"<font size=\"3\">The scheme resembles that of the code generation transformation pass. The pass iterates over all nodes in the model and if `domain=\"finn\"` and `backend=\"fpgadataflow\"` is True, the compilation is activated for that node. First an instance of the node is created and checked whether the code was generated. For this the node attribute `code_gen_dir` is checked. If it exists, the function `compile_singlenode_code()` can be executed. Then it is checked whether the path to the executable has been set. There is an exception if the custom op_type is not supported. \n",
"\n",
"The actual compilation is done with the function `compile_singlenode_code()`. What happens inside the function is shown below.</font>"
<fontsize="3">This notebook is about code generation and compilation to enable execution of FINN custom operation nodes.
Following showSrc function is used to print the source code of function calls in the Jupyter notebook:</font>
%% Cell type:code id: tags:
``` python
importinspect
defshowSrc(what):
print("".join(inspect.getsourcelines(what)[0]))
```
%% Cell type:markdown id: tags:
## Outline
-------------
*<fontsize="3">Example model</font>
*<fontsize="3">Code generation</font>
*<fontsize="3">Compilation</font>
%% Cell type:markdown id: tags:
### Example model
<fontsize="3">To show the code generation and compilation of a node, an example model with a streaming fclayer node is first created. To learn more about FINN custom operation nodes, please take a look at notebook [FINN-CustomOps](FINN-CustomOps.ipynb).
First TensorProto and helper are imported from ONNX. These functions can be used to create tensors, nodes, graphs and models in ONNX. Additional functions from `util` and the classes `DataType` and `ModelWrapper` are needed. More information about `DataType` and `ModelWrapper` can be found in Jupyter notebook [FINN-ModelWrapper](FINN-ModelWrapper.ipynb).</font>
%% Cell type:code id: tags:
``` python
fromonnximportTensorProto,helper
importfinn.core.utilsasutil
fromfinn.core.datatypeimportDataType
fromfinn.core.modelwrapperimportModelWrapper
```
%% Cell type:markdown id: tags:
<fontsize="3">Then all parameters, that are needed to create a streaming fclayer, are set. To keep the example clear small values are chosen. For more information about the parameters please take look at the documentation of the [finn-hls library](https://finn-hlslib.readthedocs.io/en/latest/).</font>
%% Cell type:code id: tags:
``` python
idt=wdt=odt=DataType.BIPOLAR
mw=8
mh=8
pe=4
simd=4
nf=mh//pe
sf=mw//simd
```
%% Cell type:markdown id: tags:
<fontsize="3">A `tensor_value_info` is created for all tensors involved. In this case there is one tensor for the weights besides the input and output tensors. Then an input list is created containing the two inputs (`"inp"`and `"weights"`).</font>
<fontsize="3">Now the node can be created. The operation type is set to `"StreamingFCLayer_Batch"` and the rest of the attributes are set appropriately. The relevant attributes for the activation of the code generation and compilation are:</font>
*<fontsize="3">**`domain="finn"`**: specifies that the created node is a FINN-Custom Op</font>
*<fontsize="3">**`backend="fpgadataflow"`**: specifies that it is a node that corresponds to a function in the finn-hls library</font>
*<fontsize="3">**`code_gen_dir"`**: specifies the path to the directory where the generated c++ files are (is set during code generation)</font>
*<fontsize="3">**`executable_path"`**: specifies the path to the executable created after compilation (is set during compilation)</font>
%% Cell type:code id: tags:
``` python
FCLayer_node=helper.make_node(
"StreamingFCLayer_Batch",
node_inp_list,
["outp"],
domain="finn",
backend="fpgadataflow",
code_gen_dir="",
executable_path="",
resType="ap_resource_lut()",
MW=mw,
MH=mh,
SIMD=simd,
PE=pe,
noActivation=1,
binaryXnorMode=1,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
)
```
%% Cell type:markdown id: tags:
<fontsize="3"> The node is packed into a graph environment and the inputs and outputs are set.</font>
<fontsize="3">A model is now created from the graph, which is then converted into a ModelWrapper object for further processing in FINN. Afterwards the ModelWrapper internal functions can be used to set the FINN data types and the initializer for the weights. Since this is an example, the weights are not taken from the training, but random values are generated using the utility function `gen_finn_dt_tensor()`. This function gets a FINN datatype and a shape and generates a tensor with values of this datatype in the desired shape.</font>
<fontsize="3">Code generation is a transformation that can be applied to the model. For more information about transformation passes, see Jupyter Notebook [FINN-HowToTransformPass](FINN-HowToTransformPass.ipynb).
The code generation transformation is shown below.</font>
<fontsize="3">The transformation pass iterates over all nodes in the model and if `domain="finn"` and `backend="fpgadataflow"` is True, the function `_codegen_single_node()` is executed which is also part of the transformation pass and is shown below. </font>
# ensure that there is generated code inside the dir
inst.code_generation(model)
except KeyError:
# exception if op_type is not supported
raise Exception("Custom op_type %s is currently not supported." % op_type)
%% Cell type:markdown id: tags:
<fontsize="3">An instance of the node is created and checked for the attribute `code_gen_dir`. If the attribute is not set, a temporary directory is created and the attribute is set accordingly.
Then the `code_generation()` function of the instance is called. If an error occurs during this process, this is probably due to the fact that the selected CustomOp is not yet supported. The following description of the code generation within the CustomOp instance may lead to overlaps with the Jupyter notebook [FINN-CustomOps](FINN-CustomOps.ipynb).
In order to clarify the individual components involved in code generation, an instance of the node is first created, as in the `_codegen_single_node` function. This is done by looking up the op_type in the [registry](https://github.com/Xilinx/finn/blob/dev/src/finn/custom_op/registry.py) of CustomOps. The instance contains a template for code generation which is shown below.</font>
%% Cell type:code id: tags:
``` python
importfinn.custom_op.registryasregistry
node=FCLayer_node
op_type=FCLayer_node.op_type
inst=registry.custom_op[op_type](node)
print(inst.docompute_template)
```
%% Output
#include "cnpy.h"
#include "npy2apintstream.hpp"
#include <vector>
#include "bnn-library.h"
// includes for network parameters
$GLOBALS$
// defines for network parameters
$DEFINES$
int main(){
$STREAMDECLARATIONS$
$READNPYDATA$
$DOCOMPUTE$
$DATAOUTSTREAM$
$SAVEASCNPY$
}
%% Cell type:markdown id: tags:
<fontsize="3">The template has some general constructs, like the inclusion of bnn-library.h, which contains the references to the finn-hls library, and of cnpy.h and npy2apintstream.hpp, which support the transfer of python numpy arrays in c++. The idea of this template is to replace the variables marked with `$ $` with c++ calls during code generation. Then the template can be written into a .cpp file and be compiled.
The sub-functions that are called during code generation are shown below.</font>
f = open(os.path.join(code_gen_dir, "execute_{}.cpp".format(node.op_type)), "w")
f.write(template)
f.close()
%% Cell type:markdown id: tags:
<fontsize="3">Except for the function `generate_params(model)` all functions needed to fill the template correspond to the `$ $` variable names, i.e. function `defines()` returns the part of the c++ code that replaces `$DEFINES$` in the template. The individual functions are member functions of the class HLSCustomOp and are defined in each CustomOp. The code for a StreamingFCLayer_Batch node can be looked up in the [code](https://github.com/Xilinx/finn/blob/dev/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py).</font>
%% Cell type:markdown id: tags:
<fontsize="3">A special function for code generation for the StreamingFCLayer_Batch node is the `generate_params(model)` function. Besides the normal input tensor, a fc layer has weight values as input and can get additional thresholds for activation. This function reads the values for the weights and thresholds via the `get_initializer` function of the ModelWrapper and writes them c++ conform in .h files, which are added to the includes.
The `generate_params` function of the StreamingFCLayer_Batch is shown below.</font>
<fontsize="3">The generated code is written to the previously created temporary directory and the node attribute `code_gen_dir` is set. This completes the code generation for executing a single CustomOp and the next step is compilation. </font>
%% Cell type:markdown id: tags:
### Compilation
%% Cell type:markdown id: tags:
<fontsize="3">The compilation is a transformation pass like the code generation. The code of this transformation is shown below. </font>
"Custom op_type %s is currently not supported." % op_type
)
return (model, False)
%% Cell type:markdown id: tags:
<fontsize="3">The scheme resembles that of the code generation transformation pass. The pass iterates over all nodes in the model and if `domain="finn"` and `backend="fpgadataflow"` is True, the compilation is activated for that node. First an instance of the node is created and checked whether the code was generated. For this the node attribute `code_gen_dir` is checked. If it exists, the function `compile_singlenode_code()` can be executed. Then it is checked whether the path to the executable has been set. There is an exception if the custom op_type is not supported.
The actual compilation is done with the function `compile_singlenode_code()`. What happens inside the function is shown below.</font>
