Add docstrings for LLMServer and related classes and examples (#142)

cacheflow/config.py

@@ -12,6 +12,20 @@ _GiB = 1 << 30
 
 
 class ModelConfig:
+    """Configuration for the model.
+
+    Args:
+        model: Name or path of the huggingface model to use.
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        use_np_weights: Save a numpy copy of model weights for faster loading.
+            This can increase the disk usage by up to 2x.
+        use_dummy_weights: Use dummy values for model weights (for profiling).
+        dtype: Data type for model weights and activations. The "auto" option
+            will use FP16 precision for FP32 and FP16 models, and BF16 precision
+            for BF16 models.
+        seed: Random seed for reproducibility.
+    """
 
     def __init__(
         self,
@@ -68,7 +82,14 @@ class ModelConfig:
 
 
 class CacheConfig:
+    """Configuration for the KV cache.
 
+    Args:
+        block_size: Size of a cache block in number of tokens.
+        gpu_memory_utilization: Fraction of GPU memory to use for the
+            CacheFlow execution.
+        swap_space: Size of the CPU swap space per GPU (in GiB).
+    """
     def __init__(
         self,
         block_size: int,
@@ -111,7 +132,15 @@ class CacheConfig:
 
 
 class ParallelConfig:
+    """Configuration for the distributed execution.
 
+    Args:
+        pipeline_parallel_size: Number of pipeline parallel groups.
+        tensor_parallel_size: Number of tensor parallel groups.
+        worker_use_ray: Whether to use Ray for model workers. Will be set to
+            True if either pipeline_parallel_size or tensor_parallel_size is
+            greater than 1.
+    """
     def __init__(
         self,
         pipeline_parallel_size: int,
@@ -134,7 +163,14 @@ class ParallelConfig:
 
 
 class SchedulerConfig:
+    """Scheduler configuration.
 
+    Args:
+        max_num_batched_tokens: Maximum number of tokens to be processed in
+            a single iteration.
+        max_num_seqs: Maximum number of sequences to be processed in a single
+            iteration.
+    """
     def __init__(
         self,
         max_num_batched_tokens: int,

cacheflow/entrypoints/openai/openai_frontend.py

@@ -96,6 +96,18 @@ def create_logprobs(token_ids: List[int],
 
 @app.post("/v1/completions")
 async def create_completion(raw_request: Request):
+    """Completion API similar to OpenAI's API.
+
+    See https://platform.openai.com/docs/api-reference/completions/create
+    for the API specification. This API mimics the OpenAI Completion API.
+
+    NOTE: Currently we do not support the following features:
+        - echo (since the cacheflow server does not currently support
+          getting the logprobs of prompt tokens)
+        - suffix (the language models we currently support do not support
+          suffix)
+        - logit_bias (to be supported in cacheflow server)
+    """
     request = CompletionRequest(**await raw_request.json())
     logger.info(f"Received completion request: {request}")
 

cacheflow/entrypoints/simple_fastapi_frontend.py

@@ -18,6 +18,12 @@ app = FastAPI()
 
 @app.post("/generate")
 async def generate_stream(request: Request) -> StreamingResponse:
+    """ Stream the results of the generation request.
+
+    The request should be a JSON object with the following fields:
+    - prompt: the prompt to use for the generation.
+    - other fields: the sampling parameters (See `SamplingParams` for details).
+    """
     request_dict = await request.json()
     prompt = request_dict.pop("prompt")
     sampling_params = SamplingParams(**request_dict)

cacheflow/server/arg_utils.py

@@ -9,6 +9,7 @@ from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
 
 @dataclass
 class ServerArgs:
+    """Arguments for CacheFlow servers."""
     model: str
     download_dir: Optional[str] = None
     use_np_weights: bool = False
@@ -117,6 +118,7 @@ class ServerArgs:
 
 @dataclass
 class AsyncServerArgs(ServerArgs):
+    """Arguments for asynchronous CacheFlow servers."""
     server_use_ray: bool = False
 
     @staticmethod

cacheflow/server/async_llm_server.py

@@ -1,6 +1,6 @@
 import asyncio
 import time
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 from cacheflow.logger import init_logger
 from cacheflow.outputs import RequestOutput
@@ -15,7 +15,25 @@ TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds
 
 
 class AsyncLLMServer:
+    """An asynchronous wrapper for LLMServer.
 
+    This class is used to wrap the LLMServer class to make it asynchronous. It
+    uses asyncio to create a background loop that keeps processing incoming
+    requests. The LLMServer is kicked by the generate method when there
+    are requests in the waiting queue. The generate method yields the outputs
+    from the LLMServer to the caller.
+
+    NOTE: For the comprehensive list of arguments, see `LLMServer`.
+
+    Args:
+        worker_use_ray: Whether to use Ray for model workers. Required for
+            distributed execution. Should be the same as
+            `parallel_config.worker_use_ray`.
+        server_use_ray: Whether to make LLMServer a Ray actor. If so, the
+            async frontend will be executed in a separate process as the
+            model workers.
+        *args, *kwargs: Arguments for LLMServer.
+    """
     def __init__(self, worker_use_ray: bool, server_use_ray: bool,
                  *args, **kwargs) -> None:
         self.worker_use_ray = worker_use_ray
@@ -35,6 +53,7 @@ class AsyncLLMServer:
         self.kicking_request_id: Optional[str] = None
 
     async def server_step(self, kicking_request_id: Optional[str] = None):
+        """Kick the server to process the waiting requests."""
         self.is_server_running = True
         self.kicking_request_id = kicking_request_id
         if self.server_use_ray:
@@ -54,8 +73,31 @@ class AsyncLLMServer:
             self.request_outputs[request_id] = request_output
             self.request_events[request_id].set()
 
-    async def generate(self, prompt: str, sampling_params: SamplingParams,
-                       request_id: str) -> RequestOutput:
+    async def generate(
+        self,
+        prompt: Optional[str],
+        sampling_params: SamplingParams,
+        request_id: str,
+        prompt_token_ids: Optional[List[int]] = None
+    ) -> RequestOutput:
+        """Generate outputs for a request.
+
+        Generate outputs for a request. This method is a coroutine. It adds the
+        request into the waiting queue of the LLMServer and streams the outputs
+        from the LLMServer to the caller.
+
+        Args:
+            prompt: The prompt string. Can be None if prompt_token_ids is
+                provided.
+            sampling_params: The sampling parameters of the request.
+            request_id: The unique id of the request.
+            prompt_token_ids: The token IDs of the prompt. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+
+        Yields:
+            The output `RequestOutput` objects from the LLMServer for the
+            request.
+        """
         # Preprocess the request.
         arrival_time = time.time()
 
@@ -66,20 +108,29 @@ class AsyncLLMServer:
 
         logger.info(f"Received request {request_id}: "
                     f"prompt: {prompt!r}, "
-                    f"sampling params: {sampling_params}.")
+                    f"sampling params: {sampling_params}, "
+                    f"prompt token ids: {prompt_token_ids}.")
 
         # Add the request into the cacheflow server's waiting queue.
         if self.server_use_ray:
             await self.server.add_request.remote(
-                request_id, prompt, sampling_params, arrival_time=arrival_time)
+                request_id, prompt, sampling_params,
+                prompt_token_ids=prompt_token_ids,
+                arrival_time=arrival_time)
         else:
             self.server.add_request(
-                request_id, prompt, sampling_params, arrival_time=arrival_time)
+                request_id, prompt, sampling_params,
+                prompt_token_ids=prompt_token_ids,
+                arrival_time=arrival_time)
 
         # The cacheflow server does not have a background loop that keeps
         # processing incoming requests. Therefore, we need to keep kicking
         # the server to process the requests.
         while True:
+            if request_id not in self.request_events:
+                # The request has been aborted.
+                return
+
             # Kick the server if the server is not running.
             if not self.is_server_running:
                 await self.server_step(request_id)
@@ -113,6 +164,14 @@ class AsyncLLMServer:
                 break
 
     async def abort(self, request_id: str) -> None:
+        """Abort a request.
+
+        Abort a submitted request. If the request is finished or not found,
+        this method will be a no-op.
+
+        Args:
+            request_id: The unique id of the request.
+        """
         if request_id not in self.request_events:
             # The request has already finished or been aborted.
             return
@@ -137,6 +196,7 @@ class AsyncLLMServer:
 
     @classmethod
     def from_server_args(cls, server_args: AsyncServerArgs) -> "AsyncLLMServer":
+        """Creates an async LLM server from the server arguments."""
         # Create the server configs.
         server_configs = server_args.create_server_configs()
         parallel_config = server_configs[2]

cacheflow/server/llm_server.py

@@ -8,7 +8,7 @@ from cacheflow.logger import init_logger
 from cacheflow.outputs import RequestOutput
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import ServerArgs
-from cacheflow.server.ray_utils import ray, initialize_cluster
+from cacheflow.server.ray_utils import DeviceID, initialize_cluster, ray
 from cacheflow.server.tokenizer_utils import (get_tokenizer,
                                               detokenize_incrementally)
 from cacheflow.sequence import Sequence, SequenceGroup, SequenceStatus
@@ -19,6 +19,33 @@ logger = init_logger(__name__)
 
 
 class LLMServer:
+    """An LLM server that receives requests and generates texts.
+
+    This is the main class for the CacheFlow LLM server. It receives requests
+    from clients and generates texts from the LLM. It includes a tokenizer, a
+    language model (possibly distributed across multiple GPUs), and GPU memory
+    space allocated for intermediate states (aka KV cache). This class utilizes
+    iteration-level scheduling and efficient memory management to maximize the
+    serving throughput.
+
+    The `LLM` class wraps this class for offline batched inference and the
+    `AsyncLLMServer` class wraps this class for online serving.
+
+    NOTE: The config arguments are derived from the `ServerArgs` class. For the
+    comprehensive list of arguments, see `ServerArgs`.
+
+    Args:
+        model_config: The configuration related to the LLM model.
+        cache_config: The configuration related to the KV cache memory
+            management.
+        parallel_config: The configuration related to distributed execution.
+        scheduler_config: The configuration related to the request scheduler.
+        distributed_init_method: The initialization method for distributed
+            execution. See `torch.distributed.init_process_group` for details.
+        stage_devices: The list of devices for each stage. Each stage is a list
+            of (rank, node_resource, device) tuples.
+        log_stats: Whether to log statistics.
+    """
 
     def __init__(
         self,
@@ -27,7 +54,7 @@ class LLMServer:
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
         distributed_init_method: str,
-        stage_devices: List[List[Any]],
+        stage_devices: List[List[DeviceID]],
         log_stats: bool,
     ) -> None:
         logger.info(
@@ -83,6 +110,7 @@ class LLMServer:
         self.cache_config.verify_with_parallel_config(self.parallel_config)
 
     def _init_cache(self) -> None:
+        """Profiles the memory usage and initializes the KV cache."""
         # Get the maximum number of blocks that can be allocated on GPU and CPU.
         num_blocks = self._run_workers(
             "profile_num_available_blocks",
@@ -108,6 +136,7 @@ class LLMServer:
 
     @classmethod
     def from_server_args(cls, server_args: ServerArgs) -> "LLMServer":
+        """Creates an LLM server from the server arguments."""
         # Create the server configs.
         server_configs = server_args.create_server_configs()
         parallel_config = server_configs[2]
@@ -126,6 +155,22 @@ class LLMServer:
         prompt_token_ids: Optional[List[int]] = None,
         arrival_time: Optional[float] = None,
     ) -> None:
+        """Add a request to the server's request pool.
+
+        The request is added to the request pool and will be processed by the
+        scheduler as `server.step()` is called. The exact scheduling policy is
+        determined by the scheduler.
+
+        Args:
+            request_id: The unique ID of the request.
+            prompt: The prompt string. Can be None if prompt_token_ids is
+                provided.
+            sampling_params: The sampling parameters for text generation.
+            prompt_token_ids: The token IDs of the prompt. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+            arrival_time: The arrival time of the request. If None, we use
+                the current time.
+        """
         if arrival_time is None:
             arrival_time = time.time()
         if prompt_token_ids is None:
@@ -148,15 +193,30 @@ class LLMServer:
         self.scheduler.add_seq_group(seq_group)
 
     def abort_request(self, request_id: str) -> None:
+        """Aborts a request with the given ID.
+
+        Args:
+            request_id: The ID of the request to abort.
+        """
         self.scheduler.abort_seq_group(request_id)
 
     def get_num_unfinished_requests(self) -> int:
+        """Gets the number of unfinished requests."""
         return self.scheduler.get_num_unfinished_seq_groups()
 
     def has_unfinished_requests(self) -> bool:
+        """Returns True if there are unfinished requests."""
         return self.scheduler.has_unfinished_seqs()
 
     def step(self) -> List[RequestOutput]:
+        """Performs one decoding iteration and returns newly generated results.
+
+        This function performs one decoding iteration for the server. It first
+        schedules the sequences to be executed in the next iteration and the
+        token blocks to be swapped in/out/copy. Then, it executes the model
+        and updates the scheduler with the model outputs. Finally, it decodes
+        the sequences and returns the newly generated results.
+        """
         seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
         if (not seq_group_metadata_list) and scheduler_outputs.is_empty():
             # Nothing to do.
@@ -188,7 +248,7 @@ class LLMServer:
         return request_outputs
 
     def _decode_sequences(self, seq_groups: List[SequenceGroup]) -> None:
-        # Decode the sequence outputs.
+        """Decodes the sequence outputs."""
         for seq_group in seq_groups:
             for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
                 new_token, new_output_text = detokenize_incrementally(
@@ -201,7 +261,7 @@ class LLMServer:
                 seq.output_text = new_output_text
 
     def _stop_sequences(self, seq_groups: List[SequenceGroup]) -> None:
-        # Stop the sequences.
+        """Stop the finished sequences."""
         for seq_group in seq_groups:
             sampling_params = seq_group.sampling_params
             for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
@@ -238,6 +298,7 @@ class LLMServer:
         *args,
         **kwargs,
     ) -> Any:
+        """Runs the given method on all workers."""
         all_outputs = []
         for worker in self.workers:
             executor = getattr(worker, method)

cacheflow/server/ray_utils.py

@@ -14,15 +14,30 @@ DeviceID = Tuple[int, Optional[str], int]  # rank, node resource (node IP), devi
 def initialize_cluster(
     parallel_config: ParallelConfig,
     server_use_ray: bool = False,
-    address: Optional[str] = None,
+    ray_server_address: Optional[str] = None,
 ) -> Tuple[str, List[List[DeviceID]]]:
+    """Initialize the distributed cluster probably with Ray.
+
+    Args:
+        parallel_config: The configurations for parallel execution.
+        server_use_ray: Whether to use Ray for async server.
+        ray_server_address: The address of the Ray cluster. If None, uses
+            the default Ray cluster address.
+
+    Returns:
+        A tuple of (`distributed_init_method`, `all_stage_devices`). The
+        `distributed_init_method` is the address for initializing the
+        distributed backend. `all_stage_devices` includes device IDs for
+        each worker in each pipeline stage. Each device ID is a tuple of
+        (rank, node resource, device id).
+    """
     if parallel_config.worker_use_ray or server_use_ray:
         if ray is None:
             raise ImportError(
                 "Ray is not installed. Please install Ray to use distributed "
                 "serving.")
         # Connect to a ray cluster.
-        ray.init(address=address)
+        ray.init(address=ray_server_address)
 
     if not parallel_config.worker_use_ray:
         # Initialize cluster locally.

cacheflow/server/tokenizer_utils.py

@@ -15,6 +15,7 @@ def get_tokenizer(
     *args,
     **kwargs,
 ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+    """Gets a tokenizer for the given model name via Huggingface."""
     config = AutoConfig.from_pretrained(model_name)
     if config.model_type == "llama" and getattr(kwargs, "use_fast", True):
         # LLaMA fast tokenizer causes protobuf errors in some environments.

examples/openai_client.py

@@ -1,14 +1,15 @@
 import openai
+
+# Modify OpenAI's API key and API base to use CacheFlow's API server.
 openai.api_key = "EMPTY"
 openai.api_base = "http://localhost:8000/v1"
 model = "facebook/opt-125m"
 
-# list models
+# Test list models API
 models = openai.Model.list()
-print(models)
-
-# create a completion
+print("Models:", models)
 
+# Test completion API
 stream = True
 completion = openai.Completion.create(
     model=model, prompt="A robot may not injure a human being", echo=False, n=2,
@@ -19,4 +20,4 @@ if stream:
     for c in completion:
         print(c)
 else:
-    print("completion:", completion)
+    print("Completion result:", completion)

examples/simple_server.py

@@ -19,7 +19,7 @@ def main(args: argparse.Namespace):
          SamplingParams(n=3, best_of=3, use_beam_search=True, temperature=0.0)),
     ]
 
-    # Run the server.
+    # Run the server by calling `server.step()` manually.
     request_id = 0
     while True:
         # To test iteration-level scheduling, we add one request at each step.