319 lines
12 KiB
Python
319 lines
12 KiB
Python
"""A GPU worker class."""
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from typing import Dict, List, Tuple
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import torch
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from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
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SchedulerConfig)
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from cacheflow.model_executor import get_model, InputMetadata, set_random_seed
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from cacheflow.model_executor.parallel_utils.parallel_state import (
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initialize_model_parallel, initialize_all_reduce_launcher)
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from cacheflow.sampling_params import SamplingParams
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from cacheflow.sequence import (SequenceData, SequenceGroupMetadata,
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SequenceOutputs)
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from cacheflow.worker.cache_engine import CacheEngine
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from cacheflow.utils import get_gpu_memory
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class Worker:
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"""A worker class that executes (a partition of) the model on a GPU.
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Each worker is associated with a single GPU. The worker is responsible for
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maintaining the KV cache and executing the model on the GPU. In case of
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distributed inference, each worker is assigned a partition of the model.
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"""
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def __init__(
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self,
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model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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rank: int,
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distributed_init_method: str,
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) -> None:
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self.model_config = model_config
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self.parallel_config = parallel_config
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self.scheduler_config = scheduler_config
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self.rank = rank
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self.distributed_init_method = distributed_init_method
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# Initialize the distributed environment.
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_init_distributed_environment(parallel_config, rank,
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distributed_init_method)
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# Initialize the model.
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set_random_seed(self.model_config.seed)
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self.model = get_model(model_config)
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initialize_all_reduce_launcher(
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self.scheduler_config.max_num_batched_tokens,
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self.model_config.get_hidden_size(),
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self.model_config.dtype,
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)
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# Uninitialized cache engine. Will be initialized by
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# self.init_cache_engine().
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self.cache_config = None
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self.block_size = None
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self.cache_engine = None
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self.cache_events = None
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self.gpu_cache = None
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@torch.inference_mode()
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def profile_num_available_blocks(
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self,
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block_size: int,
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gpu_memory_utilization: float,
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cpu_swap_space: int,
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) -> Tuple[int, int]:
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# Profile the memory usage of the model and get the maximum number of
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# cache blocks that can be allocated with the remaining free memory.
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats()
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# Profile memory usage with max_num_sequences sequences and the total
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# number of tokens equal to max_num_batched_tokens.
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# Enable top-k sampling to reflect the accurate memory usage.
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sampling_params = SamplingParams(top_p=0.99,
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top_k=self.model.config.vocab_size - 1)
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max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
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max_num_seqs = self.scheduler_config.max_num_seqs
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seqs = []
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for group_id in range(max_num_seqs):
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seq_len = (max_num_batched_tokens // max_num_seqs +
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(group_id < max_num_batched_tokens % max_num_seqs))
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seq_data = SequenceData([0] * seq_len)
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seq = SequenceGroupMetadata(
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request_id=str(group_id),
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is_prompt=True,
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seq_data={group_id: seq_data},
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sampling_params=sampling_params,
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block_tables=None,
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)
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seqs.append(seq)
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input_tokens, input_positions, input_metadata = self._prepare_inputs(seqs)
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# Execute the model.
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num_layers = self.model_config.get_num_layers(self.parallel_config)
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self.model(
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input_ids=input_tokens,
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positions=input_positions,
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kv_caches=[(None, None)] * num_layers,
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input_metadata=input_metadata,
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cache_events=None,
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)
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# Calculate the number of blocks that can be allocated with the
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# profiled peak memory.
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torch.cuda.synchronize()
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peak_memory = torch.cuda.max_memory_allocated()
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total_gpu_memory = get_gpu_memory()
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cache_block_size = CacheEngine.get_cache_block_size(
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block_size, self.model_config, self.parallel_config)
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num_gpu_blocks = int((total_gpu_memory * gpu_memory_utilization
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- peak_memory) // cache_block_size)
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num_cpu_blocks = int(cpu_swap_space // cache_block_size)
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torch.cuda.empty_cache()
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# Reset the seed to ensure that the random state is not affected by
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# the model initialization and profiling.
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set_random_seed(self.model_config.seed)
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return num_gpu_blocks, num_cpu_blocks
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def init_cache_engine(self, cache_config: CacheConfig) -> None:
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self.cache_config = cache_config
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self.block_size = cache_config.block_size
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self.cache_engine = CacheEngine(
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self.cache_config, self.model_config, self.parallel_config)
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self.cache_events = self.cache_engine.events
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self.gpu_cache = self.cache_engine.gpu_cache
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def _prepare_inputs(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> Tuple[torch.LongTensor, torch.LongTensor, InputMetadata]:
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seq_groups: List[Tuple[List[int], SamplingParams]] = []
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input_tokens: List[int] = []
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input_positions: List[int] = []
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slot_mapping: List[int] = []
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# Add prompt tokens.
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prompt_lens: List[int] = []
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for seq_group_metadata in seq_group_metadata_list:
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if not seq_group_metadata.is_prompt:
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continue
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seq_ids = list(seq_group_metadata.seq_data.keys())
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sampling_params = seq_group_metadata.sampling_params
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seq_groups.append((seq_ids, sampling_params))
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# Use any sequence in the group.
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seq_id = seq_ids[0]
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seq_data = seq_group_metadata.seq_data[seq_id]
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prompt_tokens = seq_data.get_token_ids()
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prompt_len = len(prompt_tokens)
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prompt_lens.append(prompt_len)
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input_tokens.extend(prompt_tokens)
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# NOTE(woosuk): Here we assume that the first token in the prompt
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# is always the first token in the sequence.
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input_positions.extend(range(len(prompt_tokens)))
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if seq_group_metadata.block_tables is None:
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# During memory profiling, the block tables are not initialized
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# yet. In this case, we just use a dummy slot mapping.
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slot_mapping.extend([0] * prompt_len)
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continue
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# Compute the slot mapping.
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block_table = seq_group_metadata.block_tables[seq_id]
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for i in range(prompt_len):
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block_number = block_table[i // self.block_size]
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block_offset = i % self.block_size
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slot = block_number * self.block_size + block_offset
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slot_mapping.append(slot)
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# Add generation tokens.
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max_context_len = 0
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max_num_blocks_per_seq = 0
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context_lens: List[int] = []
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generation_block_tables: List[List[int]] = []
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for seq_group_metadata in seq_group_metadata_list:
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if seq_group_metadata.is_prompt:
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continue
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seq_ids = list(seq_group_metadata.seq_data.keys())
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sampling_params = seq_group_metadata.sampling_params
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seq_groups.append((seq_ids, sampling_params))
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for seq_id in seq_ids:
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seq_data = seq_group_metadata.seq_data[seq_id]
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generation_token = seq_data.get_last_token_id()
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input_tokens.append(generation_token)
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context_len = seq_data.get_len()
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position = context_len - 1
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input_positions.append(position)
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block_table = seq_group_metadata.block_tables[seq_id]
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generation_block_tables.append(block_table)
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max_context_len = max(max_context_len, context_len)
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max_num_blocks_per_seq = max(
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max_num_blocks_per_seq, len(block_table))
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context_lens.append(context_len)
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block_number = block_table[position // self.block_size]
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block_offset = position % self.block_size
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slot = block_number * self.block_size + block_offset
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slot_mapping.append(slot)
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# Optimization: Pad the input length to be a multiple of 8.
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# This is required for utilizing the Tensor Cores in NVIDIA GPUs.
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input_tokens = _pad_to_alignment(input_tokens, multiple_of=8)
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input_positions = _pad_to_alignment(input_positions, multiple_of=8)
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# Convert to tensors.
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tokens_tensor = torch.tensor(
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input_tokens, dtype=torch.long, device='cuda')
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positions_tensor = torch.tensor(
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input_positions, dtype=torch.long, device='cuda')
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slot_mapping_tensor = torch.tensor(
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slot_mapping, dtype=torch.int, device='cuda')
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context_lens_tensor = torch.tensor(
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context_lens, dtype=torch.int, device='cuda')
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padded_block_tables = [
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_pad_to_max(block_table, max_num_blocks_per_seq)
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for block_table in generation_block_tables]
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block_tables_tensor = torch.tensor(
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padded_block_tables, dtype=torch.int, device='cuda')
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seq_data: Dict[int, SequenceData] = {}
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for seq_group_metadata in seq_group_metadata_list:
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seq_data.update(seq_group_metadata.seq_data)
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input_metadata = InputMetadata(
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seq_groups=seq_groups,
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seq_data=seq_data,
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prompt_lens=prompt_lens,
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slot_mapping=slot_mapping_tensor,
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context_lens=context_lens_tensor,
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max_context_len=max_context_len,
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block_tables=block_tables_tensor,
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)
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return tokens_tensor, positions_tensor, input_metadata
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@torch.inference_mode()
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def execute_model(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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blocks_to_swap_in: Dict[int, int],
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blocks_to_swap_out: Dict[int, int],
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blocks_to_copy: Dict[int, List[int]],
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) -> Dict[int, SequenceOutputs]:
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# Issue cache operations.
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issued_cache_op = False
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if blocks_to_swap_in:
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self.cache_engine.swap_in(blocks_to_swap_in)
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issued_cache_op = True
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if blocks_to_swap_out:
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self.cache_engine.swap_out(blocks_to_swap_out)
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issued_cache_op = True
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if blocks_to_copy:
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self.cache_engine.copy(blocks_to_copy)
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issued_cache_op = True
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if issued_cache_op:
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cache_events = self.cache_events
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else:
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cache_events = None
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# If there is no input, we don't need to execute the model.
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if not seq_group_metadata_list:
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if cache_events is not None:
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for event in cache_events:
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event.wait()
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return {}
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# Prepare input tensors.
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input_tokens, input_positions, input_metadata = self._prepare_inputs(
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seq_group_metadata_list)
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# Execute the model.
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output = self.model(
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input_ids=input_tokens,
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positions=input_positions,
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kv_caches=self.gpu_cache,
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input_metadata=input_metadata,
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cache_events=cache_events,
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)
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return output
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def _init_distributed_environment(
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parallel_config: ParallelConfig,
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rank: int,
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distributed_init_method: str,
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) -> None:
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"""Initialize the distributed environment."""
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torch.distributed.init_process_group(
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backend="nccl",
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world_size=parallel_config.world_size,
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rank=rank,
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init_method=distributed_init_method,
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)
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# A small all_reduce for warmup.
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torch.distributed.all_reduce(torch.zeros(1).cuda())
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initialize_model_parallel(parallel_config.tensor_parallel_size,
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parallel_config.pipeline_parallel_size)
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def _pad_to_alignment(x: List[int], multiple_of: int) -> List[int]:
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return x + [0] * ((-len(x)) % multiple_of)
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def _pad_to_max(x: List[int], max_len: int) -> List[int]:
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return x + [0] * (max_len - len(x))
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