[fix]修复自然语言处理模型自动压缩示例 (#1835)

Author: lizexu123

example/auto_compression/nlp/README.md

@@ -56,16 +56,16 @@
 
 #### 3.1 准备环境
 - python >= 3.6
-- PaddlePaddle >= 2.4 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
-- PaddleSlim >= 2.4
-- PaddleNLP >= 2.3
+- PaddlePaddle ==2.5 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
+- PaddleSlim ==2.5
+- PaddleNLP ==2.6
 
 安装paddlepaddle：
 ```shell
 # CPU
-pip install paddlepaddle==2.4.1
+pip install paddlepaddle==2.5.0
 # GPU 以Ubuntu、CUDA 11.2为例
-python -m pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+python -m pip install paddlepaddle-gpu==2.5.0.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
 ```
 
 安装paddleslim：
@@ -95,7 +95,6 @@ pip install paddlenlp
 |:------:|:------:|:------:|:------:|:------:|:-----------:|:------:|:------:|
 | PP-MiniLM | [afqmc](https://bj.bcebos.com/v1/paddle-slim-models/act/afqmc.tar) | [tnews](https://bj.bcebos.com/v1/paddle-slim-models/act/tnews.tar) | [iflytek](https://bj.bcebos.com/v1/paddle-slim-models/act/iflytek.tar) | [cmnli](https://bj.bcebos.com/v1/paddle-slim-models/act/cmnli.tar) | [ ocnli](https://bj.bcebos.com/v1/paddle-slim-models/act/ocnli.tar) | [cluewsc2020](https://bj.bcebos.com/v1/paddle-slim-models/act/cluewsc.tar) | [csl](https://bj.bcebos.com/v1/paddle-slim-models/act/csl.tar) |
 | ERNIE 3.0-Medium | [afqmc](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/AFQMC.tar) | [tnews](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/TNEWS.tar) | [iflytek](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/IFLYTEK.tar) | [cmnli](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/CMNLI.tar) | [ocnli](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/OCNLI.tar) | [cluewsc2020](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/CLUEWSC2020.tar) | [csl](https://bj.bcebos.com/v1/paddle-slim-models/act/NLP/ernie3.0-medium/fp32_models/CSL.tar) |
-| UIE-base | [报销工单](https://bj.bcebos.com/v1/paddle-slim-models/act/uie_base.tar) |
 
 从上表获得模型超链接, 并用以下命令下载推理模型文件:
 
@@ -119,11 +118,6 @@ export CUDA_VISIBLE_DEVICES=0
 python run.py --config_path='./configs/pp-minilm/auto/afqmc.yaml' --save_dir='./save_afqmc_pruned/'
 ```
 
-自动压缩UIE系列模型需要使用 run_uie.py 脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中训练部分的参数，将任务名称、模型类型、数据集名称、压缩参数传入，配置完成后便可对模型进行蒸馏量化训练。
-```shell
-export CUDA_VISIBLE_DEVICES=0
-python run_uie.py --config_path='./configs/uie/uie_base.yaml' --save_dir='./save_uie_qat/'
-```
 
 如仅需验证模型精度，或验证压缩之后模型精度，在启动```run.py```脚本时，将配置文件中模型文件夹 ```model_dir``` 改为压缩之后保存的文件夹路径 ```./save_afqmc_pruned``` ，命令加上```--eval True```即可：
 ```shell
@@ -217,8 +211,6 @@ QuantPost:
 
 - TensorRT预测：
 
-环境配置：如果使用 TesorRT 预测引擎，需安装 ```WITH_TRT=ON``` 的Paddle，下载地址：[Python预测库](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html#python)
-
 首先下载量化好的模型：
 ```shell
 wget https://bj.bcebos.com/v1/paddle-slim-models/act/save_ppminilm_afqmc_new_calib.tar
@@ -227,10 +219,30 @@ tar -xf save_ppminilm_afqmc_new_calib.tar
 
 ```shell
 python paddle_inference_eval.py \
-      --model_path=save_ernie3_afqmc_new_cablib \
+      --model_path=save_ppminilm_afqmc_new_calib \
+      --model_filename=inference.pdmodel \
+      --params_filename=inference.pdiparams \
+      --task_name='afqmc' \
+      --use_trt \
+      --precision=int8
+```
+
+- ERNIE 3.0-Medium:
+```shell
+python paddle_inference_eval.py \
+      --model_path=TNEWS \
       --model_filename=infer.pdmodel \
       --params_filename=infer.pdiparams \
-      --task_name='afqmc' \
+      --task_name='tnews' \
+      --use_trt \
+      --precision=fp32
+```
+```shell
+python paddle_inference_eval.py \
+      --model_path=save_tnews_pruned \
+      --model_filename=infer.pdmodel \
+      --params_filename=infer.pdiparams \
+      --task_name='tnews' \
       --use_trt \
       --precision=int8
 ```
@@ -239,9 +251,9 @@ python paddle_inference_eval.py \
 
 ```shell
 python paddle_inference_eval.py \
-      --model_path=save_ernie3_afqmc_new_cablib \
-      --model_filename=infer.pdmodel \
-      --params_filename=infer.pdiparams \
+      --model_path=save_ppminilm_afqmc_new_calib \
+      --model_filename=inference.pdmodel \
+      --params_filename=inference.pdiparams \
       --task_name='afqmc' \
       --device=cpu \
       --use_mkldnn=True \

example/auto_compression/nlp/configs/ernie3.0/tnews.yaml

@@ -6,12 +6,17 @@ Global:
   dataset: clue
   batch_size: 16
   max_seq_length: 128
-TrainConfig:
-  epochs: 6
-  eval_iter: 1110
-  learning_rate: 2.0e-5
-  optimizer_builder:
-    optimizer: 
-      type: AdamW
-    weight_decay: 0.01
-  origin_metric: 0.5700
+
+# 剪枝
+Prune:
+  prune_algo: transformer_pruner
+  pruned_ratio: 0.25
+
+# 离线量化
+QuantPost:
+  activation_bits: 8
+  quantize_op_types:
+  - depthwise_conv2d
+  - conv2d
+  weight_bits: 8
+  

example/auto_compression/nlp/configs/pp-minilm/auto/afqmc.yaml

@@ -6,17 +6,11 @@ Global:
   dataset: clue
   batch_size: 16
   max_seq_length: 128
-TransformerPrune:
-  pruned_ratio: 0.25
-HyperParameterOptimization:
-Distillation:
+
+#离线量化
 QuantPost:
-TrainConfig:
-  epochs: 6
-  eval_iter: 1070
-  learning_rate: 2.0e-5
-  optimizer_builder:
-    optimizer: 
-      type: AdamW
-    weight_decay: 0.01
-  origin_metric: 0.7403
+  activation_bits: 8
+  quantize_op_types: 
+  - conv2d
+  - depthwise_conv2d
+  weight_bits: 8

example/auto_compression/nlp/paddle_inference_eval.py

@@ -91,7 +91,8 @@ def parse_args():
         "--max_seq_length",
         default=128,
         type=int,
-        help="The maximum total input sequence length after tokenization. Sequences longer "
+        help=
+        "The maximum total input sequence length after tokenization. Sequences longer "
         "than this will be truncated, sequences shorter will be padded.", )
     parser.add_argument(
         "--perf_warmup_steps",
@@ -107,7 +108,8 @@ def parse_args():
         type=str,
         default="fp32",
         choices=["fp32", "fp16", "int8"],
-        help="The precision of inference. It can be 'fp32', 'fp16' or 'int8'. Default is 'fp16'.",
+        help=
+        "The precision of inference. It can be 'fp32', 'fp16' or 'int8'. Default is 'fp16'.",
     )
     parser.add_argument(
         "--use_mkldnn",
@@ -156,8 +158,7 @@ def _convert_example(example,
             }
         elif "target" in example:  # wsc
             text, query, pronoun, query_idx, pronoun_idx = (
-                example["text"],
-                example["target"]["span1_text"],
+                example["text"], example["target"]["span1_text"],
                 example["target"]["span2_text"],
                 example["target"]["span1_index"],
                 example["target"]["span2_index"], )
@@ -209,6 +210,12 @@ def create_predictor(cls, args):
         config = paddle.inference.Config(
             os.path.join(args.model_path, args.model_filename),
             os.path.join(args.model_path, args.params_filename))
+        config.switch_ir_debug(True)
+        # 适用于ERNIE 3.0-Medium模型
+        # config.exp_disable_tensorrt_ops(["elementwise_add"])
+        # config.exp_disable_tensorrt_ops(["fused_embedding_eltwise_layernorm"])
+        # config.exp_disable_tensorrt_ops(["tmp_3"])
+
         if args.device == "gpu":
             # set GPU configs accordingly
             config.enable_use_gpu(100, 0)
@@ -239,8 +246,8 @@ def create_predictor(cls, args):
             dynamic_shape_file = os.path.join(args.model_path,
                                               "dynamic_shape.txt")
             if os.path.exists(dynamic_shape_file):
-                config.enable_tuned_tensorrt_dynamic_shape(dynamic_shape_file,
-                                                           True)
+                config.enable_tuned_tensorrt_dynamic_shape(
+                    dynamic_shape_file, True)
                 print("trt set dynamic shape done!")
             else:
                 config.collect_shape_range_info(dynamic_shape_file)
@@ -365,4 +372,4 @@ def main():
 
 if __name__ == "__main__":
     paddle.set_device("cpu")
-    main()
+    main()

paddleslim/quant/advanced/auto_clip.py

@@ -21,14 +21,15 @@
 from .metrics import mse_loss
 from paddle.distributed.fleet.meta_parallel import (
     ColumnParallelLinear,
-    RowParallelLinear,
-)
+    RowParallelLinear, )
 __all__ = ['AutoClip']
 
+
 class AutoClip(nn.Layer):
     """
     AutoClip from AWQ[https://arxiv.org/abs/2306.00978]
     """
+
     def __init__(
             self,
             model,
@@ -39,8 +40,7 @@ def __init__(
             n_grid=20,
             max_shrink=0.5,
             n_sample_token=512,
-            group_size=128,
-            ):
+            group_size=128, ):
         super(AutoClip, self).__init__()
         self.model = model
         self.weight_bits = weight_bits
@@ -59,15 +59,17 @@ def __init__(
     def _apply_hook(self):
         self._forward_hook_list = []
         for _, sub_layer in self.model.named_sublayers():
-            if type(sub_layer) in [ColumnParallelLinear, RowParallelLinear, paddle.nn.Linear]:
+            if type(sub_layer) in [
+                    ColumnParallelLinear, RowParallelLinear, paddle.nn.Linear
+            ]:
                 forward_pre_hook_handle = sub_layer.register_forward_pre_hook(
                     self._forward_pre_hook)
                 self._forward_hook_list.append(forward_pre_hook_handle)
 
     def _forward_pre_hook(self, layer, input):
         self._sample_scale(input, layer.full_name())
         return input
-    
+
     def _sample_scale(self, input, name):
         input = input[0] if type(input) == tuple else input
         input.stop_gradient = True
@@ -80,7 +82,6 @@ def _sample_scale(self, input, name):
             else:
                 self.sampled_inputs[name] = input
 
-    
     def auto_clip(self, group_size=128, oc_batch_size=256):
         """
         search clip scale for each layer and update the layer's weight
@@ -89,7 +90,7 @@ def auto_clip(self, group_size=128, oc_batch_size=256):
             name = sub_layer.full_name()
             if name not in self.sampled_inputs or 'out_linear' in sub_name:
                 continue
-            
+
             weight = sub_layer.weight.cast('float16')
             weight_t = paddle.transpose(weight, perm=[1, 0])
             x = self.sampled_inputs[name].cast('float16')
@@ -98,33 +99,41 @@ def auto_clip(self, group_size=128, oc_batch_size=256):
             x = x.reshape([1, x.shape[0], -1, group_size])
             x = x[:, 0::x.shape[1] // self.n_sample_token]
             weight_t = weight_t.reshape([weight_t.shape[0], 1, -1, group_size])
-            oc_batch_size = oc_batch_size if weight_t.shape[0] % oc_batch_size == 0 else 128  # prevent OOM
+            oc_batch_size = oc_batch_size if weight_t.shape[
+                0] % oc_batch_size == 0 else 128  # prevent OOM
             assert weight_t.shape[0] % oc_batch_size == 0
 
             w_all = weight_t
             best_max_val_all = []
 
             for i_b in range(weight_t.shape[0] // oc_batch_size):
-                w = w_all[i_b * oc_batch_size: (i_b + 1) * oc_batch_size]
+                w = w_all[i_b * oc_batch_size:(i_b + 1) * oc_batch_size]
 
-                org_max_val = w.abs().max(axis=-1, keepdim=True)  # co, 1, n_group, 1
+                org_max_val = w.abs().max(
+                    axis=-1, keepdim=True)  # co, 1, n_group, 1
                 best_max_val = org_max_val.clone()
                 min_errs = paddle.ones_like(org_max_val, dtype='float16') * 1e9
                 org_out = (x * w).sum(axis=-1)  # co, n_token, n_group
                 for i_s in range(int(self.max_shrink * self.n_grid)):
                     max_val = org_max_val * (1 - i_s / self.n_grid)
                     max_val_tmp = max_val
                     cur_w = paddle.where(w > max_val_tmp, max_val_tmp, w)
-                    cur_w = paddle.where(cur_w < - max_val_tmp, - max_val_tmp, cur_w)
+                    cur_w = paddle.where(cur_w < -max_val_tmp, -max_val_tmp,
+                                         cur_w)
                     org_w_shape = cur_w.shape
-                    cur_w_r = cur_w.reshape([-1, self.group_size]).transpose([1, 0])
-                    quant_dequant_weight = fake_quant(cur_w_r, method='abs_max_channel_wise', weight_bits=4)
-                    quant_dequant_weight = quant_dequant_weight.transpose([1, 0]).reshape(org_w_shape)
+                    cur_w_r = cur_w.reshape([-1,
+                                             self.group_size]).transpose([1, 0])
+                    quant_dequant_weight = fake_quant(
+                        cur_w_r, method='abs_max_channel_wise', weight_bits=4)
+                    quant_dequant_weight = quant_dequant_weight.transpose(
+                        [1, 0]).reshape(org_w_shape)
                     cur_out = (x * quant_dequant_weight).sum(axis=-1)
                     # co, 1, n_group, 1
                     tmp = (cur_out - org_out).detach().clone()
-                    err = paddle.pow(tmp, 2).mean(axis=1).reshape(min_errs.shape)
-                    print('block {} search s {} err {}'.format(i_b, i_s, err.mean().item()))
+                    err = paddle.pow(tmp,
+                                     2).mean(axis=1).reshape(min_errs.shape)
+                    print('block {} search s {} err {}'.format(
+                        i_b, i_s, err.mean().item()))
                     del cur_w, cur_out, quant_dequant_weight, tmp, cur_w_r
                     paddle.device.cuda.empty_cache()
 
@@ -143,16 +152,21 @@ def auto_clip(self, group_size=128, oc_batch_size=256):
                 if 'w_0' in param.name:
                     param_tmp = param.transpose(perm=[1, 0]).cast('float16')
                     tmp_shape = param_tmp.shape
-                    param_tmp = param_tmp.reshape([best_max_val.shape[0], best_max_val.shape[1], -1])
-                    best_max_val = paddle.tile(best_max_val, repeat_times=(1, 1, param_tmp.shape[-1]))
-                    param_tmp = paddle.where(param_tmp > best_max_val, best_max_val, param_tmp)
-                    param_tmp = paddle.where(param_tmp < - best_max_val, - best_max_val, param_tmp)
+                    param_tmp = param_tmp.reshape(
+                        [best_max_val.shape[0], best_max_val.shape[1], -1])
+                    best_max_val = paddle.tile(
+                        best_max_val, repeat_times=(1, 1, param_tmp.shape[-1]))
+                    param_tmp = paddle.where(param_tmp > best_max_val,
+                                             best_max_val, param_tmp)
+                    param_tmp = paddle.where(param_tmp < -best_max_val,
+                                             -best_max_val, param_tmp)
                     param_tmp = param_tmp.reshape(tmp_shape).cast(param.dtype)
                     param_tmp = param_tmp.transpose(perm=[1, 0])
                     paddle.assign(param_tmp, output=param)
                     del param_tmp
                     paddle.device.cuda.empty_cache()
                     break
 
-            del best_max_val, weight_t, x, weight, self.sampled_inputs[name], w_all, best_max_val_all
+            del best_max_val, weight_t, x, weight, self.sampled_inputs[
+                name], w_all, best_max_val_all
             paddle.device.cuda.empty_cache()

paddleslim/quant/advanced/gptq.py

@@ -189,4 +189,4 @@ def fasterquant(self,
 
         self.quantized = True
         del H, Q, Hinv, W, Losses
-        paddle.device.cuda.empty_cache()
+        paddle.device.cuda.empty_cache()

paddleslim/quant/advanced/piecewise_search.py

@@ -71,7 +71,6 @@ def search(self, layer_name, sampled_input, act_abs_max, weight):
         origin_out = paddle.matmul(act, weight)
         w_abs_max = weight.abs().max(axis=-1, keepdim=True)
         rw_abs_max = w_abs_max.reshape(act_abs_max.shape)
-
         smooth_scale_out = None
         global_loss = float('inf')
         best_scale = None
@@ -184,5 +183,4 @@ def search(self, layer_name, sampled_input, act_abs_max, weight):
                     print('Find Better K-Piece {}'.format(k_piece))
             if not self.search_piece:
                 break
-
         return best_scale