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[Fix] Llama3 Load/Omit CheckpointIO Temporarily (#5717) 

* Fix Llama3 Load error
* Omit Checkpoint IO Temporarily
pull/5723/head
Runyu Lu 2024-05-14 20:17:43 +08:00 committed by GitHub
parent 5bbab1533a
commit 74c47921fa
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3 changed files with 65 additions and 62 deletions
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26
colossalai/inference/core/engine.py
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@ -24,7 +24,7 @@ from colossalai.inference.modeling.policy import model_policy_map
from colossalai.inference.sampler import search_tokens
from colossalai.inference.spec import Drafter, GlideInput
from colossalai.inference.struct import Sequence
from colossalai.inference.utils import get_model_size, has_index_file
from colossalai.inference.utils import get_model_size
from colossalai.interface import ModelWrapper
from colossalai.logging import get_dist_logger
from colossalai.pipeline.stage_manager import PipelineStageManager
@ -113,18 +113,15 @@ class InferenceEngine:
model_policy (Policy): the policy to replace the model
"""
casuallm = None
if isinstance(model_or_path, str):
try:
hf_config = AutoConfig.from_pretrained(model_or_path, trust_remote_code=True)
arch = getattr(hf_config, "architectures")[0]
if arch in _supported_models.keys():
casuallm = _supported_models[arch](hf_config)
if isinstance(casuallm, AutoModelForCausalLM):
# NOTE(caidi) It's necessary to add half() here, otherwise baichuan13B will overflow the memory.
model = AutoModelForCausalLM.from_pretrained(model_or_path, trust_remote_code=True).half()
else:
model = _supported_models[arch](hf_config)
# NOTE(lry89757) Currently we load the model using transformers-api,
# but we will use lazy tensor and checkpoint io to accelerate
# the model load process in the future.
model = _supported_models[arch].from_pretrained(model_or_path, trust_remote_code=True)
else:
raise ValueError(f"Model {arch} is not supported.")
@ -175,13 +172,14 @@ class InferenceEngine:
f"After the shard, Rank: [{dist.get_rank()}], model size: {get_model_size(self.model)} GB, model's device is: {model.device}"
)
if isinstance(model_or_path, str) and not isinstance(casuallm, AutoModelForCausalLM):
from colossalai.inference.core.plugin import InferCheckpoint_io
# NOTE(lry89757) Deprecated currently, will reused when introduce lazy tensor
# if isinstance(model_or_path, str) and not isinstance(casuallm, AutoModelForCausalLM):
# from colossalai.inference.core.plugin import InferCheckpoint_io
cpt_io = InferCheckpoint_io()
if_has_index_file, model_index_file = has_index_file(model_or_path)
assert if_has_index_file, "the model path is invalid"
cpt_io.load_model(self.model, model_index_file)
# cpt_io = InferCheckpoint_io()
# if_has_index_file, model_index_file = has_index_file(model_or_path)
# assert if_has_index_file, "the model path is invalid"
# cpt_io.load_model(self.model, model_index_file)
free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
peak_memory = init_gpu_memory - free_gpu_memory

32
colossalai/inference/executor/rpc_worker.py
View File

@ -1,4 +1,3 @@
import os
from typing import List, Tuple, Union
import rpyc
@ -19,7 +18,7 @@ from colossalai.inference.modeling.policy import (
model_policy_map,
)
from colossalai.inference.sampler import search_tokens
from colossalai.inference.utils import get_model_size, has_index_file
from colossalai.inference.utils import get_model_size
from colossalai.interface import ModelWrapper
from colossalai.logging import get_dist_logger
from colossalai.pipeline.stage_manager import PipelineStageManager
@ -178,15 +177,19 @@ class rpcWorkerService(rpyc.Service):
"""
if isinstance(model_or_path, str):
is_local = os.path.isdir(model_or_path)
# is_local = os.path.isdir(model_or_path)
try:
hf_config = AutoConfig.from_pretrained(model_or_path, trust_remote_code=True)
arch = getattr(hf_config, "architectures")[0]
if is_local:
model = _SUPPORTED_MODELS[arch](hf_config)
else:
# load the real checkpoint
model = _SUPPORTED_MODELS[arch].from_pretrained(model_or_path, trust_remote_code=True)
# NOTE(lry89757) Currently we load the model using transformers-api,
# but we will use lazy tensor and checkpoint io to accelerate
# the model load process in the future.
model = _SUPPORTED_MODELS[arch].from_pretrained(model_or_path, trust_remote_code=True)
# if is_local:
# model = _SUPPORTED_MODELS[arch](hf_config)
# else:
# # load the real checkpoint
# model = _SUPPORTED_MODELS[arch].from_pretrained(model_or_path, trust_remote_code=True)
except Exception as e:
logger.error(
f"An exception occurred during loading model: {e}, model should be loaded by transformers\n"
@ -235,13 +238,14 @@ class rpcWorkerService(rpyc.Service):
f"After the shard, Rank: [{dist.get_rank()}], model size: {get_model_size(self.model)} GB, model's device is: {model.device}"
)
if isinstance(model_or_path, str) and is_local:
from colossalai.inference.core.plugin import InferCheckpoint_io
# NOTE(lry89757) Deprecated currently, will reused when introduce lazy tensor
# if isinstance(model_or_path, str) and is_local:
# from colossalai.inference.core.plugin import InferCheckpoint_io
cpt_io = InferCheckpoint_io()
if_has_index_file, model_index_file = has_index_file(model_or_path)
assert if_has_index_file, "the model path is invalid"
cpt_io.load_model(self.model, model_index_file)
# cpt_io = InferCheckpoint_io()
# if_has_index_file, model_index_file = has_index_file(model_or_path)
# assert if_has_index_file, "the model path is invalid"
# cpt_io.load_model(self.model, model_index_file)
free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
peak_memory = init_gpu_memory - free_gpu_memory

69
colossalai/inference/modeling/models/nopadding_llama.py
View File

@ -646,48 +646,49 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
# NOTE This is a hack to ensure we could load the right weight from LlamaAttention checkpoint due to the use of torch.stack(q_weight, k_weight, v_weight)
for hook in self._load_state_dict_pre_hooks.values():
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
if self.num_heads == self.num_key_value_heads:
# NOTE This is a hack to ensure we could load the right weight from LlamaAttention checkpoint due to the use of torch.stack(q_weight, k_weight, v_weight)
for hook in self._load_state_dict_pre_hooks.values():
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
local_state = {k: v for k, v in local_name_params if v is not None}
persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
local_state = {k: v for k, v in local_name_params if v is not None}
key = "qkv_weight"
k1 = "q_proj.weight"
k2 = "k_proj.weight"
k3 = "v_proj.weight"
q_w = state_dict[prefix + k1]
k_w = state_dict[prefix + k2]
v_w = state_dict[prefix + k3]
key = "qkv_weight"
k1 = "q_proj.weight"
k2 = "k_proj.weight"
k3 = "v_proj.weight"
q_w = state_dict[prefix + k1]
k_w = state_dict[prefix + k2]
v_w = state_dict[prefix + k3]
device_mesh = self.helper_layout.device_mesh
sharding_spec = self.helper_layout.sharding_spec
q_w = distribute_tensor(q_w, device_mesh, sharding_spec)
k_w = distribute_tensor(k_w, device_mesh, sharding_spec)
v_w = distribute_tensor(v_w, device_mesh, sharding_spec)
device_mesh = self.helper_layout.device_mesh
sharding_spec = self.helper_layout.sharding_spec
q_w = distribute_tensor(q_w, device_mesh, sharding_spec)
k_w = distribute_tensor(k_w, device_mesh, sharding_spec)
v_w = distribute_tensor(v_w, device_mesh, sharding_spec)
qkv_w = torch.stack([q_w.T, k_w.T, v_w.T], dim=0)
qkv_w = torch.stack([q_w.T, k_w.T, v_w.T], dim=0)
input_param = nn.Parameter(
qkv_w
) # NOTE qkv_weight doesn't have to be a distensor, Like input_param = sharded_tensor_to_param(input_param)
input_param = nn.Parameter(
qkv_w
) # NOTE qkv_weight doesn't have to be a distensor, Like input_param = sharded_tensor_to_param(input_param)
param = local_state[key]
param = local_state[key]
try:
with torch.no_grad():
param.copy_(input_param)
except Exception as ex:
error_msgs.append(
'While copying the parameter named "{}", '
"whose dimensions in the model are {} and "
"whose dimensions in the checkpoint are {}, "
"an exception occurred : {}.".format(key, param.size(), input_param.size(), ex.args)
)
try:
with torch.no_grad():
param.copy_(input_param)
except Exception as ex:
error_msgs.append(
'While copying the parameter named "{}", '
"whose dimensions in the model are {} and "
"whose dimensions in the checkpoint are {}, "
"an exception occurred : {}.".format(key, param.size(), input_param.size(), ex.args)
)
strict = False # to avoid unexpected_keys
strict = False # to avoid unexpected_keys
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)