mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
119 lines
4.4 KiB
119 lines
4.4 KiB
# might want to consider combine with InferenceConfig in colossalai/ppinference/inference_config.py later
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
from transformers.tokenization_utils_base import BatchEncoding
|
|
|
|
from .kvcache_manager import MemoryManager
|
|
|
|
|
|
# adapted from: lightllm/server/router/model_infer/infer_batch.py
|
|
@dataclass
|
|
class BatchInferState:
|
|
r"""
|
|
Information to be passed and used for a batch of inputs during
|
|
a single model forward
|
|
"""
|
|
batch_size: int
|
|
max_len_in_batch: int
|
|
|
|
cache_manager: MemoryManager = None
|
|
|
|
block_loc: torch.Tensor = None
|
|
start_loc: torch.Tensor = None
|
|
seq_len: torch.Tensor = None
|
|
past_key_values_len: int = None
|
|
|
|
is_context_stage: bool = False
|
|
context_mem_index: torch.Tensor = None
|
|
decode_is_contiguous: bool = None
|
|
decode_mem_start: int = None
|
|
decode_mem_end: int = None
|
|
decode_mem_index: torch.Tensor = None
|
|
decode_layer_id: int = None
|
|
|
|
device: torch.device = torch.device("cuda")
|
|
|
|
@property
|
|
def total_token_num(self):
|
|
# return self.batch_size * self.max_len_in_batch
|
|
assert self.seq_len is not None and self.seq_len.size(0) > 0
|
|
return int(torch.sum(self.seq_len))
|
|
|
|
def set_cache_manager(self, manager: MemoryManager):
|
|
self.cache_manager = manager
|
|
|
|
# adapted from: https://github.com/ModelTC/lightllm/blob/28c1267cfca536b7b4f28e921e03de735b003039/lightllm/common/infer_utils.py#L1
|
|
@staticmethod
|
|
def init_block_loc(
|
|
b_loc: torch.Tensor, seq_len: torch.Tensor, max_len_in_batch: int, alloc_mem_index: torch.Tensor
|
|
):
|
|
"""in-place update block loc mapping based on the sequence length of the inputs in current bath"""
|
|
start_index = 0
|
|
seq_len_numpy = seq_len.cpu().numpy()
|
|
for i, cur_seq_len in enumerate(seq_len_numpy):
|
|
b_loc[i, max_len_in_batch - cur_seq_len : max_len_in_batch] = alloc_mem_index[
|
|
start_index : start_index + cur_seq_len
|
|
]
|
|
start_index += cur_seq_len
|
|
return
|
|
|
|
@classmethod
|
|
def init_from_batch(
|
|
cls,
|
|
batch: torch.Tensor,
|
|
max_input_len: int,
|
|
max_output_len: int,
|
|
cache_manager: MemoryManager,
|
|
):
|
|
if not isinstance(batch, (BatchEncoding, dict, list, torch.Tensor)):
|
|
raise TypeError(f"batch type {type(batch)} is not supported in prepare_batch_state")
|
|
|
|
input_ids_list = None
|
|
attention_mask = None
|
|
|
|
if isinstance(batch, (BatchEncoding, dict)):
|
|
input_ids_list = batch["input_ids"]
|
|
attention_mask = batch["attention_mask"]
|
|
else:
|
|
input_ids_list = batch
|
|
if isinstance(input_ids_list[0], int): # for a single input
|
|
input_ids_list = [input_ids_list]
|
|
attention_mask = [attention_mask] if attention_mask is not None else attention_mask
|
|
|
|
batch_size = len(input_ids_list)
|
|
|
|
seq_start_indexes = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
|
|
seq_lengths = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
|
|
start_index = 0
|
|
|
|
max_len_in_batch = -1
|
|
if isinstance(batch, (BatchEncoding, dict)):
|
|
for i, attn_mask in enumerate(attention_mask):
|
|
curr_seq_len = len(attn_mask)
|
|
seq_lengths[i] = curr_seq_len
|
|
seq_start_indexes[i] = start_index
|
|
start_index += curr_seq_len
|
|
max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
|
|
else:
|
|
length = max(len(input_id) for input_id in input_ids_list)
|
|
for i, input_ids in enumerate(input_ids_list):
|
|
curr_seq_len = length
|
|
seq_lengths[i] = curr_seq_len
|
|
seq_start_indexes[i] = start_index
|
|
start_index += curr_seq_len
|
|
max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
|
|
block_loc = torch.zeros((batch_size, max_input_len + max_output_len), dtype=torch.long, device="cuda")
|
|
|
|
return cls(
|
|
batch_size=batch_size,
|
|
max_len_in_batch=max_len_in_batch,
|
|
seq_len=seq_lengths.to("cuda"),
|
|
start_loc=seq_start_indexes.to("cuda"),
|
|
block_loc=block_loc,
|
|
decode_layer_id=0,
|
|
past_key_values_len=0,
|
|
is_context_stage=True,
|
|
cache_manager=cache_manager,
|
|
)
|