[feat] Update sync model by tensor, fix tMbs problem, add qwen train benchmark.

This commit is contained in:
xysheng-colossal 2025-07-17 16:29:26 +08:00
parent d9b5f10d82
commit ad1ceb0424
16 changed files with 492 additions and 60 deletions

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@ -55,3 +55,32 @@ def ray_broadcast_tensor_dict(
if rank == src:
out_dict = tensor_dict
return out_dict
def ray_broadcast_tensor_dict_and_load(
producer_obj, tensor_dict: Dict[str, torch.Tensor], src: int = 0, device=None, group_name: str = "default"
):
rank = cc.get_rank(group_name)
if rank == src:
metadata = []
for k, v in tensor_dict.items():
metadata.append((k, v.shape, v.dtype))
else:
metadata = None
metadata = ray_broadcast_object(metadata, src, device, group_name)
for k, shape, dtype in metadata:
if "consumer_global_step" == k:
continue
if rank == src:
tensor = tensor_dict[k]
else:
out_dict = {}
tensor = torch.empty(shape, dtype=dtype, device=device)
cc.broadcast(tensor, src, group_name)
if rank != src:
out_dict[k] = tensor
producer_obj.load_state_dict(out_dict)
del out_dict
torch.npu.empty_cache()
if rank == src:
out_dict = tensor_dict

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@ -15,7 +15,7 @@ from colossalai.booster.plugin import HybridParallelPlugin
from colossalai.initialize import launch
from colossalai.nn.optimizer import HybridAdam
from .comm import ray_broadcast_tensor_dict
from .comm import ray_broadcast_tensor_dict, ray_broadcast_tensor_dict_and_load
from .utils import bind_batch, post_recv, unbind_batch
@ -172,6 +172,8 @@ class BaseConsumer:
)
self.profiler.enter("step")
loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
del batch
del raw_mini_batches_metric_dict
self.profiler.exit("step")
self.buffer = self.buffer[
effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
@ -303,7 +305,8 @@ class BaseConsumer:
state_dict = self.state_dict()
if self.pp_size > 1:
if self.tp_rank == 0 and self.dp_rank == 0:
ray_broadcast_tensor_dict(
ray_broadcast_tensor_dict_and_load(
None,
state_dict,
src=self.num_producers,
device=self.device,
@ -311,8 +314,12 @@ class BaseConsumer:
)
else:
if self.rank == 0:
ray_broadcast_tensor_dict(
state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
ray_broadcast_tensor_dict_and_load(
None,
state_dict,
src=self.num_producers,
device=self.device,
group_name="sync_model",
)
del state_dict
torch.npu.empty_cache()

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@ -62,6 +62,7 @@ class GRPOConsumer(BaseConsumer):
batch_size,
model_config,
plugin_config,
generate_config,
minibatch_size,
save_interval=save_interval,
save_dir=save_dir,

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@ -17,7 +17,7 @@ from ray.util.collective.types import ReduceOp
from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer
from .comm import ray_broadcast_tensor_dict
from .comm import ray_broadcast_tensor_dict, ray_broadcast_tensor_dict_and_load
from .inference_backend import BACKEND_MAP
from .utils import safe_append_to_jsonl_file
@ -191,6 +191,7 @@ class BaseProducer:
)
else:
cc.init_collective_group(self.num_producers + 1, self.producer_idx, backend="hccl", group_name="sync_model")
cc.init_collective_group(self.num_producers, self.producer_idx, backend="hccl", group_name="producer_group")
def rollout(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs) -> Dict[str, torch.Tensor]:
raise NotImplementedError
@ -340,25 +341,16 @@ class BaseProducer:
print(
f"[P{self.producer_idx}] Sync model PP stage {pp_idx} episode {episode} step {(i + 1) // self.num_microbatches - 1}"
)
state_dict = ray_broadcast_tensor_dict(
None, self.num_producers, device=self.device, group_name=f"sync_model_{pp_idx}"
ray_broadcast_tensor_dict_and_load(
self, None, self.num_producers, device=self.device, group_name=f"sync_model_{pp_idx}"
)
if "consumer_global_step" in state_dict:
self.consumer_global_step = state_dict.pop("consumer_global_step").item()
self.load_state_dict(state_dict)
else:
print(
f"[P{self.producer_idx}] Sync model episode {episode} step {(i + 1) // self.num_microbatches - 1}"
)
state_dict = ray_broadcast_tensor_dict(
None, self.num_producers, device=self.device, group_name="sync_model"
ray_broadcast_tensor_dict_and_load(
self, None, self.num_producers, device=self.device, group_name=f"sync_model"
)
if "consumer_global_step" in state_dict:
self.consumer_global_step = state_dict.pop("consumer_global_step").item()
self.load_state_dict(state_dict)
self.profiler.exit("sync_model")
del state_dict
torch.npu.empty_cache()
if isinstance(self.model, BACKEND_MAP["vllm"]) and self.model.model_config.get(
"enable_sleep_mode", False
):

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@ -166,6 +166,7 @@ if __name__ == "__main__":
parser.add_argument(
"--enable_profiling", action="store_true", default=False, help="Enable profiling for the training process."
)
parser.add_argument("--cpu_offload", action="store_true", default=False, help="Cpu offload.")
args = parser.parse_args()
if args.train_minibatch_size is None:
@ -251,7 +252,7 @@ if __name__ == "__main__":
)
generate_config.update(
dict(
max_tokens=args.max_new_tokens, # max new tokens
max_tokens=args.max_new_tokens + args.max_prompt_tokens, # max new tokens
include_stop_str_in_output=True,
stop=["</answer>"] if args.reward_type == "think_answer_tags" else None,
ignore_eos=True if args.reward_type == "think_answer_tags" else False,
@ -344,6 +345,7 @@ if __name__ == "__main__":
1, args.train_microbatch_size // args.pipeline_parallel_size
), # microbatch size should be set to train_microbatch_size // pp_size
"zero_stage": args.zero_stage,
"cpu_offload": args.cpu_offload,
"max_norm": 1.0,
"enable_flash_attention": True,
"sp_size": args.tensor_parallel_size,

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@ -12,10 +12,7 @@ from transformers.modeling_outputs import (
)
try:
from transformers.modeling_attn_mask_utils import (
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from transformers.models.qwen2.modeling_qwen2 import (
Qwen2Attention,
Qwen2ForCausalLM,
@ -132,46 +129,20 @@ class Qwen2PipelineForwards:
else:
position_ids = position_ids.view(-1, seq_length).long()
if (
not shard_config.enable_flash_attention
and attention_mask is not None
and self._attn_implementation == "flash_attention_2"
and use_cache
):
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
# embed positions, for the first stage, hidden_states is the input embeddings,
# for the other stages, hidden_states is the output of the previous stage
if shard_config.enable_flash_attention:
# in this case, attention_mask is a dict rather than a tensor
attention_mask = None
else:
if self._attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self._attn_implementation == "sdpa" and not output_attentions:
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask,
(batch_size, seq_length),
hidden_states,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask,
(batch_size, seq_length),
hidden_states,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
if stage_manager.is_first_stage():
if shard_config.enable_sequence_parallelism:

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@ -161,18 +161,18 @@ class PerformanceEvaluator:
) * (
1.0 + (seq_len / (6.0 * self.hidden_size)) + (self.vocab_size / (16.0 * self.num_layers * self.hidden_size))
)
self.flop += batch_size * seq_len * self.model_numel * 2 * (3 + int(self.enable_grad_checkpoint))
self.flop += batch_size * (seq_len // 1024) * self.model_numel * (3 + int(self.enable_grad_checkpoint))
def on_fit_end(self) -> None:
avg_duration = all_reduce_mean(self.timer.duration, self.coordinator.world_size)
avg_throughput = self.num_samples * self.dp_world_size / (avg_duration + 1e-12)
mp_world_size = self.coordinator.world_size // self.dp_world_size
avg_tflops_per_gpu_megatron = self.flop_megatron / 1e12 / (avg_duration + 1e-12) / mp_world_size
self.flop_megatron / 1e12 / (avg_duration + 1e-12) / mp_world_size
avg_tflops_per_gpu = self.flop / 1e12 / (avg_duration + 1e-12) / mp_world_size
self.coordinator.print_on_master(
f"num_samples: {self.num_samples}, dp_world_size: {self.dp_world_size}, flop_megatron: {self.flop_megatron}, flop: {self.flop}, avg_duration: {avg_duration}, "
f"avg_throughput: {avg_throughput}"
)
self.coordinator.print_on_master(
f"Throughput: {avg_throughput:.2f} samples/sec, TFLOPS per GPU by Megatron: {avg_tflops_per_gpu_megatron:.2f}, TFLOPS per GPU: {avg_tflops_per_gpu:.2f}"
f"Throughput: {avg_throughput:.2f} samples/sec, TFLOPS per GPU: {avg_tflops_per_gpu:.2f}"
)

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@ -0,0 +1,127 @@
# Pretraining LLaMA-1/2/3: best practices for building LLaMA-1/2/3-like base models
### LLaMA3
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA3-70B-H100.png" width=600/>
</p>
- 70 billion parameter LLaMA3 model training accelerated by 18%
### LLaMA2
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png" width=600/>
</p>
- 70 billion parameter LLaMA2 model training accelerated by 195%
[[blog]](https://www.hpc-ai.tech/blog/70b-llama2-training)
### LLaMA1
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width=600/>
</p>
- 65-billion-parameter large model pretraining accelerated by 38%
[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining)
## Usage
> ⚠ This example only has benchmarking script. For training/finetuning, please refer to the [applications/Colossal-LLaMA](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA).
### 1. Installation
Please install the latest ColossalAI from source.
```bash
BUILD_EXT=1 pip install -U git+https://github.com/hpcaitech/ColossalAI
```
Then install other dependencies.
```bash
pip install -r requirements.txt
```
### 4. Shell Script Examples
For your convenience, we provide some shell scripts to run benchmark with various configurations.
You can find them in `scripts/benchmark_7B` and `scripts/benchmark_70B` directory. The main command should be in the format of:
```bash
colossalai run --nproc_per_node YOUR_GPU_PER_NODE --hostfile YOUR_HOST_FILE \
benchmark.py --OTHER_CONFIGURATIONS
```
Here we will show an example of how to run training
llama pretraining with `gemini, batch_size=16, sequence_length=4096, gradient_checkpoint=True, flash_attn=True`.
#### a. Running environment
This experiment was performed on 4 computing nodes with 32 A800/H800 80GB GPUs in total for LLaMA-1 65B or LLaMA-2 70B. The nodes are
connected with RDMA and GPUs within one node are fully connected with NVLink.
#### b. Running command
```bash
cd scripts/benchmark_7B
```
First, put your host file (`hosts.txt`) in this directory with your real host ip or host name.
Here is a sample `hosts.txt`:
```text
hostname1
hostname2
hostname3
hostname4
```
Then add environment variables to script if needed.
Finally, run the following command to start training:
```bash
bash gemini.sh
```
If you encounter out-of-memory(OOM) error during training with script `gemini.sh`, changing to script `gemini_auto.sh` might be a solution, since gemini_auto will set a upper limit on GPU memory usage through offloading part of the model parameters and optimizer states back to CPU memory. But there's a trade-off: `gemini_auto.sh` will be a bit slower, since more data are transmitted between CPU and GPU.
#### c. Results
If you run the above command successfully, you will get the following results:
`max memory usage: 55491.10 MB, throughput: 24.26 samples/s, TFLOPS/GPU: 167.43`.
## Reference
```
@article{bian2021colossal,
title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
journal={arXiv preprint arXiv:2110.14883},
year={2021}
}
```
```bibtex
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```bibtex
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```bibtex
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```

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@ -0,0 +1,282 @@
import argparse
import resource
import time
import warnings
from contextlib import nullcontext
import torch
import torch.distributed as dist
from data_utils import RandomDataset
from performance_evaluator import PerformanceEvaluator, get_profile_context
from tqdm import tqdm
from transformers import AutoConfig, Qwen2ForCausalLM
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin
from colossalai.cluster import DistCoordinator
from colossalai.lazy import LazyInitContext
from colossalai.nn.optimizer import HybridAdam
from colossalai.shardformer import PipelineGradientCheckpointConfig
warnings.filterwarnings("ignore")
# ==============================
# Constants
# ==============================
# We have lots of qwen2 for your choice!
MODEL_CONFIGS = {
"7b": Qwen2Config(
hidden_size=3584,
intermediate_size=18944,
num_hidden_layers=28,
num_attention_heads=28,
num_key_value_heads=4,
max_position_embeddings=131072,
),
"72b": Qwen2Config(
hidden_size=8192,
intermediate_size=29568,
num_hidden_layers=80,
num_attention_heads=64,
num_key_value_heads=8,
max_position_embeddings=131072,
),
}
def main():
# ==============================
# Parse Arguments
# ==============================
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", type=str, default="7b", help="Model configuration")
parser.add_argument("-model", "--model_path", type=str, help="Model path")
parser.add_argument(
"-p",
"--plugin",
choices=["gemini", "gemini_auto", "fsdp", "fsdp_cpu", "3d", "3d_cpu"],
default="gemini",
help="Choose which plugin to use",
)
parser.add_argument("-b", "--batch_size", type=int, default=2, help="Batch size")
parser.add_argument("-s", "--num_steps", type=int, default=10, help="Number of steps to run")
parser.add_argument("-i", "--ignore_steps", type=int, default=3, help="Number of steps to ignore")
parser.add_argument("-g", "--grad_checkpoint", action="store_true", help="Use gradient checkpointing")
parser.add_argument("-l", "--max_length", type=int, default=4096, help="Max sequence length")
parser.add_argument(
"-w", "--warmup_ratio", type=float, default=0.8, help="warm up ratio of non-model data. Only for gemini-auto"
)
parser.add_argument("-m", "--memory_limit", type=int, help="Gemini memory limit in mb")
parser.add_argument("-x", "--xformers", action="store_true", help="Use xformers")
parser.add_argument("--shard_param_frac", type=float, default=1.0, help="Shard param fraction. Only for gemini")
parser.add_argument("--offload_optim_frac", type=float, default=0.0, help="Offload optim fraction. Only for gemini")
parser.add_argument("--offload_param_frac", type=float, default=0.0, help="Offload param fraction. Only for gemini")
parser.add_argument("--tp", type=int, default=1, help="Tensor parallel size")
parser.add_argument("--sp", type=int, default=1, help="Sequence parallel size")
parser.add_argument("--extra_dp", type=int, default=1, help="Extra data parallel size, used for Gemini")
parser.add_argument("--pp", type=int, default=1, help="Pipeline parallel size")
parser.add_argument("--mbs", type=int, default=1, help="Micro batch size of pipeline parallel")
parser.add_argument("--zero", type=int, default=0, help="Zero Stage when hybrid plugin is enabled")
parser.add_argument("--custom-ckpt", action="store_true", help="Customize checkpoint", default=False)
parser.add_argument("--pp_style", default="1f1b", choices=["1f1b", "interleaved", "zbv"])
parser.add_argument("--n_chunks", default=1, help="number of model chunks", type=eval)
parser.add_argument("--profile", action="store_true", help="Profile the code")
parser.add_argument(
"--nsys",
action="store_true",
help="Use nsys for profiling. \
You should put something like this before colossalai launch: \
nsys profile -w true -t cuda,cudnn,cublas -s cpu --capture-range=cudaProfilerApi --capture-range-end=stop --cudabacktrace=true -x true --python-backtrace=cuda -o prof_out",
)
parser.add_argument("--disable-async-reduce", action="store_true", help="Disable the asynchronous reduce operation")
parser.add_argument("--prefetch_num", type=int, default=0, help="chunk prefetch max number")
parser.add_argument("--no_cache", action="store_true")
parser.add_argument("--use_fp8_comm", action="store_true", default=False, help="for using fp8 during communication")
parser.add_argument("--use_fp8", action="store_true", default=False, help="for using fp8 linear")
parser.add_argument("--overlap_p2p", action="store_true", default=True, help="for using overlap p2p")
parser.add_argument("--overlap_allgather", action="store_true")
parser.add_argument(
"--sp_mode",
default="all_to_all",
choices=["all_to_all", "ring_attn", "ring", "split_gather"],
help="Sequence parallelism mode",
)
args = parser.parse_args()
colossalai.launch_from_torch()
coordinator = DistCoordinator()
def empty_init():
pass
# ckpt config for LLaMA3-70B on 64 H100 GPUs
hybrid_kwargs = (
{
"gradient_checkpoint_config": PipelineGradientCheckpointConfig(
num_ckpt_layers_per_stage=[19, 19, 19, 13],
),
"num_layers_per_stage": [19, 20, 20, 21],
"pp_style": "interleaved",
}
if args.custom_ckpt
else {}
)
# ==============================
# Initialize Booster
# ==============================
if args.config in MODEL_CONFIGS:
config = MODEL_CONFIGS[args.config]
else:
config = AutoConfig.from_pretrained(args.config, trust_remote_code=True)
if args.plugin == "3d":
scheduler_nodes = None
plugin = HybridParallelPlugin(
tp_size=args.tp,
pp_size=args.pp,
pp_style=args.pp_style,
num_model_chunks=args.n_chunks,
zero_stage=args.zero,
sp_size=args.sp,
sequence_parallelism_mode=args.sp_mode,
enable_sequence_parallelism=args.sp > 1,
enable_fused_normalization=get_accelerator().is_available(),
enable_flash_attention=args.xformers,
microbatch_size=args.mbs,
precision="bf16",
enable_metadata_cache=not args.no_cache,
overlap_allgather=args.overlap_allgather,
use_fp8=args.use_fp8,
fp8_communication=args.use_fp8_comm,
scheduler_nodes=scheduler_nodes,
**hybrid_kwargs,
)
elif args.plugin == "3d_cpu":
plugin = HybridParallelPlugin(
tp_size=args.tp,
pp_size=args.pp,
pp_style=args.pp_style,
num_model_chunks=args.n_chunks,
zero_stage=args.zero,
cpu_offload=True,
enable_fused_normalization=get_accelerator().is_available(),
enable_flash_attention=args.xformers,
microbatch_size=args.mbs,
initial_scale=2**8,
precision="bf16",
overlap_p2p=args.overlap_p2p,
use_fp8=args.use_fp8,
fp8_communication=args.use_fp8_comm,
)
else:
raise ValueError(f"Unknown plugin {args.plugin}")
booster = Booster(plugin=plugin)
# ==============================
# Initialize Dataset and Dataloader
# ==============================
dp_size = getattr(plugin, "dp_size", coordinator.world_size)
if args.config in MODEL_CONFIGS:
config = MODEL_CONFIGS[args.config]
else:
config = AutoConfig.from_pretrained(args.config, trust_remote_code=True)
get_accelerator().manual_seed(42)
dataset = RandomDataset(
num_samples=args.batch_size * args.num_steps * dp_size, max_length=args.max_length, vocab_size=config.vocab_size
)
dataloader = plugin.prepare_dataloader(dataset, batch_size=args.batch_size, shuffle=True, drop_last=True, seed=42)
# ==============================
# Initialize Model and Optimizer
# ==============================
init_ctx = (
LazyInitContext(default_device=get_accelerator().get_current_device())
if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin))
else nullcontext()
)
model = Qwen2ForCausalLM.from_pretrained(
MODEL_PATH, trust_remote_code=True, use_flash_attention_2=False, use_cache=False, attn_implementation="eager"
)
if args.grad_checkpoint:
model.gradient_checkpointing_enable()
model_numel = 14480488529920
num_layers = model.config.num_hidden_layers
performance_evaluator = PerformanceEvaluator(
model_numel,
num_layers,
model.config.hidden_size,
model.config.vocab_size,
args.grad_checkpoint,
args.ignore_steps,
dp_world_size=dp_size,
)
optimizer = HybridAdam(model.parameters())
torch.set_default_dtype(torch.bfloat16)
model, optimizer, _, dataloader, _ = booster.boost(model, optimizer, dataloader=dataloader)
torch.set_default_dtype(torch.float)
coordinator.print_on_master(
f"Booster init max device memory: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB"
)
coordinator.print_on_master(
f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024:.2f} MB"
)
with get_profile_context(
args.profile,
args.ignore_steps,
1, # avoid creating massive log files
save_dir=f"./profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-qwen2-{args.config}",
nsys=args.nsys,
) as prof:
if isinstance(plugin, HybridParallelPlugin) and args.pp > 1:
data_iter = iter(dataloader)
for step in tqdm(range(len(dataloader)), desc="Step", disable=not coordinator.is_master()):
performance_evaluator.on_step_start(step)
outputs = booster.execute_pipeline(
data_iter,
model,
criterion=lambda outputs, inputs: outputs[0],
optimizer=optimizer,
return_loss=True,
)
loss = outputs["loss"]
if coordinator.is_last_process():
print(f"Step {step} loss: {loss}")
optimizer.step()
optimizer.zero_grad()
performance_evaluator.on_step_end(input_ids=torch.empty(args.batch_size, args.max_length))
prof.step()
else:
for step, batch in enumerate(tqdm(dataloader, desc="Step", disable=not coordinator.is_master())):
performance_evaluator.on_step_start(step)
outputs = model(**batch)
loss = outputs[0]
del outputs # free memory
if dist.get_rank() == dist.get_world_size() - 1:
print(f"Step {step} loss: {loss}")
booster.backward(loss, optimizer)
optimizer.step()
optimizer.zero_grad()
performance_evaluator.on_step_end(**batch)
prof.step()
performance_evaluator.on_fit_end()
coordinator.print_on_master(f"Max device memory usage: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB")
if __name__ == "__main__":
main()

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../data_utils.py

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#!/bin/bash
################
#Load your environments and modules here
################
export OMP_NUM_THREADS=8
#hybird: zero2+flash_atten+grad_ckpt+bs4
colossalai run --nproc_per_node 8 benchmark.py -m "/home/grpo/models/Qwen2.5-7B/" -p "3d" -x -g --zero 1 -b 32 --mbs 1 --tp 2 --pp 2 -l 4096

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../model_utils.py

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../performance_evaluator.py

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colossalai>=0.3.6
datasets
numpy
tqdm
transformers
flash-attn>=2.0.0
SentencePiece==0.1.99
tensorboard==2.14.0

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