temp commit

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

@@ -46,8 +46,10 @@ class MetaReferenceInferenceConfig(BaseModel):
         return model
 
     @property
-    def model_parallel_size(self) -> int:
+    def model_parallel_size(self) -> Optional[int]:
         resolved = resolve_model(self.model)
+        if resolved is None:
+            return None
         return resolved.pth_file_count
 
     @classmethod

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

@@ -25,12 +25,12 @@ from fairscale.nn.model_parallel.initialize import (
 )
 from llama_models.llama3.api.args import ModelArgs
 from llama_models.llama3.api.chat_format import ChatFormat, ModelInput
+from llama_models.llama3.api.datatypes import Model
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.llama3.reference_impl.model import Transformer
 from llama_models.llama3.reference_impl.multimodal.model import (
     CrossAttentionTransformer,
 )
-from llama_models.sku_list import resolve_model
 from pydantic import BaseModel
 
 from llama_stack.apis.inference import *  # noqa: F403
@@ -53,16 +53,16 @@ from .config import (
 log = logging.getLogger(__name__)
 
 
-def model_checkpoint_dir(model) -> str:
-    checkpoint_dir = Path(model_local_dir(model.descriptor()))
+def model_checkpoint_dir(model_id) -> str:
+    checkpoint_dir = Path(model_local_dir(model_id))
 
     paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
     if not any(p.exists() for p in paths):
         checkpoint_dir = checkpoint_dir / "original"
 
     assert checkpoint_dir.exists(), (
-        f"Could not find checkpoints in: {model_local_dir(model.descriptor())}. "
-        f"Please download model using `llama download --model-id {model.descriptor()}`"
+        f"Could not find checkpoints in: {model_local_dir(model_id)}. "
+        f"Please download model using `llama download --model-id {model_id}`"
     )
     return str(checkpoint_dir)
 
@@ -80,6 +80,7 @@ class Llama:
             MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
         ],
         model_id: str,
+        llama_model: Model,
     ):
         """
         Build a Llama instance by initializing and loading a model checkpoint.
@@ -88,14 +89,16 @@ class Llama:
             This method initializes the distributed process group, sets the device to CUDA,
             and loads the pre-trained model and tokenizer.
         """
-        model = resolve_model(model_id)
-
-        llama_model = model.core_model_id.value
-
+        llama_model_id = llama_model.core_model_id.value
         if not torch.distributed.is_initialized():
+            print("I reach torch.distributed.init_process_group")
             torch.distributed.init_process_group("nccl")
 
-        model_parallel_size = config.model_parallel_size
+        model_parallel_size = (
+            config.model_parallel_size
+            if config.model_parallel_size
+            else llama_model.pth_file_count
+        )
 
         if not model_parallel_is_initialized():
             initialize_model_parallel(model_parallel_size)
@@ -103,6 +106,8 @@ class Llama:
         local_rank = int(os.environ.get("LOCAL_RANK", 0))
         torch.cuda.set_device(local_rank)
 
+        print("torch.cuda.set_device")
+
         # seed must be the same in all processes
         if config.torch_seed is not None:
             torch.manual_seed(config.torch_seed)
@@ -114,7 +119,7 @@ class Llama:
         if config.checkpoint_dir and config.checkpoint_dir != "null":
             ckpt_dir = config.checkpoint_dir
         else:
-            ckpt_dir = model_checkpoint_dir(model)
+            ckpt_dir = model_checkpoint_dir(model_id)  # true model id
 
         checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
         assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
@@ -190,7 +195,7 @@ class Llama:
             model.load_state_dict(state_dict, strict=False)
 
         log.info(f"Loaded in {time.time() - start_time:.2f} seconds")
-        return Llama(model, tokenizer, model_args, llama_model)
+        return Llama(model, tokenizer, model_args, llama_model_id)
 
     def __init__(
         self,

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

@@ -46,13 +46,16 @@ class MetaReferenceInferenceImpl(
         self.config = config
         self.model = None
 
-    async def initialize(self, model_id) -> None:
+    async def initialize(self, model_id, llama_model) -> None:
         log.info(f"Loading model `{model_id}`")
         if self.config.create_distributed_process_group:
-            self.generator = LlamaModelParallelGenerator(self.config, model_id)
+            print("I reach create_distributed_process_group")
+            self.generator = LlamaModelParallelGenerator(
+                self.config, model_id, llama_model
+            )
             self.generator.start()
         else:
-            self.generator = Llama.build(self.config, model_id)
+            self.generator = Llama.build(self.config, model_id, llama_model)
 
         self.model = model_id
 
@@ -65,26 +68,27 @@ class MetaReferenceInferenceImpl(
             raise RuntimeError(
                 "Inference model hasn't been initialized yet, please register your requested model or add your model in the resouces first"
             )
-        inference_model = resolve_model(self.model)
-        requested_model = resolve_model(request.model)
-        if requested_model is None:
+        if request.model is None:
             raise RuntimeError(
                 f"Unknown model: {request.model}, Run `llama model list`"
             )
-        elif requested_model.descriptor() != inference_model.descriptor():
-            raise RuntimeError(
-                f"Model mismatch: {request.model} != {inference_model.descriptor()}"
-            )
+        elif request.model != self.model:
+            raise RuntimeError(f"Model mismatch: {request.model} != {self.model}")
 
     async def unregister_model(self, model_id: str) -> None:
         pass
 
     async def register_model(self, model: LlamaStackModel) -> LlamaStackModel:
-        llama_model = resolve_model(model.identifier)
+        llama_model = (
+            resolve_model(model.metadata["llama_model"])
+            if "llama_model" in model.metadata
+            else resolve_model(model.identifier)
+        )
         if llama_model is None:
             raise RuntimeError(
-                f"Unknown model: {model.identifier}, Please make sure your model is in llama-models SKU list"
+                "Please make sure your llama_model in model metadata or model identifier is in llama-models SKU list"
             )
+
         self.model_registry_helper = ModelRegistryHelper(
             [
                 build_model_alias(
@@ -94,6 +98,7 @@ class MetaReferenceInferenceImpl(
             ],
         )
         model = await self.model_registry_helper.register_model(model)
+
         if model.model_type == ModelType.embedding:
             self._load_sentence_transformer_model(model.provider_resource_id)
 
@@ -103,7 +108,7 @@ class MetaReferenceInferenceImpl(
             and model.metadata["skip_initialize"]
         ):
             return model
-        await self.initialize(model.identifier)
+        await self.initialize(model.identifier, llama_model)
         return model
 
     async def completion(

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

@@ -10,8 +10,8 @@ from functools import partial
 from typing import Any, Generator
 
 from llama_models.llama3.api.chat_format import ChatFormat
+from llama_models.llama3.api.datatypes import Model
 from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
 
 from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
 
@@ -37,8 +37,9 @@ class ModelRunner:
 def init_model_cb(
     config: MetaReferenceInferenceConfig,
     model_id: str,
+    llama_model: Model,
 ):
-    llama = Llama.build(config, model_id)
+    llama = Llama.build(config, model_id, llama_model)
     return ModelRunner(llama)
 
 
@@ -57,14 +58,15 @@ class LlamaModelParallelGenerator:
         self,
         config: MetaReferenceInferenceConfig,
         model_id: str,
+        llama_model: Model,
     ):
         self.config = config
         self.model_id = model_id
-        self.model = resolve_model(model_id)
+        self.llama_model = llama_model
 
         # this is a hack because Agent's loop uses this to tokenize and check if input is too long
         # while the tool-use loop is going
-        checkpoint_dir = model_checkpoint_dir(self.model)
+        checkpoint_dir = model_checkpoint_dir(self.model_id)
         tokenizer_path = os.path.join(checkpoint_dir, "tokenizer.model")
         self.formatter = ChatFormat(Tokenizer(tokenizer_path))
 
@@ -78,11 +80,15 @@ class LlamaModelParallelGenerator:
         if self.config.model_parallel_size:
             model_parallel_size = self.config.model_parallel_size
         else:
-            model_parallel_size = resolve_model(self.model).pth_file_count
+            model_parallel_size = self.llama_model.pth_file_count
+
+        print(f"model_parallel_size: {model_parallel_size}")
 
         self.group = ModelParallelProcessGroup(
             model_parallel_size,
-            init_model_cb=partial(init_model_cb, self.config, self.model_id),
+            init_model_cb=partial(
+                init_model_cb, self.config, self.model_id, self.llama_model
+            ),
         )
         self.group.start()
         return self

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

@@ -300,7 +300,7 @@ def start_model_parallel_process(
 
     main_process_url = request_socket.getsockopt_string(zmq.LAST_ENDPOINT)
 
-    ctx = multiprocessing.get_context("fork")
+    ctx = multiprocessing.get_context("spawn")
     process = ctx.Process(
         target=launch_dist_group,
         args=(

llama_stack/providers/tests/inference/conftest.py

@@ -69,7 +69,6 @@ def pytest_generate_tests(metafunc):
             else:
                 params = MODEL_PARAMS
 
-        # print("params", params)
         metafunc.parametrize(
             "inference_model",
             params,
@@ -83,7 +82,5 @@ 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/providers/tests/inference/test_model_registration.py

@@ -9,8 +9,8 @@ import pytest
 
 # How to run this test:
 #
-# pytest -v -s llama_stack/providers/tests/inference/test_model_registration.py
-#   -m "meta_reference"
+# torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="Llama3.1-8B-Instruct"
+#  ./llama_stack/providers/tests/inference/test_model_registration.py
 
 
 class TestModelRegistration:

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

@@ -75,7 +75,7 @@ metadata_store:
   namespace: null
   type: sqlite
   db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
-models: []
+models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}
   provider_id: meta-reference-inference