import time from typing import Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn from .configs import ModelConfig, ParallelConfig, SchedulerConfig from vllm.logger import init_logger from .model_loader import get_model from vllm.model_executor import InputMetadata, SamplingMetadata from vllm.model_executor.parallel_utils.communication_op import ( broadcast, broadcast_object_list, ) from .sampling_params import SamplingParams, SamplingType from .sequence import ( SamplerOutput, SequenceData, SequenceGroupMetadata, SequenceGroupOutput, SequenceOutput, ) from vllm.utils import in_wsl from ..embed import Embed from .sampler import Sampler from safetensors.torch import safe_open logger = init_logger(__name__) KVCache = Tuple[torch.Tensor, torch.Tensor] _PAD_SLOT_ID = -1 # 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, is_driver_worker: bool = False, post_model_path: str = None, ): self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.is_driver_worker = is_driver_worker self.post_model_path = post_model_path # model_config can be None in tests/samplers/test_sampler.py. # FIXME(woosuk): 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.model = None self.block_size = None # Set after initial profiling. 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() def load_model(self) -> None: self.model = get_model(self.model_config) self.post_model = Embed( self.model_config.get_hidden_size(), self.model_config.num_audio_tokens, self.model_config.num_text_tokens, ) state_dict_tensors = {} with safe_open(self.post_model_path, framework="pt", device=0) as f: for k in f.keys(): state_dict_tensors[k] = f.get_tensor(k) self.post_model.load_state_dict(state_dict_tensors) self.post_model.to(next(self.model.parameters())).eval() self.sampler = Sampler(self.post_model, self.model_config.num_audio_tokens, 4) 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]]: assert len(seq_group_metadata_list) > 0 input_tokens: List[List[int]] = [] input_positions: List[List[int]] = [] slot_mapping: List[List[int]] = [] prompt_lens: 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) input_tokens.append(prompt_tokens) # NOTE(woosuk): Here we assume that the first token in the prompt # is always the first token in the sequence. input_positions.append(list(range(prompt_len))) 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: start_idx = max(0, prompt_len - self.sliding_window) for i in range(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(prompt_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 ) input_metadata = InputMetadata( is_prompt=True, slot_mapping=slot_mapping, max_context_len=None, context_lens=None, block_tables=None, use_cuda_graph=False, ) return input_tokens, input_positions, input_metadata, prompt_lens def _prepare_decode( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]: 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]] = [] 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()) 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]) 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: block_tables = _make_tensor_with_pad( block_tables, max_len=max_context_len, pad=0, dtype=torch.int, device="cuda", ) input_metadata = InputMetadata( is_prompt=False, slot_mapping=slot_mapping, max_context_len=max_context_len, context_lens=context_lens, block_tables=block_tables, use_cuda_graph=use_captured_graph, ) return input_tokens, input_positions, input_metadata def _prepare_sample( self, seq_group_metadata_list: List[SequenceGroupMetadata], prompt_lens: 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_prompt_len = max(prompt_lens) if prompt_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 prompt_len = prompt_lens[i] if sampling_params.prompt_logprobs is not None: # NOTE: prompt token positions do not need sample, skip categorized_sample_indices_start_idx += prompt_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 + prompt_len - 1, ) ) selected_token_indices.append(selected_token_start_idx + prompt_len - 1) selected_token_start_idx += max_prompt_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) 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, ) return sampling_metadata def prepare_input_tensors( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, SamplingMetadata]: 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) = ( self._prepare_prompt(seq_group_metadata_list) ) else: (input_tokens, input_positions, input_metadata) = self._prepare_decode( seq_group_metadata_list ) prompt_lens = [] sampling_metadata = self._prepare_sample( seq_group_metadata_list, prompt_lens ) def get_size_or_none(x: Optional[torch.Tensor]): return x.size() if x is not None else None # Broadcast the input data. For input tensors, we first broadcast # its shape and then broadcast the tensor to avoid high # serialization cost. py_data = { "input_tokens_size": input_tokens.size(), "input_positions_size": input_positions.size(), "is_prompt": input_metadata.is_prompt, "slot_mapping_size": get_size_or_none(input_metadata.slot_mapping), "max_context_len": input_metadata.max_context_len, "context_lens_size": get_size_or_none(input_metadata.context_lens), "block_tables_size": get_size_or_none(input_metadata.block_tables), "use_cuda_graph": input_metadata.use_cuda_graph, "selected_token_indices_size": sampling_metadata.selected_token_indices.size(), } broadcast_object_list([py_data], src=0) # TODO(zhuohan): Combine the broadcasts or set async_op=True. broadcast(input_tokens, src=0) broadcast(input_positions, src=0) if input_metadata.slot_mapping is not None: broadcast(input_metadata.slot_mapping, src=0) if input_metadata.context_lens is not None: broadcast(input_metadata.context_lens, src=0) if input_metadata.block_tables is not None: broadcast(input_metadata.block_tables, src=0) broadcast(sampling_metadata.selected_token_indices, src=0) else: receving_list = [None] broadcast_object_list(receving_list, src=0) py_data = receving_list[0] input_tokens = torch.empty( *py_data["input_tokens_size"], dtype=torch.long, device="cuda" ) broadcast(input_tokens, src=0) input_positions = torch.empty( *py_data["input_positions_size"], dtype=torch.long, device="cuda" ) broadcast(input_positions, src=0) if py_data["slot_mapping_size"] is not None: slot_mapping = torch.empty( *py_data["slot_mapping_size"], dtype=torch.long, device="cuda" ) broadcast(slot_mapping, src=0) else: slot_mapping = None if py_data["context_lens_size"] is not None: context_lens = torch.empty( *py_data["context_lens_size"], dtype=torch.int, device="cuda" ) broadcast(context_lens, src=0) else: context_lens = None if py_data["block_tables_size"] is not None: block_tables = torch.empty( *py_data["block_tables_size"], dtype=torch.int, device="cuda" ) broadcast(block_tables, src=0) else: block_tables = None selected_token_indices = torch.empty( *py_data["selected_token_indices_size"], dtype=torch.long, device="cuda" ) broadcast(selected_token_indices, src=0) input_metadata = InputMetadata( is_prompt=py_data["is_prompt"], slot_mapping=slot_mapping, max_context_len=py_data["max_context_len"], context_lens=context_lens, block_tables=block_tables, use_cuda_graph=py_data["use_cuda_graph"], ) sampling_metadata = SamplingMetadata( seq_groups=None, seq_data=None, prompt_lens=None, selected_token_indices=selected_token_indices, categorized_sample_indices=None, perform_sampling=False, ) return input_tokens, input_positions, input_metadata, sampling_metadata @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 = ( self.prepare_input_tensors(seq_group_metadata_list) ) # print(sampling_metadata.seq_data) seq_groups = [] input_tokens_history = [] for i, rtn in enumerate(sampling_metadata.seq_groups): seq_groups.append(rtn[0][0]) tokens_history = sampling_metadata.seq_data[rtn[0][0]].output_token_ids if len(tokens_history) >= 1: if len(tokens_history[0]) == 1: tokens_history = [token[0] for token in tokens_history] else: tokens_history = [list(token) for token in tokens_history] input_tokens_history.append(tokens_history) input_tokens_history = torch.tensor(input_tokens_history).to( input_tokens.device ) # token_ids = rtn.outputs[0].token_ids # for j, token_id in enumerate(token_ids): # if len(token_id) == 1: # token_ids[j] = token_id[0] # else: # token_ids[j] = list(token_id) # Execute the model. # print("it1",input_tokens) if len(input_tokens.shape) == 2: input_tokens = input_tokens.unsqueeze(2).repeat(1, 1, 4) if len(input_tokens_history.shape) == 2: input_tokens_history = input_tokens_history.unsqueeze(2).repeat(1, 1, 4) # print(input_tokens_history.shape) # print("it2",input_tokens.shape) text_mask = input_tokens != 0 text_mask = text_mask[:, :, 0] 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 infer_text = sampling_metadata.seq_groups[0][1].infer_text temperture = sampling_metadata.seq_groups[0][1].temperature if not infer_text: temperture = torch.tensor(temperture).to(input_tokens.device) logits_processors, logits_warpers = sampling_metadata.seq_groups[0][ 1 ].logits_processors # print(logits_processors, logits_warpers) min_new_token = sampling_metadata.seq_groups[0][1].min_new_token eos_token = sampling_metadata.seq_groups[0][1].eos_token start_idx = sampling_metadata.seq_groups[0][1].start_idx if input_tokens.shape[-2] == 1: if infer_text: input_emb: torch.Tensor = self.post_model.emb_text( input_tokens[:, :, 0] ) else: code_emb = [ self.post_model.emb_code[i](input_tokens[:, :, i]) for i in range(self.post_model.num_vq) ] input_emb = torch.stack(code_emb, 3).sum(3) start_idx = ( input_tokens_history.shape[-2] - 1 if input_tokens_history.shape[-2] > 0 else 0 ) else: input_emb = self.post_model(input_tokens, text_mask) # print(input_emb.shape) hidden_states = model_executable( input_emb=input_emb, positions=input_positions, kv_caches=kv_caches, input_metadata=input_metadata, ) # print(hidden_states.shape) # print(input_tokens) B_NO_PAD = input_tokens_history.shape[0] input_tokens = input_tokens[:B_NO_PAD, :, :] hidden_states = hidden_states[:B_NO_PAD, :, :] idx_next, logprob, finish = self.sampler.sample( inputs_ids=( input_tokens if input_tokens_history.shape[-2] == 0 else input_tokens_history ), hidden_states=hidden_states, infer_text=infer_text, temperature=temperture, logits_processors=logits_processors, logits_warpers=logits_warpers, min_new_token=min_new_token, now_length=1, eos_token=eos_token, start_idx=start_idx, ) # print(logprob.shape, idx_next.shape) if len(logprob.shape) == 2: logprob = logprob[:, None, :] logprob = torch.gather(logprob, -1, idx_next.transpose(-1, -2))[:, :, 0] # print("测试",idx_next.shape, logprob.shape) # Sample the next token. # output = self.model.sample( # hidden_states=hidden_states, # sampling_metadata=sampling_metadata, # ) results = [] for i in range(idx_next.shape[0]): idx_next_i = idx_next[i, 0, :].tolist() logprob_i = logprob[i].tolist() tmp_hidden_states = hidden_states[i] if input_tokens[i].shape[-2] != 1: tmp_hidden_states = tmp_hidden_states[-1:, :] result = SequenceGroupOutput( samples=[ SequenceOutput( parent_seq_id=seq_groups[i], logprobs={tuple(idx_next_i): logprob_i}, output_token=tuple(idx_next_i), hidden_states=tmp_hidden_states, finished=finish[i].item(), ), ], prompt_logprobs=None, ) results.append(result) # print(results) # print(idx_next, idx_next.shape, logprob.shape) return results @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, infer_text=True ) max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens max_num_seqs = self.scheduler_config.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, ) 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 @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.info( "CUDA graphs can take additional 1~3 GiB 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_emb = torch.zeros( max_batch_size, 1, self.model_config.get_hidden_size(), dtype=next(self.model.parameters()).dtype, ).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() # NOTE: Capturing the largest batch size first may help reduce the # memory usage of CUDA graph. for batch_size in reversed(_BATCH_SIZES_TO_CAPTURE): # Create dummy input_metadata. input_metadata = InputMetadata( is_prompt=False, slot_mapping=slot_mapping[:batch_size], 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, ) graph_runner = CUDAGraphRunner(self.model) graph_runner.capture( input_emb[: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_emb: 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_emb, 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_emb, positions, kv_caches, input_metadata, ) torch.cuda.synchronize() # Save the input and output buffers. self.input_buffers = { "input_emb": input_emb, "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_emb: 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_emb"].copy_(input_emb, 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 if len(x) == max_len: return list(x) return list(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 = [] for x_i in x: pad_i = pad if isinstance(x[0][0], tuple): pad_i = (0,) * len(x[0][0]) padded_x.append(_pad_to_max(x_i, max_len, pad_i)) 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)