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fix: Correctly parse algorithm_config when launching NVIDIA customization job

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This commit is contained in:
Jash Gulabrai 2025-04-24 17:17:00 -04:00
parent 7ed137e963
commit 26c10b5ab5
1 changed files with 21 additions and 12 deletions
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33
llama_stack/providers/remote/post_training/nvidia/post_training.py
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@ -67,13 +67,18 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
self.timeout = aiohttp.ClientTimeout(total=config.timeout) self.timeout = aiohttp.ClientTimeout(total=config.timeout)
# TODO: filter by available models based on /config endpoint # TODO: filter by available models based on /config endpoint
ModelRegistryHelper.__init__(self, model_entries=_MODEL_ENTRIES) ModelRegistryHelper.__init__(self, model_entries=_MODEL_ENTRIES)
self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout) self.session = None
self.customizer_url = config.customizer_url
self.customizer_url = config.customizer_url
if not self.customizer_url: if not self.customizer_url:
warnings.warn("Customizer URL is not set, using default value: http://nemo.test", stacklevel=2) warnings.warn("Customizer URL is not set, using default value: http://nemo.test", stacklevel=2)
self.customizer_url = "http://nemo.test" self.customizer_url = "http://nemo.test"
async def _get_session(self) -> aiohttp.ClientSession:
if self.session is None or self.session.closed:
self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout)
return self.session
async def _make_request( async def _make_request(
self, self,
method: str, method: str,
@ -94,8 +99,9 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
if json and "Content-Type" not in request_headers: if json and "Content-Type" not in request_headers:
request_headers["Content-Type"] = "application/json" request_headers["Content-Type"] = "application/json"
session = await self._get_session()
for _ in range(self.config.max_retries): for _ in range(self.config.max_retries):
async with self.session.request(method, url, params=params, json=json, **kwargs) as response: async with session.request(method, url, params=params, json=json, **kwargs) as response:
if response.status >= 400: if response.status >= 400:
error_data = await response.json() error_data = await response.json()
raise Exception(f"API request failed: {error_data}") raise Exception(f"API request failed: {error_data}")
@ -122,8 +128,8 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
jobs = [] jobs = []
for job in response.get("data", []): for job in response.get("data", []):
job_id = job.pop("id") job_id = job.pop("id")
job_status = job.pop("status", "unknown").lower() job_status = job.pop("status", "scheduled").lower()
mapped_status = STATUS_MAPPING.get(job_status, "unknown") mapped_status = STATUS_MAPPING.get(job_status, "scheduled")
# Convert string timestamps to datetime objects # Convert string timestamps to datetime objects
created_at = ( created_at = (
@ -177,7 +183,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
) )
api_status = response.pop("status").lower() api_status = response.pop("status").lower()
mapped_status = STATUS_MAPPING.get(api_status, "unknown") mapped_status = STATUS_MAPPING.get(api_status, "scheduled")
return NvidiaPostTrainingJobStatusResponse( return NvidiaPostTrainingJobStatusResponse(
status=JobStatus(mapped_status), status=JobStatus(mapped_status),
@ -297,7 +303,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
User is informed about unsupported parameters via warnings. User is informed about unsupported parameters via warnings.
""" """
# Map model to nvidia model name # Map model to nvidia model name
# ToDo: only supports llama-3.1-8b-instruct now, need to update this to support other models # See `_MODEL_ENTRIES` for supported models
nvidia_model = self.get_provider_model_id(model) nvidia_model = self.get_provider_model_id(model)
# Check for unsupported method parameters # Check for unsupported method parameters
@ -389,14 +395,17 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
# Handle LoRA-specific configuration # Handle LoRA-specific configuration
if algorithm_config: if algorithm_config:
if isinstance(algorithm_config, dict) and algorithm_config.get("type") == "LoRA": algorithm_config_dict = (
warn_unsupported_params(algorithm_config, supported_params["lora_config"], "LoRA config") algorithm_config.model_dump() if hasattr(algorithm_config, "model_dump") else algorithm_config
)
if isinstance(algorithm_config_dict, dict) and algorithm_config_dict.get("type") == "LoRA":
warn_unsupported_params(algorithm_config_dict, supported_params["lora_config"], "LoRA config")
job_config["hyperparameters"]["lora"] = { job_config["hyperparameters"]["lora"] = {
k: v k: v
for k, v in { for k, v in {
"adapter_dim": algorithm_config.get("adapter_dim"), "adapter_dim": algorithm_config_dict.get("adapter_dim"),
"alpha": algorithm_config.get("alpha"), "alpha": algorithm_config_dict.get("alpha"),
"adapter_dropout": algorithm_config.get("adapter_dropout"), "adapter_dropout": algorithm_config_dict.get("adapter_dropout"),
}.items() }.items()
if v is not None if v is not None
} }