# Reading IR [![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.7/resource/_static/logo_source_en.png)](https://gitee.com/mindspore/docs/blob/r1.7/tutorials/experts/source_en/debug/mindir.md) ## Overview When a model compiled using MindSpore runs in the graph mode `context.set_context(mode=context.GRAPH_MODE)` and `context.set_context(save_graphs=True)` is set in the configuration, some intermediate files will be generated during graph compliation. These intermediate files are called IR files. Currently, there are three IR files: - .ir file: An IR file that describes the model structure in text format and can be directly viewed using any text editors. - .dat file: An IR file that describes the model structure more strictly than the .ir file. It contains more contents and can be directly viewed using any text editors. - .dot file: An IR file that describes the topology relationships between different nodes. You can use this file by [graphviz](http://graphviz.org/) as the input to generate images for users to view the model structure. For models with multiple operators, it is recommended using the visualization component [MindInsight](https://www.mindspore.cn/mindinsight/docs/en/r1.7/dashboard.html#computational-graph-visualization) to visualize computing graphs. ## Saving IR `context.set_context(save_graphs=True)` is used to save the intermediate code in each compilation phase. The intermediate code can be saved in two formats. One is the text format with the suffix `.ir`, and the other is the graphical format with the suffix `.dot`. When the network scale is small, you are advised to use the graphical format that is more intuitive. When the network scale is large, you are advised to use the text format that is more efficient. You can run the graphviz command to convert a .dot file to the picture format. For example, you can run the `dot -Tpng *.dot -o *.png` command to convert a `.dot` file to a .png file. Add the following code to `train.py`. When running the script, MindSpore will automatically store the IR files generated during compilation under the specified path. ```python if __name__ == "__main__": context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files") ``` After the training command is executed, some files are generated in the path of `save_graphs_path`. ```text . ├──00_parse_0000.ir ├──00_parse_0001.dat ├──00_parse_0002.dot ├──01_symbol_resolve_0003.ir ├──01_symbol_resolve_0004.dat ├──01_symbol_resolve_0005.dot ├──02_combine_like_graphs_0006.ir ├──02_combine_like_graphs_0007.dat ├──02_combine_like_graphs_0008.dot ├──03_inference_opt_prepare_0009.ir ├──03_inference_opt_prepare_0010.dat ├──03_inference_opt_prepare_0011.dot ├──04_abstract_specialize_0012.ir ├──04_abstract_specialize_0013.dat ├──04_abstract_specialize_0014.dot ... ``` The IR files starting with digits and underscores are generated during the ME graph compilation. The compute graph is saved in each phase of the `pipeline`. Let's see the important phases. - The `parse` phase parses the `construct` function of the entrance. If viewing the IR file, we can see that only the graph information of the top cell is parsed in this phase. - The `symbol_resolve` phase recursively parses other functions and objects directly or indirectly referenced by the entry function. When using the unsupported syntax, it will get an error in this phase. - The `abstract_specialize` phase infers every node's `data type` and `shape` by the cell's inputs. When you want to know the shape or data type of a specific operator in IR, you can view this IR file. - The `optimize` phase, hardware-independent optimization is performed, the automatic differential and automatic parallel functions are also performed. Some ir files with the prefix `opt_pass` are saved here. No need to pay too much attention to those files if you are not the framework developer. - The `validate` phase will check the temporary operators which should be removed in the prior phase. If any temporary operator exists, the process will report an error and exit. - The `task_emit` phase will transfer the compute graph to the backend for further processing. - The `execute` phase will execute the compute graph. This is the final graph in the phase of frontend. In addition, you don't need to pay too much attention to the IR files (such as files beginning with `hwopt`) if you are not the framework developer because the backend is close to the hardware. Only need pay attention to the file `graph_build_[graph_id]_[IR_id].ir`. It is the MindIR after the frontend and backend optimization. > Multiple files may be saved because the backend only can handle the single graph. > It is different with the frontend when the front save all sub-graphs in the one file. ## IR File Contents Introduction The following is an example to describe the contents of the IR file. The content may have some changes with the version upgrade of MindSpore. ```python import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore import ops from mindspore import dtype as mstype context.set_context(mode=context.GRAPH_MODE) context.set_context(save_graphs=True, save_graphs_path="./") class Net(nn.Cell): def __init__(self): super().__init__() self.add = ops.Add() self.sub = ops.Sub() self.mul = ops.Mul() self.div = ops.Div() def func(x, y): return self.div(x, y) def construct(self, x, y): a = self.sub(x, 1) b = self.add(a, y) c = self.mul(b, self.func(a, b)) return c input1 = Tensor(3, mstype.float32) input2 = Tensor(2, mstype.float32) net = Net() out = net(input1, input2) print(out) ``` ### ir Introduction Use a text editing software (for example, vi) to open the `04_abstract_specialize_0012.ir` file. The file contents are as follows: ```text 1 #IR entry : @1_construct_wrapper.21 2 #attrs : 3 #Total params : 2 4 5 %para1_x : 6 %para2_y : 7 8 #Total subgraph : 3 9 10 subgraph attr: 11 Undeterminate : 0 12 subgraph @2_construct.22(%para3_x, %para4_y) { 13 %0(a) = Sub(%para3_x, Tensor(shape=[], dtype=Float32, value= 1)) {instance name: sub} primitive_attrs: {input_names: [x, y], output_names: [output]} 14 : (, ) -> () 15 # In file train.py(34)/ a = self.sub(x, 1)/ 16 %1(b) = Add(%0, %para4_y) {instance name: add} primitive_attrs: {input_names: [x, y], output_names: [output]} 17 : (, ) -> () 18 # In file train.py(35)/ b = self.add(a, y)/ 19 %2([CNode]5) = call @3_func.23(%0, %1) 20 : (, ) -> () 21 # In file train.py(36)/ c = self.mul(b, self.func(a, b))/ 22 %3(c) = Mul(%1, %2) {instance name: mul} primitive_attrs: {input_names: [x, y], output_names: [output]} 23 : (, ) -> () 24 # In file train.py(36)/ c = self.mul(b, self.func(a, b))/ 25 Return(%3) 26 : () 27 # In file train.py(37)/ return c/ 28 } 29 30 subgraph attr: 31 Undeterminate : 0 32 subgraph @3_func.23(%para5_x, %para6_y) { 33 %0([CNode]20) = Div(%para5_x, %para6_y) {instance name: div} primitive_attrs: {input_names: [x, y], output_names: [output]} 34 : (, ) -> () 35 # In file train.py(31)/ return self.div(x, y)/ 36 Return(%0) 37 : () 38 # In file train.py(31)/ return self.div(x, y)/ 39 } 40 41 subgraph attr: 42 subgraph @1_construct_wrapper.21() { 43 %0([CNode]2) = call @2_construct.22(%para1_x, %para2_y) 44 : (, ) -> () 45 # In file train.py(37)/ return c/ 46 Return(%0) 47 : () 48 # In file train.py(37)/ return c/ 49 } ``` The above contents can be divided into two parts, the first part is the input list and the second part is the graph structure. The first line tells us the name of the top MindSpore graph about the network, `1_construct_wrapper.21`, or the entry graph. Line 3 tells us how many inputs are in the network. Line 5 to 6 are the input list, which is in the format of `%para[No.]_[name] : <[data_type]x[shape]>`. Line 8 tells us the number of subgraph parsed by the network. There are 3 graphs in this IR. Line 42 is the entry graph `1_construct_wrapper.21`. Line 32 is graph `3_func.23`, parsed from the `func(x, y)` in the source script. Line 12 is graph `2_construct.22`, parsed from the function `construct`. Taking graph `2_construct.22` as an example, Line 10 to 28 indicate the graph structure, which contains several nodes, namely, `CNode`. In this example, there are `Sub`, `Add`, `Mul`. They are defined in the function `__init__`. Line 19 calls a graph by `call @3_func.23`. It indicates calling the graph `func(x, y)` to execute a division operation. The [CNode](https://www.mindspore.cn/docs/en/r1.7/design/mindir.html#syntax) information format is as follows: including the node name, attribute, input node, the specs of the inputs and outputs, and source code parsing call stack. The ANF graph is a unidirectional acyclic graph. So, the connection between nodes is displayed only based on the input relationship. The corresponding source code reflects the relationship between the `CNode` and the script source code. For example, line 15 is parsed from `a = self.sub(x, 1)`. ```text %[No.]([debug_name]) = [op_name]([arg], ...) primitive_attrs: {[key]: [value], ...} : (<[input data_type]x[input shape]>, ...) -> (<[output data_type]x[output shape]>, ...) # Corresponding source code ``` About the corresponding source code: - There are two mode for the corresponding source code displaying. The first mode is to display the complete call stack, such as `15_execute_0141.ir` on the frontend and `graph_build_0_136.ir` on the backend. The second mode only displays one code line for reducing the size of the IR file, which eliminates the call stack, such as `04_abstract_specialize_0012.ir`. - If the operator is a back propagation operator, the associated code line will not only display its own code, but also the corresponding forward code, identified by "Corresponding forward node candidate:". - If the operator is a fusion operator, the associated code line will display the fusion related code, identified by "Corresponding code candidate:", where the separator "-" is used to distinguish different codes. > - After several optimizations by the compiler, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method. > - After the `kernel select` phase at the backend, two lines of input and output specification information (that is, the content after `:`) will appear. The first line represents the specifications on the HOST side, and the second line represents the specifications on the DEVICE side. ### dat Introduction Use a text editing software (for example, vi) to open the `04_abstract_specialize_0013.dat` file. The file contents are as follows: ```text 1 # [No.1] 1_construct_wrapper.21 2 # In file train.py(33)/ def construct(self, x, y):/ 3 funcgraph fg_21( 4 %para1 : Tensor(F32)[] # x 5 , %para2 : Tensor(F32)[] # y 6 ) { 7 %1 : Tensor(F32)[] = FuncGraph::fg_22(%para1, %para2) #(Tensor(F32)[], Tensor(F32)[]) # fg_22=2_construct.22 #scope: Default 8 # In file train.py(37)/ return c/#[CNode]2 9 Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[]) #scope: Default 10 # In file train.py(37)/ return c/#[CNode]1 11 } 12 # order: 13 # 1: 1_construct_wrapper.21:[CNode]2{[0]: ValueNode 2_construct.22, [1]: x, [2]: y} 14 # 2: 1_construct_wrapper.21:[CNode]1{[0]: ValueNode Return, [1]: [CNode]2} 15 16 17 # [No.2] 2_construct.22 18 # In file train.py(33)/ def construct(self, x, y):/ 19 funcgraph fg_22( 20 %para3 : Tensor(F32)[] # x 21 , %para4 : Tensor(F32)[] # y 22 ) { 23 %1 : Tensor(F32)[] = PrimitivePy::Sub{prim_type=2}[input_names=["x", "y"], output_names=["output"]](%para3, Tensor(43)[]) #(Tensor(F32)[], Tenso r(F32)[]) #scope: Default 24 # In file train.py(34)/ a = self.sub(x, 1)/#a 25 %2 : Tensor(F32)[] = PrimitivePy::Add{prim_type=2}[input_names=["x", "y"], output_names=["output"]](%1, %para4) #(Tensor(F32)[], Tensor(F32)[]) #scope: Default 26 # In file train.py(35)/ b = self.add(a, y)/#b 27 %3 : Tensor(F32)[] = FuncGraph::fg_23(%1, %2) #(Tensor(F32)[], Tensor(F32)[]) # fg_23=3_func.23 #scope: Default 28 # In file train.py(36)/ c = self.mul(b, self.func(a, b))/#[CNode]5 29 %4 : Tensor(F32)[] = PrimitivePy::Mul{prim_type=2}[input_names=["x", "y"], output_names=["output"]](%2, %3) #(Tensor(F32)[], Tensor(F32)[]) #sco pe: Default 30 # In file train.py(36)/ c = self.mul(b, self.func(a, b))/#c 31 Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[]) #scope: Default 32 # In file train.py(37)/ return c/#[CNode]4 33 } 34 # order: 35 # 1: 2_construct.22:a{[0]: ValueNode Sub, [1]: x, [2]: ValueNode Tensor(shape=[], dtype=Float32, value= 1)} 36 # 2: 2_construct.22:b{[0]: ValueNode Add, [1]: a, [2]: y} 37 # 3: 2_construct.22:[CNode]5{[0]: ValueNode 3_func.23, [1]: a, [2]: b} 38 # 4: 2_construct.22:c{[0]: ValueNode Mul, [1]: b, [2]: [CNode]5} 39 # 5: 2_construct.22:[CNode]4{[0]: ValueNode Return, [1]: c} 40 41 42 # [No.3] 3_func.23 43 # In file train.py(30)/ def func(x, y):/ 44 funcgraph fg_23( 45 %para5 : Tensor(F32)[] # x 46 , %para6 : Tensor(F32)[] # y 47 ) { 48 %1 : Tensor(F32)[] = PrimitivePy::Div{prim_type=2}[input_names=["x", "y"], output_names=["output"]](%para5, %para6) #(Tensor(F32)[], Tensor(F32) []) #scope: Default 49 # In file train.py(31)/ return self.div(x, y)/#[CNode]20 50 Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[]) #scope: Default 51 # In file train.py(31)/ return self.div(x, y)/#[CNode]19 52 } 53 # order: 54 # 1: 3_func.23:[CNode]20{[0]: ValueNode Div, [1]: x, [2]: y} 55 # 2: 3_func.23:[CNode]19{[0]: ValueNode Return, [1]: [CNode]20} 56 57 58 # num of total function graphs: 3 ``` Above, it lists all the graphs beginning with the entry graph. Line 1 indicates graph `1_construct_wrapper.21` whose id is `No.1`. And line 7 calls graph `2_construct.22`. line 17 to 39 shows the information of graph `2_construct.22`. Taking graph `2_construct.22` as an example. Line 18 tells us which function this graph is parsed from. Line 20 to 21 indicates the input information which is in the format of `%para[No.] : [data_type][shape] # [name]`. Line 23 to 32 indicates the graph structure, which contains several nodes, namely, `CNode`. In this example, there are `Sub`, `Add`, `Mul`. They are defined in the function `__init__`. Line 34 to 39 shows the execution order of the `CNode` from graph `2_construct.22`, corresponding to the order of code execution. The information format is: `No.: belonging graph:node name{[0]: the first input, [1]: the second input, ...}`. For `CNode`, the first input indicates how to compute for this `CNode`. Line 28 indicates the number of graphs. Here is 3. The [CNode](https://www.mindspore.cn/docs/en/r1.7/design/mindir.html#syntax) information format is as follows: including the node name, attribute, input node, output information, format and the corresponding source code. ```text %[No,] : [outputs' Spec] = [op_name]{[prim_type]}[attr0, attr1, ...](arg0, arg1, ...) #(inputs' Spec)#[scope] # Corresponding source code/#debug_name ``` ### dot Introduction We can use this file by [graphviz](http://graphviz.org/) as the input to generate images for users to view the model structure. For example, under the Linux operating system, we can convert a PNG image by the following command. ```shell dot -Tpng -o 04_abstract_specialize_0014.png 04_abstract_specialize_0014.dot ``` The transformed image is shown below, and we can visually see the model structure. The Different black boxes distinguish different subgraphs, and the blue arrows between graphs represent calling another graph. The blue area represents the parameter, the rectangle represents the parameter list of the graph, the hexagon and the black arrow represent the parameter as the input of the CNode to participate in the calculation process. The yellow rectangle represents the CNode. As can be seen from the picture, the CNode input starts from index 0, and the 0th input (that is, the purple or green area) represents what calculation the operator will perform, which is connected by a dotted arrow. The type is usually an operator primitive, or it can also be another graph. The rest inputs are the parameters required for the calculation. ![04_abstract_specialize_0014.png](./images/dot_to_png.png) For models with multiple operators, the picture will be very large. It is recommended using the visualization component [MindInsight](https://www.mindspore.cn/mindinsight/docs/en/r1.7/dashboard.html#computational-graph-visualization) to visualize computing graphs. ## Reading analyze_fail.dat In the process of `MindSpore` compiling a graph, the exceptions about graph evaluating fail usually happen. But we can find the reason by analyzing the exception information and analyze_fail.dat. For example, we run the script below. ```python 1 import mindspore.context as context 2 import mindspore.nn as nn 3 from mindspore import Tensor 4 from mindspore.nn import Cell 5 from mindspore import ops 6 from mindspore import dtype as mstype 7 8 context.set_context(mode=context.GRAPH_MODE) 9 context.set_context(save_graphs=True) 10 11 class Net(nn.Cell): 12 def __init__(self): 13 super().__init__() 14 self.add = ops.Add() 15 self.sub = ops.Sub() 16 self.mul = ops.Mul() 17 self.div = ops.Div() 18 19 def func(x, y): 20 return self.div(x, y) 21 22 def construct(self, x, y): 23 a = self.sub(x, 1) 24 b = self.add(a, y) 25 c = self.mul(b, self.func(a, a, b)) 26 return c 27 28 input1 = Tensor(3, mstype.float32) 29 input2 = Tensor(2, mstype.float32) 30 net = Net() 31 out = net(input1, input2) 32 print(out) ``` An error happens. ```text 1 [EXCEPTION] ANALYZER(31946,7f6f03941740,python):2021-09-18-15:10:49.094.863 [mindspore/ccsrc/pipeline/jit/static_analysis/stack_frame.cc:85] DoJump] The parameters number of the function is 2, but the number of provided arguments is 3. 2 FunctionGraph ID : func.18 3 NodeInfo: In file test.py(19) 4 def func(x, y): 5 6 Traceback (most recent call last): 7 File "test.py", line 31, in 8 out = net(input1, input2) 9 File "/home/workspace/mindspore/mindspore/nn/cell.py", line 404, in __call__ 10 out = self.compile_and_run(*inputs) 11 File "/home/workspace/mindspore/mindspore/nn/cell.py", line 682, in compile_and_run 12 self.compile(*inputs) 13 File "/home/workspace/mindspore/mindspore/nn/cell.py", line 669, in compile 14 _cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode) 15 File "/home/workspace/mindspore/mindspore/common/api.py", line 542, in compile 16 result = self._graph_executor.compile(obj, args_list, phase, use_vm, self.queue_name) 17 TypeError: mindspore/ccsrc/pipeline/jit/static_analysis/stack_frame.cc:85 DoJump] The parameters number of the function is 2, but the number of provided arguments is 3. 18 FunctionGraph ID : func.18 19 NodeInfo: In file test.py(19) 20 def func(x, y): 21 22 The function call stack (See file '/home/workspace/mindspore/rank_0/om/analyze_fail.dat' for more details): 23 # 0 In file test.py(26) 24 return c 25 ^ 26 # 1 In file test.py(25) 27 c = self.mul(b, self.func(a, a, b)) 28 ^ ``` Above exception is 'TypeError: mindspore/ccsrc/pipeline/jit/static_analysis/stack_frame.cc:85 DoJump] The parameters number of the function is 2, but the number of provided arguments is 3...'. And it tells us `FunctionGraph ID : func.18` only needs two parameters, but actually gives 3. We can find the related code is `self.func(a, a, b)` from 'The function call stack ... In file test.py(25)'. Easily, by checking the code, we know that we gave too much parameter to the calling function. Sometimes the exception information is not enough easy to understand. Or we want to see the part of graph information that have evaluated. Then we can open `/home/workspace/mindspore/rank_0/om/analyze_fail.dat` that indicated in the exception text by using a text editing software (for example, vi). ```text 1 # [No.1] construct_wrapper.0 2 # In file test.py(22)/ def construct(self, x, y):/ 3 funcgraph fg_0( 4 %para1 : Tensor(F32)[] # x 5 , %para2 : Tensor(F32)[] # y 6 ) { 7 8 #------------------------> 0 9 %1 = FuncGraph::fg_3(%para1, %para2) #(Tensor(F32)[], Tensor(F32)[]) # fg_3=construct.3 #scope: Default 10 # In file test.py(26)/ return c/#[CNode]2 11 Primitive::Return{prim_type=1}(%1) #(Undefined) #scope: Default 12 # In file test.py(26)/ return c/#[CNode]1 13 } 14 # order: 15 # 1: construct_wrapper.0:[CNode]2{[0]: ValueNode construct.3, [1]: x, [2]: y} 16 # 2: construct_wrapper.0:[CNode]1{[0]: ValueNode Return, [1]: [CNode]2} 17 18 19 # [No.2] construct.3 20 # In file test.py(22)/ def construct(self, x, y):/ 21 funcgraph fg_3( 22 %para3 : Tensor(F32)[] # x 23 , %para4 : Tensor(F32)[] # y 24 ) { 25 %1 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-Sub{prim_type=1}[input_names=["x", "y"], output_names=["output"]](%para3, I64(1)) #(Tensor(F32)[], I64) #scope: Default 26 # In file test.py(23)/ a = self.sub(x, 1)/#a 27 %2 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-Add{prim_type=1}[input_names=["x", "y"], output_names=["output"]](%1, %para4) #(Tensor(F32)[], Tensor(F32)[]) #scope: Default 28 # In file test.py(24)/ b = self.add(a, y)/#b 29 30 #------------------------> 1 31 %3 = FuncGraph::fg_18(%1, %1, %2) #(Tensor(F32)[], Tensor(F32)[], Tensor(F32)[]) # fg_18=func.18 #scope: Default 32 # In file test.py(25)/ c = self.mul(b, self.func(a, a, b))/#[CNode]5 33 %4 = DoSignaturePrimitive::S-Prim-Mul{prim_type=1}[input_names=["x", "y"], output_names=["output"]](%2, %3) #(Tensor(F32)[], Undefined) #scope: Default 34 # In file test.py(25)/ c = self.mul(b, self.func(a, a, b))/#c 35 Primitive::Return{prim_type=1}(%4) #(Undefined) #scope: Default 36 # In file test.py(26)/ return c/#[CNode]4 37 } 38 # order: 39 # 1: construct.3:a{[0]: a, [1]: ValueNode 1, [2]: ValueNode Float32} 40 # 2: construct.3:a{[0]: ValueNode S-Prim-Sub, [1]: x, [2]: ValueNode 1} 41 # 3: construct.3:b{[0]: ValueNode S-Prim-Add, [1]: a, [2]: y} 42 # 4: construct.3:[CNode]5{[0]: ValueNode func.18, [1]: a, [2]: a, [3]: b} 43 # 5: construct.3:c{[0]: ValueNode S-Prim-Mul, [1]: b, [2]: [CNode]5} 44 # 6: construct.3:[CNode]4{[0]: ValueNode Return, [1]: c} 45 46 47 #=============================================================================== 48 # num of function graphs in stack: 2 ``` The file `analyze_fail.dat` has the same information format with the file `.dat`. The only difference is `analyze_fail.dat` will locate the node which inferring failed. Searching the point by the text of `------------------------>`, we reach the last position of the `------------------------> 1` at line 30. The node at line 31 to 32 have an error. Its IR expression is `%3 = FuncGraph::fg_18(%1, %1, %2) ...`. We can know the node have 3 parameters from `(%1, %1, %2)`. But actually the function only need 2. So the compiler will fail when evaluating the node. To solve th problem, we should decrease the parameter number.