wehub-resource-sync
619 lines
24 KiB
Python
619 lines
24 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import logging
|
|
import os
|
|
import random
|
|
from dataclasses import dataclass
|
|
from functools import reduce
|
|
|
|
import numpy as np
|
|
from single_llama_model import LlamaForCausalLM, LlamaPretrainingCriterion
|
|
from single_lora_model import LoRAModel
|
|
|
|
import paddle
|
|
import paddle.distributed as dist
|
|
from paddle import LazyGuard
|
|
from paddle.distributed.auto_parallel.intermediate.parallelize import (
|
|
parallelize_model,
|
|
parallelize_optimizer,
|
|
)
|
|
from paddle.io import BatchSampler, DataLoader, Dataset
|
|
|
|
|
|
def is_pp_enable():
|
|
global_mesh = dist.auto_parallel.get_mesh()
|
|
return "pp" in global_mesh.dim_names
|
|
|
|
|
|
def get_mesh(pp_idx=None):
|
|
global_mesh = dist.auto_parallel.get_mesh()
|
|
assert global_mesh is not None, "global_mesh is not initialized!"
|
|
if pp_idx is None:
|
|
return global_mesh
|
|
if is_pp_enable():
|
|
mesh = global_mesh.get_mesh_with_dim("pp")[pp_idx]
|
|
return mesh
|
|
else:
|
|
return global_mesh
|
|
|
|
|
|
class Config:
|
|
vocab_size = 8192
|
|
hidden_size = 512
|
|
intermediate_size = 2048
|
|
seq_length = 512
|
|
num_hidden_layers = 2
|
|
num_attention_heads = 8
|
|
rms_norm_eps = 1e-6
|
|
use_lazy_init = False
|
|
context_parallel = False
|
|
sep_parallel = False
|
|
|
|
|
|
@dataclass
|
|
class LoRaConfig:
|
|
r = 8
|
|
lora_alpha = 8
|
|
lora_dropout = 0.0
|
|
rslora = False
|
|
lora_plus_scale = 1.0
|
|
pissa = False
|
|
use_quick_lora = False
|
|
lora_use_mixer = False
|
|
use_mora = False
|
|
trainable_bias = False
|
|
trainable_modules = None
|
|
target_modules = [
|
|
".*q_proj.*",
|
|
".*v_proj.*",
|
|
".*k_proj.*",
|
|
".*o_proj.*",
|
|
".*qkv_proj.*",
|
|
".*gate_proj.*",
|
|
".*down_proj.*",
|
|
".*up_proj.*",
|
|
".*gate_up_fused_proj.*",
|
|
]
|
|
|
|
|
|
class RandomDataset(Dataset):
|
|
def __init__(self, seq_len, num_samples=100):
|
|
super().__init__()
|
|
self.seq_len = seq_len
|
|
self.num_samples = num_samples
|
|
|
|
def __getitem__(self, index):
|
|
input = np.random.uniform(size=[self.seq_len]).astype("int64")
|
|
label = (np.random.uniform(size=[self.seq_len]) * 10).astype("int64")
|
|
return input, label
|
|
|
|
def __len__(self):
|
|
return self.num_samples
|
|
|
|
|
|
def create_optimizer(model, lr_scheduler):
|
|
decay_parameters = [
|
|
p.name
|
|
for n, p in model.named_parameters()
|
|
if not any(nd in n for nd in ["bias", "norm"])
|
|
]
|
|
|
|
def apply_decay_param_fun(x):
|
|
return x in decay_parameters
|
|
|
|
# test global_clip in auto_parallel
|
|
if os.getenv("use_param_group") == "true":
|
|
param_group = {}
|
|
param_group["params"] = list(model.parameters())
|
|
param_group["weight_decay"] = 0.01
|
|
param_group["grad_clip"] = paddle.nn.ClipGradByGlobalNorm(1.0)
|
|
optimizer = paddle.optimizer.adamw.AdamW(
|
|
learning_rate=lr_scheduler,
|
|
apply_decay_param_fun=apply_decay_param_fun,
|
|
parameters=[param_group],
|
|
)
|
|
else:
|
|
optimizer = paddle.optimizer.adamw.AdamW(
|
|
learning_rate=lr_scheduler,
|
|
apply_decay_param_fun=apply_decay_param_fun,
|
|
parameters=model.parameters(),
|
|
weight_decay=0.01,
|
|
grad_clip=paddle.nn.ClipGradByGlobalNorm(1.0),
|
|
)
|
|
return optimizer
|
|
|
|
|
|
class TestParallelAPI:
|
|
def __init__(self):
|
|
self.config = Config()
|
|
self.lora_config = LoRaConfig()
|
|
self.dp = int(os.getenv("dp"))
|
|
self.mp = int(os.getenv("mp"))
|
|
self.pp = int(os.getenv("pp"))
|
|
self.sep = int(os.getenv("sep", "1"))
|
|
if os.getenv("use_lazy_init") == "true":
|
|
self.config.use_lazy_init = True
|
|
self.gradient_accumulation_steps = int(os.getenv("acc_step"))
|
|
|
|
self.amp = False
|
|
self.amp_dtype = "float16"
|
|
self.amp_level = "O1"
|
|
self.amp_master_grad = False
|
|
if os.getenv("amp") == "true":
|
|
self.amp = True
|
|
if os.getenv("amp_dtype") in ["float16", "bfloat16"]:
|
|
self.amp_dtype = os.getenv("amp_dtype")
|
|
if os.getenv("amp_level") in ["O0", "O1", "O2"]:
|
|
self.amp_level = os.getenv("amp_level")
|
|
if os.getenv("amp_master_grad") == "true":
|
|
self.amp_master_grad = True
|
|
self.level = os.getenv("sharding_stage", "0")
|
|
self.sequence_parallel = False
|
|
if os.getenv("sequence_parallel") == "true":
|
|
self.sequence_parallel = True
|
|
self.config.context_parallel = False
|
|
if os.getenv("context_parallel", "false") == "true":
|
|
self.config.context_parallel = True
|
|
self.config.sep_parallel = False
|
|
if os.getenv("sep_parallel", "false") == "true":
|
|
self.config.sep_parallel = True
|
|
self.prepare_input_output = False
|
|
if os.getenv("prepare_input_output") == "true":
|
|
self.sequence_parallel = True
|
|
if self.sep > 1:
|
|
assert (
|
|
self.config.context_parallel is True
|
|
and self.config.sep_parallel is False
|
|
) or (
|
|
self.config.context_parallel is False
|
|
and self.config.sep_parallel is True
|
|
), (
|
|
"when sep > 1, either context_parallel or sep_parallel should be true"
|
|
)
|
|
num_hidden_layers = os.getenv("num_hidden_layers")
|
|
if num_hidden_layers:
|
|
self.config.num_hidden_layers = int(num_hidden_layers)
|
|
|
|
self.one_api = False
|
|
if os.getenv("one_api") == "true":
|
|
self.one_api = True
|
|
|
|
seed = int(os.getenv("seed", 2024))
|
|
self.share_embedding = int(os.getenv("test_share_embedding", "0"))
|
|
self.position_embedding = int(os.getenv("test_position_embedding", "0"))
|
|
self.test_lora = int(os.getenv("test_lora", "0"))
|
|
np.random.seed(seed)
|
|
random.seed(seed)
|
|
paddle.seed(seed)
|
|
self.init_dist_env()
|
|
|
|
def init_dist_env(self):
|
|
mesh_dims = [
|
|
("dp", self.dp),
|
|
("pp", self.pp),
|
|
("mp", self.mp),
|
|
("sep", self.sep),
|
|
]
|
|
if self.pp * self.mp == 1:
|
|
mesh_dims = [("dp", self.dp)]
|
|
dim_names = [mesh_dim[0] for mesh_dim in mesh_dims]
|
|
mesh_shape = [mesh_dim[1] for mesh_dim in mesh_dims]
|
|
mesh_arr = np.arange(
|
|
0, reduce(lambda x, y: x * y, mesh_shape, 1)
|
|
).reshape(mesh_shape)
|
|
global_mesh = dist.ProcessMesh(mesh_arr, dim_names)
|
|
dist.auto_parallel.set_mesh(global_mesh)
|
|
|
|
def check_mp(self, layer):
|
|
if self.mp == 1:
|
|
return
|
|
for name, sub_layer in layer.named_sublayers():
|
|
if len(sub_layer.sublayers()) == 0:
|
|
if 'q_proj' in name or 'k_proj' in name or 'v_proj' in name:
|
|
assert sub_layer.weight.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(1),
|
|
dist.Replicate(), # cp
|
|
]
|
|
assert sub_layer.bias.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(0),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if self.test_lora:
|
|
assert sub_layer.lora_B.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(1),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if 'gate_proj' in name or 'up_proj' in name:
|
|
assert sub_layer.weight.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(1),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if self.test_lora:
|
|
assert sub_layer.lora_B.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(1),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if (
|
|
'embed_tokens' in name or 'lm_head' in name
|
|
) and not self.share_embedding:
|
|
assert sub_layer.weight.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(1),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if 'o_proj' in name:
|
|
assert sub_layer.weight.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(0),
|
|
dist.Replicate(), # cp
|
|
], f'{name} , {sub_layer.weight.name} , {sub_layer.weight}'
|
|
if self.test_lora:
|
|
assert sub_layer.lora_A.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(0),
|
|
dist.Replicate(), # cp
|
|
]
|
|
# assert sub_layer.bias.placements is None
|
|
if 'down_proj' in name:
|
|
assert sub_layer.weight.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(0),
|
|
dist.Replicate(), # cp
|
|
]
|
|
if self.test_lora:
|
|
assert sub_layer.lora_A.placements == [
|
|
dist.Replicate(),
|
|
dist.Shard(0),
|
|
dist.Replicate(), # cp
|
|
]
|
|
|
|
def check_lora(self, layer):
|
|
if not self.test_lora:
|
|
return
|
|
for name, sub_layer in layer.named_sublayers():
|
|
if len(sub_layer.sublayers()) == 0:
|
|
if 'q_proj' in name or 'k_proj' in name or 'v_proj' in name:
|
|
assert sub_layer.weight.stop_gradient
|
|
assert not sub_layer.lora_A.stop_gradient
|
|
assert not sub_layer.lora_B.stop_gradient
|
|
if 'gate_proj' in name or 'up_proj' in name:
|
|
assert sub_layer.weight.stop_gradient
|
|
assert not sub_layer.lora_A.stop_gradient
|
|
assert not sub_layer.lora_B.stop_gradient
|
|
if (
|
|
'embed_tokens' in name or 'lm_head' in name
|
|
) and not self.share_embedding:
|
|
assert sub_layer.weight.stop_gradient
|
|
if 'o_proj' in name:
|
|
assert sub_layer.weight.stop_gradient, (
|
|
f'{name} , {sub_layer.weight.name} , {sub_layer.weight}'
|
|
)
|
|
assert not sub_layer.lora_A.stop_gradient
|
|
assert not sub_layer.lora_B.stop_gradient
|
|
# assert sub_layer.bias.stop_gradient is None
|
|
if 'down_proj' in name:
|
|
assert sub_layer.weight.stop_gradient
|
|
assert not sub_layer.lora_A.stop_gradient
|
|
assert not sub_layer.lora_B.stop_gradient
|
|
|
|
def parallel_model(self, layer):
|
|
dp_config = None
|
|
mp_config = None
|
|
pp_config = None
|
|
cp_config = None
|
|
prefix = "model." if self.test_lora else ""
|
|
if self.pp > 1:
|
|
# decoders_per_rank = self.config.num_hidden_layers // self.pp
|
|
# split_spec = {
|
|
# ff"{prefix}llama.layers.{i * decoders_per_rank - 1}": SplitPoint.END
|
|
# for i in range(1, self.pp)
|
|
# }
|
|
pp_config = {
|
|
'split_spec': f"{prefix}llama.layers",
|
|
"global_spec": f"{prefix}llama.global_layer",
|
|
}
|
|
if self.dp > 1:
|
|
dp_config = {'sharding_level': self.level}
|
|
if self.mp > 1:
|
|
if not self.sequence_parallel:
|
|
plan = {
|
|
f"{prefix}llama.embed_tokens": dist.ColWiseParallel(
|
|
gather_output=True
|
|
),
|
|
f"{prefix}llama.position_embedding": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.q_proj": dist.ColWiseParallel(
|
|
gather_output=True
|
|
),
|
|
f"{prefix}llama.layers.*.self_attn.q_proj.lora_B": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.k_proj": dist.ColWiseParallel(
|
|
gather_output=True
|
|
),
|
|
f"{prefix}llama.layers.*.self_attn.k_proj.lora_B": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.v_proj": dist.ColWiseParallel(
|
|
gather_output=True
|
|
),
|
|
f"{prefix}llama.layers.*.self_attn.v_proj.lora_B": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(
|
|
is_input_parallel=False
|
|
),
|
|
f"{prefix}llama.layers.*.self_attn.o_proj.lora_A": dist.RowWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.gate_proj.lora_B": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.up_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.up_proj.lora_B": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.down_proj": dist.RowWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.down_proj.lora_A": dist.RowWiseParallel(),
|
|
f"{prefix}lm_head.weight": dist.ColWiseParallel(),
|
|
}
|
|
else:
|
|
if self.prepare_input_output:
|
|
plan = {
|
|
f"{prefix}llama.embed_tokens": dist.ColWiseParallel(),
|
|
f"{prefix}llama.position_embedding": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.q_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.k_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.v_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.up_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.down_proj": dist.RowWiseParallel(),
|
|
f"{prefix}lm_head.weight": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.input_layernorm": dist.SequenceParallelEnable(),
|
|
f"{prefix}llama.layers.*.post_attention_layernorm": dist.SequenceParallelEnable(),
|
|
f"{prefix}llama.norm": dist.SequenceParallelEnable(),
|
|
}
|
|
else:
|
|
plan = {
|
|
f"{prefix}llama.embed_tokens": [
|
|
dist.ColWiseParallel(),
|
|
dist.SequenceParallelBegin(),
|
|
],
|
|
f"{prefix}llama.position_embedding": [
|
|
dist.ColWiseParallel(),
|
|
dist.SequenceParallelBegin(),
|
|
],
|
|
f"{prefix}llama.layers.*.self_attn.q_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.k_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.v_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(),
|
|
f"{prefix}llama.layers.*.self_attn": dist.SequenceParallelDisable(),
|
|
f"{prefix}llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.up_proj": dist.ColWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp.down_proj": dist.RowWiseParallel(),
|
|
f"{prefix}llama.layers.*.mlp": dist.SequenceParallelDisable(
|
|
need_transpose=False
|
|
),
|
|
f"{prefix}lm_head.weight": dist.ColWiseParallel(),
|
|
f"{prefix}lm_head": dist.SequenceParallelEnd(),
|
|
}
|
|
mp_config = {'parallelize_plan': plan}
|
|
if self.sep > 1:
|
|
if not (
|
|
self.config.context_parallel is True
|
|
and (
|
|
os.getenv("backend") != "gpu"
|
|
or not self.amp
|
|
or int(paddle.version.cuda().split(".")[0]) < 11
|
|
or paddle.device.cuda.get_device_capability()[0] < 8
|
|
)
|
|
):
|
|
bck = 'p2p'
|
|
if self.config.context_parallel is True:
|
|
bck = 'p2p'
|
|
elif self.config.sep_parallel is True:
|
|
bck = 'all2all'
|
|
else:
|
|
logging.error(
|
|
f"when sep > 1, should set context_parallel or sep_parallel, but got sep_parallel={self.config.sep_parallel}, context_parallel={self.context_parallel}"
|
|
)
|
|
plan = {
|
|
f"{prefix}llama": dist.PrepareContextParallel(backend=bck),
|
|
f"{prefix}llama.layers.*.self_attn.sdpa": dist.ContextParallel(
|
|
backend=bck
|
|
),
|
|
}
|
|
cp_config = {'parallelize_plan': plan}
|
|
lr_scheduler = paddle.optimizer.lr.LinearWarmup(
|
|
learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001
|
|
)
|
|
|
|
config = {
|
|
'dp_config': dp_config,
|
|
'mp_config': mp_config,
|
|
'pp_config': pp_config,
|
|
'cp_config': cp_config,
|
|
}
|
|
|
|
if self.one_api:
|
|
optimizer = create_optimizer(layer, lr_scheduler)
|
|
model, optimizer = dist.parallelize(
|
|
layer,
|
|
optimizer,
|
|
config=config,
|
|
)
|
|
else:
|
|
layer = parallelize_model(
|
|
layer,
|
|
config=config,
|
|
)
|
|
optimizer = create_optimizer(layer, lr_scheduler)
|
|
optimizer = parallelize_optimizer(
|
|
optimizer,
|
|
config=config,
|
|
)
|
|
self.check_mp(layer)
|
|
self.check_lora(layer)
|
|
return layer, optimizer, lr_scheduler
|
|
|
|
def run_llama(self, to_static=0):
|
|
if self.config.use_lazy_init:
|
|
with LazyGuard():
|
|
model = LlamaForCausalLM(
|
|
self.config, self.share_embedding, self.position_embedding
|
|
)
|
|
else:
|
|
model = LlamaForCausalLM(
|
|
self.config, self.share_embedding, self.position_embedding
|
|
)
|
|
if self.test_lora:
|
|
if self.config.use_lazy_init:
|
|
with LazyGuard():
|
|
model = LoRAModel(model, self.lora_config)
|
|
else:
|
|
model = LoRAModel(model, self.lora_config)
|
|
model, optimizer, lr_scheduler = self.parallel_model(model)
|
|
|
|
criterion = LlamaPretrainingCriterion(self.config)
|
|
|
|
if self.config.use_lazy_init:
|
|
for param in model.parameters():
|
|
assert not param._is_initialized()
|
|
param.initialize()
|
|
|
|
if self.amp and not to_static:
|
|
model, optimizer = paddle.amp.decorate(
|
|
models=model,
|
|
optimizers=optimizer,
|
|
level=self.amp_level,
|
|
dtype=self.amp_dtype,
|
|
master_grad=self.amp_master_grad,
|
|
)
|
|
|
|
train_dataset = RandomDataset(self.config.seq_length)
|
|
train_sampler = BatchSampler(
|
|
train_dataset,
|
|
batch_size=2,
|
|
shuffle=True,
|
|
drop_last=True,
|
|
)
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
batch_sampler=train_sampler,
|
|
num_workers=0,
|
|
)
|
|
|
|
if self.pp == 1:
|
|
meshes = [get_mesh(0)]
|
|
elif self.pp > 1:
|
|
meshes = [get_mesh(0), get_mesh(-1)]
|
|
else:
|
|
raise ValueError("pp should be greater or equal to 1")
|
|
|
|
dist_loader = dist.shard_dataloader(
|
|
dataloader=train_dataloader,
|
|
meshes=meshes,
|
|
shard_dims="dp",
|
|
)
|
|
|
|
global_step = 1
|
|
tr_loss = float(0)
|
|
|
|
if not to_static:
|
|
model.train()
|
|
scaler = None
|
|
if self.amp and self.amp_dtype == "float16":
|
|
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
|
|
scaler = dist.shard_scaler(scaler)
|
|
|
|
for step, inputs in enumerate(dist_loader()):
|
|
input_ids, labels = inputs
|
|
custom_black_list = [
|
|
"reduce_sum",
|
|
"c_softmax_with_cross_entropy",
|
|
]
|
|
custom_white_list = []
|
|
if self.amp_level == "O2":
|
|
custom_white_list.extend(
|
|
["lookup_table", "lookup_table_v2"]
|
|
)
|
|
with paddle.amp.auto_cast(
|
|
self.amp,
|
|
custom_black_list=set(custom_black_list),
|
|
custom_white_list=set(custom_white_list),
|
|
level=self.amp_level,
|
|
dtype=self.amp_dtype,
|
|
):
|
|
logits = model(input_ids)
|
|
tr_loss_step = criterion(logits, labels)
|
|
|
|
if self.gradient_accumulation_steps > 1:
|
|
tr_loss_step /= self.gradient_accumulation_steps
|
|
if scaler is not None:
|
|
scaler.scale(tr_loss_step).backward()
|
|
else:
|
|
tr_loss_step.backward()
|
|
tr_loss += tr_loss_step
|
|
|
|
if global_step % self.gradient_accumulation_steps == 0:
|
|
logging.info(
|
|
f"step: {global_step // self.gradient_accumulation_steps} loss: {tr_loss.numpy()}"
|
|
)
|
|
if scaler is not None:
|
|
scaler.step(optimizer)
|
|
scaler.update()
|
|
else:
|
|
optimizer.step()
|
|
optimizer.clear_grad()
|
|
lr_scheduler.step()
|
|
tr_loss = 0
|
|
|
|
global_step += 1
|
|
if global_step // self.gradient_accumulation_steps >= 3:
|
|
break
|
|
else:
|
|
strategy = dist.Strategy()
|
|
if self.gradient_accumulation_steps > 1:
|
|
strategy.pipeline.accumulate_steps = (
|
|
self.gradient_accumulation_steps
|
|
)
|
|
|
|
if self.amp:
|
|
amp = strategy.amp
|
|
amp.enable = self.amp
|
|
amp.dtype = self.amp_dtype
|
|
amp.level = self.amp_level.lower()
|
|
if self.amp_master_grad:
|
|
amp.use_master_grad = True
|
|
|
|
dist_model = dist.to_static(
|
|
model,
|
|
dist_loader,
|
|
criterion,
|
|
optimizer,
|
|
strategy=strategy,
|
|
)
|
|
|
|
dist_model.train()
|
|
for step, inputs in enumerate(dist_loader()):
|
|
input_ids, labels = inputs
|
|
loss = dist_model(input_ids, labels)
|
|
logging.info(f"step: {step} loss: {loss}")
|
|
if step >= 3:
|
|
break
|
|
|
|
def run_test_cases(self):
|
|
self.run_llama(0)
|
|
if self.sep == 1:
|
|
# sep now only support dynamic mode
|
|
self.run_llama(1)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
TestParallelAPI().run_test_cases()
|