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[inference] Refactor inference architecture (#5057)

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* [inference] support only TP (#4998)

* support only tp

* enable tp

* add support for bloom (#5008)

* [refactor] refactor gptq and smoothquant llama (#5012)

* refactor gptq and smoothquant llama

* fix import error

* fix linear import torch-int

* fix smoothquant llama import error

* fix import accelerate error

* fix bug

* fix import smooth cuda

* fix smoothcuda

* [Inference Refactor] Merge chatglm2 with pp and tp (#5023)

merge chatglm with pp and tp

* [Refactor] remove useless inference code (#5022)

* remove useless code

* fix quant model

* fix test import bug

* mv original inference legacy

* fix chatglm2

* [Refactor] refactor policy search and quant type controlling in inference (#5035)

* [Refactor] refactor policy search and quant type controling in inference

* [inference] update readme (#5051)

* update readme

* update readme

* fix architecture

* fix table

* fix table

* [inference] udpate example (#5053)

* udpate example

* fix run.sh

* fix rebase bug

* fix some errors

* update readme

* add some features

* update interface

* update readme

* update benchmark

* add requirements-infer

---------

Co-authored-by: Bin Jia <45593998+FoolPlayer@users.noreply.github.com>
Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
This commit is contained in:
Xu Kai
2023-11-19 21:05:05 +08:00
committed by GitHub
parent bc09b95f50
commit fd6482ad8c
115 changed files with 6027 additions and 1431 deletions
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346
colossalai/legacy/inference/dynamic_batching/infer_batch.py Normal file
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# Adapted from https://github.com/ModelTC/lightllm
import collections
from dataclasses import dataclass
from typing import Dict, List, Tuple
import numpy as np
import torch
from colossalai.inference.tensor_parallel import MemoryManager
# make batch infer state an attr of InferBatch
class InferSamplingParams:
def __init__(
self,
do_sample: bool = False,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = -1,
vocab_size: int = -1,
) -> None:
self.do_sample = do_sample
self.presence_penalty = presence_penalty
self.frequency_penalty = frequency_penalty
self.temperature = temperature
self.top_p = top_p
self.top_k = top_k
if self.top_k == -1:
self.top_k = vocab_size
return
@dataclass
class InferBatch:
batch_id: int
requests: List
requests_idx_mapping: Dict[int, int]
input_ids: torch.Tensor
all_input_ids: List[List[int]]
input_lengths: List[int]
out_token_id_counts: List
sampling_param_list: List[InferSamplingParams]
nopad_total_token_num: int
nopad_max_len_in_batch: int
nopad_b_loc: torch.Tensor
nopad_b_start_loc: torch.Tensor
nopad_b_seq_len: torch.Tensor
cache_manager: MemoryManager
max_total_len: int
@classmethod
@torch.no_grad()
def init_batch(
cls,
batch_id,
requests,
dtype: torch.dtype,
device: torch.device,
cache_manager: MemoryManager,
vocab_size: int,
max_total_len: int,
) -> "InferBatch":
input_lengths = []
all_input_ids = []
requests_idx_mapping = {}
out_token_id_counts = []
sampling_param_list = []
nopad_total_token_num = 0
nopad_max_len_in_batch = 0
nopad_b_loc = torch.empty((len(requests), max_total_len + 12), dtype=torch.long, device="cuda")
# to avoid memory leak , we pre-allocate 12 more space for each batch.
nopad_b_start_loc = torch.zeros(len(requests), dtype=torch.int32, device="cuda")
for i, r in enumerate(requests):
# request id -> idx in list mapping
requests_idx_mapping[r["request_id"]] = i
tokenized_input = r["input_id"]
input_length = len(tokenized_input)
input_lengths.append(input_length)
all_input_ids.append(tokenized_input)
out_token_id_counts.append(collections.defaultdict(int))
# postprocessor
sampling_param = r["sampling_param"]
sampling_param["vocab_size"] = vocab_size
sampling_param_list.append(InferSamplingParams(**sampling_param))
nopad_total_token_num += input_length
nopad_max_len_in_batch = max(nopad_max_len_in_batch, input_length)
nopad_b_seq_len = torch.tensor(input_lengths, dtype=torch.int32, device="cuda")
nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
if len(requests) > 1:
input_ids = np.concatenate(all_input_ids, dtype=np.int64)
else:
input_ids = all_input_ids[0]
# Create tensors on device
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
return cls(
batch_id=batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
input_lengths=input_lengths,
all_input_ids=all_input_ids,
nopad_total_token_num=nopad_total_token_num,
nopad_max_len_in_batch=nopad_max_len_in_batch,
nopad_b_loc=nopad_b_loc,
nopad_b_start_loc=nopad_b_start_loc,
nopad_b_seq_len=nopad_b_seq_len,
out_token_id_counts=out_token_id_counts,
sampling_param_list=sampling_param_list,
cache_manager=cache_manager,
max_total_len=max_total_len,
)
@torch.no_grad()
def free_self(self) -> None:
"""
Free the memory of the InferBatch itself
"""
remove_index = []
for idx in range(len(self)):
remove_index.append(
self.nopad_b_loc[
idx,
(self.nopad_max_len_in_batch - 1)
- (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
]
)
remove_index = torch.cat(remove_index, dim=-1)
self.cache_manager.free(remove_index)
@torch.no_grad()
def filter(self, request_ids: List[int]) -> "InferBatch":
"""
Filter finished batch and return a new InferBatch with left ones.
"""
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
requests_idx_mapping = {}
indices = []
requests = []
all_input_ids = []
input_lengths = []
nopad_total_token_num = 0
nopad_max_len_in_batch = 0
nopad_b_loc = torch.empty((len(request_ids), self.max_total_len + 12), dtype=torch.long, device="cuda")
nopad_b_start_loc = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
nopad_b_seq_len = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
left_idx = []
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
left_idx.append(idx)
left_idx_set = set(left_idx)
remove_index = []
for idx in range(len(self)):
if idx not in left_idx_set:
remove_index.append(
self.nopad_b_loc[
idx,
(self.nopad_max_len_in_batch - 1)
- (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
]
)
remove_index = torch.cat(remove_index, dim=-1)
self.cache_manager.free(remove_index)
nopad_max_len_in_batch = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
indices.append(idx)
nopad_b_seq_len[:] = self.nopad_b_seq_len[indices]
nopad_max_len_in_batch = torch.max(nopad_b_seq_len).item()
nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
nopad_total_token_num = torch.sum(nopad_b_seq_len).item()
nopad_b_loc[:, 0 : (nopad_max_len_in_batch - 1)] = self.nopad_b_loc[
indices,
(self.nopad_max_len_in_batch - 1) - (nopad_max_len_in_batch - 1) : (self.nopad_max_len_in_batch - 1),
]
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
requests.append(self.requests[idx])
all_input_ids.append(self.all_input_ids[idx])
input_lengths.append(self.input_lengths[idx])
input_ids = self.input_ids[indices]
return InferBatch(
batch_id=self.batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
input_lengths=input_lengths,
all_input_ids=all_input_ids,
nopad_total_token_num=nopad_total_token_num,
nopad_max_len_in_batch=nopad_max_len_in_batch,
nopad_b_loc=nopad_b_loc,
nopad_b_start_loc=nopad_b_start_loc,
nopad_b_seq_len=nopad_b_seq_len,
out_token_id_counts=[self.out_token_id_counts[_i] for _i in indices],
sampling_param_list=[self.sampling_param_list[_i] for _i in indices],
cache_manager=self.cache_manager,
max_total_len=self.max_total_len,
)
@classmethod
@torch.no_grad()
def merge(cls, batch1, batch2) -> "InferBatch":
"""
Return megerd new InferBatch
"""
requests = batch1.requests + batch2.requests
requests_idx_mapping = {}
new_batch_size = len(batch1) + len(batch2)
input_ids = batch1.input_ids.new_empty(new_batch_size)
all_input_ids = []
input_lengths = []
out_token_id_counts = []
sampling_param_list = []
cumulative_batch_size = 0
nopad_total_token_num = batch1.nopad_total_token_num + batch2.nopad_total_token_num
nopad_max_len_in_batch = max(batch1.nopad_max_len_in_batch, batch2.nopad_max_len_in_batch)
max_total_len = max(batch1.max_total_len, batch2.max_total_len)
nopad_b_loc = torch.empty((new_batch_size, batch1.max_total_len + 12), dtype=torch.long, device="cuda")
nopad_b_start_loc = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
nopad_b_seq_len = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
nopad_start_loc_len_temp = 0
batches = [batch1, batch2]
for i, batch in enumerate(batches):
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + cumulative_batch_size
start_index = cumulative_batch_size
end_index = cumulative_batch_size + len(batch)
input_ids[start_index:end_index] = batch.input_ids
nopad_b_seq_len[start_index:end_index] = batch.nopad_b_seq_len
nopad_b_start_loc[start_index:end_index] = batch.nopad_b_start_loc + nopad_start_loc_len_temp
nopad_start_loc_len_temp = nopad_b_start_loc[end_index - 1] + nopad_b_seq_len[end_index - 1]
nopad_b_loc[
start_index:end_index,
nopad_max_len_in_batch - batch.nopad_max_len_in_batch : nopad_max_len_in_batch - 1,
] = batch.nopad_b_loc[:, : batch.nopad_max_len_in_batch - 1]
all_input_ids.extend(batch.all_input_ids)
input_lengths.extend(batch.input_lengths)
out_token_id_counts.extend(batch.out_token_id_counts)
sampling_param_list.extend(batch.sampling_param_list)
# Update
cumulative_batch_size += len(batch)
nopad_b_loc[:, nopad_max_len_in_batch - 1] = (
nopad_total_token_num - new_batch_size + torch.arange(0, new_batch_size, dtype=torch.int32, device="cuda")
)
return InferBatch(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
input_lengths=input_lengths,
all_input_ids=all_input_ids,
nopad_total_token_num=nopad_total_token_num,
nopad_max_len_in_batch=nopad_max_len_in_batch,
nopad_b_loc=nopad_b_loc,
nopad_b_start_loc=nopad_b_start_loc,
nopad_b_seq_len=nopad_b_seq_len,
out_token_id_counts=out_token_id_counts,
sampling_param_list=sampling_param_list,
cache_manager=batches[0].cache_manager,
max_total_len=max_total_len,
)
def __len__(self):
return len(self.requests)
def get_post_sample_tensors(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
presence_penalties: List[float] = []
frequency_penalties: List[float] = []
temperatures: List[float] = []
top_ps: List[float] = []
top_ks: List[int] = []
p_token_ids: List[int] = []
p_token_counts: List[int] = []
p_seq_len: List[int] = [
0,
]
p_max_len_in_batch: int = 0
for i, id_to_count in enumerate(self.out_token_id_counts):
sample_param = self.sampling_param_list[i]
presence_penalties.append(sample_param.presence_penalty)
frequency_penalties.append(sample_param.frequency_penalty)
temperatures.append(sample_param.temperature)
top_ps.append(sample_param.top_p)
top_ks.append(sample_param.top_k)
for token_id, count in id_to_count.items():
p_token_ids.append(token_id)
p_token_counts.append(count)
p_seq_len.append(len(id_to_count))
p_max_len_in_batch = max(p_max_len_in_batch, len(id_to_count))
presence_penalties = torch.tensor(presence_penalties, dtype=torch.float, device="cuda")
frequency_penalties = torch.tensor(frequency_penalties, dtype=torch.float, device="cuda")
temperatures = torch.tensor(temperatures, dtype=torch.float, device="cuda")
top_ps = torch.tensor(top_ps, dtype=torch.float, device="cuda")
top_ks = torch.tensor(top_ks, dtype=torch.int32, device="cuda")
p_token_ids = torch.tensor(p_token_ids, dtype=torch.int32, device="cuda")
p_token_counts = torch.tensor(p_token_counts, dtype=torch.int32, device="cuda")
p_seq_len = torch.tensor(p_seq_len, dtype=torch.int32, device="cuda")
p_cumsum_seq_len = torch.cumsum(p_seq_len, dim=0, dtype=torch.int32)
return (
presence_penalties,
frequency_penalties,
temperatures,
top_ps,
top_ks,
p_token_ids,
p_token_counts,
p_cumsum_seq_len,
p_max_len_in_batch,
)