# Reading IR [![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.8/resource/_static/logo_source_en.png)](https://gitee.com/mindspore/docs/blob/r1.8/tutorials/experts/source_en/debug/mindir.md) ## Overview When a model compiled using MindSpore runs in the graph mode `set_context(mode=GRAPH_MODE)` and `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/r1.8/dashboard.html#computational-graph-visualization) to visualize computing graphs. ## Saving IR `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. In the training script `train.py`, we add the following code to the `set_context` function, when running the training script, MindSpore will automatically store the IR file generated during compilation to the specified path. ```python if __name__ == "__main__": set_context(save_graphs=True, save_graphs_path="path/to/ir/files") ``` After the training command is executed, several files were generated under the specified 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 entrance function and this phase generates MindIR initially. 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 entrance function, mainly recursive resolution entry functions directly or indirectly reference to other functions and objects. When using the unsupported syntax, it will get an error in this phase. - The `abstract_specialize` phase means that the data type and shape information for all nodes in the IR is deduced from the input information. 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 refers hardware-independent optimization is performed. The automatic differential and automatic parallel functions are also performed. This stage can be subdivided into several substages. In the list of IR files, where the files prefixed with `opt_pass_ [ordinal]` are IR files saved after the end of these sub-stages, non-framework developers do not need to pay too much attention. - The `validate` phase will verify the compiled compute graph and 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. The IR graph in this stage is the final graph in the phase of frontend. In addition, because the backend is closer to the bottom layer, non-framework developers do not need to pay much attention to other IR files saved during the backend optimization process (such as files that begin with `hwopt`). Non-framework developers only need to look at the file named `graph_build_[Graph Sequence Number]_[IR File Sequence Number].ir`, i.e. IR after all front and back end optimizations. > Multiple files may be saved because the backend is optimized on subgraphs, which is different from the mechanism by which multiple subgraphs on the front end are saved in the same file. ## IR File Contents Introduction The following is an example to describe the contents of the IR file. Run the script: ```python import mindspore as ms from mindspore import nn, ops ms.set_context(mode=ms.GRAPH_MODE) ms.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 = ms.Tensor(3, ms.float32) input2 = ms.Tensor(2, ms.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 output after execution. The file contents are as follows (Here is MindSpore 1.6, and the content may have some imperceptible changes with the version upgrade): ```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: - Line 1 tells us `1_construct_wrapper.21`, the name of the top MindSpore graph about the network, which is 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)` defined in the network. 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` defined in the function `__init__`. - Line 19 shows that figure `3_func.23` is called in the form of `call @3_func.23`, corresponding to the execution of the two-digit division of the function `func` in the script. The `CNode` ([check the design of ANF-IR](https://www.mindspore.cn/docs/en/r1.8/design/mindir.html#syntax)) information format is as follows: from left to right, the ordinal number, node name - debug_name, operator name - op_name, input node - arg, attributes of the node - primitive_attrs, input and output specifications, source code parsing call stack and other information. Because the ANF graph is a unidirectional acyclic graph, 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 (Here is MindSpore 1.6, and the content may have some imperceptible changes with the version upgrade): ```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 show the information of graph `2_construct.22`. Taking graph `2_construct.22` whose information is located at Line 17 to 39 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 compute nodes in the graph, 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 58 indicates the number of graphs. Here is 3. The `CNode` ([check the design of ANF-IR](https://www.mindspore.cn/docs/en/r1.8/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 by using the visualization component [MindInsight](https://www.mindspore.cn/mindinsight/docs/en/r1.8/dashboard.html#computational-graph-visualization) to visualize compute graphs. ## How to derive the cause of the failure based on the analyze_fail.dat file analysis graph 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. ### Example 1: parameters number mismatch ```python 1 import mindspore as ms 2 import mindspore.nn as nn 3 from mindspore.nn import Cell 4 from mindspore import ops 5 6 ms.set_context(mode=ms.GRAPH_MODE) 7 ms.set_context(save_graphs=True) 8 9 class Net(nn.Cell): 10 def __init__(self): 11 super().__init__() 12 self.add = ops.Add() 13 self.sub = ops.Sub() 14 self.mul = ops.Mul() 15 self.div = ops.Div() 16 17 def func(x, y): 18 return self.div(x, y) 19 20 def construct(self, x, y): 21 a = self.sub(x, 1) 22 b = self.add(a, y) 23 c = self.mul(b, self.func(a, a, b)) 24 return c 25 26 input1 = ms.Tensor(3, ms.float32) 27 input2 = ms.Tensor(2, ms.float32) 28 net = Net() 29 out = net(input1, input2) 30 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(17) 4 def func(x, y): 5 6 Traceback (most recent call last): 7 File "test.py", line 29, 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(17) 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(24) 24 return c 25 ^ 26 # 1 In file test.py(23) 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(23)'. Easily, by checking the code, we know that we gave too much parameter to the calling function. Sometimes when the exception information is not enough easy to understand, or we want to see the part of graph information that have evaluated, we use text editing software (e.g., vi) to open the file (in parentheses on line 20) that prompts in the error message: `/home/workspace/mindspore/rank_0/om/analyze_fail.dat` with the following content (Here is MindSpore 1.6, and the content may have some imperceptible changes with the version upgrade): ```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. This last arrow points to the node that derives the error, which is `%3 = FuncGraph::fg_18(%1, %1, %2)...`, which expresses the information of the node in IR. How to view the dat file has been described in the `Dat File Introduction` section earlier, and will not be repeated here. 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)`. From the source parsing call stack, it can be known that the function is actually `self.func`, which is defined in the script as `def dunc(x, y):...`. In the function definition, only two parameters are needed, so there will be a deduction failure error, and we need to modify the number of parameters passed in the script to solve the problem. ### Example 2: BiasAdd inputs shape mismatch ```python 1 import numpy as np 2 import mindspore 3 from mindspore import nn, ops, context, Tensor, Parameter 4 from mindspore.common.initializer import initializer 5 6 class Net(nn.Cell): 7 def __init__(self): 8 super(Net, self).__init__() 9 self.weight = Parameter(initializer('normal', [32, 8]), name="weight") 10 self.bias = Parameter(initializer('zeros', [4]), name="bias") 11 12 self.matmul = ops.MatMul() 13 self.bias_add = ops.BiasAdd() 14 15 def construct(self, x1): 16 x = self.matmul(x1, self.weight) 17 x = self.bias_add(x, self.bias) 18 return x 19 20 net = Net() 21 x = Tensor(np.arange(3*32).reshape(3, 32), mindspore.float32) 22 out = net(x) 23 print('out', out.shape) ``` An error happens. ```text Traceback (most recent call last): File "test.py", line 22, in out = net(x) File "/home/workspace/mindspore/build/package/mindspore/nn/cell.py", line 573, in __call__ out = self.compile_and_run(*args) File "/home/workspace/mindspore/build/package/mindspore/nn/cell.py", line 956, in compile_and_run self.compile(*inputs) File "/home/workspace/mindspore/build/package/mindspore/nn/cell.py", line 929, in compile _cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode) File "/home/workspace/mindspore/build/package/mindspore/common/api.py", line 1076, in compile result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode()) ValueError: For 'BiasAdd', bias[0] shape must be equal to input_x[1] shape when data_format is NHWC or input_x[1] shape, but got bias[0] shape: 4, input_x[1] or input_x[1] shape: 8. ---------------------------------------------------- - The Traceback of Net Construct Code: ---------------------------------------------------- The function call stack (See file '/home/workspace/mindspore/rank_0/om/analyze_fail.dat' for more details. Get instructions about `analyze_fail.dat` at https://www.mindspore.cn/search?inputValue=analyze_fail.dat): # 0 In file test.py(17) x = self.bias_add(x, self.bias) ^ ---------------------------------------------------- - C++ Call Stack: (For framework developers) ---------------------------------------------------- mindspore/core/ops/bias_add.cc:71 BiasAddInferShape ``` The above reports that the errors is caused by the mismatching of the shape of the first input and the second input of the operator `BiasAdd`. To further understand what changes have taken place in the shape of the operator, we use text editing software (e.g., vi) to open the file that prompts in the error message: `/home/workspace/mindspore/rank_0/om/analyze_fail.dat` with the following content (Here is MindSpore 1.8, and the content may have some imperceptible changes with the version upgrade): ```text 1 # 1.This file shows the parsed IR info when graph evaluating failed to help find the problem. 2 # 2.You can search the last `------------------------>` to the node which is inferred failed. 3 # 3.Refer to https://www.mindspore.cn/search?inputValue=analyze_fail.dat to get more instructions. 4 # =============================================================================== 5 6 # [No.1] construct_wrapper.1 7 # In file test.py(15)/ def construct(self, x1):/ 8 funcgraph fg_1( 9 %para1 : Tensor(F32)[3, 32] # x1 10 , %para2 : Ref[Tensor(F32)][4] # bias 11 , %para3 : Ref[Tensor(F32)][32, 8] # weight 12 ) { 13 14 #------------------------> 0 15 %1 = FuncGraph::fg_2(%para1) #(Tensor(F32)[3, 32]) # fg_2=construct.2 #scope: Default 16 #[CNode]3 17 Primitive::Return{prim_type=1}(%1) #(Undefined) #scope: Default 18 # In file test.py(18)/ return x/#[CNode]4 19 } 20 # order: 21 # 1: construct_wrapper.1:[CNode]3{[0]: ValueNode construct.2, [1]: x1} 22 # 2: construct_wrapper.1:[CNode]4{[0]: ValueNode Return, [1]: [CNode]3} 23 24 25 # [No.2] construct.2 26 # In file test.py(15)/ def construct(self, x1):/ 27 funcgraph fg_2[fg_1]( 28 %para4 : Tensor(F32)[3, 32] # x1 29 ) { 30 %1 : Tensor(F32)[3, 8] = DoSignaturePrimitive::S-Prim-MatMul{prim_type=1}[output_names=["output"], transpose_a=Bool(0), input_names=["x1", "x2"], transpose_x2=Bool(0), transpose_x1 =Bool(0), transpose_b=Bool(0)](%para4, %para3) #(Tensor(F32)[3, 32], Ref[Tensor(F32)][32, 8]) #scope: Default 31 # In file test.py(16)/ x = self.matmul(x1, self.weight)/#x 32 33 #------------------------> 1 34 %2 = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[output_names=["output"], format="NCHW", input_names=["x", "b"]](%1, %para2) #(Tensor(F32)[3, 8], Ref[Tensor(F32)][4]) #sco pe: Default 35 # In file test.py(17)/ x = self.bias_add(x, self.bias)/#x 36 Primitive::Return{prim_type=1}(%2) #(Undefined) #scope: Default 37 # In file test.py(18)/ return x/#[CNode]5 38 } 39 # order: 40 # 1: construct.2:x{[0]: ValueNode S-Prim-MatMul, [1]: x1, [2]: weight} 41 # 2: construct.2:x{[0]: ValueNode S-Prim-BiasAdd, [1]: x, [2]: bias} 42 # 3: construct.2:[CNode]5{[0]: ValueNode Return, [1]: x} 43 44 45 #=============================================================================== 46 # num of function graphs in stack: 2/3 (Ignored 1 internal frames). ``` Search `------------------------>` to the position where inferring failed at line 33. According to `...(%1, %para2) #(Tensor(F32)[3, 8], Ref[Tensor(F32)][4])`, `BiasAdd`'s inputs are `%1` and `%para2`. That `%1`' with shape `[3, 8]` and `%para2` with shape `[4]` doesn't meet the requirement about `bias (Tensor) - The bias tensor, with shape (C). C must be the same as channel dimension C of input_x...` for `BiasAdd` API. Thus, an error happens. To solve this problem, we need modify the shape of `%1` or `%para2` (namely `self.bias`). - For `%para2` (namely `self.bias`), we modify the shape of `self.bias` by `self.bias = Parameter(initializer('zeros', [8]), name="bias")`. - For `%1`, we need know what `%1` is. According to line 30, `%1` is a `MatMul` with output shape `[3, 8]`. Its inputs are `(%para4, %para3)`. The first input (namely given arg `x`) shape is `[3, 32]` and the second input (namely `self.weight`) shape is `[32, 8]`. To meet the requirement of `BiasAdd` with the data shape `[4]`, the shape of `%1` output needs to be `[3, 4]`. Therefore, we modify `self.weight` by `self.weight = Parameter(initializer('normal', [32, 4]), name="weight")`.