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chore: import upstream snapshot with attribution

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2026-07-13 12:40:42 +08:00
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# 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()