pytest.mark.parametrize("delta_softplus", [True])
 @pytest.mark.parametrize("has_z", [True])
@@ -238,7 +238,7 @@ def selective_scan_opcheck_fn(
 @pytest.mark.parametrize("varBC_groups", [1, 2])
 @pytest.mark.parametrize("is_variable_C", [True])
 @pytest.mark.parametrize("is_variable_B", [True])
-@pytest.mark.parametrize("scan_chunks", [1, 2, 3])
+@pytest.mark.parametrize("scan_chunks", [1, 3])
 def test_selective_scan(
     is_variable_B,
     is_variable_C,
@@ -375,9 +375,9 @@ def test_selective_scan(
     )
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("has_z", [False, True])
-@pytest.mark.parametrize("dstate", [16, 32, 64])
+@pytest.mark.parametrize("dstate", [16, 64])
 @pytest.mark.parametrize("dim", [2048, 2048 + 16, 4096])
 def test_selective_state_update(dim, dstate, has_z, itype):
     device = "cuda"
@@ -413,7 +413,7 @@ def test_selective_state_update(dim, dstate, has_z, itype):
 
 @pytest.mark.parametrize("wtype", [torch.float32])
 @pytest.mark.parametrize("itype", [torch.float32])
-@pytest.mark.parametrize("seqlen", [1, 128, 129, 256, 512, 1024, 2048, 4096])
+@pytest.mark.parametrize("seqlen", [1, 256, 1024, 4096])
 @pytest.mark.parametrize("return_last_state", [True])
 @pytest.mark.parametrize("has_delta_bias", [True])
 @pytest.mark.parametrize("delta_softplus", [True])
@@ -589,9 +589,9 @@ def test_selective_scan_varlen(
     )
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("has_z", [True])
-@pytest.mark.parametrize("dstate", [16, 32, 64])
+@pytest.mark.parametrize("dstate", [16, 64])
 @pytest.mark.parametrize("dim", [2048, 2048 + 16, 4096])
 # tests correctness in case subset of the sequences are padded
 @pytest.mark.parametrize("with_padding", [True, False])
@@ -679,11 +679,11 @@ def test_selective_state_update_with_batch_indices(
     assert torch.allclose(out[:batch_size], out_ref, rtol=rtol, atol=atol)
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("has_z", [False, True])
 @pytest.mark.parametrize("tie_hdim", [False, True])
-@pytest.mark.parametrize("ngroups", [1, 2, 4])
-@pytest.mark.parametrize("dstate", [16, 32, 64])
+@pytest.mark.parametrize("ngroups", [1, 4])
+@pytest.mark.parametrize("dstate", [16, 64])
 @pytest.mark.parametrize("dim", [2048, 4096])
 def test_selective_state_update_with_heads_with_batch_indices(
     dim, dstate, ngroups, has_z, tie_hdim, itype

tests/kernels/mamba/test_mamba_ssm_ssd.py

@@ -188,9 +188,9 @@ def generate_continuous_batched_examples(
         )
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("n_heads", [3, 4, 11, 16, 32])
-@pytest.mark.parametrize("d_head", [5, 8, 19, 32, 128])
+@pytest.mark.parametrize("itype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("n_heads", [4, 16, 32])
+@pytest.mark.parametrize("d_head", [5, 8, 32, 128])
 @pytest.mark.parametrize("seq_len_chunk_size", [(112, 16), (128, 32)])
 def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, itype):
     # this tests the kernels on a single example (bs=1)
@@ -254,15 +254,14 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, it
     )
 
 
-@pytest.mark.parametrize("itype", [torch.float32, torch.float16])
-@pytest.mark.parametrize("n_heads", [4, 8, 13])
-@pytest.mark.parametrize("d_head", [5, 16, 21, 32])
+@pytest.mark.parametrize("itype", [torch.float32])
+@pytest.mark.parametrize("n_heads", [4, 8])
+@pytest.mark.parametrize("d_head", [5, 16, 32])
 @pytest.mark.parametrize(
     "seq_len_chunk_size_cases",
     [
         # small-ish chunk_size (8)
         (64, 8, 2, [(64, 32), (64, 32)]),
-        (64, 8, 2, [(32, 32), (32, 32), (32, 32)]),
         (64, 8, 2, [(8, 8), (8, 8), (8, 8)]),  # chunk size boundary
         (
             64,
@@ -270,16 +269,7 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, it
             2,
             [(4, 4), (4, 4), (4, 4), (4, 4)],
         ),  # chunk_size larger than cont batches
-        (
-            64,
-            8,
-            5,
-            [
-                (64, 32, 16, 8, 8),
-                (8, 16, 32, 16, 8),
-                (8, 8, 16, 32, 16),
-            ],
-        ),  # mode examples with varied lengths
+        (64, 8, 5, [(64, 32, 16, 8, 8)]),
         # large-ish chunk_size (256)
         (64, 256, 1, [(5,), (1,), (1,), (1,)]),  # irregular sizes with small sequences
         (
@@ -359,11 +349,7 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases,
 @pytest.mark.parametrize("chunk_size", [8, 256])
 @pytest.mark.parametrize(
     "seqlens",
-    [
-        (16, 2, 8, 13),
-        (270, 88, 212, 203),
-        (16, 20),
-    ],
+    [(16, 20), (270, 88, 212, 203)],
 )
 def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens):
     # This test verifies the correctness of the chunked prefill implementation