[DCU]Skip configurations that FlashAttention does not support

Signed-off-by: zhaochao <zhaochao1@sugon.com>

tests/pytorch/attention/test_attention.py

@@ -260,21 +260,22 @@ def test_dpa_checkpoint(dtype, model_configs, model):
 
 
 model_configs_mla = {
+    #TODO:FlashAttention on ROCm only support MLA with head_dim_qk = head_dim_v
     #    test:             b,  h, hg, dqk, sq, skv,   p,      mask,      bias   # attn , backend
-    "mla_1_0": ModelConfig(8, 128, 16, 64, head_dim_v=128),  # self , 0
-    "mla_1_1": ModelConfig(4, 128, 16, 64, max_seqlen_kv=256, head_dim_v=128),  # cross, 0
-    "mla_1_2": ModelConfig(4, 128, 16, 192, max_seqlen_kv=256, head_dim_v=128),  # cross, 0
-    "mla_2_0": ModelConfig(2, 2048, 24, 128, attn_mask_type="causal", head_dim_v=64),  # self , 1
-    "mla_2_1": ModelConfig(
-        1, 2048, 24, 128, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=64
-    ),  # cross, 1
-    "mla_2_2": ModelConfig(
-        1, 2048, 24, 192, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=128
-    ),  # cross, 1
-    "mla_3_0": ModelConfig(8, 1, 16, 128, max_seqlen_kv=2048, head_dim_v=64),  # inference
-    "mla_3_1": ModelConfig(8, 1, 16, 256, max_seqlen_kv=2048, head_dim_v=128),  # inference
-    "mla_3_2": ModelConfig(8, 1, 16, 192, max_seqlen_kv=2048, head_dim_v=128),  # inference
-    "mla_3_3": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=128),  # inference
+    # "mla_1_0": ModelConfig(8, 128, 16, 64, head_dim_v=128),  # self , 0
+    # "mla_1_1": ModelConfig(4, 128, 16, 64, max_seqlen_kv=256, head_dim_v=128),  # cross, 0
+    # "mla_1_2": ModelConfig(4, 128, 16, 192, max_seqlen_kv=256, head_dim_v=128),  # cross, 0
+    # "mla_2_0": ModelConfig(2, 2048, 24, 128, attn_mask_type="causal", head_dim_v=64),  # self , 1
+    # "mla_2_1": ModelConfig(
+    #     1, 2048, 24, 128, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=64
+    # ),  # cross, 1
+    # "mla_2_2": ModelConfig(
+    #     1, 2048, 24, 192, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=128
+    # ),  # cross, 1
+    # "mla_3_0": ModelConfig(8, 1, 16, 128, max_seqlen_kv=2048, head_dim_v=64),  # inference
+    # "mla_3_1": ModelConfig(8, 1, 16, 256, max_seqlen_kv=2048, head_dim_v=128),  # inference
+    # "mla_3_2": ModelConfig(8, 1, 16, 192, max_seqlen_kv=2048, head_dim_v=128),  # inference
+    # "mla_3_3": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=128),  # inference
     "mla_3_4": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=160),  # inference
 }