132 lines
4.0 KiB
Bash
132 lines
4.0 KiB
Bash
#!/usr/bin/env bash
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set -euo pipefail
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echo "==> Show Python / Torch / CUDA"
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python - <<'PY'
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import sys, torch
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print("Python:", sys.version.split()[0])
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print("Torch:", torch.__version__)
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print("Torch CUDA tag:", torch.version.cuda)
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print("CUDA available:", torch.cuda.is_available())
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PY
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# 选择 CUDA 工具链：优先 /usr/local/cuda-11.8，其次现有的 /usr/local/cuda-12.8
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if [ -d /usr/local/cuda-11.8 ]; then
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export CUDA_HOME=/usr/local/cuda-11.8
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echo "==> Using CUDA_HOME=${CUDA_HOME} (preferred for cu118)"
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elif [ -d /usr/local/cuda-12.8 ]; then
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export CUDA_HOME=/usr/local/cuda-12.8
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echo "==> Using CUDA_HOME=${CUDA_HOME} (nvcc 12.8)"
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else
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echo "!! 未找到 /usr/local/cuda-11.8 或 /usr/local/cuda-12.8，请安装 CUDA toolkit (dev)。"
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exit 1
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fi
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export PATH="${CUDA_HOME}/bin:${PATH}"
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echo "==> nvcc version"
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nvcc --version || { echo "nvcc not found via CUDA_HOME=${CUDA_HOME}"; exit 1; }
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# 3090 = sm_86
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export TORCH_CUDA_ARCH_LIST="8.6"
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echo "==> Install build deps via mamba"
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mamba install -y -c conda-forge cmake ninja pybind11 libaio git
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echo "==> Upgrade Python build tools"
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pip install -U pip setuptools wheel
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# 固定到较稳的 DeepSpeed tag；需要最新版可改为 --branch master
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DS_TAG="v0.14.3"
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if [ ! -d DeepSpeed ]; then
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git clone --branch ${DS_TAG} https://github.com/microsoft/DeepSpeed.git
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fi
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cd DeepSpeed
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echo "==> Build & Install DeepSpeed (training kernels only)"
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export DS_BUILD_OPS=1
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export DS_BUILD_AIO=1
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export DS_BUILD_FUSED_ADAM=1
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export DS_BUILD_CPU_ADAM=1
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# 不启用推理/transformer内核，降低不必要的编译/兼容风险
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# export DS_BUILD_TRANSFORMER=1
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# 某些环境需要强制找 CUDA：export DS_FORCE_CUDA=1
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# export DS_FORCE_CUDA=1
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pip install .
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echo "==> Verify DeepSpeed env"
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python -m deepspeed.env_report
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echo "==> Smoke test: import FusedAdam"
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python - <<'PY'
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import deepspeed, torch
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from deepspeed.ops.adam import FusedAdam
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print("DeepSpeed:", deepspeed.__version__)
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print("Torch:", torch.__version__, "CUDA tag:", torch.version.cuda)
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print("GPU:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else None)
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print("FusedAdam OK")
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PY
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echo "==> Create minimal HF Trainer single-GPU test (tiny model)"
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cd ..
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cat > ds_config_stage2_single.json <<'JSON'
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{
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"train_batch_size": 8,
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"train_micro_batch_size_per_gpu": 1,
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"gradient_accumulation_steps": 8,
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"zero_optimization": { "stage": 2, "overlap_comm": true, "contiguous_gradients": true },
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"fp16": { "enabled": true },
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"aio": {
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"block_size": 1048576,
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"queue_depth": 16,
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"thread_count": 1,
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"single_submit": false,
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"overlap_events": true,
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"verbose": false
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},
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"gradient_clipping": 1.0,
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"steps_per_print": 1000,
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"wall_clock_breakdown": false
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}
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JSON
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cat > train_single_gpu_min.py <<'PY'
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from datasets import Dataset
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from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
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import torch
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model_name = "sshleifer/tiny-gpt2" # 极小模型，快速验证
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tok = AutoTokenizer.from_pretrained(model_name)
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if tok.pad_token is None:
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tok.pad_token = tok.eos_token
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data = ["hello world", "deepspeed single gpu", "trainer test", "fast check"] * 200
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ds = Dataset.from_dict({"text": data})
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def enc(e): return tok(e["text"], truncation=True, max_length=128)
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ds = ds.map(enc)
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collator = DataCollatorForLanguageModeling(tok, mlm=False)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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args = TrainingArguments(
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output_dir="out-ds-single",
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per_device_train_batch_size=1,
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gradient_accumulation_steps=8,
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learning_rate=5e-4,
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num_train_epochs=1,
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logging_steps=10,
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save_steps=0,
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fp16=True,
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deepspeed="ds_config_stage2_single.json"
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)
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trainer = Trainer(model=model, args=args, tokenizer=tok,
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train_dataset=ds, data_collator=collator)
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trainer.train()
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print("OK: single-GPU training finished.")
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PY
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echo "==> Run minimal single-GPU training with DeepSpeed Stage-2"
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CUDA_VISIBLE_DEVICES=0 python train_single_gpu_min.py
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echo "==> ALL DONE (single-GPU)."
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