import gc import time from typing import TYPE_CHECKING, Dict, List, Optional, Tuple import numpy as np import torch import torch.distributed import torch.nn as nn from vllm.config import CompilationLevel, VllmConfig from vllm.distributed.parallel_state import graph_capture from vllm.forward_context import set_forward_context from vllm.inputs import INPUT_REGISTRY, InputRegistry from vllm.logger import init_logger from vllm.model_executor.model_loader import get_model from vllm.multimodal import MultiModalKwargs from vllm.sampling_params import SamplingType from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, LayerBlockType, cdiv, is_pin_memory_available) from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend, FlashAttentionMetadata) from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.sample.metadata import SamplingMetadata from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch if TYPE_CHECKING: from vllm.v1.core.scheduler import SchedulerOutput logger = init_logger(__name__) class GPUModelRunner: def __init__( self, vllm_config: VllmConfig, device: torch.device, input_registry: InputRegistry = INPUT_REGISTRY, ): self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config self.load_config = vllm_config.load_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config self.speculative_config = vllm_config.speculative_config self.prompt_adapter_config = vllm_config.prompt_adapter_config self.observability_config = vllm_config.observability_config model_config = self.model_config cache_config = self.cache_config scheduler_config = self.scheduler_config parallel_config = self.parallel_config self.device = device self.pin_memory = is_pin_memory_available() self.dtype = self.model_config.dtype if cache_config.cache_dtype == "auto": self.kv_cache_dtype = self.dtype else: self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ cache_config.cache_dtype] self.is_multimodal_model = model_config.is_multimodal_model self.sliding_window = model_config.get_sliding_window() self.block_size = cache_config.block_size self.max_model_len = model_config.max_model_len self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size) self.max_num_tokens = scheduler_config.max_num_batched_tokens # Model-related. self.num_attn_layers = model_config.get_num_layers_by_block_type( parallel_config, LayerBlockType.attention) self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) self.head_size = model_config.get_head_size() self.hidden_size = model_config.get_hidden_size() # Multi-modal data support self.input_registry = input_registry # Lazy initialization # self.model: nn.Module # Set after load_model self.kv_caches: List[torch.Tensor] = [] # req_id -> (input_id -> encoder_output) self.encoder_cache: Dict[str, Dict[int, torch.Tensor]] = {} # Request states. self.requests: Dict[str, CachedRequestState] = {} # Persistent batch. self.input_batch = InputBatch( max_num_reqs=self.scheduler_config.max_num_seqs, max_model_len=self.max_model_len, max_num_blocks_per_req=self.max_num_blocks_per_req, device=self.device, pin_memory=self.pin_memory, ) self.use_cuda_graph = (self.vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and not self.model_config.enforce_eager) # TODO(woosuk): Provide an option to tune the max cudagraph batch size. # The convention is different. # self.cudagraph_batch_sizes sorts in ascending order. # The batch sizes in the config are in descending order. self.cudagraph_batch_sizes = list( reversed(self.vllm_config.compilation_config.capture_sizes)) # Persistent buffers for CUDA graphs. self.input_ids = torch.zeros(self.max_num_tokens, dtype=torch.int32, device=self.device) self.positions = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=self.device) self.inputs_embeds = torch.zeros( (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=self.device) def _update_states(self, scheduler_output: "SchedulerOutput") -> None: # Remove stopped requests from the cached states. # Keep the states of the pre-empted requests. for req_id in scheduler_output.finished_req_ids: self.requests.pop(req_id, None) self.encoder_cache.pop(req_id, None) # Free the cached encoder outputs. for req_id, input_id in scheduler_output.free_encoder_input_ids: encoder_outputs = self.encoder_cache.get(req_id) if encoder_outputs is not None: encoder_outputs.pop(input_id, None) if not encoder_outputs: self.encoder_cache.pop(req_id, None) # Remove the requests from the persistent batch. stopped_req_ids = set().union( scheduler_output.preempted_req_ids, scheduler_output.finished_req_ids, ) removed_req_indices: List[int] = [] for req_id in stopped_req_ids: req_index = self.input_batch.remove_request(req_id) if req_index is not None: removed_req_indices.append(req_index) # Update the states of the running requests. for req_data in scheduler_output.scheduled_running_reqs: req_id = req_data.req_id req_state = self.requests[req_id] req_index = self.input_batch.req_id_to_index[req_id] # Update the num_computed_tokens. req_state.num_computed_tokens = req_data.num_computed_tokens self.input_batch.num_computed_tokens_cpu[req_index] = ( req_data.num_computed_tokens) # Update the block table. num_new_blocks = len(req_data.new_block_ids) if num_new_blocks == 0: continue start_index = len(req_state.block_ids) end_index = start_index + num_new_blocks req_state.block_ids.extend(req_data.new_block_ids) self.input_batch.block_table_cpu[ req_index, start_index:end_index] = req_data.new_block_ids req_ids_to_add: List[str] = [] # Add new requests to the cached states. for req_data in scheduler_output.scheduled_new_reqs: req_id = req_data.req_id sampling_params = req_data.sampling_params if sampling_params.sampling_type == SamplingType.RANDOM_SEED: generator = torch.Generator(device=self.device) generator.manual_seed(sampling_params.seed) else: generator = None self.requests[req_id] = CachedRequestState( req_id=req_id, prompt_token_ids=req_data.prompt_token_ids, prompt=req_data.prompt, mm_inputs=req_data.mm_inputs, mm_positions=req_data.mm_positions, sampling_params=sampling_params, generator=generator, block_ids=req_data.block_ids, num_computed_tokens=req_data.num_computed_tokens, output_token_ids=[], ) req_ids_to_add.append(req_id) # Update the cached states of the resumed requests. for req_data in scheduler_output.scheduled_resumed_reqs: req_id = req_data.req_id req_state = self.requests[req_id] req_state.block_ids = req_data.block_ids req_state.num_computed_tokens = req_data.num_computed_tokens req_ids_to_add.append(req_id) # Add the new or resumed requests to the persistent batch. # The smaller empty indices are filled first. removed_req_indices = sorted(removed_req_indices, reverse=True) for req_id in req_ids_to_add: req_state = self.requests[req_id] if removed_req_indices: # Fill the empty index. req_index = removed_req_indices.pop() else: # Append to the end. req_index = None self.input_batch.add_request(req_state, req_index) # Condense the batched states if there are empty indices. if removed_req_indices: self.input_batch.condense(removed_req_indices) def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens assert total_num_scheduled_tokens > 0 num_reqs = self.input_batch.num_reqs assert num_reqs > 0 # OPTIMIZATION: Start copying the block table first. # This way, we can overlap the copy with the following CPU operations. self.input_batch.block_table[:num_reqs].copy_( self.input_batch.block_table_cpu_tensor[:num_reqs], non_blocking=True) # Get the number of scheduled tokens for each request. # TODO: The Python loop can be slow. Optimize. num_scheduled_tokens = [] max_num_scheduled_tokens = 0 for req_id in self.input_batch.req_ids[:num_reqs]: num_tokens = scheduler_output.num_scheduled_tokens[req_id] num_scheduled_tokens.append(num_tokens) max_num_scheduled_tokens = max(max_num_scheduled_tokens, num_tokens) num_scheduled_tokens = np.array(num_scheduled_tokens, dtype=np.int32) assert max_num_scheduled_tokens > 0 # Get request indices. # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2] indices = np.arange(num_reqs) req_indices = np.repeat(indices, num_scheduled_tokens) # Get batched arange. # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] arange_matrix = np.tile(np.arange(max_num_scheduled_tokens), (num_reqs, 1)) mask = arange_matrix < num_scheduled_tokens[:, np.newaxis] arange = arange_matrix[mask] # Get positions. positions = torch.empty((total_num_scheduled_tokens, ), dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) positions_np = positions.numpy() np.add(self.input_batch.num_computed_tokens_cpu[req_indices], arange, out=positions_np) # Get token indices. # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2] # where M is the max_model_len. token_indices = (positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]) token_indices = torch.from_numpy(token_indices) input_ids = torch.empty((total_num_scheduled_tokens, ), dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) torch.index_select(torch.from_numpy( self.input_batch.token_ids_cpu).flatten(), 0, token_indices, out=input_ids) # Calculate the slot mapping. # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1] # where K is the max_num_blocks_per_req and the block size is 2. # NOTE(woosuk): We can't simply use `token_indices // block_size` here # because M (max_model_len) is not necessarily divisible by block_size. block_numbers = self.input_batch.block_table_cpu_tensor.flatten()[ req_indices * self.max_num_blocks_per_req + positions_np // self.block_size] block_offsets = torch.from_numpy(positions_np % self.block_size) slot_mapping = torch.empty((total_num_scheduled_tokens, ), dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) torch.add(block_numbers * self.block_size, block_offsets, out=slot_mapping) # Prepare the attention metadata. query_start_loc = torch.empty((num_reqs + 1, ), dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) query_start_loc_np = query_start_loc.numpy() query_start_loc_np[0] = 0 np.cumsum(num_scheduled_tokens, out=query_start_loc_np[1:]) seq_lens = (self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens) max_seq_len = seq_lens.max() seq_start_loc = torch.empty((num_reqs + 1, ), dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) seq_start_loc_np = seq_start_loc.numpy() seq_start_loc_np[0] = 0 np.cumsum(seq_lens, out=seq_start_loc_np[1:]) self.input_ids[:total_num_scheduled_tokens].copy_(input_ids, non_blocking=True) self.positions[:total_num_scheduled_tokens].copy_(positions, non_blocking=True) query_start_loc = query_start_loc.to(self.device, non_blocking=True) seq_start_loc = seq_start_loc.to(self.device, non_blocking=True) slot_mapping = slot_mapping.to(self.device, non_blocking=True).long() attn_metadata = FlashAttentionMetadata( num_actual_tokens=total_num_scheduled_tokens, max_query_len=max_num_scheduled_tokens, query_start_loc=query_start_loc, max_seq_len=max_seq_len, seq_start_loc=seq_start_loc, block_table=self.input_batch.block_table[:num_reqs], slot_mapping=slot_mapping, ) # NOTE(woosuk): Due to chunked prefills, there can be at most 1 partial # request in the batch. While we should not sample any token from this # partial request, we do so for simplicity. We will ignore the sampled # token from the partial request. # TODO: Support prompt logprobs. logits_indices = query_start_loc[1:] - 1 return attn_metadata, logits_indices def _prepare_sampling( self, scheduler_output: "SchedulerOutput", ) -> SamplingMetadata: skip_copy = True if (scheduler_output.finished_req_ids or scheduler_output.preempted_req_ids): skip_copy = False if (scheduler_output.scheduled_new_reqs or scheduler_output.scheduled_resumed_reqs): skip_copy = False # Create the sampling metadata. sampling_metadata = self.input_batch.make_sampling_metadata(skip_copy) return sampling_metadata def _execute_encoder(self, scheduler_output: "SchedulerOutput"): scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs if not scheduled_encoder_inputs: return # Batch the multi-modal inputs. mm_inputs: List[MultiModalKwargs] = [] req_input_ids: List[Tuple[int, int]] = [] for req_id, encoder_input_ids in scheduled_encoder_inputs.items(): req_state = self.requests[req_id] for input_id in encoder_input_ids: mm_inputs.append(req_state.mm_inputs[input_id]) req_input_ids.append((req_id, input_id)) batched_mm_inputs = MultiModalKwargs.batch(mm_inputs) batched_mm_inputs = MultiModalKwargs.as_kwargs(batched_mm_inputs, device=self.device) # Run the encoder. # `encoder_outputs` is either of the following: # 1. A tensor of shape [num_images, feature_size, hidden_size] # in case when feature_size is fixed across all images. # 2. A list (length: num_images) of tensors, each of shape # [feature_size, hidden_size] in case when the feature size is # dynamic depending on input images. encoder_outputs = self.model.get_multimodal_embeddings( **batched_mm_inputs) # Cache the encoder outputs. for (req_id, input_id), output in zip(req_input_ids, encoder_outputs): if req_id not in self.encoder_cache: self.encoder_cache[req_id] = {} self.encoder_cache[req_id][input_id] = output def _gather_encoder_outputs( self, scheduler_output: "SchedulerOutput", ) -> List[torch.Tensor]: encoder_outputs: List[torch.Tensor] = [] num_reqs = self.input_batch.num_reqs for req_id in self.input_batch.req_ids[:num_reqs]: num_scheduled_tokens = scheduler_output.num_scheduled_tokens[ req_id] req_state = self.requests[req_id] num_computed_tokens = req_state.num_computed_tokens mm_positions = req_state.mm_positions for i, pos_info in enumerate(mm_positions): start_pos = pos_info["offset"] num_encoder_tokens = pos_info["length"] # The encoder output is needed if the two ranges overlap: # [num_computed_tokens, # num_computed_tokens + num_scheduled_tokens) and # [start_pos, start_pos + num_encoder_tokens) if start_pos >= num_computed_tokens + num_scheduled_tokens: # The encoder output is not needed in this step. break if start_pos + num_encoder_tokens <= num_computed_tokens: # The encoder output is already processed and stored # in the decoder's KV cache. continue start_idx = max(num_computed_tokens - start_pos, 0) end_idx = min( num_computed_tokens - start_pos + num_scheduled_tokens, num_encoder_tokens) assert start_idx < end_idx assert req_id in self.encoder_cache assert i in self.encoder_cache[req_id] encoder_output = self.encoder_cache[req_id][i] encoder_outputs.append(encoder_output[start_idx:end_idx]) return encoder_outputs @torch.inference_mode() def execute_model( self, scheduler_output: "SchedulerOutput", ) -> ModelRunnerOutput: self._update_states(scheduler_output) if self.is_multimodal_model: # Run the multimodal encoder if any. self._execute_encoder(scheduler_output) encoder_outputs = self._gather_encoder_outputs(scheduler_output) else: encoder_outputs = [] # Prepare the decoder inputs. attn_metadata, logits_indices = self._prepare_inputs(scheduler_output) num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if (self.use_cuda_graph and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]): # Use piecewise CUDA graphs. # Add padding to the batch size. num_input_tokens = self._get_padded_batch_size( num_scheduled_tokens) else: # Eager mode. num_input_tokens = num_scheduled_tokens attn_metadata.num_input_tokens = num_input_tokens if self.is_multimodal_model: # NOTE(woosuk): To unify token ids and soft tokens (vision # embeddings), we always use embeddings (rather than token ids) # as input to the multimodal model, even when the input is text. input_ids = self.input_ids[:num_scheduled_tokens] if encoder_outputs: inputs_embeds = self.model.get_input_embeddings( input_ids, encoder_outputs) else: inputs_embeds = self.model.get_input_embeddings(input_ids) # TODO(woosuk): Avoid the copy. Optimize. self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) inputs_embeds = self.inputs_embeds[:num_input_tokens] input_ids = None else: # For text-only models, we use token ids as input. # While it is possible to use embeddings as input just like the # multimodal models, it is not desirable for performance since # then the embedding layer is not included in the CUDA graph. input_ids = self.input_ids[:num_input_tokens] inputs_embeds = None # Run the decoder. # Use persistent buffers for CUDA graphs. with set_forward_context(attn_metadata, self.vllm_config): hidden_states = self.model( input_ids=input_ids, positions=self.positions[:num_input_tokens], kv_caches=self.kv_caches, attn_metadata=None, inputs_embeds=inputs_embeds, ) hidden_states = hidden_states[:num_scheduled_tokens] hidden_states = hidden_states[logits_indices] logits = self.model.compute_logits(hidden_states, None) # Sample the next token and get logprobs if needed. sampling_metadata = self._prepare_sampling(scheduler_output) sampler_output = self.model.sample( logits=logits, sampling_metadata=sampling_metadata, ) sampled_token_ids = sampler_output.sampled_token_ids # TODO(woosuk): The following loop can be slow since it iterates over # the requests one by one. Optimize. num_reqs = self.input_batch.num_reqs for i, req_id in enumerate(self.input_batch.req_ids[:num_reqs]): req_state = self.requests[req_id] seq_len = (req_state.num_computed_tokens + scheduler_output.num_scheduled_tokens[req_id]) assert seq_len <= req_state.num_tokens if seq_len == req_state.num_tokens: # Append the sampled token to the output token ids. token_id = sampled_token_ids[i] self.input_batch.token_ids_cpu[i, seq_len] = token_id req_state.output_token_ids.append(token_id) else: # Ignore the sampled token from the partial request. # Rewind the generator state as if the token was not sampled. generator = self.input_batch.generators.get(i) if generator is not None: # This relies on cuda-specific torch-internal impl details generator.set_offset(generator.get_offset() - 4) if sampler_output.logprob_token_ids is None: logprob_token_ids = None else: logprob_token_ids = sampler_output.logprob_token_ids.cpu() if sampler_output.logprobs is None: logprobs = None else: logprobs = sampler_output.logprobs.cpu() model_runner_output = ModelRunnerOutput( req_ids=self.input_batch.req_ids[:num_reqs], req_id_to_index=self.input_batch.req_id_to_index, sampled_token_ids=sampled_token_ids, logprob_token_ids_cpu=logprob_token_ids, logprobs_cpu=logprobs, ) return model_runner_output def load_model(self) -> None: logger.info("Starting to load model %s...", self.model_config.model) with DeviceMemoryProfiler() as m: # noqa: SIM117 self.model = get_model(vllm_config=self.vllm_config) self.model_memory_usage = m.consumed_memory logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) @torch.inference_mode() def _dummy_run( self, model: nn.Module, num_tokens: int, kv_caches: List[torch.Tensor], ) -> torch.Tensor: if self.is_multimodal_model: input_ids = None inputs_embeds = self.inputs_embeds[:num_tokens] else: input_ids = self.input_ids[:num_tokens] inputs_embeds = None with set_forward_context(None, self.vllm_config): hidden_states = model( input_ids=input_ids, positions=self.positions[:num_tokens], kv_caches=kv_caches, attn_metadata=None, inputs_embeds=inputs_embeds, ) return hidden_states def profile_run(self) -> None: # TODO(woosuk): Profile the max memory usage of the encoder and # the encoder cache. # use an empty tensor instead of `None`` to force Dynamo to pass # it by reference, rather by specializing on the value `None`. # the `dtype` argument does not matter, and we use `float32` as # a placeholder (it has wide hardware support). # it is important to create tensors inside the loop, rather than # multiplying the list, to avoid Dynamo from treating them as # tensor aliasing. dummy_kv_caches = [ torch.tensor([], dtype=torch.float32, device=self.device) for _ in range(self.num_attn_layers) ] # Trigger compilation for general shape. hidden_states = self._dummy_run(self.model, self.max_num_tokens, dummy_kv_caches) logits = self.model.compute_logits(hidden_states, None) logits = logits[:self.max_num_tokens] # TODO(woosuk): Consider the memory usage of the sampler. torch.cuda.synchronize() del hidden_states, logits gc.collect() def capture_model(self) -> None: if not self.use_cuda_graph: logger.warning( "Skipping CUDA graph capture. Please add " "-O %s to use CUDA graphs.", CompilationLevel.PIECEWISE) return start_time = time.perf_counter() start_free_gpu_memory = torch.cuda.mem_get_info()[0] # Trigger CUDA graph capture for specific shapes. # Capture the large shapes first so that the smaller shapes # can reuse the memory pool allocated for the large shapes. with graph_capture(): for num_tokens in reversed(self.cudagraph_batch_sizes): for _ in range(self.vllm_config.compilation_config. cudagraph_num_of_warmups): self._dummy_run(self.model, num_tokens, self.kv_caches) self._dummy_run(self.model, num_tokens, self.kv_caches) end_time = time.perf_counter() end_free_gpu_memory = torch.cuda.mem_get_info()[0] elapsed_time = end_time - start_time cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory # This usually takes 5~20 seconds. logger.info("Graph capturing finished in %.0f secs, took %.2f GiB", elapsed_time, cuda_graph_size / (1 << 30)) def initialize_kv_cache(self, num_blocks: int) -> None: assert len(self.kv_caches) == 0 kv_cache_shape = FlashAttentionBackend.get_kv_cache_shape( num_blocks, self.block_size, self.num_kv_heads, self.head_size) for _ in range(self.num_attn_layers): self.kv_caches.append( torch.zeros(kv_cache_shape, dtype=self.kv_cache_dtype, device=self.device)) def _get_padded_batch_size(self, batch_size: int) -> Optional[int]: # TODO: Optimize this? for size in self.cudagraph_batch_sizes: if batch_size <= size: return size return None