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Unverified Commit 7cbfe2ea authored by q.yao's avatar q.yao Committed by GitHub
Browse files

Tensor Parallel python api (#82) 

* wip

* profile disable tp

* fix profile

* lint

* fix dlpack

* remove comment

* add tp flag

* add session len check

* add eos

* remove tp and session len inputs

* warp tokenizer

* multithread load weight

* update profile

* refactor tokenizer

* remove pre/post process

* remove mpi4py requirement

* remove

* remove bind

* remove mpi requirement

* check backend_tokenizer
parent 1f88baa5
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benchmark/profile_generation.py
View file @ 7cbfe2ea
......@@ -6,10 +6,9 @@ from threading import Thread
import fire
import numpy as np
from transformers import AutoTokenizer
from lmdeploy.model import MODELS
from lmdeploy.turbomind import TurboMind
from lmdeploy.turbomind import Tokenizer, TurboMind
def infer(model, session_id: int, input_ids: str, output_seqlen: int,
......@@ -42,11 +41,7 @@ def infer(model, session_id: int, input_ids: str, output_seqlen: int,
que.put((session_id, stats))
def warmup(model,
concurrency: int,
session_len: int,
output_seqlen: int,
warmup_round: int = 4):
def warmup(model, concurrency: int, output_seqlen: int, warmup_round: int = 4):
print('start to warmup ...')
def _infer(model, session_id):
......@@ -81,18 +76,16 @@ def warmup(model,
def main(model_path: str,
model_name: str,
concurrency: int = 1,
session_len: int = 2056,
input_seqlen: int = 0,
output_seqlen: int = 512,
test_round: int = 10):
tokenizer_model_path = osp.join(model_path, 'triton_models', 'tokenizer')
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_path,
trust_remote_code=True)
tokenizer = Tokenizer(tokenizer_model_path)
model = MODELS.get(model_name)()
stop_words = model.stop_words
tm_model = TurboMind(model_path=model_path, stop_words=stop_words)
warmup(tm_model, concurrency, session_len, output_seqlen)
warmup(tm_model, concurrency, output_seqlen)
# make up a prompt that can be tokenized into {input_seqlen} tokens
prompt = '' if input_seqlen == 0 else 'hi' + ' hi' * (input_seqlen - 1)
......
lmdeploy/turbomind/__init__.py
View file @ 7cbfe2ea
# Copyright (c) OpenMMLab. All rights reserved.
from .tokenizer import Postprocessor, Preprocessor, Tokenizer
from .tokenizer import Tokenizer
from .turbomind import TurboMind
__all__ = ['Postprocessor', 'Preprocessor', 'Tokenizer', 'TurboMind']
__all__ = ['Tokenizer', 'TurboMind']
lmdeploy/turbomind/chat.py
View file @ 7cbfe2ea
......@@ -4,10 +4,10 @@ import os.path as osp
import random
import fire
from transformers import AutoTokenizer
from lmdeploy import turbomind as tm
from lmdeploy.model import MODELS
from lmdeploy.turbomind.tokenizer import Tokenizer
os.environ['TM_LOG_LEVEL'] = 'ERROR'
......@@ -39,12 +39,12 @@ def main(model_name, model_path, session_id: int = 1):
session_id (int): the identical id of a session
"""
model = MODELS.get(model_name)()
tm_model = tm.TurboMind(model_path, stop_words=model.stop_words)
generator = tm_model.create_instance()
tokenizer_model_path = osp.join(model_path, 'triton_models', 'tokenizer')
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_path,
trust_remote_code=True)
model = MODELS.get(model_name)()
tokenizer = Tokenizer(tokenizer_model_path)
tm_model = tm.TurboMind(model_path,
eos_id=tokenizer.eos_token_id,
stop_words=model.stop_words)
generator = tm_model.create_instance()
nth_round = 1
step = 0
......@@ -56,7 +56,7 @@ def main(model_name, model_path, session_id: int = 1):
exit(0)
elif prompt == 'end':
prompt = model.get_prompt('', nth_round == 1)
input_ids = tokenizer.encode(prompt, add_special_tokens=False)
input_ids = tokenizer.encode(prompt)
for outputs in generator.stream_infer(session_id=session_id,
input_ids=[input_ids],
request_output_len=512,
......@@ -67,10 +67,14 @@ def main(model_name, model_path, session_id: int = 1):
step = 0
seed = random.getrandbits(64)
else:
prompt = model.get_prompt(prompt, nth_round == 1)
input_ids = tokenizer.encode(prompt, add_special_tokens=False)
print(f'session {session_id}')
print(f'{prompt}', end='', flush=True)
if step >= tm_model.session_len:
print('WARNING: exceed session max length.'
' Please end the session.')
continue
prompt = model.get_prompt(prompt, nth_round == 1)
input_ids = tokenizer.encode(prompt)
print(f'{prompt} ', end='', flush=True)
response_size = 0
for outputs in generator.stream_infer(
session_id=session_id,
......@@ -89,8 +93,7 @@ def main(model_name, model_path, session_id: int = 1):
random_seed=seed if nth_round == 1 else None):
res, tokens = outputs[0]
# decode res
response = tokenizer.decode(
res, skip_special_tokens=True)[response_size:]
response = tokenizer.decode(res)[response_size:]
response = valid_str(response)
print(f'{response}', end='', flush=True)
response_size += len(response)
......
lmdeploy/turbomind/tokenizer.py
View file @ 7cbfe2ea
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Sequence, Union
from typing import Sequence
import torch
from torch.nn.utils.rnn import pad_sequence
class Tokenizer:
"""Tokenize prompts or de-tokenize tokens into texts.
class SentencePieceTokenizer:
"""Tokenizer of sentencepiece.
Args:
model_file (str): the path of the tokenizer model
"""
def __init__(self, model_file: str):
if model_file.endswith('.model'):
model_folder = osp.split(model_file)[0]
else:
model_folder = model_file
tokenizer_config_file = osp.join(model_folder, 'tokenizer_config.json')
from sentencepiece import SentencePieceProcessor
self.model = SentencePieceProcessor(model_file=model_file)
model_file_exists = osp.exists(model_file)
config_exists = osp.exists(tokenizer_config_file)
use_hf_model = not config_exists or not model_file_exists
self.use_hf_model = use_hf_model
if not self.use_hf_model:
from sentencepiece import SentencePieceProcessor
self.model = SentencePieceProcessor(model_file=model_file)
self.vocab_size = self.model.vocab_size()
self.bos_token_id = self.model.bos_id()
self.eos_token_id = self.model.eos_id()
else:
from transformers import AutoTokenizer
backend_tokenizer_file = osp.join(model_folder, 'tokenizer.json')
if not osp.exists(backend_tokenizer_file) and model_file_exists:
print('WARNING: Can not find tokenizer.json. '
'It may take long time to initialize the tokenizer.')
self.model = AutoTokenizer.from_pretrained(model_folder)
self.vocab_size = self.model.vocab_size
self.bos_token_id = self.model.bos_token_id
self.eos_token_id = self.model.eos_token_id
# save tokenizer.json to reuse
if not osp.exists(backend_tokenizer_file) and model_file_exists:
self.model.backend_tokenizer.save(backend_tokenizer_file)
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size()
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_id()
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_id()
def encode(self, s: str):
"""Tokenize a prompt.
......@@ -53,25 +39,15 @@ class Tokenizer:
Returns:
list[int]: token ids
"""
if not self.use_hf_model:
add_bos = False
add_eos = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '')
add_bos = True
if s == '<EOS>':
s = ''
add_eos = True
return self.model.Encode(s, add_bos=add_bos, add_eos=add_eos)
else:
add_special_tokens = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '<s>')
if s == '<EOS>':
s = '</s>'
if len(s) == 0:
add_special_tokens = True
return self.model.encode(s, add_special_tokens=add_special_tokens)
add_bos = False
add_eos = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '')
add_bos = True
if s == '<EOS>':
s = ''
add_eos = True
return self.model.Encode(s, add_bos=add_bos, add_eos=add_eos)
def decode(self, t: Sequence[int]):
"""De-tokenize.
......@@ -81,85 +57,132 @@ class Tokenizer:
Returns:
str: text of decoding tokens
"""
if not self.use_hf_model:
return self.model.Decode(t)
else:
skip_special_tokens = False
return self.model.decode(t,
skip_special_tokens=skip_special_tokens)
if isinstance(t, torch.Tensor):
t = t.tolist()
return self.model.Decode(t)
class Preprocessor:
"""Tokenize prompts.
class HuggingFaceTokenizer:
"""Tokenizer of sentencepiece.
Args:
tokenizer (Tokenizer): an instance of tokenizer
model_dir (str): the directory of the tokenizer model
"""
def __init__(self, tokenizer: Tokenizer):
self.tokenizer = tokenizer
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
def __init__(self, model_dir: str):
from transformers import AutoTokenizer
model_file = osp.join(model_dir, 'tokenizer.model')
backend_tokenizer_file = osp.join(model_dir, 'tokenizer.json')
model_file_exists = osp.exists(model_file)
if not osp.exists(backend_tokenizer_file) and model_file_exists:
print('WARNING: Can not find tokenizer.json. '
'It may take long time to initialize the tokenizer.')
self.model = AutoTokenizer.from_pretrained(model_dir,
trust_remote_code=True)
# save tokenizer.json to reuse
if not osp.exists(backend_tokenizer_file) and model_file_exists:
if hasattr(self.model, 'backend_tokenizer'):
self.model.backend_tokenizer.save(backend_tokenizer_file)
def __call__(self, *args, **kwargs):
return self.infer(*args, **kwargs)
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
def infer(self, prompts: Union[str, Sequence[str]]) -> tuple:
"""Tokenize the input prompts.
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
Args:
prompts(str | Sequence[str]): user's prompt, or a batch prompts
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
def encode(self, s: str):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
Tuple(torch.Tensor, torch.Tensor): prompt's token
ids, ids' length and requested output length
list[int]: token ids
"""
if isinstance(prompts, str):
_ = [[prompts]]
elif isinstance(prompts, Sequence):
_ = [[prompt] for prompt in prompts]
else:
assert 0, f'str or Sequence[str] prompts are expected but got ' \
f'{type(prompts)}'
add_special_tokens = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '<s>')
if s == '<EOS>':
s = '</s>'
if len(s) == 0:
add_special_tokens = True
return self.model.encode(s, add_special_tokens=add_special_tokens)
start_ids = [
torch.IntTensor(self.tokenizer.encode(prompt))
for prompt in prompts
]
start_lengths = torch.IntTensor([[len(ids)] for ids in start_ids])
start_ids = pad_sequence(start_ids,
batch_first=True,
padding_value=self.eos_token_id)
return start_ids, start_lengths
def decode(self, t: Sequence[int]):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
Returns:
str: text of decoding tokens
"""
skip_special_tokens = True
return self.model.decode(t, skip_special_tokens=skip_special_tokens)
class Postprocessor:
"""De-tokenize token ids.
class Tokenizer:
"""Tokenize prompts or de-tokenize tokens into texts.
Args:
tokenizer (Tokenizer): an instance of tokenizer
model_file (str): the path of the tokenizer model
"""
def __init__(self, tokenizer: Tokenizer):
self.tokenizer = tokenizer
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
def __init__(self, model_file: str):
if model_file.endswith('.model'):
model_folder = osp.split(model_file)[0]
else:
model_folder = model_file
model_file = osp.join(model_folder, 'tokenizer.model')
tokenizer_config_file = osp.join(model_folder, 'tokenizer_config.json')
model_file_exists = osp.exists(model_file)
config_exists = osp.exists(tokenizer_config_file)
use_hf_model = config_exists or not model_file_exists
if not use_hf_model:
self.model = SentencePieceTokenizer(model_file)
else:
self.model = HuggingFaceTokenizer(model_folder)
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
def __call__(self, *args, **kwargs):
return self.infer(*args, **kwargs)
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
def infer(self, output_ids: torch.Tensor, seqlen: torch.Tensor):
"""De-tokenize tokens for text.
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
def encode(self, s: str):
"""Tokenize a prompt.
Args:
output_ids(torch.Tensor): tokens' id
seqlen(torch.Tensor): sequence length
s (str): a prompt
Returns:
list[int]: token ids
"""
return self.model.encode(s)
def decode(self, t: Sequence[int]):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
Returns:
str: decoded tokens
str: text of decoding tokens
"""
outputs = []
for tokens, _len in zip(output_ids, seqlen):
output = self.tokenizer.decode(tokens[:_len])
outputs.append(output)
return outputs
return self.model.decode(t)
lmdeploy/turbomind/turbomind.py
View file @ 7cbfe2ea
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import sys
from configparser import ConfigParser
from contextlib import contextmanager
from queue import Queue
from threading import Thread
from typing import Iterable, List
......@@ -53,47 +55,80 @@ def _tm_dict_to_torch_dict(tm_dict: _tm.TensorMap):
return ret
@contextmanager
def cuda_ctx(device_id):
old_device = torch.cuda.current_device()
torch.cuda.set_device(device_id)
yield
torch.cuda.set_device(old_device)
class TurboMind:
"""LMDeploy's inference engine.
Args:
model_path (str): the path of turbomind's model
data_type (str): the data type
session_len (int): the max length of a session
eos_id (int): eos token id
stop_words (List[int]): token ids of stop-words
device_id (int): the id of a gpu card
node_id (int): the id of a node
device_num (int): the number of gpu cards
node_num (int): the number of node
"""
def __init__(self,
model_path: str,
data_type: str = 'fp16',
session_len: int = 2048,
eos_id: int = 2,
stop_words: List[int] = None,
device_id: int = 0,
node_id: int = 0,
device_num: int = 1,
node_num: int = 1):
stop_words: List[int] = None):
self.eos_id = eos_id
# create model instance
# TODO: support mpi
node_id = 0
node_num = 1
# read meta from model path
self.gpu_count = 1
self.session_len = 2048
ini_path = osp.join(model_path, 'triton_models/weights/config.ini')
with open(ini_path, 'r') as f:
parser = ConfigParser()
parser.read_file(f)
section_name = ''
if 'turbomind' in parser:
section_name = 'turbomind'
elif 'llama' in parser:
section_name = 'llama'
if len(section_name) > 0:
self.gpu_count = parser.getint(section_name,
'tensor_para_size')
self.session_len = parser.getint(section_name, 'session_len')
# params
self.node_id = node_id
self.node_num = node_num
self.gpu_count = device_num
self.device_id = device_id
self.world_size = self.node_num * self.gpu_count
self.rank = self.node_id * self.gpu_count + self.device_id
self.session_len = session_len
# create model
weight_dir = osp.join(model_path, 'triton_models', 'weights')
model = _tm.AbstractTransformerModel.create_llama_model(
weight_dir, tensor_para_size=self.gpu_count, data_type=data_type)
model.create_shared_weights(self.device_id, self.rank)
self.model = model
self.nccl_params = model.create_nccl_params(self.node_id)
torch.cuda.synchronize()
# create weight
def _create_weight(device_id):
with cuda_ctx(device_id):
rank = self.node_id * self.gpu_count + device_id
model.create_shared_weights(device_id, rank)
threads = []
for device_id in range(self.gpu_count):
t = Thread(target=_create_weight, args=(device_id, ))
t.start()
threads.append(t)
for t in threads:
t.join()
self.stop_words = _stop_words(stop_words)
def create_instance(self, cuda_stream_id=0):
......@@ -117,40 +152,57 @@ class TurboMindInstance:
def __init__(self, tm_model, cuda_stream_id=0):
self.tm_model = tm_model
self.cuda_stream_id = cuda_stream_id
self.node_id = tm_model.node_id
self.gpu_count = tm_model.gpu_count
self.device_id = tm_model.device_id
self.rank = tm_model.rank
self.stop_words = tm_model.stop_words
self.eos_id = tm_model.eos_id
self.session_len = tm_model.session_len
self.cuda_stream_id = cuda_stream_id
# create instance
model = tm_model.model
nccl_params = model.create_nccl_params(tm_model.node_id)
custom_comms = model.create_custom_comms(tm_model.world_size)
instance_comm = model.create_instance_comm(tm_model.gpu_count)
model_inst = model.create_model_instance(self.device_id, self.rank,
self.cuda_stream_id,
nccl_params, custom_comms[0])
# model_inst.register_callback(self._forward_callback)
self.model_inst = model_inst
self.instance_comm = instance_comm
self.nccl_params = tm_model.nccl_params
self.instance_comm = tm_model.model.create_instance_comm(
self.gpu_count)
# create model instances
model_insts = [None] * self.gpu_count
threads = []
for device_id in range(self.gpu_count):
t = Thread(target=self._create_model_instance,
args=(device_id, model_insts))
t.start()
threads.append(t)
for t in threads:
t.join()
self.model_insts = model_insts
self.que = Queue()
self.thread = None
self.threads = [None] * self.gpu_count
def _create_model_instance(self, device_id, model_insts):
with cuda_ctx(device_id):
rank = self.node_id * self.gpu_count + device_id
model_inst = self.tm_model.model.create_model_instance(
device_id, rank, self.cuda_stream_id, self.nccl_params)
model_insts[device_id] = model_inst
def _forward_callback(self, result, ctx):
self.que.put((False, result))
def _forward_thread(self, inputs):
def _func():
output = self.model_inst.forward(inputs, self.instance_comm)
self.que.put((True, output))
def _func(device_id, enque_output):
with cuda_ctx(device_id):
output = self.model_insts[device_id].forward(
inputs, self.instance_comm)
if enque_output:
self.que.put((True, output))
self.thread = Thread(target=_func)
self.thread.start()
for device_id in range(self.gpu_count):
t = Thread(target=_func, args=(device_id, device_id == 0))
t.start()
self.threads[device_id] = t
def stream_infer(self,
session_id,
......@@ -190,7 +242,7 @@ class TurboMindInstance:
stream_output (bool): indicator for stream output
"""
if stream_output:
self.model_inst.register_callback(self._forward_callback)
self.model_insts[0].register_callback(self._forward_callback)
if len(input_ids) == 0:
input_ids = []
......@@ -281,10 +333,11 @@ class TurboMindInstance:
for output, l in zip(output_ids, sequence_length)]
if finish:
for t in self.threads:
t.join()
while self.que.qsize() > 0:
self.que.get()
self.thread.join()
break
if stream_output:
self.model_inst.unregister_callback()
self.model_insts[0].unregister_callback()
src/turbomind/python/CMakeLists.txt
View file @ 7cbfe2ea
......@@ -14,7 +14,7 @@ endif()
pybind11_add_module(${PROJECT_NAME} bind.cpp)
target_link_libraries(${PROJECT_NAME} PRIVATE TransformerTritonBackend
LlamaTritonBackend custom_ar_comm memory_utils)
LlamaTritonBackend)
target_compile_features(${PROJECT_NAME} PRIVATE cxx_std_14)
set_target_properties(${PROJECT_NAME} PROPERTIES
......
src/turbomind/python/bind.cpp
View file @ 7cbfe2ea
#include "src/turbomind/python/dlpack.h"
#include "src/turbomind/triton_backend/llama/LlamaTritonModel.h"
#include "src/turbomind/triton_backend/transformer_triton_backend.hpp"
#include "src/turbomind/utils/nccl_utils.h"
#include <cuda_runtime.h>
#include <memory>
#include <pybind11/functional.h>
#include <pybind11/pybind11.h>
......@@ -26,7 +28,14 @@ std::shared_ptr<T> make_shared_nodel(T data)
DLDevice getDLDevice(triton::Tensor& tensor)
{
DLDevice device{.device_id = 0};
int device_id = 0;
if (tensor.where == triton::MEMORY_GPU) {
cudaPointerAttributes ptr_attr;
cudaPointerGetAttributes(&ptr_attr, tensor.data);
device_id = ptr_attr.device;
}
DLDevice device{.device_id = device_id};
switch (tensor.where) {
case triton::MEMORY_CPU:
......@@ -204,7 +213,6 @@ std::shared_ptr<triton::Tensor> DLManagedTensorToTritonTensor(DLManagedTensor* t
PYBIND11_MODULE(_turbomind, m)
{
// nccl param
py::class_<ft::NcclParam>(m, "NcclParam")
.def(py::init<int, int>(), "rank"_a = 0, "world_size"_a = 1)
......@@ -320,7 +328,6 @@ PYBIND11_MODULE(_turbomind, m)
// transformer model
py::class_<AbstractTransformerModel, std::shared_ptr<AbstractTransformerModel>>(m, "AbstractTransformerModel")
// .def_static("create_llama_model", &AbstractTransformerModel::createLlamaModel, "model_dir"_a)
.def_static(
"create_llama_model",
[](std::string model_dir,
......@@ -349,7 +356,7 @@ PYBIND11_MODULE(_turbomind, m)
"multi_node"_a = false)
.def(
"create_custom_comms",
[](std::shared_ptr<AbstractTransformerModel>& model, int world_size) {
[](AbstractTransformerModel* model, int world_size) {
std::vector<std::shared_ptr<ft::AbstractCustomComm>> ret;
model->createCustomComms(&ret, world_size);
return ret;
......@@ -358,7 +365,7 @@ PYBIND11_MODULE(_turbomind, m)
.def("create_instance_comm", &AbstractTransformerModel::createInstanceComm, "size"_a)
.def(
"create_model_instance",
[](std::shared_ptr<AbstractTransformerModel>& model,
[](AbstractTransformerModel* model,
int deviceId,
int rank,
long stream_id,
......@@ -367,12 +374,17 @@ PYBIND11_MODULE(_turbomind, m)
cudaStream_t stream = reinterpret_cast<cudaStream_t>(stream_id);
return model->createModelInstance(deviceId, rank, stream, nccl_params, custom_all_reduce_comm);
},
py::call_guard<py::gil_scoped_release>(),
"device_id"_a,
"rank"_a,
"stream"_a,
"nccl_params"_a,
"custom_all_reduce_comm"_a = nullptr)
.def("create_shared_weights", &AbstractTransformerModel::createSharedWeights, "device_id"_a, "rank"_a)
.def("create_shared_weights",
&AbstractTransformerModel::createSharedWeights,
py::call_guard<py::gil_scoped_release>(),
"device_id"_a,
"rank"_a)
.def("__str__", &AbstractTransformerModel::toString)
.def("__repr__", &AbstractTransformerModel::toString)
.def("get_tensor_para_size", &AbstractTransformerModel::getTensorParaSize)
......
src/turbomind/triton_backend/CMakeLists.txt
View file @ 7cbfe2ea
......@@ -283,7 +283,7 @@ export(PACKAGE TritonTurboMindBackend)
# limitations under the License.
add_library(TransformerTritonBackend SHARED transformer_triton_backend.cpp)
target_link_libraries(TransformerTritonBackend PRIVATE nccl_utils mpi_utils)
target_link_libraries(TransformerTritonBackend PRIVATE nccl_utils)
install(TARGETS TransformerTritonBackend DESTINATION ${CMAKE_INSTALL_LIBDIR})
add_subdirectory(llama)
src/turbomind/triton_backend/transformer_triton_backend.cpp
View file @ 7cbfe2ea
......@@ -39,9 +39,6 @@ AbstractTransformerModel::createNcclParams(const int node_id, const int device_i
ft::ftNcclGetUniqueId(nccl_ids[i]);
}
}
for (size_t i = 0; i < nccl_ids.size(); i++) {
ft::mpi::bcast(&nccl_ids[i], sizeof(nccl_ids[i]), ft::mpi::MPI_TYPE_BYTE, 0, ft::mpi::COMM_WORLD);
}
}
std::vector<ft::NcclParam> tensor_para_params(local_comm_size);
......
src/turbomind/triton_backend/transformer_triton_backend.hpp
View file @ 7cbfe2ea
......@@ -29,7 +29,6 @@
#include "src/turbomind/utils/Tensor.h"
#include "src/turbomind/utils/custom_ar_comm.h"
#include "src/turbomind/utils/instance_comm.h"
#include "src/turbomind/utils/mpi_utils.h"
#include "src/turbomind/utils/nccl_utils.h"
namespace ft = turbomind;
......
src/turbomind/utils/CMakeLists.txt
View file @ 7cbfe2ea
......@@ -64,7 +64,7 @@ add_library(nccl_utils STATIC nccl_utils.cc)
set_property(TARGET nccl_utils PROPERTY POSITION_INDEPENDENT_CODE ON)
set_property(TARGET nccl_utils PROPERTY CUDA_RESOLVE_DEVICE_SYMBOLS ON)
if (BUILD_MULTI_GPU)
target_link_libraries(nccl_utils PUBLIC ${NCCL_LIBRARIES} mpi_utils logger)
target_link_libraries(nccl_utils PUBLIC ${NCCL_LIBRARIES} logger)
endif()
add_library(cublasINT8MMWrapper STATIC cublasINT8MMWrapper.cc)
......
src/turbomind/utils/nccl_utils.cc
View file @ 7cbfe2ea
......@@ -306,118 +306,6 @@ void ftNcclParamDestroy(NcclParam& param)
#endif
}
void ftNcclInitialize(NcclParam& tensor_para,
NcclParam& pipeline_para,
const int tensor_para_size,
const int pipeline_para_size)
{
TM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
// Initialize nccl communication grid of tensor and pipeline parallel groups.
#ifndef BUILD_MULTI_GPU
FT_CHECK_WITH_INFO(tensor_para_size == 1,
fmtstr("tensor_para_size=%d although BUILD_MULTI_GPU is disabled. "
"Please use the cmake flag -DBUILD_MULTI_GPU=ON if you want "
"to use tensor/pipeline parallelism.",
tensor_para_size));
FT_CHECK_WITH_INFO(pipeline_para_size == 1,
fmtstr("pipeline_para_size=%d although BUILD_MULTI_GPU is disabled. "
"Please use the cmake flag -DBUILD_MULTI_GPU=ON if you want "
"to use tensor/pipeline parallelism.",
pipeline_para_size));
tensor_para.rank_ = 0;
tensor_para.world_size_ = tensor_para_size;
pipeline_para.rank_ = 0;
pipeline_para.world_size_ = pipeline_para_size;
#else
// Initialize a nccl communicator.
if (tensor_para.nccl_comm_ != nullptr && pipeline_para.nccl_comm_ != nullptr) {
TM_LOG_WARNING("NcclParam is already initialized. Skip NCCL initialization.");
return;
}
FT_CHECK(tensor_para.nccl_comm_ == nullptr);
FT_CHECK(pipeline_para.nccl_comm_ == nullptr);
FT_CHECK(tensor_para_size > 0);
FT_CHECK(pipeline_para_size > 0);
if (tensor_para_size == 1 && pipeline_para_size == 1) {
TM_LOG_WARNING("Skip NCCL initialization since requested tensor/pipeline parallel sizes are equals to 1.");
tensor_para.rank_ = 0;
tensor_para.world_size_ = tensor_para_size;
pipeline_para.rank_ = 0;
pipeline_para.world_size_ = pipeline_para_size;
return;
}
int mpi_initialized;
MPICHECK(MPI_Initialized(&mpi_initialized));
FT_CHECK_WITH_INFO(mpi_initialized, "Fail to nccl initialization because MPI is not initialized.");
int rank, world_size;
MPICHECK(MPI_Comm_rank(MPI_COMM_WORLD, &rank));
MPICHECK(MPI_Comm_size(MPI_COMM_WORLD, &world_size));
FT_CHECK_WITH_INFO(tensor_para_size * pipeline_para_size <= world_size,
fmtstr("tensor_para_size (%d) * pipeline_para_size (%d) should equal to the world size (%d).",
tensor_para_size,
pipeline_para_size,
world_size));
// Convert WORLD communicator into 2D grid (k * n) communicator.
// row = a tensor parallel group, col = a pipeline parallel group.
MPI_Comm grid_comm, tp_comm, pp_comm;
int dims[2] = {pipeline_para_size, tensor_para_size};
int periods[2] = {0, 0};
MPI_Cart_create(MPI_COMM_WORLD, 2, dims, periods, 0, &grid_comm);
// Split 2D communicator into rows and cols.
int tp_remain_dims[2] = {false, true};
int pp_remain_dims[2] = {true, false};
MPI_Cart_sub(grid_comm, tp_remain_dims, &tp_comm);
MPI_Cart_sub(grid_comm, pp_remain_dims, &pp_comm);
int tp_rank, pp_rank;
MPI_Comm_rank(tp_comm, &tp_rank);
MPI_Comm_rank(pp_comm, &pp_rank);
ncclUniqueId tp_uid;
ncclUniqueId pp_uid;
// The root of each group creates a nccl uid.
if (tp_rank == 0) {
TM_LOG_DEBUG("rank %d pp rank %d creates nccl uid.", rank, tp_rank);
NCCLCHECK(ncclGetUniqueId(&tp_uid));
}
if (pp_rank == 0) {
TM_LOG_DEBUG("rank %d pp rank %d creates nccl uid.", rank, pp_rank);
NCCLCHECK(ncclGetUniqueId(&pp_uid));
}
// Broadcast nccl uid to share the same nccl uid across gpus in the same group.
TM_LOG_DEBUG("Broadcast nccl uid to the others in the same parallel groups.");
MPI_Bcast(&tp_uid, sizeof(tp_uid), MPI_BYTE, 0, tp_comm);
MPI_Bcast(&pp_uid, sizeof(pp_uid), MPI_BYTE, 0, pp_comm);
TM_LOG_DEBUG("Initialize NCCL communicators.");
ncclComm_t tp_nccl_comm, pp_nccl_comm;
NCCLCHECK(ncclCommInitRank(&tp_nccl_comm, tensor_para_size, tp_uid, tp_rank));
NCCLCHECK(ncclCommInitRank(&pp_nccl_comm, pipeline_para_size, pp_uid, pp_rank));
tensor_para.world_size_ = tensor_para_size;
tensor_para.rank_ = tp_rank;
tensor_para.nccl_uid_ = tp_uid;
tensor_para.nccl_comm_ = tp_nccl_comm;
pipeline_para.world_size_ = pipeline_para_size;
pipeline_para.rank_ = pp_rank;
pipeline_para.nccl_uid_ = pp_uid;
pipeline_para.nccl_comm_ = pp_nccl_comm;
TM_LOG_INFO("NCCL initialized rank=%d world_size=%d tensor_para=%s pipeline_para=%s",
rank,
world_size,
tensor_para.toString().c_str(),
pipeline_para.toString().c_str());
#endif
TM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
}
static std::atomic<int>& ncclGroupCount()
{
static std::atomic<int> value{};
......
src/turbomind/utils/nccl_utils.h
View file @ 7cbfe2ea
......@@ -18,11 +18,9 @@
#include "src/turbomind/utils/cuda_utils.h"
#include "src/turbomind/utils/logger.h"
#include "src/turbomind/utils/mpi_utils.h"
#include <cuda_runtime.h>
#ifdef BUILD_MULTI_GPU
#include <mpi.h>
#include <nccl.h>
#endif
#include <stdio.h>
......@@ -118,11 +116,6 @@ void ftNcclGetUniqueId(NcclUid& uid);
void ftNcclCommInitRank(NcclParam& param, const int rank, const int world_size, const NcclUid uid);
void ftNcclParamDestroy(NcclParam& param);
void ftNcclInitialize(NcclParam& tensor_para,
NcclParam& pipeline_para,
const int tensor_para_size,
const int pipeline_para_size);
int ftNcclNextGroupId();
int ftNcclGroupCount();
......
tests/python/test_tokenizer.py
View file @ 7cbfe2ea
from lmdeploy.turbomind.tokenizer import Postprocessor, Preprocessor, Tokenizer
from lmdeploy.turbomind.tokenizer import Tokenizer
def main():
tokenizer = Tokenizer('huggyllama/llama-7b')
preprocessor = Preprocessor(tokenizer)
postprocessor = Postprocessor(tokenizer)
prompts = ['cest la vie', '上帝已死']
tokens = preprocessor(prompts)
print(tokens)
decode_prompts = postprocessor(*tokens)
print(decode_prompts)
for prompt in prompts:
tokens = tokenizer.encode(prompt)
output = tokenizer.decode(tokens)
print(output)
if __name__ == '__main__':
......
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