[BugFix] Fix `VllmConfig()` construction on all platforms (#20695)

Signed-off-by: Nick Hill <nhill@redhat.com>

vllm/config.py

@@ -4722,7 +4722,6 @@ class VllmConfig:
         # calculate the default `batch_size_capture_list`
         if not envs.VLLM_USE_V1:
             batch_size_capture_list = []
-            max_batchsize_to_capture = 0
             if self.scheduler_config is not None and \
                 self.model_config is not None and \
                     not self.model_config.enforce_eager:

vllm/platforms/cpu.py

@@ -96,7 +96,8 @@ class CpuPlatform(Platform):
         from vllm.utils import GiB_bytes
         model_config = vllm_config.model_config
 
-        model_config.disable_cascade_attn = True
+        if model_config is not None:
+            model_config.disable_cascade_attn = True
 
         cache_config = vllm_config.cache_config
 
@@ -123,7 +124,7 @@ class CpuPlatform(Platform):
                 "CPU backend doesn't support fp8_e4m3 KV cache type, "
                 "cast to fp8_e5m2.")
 
-        if (cache_config.cache_dtype != "auto"
+        if (cache_config.cache_dtype != "auto" and model_config is not None
                 and model_config.dtype == torch.half):
             logger.warning("FP8 KV cache on the CPU backend only does not"
                            " support fp16 for now, cast to bf16.")
@@ -229,7 +230,7 @@ class CpuPlatform(Platform):
         os.environ["LOCAL_WORLD_SIZE"] = str(
             vllm_config.parallel_config.tensor_parallel_size)
 
-        if vllm_config.model_config and vllm_config.model_config.use_mla:
+        if model_config is not None and model_config.use_mla:
             logger.info(
                 "MLA is enabled on a non-GPU platform; forcing chunked "
                 "prefill and prefix caching to be disabled.")

vllm/platforms/cuda.py

@@ -166,17 +166,19 @@ class CudaPlatformBase(Platform):
                 logger.info(
                     "Forcing kv cache block size to 64 for FlashMLA backend.")
 
+        compilation_config = vllm_config.compilation_config
         if (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput"
                 and parallel_config.data_parallel_size > 1
-                and vllm_config.compilation_config.use_cudagraph):
+                and compilation_config.use_cudagraph):
             logger.info(
                 "Data Parallel: Forcing enforce eager to be True since DP "
                 "with DeepEP high-throughput kernels are not CUDA Graph "
                 "compatible. The DeepEP low-latency kernels are CUDA Graph "
                 "compatible. Set the all_to_all backend to deepep_low_latency "
                 "to use those kernels instead.")
-            vllm_config.compilation_config.use_cudagraph = False
-            vllm_config.model_config.enforce_eager = True
+            compilation_config.use_cudagraph = False
+            if model_config is not None:
+                model_config.enforce_eager = True
             # TODO (varun): Turning this ON gives incorrect results for the
             # Deepseek-V2-lite model.
             vllm_config.compilation_config.use_inductor = False

vllm/platforms/tpu.py

@@ -116,11 +116,13 @@ class TpuPlatform(Platform):
         assert vllm_config.speculative_config is None, \
             "TPU does not support speculative decoding"
 
-        if vllm_config.model_config.dtype in (torch.float16, torch.float32):
+        model_config = vllm_config.model_config
+        if model_config is not None and model_config.dtype in (torch.float16,
+                                                               torch.float32):
             logger.warning(
                 "The TPU backend currently does not support %s. "
-                "Using bfloat16 instead.", vllm_config.model_config.dtype)
-            vllm_config.model_config.dtype = torch.bfloat16
+                "Using bfloat16 instead.", model_config.dtype)
+            model_config.dtype = torch.bfloat16
 
         from vllm.v1.attention.backends.pallas import PallasAttentionBackend
         cache_config.block_size = PallasAttentionBackend.get_page_size(
@@ -146,7 +148,7 @@ class TpuPlatform(Platform):
             "Forcing --disable_chunked_mm_input.")
             scheduler_config.disable_chunked_mm_input = True
 
-        if vllm_config.model_config and vllm_config.model_config.use_mla:
+        if model_config and model_config.use_mla:
             logger.info(
                 "MLA is enabled on a non-GPU platform; forcing chunked "
                 "prefill and prefix caching to be disabled.")

vllm/platforms/xpu.py

@@ -85,14 +85,14 @@ class XPUPlatform(Platform):
     @classmethod
     def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         cache_config = vllm_config.cache_config
+        model_config = vllm_config.model_config
         # in V1(or with ipex chunked prefill) block_size is 64
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 64
 
         # FIXME: Temporarily forcing eager mode
         # remove after t.compile support stabilizes.
-
-        if (envs.VLLM_USE_V1 and vllm_config.model_config is not None
+        if (envs.VLLM_USE_V1 and model_config is not None
                 and not vllm_config.model_config.enforce_eager):
             from vllm.config import CompilationLevel
             vllm_config.compilation_config.level = CompilationLevel.NO_COMPILATION  # noqa: E501
@@ -100,8 +100,7 @@ class XPUPlatform(Platform):
         # Instances created using VllmConfig() typically have model_config as
         # None by default. The modification involves adding a check to prevent
         # potential null exceptions check and update model config.
-        if vllm_config.model_config is not None:
-            model_config = vllm_config.model_config
+        if model_config is not None:
             if model_config.dtype == torch.bfloat16:
                 bf16_supported = cls.device_support_bf16()
                 if not bf16_supported:
@@ -139,7 +138,7 @@ class XPUPlatform(Platform):
                 parallel_config.distributed_executor_backend)
             parallel_config.distributed_executor_backend = "ray"
 
-        if vllm_config.model_config and vllm_config.model_config.use_mla:
+        if model_config and model_config.use_mla:
             logger.info(
                 "MLA is enabled on a non-GPU platform; forcing chunked "
                 "prefill and prefix caching to be disabled.")