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已合并
fix: replace Chinese log messages with English in examples #4883
wangyuchen创建于 9月10日
fix: replace Chinese log messages with English in examples #4883
已合并
wangyuchen创建于 9月10日
从yuchen97/gefix/example_log合入到cann/gedevelop
共 60 个文件变更+784-562
Mexamples/es/control_edge/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeControlEdgeGraphByEsAndDump();40 es_showcase::MakeControlEdgeGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/control_edge/python/src/make_control_edge_graph.py+27-15
@@ -22,12 +22,18 @@ def build_control_edge_graph():
22 # 1. 创建图构建器22 # 1. 创建图构建器
23 builder = GraphBuilder("control_dep_scope_example")23 builder = GraphBuilder("control_dep_scope_example")
24 # 2. 创建依赖源节点24 # 2. 创建依赖源节点
25- input_tensor_holder0 = builder.create_input(index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 2])25+ input_tensor_holder0 = builder.create_input(
26- input_tensor_holder1 = builder.create_input(index=1, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 2])26+ index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 2]
27+ )
28+ input_tensor_holder1 = builder.create_input(
29+ index=1, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 2]
30+ )
27 # 3. 使用 scope：在 scope 内创建的所有节点自动依赖 input_tensor_holder0 和 input_tensor_holder131 # 3. 使用 scope：在 scope 内创建的所有节点自动依赖 input_tensor_holder0 和 input_tensor_holder1
28 with control_dependency_scope([input_tensor_holder0, input_tensor_holder1]):32 with control_dependency_scope([input_tensor_holder0, input_tensor_holder1]):
29 # 在此 scope 内创建的节点会自动添加控制依赖33 # 在此 scope 内创建的节点会自动添加控制依赖
30- input_tensor_holder2 = builder.create_input(index=2, name="input2", data_type=DataType.DT_FLOAT, shape=[2, 2])34+ input_tensor_holder2 = builder.create_input(
35+ index=2, name="input2", data_type=DataType.DT_FLOAT, shape=[2, 2]
36+ )
31 input_tensor_holder3 = input_tensor_holder0 + input_tensor_holder237 input_tensor_holder3 = input_tensor_holder0 + input_tensor_holder2
32 # 4. 设置输出并构建38 # 4. 设置输出并构建
33 builder.set_graph_output(input_tensor_holder3, 0)39 builder.set_graph_output(input_tensor_holder3, 0)
@@ -46,34 +52,40 @@ def run_graph(graph) -> None:
46 ge_api = GeApi()52 ge_api = GeApi()
47 ret = ge_api.ge_initialize(config)53 ret = ge_api.ge_initialize(config)
48 if ret != 0:54 if ret != 0:
49- print(f"GE初始化失败，返回码: {ret}")55+ print(f"GE initialization failed, return code: {ret}")
50 return ret56 return ret
51- print("GE环境初始化成功 (Device ID: 0)")57+ print("GE environment initialized successfully (Device ID: 0)")
52 58 
53 try:59 try:
54 session = Session()60 session = Session()
55 graph_id = 161 graph_id = 1
56 ret = session.add_graph(graph_id, graph)62 ret = session.add_graph(graph_id, graph)
57 if ret != 0:63 if ret != 0:
58- print(f"添加图失败，返回码: {ret}")64+ print(f"Failed to add graph, return code: {ret}")
59 return ret65 return ret
60- print(f"图已添加到Session (Graph ID: {graph_id})")66+ print(f"Graph added to Session (Graph ID: {graph_id})")
61 67 
62 # 4. 准备输入数据68 # 4. 准备输入数据
63- tensor1 = Tensor([2.0, 2.0, 2.0, 2.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2])69+ tensor1 = Tensor(
64- tensor2 = Tensor([1.0, 1.0, 1.0, 1.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2])70+ [2.0, 2.0, 2.0, 2.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2]
65- tensor3 = Tensor([1.0, 1.0, 1.0, 1.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2])71+ )
72+ tensor2 = Tensor(
73+ [1.0, 1.0, 1.0, 1.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2]
74+ )
75+ tensor3 = Tensor(
76+ [1.0, 1.0, 1.0, 1.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2, 2]
77+ )
66 # 创建Tensor对象78 # 创建Tensor对象
67 inputs = [tensor1, tensor2, tensor3]79 inputs = [tensor1, tensor2, tensor3]
68 # 5. 运行图80 # 5. 运行图
69 ret = session.run_graph(graph_id, inputs)81 ret = session.run_graph(graph_id, inputs)
70- print("[Info] 图运行成功！")82+ print("[Info] Graph executed successfully!")
71 for idx, tensor in enumerate(ret, start=1):83 for idx, tensor in enumerate(ret, start=1):
72- print(f"Tensor{idx}详情：{tensor}")84+ print(f"Tensor{idx} details: {tensor}")
73 return 085 return 0
74 86 
75 except Exception as e:87 except Exception as e:
76- print(f"[Error] 执行过程中出错: {e}")88+ print(f"[Error] Error during execution: {e}")
77 import traceback89 import traceback
78 90 
79 traceback.print_exc()91 traceback.print_exc()
@@ -81,9 +93,9 @@ def run_graph(graph) -> None:
81 93 
82 finally:94 finally:
83 # 6. 清理GE环境95 # 6. 清理GE环境
84- print("[Info] 清理GE环境...")96+ print("[Info] Cleaning up GE environment...")
85 ge_api.ge_finalize()97 ge_api.ge_finalize()
86- print("[Success] GE环境已清理")98+ print("[Success] GE environment cleaned up")
87 99 
88 100 
89graph = build_control_edge_graph()101graph = build_control_edge_graph()
Mexamples/es/control_op/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeIfGraphByEsAndDump();40 es_showcase::MakeIfGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/control_op/python/src/make_if_graph.py+15-11
@@ -23,8 +23,12 @@ def build_if_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeIfGraph")24 builder = GraphBuilder("MakeIfGraph")
25 # 2、MakeIfGraph实例创建输入节点25 # 2、MakeIfGraph实例创建输入节点
26- input_tensor_holder = builder.create_input(index=0, name="test_input", data_type=DataType.DT_FLOAT, shape=[2])26+ input_tensor_holder = builder.create_input(
27- cond = builder.create_input(index=1, name="cond", data_type=DataType.DT_INT32, shape=[])27+ index=0, name="test_input", data_type=DataType.DT_FLOAT, shape=[2]
28+ )
29+ cond = builder.create_input(
30+ index=1, name="cond", data_type=DataType.DT_INT32, shape=[]
31+ )
28 # 初始化图构建器实例，用于构建子图then_branch32 # 初始化图构建器实例，用于构建子图then_branch
29 then_branch_builder = GraphBuilder("then_branch")33 then_branch_builder = GraphBuilder("then_branch")
30 then_branch_input_tensor = then_branch_builder.create_input(34 then_branch_input_tensor = then_branch_builder.create_input(
@@ -63,9 +67,9 @@ def run_graph(graph) -> None:
63 ge_api = GeApi()67 ge_api = GeApi()
64 ret = ge_api.ge_initialize(config)68 ret = ge_api.ge_initialize(config)
65 if ret != 0:69 if ret != 0:
66- print(f"GE初始化失败，返回码: {ret}")70+ print(f"GE initialization failed, return code: {ret}")
67 return ret71 return ret
68- print("GE环境初始化成功 (Device ID: 0)")72+ print("GE environment initialized successfully (Device ID: 0)")
69 73 
70 try:74 try:
71 # 2. 创建Session75 # 2. 创建Session
@@ -74,9 +78,9 @@ def run_graph(graph) -> None:
74 graph_id = 178 graph_id = 1
75 ret = session.add_graph(graph_id, graph)79 ret = session.add_graph(graph_id, graph)
76 if ret != 0:80 if ret != 0:
77- print(f"添加图失败，返回码: {ret}")81+ print(f"Failed to add graph, return code: {ret}")
78 return ret82 return ret
79- print(f"图已添加到Session (Graph ID: {graph_id})")83+ print(f"Graph added to Session (Graph ID: {graph_id})")
80 84 
81 # 4. 准备输入数据85 # 4. 准备输入数据
82 tensor1 = Tensor([1.0, 2.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2])86 tensor1 = Tensor([1.0, 2.0], None, DataType.DT_FLOAT, Format.FORMAT_ND, [2])
@@ -85,22 +89,22 @@ def run_graph(graph) -> None:
85 inputs = [tensor1, tensor_const]89 inputs = [tensor1, tensor_const]
86 # 5. 运行图90 # 5. 运行图
87 ret = session.run_graph(graph_id, inputs)91 ret = session.run_graph(graph_id, inputs)
88- print("[Info] 图运行成功！")92+ print("[Info] Graph executed successfully!")
89 for idx, tensor in enumerate(ret, start=1):93 for idx, tensor in enumerate(ret, start=1):
90- print(f"Tensor{idx}详情：{tensor}")94+ print(f"Tensor{idx} details: {tensor}")
91 return 095 return 0
92 96 
93 except Exception as e:97 except Exception as e:
94- print(f"[Error] 执行过程中出错: {e}")98+ print(f"[Error] Error during execution: {e}")
95 import traceback99 import traceback
96 100 
97 traceback.print_exc()101 traceback.print_exc()
98 return -1102 return -1
99 finally:103 finally:
100 # 6. 清理GE环境104 # 6. 清理GE环境
101- print("[Info] 清理GE环境...")105+ print("[Info] Cleaning up GE environment...")
102 ge_api.ge_finalize()106 ge_api.ge_finalize()
103- print("[Success] GE环境已清理")107+ print("[Success] GE environment cleaned up")
104 108 
105 109 
106graph = build_if_graph()110graph = build_if_graph()
Mexamples/es/dynamic_input/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeConcatV2GraphByEsAndDump();40 es_showcase::MakeConcatV2GraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/dynamic_input/python/src/make_concatv2_graph.py+18-12
@@ -23,9 +23,15 @@ def build_concat_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeConcatV2Graph")24 builder = GraphBuilder("MakeConcatV2Graph")
25 # 2、创建图输入节点25 # 2、创建图输入节点
26- tensor_holder1 = builder.create_input(index=0, name="tensor1", data_type=DataType.DT_FLOAT, shape=[8, 64, 128])26+ tensor_holder1 = builder.create_input(
27- tensor_holder2 = builder.create_input(index=1, name="tensor2", data_type=DataType.DT_FLOAT, shape=[2, 64, 128])27+ index=0, name="tensor1", data_type=DataType.DT_FLOAT, shape=[8, 64, 128]
28- tensor_holder3 = builder.create_input(index=2, name="tensor3", data_type=DataType.DT_FLOAT, shape=[6, 64, 128])28+ )
29+ tensor_holder2 = builder.create_input(
30+ index=1, name="tensor2", data_type=DataType.DT_FLOAT, shape=[2, 64, 128]
31+ )
32+ tensor_holder3 = builder.create_input(
33+ index=2, name="tensor3", data_type=DataType.DT_FLOAT, shape=[6, 64, 128]
34+ )
29 tensor_holder_list = [tensor_holder1, tensor_holder2, tensor_holder3]35 tensor_holder_list = [tensor_holder1, tensor_holder2, tensor_holder3]
30 concat_tensor_holder = ConcatV2(tensor_holder_list, 0, N=3)36 concat_tensor_holder = ConcatV2(tensor_holder_list, 0, N=3)
31 # 3、设置图输出节点37 # 3、设置图输出节点
@@ -46,9 +52,9 @@ def run_graph(graph) -> int:
46 ge_api = GeApi()52 ge_api = GeApi()
47 ret = ge_api.ge_initialize(config)53 ret = ge_api.ge_initialize(config)
48 if ret != 0:54 if ret != 0:
49- print(f"GE初始化失败，返回码: {ret}")55+ print(f"GE initialization failed, return code: {ret}")
50 return ret56 return ret
51- print("GE环境初始化成功 (Device ID: 0)")57+ print("GE environment initialized successfully (Device ID: 0)")
52 58 
53 try:59 try:
54 # 2. 创建Session60 # 2. 创建Session
@@ -57,9 +63,9 @@ def run_graph(graph) -> int:
57 graph_id = 163 graph_id = 1
58 ret = session.add_graph(graph_id, graph)64 ret = session.add_graph(graph_id, graph)
59 if ret != 0:65 if ret != 0:
60- print(f"添加图失败，返回码: {ret}")66+ print(f"Failed to add graph, return code: {ret}")
61 return ret67 return ret
62- print(f"图已添加到Session (Graph ID: {graph_id})")68+ print(f"Graph added to Session (Graph ID: {graph_id})")
63 69 
64 # 4. 准备输入数据70 # 4. 准备输入数据
65 tensor1_data = np.full(8 * 64 * 128, 1.0, dtype=np.float32)71 tensor1_data = np.full(8 * 64 * 128, 1.0, dtype=np.float32)
@@ -89,22 +95,22 @@ def run_graph(graph) -> int:
89 input_tensor = [tensor1, tensor2, tensor3]95 input_tensor = [tensor1, tensor2, tensor3]
90 # 5. 运行图96 # 5. 运行图
91 ret = session.run_graph(graph_id, input_tensor)97 ret = session.run_graph(graph_id, input_tensor)
92- print("[Info] 图运行成功！")98+ print("[Info] Graph executed successfully!")
93 for idx, tensor in enumerate(ret, start=1):99 for idx, tensor in enumerate(ret, start=1):
94- print(f"Tensor{idx}详情： {tensor}")100+ print(f"Tensor{idx} details: {tensor}")
95 return 0101 return 0
96 102 
97 except Exception as e:103 except Exception as e:
98- print(f"[Error] 执行过程中出错: {e}")104+ print(f"[Error] Error during execution: {e}")
99 import traceback105 import traceback
100 106 
101 traceback.print_exc()107 traceback.print_exc()
102 return -1108 return -1
103 finally:109 finally:
104 # 6. 清理GE环境110 # 6. 清理GE环境
105- print("[Info] 清理GE环境...")111+ print("[Info] Cleaning up GE environment...")
106 ge_api.ge_finalize()112 ge_api.ge_finalize()
107- print("[Success] GE环境已清理")113+ print("[Success] GE environment cleaned up")
108 114 
109 115 
110graph = build_concat_graph()116graph = build_concat_graph()
Mexamples/es/dynamic_output/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeSplitGraphByEsAndDump();40 es_showcase::MakeSplitGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/dynamic_output/python/src/make_split_graph.py+12-10
@@ -23,7 +23,9 @@ def build_split_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeSplitGraph")24 builder = GraphBuilder("MakeSplitGraph")
25 # 2、创建图输入节点25 # 2、创建图输入节点
26- input_tensor_holder = builder.create_input(index=0, name="input", data_type=DataType.DT_FLOAT, shape=[8, 16, 64])26+ input_tensor_holder = builder.create_input(
27+ index=0, name="input", data_type=DataType.DT_FLOAT, shape=[8, 16, 64]
28+ )
27 split_list = Split(1, input_tensor_holder, 4, num_split=4)29 split_list = Split(1, input_tensor_holder, 4, num_split=4)
28 # 3、设置图输出节点30 # 3、设置图输出节点
29 for i, tensor in enumerate(split_list):31 for i, tensor in enumerate(split_list):
@@ -44,9 +46,9 @@ def run_graph(graph) -> int:
44 ge_api = GeApi()46 ge_api = GeApi()
45 ret = ge_api.ge_initialize(config)47 ret = ge_api.ge_initialize(config)
46 if ret != 0:48 if ret != 0:
47- print(f"GE初始化失败，返回码: {ret}")49+ print(f"GE initialization failed, return code: {ret}")
48 return ret50 return ret
49- print("GE环境初始化成功 (Device ID: 0)")51+ print("GE environment initialized successfully (Device ID: 0)")
50 52 
51 try:53 try:
52 # 2. 创建Session54 # 2. 创建Session
@@ -55,9 +57,9 @@ def run_graph(graph) -> int:
55 graph_id = 157 graph_id = 1
56 ret = session.add_graph(graph_id, graph)58 ret = session.add_graph(graph_id, graph)
57 if ret != 0:59 if ret != 0:
58- print(f"添加图失败，返回码: {ret}")60+ print(f"Failed to add graph, return code: {ret}")
59 return ret61 return ret
60- print(f"图已添加到Session (Graph ID: {graph_id})")62+ print(f"Graph added to Session (Graph ID: {graph_id})")
61 63 
62 # 4. 准备输入数据64 # 4. 准备输入数据
63 tensor_data = np.full(8 * 16 * 64, 1.0, dtype=np.float32)65 tensor_data = np.full(8 * 16 * 64, 1.0, dtype=np.float32)
@@ -72,22 +74,22 @@ def run_graph(graph) -> int:
72 input_tensor = [tensor1]74 input_tensor = [tensor1]
73 # 5. 运行图75 # 5. 运行图
74 ret = session.run_graph(graph_id, input_tensor)76 ret = session.run_graph(graph_id, input_tensor)
75- print("[Info] 图运行成功！")77+ print("[Info] Graph executed successfully!")
76 for idx, tensor in enumerate(ret, start=1):78 for idx, tensor in enumerate(ret, start=1):
77- print(f"Tensor{idx}详情：{tensor}")79+ print(f"Tensor{idx} details: {tensor}")
78 return 080 return 0
79 81 
80 except Exception as e:82 except Exception as e:
81- print(f"[Error] 执行过程中出错: {e}")83+ print(f"[Error] Error during execution: {e}")
82 import traceback84 import traceback
83 85 
84 traceback.print_exc()86 traceback.print_exc()
85 return -187 return -1
86 finally:88 finally:
87 # 6. 清理GE环境89 # 6. 清理GE环境
88- print("[Info] 清理GE环境...")90+ print("[Info] Cleaning up GE environment...")
89 ge_api.ge_finalize()91 ge_api.ge_finalize()
90- print("[Success] GE环境已清理")92+ print("[Success] GE environment cleaned up")
91 93 
92 94 
93graph = build_split_graph()95graph = build_split_graph()
Mexamples/es/hccl_ep/cpp/main.cpp+3-3
@@ -51,7 +51,7 @@ int main(int argc, char **argv) {
51 {"ge.exec.rankId", rank_id}};51 {"ge.exec.rankId", rank_id}};
52 auto ret = ge::GEInitialize(config);52 auto ret = ge::GEInitialize(config);
53 if (ret != ge::SUCCESS) {53 if (ret != ge::SUCCESS) {
54- std::cerr << "GE 初始化失败\n";54+ std::cerr << "GE initialization failed\n";
55 return -1;55 return -1;
56 }56 }
57 int result = -1;57 int result = -1;
@@ -59,10 +59,10 @@ int main(int argc, char **argv) {
59 result = 0;59 result = 0;
60 }60 }
61 ge::GEFinalize();61 ge::GEFinalize();
62- std::cout << "执行结束" << std::endl;62+ std::cout << "Execution completed" << std::endl;
63 return result;63 return result;
64 } else {64 } else {
65- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;65+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
66 return -1;66 return -1;
67 }67 }
68}68}
Mexamples/es/hccl_ep/python/src/make_ep_graph.py+52-25
@@ -10,6 +10,7 @@
10# See LICENSE in the root of the software repository for the full text of the License.10# See LICENSE in the root of the software repository for the full text of the License.
11# ----------------------------------------------------------------------------11# ----------------------------------------------------------------------------
12 12 
13+# ruff: noqa: F403, F405
13import os14import os
14import sys15import sys
15 16 
@@ -46,13 +47,19 @@ def build_ep_graph():
46 # 3、创建输入节点47 # 3、创建输入节点
47 inputs = []48 inputs = []
48 for idx, (name, shape, dtype) in enumerate(input_configs):49 for idx, (name, shape, dtype) in enumerate(input_configs):
49- input_node = builder.create_input(index=idx, name=name, data_type=dtype, shape=shape)50+ input_node = builder.create_input(
51+ index=idx, name=name, data_type=dtype, shape=shape
52+ )
50 inputs.append(input_node)53 inputs.append(input_node)
51 54 
52- hidden_states, gate_weight, finished, shared_output, expert_weight, group_list = inputs55+ hidden_states, gate_weight, finished, shared_output, expert_weight, group_list = (
56+ inputs
57+ )
53 58 
54 # 4、构建计算图59 # 4、构建计算图
55- quant_result = DynamicQuant(hidden_states, smooth_scales=None, group_index=None, dst_type=DataType.DT_INT8)60+ quant_result = DynamicQuant(
61+ hidden_states, smooth_scales=None, group_index=None, dst_type=DataType.DT_INT8
62+ )
56 hidden_states_int8 = quant_result.y63 hidden_states_int8 = quant_result.y
57 pertoken_scale = quant_result.scale64 pertoken_scale = quant_result.scale
58 65 
@@ -79,28 +86,38 @@ def build_ep_graph():
79 N=4,86 N=4,
80 )87 )
81 88 
82- topk_all = HcomAllGather(topk_cat, rank_size=RANK_SIZE, group="hccl_world_group", fusion=0, fusion_id=-1)89+ topk_all = HcomAllGather(
90+ topk_cat, rank_size=RANK_SIZE, group="hccl_world_group", fusion=0, fusion_id=-1
91+ )
83 92 
84 size_splits = builder.create_vector_int64([TOP_K, TOP_K, TOP_K, 1])93 size_splits = builder.create_vector_int64([TOP_K, TOP_K, TOP_K, 1])
85 split_results = SplitV(topk_all, size_splits, -1, 4, num_split=4)94 split_results = SplitV(topk_all, size_splits, -1, 4, num_split=4)
86 global_topk_weights = split_results[0]95 global_topk_weights = split_results[0]
87- global_topk_ids_float = split_results[1]
88 global_row_idx_float = split_results[2]96 global_row_idx_float = split_results[2]
89 global_pertoken_scale = split_results[3]97 global_pertoken_scale = split_results[3]
90 98 
91- topk_ids_rounded = Round(global_topk_ids_float)
92 row_idx_rounded = Round(global_row_idx_float)99 row_idx_rounded = Round(global_row_idx_float)
93 global_row_idx = Cast(row_idx_rounded, dst_type=DataType.DT_INT64)100 global_row_idx = Cast(row_idx_rounded, dst_type=DataType.DT_INT64)
94 global_row_idx_flat = Reshape(global_row_idx, builder.create_vector_int64([-1]))101 global_row_idx_flat = Reshape(global_row_idx, builder.create_vector_int64([-1]))
95 102 
96 axis_const = builder.create_const_int32([0], [])103 axis_const = builder.create_const_int32([0], [])
97- dispatched_hidden_states = GatherV2(global_hidden_states, global_row_idx_flat, axis_const)104+ dispatched_hidden_states = GatherV2(
98- global_pertoken_scale_squeezed = Reshape(global_pertoken_scale, builder.create_vector_int64([-1]))105+ global_hidden_states, global_row_idx_flat, axis_const
99- dispatched_pertoken_scale = GatherV2(global_pertoken_scale_squeezed, global_row_idx_flat, axis_const)106+ )
100- global_topk_weights_flat = Reshape(global_topk_weights, builder.create_vector_int64([-1]))107+ global_pertoken_scale_squeezed = Reshape(
108+ global_pertoken_scale, builder.create_vector_int64([-1])
109+ )
110+ dispatched_pertoken_scale = GatherV2(
111+ global_pertoken_scale_squeezed, global_row_idx_flat, axis_const
112+ )
113+ global_topk_weights_flat = Reshape(
114+ global_topk_weights, builder.create_vector_int64([-1])
115+ )
101 group_list_int64 = Cast(group_list, dst_type=DataType.DT_INT64)116 group_list_int64 = Cast(group_list, dst_type=DataType.DT_INT64)
102 scale_data = np.ones([NUM_EXPERTS, HIDDEN_SIZE], dtype=np.float32)117 scale_data = np.ones([NUM_EXPERTS, HIDDEN_SIZE], dtype=np.float32)
103- weight_scale = builder.create_const_float(scale_data.flatten().tolist(), [NUM_EXPERTS, HIDDEN_SIZE])118+ weight_scale = builder.create_const_float(
119+ scale_data.flatten().tolist(), [NUM_EXPERTS, HIDDEN_SIZE]
120+ )
104 121 
105 moe_output = GroupedMatmulFinalizeRouting(122 moe_output = GroupedMatmulFinalizeRouting(
106 dispatched_hidden_states, # x (int8)123 dispatched_hidden_states, # x (int8)
@@ -153,17 +170,23 @@ def run_graph(graph, device_id="0") -> int:
153 if rank_id is not None and rank_table_file is not None:170 if rank_id is not None and rank_table_file is not None:
154 config["ge.exec.rankTableFile"] = rank_table_file171 config["ge.exec.rankTableFile"] = rank_table_file
155 config["ge.exec.rankId"] = rank_id172 config["ge.exec.rankId"] = rank_id
156- print(f"[Info] 多卡模式 - RANK_ID: {rank_id}, RANK_TABLE_FILE: {rank_table_file}")173+ print(
174+ f"[Info] Multi-card mode - RANK_ID: {rank_id}, RANK_TABLE_FILE: {rank_table_file}"
175+ )
157 else:176 else:
158- print("[Info] 单卡模式 - 未检测到RANK_ID和RANK_TABLE_FILE环境变量")177+ print(
178+ "[Info] Single-card mode - RANK_ID and RANK_TABLE_FILE environment variables not detected"
179+ )
159 180 
160 ge_api = GeApi()181 ge_api = GeApi()
161 ret = ge_api.ge_initialize(config)182 ret = ge_api.ge_initialize(config)
162 if ret != 0:183 if ret != 0:
163- print(f"[Error] GE初始化失败，返回码: {ret}")184+ print(f"[Error] GE initialization failed, return code: {ret}")
164 return ret185 return ret
165 186 
166- print(f"[Info] GE环境初始化成功 (Device ID: {device_id}, RANK_ID: {rank_id if rank_id else 'N/A'})")187+ print(
188+ f"[Info] GE environment initialized successfully (Device ID: {device_id}, RANK_ID: {rank_id if rank_id else 'N/A'})"
189+ )
167 190 
168 try:191 try:
169 # 2. 创建Session192 # 2. 创建Session
@@ -173,9 +196,9 @@ def run_graph(graph, device_id="0") -> int:
173 graph_id = 1196 graph_id = 1
174 ret = session.add_graph(graph_id, graph)197 ret = session.add_graph(graph_id, graph)
175 if ret != 0:198 if ret != 0:
176- print(f"[Error] 添加图失败，返回码: {ret}")199+ print(f"[Error] Failed to add graph, return code: {ret}")
177 return ret200 return ret
178- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")201+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
179 202 
180 # 4. 准备输入数据203 # 4. 准备输入数据
181 # hidden_states204 # hidden_states
@@ -200,7 +223,9 @@ def run_graph(graph, device_id="0") -> int:
200 223 
201 # finished (BOOL 类型)224 # finished (BOOL 类型)
202 finished_data = [False] * LOCAL_BATCH225 finished_data = [False] * LOCAL_BATCH
203- finished_tensor = Tensor(finished_data, None, DataType.DT_BOOL, Format.FORMAT_ND, [LOCAL_BATCH])226+ finished_tensor = Tensor(
227+ finished_data, None, DataType.DT_BOOL, Format.FORMAT_ND, [LOCAL_BATCH]
228+ )
204 229 
205 # shared_output230 # shared_output
206 shared_output_data = np.full([LOCAL_BATCH, HIDDEN_SIZE], 0.5, dtype=np.float32)231 shared_output_data = np.full([LOCAL_BATCH, HIDDEN_SIZE], 0.5, dtype=np.float32)
@@ -213,7 +238,9 @@ def run_graph(graph, device_id="0") -> int:
213 )238 )
214 239 
215 # expert_weight240 # expert_weight
216- expert_weight_data = np.ones([NUM_EXPERTS, HIDDEN_SIZE, HIDDEN_SIZE], dtype=np.int8)241+ expert_weight_data = np.ones(
242+ [NUM_EXPERTS, HIDDEN_SIZE, HIDDEN_SIZE], dtype=np.int8
243+ )
217 expert_weight_tensor = Tensor(244 expert_weight_tensor = Tensor(
218 expert_weight_data.flatten().tolist(),245 expert_weight_data.flatten().tolist(),
219 None,246 None,
@@ -241,17 +268,17 @@ def run_graph(graph, device_id="0") -> int:
241 group_list_tensor,268 group_list_tensor,
242 ]269 ]
243 270 
244- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor")271+ print(f"[Info] Prepared {len(inputs)} input tensor(s)")
245 272 
246 # 5. 运行图273 # 5. 运行图
247 ret = session.run_graph(graph_id, inputs)274 ret = session.run_graph(graph_id, inputs)
248- print("[Info] 图运行成功！")275+ print("[Info] Graph executed successfully!")
249 for idx, tensor in enumerate(ret, start=1):276 for idx, tensor in enumerate(ret, start=1):
250- print(f"Tensor{idx}详情：{tensor}")277+ print(f"Tensor{idx} details: {tensor}")
251 return 0278 return 0
252 279 
253 except Exception as e:280 except Exception as e:
254- print(f"[Error] 执行过程中出错: {e}")281+ print(f"[Error] Error during execution: {e}")
255 import traceback282 import traceback
256 283 
257 traceback.print_exc()284 traceback.print_exc()
@@ -259,9 +286,9 @@ def run_graph(graph, device_id="0") -> int:
259 286 
260 finally:287 finally:
261 # 6. 清理GE环境288 # 6. 清理GE环境
262- print("[Info] 清理GE环境...")289+ print("[Info] Cleaning up GE environment...")
263 ge_api.ge_finalize()290 ge_api.ge_finalize()
264- print("[Success] GE环境已清理")291+ print("[Success] GE environment cleaned up")
265 292 
266 293 
267if __name__ == "__main__":294if __name__ == "__main__":
Mexamples/es/hccl_tp/cpp/main.cpp+3-3
@@ -51,7 +51,7 @@ int main(int argc, char **argv) {
51 {"ge.exec.rankId", rank_id}};51 {"ge.exec.rankId", rank_id}};
52 auto ret = ge::GEInitialize(config);52 auto ret = ge::GEInitialize(config);
53 if (ret != ge::SUCCESS) {53 if (ret != ge::SUCCESS) {
54- std::cerr << "GE 初始化失败\n";54+ std::cerr << "GE initialization failed\n";
55 return -1;55 return -1;
56 }56 }
57 int result = -1;57 int result = -1;
@@ -59,10 +59,10 @@ int main(int argc, char **argv) {
59 result = 0;59 result = 0;
60 }60 }
61 ge::GEFinalize();61 ge::GEFinalize();
62- std::cout << "执行结束" << std::endl;62+ std::cout << "Execution completed" << std::endl;
63 return result;63 return result;
64 } else {64 } else {
65- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;65+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
66 return -1;66 return -1;
67 }67 }
68}68}
Mexamples/es/hccl_tp/python/src/make_pfa_hcom_graph.py+22-13
@@ -10,6 +10,7 @@
10# See LICENSE in the root of the software repository for the full text of the License.10# See LICENSE in the root of the software repository for the full text of the License.
11# ----------------------------------------------------------------------------11# ----------------------------------------------------------------------------
12 12 
13+# ruff: noqa: F403, F405
13import os14import os
14import sys15import sys
15 16 
@@ -47,7 +48,9 @@ def build_pfa_hcom_graph():
47 inputs_fp32 = []48 inputs_fp32 = []
48 inputs_fp16 = []49 inputs_fp16 = []
49 for idx, (name, shape) in enumerate(input_configs):50 for idx, (name, shape) in enumerate(input_configs):
50- input_fp32 = builder.create_input(index=idx, name=name, data_type=DataType.DT_FLOAT, shape=shape)51+ input_fp32 = builder.create_input(
52+ index=idx, name=name, data_type=DataType.DT_FLOAT, shape=shape
53+ )
51 inputs_fp32.append(input_fp32)54 inputs_fp32.append(input_fp32)
52 inputs_fp16.append(Cast(input_fp32, dst_type=DataType.DT_FLOAT16))55 inputs_fp16.append(Cast(input_fp32, dst_type=DataType.DT_FLOAT16))
53 56 
@@ -134,17 +137,23 @@ def run_graph(graph, device_id="0") -> int:
134 if rank_id is not None and rank_table_file is not None:137 if rank_id is not None and rank_table_file is not None:
135 config["ge.exec.rankTableFile"] = rank_table_file138 config["ge.exec.rankTableFile"] = rank_table_file
136 config["ge.exec.rankId"] = rank_id139 config["ge.exec.rankId"] = rank_id
137- print(f"[Info] 多卡模式 - RANK_ID: {rank_id}, RANK_TABLE_FILE: {rank_table_file}")140+ print(
141+ f"[Info] Multi-card mode - RANK_ID: {rank_id}, RANK_TABLE_FILE: {rank_table_file}"
142+ )
138 else:143 else:
139- print("[Info] 单卡模式 - 未检测到RANK_ID和RANK_TABLE_FILE环境变量")144+ print(
145+ "[Info] Single-card mode - RANK_ID and RANK_TABLE_FILE environment variables not detected"
146+ )
140 147 
141 ge_api = GeApi()148 ge_api = GeApi()
142 ret = ge_api.ge_initialize(config)149 ret = ge_api.ge_initialize(config)
143 if ret != 0:150 if ret != 0:
144- print(f"[Error] GE初始化失败，返回码: {ret}")151+ print(f"[Error] GE initialization failed, return code: {ret}")
145 return ret152 return ret
146 153 
147- print(f"[Info] GE环境初始化成功 (Device ID: {device_id}, RANK_ID: {rank_id if rank_id else 'N/A'})")154+ print(
155+ f"[Info] GE environment initialized successfully (Device ID: {device_id}, RANK_ID: {rank_id if rank_id else 'N/A'})"
156+ )
148 157 
149 try:158 try:
150 # 2. 创建Session159 # 2. 创建Session
@@ -154,9 +163,9 @@ def run_graph(graph, device_id="0") -> int:
154 graph_id = 1163 graph_id = 1
155 ret = session.add_graph(graph_id, graph)164 ret = session.add_graph(graph_id, graph)
156 if ret != 0:165 if ret != 0:
157- print(f"[Error] 添加图失败，返回码: {ret}")166+ print(f"[Error] Failed to add graph, return code: {ret}")
158 return ret167 return ret
159- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")168+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
160 169 
161 # 4. 准备输入数据170 # 4. 准备输入数据
162 input_data_configs = [171 input_data_configs = [
@@ -184,17 +193,17 @@ def run_graph(graph, device_id="0") -> int:
184 )193 )
185 inputs.append(tensor)194 inputs.append(tensor)
186 195 
187- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor")196+ print(f"[Info] Prepared {len(inputs)} input tensor(s)")
188 197 
189 # 5. 运行图198 # 5. 运行图
190 ret = session.run_graph(graph_id, inputs)199 ret = session.run_graph(graph_id, inputs)
191- print("[Info] 图运行成功！")200+ print("[Info] Graph executed successfully!")
192 for idx, tensor in enumerate(ret, start=1):201 for idx, tensor in enumerate(ret, start=1):
193- print(f"Tensor{idx}详情：{tensor}")202+ print(f"Tensor{idx} details: {tensor}")
194 return 0203 return 0
195 204 
196 except Exception as e:205 except Exception as e:
197- print(f"[Error] 执行过程中出错: {e}")206+ print(f"[Error] Error during execution: {e}")
198 import traceback207 import traceback
199 208 
200 traceback.print_exc()209 traceback.print_exc()
@@ -202,9 +211,9 @@ def run_graph(graph, device_id="0") -> int:
202 211 
203 finally:212 finally:
204 # 6. 清理GE环境213 # 6. 清理GE环境
205- print("[Info] 清理GE环境...")214+ print("[Info] Cleaning up GE environment...")
206 ge_api.ge_finalize()215 ge_api.ge_finalize()
207- print("[Success] GE环境已清理")216+ print("[Success] GE environment cleaned up")
208 217 
209 218 
210if __name__ == "__main__":219if __name__ == "__main__":
Mexamples/es/normal_attributes/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeMatMulGraphByEsAndDump();40 es_showcase::MakeMatMulGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/normal_attributes/python/src/make_matmul_graph.py+16-12
@@ -23,10 +23,14 @@ def build_matmul_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeMatMulGraph")24 builder = GraphBuilder("MakeMatMulGraph")
25 # 2、创建图输入节点25 # 2、创建图输入节点
26- input_tensor_holder = builder.create_input(index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3])26+ input_tensor_holder = builder.create_input(
27+ index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3]
28+ )
27 weight = builder.create_const_float([1.0] * 6, shape=[2, 3])29 weight = builder.create_const_float([1.0] * 6, shape=[2, 3])
28 # transpose_x1 和 transpose_x2 为 MatMul 的属性30 # transpose_x1 和 transpose_x2 为 MatMul 的属性
29- matmul_tensor_holder = MatMul(weight, input_tensor_holder, None, transpose_x1=True, transpose_x2=False)31+ matmul_tensor_holder = MatMul(
32+ weight, input_tensor_holder, None, transpose_x1=True, transpose_x2=False
33+ )
30 # 3、设置图输出节点34 # 3、设置图输出节点
31 builder.set_graph_output(matmul_tensor_holder, 0)35 builder.set_graph_output(matmul_tensor_holder, 0)
32 # 4、构建图36 # 4、构建图
@@ -44,10 +48,10 @@ def run_graph(graph, device_id="0") -> int:
44 ge_api = GeApi()48 ge_api = GeApi()
45 ret = ge_api.ge_initialize(config)49 ret = ge_api.ge_initialize(config)
46 if ret != 0:50 if ret != 0:
47- print(f"[Error] GE初始化失败，返回码: {ret}")51+ print(f"[Error] GE initialization failed, return code: {ret}")
48 return ret52 return ret
49 53 
50- print(f"[Info] GE环境初始化成功 (Device ID: {device_id})")54+ print(f"[Info] GE environment initialized successfully (Device ID: {device_id})")
51 55 
52 try:56 try:
53 # 2. 创建Session57 # 2. 创建Session
@@ -57,9 +61,9 @@ def run_graph(graph, device_id="0") -> int:
57 graph_id = 161 graph_id = 1
58 ret = session.add_graph(graph_id, graph)62 ret = session.add_graph(graph_id, graph)
59 if ret != 0:63 if ret != 0:
60- print(f"[Error] 添加图失败，返回码: {ret}")64+ print(f"[Error] Failed to add graph, return code: {ret}")
61 return ret65 return ret
62- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")66+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
63 67 
64 # 4. 准备输入数据68 # 4. 准备输入数据
65 input_data = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)69 input_data = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
@@ -73,17 +77,17 @@ def run_graph(graph, device_id="0") -> int:
73 )77 )
74 78 
75 inputs = [input_tensor]79 inputs = [input_tensor]
76- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor")80+ print(f"[Info] Prepared {len(inputs)} input tensor(s)")
77 81 
78 # 5. 运行图82 # 5. 运行图
79 ret = session.run_graph(graph_id, inputs)83 ret = session.run_graph(graph_id, inputs)
80- print("[Info] 图运行成功！")84+ print("[Info] Graph executed successfully!")
81 for idx, tensor in enumerate(ret, start=1):85 for idx, tensor in enumerate(ret, start=1):
82- print(f"Tensor{idx}详情：{tensor}")86+ print(f"Tensor{idx} details: {tensor}")
83 return 087 return 0
84 88 
85 except Exception as e:89 except Exception as e:
86- print(f"[Error] 执行过程中出错: {e}")90+ print(f"[Error] Error during execution: {e}")
87 import traceback91 import traceback
88 92 
89 traceback.print_exc()93 traceback.print_exc()
@@ -91,9 +95,9 @@ def run_graph(graph, device_id="0") -> int:
91 95 
92 finally:96 finally:
93 # 6. 清理GE环境97 # 6. 清理GE环境
94- print("[Info] 清理GE环境...")98+ print("[Info] Cleaning up GE environment...")
95 ge_api.ge_finalize()99 ge_api.ge_finalize()
96- print("[Success] GE环境已清理")100+ print("[Success] GE environment cleaned up")
97 101 
98 102 
99if __name__ == "__main__":103if __name__ == "__main__":
Mexamples/es/normal_input/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeReluAddGraphByEsAndDump();40 es_showcase::MakeReluAddGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/normal_input/python/src/make_relu_add_graph.py+13-11
@@ -23,7 +23,9 @@ def build_input_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeMatMulGraph")24 builder = GraphBuilder("MakeMatMulGraph")
25 # 2、创建图输入节点25 # 2、创建图输入节点
26- input_tensor_holder1 = builder.create_input(index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3])26+ input_tensor_holder1 = builder.create_input(
27+ index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3]
28+ )
27 relu1 = Relu(input_tensor_holder1)29 relu1 = Relu(input_tensor_holder1)
28 relu2 = Relu(relu1)30 relu2 = Relu(relu1)
29 add_tensor_holder = Add(input_tensor_holder1, relu2)31 add_tensor_holder = Add(input_tensor_holder1, relu2)
@@ -44,10 +46,10 @@ def run_graph(graph, device_id="0") -> int:
44 ge_api = GeApi()46 ge_api = GeApi()
45 ret = ge_api.ge_initialize(config)47 ret = ge_api.ge_initialize(config)
46 if ret != 0:48 if ret != 0:
47- print(f"[Error] GE初始化失败，返回码: {ret}")49+ print(f"[Error] GE initialization failed, return code: {ret}")
48 return ret50 return ret
49 51 
50- print(f"[Info] GE环境初始化成功 (Device ID: {device_id})")52+ print(f"[Info] GE environment initialized successfully (Device ID: {device_id})")
51 53 
52 try:54 try:
53 # 2. 创建Session55 # 2. 创建Session
@@ -57,9 +59,9 @@ def run_graph(graph, device_id="0") -> int:
57 graph_id = 159 graph_id = 1
58 ret = session.add_graph(graph_id, graph)60 ret = session.add_graph(graph_id, graph)
59 if ret != 0:61 if ret != 0:
60- print(f"[Error] 添加图失败，返回码: {ret}")62+ print(f"[Error] Failed to add graph, return code: {ret}")
61 return ret63 return ret
62- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")64+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
63 65 
64 # 4. 准备输入数据66 # 4. 准备输入数据
65 input_data = np.full([2, 3], 1.0, dtype=np.float32)67 input_data = np.full([2, 3], 1.0, dtype=np.float32)
@@ -72,17 +74,17 @@ def run_graph(graph, device_id="0") -> int:
72 )74 )
73 75 
74 inputs = [input_tensor]76 inputs = [input_tensor]
75- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor")77+ print(f"[Info] Prepared {len(inputs)} input tensor(s)")
76 78 
77 # 5. 运行图79 # 5. 运行图
78 ret = session.run_graph(graph_id, inputs)80 ret = session.run_graph(graph_id, inputs)
79- print("[Info] 图运行成功！")81+ print("[Info] Graph executed successfully!")
80 for idx, tensor in enumerate(ret, start=1):82 for idx, tensor in enumerate(ret, start=1):
81- print(f"Tensor{idx}详情：{tensor}")83+ print(f"Tensor{idx} details: {tensor}")
82 return 084 return 0
83 85 
84 except Exception as e:86 except Exception as e:
85- print(f"[Error] 执行过程中出错: {e}")87+ print(f"[Error] Error during execution: {e}")
86 import traceback88 import traceback
87 89 
88 traceback.print_exc()90 traceback.print_exc()
@@ -90,9 +92,9 @@ def run_graph(graph, device_id="0") -> int:
90 92 
91 finally:93 finally:
92 # 6. 清理GE环境94 # 6. 清理GE环境
93- print("[Info] 清理GE环境...")95+ print("[Info] Cleaning up GE environment...")
94 ge_api.ge_finalize()96 ge_api.ge_finalize()
95- print("[Success] GE环境已清理")97+ print("[Success] GE environment cleaned up")
96 98 
97 99 
98if __name__ == "__main__":100if __name__ == "__main__":
Mexamples/es/operator_overload/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeAddSubMulDivGraphByEsAndDump();40 es_showcase::MakeAddSubMulDivGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/operator_overload/python/src/make_add_graph.py+21-13
@@ -22,11 +22,19 @@ def build_overload_graph():
22 # 1、创建图构建器22 # 1、创建图构建器
23 builder = GraphBuilder("MakeAddGraph")23 builder = GraphBuilder("MakeAddGraph")
24 # 2、创建图输入节点24 # 2、创建图输入节点
25- input_tensor_holder1 = builder.create_input(index=0, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 3])25+ input_tensor_holder1 = builder.create_input(
26- input_tensor_holder2 = builder.create_input(index=1, name="input2", data_type=DataType.DT_INT64, shape=[2, 3])26+ index=0, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 3]
27- input_tensor_holder3 = builder.create_input(index=2, name="input3", data_type=DataType.DT_INT64, shape=[2, 3])27+ )
28+ input_tensor_holder2 = builder.create_input(
29+ index=1, name="input2", data_type=DataType.DT_INT64, shape=[2, 3]
30+ )
31+ input_tensor_holder3 = builder.create_input(
32+ index=2, name="input3", data_type=DataType.DT_INT64, shape=[2, 3]
33+ )
28 # 操作符重载34 # 操作符重载
29- add_tensor_holder2 = input_tensor_holder1 + input_tensor_holder2 + input_tensor_holder335+ add_tensor_holder2 = (
36+ input_tensor_holder1 + input_tensor_holder2 + input_tensor_holder3
37+ )
30 # 3、设置图输出节点38 # 3、设置图输出节点
31 builder.set_graph_output(add_tensor_holder2, 0)39 builder.set_graph_output(add_tensor_holder2, 0)
32 # 4、构建图40 # 4、构建图
@@ -45,9 +53,9 @@ def run_graph(graph) -> int:
45 ge_api = GeApi()53 ge_api = GeApi()
46 ret = ge_api.ge_initialize(config)54 ret = ge_api.ge_initialize(config)
47 if ret != 0:55 if ret != 0:
48- print(f"GE初始化失败，返回码: {ret}")56+ print(f"GE initialization failed, return code: {ret}")
49 return ret57 return ret
50- print("GE环境初始化成功 (Device ID: 0)")58+ print("GE environment initialized successfully (Device ID: 0)")
51 59 
52 try:60 try:
53 # 2. 创建Session61 # 2. 创建Session
@@ -56,9 +64,9 @@ def run_graph(graph) -> int:
56 graph_id = 164 graph_id = 1
57 ret = session.add_graph(graph_id, graph)65 ret = session.add_graph(graph_id, graph)
58 if ret != 0:66 if ret != 0:
59- print(f"添加图失败，返回码: {ret}")67+ print(f"Failed to add graph, return code: {ret}")
60 return ret68 return ret
61- print(f"图已添加到Session (Graph ID: {graph_id})")69+ print(f"Graph added to Session (Graph ID: {graph_id})")
62 70 
63 # 4. 准备输入数据71 # 4. 准备输入数据
64 tensor1 = Tensor(72 tensor1 = Tensor(
@@ -85,22 +93,22 @@ def run_graph(graph) -> int:
85 input_tensor = [tensor1, tensor2, tensor3]93 input_tensor = [tensor1, tensor2, tensor3]
86 # 5. 运行图94 # 5. 运行图
87 ret = session.run_graph(graph_id, input_tensor)95 ret = session.run_graph(graph_id, input_tensor)
88- print("[Info] 图运行成功！")96+ print("[Info] Graph executed successfully!")
89 for idx, tensor in enumerate(ret, start=1):97 for idx, tensor in enumerate(ret, start=1):
90- print(f"Tensor{idx}详情：{tensor}")98+ print(f"Tensor{idx} details: {tensor}")
91 return 099 return 0
92 100 
93 except Exception as e:101 except Exception as e:
94- print(f"[Error] 执行过程中出错: {e}")102+ print(f"[Error] Error during execution: {e}")
95 import traceback103 import traceback
96 104 
97 traceback.print_exc()105 traceback.print_exc()
98 return -1106 return -1
99 finally:107 finally:
100 # 6. 清理GE环境108 # 6. 清理GE环境
101- print("[Info] 清理GE环境...")109+ print("[Info] Cleaning up GE environment...")
102 ge_api.ge_finalize()110 ge_api.ge_finalize()
103- print("[Success] GE环境已清理")111+ print("[Success] GE environment cleaned up")
104 112 
105 113 
106graph = build_overload_graph()114graph = build_overload_graph()
Mexamples/es/operator_overload_async/cpp/main.cpp+8-8
@@ -26,28 +26,28 @@ int main(int argc, char **argv) {
26 std::string command = argv[1];26 std::string command = argv[1];
27 if (command == "run") {27 if (command == "run") {
28 if (argc >= 3) {28 if (argc >= 3) {
29- std::cerr << "错误: run 命令不需要 mode 参数，当前固定为 copy 模式\n";29+ std::cerr << "Error: run command does not accept a mode argument, currently fixed to copy mode\n";
30 return -1;30 return -1;
31 }31 }
32- std::cout << "运行模式: copy" << std::endl;32+ std::cout << "Run mode: copy" << std::endl;
33 constexpr int32_t kDeviceId = 0;33 constexpr int32_t kDeviceId = 0;
34 const std::string device_id = std::to_string(kDeviceId);34 const std::string device_id = std::to_string(kDeviceId);
35 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", ge::AscendString(device_id.c_str())},35 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", ge::AscendString(device_id.c_str())},
36 {"ge.graphRunMode", "0"}};36 {"ge.graphRunMode", "0"}};
37 auto ret = ge::GEInitialize(config);37 auto ret = ge::GEInitialize(config);
38 if (ret != ge::SUCCESS) {38 if (ret != ge::SUCCESS) {
39- std::cerr << "GE 初始化失败\n";39+ std::cerr << "GE initialization failed\n";
40 return -1;40 return -1;
41 }41 }
42 const aclError acl_init_ret = aclInit(nullptr);42 const aclError acl_init_ret = aclInit(nullptr);
43 if (acl_init_ret != ACL_SUCCESS) {43 if (acl_init_ret != ACL_SUCCESS) {
44- std::cerr << "ACL 初始化失败, error=" << acl_init_ret << std::endl;44+ std::cerr << "ACL initialization failed, error=" << acl_init_ret << std::endl;
45 (void)ge::GEFinalize();45 (void)ge::GEFinalize();
46 return -1;46 return -1;
47 }47 }
48 const aclError set_device_ret = aclrtSetDevice(kDeviceId);48 const aclError set_device_ret = aclrtSetDevice(kDeviceId);
49 if (set_device_ret != ACL_SUCCESS) {49 if (set_device_ret != ACL_SUCCESS) {
50- std::cerr << "aclrtSetDevice 失败, error=" << set_device_ret << std::endl;50+ std::cerr << "aclrtSetDevice failed, error=" << set_device_ret << std::endl;
51 (void)aclFinalize();51 (void)aclFinalize();
52 (void)ge::GEFinalize();52 (void)ge::GEFinalize();
53 return -1;53 return -1;
@@ -57,18 +57,18 @@ int main(int argc, char **argv) {
57 result = 0;57 result = 0;
58 }58 }
59 if (ge::GEFinalize() != ge::SUCCESS) {59 if (ge::GEFinalize() != ge::SUCCESS) {
60- std::cerr << "GE 反初始化失败\n";60+ std::cerr << "GE finalization failed\n";
61 result = -1;61 result = -1;
62 }62 }
63 (void)aclrtResetDevice(kDeviceId);63 (void)aclrtResetDevice(kDeviceId);
64 (void)aclFinalize();64 (void)aclFinalize();
65- std::cout << "执行结束" << std::endl;65+ std::cout << "Execution completed" << std::endl;
66 return result;66 return result;
67 } else if (command == "dump") {67 } else if (command == "dump") {
68 es_showcase::MakeAddGraphByEsAndDump();68 es_showcase::MakeAddGraphByEsAndDump();
69 return 0;69 return 0;
70 } else {70 } else {
71- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;71+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
72 return -1;72 return -1;
73 }73 }
74}74}
Mexamples/es/operator_overload_async/cpp/run_sample.sh+26-26
@@ -45,7 +45,7 @@ while [[ $# -gt 0 ]]; do
45 usage45 usage
46 ;;46 ;;
47 *)47 *)
48- echo "未知选项: $1" >&248+ echo "Unknown option: $1" >&2
49 usage49 usage
50 exit 150 exit 1
51 ;;51 ;;
@@ -55,17 +55,17 @@ done
55# 验证目标有效性55# 验证目标有效性
56VALID_TARGETS=("sample" "sample_and_run")56VALID_TARGETS=("sample" "sample_and_run")
57if [[ ! " ${VALID_TARGETS[@]} " =~ " ${TARGET} " ]]; then57if [[ ! " ${VALID_TARGETS[@]} " =~ " ${TARGET} " ]]; then
58- echo "错误: 无效目标 '${TARGET}'。有效目标: ${VALID_TARGETS[*]}" >&258+ echo "Error: invalid target '${TARGET}'. Valid targets: ${VALID_TARGETS[*]}" >&2
59 exit 159 exit 1
60fi60fi
61 61 
62-echo "[Info] 目标设置为: ${TARGET}"62+echo "[Info] Target set to: ${TARGET}"
63-echo "[Info] 运行模式设置为: copy输入输出"63+echo "[Info] Run mode set to: copy input/output"
64 64 
65set +u65set +u
66if [[ -z "${ASCEND_HOME_PATH}" ]]; then66if [[ -z "${ASCEND_HOME_PATH}" ]]; then
67- echo -e "ERROR 环境变量ASCEND_HOME_PATH 未配置" >&267+ echo -e "ERROR Environment variable ASCEND_HOME_PATH is not set" >&2
68- echo -e "ERROR 请先执行: source /usr/local/Ascend/cann/set_env.sh " >&268+ echo -e "ERROR Please source the environment first: source /usr/local/Ascend/cann/set_env.sh " >&2
69 exit 169 exit 1
70fi70fi
71 71 
@@ -81,79 +81,79 @@ case "${ARCH}" in
81 ASCEND_ARCH="aarch64-linux"81 ASCEND_ARCH="aarch64-linux"
82 ;;82 ;;
83 *)83 *)
84- echo "WARNING: 未识别的架构 ${ARCH}，使用默认值 x86_64-linux" >&284+ echo "WARNING: Unrecognized architecture ${ARCH}, using default x86_64-linux" >&2
85 ASCEND_ARCH="x86_64-linux"85 ASCEND_ARCH="x86_64-linux"
86 ;;86 ;;
87esac87esac
88 88 
89-echo "[Info] 检测到系统架构: ${ARCH}"89+echo "[Info] Detected architecture: ${ARCH}"
90-echo "[Info] 使用 ASCEND 架构: ${ASCEND_ARCH}"90+echo "[Info] Using ASCEND architecture: ${ASCEND_ARCH}"
91 91 
92ASCEND_LIB_DIR="${ASCEND_HOME_PATH}/lib64"92ASCEND_LIB_DIR="${ASCEND_HOME_PATH}/lib64"
93echo "[Info] ASCEND_LIB_DIR = ${ASCEND_LIB_DIR}"93echo "[Info] ASCEND_LIB_DIR = ${ASCEND_LIB_DIR}"
94 94 
95# 预先设置 LD_LIBRARY_PATH，保证 gen_esb 能加载95# 预先设置 LD_LIBRARY_PATH，保证 gen_esb 能加载
96export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"96export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"
97-echo "[Info] 预先设置 LD_LIBRARY_PATH=${LD_LIBRARY_PATH} 以支持 gen_esb 运行"97+echo "[Info] LD_LIBRARY_PATH preset to ${LD_LIBRARY_PATH} for gen_esb"
98 98 
99# ---------- 3. 生成 build 目录 ----------99# ---------- 3. 生成 build 目录 ----------
100BUILD_DIR="build"100BUILD_DIR="build"
101if [[ ! -d "${BUILD_DIR}" ]]; then101if [[ ! -d "${BUILD_DIR}" ]]; then
102- echo "[Info] 创建构建目录 ${BUILD_DIR}"102+ echo "[Info] Creating build directory ${BUILD_DIR}"
103 mkdir -p "${BUILD_DIR}"103 mkdir -p "${BUILD_DIR}"
104fi104fi
105 105 
106# ---------- 5. 设置 LD_LIBRARY_PATH ----------106# ---------- 5. 设置 LD_LIBRARY_PATH ----------
107export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"107export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"
108-echo "[Info] LD_LIBRARY_PATH 已设置为: ${LD_LIBRARY_PATH}"108+echo "[Info] LD_LIBRARY_PATH set to: ${LD_LIBRARY_PATH}"
109# ---------- 6. 运行指定目标 ----------109# ---------- 6. 运行指定目标 ----------
110case "${TARGET}" in110case "${TARGET}" in
111 sample)111 sample)
112- echo "[Info] 开始准备并编译目标: sample"112+ echo "[Info] Preparing and building target: sample"
113- echo "[Info] 清理旧的 ${BUILD_DIR}..."113+ echo "[Info] Cleaning old ${BUILD_DIR}..."
114 [ -n "${BUILD_DIR}" ] && rm -rf "${BUILD_DIR}" || true114 [ -n "${BUILD_DIR}" ] && rm -rf "${BUILD_DIR}" || true
115 mkdir -p "${BUILD_DIR}"115 mkdir -p "${BUILD_DIR}"
116 cmake -S . -B "${BUILD_DIR}" -DCMAKE_BUILD_TYPE=Release116 cmake -S . -B "${BUILD_DIR}" -DCMAKE_BUILD_TYPE=Release
117 cmake --build "${BUILD_DIR}" --target sample -j"$(nproc)"117 cmake --build "${BUILD_DIR}" --target sample -j"$(nproc)"
118 118 
119- echo "[Info] 运行 ${BUILD_DIR}/sample dump"119+ echo "[Info] Running ${BUILD_DIR}/sample dump"
120 if [[ -x "${BUILD_DIR}/sample" ]]; then120 if [[ -x "${BUILD_DIR}/sample" ]]; then
121 "${BUILD_DIR}/sample" dump121 "${BUILD_DIR}/sample" dump
122- echo "[Success] sample 执行成功，pbtxt dump 已生成在当前目录。该文件以 ge_onnx_ 开头，可以在 netron 中打开显示"122+ echo "[Success] sample execution succeeded, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be viewed in netron."
123 else123 else
124- echo "ERROR: 找不到或不可执行 ${BUILD_DIR}/sample" >&2124+ echo "ERROR: ${BUILD_DIR}/sample not found or not executable" >&2
125 exit 1125 exit 1
126 fi126 fi
127 ;;127 ;;
128 sample_and_run)128 sample_and_run)
129- echo "[Info] 开始准备并编译目标: sample_and_run"129+ echo "[Info] Preparing and building target: sample_and_run"
130 bash "$0" -t sample130 bash "$0" -t sample
131- echo "[Info] 设置NPU设备下的环境变量 ${ASCEND_HOME_PATH}/set_env.sh"131+ echo "[Info] Setting NPU device environment ${ASCEND_HOME_PATH}/set_env.sh"
132- echo "[Info] 检查环境变量和文件"132+ echo "[Info] Checking environment variables and files"
133 if [ -z "${ASCEND_HOME_PATH:-}" ]; then133 if [ -z "${ASCEND_HOME_PATH:-}" ]; then
134- echo "[Error] ASCEND_HOME_PATH 未设置"134+ echo "[Error] ASCEND_HOME_PATH is not set"
135 exit 1135 exit 1
136 fi136 fi
137 137 
138 if [ -z "${ASCEND_ARCH:-}" ]; then138 if [ -z "${ASCEND_ARCH:-}" ]; then
139- echo "[Error] ASCEND_ARCH 未设置"139+ echo "[Error] ASCEND_ARCH is not set"
140 exit 1140 exit 1
141 fi141 fi
142 142 
143 SETENV_FILE="${ASCEND_HOME_PATH}/set_env.sh"143 SETENV_FILE="${ASCEND_HOME_PATH}/set_env.sh"
144 if [ ! -f "$SETENV_FILE" ]; then144 if [ ! -f "$SETENV_FILE" ]; then
145- echo "[Error] set_env.sh 不存在: $SETENV_FILE"145+ echo "[Error] set_env.sh does not exist: $SETENV_FILE"
146 exit 1146 exit 1
147 fi147 fi
148 # 临时禁用错误退出进行 source148 # 临时禁用错误退出进行 source
149 set +e149 set +e
150 source "$SETENV_FILE"150 source "$SETENV_FILE"
151 set -e151 set -e
152- echo "[Info] 运行 ${BUILD_DIR}/sample run"152+ echo "[Info] Running ${BUILD_DIR}/sample run"
153- "${BUILD_DIR}/sample" run && echo "[Success] sample_and_run 执行成功，pbtxt和data输出dump 已生成在当前目录"153+ "${BUILD_DIR}/sample" run && echo "[Success] sample_and_run execution succeeded, pbtxt and data output dump generated in current directory"
154 ;;154 ;;
155 *)155 *)
156- echo "错误: 未知目标 ${TARGET}" >&2156+ echo "Error: unknown target ${TARGET}" >&2
157 exit 1157 exit 1
158 ;;158 ;;
159esac159esac
Mexamples/es/operator_overload_async/python/README.md+7-7
@@ -46,14 +46,14 @@ bash run_sample.sh -t sample_and_run_python_custom_allocator
46 46 
47执行成功后，终端会出现以下格式的打印（具体行数取决于 GE 对输出 Tensor 的分配/释放；地址与 `size` 以实际运行为准）：47执行成功后，终端会出现以下格式的打印（具体行数取决于 GE 对输出 Tensor 的分配/释放；地址与 `size` 以实际运行为准）：
48```48```
49-[Info] SamplePoolAllocator 已注册到 stream49+[Info] SamplePoolAllocator registered to stream
50-[SamplePool] new : addr=0x<设备内存地址> size=<字节数> B50+[SamplePool] new : addr=0x<addr> size=<N> B
51-[Info] 异步执行 Graph 成功！51+[Info] Asynchronous execution of the graph succeeded.
52-[SamplePool] cache : addr=0x<设备内存地址> size=<字节数> B52+[SamplePool] cache : addr=0x<addr> size=<N> B
53-[Info] SamplePoolAllocator 已注销53+[Info] SamplePoolAllocator unregistered
54[SamplePool] destroy: freed <N> cached blocks54[SamplePool] destroy: freed <N> cached blocks
55-[Info] 运行环境已清理55+[Info] Runtime environment cleaned up
56-[Success] sample 执行成功，pbtxt dump 已生成在当前目录。该文件以 ge_onnx_ 开头，可以在 netron 中打开显示56+[Success] sample execution succeeded, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be viewed in netron.
57```57```
58#### 输出文件说明58#### 输出文件说明
59 59 
Mexamples/es/operator_overload_async/python/README_en.md+2-2
@@ -62,11 +62,11 @@ After successful execution, terminal will show output in following format (speci
62```text62```text
63[Info] SamplePoolAllocator registered to stream63[Info] SamplePoolAllocator registered to stream
64[SamplePool] new : addr=0x<device memory address> size=<byte count> B64[SamplePool] new : addr=0x<device memory address> size=<byte count> B
65-[Info] Asynchronous Graph execution successful!65+[Info] Asynchronous execution of the graph succeeded.
66[SamplePool] cache : addr=0x<device memory address> size=<byte count> B66[SamplePool] cache : addr=0x<device memory address> size=<byte count> B
67[Info] SamplePoolAllocator unregistered67[Info] SamplePoolAllocator unregistered
68[SamplePool] destroy: freed <N> cached blocks68[SamplePool] destroy: freed <N> cached blocks
69-[Info] Running environment cleaned69+[Info] Runtime environment cleaned up
70[Success] sample execution successful, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be opened in netron for display70[Success] sample execution successful, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be opened in netron for display
71```71```
72 72 
Mexamples/es/operator_overload_async/python/run_sample.sh+19-19
@@ -41,7 +41,7 @@ while [[ $# -gt 0 ]]; do
41 usage41 usage
42 ;;42 ;;
43 *)43 *)
44- echo "未知选项: $1" >&244+ echo "Unknown option: $1" >&2
45 usage45 usage
46 exit 146 exit 1
47 ;;47 ;;
@@ -51,17 +51,17 @@ done
51# 验证目标有效性51# 验证目标有效性
52VALID_TARGETS=("sample_and_run_python" "sample_and_run_python_custom_allocator")52VALID_TARGETS=("sample_and_run_python" "sample_and_run_python_custom_allocator")
53if [[ ! " ${VALID_TARGETS[@]} " =~ " ${TARGET} " ]]; then53if [[ ! " ${VALID_TARGETS[@]} " =~ " ${TARGET} " ]]; then
54- echo "错误: 无效目标 '${TARGET}'。有效目标: ${VALID_TARGETS[*]}" >&254+ echo "Error: invalid target '${TARGET}'. Valid targets: ${VALID_TARGETS[*]}" >&2
55 exit 155 exit 1
56fi56fi
57 57 
58-echo "[Info] 目标设置为: ${TARGET}"58+echo "[Info] Target set to: ${TARGET}"
59-echo "[Info] 测试用例设置为: add"59+echo "[Info] Test case set to: add"
60 60 
61set +u61set +u
62if [[ -z "${ASCEND_HOME_PATH}" ]]; then62if [[ -z "${ASCEND_HOME_PATH}" ]]; then
63- echo -e "ERROR 环境变量ASCEND_HOME_PATH 未配置" >&263+ echo -e "ERROR Environment variable ASCEND_HOME_PATH is not set" >&2
64- echo -e "ERROR 请先执行: source /usr/local/Ascend/cann/set_env.sh " >&264+ echo -e "ERROR Please source the environment first: source /usr/local/Ascend/cann/set_env.sh " >&2
65 exit 165 exit 1
66fi66fi
67 67 
@@ -76,33 +76,33 @@ case "${ARCH}" in
76 ASCEND_ARCH="aarch64-linux"76 ASCEND_ARCH="aarch64-linux"
77 ;;77 ;;
78 *)78 *)
79- echo "WARNING: 未识别的架构 ${ARCH}，使用默认值 x86_64-linux" >&279+ echo "WARNING: Unrecognized architecture ${ARCH}, using default x86_64-linux" >&2
80 ASCEND_ARCH="x86_64-linux"80 ASCEND_ARCH="x86_64-linux"
81 ;;81 ;;
82esac82esac
83 83 
84-echo "[Info] 检测到系统架构: ${ARCH}"84+echo "[Info] Detected architecture: ${ARCH}"
85-echo "[Info] 使用 ASCEND 架构: ${ASCEND_ARCH}"85+echo "[Info] Using ASCEND architecture: ${ASCEND_ARCH}"
86 86 
87ASCEND_LIB_DIR="${ASCEND_HOME_PATH}/lib64"87ASCEND_LIB_DIR="${ASCEND_HOME_PATH}/lib64"
88echo "[Info] ASCEND_LIB_DIR = ${ASCEND_LIB_DIR}"88echo "[Info] ASCEND_LIB_DIR = ${ASCEND_LIB_DIR}"
89 89 
90export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"90export LD_LIBRARY_PATH="${ASCEND_LIB_DIR}:${LD_LIBRARY_PATH:-}"
91-echo "[Info] LD_LIBRARY_PATH 已设置为: ${LD_LIBRARY_PATH}"91+echo "[Info] LD_LIBRARY_PATH set to: ${LD_LIBRARY_PATH}"
92 92 
93# ---------- 运行单个 Python 文件 ----------93# ---------- 运行单个 Python 文件 ----------
94run_python_file() {94run_python_file() {
95 local py_file="$1"95 local py_file="$1"
96 if [[ ! -f "${py_file}" ]]; then96 if [[ ! -f "${py_file}" ]]; then
97- echo "[Error] 未找到 Python 用例 ${py_file}" >&297+ echo "[Error] Python test file not found: ${py_file}" >&2
98 return 198 return 1
99 fi99 fi
100- echo "[Info] 运行：${py_file}"100+ echo "[Info] Running: ${py_file}"
101 if python3 "${py_file}"; then101 if python3 "${py_file}"; then
102- echo "[Success] ${py_file} 执行成功"102+ echo "[Success] ${py_file} execution succeeded"
103 return 0103 return 0
104 else104 else
105- echo "[Error] ${py_file} 执行失败" >&2105+ echo "[Error] ${py_file} execution failed" >&2
106 return 1106 return 1
107 fi107 fi
108}108}
@@ -110,22 +110,22 @@ run_python_file() {
110case "${TARGET}" in110case "${TARGET}" in
111 sample_and_run_python)111 sample_and_run_python)
112 if run_python_file "src/make_add_graph.py"; then112 if run_python_file "src/make_add_graph.py"; then
113- echo "[Success] sample 执行成功，pbtxt dump 已生成在当前目录。该文件以 ge_onnx_ 开头，可以在 netron 中打开显示"113+ echo "[Success] sample execution succeeded, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be viewed in netron."
114 else114 else
115- echo "[Error] sample 执行失败，请检查上述错误信息" >&2115+ echo "[Error] sample execution failed, check the error messages above" >&2
116 exit 1116 exit 1
117 fi117 fi
118 ;;118 ;;
119 sample_and_run_python_custom_allocator)119 sample_and_run_python_custom_allocator)
120 if run_python_file "src/make_add_graph_custom_allocator.py"; then120 if run_python_file "src/make_add_graph_custom_allocator.py"; then
121- echo "[Success] sample 执行成功，pbtxt dump 已生成在当前目录。该文件以 ge_onnx_ 开头，可以在 netron 中打开显示"121+ echo "[Success] sample execution succeeded, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be viewed in netron."
122 else122 else
123- echo "[Error] sample 执行失败，请检查上述错误信息" >&2123+ echo "[Error] sample execution failed, check the error messages above" >&2
124 exit 1124 exit 1
125 fi125 fi
126 ;;126 ;;
127 *)127 *)
128- echo "错误: 未知目标 ${TARGET}" >&2128+ echo "Error: unknown target ${TARGET}" >&2
129 exit 1129 exit 1
130 ;;130 ;;
131esac131esac
Mexamples/es/operator_overload_async/python/src/common.py+12-6
@@ -36,9 +36,15 @@ def check_ret(name: str, ret: int) -> None:
36def build_overload_graph() -> Graph:36def build_overload_graph() -> Graph:
37 """使用操作符重载构建静态 shape 加法图（shape 固定为 [2, 3]）。"""37 """使用操作符重载构建静态 shape 加法图（shape 固定为 [2, 3]）。"""
38 builder = GraphBuilder("MakeAddGraph")38 builder = GraphBuilder("MakeAddGraph")
39- h1 = builder.create_input(index=0, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 3])39+ h1 = builder.create_input(
40- h2 = builder.create_input(index=1, name="input2", data_type=DataType.DT_INT64, shape=[2, 3])40+ index=0, name="input1", data_type=DataType.DT_FLOAT, shape=[2, 3]
41- h3 = builder.create_input(index=2, name="input3", data_type=DataType.DT_INT64, shape=[2, 3])41+ )
42+ h2 = builder.create_input(
43+ index=1, name="input2", data_type=DataType.DT_INT64, shape=[2, 3]
44+ )
45+ h3 = builder.create_input(
46+ index=2, name="input3", data_type=DataType.DT_INT64, shape=[2, 3]
47+ )
42 builder.set_graph_output(h1 + h2 + h3, 0)48 builder.set_graph_output(h1 + h2 + h3, 0)
43 return builder.build_and_reset()49 return builder.build_and_reset()
44 50 
@@ -82,7 +88,7 @@ def run_graph(graph: Graph, session_runner: Callable[[Graph, Session], int]) ->
82 }88 }
83 ge_api = GeApi()89 ge_api = GeApi()
84 ge_api.ge_initialize(config)90 ge_api.ge_initialize(config)
85- print(f"[Info] GE 环境初始化成功 (Device ID: {DEVICE_ID})")91+ print(f"[Info] GE environment initialized successfully (Device ID: {DEVICE_ID})")
86 92 
87 try:93 try:
88 check_ret("acl.init", acl.init())94 check_ret("acl.init", acl.init())
@@ -90,11 +96,11 @@ def run_graph(graph: Graph, session_runner: Callable[[Graph, Session], int]) ->
90 check_ret("acl.rt.set_device", acl.rt.set_device(DEVICE_ID))96 check_ret("acl.rt.set_device", acl.rt.set_device(DEVICE_ID))
91 return session_runner(graph, session)97 return session_runner(graph, session)
92 except Exception as e:98 except Exception as e:
93- print(f"[Error] 执行过程中出错: {e}")99+ print(f"[Error] Error during execution: {e}")
94 traceback.print_exc()100 traceback.print_exc()
95 return -1101 return -1
96 finally:102 finally:
97 acl.rt.reset_device(DEVICE_ID)103 acl.rt.reset_device(DEVICE_ID)
98 acl.finalize()104 acl.finalize()
99 ge_api.ge_finalize()105 ge_api.ge_finalize()
100- print("[Info] 运行环境已清理")106+ print("[Info] Runtime environment cleaned up")
Mexamples/es/operator_overload_async/python/src/make_add_graph.py+5-3
@@ -36,16 +36,18 @@ def run_with_default_allocator(ge_graph: Graph, session: Session) -> int:
36 # 3. 添加并异步执行 Graph36 # 3. 添加并异步执行 Graph
37 session.add_graph(GRAPH_ID, ge_graph)37 session.add_graph(GRAPH_ID, ge_graph)
38 graph_added = True38 graph_added = True
39- device_outputs = session.run_graph_with_stream_async(GRAPH_ID, stream, list(inputs))39+ device_outputs = session.run_graph_with_stream_async(
40+ GRAPH_ID, stream, list(inputs)
41+ )
40 42 
41 # 4. 等待 Stream 上的任务完成43 # 4. 等待 Stream 上的任务完成
42 check_ret("acl.rt.synchronize_stream", acl.rt.synchronize_stream(stream))44 check_ret("acl.rt.synchronize_stream", acl.rt.synchronize_stream(stream))
43- print("[Info] 异步执行 Graph 成功！")45+ print("[Info] Asynchronous execution of the graph succeeded.")
44 46 
45 # 5. 将 Device 数据传回 Host 并打印47 # 5. 将 Device 数据传回 Host 并打印
46 host_outputs = [out.to_host() for out in device_outputs]48 host_outputs = [out.to_host() for out in device_outputs]
47 for idx, tensor in enumerate(host_outputs, start=1):49 for idx, tensor in enumerate(host_outputs, start=1):
48- print(f"Tensor{idx} 详情：{tensor}")50+ print(f"Tensor{idx} details: {tensor}")
49 return 051 return 0
50 finally:52 finally:
51 if stream is not None:53 if stream is not None:
Mexamples/es/operator_overload_async/python/src/make_add_graph_custom_allocator.py+7-5
@@ -78,7 +78,7 @@ def run_with_sample_allocator(sample_graph: Graph, session: Session) -> int:
78 allocator = SamplePoolAllocator()78 allocator = SamplePoolAllocator()
79 session.register_external_allocator(stream, allocator)79 session.register_external_allocator(stream, allocator)
80 allocator_registered = True80 allocator_registered = True
81- print("[Info] SamplePoolAllocator 已注册到 stream")81+ print("[Info] SamplePoolAllocator registered to stream")
82 82 
83 # 3. 创建 Device 输入83 # 3. 创建 Device 输入
84 inputs = create_input_tensors()84 inputs = create_input_tensors()
@@ -86,21 +86,23 @@ def run_with_sample_allocator(sample_graph: Graph, session: Session) -> int:
86 # 4. 添加并异步执行 Graph86 # 4. 添加并异步执行 Graph
87 session.add_graph(GRAPH_ID, sample_graph)87 session.add_graph(GRAPH_ID, sample_graph)
88 graph_added = True88 graph_added = True
89- device_outputs = session.run_graph_with_stream_async(GRAPH_ID, stream, list(inputs))89+ device_outputs = session.run_graph_with_stream_async(
90+ GRAPH_ID, stream, list(inputs)
91+ )
90 92 
91 # 5. 等待 Stream 上的任务完成93 # 5. 等待 Stream 上的任务完成
92 check_ret("acl.rt.synchronize_stream", acl.rt.synchronize_stream(stream))94 check_ret("acl.rt.synchronize_stream", acl.rt.synchronize_stream(stream))
93- print("[Info] 异步执行 Graph 成功！")95+ print("[Info] Asynchronous execution of the graph succeeded.")
94 96 
95 # 6. 将 Device 数据传回 Host 并打印97 # 6. 将 Device 数据传回 Host 并打印
96 host_outputs = [out.to_host() for out in device_outputs]98 host_outputs = [out.to_host() for out in device_outputs]
97 for idx, tensor in enumerate(host_outputs, start=1):99 for idx, tensor in enumerate(host_outputs, start=1):
98- print(f"Tensor{idx} 详情：{tensor}")100+ print(f"Tensor{idx} details: {tensor}")
99 return 0101 return 0
100 finally:102 finally:
101 if allocator_registered:103 if allocator_registered:
102 session.unregister_external_allocator(stream)104 session.unregister_external_allocator(stream)
103- print("[Info] SamplePoolAllocator 已注销")105+ print("[Info] SamplePoolAllocator unregistered")
104 if stream is not None:106 if stream is not None:
105 check_ret("acl.rt.destroy_stream", acl.rt.destroy_stream(stream))107 check_ret("acl.rt.destroy_stream", acl.rt.destroy_stream(stream))
106 if graph_added and session is not None:108 if graph_added and session is not None:
Mexamples/es/optional_input/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeBatchNormGraphByEsAndDump();40 es_showcase::MakeBatchNormGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/optional_input/python/src/make_batchnorm_graph.py+22-14
@@ -30,8 +30,12 @@ def build_batch_norm_graph():
30 format=Format.FORMAT_NCHW,30 format=Format.FORMAT_NCHW,
31 shape=[1, 3, 1, 2],31 shape=[1, 3, 1, 2],
32 )32 )
33- mean = builder.create_input(index=1, name="mean", data_type=DataType.DT_FLOAT, shape=[3])33+ mean = builder.create_input(
34- variance = builder.create_input(index=2, name="variance", data_type=DataType.DT_FLOAT, shape=[3])34+ index=1, name="mean", data_type=DataType.DT_FLOAT, shape=[3]
35+ )
36+ variance = builder.create_input(
37+ index=2, name="variance", data_type=DataType.DT_FLOAT, shape=[3]
38+ )
35 scale = builder.create_const_float([1.0, 1.0, 1.0], shape=[3])39 scale = builder.create_const_float([1.0, 1.0, 1.0], shape=[3])
36 offset = builder.create_const_float([0.0, 0.0, 0.0], shape=[3])40 offset = builder.create_const_float([0.0, 0.0, 0.0], shape=[3])
37 batchNorm_tensor_holder = BatchNorm(41 batchNorm_tensor_holder = BatchNorm(
@@ -61,10 +65,10 @@ def run_graph(graph, device_id="0") -> int:
61 ge_api = GeApi()65 ge_api = GeApi()
62 ret = ge_api.ge_initialize(config)66 ret = ge_api.ge_initialize(config)
63 if ret != 0:67 if ret != 0:
64- print(f"[Error] GE初始化失败，返回码: {ret}")68+ print(f"[Error] GE initialization failed, return code: {ret}")
65 return ret69 return ret
66 70 
67- print(f"[Info] GE环境初始化成功 (Device ID: {device_id})")71+ print(f"[Info] GE environment initialized successfully (Device ID: {device_id})")
68 72 
69 try:73 try:
70 # 2. 创建Session74 # 2. 创建Session
@@ -74,9 +78,9 @@ def run_graph(graph, device_id="0") -> int:
74 graph_id = 178 graph_id = 1
75 ret = session.add_graph(graph_id, graph)79 ret = session.add_graph(graph_id, graph)
76 if ret != 0:80 if ret != 0:
77- print(f"[Error] 添加图失败，返回码: {ret}")81+ print(f"[Error] Failed to add graph, return code: {ret}")
78 return ret82 return ret
79- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")83+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
80 84 
81 # 4. 准备输入数据85 # 4. 准备输入数据
82 input_data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=np.float32)86 input_data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=np.float32)
@@ -90,21 +94,25 @@ def run_graph(graph, device_id="0") -> int:
90 Format.FORMAT_NCHW,94 Format.FORMAT_NCHW,
91 [1, 3, 1, 2],95 [1, 3, 1, 2],
92 )96 )
93- mean_tensor = Tensor(mean_data.tolist(), None, DataType.DT_FLOAT, Format.FORMAT_ND, [3])97+ mean_tensor = Tensor(
94- variance_tensor = Tensor(variance_data.tolist(), None, DataType.DT_FLOAT, Format.FORMAT_ND, [3])98+ mean_data.tolist(), None, DataType.DT_FLOAT, Format.FORMAT_ND, [3]
99+ )
100+ variance_tensor = Tensor(
101+ variance_data.tolist(), None, DataType.DT_FLOAT, Format.FORMAT_ND, [3]
102+ )
95 103 
96 inputs = [input_tensor, mean_tensor, variance_tensor]104 inputs = [input_tensor, mean_tensor, variance_tensor]
97- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor (input, mean, variance)")105+ print(f"[Info] Prepared {len(inputs)} input tensor(s) (input, mean, variance)")
98 106 
99 # 5. 运行图107 # 5. 运行图
100 ret = session.run_graph(graph_id, inputs)108 ret = session.run_graph(graph_id, inputs)
101- print("[Info] 图运行成功！")109+ print("[Info] Graph executed successfully!")
102 for idx, tensor in enumerate(ret, start=1):110 for idx, tensor in enumerate(ret, start=1):
103- print(f"Tensor{idx}详情：{tensor}")111+ print(f"Tensor{idx} details: {tensor}")
104 return 0112 return 0
105 113 
106 except Exception as e:114 except Exception as e:
107- print(f"[Error] 执行过程中出错: {e}")115+ print(f"[Error] Error during execution: {e}")
108 import traceback116 import traceback
109 117 
110 traceback.print_exc()118 traceback.print_exc()
@@ -112,9 +120,9 @@ def run_graph(graph, device_id="0") -> int:
112 120 
113 finally:121 finally:
114 # 6. 清理GE环境122 # 6. 清理GE环境
115- print("[Info] 清理GE环境...")123+ print("[Info] Cleaning up GE environment...")
116 ge_api.ge_finalize()124 ge_api.ge_finalize()
117- print("[Success] GE环境已清理")125+ print("[Success] GE environment cleaned up")
118 126 
119 127 
120if __name__ == "__main__":128if __name__ == "__main__":
Mexamples/es/private_attributes/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeSigmoidAddGraphByEsAndDump();40 es_showcase::MakeSigmoidAddGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/private_attributes/python/src/make_sigmoid_graph.py+13-11
@@ -23,7 +23,9 @@ def build_sigmoid_add_graph():
23 # 1、创建图构建器23 # 1、创建图构建器
24 builder = GraphBuilder("MakeSigmoidAddGraph")24 builder = GraphBuilder("MakeSigmoidAddGraph")
25 # 2、创建图输入节点25 # 2、创建图输入节点
26- input_tensor_holder1 = builder.create_input(index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3])26+ input_tensor_holder1 = builder.create_input(
27+ index=0, name="input", data_type=DataType.DT_FLOAT, shape=[2, 3]
28+ )
27 with attr_scope({"_stream_label": "label_0"}):29 with attr_scope({"_stream_label": "label_0"}):
28 sigmoid0 = Sigmoid(input_tensor_holder1)30 sigmoid0 = Sigmoid(input_tensor_holder1)
29 # 添加私有属性31 # 添加私有属性
@@ -55,10 +57,10 @@ def run_graph(graph, device_id="0") -> int:
55 ge_api = GeApi()57 ge_api = GeApi()
56 ret = ge_api.ge_initialize(config)58 ret = ge_api.ge_initialize(config)
57 if ret != 0:59 if ret != 0:
58- print(f"[Error] GE初始化失败，返回码: {ret}")60+ print(f"[Error] GE initialization failed, return code: {ret}")
59 return ret61 return ret
60 62 
61- print(f"[Info] GE环境初始化成功 (Device ID: {device_id})")63+ print(f"[Info] GE environment initialized successfully (Device ID: {device_id})")
62 64 
63 try:65 try:
64 # 2. 创建Session66 # 2. 创建Session
@@ -68,9 +70,9 @@ def run_graph(graph, device_id="0") -> int:
68 graph_id = 170 graph_id = 1
69 ret = session.add_graph(graph_id, graph)71 ret = session.add_graph(graph_id, graph)
70 if ret != 0:72 if ret != 0:
71- print(f"[Error] 添加图失败，返回码: {ret}")73+ print(f"[Error] Failed to add graph, return code: {ret}")
72 return ret74 return ret
73- print(f"[Info] 图已添加到Session (Graph ID: {graph_id})")75+ print(f"[Info] Graph added to Session (Graph ID: {graph_id})")
74 76 
75 # 4. 准备输入数据77 # 4. 准备输入数据
76 input_data = np.full([2, 3], 1.0, dtype=np.float32)78 input_data = np.full([2, 3], 1.0, dtype=np.float32)
@@ -83,17 +85,17 @@ def run_graph(graph, device_id="0") -> int:
83 )85 )
84 86 
85 inputs = [input_tensor]87 inputs = [input_tensor]
86- print(f"[Info] 输入数据已准备，共{len(inputs)}个输入tensor")88+ print(f"[Info] Prepared {len(inputs)} input tensor(s)")
87 89 
88 # 5. 运行图90 # 5. 运行图
89 ret = session.run_graph(graph_id, inputs)91 ret = session.run_graph(graph_id, inputs)
90- print("[Info] 图运行成功！")92+ print("[Info] Graph executed successfully!")
91 for idx, tensor in enumerate(ret, start=1):93 for idx, tensor in enumerate(ret, start=1):
92- print(f"Tensor{idx}详情：{tensor}")94+ print(f"Tensor{idx} details: {tensor}")
93 return 095 return 0
94 96 
95 except Exception as e:97 except Exception as e:
96- print(f"[Error] 执行过程中出错: {e}")98+ print(f"[Error] Error during execution: {e}")
97 import traceback99 import traceback
98 100 
99 traceback.print_exc()101 traceback.print_exc()
@@ -101,9 +103,9 @@ def run_graph(graph, device_id="0") -> int:
101 103 
102 finally:104 finally:
103 # 6. 清理GE环境105 # 6. 清理GE环境
104- print("[Info] 清理GE环境...")106+ print("[Info] Cleaning up GE environment...")
105 ge_api.ge_finalize()107 ge_api.ge_finalize()
106- print("[Success] GE环境已清理")108+ print("[Success] GE environment cleaned up")
107 109 
108 110 
109if __name__ == "__main__":111if __name__ == "__main__":
Mexamples/es/transformer/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeTransformerGraphByEsAndDump();40 es_showcase::MakeTransformerGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/transformer/python/src/make_transformer_graph.py+19-12
@@ -9,6 +9,7 @@
9# See LICENSE in the root of the software repository for the full text of the License.9# See LICENSE in the root of the software repository for the full text of the License.
10# ----------------------------------------------------------------------------10# ----------------------------------------------------------------------------
11 11 
12+# ruff: noqa: F403, F405
12import numpy as np13import numpy as np
13from ge.es import GraphBuilder14from ge.es import GraphBuilder
14from ge.es.all import *15from ge.es.all import *
@@ -34,7 +35,9 @@ def build_transformer_graph():
34 transpose_x2=False,35 transpose_x2=False,
35 )36 )
36 sigmoid_result1 = Sigmoid(matmul_result1)37 sigmoid_result1 = Sigmoid(matmul_result1)
37- add_result1 = Reshape(sigmoid_result1, [-1, 256]) + Cast(Unsqueeze(input3, axes=[0]), dst_type=DataType.DT_FLOAT)38+ add_result1 = Reshape(sigmoid_result1, [-1, 256]) + Cast(
39+ Unsqueeze(input3, axes=[0]), dst_type=DataType.DT_FLOAT
40+ )
38 41 
39 topkv2_result1 = TopKV2(add_result1, 2)42 topkv2_result1 = TopKV2(add_result1, 2)
40 reducesum_result1 = ReduceSum(topkv2_result1.values, [-1])43 reducesum_result1 = ReduceSum(topkv2_result1.values, [-1])
@@ -49,7 +52,9 @@ def build_transformer_graph():
49 Cast(builder.create_scalar_float(1.0), dst_type=DataType.DT_FLOAT),52 Cast(builder.create_scalar_float(1.0), dst_type=DataType.DT_FLOAT),
50 ),53 ),
51 )54 )
52- identity_result1 = Identity(BroadcastTo(Unsqueeze(scatterelements_result1, axes=[-1]), [256, 256]))55+ identity_result1 = Identity(
56+ BroadcastTo(Unsqueeze(scatterelements_result1, axes=[-1]), [256, 256])
57+ )
53 maskedfill_result1 = MaskedFill(58 maskedfill_result1 = MaskedFill(
54 add_result1,59 add_result1,
55 LogicalNot(60 LogicalNot(
@@ -60,7 +65,9 @@ def build_transformer_graph():
60 ),65 ),
61 builder.create_scalar_float(0.0),66 builder.create_scalar_float(0.0),
62 )67 )
63- cast_result3 = Cast(TopKV2(maskedfill_result1, 4, sorted=False).indices, dst_type=DataType.DT_INT64)68+ cast_result3 = Cast(
69+ TopKV2(maskedfill_result1, 4, sorted=False).indices, dst_type=DataType.DT_INT64
70+ )
64 # 3、设置图输出节点71 # 3、设置图输出节点
65 gatherelements_result1 = GatherElements(sigmoid_result1, cast_result3, dim=1)72 gatherelements_result1 = GatherElements(sigmoid_result1, cast_result3, dim=1)
66 realdiv_result1 = RealDiv(gatherelements_result1, 1e-20)73 realdiv_result1 = RealDiv(gatherelements_result1, 1e-20)
@@ -98,9 +105,9 @@ def run_graph(graph) -> int:
98 ge_api = GeApi()105 ge_api = GeApi()
99 ret = ge_api.ge_initialize(config)106 ret = ge_api.ge_initialize(config)
100 if ret != 0:107 if ret != 0:
101- print(f"GE初始化失败，返回码: {ret}")108+ print(f"GE initialization failed, return code: {ret}")
102 return ret109 return ret
103- print("GE环境初始化成功 (Device ID: 0)")110+ print("GE environment initialized successfully (Device ID: 0)")
104 111 
105 try:112 try:
106 # 2. 创建Session113 # 2. 创建Session
@@ -110,9 +117,9 @@ def run_graph(graph) -> int:
110 graph_id = 1117 graph_id = 1
111 ret = session.add_graph(graph_id, graph)118 ret = session.add_graph(graph_id, graph)
112 if ret != 0:119 if ret != 0:
113- print(f"添加图失败，返回码: {ret}")120+ print(f"Failed to add graph, return code: {ret}")
114 return ret121 return ret
115- print(f"图已添加到Session (Graph ID: {graph_id})")122+ print(f"Graph added to Session (Graph ID: {graph_id})")
116 123 
117 # 4. 准备输入数据124 # 4. 准备输入数据
118 input1_data = np.random.randn(32, 8, 7168).astype(np.float32)125 input1_data = np.random.randn(32, 8, 7168).astype(np.float32)
@@ -145,13 +152,13 @@ def run_graph(graph) -> int:
145 inputs = [tensor1, tensor2, tensor3]152 inputs = [tensor1, tensor2, tensor3]
146 # 5. 运行图153 # 5. 运行图
147 ret = session.run_graph(graph_id, inputs)154 ret = session.run_graph(graph_id, inputs)
148- print("[Info] 图运行成功！")155+ print("[Info] Graph executed successfully!")
149 for idx, tensor in enumerate(ret, start=1):156 for idx, tensor in enumerate(ret, start=1):
150- print(f"Tensor{idx}详情：{tensor}")157+ print(f"Tensor{idx} details: {tensor}")
151 return 0158 return 0
152 159 
153 except Exception as e:160 except Exception as e:
154- print(f"[Error] 执行过程中出错: {e}")161+ print(f"[Error] Error during execution: {e}")
155 import traceback162 import traceback
156 163 
157 traceback.print_exc()164 traceback.print_exc()
@@ -159,9 +166,9 @@ def run_graph(graph) -> int:
159 166 
160 finally:167 finally:
161 # 6. 清理GE环境168 # 6. 清理GE环境
162- print("[Info] 清理GE环境...")169+ print("[Info] Cleaning up GE environment...")
163 ge_api.ge_finalize()170 ge_api.ge_finalize()
164- print("[Success] GE环境已清理")171+ print("[Success] GE environment cleaned up")
165 172 
166 173 
167graph = build_transformer_graph()174graph = build_transformer_graph()
Mexamples/es/transformer_nz/cpp/main.cpp+3-3
@@ -26,7 +26,7 @@ int main(int argc, char **argv) {
26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};26 std::map<ge::AscendString, ge::AscendString> config = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "0"}};
27 auto ret = ge::GEInitialize(config);27 auto ret = ge::GEInitialize(config);
28 if (ret != ge::SUCCESS) {28 if (ret != ge::SUCCESS) {
29- std::cerr << "GE 初始化失败\n";29+ std::cerr << "GE initialization failed\n";
30 return -1;30 return -1;
31 }31 }
32 int result = -1;32 int result = -1;
@@ -34,13 +34,13 @@ int main(int argc, char **argv) {
34 result = 0;34 result = 0;
35 }35 }
36 ge::GEFinalize();36 ge::GEFinalize();
37- std::cout << "执行结束" << std::endl;37+ std::cout << "Execution completed" << std::endl;
38 return result;38 return result;
39 } else if (command == "dump") {39 } else if (command == "dump") {
40 es_showcase::MakeTransformerNzGraphByEsAndDump();40 es_showcase::MakeTransformerNzGraphByEsAndDump();
41 return 0;41 return 0;
42 } else {42 } else {
43- std::cout << "错误: 未知命令 '" << command << "'" << std::endl;43+ std::cout << "Error: unknown command '" << command << "'" << std::endl;
44 return -1;44 return -1;
45 }45 }
46}46}
Mexamples/es/transformer_nz/python/src/make_transformer_nz_graph.py+22-13
@@ -9,6 +9,7 @@
9# See LICENSE in the root of the software repository for the full text of the License.9# See LICENSE in the root of the software repository for the full text of the License.
10# ----------------------------------------------------------------------------10# ----------------------------------------------------------------------------
11 11 
12+# ruff: noqa: F403, F405
12import numpy as np13import numpy as np
13from ge.es import GraphBuilder14from ge.es import GraphBuilder
14from ge.es.all import *15from ge.es.all import *
@@ -58,7 +59,9 @@ def build_transformer_nz_graph():
58 transpose_x2=False,59 transpose_x2=False,
59 )60 )
60 sigmoid_result1 = Sigmoid(matmul_result1)61 sigmoid_result1 = Sigmoid(matmul_result1)
61- add_result1 = Reshape(sigmoid_result1, [-1, 256]) + Cast(input3, dst_type=DataType.DT_FLOAT)62+ add_result1 = Reshape(sigmoid_result1, [-1, 256]) + Cast(
63+ input3, dst_type=DataType.DT_FLOAT
64+ )
62 65 
63 topkv2_result1 = TopKV2(add_result1, 2)66 topkv2_result1 = TopKV2(add_result1, 2)
64 reducesum_result1 = ReduceSum(topkv2_result1.values, [-1])67 reducesum_result1 = ReduceSum(topkv2_result1.values, [-1])
@@ -73,7 +76,9 @@ def build_transformer_nz_graph():
73 Cast(builder.create_scalar_float(1.0), dst_type=DataType.DT_FLOAT),76 Cast(builder.create_scalar_float(1.0), dst_type=DataType.DT_FLOAT),
74 ),77 ),
75 )78 )
76- identity_result1 = Identity(BroadcastTo(Unsqueeze(scatterelements_result1, axes=[-1]), [256, 256]))79+ identity_result1 = Identity(
80+ BroadcastTo(Unsqueeze(scatterelements_result1, axes=[-1]), [256, 256])
81+ )
77 maskedfill_result1 = MaskedFill(82 maskedfill_result1 = MaskedFill(
78 add_result1,83 add_result1,
79 LogicalNot(84 LogicalNot(
@@ -84,11 +89,15 @@ def build_transformer_nz_graph():
84 ),89 ),
85 builder.create_scalar_float(0.0),90 builder.create_scalar_float(0.0),
86 )91 )
87- cast_result3 = Cast(TopKV2(maskedfill_result1, 4, sorted=False).indices, dst_type=DataType.DT_INT64)92+ cast_result3 = Cast(
93+ TopKV2(maskedfill_result1, 4, sorted=False).indices, dst_type=DataType.DT_INT64
94+ )
88 # 3、设置图输出节点95 # 3、设置图输出节点
89 gatherelements_result1 = GatherElements(sigmoid_result1, cast_result3, dim=1)96 gatherelements_result1 = GatherElements(sigmoid_result1, cast_result3, dim=1)
90 realdiv_result1 = RealDiv(gatherelements_result1, 1e-20)97 realdiv_result1 = RealDiv(gatherelements_result1, 1e-20)
91- output = Cast(realdiv_result1 * builder.create_scalar_float(2.5), dst_type=DataType.DT_FLOAT)98+ output = Cast(
99+ realdiv_result1 * builder.create_scalar_float(2.5), dst_type=DataType.DT_FLOAT
100+ )
92 # 4、构建图101 # 4、构建图
93 return builder.build_and_reset([cast_result3, output])102 return builder.build_and_reset([cast_result3, output])
94 103 
@@ -115,9 +124,9 @@ def run_graph(graph) -> int:
115 ge_api = GeApi()124 ge_api = GeApi()
116 ret = ge_api.ge_initialize(config)125 ret = ge_api.ge_initialize(config)
117 if ret != 0:126 if ret != 0:
118- print(f"GE初始化失败，返回码: {ret}")127+ print(f"GE initialization failed, return code: {ret}")
119 return ret128 return ret
120- print("GE环境初始化成功 (Device ID: 0)")129+ print("GE environment initialized successfully (Device ID: 0)")
121 130 
122 try:131 try:
123 # 2. 创建Session132 # 2. 创建Session
@@ -127,9 +136,9 @@ def run_graph(graph) -> int:
127 graph_id = 1136 graph_id = 1
128 ret = session.add_graph(graph_id, graph)137 ret = session.add_graph(graph_id, graph)
129 if ret != 0:138 if ret != 0:
130- print(f"添加图失败，返回码: {ret}")139+ print(f"Failed to add graph, return code: {ret}")
131 return ret140 return ret
132- print(f"图已添加到Session (Graph ID: {graph_id})")141+ print(f"Graph added to Session (Graph ID: {graph_id})")
133 142 
134 # 4. 准备输入数据143 # 4. 准备输入数据
135 input1_data = np.random.randn(256, 7168).astype(np.float32)144 input1_data = np.random.randn(256, 7168).astype(np.float32)
@@ -144,13 +153,13 @@ def run_graph(graph) -> int:
144 inputs = [tensor1, tensor2, tensor3]153 inputs = [tensor1, tensor2, tensor3]
145 # 5. 运行图154 # 5. 运行图
146 ret = session.run_graph(graph_id, inputs)155 ret = session.run_graph(graph_id, inputs)
147- print("[Info] 图运行成功！")156+ print("[Info] Graph executed successfully!")
148 for idx, tensor in enumerate(ret, start=1):157 for idx, tensor in enumerate(ret, start=1):
149- print(f"Tensor{idx}详情：{tensor}")158+ print(f"Tensor{idx} details: {tensor}")
150 return 0159 return 0
151 160 
152 except Exception as e:161 except Exception as e:
153- print(f"[Error] 执行过程中出错: {e}")162+ print(f"[Error] Error during execution: {e}")
154 import traceback163 import traceback
155 164 
156 traceback.print_exc()165 traceback.print_exc()
@@ -158,9 +167,9 @@ def run_graph(graph) -> int:
158 167 
159 finally:168 finally:
160 # 6. 清理GE环境169 # 6. 清理GE环境
161- print("[Info] 清理GE环境...")170+ print("[Info] Cleaning up GE environment...")
162 ge_api.ge_finalize()171 ge_api.ge_finalize()
163- print("[Success] GE环境已清理")172+ print("[Success] GE environment cleaned up")
164 173 
165 174 
166graph = build_transformer_nz_graph()175graph = build_transformer_nz_graph()
Mexamples/fusion_pass/graph_base_pass/2_move_relu_before_concat_pass/python/src/python_move_relu_before_concat_pass.py+8-6
@@ -41,13 +41,15 @@ except ImportError:
41def _require_es_apis() -> None:41def _require_es_apis() -> None:
42 if ConcatV2 is None:42 if ConcatV2 is None:
43 raise RuntimeError(43 raise RuntimeError(
44- "未找到 ge.es.math.ConcatV2。请先 source CANN 环境；如仍缺失，请参考 README 的"44+ "ge.es.math.ConcatV2 not found. Please source the CANN environment first; "
45- "“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"45+ "if still missing, refer to the README section 'Handling Missing ES APIs (Optional)' "
46+ "to generate and load es_all, then run again."
46 )47 )
47 if Relu is None:48 if Relu is None:
48 raise RuntimeError(49 raise RuntimeError(
49- "未找到 ge.es.nn.Relu。请先 source CANN 环境；如仍缺失，请参考 README 的"50+ "ge.es.nn.Relu not found. Please source the CANN environment first; "
50- "“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"51+ "if still missing, refer to the README section 'Handling Missing ES APIs (Optional)' "
52+ "to generate and load es_all, then run again."
51 )53 )
52 54 
53 55 
@@ -128,8 +130,8 @@ class PythonMoveReluBeforeConcatPass(FusionBasePass):
128 130 
129 131 
130if __name__ == "__main__":132if __name__ == "__main__":
131- print("PythonMoveReluBeforeConcatPass 已注册。")133+ print("PythonMoveReluBeforeConcatPass registered.")
132- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")134+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
133 print(135 print(
134 " export ASCEND_GE_PY_PASS_PATH=$PWD/python/src/python_move_relu_before_concat_pass.py"136 " export ASCEND_GE_PY_PASS_PATH=$PWD/python/src/python_move_relu_before_concat_pass.py"
135 )137 )
Mexamples/fusion_pass/graph_base_pass/3_modify_conv_data_format_pass/python/src/python_modify_conv_data_format_pass.py+2-2
@@ -199,8 +199,8 @@ class PythonConvTransFormatPass(FusionBasePass):
199 199 
200 200 
201if __name__ == "__main__":201if __name__ == "__main__":
202- print("PythonConvTransFormatPass 已注册。")202+ print("PythonConvTransFormatPass registered.")
203- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")203+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
204 print(204 print(
205 " export ASCEND_GE_PY_PASS_PATH=$PWD/python/src/python_modify_conv_data_format_pass.py"205 " export ASCEND_GE_PY_PASS_PATH=$PWD/python/src/python_modify_conv_data_format_pass.py"
206 )206 )
Mexamples/fusion_pass/pattern_base_pass/1_fuse_matmul_add_pass/python/src/python_fuse_matmul_add_pass.py+8-5
@@ -50,13 +50,16 @@ def _require_es_apis() -> None:
50 missing = [name for name, obj in pairs if obj is None]50 missing = [name for name, obj in pairs if obj is None]
51 if missing:51 if missing:
52 raise RuntimeError(52 raise RuntimeError(
53- "未找到 ES API: "53+ "ES API not found: "
54 + ", ".join(missing)54 + ", ".join(missing)
55- + "。请先 source CANN 环境；如仍缺失，请参考 README 的“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"55+ + ". Please source the CANN environment first; if still missing, refer to the README "
56+ + "section 'Handling Missing ES APIs (Optional)' to generate and load es_all, then run again."
56 )57 )
57 58 
58 59 
59-@register_fusion_pass(name="PythonFuseMatMulAndAddPass", stage=PassStage.BEFORE_INFER_SHAPE)60+@register_fusion_pass(
61+ name="PythonFuseMatMulAndAddPass", stage=PassStage.BEFORE_INFER_SHAPE
62+)
60class PythonFuseMatMulAndAddPass(PatternFusionPass):63class PythonFuseMatMulAndAddPass(PatternFusionPass):
61 @pattern64 @pattern
62 def matmul_add(self, inputs):65 def matmul_add(self, inputs):
@@ -83,6 +86,6 @@ class PythonFuseMatMulAndAddPass(PatternFusionPass):
83 86 
84 87 
85if __name__ == "__main__":88if __name__ == "__main__":
86- print("PythonFuseMatMulAndAddPass 已注册。")89+ print("PythonFuseMatMulAndAddPass registered.")
87- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")90+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
88 print(" export ASCEND_GE_PY_PASS_PATH=$(pwd)/src/python_fuse_matmul_add_pass.py")91 print(" export ASCEND_GE_PY_PASS_PATH=$(pwd)/src/python_fuse_matmul_add_pass.py")
Mexamples/fusion_pass/pattern_base_pass/2_fuse_matmul_add_pass_with_capture_tensor/python/src/python_fuse_matmul_add_pass.py+8-5
@@ -53,9 +53,10 @@ def _require_es_apis() -> None:
53 missing = [name for name, obj in pairs if obj is None]53 missing = [name for name, obj in pairs if obj is None]
54 if missing:54 if missing:
55 raise RuntimeError(55 raise RuntimeError(
56- "未找到 ES API: "56+ "ES API not found: "
57 + ", ".join(missing)57 + ", ".join(missing)
58- + "。请先 source CANN 环境；如仍缺失，请参考 README 的“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"58+ + ". Please source the CANN environment first; if still missing, refer to the README "
59+ + "section 'Handling Missing ES APIs (Optional)' to generate and load es_all, then run again."
59 )60 )
60 61 
61 62 
@@ -82,7 +83,9 @@ class PythonFuseMatMulAndAddCaptureTensorPass(PatternFusionPass):
82 return [pat0, pat1]83 return [pat0, pat1]
83 84 
84 def meet_requirements(self, match_result):85 def meet_requirements(self, match_result):
85- print("Define MeetRequirements for FuseMatMulAndAddPass in capture tensor sample")86+ print(
87+ "Define MeetRequirements for FuseMatMulAndAddPass in capture tensor sample"
88+ )
86 add_io = match_result.get_captured_tensor(_K_ADD_CAPTURE_IDX)89 add_io = match_result.get_captured_tensor(_K_ADD_CAPTURE_IDX)
87 add_node = add_io.node90 add_node = add_io.node
88 add_input0_dt = add_node.get_input_desc(0).get_data_type()91 add_input0_dt = add_node.get_input_desc(0).get_data_type()
@@ -127,6 +130,6 @@ class PythonFuseMatMulAndAddCaptureTensorPass(PatternFusionPass):
127 130 
128 131 
129if __name__ == "__main__":132if __name__ == "__main__":
130- print("PythonFuseMatMulAndAddCaptureTensorPass 已注册。")133+ print("PythonFuseMatMulAndAddCaptureTensorPass registered.")
131- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")134+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
132 print(" export ASCEND_GE_PY_PASS_PATH=$(pwd)/src/python_fuse_matmul_add_pass.py")135 print(" export ASCEND_GE_PY_PASS_PATH=$(pwd)/src/python_fuse_matmul_add_pass.py")
Mexamples/fusion_pass/pattern_base_pass/3_fuse_matmul_add_pass_with_pattern_matcher_config/cpp/data/es_forward_1.py+13-10
@@ -10,6 +10,7 @@
10# See LICENSE in the root of the software repository for the full text of the License.10# See LICENSE in the root of the software repository for the full text of the License.
11# ----------------------------------------------------------------------------11# ----------------------------------------------------------------------------
12 12 
13+# ruff: noqa: F403, F405
13import numpy as np14import numpy as np
14from ge.es import GraphBuilder15from ge.es import GraphBuilder
15from ge.es.all import *16from ge.es.all import *
@@ -32,7 +33,9 @@ def build_matmul_add_graph():
32 Format.FORMAT_ND,33 Format.FORMAT_ND,
33 [2, 2],34 [2, 2],
34 )35 )
35- matmul_tensor_holder = MatMul(input1, input2, None, transpose_x1=False, transpose_x2=False)36+ matmul_tensor_holder = MatMul(
37+ input1, input2, None, transpose_x1=False, transpose_x2=False
38+ )
36 add_tensor_holder = Add(matmul_tensor_holder, Const(builder, value=input3))39 add_tensor_holder = Add(matmul_tensor_holder, Const(builder, value=input3))
37 # 3、设置图输出节点40 # 3、设置图输出节点
38 builder.set_graph_output(add_tensor_holder, 0)41 builder.set_graph_output(add_tensor_holder, 0)
@@ -61,9 +64,9 @@ def run_matmul_add_graph(graph) -> int:
61 ge_api = GeApi()64 ge_api = GeApi()
62 ret = ge_api.ge_initialize(config)65 ret = ge_api.ge_initialize(config)
63 if ret != 0:66 if ret != 0:
64- print(f"GE初始化失败，返回码: {ret}")67+ print(f"GE initialization failed, return code: {ret}")
65 return ret68 return ret
66- print("GE环境初始化成功 (Device ID: 0)")69+ print("GE environment initialized successfully (Device ID: 0)")
67 70 
68 try:71 try:
69 # 2. 创建Session72 # 2. 创建Session
@@ -73,9 +76,9 @@ def run_matmul_add_graph(graph) -> int:
73 graph_id = 176 graph_id = 1
74 ret = session.add_graph(graph_id, graph)77 ret = session.add_graph(graph_id, graph)
75 if ret != 0:78 if ret != 0:
76- print(f"添加图失败，返回码: {ret}")79+ print(f"Failed to add graph, return code: {ret}")
77 return ret80 return ret
78- print(f"图已添加到Session (Graph ID: {graph_id})")81+ print(f"Graph added to Session (Graph ID: {graph_id})")
79 82 
80 # 4. 准备输入数据83 # 4. 准备输入数据
81 input1_data = np.random.randn(2, 3).astype(np.float32)84 input1_data = np.random.randn(2, 3).astype(np.float32)
@@ -101,13 +104,13 @@ def run_matmul_add_graph(graph) -> int:
101 # 5. 运行图104 # 5. 运行图
102 ret = session.run_graph(graph_id, inputs)105 ret = session.run_graph(graph_id, inputs)
103 if not isinstance(ret, list):106 if not isinstance(ret, list):
104- print(f"运行图失败，返回码: {ret}")107+ print(f"Failed to run graph, return code: {ret}")
105 return ret108 return ret
106- print("图运行成功！")109+ print("Graph ran successfully!")
107 return 0110 return 0
108 111 
109 except Exception as e:112 except Exception as e:
110- print(f"[Error] 执行过程中出错: {e}")113+ print(f"[Error] Error during execution: {e}")
111 import traceback114 import traceback
112 115 
113 traceback.print_exc()116 traceback.print_exc()
@@ -115,9 +118,9 @@ def run_matmul_add_graph(graph) -> int:
115 118 
116 finally:119 finally:
117 # 6. 清理GE环境120 # 6. 清理GE环境
118- print("[Info] 清理GE环境...")121+ print("[Info] Cleaning up GE environment...")
119 ge_api.ge_finalize()122 ge_api.ge_finalize()
120- print("[Success] GE环境已清理")123+ print("[Success] GE environment cleaned up")
121 124 
122 125 
123origin_graph = build_matmul_add_graph()126origin_graph = build_matmul_add_graph()
Mexamples/fusion_pass/pattern_base_pass/3_fuse_matmul_add_pass_with_pattern_matcher_config/cpp/data/es_forward_2.py+10-9
@@ -10,6 +10,7 @@
10# See LICENSE in the root of the software repository for the full text of the License.10# See LICENSE in the root of the software repository for the full text of the License.
11# ----------------------------------------------------------------------------11# ----------------------------------------------------------------------------
12 12 
13+# ruff: noqa: F403, F405
13import numpy as np14import numpy as np
14from ge.es import GraphBuilder15from ge.es import GraphBuilder
15from ge.es.all import *16from ge.es.all import *
@@ -67,9 +68,9 @@ def run_matmul_add_graph(graph) -> int:
67 ge_api = GeApi()68 ge_api = GeApi()
68 ret = ge_api.ge_initialize(config)69 ret = ge_api.ge_initialize(config)
69 if ret != 0:70 if ret != 0:
70- print(f"GE初始化失败，返回码: {ret}")71+ print(f"GE initialization failed, return code: {ret}")
71 return ret72 return ret
72- print("GE环境初始化成功 (Device ID: 0)")73+ print("GE environment initialized successfully (Device ID: 0)")
73 74 
74 try:75 try:
75 # 2. 创建Session76 # 2. 创建Session
@@ -79,9 +80,9 @@ def run_matmul_add_graph(graph) -> int:
79 graph_id = 180 graph_id = 1
80 ret = session.add_graph(graph_id, graph)81 ret = session.add_graph(graph_id, graph)
81 if ret != 0:82 if ret != 0:
82- print(f"添加图失败，返回码: {ret}")83+ print(f"Failed to add graph, return code: {ret}")
83 return ret84 return ret
84- print(f"图已添加到Session (Graph ID: {graph_id})")85+ print(f"Graph added to Session (Graph ID: {graph_id})")
85 86 
86 # 4. 准备输入数据87 # 4. 准备输入数据
87 input1_data = np.random.randn(3, 2).astype(np.float32)88 input1_data = np.random.randn(3, 2).astype(np.float32)
@@ -107,13 +108,13 @@ def run_matmul_add_graph(graph) -> int:
107 # 5. 运行图108 # 5. 运行图
108 ret = session.run_graph(graph_id, inputs)109 ret = session.run_graph(graph_id, inputs)
109 if not isinstance(ret, list):110 if not isinstance(ret, list):
110- print(f"运行图失败，返回码: {ret}")111+ print(f"Failed to run graph, return code: {ret}")
111 return ret112 return ret
112- print("图运行成功！")113+ print("Graph ran successfully!")
113 return 0114 return 0
114 115 
115 except Exception as e:116 except Exception as e:
116- print(f"[Error] 执行过程中出错: {e}")117+ print(f"[Error] Error during execution: {e}")
117 import traceback118 import traceback
118 119 
119 traceback.print_exc()120 traceback.print_exc()
@@ -121,9 +122,9 @@ def run_matmul_add_graph(graph) -> int:
121 122 
122 finally:123 finally:
123 # 6. 清理GE环境124 # 6. 清理GE环境
124- print("[Info] 清理GE环境...")125+ print("[Info] Cleaning up GE environment...")
125 ge_api.ge_finalize()126 ge_api.ge_finalize()
126- print("[Success] GE环境已清理")127+ print("[Success] GE environment cleaned up")
127 128 
128 129 
129origin_graph = build_matmul_add_graph()130origin_graph = build_matmul_add_graph()
Mexamples/fusion_pass/pattern_base_pass/3_fuse_matmul_add_pass_with_pattern_matcher_config/cpp/data/es_forward_3.py+13-10
@@ -10,6 +10,7 @@
10# See LICENSE in the root of the software repository for the full text of the License.10# See LICENSE in the root of the software repository for the full text of the License.
11# ----------------------------------------------------------------------------11# ----------------------------------------------------------------------------
12 12 
13+# ruff: noqa: F403, F405
13import numpy as np14import numpy as np
14from ge.es import GraphBuilder15from ge.es import GraphBuilder
15from ge.es.all import *16from ge.es.all import *
@@ -33,7 +34,9 @@ def build_matmul_add_graph():
33 Format.FORMAT_ND,34 Format.FORMAT_ND,
34 [2, 2],35 [2, 2],
35 )36 )
36- matmul_tensor_holder = MatMul(input1, input2, None, transpose_x1=False, transpose_x2=False)37+ matmul_tensor_holder = MatMul(
38+ input1, input2, None, transpose_x1=False, transpose_x2=False
39+ )
37 add_tensor_holder = Add(matmul_tensor_holder, Const(builder, value=input3))40 add_tensor_holder = Add(matmul_tensor_holder, Const(builder, value=input3))
38 # 3、设置图输出节点41 # 3、设置图输出节点
39 builder.set_graph_output(add_tensor_holder, 0)42 builder.set_graph_output(add_tensor_holder, 0)
@@ -62,9 +65,9 @@ def run_matmul_add_graph(graph) -> int:
62 ge_api = GeApi()65 ge_api = GeApi()
63 ret = ge_api.ge_initialize(config)66 ret = ge_api.ge_initialize(config)
64 if ret != 0:67 if ret != 0:
65- print(f"GE初始化失败，返回码: {ret}")68+ print(f"GE initialization failed, return code: {ret}")
66 return ret69 return ret
67- print("GE环境初始化成功 (Device ID: 0)")70+ print("GE environment initialized successfully (Device ID: 0)")
68 71 
69 try:72 try:
70 # 2. 创建Session73 # 2. 创建Session
@@ -74,9 +77,9 @@ def run_matmul_add_graph(graph) -> int:
74 graph_id = 177 graph_id = 1
75 ret = session.add_graph(graph_id, graph)78 ret = session.add_graph(graph_id, graph)
76 if ret != 0:79 if ret != 0:
77- print(f"添加图失败，返回码: {ret}")80+ print(f"Failed to add graph, return code: {ret}")
78 return ret81 return ret
79- print(f"图已添加到Session (Graph ID: {graph_id})")82+ print(f"Graph added to Session (Graph ID: {graph_id})")
80 83 
81 # 4. 准备输入数据84 # 4. 准备输入数据
82 input1_data = np.random.randn(2, 3).astype(np.float32)85 input1_data = np.random.randn(2, 3).astype(np.float32)
@@ -102,13 +105,13 @@ def run_matmul_add_graph(graph) -> int:
102 # 5. 运行图105 # 5. 运行图
103 ret = session.run_graph(graph_id, inputs)106 ret = session.run_graph(graph_id, inputs)
104 if not isinstance(ret, list):107 if not isinstance(ret, list):
105- print(f"运行图失败，返回码: {ret}")108+ print(f"Failed to run graph, return code: {ret}")
106 return ret109 return ret
107- print("图运行成功！")110+ print("Graph ran successfully!")
108 return 0111 return 0
109 112 
110 except Exception as e:113 except Exception as e:
111- print(f"[Error] 执行过程中出错: {e}")114+ print(f"[Error] Error during execution: {e}")
112 import traceback115 import traceback
113 116 
114 traceback.print_exc()117 traceback.print_exc()
@@ -116,9 +119,9 @@ def run_matmul_add_graph(graph) -> int:
116 119 
117 finally:120 finally:
118 # 6. 清理GE环境121 # 6. 清理GE环境
119- print("[Info] 清理GE环境...")122+ print("[Info] Cleaning up GE environment...")
120 ge_api.ge_finalize()123 ge_api.ge_finalize()
121- print("[Success] GE环境已清理")124+ print("[Success] GE environment cleaned up")
122 125 
123 126 
124origin_graph = build_matmul_add_graph()127origin_graph = build_matmul_add_graph()
Mexamples/fusion_pass/pattern_base_pass/3_fuse_matmul_add_pass_with_pattern_matcher_config/python/src/python_fuse_matmul_add_pass.py+14-6
@@ -52,16 +52,24 @@ def _require_es_apis() -> None:
52 missing = [name for name, obj in pairs if obj is None]52 missing = [name for name, obj in pairs if obj is None]
53 if missing:53 if missing:
54 raise RuntimeError(54 raise RuntimeError(
55- "未找到 ES API: "55+ "ES API not found: "
56 + ", ".join(missing)56 + ", ".join(missing)
57- + "。请先 source CANN 环境；如仍缺失，请参考 README 的“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"57+ + ". Please source the CANN environment first; if still missing, refer to the README "
58+ + "section 'Handling Missing ES APIs (Optional)' to generate and load es_all, then run again."
58 )59 )
59 60 
60 61 
61-@register_fusion_pass(name="PythonMatmulAddFusionPass", stage=PassStage.BEFORE_INFER_SHAPE)62+@register_fusion_pass(
63+ name="PythonMatmulAddFusionPass", stage=PassStage.BEFORE_INFER_SHAPE
64+)
62class PythonMatmulAddFusionPass(PatternFusionPass):65class PythonMatmulAddFusionPass(PatternFusionPass):
63 def __init__(self):66 def __init__(self):
64- super().__init__(PatternMatcherConfigBuilder().enable_const_value_match().enable_ir_attr_match().build())67+ super().__init__(
68+ PatternMatcherConfigBuilder()
69+ .enable_const_value_match()
70+ .enable_ir_attr_match()
71+ .build()
72+ )
65 73 
66 @pattern74 @pattern
67 def matmul_add(self, inputs):75 def matmul_add(self, inputs):
@@ -89,6 +97,6 @@ class PythonMatmulAddFusionPass(PatternFusionPass):
89 97 
90 98 
91if __name__ == "__main__":99if __name__ == "__main__":
92- print("PythonMatmulAddFusionPass 已注册。")100+ print("PythonMatmulAddFusionPass registered.")
93- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")101+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
94 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_fuse_matmul_add_pass.py")102 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_fuse_matmul_add_pass.py")
Mexamples/fusion_pass/pattern_base_pass/4_add_zero_pass/python/src/python_add_zero_pass.py+2-2
@@ -73,6 +73,6 @@ class PythonAddZeroPass(PatternFusionPass):
73 73 
74 74 
75if __name__ == "__main__":75if __name__ == "__main__":
76- print("PythonAddZeroPass 已注册。")76+ print("PythonAddZeroPass registered.")
77- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")77+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
78 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_add_zero_pass.py")78 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_add_zero_pass.py")
Mexamples/fusion_pass/pattern_base_pass/4_add_zero_pass/python/src/python_add_zero_pass_const_value_match.py+11-5
@@ -21,12 +21,16 @@ from ge.passes import (
21)21)
22 22 
23 23 
24-@register_fusion_pass(name="PythonAddZeroConstValueMatchPass", stage=PassStage.BEFORE_INFER_SHAPE)24+@register_fusion_pass(
25+ name="PythonAddZeroConstValueMatchPass", stage=PassStage.BEFORE_INFER_SHAPE
26+)
25class PythonAddZeroConstValueMatchPass(PatternFusionPass):27class PythonAddZeroConstValueMatchPass(PatternFusionPass):
26 """Recognize Add(x, 0.0f) with strict const-value-match and replace it with x."""28 """Recognize Add(x, 0.0f) with strict const-value-match and replace it with x."""
27 29 
28 def __init__(self):30 def __init__(self):
29- super().__init__(PatternMatcherConfigBuilder().enable_const_value_match().build())31+ super().__init__(
32+ PatternMatcherConfigBuilder().enable_const_value_match().build()
33+ )
30 34 
31 @pattern35 @pattern
32 def add_zero(self, inputs):36 def add_zero(self, inputs):
@@ -45,6 +49,8 @@ class PythonAddZeroConstValueMatchPass(PatternFusionPass):
45 49 
46 50 
47if __name__ == "__main__":51if __name__ == "__main__":
48- print("PythonAddZeroConstValueMatchPass 已注册。")52+ print("PythonAddZeroConstValueMatchPass registered.")
49- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")53+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
50- print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_add_zero_pass_const_value_match.py")54+ print(
55+ " export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_add_zero_pass_const_value_match.py"
56+ )
Mexamples/fusion_pass/pattern_base_pass/5_add_zero_pass_in_custom_op/python/src/python_addcustom_zero_pass.py+12-8
@@ -42,8 +42,8 @@ except ImportError:
42def _require_es_custom_apis() -> None:42def _require_es_custom_apis() -> None:
43 if AddCustom is None:43 if AddCustom is None:
44 raise RuntimeError(44 raise RuntimeError(
45- "未找到 ge.es.custom.AddCustom。请先参考 README 的使用方式生成 es_custom Python ES 包，"45+ "ge.es.custom.AddCustom not found. Please generate the es_custom Python ES package "
46- "设置 PYTHONPATH 与 LD_LIBRARY_PATH 后重新执行。"46+ "as described in the README, set PYTHONPATH and LD_LIBRARY_PATH, then run again."
47 )47 )
48 48 
49 49 
@@ -51,11 +51,13 @@ def _require_es_apis() -> None:
51 _require_es_custom_apis()51 _require_es_custom_apis()
52 if Identity is None:52 if Identity is None:
53 raise RuntimeError(53 raise RuntimeError(
54- "未找到 ge.es.math.Identity。请先 source CANN 环境；如仍缺失，请参考 README 的“ES API 说明”后重新执行。"54+ "ge.es.math.Identity not found. Please source the CANN environment first; "
55+ "if still missing, refer to the README section 'ES API Description' and run again."
55 )56 )
56 if TensorMove is None:57 if TensorMove is None:
57 raise RuntimeError(58 raise RuntimeError(
58- "未找到 ge.es.math.TensorMove。请先 source CANN 环境；如仍缺失，请参考 README 的“ES API 说明”后重新执行。"59+ "ge.es.math.TensorMove not found. Please source the CANN environment first; "
60+ "if still missing, refer to the README section 'ES API Description' and run again."
59 )61 )
60 62 
61 63 
@@ -83,7 +85,9 @@ def _is_tensor_value_equal_to_zero(tensor: Tensor):
83 return False85 return False
84 86 
85 87 
86-@register_fusion_pass(name="PythonAddCustomZeroPass", stage=PassStage.BEFORE_INFER_SHAPE)88+@register_fusion_pass(
89+ name="PythonAddCustomZeroPass", stage=PassStage.BEFORE_INFER_SHAPE
90+)
87class PythonAddCustomZeroPass(PatternFusionPass):91class PythonAddCustomZeroPass(PatternFusionPass):
88 """Recognize AddCustom(AddCustom(x, zero), y) and replace it with AddCustom(x, y)."""92 """Recognize AddCustom(AddCustom(x, zero), y) and replace it with AddCustom(x, y)."""
89 93 
@@ -132,7 +136,7 @@ class PythonAddCustomZeroPass(PatternFusionPass):
132 136 
133 137 
134if __name__ == "__main__":138if __name__ == "__main__":
135- print("PythonAddCustomZeroPass 已注册。")139+ print("PythonAddCustomZeroPass registered.")
136- print("运行前请先生成 es_custom，并设置 PYTHONPATH 与 LD_LIBRARY_PATH。")140+ print("Generate es_custom and set PYTHONPATH and LD_LIBRARY_PATH before running.")
137- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")141+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
138 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_addcustom_zero_pass.py")142 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_addcustom_zero_pass.py")
Mexamples/fusion_pass/pattern_base_pass/6_decompose_grouped_conv_to_splited_pass/python/src/python_decompose_pass.py+11-8
@@ -47,18 +47,21 @@ except ImportError:
47def _require_es_apis() -> None:47def _require_es_apis() -> None:
48 if Split is None:48 if Split is None:
49 raise RuntimeError(49 raise RuntimeError(
50- "未找到 ge.es.math.Split。请先 source CANN 环境；如仍缺失，请参考 README 的"50+ "ge.es.math.Split not found. Please source the CANN environment first; "
51- "“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"51+ "if still missing, refer to the README section 'Handling Missing ES APIs (Optional)' "
52+ "to generate and load es_all, then run again."
52 )53 )
53 if Conv2D is None:54 if Conv2D is None:
54 raise RuntimeError(55 raise RuntimeError(
55- "未找到 ge.es.nn.Conv2D / ge.es.all.Conv2D。请先 source CANN 环境；如仍缺失，"56+ "ge.es.nn.Conv2D / ge.es.all.Conv2D not found. Please source the CANN environment first; "
56- "请参考 README 的“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"57+ "if still missing, refer to the README section 'Handling Missing ES APIs (Optional)' "
58+ "to generate and load es_all, then run again."
57 )59 )
58 if Concat is None and ConcatV2 is None:60 if Concat is None and ConcatV2 is None:
59 raise RuntimeError(61 raise RuntimeError(
60- "未找到 ge.es.math.Concat / ConcatV2。请先 source CANN 环境；如仍缺失，请参考 README 的"62+ "ge.es.math.Concat / ConcatV2 not found. Please source the CANN environment first; "
61- "“ES API 缺失时处理（可选）”生成并加载 es_all 后重新执行。"63+ "if still missing, refer to the README section 'Handling Missing ES APIs (Optional)' "
64+ "to generate and load es_all, then run again."
62 )65 )
63 66 
64 67 
@@ -133,6 +136,6 @@ class PythonDecomposeGroupedConvToSplitedPass(DecomposePass):
133 136 
134 137 
135if __name__ == "__main__":138if __name__ == "__main__":
136- print("PythonDecomposeGroupedConvToSplitedPass 已注册。")139+ print("PythonDecomposeGroupedConvToSplitedPass registered.")
137- print("请通过 ASCEND_GE_PY_PASS_PATH 指向本文件，例如：")140+ print("Please add this file to ASCEND_GE_PY_PASS_PATH, for example:")
138 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_decompose_pass.py")141 print(" export ASCEND_GE_PY_PASS_PATH=$PWD/src/python_decompose_pass.py")
Mexamples/multi_stream_autotune/README.md+10-10
@@ -127,20 +127,20 @@ python3 ge_ms_autotune.py \
127→ 执行命令 → 从 stdout 与该轮 plog 中收集 STEP → 校验后统计。控制台输出形如：127→ 执行命令 → 从 stdout 与该轮 plog 中收集 STEP → 校验后统计。控制台输出形如：
128 128 
129```129```
130-候选配置（7 个 × 3 轮）：default, LoadBalance:2, LoadBalance:4, ...130+candidate configs (7 x 3 rounds, online mode): default, LoadBalance:2, LoadBalance:4, ...
131 131 
132-[000] 配置=default 第 1 轮：python3 sample_run.py --steps 12132+[000] config=default round 1: python3 sample_run.py --steps 12
133- 退出码=0 STEP=13 有效=是 耗时=21.4s133+ exit_code=0 STEP=13 valid=yes wall=21.4s
134...134...
135 135 
136-寻优结果（按中位耗时升序）：136+Tuning results (sorted by median cost):
137-配置 有效轮次 步数 平均(ms) 中位(ms) P90(ms) CV 加速比 结论137+config valid_runs steps mean(ms) median(ms) P90(ms) CV speedup verdict
138-LoadBalance:4 3/3 36 12.104 12.088 12.301 0.014 1.243 提升138+LoadBalance:4 3/3 36 12.104 12.088 12.301 0.014 1.243 improvement
139-MainStream:4 3/3 36 13.552 13.489 13.702 0.011 1.114 提升139+MainStream:4 3/3 36 13.552 13.489 13.702 0.011 1.114 improvement
140-default 3/3 36 15.037 15.028 15.311 0.009 1.000 持平140+default 3/3 36 15.037 15.028 15.311 0.009 1.000 neutral
141 141 
142-[结论] 推荐配置：LoadBalance:4，相对 default 加速比 1.243，中位耗时 12.088 ms。142+[result] recommended config: LoadBalance:4, speedup over default: 1.243, median cost: 12.088 ms.
143-[复现] GE_AUTO_MULTISTREAM_PARALLEL_MODE=LoadBalance:4 python3 sample_run.py --steps 12143+[reproduce] GE_AUTO_MULTISTREAM_PARALLEL_MODE=LoadBalance:4 python3 sample_run.py --steps 12
144```144```
145 145 
146一阶段扫完后若想细化流数，指定相邻取值再跑一次即可：146一阶段扫完后若想细化流数，指定相邻取值再跑一次即可：
Mexamples/multi_stream_autotune/README_en.md+10-10
@@ -137,20 +137,20 @@ For every candidate the driver injects `GE_AUTO_MULTISTREAM_PARALLEL_MODE` and a
137run's plog directory, then validates and aggregates them. The console output looks like:137run's plog directory, then validates and aggregates them. The console output looks like:
138 138 
139```139```
140-候选配置（7 个 × 3 轮）：default, LoadBalance:2, LoadBalance:4, ...140+candidate configs (7 x 3 rounds, online mode): default, LoadBalance:2, LoadBalance:4, ...
141 141 
142-[000] 配置=default 第 1 轮：python3 sample_run.py --steps 12142+[000] config=default round 1: python3 sample_run.py --steps 12
143- 退出码=0 STEP=13 有效=是 耗时=21.4s143+ exit_code=0 STEP=13 valid=yes wall=21.4s
144...144...
145 145 
146-寻优结果（按中位耗时升序）：146+Tuning results (sorted by median cost):
147-配置 有效轮次 步数 平均(ms) 中位(ms) P90(ms) CV 加速比 结论147+config valid_runs steps mean(ms) median(ms) P90(ms) CV speedup verdict
148-LoadBalance:4 3/3 36 12.104 12.088 12.301 0.014 1.243 提升148+LoadBalance:4 3/3 36 12.104 12.088 12.301 0.014 1.243 improvement
149-MainStream:4 3/3 36 13.552 13.489 13.702 0.011 1.114 提升149+MainStream:4 3/3 36 13.552 13.489 13.702 0.011 1.114 improvement
150-default 3/3 36 15.037 15.028 15.311 0.009 1.000 持平150+default 3/3 36 15.037 15.028 15.311 0.009 1.000 neutral
151 151 
152-[结论] 推荐配置：LoadBalance:4，相对 default 加速比 1.243，中位耗时 12.088 ms。152+[result] recommended config: LoadBalance:4, speedup over default: 1.243, median cost: 12.088 ms.
153-[复现] GE_AUTO_MULTISTREAM_PARALLEL_MODE=LoadBalance:4 python3 sample_run.py --steps 12153+[reproduce] GE_AUTO_MULTISTREAM_PARALLEL_MODE=LoadBalance:4 python3 sample_run.py --steps 12
154```154```
155 155 
156To refine the stream count around the winner, run the neighbouring values again:156To refine the stream count around the winner, run the neighbouring values again:
Mexamples/multi_stream_autotune/ge_ms_autotune.py+161-101
@@ -139,16 +139,22 @@ def validate_config(config: str) -> None:
139 return139 return
140 if config.count(":") != 1:140 if config.count(":") != 1:
141 raise AutotuneError(141 raise AutotuneError(
142- "非法配置 {!r}：格式应为“策略名:流数”，例如 LoadBalance:2。".format(config)142+ "Invalid config {!r}: format must be 'strategy:streams', e.g. LoadBalance:2.".format(
143+ config
144+ )
143 )145 )
144 strategy, streams = config.split(":", 1)146 strategy, streams = config.split(":", 1)
145 if strategy not in STRATEGIES or strategy == "cv":147 if strategy not in STRATEGIES or strategy == "cv":
146 raise AutotuneError(148 raise AutotuneError(
147- "未知策略 {!r}，可选：{}。".format(strategy, "/".join(STRATEGIES))149+ "Unknown strategy {!r}, available: {}.".format(
150+ strategy, "/".join(STRATEGIES)
151+ )
148 )152 )
149 if not streams.isdigit() or not 1 <= int(streams) <= MAX_STREAMS:153 if not streams.isdigit() or not 1 <= int(streams) <= MAX_STREAMS:
150 raise AutotuneError(154 raise AutotuneError(
151- "配置 {!r} 的流数必须是 [1,{}] 的十进制整数。".format(config, MAX_STREAMS)155+ "Stream count in config {!r} must be a decimal integer in [1,{}].".format(
156+ config, MAX_STREAMS
157+ )
152 )158 )
153 159 
154 160 
Mexamples/multi_stream_autotune/sample_run.py+4-4
@@ -93,17 +93,17 @@ def execute(args: argparse.Namespace) -> int:
93 session = Session()93 session = Session()
94 session.add_graph(graph_id, build_graph(args.dim))94 session.add_graph(graph_id, build_graph(args.dim))
95 inputs = create_inputs(args.dim)95 inputs = create_inputs(args.dim)
96- outputs = session.run_graph(graph_id, inputs) # STEP 0：预热96+ outputs = session.run_graph(graph_id, inputs) # STEP 0: warmup
97 for _ in range(args.steps):97 for _ in range(args.steps):
98 outputs = session.run_graph(graph_id, inputs)98 outputs = session.run_graph(graph_id, inputs)
99 print(99 print(
100- "[Info] 样例执行完成：device={}, steps={}, outputs={}".format(100+ "[Info] sample execution completed: device={}, steps={}, outputs={}".format(
101 args.device, args.steps, [output.get_shape() for output in outputs]101 args.device, args.steps, [output.get_shape() for output in outputs]
102 )102 )
103 )103 )
104 return 0104 return 0
105- except Exception as error: # noqa: BLE001 - 样例进程以退出码反馈失败即可105+ except Exception as error: # noqa: BLE001 - the sample reports failure via its exit code
106- print("[Error] 样例执行失败：{}".format(error), file=sys.stderr)106+ print("[Error] sample execution failed: {}".format(error), file=sys.stderr)
107 return 1107 return 1
108 finally:108 finally:
109 # Session 必须先于 GE 去初始化释放。109 # Session 必须先于 GE 去初始化释放。
Mexamples/offline_compile_run/cpp/main.cpp+3-3
@@ -22,7 +22,7 @@ bool ParseSocVersion(int argc, char **argv, int start_index, std::string *soc_ve
22 for (int i = start_index; i < argc; ++i) {22 for (int i = start_index; i < argc; ++i) {
23 if (std::strcmp(argv[i], "--soc-version") == 0) {23 if (std::strcmp(argv[i], "--soc-version") == 0) {
24 if (i + 1 >= argc) {24 if (i + 1 >= argc) {
25- std::cerr << "[Error] --soc-version 参数为空\n";25+ std::cerr << "[Error] --soc-version argument is empty\n";
26 return false;26 return false;
27 }27 }
28 *soc_version = argv[i + 1];28 *soc_version = argv[i + 1];
@@ -36,7 +36,7 @@ bool ParseSocVersion(int argc, char **argv, int start_index, std::string *soc_ve
36 36 
37int main(int argc, char **argv) {37int main(int argc, char **argv) {
38 if (argc < 2) {38 if (argc < 2) {
39- std::cerr << "[Error] 用法: offline_compile_sample <命令> [--soc-version <版本>]\n"39+ std::cerr << "[Error] Usage: offline_compile_sample <command> [--soc-version <version>]\n"
40 << " 命令: build-model | build-bundle | run-infer | run-bundle-infer\n";40 << " 命令: build-model | build-bundle | run-infer | run-bundle-infer\n";
41 return -1;41 return -1;
42 }42 }
@@ -57,6 +57,6 @@ int main(int argc, char **argv) {
57 if (cmd == "run-bundle-infer") {57 if (cmd == "run-bundle-infer") {
58 return RunBundleModelInfer();58 return RunBundleModelInfer();
59 }59 }
60- std::cerr << "[Error] 未知命令: " << cmd << std::endl;60+ std::cerr << "[Error] Unknown command: " << cmd << std::endl;
61 return -1;61 return -1;
62}62}
Mexamples/offline_compile_run/cpp/src/bundle_model/build_bundle_model.cpp+8-7
@@ -37,10 +37,10 @@ int BuildBundleModel(const std::string &soc_version) {
37 auto global_options = GlobalOptionsWithSoc(soc_version);37 auto global_options = GlobalOptionsWithSoc(soc_version);
38 auto ret = ge::aclgrphBuildInitialize(global_options);38 auto ret = ge::aclgrphBuildInitialize(global_options);
39 if (ret != ge::GRAPH_SUCCESS) {39 if (ret != ge::GRAPH_SUCCESS) {
40- std::cerr << "[Error] aclgrphBuildInitialize 失败" << static_cast<int>(ret) << std::endl;40+ std::cerr << "[Error] aclgrphBuildInitialize failed" << static_cast<int>(ret) << std::endl;
41 return -1;41 return -1;
42 }42 }
43- std::cout << "[Info] 系统初始化成功\n";43+ std::cout << "[Info] System initialized successfully\n";
44 44 
45 auto add_graph = MakeOfflineAddGraph();45 auto add_graph = MakeOfflineAddGraph();
46 auto mul_graph = MakeOfflineMulGraph();46 auto mul_graph = MakeOfflineMulGraph();
@@ -51,22 +51,23 @@ int BuildBundleModel(const std::string &soc_version) {
51 ge::ModelBufferData model;51 ge::ModelBufferData model;
52 ret = ge::aclgrphBundleBuildModel(bundle, model);52 ret = ge::aclgrphBundleBuildModel(bundle, model);
53 if (ret != ge::GRAPH_SUCCESS) {53 if (ret != ge::GRAPH_SUCCESS) {
54- std::cerr << "[Error] aclgrphBundleBuildModel 失败, graphStatus=" << static_cast<int>(ret) << std::endl;54+ std::cerr << "[Error] aclgrphBundleBuildModel failed, graphStatus=" << static_cast<int>(ret) << std::endl;
55 ge::aclgrphBuildFinalize();55 ge::aclgrphBuildFinalize();
56 return -1;56 return -1;
57 }57 }
58- std::cout << "[Info] Bundle 构建成功，模型大小: " << model.length << " bytes\n";58+ std::cout << "[Info] Bundle built successfully, model size: " << model.length << " bytes\n";
59 59 
60 const char *const output_file = "bundle_sample";60 const char *const output_file = "bundle_sample";
61 ret = ge::aclgrphBundleSaveModel(output_file, model);61 ret = ge::aclgrphBundleSaveModel(output_file, model);
62 if (ret != ge::GRAPH_SUCCESS) {62 if (ret != ge::GRAPH_SUCCESS) {
63- std::cerr << "[Error] aclgrphBundleSaveModel 失败, graphStatus=" << static_cast<int>(ret) << std::endl;63+ std::cerr << "[Error] aclgrphBundleSaveModel failed, graphStatus=" << static_cast<int>(ret) << std::endl;
64 ge::aclgrphBuildFinalize();64 ge::aclgrphBuildFinalize();
65 return -1;65 return -1;
66 }66 }
67- std::cout << "[Info] 模型保存成功，" << output_file << ".om 已生成在当前目录。\n";67+ std::cout << "[Info] Model saved successfully, " << output_file
68+ << ".om has been generated in the current directory.\n";
68 69 
69 ge::aclgrphBuildFinalize();70 ge::aclgrphBuildFinalize();
70- std::cout << "[Info] 系统释放成功\n";71+ std::cout << "[Info] System resources released successfully\n";
71 return 0;72 return 0;
72}73}
Mexamples/offline_compile_run/cpp/src/bundle_model/run_bundle_model.cpp+13-13
@@ -21,14 +21,14 @@ ge::Status LoadBundleModelIds(uint32_t bundle_id, std::vector<uint32_t> *model_i
21 size_t model_num = 0;21 size_t model_num = 0;
22 aclError err = aclmdlBundleGetModelNum(bundle_id, &model_num);22 aclError err = aclmdlBundleGetModelNum(bundle_id, &model_num);
23 if (err != ACL_SUCCESS) {23 if (err != ACL_SUCCESS) {
24- std::cerr << "[Error] aclmdlBundleGetModelNum 失败, aclError=" << err << std::endl;24+ std::cerr << "[Error] aclmdlBundleGetModelNum failed, aclError=" << err << std::endl;
25 return ge::FAILED;25 return ge::FAILED;
26 }26 }
27 model_ids->resize(model_num);27 model_ids->resize(model_num);
28 for (size_t i = 0; i < model_num; ++i) {28 for (size_t i = 0; i < model_num; ++i) {
29 err = aclmdlBundleGetModelId(bundle_id, i, &(*model_ids)[i]);29 err = aclmdlBundleGetModelId(bundle_id, i, &(*model_ids)[i]);
30 if (err != ACL_SUCCESS) {30 if (err != ACL_SUCCESS) {
31- std::cerr << "[Error] aclmdlBundleGetModelId 失败, aclError=" << err << std::endl;31+ std::cerr << "[Error] aclmdlBundleGetModelId failed, aclError=" << err << std::endl;
32 return ge::FAILED;32 return ge::FAILED;
33 }33 }
34 }34 }
@@ -37,15 +37,15 @@ ge::Status LoadBundleModelIds(uint32_t bundle_id, std::vector<uint32_t> *model_i
37 37 
38ge::Status RunBundleSubmodelInfer(const uint32_t model_id, const char *label,38ge::Status RunBundleSubmodelInfer(const uint32_t model_id, const char *label,
39 const std::vector<std::vector<float>> &host_inputs) {39 const std::vector<std::vector<float>> &host_inputs) {
40- std::cout << "[Info] 执行子模型推理，model_id: " << model_id << "\n";40+ std::cout << "[Info] Executing sub-model inference, model_id: " << model_id << "\n";
41 aclmdlDesc *model_desc = aclmdlCreateDesc();41 aclmdlDesc *model_desc = aclmdlCreateDesc();
42 if (model_desc == nullptr) {42 if (model_desc == nullptr) {
43- std::cerr << "[Error] aclmdlCreateDesc 失败\n";43+ std::cerr << "[Error] aclmdlCreateDesc failed\n";
44 return ge::FAILED;44 return ge::FAILED;
45 }45 }
46 aclError e = aclmdlGetDesc(model_desc, model_id);46 aclError e = aclmdlGetDesc(model_desc, model_id);
47 if (e != ACL_SUCCESS) {47 if (e != ACL_SUCCESS) {
48- std::cerr << "[Error] aclmdlGetDesc 失败, aclError=" << e << std::endl;48+ std::cerr << "[Error] aclmdlGetDesc failed, aclError=" << e << std::endl;
49 TeardownBundleSubmodelSession(model_desc, nullptr, nullptr);49 TeardownBundleSubmodelSession(model_desc, nullptr, nullptr);
50 return ge::FAILED;50 return ge::FAILED;
51 }51 }
@@ -65,11 +65,11 @@ ge::Status RunBundleSubmodelInfer(const uint32_t model_id, const char *label,
65 }65 }
66 e = aclmdlExecute(model_id, input_io.dataset, output_io.dataset);66 e = aclmdlExecute(model_id, input_io.dataset, output_io.dataset);
67 if (e != ACL_SUCCESS) {67 if (e != ACL_SUCCESS) {
68- std::cerr << "[Error] aclmdlExecute 失败, aclError=" << e << std::endl;68+ std::cerr << "[Error] aclmdlExecute failed, aclError=" << e << std::endl;
69 TeardownBundleSubmodelSession(model_desc, &input_io, &output_io);69 TeardownBundleSubmodelSession(model_desc, &input_io, &output_io);
70 return ge::FAILED;70 return ge::FAILED;
71 }71 }
72- std::cout << "[Info] " << label << " 推理结果:\n";72+ std::cout << "[Info] " << label << " inference result:\n";
73 if (PrintModelOutputs(model_desc, output_io) != ge::SUCCESS) {73 if (PrintModelOutputs(model_desc, output_io) != ge::SUCCESS) {
74 TeardownBundleSubmodelSession(model_desc, &input_io, &output_io);74 TeardownBundleSubmodelSession(model_desc, &input_io, &output_io);
75 return ge::FAILED;75 return ge::FAILED;
@@ -80,15 +80,15 @@ ge::Status RunBundleSubmodelInfer(const uint32_t model_id, const char *label,
80} // namespace80} // namespace
81 81 
82int RunBundleModelInfer() {82int RunBundleModelInfer() {
83- std::cout << "[Info] 初始化运行环境及数据\n";83+ std::cout << "[Info] Initializing runtime environment and data\n";
84 aclError err = aclInit(nullptr);84 aclError err = aclInit(nullptr);
85 if (err != ACL_SUCCESS) {85 if (err != ACL_SUCCESS) {
86- std::cerr << "[Error] aclInit 失败, aclError=" << err << std::endl;86+ std::cerr << "[Error] aclInit failed, aclError=" << err << std::endl;
87 return -1;87 return -1;
88 }88 }
89 err = aclrtSetDevice(kDeviceId);89 err = aclrtSetDevice(kDeviceId);
90 if (err != ACL_SUCCESS) {90 if (err != ACL_SUCCESS) {
91- std::cerr << "[Error] aclrtSetDevice 失败, aclError=" << err << std::endl;91+ std::cerr << "[Error] aclrtSetDevice failed, aclError=" << err << std::endl;
92 (void)aclFinalize();92 (void)aclFinalize();
93 return -1;93 return -1;
94 }94 }
@@ -96,7 +96,7 @@ int RunBundleModelInfer() {
96 uint32_t bundle_id = 0;96 uint32_t bundle_id = 0;
97 err = aclmdlBundleLoadFromFile("bundle_sample.om", &bundle_id);97 err = aclmdlBundleLoadFromFile("bundle_sample.om", &bundle_id);
98 if (err != ACL_SUCCESS) {98 if (err != ACL_SUCCESS) {
99- std::cerr << "[Error] aclmdlBundleLoadFromFile 失败, aclError=" << err << std::endl;99+ std::cerr << "[Error] aclmdlBundleLoadFromFile failed, aclError=" << err << std::endl;
100 (void)aclrtResetDevice(kDeviceId);100 (void)aclrtResetDevice(kDeviceId);
101 (void)aclFinalize();101 (void)aclFinalize();
102 return -1;102 return -1;
@@ -107,7 +107,7 @@ int RunBundleModelInfer() {
107 TeardownAclBundleInfer(bundle_id);107 TeardownAclBundleInfer(bundle_id);
108 return -1;108 return -1;
109 }109 }
110- std::cout << "[Info] Bundle 模型加载成功，子模型个数: " << model_ids.size() << "\n";110+ std::cout << "[Info] Bundle model loaded successfully, number of sub-models: " << model_ids.size() << "\n";
111 111 
112 const auto inputs = SampleInputs();112 const auto inputs = SampleInputs();
113 if (RunBundleSubmodelInfer(model_ids[0], "Add 子模型", inputs) != ge::SUCCESS) {113 if (RunBundleSubmodelInfer(model_ids[0], "Add 子模型", inputs) != ge::SUCCESS) {
@@ -120,6 +120,6 @@ int RunBundleModelInfer() {
120 }120 }
121 121 
122 TeardownAclBundleInfer(bundle_id);122 TeardownAclBundleInfer(bundle_id);
123- std::cout << "[Info] 所有资源释放成功\n";123+ std::cout << "[Info] All resources released successfully\n";
124 return 0;124 return 0;
125}125}
Mexamples/offline_compile_run/cpp/src/common.cpp+12-12
@@ -19,20 +19,20 @@ ge::Status PrintOneInferOutputBuffer(aclmdlDesc *model_desc, size_t buffer_index
19 void *data = aclGetDataBufferAddr(data_buffer);19 void *data = aclGetDataBufferAddr(data_buffer);
20 const size_t len = aclGetDataBufferSizeV2(data_buffer);20 const size_t len = aclGetDataBufferSizeV2(data_buffer);
21 if (data == nullptr || len == 0U) {21 if (data == nullptr || len == 0U) {
22- std::cerr << "[Error] 输出 DataBuffer 无效, index=" << buffer_index << "\n";22+ std::cerr << "[Error] Output DataBuffer is invalid, index=" << buffer_index << "\n";
23 return ge::FAILED;23 return ge::FAILED;
24 }24 }
25 aclmdlIODims dims{};25 aclmdlIODims dims{};
26 aclError err = aclmdlGetOutputDims(model_desc, buffer_index, &dims);26 aclError err = aclmdlGetOutputDims(model_desc, buffer_index, &dims);
27 if (err != ACL_SUCCESS) {27 if (err != ACL_SUCCESS) {
28- std::cerr << "[Error] aclmdlGetOutputDims 失败, aclError=" << err << "\n";28+ std::cerr << "[Error] aclmdlGetOutputDims failed, aclError=" << err << "\n";
29 return ge::FAILED;29 return ge::FAILED;
30 }30 }
31 const size_t elem_count = len >= sizeof(float) ? len / sizeof(float) : 0U;31 const size_t elem_count = len >= sizeof(float) ? len / sizeof(float) : 0U;
32 std::vector<float> host(elem_count);32 std::vector<float> host(elem_count);
33 err = aclrtMemcpy(host.data(), len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);33 err = aclrtMemcpy(host.data(), len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
34 if (err != ACL_SUCCESS) {34 if (err != ACL_SUCCESS) {
35- std::cerr << "[Error] aclrtMemcpy(output D2H) 失败, aclError=" << err << "\n";35+ std::cerr << "[Error] aclrtMemcpy(output D2H) failed, aclError=" << err << "\n";
36 return ge::FAILED;36 return ge::FAILED;
37 }37 }
38 const float *out_data = host.data();38 const float *out_data = host.data();
@@ -57,7 +57,7 @@ ge::Status CreateIoDataset(aclmdlDesc *model_desc, bool is_input, IoBuffers *out
57 const size_t io_num = is_input ? aclmdlGetNumInputs(model_desc) : aclmdlGetNumOutputs(model_desc);57 const size_t io_num = is_input ? aclmdlGetNumInputs(model_desc) : aclmdlGetNumOutputs(model_desc);
58 out->dataset = aclmdlCreateDataset();58 out->dataset = aclmdlCreateDataset();
59 if (out->dataset == nullptr) {59 if (out->dataset == nullptr) {
60- std::cerr << "[Error] aclmdlCreateDataset 失败\n";60+ std::cerr << "[Error] aclmdlCreateDataset failed\n";
61 return ge::FAILED;61 return ge::FAILED;
62 }62 }
63 for (size_t i = 0; i < io_num; ++i) {63 for (size_t i = 0; i < io_num; ++i) {
@@ -66,14 +66,14 @@ ge::Status CreateIoDataset(aclmdlDesc *model_desc, bool is_input, IoBuffers *out
66 void *dev = nullptr;66 void *dev = nullptr;
67 aclError err = aclrtMalloc(&dev, buf_size, ACL_MEM_MALLOC_NORMAL_ONLY);67 aclError err = aclrtMalloc(&dev, buf_size, ACL_MEM_MALLOC_NORMAL_ONLY);
68 if (err != ACL_SUCCESS) {68 if (err != ACL_SUCCESS) {
69- std::cerr << "[Error] aclrtMalloc 失败, aclError=" << err << "\n";69+ std::cerr << "[Error] aclrtMalloc failed, aclError=" << err << "\n";
70 ReleaseDataset(out->dataset);70 ReleaseDataset(out->dataset);
71 *out = IoBuffers{};71 *out = IoBuffers{};
72 return ge::FAILED;72 return ge::FAILED;
73 }73 }
74 aclDataBuffer *data_buffer = aclCreateDataBuffer(dev, buf_size);74 aclDataBuffer *data_buffer = aclCreateDataBuffer(dev, buf_size);
75 if (data_buffer == nullptr) {75 if (data_buffer == nullptr) {
76- std::cerr << "[Error] aclCreateDataBuffer 失败\n";76+ std::cerr << "[Error] aclCreateDataBuffer failed\n";
77 (void)aclrtFree(dev);77 (void)aclrtFree(dev);
78 ReleaseDataset(out->dataset);78 ReleaseDataset(out->dataset);
79 *out = IoBuffers{};79 *out = IoBuffers{};
@@ -81,7 +81,7 @@ ge::Status CreateIoDataset(aclmdlDesc *model_desc, bool is_input, IoBuffers *out
81 }81 }
82 err = aclmdlAddDatasetBuffer(out->dataset, data_buffer);82 err = aclmdlAddDatasetBuffer(out->dataset, data_buffer);
83 if (err != ACL_SUCCESS) {83 if (err != ACL_SUCCESS) {
84- std::cerr << "[Error] aclmdlAddDatasetBuffer 失败, aclError=" << err << "\n";84+ std::cerr << "[Error] aclmdlAddDatasetBuffer failed, aclError=" << err << "\n";
85 (void)aclDestroyDataBuffer(data_buffer);85 (void)aclDestroyDataBuffer(data_buffer);
86 (void)aclrtFree(dev);86 (void)aclrtFree(dev);
87 ReleaseDataset(out->dataset);87 ReleaseDataset(out->dataset);
@@ -96,19 +96,19 @@ ge::Status CreateIoDataset(aclmdlDesc *model_desc, bool is_input, IoBuffers *out
96 96 
97ge::Status CopyFloatInputs(const std::vector<std::vector<float>> &host_inputs, const IoBuffers &inputs) {97ge::Status CopyFloatInputs(const std::vector<std::vector<float>> &host_inputs, const IoBuffers &inputs) {
98 if (host_inputs.size() > inputs.device_ptrs.size()) {98 if (host_inputs.size() > inputs.device_ptrs.size()) {
99- std::cerr << "[Error] CopyFloatInputs: host 输入个数超过 device buffer 数\n";99+ std::cerr << "[Error] CopyFloatInputs: number of host inputs exceeds device buffer count\n";
100 return ge::FAILED;100 return ge::FAILED;
101 }101 }
102 for (size_t i = 0; i < host_inputs.size(); ++i) {102 for (size_t i = 0; i < host_inputs.size(); ++i) {
103 const auto &data = host_inputs[i];103 const auto &data = host_inputs[i];
104 const size_t bytes = data.size() * sizeof(float);104 const size_t bytes = data.size() * sizeof(float);
105 if (bytes > inputs.sizes[i]) {105 if (bytes > inputs.sizes[i]) {
106- std::cerr << "[Error] CopyFloatInputs: 输入 " << i << " 超过 buffer 大小\n";106+ std::cerr << "[Error] CopyFloatInputs: input " << i << " exceeds buffer size\n";
107 return ge::FAILED;107 return ge::FAILED;
108 }108 }
109 aclError err = aclrtMemcpy(inputs.device_ptrs[i], inputs.sizes[i], data.data(), bytes, ACL_MEMCPY_HOST_TO_DEVICE);109 aclError err = aclrtMemcpy(inputs.device_ptrs[i], inputs.sizes[i], data.data(), bytes, ACL_MEMCPY_HOST_TO_DEVICE);
110 if (err != ACL_SUCCESS) {110 if (err != ACL_SUCCESS) {
111- std::cerr << "[Error] aclrtMemcpy(input) 失败, aclError=" << err << "\n";111+ std::cerr << "[Error] aclrtMemcpy(input) failed, aclError=" << err << "\n";
112 return ge::FAILED;112 return ge::FAILED;
113 }113 }
114 }114 }
@@ -117,14 +117,14 @@ ge::Status CopyFloatInputs(const std::vector<std::vector<float>> &host_inputs, c
117 117 
118ge::Status PrintInferOutputDataset(aclmdlDesc *model_desc, aclmdlDataset *output_dataset) {118ge::Status PrintInferOutputDataset(aclmdlDesc *model_desc, aclmdlDataset *output_dataset) {
119 if (output_dataset == nullptr) {119 if (output_dataset == nullptr) {
120- std::cerr << "[Error] output dataset 为空\n";120+ std::cerr << "[Error] output dataset is empty\n";
121 return ge::FAILED;121 return ge::FAILED;
122 }122 }
123 const size_t num_buffers = aclmdlGetDatasetNumBuffers(output_dataset);123 const size_t num_buffers = aclmdlGetDatasetNumBuffers(output_dataset);
124 for (size_t i = 0; i < num_buffers; ++i) {124 for (size_t i = 0; i < num_buffers; ++i) {
125 aclDataBuffer *data_buffer = aclmdlGetDatasetBuffer(output_dataset, i);125 aclDataBuffer *data_buffer = aclmdlGetDatasetBuffer(output_dataset, i);
126 if (data_buffer == nullptr) {126 if (data_buffer == nullptr) {
127- std::cerr << "[Error] aclmdlGetDatasetBuffer 失败, index=" << i << "\n";127+ std::cerr << "[Error] aclmdlGetDatasetBuffer failed, index=" << i << "\n";
128 return ge::FAILED;128 return ge::FAILED;
129 }129 }
130 const ge::Status st = PrintOneInferOutputBuffer(model_desc, i, data_buffer);130 const ge::Status st = PrintOneInferOutputBuffer(model_desc, i, data_buffer);
Mexamples/offline_compile_run/cpp/src/single_model/build_add_model.cpp+8-7
@@ -28,10 +28,10 @@ int BuildAddModel(const std::string &soc_version) {
28 auto global_options = GlobalOptionsWithSoc(soc_version);28 auto global_options = GlobalOptionsWithSoc(soc_version);
29 auto ret = ge::aclgrphBuildInitialize(global_options);29 auto ret = ge::aclgrphBuildInitialize(global_options);
30 if (ret != ge::GRAPH_SUCCESS) {30 if (ret != ge::GRAPH_SUCCESS) {
31- std::cerr << "[Error] aclgrphBuildInitialize 失败, graphStatus=" << static_cast<int>(ret) << std::endl;31+ std::cerr << "[Error] aclgrphBuildInitialize failed, graphStatus=" << static_cast<int>(ret) << std::endl;
32 return -1;32 return -1;
33 }33 }
34- std::cout << "[Info] 系统初始化成功\n";34+ std::cout << "[Info] System initialized successfully\n";
35 35 
36 auto graph = MakeOfflineAddGraph();36 auto graph = MakeOfflineAddGraph();
37 ge::ModelBufferData model;37 ge::ModelBufferData model;
@@ -39,22 +39,23 @@ int BuildAddModel(const std::string &soc_version) {
39 build_options.emplace(ge::AscendString("input_format"), ge::AscendString("ND"));39 build_options.emplace(ge::AscendString("input_format"), ge::AscendString("ND"));
40 ret = ge::aclgrphBuildModel(*graph, build_options, model);40 ret = ge::aclgrphBuildModel(*graph, build_options, model);
41 if (ret != ge::GRAPH_SUCCESS) {41 if (ret != ge::GRAPH_SUCCESS) {
42- std::cerr << "[Error] aclgrphBuildModel 失败, graphStatus=" << static_cast<int>(ret) << std::endl;42+ std::cerr << "[Error] aclgrphBuildModel failed, graphStatus=" << static_cast<int>(ret) << std::endl;
43 ge::aclgrphBuildFinalize();43 ge::aclgrphBuildFinalize();
44 return -1;44 return -1;
45 }45 }
46- std::cout << "[Info] 模型构建成功，模型大小: " << model.length << " bytes\n";46+ std::cout << "[Info] Model built successfully, model size: " << model.length << " bytes\n";
47 47 
48 const char *const output_file = "add_sample";48 const char *const output_file = "add_sample";
49 ret = ge::aclgrphSaveModel(output_file, model);49 ret = ge::aclgrphSaveModel(output_file, model);
50 if (ret != ge::GRAPH_SUCCESS) {50 if (ret != ge::GRAPH_SUCCESS) {
51- std::cerr << "[Error] aclgrphSaveModel 失败, graphStatus=" << static_cast<int>(ret) << std::endl;51+ std::cerr << "[Error] aclgrphSaveModel failed, graphStatus=" << static_cast<int>(ret) << std::endl;
52 ge::aclgrphBuildFinalize();52 ge::aclgrphBuildFinalize();
53 return -1;53 return -1;
54 }54 }
55- std::cout << "[Info] 模型保存成功，" << output_file << ".om 已生成在当前目录。\n";55+ std::cout << "[Info] Model saved successfully, " << output_file
56+ << ".om has been generated in the current directory.\n";
56 57 
57 ge::aclgrphBuildFinalize();58 ge::aclgrphBuildFinalize();
58- std::cout << "[Info] 系统释放成功\n";59+ std::cout << "[Info] System resources released successfully\n";
59 return 0;60 return 0;
60}61}
Mexamples/offline_compile_run/cpp/src/single_model/run_add_model.cpp+10-10
@@ -24,59 +24,59 @@ int ExecuteSingleLoadedModel(aclmdlDesc *model_desc, uint32_t model_id) {
24 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);24 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);
25 return -1;25 return -1;
26 }26 }
27- std::cout << "[Info] 执行模型推理\n";27+ std::cout << "[Info] Executing model inference\n";
28 if (CopyFloatInputs(SampleInputs(), input_io) != ge::SUCCESS) {28 if (CopyFloatInputs(SampleInputs(), input_io) != ge::SUCCESS) {
29 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);29 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);
30 return -1;30 return -1;
31 }31 }
32 const aclError err = aclmdlExecute(model_id, input_io.dataset, output_io.dataset);32 const aclError err = aclmdlExecute(model_id, input_io.dataset, output_io.dataset);
33 if (err != ACL_SUCCESS) {33 if (err != ACL_SUCCESS) {
34- std::cerr << "[Error] aclmdlExecute 失败, aclError=" << err << std::endl;34+ std::cerr << "[Error] aclmdlExecute failed, aclError=" << err << std::endl;
35 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);35 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);
36 return -1;36 return -1;
37 }37 }
38- std::cout << "[Info] 模型推理结果:\n";38+ std::cout << "[Info] Model inference result:\n";
39 if (PrintModelOutputs(model_desc, output_io) != ge::SUCCESS) {39 if (PrintModelOutputs(model_desc, output_io) != ge::SUCCESS) {
40 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);40 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);
41 return -1;41 return -1;
42 }42 }
43 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);43 TeardownAclSingleModelInfer(&input_io, &output_io, model_desc, model_id);
44- std::cout << "[Info] 所有资源释放成功\n";44+ std::cout << "[Info] All resources released successfully\n";
45 return 0;45 return 0;
46}46}
47 47 
48} // namespace48} // namespace
49 49 
50int RunSingleModelInfer() {50int RunSingleModelInfer() {
51- std::cout << "[Info] 初始化运行环境及数据\n";51+ std::cout << "[Info] Initializing runtime environment and data\n";
52 aclError err = aclInit(nullptr);52 aclError err = aclInit(nullptr);
53 if (err != ACL_SUCCESS) {53 if (err != ACL_SUCCESS) {
54- std::cerr << "[Error] aclInit 失败, aclError=" << err << std::endl;54+ std::cerr << "[Error] aclInit failed, aclError=" << err << std::endl;
55 return -1;55 return -1;
56 }56 }
57 err = aclrtSetDevice(kDeviceId);57 err = aclrtSetDevice(kDeviceId);
58 if (err != ACL_SUCCESS) {58 if (err != ACL_SUCCESS) {
59- std::cerr << "[Error] aclrtSetDevice 失败, aclError=" << err << std::endl;59+ std::cerr << "[Error] aclrtSetDevice failed, aclError=" << err << std::endl;
60 (void)aclFinalize();60 (void)aclFinalize();
61 return -1;61 return -1;
62 }62 }
63 uint32_t model_id = 0;63 uint32_t model_id = 0;
64 err = aclmdlLoadFromFile("add_sample.om", &model_id);64 err = aclmdlLoadFromFile("add_sample.om", &model_id);
65 if (err != ACL_SUCCESS) {65 if (err != ACL_SUCCESS) {
66- std::cerr << "[Error] aclmdlLoadFromFile 失败, aclError=" << err << std::endl;66+ std::cerr << "[Error] aclmdlLoadFromFile failed, aclError=" << err << std::endl;
67 (void)aclrtResetDevice(kDeviceId);67 (void)aclrtResetDevice(kDeviceId);
68 (void)aclFinalize();68 (void)aclFinalize();
69 return -1;69 return -1;
70 }70 }
71 aclmdlDesc *model_desc = aclmdlCreateDesc();71 aclmdlDesc *model_desc = aclmdlCreateDesc();
72 if (model_desc == nullptr) {72 if (model_desc == nullptr) {
73- std::cerr << "[Error] aclmdlCreateDesc 失败\n";73+ std::cerr << "[Error] aclmdlCreateDesc failed\n";
74 TeardownAclSingleModelInfer(nullptr, nullptr, nullptr, model_id);74 TeardownAclSingleModelInfer(nullptr, nullptr, nullptr, model_id);
75 return -1;75 return -1;
76 }76 }
77 err = aclmdlGetDesc(model_desc, model_id);77 err = aclmdlGetDesc(model_desc, model_id);
78 if (err != ACL_SUCCESS) {78 if (err != ACL_SUCCESS) {
79- std::cerr << "[Error] aclmdlGetDesc 失败, aclError=" << err << std::endl;79+ std::cerr << "[Error] aclmdlGetDesc failed, aclError=" << err << std::endl;
80 TeardownAclSingleModelInfer(nullptr, nullptr, model_desc, model_id);80 TeardownAclSingleModelInfer(nullptr, nullptr, model_desc, model_id);
81 return -1;81 return -1;
82 }82 }
Mexamples/offline_compile_run/python/src/bundle_model/build_bundle_model.py+9-5
@@ -22,7 +22,9 @@ from ge.offline_compile import (
22 22 
23 23 
24def parse_bundle_args():24def parse_bundle_args():
25- parser = argparse.ArgumentParser(description="Build an offline bundle_model OM model from GE Python Graphs.")25+ parser = argparse.ArgumentParser(
26+ description="Build an offline bundle_model OM model from GE Python Graphs."
27+ )
26 parser.add_argument(28 parser.add_argument(
27 "--soc-version",29 "--soc-version",
28 type=str,30 type=str,
@@ -38,7 +40,7 @@ def main():
38 if args.soc_version:40 if args.soc_version:
39 global_options["ge.socVersion"] = args.soc_version41 global_options["ge.socVersion"] = args.soc_version
40 build_initialize(global_options)42 build_initialize(global_options)
41- print("[Info] 系统初始化成功")43+ print("[Info] System initialized successfully")
42 44 
43 graph_with_options = [45 graph_with_options = [
44 GraphWithOptions(build_add_graph(), {"input_format": "ND"}),46 GraphWithOptions(build_add_graph(), {"input_format": "ND"}),
@@ -46,14 +48,16 @@ def main():
46 ]48 ]
47 try:49 try:
48 model = bundle_build_model(graph_with_options)50 model = bundle_build_model(graph_with_options)
49- print(f"[Info] 模型构建成功，模型大小: {model.length} bytes")51+ print(f"[Info] Model built successfully, model size: {model.length} bytes")
50 52 
51 file_name = "bundle_sample"53 file_name = "bundle_sample"
52 bundle_save_model(file_name, model)54 bundle_save_model(file_name, model)
53- print(f"[Info] 模型保存成功，{file_name}.om 模型文件已生成在当前目录。")55+ print(
56+ f"[Info] Model saved successfully, {file_name}.om model file has been generated in the current directory."
57+ )
54 finally:58 finally:
55 build_finalize()59 build_finalize()
56- print("[Info] 系统释放成功")60+ print("[Info] System resources released successfully")
57 61 
58 62 
59if __name__ == "__main__":63if __name__ == "__main__":
Mexamples/offline_compile_run/python/src/bundle_model/run_bundle_model.py+8-6
@@ -30,7 +30,7 @@ class AclBundleModelRunner:
30 30 
31 @staticmethod31 @staticmethod
32 def run_model(model_id: int, inputs):32 def run_model(model_id: int, inputs):
33- print(f"[Info] 执行子模型推理，model_id: {model_id}")33+ print(f"[Info] Executing sub-model inference, model_id: {model_id}")
34 model_desc = acl.mdl.create_desc()34 model_desc = acl.mdl.create_desc()
35 input_dataset = None35 input_dataset = None
36 output_dataset = None36 output_dataset = None
@@ -52,7 +52,7 @@ class AclBundleModelRunner:
52 check_ret("acl.mdl.destroy_desc", acl.mdl.destroy_desc(model_desc))52 check_ret("acl.mdl.destroy_desc", acl.mdl.destroy_desc(model_desc))
53 53 
54 def init(self) -> None:54 def init(self) -> None:
55- print("[Info] 初始化运行环境及数据")55+ print("[Info] Initializing runtime environment and data")
56 # 初始化 acl56 # 初始化 acl
57 check_ret("acl.init", acl.init())57 check_ret("acl.init", acl.init())
58 # 指定运行的 Device58 # 指定运行的 Device
@@ -68,10 +68,12 @@ class AclBundleModelRunner:
68 model_id, ret = acl.mdl.bundle_get_model_id(self.bundle_id, index)68 model_id, ret = acl.mdl.bundle_get_model_id(self.bundle_id, index)
69 check_ret("acl.mdl.bundle_get_model_id", ret)69 check_ret("acl.mdl.bundle_get_model_id", ret)
70 self.model_ids.append(model_id)70 self.model_ids.append(model_id)
71- print(f"[Info] Bundle 模型加载成功，子模型个数: {len(self.model_ids)}")71+ print(
72+ f"[Info] Bundle model loaded successfully, number of sub-models: {len(self.model_ids)}"
73+ )
72 74 
73 def release(self) -> None:75 def release(self) -> None:
74- print("[Info] 释放所有资源")76+ print("[Info] Releasing all resources")
75 if self.bundle_id is not None:77 if self.bundle_id is not None:
76 check_ret("acl.mdl.bundle_unload", acl.mdl.bundle_unload(self.bundle_id))78 check_ret("acl.mdl.bundle_unload", acl.mdl.bundle_unload(self.bundle_id))
77 acl.rt.reset_device(self.device_id)79 acl.rt.reset_device(self.device_id)
@@ -83,12 +85,12 @@ def main():
83 try:85 try:
84 runner.init()86 runner.init()
85 add_outputs = runner.run_model(runner.model_ids[0], create_sample_inputs())87 add_outputs = runner.run_model(runner.model_ids[0], create_sample_inputs())
86- print("[Info] Add 子模型推理结果:")88+ print("[Info] Add sub-model inference result:")
87 for i, output in enumerate(add_outputs, start=1):89 for i, output in enumerate(add_outputs, start=1):
88 print(f"Output[{i}]: \n{output}")90 print(f"Output[{i}]: \n{output}")
89 91 
90 mul_outputs = runner.run_model(runner.model_ids[1], create_sample_inputs())92 mul_outputs = runner.run_model(runner.model_ids[1], create_sample_inputs())
91- print("[Info] Mul 子模型推理结果:")93+ print("[Info] Mul sub-model inference result:")
92 for i, output in enumerate(mul_outputs, start=1):94 for i, output in enumerate(mul_outputs, start=1):
93 print(f"Output[{i}]: \n{output}")95 print(f"Output[{i}]: \n{output}")
94 finally:96 finally:
Mexamples/offline_compile_run/python/src/single_model/build_add_model.py+9-5
@@ -16,7 +16,9 @@ from ge.offline_compile import build_finalize, build_initialize, build_model, sa
16 16 
17 17 
18def parse_args():18def parse_args():
19- parser = argparse.ArgumentParser(description="Build an offline OM model from a GE Python Graph.")19+ parser = argparse.ArgumentParser(
20+ description="Build an offline OM model from a GE Python Graph."
21+ )
20 parser.add_argument(22 parser.add_argument(
21 "--soc-version",23 "--soc-version",
22 type=str,24 type=str,
@@ -32,20 +34,22 @@ def main():
32 if args.soc_version:34 if args.soc_version:
33 global_options["ge.socVersion"] = args.soc_version35 global_options["ge.socVersion"] = args.soc_version
34 build_initialize(global_options)36 build_initialize(global_options)
35- print("[Info] 系统初始化成功")37+ print("[Info] System initialized successfully")
36 38 
37 build_options = {"input_format": "ND"}39 build_options = {"input_format": "ND"}
38 graph = build_add_graph()40 graph = build_add_graph()
39 try:41 try:
40 model = build_model(graph, build_options)42 model = build_model(graph, build_options)
41- print(f"[Info] 模型构建成功，模型大小: {model.length} bytes")43+ print(f"[Info] Model built successfully, model size: {model.length} bytes")
42 44 
43 file_name = "add_sample"45 file_name = "add_sample"
44 save_model(file_name, model)46 save_model(file_name, model)
45- print(f"[Info] 模型保存成功，{file_name}.om 模型文件已生成在当前目录。")47+ print(
48+ f"[Info] Model saved successfully, {file_name}.om model file has been generated in the current directory."
49+ )
46 finally:50 finally:
47 build_finalize()51 build_finalize()
48- print("[Info] 系统释放成功")52+ print("[Info] System resources released successfully")
49 53 
50 54 
51if __name__ == "__main__":55if __name__ == "__main__":
Mexamples/offline_compile_run/python/src/single_model/run_add_model.py+10-6
@@ -33,7 +33,7 @@ class AclModelRunner:
33 self.output_data = []33 self.output_data = []
34 34 
35 def init(self) -> None:35 def init(self) -> None:
36- print("[Info] 初始化运行环境及数据")36+ print("[Info] Initializing runtime environment and data")
37 # 初始化 acl37 # 初始化 acl
38 check_ret("acl.init", acl.init())38 check_ret("acl.init", acl.init())
39 # 指定运行的 Device39 # 指定运行的 Device
@@ -45,11 +45,15 @@ class AclModelRunner:
45 self.model_desc = acl.mdl.create_desc()45 self.model_desc = acl.mdl.create_desc()
46 check_ret("acl.mdl.get_desc", acl.mdl.get_desc(self.model_desc, self.model_id))46 check_ret("acl.mdl.get_desc", acl.mdl.get_desc(self.model_desc, self.model_id))
47 47 
48- self.input_dataset, self.input_data = prepare_acl_mdl_dataset(self.model_desc, "input")48+ self.input_dataset, self.input_data = prepare_acl_mdl_dataset(
49- self.output_dataset, self.output_data = prepare_acl_mdl_dataset(self.model_desc, "output")49+ self.model_desc, "input"
50+ )
51+ self.output_dataset, self.output_data = prepare_acl_mdl_dataset(
52+ self.model_desc, "output"
53+ )
50 54 
51 def forward(self, inputs):55 def forward(self, inputs):
52- print("[Info] 执行模型推理")56+ print("[Info] Executing model inference")
53 copy_inputs_to_acl_dataset(self.input_data, inputs)57 copy_inputs_to_acl_dataset(self.input_data, inputs)
54 check_ret(58 check_ret(
55 "acl.mdl.execute",59 "acl.mdl.execute",
@@ -58,7 +62,7 @@ class AclModelRunner:
58 return collect_acl_model_outputs(self.model_desc, self.output_data)62 return collect_acl_model_outputs(self.model_desc, self.output_data)
59 63 
60 def release(self) -> None:64 def release(self) -> None:
61- print("[Info] 释放所有资源")65+ print("[Info] Releasing all resources")
62 # 释放模型推理的输入、输出资源66 # 释放模型推理的输入、输出资源
63 release_acl_mdl_dataset(self.input_dataset)67 release_acl_mdl_dataset(self.input_dataset)
64 release_acl_mdl_dataset(self.output_dataset)68 release_acl_mdl_dataset(self.output_dataset)
@@ -77,7 +81,7 @@ def main():
77 try:81 try:
78 runner.init()82 runner.init()
79 outputs = runner.forward(create_sample_inputs())83 outputs = runner.forward(create_sample_inputs())
80- print("[Info] 模型推理结果:")84+ print("[Info] Model inference result:")
81 for i, output in enumerate(outputs, start=1):85 for i, output in enumerate(outputs, start=1):
82 print(f"Output[{i}]: \n{output}")86 print(f"Output[{i}]: \n{output}")
83 finally:87 finally: