temp commit

llama_stack/distribution/routers/routers.py

@@ -74,7 +74,6 @@ class InferenceRouter(Inference):
         self,
         routing_table: RoutingTable,
     ) -> None:
-        print("InferenceRouter init")
         self.routing_table = routing_table
 
     async def initialize(self) -> None:
@@ -91,7 +90,6 @@ class InferenceRouter(Inference):
         metadata: Optional[Dict[str, Any]] = None,
         model_type: Optional[ModelType] = None,
     ) -> None:
-        print("inference router")
         await self.routing_table.register_model(
             model_id, provider_model_id, provider_id, metadata, model_type
         )

llama_stack/providers/inline/inference/meta_reference/__init__.py

@@ -15,9 +15,6 @@ async def get_provider_impl(
 ):
     from .inference import MetaReferenceInferenceImpl
 
-    print("get_provider_impl")
-
     impl = MetaReferenceInferenceImpl(config)
 
-    print("after MetaReferenceInferenceImpl")
     return impl

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -94,7 +94,6 @@ class MetaReferenceInferenceImpl(
             ],
         )
         model = await self.model_registry_helper.register_model(model)
-        print("model type", type(model))
         if model.model_type == ModelType.embedding:
             self._load_sentence_transformer_model(model.provider_resource_id)
 
@@ -304,7 +303,6 @@ class MetaReferenceInferenceImpl(
 
         if self.config.create_distributed_process_group:
             async with SEMAPHORE:
-                print("after SEMAPHORE")
                 return impl()
         else:
             return impl()

llama_stack/providers/inline/inference/meta_reference/model_parallel.py

@@ -58,7 +58,6 @@ class LlamaModelParallelGenerator:
         config: MetaReferenceInferenceConfig,
         model_id: str,
     ):
-        print("LlamaModelParallelGenerator init")
         self.config = config
         self.model_id = model_id
         self.model = resolve_model(model_id)
@@ -76,7 +75,6 @@ class LlamaModelParallelGenerator:
         self.__exit__(None, None, None)
 
     def __enter__(self):
-        print("enter LlamaModelParallelGenerator")
         if self.config.model_parallel_size:
             model_parallel_size = self.config.model_parallel_size
         else:

llama_stack/providers/tests/inference/conftest.py

@@ -69,6 +69,7 @@ def pytest_generate_tests(metafunc):
             else:
                 params = MODEL_PARAMS
 
+        # print("params", params)
         metafunc.parametrize(
             "inference_model",
             params,
@@ -82,5 +83,7 @@ def pytest_generate_tests(metafunc):
                 "inference": INFERENCE_FIXTURES,
             },
         ):
+            # print("I reach here")
             fixtures = [stack.values[0]["inference"] for stack in filtered_stacks]
+        print("fixtures", fixtures)
         metafunc.parametrize("inference_stack", fixtures, indirect=True)

llama_stack/templates/experimental-post-training/run.yaml

@@ -3,10 +3,17 @@ image_name: experimental-post-training
 docker_image: null
 conda_env: experimental-post-training
 apis:
+- inference
 - telemetry
 - datasetio
 - post_training
 providers:
+  inference:
+  - provider_id: meta-reference-inference
+    provider_type: inline::meta-reference
+    config:
+      max_seq_len: 4096
+      checkpoint_dir: null
   datasetio:
   - provider_id: huggingface-0
     provider_type: remote::huggingface
@@ -24,11 +31,11 @@ metadata_store:
   namespace: null
   type: sqlite
   db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
-models:
-- metadata: {}
-  model_id: ${env.POST_TRAINING_MODEL}
-  provider_id: meta-reference-inference
-  provider_model_id: null
+models: []
+# - metadata: {}
+#   model_id: ${env.POST_TRAINING_MODEL}
+#   provider_id: meta-reference-inference
+#   provider_model_id: null
 shields: []
 memory_banks: []
 datasets:

llama_stack/templates/meta-reference-quantized-gpu/run.yaml

@@ -16,7 +16,7 @@ providers:
   - provider_id: meta-reference-inference
     provider_type: inline::meta-reference-quantized
     config:
-      model: ${env.INFERENCE_MODEL}
+      model: ${env.INFERENCE_MODEL} # please make sure your inference model here is added as resource
       max_seq_len: 4096
       checkpoint_dir: ${env.INFERENCE_CHECKPOINT_DIR:null}
       quantization: