fix: profiler deployment timeout handling for MoE models (#6086)

Wrap wait_for_deployment_ready() in try/except TimeoutError for both prefill and decode profiling sweeps
On timeout: log error, record via add_profiling_error(), clean up the timed-out deployment, and continue to the next parallelization mapping
Previously, a single deployment timeout would crash the entire profiler job

benchmarks/profiler/profile_sla.py

@@ -243,7 +243,8 @@ async def run_profile(args):
         for num_gpus in profile_num_gpus:
             logger.info(f"Profiling prefill with {num_gpus} GPUs...")
             candidate_mappings = get_candidate_parallel_mappings(
-                num_gpus, args.model_info, EngineType.PREFILL
+                num_gpus,
+                args.model_info,
             )
 
             for mapping in candidate_mappings:
@@ -296,9 +297,23 @@ async def run_profile(args):
                     deployment_clients.append(client)  # Track for cleanup
                     await client.create_deployment(prefill_config_fn)
                     logger.info("Waiting for deployment to be ready...")
+                    try:
                         await client.wait_for_deployment_ready(
                             timeout=getattr(args, "deployment_timeout", 1800)
                         )
+                    except TimeoutError:
+                        logger.error(
+                            f"Deployment for mapping {mapping.label()} with {num_gpus} GPUs "
+                            f"failed to become ready within timeout during prefill profiling, skipping"
+                        )
+                        add_profiling_error(
+                            f"Mapping {mapping.label()} with {num_gpus} GPUs timed out "
+                            f"during prefill profiling"
+                        )
+                        logger.info("Cleaning up timed-out deployment...")
+                        await client.delete_deployment()
+                        deployment_clients.remove(client)
+                        continue
                     logger.info("Deployment is ready")
 
                     logger.info("Getting deployment logs...")
@@ -350,7 +365,8 @@ async def run_profile(args):
         for num_gpus in profile_num_gpus:
             logger.info(f"Profiling decode with {num_gpus} GPUs...")
             candidate_mappings = get_candidate_parallel_mappings(
-                num_gpus, args.model_info, EngineType.DECODE
+                num_gpus,
+                args.model_info,
             )
 
             for mapping in candidate_mappings:
@@ -401,9 +417,23 @@ async def run_profile(args):
                     deployment_clients.append(client)  # Track for cleanup
                     await client.create_deployment(decode_config_fn)
                     logger.info("Waiting for deployment to be ready...")
+                    try:
                         await client.wait_for_deployment_ready(
                             timeout=getattr(args, "deployment_timeout", 1800)
                         )
+                    except TimeoutError:
+                        logger.error(
+                            f"Deployment for mapping {mapping.label()} with {num_gpus} GPUs "
+                            f"failed to become ready within timeout during decode profiling, skipping"
+                        )
+                        add_profiling_error(
+                            f"Mapping {mapping.label()} with {num_gpus} GPUs timed out "
+                            f"during decode profiling"
+                        )
+                        logger.info("Cleaning up timed-out deployment...")
+                        await client.delete_deployment()
+                        deployment_clients.remove(client)
+                        continue
                     logger.info("Deployment is ready")
 
                     logger.info("Getting deployment logs...")

benchmarks/profiler/utils/config_modifiers/parallelization_mapping.py

@@ -169,10 +169,11 @@ def _validate_intermediate_size(
 
 
 def get_candidate_parallel_mappings(
-    num_gpus: int, model_info: ModelInfo, phase: str
+    num_gpus: int,
+    model_info: ModelInfo,
 ) -> list[ParallelizationMapping]:
     """
-    Return a list of candidate parallelization mappings for a given GPU count and phase,
+    Return a list of candidate parallelization mappings for a given GPU count,
     verified against model properties.
 
     Verification rules: