[NPU] support npu llama2-13B export & inference

Author: ronny1996

csrc_npu/README.md

@@ -0,0 +1,14 @@
+# PaddleNLP 自定义 OP
+
+此文档介绍如何编译安装 PaddleNLP NPU 自定义 OP。
+
+# 1. 安装 PaddleCustomDevice
+
+参考 [PaddleCustomDevice NPU 安装文档](https://github.com/PaddlePaddle/PaddleCustomDevice/blob/develop/backends/npu/README_cn.md) 进行安装
+
+# 2. 安装 paddlenlp_ops
+```shell
+python setup.py build bdist_wheel
+
+pip install dist/paddlenlp_ops*.whl
+```

csrc_npu/python/paddlenlp_ops/__init__.py

@@ -0,0 +1,15 @@
+# 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.
+
+from paddle_custom_device.npu.ops import *

csrc_npu/setup.py

@@ -0,0 +1,59 @@
+# 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 os
+
+from setuptools import Distribution, setup
+
+packages = []
+package_data = {}
+
+
+class BinaryDistribution(Distribution):
+    def has_ext_modules(self):
+        return True
+
+
+def main():
+    setup(
+        name="paddlenlp_ops",
+        version="0.0.0",
+        description="PaddleNLP NPU CustomOps",
+        long_description="",
+        long_description_content_type="text/markdown",
+        author_email="Paddle-better@baidu.com",
+        maintainer="PaddlePaddle",
+        maintainer_email="Paddle-better@baidu.com",
+        project_urls={},
+        license="Apache Software License",
+        packages=[
+            "paddlenlp_ops",
+        ],
+        include_package_data=True,
+        package_data={
+            "": ["*.py"],
+        },
+        package_dir={
+            "": "python",
+        },
+        zip_safe=False,
+        distclass=BinaryDistribution,
+        entry_points={"console_scripts": []},
+        classifiers=[],
+        keywords="PaddleNLP NPU CustomOps",
+    )
+
+
+if __name__ == "__main__":
+    main()

llm/export_model.py

@@ -40,9 +40,12 @@ def load_inference_model(model_path, model_name, param_name, exe):
         return paddle.static.io.load_inference_model(model_path, exe)
 
 
-def validate_pdmodel(model_path, model_prefix):
+def validate_pdmodel(model_path, model_prefix, device):
     paddle.enable_static()
-    place = paddle.CUDAPlace(0)
+    if device == "gpu":
+        place = paddle.CUDAPlace(0)
+    else:
+        place = paddle.CustomPlace(device, 0)
     exe = paddle.static.Executor(place)
     scope = paddle.static.Scope()
 
@@ -95,7 +98,12 @@ def main():
 
     if tensor_parallel_degree > 1:
         export_args.output_path = os.path.join(export_args.output_path, f"rank_{tensor_parallel_rank}")
-    validate_pdmodel(export_args.output_path, predictor_args.model_prefix)
+    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
+
+        process_params(os.path.join(export_args.output_path, predictor_args.model_prefix))
 
 
 if __name__ == "__main__":

llm/llama/npu/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 op.attr("trans_y") == False:
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("out_proj_weight") and op.attr("trans_y") == False:
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("ffn1_weight") and op.attr("trans_y") == False:
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name.endswith("ffn2_weight") and op.attr("trans_y") == False:
+                        op._set_attr("trans_y", True)
+                        w = block.var(w_name)
+                        trans_weight(w)
+                    elif w_name == "llama_lm_head_0.w_0" and op.attr("trans_y") == False:
+                        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/predictor.py

@@ -656,8 +656,12 @@ def _create_predictor(self, predictor_args: PredictorArgument):
         if predictor_args.dtype == "bfloat16":
             config.delete_pass("gpu_cpu_map_matmul_v2_to_matmul_pass")
 
-        device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
-        config.enable_use_gpu(100, device_id)
+        if predictor_args.device in paddle.device.get_all_custom_device_type():
+            device_id = int(os.environ.get("FLAGS_selected_{}s".format(predictor_args.device), 0))
+            config.enable_custom_device(predictor_args.device, device_id)
+        else:
+            device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
+            config.enable_use_gpu(100, device_id)
         config.enable_new_executor()
 
         if self.tensor_parallel_degree > 1:
@@ -1076,11 +1080,23 @@ def _create_predictor(self, predictor_args: PredictorArgument):
         config = paddle.inference.Config(infer_model_path + ".pdmodel", infer_model_path + ".pdiparams")
 
         config.switch_ir_optim(False)
-        device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
-        config.enable_use_gpu(100, device_id)
+        if predictor_args.device in paddle.device.get_all_custom_device_type():
+            device_id = int(os.environ.get("FLAGS_selected_{}s".format(predictor_args.device), 0))
+            config.enable_custom_device(predictor_args.device, device_id)
+        else:
+            device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
+            config.enable_use_gpu(100, device_id)
         # config.disable_glog_info()
         # config.enable_memory_optim()
 
+        if predictor_args.device == "npu":
+            import paddle_custom_device.npu.passes as passes
+
+            config.switch_ir_optim(True)
+            pass_builder = config.pass_builder()
+            passes.addPasses(pass_builder, self.model_config.model_type, self.model_config.quant_type)
+            pass_builder.turn_on_debug()
+
         if self.tensor_parallel_degree > 1:
             trainer_endpoints = fleet.worker_endpoints()
             current_endpoint = trainer_endpoints[self.tensor_parallel_rank]
@@ -1527,6 +1543,11 @@ def predict():
         fleet.init(is_collective=True, strategy=strategy)
 
     predictor = create_predictor(predictor_args, model_args)
+
+    if predictor_args.benchmark:
+        benchmark(predictor, predictor_args, model_args)
+        return
+
     source_texts = []
     target_texts = []
     if model_args.data_file:
@@ -1570,14 +1591,10 @@ def predict():
                 out = {"src": source, "tgt": target, "output": output}
                 f.write(json.dumps(out, ensure_ascii=False) + "\n")
 
-    if predictor_args.benchmark:
-        benchmark(predictor, predictor_args, model_args)
-
 
 def benchmark(predictor, predictor_args, model_args):
     # Just construct a simple benchmark input. We pad input to the src_length.
-    test_texts = "hello world, how are you?"
-    benchmark_texts = [test_texts + "<pad>" * predictor_args.src_length for _ in range(predictor_args.batch_size)]
+    benchmark_texts = [predictor.tokenizer.pad_token * (predictor_args.src_length - 1) for _ in range(predictor_args.batch_size)]
 
     batch_benchmark_texts = batchfy_text(benchmark_texts, predictor_args.batch_size)
     print("***********Start Benchmark**********")

paddlenlp/experimental/transformers/fused_transformer_layers.py

@@ -570,7 +570,7 @@ def compute_layernorm_before_qkv(self, src, i):
         return ln_out
 
     def compute_qkv_linear(self, ln_out, i):
-        if float(paddle.version.cuda()) < 11.6:
+        if paddle.version.cuda() == "False" or float(paddle.version.cuda()) < 11.6:
             qkv_out = paddle.matmul(ln_out, self.qkv_weights[i], False, True)
             if self.qkv_biases[i] is not None:
                 qkv_out = paddle.add(qkv_out, self.qkv_biases[i])