--- title: DUMP_GRAPH_FORMAT description: "控制需要生成的dump文件类型。" url: https://www.hiascend.com/document/detail/zh/canncommercial/latest/maintenref/envvar/envref_07_0155.html sourcePath: /source/zh/canncommercial/900/maintenref/envvar/envref_07_0155.html indexId: b7e3f0463f5b21c0927e6e311906ab22c45defac882227a8ea2be7be5652d13f67 --- # DUMP_GRAPH_FORMAT #### 功能描述 控制需要生成的dump文件类型。 支持的取值包括： - onnx：基于ONNX的模型描述结构，可以使用Netron等可视化软件打开，生成文件名为ge_onnx*.pbtxt。 - ge_proto：protobuf格式存储的文本文件，生成文件名为ge_proto*.txt。 - readable：类似Dynamo fx图风格的高可读性文本文件，生成文件名为ge_readable*.txt。文件内容解析请参见 readable文件解析 。 使用方式： 配置按照“|”分隔的字符串，类型全部为小写，支持组合配置，例如“ge_proto|onnx|readable”，表示dump全量类型的文件；也支持单独配置，例如“ge_proto”，表示只dump ge_proto类型的文件ge_proto*.txt。 DUMP_GRAPH_FORMAT环境变量只有在DUMP_GE_GRAPH开启时才生效，默认为“ge_proto|onnx”。 #### 配置示例 ``` export DUMP_GRAPH_FORMAT="ge_proto|onnx" ``` #### 使用约束 - 如果此环境变量设置了其他非法值，可能会导致未定义的行为发生。 - 如果开启了采集算子dump数据功能，可以参考《图开发(https://www.hiascend.com/document/detail/zh/canncommercial/900/programug/graphdevg/atlasag_25_0081.html)》>“ge.exec.enableDump(https://www.hiascend.comdocument/detail/zh/canncommercial/900/API/ascendgraphapi/atlasgeapi_07_0150.html)”参数， 即使DUMP_GRAPH_FORMAT环境变量取值不包括“ge_proto”，最终都会dump子图ge_proto_xxxx_Build.txt。 #### readable文件解析 - 如下为一个完整的ge_readable*.txt文件： ``` graph("MakeTransformerSubGraph"): %input_0 : [#users=1] = Node[type=Data] (attrs = {index: 0}) %input_1 : [#users=1] = Node[type=Data] (attrs = {index: 1}) %input_2 : [#users=1] = Node[type=Data] (attrs = {index: 2}) %Const_0 : [#users=1] = Node[type=Const] (attrs = {value: [-1 7168]}) %Reshape_1 : [#users=1] = Node[type=Reshape] (inputs = (x=%input_0, shape=%Const_0), attrs = {axis: 0, num_axes: -1}) %Cast_2 : [#users=1] = Node[type=Cast] (inputs = (x=%Reshape_1), attrs = {dst_type: 0}) %Cast_3 : [#users=1] = Node[type=Cast] (inputs = (x=%input_1), attrs = {dst_type: 0}) %Const_4 : [#users=1] = Node[type=Const] (attrs = {value: [1 0]}) %Transpose_5 : [#users=1] = Node[type=Transpose] (inputs = (x=%Cast_3, perm=%Const_4)) %MatMul_6 : [#users=1] = Node[type=MatMul] (inputs = (x1=%Cast_2, x2=%Transpose_5), attrs = {transpose_x1: false, transpose_x2: false}) %Sigmoid_7 : [#users=1] = Node[type=Sigmoid] (inputs = (x=%MatMul_6)) %Const_8 : [#users=1] = Node[type=Const] (attrs = {value: [-1 256]}) %Reshape_9 : [#users=1] = Node[type=Reshape] (inputs = (x=%Sigmoid_7, shape=%Const_8), attrs = {axis: 0, num_axes: -1}) %Unsqueeze_10 : [#users=1] = Node[type=Unsqueeze] (inputs = (x=%input_2), attrs = {axes: {0}}) %Cast_11 : [#users=1] = Node[type=Cast] (inputs = (x=%Unsqueeze_10), attrs = {dst_type: 0}) %Add_12 : [#users=1] = Node[type=Add] (inputs = (x1=%Reshape_9, x2=%Cast_11)) %Const_13 : [#users=1] = Node[type=Const] (attrs = {value: [2]}) %TopKV2_14 : [#users=2] = Node[type=TopKV2] (inputs = (x=%Add_12, k=%Const_13), attrs = {sorted: true, dim: -1, largest: true, indices_dtype: 3}) %ret : [users=1] = get_element[node=%TopKV2_14](0) %ret_1 : [users=0] = get_element[node=%TopKV2_14](1) %Const_15 : [#users=1] = Node[type=Const] (attrs = {value: [-1]}) %ReduceSum_16 : [#users=1] = Node[type=ReduceSum] (inputs = (x=%ret, axes=%Const_15), attrs = {keep_dims: false, noop_with_empty_axes: true}) %Const_17 : [#users=1] = Node[type=Const] (attrs = {value: [4]}) %TopKV2_18 : [#users=2] = Node[type=TopKV2] (inputs = (x=%ReduceSum_16, k=%Const_17), attrs = {sorted: false, dim: -1, largest: true, indices_dtype: 3}) %ret_2 : [users=0] = get_element[node=%TopKV2_18](0) %ret_3 : [users=1] = get_element[node=%TopKV2_18](1) %Cast_19 : [#users=1] = Node[type=Cast] (inputs = (x=%ret_3), attrs = {dst_type: 9}) %ZerosLike_20 : [#users=1] = Node[type=ZerosLike] (inputs = (x=%ReduceSum_16)) %Shape_21 : [#users=1] = Node[type=Shape] (inputs = (x=%Cast_19), attrs = {dtype: 3}) %Const_22 : [#users=1] = Node[type=Const] (attrs = {value: [1.000000]}) %Cast_23 : [#users=1] = Node[type=Cast] (inputs = (x=%Const_22), attrs = {dst_type: 0}) %Fill_24 : [#users=1] = Node[type=Fill] (inputs = (dims=%Shape_21, value=%Cast_23)) %ScatterElements_25 : [#users=1] = Node[type=ScatterElements] (inputs = (data=%ZerosLike_20, indices=%Cast_19, updates=%Fill_24), attrs = {axis: 0, reduction: "none"}) %Unsqueeze_26 : [#users=1] = Node[type=Unsqueeze] (inputs = (x=%ScatterElements_25), attrs = {axes: {-1}}) %Const_27 : [#users=1] = Node[type=Const] (attrs = {value: [256 256]}) %BroadcastTo_28 : [#users=1] = Node[type=BroadcastTo] (inputs = (x=%Unsqueeze_26, shape=%Const_27)) %Identity_29 : [#users=1] = Node[type=Identity] (inputs = (x=%BroadcastTo_28)) %Const_30 : [#users=1] = Node[type=Const] (attrs = {value: [256 256]}) %Reshape_31 : [#users=1] = Node[type=Reshape] (inputs = (x=%Identity_29, shape=%Const_30), attrs = {axis: 0, num_axes: -1}) %Cast_32 : [#users=1] = Node[type=Cast] (inputs = (x=%Reshape_31), attrs = {dst_type: 12}) %LogicalNot_33 : [#users=1] = Node[type=LogicalNot] (inputs = (x=%Cast_32)) %Const_34 : [#users=1] = Node[type=Const] (attrs = {value: [0.000000]}) %MaskedFill_35 : [#users=1] = Node[type=MaskedFill] (inputs = (x=%Add_12, mask=%LogicalNot_33, value=%Const_34)) %Const_36 : [#users=1] = Node[type=Const] (attrs = {value: [4]}) %TopKV2_37 : [#users=2] = Node[type=TopKV2] (inputs = (x=%MaskedFill_35, k=%Const_36), attrs = {sorted: false, dim: -1, largest: true, indices_dtype: 3}) %ret_4 : [users=0] = get_element[node=%TopKV2_37](0) %ret_5 : [users=1] = get_element[node=%TopKV2_37](1) %Cast_38 : [#users=1] = Node[type=Cast] (inputs = (x=%ret_5), attrs = {dst_type: 9}) %GatherElements_39 : [#users=1] = Node[type=GatherElements] (inputs = (x=%Sigmoid_7, index=%Cast_38), attrs = {dim: 1}) %Const_40 : [#users=1] = Node[type=Const] (attrs = {value: [0.000001]}) %RealDiv_41 : [#users=1] = Node[type=RealDiv] (inputs = (x1=%GatherElements_39, x2=%Const_40)) %Const_42 : [#users=1] = Node[type=Const] (attrs = {value: [2.500000]}) %Mul_43 : [#users=1] = Node[type=Mul] (inputs = (x1=%RealDiv_41, x2=%Const_42)) %Cast_44 : [#users=1] = Node[type=Cast] (inputs = (x=%Mul_43), attrs = {dst_type: 0}) return (output_0=%Cast_38, output_1=%Cast_44) ``` - 如下为一个包含子图的文件： ``` graph("TransformerBlockSubgraph"): %input_0 : [#users=1] = Node[type=Data] (attrs = {index: 0}) %pred_1 : [#users=1] = Node[type=Data] (attrs = {index: 1}) %If_0 : [#users=1] = Node[type=If] (inputs = (cond=%pred_1, input_0=%input_0), attrs = {then_branch: %If_then, else_branch: %If_else}) %Const_1 : [#users=1] = Node[type=Const] (attrs = {value: [0]}) %Const_2 : [#users=1] = Node[type=Const] (attrs = {value: [4]}) %Const_3 : [#users=1] = Node[type=Const] (attrs = {value: [1]}) %For_6 : [#users=2] = Node[type=For] (inputs = (start=%Const_1, limit=%Const_2, delta=%Const_3, input_1=%If_0), attrs = {body: %For_body}) %ret : [users=1] = get_element[node=%For_6](0) %ret_1 : [users=1] = get_element[node=%For_6](1) return (output_0=%ret, output_1=%ret_1) graph("If_then"): %input_0 : [#users=1] = Node[type=Data] (attrs = {index: 0}) %Const_0 : [#users=1] = Node[type=Const] (attrs = {value: [0.900000]}) %Mul_1 : [#users=1] = Node[type=Mul] (inputs = (x1=%input_0, x2=%Const_0)) return (%Mul_1) graph("If_else"): %input_0 : [#users=1] = Node[type=Data] (attrs = {index: 0}) %Identity_0 : [#users=1] = Node[type=Identity] (inputs = (x=%input_0)) return (%Identity_0) graph("For_body"): %iter : [#users=1] = Node[type=Data] (attrs = {index: 0}) %hidden : [#users=1] = Node[type=Data] (attrs = {index: 1}) %Const_0 : [#users=1] = Node[type=Const] (attrs = {value: [1]}) %Add_0 : [#users=1] = Node[type=Add] (inputs = (x1=%iter, x2=%Const_0)) %Const_1 : [#users=1] = Node[type=Const] (attrs = {value: [0.500000]}) %Mul_1 : [#users=1] = Node[type=Mul] (inputs = (x1=%hidden, x2=%Const_1)) %Add_2 : [#users=1] = Node[type=Add] (inputs = (x1=%hidden, x2=%Mul_1)) return (output_0=%Add_0, output_1=%Add_2) ``` 字段解释如下： - 图名称：graph("<图名称>")。 - 节点实例：%<节点实例名称> : [#users=<出度>] = Node[type=<节点类型>](inputs = (<输入名称1>=%<输入实例1>, ...), attrs = {<属性名称1>: <属性值1>, ...})。 - <节点实例名称>：节点实例名称。 - #users=<出度> ：节点输出个数。 - Node[type=<节点类型>]：节点对应的算子类型，例如MatMul节点显示为Node[type=MatMul]。 - inputs = (<输入名称1>=%<输入实例1>, ...)：节点输入以“参数名=实例名”形式展示；参数名解析异常时，回退为 _input_N 序列。当节点无输入时该字段缺省。 动态输入：对动态输入参数使用输入名称_#cnt形式编号（输入名称_0, 输入名称_1, ...）。 - attrs = {<属性名称1>: <属性值1>, ...}：节点属性集合，包含子图属性项与普通属性项，当属性为空时该字段缺省。 - 多输出节点的输出引用：%ret/ret_#cnt : [#users=<消费者个数>] = get_element[node=%<节点实例名称>](<输出index>)。 - %ret/ret_#cnt：多输出节点的每一路输出统一使用ret命名ret, ret_1, ret_2, ...。 - [#users=<消费者个数>]：该输出的消费者数量。 - get_element[node=%<节点实例名称>](<输出index>)：以get_element表示“从多输出节点提取第 <输出index> 路输出”。 - 图输出：return (<输出列表>) - 表示图的输出，对应NetOutput节点的输入。 - 单输出：return (%<输出实例>)。 - 多输出：return (output_0=%<输出实例0>, output_1=%<输出实例1>, ...)。输出使用output_#cnt形式编号（output_0, output_1, ...）。 - 子图表示：包含子图的节点按以下规则表示： - 子图声明：在父节点的attrs中声明：attrs = {<子图属性名称>: %<子图实例名>, ... }；子图属性名称解析异常时，回退为_graph_N序列。 - 输入对应关系：父节点的输入input_#cnt（或 args_#cnt）对应子图中index属性值为cnt的Data节点。例如：父节点的input_0 →子图的Data(attrs = {index: 0})。 - 输出对应关系： - 单输出：子图返回值直接作为父节点输出。 - 多输出：子图的输出output_#cnt对应父节点的第cnt路输出，父节点通过get_element[node=%<父节点>](cnt)提取对应输出。 - 子图展示位置：子图内容在父图主体输出结束后单独展示；每个子图以 graph("<子图实例名>"):开始。 #### 支持的型号 Atlas 训练系列产品 Atlas 推理系列产品 Atlas A2 训练系列产品 / Atlas A2 推理系列产品 Atlas A3 训练系列产品 / Atlas A3 推理系列产品 Atlas 350 加速卡 Atlas 200I/500 A2 推理产品