import time from typing import Dict, List, Optional, Tuple, Set, Union import numpy as np import torch import torch.nn as nn from aphrodite.common.config import ModelConfig, LoRAConfig, ParallelConfig, SchedulerConfig from aphrodite.common.logger import init_logger from aphrodite.modeling import get_model, InputMetadata, SamplingMetadata from aphrodite.modeling.megatron.communication_op import (broadcast_tensor_dict ) from aphrodite.modeling.megatron import custom_all_reduce from aphrodite.common.sampling_params import SamplingParams, SamplingType from aphrodite.common.sequence import (SamplerOutput, SequenceData, SequenceGroupMetadata) from aphrodite.modeling.sampling_metadata import PersistentMetadata from aphrodite.lora.worker_manager import LRUCacheWorkerLoRAManager from aphrodite.lora.layers import LoRAMapping from aphrodite.lora.request import LoRARequest from aphrodite.common.utils import in_wsl logger = init_logger(__name__) KVCache = Tuple[torch.Tensor, torch.Tensor] _PAD_SLOT_ID = -1 LORA_WARMUP_RANK = 8 # Capture graphs for batch size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256. # NOTE: _get_graph_batch_size needs to be updated if this list is changed. _BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [8 * i for i in range(1, 33)] class ModelRunner: def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, ): self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.lora_config = lora_config self.is_driver_worker = is_driver_worker # model_config can be None in tests/samplers/test_sampler.py. # FIXME: This is a hack to make the tests work. Refactor this. self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None) self.device = torch.device(torch.cuda.current_device()) self.model = None self.block_size = None # Set after initial profiling. self.lora_manager = None self.graph_runners: Dict[int, CUDAGraphRunner] = {} self.graph_memory_pool = None # Set during graph capture. self.max_context_len_to_capture = ( self.model_config.max_context_len_to_capture if self.model_config is not None else 0) # When using CUDA graph, the input block tables must be padded to # max_context_len_to_capture. However, creating the block table in # Python can be expensive. To optimize this, we cache the block table # in numpy and only copy the actual input content at every iteration. # The shape of the cached block table will be # (max batch size to capture, max context len to capture / block size). self.graph_block_tables = None # Set after initial profiling. # cache in_wsl result self.in_wsl = in_wsl() self.kv_cache_dtype = kv_cache_dtype def load_model(self) -> None: self.model = get_model(self.model_config, self.lora_config) vocab_size = self.model.config.vocab_size if self.lora_config: self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens + self.scheduler_config.max_paddings, vocab_size, self.lora_config, self.device) self.model = self.lora_manager.create_lora_manager(self.model) def set_block_size(self, block_size: int) -> None: self.block_size = block_size max_num_blocks = (self.max_context_len_to_capture + block_size - 1) // block_size self.graph_block_tables = np.zeros( (max(_BATCH_SIZES_TO_CAPTURE), max_num_blocks), dtype=np.int32) def _prepare_prompt( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, List[int], List[int], List[int], List[int], Set[LoRARequest]]: assert len(seq_group_metadata_list) > 0 input_tokens: List[List[int]] = [] input_positions: List[List[int]] = [] slot_mapping: List[List[int]] = [] lora_index_mapping: List[int] = [] lora_prompt_mapping: List[int] = [] lora_requests: Set[LoRARequest] = set() prompt_lens: List[int] = [] context_lens: List[int] = [] subquery_lens: List[int] = [] prefix_block_tables: List[List[int]] = [] for seq_group_metadata in seq_group_metadata_list: assert seq_group_metadata.is_prompt seq_ids = list(seq_group_metadata.seq_data.keys()) assert len(seq_ids) == 1 seq_id = seq_ids[0] seq_data = seq_group_metadata.seq_data[seq_id] prompt_tokens = seq_data.get_token_ids() prompt_len = len(prompt_tokens) prompt_lens.append(prompt_len) prefix_len = 0 prefix = seq_group_metadata.prefix if prefix is not None and prefix.computed: prefix_len = prefix.get_length() prompt_tokens = prompt_tokens[prefix_len:] prefix_block_tables.append(prefix.get_block_numbers()) else: prefix_block_tables.append([]) # actual prompt lens context_lens.append(prefix_len) subquery_lens.append(prompt_len - prefix_len) input_tokens.append(prompt_tokens) # NOTE: Here we assume that the first token in the prompt # is always the first token in the sequence. input_positions.append( list(range(prefix_len, prefix_len + len(prompt_tokens)))) lora_id = seq_group_metadata.lora_int_id if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) lora_index_mapping.append([lora_id] * prompt_len) lora_prompt_mapping.extend( [lora_id] * (prompt_len if seq_group_metadata.sampling_params.prompt_logprobs else 1)) if seq_group_metadata.block_tables is None: # During memory profiling, the block tables are not initialized # yet. In this case, we just use a dummy slot mapping. slot_mapping.append([_PAD_SLOT_ID] * prompt_len) continue # Compute the slot mapping. slot_mapping.append([]) block_table = seq_group_metadata.block_tables[seq_id] # Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID, # where start_idx is max(0, prompt_len - sliding_window). # For example, if the prompt len is 10, sliding window is 8, and # block size is 4, the first two tokens are masked and the slot # mapping will be [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1]. start_idx = 0 if self.sliding_window is not None: assert prefix_len == 0, ( "Prefix caching is currently not supported with " "sliding window attention") start_idx = max(0, prompt_len - self.sliding_window) for i in range(prefix_len, prompt_len): if i < start_idx: slot_mapping[-1].append(_PAD_SLOT_ID) continue block_number = block_table[i // self.block_size] block_offset = i % self.block_size slot = block_number * self.block_size + block_offset slot_mapping[-1].append(slot) max_prompt_len = max(subquery_lens) input_tokens = _make_tensor_with_pad(input_tokens, max_prompt_len, pad=0, dtype=torch.long) input_positions = _make_tensor_with_pad(input_positions, max_prompt_len, pad=0, dtype=torch.long) slot_mapping = _make_tensor_with_pad(slot_mapping, max_prompt_len, pad=_PAD_SLOT_ID, dtype=torch.long) lora_index_mapping = [ _pad_to_max(mapping, max_prompt_len, pad=0) for mapping in lora_index_mapping ] context_lens_tensor = torch.tensor(context_lens, dtype=torch.int, device="cuda") # Prepare prefix block tables max_prompt_block_table_len = max(len(t) for t in prefix_block_tables) block_tables = _make_tensor_with_pad( prefix_block_tables, max_len=max_prompt_block_table_len, pad=0, dtype=torch.int, ) start_loc_tensor = torch.arange(0, len(prompt_lens) * max_prompt_len, max_prompt_len, dtype=torch.long, device="cuda") prompt_lens_tensor = torch.tensor(prompt_lens, dtype=torch.long, device="cuda") input_metadata = InputMetadata( is_prompt=True, slot_mapping=slot_mapping, prompt_lens=prompt_lens_tensor, max_seq_len=max_prompt_len, start_loc=start_loc_tensor, max_context_len=None, context_lens=context_lens_tensor, block_tables=block_tables, use_cuda_graph=False, kv_cache_dtype=self.kv_cache_dtype, ) return (input_tokens, input_positions, input_metadata, prompt_lens, subquery_lens, lora_index_mapping, lora_prompt_mapping, lora_requests) def _prepare_decode( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, List[int], List[int], Set[LoRARequest]]: assert len(seq_group_metadata_list) > 0 input_tokens: List[List[int]] = [] input_positions: List[List[int]] = [] slot_mapping: List[List[int]] = [] context_lens: List[int] = [] block_tables: List[List[int]] = [] lora_index_mapping: List[int] = [] lora_prompt_mapping: List[int] = [] lora_requests: Set[LoRARequest] = set() for seq_group_metadata in seq_group_metadata_list: assert not seq_group_metadata.is_prompt seq_ids = list(seq_group_metadata.seq_data.keys()) lora_id = seq_group_metadata.lora_int_id if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) for seq_id in seq_ids: seq_data = seq_group_metadata.seq_data[seq_id] generation_token = seq_data.get_last_token_id() input_tokens.append([generation_token]) seq_len = seq_data.get_len() position = seq_len - 1 input_positions.append([position]) context_len = seq_len if self.sliding_window is None else min( seq_len, self.sliding_window) context_lens.append(context_len) block_table = seq_group_metadata.block_tables[seq_id] block_number = block_table[position // self.block_size] block_offset = position % self.block_size slot = block_number * self.block_size + block_offset slot_mapping.append([slot]) lora_index_mapping.append([lora_id]) lora_prompt_mapping.append(lora_id) if self.sliding_window is not None: sliding_window_blocks = (self.sliding_window // self.block_size) block_table = block_table[-sliding_window_blocks:] block_tables.append(block_table) batch_size = len(input_tokens) max_context_len = max(context_lens) use_captured_graph = ( not self.model_config.enforce_eager and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1] and max_context_len <= self.max_context_len_to_capture) if use_captured_graph: # Pad the input tokens, positions, and slot mapping to match the # batch size of the captured graph. graph_batch_size = _get_graph_batch_size(batch_size) assert graph_batch_size >= batch_size for _ in range(graph_batch_size - batch_size): input_tokens.append([]) input_positions.append([]) slot_mapping.append([]) context_lens.append(1) block_tables.append([]) batch_size = graph_batch_size input_tokens = _make_tensor_with_pad(input_tokens, max_len=1, pad=0, dtype=torch.long, device="cuda") input_positions = _make_tensor_with_pad(input_positions, max_len=1, pad=0, dtype=torch.long, device="cuda") slot_mapping = _make_tensor_with_pad(slot_mapping, max_len=1, pad=_PAD_SLOT_ID, dtype=torch.long, device="cuda") context_lens = torch.tensor(context_lens, dtype=torch.int, device="cuda") if use_captured_graph: # The shape of graph_block_tables is # [max batch size, max context len // block size]. input_block_tables = self.graph_block_tables[:batch_size] for i, block_table in enumerate(block_tables): if block_table: input_block_tables[i, :len(block_table)] = block_table block_tables = torch.tensor(input_block_tables, device="cuda") else: max_block_table_len = max( len(block_table) for block_table in block_tables) block_tables = _make_tensor_with_pad( block_tables, max_len=max_block_table_len, pad=0, dtype=torch.int, device="cuda", ) lora_index_mapping = [ _pad_to_max(mapping, 1, pad=0) for mapping in lora_index_mapping ] input_metadata = InputMetadata( is_prompt=False, slot_mapping=slot_mapping, prompt_lens=None, max_seq_len=None, start_loc=None, max_context_len=max_context_len, context_lens=context_lens, block_tables=block_tables, use_cuda_graph=use_captured_graph, kv_cache_dtype=self.kv_cache_dtype, ) return (input_tokens, input_positions, input_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests) def _prepare_sample( self, seq_group_metadata_list: List[SequenceGroupMetadata], prompt_lens: List[int], subquery_lens: Optional[List[int]], ) -> SamplingMetadata: seq_groups: List[Tuple[List[int], SamplingParams]] = [] selected_token_indices: List[int] = [] selected_token_start_idx = 0 categorized_sample_indices = {t: [] for t in SamplingType} categorized_sample_indices_start_idx = 0 max_subquery_len = max(subquery_lens) if subquery_lens else 1 for i, seq_group_metadata in enumerate(seq_group_metadata_list): seq_ids = list(seq_group_metadata.seq_data.keys()) sampling_params = seq_group_metadata.sampling_params seq_groups.append((seq_ids, sampling_params)) if seq_group_metadata.is_prompt: assert len(seq_ids) == 1 assert subquery_lens is not None subquery_len = subquery_lens[i] if sampling_params.prompt_logprobs is not None: # NOTE: prompt token positions do not need sample, skip categorized_sample_indices_start_idx += subquery_len - 1 categorized_sample_indices[ sampling_params.sampling_type].append( categorized_sample_indices_start_idx) categorized_sample_indices_start_idx += 1 if sampling_params.prompt_logprobs is not None: selected_token_indices.extend( range(selected_token_start_idx, selected_token_start_idx + subquery_len - 1)) selected_token_indices.append(selected_token_start_idx + subquery_len - 1) selected_token_start_idx += max_subquery_len else: num_seqs = len(seq_ids) selected_token_indices.extend( range(selected_token_start_idx, selected_token_start_idx + num_seqs)) selected_token_start_idx += num_seqs categorized_sample_indices[ sampling_params.sampling_type].extend( range(categorized_sample_indices_start_idx, categorized_sample_indices_start_idx + num_seqs)) categorized_sample_indices_start_idx += num_seqs selected_token_indices = _async_h2d(selected_token_indices, dtype=torch.long, pin_memory=not self.in_wsl) categorized_sample_indices = { t: _async_h2d(seq_ids, dtype=torch.int, pin_memory=not self.in_wsl) for t, seq_ids in categorized_sample_indices.items() } seq_data: Dict[int, SequenceData] = {} for seq_group_metadata in seq_group_metadata_list: seq_data.update(seq_group_metadata.seq_data) seq_persistence_data: Dict[int, dict] = {} for grp in seq_group_metadata_list: seq_persistence_data.update(grp.persistent_data) sampling_metadata = SamplingMetadata( seq_groups=seq_groups, seq_data=seq_data, prompt_lens=prompt_lens, selected_token_indices=selected_token_indices, categorized_sample_indices=categorized_sample_indices, persistent_metadata=PersistentMetadata(seq_persistence_data), ) return sampling_metadata def prepare_input_tensors( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, SamplingMetadata, Set[int], LoRAMapping]: if self.is_driver_worker: # NOTE: We assume that all sequences in the group are all prompts or # all decodes. is_prompt = seq_group_metadata_list[0].is_prompt # Prepare input tensors. if is_prompt: (input_tokens, input_positions, input_metadata, prompt_lens, subquery_lens, lora_index_mapping, lora_prompt_mapping, lora_requests) = self._prepare_prompt(seq_group_metadata_list) else: (input_tokens, input_positions, input_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests) = self._prepare_decode(seq_group_metadata_list) prompt_lens = [] subquery_lens = None sampling_metadata = self._prepare_sample(seq_group_metadata_list, prompt_lens, subquery_lens) if self.lora_config: flat_lora_index_mapping = [ item for sublist in lora_index_mapping for item in sublist ] lora_mapping = LoRAMapping( flat_lora_index_mapping, lora_prompt_mapping, ) else: lora_mapping = None # Broadcast the metadata. metadata_dict = { "input_tokens": input_tokens, "input_positions": input_positions, "is_prompt": input_metadata.is_prompt, "slot_mapping": input_metadata.slot_mapping, "prompt_lens": input_metadata.prompt_lens, "max_seq_len": input_metadata.max_seq_len, "start_loc": input_metadata.start_loc, "max_context_len": input_metadata.max_context_len, "context_lens": input_metadata.context_lens, "block_tables": input_metadata.block_tables, "use_cuda_graph": input_metadata.use_cuda_graph, "kv_cache_dtype": input_metadata.kv_cache_dtype, "selected_token_indices": sampling_metadata.selected_token_indices, "lora_requests": lora_requests, "lora_mapping": lora_mapping, } broadcast_tensor_dict(metadata_dict, src=0) else: metadata_dict = broadcast_tensor_dict(src=0) input_tokens = metadata_dict["input_tokens"] input_positions = metadata_dict["input_positions"] lora_mapping = metadata_dict["lora_mapping"] lora_requests = metadata_dict["lora_requests"] input_metadata = InputMetadata( is_prompt=metadata_dict["is_prompt"], slot_mapping=metadata_dict["slot_mapping"], prompt_lens=metadata_dict["prompt_lens"], max_seq_len=metadata_dict["max_seq_len"], start_loc=metadata_dict["start_loc"], max_context_len=metadata_dict["max_context_len"], context_lens=metadata_dict["context_lens"], block_tables=metadata_dict["block_tables"], use_cuda_graph=metadata_dict["use_cuda_graph"], kv_cache_dtype=metadata_dict["kv_cache_dtype"], ) sampling_metadata = SamplingMetadata( seq_groups=None, seq_data=None, prompt_lens=None, selected_token_indices=metadata_dict["selected_token_indices"], categorized_sample_indices=None, perform_sampling=False, ) return (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests, lora_mapping) @torch.inference_mode() def execute_model( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], kv_caches: List[Tuple[torch.Tensor, torch.Tensor]], ) -> Optional[SamplerOutput]: (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests, lora_mapping) = (self.prepare_input_tensors(seq_group_metadata_list)) if self.lora_config: self.set_active_loras(lora_requests, lora_mapping) # Execute the model. if input_metadata.use_cuda_graph: graph_batch_size = input_tokens.shape[0] model_executable = self.graph_runners[graph_batch_size] else: model_executable = self.model hidden_states = model_executable( input_ids=input_tokens, positions=input_positions, kv_caches=kv_caches, input_metadata=input_metadata, ) # Sample the next token. output = self.model.sample( hidden_states=hidden_states, sampling_metadata=sampling_metadata, ) return output @torch.inference_mode() def profile_run(self) -> None: # Enable top-k sampling to reflect the accurate memory usage. vocab_size = self.model_config.get_vocab_size() sampling_params = SamplingParams(top_p=0.99, top_k=vocab_size - 1) max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens max_num_seqs = self.scheduler_config.max_num_seqs # This represents the maximum number of different requests # that will have unique loras, an therefore the max amount of memory # consumption create dummy lora request copies from the lora request # passed in, which contains a lora from the lora warmup path. dummy_lora_requests = [] dummy_lora_requests_per_seq = [] if self.lora_config: for idx in range(self.lora_config.max_loras): lora_id = idx + 1 dummy_lora_request = LoRARequest( lora_name=f"warmup_{lora_id}", lora_int_id=lora_id, lora_local_path="/not/a/real/path", ) self.lora_manager.add_dummy_lora(dummy_lora_request, rank=LORA_WARMUP_RANK) dummy_lora_requests.append(dummy_lora_request) dummy_lora_requests_per_seq = [ dummy_lora_requests[idx % len(dummy_lora_requests)] for idx in range(max_num_seqs) ] # Profile memory usage with max_num_sequences sequences and the total # number of tokens equal to max_num_batched_tokens. seqs: List[SequenceGroupMetadata] = [] for group_id in range(max_num_seqs): seq_len = (max_num_batched_tokens // max_num_seqs + (group_id < max_num_batched_tokens % max_num_seqs)) seq_data = SequenceData([0] * seq_len) seq = SequenceGroupMetadata( request_id=str(group_id), is_prompt=True, seq_data={group_id: seq_data}, sampling_params=sampling_params, block_tables=None, persistent_data={}, lora_request=dummy_lora_requests_per_seq[group_id] if dummy_lora_requests_per_seq else None, ) seqs.append(seq) # Run the model with the dummy inputs. num_layers = self.model_config.get_num_layers(self.parallel_config) kv_caches = [(None, None)] * num_layers self.execute_model(seqs, kv_caches) torch.cuda.synchronize() return def remove_all_loras(self) -> bool: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.remove_all_loras() def set_active_loras(self, lora_requests: List[LoRARequest], lora_mapping: LoRAMapping) -> None: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") self.lora_manager.set_active_loras(lora_requests, lora_mapping) def add_lora(self, lora_request: LoRARequest) -> bool: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.remove_lora(lora_id) def list_loras(self) -> Set[int]: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.list_loras() @torch.inference_mode() def capture_model(self, kv_caches: List[KVCache]) -> None: assert not self.model_config.enforce_eager logger.info("Capturing the model for CUDA graphs. This may lead to " "unexpected consequences if the model is not static. To " "run the model in eager mode, set 'enforce_eager=True' or " "use '--enforce-eager' in the CLI.") logger.warning("CUDA graphs can take additional 1~3 GiB of memory " "per GPU. If you are running out of memory, consider " "decreasing `gpu_memory_utilization` or enforcing " "eager mode.") start_time = time.perf_counter() # Prepare dummy inputs. These will be reused for all batch sizes. max_batch_size = max(_BATCH_SIZES_TO_CAPTURE) input_tokens = torch.zeros(max_batch_size, 1, dtype=torch.long).cuda() input_positions = torch.zeros(max_batch_size, 1, dtype=torch.long).cuda() slot_mapping = torch.empty(max_batch_size, 1, dtype=torch.long).cuda() slot_mapping.fill_(_PAD_SLOT_ID) context_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda() block_tables = torch.from_numpy(self.graph_block_tables).cuda() graph_batch_size = _get_graph_batch_size( self.scheduler_config.max_num_seqs) batch_size_capture_list = [ bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size ] # NOTE: Capturing the largest batch size first may help reduce the # memory usage of CUDA graph. with custom_all_reduce.capture(): for batch_size in reversed(batch_size_capture_list): if batch_size > self.scheduler_config.max_num_seqs: continue # Create dummy input_metadata. input_metadata = InputMetadata( is_prompt=False, slot_mapping=slot_mapping[:batch_size], prompt_lens=None, max_seq_len=None, start_loc=None, max_context_len=self.max_context_len_to_capture, context_lens=context_lens[:batch_size], block_tables=block_tables[:batch_size], use_cuda_graph=True, kv_cache_dtype=self.kv_cache_dtype, ) if self.lora_config: lora_mapping = LoRAMapping( [0] * batch_size, [0] * batch_size, ) self.set_active_loras(set(), lora_mapping) graph_runner = CUDAGraphRunner(self.model) graph_runner.capture( input_tokens[:batch_size], input_positions[:batch_size], kv_caches, input_metadata, memory_pool=self.graph_memory_pool, ) self.graph_memory_pool = graph_runner.graph.pool() self.graph_runners[batch_size] = graph_runner end_time = time.perf_counter() elapsed_time = end_time - start_time # This usually takes < 10 seconds. logger.info(f"Graph capturing finished in {elapsed_time:.0f} secs.") class CUDAGraphRunner: def __init__(self, model: nn.Module): self.model = model self.graph = None self.input_buffers: Dict[str, torch.Tensor] = {} self.output_buffers: Dict[str, torch.Tensor] = {} def capture( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, memory_pool, ) -> None: assert self.graph is None # Run the model once without capturing the graph. # This is to make sure that the captured graph does not include the # kernel launches for initial benchmarking (e.g., Triton autotune). self.model( input_ids, positions, kv_caches, input_metadata, ) torch.cuda.synchronize() # Capture the graph. self.graph = torch.cuda.CUDAGraph() with torch.cuda.graph(self.graph, pool=memory_pool): hidden_states = self.model( input_ids, positions, kv_caches, input_metadata, ) torch.cuda.synchronize() # Save the input and output buffers. self.input_buffers = { "input_ids": input_ids, "positions": positions, "kv_caches": kv_caches, "slot_mapping": input_metadata.slot_mapping, "context_lens": input_metadata.context_lens, "block_tables": input_metadata.block_tables, } self.output_buffers = {"hidden_states": hidden_states} return def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[Tuple[torch.Tensor, torch.Tensor]], input_metadata: InputMetadata, ) -> torch.Tensor: # KV caches are fixed tensors, so we don't need to copy them. del kv_caches # Copy the input tensors to the input buffers. self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True) self.input_buffers["positions"].copy_(positions, non_blocking=True) self.input_buffers["slot_mapping"].copy_(input_metadata.slot_mapping, non_blocking=True) self.input_buffers["context_lens"].copy_(input_metadata.context_lens, non_blocking=True) self.input_buffers["block_tables"].copy_(input_metadata.block_tables, non_blocking=True) # Run the graph. self.graph.replay() # Return the output tensor. return self.output_buffers["hidden_states"] def __call__(self, *args, **kwargs): return self.forward(*args, **kwargs) def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]: assert len(x) <= max_len return x + [pad] * (max_len - len(x)) def _make_tensor_with_pad( x: List[List[int]], max_len: int, pad: int, dtype: torch.dtype, device: Union[str, torch.device] = "cuda", pin_memory: bool = False, ) -> torch.Tensor: padded_x = [_pad_to_max(x_i, max_len, pad) for x_i in x] return torch.tensor(padded_x, dtype=dtype, device=device, pin_memory=pin_memory and str(device) == "cpu") def _get_graph_batch_size(batch_size: int) -> int: if batch_size <= 2: return batch_size elif batch_size <= 4: return 4 else: return (batch_size + 7) // 8 * 8 def _async_h2d(data: list, dtype, pin_memory): t = torch.tensor(data, dtype=dtype, pin_memory=pin_memory) return t.to(device="cuda", non_blocking=True)