Enhance model loader

cacheflow/core/server.py

@@ -12,8 +12,8 @@
 from cacheflow.frontend.simple_frontend import SimpleFrontend
 from cacheflow.logger import init_logger
 from cacheflow.model_executor import get_memory_analyzer
-from cacheflow.sequence import SequenceGroup
 from cacheflow.sampling_params import SamplingParams
+from cacheflow.sequence import SequenceGroup
 from cacheflow.utils import get_gpu_memory, get_cpu_memory
 from cacheflow.worker.controller import Controller, DeviceID
 

cacheflow/model_executor/model_loader.py

@@ -14,32 +14,51 @@
 from cacheflow.model_executor.weight_utils import initialize_dummy_weights
 
 
-_MODELS = {
-    'gpt2': GPT2LMHeadModel,
-    'llama': LlamaForCausalLM,
-    'opt': OPTForCausalLM,
-    'stablelm': GPTNeoXForCausalLM,
-    'pythia': GPTNeoXForCausalLM,
-    'dolly-v2': GPTNeoXForCausalLM,
+# TODO(woosuk): Lazy-load the model classes.
+_MODEL_REGISTRY = {
+    "GPT2LMHeadModel": GPT2LMHeadModel,
+    "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
+    "LlamaForCausalLM": LlamaForCausalLM,
+    "OPTForCausalLM": OPTForCausalLM,
 }
 
 _MEMORY_ANALYZERS = {
-    'gpt2': GPT2MemoryAnalyzer,
-    'llama': LlamaMemoryAnalyzer,
-    'opt': OPTMemoryAnalyzer,
-    'stablelm': GPTNeoXMemoryAnalyzer,
-    'pythia': GPTNeoXMemoryAnalyzer,
-    'dolly-v2': GPTNeoXMemoryAnalyzer,
+    "GPT2LMHeadModel": GPT2MemoryAnalyzer,
+    "GPTNeoXForCausalLM": GPTNeoXMemoryAnalyzer,
+    "LlamaForCausalLM": LlamaMemoryAnalyzer,
+    "OPTForCausalLM": OPTMemoryAnalyzer,
 }
 
 
+def _get_model_architecture(config: PretrainedConfig) -> nn.Module:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in _MODEL_REGISTRY:
+            return _MODEL_REGISTRY[arch]
+    raise ValueError(
+        f"Model architectures {architectures} are not supported for now. "
+        f"Supported architectures: {list(_MODEL_REGISTRY.keys())}"
+    )
+
+
+def _get_memory_analyzer(config: PretrainedConfig) -> CacheFlowMemoryAnalyzer:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in _MEMORY_ANALYZERS:
+            return _MEMORY_ANALYZERS[arch]
+    raise ValueError(
+        f"Model architectures {architectures} are not supported for now. "
+        f"Supported architectures: {list(_MEMORY_ANALYZERS.keys())}"
+    )
+
+
 def _get_dtype(config: PretrainedConfig, dtype: str) -> torch.dtype:
-    # NOTE: getattr(config, 'torch_dtype', torch.float32) is not correct
+    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
     # because config.torch_dtype can be None.
-    config_dtype = getattr(config, 'torch_dtype', None)
+    config_dtype = getattr(config, "torch_dtype", None)
     if config_dtype is None:
         config_dtype = torch.float32
-    if dtype == 'default':
+    if dtype == "default":
         if config_dtype == torch.float32:
             # Following the common practice, we use float16 for float32 models.
             torch_dtype = torch.float16
@@ -51,7 +70,7 @@ def _get_dtype(config: PretrainedConfig, dtype: str) -> torch.dtype:
             # TODO(woosuk): Allow using float16 for bfloat16 models and
             # vice versa. Print a warning message and continue.
             raise ValueError(
-                f'Cannot use {torch_dtype} for {config_dtype} model.')
+                f"Cannot use {torch_dtype} for {config_dtype} model.")
     return torch_dtype
 
 
@@ -65,24 +84,21 @@ def get_model(
     config = AutoConfig.from_pretrained(model_name)
     torch_dtype = _get_dtype(config, dtype)
     torch.set_default_dtype(torch_dtype)
-    for model_class_name, model_class in _MODELS.items():
-        if model_class_name in model_name:
-            if use_dummy_weights:
-                # Create a model instance.
-                # The weights will be initialized as empty tensors.
-                model = model_class(config)
-                model = model.cuda()
-                # NOTE(woosuk): For precise performance evaluation, we assign
-                # random values to the weights.
-                initialize_dummy_weights(model)
-            else:
-                # Create a model instance.
-                model = model_class(config)
-                # Load the weights from the cached or downloaded files.
-                model.load_weights(model_name, cache_dir, use_np_cache)
-                model = model.cuda()
-            return model.eval(), torch_dtype
-    raise ValueError(f'Unsupported model name: {model_name}')
+    model_class = _get_model_architecture(config)
+
+    # Create a model instance.
+    # The weights will be initialized as empty tensors.
+    model = model_class(config)
+    if use_dummy_weights:
+        model = model.cuda()
+        # NOTE(woosuk): For accurate performance evaluation, we assign
+        # random values to the weights.
+        initialize_dummy_weights(model)
+    else:
+        # Load the weights from the cached or downloaded files.
+        model.load_weights(model_name, cache_dir, use_np_cache)
+        model = model.cuda()
+    return model.eval(), torch_dtype
 
 
 def get_memory_analyzer(
@@ -95,9 +111,7 @@ def get_memory_analyzer(
 ) -> CacheFlowMemoryAnalyzer:
     config = AutoConfig.from_pretrained(model_name)
     torch_dtype = _get_dtype(config, dtype)
-    for model_class, memory_analyzer in _MEMORY_ANALYZERS.items():
-        if model_class in model_name:
-            return memory_analyzer(
-                model_name, block_size, torch_dtype, gpu_memory, cpu_memory,
-                tensor_parallel_size)
-    raise ValueError(f'Unsupported model name: {model_name}')
+    memory_analyzer = _get_memory_analyzer(config)
+    return memory_analyzer(
+        model_name, block_size, torch_dtype, gpu_memory, cpu_memory,
+        tensor_parallel_size)