[LLM][NPU] reformat npu scripts

Author: SylarTiaNII

llm/npu/llama/README.md

@@ -86,15 +86,15 @@ python -m pip install -e .
 # PaddleNLP仓库内置了部分昇腾专用的融合算子，以便用户享受到极致压缩的推理成本
 cd csrc/npu
 python setup.py build bdist_wheel
-pip install dist/paddlenlp_ops-0.0.0-cp39-cp39-linux_aarch64.whl
+pip install dist/paddlenlp_ops-0.0.0-cp39-cp39-linux_x86_64.whl
 cd -
 ```
 
 ### （2）数据准备：(这将花费您2～5min时间)
 sft为精调策略，我们提供了广告生成数据集demo便于您调试使用
 ```
 #精调：为了方便测试，我们也提供了广告生成数据集可以直接使用：
-cd llm/llama/npu
+cd llm/npu/llama
 wget https://bj.bcebos.com/paddlenlp/datasets/examples/AdvertiseGen.tar.gz
 tar -zxvf AdvertiseGen.tar.gz
 ```
@@ -145,8 +145,7 @@ bash llama_npu_sft_N1C8.sh
 ```
 为了保障极致压缩的推理成本，我们使用了静态图实现。因此需要从训练产出的动态图模型中导出静态图模型，执行如下命令进行导出：
 ```
-cd ../..
-bash export_npu.sh ./llama/npu/output/sft_bf16_llama_N1C8/ ./inference
+bash export_npu.sh ./output/sft_bf16_llama_N1C8/ ./inference
 ```
 最终，我们通过静态图的模型执行推理：
 ```

llm/export_npu.sh renamed to llm/npu/llama/export_npu.sh

@@ -14,11 +14,16 @@
 
 set -x
 
-src_path=${1:-"./llama/npu/output/sft_bf16_llama_N1C8/"}
+src_path=${1:-".npu/llama/output/sft_bf16_llama_N1C8/"}
 dst_path=${2:-"./inference"}
 
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 source /usr/local/Ascend/atb/set_env.sh
 
+src_path=`realpath $src_path`
+dst_path=`realpath $dst_path`
+cd ../../
+
 export PYTHONPATH=../:$PYTHONPATH
-python export_model.py --model_name_or_path ${src_path}  --inference_model --output_path ${dst_path} --dtype float16  --device npu  --block_attn
+python predict/export_model.py --model_name_or_path ${src_path}  --inference_model --output_path ${dst_path} --dtype float16  --device npu  --block_attn
+cd -

llm/npu/llama/export_utils.py

@@ -0,0 +1,110 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import numpy as np
+import paddle
+from tqdm import tqdm
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", default="inference/model", help="The directory of exported model.")
+    return parser.parse_args()
+
+
+def trans_weight(var):
+    shape = var.desc.shape()
+    new_shape = [shape[1], shape[0]]
+    var.desc.set_shape(new_shape)
+
+    var_data = np.array(var.get_value())
+    var.get_value().set(var_data.T, paddle.CPUPlace())
+
+
+def convert_dequant_scale(var):
+    deq_scale = np.array(var.get_value()).astype(np.float32)
+    new_deq_scale = np.stack([deq_scale.reshape(-1, 1), np.zeros_like(deq_scale).reshape(-1, 1)], axis=-1).reshape(-1)
+    var.get_value().set(np.frombuffer(new_deq_scale.tobytes(), dtype=np.int64), paddle.CPUPlace())
+
+
+def process_params(model_path):
+    paddle.enable_static()
+    exe = paddle.static.Executor(paddle.CPUPlace())
+
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    scope = paddle.static.Scope()
+    with paddle.base.scope_guard(scope):
+        with paddle.base.program_guard(prog, startup_prog):
+            [program, feed_target_names, fetch_targets] = paddle.static.io.load_inference_model(model_path, exe)
+
+            feed_targets = []
+            for var in program.list_vars():
+                if var.name in feed_target_names:
+                    feed_targets.append(var)
+
+            block = program.global_block()
+
+            for op in tqdm(block.ops, desc="processing the linear layer for NPU"):
+                if op.type == "matmul_v2":
+                    w_name = op.input_arg_names[-1]
+                    if w_name.endswith("qkv_weight") and not op.attr("trans_y"):
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("out_proj_weight") and not op.attr("trans_y"):
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("ffn1_weight") and not op.attr("trans_y"):
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("ffn2_weight") and not op.attr("trans_y"):
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name == "llama_lm_head_0.w_0" and not op.attr("trans_y"):
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+
+            for var_name in tqdm(block.vars, desc="processing the dequant layer for NPU"):
+                if var_name.endswith("qkv_out_scale"):
+                    var = block.var(var_name)
+                    convert_dequant_scale(var)
+                elif var_name.endswith("linear_out_scale"):
+                    var = block.var(var_name)
+                    convert_dequant_scale(var)
+                elif var_name.endswith("ffn1_out_scale"):
+                    var = block.var(var_name)
+                    convert_dequant_scale(var)
+                elif var_name.endswith("ffn2_out_scale"):
+                    var = block.var(var_name)
+                    convert_dequant_scale(var)
+
+            paddle.static.save_inference_model(
+                model_path, feed_targets, fetch_targets, exe, program=program, skip_prune_program=True
+            )
+
+
+def main():
+    args = parse_arguments()
+    process_params(args.model_path)
+
+
+if __name__ == "__main__":
+    main()

llm/npu/llama/llama_npu_lora_N1C8.sh

@@ -30,12 +30,12 @@ export MC2_Recompute=1
 
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 export PYTHONPATH=../../../:$PYTHONPATH
-ps aux | grep finetune_generation.py | grep -v grep | awk '{print $2}' | xargs kill -9
+ps aux | grep run_finetune.py | grep -v grep | awk '{print $2}' | xargs kill -9
 
 python -u  -m paddle.distributed.launch \
     --devices "0,1,2,3,4,5,6,7" \
     --log_dir "./lora_bf16_llama_N1C8" \
-    ../../finetune_generation.py \
+    ../../run_finetune.py \
     --device "npu" \
     --model_name_or_path "meta-llama/Llama-2-13b-chat" \
     --dataset_name_or_path "data/" \

llm/npu/llama/llama_npu_sft_N1C8.sh

@@ -30,12 +30,12 @@ export MC2_Recompute=1
 
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 export PYTHONPATH=../../../:$PYTHONPATH
-ps aux | grep finetune_generation.py | grep -v grep | awk '{print $2}' | xargs kill -9
+ps aux | grep run_finetune.py | grep -v grep | awk '{print $2}' | xargs kill -9
 
 python -u  -m paddle.distributed.launch \
     --devices "0,1,2,3,4,5,6,7" \
     --log_dir "./sft_bf16_llama_N1C8" \
-    ../../finetune_generation.py \
+    ../../run_finetune.py \
     --device "npu" \
     --model_name_or_path "meta-llama/Llama-2-13b" \
     --dataset_name_or_path "data/" \

llm/predict_npu.sh renamed to llm/npu/llama/predict_npu.sh

@@ -18,6 +18,8 @@ model_path=${1:-"./inference"}
 
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 source /usr/local/Ascend/atb/set_env.sh
-
+model_path=`realpath $model_path`
+cd ../../
 export PYTHONPATH=../:$PYTHONPATH
-python predictor.py  --model_name_or_path ${model_path} --inference_model --dtype "float16" --mode "static" --block_attn --device npu
+python predict/predictor.py  --model_name_or_path ${model_path} --inference_model --dtype "float16" --mode "static" --block_attn --device npu
+cd -

llm/predict/export_model.py

@@ -101,7 +101,7 @@ def main():
     validate_pdmodel(export_args.output_path, predictor_args.model_prefix, predictor_args.device)
 
     if predictor_args.device == "npu":
-        from llama.npu.export_utils import process_params
+        from npu.llama.export_utils import process_params
 
         process_params(os.path.join(export_args.output_path, predictor_args.model_prefix))