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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)
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