deepspeed zero3 QLoRA finetuning (#11625)
* deepspeed zero3 QLoRA finetuning * Update convert.py * Update low_bit_linear.py * Update utils.py * Update qlora_finetune_llama2_13b_arch_2_card.sh * Update low_bit_linear.py * Update alpaca_qlora_finetuning.py * Update low_bit_linear.py * Update utils.py * Update convert.py * Update alpaca_qlora_finetuning.py * Update alpaca_qlora_finetuning.py * Update low_bit_linear.py * Update deepspeed_zero3.json * Update qlora_finetune_llama2_13b_arch_2_card.sh * Update low_bit_linear.py * Update low_bit_linear.py * Update utils.py * fix style * fix style * Update alpaca_qlora_finetuning.py * Update qlora_finetune_llama2_13b_arch_2_card.sh * Update convert.py * Update low_bit_linear.py * Update model.py * Update alpaca_qlora_finetuning.py * Update low_bit_linear.py * Update low_bit_linear.py * Update low_bit_linear.py
This commit is contained in:
Heyang Sun
GitHub
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commit
70c828b87c
5 changed files with 119 additions and 14 deletions
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@ -144,6 +144,14 @@ def train(
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prompter = Prompter(prompt_template_name)
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if deepspeed is not None and "zero3" in deepspeed:
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from ipex_llm.transformers.utils \
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import _constant_buffered_norm2
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from ipex_llm.llm_patching import replace_attr
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import deepspeed as ds
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replace_attr(ds.runtime.zero.stage3.DeepSpeedZeroOptimizer_Stage3,
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"_constant_buffered_norm2", _constant_buffered_norm2)
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device_map = "auto"
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world_size = int(os.environ.get("WORLD_SIZE", 1))
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ddp = world_size != 1
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@ -161,7 +169,7 @@ def train(
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optimize_model=False,
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torch_dtype=torch.bfloat16,
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modules_to_not_convert=["lm_head"],
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trust_remote_code=True,
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trust_remote_code=True
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)
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else:
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# According to the QLoRA paper, using "nf4" could yield better model quality than "int4"
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@ -186,9 +194,10 @@ def train(
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# # device_map=device_map,
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# modules_to_not_convert=["lm_head"],
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# )
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print(f"Model loaded on rank {os.environ.get('LOCAL_RANK')}")
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model = model.to(f'xpu:{os.environ.get("LOCAL_RANK", 0)}')
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print(f"Model moved to rank {os.environ.get('LOCAL_RANK')}")
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if deepspeed is not None and not "zero3" in deepspeed:
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print(f"Model loaded on rank {os.environ.get('LOCAL_RANK')}")
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model = model.to(f'xpu:{os.environ.get("LOCAL_RANK", 0)}')
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print(f"Model moved to rank {os.environ.get('LOCAL_RANK')}")
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tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
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print(f"Tokenizer loaded on rank {os.environ.get('LOCAL_RANK')}")
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@ -0,0 +1,15 @@
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{
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"zero_optimization": {
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"stage": 3,
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"contiguous_gradients": true,
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"overlap_comm": true,
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"offload_optimizer": {"device": "cpu"}
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},
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"bf16": {
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"enabled": true
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},
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"world_size": 2,
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"train_batch_size": 32,
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"train_micro_batch_size_per_gpu": 2,
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"gradient_accumulation_steps": 8
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}
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@ -0,0 +1,41 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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export MASTER_ADDR=127.0.0.1
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export MASTER_PORT=29503
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export FI_PROVIDER=tcp
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export CCL_ATL_TRANSPORT=ofi
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export CCL_ZE_IPC_EXCHANGE=sockets
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export UR_L0_IN_ORDER_BARRIER_BY_SIGNAL=0
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basekit_root=/opt/intel/oneapi
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source $basekit_root/setvars.sh --force
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source $basekit_root/ccl/latest/env/vars.sh --force
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NUM_GPUS=2 # number of used GPU
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=2
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export TORCH_LLM_ALLREDUCE=0 # Different from PVC
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export DS_SKIP_CUDA_CHECK=1
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mpirun -n $NUM_GPUS \
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python -u ./alpaca_qlora_finetuning.py \
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--base_model "meta-llama/Llama-2-13b-hf" \
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--data_path "yahma/alpaca-cleaned" \
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--output_dir "./ipex-llm-qlora-alpaca" \
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--gradient_checkpointing True \
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--micro_batch_size 2 \
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--batch_size 32 \
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--deepspeed ./deepspeed_zero3.json
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@ -229,6 +229,13 @@ def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
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dst_tensor = dst_tensor.reshape(tensor.shape[0], tensor.shape[-1] // QK)
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scale = torch.empty(n // k, dtype=torch.float32,
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device=device)
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elif qtype == NF4:
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# Deepspeed zero3 requires unified dtype,
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# thus here uses bfloat16 consistent to other layers
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# dst_size above is computed based on uint8, and for bfloat16,
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# buffer size should be half
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dst_tensor = torch.empty(dst_size // 2, dtype=torch.bfloat16,
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device=device)
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else:
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dst_tensor = torch.empty(dst_size, dtype=torch.uint8,
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device=device)
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@ -260,12 +267,15 @@ def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
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def ggml_q_format_convet_cpu2xpu(tensor: torch.Tensor, num_elem: int, qtype: int):
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invalidInputError(tensor.dtype == torch.uint8,
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"Input tensor must be uint8")
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if qtype == NF4:
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invalidInputError(tensor.dtype == torch.bfloat16,
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"NF4 Input tensor must be bfloat16")
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else:
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invalidInputError(tensor.dtype == torch.uint8,
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"Input tensor except NF4 must be uint8")
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invalidInputError(tensor.device == torch.device('cpu'),
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"Input tensor must be uint8")
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"Input tensor must be on cpu")
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src = ctypes.c_void_p(tensor.data.data_ptr())
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@ -370,7 +380,6 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
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# Rename to FP4Params to trigger initializing
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# the params layer with all parameters on the CPU
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# https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/modeling.py#L333
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class FP4Params(torch.nn.Parameter):
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def __new__(cls,
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data=None,
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@ -582,7 +591,13 @@ class MatMulLowBit(torch.autograd.Function):
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def forward(ctx, A, weight, input_seq_size):
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ctx.is_empty = False
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import xe_linear
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result = xe_linear.forward_new(A, weight.data, weight.qtype, input_seq_size)
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if weight.qtype == NF4:
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result = xe_linear.forward_new(A,
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weight.data.view(torch.uint8),
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weight.qtype,
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input_seq_size)
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else:
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result = xe_linear.forward_new(A, weight.data, weight.qtype, input_seq_size)
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if any(ctx.needs_input_grad[:2]):
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ctx.tensors = (A, weight)
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else:
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@ -602,7 +617,12 @@ class MatMulLowBit(torch.autograd.Function):
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if req_gradA:
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if torch.xpu.is_autocast_xpu_enabled():
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grad_output = grad_output.to(torch.xpu.get_autocast_xpu_dtype())
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dequant_weight = xe_linear.dequant(A, weight.data, weight.qtype)
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if weight.qtype == NF4:
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dequant_weight = xe_linear.dequant(A,
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weight.data.view(torch.uint8),
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weight.qtype)
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else:
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dequant_weight = xe_linear.dequant(A, weight.data, weight.qtype)
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grad_A = torch.matmul(grad_output, dequant_weight.reshape(weight._shape))
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return grad_A, grad_weight, None
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@ -737,9 +757,16 @@ class LowBitLinear(nn.Linear):
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if x_2d.requires_grad:
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result = MatMulLowBit.apply(x_2d, self.weight, input_seq_size)
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else:
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result = xe_linear.forward_new(x_2d, self.weight.data,
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self.weight.qtype,
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input_seq_size)
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if self.weight.qtype == NF4:
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result = xe_linear.forward_new(x_2d,
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self.weight.data.view(torch.uint8),
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self.weight.qtype,
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input_seq_size)
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else:
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result = xe_linear.forward_new(x_2d,
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self.weight.data,
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self.weight.qtype,
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input_seq_size)
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elif self.enable_xetla:
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x_2d = x_2d.half()
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result = xe_linear.mm_xetla(x_2d, self.weight.data, self.qtype)
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@ -382,3 +382,16 @@ def check_hidden_size(qtype, hidden_size):
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"required for fq6_k - using fallback quantization fp6.")
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return ggml_tensor_qtype["fp6"]
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return qtype
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# Arc platfrom does not support FP64,
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# Disable FP64 in DeepSpeedZeroOptimizer_Stage3's _constant_buffered_norm2 method
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# https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/runtime/zero/stage3.py#L1365
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def _constant_buffered_norm2(self, input, buffer_size=250000000):
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norm = None
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for part in input.view(-1).split(buffer_size):
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if norm is None:
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norm = part.data.norm(2)**2.0
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else:
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norm += part.data.norm(2)**2.0
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return norm**0.5
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