fix qwen2.5 finetune precision with sdpa

examples/transformers/peft/lora/Qwen2.5-7B-Instruct-Lora.ipynb

@@ -18,9 +18,6 @@
    "outputs": [],
    "source": [
     "import mindnlp\n",
-    "import mindspore\n",
-    "\n",
-    "# mindspore.set_context(pynative_synchronize=True)\n",
     "from datasets import Dataset\n",
     "import pandas as pd\n",
     "from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig"
@@ -167,8 +164,7 @@
    "source": [
     "import torch\n",
     "\n",
-    "model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', torch_dtype=torch.float16)\n",
-    "model = model.npu()"
+    "model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', torch_dtype=torch.float16, device_map='auto')"
    ]
   },
   {
@@ -250,22 +246,6 @@
     "model.print_trainable_parameters()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6b6aebd2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 待训练的lora参数需转成fp32\n",
-    "print_flag = True\n",
-    "for param in filter(lambda p: p.requires_grad, model.parameters()):\n",
-    "    if print_flag:\n",
-    "        print(param.data.dtype)\n",
-    "        print_flag = False\n",
-    "    param.data = param.data.to(torch.float32)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "ca055683-837f-4865-9c57-9164ba60c00f",
@@ -362,11 +342,14 @@
     "tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)\n",
     "\n",
     "# 加载模型\n",
-    "model = AutoModelForCausalLM.from_pretrained(mode_path, device_map=\"auto\",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()\n",
+    "model = AutoModelForCausalLM.from_pretrained(mode_path, torch_dtype=torch.float16, trust_remote_code=True).eval()\n",
     "\n",
     "# 加载lora权重\n",
     "model = PeftModel.from_pretrained(model, model_id=lora_path)\n",
     "\n",
+    "# host to device\n",
+    "model = model.npu()\n",
+    "\n",
     "prompt = \"你是谁？\"\n",
     "inputs = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": \"假设你是皇帝身边的女人--甄嬛。\"},{\"role\": \"user\", \"content\": prompt}],\n",
     "                                       add_generation_prompt=True,\n",

examples/transformers/peft/lora/Qwen2.5-7B-Instruct-Lora.py

@@ -1,11 +1,7 @@
-#!/usr/bin/env python
+# !/usr/bin/env python
 # coding: utf-8
 
-# # 导入环境
-
-# In[ ]:
-
-
+# 导入环境
 import mindnlp
 import mindspore
 
@@ -14,30 +10,12 @@
 import pandas as pd
 from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig
 
-
-
 # 将JSON文件转换为CSV文件
 df = pd.read_json('/home/lvyufeng/lvyufeng/mindnlp/examples/transformers/peft/lora/huanhuan.json')
 ds = Dataset.from_pandas(df)
 
-
-# In[ ]:
-
-
-ds[:3]
-
-
-# # 处理数据集
-
-# In[ ]:
-
-
+# 处理数据集
 tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-7B-Instruct', use_fast=False, trust_remote_code=True)
-tokenizer
-
-
-# In[ ]:
-
 
 def process_func(example):
     MAX_LENGTH = 384    # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性
@@ -57,55 +35,14 @@ def process_func(example):
         "labels": labels
     }
 
-
-# In[ ]:
-
-
 tokenized_id = ds.map(process_func, remove_columns=ds.column_names)
 
-print(len(tokenized_id))
-
-# In[ ]:
-
-
-tokenizer.decode(tokenized_id[0]['input_ids'])
-
-
-# In[ ]:
-
-
-tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1]["labels"])))
-
-
-# # 创建模型
-
-# In[ ]:
-
 
 import torch
-
-model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', torch_dtype=torch.float16, attn_implementation='eager')
-# model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', torch_dtype=torch.float16)
-model = model.npu()
-
-
-# In[ ]:
-
-
+model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', torch_dtype=torch.float16, device_map=0)
 model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法
 
-
-# In[ ]:
-
-
-model.dtype
-
-
-# # lora 
-
-# In[ ]:
-
-
+# lora 
 from peft import LoraConfig, TaskType, get_peft_model
 
 config = LoraConfig(
@@ -116,30 +53,11 @@ def process_func(example):
     lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理
     lora_dropout=0.1# Dropout 比例
 )
-config
-
-
-# In[ ]:
-
 
 model = get_peft_model(model, config)
-config
-
-
-# In[ ]:
-
-
 model.print_trainable_parameters()
 
-
-# In[ ]:
-
-
-# # 配置训练参数
-
-# In[ ]:
-
-
+# 配置训练参数
 args = TrainingArguments(
     output_dir="./output/Qwen2.5_instruct_lora",
     per_device_train_batch_size=4,
@@ -149,39 +67,21 @@ def process_func(example):
     save_steps=100, 
     learning_rate=1e-4,
     save_on_each_node=True,
-    # fp16=True,
     # gradient_checkpointing=True
 )
 
-
-# In[ ]:
-
-
 trainer = Trainer(
     model=model,
     args=args,
     train_dataset=tokenized_id,
     data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
 )
 
-
-# In[ ]:
-
-
-trainer.accelerator.state
-
-
-# In[ ]:
-
-
 trainer.train()
 
+# 合并加载模型
 
-# # 合并加载模型
-
-# In[ ]:
-
-
+import mindnlp
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 from peft import PeftModel
@@ -193,11 +93,13 @@ def process_func(example):
 tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)
 
 # 加载模型
-model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()
+model = AutoModelForCausalLM.from_pretrained(mode_path, torch_dtype=torch.float16, trust_remote_code=True).eval()
 
 # 加载lora权重
 model = PeftModel.from_pretrained(model, model_id=lora_path)
 
+model = model.npu()
+
 prompt = "你是谁？"
 inputs = tokenizer.apply_chat_template([{"role": "user", "content": "假设你是皇帝身边的女人--甄嬛。"},{"role": "user", "content": prompt}],
                                        add_generation_prompt=True,

mindnlp/core/_C/__init__.py

@@ -1,6 +1,6 @@
 from typing import Any
-from mindspore import Generator as msGenerator
 import mindspore
+from mindspore.ops.operations._inner_ops import Generator as GeneratorOp
 
 from mindnlp import core
 from . import _nn
@@ -105,19 +105,101 @@ def __exit__(self, type: Any, value: Any, traceback: Any):
 
 device_ = device
 
-class Generator(msGenerator):
+STEP = 0
+SEED = 1
+GET_STATE = 2
+SET_STATE = 3
+MANUAL_SEED = 4
+INITIAL_SEED = 5
+
+class Generator:
     def __init__(self, device='cpu'):
-        super().__init__()
         if device == 'cuda' and DEVICE_TARGET == 'Ascend':
             device = 'npu'
         self._device = device_(device) if isinstance(device, str) else device
 
+        self._seed = mindspore.Tensor(0)
+        self._offset = mindspore.Tensor(0)
+        self._generator = GeneratorOp().set_device("CPU")
+        self._generator.add_prim_attr("manual_seed", False)
+
+
     @property
     def device(self):
         if hasattr(self, '_device'):
             return self._device
         return device('cpu')
 
+    def set_state(self, state):
+        """
+        Sets the generator state.
+
+        Args:
+            state (tensor): target state of the generator.
+        """
+        self._generator(SET_STATE, (self._seed, self._offset, state))
+
+    def get_state(self):
+        """
+        Get the generator state.
+
+        Returns:
+            Tensor, generator state.
+        """
+        return self._generator(GET_STATE, (self._seed, self._offset))[2]
+
+    def seed(self):  # pylint: disable=redefined-outer-name
+        """
+        Seed generator with random number.
+
+        Returns:
+            Randomly generated seeds, the type is int.
+        """
+        current_seed = self._generator(
+            SEED, (self._seed, self._offset))[0]
+        return current_seed.item()
+
+    def manual_seed(self, seed):  # pylint: disable=redefined-outer-name
+        """
+        Set the generator seed.
+
+        Args:
+            seed (int): Set the generator seed.
+
+        Returns:
+            Generator, the generator instance.
+        """
+        if not isinstance(seed, int):
+            raise TypeError("Seed must be an integer.")
+        seed = mindspore.Tensor(seed, mindspore.int64)
+        self._generator(MANUAL_SEED, (self._seed, self._offset, seed))
+        self._generator.add_prim_attr("manual_seed", True)
+        return self
+
+    def initial_seed(self):
+        """
+        Return the initial seed of generator.
+
+        Returns:
+            The initial seed of generator.
+        """
+        current_seed = self._generator(
+            INITIAL_SEED, (self._seed, self._offset))[0]
+        return current_seed.item()
+
+
+    def _step(self, step):
+        """
+        Return current seed and offset, and update offset for the next call.
+
+        Args:
+            step (Tensor): Update offset by step.
+
+        Returns:
+            Current seed and offset.
+        """
+        return self._generator(STEP, (self._seed, self._offset, step,))[:2]
+
 default_generator = Generator()
 
 class Tag: pass

mindnlp/core/__init__.py

@@ -43,12 +43,12 @@
 
 
 
-from ._C import *
-from ._C.size import Size
 from ._dtype import *
-from .ops import *
 from ._tensor import Tensor, tensor, is_tensor, \
     LongTensor, FloatTensor, BoolTensor, HalfTensor, BFloat16Tensor, IntTensor
+from ._C import *
+from ._C.size import Size
+from .ops import *
 from ._tensor import enable_mindspore_patch
 enable_mindspore_patch()