merge main

vllm/worker/worker.py

+"""A GPU worker class."""
+import os
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.distributed
+
+from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig)
+from vllm.model_executor import set_random_seed
+from vllm.model_executor.parallel_utils.parallel_state import (
+    initialize_model_parallel)
+from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.worker.cache_engine import CacheEngine
+from vllm.worker.model_runner import ModelRunner
+
+
+class Worker:
+    """A worker class that executes (a partition of) the model on a GPU.
+
+    Each worker is associated with a single GPU. The worker is responsible for
+    maintaining the KV cache and executing the model on the GPU. In case of
+    distributed inference, each worker is assigned a partition of the model.
+    """
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        rank: Optional[int] = None,
+        distributed_init_method: Optional[str] = None,
+    ) -> None:
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+
+        self.model_runner = ModelRunner(model_config, parallel_config,
+                                        scheduler_config)
+        # Uninitialized cache engine. Will be initialized by
+        # self.init_cache_engine().
+        self.cache_config = None
+        self.cache_engine = None
+        self.cache_events = None
+        self.gpu_cache = None
+
+    def init_model(self) -> None:
+        # torch.distributed.all_reduce does not free the input tensor until
+        # the synchronization point. This causes the memory usage to grow
+        # as the number of all_reduce calls increases. This env var disables
+        # this behavior.
+        # Related issue:
+        # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+        os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+        # This env var set by Ray causes exceptions with graph building.
+        os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
+        # Env vars will be set by Ray.
+        self.rank = self.rank if self.rank is not None else int(
+            os.getenv("RANK", "-1"))
+        local_rank = int(os.getenv("LOCAL_RANK", "0"))
+        self.device = torch.device(f"cuda:{local_rank}")
+        if self.rank < 0:
+            raise ValueError("Invalid or unspecified rank.")
+        torch.cuda.set_device(self.device)
+
+        _check_if_gpu_supports_dtype(self.model_config.dtype)
+
+        # Initialize the distributed environment.
+        _init_distributed_environment(self.parallel_config, self.rank,
+                                      self.distributed_init_method)
+
+        # Initialize the model.
+        set_random_seed(self.model_config.seed)
+
+    def load_model(self):
+        self.model_runner.load_model()
+
+    @torch.inference_mode()
+    def profile_num_available_blocks(
+        self,
+        block_size: int,
+        gpu_memory_utilization: float,
+        cpu_swap_space: int,
+    ) -> Tuple[int, int]:
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        torch.cuda.synchronize()
+        free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
+        peak_memory = total_gpu_memory - free_gpu_memory
+
+        cache_block_size = CacheEngine.get_cache_block_size(
+            block_size, self.model_config, self.parallel_config)
+        num_gpu_blocks = int(
+            (total_gpu_memory * gpu_memory_utilization - peak_memory) //
+            cache_block_size)
+        num_cpu_blocks = int(cpu_swap_space // cache_block_size)
+        num_gpu_blocks = max(num_gpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+        torch.cuda.empty_cache()
+        return num_gpu_blocks, num_cpu_blocks
+
+    def init_cache_engine(self, cache_config: CacheConfig) -> None:
+        self.cache_config = cache_config
+        self.cache_engine = CacheEngine(self.cache_config, self.model_config,
+                                        self.parallel_config)
+        self.cache_events = self.cache_engine.events
+        self.gpu_cache = self.cache_engine.gpu_cache
+        self.model_runner.set_block_size(self.cache_engine.block_size)
+
+    def warm_up_model(self) -> None:
+        if not self.model_config.enforce_eager:
+            self.model_runner.capture_model(self.gpu_cache)
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        blocks_to_swap_in: Dict[int, int],
+        blocks_to_swap_out: Dict[int, int],
+        blocks_to_copy: Dict[int, List[int]],
+    ) -> SamplerOutput:
+        # Issue cache operations.
+        issued_cache_op = False
+        if blocks_to_swap_in:
+            self.cache_engine.swap_in(blocks_to_swap_in)
+            issued_cache_op = True
+        if blocks_to_swap_out:
+            self.cache_engine.swap_out(blocks_to_swap_out)
+            issued_cache_op = True
+        if blocks_to_copy:
+            self.cache_engine.copy(blocks_to_copy)
+            issued_cache_op = True
+
+        cache_events = self.cache_events if issued_cache_op else None
+
+        # Wait for cache operations to finish.
+        # TODO(woosuk): Profile swapping overhead and optimize if needed.
+        if cache_events is not None:
+            for event in cache_events:
+                event.wait()
+        # If there is no input, we don't need to execute the model.
+        if not seq_group_metadata_list:
+            return {}
+
+        output = self.model_runner.execute_model(seq_group_metadata_list,
+                                                 self.gpu_cache)
+        return output
+
+
+def _init_distributed_environment(
+    parallel_config: ParallelConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = None,
+) -> None:
+    """Initialize the distributed environment."""
+    if torch.distributed.is_initialized():
+        torch_world_size = torch.distributed.get_world_size()
+        if torch_world_size != parallel_config.world_size:
+            raise RuntimeError(
+                "torch.distributed is already initialized but the torch world "
+                "size does not match parallel_config.world_size "
+                f"({torch_world_size} vs. {parallel_config.world_size}).")
+    elif not distributed_init_method:
+        raise ValueError(
+            "distributed_init_method must be set if torch.distributed "
+            "is not already initialized")
+    else:
+        torch.distributed.init_process_group(
+            backend="nccl",
+            world_size=parallel_config.world_size,
+            rank=rank,
+            init_method=distributed_init_method,
+        )
+
+    # A small all_reduce for warmup.
+    torch.distributed.all_reduce(torch.zeros(1).cuda())
+    initialize_model_parallel(parallel_config.tensor_parallel_size,
+                              parallel_config.pipeline_parallel_size)
+
+
+def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
+    # Check if the GPU supports the dtype.
+    if torch_dtype == torch.bfloat16:
+        compute_capability = torch.cuda.get_device_capability()
+        if compute_capability[0] < 8:
+            gpu_name = torch.cuda.get_device_name()
+            raise ValueError(
+                "Bfloat16 is only supported on GPUs with compute capability "
+                f"of at least 8.0. Your {gpu_name} GPU has compute capability "
+                f"{compute_capability[0]}.{compute_capability[1]}.")