fix(attention): read per-runner kv cache dtype off model_runner (#32251)

python/sglang/test/kits/attention_unittest/attention_methods/dense_attention.py

@@ -318,6 +318,7 @@ class MockModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.canary_manager = None
         self.page_size = case.page_size

python/sglang/test/kits/attention_unittest/attention_methods/dsa_attention.py

@@ -289,6 +289,7 @@ class DSAMockModelRunner(ModelRunner):
         # 656 bytes/token while the model still projects K/V in BF16;
         # `set_mla_kv_buffer` does the quantize on the way in.
         self.kv_cache_dtype = torch.float8_e4m3fn if fp8_kv_cache else dtype
+        self.kv_cache_dtype_str = "auto"
         # For TARGET_VERIFY / DRAFT_EXTEND, the DSA backend uses
         # `self.speculative_num_draft_tokens` to size `seqlens_expanded`
         # (`dsa_backend.py:482-486,510-515`). When zero, deep_gemm's

python/sglang/test/kits/attention_unittest/attention_methods/dsv4_attention.py

@@ -328,6 +328,7 @@ class MockDSV4ModelRunner:
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.canary_manager = None
         self.page_size = case.page_size

python/sglang/test/kits/attention_unittest/attention_methods/dual_chunk_attention.py

@@ -318,6 +318,7 @@ class DualChunkMockModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.canary_manager = None
         self.page_size = case.page_size

python/sglang/test/kits/attention_unittest/attention_methods/gdn_attention.py

@@ -212,6 +212,7 @@ class MockGDNModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.ps = ParallelState.trivial()
         self.canary_manager = None

python/sglang/test/kits/attention_unittest/attention_methods/kda_attention.py

@@ -218,6 +218,7 @@ class MockKDAModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.ps = ParallelState.trivial()
         self.canary_manager = None

python/sglang/test/kits/attention_unittest/attention_methods/lightning_attention.py

@@ -226,6 +226,7 @@ class MockLightningModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.ps = ParallelState.trivial()
         self.canary_manager = None

python/sglang/test/kits/attention_unittest/attention_methods/mamba2_attention.py

@@ -311,6 +311,7 @@ class MockMamba2ModelRunner(ModelRunner):
         self.device = device
         self.dtype = dtype
         self.kv_cache_dtype = dtype
+        self.kv_cache_dtype_str = "auto"
         self.gpu_id = 0
         self.ps = ParallelState.trivial()
         self.canary_manager = None

python/sglang/test/kits/attention_unittest/attention_methods/mla_attention.py

@@ -229,6 +229,7 @@ class MockMLAModelRunner(ModelRunner):
         # while the model still projects K/V in bf16; `set_mla_kv_buffer`
         # does the BF16->FP8 cast on the way in.
         self.kv_cache_dtype = torch.float8_e4m3fn if fp8_kv_cache else dtype
+        self.kv_cache_dtype_str = "fp8_e4m3" if fp8_kv_cache else "auto"
         self.gpu_id = 0
         self.canary_manager = None
         self.page_size = case.page_size