diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index 120ec826..cae0ed9a 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/workload.doctree b/.doctrees/workload.doctree index e634f490..330fc781 100644 Binary files a/.doctrees/workload.doctree and b/.doctrees/workload.doctree differ diff --git a/_sources/workload.rst.txt b/_sources/workload.rst.txt index 2a88c9f7..97e6ab6f 100644 --- a/_sources/workload.rst.txt +++ b/_sources/workload.rst.txt @@ -2,7 +2,7 @@ Workload ======== -The recommended way of defining an algorithmic workload is through an onnx model. An onnx model can contain multiple operator types, which in the context of ML are often referred to as layers, some of which are automatically recognised and parsed by ZigZag. Alternatively, the layers can be manually defined for more customization. +The recommended way of defining an algorithmic workload is through an onnx model. An onnx model can contain multiple operator types, which in the context of ML are often referred to as different layers, some of which are automatically recognized and parsed by ZigZag. Alternatively, the layers can be manually defined for more customization. Onnx models =========== @@ -12,18 +12,19 @@ Supported onnx operators A complete list of all onnx operators can be found `here `_. -Following operators are supported by ZigZag and will automatically be parsed into ``LayerNode`` objects when using your onnx model within the framework: +The following operators are supported by ZigZag and will automatically be parsed into ``LayerNode`` objects when using your onnx model within the framework: * `Conv `_ * `QLinearConv `_ * `MatMul `_ +* `Gemm `_ -All other operators will be parsed into a ``DummyNode`` object, which is assumed to not be accelerateable, incurring 0 hardware cost. If you have an onnx operator you would like to see supported, feel free to `open an issue `_ or manually add it yourself in the `ONNXModelParserStage `_ taking into account the :ref:`contributing guidelines`. +All other operators will be parsed into a ``DummyNode`` object, which is assumed to not be accelerateable, incurring zero hardware costs. If you have an onnx operator you would like to see supported, feel free to `open an issue `_ or manually add it yourself in the `ONNXModelParserStage `_ taking into account the :ref:`contributing guidelines`. Saving your onnx model with external data ----------------------------------------- -If your onnx model is rather large, and you want to avoid having it inside of your ZigZag repo, you can save it with external data, which saves the weights as an external file, which can be discarded as ZigZag doesn't require the weight values. You can do so as follows: +If your onnx model is rather large, and you want to avoid having it inside of your ZigZag repo, you can save the original model's weights as an external file, which can be discarded (ZigZag doesn't require the weight values but only the layers' shape). You can do so as follows: .. code:: python @@ -31,7 +32,7 @@ If your onnx model is rather large, and you want to avoid having it inside of yo # onnx_model is an in-memory ModelProto model = onnx.load('my_model_with_internal_data.onnx') onnx.save_model(model, 'path/to/save/the/model.onnx', save_as_external_data=True, all_tensors_to_one_file=True, location='external_data_filename', size_threshold=1024, convert_attribute=False) - # Then the onnx_model has converted raw data as external data and saved to specific directory + # Then the onnx_model has converted raw data as external data and saved to a specific directory Inferring an onnx model's shapes -------------------------------- @@ -50,16 +51,16 @@ ZigZag requires an inferred onnx model, as it needs to know the shapes of all in Manual layer definition ======================= -It is also possible to manually define your own workload layers. In that case there the ``main.py`` file should be executed instead of ``main_onnx.py``. Moreover, the workload file should be provided as input together with the accelerator, thus there is no onnx model and mapping file loaded. The mapping information is inserted for each layer alongside the layer shape definition, identically to how it was defined in the mapping file. +It is also possible to manually define your own workload layers. In that case, the ``main.py`` file should be executed instead of ``main_onnx.py``. Moreover, the workload file should be provided as input together with the accelerator, and there is no onnx model loaded. -Each layer definition is represented as a dict which should have the following attributes: +Each layer definition is represented as a Python dictionary, which should have the following attributes: -* **equation**: The operational equation for this layer. The dimensions should be small letters, where as the operands are large letters. 'O' should always be used for the output operand, the input operands can be named freely. +* **equation**: The operational equation for this layer. The dimensions should be small letters, whereas the operands are large letters. 'O' should always be used for the output operand; the input operands can be named freely. * **dimension_relations**: The relationship between different dimensions present in the equation. This is often used in convolutional layers, where there is a relationship between the spatial input indices and the spatial output indices through the stride and with the filter indices through the dilation rate. -* **loop_dim_size**: The size of the different dimensions present in the equation. Dimensions defined (i.e. on the left hand side) in the dimension_relations are not to be provided and are inferred automatically. +* **loop_dim_size**: The size of the different dimensions present in the equation. Dimensions defined (i.e. on the left-hand side) in the dimension_relations are not to be provided and are inferred automatically. * **operand_precision**: The bit precision of the different operands present in the equation. 'O' should always be used, which represents the partial output precision. 'O_final' represents the final output precision. * **operand_source**: The layer id the input operands of this layer come from. This is important to correctly build the NN graph edges. -* **constant_operands**: The operands of this layer which are constants and do not depend on prior computations. +* **constant_operands**: The operands of this layer which are constants and do not depend on prior computations, like the weight. * **core_allocation**: The core that will execute this layer. * **spatial_mapping**: The spatial parallelization strategy used for this layer. If none is provided, the SpatialMappingGeneratorStage should be used within ZigZag's execution pipeline. * **memory_operand_links**: The link between the virtual memory operands and the actual algorithmic operands. For more information, read the hardware readme. @@ -74,4 +75,4 @@ The following loop notation has to be used to describe a layer of the workload ( * **FY**: Kernel rows * **FX**: Kernel columns -An example of this manual layer defintion can be found at: `inputs/examples/workloads/resnet18.py `_. \ No newline at end of file +An example of this manual layer definition can be found at: `inputs/examples/workloads/resnet18.py `_. diff --git a/searchindex.js b/searchindex.js index a61baf26..92581123 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "code-documentation", "contribute", "future", "getting-started", "hardware", "index", "installation", "mapping", "outputs", "publications", "stages", "user-guide", "workload"], "filenames": ["api.rst", "code-documentation.rst", "contribute.rst", "future.rst", "getting-started.rst", "hardware.rst", "index.rst", "installation.rst", "mapping.rst", "outputs.rst", "publications.rst", "stages.rst", 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"Creating your custom stage": [[11, "creating-your-custom-stage"]], "User Guide": [[12, "user-guide"]], "Workload": [[13, "workload"]], "Onnx models": [[13, "onnx-models"]], "Supported onnx operators": [[13, "supported-onnx-operators"]], "Saving your onnx model with external data": [[13, "saving-your-onnx-model-with-external-data"]], "Inferring an onnx model\u2019s shapes": [[13, "inferring-an-onnx-model-s-shapes"]], "Manual layer definition": [[13, "manual-layer-definition"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/workload.html b/workload.html index 4333f11a..654ca720 100644 --- a/workload.html +++ b/workload.html @@ -1,11 +1,11 @@ - Workload — ZigZag 2.0.0 documentation Skip to content

Workload

The recommended way of defining an algorithmic workload is through an onnx model. An onnx model can contain multiple operator types, which in the context of ML are often referred to as layers, some of which are automatically recognised and parsed by ZigZag. Alternatively, the layers can be manually defined for more customization.

Onnx models

Supported onnx operators

A complete list of all onnx operators can be found here.

Following operators are supported by ZigZag and will automatically be parsed into LayerNode objects when using your onnx model within the framework:

All other operators will be parsed into a DummyNode object, which is assumed to not be accelerateable, incurring 0 hardware cost. If you have an onnx operator you would like to see supported, feel free to open an issue or manually add it yourself in the ONNXModelParserStage taking into account the Contributing guidelines.

Saving your onnx model with external data

If your onnx model is rather large, and you want to avoid having it inside of your ZigZag repo, you can save it with external data, which saves the weights as an external file, which can be discarded as ZigZag doesn’t require the weight values. You can do so as follows:

import onnx
+                          Workload — ZigZag 2.0.0 documentation                     Skip to content  
  
 
  
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
Workload
 
The recommended way of defining an algorithmic workload is through an onnx model. An onnx model can contain multiple operator types, which in the context of ML are often referred to as different layers, some of which are automatically recognized and parsed by ZigZag. Alternatively, the layers can be manually defined for more customization.
 
 
Onnx models
 
 
Supported onnx operators
 
A complete list of all onnx operators can be found here.
 
The following operators are supported by ZigZag and will automatically be parsed into LayerNode objects when using your onnx model within the framework:
  
All other operators will be parsed into a DummyNode object, which is assumed to not be accelerateable, incurring zero hardware costs. If you have an onnx operator you would like to see supported, feel free to open an issue or manually add it yourself in the ONNXModelParserStage taking into account the Contributing guidelines.
 
 
 
Saving your onnx model with external data
 
If your onnx model is rather large, and you want to avoid having it inside of your ZigZag repo, you can save the original model’s weights as an external file, which can be discarded (ZigZag doesn’t require the weight values but only the layers’ shape). You can do so as follows:
 
import onnx
 # onnx_model is an in-memory ModelProto
 model = onnx.load('my_model_with_internal_data.onnx')
 onnx.save_model(model, 'path/to/save/the/model.onnx', save_as_external_data=True, all_tensors_to_one_file=True, location='external_data_filename', size_threshold=1024, convert_attribute=False)
-# Then the onnx_model has converted raw data as external data and saved to specific directory
+# Then the onnx_model has converted raw data as external data and saved to a specific directory
 
 
 
 
 
Inferring an onnx model’s shapes
 
ZigZag requires an inferred onnx model, as it needs to know the shapes of all intermediate tensors to correctly infer the layer shapes. If you have an onnx model that is not shape inferred, you can do so by the following commands:
 
import onnx
 from onnx import shape_inference
 model = onnx.load("my_model.onnx")
 inferred_model = shape_inference.infer_shapes(model)
 onnx.save(inferred_model, "my_inferred_model.onnx")
-
 
 
 
 
 
Manual layer definition
 
It is also possible to manually define your own workload layers. In that case there the main.py file should be executed instead of main_onnx.py. Moreover, the workload file should be provided as input together with the accelerator, thus there is no onnx model and mapping file loaded. The mapping information is inserted for each layer alongside the layer shape definition, identically to how it was defined in the mapping file.
 
Each layer definition is represented as a dict which should have the following attributes:
 
     
  • equation: The operational equation for this layer. The dimensions should be small letters, where as the operands are large letters. ‘O’ should always be used for the output operand, the input operands can be named freely.
     
  • dimension_relations: The relationship between different dimensions present in the equation. This is often used in convolutional layers, where there is a relationship between the spatial input indices and the spatial output indices through the stride and with the filter indices through the dilation rate.
     
  • loop_dim_size: The size of the different dimensions present in the equation. Dimensions defined (i.e. on the left hand side) in the dimension_relations are not to be provided and are inferred automatically.
     
  • operand_precision: The bit precision of the different operands present in the equation. ‘O’ should always be used, which represents the partial output precision. ‘O_final’ represents the final output precision.
     
  • operand_source: The layer id the input operands of this layer come from. This is important to correctly build the NN graph edges.
     
  • constant_operands: The operands of this layer which are constants and do not depend on prior computations.
     
  • core_allocation: The core that will execute this layer.
     
  • spatial_mapping: The spatial parallelization strategy used for this layer. If none is provided, the SpatialMappingGeneratorStage should be used within ZigZag’s execution pipeline.
     
  • memory_operand_links: The link between the virtual memory operands and the actual algorithmic operands. For more information, read the hardware readme.
     
 
The following loop notation has to be used to describe a layer of the workload (see loop notation in this paper):
 
     
  • B: Batch size
     
  • K: Output channels
     
  • C: Input channels
     
  • OY: Output rows
     
  • OX: Output columns
     
  • FY: Kernel rows
     
  • FX: Kernel columns
     
 
An example of this manual layer defintion can be found at: inputs/examples/workloads/resnet18.py.
 
 
 
 
 
 
 
   
\ No newline at end of file
+

Manual layer definition

It is also possible to manually define your own workload layers. In that case, the main.py file should be executed instead of main_onnx.py. Moreover, the workload file should be provided as input together with the accelerator, and there is no onnx model loaded.

Each layer definition is represented as a Python dictionary, which should have the following attributes:

  • equation: The operational equation for this layer. The dimensions should be small letters, whereas the operands are large letters. ‘O’ should always be used for the output operand; the input operands can be named freely.

  • dimension_relations: The relationship between different dimensions present in the equation. This is often used in convolutional layers, where there is a relationship between the spatial input indices and the spatial output indices through the stride and with the filter indices through the dilation rate.

  • loop_dim_size: The size of the different dimensions present in the equation. Dimensions defined (i.e. on the left-hand side) in the dimension_relations are not to be provided and are inferred automatically.

  • operand_precision: The bit precision of the different operands present in the equation. ‘O’ should always be used, which represents the partial output precision. ‘O_final’ represents the final output precision.

  • operand_source: The layer id the input operands of this layer come from. This is important to correctly build the NN graph edges.

  • constant_operands: The operands of this layer which are constants and do not depend on prior computations, like the weight.

  • core_allocation: The core that will execute this layer.

  • spatial_mapping: The spatial parallelization strategy used for this layer. If none is provided, the SpatialMappingGeneratorStage should be used within ZigZag’s execution pipeline.

  • memory_operand_links: The link between the virtual memory operands and the actual algorithmic operands. For more information, read the hardware readme.

The following loop notation has to be used to describe a layer of the workload (see loop notation in this paper):

  • B: Batch size

  • K: Output channels

  • C: Input channels

  • OY: Output rows

  • OX: Output columns

  • FY: Kernel rows

  • FX: Kernel columns

An example of this manual layer definition can be found at: inputs/examples/workloads/resnet18.py.

\ No newline at end of file