feat: add parallelization filters (#4144)

Signed-off-by: hongkuanz <hongkuanz@nvidia.com>
Signed-off-by: Hongkuan Zhou <tedzhouhk@gmail.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

benchmarks/profiler/deploy/profile_sla_moe_dgdr.yaml

@@ -15,10 +15,6 @@ spec:
   profilingConfig:
     profilerImage: "nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.6.1"
     config:
-      # Engine configuration
-      engine:
-        is_moe_model: true  # Enable MoE model support (uses TEP/DEP instead of TP)
-
       # Sweep/profiling configuration
       sweep:
         # Standard online profiling (not using AI Configurator)

benchmarks/profiler/profile_endpoint.py

@@ -5,6 +5,7 @@ import argparse
 import logging
 import os
 
+from benchmarks.profiler.utils.defaults import EngineType
 from benchmarks.profiler.utils.profile_decode import profile_decode
 from benchmarks.profiler.utils.profile_prefill import profile_prefill
 
@@ -91,7 +92,11 @@ if __name__ == "__main__":
     os.makedirs(args.work_dir, exist_ok=True)
     if args.tokenizer_path == "":
         args.tokenizer_path = args.model_name
-    if args.mode == "prefill":
+
+    # Convert string mode to EngineType
+    mode = EngineType(args.mode)
+
+    if mode == EngineType.PREFILL:
         profile_prefill(
             args.work_dir,
             args.model_name,
@@ -101,7 +106,7 @@ if __name__ == "__main__":
             args.max_context_length,
             args.interpolation_granularity,
         )
-    elif args.mode == "decode":
+    elif mode == EngineType.DECODE:
         assert args.max_kv_tokens > 0, "max_kv_tokens must be provided for decode"
         profile_decode(
             args.work_dir,
@@ -115,4 +120,4 @@ if __name__ == "__main__":
             args.attention_dp_size,
         )
     else:
-        raise ValueError(f"Invalid mode: {args.mode}")
+        raise ValueError(f"Invalid mode: {mode}")

benchmarks/profiler/utils/config.py

@@ -17,7 +17,7 @@ import json
 import logging
 import math
 import shlex
-from typing import Literal, Optional, Protocol
+from typing import Optional
 
 from pydantic import BaseModel
 
@@ -378,69 +378,3 @@ def update_image(config: dict, image: str) -> dict:
             logger.debug(f"Updated image for {service_name} to {image}")
 
     return cfg.model_dump()
-
-
-class ConfigModifierProtocol(Protocol):
-    @classmethod
-    def convert_config(
-        cls,
-        config: dict,
-        target: Literal["prefill", "decode"],
-        is_moe_model: bool = False,
-    ) -> dict:
-        ...
-
-    @classmethod
-    def set_config_tp_size(
-        cls,
-        config: dict,
-        tp_size: int,
-        component_type: SubComponentType = SubComponentType.DECODE,
-    ) -> dict:
-        ...
-
-    @classmethod
-    def set_config_tep_size(
-        cls,
-        config: dict,
-        tep_size: int,
-        num_gpus_per_node: int,
-        component_type: SubComponentType = SubComponentType.DECODE,
-    ) -> dict:
-        ...
-
-    @classmethod
-    def set_config_dep_size(
-        cls,
-        config: dict,
-        dep_size: int,
-        num_gpus_per_node: int,
-        component_type: SubComponentType = SubComponentType.DECODE,
-    ) -> dict:
-        ...
-
-    @classmethod
-    def get_model_name(cls, config: dict) -> str:
-        ...
-
-    @classmethod
-    def get_port(cls, config: dict) -> int:
-        ...
-
-    @classmethod
-    def get_kv_cache_size_from_dynamo_log(
-        cls, dynamo_log_fn: str, attention_dp_size: int = 1
-    ) -> int:
-        ...
-
-    @classmethod
-    def load_default_config(cls) -> dict:
-        ...
-
-    @classmethod
-    def update_model(cls, config: dict, model_name: str) -> dict:
-        ...
-
-    @classmethod
-    def update_image(cls, config: dict, image: str) -> dict:
-        ...

benchmarks/profiler/utils/config_modifiers/__init__.py

@@ -16,7 +16,9 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from benchmarks.profiler.utils.config import ConfigModifierProtocol
+    from benchmarks.profiler.utils.config_modifiers.protocol import (
+        ConfigModifierProtocol,
+    )
 
 from benchmarks.profiler.utils.config_modifiers.sglang import SGLangConfigModifier
 from benchmarks.profiler.utils.config_modifiers.trtllm import TrtllmConfigModifier

benchmarks/profiler/utils/config_modifiers/parallelization_mapping.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import copy
+import logging
+from dataclasses import dataclass
+from enum import Enum
+
+from benchmarks.profiler.utils.defaults import EngineType
+from benchmarks.profiler.utils.model_info import ModelInfo
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+console_handler = logging.StreamHandler()
+console_handler.setLevel(logging.INFO)
+formatter = logging.Formatter(
+    "%(asctime)s - %(name)s - %(levelname)s - %(message)s", "%Y-%m-%d %H:%M:%S"
+)
+console_handler.setFormatter(formatter)
+logger.addHandler(console_handler)
+
+
+class ParallelizationStrategy(Enum):
+    """Enum for parallelization strategy types."""
+
+    TP = "TP"
+    TEP = "TEP"
+    DEP = "DEP"
+
+
+@dataclass(frozen=True)
+class ParallelizationMapping:
+    """
+    Represents parallelization mapping of configs
+    """
+
+    tp: int | None = None
+    tep: int | None = None
+    dep: int | None = None
+
+    def label(self) -> str:
+        if self.tp is not None:
+            return f"{ParallelizationStrategy.TP.value}={self.tp}"
+        if self.tep is not None:
+            return f"{ParallelizationStrategy.TEP.value}={self.tep}"
+        if self.dep is not None:
+            return f"{ParallelizationStrategy.DEP.value}={self.dep}"
+        return "default"
+
+    def get_tp_size(self) -> int:
+        """
+        Get the effective TP size for KV heads splitting.
+        Both TP and TEP split KV heads, DEP doesn't (returns 1).
+        """
+        if self.tp is not None:
+            return self.tp
+        if self.tep is not None:
+            return self.tep
+        return 1  # DEP has TP split of 1
+
+    def get_expert_split(self) -> int:
+        """
+        Get the effective expert split size.
+        Both TEP and DEP split experts, TP doesn't (returns 1).
+        """
+        if self.tep is not None:
+            return self.tep
+        if self.dep is not None:
+            return self.dep
+        return 1  # TP has expert split of 1
+
+    def get_attn_dp_size(self, num_gpus: int) -> int:
+        """
+        Get the attention data parallelism size.
+        DEP uses data parallelism for attention (returns num_gpus).
+        TP and TEP don't use data parallelism for attention (returns 1).
+
+        Args:
+            num_gpus: Total number of GPUs being used
+
+        Returns:
+            The attention data parallelism size
+        """
+        if self.dep is not None:
+            return num_gpus
+        return 1  # TP and TEP have attention DP size of 1
+
+
+def _check_divisibility(
+    value: int | None,
+    divisor: int,
+    value_name: str,
+    divisor_name: str,
+    mapping_label: str,
+) -> bool:
+    """
+    Check if value is divisible by divisor.
+    Returns True if valid (or value is None), False if invalid.
+
+    Args:
+        value: The value to check (e.g., num_kv_heads, num_experts)
+        divisor: The divisor to check against
+        value_name: Name of the value for error messages
+        divisor_name: Name of the divisor for error messages (e.g., "tp_size", "expert_split")
+        mapping_label: Label of the mapping for error messages
+    """
+    if value is None:
+        logger.warning(
+            f"Skipping {value_name} divisibility check for {mapping_label}: {value_name} is unknown"
+        )
+        return True
+
+    if divisor > 1 and int(value) % divisor != 0:
+        logger.warning(
+            f"Invalid mapping {mapping_label}: {value_name}={value} not divisible by {divisor_name}={divisor}"
+        )
+        return False
+
+    return True
+
+
+def _validate_intermediate_size(
+    mapping: ParallelizationMapping,
+    intermediate_size: int | None,
+    quant_block: int | None,
+) -> bool:
+    """
+    Validate intermediate size and quantization block for TP and TEP strategies.
+    Checks:
+    - intermediate_size % tp_size == 0
+    - (intermediate_size // tp_size) divides quant_block (if quant_block is known)
+    """
+    tp_size = mapping.get_tp_size()
+
+    # Check basic divisibility
+    if not _check_divisibility(
+        intermediate_size, tp_size, "intermediate_size", "tp_size", mapping.label()
+    ):
+        return False
+
+    # Additional check for quantization block constraint
+    if intermediate_size is not None and quant_block is not None and tp_size > 1:
+        per_shard = int(intermediate_size) // tp_size
+        if not _check_divisibility(
+            per_shard, quant_block, "per_shard", "quant_block", mapping.label()
+        ):
+            return False
+
+    return True
+
+
+def get_candidate_parallel_mappings(
+    num_gpus: int, model_info: ModelInfo, phase: str
+) -> list[ParallelizationMapping]:
+    """
+    Return a list of candidate parallelization mappings for a given GPU count and phase,
+    verified against model properties.
+
+    Verification rules:
+    - TP and TEP must divide num_kv_heads (if available)
+    - TEP and DEP must divide num_experts (if available)
+    """
+    is_moe = bool(model_info.is_moe)
+    num_kv_heads = model_info.num_kv_heads
+    num_experts = model_info.num_experts
+    intermediate_size = model_info.intermediate_size
+    quant_block = model_info.quantization_block_size
+
+    candidates: list[ParallelizationMapping] = []
+    if is_moe:
+        if phase == EngineType.PREFILL:
+            candidates = [ParallelizationMapping(tep=num_gpus)]
+        elif phase == EngineType.DECODE:
+            candidates = [
+                ParallelizationMapping(dep=num_gpus),
+                ParallelizationMapping(tep=num_gpus),
+            ]
+    else:
+        candidates = [ParallelizationMapping(tp=num_gpus)]
+
+    # Verify candidates against model constraints
+    verified: list[ParallelizationMapping] = []
+    for m in candidates:
+        # Check KV heads divisibility
+        if not _check_divisibility(
+            num_kv_heads, m.get_tp_size(), "num_kv_heads", "tp_size", m.label()
+        ):
+            continue
+
+        # Check experts divisibility
+        if not _check_divisibility(
+            num_experts, m.get_expert_split(), "num_experts", "expert_split", m.label()
+        ):
+            continue
+
+        # Check intermediate size and quantization block
+        if not _validate_intermediate_size(m, intermediate_size, quant_block):
+            continue
+
+        verified.append(m)
+
+    return verified
+
+
+def apply_parallel_mapping_to_config(
+    base_config: dict,
+    mapping: ParallelizationMapping,
+    phase: str,
+    config_modifier,
+    num_gpus_per_node: int | None,
+) -> dict:
+    cfg = copy.deepcopy(base_config)
+    if mapping.tp is not None:
+        cfg = config_modifier.set_config_tp_size(cfg, mapping.tp)
+    elif phase == EngineType.PREFILL and mapping.tep is not None:
+        cfg = config_modifier.set_config_tep_size(cfg, mapping.tep, num_gpus_per_node)
+    elif phase == EngineType.DECODE and mapping.dep is not None:
+        cfg = config_modifier.set_config_dep_size(cfg, mapping.dep, num_gpus_per_node)
+    else:
+        pass
+    return cfg

benchmarks/profiler/utils/config_modifiers/protocol.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Protocol
+
+from benchmarks.profiler.utils.defaults import EngineType
+from dynamo.planner.defaults import SubComponentType
+
+
+class ConfigModifierProtocol(Protocol):
+    @classmethod
+    def convert_config(
+        cls,
+        config: dict,
+        target: EngineType,
+        is_moe_model: bool = False,
+    ) -> dict:
+        ...
+
+    @classmethod
+    def set_config_tp_size(
+        cls,
+        config: dict,
+        tp_size: int,
+        component_type: SubComponentType = SubComponentType.DECODE,
+    ) -> dict:
+        ...
+
+    @classmethod
+    def set_config_tep_size(
+        cls,
+        config: dict,
+        tep_size: int,
+        num_gpus_per_node: int,
+        component_type: SubComponentType = SubComponentType.DECODE,
+    ) -> dict:
+        ...
+
+    @classmethod
+    def set_config_dep_size(
+        cls,
+        config: dict,
+        dep_size: int,
+        num_gpus_per_node: int,
+        component_type: SubComponentType = SubComponentType.DECODE,
+    ) -> dict:
+        ...
+
+    @classmethod
+    def get_model_name(cls, config: dict) -> str:
+        ...
+
+    @classmethod
+    def get_port(cls, config: dict) -> int:
+        ...
+
+    @classmethod
+    def get_kv_cache_size_from_dynamo_log(
+        cls, dynamo_log_fn: str, attention_dp_size: int = 1
+    ) -> int:
+        ...
+
+    @classmethod
+    def load_default_config(cls) -> dict:
+        ...
+
+    @classmethod
+    def update_model(cls, config: dict, model_name: str) -> dict:
+        ...
+
+    @classmethod
+    def update_image(cls, config: dict, image: str) -> dict:
+        ...

benchmarks/profiler/utils/config_modifiers/sglang.py

@@ -3,7 +3,6 @@
 
 import logging
 import re
-from typing import Literal
 
 import yaml
 
@@ -22,6 +21,7 @@ from benchmarks.profiler.utils.config import (
 from benchmarks.profiler.utils.defaults import (
     DEFAULT_MODEL_NAME,
     DYNAMO_RUN_DEFAULT_PORT,
+    EngineType,
 )
 from dynamo.planner.defaults import SubComponentType
 
@@ -82,7 +82,7 @@ class SGLangConfigModifier:
     def convert_config(
         cls,
         config: dict,
-        target: Literal["prefill", "decode"],
+        target: EngineType,
         is_moe_model: bool = False,
     ) -> dict:
         cfg = Config.model_validate(config)
@@ -94,7 +94,7 @@ class SGLangConfigModifier:
         if "Planner" in cfg.spec.services:
             del cfg.spec.services["Planner"]
 
-        if target == "prefill":
+        if target == EngineType.PREFILL:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "sglang", SubComponentType.PREFILL
@@ -131,7 +131,7 @@ class SGLangConfigModifier:
 
             worker_service.extraPodSpec.mainContainer.args = args
 
-        elif target == "decode":
+        elif target == EngineType.DECODE:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "sglang", SubComponentType.PREFILL
@@ -292,6 +292,12 @@ class SGLangConfigModifier:
             return DEFAULT_MODEL_NAME
 
         args = break_arguments(args)
+        # Check for --model-path first (primary argument for SGLang)
+        for i, arg in enumerate(args):
+            if arg == "--model-path" and i + 1 < len(args):
+                return args[i + 1]
+
+        # Fall back to --served-model-name if --model-path not found
         for i, arg in enumerate(args):
             if arg == "--served-model-name" and i + 1 < len(args):
                 return args[i + 1]

benchmarks/profiler/utils/config_modifiers/trtllm.py

@@ -4,7 +4,6 @@
 import json
 import logging
 import re
-from typing import Literal
 
 import yaml
 
@@ -24,6 +23,7 @@ from benchmarks.profiler.utils.config import (
 from benchmarks.profiler.utils.defaults import (
     DEFAULT_MODEL_NAME,
     DYNAMO_RUN_DEFAULT_PORT,
+    EngineType,
 )
 from dynamo.planner.defaults import SubComponentType
 
@@ -84,7 +84,7 @@ class TrtllmConfigModifier:
     def convert_config(
         cls,
         config: dict,
-        target: Literal["prefill", "decode"],
+        target: EngineType,
         is_moe_model: bool = False,
     ) -> dict:
         if is_moe_model:
@@ -101,7 +101,7 @@ class TrtllmConfigModifier:
         if "Planner" in cfg.spec.services:
             del cfg.spec.services["Planner"]
 
-        if target == "prefill":
+        if target == EngineType.PREFILL:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "trtllm", SubComponentType.PREFILL
@@ -157,7 +157,7 @@ class TrtllmConfigModifier:
 
             worker_service.extraPodSpec.mainContainer.args = args
 
-        elif target == "decode":
+        elif target == EngineType.DECODE:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "trtllm", SubComponentType.PREFILL

benchmarks/profiler/utils/config_modifiers/vllm.py

@@ -2,7 +2,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging
-from typing import Literal
 
 import yaml
 
@@ -20,6 +19,7 @@ from benchmarks.profiler.utils.config import (
 from benchmarks.profiler.utils.defaults import (
     DEFAULT_MODEL_NAME,
     DYNAMO_RUN_DEFAULT_PORT,
+    EngineType,
 )
 from dynamo.planner.defaults import SubComponentType
 
@@ -79,7 +79,7 @@ class VllmV1ConfigModifier:
     def convert_config(
         cls,
         config: dict,
-        target: Literal["prefill", "decode"],
+        target: EngineType,
         is_moe_model: bool = False,
     ) -> dict:
         if is_moe_model:
@@ -96,7 +96,7 @@ class VllmV1ConfigModifier:
         if "Planner" in cfg.spec.services:
             del cfg.spec.services["Planner"]
 
-        if target == "prefill":
+        if target == EngineType.PREFILL:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "vllm", SubComponentType.PREFILL
@@ -133,7 +133,7 @@ class VllmV1ConfigModifier:
 
             worker_service.extraPodSpec.mainContainer.args = args
 
-        elif target == "decode":
+        elif target == EngineType.DECODE:
             # Get service names by inferring from subComponentType first
             prefill_service_name = get_service_name_by_type(
                 cfg, "vllm", SubComponentType.PREFILL

benchmarks/profiler/utils/defaults.py

@@ -13,9 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from enum import Enum
+
 DEFAULT_MODEL_NAME = "Qwen/Qwen3-0.6B"
 DYNAMO_RUN_DEFAULT_PORT = 8000
 
 # set a decode maximum concurrency due to limits of profiling tools
 # for MoE models with attn-dp, we might hit this limit
 DECODE_MAX_CONCURRENCY = 2000
+
+
+class EngineType(str, Enum):
+    PREFILL = "prefill"
+    DECODE = "decode"

benchmarks/profiler/utils/model_info.py

@@ -5,6 +5,7 @@ from pathlib import Path
 from typing import Optional, Union
 
 from huggingface_hub import model_info
+from pydantic import BaseModel
 from transformers import AutoConfig
 
 DTYPE_BYTES_MAP = {
@@ -103,10 +104,21 @@ def get_model_weight_size(
         return get_model_weight_size_from_hub(str(model_name_or_path))
 
 
+class ModelInfo(BaseModel):
+    model_size: float
+    architecture: str
+    is_moe: bool
+    max_context_length: Optional[int] = None
+    num_experts: Optional[int] = None
+    intermediate_size: Optional[int] = None
+    num_kv_heads: Optional[int] = None
+    quantization_block_size: Optional[int] = None
+
+
 def get_model_info(
     model_name_or_path: Union[str, Path],
     trust_remote_code: bool = False,
-) -> dict:
+) -> ModelInfo:
     model_size = get_model_weight_size(model_name_or_path)
 
     config = AutoConfig.from_pretrained(
@@ -114,10 +126,11 @@ def get_model_info(
         trust_remote_code=trust_remote_code,
     )
 
-    if config.architectures[0] in MOE_ARCHITECTURES:
-        config.is_moe = True
+    architecture = config.architectures[0]
+    if architecture in MOE_ARCHITECTURES:
+        is_moe = True
     else:
-        config.is_moe = False
+        is_moe = False
 
     # Detect max context length from config
     # Different models use different attribute names for max context length
@@ -132,7 +145,7 @@ def get_model_info(
     # Detect number of experts for MoE models
     # Different models use different attribute names
     num_experts = None
-    if config.is_moe:
+    if is_moe:
         expert_attrs = [
             "n_routed_experts",  # DeepSeek V3/R1
             "num_local_experts",  # Mixtral, Qwen
@@ -145,12 +158,78 @@ def get_model_info(
                     num_experts = value
                     break
 
-    return {
-        "model_size": model_size,
-        "is_moe": config.is_moe,
-        "max_context_length": max_context_length,
-        "num_experts": num_experts,
-    }
+    # Detect intermediate size (FFN hidden dimension)
+    intermediate_size = None
+    intermediate_attrs = [
+        "intermediate_size",  # Most common (BERT, LLaMA, etc.)
+        "ffn_dim",  # Some transformer models
+    ]
+    for attr in intermediate_attrs:
+        if hasattr(config, attr):
+            value = getattr(config, attr)
+            if value is not None:
+                intermediate_size = value
+                break
+
+    # Detect number of key-value heads (for GQA)
+    num_kv_heads = None
+    kv_head_attrs = [
+        "num_key_value_heads",  # LLaMA 2/3, Mistral, etc.
+        "num_kv_heads",  # Alternative name
+    ]
+    for attr in kv_head_attrs:
+        if hasattr(config, attr):
+            value = getattr(config, attr)
+            if value is not None:
+                num_kv_heads = value
+                break
+    # If not found, check if it equals num_attention_heads (standard MHA)
+    if num_kv_heads is None and hasattr(config, "num_attention_heads"):
+        num_kv_heads = config.num_attention_heads
+
+    # Detect quantization block size
+    quantization_block_size = None
+    if hasattr(config, "quantization_config"):
+        quant_config = config.quantization_config
+        if isinstance(quant_config, dict):
+            # Check for common quantization block size attributes
+            quantization_block_size = (
+                quant_config.get("weight_block_size")
+                or quant_config.get("block_size")
+                or quant_config.get("group_size")
+                or quant_config.get("q_group_size")
+            )
+        elif quant_config is not None:
+            # Handle object-based quantization config
+            for attr in [
+                "weight_block_size",
+                "block_size",
+                "group_size",
+                "q_group_size",
+            ]:
+                if hasattr(quant_config, attr):
+                    value = getattr(quant_config, attr)
+                    if value is not None:
+                        quantization_block_size = value
+                        break
+
+        # Handle case where block size is a list (e.g., [128, 128] for [input, output] block sizes)
+        if (
+            isinstance(quantization_block_size, list)
+            and len(quantization_block_size) > 0
+        ):
+            quantization_block_size = max(quantization_block_size)
+
+    return ModelInfo(
+        model_size=model_size,
+        architecture=architecture,
+        is_moe=is_moe,
+        max_context_length=max_context_length,
+        num_experts=num_experts,
+        intermediate_size=intermediate_size,
+        num_kv_heads=num_kv_heads,
+        quantization_block_size=quantization_block_size,
+    )
 
 
 if __name__ == "__main__":

benchmarks/profiler/utils/plot.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import logging
+from collections import defaultdict
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -33,22 +34,27 @@ console_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 
 
-def plot_prefill_performance(prefill_results, target_ttft, output_dir):
+def plot_prefill_performance(prefill_data, target_ttft, output_dir):
     """
-    Plot prefill performance as a 2D scatter plot with GPU count annotations.
+    Plot prefill performance as a 2D scatter plot with GPU count and mapping annotations.
 
     Args:
-        prefill_results: tuple of (prefill_num_gpu, prefill_ttft, prefill_thpt_per_gpu)
+        prefill_data: PrefillProfileData instance containing profiling results
         target_ttft: target TTFT value for the vertical line
         output_dir: directory to save the plot
     """
-    prefill_num_gpu, prefill_ttft, prefill_thpt_per_gpu = prefill_results
     plt.figure(figsize=(10, 6))
-    plt.scatter(prefill_ttft, prefill_thpt_per_gpu, s=100)
-    for i, num_gpu in enumerate(prefill_num_gpu):
+    plt.scatter(prefill_data.ttft, prefill_data.thpt_per_gpu, s=100)
+    for i, num_gpu in enumerate(prefill_data.num_gpus):
+        label_suffix = (
+            f" [{prefill_data.parallel_mapping_labels[i]}]"
+            if prefill_data.parallel_mapping_labels
+            and i < len(prefill_data.parallel_mapping_labels)
+            else ""
+        )
         plt.annotate(
-            f"{num_gpu} GPU(s)",
-            (prefill_ttft[i], prefill_thpt_per_gpu[i]),
+            f"{num_gpu} GPU(s){label_suffix}",
+            (prefill_data.ttft[i], prefill_data.thpt_per_gpu[i]),
             xytext=(10, 0),
             textcoords="offset points",
             fontsize=10,
@@ -70,19 +76,46 @@ def plot_prefill_performance(prefill_results, target_ttft, output_dir):
     plt.close()
 
 
-def plot_decode_performance(decode_results, target_itl, output_dir):
+def plot_decode_performance(decode_data, target_itl, output_dir):
     """
     Plot decode performance with multiple GPU count lines.
 
     Args:
-        decode_results: list of tuples (num_gpu, itl_list, thpt_per_gpu_list)
+        decode_data: DecodeProfileData instance containing profiling results
         target_itl: target ITL value for the vertical line
         output_dir: directory to save the plot
     """
     plt.figure(figsize=(10, 6))
 
-    for num_gpu, itl_list, thpt_per_gpu_list in decode_results:
-        plt.plot(itl_list, thpt_per_gpu_list, label=f"{num_gpu} GPU(s)")
+    # Group data by (num_gpus, parallel_mapping_label) combination
+    grouped_data: defaultdict[tuple[int, str], dict[str, list[float]]] = defaultdict(
+        lambda: {"itl": [], "thpt": []}
+    )
+
+    for i in range(len(decode_data.num_gpus)):
+        num_gpu = decode_data.num_gpus[i]
+        label = (
+            decode_data.parallel_mapping_labels[i]
+            if decode_data.parallel_mapping_labels
+            else ""
+        )
+        key = (num_gpu, label)
+        grouped_data[key]["itl"].append(decode_data.itl[i])
+        grouped_data[key]["thpt"].append(decode_data.thpt_per_gpu[i])
+
+    # Plot each group as a line
+    for (num_gpu, parallel_mapping_label), data in sorted(grouped_data.items()):
+        if parallel_mapping_label:
+            label = f"{num_gpu} GPU(s) [{parallel_mapping_label}]"
+        else:
+            label = f"{num_gpu} GPU(s)"
+
+        # Sort by ITL for proper line plotting
+        sorted_pairs = sorted(zip(data["itl"], data["thpt"]))
+        itl_sorted = [x[0] for x in sorted_pairs]
+        thpt_sorted = [x[1] for x in sorted_pairs]
+
+        plt.plot(itl_sorted, thpt_sorted, label=label, marker="o")
 
     plt.axvline(
         x=target_itl, color="r", linestyle="--", label=f"Target ITL: {target_itl} ms"
@@ -253,18 +286,24 @@ def plot_decode_3d_surface(
     plt.close()
 
 
-def plot_pd_joint_results(isl, osl, prefill_results, decode_results, output_dir):
+def plot_pd_joint_results(isl, osl, prefill_data, decode_data, output_dir):
+    """
+    Plot joint prefill and decode results showing cost per 1000 requests under different SLA.
+
+    Args:
+        isl: input sequence length
+        osl: output sequence length
+        prefill_data: PrefillProfileData instance containing profiling results
+        decode_data: DecodeProfileData instance containing profiling results
+        output_dir: directory to save the plot
+    """
     GPU_COST_PER_HOUR = 3.0  # $3/hour
 
     # compute pareto front for prefill
-    p_ttft, p_thpt = compute_pareto(prefill_results[1], prefill_results[2])
+    p_ttft, p_thpt = compute_pareto(prefill_data.ttft, prefill_data.thpt_per_gpu)
 
     # compute pareto front for decode
-    _d_itl, _d_thpt = [], []
-    for _d_result in decode_results:
-        _d_itl.extend(_d_result[1])
-        _d_thpt.extend(_d_result[2])
-    d_itl, d_thpt = compute_pareto(_d_itl, _d_thpt)
+    d_itl, d_thpt = compute_pareto(decode_data.itl, decode_data.thpt_per_gpu)
 
     # convert to cost per thousand requests
     p_ttft = np.array(p_ttft)

benchmarks/profiler/utils/profile_cache.py

-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import glob
-import json
-import logging
-import os
-import re
-from typing import List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-
-def check_prefill_results_exist(output_dir: str, tp_size: int, isl: int) -> bool:
-    """Check if prefill results already exist for a given TP size."""
-    work_dir = f"{output_dir}/prefill_tp{tp_size}"
-    result_file = f"{work_dir}/aiperf_isl{isl}/*/profile_export_aiperf.json"
-
-    # Check if the work directory exists
-    if not os.path.exists(work_dir):
-        return False
-
-    # Look for the aiperf result file
-    result_files = glob.glob(result_file)
-    if not result_files:
-        return False
-
-    # Verify the result file has valid data
-    try:
-        with open(result_files[0], "r") as f:
-            data = json.load(f)
-            # Check if it has the required metrics
-            if "time_to_first_token" in data and "avg" in data["time_to_first_token"]:
-                logger.info(
-                    f"Found existing prefill results for TP{tp_size} at {result_files[0]}"
-                )
-                return True
-    except (json.JSONDecodeError, KeyError, FileNotFoundError):
-        pass
-
-    return False
-
-
-def check_decode_results_exist(
-    output_dir: str, tp_size: int, isl: int, osl: int
-) -> bool:
-    """Check if decode results already exist for a given TP size."""
-    work_dir = f"{output_dir}/decode_tp{tp_size}"
-
-    # Check if the work directory exists
-    if not os.path.exists(work_dir):
-        return False
-
-    # Look for at least one decode result file
-    result_pattern = (
-        f"{work_dir}/aiperf_request*_isl{isl}_osl{osl}_n*/*/profile_export_aiperf.json"
-    )
-    result_files = glob.glob(result_pattern)
-
-    if not result_files:
-        return False
-
-    # Verify at least one result file has valid data
-    try:
-        with open(result_files[0], "r") as f:
-            data = json.load(f)
-            # Check if it has the required metrics
-            if "inter_token_latency" in data and "avg" in data["inter_token_latency"]:
-                logger.info(
-                    f"Found existing decode results for TP{tp_size} at {result_files[0]} (and {len(result_files)-1} others)"
-                )
-                return True
-    except (json.JSONDecodeError, KeyError, FileNotFoundError):
-        pass
-
-    return False
-
-
-def load_existing_prefill_results(
-    output_dir: str, tp_size: int, isl: int
-) -> Tuple[Optional[float], Optional[float]]:
-    """Load existing prefill results from disk."""
-    work_dir = f"{output_dir}/prefill_tp{tp_size}"
-    result_file = f"{work_dir}/aiperf_isl{isl}/*/profile_export_aiperf.json"
-
-    result_files = glob.glob(result_file)
-    if result_files:
-        try:
-            with open(result_files[0], "r") as f:
-                data = json.load(f)
-                ttft = data["time_to_first_token"]["avg"]
-                thpt_per_gpu = isl / ttft / tp_size * 1000
-                return ttft, thpt_per_gpu
-        except (json.JSONDecodeError, KeyError, FileNotFoundError):
-            pass
-    return None, None
-
-
-def load_existing_decode_results(
-    output_dir: str, tp_size: int, isl: int, osl: int
-) -> List[Tuple[float, float, int]]:
-    """Load existing decode results from disk."""
-    work_dir = f"{output_dir}/decode_tp{tp_size}"
-
-    result_pattern = (
-        f"{work_dir}/aiperf_request*_isl{isl}_osl{osl}_n*/*/profile_export_aiperf.json"
-    )
-    result_files = glob.glob(result_pattern)
-
-    decode_results = []
-    for result_file in result_files:
-        try:
-            with open(result_file, "r") as f:
-                data = json.load(f)
-                itl = data["inter_token_latency"]["avg"]
-                thpt_per_gpu = data["output_token_throughput"]["avg"] / tp_size
-
-                # Extract concurrency from filename
-                match = re.search(r"aiperf_request(\d+)_", result_file)
-                if match:
-                    concurrency = int(match.group(1))
-                    decode_results.append((itl, thpt_per_gpu, concurrency))
-        except (json.JSONDecodeError, KeyError, FileNotFoundError):
-            continue
-
-    return decode_results

benchmarks/profiler/utils/profiler_argparse.py

@@ -76,8 +76,6 @@ def create_profiler_parser() -> argparse.Namespace:
             max_num_gpus_per_engine: Int (maximum number of GPUs per engine, default: 0)
             num_gpus_per_node: Int (number of GPUs per node for MoE models - this will be the granularity when searching for the best TEP/DEP size, default: 0)
         sweep:
-            skip_existing_results: Boolean (skip TP sizes that already have results in the output directory, default: False)
-            force_rerun: Boolean (force re-running all tests even if results already exist (overrides --skip-existing-results), default: False)
             prefill_interpolation_granularity: Int (how many samples to benchmark to interpolate TTFT under different ISL, default: 16)
             decode_interpolation_granularity: Int (how many samples to benchmark to interpolate ITL under different active kv cache size and decode context length, default: 6)
             use_ai_configurator: Boolean (use ai-configurator to estimate benchmarking results instead of running actual deployment, default: False)
@@ -158,26 +156,20 @@ def create_profiler_parser() -> argparse.Namespace:
     parser.add_argument(
         "--min-num-gpus-per-engine",
         type=int,
-        default=config.get("hardware", {}).get("min_num_gpus_per_engine", 1),
+        default=config.get("hardware", {}).get("min_num_gpus_per_engine", 0),
         help="minimum number of GPUs per engine",
     )
     parser.add_argument(
         "--max-num-gpus-per-engine",
         type=int,
-        default=config.get("hardware", {}).get("max_num_gpus_per_engine", 8),
+        default=config.get("hardware", {}).get("max_num_gpus_per_engine", 0),
         help="maximum number of GPUs per engine",
     )
     parser.add_argument(
-        "--skip-existing-results",
-        action="store_true",
-        default=config.get("sweep", {}).get("skip_existing_results", False),
-        help="Skip TP sizes that already have results in the output directory",
-    )
-    parser.add_argument(
-        "--force-rerun",
-        action="store_true",
-        default=config.get("sweep", {}).get("force_rerun", False),
-        help="Force re-running all tests even if results already exist (overrides --skip-existing-results)",
+        "--num-gpus-per-node",
+        type=int,
+        default=config.get("hardware", {}).get("num_gpus_per_node", 0),
+        help="Number of GPUs per node for MoE models - this will be the granularity when searching for the best TEP/DEP size",
     )
     parser.add_argument(
         "--isl",
@@ -235,19 +227,6 @@ def create_profiler_parser() -> argparse.Namespace:
         default=config.get("sweep", {}).get("dry_run", False),
         help="Dry run the profile job",
     )
-    parser.add_argument(
-        "--is-moe-model",
-        action="store_true",
-        dest="is_moe_model",
-        default=config.get("engine", {}).get("is_moe_model", False),
-        help="Enable MoE (Mixture of Experts) model support, use TEP for prefill and DEP for decode",
-    )
-    parser.add_argument(
-        "--num-gpus-per-node",
-        type=int,
-        default=config.get("hardware", {}).get("num_gpus_per_node", 8),
-        help="Number of GPUs per node for MoE models - this will be the granularity when searching for the best TEP/DEP size",
-    )
     parser.add_argument(
         "--enable-gpu-discovery",
         action="store_true",
@@ -311,9 +290,5 @@ def create_profiler_parser() -> argparse.Namespace:
     if not args.model and not args.config:
         parser.error("--model or --config is required (provide at least one)")
 
-    # Run auto-generation if GPU discovery is enabled
-    # This will override any manually specified hardware parameters
-    if args.enable_gpu_discovery:
-        auto_generate_search_space(args)
-
+    auto_generate_search_space(args)
     return args

benchmarks/profiler/utils/search_space_autogen.py

@@ -9,7 +9,7 @@ import os
 import yaml
 
 from benchmarks.profiler.utils.config_modifiers import CONFIG_MODIFIERS
-from benchmarks.profiler.utils.model_info import get_model_info
+from benchmarks.profiler.utils.model_info import ModelInfo, get_model_info
 from deploy.utils.gpu_inventory import get_gpu_summary
 
 logger = logging.getLogger(__name__)
@@ -23,7 +23,9 @@ console_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 
 MODEL_GPU_MEM_FRAC_MAX = 0.9
-MOE_MODEL_MAX_NUM_GPUS = 32
+
+# for MoE models, we sweep up to number of GPUs that can hold 8x the model weights
+MOE_MODEL_MAX_NUM_GPU_FACTOR = 8
 
 
 def auto_generate_search_space(args: argparse.Namespace) -> None:
@@ -31,17 +33,16 @@ def auto_generate_search_space(args: argparse.Namespace) -> None:
         args.backend
     ]  # args.backend is already validated in argparse
 
-    # first check if config file exists
-    if args.model:
-        if not args.config:
-            # modify config file from default config file
-            logger.info("DGD config file not provided, using default config file")
-            config = config_modifier.load_default_config()
-
-        else:
-            with open(args.config, "r") as f:
-                config = yaml.safe_load(f)
+    # first get the config
+    if not args.config:
+        # modify config file from default config file
+        logger.info("DGD config file not provided, using default config file")
+        config = config_modifier.load_default_config()
+    else:
+        with open(args.config, "r") as f:
+            config = yaml.safe_load(f)
 
+    if args.model:
         logger.info(f"Updating model in DGD config file to {args.model}")
         config = config_modifier.update_model(config, args.model)
         if args.dgd_image:
@@ -55,62 +56,84 @@ def auto_generate_search_space(args: argparse.Namespace) -> None:
             yaml.dump(config, f)
         args.config = config_fn
 
-    # now determine the search space
-    model_info = None
-    if args.model:
-        logger.info(f"Getting model info for {args.model}...")
-        model_info = get_model_info(args.model)
-
-        num_experts_str = (
-            f", num_experts={model_info['num_experts']}"
-            if model_info.get("num_experts")
-            else ""
-        )
-        logger.info(
-            f"Model {args.model} has size {model_info['model_size']}, is_moe={model_info['is_moe']}, and max_context_length={model_info['max_context_length']}{num_experts_str}"
-        )
-        args.is_moe_model = model_info["is_moe"]  # type: ignore[assignment]
-        args.max_context_length = model_info["max_context_length"]  # type: ignore[assignment]
-
-    if (
-        args.min_num_gpus_per_engine == 0
-        or args.max_num_gpus_per_engine == 0
-        or args.num_gpus_per_node == 0
-    ):
-        if not args.model:
-            # TODO: get model info provided DGD config
-            error_msg = "No model provided, cannot auto-generate GPU search space. Please provide `--model` or GPU info"
-            logger.error(error_msg)
-            raise RuntimeError(error_msg)
-
-        logger.info("Getting GPU info from k8s cluster...")
-        gpu_info = get_gpu_summary()
-        logger.info(
-            f"Cluster has {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node with {gpu_info['vram']} VRAM"
-        )
-
-        # model_info should be set by now (checked above), but mypy needs explicit verification
-        assert model_info is not None, "model_info must be set when model is provided"
-
-        min_gpu = math.ceil(
-            model_info["model_size"] / MODEL_GPU_MEM_FRAC_MAX / gpu_info["vram"]  # type: ignore[operator]
-        )
-        max_gpu = (
-            gpu_info["gpus_per_node"]  # type: ignore[misc]
-            if not model_info["is_moe"]
-            else MOE_MODEL_MAX_NUM_GPUS
-        )
-        if min_gpu > max_gpu:
-            error_msg = f"No valid GPU configuration found for model {args.model} on the cluster with {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node"
-            logger.error(error_msg)
-            raise RuntimeError(error_msg)
-
-        logger.info(
-            f"Auto-generated search space for model {args.model} on the cluster with {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node: {min_gpu} to {max_gpu}"
-        )
-        args.min_num_gpus_per_engine = min_gpu
-        args.max_num_gpus_per_engine = max_gpu
-        args.num_gpus_per_node = gpu_info["gpus_per_node"]  # type: ignore[assignment]
-        args.num_experts = model_info.get("num_experts")  # type: ignore[assignment]
+    # get model info and update args
+    model_info: ModelInfo | None = None
+    if not args.model:
+        # get the model name from config
+        args.model = config_modifier.get_model_name(config)
+    logger.info(f"Getting model info for {args.model}...")
+    model_info = get_model_info(args.model)
+
+    num_experts_str = (
+        f", num_experts={model_info.num_experts}"
+        if model_info.num_experts is not None
+        else ""
+    )
+    logger.info(
+        f"Model {args.model} has size {model_info.model_size}, is_moe={model_info.is_moe}, and max_context_length={model_info.max_context_length}{num_experts_str}"
+    )
+    args.model_info = model_info
 
+    # now determine the search space
+    if args.enable_gpu_discovery:
+        if (
+            args.min_num_gpus_per_engine == 0
+            or args.max_num_gpus_per_engine == 0
+            or args.num_gpus_per_node == 0
+        ):
+            if not args.model:
+                # TODO: get model info provided DGD config
+                error_msg = "No model provided, cannot auto-generate GPU search space. Please provide `--model` or GPU info"
+                logger.error(error_msg)
+                raise RuntimeError(error_msg)
+
+            logger.info("Getting GPU info from k8s cluster...")
+            gpu_info = get_gpu_summary()
+            logger.info(
+                f"Cluster has {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node with {gpu_info['vram']} VRAM"
+            )
+
+            # model_info should be set by now (checked above), but mypy needs explicit verification
+            assert (
+                model_info is not None
+            ), "model_info must be set when model is provided"
+
+            vram_mib = int(gpu_info["vram"])  # type: ignore[call-overload]
+            gpus_per_node = int(gpu_info["gpus_per_node"])  # type: ignore[call-overload]
+
+            min_gpu = math.ceil(
+                model_info.model_size / MODEL_GPU_MEM_FRAC_MAX / vram_mib
+            )
+            if not model_info.is_moe:
+                max_gpu = gpus_per_node
+            else:
+                max_gpu = max(min_gpu * MOE_MODEL_MAX_NUM_GPU_FACTOR, gpus_per_node)
+            if min_gpu > max_gpu:
+                error_msg = f"No valid GPU configuration found for model {args.model} on the cluster with {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node"
+                logger.error(error_msg)
+                raise RuntimeError(error_msg)
+
+            logger.info(
+                f"Auto-generated search space for model {args.model} on the cluster with {gpu_info['gpus_per_node']}x{gpu_info['model']} GPUs per node: {min_gpu} to {max_gpu}"
+            )
+            args.min_num_gpus_per_engine = min_gpu
+            args.max_num_gpus_per_engine = max_gpu
+            args.num_gpus_per_node = gpus_per_node  # type: ignore[assignment]
+    else:
+        # use default values for GPUs
+        if args.min_num_gpus_per_engine == 0:
+            logger.warning(
+                "GPU discover is disabled and min_num_gpus_per_engine is not specified, setting to 1"
+            )
+            args.min_num_gpus_per_engine = 1
+        if args.max_num_gpus_per_engine == 0:
+            logger.warning(
+                "GPU discover is disabled and max_num_gpus_per_engine is not specified, setting to 4"
+            )
+            args.max_num_gpus_per_engine = 4
+        if args.num_gpus_per_node == 0:
+            logger.warning(
+                "GPU discover is disabled and num_gpus_per_node is not specified, setting to 8"
+            )
+            args.num_gpus_per_node = 8
     return

deploy/cloud/operator/config/samples/nvidia.com_v1alpha1_dynamographdeploymentrequest.yaml

@@ -37,8 +37,7 @@ spec:
 
       # Engine configuration
       engine:
-        max_context_length: 16384  # Maximum context length supported by the model
-        is_moe_model: false  # Enable MoE model support (uses TEP/DEP instead of TP)
+        max_context_length: 16384  # will override max context length of the model if provided
 
       # Hardware configuration
       hardware:

deploy/cloud/operator/internal/controller/dynamographdeploymentrequest_controller_test.go

@@ -852,7 +852,7 @@ var _ = Describe("DGDR Helper Functions", func() {
 					ProfilingConfig: nvidiacomv1alpha1.ProfilingConfigSpec{
 						Config: createTestConfig(map[string]interface{}{
 							"sweep": map[string]interface{}{
-								"force_rerun": true,
+								"prefill_interpolation_granularity": 16,
 							},
 						}),
 					},

docs/planner/sla_planner_quickstart.md

@@ -315,7 +315,8 @@ profilingConfig:
 
     # Profiling sweep settings (optional)
     sweep:
-      force_rerun: false
+      prefill_interpolation_granularity: 16  # Number of samples for prefill ISL sweep
+      decode_interpolation_granularity: 6    # Number of samples for decode sweep
 ```
 
 > **Note**: `engine.config` is a **file path** to a DGD YAML file, not inline configuration. Use ConfigMapRef (recommended) or leave it unset to auto-generate.

tests/profiler/test_profile_sla_aiconfigurator.py

@@ -18,6 +18,19 @@ project_root = Path(__file__).parent.parent.parent
 sys.path.insert(0, str(project_root))
 
 from benchmarks.profiler.profile_sla import run_profile  # noqa: E402
+from benchmarks.profiler.utils.model_info import ModelInfo  # noqa: E402
+
+
+# Override the logger fixture from conftest.py to prevent directory creation
+@pytest.fixture(autouse=True)
+def logger(request):
+    """Override the logger fixture to prevent test directory creation.
+
+    This replaces the logger fixture from tests/conftest.py that creates
+    directories named after each test.
+    """
+    # Simply do nothing - no directories created, no file handlers added
+    yield
 
 
 class TestProfileSlaAiconfigurator:
@@ -41,11 +54,9 @@ class TestProfileSlaAiconfigurator:
                 self.osl = 500
                 self.ttft = 50
                 self.itl = 10
-                self.max_context_length = 16384
                 self.prefill_interpolation_granularity = 16
                 self.decode_interpolation_granularity = 6
                 self.service_name = ""
-                self.is_moe_model = False
                 self.dry_run = False
                 self.use_ai_configurator = True
                 self.aic_system = "h200_sxm"
@@ -54,6 +65,13 @@ class TestProfileSlaAiconfigurator:
                 self.aic_backend_version = "0.20.0"
                 self.num_gpus_per_node = 8
                 self.deploy_after_profile = False
+                # Provide minimal model_info to avoid HF queries
+                self.model_info = ModelInfo(
+                    model_size=16384.0,
+                    architecture="TestArchitecture",
+                    is_moe=False,
+                    max_context_length=16384,
+                )
 
         return Args()