requested changes/refactor

.runpod/README.md

@@ -28,7 +28,7 @@ All behaviour is controlled through environment variables:
 | `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API                 |                     | String                                                             |
 | `MAX_CONCURRENCY`                   | Maximum concurrent requests                       | 300                 | Integer                                                            |
 
-**Pass any vLLM engine arg** not listed above by prefixing it with `VLLM_RUNPOD_`. The suffix maps to the vLLM `AsyncEngineArgs` field name (case-insensitive). For example, `VLLM_RUNPOD_ENABLE_CHUNKED_PREFILL=true` sets `enable_chunked_prefill`. See the [vLLM engine args docs](https://docs.vllm.ai/en/latest/configuration/engine_args) for all available options.
+**Pass any vLLM engine arg** not listed above by setting an env var with the **UPPERCASED** field name (e.g. `MAX_MODEL_LEN=4096`, `ENABLE_CHUNKED_PREFILL=true`). The worker auto-discovers all `AsyncEngineArgs` fields from env. See the [vLLM engine args docs](https://docs.vllm.ai/en/latest/configuration/engine_args) for all available options.
 
 For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
 

README.md

@@ -59,15 +59,15 @@ Configure worker-vllm using environment variables:
 | `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API                 |                     | String                                                             |
 | `MAX_CONCURRENCY`                   | Maximum concurrent requests                       | 30                  | Integer                                                            |
 
-**Pass any vLLM engine arg** not listed above by prefixing it with `VLLM_`. The suffix maps directly to the vLLM `AsyncEngineArgs` field name (case-insensitive). For example:
+**Pass any vLLM engine arg** not listed above by setting an environment variable with the **UPPERCASED** field name (same names vLLM uses). The worker auto-discovers all `AsyncEngineArgs` fields from env. For example:
 
 | Environment Variable      | vLLM Engine Arg          | Example Value |
 | ------------------------- | ------------------------ | ------------- |
-| `VLLM_RUNPOD_MAX_MODEL_LEN`      | `max_model_len`          | `4096`        |
+| `MAX_MODEL_LEN`           | `max_model_len`          | `4096`        |
-| `VLLM_RUNPOD_ENFORCE_EAGER`      | `enforce_eager`          | `true`        |
+| `ENFORCE_EAGER`           | `enforce_eager`          | `true`        |
-| `VLLM_RUNPOD_ENABLE_CHUNKED_PREFILL` | `enable_chunked_prefill` | `true`    |
+| `ENABLE_CHUNKED_PREFILL`  | `enable_chunked_prefill` | `true`        |
 
-Any `VLLM_RUNPOD_<ARG>` that matches a valid vLLM engine arg will be applied automatically. This lets you configure any vLLM option without waiting for explicit worker support.
+Any env var whose name matches a valid `AsyncEngineArgs` field (uppercased) is applied automatically. Backward-compat aliases: `MODEL_NAME`, `TOKENIZER_NAME`, `MAX_CONTEXT_LEN_TO_CAPTURE`. This lets you configure any vLLM option without waiting for explicit worker support.
 
 For the complete list of all available environment variables, examples, and detailed descriptions: **[Configuration](docs/configuration.md)**
 

docs/configuration.md

@@ -156,26 +156,27 @@ The way this works is that the first request will have a batch size of `DEFAULT_
 | `DISABLE_LOGGING_REQUEST`   | False   | `bool`  | Disable logging requests.                                                                                                                              |
 | `MAX_LOG_LEN`               | None    | `int`   | Max number of prompt characters or prompt ID numbers being printed in log.                                                                             |
 
-## VLLM_RUNPOD_ Prefix: Pass Any Engine Arg
+## UPPERCASED env vars: Pass any engine arg
 
-Any vLLM `AsyncEngineArgs` field can be set via an environment variable using the `VLLM_RUNPOD_` prefix. The suffix maps directly to the field name (case-insensitive, underscores preserved).
+Any vLLM `AsyncEngineArgs` field can be set via an environment variable using the **UPPERCASED** field name (the same names vLLM uses). The worker auto-discovers all fields from env — no prefix.
 
-**Format:** `VLLM_RUNPOD_<ARG_NAME>=<value>`
+**Format:** `<FIELD_NAME_UPPERCASED>=<value>` (e.g. `MAX_MODEL_LEN=4096`)
 
 **Examples:**
 
-| Environment Variable                  | vLLM Engine Arg            | Value Example |
+| Environment Variable     | vLLM Engine Arg          | Value Example |
-| ------------------------------------- | -------------------------- | ------------- |
+| ------------------------ | ------------------------ | ------------- |
-| `VLLM_RUNPOD_MAX_MODEL_LEN`           | `max_model_len`            | `4096`        |
+| `MAX_MODEL_LEN`          | `max_model_len`          | `4096`        |
-| `VLLM_RUNPOD_ENFORCE_EAGER`           | `enforce_eager`            | `true`        |
+| `ENFORCE_EAGER`          | `enforce_eager`          | `true`        |
-| `VLLM_RUNPOD_ENABLE_CHUNKED_PREFILL`  | `enable_chunked_prefill`   | `true`        |
+| `ENABLE_CHUNKED_PREFILL` | `enable_chunked_prefill` | `true`        |
-| `VLLM_RUNPOD_NUM_SCHEDULER_STEPS`     | `num_scheduler_steps`      | `8`           |
+| `NUM_SCHEDULER_STEPS`    | `num_scheduler_steps`    | `8`           |
-| `VLLM_RUNPOD_TOKENIZER_POOL_SIZE`     | `tokenizer_pool_size`      | `4`           |
+| `TOKENIZER_POOL_SIZE`    | `tokenizer_pool_size`   | `4`           |
+
+**Backward-compat aliases:** `MODEL_NAME` → `model`, `TOKENIZER_NAME` → `tokenizer`, `MAX_CONTEXT_LEN_TO_CAPTURE` → `max_seq_len_to_capture`, `MODEL_REVISION` → `revision`.
 
 **Notes:**
 - Only valid `AsyncEngineArgs` fields are applied. Unknown keys are silently ignored.
-- Values are automatically cast to the correct type (`int`, `float`, `bool`, `str`, or JSON for `dict`/`list`).
+- Values are automatically cast to the correct type (`int`, `float`, `bool`, `str`, or JSON for `dict`/`list`/`tuple`).
-- `VLLM_RUNPOD_` overrides are applied **after** all other worker env vars, so they take precedence.
 - For a full list of available engine args, see the [vLLM AsyncEngineArgs documentation](https://docs.vllm.ai/en/latest/serving/engine_args.html).
 
 ## Docker Build Arguments

src/engine_args.py

@@ -7,14 +7,93 @@ from vllm import AsyncEngineArgs
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 from src.utils import convert_limit_mm_per_prompt
 
-RENAME_ARGS_MAP = {
+# Backward-compat: env var names users already know → engine arg name
+ENV_ALIASES = {
     "MODEL_NAME": "model",
     "MODEL_REVISION": "revision",
     "TOKENIZER_NAME": "tokenizer",
-    "MAX_CONTEXT_LEN_TO_CAPTURE": "max_seq_len_to_capture"
+    "MAX_CONTEXT_LEN_TO_CAPTURE": "max_seq_len_to_capture",
 }
 
-VLLM_ENV_PREFIX = "VLLM_RUNPOD_"
+# Literal defaults from original worker (used when env/local do not set a value)
+DEFAULT_ARGS = {
+    "disable_log_stats": False,
+    "enable_log_requests": False,
+    "gpu_memory_utilization": 0.95,
+    "pipeline_parallel_size": 1,
+    "tensor_parallel_size": 1,
+    "served_model_name": None,
+    "tokenizer": None,
+    "skip_tokenizer_init": False,
+    "tokenizer_mode": "auto",
+    "trust_remote_code": False,
+    "download_dir": None,
+    "load_format": "auto",
+    "dtype": "auto",
+    "kv_cache_dtype": "auto",
+    "quantization_param_path": None,
+    "seed": 0,
+    "max_model_len": None,
+    "worker_use_ray": False,
+    "distributed_executor_backend": None,
+    "max_parallel_loading_workers": None,
+    "block_size": 16,
+    "enable_prefix_caching": False,
+    "disable_sliding_window": False,
+    "swap_space": 4,
+    "cpu_offload_gb": 0,
+    "max_num_batched_tokens": None,
+    "max_num_seqs": 256,
+    "max_logprobs": 20,
+    "revision": None,
+    "code_revision": None,
+    "rope_scaling": None,
+    "rope_theta": None,
+    "tokenizer_revision": None,
+    "quantization": None,
+    "enforce_eager": False,
+    "max_seq_len_to_capture": 8192,
+    "disable_custom_all_reduce": False,
+    "tokenizer_pool_size": 0,
+    "tokenizer_pool_type": "ray",
+    "tokenizer_pool_extra_config": None,
+    "enable_lora": False,
+    "max_loras": 1,
+    "max_lora_rank": 16,
+    "enable_prompt_adapter": False,
+    "max_prompt_adapters": 1,
+    "max_prompt_adapter_token": 0,
+    "fully_sharded_loras": False,
+    "lora_extra_vocab_size": 256,
+    "long_lora_scaling_factors": None,
+    "lora_dtype": "auto",
+    "max_cpu_loras": None,
+    "device": "auto",
+    "ray_workers_use_nsight": False,
+    "num_gpu_blocks_override": None,
+    "num_lookahead_slots": 0,
+    "model_loader_extra_config": None,
+    "ignore_patterns": None,
+    "preemption_mode": None,
+    "scheduler_delay_factor": 0.0,
+    "enable_chunked_prefill": None,
+    "guided_decoding_backend": "outlines",
+    "speculative_model": None,
+    "speculative_draft_tensor_parallel_size": None,
+    "num_speculative_tokens": None,
+    "speculative_max_model_len": None,
+    "speculative_disable_by_batch_size": None,
+    "ngram_prompt_lookup_max": None,
+    "ngram_prompt_lookup_min": None,
+    "spec_decoding_acceptance_method": "rejection_sampler",
+    "typical_acceptance_sampler_posterior_threshold": None,
+    "typical_acceptance_sampler_posterior_alpha": None,
+    "qlora_adapter_name_or_path": None,
+    "disable_logprobs_during_spec_decoding": None,
+    "otlp_traces_endpoint": None,
+    "stream_interval": 1,
+
+}
 
 
 def _resolve_field_type(field_type: type) -> type:
@@ -57,6 +136,18 @@ def _convert_env_value_to_field_type(value: str, field_name: str, field_type: ty
             return json.loads(val)
         except json.JSONDecodeError:
             return val
+    # tuple (e.g. long_lora_scaling_factors) — comma-separated or JSON array
+    if effective_type is tuple or origin is tuple:
+        args = get_args(field_type) if hasattr(field_type, "__args__") else ()
+        elem_types = [a for a in args if a is not Ellipsis]
+        elem_type = elem_types[0] if elem_types else str
+        try:
+            parsed = json.loads(val)
+            if isinstance(parsed, list):
+                return tuple(elem_type(x) for x in parsed)
+        except (json.JSONDecodeError, TypeError):
+            pass
+        return tuple(elem_type(x.strip()) for x in str(val).split(",") if x.strip())
     # Fallback: try int, float, then str
     try:
         return int(val)
@@ -69,128 +160,44 @@ def _convert_env_value_to_field_type(value: str, field_name: str, field_type: ty
     return str(val)
 
 
-def _get_vllm_env_overrides() -> dict:
+def _get_args_from_env_auto_discover() -> dict:
-    """Collect engine arg overrides from env vars with prefix VLLM_RUNPOD_.
+    """Auto-discover engine args from env vars using UPPERCASED field names.
 
-    Any env var VLLM_RUNPOD_<ARG> maps to the engine arg <arg> (lowercase).
+    For every field in AsyncEngineArgs, check os.getenv(FIELD_NAME).
-    E.g. VLLM_RUNPOD_MAX_MODEL_LEN=4096 -> max_model_len=4096.
+    E.g. MAX_MODEL_LEN=4096 -> max_model_len=4096.
-    Only keys that exist on AsyncEngineArgs are applied; values are
+    Uses same type conversion as before; supports all vLLM engine args without manual listing.
-    converted to the field type (int, float, bool, str, json for dict/list).
     """
-    overrides = {}
+    args = {}
     valid_fields = AsyncEngineArgs.__dataclass_fields__
-    for key, value in os.environ.items():
+    for field_name, field in valid_fields.items():
-        if not key.startswith(VLLM_ENV_PREFIX) or len(key) <= len(VLLM_ENV_PREFIX):
+        env_key = field_name.upper()
+        value = os.environ.get(env_key)
+        if value is None:
             continue
-        suffix = key[len(VLLM_ENV_PREFIX) :]
-        arg_name = suffix.lower()
-        if arg_name not in valid_fields:
-            continue
-        field = valid_fields[arg_name]
         try:
-            overrides[arg_name] = _convert_env_value_to_field_type(
+            args[field_name] = _convert_env_value_to_field_type(
-                value, arg_name, field.type
+                value, field_name, field.type
             )
         except (ValueError, TypeError, json.JSONDecodeError) as e:
             logging.warning(
-                "Skip VLLM_RUNPOD_ env override %s=%r: %s", key, value, e
+                "Skip env %s=%r: %s", env_key, value, e
             )
+    return args
+
+
+def _apply_env_aliases(args: dict) -> None:
+    """Apply ENV_ALIASES: if MODEL_NAME etc. are set, set the target engine arg."""
+    valid_fields = AsyncEngineArgs.__dataclass_fields__
+    for alias, target in ENV_ALIASES.items():
+        value = os.environ.get(alias)
+        if value is None or target not in valid_fields:
             continue
-    if overrides:
+        try:
-        logging.info(
+            args[target] = _convert_env_value_to_field_type(
-            "Applying engine arg overrides from VLLM_RUNPOD_ env vars: %s",
+                value, target, valid_fields[target].type
-            list(overrides.keys()),
+            )
-        )
+        except (ValueError, TypeError, json.JSONDecodeError) as e:
-    return overrides
+            logging.warning("Skip env alias %s=%r: %s", alias, value, e)
-
-
-DEFAULT_ARGS = {
-    "disable_log_stats": os.getenv('DISABLE_LOG_STATS', 'False').lower() == 'true',
-    # disable_log_requests is deprecated, use enable_log_requests instead
-    "enable_log_requests": os.getenv('ENABLE_LOG_REQUESTS', 'False').lower() == 'true',
-    "gpu_memory_utilization": float(os.getenv('GPU_MEMORY_UTILIZATION', 0.95)),
-    "pipeline_parallel_size": int(os.getenv('PIPELINE_PARALLEL_SIZE', 1)),
-    "tensor_parallel_size": int(os.getenv('TENSOR_PARALLEL_SIZE', 1)),
-    "served_model_name": os.getenv('SERVED_MODEL_NAME', None),
-    "tokenizer": os.getenv('TOKENIZER', None),
-    "skip_tokenizer_init": os.getenv('SKIP_TOKENIZER_INIT', 'False').lower() == 'true',
-    "tokenizer_mode": os.getenv('TOKENIZER_MODE', 'auto'),
-    "trust_remote_code": os.getenv('TRUST_REMOTE_CODE', 'False').lower() == 'true',
-    "download_dir": os.getenv('DOWNLOAD_DIR', None),
-    "load_format": os.getenv('LOAD_FORMAT', 'auto'),
-    "config_format": os.getenv('CONFIG_FORMAT', 'auto'),
-    "dtype": os.getenv('DTYPE', 'auto'),
-    "kv_cache_dtype": os.getenv('KV_CACHE_DTYPE', 'auto'),
-    "quantization_param_path": os.getenv('QUANTIZATION_PARAM_PATH', None),
-    "seed": int(os.getenv('SEED', 0)),
-    "max_model_len": int(os.getenv('MAX_MODEL_LEN', 0)) or None,
-    "worker_use_ray": os.getenv('WORKER_USE_RAY', 'False').lower() == 'true',
-    "distributed_executor_backend": os.getenv('DISTRIBUTED_EXECUTOR_BACKEND', None),
-    "max_parallel_loading_workers": int(os.getenv('MAX_PARALLEL_LOADING_WORKERS', 0)) or None,
-    "block_size": int(os.getenv('BLOCK_SIZE', 16)),
-    "enable_prefix_caching": os.getenv('ENABLE_PREFIX_CACHING', 'False').lower() == 'true',
-    "disable_sliding_window": os.getenv('DISABLE_SLIDING_WINDOW', 'False').lower() == 'true',
-    # attention_backend replaces deprecated VLLM_ATTENTION_BACKEND env var
-    "attention_backend": os.getenv('ATTENTION_BACKEND', None),
-    # Enabled by default for improved throughput. Set to False to disable if experiencing issues
-    "async_scheduling": None if os.getenv('ASYNC_SCHEDULING') is None else os.getenv('ASYNC_SCHEDULING', 'True').lower() == 'true',
-    # Controls how often to yield streaming results
-    "stream_interval": int(os.getenv('STREAM_INTERVAL', 1)),
-    "swap_space": int(os.getenv('SWAP_SPACE', 4)),  # GiB
-    "cpu_offload_gb": int(os.getenv('CPU_OFFLOAD_GB', 0)),  # GiB
-    # vLLM defaults None to 2048; keep 0 as None to let vLLM auto-calculate
-    "max_num_batched_tokens": int(os.getenv('MAX_NUM_BATCHED_TOKENS', 0)) or None,
-    "max_num_seqs": int(os.getenv('MAX_NUM_SEQS', 256)),
-    "max_logprobs": int(os.getenv('MAX_LOGPROBS', 20)),  # Default value for OpenAI Chat Completions API
-    "revision": os.getenv('REVISION', None),
-    "code_revision": os.getenv('CODE_REVISION', None),
-    "rope_scaling": os.getenv('ROPE_SCALING', None),
-    "rope_theta": float(os.getenv('ROPE_THETA', 0)) or None,
-    "tokenizer_revision": os.getenv('TOKENIZER_REVISION', None),
-    "quantization": os.getenv('QUANTIZATION', None),
-    "enforce_eager": os.getenv('ENFORCE_EAGER', 'False').lower() == 'true',
-    "max_context_len_to_capture": int(os.getenv('MAX_CONTEXT_LEN_TO_CAPTURE', 0)) or None,
-    "max_seq_len_to_capture": int(os.getenv('MAX_SEQ_LEN_TO_CAPTURE', 8192)),
-    "disable_custom_all_reduce": os.getenv('DISABLE_CUSTOM_ALL_REDUCE', 'False').lower() == 'true',
-    "tokenizer_pool_size": int(os.getenv('TOKENIZER_POOL_SIZE', 0)),
-    "tokenizer_pool_type": os.getenv('TOKENIZER_POOL_TYPE', 'ray'),
-    "tokenizer_pool_extra_config": os.getenv('TOKENIZER_POOL_EXTRA_CONFIG', None),
-    "enable_lora": os.getenv('ENABLE_LORA', 'False').lower() == 'true',
-    "max_loras": int(os.getenv('MAX_LORAS', 1)),
-    "max_lora_rank": int(os.getenv('MAX_LORA_RANK', 16)),
-    "enable_prompt_adapter": os.getenv('ENABLE_PROMPT_ADAPTER', 'False').lower() == 'true',
-    "max_prompt_adapters": int(os.getenv('MAX_PROMPT_ADAPTERS', 1)),
-    "max_prompt_adapter_token": int(os.getenv('MAX_PROMPT_ADAPTER_TOKEN', 0)),
-    "fully_sharded_loras": os.getenv('FULLY_SHARDED_LORAS', 'False').lower() == 'true',
-    "lora_extra_vocab_size": int(os.getenv('LORA_EXTRA_VOCAB_SIZE', 256)),
-    "long_lora_scaling_factors": tuple(map(float, os.getenv('LONG_LORA_SCALING_FACTORS', '').split(','))) if os.getenv('LONG_LORA_SCALING_FACTORS') else None,
-    "lora_dtype": os.getenv('LORA_DTYPE', 'auto'),
-    "max_cpu_loras": int(os.getenv('MAX_CPU_LORAS', 0)) or None,
-    "device": os.getenv('DEVICE', 'auto'),
-    "ray_workers_use_nsight": os.getenv('RAY_WORKERS_USE_NSIGHT', 'False').lower() == 'true',
-    "num_gpu_blocks_override": int(os.getenv('NUM_GPU_BLOCKS_OVERRIDE', 0)) or None,
-    "num_lookahead_slots": int(os.getenv('NUM_LOOKAHEAD_SLOTS', 0)),
-    "model_loader_extra_config": os.getenv('MODEL_LOADER_EXTRA_CONFIG', None),
-    "ignore_patterns": os.getenv('IGNORE_PATTERNS', None),
-    "preemption_mode": os.getenv('PREEMPTION_MODE', None),
-    "scheduler_delay_factor": float(os.getenv('SCHEDULER_DELAY_FACTOR', 0.0)),
-    "enable_chunked_prefill": os.getenv('ENABLE_CHUNKED_PREFILL', None),
-    "guided_decoding_backend": os.getenv('GUIDED_DECODING_BACKEND', 'outlines'),
-    "speculative_model": os.getenv('SPECULATIVE_MODEL', None),
-    "speculative_draft_tensor_parallel_size": int(os.getenv('SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE', 0)) or None,
-    "enable_expert_parallel": bool(os.getenv('ENABLE_EXPERT_PARALLEL', 'False').lower() == 'true'),
-    "num_speculative_tokens": int(os.getenv('NUM_SPECULATIVE_TOKENS', 0)) or None,
-    "speculative_max_model_len": int(os.getenv('SPECULATIVE_MAX_MODEL_LEN', 0)) or None,
-    "speculative_disable_by_batch_size": int(os.getenv('SPECULATIVE_DISABLE_BY_BATCH_SIZE', 0)) or None,
-    "ngram_prompt_lookup_max": int(os.getenv('NGRAM_PROMPT_LOOKUP_MAX', 0)) or None,
-    "ngram_prompt_lookup_min": int(os.getenv('NGRAM_PROMPT_LOOKUP_MIN', 0)) or None,
-    "spec_decoding_acceptance_method": os.getenv('SPEC_DECODING_ACCEPTANCE_METHOD', 'rejection_sampler'),
-    "typical_acceptance_sampler_posterior_threshold": float(os.getenv('TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD', 0)) or None,
-    "typical_acceptance_sampler_posterior_alpha": float(os.getenv('TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA', 0)) or None,
-    "qlora_adapter_name_or_path": os.getenv('QLORA_ADAPTER_NAME_OR_PATH', None),
-    "disable_logprobs_during_spec_decoding": os.getenv('DISABLE_LOGPROBS_DURING_SPEC_DECODING', None),
-    "otlp_traces_endpoint": os.getenv('OTLP_TRACES_ENDPOINT', None),
-}
 
 def get_speculative_config():
     """Build speculative decoding configuration from environment variables.
@@ -282,6 +289,7 @@ def get_speculative_config():
 
     return None
 
+
 def _resolve_max_model_len(model, trust_remote_code=False, revision=None):
     """Resolve max_model_len from the model's HuggingFace config."""
     try:
@@ -300,25 +308,19 @@ def _resolve_max_model_len(model, trust_remote_code=False, revision=None):
         logging.warning(f"Could not resolve max_model_len from model config: {e}")
     return None
 
-limit_mm_env = os.getenv('LIMIT_MM_PER_PROMPT')
-if limit_mm_env is not None:
-    DEFAULT_ARGS["limit_mm_per_prompt"] = convert_limit_mm_per_prompt(limit_mm_env)
 
-def match_vllm_args(args):
+def _local_args_to_engine_args(local: dict) -> dict:
-    """Rename args to match vllm by:
+    """Map local args (e.g. from /local_model_args.json) to engine arg names and filter."""
-    1. Renaming keys to lower case
+    valid = AsyncEngineArgs.__dataclass_fields__
-    2. Renaming keys to match vllm
+    out = {}
-    3. Filtering args to match vllm's AsyncEngineArgs
+    for k, v in local.items():
+        target = ENV_ALIASES.get(k, k.lower().replace("-", "_"))
+        if target not in valid or v in (None, "", "None"):
+            continue
+        out[target] = v
+    return out
 
-    Args:
-        args (dict): Dictionary of args
 
-    Returns:
-        dict: Dictionary of args with renamed keys
-    """
-    renamed_args = {RENAME_ARGS_MAP.get(k, k): v for k, v in args.items()}
-    matched_args = {k: v for k, v in renamed_args.items() if k in AsyncEngineArgs.__dataclass_fields__}
-    return {k: v for k, v in matched_args.items() if v not in [None, "", "None"]}
 def get_local_args():
     """
     Retrieve local arguments from a JSON file.
@@ -341,26 +343,36 @@ def get_local_args():
 
     return local_args
 def get_engine_args():
-    # Start with default args
+    # Start with worker custom defaults (only where we differ from vLLM)
-    args = DEFAULT_ARGS
+    args = dict(DEFAULT_ARGS)
-    
+
-    # Get env args that match keys in AsyncEngineArgs
+    # Auto-discover: every AsyncEngineArgs field from env UPPERCASED (e.g. MAX_MODEL_LEN)
-    args.update(os.environ)
+    args.update(_get_args_from_env_auto_discover())
-    
+
-    # Get local args if model is baked in and overwrite env args
+    # Backward-compat aliases (MODEL_NAME → model, etc.)
-    args.update(get_local_args())
+    _apply_env_aliases(args)
-    
+
+    # Local baked-in model overrides
+    local = get_local_args()
+    if local:
+        args.update(_local_args_to_engine_args(local))
+
+    # Filter to valid engine args and drop sentinel empty values
+    valid_fields = AsyncEngineArgs.__dataclass_fields__
+    args = {
+        k: v for k, v in args.items()
+        if k in valid_fields and v not in (None, "", "None")
+    }
+
+    # Special conversion for limit_mm_per_prompt (e.g. "image=1,video=0")
+    limit_mm_env = os.getenv("LIMIT_MM_PER_PROMPT")
+    if limit_mm_env is not None:
+        args["limit_mm_per_prompt"] = convert_limit_mm_per_prompt(limit_mm_env)
+
     # if args.get("TENSORIZER_URI"): TODO: add back once tensorizer is ready
     #     args["load_format"] = "tensorizer"
     #     args["model_loader_extra_config"] = TensorizerConfig(tensorizer_uri=args["TENSORIZER_URI"], num_readers=None)
     #     logging.info(f"Using tensorized model from {args['TENSORIZER_URI']}")
-    
-    
-    # Rename and match to vllm args
-    args = match_vllm_args(args)
-
-    # Apply any VLLM_RUNPOD_* env vars as overrides (e.g. VLLM_RUNPOD_MAX_MODEL_LEN=4096 -> max_model_len=4096)
-    args.update(_get_vllm_env_overrides())
 
     if args.get("load_format") == "bitsandbytes":
         args["quantization"] = args["load_format"]