diff --git a/docs/website/.gitignore b/docs/website/.gitignore deleted file mode 100644 index 7026e5e212..0000000000 --- a/docs/website/.gitignore +++ /dev/null @@ -1,22 +0,0 @@ -# Dependencies -/node_modules - -# Production -/build - -# Generated files -.docusaurus -.cache-loader -/static/py_api -/static/cpp_api - -# Misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/docs/website/.npmrc b/docs/website/.npmrc deleted file mode 100644 index a16e3e176f..0000000000 --- a/docs/website/.npmrc +++ /dev/null @@ -1,2 +0,0 @@ -# Stop people use npm instead of yarn by accident -engine-strict = true diff --git a/docs/website/README.md b/docs/website/README.md deleted file mode 100644 index 07f432df8e..0000000000 --- a/docs/website/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Website - -This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. - -### Installation - -``` -$ yarn -``` - -### Local Development - -``` -$ yarn start -``` - -This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. - -### Build - -``` -$ yarn build -``` - -This command generates static content into the `build` directory and can be served using any static contents hosting service. diff --git a/docs/website/babel.config.js b/docs/website/babel.config.js deleted file mode 100644 index 06cff29cac..0000000000 --- a/docs/website/babel.config.js +++ /dev/null @@ -1,11 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -module.exports = { - presets: [require.resolve('@docusaurus/core/lib/babel/preset')], -}; diff --git a/docs/website/docs/basics/terminology.md b/docs/website/docs/basics/terminology.md deleted file mode 100644 index 194932f1ec..0000000000 --- a/docs/website/docs/basics/terminology.md +++ /dev/null @@ -1,37 +0,0 @@ -# Basics -## Terminology - -### ATen mode -ATen mode uses the ATen (pytorch core) implementation of Tensor (`at::Tensor`) -along with related types (ScalarType, etc.) -* `at::Tensor` is big and complex, and often allocates memory with new/malloc -* The ATen kernels, which rely on the full `at::Tensor` API, are usable in this - configuration -* Those kernels also tend to do dynamic memory allocation, and often have extra - flexibility (and thus overhead) to handle things not needed by mobile/embedded - clients: e.g., CUDA support, sparse tensor support, dtype promotion - -### Portable mode -Portable mode uses ExecuTorch's smaller `torch::executor::Tensor` (aka ETensor) -implementation, along with related types (`torch::executor::ScalarType`, etc.) -* ETensor's API is a source-compatible subset of `at::Tensor`. Code that is - written against ETensor can also build against `at::Tensor`. -* "portable mode kernels" are any operator implementations that are written to be - compatible with ETensor. But that means they can also be built against - `at::Tensor` if desired, and used in the same model as ATen kernels. -* ETensor does not own or allocate memory on its own - * To support dynamic shapes, kernels can allocate - Tensor data using the MemoryAllocator provided by the client. - -### Portable kernels -See ([`executorch/kernels/portable/README.md`](https://github.com/pytorch/executorch/blob/main/kernels/portable/README.md)) for technical details. -Portable kernels, which live under `executorch/kernels/portable`, are: -* Portable mode kernels -* Compatible with ATen operator signatures -* Written in portable C++ so that they can build for any target -* Written as reference implementations, prioritizing clarity and simplicity - over optimization -* Generally much smaller in code size than ATen kernels -* Written to avoid dynamically allocating memory using new/malloc - * To support dynamic shapes, some kernels may allocate - Tensor data using the MemoryAllocator provided by the client. diff --git a/docs/website/docs/contributors/code_style.md b/docs/website/docs/contributors/code_style.md deleted file mode 100644 index 8bb2a6b0f1..0000000000 --- a/docs/website/docs/contributors/code_style.md +++ /dev/null @@ -1,5 +0,0 @@ -# Code Style - -See the top-level -[CONTRIBUTING.md](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md) -file. diff --git a/docs/website/docs/export/00_export_manual.md b/docs/website/docs/export/00_export_manual.md deleted file mode 100644 index 927d494580..0000000000 --- a/docs/website/docs/export/00_export_manual.md +++ /dev/null @@ -1,31 +0,0 @@ -# PT2.0 Export Manual - -## Context -At a high level, the goal of PT2 Export is to enable the execution of entire -PyTorch programs by other means than the “eager” PyTorch runtime, with a -representation that is amenable to meet the optimization and targeting goals of -specialized use cases. Specifically, we want to enable users to convert their -PyTorch models to a standardized IR, decoupled from its execution, that various -domain-specific runtimes can transform and execute independently. This -conversion is powered by Dynamo’s technique for sound whole-graph -capture—capturing a graph without any “breaks” that would require the eager -runtime to fall back to Python. The rest of this wiki documents a snapshot of -PT2 Export as of early May 2023—what we consider an "MVP" release. Please note -that this project is under active and heavy development: while this snapshot -should give a fairly accurate picture of the final state, some some details -might change in the coming weeks / months based on feedback. If you have any -issues, please file an issue on Github and tag "export". - -## Documentation -- [Overview](./overview.md) - - [Background](./background.md) - - [Overall Workflow](./overall_workflow.md) - - [Soundness](./soundness.md) - - [Errors](./errors.md) -- [Export API Reference](./export_api_reference.md) - - [Modules and Entrypoints](./modules_and_entrypoints.md) - - [Constraints API](./constraint_apis.md) - - [Exported Program](../ir_spec/00_exir.md#exportedprogram) - - [Control Flow Operators](../ir_spec/control_flow.md) - - [Custom Operators](./custom_operators.md) -- [ExportDB](./exportdb.md) diff --git a/docs/website/docs/export/README.md b/docs/website/docs/export/README.md deleted file mode 100644 index ef0d039ae3..0000000000 --- a/docs/website/docs/export/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Deprecated folder - -This folder is deprecated. For up-to-date comprehensive technical overview of ExecuTorch and step-by-step tutorials, -please visit our [documentation website](https://pytorch.org/executorch). For export tutorial, please visit [`torch.export()`](https://pytorch.org/docs/main/export.html). diff --git a/docs/website/docs/export/background.md b/docs/website/docs/export/background.md deleted file mode 100644 index 612047b03e..0000000000 --- a/docs/website/docs/export/background.md +++ /dev/null @@ -1,61 +0,0 @@ -# Background - -## Setup -Let's say you have a function that you want to export. You export it by passing -example inputs (tensors) as arguments to `torch._export.export`. The exported -program can then be called with other inputs. -```python -from torch._export import export - -def foo(x): # expect x to be a tensor - ... - -t = torch.rand(4, 8, 32) # example input tensor -exported_foo = export(foo, t) - -# expect that exported_foo can now be called with other input tensor -``` -More generally, the function to be exported can take multiple inputs, and the -function itself could be a torch.nn.Module (with a forward method). See [Export -API Reference](./export_api_reference.md). - -## Graph Breaks - -The PT2 compiler is a "tracing" compiler, which means that it compiles the -execution path—or "trace"—of your function on your example inputs. The -intermediate representation of such a trace is a graph. In eager mode it is -usual to have graph breaks, where the compiler can fail to trace some parts of -the code; this is fine because it can always fall back to the Python interpreter -to fill these gaps. However in export mode we do not want any graph breaks: we -want the compiler to capture the entire execution in a single graph. - -### Rewriting Code -s -Graph breaks can arise either because of missing support for Python features, or -because the compiler cannot decide which control flow path to continue tracing -on. In most cases, it is possible to rewrite code to avoid such graph breaks and -complete the export.When the compiler's tracing mechanism does not support some -Python feature, we strive to provide a workaround as part of the error message. -Over time, we expect to fill in such gaps. On the other hand, not being able to -decide which control flow path to continue tracing on is a necessary limitation -of the compiler. You are required to use special operators to unblock such -cases. See [Control Flow Operators](../ir_spec/control_flow.md). - -## Shapes - -Recall that while we need example inputs for export, we must generalize the -exported program to be callable with other inputs. The main mechanism for this -generalization is through reuse of shapes (of tensors). Next, let us dive deeper -into shapes. - -### Static and Dynamic Dimensions - -The shape of a tensor is a tuple of dimensions. Roughly speaking, the exported -program can be called with inputs of the same shape as the example inputs. By -default, we assume that dimensions are static: the compiler assumes they are -going to be the same, and specializes the exported program to those -dimensions.However, some dimensions, such as a batch dimension, are expected to -not be the same—the example inputs passed to export may have a different batch -size as inputs to the exported program. Such dimensions must be marked dynamic. -See [Soundness](./soundness.md) to learn how to specify properties of such -dimensions. diff --git a/docs/website/docs/export/constraint_apis.md b/docs/website/docs/export/constraint_apis.md deleted file mode 100644 index dd12a26948..0000000000 --- a/docs/website/docs/export/constraint_apis.md +++ /dev/null @@ -1,91 +0,0 @@ -# Constraint APIs - -To enable the export of input shape-dependent models, it is necessary for users -to impose constraints on the tracing inputs, ensuring the safe traversal of the -model during the export process. For a comprehensive understanding of how to -utilize this feature, please refer to [this overview](./soundness.md). To -express these constraints, we have developed the following set of APIs. - -## dynamic_dim -```python -def dynamic_dim(x: torch.Tensor, dim: int): - """ - Marks the dimension `dim` of input `x` as unbounded dynamic - """ - pass -``` - -It is possible to impose specific bounds on the marked dynamic dimension. For -example: -```python -constraints = [dynamic_dim(x, 0) >= 3, dynamic_dim(x, 0) <= 6] -``` - -By passing the above `constraints` to export, we effectively establish the range -of acceptable values for the 0th dimension of tensor x, constraining it between -3 and 6. Consequently, the PT2 Export functionality can safely trace through the -following program: -```python -def f(x): - if x.shape[0] > 3: - return x.cos() - return x.sin() -``` - -Moreover, it is possible to impose specific equalities between marked dynamic -dimensions. For example: -```python -constraints = [dynamic_dim(x, 0) == dynamic_dim(y, 0)] -``` - -This means that whatever the 0th dimensions of tensors x and y may be, they must -be the same. This is useful to export the following program, which implicitly -requires this condition because of the semantics of `torch.add` - -```python -def f(x, y): - return x + y -``` - -## constrain_as_value -```python -def constrain_as_value(symbol, min: Optional[int], max: Optional[int]): - """ - Adds a minimum and/or maximum constraint on the intermediate symbol during the tracing phase. - """ -``` - -The `constrain_as_value` function informs us that the specified symbol is -guaranteed to fall within the provided minimum and maximum values. If no minimum -or maximum values are provided, the symbol is assumed to be unbounded. Here's a -concrete example of its usage within a model: -```python -def f(x, y): - b = y.item() - constrain_as_value(b, 3, 5) - if b > 3: - return x.cos() - return x.sin() -``` - -## constrain_as_size -```python -def constrain_as_size(symbol, min: Optional[int] = 2, max: Optional[int]): - """ - Adds a minimum and/or maximum constraint on the intermediate symbol during the tracing phase, - with additional checks to ensure the constrained value can be used to construct a tensor shape. - """ -``` - -The `constrain_as_size` API is similar to constrain_as_value but includes -additional verifications to ensure that the constrained value can be used to -construct a tensor shape. For instance, our tracer specializes in handling shape -sizes of 0 or 1, so this API explicitly raises an error if the constrain_as_size -is used with a minimum value less than 2. Here's an example of its usage: -```python -def f(x, y): - b = y.item() - constrain_as_size(b, 3, 5) - z = torch.ones(b, 4) - return x.sum() + z.sum() -``` diff --git a/docs/website/docs/export/custom_operators.md b/docs/website/docs/export/custom_operators.md deleted file mode 100644 index 1e3c7c1ab3..0000000000 --- a/docs/website/docs/export/custom_operators.md +++ /dev/null @@ -1,19 +0,0 @@ -# Custom Operators - -To ensure a successful export of your model, it is necessary to provide a META -implementation for any custom operators used. Custom operators refer to -operators that are not part of the "aten" or "prim" namespaces. You have the -flexibility to implement the META functionality in either Python or C++. - -Note that the official API for registering custom meta kernels is currently -undergoing intensive development. While the final API is being refined, you can -refer to the documentation -[here](https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit#heading=h.64r4npvq0w0). -In this document, you can find detailed instructions on how to write a Python -meta function by searching for the "Out-of-tree" section within the "How to -write a Python meta function" section. - -By following the guidelines outlined in the documentation, you can ensure that -your custom operators are properly registered and integrated into the export -process. We recommend staying updated with our latest announcements and -documentation for any updates or improvements to the official API. diff --git a/docs/website/docs/export/errors.md b/docs/website/docs/export/errors.md deleted file mode 100644 index 7e0d2c3881..0000000000 --- a/docs/website/docs/export/errors.md +++ /dev/null @@ -1,72 +0,0 @@ -# Errors - -In this section we discuss errors that can commonly arise during export. - -## Expected Graph Breaks - -### Unsupported Features -In PT2 Export, we are primarily reusing the same tracing mechanism—Dynamo—that -we use in eager mode. Recall that in eager mode, graph breaks are expected—we -always have a fallback option. A consequence of this design is that Dynamo has -incomplete coverage of PyTorch and Python features. (That said, the fewer graph -breaks there are, generally speaking, the better performance we can expect—even -in eager mode—because it enables optimizations to apply over larger regions of -code. Thus we are actively working on filling in coverage gaps to avoid graph -breaks where possible.)Unfortunately, this means that you may encounter graph -breaks during export due to Dynamo coverage gaps. In such cases, you should -expect to get an error that includes a link to [ExportDB](./exportdb.md). The -corresponding entry should show a minimal negative example (failure) and a -minimal positive example (success) that should help you understand the -limitation and the workaround, i.e., how to fix the error by rewriting code. - -## Constraint Violations -Recall that you can specify constraints on dynamic dimensions, which encode the -soundness conditions for export. It is possible that these constraints are not -valid. - -### Various Cases -Specifically, the compiler may find that: -- A dynamic dimension must be equal to a constant. - - In this case, this dimension must be static: you cannot mark it dynamic. -- A dynamic dimension must be in a range that does not follow the specified range, i.e., is not entirely included between the specified lower and upper bounds. - - In this case, you need to adjust the specified bounds. - - Note that when bounds are not specified, they are implicitly assumed to be [2, infinity). - - For technical reasons that are difficult to explain here, they are assumed to be not 0 or 1. This is not a bug, and does not necessarily mean that your exported program will not work for dimensions 0 or 1. It does mean, though, that you should test for these cases. -- A dynamic dimension must be equal to another dynamic dimension that it is not specified equal to. - - In this case, you need to add the missing equality. - - By default, all dynamic dimensions are assumed to be independent. - - For legacy reasons that are difficult to explain here, you might find spurious implicitly assumed equalities when dimensions in your example inputs happen to be equal. If you ever encounter such a case, please report it as a bug. - -## Using the Compiler as a Guide - -See [this overview](./soundness.md#constraint-violations-and-how-to-fix-them) of -how to fix such errors. Briefly: -* You should see generated functions specializations and specify_constraints on the console that respectively summarize which dimensions are assumed static and what the necessary constraints on the remaining dynamic dimensions are. -* If you agree with this information, you can copy-paste and call specify_constraints with your example inputs to specify constraints, and you can copy-paste and call specializations on your example inputs to assert their constant values. -* If you do not agree and would like to provide tighter constraints, feel free to modify specify_constraints; the compiler will be happy to accept. -* If you do not agree and would like looser constraints, please use TORCH_LOGs=dynamic to enable INFO-level dynamic-shape logging, which will guide you to where the inferred constraints come from. You can also try TORCH_LOGs=+dynamic to enable (further, verbose) DEBUG-level logging. - * Note that you might have to change your code or your expectations based on this information. If you are absolutely convinced that the compiler has a bug, please report it! For example, there are tricky cases where the constraints may come from non-user code, like a fast path in the compiler itself. We encourage you to try different example inputs to avoid such constraints. - -## Missing META Kernels for Operators - -### ATen Operators -In the unfortunate case where your model uses an ATen operator that is not -supported yet, you may get an obscure error of the form: -```python -Unable to find op(FakeTensor, FakeTensor, ...) -``` -Please report a bug if you encounter this error. - -### Custom Operators -In this case you should follow the instructions at [Custom -Operators](./custom_operators.md). Note that the current mechanism is not ideal, -but will be updated soon to make it easy for you to register custom operators. - -## Validation Errors -Note that we do not do any validation of the exported program yet; this is -planned for the near future. In these cases you should report a bug since the -issue is likely in PyTorch. - -### Correctness -The export workflow should complain when the exported program behaves -differently than the eager program by running the example inputs through both. diff --git a/docs/website/docs/export/export_api_reference.md b/docs/website/docs/export/export_api_reference.md deleted file mode 100644 index 85aea0d94c..0000000000 --- a/docs/website/docs/export/export_api_reference.md +++ /dev/null @@ -1,6 +0,0 @@ -# Export API Reference - -- [Modules and Entrypoints](./modules_and_entrypoints.md) -- [Constraints API](./constraint_apis.md) -- [Control Flow Operators](../ir_spec/control_flow.md) -- [Custom Operators](./custom_operators.md) diff --git a/docs/website/docs/export/exportdb.md b/docs/website/docs/export/exportdb.md deleted file mode 100644 index d4bf10454d..0000000000 --- a/docs/website/docs/export/exportdb.md +++ /dev/null @@ -1,10 +0,0 @@ -# ExportDB - -ExportDB is a [centralized] -(https://pytorch.org/docs/main/generated/exportdb/index.html) dataset for -recording working and non working export usage examples. - -We try to cover a lot of corner cases from PyTorch and Python so that users can -have a better understanding of the capabilities and caveats of export, by -browsing the example database. We also expect that errors that arise during -export will have actionable messages that point to examples in this database. diff --git a/docs/website/docs/export/modules_and_entrypoints.md b/docs/website/docs/export/modules_and_entrypoints.md deleted file mode 100644 index 119907c5ef..0000000000 --- a/docs/website/docs/export/modules_and_entrypoints.md +++ /dev/null @@ -1,42 +0,0 @@ -# Modules and Entrypoints - - Disclaimer: Please note that at present, we do not offer any backward -compatibility guarantees for the following APIs. While we are committed to -minimizing significant API changes, it is important to understand that we are -currently in an intensive development phase, and as such, we reserve the right -to modify implementation details and top-level API parameters. We are -constantly striving to enhance our offerings and deliver the best possible -experience to our users. However, during this phase, it is essential to remain -aware that certain adjustments may be necessary to improve functionality, -stability, or meet evolving requirements. - -## Export API - -At the top level, the export API is defined as follows: - -```python -def export( - m: Union[torch.nn.Module, Callable[..., Any]], - args: Union[Dict[str, Tuple[Value, ...]], Tuple[Value, ...]], - constraints: Optional[List[Constraint]] = None, -) -> ExportedProgram: - """ - Traces either an nn.Module's forward function or just a callable with PyTorch - operations inside and produce a ExportedProgram. - - Args: - m: the `nn.Module` or callable to trace. - - args: Tracing example inputs. - - constraints: A list of constraints on the dynamic arguments specifying - their possible range of their shapes - - Returns: - An ExportedProgram containing the traced method. - """ -``` - -## Exported Artifact -The export call returns a custom export artifact called [Exported -Program](../ir_spec/00_exir.md#exportedprogram). diff --git a/docs/website/docs/export/overall_workflow.md b/docs/website/docs/export/overall_workflow.md deleted file mode 100644 index bb3381a5ee..0000000000 --- a/docs/website/docs/export/overall_workflow.md +++ /dev/null @@ -1,41 +0,0 @@ -# Overall Workflow - -## Step 0: Preparation -To export a model, you need to ensure that: -- You have some example inputs you expect to work for your model. -- You are able to rewrite some of the model's code as necessary to successfully capture a single graph. See [Graph Breaks](./background.md/#graph-breaks). (NOTE: We do not have a story for how to deal with graph breaks in third-party libraries.) -- You know which shape dimensions (if any) of the model's inputs should be dynamic. See [Shapes](./background.md/#shapes). - -## Step 1: Specification -Next, you express which dimensions (if any) you expect to be dynamic, and -(optionally) specify constraints on them to the best of your knowledge. (The -compiler will guide you on whether your constraints are sufficient, so if you do -not know anything, it's fine. However, the compiler will not infer which -dimensions should be dynamic.) These constraints encode conditions for soundness -of the exported program. See [Soundness](./soundness.md). - -## Step 2: Trial and Error -You now call export on your model with your example inputs and constraints. See -[Export API Reference](./export_api_reference.md). At this point you may hit -various kinds of errors—typically, due to graph breaks or insufficient -constraints. See [Errors](./errors.md). We expect that the error messages should -be actionable, so you can learn how to fix them—usually, by looking at linked -examples in [ExportDB](./exportdb.md)—and fix them by rewriting code. - -## Step 3. Inspection -At this point you will have an exported program, and you will be warned about -the assertions it makes on inputs: in particular, which dimensions are static -and have been specialized, and what conditions are expected on the remaining -dynamic dimensions. Make sure they make sense; otherwise you should debug them. - -## Step 4: Testing -Finally, we would encourage you to try out other inputs whose shapes are -valid—i.e., they satisfy the assertions emitted by the compiler—yet different -from the example inputs you provided to export. (This only makes sense if you -had some dynamic dimensions.)* If all the inputs you try pass, great! Consider -your workflow complete. - -* Otherwise, you have hit what is almost surely a over-specialization bug. - Please file a bug on github with a pointer to your model, which example inputs - and constraints you used for export, and which inputs it failed on. We will - try to unblock you as best as possible. diff --git a/docs/website/docs/export/overview.md b/docs/website/docs/export/overview.md deleted file mode 100644 index 162acaa5b0..0000000000 --- a/docs/website/docs/export/overview.md +++ /dev/null @@ -1,9 +0,0 @@ -# Overview - -We begin with a brief overview of the experience of using PT2 Export and the key -concepts in its design. - -- [Background](./background.md) -- [Overall Workflow](./overall_workflow.md) -- [Soundness](./soundness.md) -- [Errors](./errors.md) diff --git a/docs/website/docs/export/soundness.md b/docs/website/docs/export/soundness.md deleted file mode 100644 index 911a59d124..0000000000 --- a/docs/website/docs/export/soundness.md +++ /dev/null @@ -1,123 +0,0 @@ -# Soundness - -The main mechanism to ensure the correctness of the exported program when called -with other inputs is the specification of constraints on the shapes of inputs. -Concretely, dimensions that are static are specialized, and other dimensions -must be marked dynamic, along with any constraints they must satisfy. This -information is then converted into assertions on each dimension that other -inputs must satisfy. - -## Specifying Constraints -To mark a dimension dynamic, you pass the relevant example input tensor and -dimension to torch._export.dynamic_dim. It is possible to specify bounds on and -equalities between such dimensions. In particular, you can use Python relational -operators to provide: -- expected upper and lower bounds of dynamic dimensions; -- expected equalities between dynamic dimensions. - -You then pass these specifications as constraints to export. -```python -from torch._export import dynamic_dim, export - -def foo(x): # expect x to be a tensor - ... - -t = torch.rand(4, 8, 32) # example input tensor -# mark some dimensions of input tensor to be dynamic (assumed static by default) -constraints = [ - dynamic_dim(t, 0), - dynamic_dim(t, 1) <= 256, -] -exported_foo = export(foo, t, constraints=constraints) - -# expect that exported_foo can now be called with other input tensors -# and constraints encode conditions on such input tensors for correctness -``` -Note that dynamic dimensions are tracked "symbolically" by the compiler—for -correctness, it cannot use their "concrete" values in example inputs in the -exported program, but only the specified constraints on them. When the compiler -finds any additional necessary conditions on them as it traces through the code, -it reports them back as part of a ConstraintViolationError. Next, let us look at -how to fix such an error. - -## Constraint Violations and How to Fix Them -Usually you will have some idea of which dimensions you want to be dynamic, and -what bounds you want on them. But suppose that you want the compiler to guide -you. In that case, just specify what you think is reasonable—the compiler will -emit actionable error messages where needed. In the limit, you can specify all -dimensions to be dynamic, with no bounds, and see where that leads! -```python - -from torch._export import dynamic_dim, export - -def foo(x): # expect x to be a tensor - ... - -t = torch.rand(4, 8, 32) # example input tensor -# I want the compiler to guide me on what ranges to specify -constraints = [dynamic_dim(t, i) for i in range(t.dim())] -exported_foo = export(foo, t, constraints=constraints) -``` -Suppose that when tracing the code, the compiler finds that dimension 1 must -have a non-trivial upper bound and dimension 2 must be a constant. The compiler -will emit an error of the following form: - -```python -torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated! - ... - -The following dimensions have been specialized. They CANNOT be dynamic. -def specializations(x): - return x.size()[2] == 32 - -The following dimensions CAN be dynamic. Here’s how to specify constraints on them: -def specify_constraints(x): - return [ - dynamic_dim(x, 0), - dynamic_dim(x, 1) <= 256, - ] -``` -In other words, this error means that: -- Dimension 2 of the input was found to be constrained to be 32. The generated code will assume that `x.size()[2] == 32`, possibly use this value for specialization, and will assert this condition on other inputs. -- Dimension 0 and 1 of the input can range over different values. Moreover, dimension 1 cannot be more than 256. The generated code will assume that `x.size()[1] <= 256`, possibly use this upper bound for memory planning, and will assert this condition on other inputs. - -At this point, you are free to use these discovered facts as you choose for the -final specification:* You may use them "as is." -- You may include further knowledge based on the intended use of the exported program, such as: - - upper-bounding dimension 0, say with 1024; - - tightening the upper bound on dimension 1, say with 128; - - deciding that one or both should not be considered dynamic: you do this by taking them out of constraints, effectively asking the compiler to specialize on their concrete value in the input. - -Or you may be surprised and want to dig in further, to try to find out why the -compiler discovered these facts. For that, you can re-run the export script with -prefix `TORCH_LOGS=dynamic,dynamo` on the command line.) You will see log messages -such as the following: - -```python -[INFO] creating symbol: s0 = 4 with source: x.size()[0] -[INFO] creating symbol: s1 = ... x.size()[1] -[INFO] creating symbol: s2 = ... x.size()[2] - -[INFO] adding guard: s1 <= s2 * 8 at: -File "example.py", line 629, in foo - if x.shape[1] <= x.shape[2] * 8: -[INFO] adding guard: s1 * 4 <= 2048 at: ... -[INFO] adding guard: s2 // 8 >= 2 at: ... -[INFO] adding guard: s2 * s2 + s1 * 8 <= 4096 at: ... -[INFO] adding guard: s2 % 4 == 0 at: ... -[INFO] adding guard: s2 * 2 == 64 at: ... -[INFO] Summary of dimension constraints: -The following dimensions have been specialized and CANNOT be dynamic. - -def specializations(x: torch.Tensor): - assert x.size()[0] == 2 - assert x.size()[1] == 2 - -``` -Under the hood, the compiler creates symbols for each dimension—in this case, -`(s0, s1, s2)`—and generates conditions involving these symbols. For example, the -condition `s1 <= s2 * 8` is generated when tracing the shown line of code in -function `foo`. You can also see a list of specializations we have assumed on -input shapes. This can give you an idea of how your code led to the individual -pieces of information being discovered, which were ultimately simplified to -produce the final error message. diff --git a/docs/website/docs/ir_spec/00_exir.md b/docs/website/docs/ir_spec/00_exir.md deleted file mode 100644 index cef646d82a..0000000000 --- a/docs/website/docs/ir_spec/00_exir.md +++ /dev/null @@ -1,620 +0,0 @@ -# EXIR Reference - -**Last Update:** July 21, 2023 - -EXIR is an intermediate representation (IR) for compilers, which bears -similarities to MLIR and TorchScript. It is specifically designed to express the -semantics of PyTorch programs that are written in Python. EXIR primarily -represents computation in a streamlined list of operations, with limited support -for dynamism such as control flows. - -To create an EXIR, a frontend can be used that soundly captures a PyTorch -program via a trace-specializing mechanism. The resulting EXIR can then be -optimized and executed by a backend. - - The key concepts that will be covered in this document include: - - ExportedProgram: the data structure containing the EXIR program - - Graph: which consists of a list of nodes. - - Nodes represent operations, control flow, and metadata stored on this node. - - Values are produced and consumed by nodes. - - Types are associated with values and nodes. - - The size and memory layout of values are also defined. - - -## Assumptions: - -This doc assumes that the audience is sufficiently familiar with PyTorch -specifically with `torch.fx` and its related toolings. Thus it will stop -describing contents present in torch.fx documentation and paper. [1](#torchfx) - - -## What is EXIR: - -EXIR is a graph-based intermediate representation IR of PyTorch programs. EXIR -is realized on top of `torch.fx` Graph. In other words, **all EXIR graphs are -also valid FX graphs**, and if interpreted using standard FX semantics -[1](#torchfx), EXIR can be interpreted soundly. One implication is that it can -be converted to a valid Python program via standard FX codegen. - -This documentation will primarily focus on highlighting areas where EXIR differs -from FX in terms of its strictness, while skipping parts where it shares -similarities with FX. - -You can follow the [tutorial](../tutorials/frontend.md) to play around with what is said here. - - -## ExportedProgram - -The top-level EXIR construct is an `ExportedProgram` class. It bundles the -computational graph of a PyTorch model (which is usually a `torch.nn.Module`) -with the parameters or weights that this model consumes. - -The `ExportedProgram` has the following attributes: - -* `graph_module (torch.fx.GraphModule)`: Data structure containing the flattened - computational graph of the PyTorch model. The graph can be directly accessed - through `ExportedProgram.graph`. -* `graph_signature (ExportGraphSignature)`: The graph signature specifies the - parameters and buffer names used and mutated within the graph. Instead of - storing parameters and buffers as attributes of the graph, they are lifted as - inputs to the graph. The graph_signature is utilized to keep track of - additional information on these parameters and buffers. -* `call_spec (CallSpec)`: When running the exported program in eager mode, the - call spec defines the format specification of inputs and outputs. The graph - itself accepts a flattened list of inputs and returns a flattened list of - outputs. In cases where inputs/outputs are not in a flattened list format - (e.g., a list of lists), we use `call_spec.in_spec` to flatten the inputs and - `call_spec.out_spec` to unflatten the outputs into the format expected by the - models when running eagerly. -* `state_dict (Dict[str, Union[torch.Tensor, torch.nn.Parameter]])`: Data structure - containing the parameters and buffers. -* `range constraints (Dict[sympy.Symbol, RangeConstraint])`: For programs that - are exported with data dependent behavior, the metadata on each node will - contain symbolic shapes (which look like `s0`, `i0`). This attribute maps the - symbolic shapes to their lower/upper ranges. -* `equality_constraints (List[Tuple[InputDim, InputDim]])`: A list of nodes in - the graph and dimensions that have the same shape. - - -## Graph - -An EXIR Graph is a PyTorch program represented in the form of a DAG (directed acyclic graph). -Each node in this graph represents a particular computation or operation, and -edges of this graph consist of references between nodes. - -We can view Graph having this schema: - -```python -class Graph: - nodes: List[Node] -``` - -In practice, EXIR's graph is realized as `torch.fx.Graph` Python class. - -An EXIR graph contains the following nodes (Nodes will be described in more -details in the next section): - -* 0 or more nodes of op type `placeholder` -* 0 or more nodes of op type `call_function` -* exactly 1 node of op type `output` - -**Collorary:** The smallest valid Graph will be of one node. i.e. nodes is never empty. - -**Definition:** -The set of `placeholder` nodes of a Graph represents the **inputs** of the Graph of GraphModule. -The `output` node of a Graph represents the **outputs** of the Graph of GraphModule. - -Example: -```python -from torch import nn - -class MyModule(nn.Module): - - def forward(self, x, y): - return x + y - -mod = torch._export.export(MyModule()) -print(mod.graph) -``` -Output: -``` -graph(): - %arg0_1 : [#users=1] = placeholder[target=arg0_1] - %arg1_1 : [#users=1] = placeholder[target=arg1_1] - %add : [#users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}) - return [add] -``` - -The above is the textual representation of a Graph, with each line being a node. - -## Node - -A Node represents a particular computation or operation, and is represented in -Python using `torch.fx.Node` class. Edges between nodes are represented as -direct references to other nodes via the args property of the Node class. Using -the same FX machinery, we can represent the following operations that a -computational graph typically needs, such as operator calls, placeholders (aka -inputs), conditionals, and loops. - -The Node has the following schema: - -```python -class Node: - name: str # name of node - op_name: str # type of operation - - # interpretation of the fields below depends on op_name - target: [str|Callable] - args: List[object] - kwargs: Dict[str, object] - meta: Dict[str, object] -``` - -### FX Text Format - -As the example above, notice that each line has this format -``` - %:[...] = [target=](args = (%arg1, %arg2, arg3, arg4, …)), kwargs = {}) -``` - -This format captures everything present in the Node class, with exception of `meta`, in a compact format. - -Concretely: - -**<name>** is the name of node as would appear in `node.name` - -**<op_type>** is the `node.op_name` field, which must be one of these: -[call_function](#callfunction), call_method, [placeholder](#placeholder), -[get_attr](#getattr), or [output](#output). - -**<target>** is the target of the node as `node.target`. The meaning of this -field depends on `op_type`. - -**args1, … args 4…** are what listed in `node.args` tuple, if a value in the list -is a `fx.Node`, then it will be especially indicated with a leading **%.** - -For example, a call to the add operator would appear as - -``` -%add1 = call_function[target = torch.op.aten.add.Tensor](args = (%x, %y), kwargs = {}) -``` -Where `%x`, `%y` are 2 other Nodes that have names x and y. -Worth noting that, the string `torch.op.aten.add.Tensor` represents the -callable object that is actually stored in the target field, not merely its string -name. - -The final line of this text format is -``` -return [add] -``` -is a Node with `op_name = output`, this is used to indicate that we are returning this one element. - -### call_function -A `call_function` node represents a call to an operator. - -#### Definitions - -* **Functional:** We say a callable is “functional” if it satisfy all following requirements: - * Non-aliasing, ie output tensors do not share data storage with each other or with inputs of the operator - * Non-mutating, ie the operator does not mutate value of its input (for tensors, this includes both metadata and data) - * No side effects, ie the operator does not mutate states that are visible from outside, like changing values of module parameters. - -* **Operator:** is a functional callable with a predefined schema. Examples of - such operators include functional ATen operators. - - -#### Representation in FX -``` -%name = call_function[target = operator](args = (%x, %y, …), kwargs = {}) -``` - -#### Differences from vanilla FX call_function - -1. In FX graph, a call_function can refer to any callable, in EXIR, we restrict -this to only Canonical ATen operators (a select subset of PyTorch ATen operator -library), custom operators and control flow operators. -2. In EXIR, constant arguments will be embedded within the graph. -3. In FX graph, a get_attr node can represent reading any attribute stored in -the graph module. However, in EXIR this will only be reading submodules as all -parameters/buffers will be passed in as inputs to the graph module. - - -#### Metadata - -`Node.meta` is a dict attached to every FX node. However, FX spec does not -specify what metadata can or will be there. EXIR provides a stronger contract, -specifically all `call_function` nodes will guarantee to have and only have -the following metadata fields: - -* `node.meta["stack_trace"]` is string containing the python stack trace - referencing the original python source code. An example stack trace looks like: - ``` - File "my_module.py", line 19, in forward - return x + dummy_helper(y) - File "helper_utility.py", line 89, in dummy_helper - return y + 1 - ``` -* `node.meta["val"]` describes the output of running the operation. It can be - of type [`SymInt`](#symint), [`FakeTensor`](#faketensor), a - `List[Union[FakeTensor, SymInt]]`, or `None`. - -* `node.meta["nn_module_stack"]` describes the "stacktrace" of the `torch.nn.Module` - from which the node came from, if it was from a `torch.nn.Module` call. For - example, if a node containing the `addmm` op called from a `torch.nn.Linear` - module inside of a `torch.nn.Sequential` module, the `nn_module_stack` would - look something like: - ```python - {'self_linear': ('self.linear', ), 'self_sequential': ('self.sequential', )} - ``` - -* `node.meta["source_fn_stack"]` contains the stack of torch function or the leaf - `torch.nn.Module` class this node was called from before decomposition. For - example, a node containing the `addmm` op from a `torch.nn.Linear` module call - would contain `torch.nn.Linear` in their `source_fn`, and a node containing - the `addmm` op from a `torch.nn.functional.Linear` module call would contain - `torch.nn.functional.Linear` in their `source_fn`. The stack records the higher order - operator stack that this source_fn belongs to. For example, if a `torch.nn.Linear` module - call is within the true branch of `cond`, then the stack will contain `['cond', 'torch.nn.Linear']`. - - -### placeholder - -Placeholder represents input to a graph. Its semantics are exactly the same as in FX. -Placeholder nodes must be the first N nodes in the nodes list of a graph. N can be zero. - -#### Representation in FX: - -``` -%name = placeholder[target = name](args = ()) -``` - -The target field is a string which is the name of input. - -`args`, if non empty; should be of size 1 representing the default value of this input. - -#### Metadata - -Placeholder nodes also have `meta[‘val’]`, like `call_function` nodes. The val field -in this case represents the input shape/dtype that the graph is expected to -receive for this input parameter. - -### output - -An output call represents a return statement in a function; thus terminates the current graph. -There is one and only one output node, and it will always be the last node of the graph. - -#### Representation in FX - -``` -output[](args = (%something, …)) -``` - -This is the exact semantics as in FX [1](#torchfx). args represents the node to be returned. - -#### Metadata - -Output node has the same metadata as `call_function` nodes. - -### get_attr - -`get_attr` nodes represent reading a submodule from the encapsulating -`GraphModule`. Unlike a vanilla FX graph from `torch.fx.symbolic_trace` in which -`get_attr` nodes are used to read attributes such as parameters and buffers from -the top-level `GraphModule`, parameters and buffers will be passed in as inputs -to the graph module, and stored in the toplevel `ExportedProgram`. - -#### Representation in FX: - -``` -%name = get_attr[target = name](args = ()) -``` - -#### Example: -Consider the following model: - -```python -class TrueModule(torch.nn.Module): - def forward(self, x): - return x.sin() - -class FalseModule(torch.nn.Module): - def forward(self, x): - return x.cos() - -class Module(torch.nn.Module): - def __init__(self): - self.true_module = TrueModule() - self.false_module = FalseModule() - - def forward(self, x): - return torch.ops.higher_order.cond(x.shape[0] == 1, self.true_module, self.false_module, x) -``` - -Then, `%name = get_attr[target = true_module](args = ())` appears in the corresponding -graph to read the attribute `self.true_module`. - - -## EXIR Dialects - -EXIR is a specification that consists of the following parts: - -1. A definition of computation graph model. -2. Set of operators allowed in the graph. - -An EXIR dialect is a EXIR graph composed with the operations defined below, but -with additional properties (such as restrictions on operator set or metadata) -that are meant for a specific purpose. - -The EXIR dialects that currently exist are: - -* [ATen Dialect](./01_aten_dialect.md) -* [Edge Dialect](./02_edge_dialect.md) -* [Backend Dialect](./03_backend_dialect.md) - -These dialects represent stages that a captured program goes through from -program capture to be converted into an executable format. For example, -a compilation pipeline targeting Edge devices may look like this: a Python program -is first captured as ATen dialect, then ATen is converted to Edge Diaelct, Edge -to Backend, and finally converted from EXIR to a binary format for execution. - - -## References: - -### torch.fx - -Documentation of torch.fx: [https://pytorch.org/docs/stable/fx.html](https://pytorch.org/docs/stable/fx.html) - - -### SymInt - -A SymInt is an object that can either be a literal integer or a symbol that represents -an Integer (represented in python by `sympy.Symbol` class). When SymInt is a -symbol, it describes a variable of type integer that is unknown to the graph at -compile time, and its value is only know at runtime. - -### FakeTensor - -A FakeTensor is a object that contains metadata of a tensor. It can be viewed as -having the following metadata. - -```python -class FakeTensor: - size: List[SymInt] - dtype: dtype - dim_order: List[int] # This doesn't exist yet -``` - -The size field of FakeTensor is a list of integers or SymInts. If SymInts are -present, this means this tensor has a dynamic shape. If integers are present, it -is assumed that that tensor will have that exact static shape. The rank of the -TensorMeta is never dynamic. The dtype field represents the dtype of the -output of that node. There are no implicit type promotions in Edge IR. There -are no strides in FakeTensor. - -In other words: - -* If the operator in node.target returns a Tensor, then, node.meta['val'] is a - FakeTensor describing that tensor. -* If the operator in node.target returns a n-tuple of Tensors, then, - node.meta['val'] is a n-tuple of FakeTensors describing each tensor. -* If the operator in node.target returns a int/float/scalar that is known at - compile time, then, node.meta['val'] is None. -* If the operator in node.target returns a int/float/scalar that is not known - at compile time, then, node.meta['val'] is of type SymInt. - -For example: -* `aten::add` returns a Tensor; so its spec will be a FakeTensor with dtype - and size of the tensor returned by this operators. -* `aten::sym_size` returns an integer; so its val will be a SymInt because its - value are only available at runtime. -* `max_pool2d_with_indexes` returns a tuple of (Tensor, Tensor); so the spec - will be also a 2-tuple of FakeTensor object, the first TensorMeta describes - the first element of the return value etc. - -Python code: -```python -def add_one(x): - return torch.ops.aten(x, 1) -``` -Graph: -``` -graph(): - %ph_0 : [#users=1] = placeholder[target=ph_0] - %add_tensor : [#users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%ph_0, 1), kwargs = {}) - return [add_tensor] -``` - -FakeTensor -``` -FakeTensor(dtype=torch.int, size=[2,], device=CPU) -``` - -### Pytree-able types - -The following types are defined as **leaf type:** - -**Source: ** - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Type - Definition - Notes -
Tensor - Pytorch Tensor type - https://pytorch.org/docs/stable/tensors.html#torch.Tensor -
Scalar - Any numerical types from Python, including integral types, floating point types, and zero dimensional tensors. - float and int argument types should suffice for most algorithms (you should only use Scalar if the operator truly may accept either type) -
int - python int (binded as int64_t in C++) - -
float - python float (binded as double in C++) - -
bool - python bool - -
str - python string - -
ScalarType - Enum type for all permissible dtype - https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype -
Layout - Enum type for all permissible Layout - https://pytorch.org/docs/stable/tensor_attributes.html#torch.layout -
MemoryFormat - Enum type for all permissible MemoryFormat - https://pytorch.org/docs/stable/tensor_attributes.html#torch-memory-format -
Device - torch.device -> str - https://pytorch.org/docs/stable/tensor_attributes.html#torch.device -
- - -The following types are defined as **container type:** - - - - - - - - - - - - - - - - - - -
Tuple - Python tuple - -
List - Python list - -
Dict - Python dict with keys Scalar as defined in the table above. - -
- - -**We define a type “Pytree-able”, if it is either a leaf type, or a container type that contains other Pytree-able types.** - -NOTE: The concept of pytree is the same as the one documented here for JAX: [https://jax.readthedocs.io/en/latest/pytrees.html](https://jax.readthedocs.io/en/latest/pytrees.html) - - - -### Memory formats - -We use the term **Pytorch Default Dims Order** to describe the memory format represented by `torch.contiguous_format`. In other words, Let N, C, H, W be number of images, channel, height and weight, then `torch.contiguous_format `will tensor dimensions be in NCHW ordering. - -Other memory formats available in torch are: torch.channels_last: = NHWC - -Other permutations of NCHW are allowed but we don’t have explicit names for them. - - - - - - - - - - - - - - - - - - - -
Format - Column Order -
contiguous_format - NCHW -
Channels_last - NHWC -
(no name) - Other permutations -
- - -See more on channel_last mem format: [https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) - -For ExecuTorch, we have introduced a concept of dim orders to convey how a dense tensor is laid out in memory. Tensor's memory layout and memory format representation using the dim order in the ExecuTorch the stack is still a WIP. We will update this doc and the IR spec very shortly. - -### Tensor -A Tensor type describes a mathematical tensor. -Let `t` be a Tensor, then, conceptually `t` provides the following interfaces: - -1. dtype: t.dtype returns the type of the Scalar associated with this tensor. - dtype can be one of {int8, int16, int32, int64, float32, float64, bool...},` - The list of all supported dtypes is listed in this page: https://pytorch.org/docs/stable/tensors.html -2. size: (also known as shape) t.size is a list of integers. - ** `len(t.size)` is known as the "rank" of tensor - ** `prod(t.size)` the product of the sizes is the total number of elements in this tensor. - -In Python, we use `torch.Tensor` class to represent a Tensor: https://pytorch.org/docs/stable/tensors.html diff --git a/docs/website/docs/ir_spec/01_aten_dialect.md b/docs/website/docs/ir_spec/01_aten_dialect.md deleted file mode 100644 index 449ed90d71..0000000000 --- a/docs/website/docs/ir_spec/01_aten_dialect.md +++ /dev/null @@ -1,80 +0,0 @@ -# ATen Dialect - - -## Properties: - -An ATen dialect graph is a valid EXIR graph with the following additional properties: - - -1. All operators in OpCall nodes are either from a predefined operator set, - called ["Core ATen Operators”](https://pytorch.org/docs/stable/ir.html), or a - registered custom operator. A registered custom operator is an operator - registered into the current Pytorch eager mode runtime, usually with - TORCH_LIBRARY call (implies schema). -2. Every ATen operator must also have a meta kernel. A meta kernel is a - function that, given the shapes of the input tensors, can return the shape of - output tensor. -3. Input value type must be “Pytree-able[See 2]”. As a consequence, the output - types are also Pytree-able because all the operators output are pytree-able. -4. Ops of Aten dialect can choose to work Dynamic dtypes, implicit type - promotions and implicit broadcasting of tensors. -5. All tensors memory formats are in [**Pytorch Default Dims Format:**](./00_exir.md#memory-formats) - i.e. torch.contiguous_format. - - - - - - - - - - - - - - - - - - - - -
-Op Set - Canonical ATen - Custom Op - All ATen Ops -
ATen - Allowed - Allowed, must have meta kernel - -
Edge - Aten + Type specializations - Allowed - -
- - - -## Intent - -This section describes what we envision ATen dialect is used for. - -ATen dialect will be used as the entry point of the executorch compilation -pipeline, it is the first time an eager mode Pytorch program becomes an EXIR -graph. At this stage, functionalization is performed, so all the tensor aliases -are made a copy of. Therefore, all tensors are converted to continuous format. - -The goal of this dialect is to capture users' programs as faithfully as possible -(while remaining valid EXIR). Registered Custom Operators that user has called -in eager mode will preserve as-is in ATen dialect. However, we should refrain -from adding custom ops in the graph via passes. - -For now, the function of ATen dialect is to further lower to edge dialect. -However, in the future we can see this one as the common integration point for -other export use cases. - -## ATen Operator Definition - -[under construction] diff --git a/docs/website/docs/ir_spec/02_edge_dialect.md b/docs/website/docs/ir_spec/02_edge_dialect.md deleted file mode 100644 index 838f71e092..0000000000 --- a/docs/website/docs/ir_spec/02_edge_dialect.md +++ /dev/null @@ -1,86 +0,0 @@ -# Edge dialect - -Edge dialect is a dialect of EXIR satifying the following properties: - -## Properties - -1. All operators in OpCall nodes are either from a predefined operator set, - called **“Edge Operators”**, or a registered custom operator. An Edge operator is a - ATen operator with dtype specialization. -2. Input and output of the graph, and as well as to every node, cannot be Scalar. I.e. - All scalar types (such as float, int) are converted to Tensor. - -## Intent - -This dialect is meant to introduce specializations that are useful for Edge -devices but not necessarily for general (server) export. -However, we still withhold specializing further to each different hardware. -In other words, we don’t want to introduce any new hardware dependent concepts or data; -besides those already present in users’ original python program. - -## How to use - -A GraphModule in EXIR edge dialect is represented with `torch.fx.GraphModule` Python class -in memory. To obtain such a class, one start with a `torch.nn.Module`: - -```python -import torch -from executorch import exir - -class MyModule(torch.nn.Module): - ... -a = MyModule() -tracing_inputs = (torch.rand(2, 2),) -edge_dialect_module = exir.capture(a, tracing_inputs).to_edge().module -``` - -As we can see if no input is provided to `to_edge()` API, the lowering process from ATen dialect to edge dialect should be invisible to the user. However we provide some knobs for advanced usage: - -* `EdgeCompileConfig.passes` -User defined graph transformation goes in here. Order matters. Note: if the custom pass is touching `node.target`, be aware that all of the `node.target` at this stage are "Edge ops" (more details below) and not torch ops like in ATen dialect. Tutorial on pass writing can be found [here](../tutorials/passes). After all these passes are executed, `to_edge()` will make sure the graph is still valid. - -* `EdgeCompileConfig._check_ir_validity` -Default value is true. If set to false, graph validaity check will be turned off. Turn this flag off with caution, since the graph may become invalid after `to_edge()`. - -## Edge Operator - -As mentioned before, an edge operator is an ATen core operator with type specialization. This means the instance of edge operator contains a set of dtype constraints, to describe all the tensor dtypes supported by both ExecuTorch runtime and their ATen kernels. These dtype constraints are expressed in a DSL defined in [edge.yaml](https://github.com/pytorch/executorch/blob/main/exir/dialects/edge/edge.yaml). Here's an example of the dtype constraints: - -``` -- func: sigmoid - namespace: edge - inherits: aten::sigmoid - type_alias: - T0: [Bool, Byte, Char, Int, Long, Short] - T1: [Double, Float] - T2: [Float] - type_constraint: - - self: T0 - __ret_0: T2 - - self: T1 - __ret_0: T1 -``` -This is saying if `self` tensor is one of the type `Bool, Byte, Char, Int, Long, Short`, then the return tensor would be `Float`. If `self` is one of `Double, Float`, the return tensor will be the same dtype. - -After these dtype constraints are collected and documented in edge.yaml, EXIR consumes it, load them into EXIR Edge operators. This is convenient for developers to learn the supported dtypes of any argument in Edge op schema. For example we can do: - -```python -from executorch.exir.dialects._ops import ops as exir_ops # import dialects ops -sigmoid = exir_ops.edge.aten.sigmoid.default -print(sigmoid._schema) -# aten::sigmoid(Tensor self) -> Tensor -self_arg = sigmoid._schema.arguments[0] -_return = sigmoid._schema.returns[0] - -print(self_arg.allowed_types) -# {torch.float32, torch.int8, torch.float64, torch.int16, torch.int32, torch.int64, torch.uint8, torch.bool} - -print(_return.allowed_types) -# {torch.float32, torch.float64} -``` - -These constraints are helpful for someone who wants to write a custom kernel for this operator. Also inside EXIR, we offer a validator to check if the graph is still complying with these dtype constraints, after custom transformations. - -## Op Set (WIP) - -Check out [edge.yaml](https://github.com/pytorch/executorch/blob/main/exir/dialects/edge/edge.yaml) for the complete list of operators having dtype constraints specified. We are gradually expanding this operator set and targeting to provide dtype constraints for all core ATen ops. diff --git a/docs/website/docs/ir_spec/03_backend_dialect.md b/docs/website/docs/ir_spec/03_backend_dialect.md deleted file mode 100644 index 3bd3a7ca59..0000000000 --- a/docs/website/docs/ir_spec/03_backend_dialect.md +++ /dev/null @@ -1,81 +0,0 @@ -# Backend Dialect - - -## Properties -Backend dialect is the name we gave to the `ExportedProgram` in Edge dialect, after optional **target specific** passes. The difference between backend dialect and edge dialect is that backend dialect is target-aware and may contain operators or submodules that are only meaningful to the target backend. Backend specific operators are new components we may see in a backend dialect, comparing with Edge dialect. They are a set of operators for the target backend. - -Another property to notice is that the memory formats of the tensor can be any format (this is subject to change in the near future when we introduce dim order to backend dialect). - - -## Intent - -This dialect allows introduction of operators that do not conform to the schema defined in the canonical ATen operator set, and are not showing up in any of the dialects above (ATen dialect and edge dialect). Consider to use backend operators if your use case satisfies one or more of the following criteria: - -1. Your backend provides a library that optimizes a certain operator that is equivalent to a subgraph. E.g., linear_relu (equivalent to linear + relu) that can be executed faster on a certain backend. -2. There's a need to retrace the graph module after it is already lowered to a backend. When we retrace, backend operators can transform back to the original subgraph (in ATen dialect) where normal custom op doesn't take care of that. -3. Your backend specific operator doesn't have a generic CPU kernel but only a kernel for a certain backend. Using backend operator can workaround this issue by using the original subgraph as default kernel and keep the graph module runnable. - - -## How to use - -To lower edge ops to backend ops, a pass will perform pattern matching to identify the edge ops of interest in the graph, and then replace them with equivalent backend operators. There are two APIs to register such passes: - -* `transform()`. An API on `ExportProgram` that allows users to provide custom passes. Note that this is not guarded by any validator so the soundness of the program is not guaranteed. -* [`ExecutorchBackendConfig.passes`](https://github.com/pytorch/executorch/blob/main/exir/capture/_config.py#L40). If added here, the pass will be part of the lowering process from backend dialect to `ExecutorchProgram`. - -Example: one of such passes is `QuantFusion`. This pass takes a "canonical quantization pattern", ie. "dequant - some_op - quant" and fuse this pattern into a single operator that is backend specific, i.e. `quantized_decomposed::some_op`. You can find more details [here](../tutorials/short_term_quantization_flow.md). Another simpler example is [here](https://github.com/pytorch/executorch/blob/main/exir/passes/replace_edge_with_backend_pass.py#L20) where we replace sym_size operators to the ones that are understood by ExecuTorch. - -## API - -We provide a decorator `bind_pattern_to_op` to help users to easily register their backend operators into EXIR. This decorator takes: -* a `torch.Library` object, it indicates which library or namespace this backend operator belongs to. -* a name or schema. If we already defined the schema of the backend operator in the `torch.Library` object, only a name is needed. Otherwise we can register the schema if a schema string is being passed in. - -This decorator should be added to the pattern we are trying to match (and then lower to this backend op) on edge dialect. This way we are registering this pattern as a `CompositeImplicitAutograd` kernel for this backend operator. - -Then the operator can be accessed/used from the passes. The `CompositeImplicitAutograd` kernel makes sure: -1. No need for the user to write a (CPU) runnable kernel -2. Ensures the retracability of `ExportProgram`. Once retraced, the backend operator will be decomposed into the ATen ops used in the pattern. - -## Op Set -Unlike edge dialect where we have a well defined op set, for backend dialect, since it is target-aware we will be allowing user to use our API to register target-aware ops and they will be grouped by namespaces. Here are some examples: `executorch_prims` are ops that are used by ExecuTorch runtime to perform operation on `SymInt`s. `quantized_decomposed` are ops that fuses edge operators for quantization purpose and are meaningful to targets that support quantization. - -* `executorch_prims::add.int(SymInt a, SymInt b) -> SymInt` - * pattern: builtin.add - * backend: executor -* `executorch_prims::mul.int(SymInt a, SymInt b) -> SymInt` - * pattern: builtin.mul - * backend: executor -* `executorch_prims::sub.int(SymInt a, SymInt b) -> SymInt` - * pattern: builtin.sub - * backend: executor -* `executorch_prims::floordiv.int(SymInt a, SymInt b) -> SymInt` - * pattern: builtin.floordiv - * backend: executor -* `executorch_prims::gt.int(SymInt a, SymInt b) -> bool` - * pattern: builtin.gt - * backend: executor -* `executorch_prims::lt.int(SymInt a, SymInt b) -> bool` - * pattern: builtin.lt - * backend: executor -* `executorch_prims::ge.int(SymInt a, SymInt b) -> bool` - * pattern: builtin.ge - * backend: executor -* `executorch_prims::le.int(SymInt a, SymInt b) -> bool` - * pattern: builtin.le - * backend: executor -* `executorch_prims::eq.int(SymInt a, SymInt b) -> bool` - * pattern: builtin.eq - * backend: executor -* `quantized_decomposed::embedding_byte(Tensor weight, Tensor weight_scales, Tensor weight_zero_points, int weight_quant_min, int weight_quant_max, Tensor indices) -> Tensor` - * pattern: [source](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py) - * backend: quantization -* `quantized_decomposed::add(Tensor a, float a_scale, int a_zero_point, int a_quant_min, int a_quant_max, Tensor b, float b_scale, int b_zero_point, int b_quant_min, int b_quant_max, float out_scale, int out_zero_point, int out_quant_min, int out_quant_max) -> Tensor qc` - * pattern: [source](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py) - * backend: quantization -* `quantized_decomposed::add.scalar(Tensor qa, float a_scale, int a_zero_point, int a_quant_min, int a_quant_max, ScalarType a_dtype, Scalar b, float out_scale, int out_zero_point, int out_quant_min, int out_quant_max, ScalarType out_dtype) -> Tensor` - * pattern: [source](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py) - * backend: quantization -* `quantized_decomposed::add_relu(Tensor a, float a_scale, int a_zero_point, int a_quant_min, int a_quant_max, Tensor b, float b_scale, int b_zero_point, int b_quant_min, int b_quant_max, float out_scale, int out_zero_point, int out_quant_min, int out_quant_max) -> Tensor qc` - * pattern: [source](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py) - * backend: quantization diff --git a/docs/website/docs/ir_spec/README.md b/docs/website/docs/ir_spec/README.md deleted file mode 100644 index c22e70e138..0000000000 --- a/docs/website/docs/ir_spec/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Deprecated folder - -This folder is deprecated. For up-to-date comprehensive technical overview of ExecuTorch and step-by-step tutorials, -please visit our [documentation website](https://pytorch.org/executorch). diff --git a/docs/website/docs/ir_spec/control_flow.md b/docs/website/docs/ir_spec/control_flow.md deleted file mode 100644 index 6f32f096a0..0000000000 --- a/docs/website/docs/ir_spec/control_flow.md +++ /dev/null @@ -1,201 +0,0 @@ -# (Prototype) Control Flow - -EXIR has a couple of special operators used to help specify control flow within -some code, similar to jax's control flow operators. Currently these operators -are only supported for inference. - -## torch.ops.higher_order.cond - -The `cond` function represents an “if” statement in other programming languages. -It can logically be seen as implemented as follows: - -```python -def cond( - pred: Union[bool, torch.Tensor], - true_branch: Callable, - false_branch: Callable, - operands: List[torch.Tensor] -): - if pred: - return true_branch(*operands) - else: - return false_branch(*operands) -``` - -Parameters -* `pred (Union[bool, torch.Tensor])`: A boolean expression or a tensor with one element, - indicating which branch function to apply, or a boolean expression -* `true_branch (Callable)`: A callable function (a -> b) that is within the - scope that is being traced. -* `false_branch (Callable)`: A callable function (a -> b) that is within the - scope that is being traced. The true branch and false branch must have - consistent input and outputs, meaning the inputs have to be the same, and - the outputs have to be the same type and shape. -* `operands (List[torch.Tensor])`: A list of inputs to the true/false - branches. - -Returns: -* Value (b) of either `true_branch(*operands)` or `false_branch(*operands)`, - depending on the value of `pred`. - -### Limitations -* The conditional statement (aka `pred`) must meet one of the following constraints: - * It's a `torch.Tensor` with only one element, e.g. `torch.tensor(10)` or - `torch.tensor([[10]])`, etc. - * It's a boolean expression, e.g. `x.shape[0] > 10` or `x.dim() > 1 and x.shape[1] > 10` -* The operands must be a list of tensors -* The branch function must meet all of the following constraints: - * The function signature must match with operands - * The function must return a single tensor with same metadata, e.g. shape, - dtype, etc. - * The function can not have closure variables (except `self` variable) - * The function can not have inplace mutations on inputs or global variables - * The function can not be static method - -### Examples -An basic example of how to use the `cond()` operator: - -```python -from functorch.experimental.control_flow import cond - -class DynamicShapeCondPredicate(torch.nn.Module): - """ - A basic usage of control flow based on dynamic shape predicate. - """ - - def __init__(self): - super().__init__() - - def forward(self, x): - def true_fn(x): - return x.cos() - - def false_fn(x): - return x.sin() - - return cond(x.shape[0] > 4, true_fn, false_fn, [x]) -``` - -This results in the following GraphModule: - -``` -# Toplevel graph module -class GraphModule(torch.nn.Module): - graph(): - %arg0 : f32[s0, s1] = placeholder[target=arg0] - %sym_size : Sym(s0) = call_function[target=torch.ops.aten.sym_size](args = (%arg0, 0), kwargs = {}) - %gt : Sym(s0 > 4) = call_function[target=operator.gt](args = (%sym_size, 4), kwargs = {}) - %true_graph_0 = get_attr[target=true_graph_0] - %false_graph_0 = get_attr[target=false_graph_0] - %cond : f32[s0, s1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, [%arg0]), kwargs = {}) - return [cond] - - # true_graph_0 - class (torch.nn.Module): - graph(): - %arg0_1 : f32[s0, s1] = placeholder[target=arg0_1] - %cos : f32[s0, s1] = call_function[target=torch.ops.aten.cos.default](args = (%arg0_1,), kwargs = {}) - return cos - - # false_graph_0 - class (torch.nn.Module): - graph(): - %arg0_1 : f32[s0, s1] = placeholder[target=arg0_1] - %sin : f32[s0, s1] = call_function[target=torch.ops.aten.sin.default](args = (%arg0_1,), kwargs = {}) - return sin -``` - -**See examples of advanced usage of `cond()` operator in ExportDB: [cond tag](https://www.internalfb.com/intern/staticdocs/exportdb/cond.html)** - - -## torch.ops.higher_order.map - -The `map` function is similar to Python's builtin `map`, where it represents -applying an operation in the first dimension of a tensor. -It can logically be seen as implemented as follows: - -```python -def map(f: Callable, xs: Tensor, *args: Any) -> Tensor: - return torch.stack([f(x, *args) for x in xs]) -``` - -Parameters -* `f (Callable)`: A callable function that is applied element-wise over the - first dimension of `xs`. It should not consume keyword-only args, and should - take `1 + len(args)` number of arguments. -* `xs (Tensor)`: The tensor to map over. If `xs` is a TensorList, then it has to - contain tensors of the same shape. -* `args`: Inputs that are needed for the map function `f` in addition to each - axis of `xs` - -Returns -* A mapped tensor or tensor-list. The return tensor's has shape `xs.size[0]` in - the first dimension, and the other dimensions depend on the return shape of - `f`. - - -An example of how to use the map operator: - -``` -from functorch.experimental.control_flow import map - -def dynamic_shape_map(xs, y): - """ - functorch map() maps a function over the first tensor dimension. - """ - - def body(x, y): - return x + y - - return map(body, xs, y) -``` - -This results in the following GraphModule: - -``` -# Toplevel graph module -class GraphModule(torch.nn.Module): - graph(): - %arg0: f32[3, s1] = placeholder[target=arg0] - %arg1: f32[s1] = placeholder[target=arg1] - %body_graph_0 = get_attr[target=body_graph_0] - map_1: f32[3, s1] = call_function[target=torch.ops.higher_order.map](args = (%body_graph_0, %arg0, %arg1), kwargs = {}) - return [map_1] - - # body_graph_0 - class (torch.nn.Module): - graph(): - %arg0_1: f32[s1] = placeholder[target=arg0_1] - %arg1_1: f32[s1] = placeholder[target=arg1_1] - %add: f32[s1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}) - return [%add] -``` - -**See examples of advanced usage of `map()` operator in ExportDB: [map tag](https://www.internalfb.com/intern/staticdocs/exportdb/map.html)** - -## torch.ops.while - -TODO - diff --git a/docs/website/docs/sdk/00_getting_started.md b/docs/website/docs/sdk/00_getting_started.md deleted file mode 100644 index 336ac718ce..0000000000 --- a/docs/website/docs/sdk/00_getting_started.md +++ /dev/null @@ -1,60 +0,0 @@ -# Getting started - -The ExecuTorch SDK is designed to provide ExecuTorch users with the tooling they need to profile, debug and visualize models that they have onboarded onto ExecuTorch. - -The fundamental features that the SDK provides today is: -- Visualization of models based on the exported graphs in various dialects (ATen, Edge etc.) -- Correlating and displaying the performance data along with the graph visualizations in TensorBoard -- Tabular rendering of all performance data in TensorBoard -- Command line tooling available to dive deeper into graph modules and performance data -- E2E runs on AIBench with direct pointers to TensorBoard visualization on the job results page. - -### SDK entry points: - -We primarily offer two entry points into the SDK tooling shown below: - -- **Python API & CLI tools** - With an ETrecord and an optional ETdump users will be able to leverage all the SDK tooling such as generating a TensorBoard visualization or digging into performance data via the Python API's we provide or also via our CLI utility. - -
- -
- -- **AIBench** - With an ETrecord users can benchmark their model on devices in the AIBench lab. We'll behind the scenes generate an ETdump for you with all the performance data, retrieve it from the device and then display the corresponding URL on the AIBench job result page that the users can open for graph and performance data visualization. - -
- -
- -## SDK tutorials: -The SDK tutorial is overall broken into the following components: - -- [Generating an ETrecord](./01_generating_etrecord.md) -- [Generating an ETdump](./02_generating_etdump.md) -- [Library and CLI](./03_using_sdk_cli_tools.md) -- [Visualizing models along with performance data](./04_visualizing_models.mdx) -- [Debugging with ETDB](./05_ETDB.md) -- [Using AIBench to profile and visualize models](./06_aibench_sdk.md) - -## SDK fundamentals: -Before we dive into to understand how to use the tooling let's understand some fundamental components of the SDK that are integral for fully leveraging all the functionality it provides. These are: - -- **ETrecord** -- **ETdump** - - -### ETrecord: - - -ETrecord is intended to be the debug artifact that is generated by users ahead of time. To draw a rough equivalent to conventional software development ETrecord can be considered as the binary built with debug symbols that is used for debugging in gdb. - -When generating an ETrecord the user will be expected to provide the graph modules that they are interested in working with via our tooling. The user can also provide their ExecuTorch program to store in ETrecord which can then later also be used to run the actual model on device or anywhere the user intends to. Further details on how to generate an ETrecord are provided in the ETrecord section. - - -### ETdump - - -ETdump is the serialized object that is generated by the runtime after running a model. Similarly as above to draw a rough equivalent to conventional software development ETdump could be considered as the coredump of ExecuTorch, but in this case within ETdump we store all the performance and debug data that was generated by the runtime. - -ETdump is then provided to our tooling to visualize the performance and debug data generated by the runtime. When ETdump and ETrecord are both passed to our tooling we are able to correlate this performance and debug data with the corresponding nodes in the graph that generated them. - -*** NOTE:*** If you only want to visualize the graph then you don't need an ETDump. You will only need an ETDump if you want to visualize the graph along with model performance and debug data. diff --git a/docs/website/docs/sdk/01_generating_etrecord.md b/docs/website/docs/sdk/01_generating_etrecord.md deleted file mode 100644 index 8347832513..0000000000 --- a/docs/website/docs/sdk/01_generating_etrecord.md +++ /dev/null @@ -1,55 +0,0 @@ -# Generating an ETrecord - -Make sure to read and understand what an ETrecord is and why we need it for the SDK in the [getting started page](./00_getting_started.md#etrecord) - - -For a demo on how to generate an ETrecord, please refer to this [notebook](https://www.internalfb.com/intern/anp/view/?id=3799219). - -There are two important API's users must be aware of when dealing with ETrecord: `generate_etrecord` and `parse_etrecord`. - ---- -### Generating an ETrecord - -```python -generate_etrecord( - etrecord_path: str, - edge_dialect_program: ExirExportedProgram - executorch_program: Union[ExecutorchProgram, MultiMethodExecutorchProgram], - export_modules: Optional[ - Dict[ - str, Union[MultiMethodExirExportedProgram, ExirExportedProgram] - ] - ] = None, -) -> None: -``` - -Generates an ETRecord from the given objects and saves it to the given path. -The objects that will be serialized to an ETRecord are all the graph modules present in the export_modules dict, the graph module present in the edge dialect program object, -and also the graph module present in the executorch program object, which is the closest graph module representation of what is eventually run on the device. - -In addition to all the graph modules we also serialize the program buffer which the users can provide to the ExecuTorch runtime to run the model. - -#### Parameters: -- `etrecord_path` : Path to where the ETRecord file will be saved to. -- `edge_dialect_program`: ExirExportedProgram for this model returned by the call to to_edge() -- `executorch_program`: ExecutorchProgram or MultiMethodExecutorchProgram for this model returned by the call to to_executorch() -- `export_modules`: Dictionary of graph modules with the key being the user provided name and the value is the corresponding exported module. The exported graph modules can be either the output of capture() or to_edge(). - -#### Returns: -None - ---- - -### Parsing an ETrecord - -```python -parse_etrecord(etrecord_path: str) -``` - -Parses an ETRecord file and returns a ETRecord object that contains the deserialized graph modules, program buffer and debug handle map. In the graph map in the returned ETRecord object if a model with multiple entry points was provided originally by the user during ETRecord generation then each entry point will be stored as a separate graph module in the ETRecord object with the name being the original module name + "/" + the name of the entry point. - -#### Parameters: - - `etrecord_path`: Path to the ETRecord file. - -#### Returns: - - ETRecord object diff --git a/docs/website/docs/sdk/02_generating_etdump.md b/docs/website/docs/sdk/02_generating_etdump.md deleted file mode 100644 index 0ddc3b74d0..0000000000 --- a/docs/website/docs/sdk/02_generating_etdump.md +++ /dev/null @@ -1,49 +0,0 @@ -# Generating an ETdump -> Note: If you're using AIBench with one of the pre-built binaries then you can skip this step. -AIBench will handle the retrieval and processing of ETdump for you. - -Generating an ETdump from the runtime is very trivial. It consists of two steps. - -1) Integrate the etdump generation code into your application to generate an etdump after the model has been executed. - -2) Compile your binary with the profiling enabled flag to ensure that the profiling data is written out to etdump. - ---- - -#### 1. Integrating ETdump generation code into your application - -- Add this header to your file -```c -#include -``` - -- Create an instance of `ETDump` by passing in a `MemoryAllocator`. - -```c -MemoryAllocator etdump_allocator{MemoryAllocator(ETDumpMemPoolSize, etdump_mem_pool)};} -ETDump et_dump(etdump_allocator); -``` - -- Write out the `ETDump` to the filesystem. - -```c -auto ret = et_dump.serialize_prof_results_to_etdump(etdump_path) -if (ret != torch::executor::Error::Ok) { - ET_LOG(Error, "Failed to serialize and write out etdump data."); - return -1; - } -} -``` - -#### 2. Generating profiling data into an ETdump - -To make sure that the profiling data collected in the runtime is written out to the ETDump your application will have to be compiled with the following flag. - -```c --DPROFILING_ENABLED -``` - -In buck this is as simple as compiling your target with the following buck config: -``` --c executorch.prof_enabled=true -``` diff --git a/docs/website/docs/sdk/03_using_sdk_cli_tools.md b/docs/website/docs/sdk/03_using_sdk_cli_tools.md deleted file mode 100644 index f9d9623500..0000000000 --- a/docs/website/docs/sdk/03_using_sdk_cli_tools.md +++ /dev/null @@ -1,105 +0,0 @@ -# Library and CLI - -There are 2 main ways of accessing the SDK: **SDK Library** and **Buck CLI** - -> Note: If using AIBench, TensorBoard generation (via SDK) is already integrated. Read more [here](./06_aibench_sdk.md) - -## SDK Library - -The SDK Library provides functions for interacting with ETRecord, TensorBoard Visualization, and ETDB. - -For a full walk through, please refer to this [notebook](https://www.internalfb.com/intern/anp/view/?id=3799219). - ---- - -### Visualizer -```python -from executorch.sdk.fb import visualize_etrecord, visualize_etrecord_path - -async def visualize_etrecord( - etrecord: ETRecord, - et_dump_path: Optional[str] = None, - run_name: Optional[str] = None, -) -> str - -async def visualize_etrecord_path( - etrecord_path: str, - et_dump_path: Optional[str] = None, - run_name: Optional[str] = None, -) -> str -``` - -Generates an internal link to a TensorBoard instance visualizing the model graphs in the ETRecord. - -If an `et_dump_path` is provided, associate the profiling information from the et_dump to the model_graphs. If a `run_name` is provided, use it to label the generated TB instance. - -#### Parameters -- `etrecord/etrecord_path`: Instance/Path of the ETRecord to be visualized -- `et_dump_path`: Optional path to the ETDump to be associated with the ETRecord -- `run_name`: Optional name to associate with the TB instance. One will be genereated if not provided - -#### Returns - -- Path to an internal TB link - ---- - -### ETDB - -```python -from executorch.sdk.fb import debug_etrecord, debug_etrecord_path - -async def debug_etrecord( - etrecord: ETRecord, et_dump_path: Optional[str] = None, verbose: bool = False -) - -async def debug_etrecord_path( - etrecord_path: str, et_dump_path: Optional[str] = None, verbose: bool = False -) -``` -Kicks off an interactive ETDB terminal instance for the model graphs in the ETRecord. - -If an `et_dump_path` is provided, associate the profiling information from the et_dump to the model_graphs. If a `verbose` flag is provided, the terminal instance will be ran in verbose mode. - -#### Parameters -- `etrecord/etrecord_path`: Instance/Path of the ETRecord to be visualized -- `et_dump_path`: Optional path to the ETDump to be associated with the ETRecord -- `verbose`: Optional flag to enable verbose printing. - -#### Returns - -- None - ---- - -### ETRecord Helper -```python -from executorch.sdk.fb import ETRecord, generate_etrecord, parse_etrecord -``` -See [ETRecord](./01_generating_etrecord.md) for more information - ---- -## Buck CLI - -The Buck CLI is perfect for ad hoc visualization and debugging saved ETRecord's (and associated ETDump's). The CLI operates as a convenient wrapper for calling `visualize_etrecord_path` and `debug_etrecord_path` - -``` bash -buck run //executorch/sdk/fb:cli ... - - : Mandatory Path to ETRecord - - [--et_dump ET_DUMP]: Optional Path to ETDump - [--run_name RUN_NAME]: Optional Name for TB Run - [--terminal_mode]: Toggle for using ETDB instead of generating a TB Link - [--verbose]: Toggle for verbose format when using ETDB -``` - -Example: Generate TB Visualization via CLI: -``` bash -buck run //executorch/sdk/fb:cli et_record.bin --et_dump et_dump.etdp -``` - -Example: Kickoff ETDB in Verbose mode via CLI: -``` bash -buck run //executorch/sdk/fb:cli et_record.bin --et_dump et_dump.etdp --terminal_mode --verbose -``` diff --git a/docs/website/docs/sdk/04_visualizing_models.mdx b/docs/website/docs/sdk/04_visualizing_models.mdx deleted file mode 100644 index ebda4499ee..0000000000 --- a/docs/website/docs/sdk/04_visualizing_models.mdx +++ /dev/null @@ -1,18 +0,0 @@ -# Visualizing models along with performance data - -Visualization of models in the ExecuTorch SDK is done using TensorBoard. The process to generate the visualization of a model can be broadly broken down into three steps. - -1. Generate an etrecord with the exported graph modules of your model that you want to visualize. Refer to the [etrecord tutorial](./01_generating_etrecord.md) for more details on how to do this. - -2. *Optionally* generate an etdump to also visualize the performance data of your model run along with the graph. Refer to the [etdump tutorial](./02_generating_etdump.md) for more details on how to do this. - -3. Generate a TensorBoard url by calling a `visualize_etrecord` API, passing in the ETrecord you generated in step 1 and also the optional ETdump you generated in step 2. This will return a url that you can open in your browser to visualize the model and view the performance data. See [library documentation](./03_using_sdk_cli_tools.md) for API details - - -For a complete end-to-end tutorial on how to do this please refer to this [notebook](https://www.internalfb.com/intern/anp/view/?id=3799219). - -Here's a video tutorial walking through all the features we have support for in our visualization integration with TensorBoard. - -
- -
diff --git a/docs/website/docs/sdk/05_ETDB.md b/docs/website/docs/sdk/05_ETDB.md deleted file mode 100644 index 2ca32c6a59..0000000000 --- a/docs/website/docs/sdk/05_ETDB.md +++ /dev/null @@ -1,101 +0,0 @@ - # Debugging with ETDB - -ETDB (ExecuTorch Debugger) is an interactive text-based tool used for investigating models (`ETRecord`) and profiling information (`ETDump`). Features provided include: -- **Tabular Visualization** of model graphs -- **Drill-in Selection** of components for detailed investigation -- **Aggregated Operator Statistics** based on results from ETDump -- **Module Architecture** grouped by a node's immediate SubModule - -To kick off an ETDB instance follow these steps: - - -1. Generate an etrecord with the exported graph modules of your model that you want to visualize. Refer to the [etrecord tutorial](./01_generating_etrecord.md) for more details on how to do this. - -2. *Optionally* generate an etdump to also visualize the performance data of your model run along with the graph. Refer to the [etdump tutorial](./02_generating_etdump.md) for more details on how to do this. - -3. Initiate the ETDB instance by calling a `debug_etrecord` API, passing in the ETrecord you generated in step 1 and also the optional ETdump you generated in step 2. This will kick off the text tool. See [library documentation](./03_using_sdk_cli_tools.md) for API details - -For a complete end-to-end tutorial on how to do this please refer to this [notebook](https://www.internalfb.com/intern/anp/view/?id=3799219). - ---- - -## ETDB Flow - -There are 3 stages to the ETDB flow: -1. **Graph Selection**: Prompts for which model graph to investigate -2. **Graph Overview**: Prints an overview of the selected graph -3. **Graph Investigation**: Interactive mode; User input is used to generate a focused view of the graph - - -### Graph Selection - -Given a list of the graphs found in ETRecord, choose one to investigate. - -Note: In the future it will be possible to toggle between graphs during the Graph Investigation stage. - -### Graph Overview - -Once a graph is selected, a set of overview tables will be displayed. Specifically the following: -- **Graph Counts**: Graph input + output counts, and a breakdown of node types in the model -- **Enumerated Nodes**: Tables listing each of the top level model nodes -- **Aggregated Summaries**: An aggregated summary table grouped by op/module type - -If profiling information was attached to this graph, a **Run Summary** table with aggregated run stats will also be shown. - - -### Graph Investigation - -Graph Investigation is the interactive segment of ETDB. With reference to the row entries in **Graph Overview**, ETDB takes as input any of the following: -- **Node Name** (e.g. add\_tensor_) -- **Module Name** (e.g. l_\_self___model_backbone_encoder_module) -- **Op Type** (e.g. add.Tensor) -- **Module Type** (e.g. Linear) - -From the selection, a new set of tables containing related information will be generated. - ---- - -#### Selection: Node Name - -If an node name is provided, a few things are returned: -- **Related Nodes**: Tables containing the inputs/outputs of the selected Node -- **Parent Module Summary**: Basic information about this node's parent module (if exists) -- **Operator Summary**: Count (and summary stats if ETDump is provided) of this node's type - -For example, if ETDump was provided and `addmm_default_` was selected: -- Related Nodes: Tables of Input Nodes, Tables of Output Nodes -- Parent Module Summary: Summary of `l__self___linear` -- Operator Summary: # of `addmm.default` instances in this model and runtime stats of those instances (e.g. Avg, P10, P90) - ---- - -#### Selection: Module Name - -If an module name is provided, a few things are returned: -- **Related Nodes**: Tables containing the inputs/outputs of the selected module -- **Module Contents**: Table listing all nodes within this module instance -- **Parent Module Summary**: Basic information about this node's parent module (if exists) -- **Module Summary**: Count (and summary stats if ETDump is provided) of this module's type - -For example, if ETDump was provided and `l__self___linear` was selected: -- Related Nodes/Modules: Tables of Input Nodes, Tables of Output Nodes -- Module Contents: List of all nodes in `l__self___linear` -- Parent Module Summary: Summary of `l__self___wrapped_linear` -- Module Summary: # of instances of `Linear` - ---- - -#### Selection: Op Type + Module Type - -If either an op type or module type are provided, two tables are returned: -- **Aggregated Stats**: Count (and summary stats if ETDump is provided) for that node type -- **Enumerated Nodes**: Table listing all nodes of the selected type - -For example, if ETDump was provided and `slice_copy.Tensor_out` was selected: -- Aggregated Stats: # of `slice_copy.Tensor_out` instances and runtime stats of those instances (e.g. Avg, P10, P90) -- Enumerated Nodes: List of all `slice_copy.Tensor_out` instances in this model ---- -## Ease of Use - -Below are a few ease of use features -- **Undo Investigation**: Entering `b` or `back` while in the Graph Investigation flow, replays the last user input provided diff --git a/docs/website/docs/sdk/06_aibench_sdk.md b/docs/website/docs/sdk/06_aibench_sdk.md deleted file mode 100644 index 5d5fcf9a56..0000000000 --- a/docs/website/docs/sdk/06_aibench_sdk.md +++ /dev/null @@ -1,23 +0,0 @@ -# Using AIBench along with the SDK - -AIBench is a benchmarking framework used to execute binaries on-device and collect output. It's an intuitive tool for on-device workloads, including testing, benchmarking ML models, measuring PyTorch operator performance, and scheduling continuous benchmark runs. -Get started at the [AIBench wiki](https://www.internalfb.com/intern/wiki/AIBench/Mobile_Overview/Submitting_a_Job/) - -If you're interested in benchmarking your model on a device using AIBench and then visualizing the performance data along with the model visualization then the `ExecutorchAIBenchAPI` is your one-stop solution for all of this. - -AIBench handles all of the plumbing required to generate an ETdump and also to subsequently generate the visualization along with the performance data. -Pass in the ETRecord (please refer to [this page](./01_generating_etrecord.md) for instructions on how to generate en ETrecord) along with your device selection to the `ExecutorchAIBenchAPI`. - -Behind the scenes we will then run your model on the device you selected, fetch the etdump that was generated by the runtime, generate the visualization along with the performance data visualization on a TensorBoard and the link to this will be populated on the AIBench Job Result page. - -### Sample Notebook: -A sample notebook that goes through the entire flow of generating an ETRecord and then running an AIBench job using that. [Link](https://www.internalfb.com/intern/anp/view/?id=3799219) - -### Visualization and ETDump links in AIBench job result page: -As shown in the image below you can click on the link that says "Open etdump.etdp in TensorBoard" which will take you to the TensorBoard page with the visualization. - -Alternatively if you wish to you can also download the etdump from the link thay says "etdump.etdp" and use it in our command line tooling. - -
- -
diff --git a/docs/website/docs/sdk/README.md b/docs/website/docs/sdk/README.md deleted file mode 100644 index c22e70e138..0000000000 --- a/docs/website/docs/sdk/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Deprecated folder - -This folder is deprecated. For up-to-date comprehensive technical overview of ExecuTorch and step-by-step tutorials, -please visit our [documentation website](https://pytorch.org/executorch). diff --git a/docs/website/docs/tutorials/00_setting_up_executorch.md b/docs/website/docs/tutorials/00_setting_up_executorch.md deleted file mode 100644 index 6277e2c49e..0000000000 --- a/docs/website/docs/tutorials/00_setting_up_executorch.md +++ /dev/null @@ -1,163 +0,0 @@ - - -# Setting up ExecuTorch - -This is a tutorial for building and installing ExecuTorch from the GitHub -repository. - -## Ahead-of-Time Setup [(Open on Google Colab)](https://colab.research.google.com/drive/1m8iU4y7CRVelnnolK3ThS2l2gBo7QnAP#scrollTo=1o2t3LlYJQY5) - -This will install an `executorch` pip package to your conda environment and -allow you to export your PyTorch model to a flatbuffer file using ExecuTorch. - -### Step 1: Clone the ExecuTorch repo - -```bash -# Do one of these, depending on how your auth is set up -git clone https://github.com/pytorch/executorch.git -git clone git@github.com:pytorch/executorch.git -``` - -Ensure that git has fetched and updated the submodules. This is necessary -anytime commit hash of any of the submodules changes. Thus it is safe and -necessary at times to apply this step after you pull changes from upstream. - -```bash -cd executorch -git submodule sync -git submodule update --init -``` - -### Step 2: Set up a dev environment - -Install -[Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) -and activate a new virtual environment: - -```bash -conda create -yn executorch python=3.10.0 -conda activate executorch -``` - -Or alternatively, activate a new Python virtual environment: - -```bash -python3 -m venv .executorch -source .executorch/bin/activate -``` - -### Step 3: Install [Cmake](https://cmake.org/download) - -```bash -conda install cmake -``` - -Alternatively: - -```bash -pip install cmake -``` - -### Step 4: Install `executorch` pip package and dependencies - -Install all required python dependencies and PyTorch dependencies. - -```bash -# Note: if you are behind a firewall an appropriate proxy server must be setup -# for all subsequent steps. -./install_requirements.sh -``` - -When getting a new version of the executorch repo (via clone, fetch, or pull), -you may need to re-install a new version the PyTorch nightly pip package. The -`TORCH_VERSION` value in this document will be the correct version for the -corresponsing version of the repo. - -### Step 5: Expose FlatBuffers compiler - -ExecuTorch uses `flatc` to export models and builds it from sources at -`third-party/flatbuffers`. Make it's available by referring in `$PATH`, -as prompted by the previous step, or exporting as `$FLATC_EXECUTABLE` -enironment variable. -Run `./build/install_flatc.sh` to make sure `flatc` is installed correctly. - -## Testing Model Export - -Generate a test program file from an `nn.Module` via Python script: - -```bash -# Creates the file `add.pte` -python3 -m examples.portable.scripts.export --model_name="add" - -# Creates the delegated program `composite_model.pte`, other options are "whole" and "partition" -python3 -m examples.portable.scripts.export_and_delegate --option "composite" -``` - -Or via Python interpreter: - -```python -$ python3 ->>> import executorch.exir as exir ->>> from executorch.exir.tests.models import Mul ->>> m = Mul() ->>> print(exir.capture(m, m.get_random_inputs()).to_edge()) ->>> open("mul.pte", "wb").write(exir.capture(m, m.get_random_inputs()).to_edge().to_executorch().buffer) -``` - -Please refer to the [More Examples](./00_setting_up_executorch.md#more-examples) -section for running with more popular models. - -## Runtime Setup - -Follow -[Ahead-of-Time Setup: Step 1](./00_setting_up_executorch.md#step-1-clone-the-executorch-repo) -above to clone the `executorch` repo if you haven't already. - -### Step 1: Install buck2 - -- If you don't have the `zstd` commandline tool, install it with `pip install zstd` -- Download a prebuilt buck2 archive for your system from the https://github.com/facebook/buck2/releases/tag/2023-07-18 release page. Note that the version is important, and newer or older versions may not work with the version of the buck2 prelude used by the ExecuTorch repo. -- Decompress with the following command (filename depends on your system) - -```bash -# For example, buck2-x86_64-unknown-linux-musl.zst or buck2-aarch64-apple-darwin.zst -zstd -cdq buck2-DOWNLOADED_FILENAME.zst > /tmp/buck2 && chmod +x /tmp/buck2 -``` - -You may want to copy the `buck2` binary into your `$PATH` so you can run it as `buck2`. - -### Step 2: Build a binary - -`executor_runner` is an example wrapper around executorch runtime which includes all the operators and backends - -```bash -/tmp/buck2 build //examples/portable/executor_runner:executor_runner --show-output -``` - -The `--show-output` flag will print the path to the executable if you want to run it directly. - -If you run into `Stderr: clang-14: error: invalid linker name in argument '-fuse-ld=lld'`, do -```bash -conda install -c conda-forge lld -``` - -### Step 3: Run a binary - -```bash -# add.pte is the program generated from portable/scripts/export.py during AOT Setup Step 3 -/tmp/buck2 run //examples/portable/executor_runner:executor_runner -- --model_path add.pte - -# To run a delegated model -/tmp/buck2 run //examples/portable/executor_runner:executor_runner -- --model_path composite_model.pte -``` - -or execute the binary directly from the `--show-output` path shown when building. - -```bash -./buck-out/.../executor_runner --model_path add.pte -``` - -## More Examples - -The [`executorch/examples`](https://github.com/pytorch/executorch/blob/main/examples) directory contains useful examples with a guide to lower and run -popular models like MobileNet V3, Torchvision ViT, Wav2Letter, etc. on ExecuTorch. diff --git a/docs/website/docs/tutorials/README.md b/docs/website/docs/tutorials/README.md deleted file mode 100644 index c22e70e138..0000000000 --- a/docs/website/docs/tutorials/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Deprecated folder - -This folder is deprecated. For up-to-date comprehensive technical overview of ExecuTorch and step-by-step tutorials, -please visit our [documentation website](https://pytorch.org/executorch). diff --git a/docs/website/docs/tutorials/aten_ops_and_aten_mode.md b/docs/website/docs/tutorials/aten_ops_and_aten_mode.md deleted file mode 100644 index 42686d42fa..0000000000 --- a/docs/website/docs/tutorials/aten_ops_and_aten_mode.md +++ /dev/null @@ -1,182 +0,0 @@ -# ATen-compliant Operator Registration & ATen mode - - -## Introduction - -ExecuTorch supports a subset of ATen-compliant operators. -ATen-compliant operators are those defined in -[`native_functions.yaml`](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml), -with their native functions (or kernels, we use these two terms interchangeably) -either defined in ATen library or other user defined libraries. The ATen-compliant operators supported by ExecuTorch have these traits (actually same for custom ops): -1. Out variant, means these ops take an `out` argument -2. Functional except `out`. These ops shouldn't mutate input tensors other than `out`, shouldn't create aliasing views. - -To give an example, `aten::add_.Tensor` is not supported since it mutates an input tensor, `aten::add.out` is supported. - -ATen mode is a build-time option to link ATen library into ExecuTorch runtime, so those registered ATen-compliant ops can use their original ATen kernels. - -On the other hand we need to provide our custom kernels if ATen mode is off (a.k.a. lean mode). - -In the next section we will walk through the steps to register ATen-compliant ops into ExecuTorch runtime. - -## Step by step guide -There are two branches for this use case: -* ATen mode. In this case we expect the exported model to be able to run with ATen kernels . -* Lean mode. This requires ATen-compliant op implementations using `ETensor`. - -In a nutshell, we need the following steps in order for a ATen-compliant op to work on ExecuTorch: - -#### ATen mode: -1. Define a target for selective build (`et_operator_library` macro) -2. Pass this target to codegen using `executorch_generated_lib` macro -3. Hookup the generated lib into ExecuTorch runtime. - -For more details on how to use selective build, check [Selective Build](https://www.internalfb.com/intern/staticdocs/executorch/docs/tutorials/custom_ops/#selective-build). -#### Lean mode: -1. Declare the op name in `functions.yaml`. Detail instruction can be found in [Declare the operator in a YAML file](https://www.internalfb.com/code/fbsource/xplat/executorch/kernels/portable/README.md). -2. (not required if using ATen mode) Implement the kernel for your operator using `ETensor`. ExecuTorch provides a portable library for frequently used ATen-compliant ops. Check if the op you need is already there, or you can write your own kernel. -3. Specify the kernel namespace and function name in `functions.yaml` so codegen knows how to bind operator to its kernel. -4. Let codegen machinery generate code for either ATen mode or lean mode, and hookup the generated lib into ExecuTorch runtime. - -### Case Study -Let's say a model uses an ATen-compliant operator `aten::add.out`. - -We can either reuse the kernel written in portable library [here](https://www.internalfb.com/code/fbsource/xplat/executorch/kernels/portable/cpu/op_add.cpp) or choose to write our own. - -#### ATen mode - -For ATen mode we don't need to define any `functions.yaml` file or write any kernel, since they are already defined in `native_functions.yaml`. All we need to do is to use selective build to choose the ops we want: -```python -et_operator_library( - name = "selected_ops", - ops = [ - "aten::add.out", - ], -) - -executorch_generated_lib( - name = "add_lib", - deps = [ - ":selected_ops", - ], - aten_mode = True, -) -``` - -#### Lean mode - -Let's say we like to write our own kernel: - -```cpp -// add.cpp -namespace custom { -namespace native { - -Tensor& add_out(const Tensor& a, const Tensor& b, const Scalar& alpha, Tensor& out) { - // do something - return out; -} - -} // namespace native -} // namespace custom -``` - -The corresponding `functions.yaml` for this operator looks like: - -```yaml -- op: add.out - dispatch: - CPU: custom::add_out -``` -Notice that there are some caveats: -#### Caveats -* `dispatch` and `CPU` are legacy fields and they don't mean anything in ExecuTorch context. -* Namespace `aten` is omitted. -* We don't need to write `aten::add.out` function schema because we will use the schema definition in `native_functions.yaml` as our source of truth. -* Kernel namespace in the yaml file is `custom` instead of `custom::native`. This is because codegen will append a `native` namespace automatically. It also means the kernel always needs to be defined under `::native`. - -Now we need to trigger codegen to generate operator library: -```python -export_file( - name = "functions.yaml" -) - -cxx_library( - name = "add_kernel", - srcs = ["add.cpp"], -) - -et_operator_library( - name = "all_ops", - include_all_ops = True, # Select all ops in functions.yaml -) - -executorch_generated_lib( - name = "add_lib", - functions_yaml_target = ":functions.yaml", - deps = [ - ":all_ops", - ":add_kernel", - ], -) -``` -### Usage of generated lib -In the case study above, eventually we have `add_lib` which is a C++ library responsible to register `aten::add.out` into ExecuTorch runtime. - -In our ExecuTorch binary target, add `add_lib` as a dependency: -```python -cxx_binary( - name = "executorch_bin", - deps = [ - "//executorch/runtime/executor:program", # Program and Method - "//path/to:add_lib", # operator library - ], -) -``` -## Common APIs - -To facilitate custom operator registration, we provide the following APIs: - -- `functions.yaml`: ATen-compliant operator schema and kernel metadata are defined in this file. -- `executorch_generated_lib`: the Buck rule to call ExecuTorch codegen system and encapsulate generated C++ source files into libraries. If only include ATen-compliant operators, only one library will be generated: - - ``: contains C++ source files to register ATen-compliant operators. Required by ExecuTorch runtime. - - Input: most of the input fields are self-explainatory. - - `deps`: kernel libraries - can be custom kernels or portable kernels (see portable kernel library [README.md](https://fburl.com/code/zlgs6zzf) on how to add more kernels) - needs to be provided. Selective build related targets should also be passed into the generated libraries through `deps`. - - `define_static_targets`: if true we will generate a `_static` library with static linkage. See docstring for more information. - - `functions_yaml_target`: the target pointing to `functions.yaml`. See `ATen-compliant Operator Registration` section for more details. - - -We also provide selective build system to allow user to select operators from both `functions.yaml` and `custom_ops.yaml` into ExecuTorch build. See [Selective Build](https://www.internalfb.com/intern/staticdocs/executorch/docs/tutorials/custom_ops/#selective-build) section. - - - -## Common Issues - - -### Missing operator - -Example error message: -``` -Nov 14 16:48:07 devvm11149.prn0.facebook.com bento[1985271]: [354870826409]Executor.cpp:260 Missing operator: [1] aten::squeeze_copy.dim_out - -Nov 14 16:48:07 devvm11149.prn0.facebook.com bento[1985271]: [354870830000]Executor.cpp:267 In function init(), assert failed (num_missing_ops == 0): There are 1 operators missing from registration to Executor. See logs for details -``` - -This error message indicates that the operators are not registered into the ExecuTorch runtime. - -For lean mode mode, please make sure the ATen-compliant operator schema is being added to your `functions.yaml`. For more guidance of how to write a `functions.yaml` file, please refer to [Declare the operator in a YAML file](https://www.internalfb.com/code/fbsource/xplat/executorch/kernels/portable/README.md). - -For both ATen mode and lean mode, double check whether the operator is being selected correctly. One way to debug is to select all operator (follow the instruction in [et_operator_library](https://www.internalfb.com/intern/staticdocs/executorch/docs/tutorials/custom_ops/#apis)) and check if the error is gone. - -### Undefined symbols - -Example error message: -``` -ld.lld: error: undefined symbol: torch::executor::native::add_out(torch::executor::Tensor const&, torch::executor::Tensor const&, torch::executor::Scalar const&, torch::executor::Tensor&) ->>> referenced by Functions.h:34 (buck-out/v2/gen/fbsource/9f23200ddcddc3cb/xplat/executorch/codegen/__generated_lib_combined__/out/Functions.h:34) ->>> __objects__/UnboxingFunctionsEverything.cpp.o:(torch::executor::add_outf(torch::executor::Tensor const&, torch::executor::Tensor const&, torch::executor::Scalar const&, torch::executor::Tensor&)) in archive buck-out/v2/gen/fbsource/9f23200ddcddc3cb/xplat/executorch/codegen/__generated_lib__/libgenerated_lib.a -``` -This is likely caused by one of the following: -* a typo in kernel definition that causes type mismatch. -* namespace caveats described in [Caveats](#caveats). -* other build isses such as dependency not being added to runtime target. diff --git a/docs/website/docs/tutorials/backend_delegate.md b/docs/website/docs/tutorials/backend_delegate.md deleted file mode 100644 index 32c92f60e4..0000000000 --- a/docs/website/docs/tutorials/backend_delegate.md +++ /dev/null @@ -1,397 +0,0 @@ -# Backend and Delegate - -Backend delegation is an entry point for backends to process and execute PyTorch programs --to leverage performance and efficiency benefits of specialized backends and hardware--, while still providing PyTorch users with an experience close to that of the PyTorch runtime. - -At a high level, the entry point for backends is defined by 2 components: - -- An IR to represent the program: Edge Dialect (and the related producer API: exir.capture) -- A couple of interfaces for backends to implement: - - AOT - - Program preprocess (e.g. ahead of time compilation, transformation, optimization...) - - Runtime - - Program init (e.g. runtime compilation). - - Program execution. - - [optional] destroy (e.g. release backend owned resource) - -## Backend interfaces - -A delegate backend implementation is composed of an implementation of 1) An ahead-of-time preprocessing interface, and 2) A runtime initialization and execution interface. - -```python -# ahead-of-time preprocessing interface -# defined in backend_details.py -def preprocess( - edge_program: ExportedProgram, - compile_specs: List[CompileSpec], -) -> bytes: -``` - -The demo preprocess is implemented here: executorch/backends/tests/backend_with_compiler_demo.py. The demo loops through the nodes in the graph module of the `edge_program` and serializes the add, mul, and sin instructions into a string, which is later parsed and executed at runtime. - -**Runtime initialization and execution interface** - -```cpp -// Following apis are defined in backend_registry.h -// runtime initialization -__ET_NODISCARD virtual Result init( - FreeableBuffer* processed, - ArrayRef compile_specs, - MemoryAllocator* memory_allocator); - -// runtime initialization -__ET_NODISCARD virtual Error execute(DelegateHandle* handle, EValue** args); - -// [Optional] runtime destroy. Destroy the resource held by the backend -virtual void destroy(__ET_UNUSED DelegateHandle* handle); -``` - -When backend is ready, to_backend API is the way to lower partitionally or the whole model to go through backend preprocess lower to the backend. We provide 2 to_backend APIs (multiple methods to_backend APIs are WIP), one to lower the whole model to the backend and the other one is together with a partitioner to lowered the tagged nodes to the backend. - -```python -# defined in backend_api.py - -# Lower the whole program to the target backend -def to_backend( - backend_id: str, - edge_program: ExportedProgram, - compile_specs: List[CompileSpec], -) -> LoweredBackendModule: - -# Lower the tagged nodes (done by partitioenr) in the program to the target backend -def to_backend( - edge_program: ExportedProgram, - partitioner: Type[TPartitioner], -) -> ExportedProgram: -``` - -To register the backend for ahead of time, just simply lowered the module - -```python -from executorch.exir.backend.test.backend_with_compiler_demo import BackendWithCompilerDemo -``` - -To register the backend for runtime, it's done via register_backend APIs -```cpp -__ET_NODISCARD Error register_backend(const Backend& backend); -``` - - -# How to delegate a PyTorch module to a different backend in ExecuTorch for Model Authors - -This note is to demonstrate the basic end-to-end flow of backend delegation in -the ExecuTorch runtime. - -At a high level, here are the steps needed for delegation: - -1. Add your backend to ExecuTorch. -2. Frontend: lower the PyTorch module or part of the module to a backend. -3. Deployment: load and run the lowered module through ExecuTorch runtime -interface. - - -## Frontend - -There are three flows for delegation: - -1. Lower the whole module. Good for testing a fully lowereable module. -1. Lower the whole module and compose it with another module. Good for reusing lowered module exported from other flow. -2. After getting the module, lowering the subgraph partitioned by the according partitioner, like XNNPACK partitioner. Good for lowering a model including both lowerable and non-lowerable nodes. - -### Flow 1: Lowering the whole module - -The flow starts from a traced graph module with Edge Dialect representation. To lower -it, we call the following function which returns a `LoweredBackendModule` (more -documentation on this function can be found in the Python API reference): - -```python -# defined in backend_api.py -def to_backend( - backend_id: str, - edge_program: ExportedProgram, - compile_spec: List[CompileSpec], -) -> LoweredBackendModule: -``` - -Within this function, the backend's `preprocess()` function is called which -produces a compiled blob which will be emitted to the flatbuffer binary. The -lowered module can be directly captured, or be put back in a parent module to be -captured. Eventually the captured module is serialized in the flatbuffers model -that can be loaded by the runtime. - -The following is an example of this flow: - -```python -from executorch.exir.backend.backend_api import to_backend, MethodCompileSpec -import executorch.exir as exir -import torch - -# The submodule runs in a specific backend. In this example, `BackendWithCompilerDemo` backend -class LowerableSubModel(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x): - return torch.sin(x) - -# Convert the lowerable module to Edge IR Representation -to_be_lowered = LowerableSubModel() -example_input = (torch.ones(1), ) -to_be_lowered_exir_submodule = exir.capture(to_be_lowered, example_input).to_edge() - -# Import the backend implementation -from executorch.exir.backend.test.backend_with_compiler_demo import ( - BackendWithCompilerDemo, -) -lowered_module = to_backend('BackendWithCompilerDemo', to_be_lowered_exir_submodule, []) -``` - -We can emit the program directly by running - -```python -# API to be added -program = lowered_module.program -``` - -### Flow 2: Lowering the whole module and composite - -After flow 1, alternatively we can compose this lowered module with another module: - -```python -# This submodule runs in executor runtime -class NonLowerableSubModel(torch.nn.Module): - def __init__(self, bias): - super().__init__() - self.bias = bias - - def forward(self, a, b): - return torch.add(torch.add(a, b), self.bias) - - -# The composite module, including lower part and non-lowerpart -class CompositeModel(torch.nn.Module): - def __init__(self): - super().__init__() - self.non_lowerable = NonLowerableSubModel(torch.ones(1) * 0.3) - self.lowerable = lowered_module - - def forward(self, x): - a = self.lowerable(x) - b = self.lowerable(a) - ret = self.non_lowerable(a, b) - return a, b, ret - -composite_model = CompositeModel() -model_inputs = (torch.ones(1), ) -exec_prog = exir.capture(composite_model, model_inputs).to_edge().to_executorch() - -# Save the flatbuffer to a local file -save_path = "delegate.pte" -with open(save_path, "wb") as f: - f.write(exec_prog.buffer) -``` - -### Flow 3: Partitioning - -The flow starts from a traced graph module with Edge Dialect representation. To lower -certain nodes in this graph module, we can use the overloaded `to_backend` -function (more documentation on this function can be found in the Python API -reference): - -```python -def to_backend( - edge_program: ExportedProgram, - partitioner: Type[TPartitioner], -) -> ExportedProgram: -``` - -This function takes in a `Partitioner` which adds a tag to all the nodes that -are meant to be lowered. The `Partitioner.partition` function will return both the tagged graph module and the `partition_tags` mapping tags to -backend names and module compile specs. The tagged nodes will then be -partitioned and lowered to their mapped backends using Flow 1's process. -Available helper partitioner are documented [here](./passes.md#partitioner). These -lowered modules will be inserted into the toplevel module and serialized. - -The following is an example of the flow: -```python -from executorch.exir.backend.backend_api import to_backend -import executorch.exir as exir -import torch - -class Model(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x, y): - x = x + y - x = x * y - x = x - y - x = x / y - x = x * y - x = x + y - return x - -model = Model() -model_inputs = (torch.randn(1, 3), torch.randn(1, 3)) -gm = exir.capture(model, model_inputs).to_edge() - -from executorch.exir.backend.test.op_partitioner_demo import AddMulPartitionerDemo -exec_prog = to_backend(gm, AddMulPartitionerDemo).to_executorch( - exir.ExecutorchBackendConfig(passes=SpecPropPass()) -) - -# Save the flatbuffer to a local file -save_path = "delegate.pte" -with open(save_path, "wb") as f: - f.write(exec_prog.buffer) -``` - -## Runtime - -The serialized flatbuffer model is loaded by the ExecuTorch runtime. The -preprocessed blob is directly stored in the flatbuffer, which is loaded into a -call to the backend's `init()` function during model initialization stage. At -the model execution stage, the initialized handled can be executed through the -backend's `execute()` function. - -To run the real model with executor: - -``` -> :warning: **pybind is not ready for partner preview**: please use size_test_all_ops or executor_runner cpp binary for now. pybind to run executor will be ready before MVP -``` - - -```python -# Load the program with executor runtime -executorch_module = _load_for_executorch_from_buffer(flatbuffer) -print("model_inputs: ", model_inputs) -# Execute the program -model_outputs = executorch_module.forward([*model_inputs]) -``` - -## Error Messages - -If there is an error in the backend, for example, if there is any operator that -is not supported by the backend, a debug handler can be thrown. It can surface -back to the Python frontend with source code information. Below is an example -where operator `tan` is not supported in `BackendWithCompilerDemo` backend. - -A problematic program: -```python -class TanModule(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x): - return torch.tan(x) - -tan_module = TanModule() -model_inputs = (torch.ones(1),) -edgeir_m = exir.capture(tan_module, model_inputs).to_edge() -lowered_tan_module = to_backend( - "BackendWithCompilerDemo", edgeir_m, [] -) - -class CompositeModelWithTan(torch.nn.Module): - def __init__(self): - super().__init__() - self.lowered_tan = lowered_tan_module - - def forward(self, x): - output_from_submodule = self.lowered_tan(x) - return output_from_submodule - -composite_model_with_tan = CompositeModelWithTan() -model_inputs = (torch.ones(1),) - -composite_model_with_tan(*model_inputs) - -exec_prog = ( - exir.capture(composite_model_with_tan, model_inputs).to_edge().to_executorch() -) - -buff = exec_prog.buffer -model_inputs = torch.ones(1) - -# Load and init the program in executor -executorch_module = _load_for_executorch_from_buffer(buff) - -# Expect to throw with debug handler here. -model_outputs = executorch_module.forward([model_inputs]) -``` - -It's expected to capture debug handler like `instruction demo::tan_default1 is not supported, debug handler is: 1` - - -## Common Questions - -1. How to get data in backend.preprocess - -The graph module being preprocessed is a lifted graph, this means that static data like weights and biases are supplied as inputs to the graph. However, we can access the weights and biases ahead-of-time through the exported program. To access these parameters from a given node, we can use the function `get_params` provided in `torch/_export/utils.py` - -2. How to embed the data (like weight/bias) to the backend? - -It's common that backend have some ways optimize the const data. In this case, we'd need to tag the placeholder node which are also the state in the partitioner, and during backend.preprocess, we can follow the description in the first question to get the weight. - -3. How to run the lowered module in Python? - -We haven't added the support yet but that's the plan! - -4. Should we expect to see `get_attr` node in exir? - -The`get_attr` will only show up for control flow or after to_backend call. It won't hold any data. - -5. Can we delegate to multiple backends? - -Yes! There are two ways to do this: - -Option 1: Run to_backend multiple times for different backends - -If we have two backends, backend_1 and backend_2, and they have their own parititioners: backend_1_parititioner and backend_2_partitioner, we can run it like - -```python -# Will first lower nodes to backend_1 depending on the backend_1_parititioner depending on partitioner algorithm -exported_program_backend_1 = to_backend(exported_program, backend_1_parititioner) -# For the rest of nodes, they will be lowered to backend_2 depending on backend_2_parititioner -exported_program_backend_1_and_2 = to_backend(exported_program_backend_1, backend_2_parititioner) -``` - -A more conrete example be found in executorch/exir/backend/test/demos/test_xnnpack_qnnpack.py. In this example, qnnpack is one backend and xnnpack is another backend. We haven't open-sourced these two backends delegates yet, and this example won't run out of box. It can be used as a reference to see how it can be done. - -This option is easy to try becuase usually all backends will implement their own parititioner. However this option may get different results if we change the order of to_backend call. If we want to have a better control on the nodes, like which backend they should go, option 2 is better. - -Option 2: -Another option is to create a customized partitioner, say parititioner `backend_1_2_parittioner`, and inside the partitioner logic, - -```python -class Backend_1_2_Partitioner(Partitioner): - """ - Partitions all add/mul nodes regardless of order for Backend2 - """ - - def __init__(self) -> None: - self.delegation_spec_1 = DelegationSpec("Backend1", []) - self.delegation_spec_2 = DelegationSpec("Backend2", []) - - def partition( - self, exported_program: ExportedProgram - ) -> PartitionResult: - partition_tags: Dict[str, DelegationSpec] = {} - # Tag all nodes in the first partiton to backend 1 - node_to_backend_1 = ... # some logic to select the nodes from the graph - delegation_tag = f"backend2_tag{partitioner_1.id}" - node.meta["delegation_tag"] = delegation_tag - partition_tags[delegation_tag] = self.delegation_spec_1 - - # Tag all nodes in the first partiton to backend 2 - node_to_backend_2 = ... # some logic to select the nodes from the graph - delegation_tag = f"backend2_tag{partitioner_2.id}" - node.meta["delegation_tag"] = delegation_tag - partition_tags[delegation_tag] = self.delegation_spec_2 - return PartitionResult( - tagged_exported_program=exported_program, partition_tags=partition_tags - ) -``` - -6. Is there an easy way to write partitioner? - -We provide canonical partitioners in "Partitioner Sesssion" in passes to make it easy to find nodes from decomposed operators. diff --git a/docs/website/docs/tutorials/bundled_program.md b/docs/website/docs/tutorials/bundled_program.md deleted file mode 100644 index 98a6a2a0e4..0000000000 --- a/docs/website/docs/tutorials/bundled_program.md +++ /dev/null @@ -1,177 +0,0 @@ -DEPRECATED: This document is moving to //executorch/docs/source/sdk-bundled-io.md - -# Bundled Program - -## Introduction -Bundled Program is a wrapper around the core ExecuTorch program designed to help users wrapping test cases and other related info with the models they deploy. Bundled Program is not necessarily a core part of the program and not needed for its execution but is more necessary for various other use-cases, especially for model correctness evaluation such as e2e testing during model bring-up etc. - -Overall procedure can be broken into two stages, and in each stage we are supporting: -* **Emit stage**: Bundling test I/O cases as well as other useful info in key-value pairs along with the ExecuTorch program. -* **Runtime stage**: Accessing, executing and verifying the bundled test cases during runtime. - -## Emit stage - - This stage mainly focuses on the creation of a BundledProgram, and dump it out to the disk as a flatbuffer file. Please refer to Bento notebook [N2744997](https://www.internalfb.com/intern/anp/view/?id=2744997) for details on how to create a bundled program. - -## Runtime Stage -This stage mainly focuses on executing the model with the bundled inputs and and comparing the model's output with the bundled expected output. We provide multiple APIs to handle the key parts of it. - -### Get executorch program ptr from BundledProgram buffer -We need the pointer to executorch program to do the execution. To unify the process of loading and executing BundledProgram and Program flatbuffer, we create an API: - ```c++ - -/** - * Finds the serialized ExecuTorch program data in the provided file data. - * - * The returned buffer is appropriate for constructing a - * torch::executor::Program. - * - * Calling this is only necessary if the file could be a bundled program. If the - * file will only contain an unwrapped ExecuTorch program, callers can construct - * torch::executor::Program with file_data directly. - * - * @param[in] file_data The contents of an ExecuTorch program or bundled program - * file. - * @param[in] file_data_len The length of file_data, in bytes. - * @param[out] out_program_data The serialized Program data, if found. - * @param[out] out_program_data_len The length of out_program_data, in bytes. - * - * @returns Error::Ok if the program was found, and - * out_program_data/out_program_data_len point to the data. Other values - * on failure. - */ -Error GetProgramData( - void* file_data, - size_t file_data_len, - const void** out_program_data, - size_t* out_program_data_len); -``` - -Here's an example of how to use the GetProgramData API: -```c++ - std::shared_ptr buff_ptr; - size_t buff_len; - -// FILE_PATH here can be either BundledProgram or Program flatbuffer file. - Error status = torch::executor::util::read_file_content( - FILE_PATH, &buff_ptr, &buff_len); - ET_CHECK_MSG( - status == Error::Ok, - "read_file_content() failed with status 0x%" PRIx32, - status); - - uint32_t prof_tok = EXECUTORCH_BEGIN_PROF("de-serialize model"); - - const void* program_ptr; - size_t program_len; - status = torch::executor::util::GetProgramData( - buff_ptr.get(), buff_len, &program_ptr, &program_len); - ET_CHECK_MSG( - status == Error::Ok, - "GetProgramData() failed with status 0x%" PRIx32, - status); -``` - -### Load bundled input to ExecutionPlan -To execute the program on the bundled input, we need to load the bundled input into the ExecutionPlan. Here we provided an API called `torch::executor::util::LoadBundledInput`: - -```c++ - -/** - * Load testset_idx-th bundled input of method_idx-th Method test in - * bundled_program_ptr to given Method. - * - * @param[in] method The Method to verify. - * @param[in] bundled_program_ptr The bundled program contains expected output. - * @param[in] method_name The name of the Method being verified. - * @param[in] testset_idx The index of input needs to be set into given Method. - * - * @returns Return Error::Ok if load successfully, or the error happens during - * execution. - */ -__ET_NODISCARD Error LoadBundledInput( - Method& method, - serialized_bundled_program* bundled_program_ptr, - MemoryAllocator* memory_allocator, - const char* method_name, - size_t testset_idx); -``` - -### Verify the plan's output. -We call `torch::executor::util::VerifyResultWithBundledExpectedOutput` to verify the method's output with bundled expected outputs. Here's the details of this API: - -```c++ -/** - * Compare the Method's output with testset_idx-th bundled expected - * output in method_idx-th Method test. - * - * @param[in] method The Method to extract outputs from. - * @param[in] bundled_program_ptr The bundled program contains expected output. - * @param[in] method_name The name of the Method being verified. - * @param[in] testset_idx The index of expected output needs to be compared. - * @param[in] rtol Relative tolerance used for data comparsion. - * @param[in] atol Absolute tolerance used for data comparsion. - * - * @returns Return Error::Ok if two outputs match, or the error happens during - * execution. - */ -__ET_NODISCARD Error VerifyResultWithBundledExpectedOutput( - Method& method, - serialized_bundled_program* bundled_program_ptr, - MemoryAllocator* memory_allocator, - const char* method_name, - size_t testset_idx, - double rtol = 1e-5, - double atol = 1e-8); - -``` - -### Example - -Here we provide an example about how to run the bundled program step by step. Most of the code are borrowed from "fbcode/executorch/sdk/fb/runners/executor_runner.cpp" and please review that file if you need more info and context: - -```c++ - // method_name is the name for the method we want to test - // memory_manager is the executor::MemoryManager variable for executor memory allocation. - // program is the executorch program. - Result method = program->load_method(method_name, &memory_manager); - EXECUTORCH_END_PROF(prof_tok); - ET_CHECK_MSG( - method.ok(), - "load_method() failed with status 0x%" PRIx32, - method.error()); - - // Load testset_idx-th input in the buffer to plan - status = torch::executor::util::LoadBundledInput( - *method, - program_data.bundled_program_data(), - &bundled_input_allocator, - method_name, - FLAGS_testset_idx); - ET_CHECK_MSG( - status == Error::Ok, - "LoadBundledInput failed with status 0x%" PRIx32, - status); - - // Execute the plan - status = method->execute(); - ET_CHECK_MSG( - status == Error::Ok, - "method->execute() failed with status 0x%" PRIx32, - status); - - // Verify the result. - status = torch::executor::util::VerifyResultWithBundledExpectedOutput( - *method, - program_data.bundled_program_data(), - &bundled_input_allocator, - method_name, - FLAGS_testset_idx, - FLAGS_rtol, - FLAGS_atol); - ET_CHECK_MSG( - status == Error::Ok, - "Bundle verification failed with status 0x%" PRIx32, - status); - -``` diff --git a/docs/website/docs/tutorials/cmake_build_system.md b/docs/website/docs/tutorials/cmake_build_system.md deleted file mode 100644 index c98cff551f..0000000000 --- a/docs/website/docs/tutorials/cmake_build_system.md +++ /dev/null @@ -1,209 +0,0 @@ -DEPRECATED: This document is moving to //executorch/docs/source/runtime-build-and-cross-compilation.md -# Building with CMake - -Although buck2 is the main build system for the ExecuTorch project, it's also -possible to build core pieces of the runtime using [CMake](https://cmake.org/) -for easier integration with other build systems. Even if you don't use CMake -directly, CMake can emit scripts for other format like Make or Ninja. (see -[cmake-generators(7)](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html)). - -## Targets built by the CMake build system - -ExecuTorch's CMake build system doesn't cover everything that the buck2 build -system covers. It can only build pieces of the runtime that are likely to be -useful to embedded systems users. - -- `libexecutorch.a`: The core of the ExecuTorch runtime. Does not contain any - operator/kernel definitions or backend definitions. -- `libportable_kernels.a`: The implementations of ATen-compatible operators, - following the signatures in `//kernels/portable/functions.yaml`. -- `libportable_kernels_bindings.a`: Generated code that registers the contents - of `libportable_kernels.a` with the runtime. - - NOTE: This must be linked into your application with a flag like - `-Wl,-force_load` or `-Wl,--whole-archive`. It contains load-time functions - that automatically register the kernels, but linkers will often prune those - functions by default because there are no direct calls to them. -- `executor_runner`: An example tool that runs a `.pte` program file using all - `1` values as inputs, and prints the outputs to stdout. It is linked with - `libportable_kernels.a`, so the program may use any of the operators it - implements. - -## One-time setup - -1. Clone the repo and install buck2 as described in the "Runtime Setup" section - of [Setting up ExecuTorch](00_setting_up_executorch.md#runtime-setup) - - `buck2` is necessary because the CMake build system runs `buck2` commands - to extract source lists from the primary build system. It will be possible - to configure the CMake system to avoid calling `buck2`, though. -1. If your system's version of python3 is older than 3.11: - - Run `pip install tomli` - - This provides an import required by a script that the CMake build system - calls to extract source lists from `buck2`. Consider doing this `pip - install` inside your conda environment if you created one during AOT Setup - (see [Setting up - ExecuTorch](00_setting_up_executorch.md#aot-setup-open-on-google-colab)). -1. Install CMake version 3.19 or later - -## Configure the CMake build - -Follow these steps after cloning or pulling the upstream repo, since the build -dependencies may have changed. - -```bash -# cd to the root of the executorch repo -cd executorch - -# Clean and configure the CMake build system. It's good practice to do this -# whenever cloning or pulling the upstream repo. -# -# NOTE: If your `buck2` binary is not on the PATH, you can change this line to -# say something like `-DBUCK2=/tmp/buck2` to point directly to the tool. -(rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake -DBUCK2=buck2 ..) -``` - -Once this is done, you don't need to do it again until you pull from the -upstream repo again. - -## Build the runtime components - -Build all targets with - -```bash -# cd to the root of the executorch repo -cd executorch - -# Build using the configuration that you previously generated under the -# `cmake-out` directory. -# -# NOTE: The `-j` argument specifies how many jobs/processes to use when -# building, and tends to speed up the build significantly. It's typical to use -# "core count + 1" as the `-j` value. -cmake --build cmake-out -j9 -``` - -## Try using `executor_runner` - -First, generate an `add.pte` or other ExecuTorch program file using the -instructions in the "AOT Setup" section of -[Setting up ExecuTorch](00_setting_up_executorch.md#aot-setup-open-on-google-colab). - -Then, pass it to the commandline tool: - -```bash -./cmake-out/executor_runner --model_path path/to/add.pte -``` - -If it worked, you should see the message "Model executed successfully" followed -by the output values. - -``` -I 00:00:00.002052 executorch:executor_runner.cpp:75] Model file add.pte is loaded. -I 00:00:00.002086 executorch:executor_runner.cpp:85] Running method forward -I 00:00:00.002092 executorch:executor_runner.cpp:140] Setting up non-const buffer 1, size 48. -I 00:00:00.002149 executorch:executor_runner.cpp:181] Method loaded. -I 00:00:00.002154 executorch:util.h:105] input already initialized, refilling. -I 00:00:00.002157 executorch:util.h:105] input already initialized, refilling. -I 00:00:00.002159 executorch:executor_runner.cpp:186] Inputs prepared. -I 00:00:00.011684 executorch:executor_runner.cpp:195] Model executed successfully. -I 00:00:00.011709 executorch:executor_runner.cpp:210] 8.000000 -``` - - -## Cross compilation - - -### Android -- Prerequisite: [Android NDK](https://developer.android.com/ndk), choose one of the following: - - Option 1: Download Android Studio, [follow instruction here to install ndk](https://developer.android.com/studio/projects/install-ndk) - - Option 2: Download NDK directly from [here](https://developer.android.com/ndk/downloads) - -Assuming NDK is available on installed, run -```bash -# Run the following lines from executorch folder -rm -rf cmake-android-out && mkdir cmake-android-out && cd cmake-android-out - -BUCK2=/path/to/buck2 -FLATC=/path/to/flatc -NDK_ROOT=${HOME}/Library/Android/sdk/ndk/25.2.9519653 - -# Point -DCMAKE_TOOLCHAIN_FILE to the location where ndk is installed. -# If BUCK2 or FLATC aren't specified, fall back to using -# the commands on the PATH. -cmake \ - -DCMAKE_TOOLCHAIN_FILE=${NDK_ROOT}/build/cmake/android.toolchain.cmake \ - -DANDROID_ABI=arm64-v8a \ - -DBUCK2="${BUCK2:-$(which buck2)}" \ - -DFLATC_EXECUTABLE="${FLATC:-$(which flatc)}" \ - .. - -# Move to the executorch folder and build -cd .. && cmake --build cmake-android-out -j9 - -# push the binary to an Android device -adb push cmake-android-out/executor_runner /data/local/tmp/executorch - -adb shell "/data/local/tmp/executorch/executor_runner --model_path /data/local/tmp/executorch/add.ff" -``` - -### IOS -``` ---- -**NOTE** -While we're working on making it a smoother experience, here is an early workflow to try out cross compilation for iOS. ---- - -``` -Only supported in mac - -Prerequisites: -- XCode - -After XCode is installed, - -1. Get the iOS cmake toolchain, options are -- Option 1 [recommended] : use the `ios.toolchain.cmake` from the following github repo -```bash -git clone https://github.com/leetal/ios-cmake.git -``` -- Option2 [wip], use the `iOS.cmake` from pytorch, the tool chain is located in `executorch/third-party/pytorch/pytorch/blob/main/cmake/iOS.cmake` - - -2. Use the tool chain provided in the repro to build the executorch library. -```bash -rm -rf cmake-ios-out && mkdir cmake-ios-out && cd cmake-ios-out - -BUCK2=/path/to/buck2 -FLATC=/path/to/flatc -PYTHON=/path/to/miniconda/envs/executorch/bin/python3 -# The path to the cloned ios-cmake repo -IOS_CMAKE=/path/to/ios-cmake - -# Change the PLATFORM accordingly; please refer to the table listed in https://github.com/leetal/ios-cmake#readme -cmake \ - -G Xcode \ - -DCMAKE_TOOLCHAIN_FILE=${IOS_CMAKE}/ios.toolchain.cmake \ - -DPLATFORM=SIMULATOR \ - -DBUCK2="${BUCK2:-$(which buck2)}" \ - -DFLATC_EXECUTABLE="${FLATC:-$(which flatc)}" \ - -DPYTHON_EXECUTABLE="${PYTHON:-$(which python3)}" \ - .. - -# Move to the executorch folder and build -cd .. && cmake --build cmake-ios-out -j9 - -# Create an include folder in cmake-ios-out to include all header files -(cd cmake-ios-out && mkdir include && cd include && \ - ln -s ../../runtime ../../util ../../extension .) -``` - - -3. XCode setup - -If using ios cmake tool chain from `https://github.com/leetal/ios-cmake.git`, after build: - -- Open the project in XCode, drag the `executorch.xcodeproj` generated from Step 2 to `Frameworks`, -- Go to project Target’s `Build Phases` - `Link Binaries With Libraries`, click the + sign and add all the library files located in `cmake-ios-out/build`. -- Navigate to the project `Build Settings` - - Set the value **Header Search Paths** to `cmake-ios-out/include` - - Set **Library Search Paths** to `cmake-ios-out/build` - - In **other linker flags**, add a custom linker flag `-all_load` diff --git a/docs/website/docs/tutorials/custom_ops.md b/docs/website/docs/tutorials/custom_ops.md deleted file mode 100644 index 0a5d0ba718..0000000000 --- a/docs/website/docs/tutorials/custom_ops.md +++ /dev/null @@ -1,223 +0,0 @@ -# Custom Operator Registration - - -## Introduction - -Custom operator is defined in contrast with ATen-compliant operator. -ATen-compliant operators are those defined in -[`native_functions.yaml`](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml), -with their native functions (or kernels, we use these two terms interchangeably) -defined in ATen library. Custom operator lives out of ATen library and is most -likely associated with target model and hardware platform. - -There are two types of usages of custom ops. The first type introduces custom ops into PyTorch model eager mode, whilst the second type only introduces custom ops during graph transformation. - -## Step by step guide -There are two branches for this use case: -* ATen mode. In this case we expect the exported model to be able to run with ATen kernels (as well as custom kernels for custom ops). -* Lean mode. This requires custom op implementations using `ETensor`. - -This also gets entangled with out variants of these custom ops. Generally, a custom op will require an out variant (with C++ implementation using `ETensor`) to be able to run on ExecuTorch runtime. - -In a nutshell, we need the following steps in order for a custom op to work on ExecuTorch: -1. Register the custom op definition into PyTorch runtime so that they are visible to ExecuTorch compiler. -2. Implement and register the implementation of it to PyTorch runtime. Do one of the following: - 1. Implement in Python and use [`library.py`](https://github.com/pytorch/pytorch/blob/main/torch/library.py) API to register it - 2. Implement in C++, use `at::Tensor` or `exec_aten::Tensor` and use [`library.h`](https://github.com/pytorch/pytorch/blob/main/torch/library.h) API to register it. - 3. You will also need to provide META implementatio for your custom operator, so that export tracer knows how to reason about the input/output of your custom operator. For more instructions, you can refer [here](../export/custom_operators.md) -3. Define an out variant of the custom op, implement it in C++ using `ETensor`. This step should also be trivial if we used `exec_aten::Tensor` in step 2.ii, since we can share the same logic for the two variants. -4. Create `custom_ops.yaml` for this operator, both functional and out variant, specify function schema and corresponding kernels. (See Common APIs for more info). - 1. In ATen mode, the C++ kernel implementation using `at::Tensor` will be linked. - 2. In lean mode, the C++ kernel implementation using `ETensor` will be linked. - -### Case Study -Let's say a model uses a custom operator called `my_op::foo`. It's very common that we register them in eager mode like the following: -```c -// foo.cpp -at::Tensor foo(const at::Tensor& a) { - at::Tensor result = a; - // do something - return result; -} - -TORCH_LIBRARY(my_op, m) { - m.def("foo", foo); -} - -TORCH_LIBRARY_IMPL(my_op, CPU, m) { - m.impl("foo), TORCH_FN(foo)); -} -``` - -Alternatively this operator can be registered through Python API: -```python -# foo.py -from torch.library import impl, Library -lib = Library("my_op", "DEF") - -lib.define("foo(Tensor a) -> Tensor") - -@impl(lib, "foo", "CPU") -def foo(a): - # do something - return a -``` -If we already have similar code checked in, that satisfies our steps 1 and 2 described above. If we start to implement this custom operator from scratch, we can implement it in either Python or C++, as long as we register it into the dispatcher. - -Now if we want to use `my_op::foo` in ExecuTorch, first we need to define a out variant of it and implement it in C++. Notice that we should implement it using `exec_aten` namespace so that it can be treated as either an `at::tensor` or an `ETensor` under the hood: -```c -// foo.cpp -namespace my_op { -namespace native { -exec_aten::Tensor& foo_out(const exec_aten::Tensor& a, exec_aten::Tensor& out) { - // do something - return out; -} -} // namespace native -} // namespace my_op -``` -Along with a `custom_ops.yaml` that binds the function schema with the C++ implementation: -```yaml -- func: my_op::foo.out(Tensor a, *, Tensor(a!) out) -> Tensor(a!) - variatns: function - dispatch: - CPU: my_op::foo_out # pointing to my_op::native::foo_out -``` -We can generate code similar to the functional variant to register this out variant into PyTorch runtime. We can also generate code to register it into ExecuTorch runtime. - -For ATen mode and lean mode we can have two sets of generated libs: -```python -export_file( - name = "custom_ops.yaml" -) - -cxx_library( - name = "foo", - srcs = ["foo.cpp"], -) - -executorch_generated_lib( - name = "my_op_aten", - custom_ops_yaml_target = ":custom_ops.yaml", - custom_ops_aten_kernel_deps = [ - ":foo", # This means foo_out can be registered into PyTorch dispatcher as well. - ], - aten_mode = True, - deps = [ - ":foo", # Kernel for ExecuTorch runtime - ], -) - -executorch_generated_lib( - name = "my_op_lean", - custom_ops_yaml_target = ":custom_ops.yaml", - custom_ops_aten_kernel_deps = [ - ":foo", # This means foo_out can be registered into PyTorch dispatcher as well. - ], - aten_mode = False, - deps = [ - ":foo", - ], -) -``` -### Usage of generated lib -Here's a breakdown on what libraries we will generate and how to use them. -* `my_op_aten`: - * `custom_ops_my_op_aten`: C++ library responsible to register `my_op::foo.out` into PyTorch runtime. - * `my_op_aten`: C++ library responsible to register `my_op::foo.out` into ExecuTorch runtime. -* `my_op_lean`: - * `custom_ops_my_op_lean`: C++ library responsible to register `my_op::foo.out` into PyTorch runtime - * `my_op_lean`: C++ library responsible to register `my_op::foo.out` into ExecuTorch runtime. - -So in the compiler we can register `my_op::foo.out` by loading a shared library: -```python -# custom_passes.py -torch.ops.load_library("//path/to:custom_ops_my_op_aten") - -# we have access to my_op::foo.out -print(torch.ops.my_op.foo.out) -``` -If we want to use this in Python unit test, we can add the library as a `preload_deps`: -```python -python_unittest( - name = "test", - srcs = ["test.py"], - # Only use preload_deps to load "custom_ops_my_op_aten" in python_unittest - # instead of in RnntModel.py. See D40133925 for more detail. - preload_deps = [ - "//path/to:custom_ops_my_op_aten", - ], - deps = [ - ... - ], -) -``` -## Common APIs - -To facilitate custom operator registration, we provide the following APIs: - -- `custom_ops.yaml`: operator schema and kernel metadata are defined in this file, following the same syntax as in `native_functions.yaml`. -- `executorch_generated_lib`: the Buck rule to call ExecuTorch codegen system and encapsulate generated C++ source files into libraries. Two libraries will be generated: - - ``: contains C++ source files to register both ATen-compliant operators and custom operators into ExecuTorch runtime. Required by ExecuTorch runtime. - - `custom_ops_`: contains C++ source files to register custom operators into PyTorch runtime. This library will be used by compiler but not necessarily ExecuTorch runtime. - - Input: most of the input fields are self-explainatory. - - `deps`: kernel libraries - can be custom kernels or portable kernels (see portable kernel library [README.md](https://fburl.com/code/zlgs6zzf) on how to add more kernels) - needs to be provided. Selective build related targets can also be passed into the generated libraries through `deps`. - - `define_static_targets`: if true we will generate a `_static` library with static linkage. See docstring for more information. - - `functions_yaml_target`: the target pointing to `functions.yaml`. See `ATen-compliant Operator Registration` section for more details. - - `custom_ops_target`: the target pointing to `custom_ops.yaml`. Since custom operators are tightly coupled with ExecuTorch users, each user will need to maintain their own `custom_ops.yaml` file. - - -We also provide selective build system to allow user to select operators from both `functions.yaml` and `custom_ops.yaml` into ExecuTorch build. See [Selective Build](https://www.internalfb.com/intern/staticdocs/executorch/docs/tutorials/selective_build/) tutorial. - -## Best Practices - -- Out variant vs. functional variant: currently functionalization is not supported on custom mutating operators (e.g., out variants), hence the recommended way is to register both functional and out variants into `custom_ops.yaml`. During model authoring stage, the compiler recognize both variants and will perform a transform to replace functional variant with its corresponding out variant. -- Custom namespace support: we support custom namespace for both operator level and kernel level. It's recommended to use a different namespace than `aten` for custom operators, the definition should also live in a different namespace than `at::native`. See [README.md](https://fburl.com/code/kn4zexqm) for more information on namespaces. -- ATen library missing out variant: a lot of the need for custom operator is due to missing out variant in ATen library. If that happens, custom operator registration can be used to register those missing out variants as a short-term workaround. Please contact PyTorch Edge Team Portibility pillar to discuss a long-term approach. You can post in this [PyTorch Edge Users group](https://fb.workplace.com/groups/pytorch.edge.users) or reach out to [ExecuTorch oncall](https://www.internalfb.com/omh/view/executorch/oncall_profile). - -## Common Issues - -### Missing out variants - -Example error message: -``` -[2022-11-14T11:57:40.588-08:00] ====================================================================== -[2022-11-14T11:57:40.588-08:00] ERROR: test_end_to_end_executorch_dynamo (executorch.test.end2end.test_end2end_real_model_dynamo.ExecutorchDynamoTests) -[2022-11-14T11:57:40.588-08:00] ---------------------------------------------------------------------- -[2022-11-14T11:57:40.588-08:00] Traceback (most recent call last): -[2022-11-14T11:57:40.588-08:00] File "/data/sandcastle/boxes/eden-trunk-hg-fbcode-fbsource/buck-out/v2/gen/fbcode/9f23200ddcddc3cb/executorch/test/end2end/__test_end2end_real_model_dynamo__/test_end2end_real_model_dynamo#link-tree/torch/fx/passes/infra/pass_manager.py", line 271, in __call__ -[2022-11-14T11:57:40.588-08:00] res = fn(module) -[2022-11-14T11:57:40.588-08:00] File "/data/sandcastle/boxes/eden-trunk-hg-fbcode-fbsource/buck-out/v2/gen/fbcode/9f23200ddcddc3cb/executorch/test/end2end/__test_end2end_real_model_dynamo__/test_end2end_real_model_dynamo#link-tree/torch/fx/passes/infra/pass_manager.py", line 35, in wrapped_fn -[2022-11-14T11:57:40.588-08:00] res = fn(gm) -[2022-11-14T11:57:40.588-08:00] File "/data/sandcastle/boxes/eden-trunk-hg-fbcode-fbsource/buck-out/v2/gen/fbcode/9f23200ddcddc3cb/executorch/test/end2end/__test_end2end_real_model_dynamo__/test_end2end_real_model_dynamo#link-tree/torch/fx/passes/infra/pass_base.py", line 44, in __call__ -[2022-11-14T11:57:40.588-08:00] self.ensures(graph_module) -[2022-11-14T11:57:40.588-08:00] File "/data/sandcastle/boxes/eden-trunk-hg-fbcode-fbsource/buck-out/v2/gen/fbcode/9f23200ddcddc3cb/executorch/test/end2end/__test_end2end_real_model_dynamo__/test_end2end_real_model_dynamo#link-tree/executorch/exir/passes/__init__.py", line 212, in ensures -[2022-11-14T11:57:40.588-08:00] raise RuntimeError(f"Missing out variants: {self.missing_out_vars}") -[2022-11-14T11:57:40.588-08:00] RuntimeError: Missing out variants: {'not_a_real_op::fake_op'} -``` -This is likely caused by the out variant operator not defined or not linked to the binary/unittest target, so that it's missing from PyTorch runtime. Please follow the [Step by step guide](#step-by-step-guide) step 3 & 4 and [Case Study](#case-study), add the generated lib in the dependency and load the shared library like explained in [Usage of generated lib](#usage-of-generated-lib). - -If the operator is still missing, double check whether the operator is being selected correctly. One way to debug is to select all operator (follow the instruction in [et_operator_library](#et_operator_library)) and check if the error is gone. - -### Missing operator - -Example error message: -``` -Nov 14 16:48:07 devvm11149.prn0.facebook.com bento[1985271]: [354870826409]Executor.cpp:260 Missing operator: [1] aten::squeeze_copy.dim_out\n - -Nov 14 16:48:07 devvm11149.prn0.facebook.com bento[1985271]: [354870830000]Executor.cpp:267 In function init(), assert failed (num_missing_ops == 0): There are 1 operators missing from registration to Executor. See logs for details\n -``` - -This error message indicates that the operators are not registered into the ExecuTorch runtime. It's important to identify whether the missing operator is from ATen library or is a custom op. If the missing operator name starts with `aten` then it's an ATen operator, otherwise it's a custom op. - -#### Missing ATen operator - -Please make sure the ATen operator schema is being added to your `functions.yaml`. For more guidance of how to write a `functions.yaml` file, please refer to [Declare the operator in a YAML file](https://www.internalfb.com/code/fbsource/xplat/executorch/kernels/portable/README.md). - -#### Missing custom operator - -This means the custom operator is not being registered to ExecuTorch runtime. A custom operator (functional variant) needs to be registered into PyTorch runtime so that it's available in compiler; a custom operator (out variant) needs to be registered into both PyTorch runtime and ExecuTorch runtime, so that it can be lowered and executed. - -To fix this, -1. make sure the operator is being added to `custom_ops.yaml`, refer to [Declare the operator in a YAML file](https://www.internalfb.com/code/fbsource/xplat/executorch/kernels/portable/README.md) for details. -2. double check whether the operator is being selected correctly. One way to debug is to select all operator (follow the instruction in [et_operator_library](#et_operator_library)) and check if the error is gone. diff --git a/docs/website/docs/tutorials/executionplan_io_apis.md b/docs/website/docs/tutorials/executionplan_io_apis.md deleted file mode 100644 index e692e59ff0..0000000000 --- a/docs/website/docs/tutorials/executionplan_io_apis.md +++ /dev/null @@ -1,38 +0,0 @@ -# I/O APIs for Execution Plan - -Here we present several unified APIs for execution plan I/O with different memory planning schemas. - -```c++ -Error set_input(const EValue& input_evalue, size_t input_idx); -``` -This function sets the `input_idx`-th input of the execution plan to be `input_evalue`. - -`input_idx` should be smaller than the number of plan's input. The data type of `input_evalue` should be the same as the `input_idx`-th input. If it is a tensor, dynamic shape is supported and its dtype should follow the execution plan. - -Return `Error::Ok` if input setting completed successfully, otherwise return error occurs during execution. - -Caution: the `execution_plan` may or may not have a buffer for the input data based on its memory plan. Users should double check the memory and take care of data lifecycle if needed. - -```c++ -Error set_inputs(const exec_aten::ArrayRef& input_evalues); -``` - -This function sets the input of the execution plan to be `input_evalues`. - -Expect the data types of EValue elements in the `input_evalues` to be aligned with the plan’s input, and the length of `input_evalues` should be the same as the number of the plan’s input. - -Return `Error::Ok` if input setting completed successfully, otherwise return the error occurs during execution. Other things worth to be considered are same as above. - -```c++ -Error get_outputs(EValue* output_evalues, size_t length); -``` - -This function retrieves the plan’s output and write into the given EValue list `output_evalues` with given `length`. - -Expect the data types of EValue elements in the `output_evalues` to be aligned with the plan’s input, and the `length` should be larger than or equal to the number of plan’s output. - -Return `Error::Ok` if input setting completed successfully, otherwise return the error occurs during execution. - -Caution: This function exposes the data pointer of inner output tensors. Uses should be careful when using it and not mutate the data in place. We are working on updating the implementation to remove such constraints. - -More details can be found on [T132716305](https://www.internalfb.com/tasks/?t=132716305). diff --git a/docs/website/docs/tutorials/executorch_stack.png b/docs/website/docs/tutorials/executorch_stack.png deleted file mode 100644 index 2df21fc243..0000000000 Binary files a/docs/website/docs/tutorials/executorch_stack.png and /dev/null differ diff --git a/docs/website/docs/tutorials/exporting_to_executorch.md b/docs/website/docs/tutorials/exporting_to_executorch.md deleted file mode 100644 index 42690f11ad..0000000000 --- a/docs/website/docs/tutorials/exporting_to_executorch.md +++ /dev/null @@ -1,218 +0,0 @@ -# Exporting to ExecuTorch Runtime - -On a high-level, the workflow for executorch is split to an ahead-of-time (AOT) -compilation, and a runtime. - -![](./executorch_stack.png) - -## 1. AOT Compilation - -AOT compliation offers various advantages, including the ability to apply -optimizations that are only feasible during compilation, such as graph -transformations and hardware-specific optimizations. AOT compilation offers -faster cold-start times and requires less memory compared to JIT compilation. -This makes it a particularly useful technique for optimizing the performance of -embedded devices. At a high level, the AOT steps are the following: - -1. Given an eager module, export it to a graph representation, which we will - call the `EXIR ATen Dialect`. - - a. Optionally apply passes like quantization on this graph. -2. Run passes on this graph to lower the graph representation to the - `EXIR Edge Dialect`. - - a. Optionally apply backend-agnostic passes. -3. Optionally delegate parts of the graph or the whole graph to a specific - backend, such as DSP, NPU or other hardware. -4. Run optional backend-specific passes, lower the graph representation to the - `EXIR Backend Dialect`, and run memory planning. - -5. Save the graph to a flatbuffer file. - - -### 1.1 Exporting to EXIR ATen Dialect - -NB: Export APIs are undergoing changes to align better with long term state of export. Please refer to https://github.com/pytorch/executorch/issues/290, for more details. - -The entrypoint to ExecuTorch is through the `torch._export.capture_pre_autograd_graph` API, which is used -to fully capture a PyTorch Model (either `torch.nn.Module` or a callable) into a -`torch.fx` graph representation. - -In order for the model to -be fully captured into a graph, it may require minor modifications to the user -code, such as using [control flow operators](../ir_spec/control_flow.md) in -place of if-statements to capture dynamic behavior. To learn more about what -is supported in the export flow, you can take a look at the examples in -[exportdb](https://pytorch.org/docs/main/generated/exportdb/index.html). -[Custom operators](./custom_ops.md) can also be traced into the graph -through registering the custom operator to a torch library and providing a meta -kernel. To learn more about exporting a model or if you have trouble exporting, -you can look at [these docs](../export/00_export_manual.md) - -To enable exporting input shape-dependent models, this API also -takes in a list of constraints, where users can specify which input shapes are -dynamic and impose ranges on them. To learn more about constraints, you can look -at [these docs](../export/constraint_apis.md) - -The output of `torch._export.capture_pre_autograd_graph` is a fully flattened graph (meaning the graph does -not contain any module hierachy, except in the case of control flow operators). -Furthermore, the captured graph is in ATen dialect with ATen opset which is autograd safe, i.e. safe for eager mode training. -This is important for quantization as noted in https://github.com/pytorch/executorch/issues/290. - -ATen operator set of graph obtained via `torch._export.capture_pre_autograd_graph` is full set of ATen ops (~3000). This -operator set is further refined into [Core ATen Operators](https://pytorch.org/docs/master/ir.html) via `exir.capture`. -Resulting IR is functional, i.e. does not contain any mutations or aliasing on operator outputs. -The detailed specification for the result of `exir.capture` can be -found in the [EXIR Reference](../ir_spec/00_exir.md) and is specifically in the -[EXIR ATen Dialect](../ir_spec/01_aten_dialect.md). - -```python -import torch -from torch import _export as export -import executorch.exir as exir - -class MyModule(torch.nn.Module): - def __init__(self): - super().__init__() - self.param = torch.nn.Parameter(torch.rand(3, 4)) - self.linear = torch.nn.Linear(4, 5) - - def forward(self, x): - return self.linear(x + self.param).clamp(min=0.0, max=1.0) - -pre_autograd_aten_dialect = export.capture_pre_autograd_graph(MyModule(), (torch.randn(3, 4),), constraints) -# Quantization APIs can optionally be invoked on top of pre_autograd_aten_dialect -aten_dialect = exir.capture(pre_autograd_aten_dialect, (torch.randn(3, 4),), constraints) - -print(aten_dialect.exported_program) -""" -ExportedProgram: - class GraphModule(torch.nn.Module): - def forward(self, arg0_1: f32[3, 4], arg1_1: f32[5, 4], arg2_1: f32[5], arg3_1: f32[3, 4]): - add: f32[3, 4] = torch.ops.aten.add.Tensor(arg3_1, arg0_1); - permute: f32[4, 5] = torch.ops.aten.permute_copy.default(arg1_1, [1, 0]); - addmm: f32[3, 5] = torch.ops.aten.addmm.default(arg2_1, add, permute); - clamp: f32[3, 5] = torch.ops.aten.clamp.default(addmm, 0.0, 1.0); - return (clamp,) -""" -``` - -At this point, users can choose to run additional passes through the -`exported_program._transform(passes)` function. A tutorial on how to write -transformations can be found [here](./passes.md). - -For quantization API usage, a tutorial for doing so can be found [here](./quantization_flow.md). - -### 1.2 Lower to EXIR Edge Dialect - -The next step is to lower the `EXIR ATen Dialect` graph to the -`EXIR Edge Dialect`, in which specializations that are useful for Edge devices but not -necessary for general (server) will be applied. Some of these specializations -include dtype specialization, scalar to tensor conversion, and converting all -operators to the `executorch.exir.dialects.edge` namespace. Note that this -dialect is still backend (or target) agnostic. Detailed -documentation on the Edge Dialect can be found -[here](../ir_spec/02_edge_dialect.md). - -This lowering will be done through the `to_edge()` API. - -```python -pre_autograd_aten_dialect = export.capture_pre_autograd_graph(MyModule(), (torch.randn(3, 4),), constraints) -aten_dialect = exir.capture(pre_autograd_aten_dialect, (torch.randn(3, 4),)) -edge_dialect = aten_dialect.to_edge() - -print(edge_dialect.exported_program) -""" -ExportedProgram: - class GraphModule(torch.nn.Module): - def forward(self, arg0_1: f32[3, 4], arg1_1: f32[5, 4], arg2_1: f32[5], arg3_1: f32[3, 4]): - add: f32[3, 4] = executorch_exir_dialects_edge__ops_aten_add_Tensor(arg3_1, arg0_1); - permute: f32[4, 5] = executorch_exir_dialects_edge__ops_permute_copy_default(arg1_1, [1, 0]); - addmm: f32[3, 5] = executorch_exir_dialects_edge__ops_addmm_default(arg2_1, add, permute); - clamp: f32[3, 5] = executorch_exir_dialects_edge__ops_clamp_default(addmm, 0.0, 1.0); - return (clamp,) -""" -``` - -Users can also choose to run additional passes through the -`exported_program._transform(passes)` function. A tutorial on how to write -transformations can be found [here](./passes.md). Note that since the graph is -now in Edge Dialect, all passes must also result in a valid Edge Dialect graph -(specifically one thing to point out is that the operators are now in the -`executorch.exir.dialects.edge` namespace, rather than the `torch.ops.aten` -namespace). More information about the Edge Operators can be found [here](../ir_spec/02_edge_dialect.md#edge-operator). - -### 1.3 Delegate the graph to a backend - -Users can then delegate parts of their graph or the whole graph to a specific -third party delegate using our [delegation APIs](./backend_delegate.md). - -### 1.4 Run backend-specific passes and memory planning - -Through the `to_executorch()` API, users can pass in backend-specific -passes, such as replacing sets of operators with a custom backend operator, and -run it on the Edge Dialect graph. - -Afterwards, an out-variant pass will be run on the graph to convert all of the -operators to their out variants. The purpose of this is to prepare the graph for -memory planning. Instead of allocating returned tensors in the kernel -implementations, an operator's out variant will take in a prealloacated tensor -to its `out` kwarg, and store the result there, making it easier for memory -planners to do tensor lifetime analysis. `alloc` nodes containing a call to a -`executorch.exir.memory.alloc` operator will be inserted into the graph to pass -to the out variants. - -Lastly, a memory planning pass is run to assign a memory ID to each tensor, and -an offset in the buffer, marking where the storage for the tensor starts. A -default greedy and naive implementation is provided, but a user-provided memory -planning pass can also be passed into `to_executorch`. A tutorial on how to -write a memory plnaning pass is here (TODO). - -```python -pre_autograd_aten_dialect = export.capture_pre_autograd_graph(MyModule(), (torch.randn(3, 4),), constraints) -aten_dialect = exir.capture(pre_autograd_aten_dialect, (torch.randn(3, 4),)) -edge_dialect = aten_dialect.to_edge() -# edge_dialect = to_backend(edge_dialect.exported_program, CustomBackendPartitioner) -executorch_program = edge_dialect.to_executorch(executorch_backend_config) - -print(executorch_program.dump_exported_program()) -""" -ExportedProgram: - class GraphModule(torch.nn.Module): - def forward(self, arg0_1: f32[3, 4], arg1_1: f32[5, 4], arg2_1: f32[5], arg3_1: f32[3, 4]): - alloc: f32[3, 4] = executorch_exir_memory_alloc(((3, 4), torch.float32)) - add: f32[3, 4] = torch.ops.aten.add.out(arg3_1, arg0_1, out = alloc); - alloc_1: f32[4, 5] = executorch_exir_memory_alloc(((4, 5), torch.float32)) - permute: f32[4, 5] = torch.ops.aten.permute_copy.out(arg1_1, [1, 0], out = alloc_1); - alloc_2: f32[3, 5] = executorch_exir_memory_alloc(((3, 5), torch.float32)) - addmm: f32[3, 5] = torch.ops.aten.addmm.out(arg2_1, add, permute, out = alloc_2); - alloc_3: f32[3, 5] = executorch_exir_memory_alloc(((3, 5), torch.float32)) - clamp: f32[3, 5] = torch.ops.aten.clamp.out(addmm, 0.0, 1.0, out = alloc3); - return (clamp,) -""" -``` - -### 1.5 Save the graph to a flatbuffer file. - -Finally, the exported and delegated graph can be saved to a flatbuffer file to -be loaded in the ExecuTorch runtime. - -```python -pre_autograd_aten_dialect = export.capture_pre_autograd_graph(MyModule(), (torch.randn(3, 4),), constraints) -edge_dialect = exir.capture(pre_autograd_aten_dialect, (torch.randn(3, 4),)).to_edge() -# edge_dialect = to_backend(edge_dialect.exported_program, CustomBackendPartitioner) -executorch_program = edge_dialect.to_executorch(executorch_backend_config) -buffer = executorch_program.buffer - -# Save it to a file and load it in the ExecuTorch runtime -with open("model.pte", "wb") as file: - file.write(buffer) - -# Or you can run the ExecuTorch runtime in python to try it out -from executorch.extension.pybindings.portable_lib import ( - _load_for_executorch_from_buffer, -) - -executorch_module = _load_for_executorch_from_buffer(buff) -model_outputs = executorch_module.forward([torch.randn(3, 4)]) -``` diff --git a/docs/website/docs/tutorials/frontend.md b/docs/website/docs/tutorials/frontend.md deleted file mode 100644 index 9be51fd5c8..0000000000 --- a/docs/website/docs/tutorials/frontend.md +++ /dev/null @@ -1,111 +0,0 @@ -# Tutorial for creating a toy runtime for a subset of EXIR - -## Introduction - -The goal of EXIR and pytorch export in general is to capture the -computational graph and have it exported outside of pytorch. -Once exported, that graph can be consumed to perform a variety of tasks, -the most common being to generate a bytecode for a different runtime. - -## Toy runtime - -For simplicity, our toy runtime will only support scalars and have the following bytecode: - -1. binary ops `+ - * /`: meaning pop 2 operands off the stack, and push the result into stack -2. A number: meaning to push that number into the stack. - -I.e. our runtime is exact the reverse polish calculator as provided by `dc` Unix command. - -## Front end: - -The front end will be pytorch program that operates on scalar tensors. -i.e. we want to be able to compile simple programs like: - -```python -from torch import nn - -class MyModule(nn.Module): - - def forward(self, x, y): - a = 2 * x - b = 3 * y + x - c = a + (x + y) - return c -``` - -### Step 1. Produce EXIR via `exir.capture` - -```python -# NOTE import path TBD -from executorch import exir - -my_module = MyModule() - -print(my_module(torch.Tensor(3), torch.tensor(4))) - -my_module_exir = exir.capture(my_module, (torch.Tensor(3), torch.Tensor(4))) - -print(my_module_exir.module.print_tabular()) -``` - -`my_module_exir` above is an instance of `ExportProgram` instance, which have -a `module` field of type `torch.fx.GraphModule`. - -### (optional) Step 2. Passes - -A *pass* is a transform on EXIR Graphs. It can be thought as a function that takes -a `torch.fx.GraphModule` as input, modify it, and returns the modified `torch.fx.GraphModule`. - -Usually a pass is used to implement an optimization, but can also be used for other things like -computing statistics or infering extra metadata. - -For sake of example, we will implement a pass that replace the operation `2 * x` with `x + x`, because say, -in our toy runtime the operator of sum is much faster than multiplication. - -You can see example of passes [here](../tutorials/passes.md). - -### Step 3. Emit code - -To create equivalent reverse polish notation code from our graph, we will create a function that takes -a `torch.fx.GraphModule` and return a list of characters that represents the bytecode of our runtime. - -```python -def emit_rpn(graphmodule: torch.fx.GraphModule): - # find output node - - code = [] - def emit_code_recursive(node): - nonlocal code - if isinstance(node, torch.Tensor): # constant - code.append(str(node)) - if node.op == 'call_function': - for a in node.args: - # first emit for all my operands - emit_code_recursive(a) - if node.target == torch.ops.aten.add: - code.append('+') - elif node.target == torch.ops.aten.add: - code.append('+') - elif node.target == torch.ops.aten.add: - code.append('+') - elif node.target == torch.ops.aten.add: - code.append('+') - else: - raise AssertError('Unknown OP: ', node.target) - - output_node = [n for n in graphmodule.graph.nodes if n.op == 'output'] - emit_code_recursive(output_node[0].args[0]) - return code -``` - -### Step 4: Run the generated code in our runtime - -```python - -def run_bytecode(bytecode, *args): - program = [] - program.extend(map(str, args)) - program.extend(bytecode) - program.append('p') # p for print in dc - os.system(['echo {} | dc'.format(' '.join(program))]) -``` diff --git a/docs/website/docs/tutorials/pal.md b/docs/website/docs/tutorials/pal.md deleted file mode 100644 index cae26d119e..0000000000 --- a/docs/website/docs/tutorials/pal.md +++ /dev/null @@ -1,33 +0,0 @@ -# Platform Abstraction Layer - -DEPRECATED: This document is moving to //executorch/docs/source/runtime-platform-abstraction-layer.md - -The ExecuTorch Platform Abstraction Layer (PAL) provides a way for execution -environments to override operations like: -- Getting the current time -- Printing a log statement -- Panicking the process/system - -The PAL function declarations are in `//executorch/runtime/platform/platform.h`. - -## Overriding the default PAL - -The default PAL implementation is in `//executorch/runtime/platform/target/Posix.cpp`. -It uses `std::chrono::steady_clock` for the time, prints log messages to -`stderr`, and makes other default assumptions. - -But, if they don't work for your system, you can override the default PAL by: -- Including `executorch/runtime/platform/platform.h` -- Defining an implementation of one or more of the `et_pal_*()` functions - -No build system changes necessary. The default PAL functions are weak symbols, -so providing your own strong-symbol definition will override them at link time. - -## Minimal PAL -If you run into build problems because your system doesn't support the functions -called by `Posix.cpp`, you can instead use the no-op minimal PAL at -`//executorch/runtime/platform/target/Posix.cpp` by building with `-c -executorch.pal_default=minimal`. This will avoid calling `fprintf()`, -`std::chrono::steady_clock`, and anything else that `Posix.cpp` uses. But since -the `Minimal.cpp` `et_pal_*()` functions are no-ops, you will need to override -all of them. diff --git a/docs/website/docs/tutorials/passes.md b/docs/website/docs/tutorials/passes.md deleted file mode 100644 index 1c6063c772..0000000000 --- a/docs/website/docs/tutorials/passes.md +++ /dev/null @@ -1,540 +0,0 @@ -# EXIR Infrastructure - -## Passes - -Passes can be roughly categorized into a couple of axis: - -Axis A: -1. Creating one-to-X mapping (eg. decomposition) -2. Creating many-to-one mapping (eg. fusion) - -Axis B: -1. Doing forwards iteration (eg. shape propagation) -2. Doing backwards iteration (eg. dead code elimination) - -Axis C: -1. Dependent on local node information (eg. out-variant conversion) -2. Dependent on global graph information (eg. memory planning) - -Our projection on the frequency of these use cases are: -1. A.1, B.1, C.1 -2. A.2 -3. B.2, C.2 - -### Level 1 - -For level 1 uses cases (creating one-to-X mappings, doing forwards iterations, -and looking at local node information), we can utilize a helper class called -[`ExportPass`](https://fburl.com/code/ecf4kyax). This is an -[interpreter-based](https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern) -way where we execute each node and recreate the graph except with -transformations specified. This allows us to preserve the IR Spec by ensuring -that all nodes created while in the pass meet the IR Spec such as making sure that -metadata such as stack trace, FakeTensor values, and torch.nn.Module heirarchy -are preserved and updated depending on the transformations made. - -To implement this pass, we can subclass -[`ExportPass`](https://fburl.com/code/ecf4kyax) and implement the exposed -functions. When called with a graph module, it will run the graph module and -create a new graph containing the changes specified by the pass. This means that -the graph module passed in must be runnable on CPU, and this invariant will be -maintained after the pass is run. - -#### One-to-One Pass - -An example for one-to-one mappings, if we wanted to replace an op A with another op B, -we can run the given -`fx.GraphModule`, and very time we see op A, return op B. - -An example is: - -```python -class ReplaceInPlaceReluWithOutOfPlaceReluPass(ExportPass): - """ - relu_ is the in-place version. Replace it with relu, which is the - out-of-place version - """ - - def call_operator(self, op, args, kwargs, meta): - if op != torch.ops.aten.relu_.default: - return super().call_operator(op, args, kwargs, meta) - return super().call_operator(Op(torch.ops.aten.relu.default), args, kwargs, meta) - -# To create a pass -replace_pass = ReplaceInPlaceReluWithOutOfPlaceReluPass() -# To run a pass -new_graph_module = replace_pass(graph_module).graph_module -``` - -The `super().call_operator(op, args, kwargs, meta)` call creates a -`call_function` FX node, and returns the result of running the operator with the -given arguments. - -#### One-to-X Pass - -If we wanted to do one-to-X mappings, like replacing op A with 2 other ops B and -C, we would then make 2 calls to `super().call_operator` to create 2 FX nodes, -one with op B and another with op C, and return the result of running op C. - -For example: -```python -class ReplaceAddWithMulSub(ExportPass): - """ - Original: - def f(x, y): - return x + y - - After pass: - def f(x, y): - z = x * y - return z - y - """ - def call_operator(self, op, args, kwargs, meta): - if op != torch.ops.aten.add.default: - return super().call_operator(op, args, kwargs, meta) - - x, y = args - - mul_res = super().call_operator( - torch.ops.aten.mul.default, - args, - {}, - meta - ) - - return super().call_operator( - torch.ops.aten.sub.default, - (mul_res, y), - {}, - meta - ) -``` - -#### One-to-None Pass - -If we wanted to remove an op, we can just return the value passed into the -function: - -```python -class RemoveDetachPass(ExportPass): - def call_operator(self, op, args, kwargs, meta): - if op not in ( - torch.ops.aten.detach.default, - torch.ops.aten.detach_copy.default, - ): - return super().call_operator(op, args, kwargs, meta) - - assert len(args) == 1 - return args[0] -``` - -#### Utilizing Local Information - -An example of utilizing local node information is, if we wanted to convert all the -scalars within the graph to tensors, we -can run the given `fx.GraphModule`, and for every argument that contains a scalar, -we convert it to a tensor. It might look something like: - -```python -def args_map(op, fn, args, kwargs): - assert isinstance(args, tuple) - assert isinstance(kwargs, dict) - args = list(args) - kwargs = kwargs.copy() - - # Update the argument based on the function passed - def update(key, args, schema): - args[key] = fn(args[key], schema) - - # Update each argument in the schema - for i, schema in enumerate(self.op._schema.arguments): - if schema.name in kwargs: - update(schema.name, kwargs, schema) - elif not schema.kwarg_only and i < len(args): - update(i, args, schema) - -class ScalarToTensorPass(ExportPass): - def call_operator(self, op, args, kwargs): - def try_coerce(value, arg): - return ( - torch.tensor(value) - if isinstance(value, (float, int, bool)) - and type(arg.type) == torch.TensorType - else value - ) - - args, kwargs = args_map(op, try_coerce, args, kwargs) - return super().call_operator(op, args, kwargs) -``` - -### Level 2 - -For creating many-to-one mappings, we can utilize FX's [subgraph -rewriter](https://fburl.com/code/9ld92myj). Given a `pattern`, it creates a -subgraph of operators matching to the pattern, and then replaces each matched -subgraph with the `replacement`. - -Note: - - This is an inplace operation. - -The `pattern` and `replacement` inputs must be callable functions written with -the same ops that are used in the EXIR graph you are matching with (ATen ops) -so that the subgraph rewriter can find the correct pattern in the graph. Inputs -to the pattern/replacement callables will be treated as wildcards. - -An example: - -```python -from torch.fx import subgraph_rewriter - -def replace_patterns(graph_module): - def pattern(x, y): - x = torch.ops.aten.add.Tensor(x, y) - x = torch.ops.aten.mul.Tensor(x, y) - return x - - def replacement(x, y): - return torch.ops.aten.sub.Tensor(x, y) - -replaced_patterns = subgraph_rewriter.replace_pattern_with_filters( - traced_module, pattern, replacement -) -``` - -The subgraph rewriter returns a list of `ReplacedPatterns`: - -```python -@dataclass -class ReplacedPatterns: - # Node from which the match was found - anchor: Node - # Maps nodes in the pattern subgraph to nodes in the larger graph - nodes_map: Dict[Node, Node] - # List of nodes that were added into the graph - replacements: List[Node] -``` - -Note: - - The nodes created by the subgraph rewriter will not have the metadata that - is normally in EXIR nodes (`stack_trace`, `val`, `nn_module_stack`). - - -### Level 3 - -For the third way of creating a pass, we can utilize the most basic -[`PassBase`](https://fburl.com/code/qtuz4l47). To create a -pass, we can subclass this and implement the function `call` with the pass -contents. Additionally, we can implement the functions `requires` and `ensures` -which will be called before and after the function `call`. Note that these -functions can also be overridden in `ExportPass`. To run a pass on a -graph module, we can pass the graph module directly to an instance of the class. - -An example: -```python -class ReplaceAddPass(PassBase): - - def __init__(self, replace_op): - self.replace_op = replace_op - - def call(self, graph_module): - for node in gm.graph.nodes: - if node.op == "call_function" and node.target == torch.add: - node.target = self.replace_op - - # Optional to implement, will be called before call() - def requires(self, graph_module) -> None: - for node in graph_module.graph.nodes: - if node.op == "call_function" and node.target == torch.add: - return - raise ValueError("No torch.add ops!") - - # Optional to implement, will be called after call() - def ensures(self, graph_module: torch.fx.GraphModule) -> None: - pass - -# To create a pass -replace_add_with_div = ReplaceAddPass(torch.div) -# To run a pass -replace_add_with_div(graph_module) -``` - -## Pass Manager - -The `PassManager` is a class used to run multiple passes on a given graph -module. When initializing a `PassManager` instance, we pass in a list of passes -that we want to run and set a couple of flags. To run the collection of passes -on a graph module, we can pass the graph module directly to the `PassManager` -instance. - -An example: -```python -from executorch.exir.pass_manager import PassManager - -pm = PassManager( - passes=[replace_add_with_div, replace_div_with_mul], - run_checks_after_each_pass=True, - suppress_check_failures=False, -) -graph_module_out = pm(graph_module) -``` - -To add a common set of checks that are run after each pass, we can call the -function `set_checks(check: Callable)` which takes in a callable function as -input. If the `run_checks_after_each_pass` flag is set, the `check` will be -called after each pass is run on the graph module. - -An example: -```python -pm = PassManager(passes=[replace_add_with_div, replace_div_with_mul]) - -def check_div_target(graph_module): - for node in graph_module.graph.nodes: - if node.op == "call_function" and node.target != torch.div: - raise ValueError("Target should be div!") - -pm.add_checks(check_div_target) - -pm(graph_module) # raises ValueError after replace_div_with_mul pass -``` - -## Partitioner - -There are a couple of common FX graph based partitioners we can use to partition -the graph. However, these do not necessarily produce a graph that is compliant -with IR Spec, so be careful when using them. - -### Subgraph Matcher - -For finding subgraphs within a graph that match a specific pattern, we can -utilize FX's [`SubgraphMatcher`](https://fburl.com/code/9ccshnvi). - -Class Attributes: - -* `pattern (Graph)`: The targeted matching pattern. Placeholder nodes in the - graph will be treated as wildcards when matching. -* `match_output (bool)`: If True, output node in the pattern graph will be - treated as a part of the targeted pattern. If False, output node is ignored - during match. -* `match_placeholder (bool)`: If True, placeholder node in the pattern graph - will be treated as a part of the targeted pattern. If False, placeholder - nodes will be used a wildcard. -* `remove_overlapping_matches (bool)`: If True, in the case of overlapping - matches, only the first match will be returned. -* `ignore_literals (bool)`: If True, will not check if literals are equal and - will instead treat them as wildcards. - -An example: - -```python -from torch.fx.passes.utils.matcher_utils import SubgraphMatcher - -class LargeModel(torch.nn.Module): - def __init__(self): - super().__init__() - self._weight = torch.nn.Parameter(torch.ones(3, 3)) - self._bias = torch.nn.Parameter(torch.ones(3, 3)) - - def forward(self, x): - return torch.ops.aten.addmm.default(self._bias, x, self._weight) - -large_model_graph = exir.capture(LargeModel(), large_inputs).to_edge().graph - -class PatternModel(torch.nn.Module): - def __init__(self): - super().__init__() - self._weight_1 = torch.nn.Parameter(torch.ones(5, 5)) - self._bias_1 = torch.nn.Parameter(torch.ones(5, 5)) - - def forward(self, x): - return torch.ops.aten.addmm.default(self._bias_1, x, self._weight_1) - -pattern_graph = exir.capture(PatternModel(), pattern_inputs).to_edge().graph - -subgraph_matcher = SubgraphMatcher(pattern_graph) -match_result = subgraph_matcher.match(large_model_graph) -``` - -The `match` function returns a list of `InternalMatch`: - -```python -@dataclass -class InternalMatch(): - # Nodes from which the match was found - anchors: List[Node] - # Maps nodes in the pattern subgraph to nodes in the larger graph - nodes_map: Dict[Node, Node] = field(default_factory=dict) - # Nodes in target graph that are matched placeholder in pattern - placeholder_nodes: List[Node] = field(default_factory=list) - # Nodes in matched subgraph returned by output - returning_nodes: List[Node] = field(default_factory=list) -``` - -### Capability Based Partitioner - -To find the largest subgraphs of nodes that support a specific invariant, we can -utilize FX's [`CapabilityBasedPartitioner`](https://fburl.com/code/hrw8h4r1). - -Class Attributes - -* `graph_module (torch.fx.GraphModule)`: The graph module we are partitioning on. -* `operator_support (OperatorSupportBase)`: The object used to determine if a - node in the graph is supported in the partition. -* `allows_single_node_partition (bool)`: If True, allows single node - partitions to be formed. -* `non_compute_ops (Optional[Sequence[str]])`: A set of ops that are - considered to be "non-compute" (ex `torch.ops.aten.view` and - `_operator.getitem`, so that the partitioner will not create graphs that only - contain these non-compute ops -* `allowed_single_node_partition_ops (Optional[Sequence[str]])`: A set of ops - that are allowed to be in a single node partition. - -The [`OperatorSupportBase`](https://fburl.com/code/xumdm1qc) class is used by -the partitioner to determine if a specific node in the graph belongs in the -partition. This is done by overriding the `is_node_supported` function. You can -chain multiple `OperatorSuppportBase` by using -[`chain`](https://fburl.com/code/cfmcj8bb)(which returns False if any of the -OperatorSupportBase return False) and -[`any_chain`](https://fburl.com/code/5bwe3364) (which returns True if any of the -OperatorSupportBase returns True). - -An example: - -```python -from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner -from torch.fx.passes.operator_support import any_chain, OperatorSupportBase - -class AddMulOperatorSupport(OperatorSupportBase): - def is_node_supported(self, submodules, node: torch.fx.Node) -> bool: - return node.op == "call_function" and node.target in [ - torch.ops.aten.add.Tensor, torch.ops.aten.mul.Tensor, - ] - -capability_partitioner = CapabilityBasedPartitioner( - graph_module, - op_support, -) - -# Returns a list of partitions (list of nodes that belong in each partition) -partition_list = capability_partitioner.propose_partitions() -``` - -If you look at the capability based partitioner, you may also find a -`fuse_partition` function which will return a modified graph with the partitions -as submodules, and calls to these submodules in the toplevel graph through -`call_module` nodes. However, this is not compliant to the IR Spec because we do -not allow `call_module` nodes. - - -### Combined - -We also provide a combined helper function: -[`generate_pattern_op_partitions`](https://fburl.com/code/pquxdl24) - -Args: -* `graph_module (fx.GraphModule)`: Module that we want to partition -* `patterns (List[torch.fx.Graph])`: A list of patterns in the form of - torch.fx.Graph. These graphs can be obtained through the `graph` field from a - GraphModule obtained by exir.capture (recommended) or symbolic tracing (which - might not result in an accurate edge dialect graph), or by manual crafting a - graph module. -* `op_support (OperatorSupportBase)`: A OperatorSupportBase that can be created - in the following ways: - * Subclassing it directly and implementing `is_node_supported()` - * Getting the result of `create_op_support()` - * Getting the result of `create_pattern_support()` - * Multiple OperatorSupportBase classes chained together with `chain()` or `any_chain()` - -Returns -* A list of partitions (largest possible subgraphs) containing nodes are - supported by the union of the given OperatorSupportBase object and the - given pattern graphs - - -### Source Partitioner - -For more complicated use cases in which users want to partition based on higher -level modules (`torch.nn.Linear` or `torch.nn.functional.Linear`) which are now -decomposed into their operators (`aten.permute`, `aten.addmm`), we have the -following [helper function](https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/utils/source_matcher_utils.py#L51): - -`get_source_partitions(graph: torch.fx.Graph, wanted_sources: List[Any]) -> Dict[Any, SourcePartition]` - -Args: -* `graph`: The graph we want to partition -* `wanted_sources`: List of sources of nodes that were decomposed from this - source. This can be a function (ex. `torch.nn.functional.linear`) or a leaf - module type (ex. `torch.nn.Linear`) - -Returns: -* Dictionary mapping sources (ex. `torch.nn.modules.linear.Linear`) to a list of - `SourcePartitions` that correspond to the list of nodes that were flattened from - a module of that type. - -```python -@dataclass -class SourcePartition(): - # Nodes in a particular partition - nodes: List[Node] - # Module type - module_type: Type - # Nodes in the graph that are needed as inputs to the partition - input_nodes: List[Node] = field(default_factory=list) - # Nodes in the partition that are being used by nodes outside of the partition - output_nodes: List[Node] = field(default_factory=list) - # Parameters that are being used - params: List[str] = field(default_factory=list) -``` - -An example: - -```python -class M(torch.nn.Module): - def __init__(self): - super().__init__() - self.linear1 = torch.nn.Linear(3, 3) - self.relu = torch.nn.ReLU() - self.linear2 = torch.nn.Linear(3, 5) - - def forward(self, x): - x = self.linear1(x) - x = self.linear1(x) - x = self.relu(x) - x = self.linear2(x) - return x - -inputs = (torch.randn(3, 3),) -edge_graph = exir.capture(M(), inputs).to_edge().graph -print(edge_graph) -""" -graph(): - %arg0 : [#users=1] = placeholder[target=arg0] - %_param_constant0 : [#users=1] = get_attr[target=_param_constant0] - %permute_default : [#users=1] = call_function[target=torch.ops.aten.permute_copy.default](args = (%_param_constant0,), kwargs = {}) - %_param_constant1 : [#users=1] = get_attr[target=_param_constant1] - %addmm_default : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant1, %arg0, %t_default), kwargs = {}) - %_param_constant0_1 : [#users=1] = get_attr[target=_param_constant0] - %permute_default_1 : [#users=1] = call_function[target=torch.ops.aten.permute_copy.default](args = (%_param_constant0_1,), kwargs = {}) - %_param_constant1_1 : [#users=1] = get_attr[target=_param_constant1] - %addmm_default_1 : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant1_1, %addmm_default, %t_default_1), kwargs = {}) - %relu_default : [#users=1] = call_function[target=torch.ops.aten.relu.default](args = (%addmm_default_1,), kwargs = {}) - %_param_constant2 : [#users=1] = get_attr[target=_param_constant2] - %permute_default_2 : [#users=1] = call_function[target=torch.ops.aten.permute_copy.default](args = (%_param_constant2,), kwargs = {}) - %_param_constant3 : [#users=1] = get_attr[target=_param_constant3] - %addmm_default_2 : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant3, %relu_default, %t_default_2), kwargs = {}) - return [addmm_default_2] -""" - -module_partitions = get_source_partitions(edge_graph, [torch.nn.Linear, torch.nn.ReLU]) -print(module_partitions) -""" -{: [ - ModulePartition(nodes=[_param_constant0, t_default, _param_constant1, addmm_default], module_type=, input_nodes=[arg0], output_nodes=[addmm_default], params=["_param_constant0", "_param_constant1"]), - ModulePartition(nodes=[_param_constant0_1, t_default_1, _param_constant1_1, addmm_default_1], module_type=, input_nodes=[addmm_default], output_nodes=[addmm_default_1], params=["_param_constant0_1", "_param_constant1_1"]), - ModulePartition(nodes=[_param_constant2, t_default_2, _param_constant3, addmm_default_2], module_type=, input_nodes=[relu_default], output_nodes=[addmm_default_2], params=["_param_constant2", "_param_constant3"])], - - : [ - ModulePartition(nodes=[relu_default], module_type=, input_nodes=[addmm_default_1], output_nodes=[relu_default], params=[])]} -""" -``` diff --git a/docs/website/docs/tutorials/profiling.md b/docs/website/docs/tutorials/profiling.md deleted file mode 100644 index 8726bd90ab..0000000000 --- a/docs/website/docs/tutorials/profiling.md +++ /dev/null @@ -1,189 +0,0 @@ -# Profiling in ExecuTorch - -## Introduction -Profiling in ExecuTorch is broken down into two stages. - -1) Running your test binary/application and collecting the profiling buffer dump. -2) Using the post-processing library/tool to generate the profiling metrics from the profiling buffer dump. - -## Important concepts -There are two main concepts to understand in ExecuTorch profiling which are profiling blocks and events. We elaborate on what these are below: - -### Profiling Events -A profiling event can be either of these: -- Code execution (time taken for execution) event -- Memory allocation event - -For code execution events it's required that the user explictily marks the segment of the code that they want profiled. (There are a set of core events that are already profiled out of the box which will be described later). - -For memory allocation events, the user will be explictily required to enable profiling on their memory allocator. The interface for this will be described later in the API section. - -### Profiling Blocks -A profiling block is essentially a block of code that consists of a set of profiling events that take place within it. The user can decide to mark different parts of their code as different profiling blocks by tagging them with a certain name. During post-processing all the metrics for profiling blocks with the same name will be aggregated and presented to the user. It's easy to derive the p10, p90, mean, median etc. from these aggregated values. The API to create new profiling blocks will be described below in the API section. - -## Build time configuration - -There are 3 pre-processor flags that are of importance to profiling in ExecuTorch. These are: -- **PROFILING_ENABLED** - Passing in this flag enables profliling in ExecuTorch and all profiling events will be logged to the proifling buffer. If this flag isn't passed in then all the profiling hooks that have been placed in the code (including the core runtime) will all resolve to no-ops and have no effect on the binary size. -- **MAX_PROFILE_EVENTS** - This flag sets the maximum number of events that can be logged into a profiling block. If not passed in the default value resolves to 1024 -- **MAX_PROFILE_BLOCKS** - This flag sets the maximum number of profiling blocks supported. If not passed in the default value resolves to 2. - -While building your target application with Buck the above pre-processor flags can be controlled by these Buck configs. - -| Pre-processor flag | Buck config | -| -------------------------| ----------- | -| `PROFILING_ENABLED` | `-c executorch.prof_enabled=\` | -| `MAX_PROFILE_EVENTS` | `-c executorch.prof_buf_size=` | -| `MAX_PROFILE_BLOCKS`. | `-c executorch.num_prof_blocks=` | - -***Important Note***: When `PROFILING_ENABLED` is not passed in none of the profiling hooks will be enabled as they will all resolve to no-ops and there will be no impact on the binary size of the resulting target. - -## Events tracked by the core runtime -In the core runtime we already have profiling hooks in place to track and profile a set of events that we believe provide good insight into how your model is performing in ExecuTorch. Once you have onboarded onto ExecuTorch all you have to do is do a test run with profiling enabled and pass in the profiling buffer dump to the post-procesing tool to get this data. - -The events that are tracked right out of the box in the core runtime are: -- Model de-serialization time -- Model loading time -- Inference loop execution time -- Individual operator execution time -- Framework overhead -- Memory consumed (memory allocated through ExecuTorch allocators) - -## Running an example with Buck - -Here is an example of what a ExecuTorch run + profile + post-procesing workflow looks like.: - -This runs the sample program with profiling enabled -```bash -cd executorch -buck2 run -c executorch.prof_enabled=true examples/portable/executor_runner:executor_runner -- --model_path add.pte -``` -Run the post-processing CLI tool that calls into the same API's listed above and prints out the profiling results in a tabulated format in the terminal. - -```bash -cd executorch -buck2 run profiler:profiler_results_cli -- --prof_results_bin prof_result.bin -``` - -## Runtime Profiling API's - -In this section we'll describe the API's that can be used to create new profiling blocks, enable profiling on memory allocators or profile custom events in your application that are outside of the scope of the events already tracked in the core runtime listed above. - -`EXECUTORCH_PROFILE_CREATE_BLOCK(name)` - -Creates a new profiling block. During post-processing events from all blocks with the same names will be aggregated together. Every block with the same name should have the same number of events, this means that if you have multiple test cases that can take different branches and hence execute different operations due to control flow then each test case that takes a certain branch must be labelled differently. - -**Input**: - -*name* (const char \*) - Name of this profiling block - -**Returns**: - -N/A - -#### `EXECUTORCH_DUMP_PROFILE_RESULTS(prof_result)` -After finishing your test run you will need access to the profiling buffer dump which will then be used in the post-processing step to de-serialize and generate the metrics that were tracked in your test run. A call to this API does some background work to serialize the profiling results into a binary dump and updates prof_result with the pointer to this dump and the size of it. - -**Input**: - -*prof_result* (struct prof_result_t *) - prof_data inside prof_result will be updated to point to the profiling buffer dump and num_bytes inside prof_result will be updated to indicate the size of the profiling buffer dump. - -**Returns**: - -void - -#### `EXECUTORCH_RESET_PROFILE_RESULTS()` - -Resets the current profiling block to clear all the events in it. - -**Input**: - -void - -**Returns**: - -void - -To enable memory profiling all you need to do is call the interface below on the ExecuTorch MemoryAllocator instance that you have created. This will then track all the memory allocations done via this allocator and generate the total size of allocations done after the profiling dump is post-processed. - -`enable_profiling(name)` - -**Input**: - -*name* (const char \*) - Name of this allocator to tag it with. - -**Returns**: - -void - -## Post-processing profiling API's - -Once you have completed your test run and obtained the profiling buffer dump users can use the following Python API's to deserialize the profiling data, aggregate metrics and also print out the results in a pretty table format in the terminal. - -#### `deserialize_profile_results(buff: bytes, time_scale: TimeScale = TimeScale.TIME_IN_NS)` - -**Input**: - -*buff : bytes* - Bytes read from the profiling buffer dump - -*time_scale : TimeScale* - Time scale of the profiling data that was collected from the target platform. Refer to the [source code](https://fburl.com/code/zygsx318) for the list of valid values. - -**Returns**: - -*result : Tuple[Dict[str, List[ProfileEvent]], Dict[str, List[MemEvent]]]* The result returned is a tuple of dictionaries. The first dictionary maps the block name to the corresponding list of profiling events that were aggregated from that block. The second dictionary maps the block name to the corresponding list of memory allocation events that were aggregated from that block. - -#### `deserialize_profile_results_files(profile_results_path: str, model_ff_path: str, time_scale: TimeScale = TimeScale.TIME_IN_NS)`: - -In this API you pass in the path to the profiling buffer dump and the path to the model flatbuffer file along with the time scale. If this model was emitted and subsquently serialized into a flatbuffer with stacktraces enabled then during the post-processing we'll map the operators that were executed in the model to the corresponding line of python code that this operator maps back to. - -*profile_results_path : str* - Path to the profiling buffer dump - -*model_ff_path : str* - Path to the model flatbuffer file - -*time_scale: TimeScale* - Time scale of the profiling data that was collected from the target platform. Refer to the [source code](https://fburl.com/code/zygsx318) for the list of valid values. - -**Returns**: - -*result : Tuple[Dict[str, List[ProfileEvent]], Dict[str, List[MemEvent]]]* The result returned is a tuple of dictionaries. The first dictionary maps the block name to the corresponding list of profiling events that were aggregated from that block. The second dictionary maps the block name to the corresponding list of memory allocation events that were aggregated from that block. - -#### `profile_aggregate_framework_tax(prof_data: Dict[str, List[ProfileEvent]])` -Through this interface users will be able to generate metrics about the framework overhead that was incurred while executing this model. - -**Input**: - -*prof_data: Dict[str, List[ProfileEvent]]* - The profiling events dictionary that was returned by the deserialization of the profiling results. - -**Returns**: - -*result : Dict[str, ProfileEventFrameworkTax]* - A dictionary is returned that maps the block name to the profiling overhead metrics parsed from the profiling data of this block. If this block had no operators executed in it then this will not generate any useful data. - -#### `profile_table(profile_data: Dict[str, List[ProfileEvent]], model_buffer=None)` - -This will return a list of instances of `PrettyTable`'s on which `print()` can be called to print out to the terminal the data in a tabulated format. Each table in the list contains all the profiling events that were aggregated for blocks with the same names. - -**Input**: - -*profile_data: Dict[str, List[ProfileEvent]]* - The profiling events dictionary that was returned after the deserialization of the profiling results. - -**Returns**: - -*table: List[PrettyTable]* - A list of `PrettyTable`'s where each table contains all the profiling events that were aggregated for blocks with the same names. - - -#### `profile_framework_tax_table(prof_framework_tax_data: Dict[str, ProfileEventFrameworkTax]):` - -This will return a list of instances of `PrettyTable`'s on which `print()` can be called to print out to the terminal the data in a tabulated format. Each table in the list contains the framework overhead metrics that were aggregated for blocks with the same names. - -**Input**: - -*prof_framework_tax_data: Dict[str, ProfileEventFrameworkTax]* - The profiling framework overhead dictionary that was returned by `profile_aggregate_framework_tax`. - -*table: List[PrettyTable]* - A list of `PrettyTable`'s where each table contains all the framework overhead metrics that were aggregated for blocks with the same names. - -#### `mem_profile_table(mem_allocations: Dict[str, List[MemEvent]])`: - -This will return a list of instances of `PrettyTable`'s on which `print()` can be called to print out to the terminal the data in a tabulated format. Each table in the list contains the total memory allocations done from the memory allocators aggregated for blocks with the same names. - -**Input**: - -*mem_allocations: Dict[str, List[MemEvent]]* - The memory allocations dictionary that was returned after the deserialization of the profiling results. diff --git a/docs/website/docs/tutorials/profiling_and_debugging_delegates.md b/docs/website/docs/tutorials/profiling_and_debugging_delegates.md deleted file mode 100644 index 5bc4e314dc..0000000000 --- a/docs/website/docs/tutorials/profiling_and_debugging_delegates.md +++ /dev/null @@ -1,142 +0,0 @@ - - -# Profiling and debugging delegates - -Delegate backends are a prominent component of Edge Models. One attribute of -delegated backends is that they operate mostly as an opaque transformation. -This gives delegate authors greater freedom when defining backend behavior, -but also prevents the ExecuTorch authoring flow from tracking underlying changes. - -This makes associating profiling and debug information through delegated -graphs difficult. We have provided a framework that will enable delegate authors -to propagate this information and retrieve it for post run analysis. The process is -broken down into two stages: - -1) **Ahead-of-time delegation stage** - Delegate authors need to generate -a debug handle map using the process described below. - -2) **Runtime stage** - Delegate authors need to log the profiling data along with the -delegate debug identifiers generated in stage 1 using the API's described below in -the runtime section. - -## 1). AOT (ahead-of-time) delegation Stage -### Generating a debug handle map: -**Delegate debug identifiers** are used by delegate authors to mark points of -interest in the lowered graph. Identifiers are associated with operator -nodes of the pre-lowered model graph. - -- *For example: If a delegate author wants to signal the fusion of 3 operators -into a single operator of the lowered graph, they would register the 3 -original operators to the delegate debug identifier ahead-of-time and then log using the -delegate debug identifier at runtime.* - -This is tracked by the `debug_handle_map` and returned as a part of -**PreprocessResult** by the call to `preprocess` from the ahead-of-time implementation of the delegated -backends. The `debug_handle_map` is essentially used as a mechanism to communicate what transformations -occurred in the backend. - -```python -class PreprocessResult: - processed_bytes: bytes = bytes() - - debug_handle_map: Optional[ - Union[Dict[int, Tuple[int]], Dict[str, Tuple[int]]] - ] = None - - ... -``` - -The construction of this map is done via a **DelegateMappingBuilder**. - - -### DelegateMappingBuilder - -**DelegateMappingBuilder** is a helper class for managing and constructing -`delegate_handle_map`. A new instance should be used in each `preprocess` call -and the result of this builder should be passed in when constructing -`PreprocessResult` - -First, create a DelegateMappingBuilder instance that uses either -manually provided identifiers or generated identifiers for node association. - -- `DelegateMappingBuilder()` - - With __manual identifiers__, users pass in a str or int delegate debug identifier - when creating entries -- `DelegateMappingBuilder(generated_identifiers=True)` - - With __generated identifier__, the builder will auto-assign an delegate debug identifier - -**Note: A single DelegateMappingBuilder instance can use either manual -or generated identifiers, but not both** - - -Next, use `insert_delegate_mapping_entry` to iteratively construct the -delegate_map. It takes Node(s) to associate and an optional -delegate debug identifier (only intended to be used for the manual identifiers case described above). -The identifier used is returned from the call. - -```python -def insert_delegate_mapping_entry( - self, - nodes: Union[fx.Node, List[fx.Node]], - identifier: Optional[Union[int, str]] = None, -) -> Union[int, str]: -``` - -Finally, use `get_delegate_mapping` to retrieve the constructed map. -The return value can be directly passed to **PreprocessResults**. - -```python -def get_delegate_mapping( - self, -) -> Union[Dict[int, Tuple[int]], Dict[str, Tuple[int]]] -``` - -## 2). Runtime stage - -NOTE : These API's are not available yet but shown here to give a representation of what the -runtime side of things looks like. - -### ID based API's: - -If users used integer ID's to generate delegate_debug_identifiers during the AOT process then -they should log their profiling events using the following API's. - -Option 1 (For when users can explicitly mark the start and end of an event): -```C++ -EventEntry event_entry = EVENT_TRACER_BEGIN_DELEGATE_PROFILING_EVENT_ID(event_tracer, id) -EVENT_TRACER_END_DELEGATE_PROFILING_EVENT_ID(event_entry) -``` - -Option 2 (For when users only have access to the start and end time of the events after they have occurred.) -```C++ -EVENT_TRACER_LOG_DELEGATE_PROFILING_EVENT_ID(event_tracer, id, start_time, end_time) -``` - -### String based API's: - -If users used strings to generate delegate_debug_identifiers during the AOT process then they -should log their profiling events using the following API's. - -Option 1 (For when users can explicitly mark the start and end of an event): -```C++ -EventEntry = EVENT_TRACER_BEGIN_DELEGATE_PROFILING_EVENT_NAME(event_tracer, name) -EVENT_TRACER_END_DELEGATE_PROFILING_EVENT_NAME(event_entry) -``` - -Option 2 (For when users only have access to the start and end time of the events after they have occurred.) -```C++ -EVENT_TRACER_LOG_DELEGATE_PROFILING_EVENT_NAME(event_tracer, name, start_time, end_time) -``` - -## Examples: - -To indicate how these API's can be used we have provided an end-to-end representative example. - -Demo backend that generates delegate mapping for a model that undergoes some simple transformations -in the backend. -`executorch/exir/backend/test/backend_with_delegate_mapping_demo.py` - -Corresponding runtime backend code that logs the delegate debug identifiers that were generated -during the ahead-of-time processing done in the above backend example. - -`executorch/runtime/executor/test/test_backend_with_delegate_mapping.cpp` diff --git a/docs/website/docs/tutorials/quantization_flow.md b/docs/website/docs/tutorials/quantization_flow.md deleted file mode 100644 index 3075027e53..0000000000 --- a/docs/website/docs/tutorials/quantization_flow.md +++ /dev/null @@ -1,68 +0,0 @@ -# Quantization Flow in Executorch - -## 1. Capture the model with `export.capture_pre_autograd_graph` -### Process -The flow uses `PyTorch 2.0 Export Quantization` to quantize the model, that works on a model captured by `exir.capture`. If the model is not traceable, please see [here](https://pytorch.org/docs/main/generated/exportdb/index.html) for supported constructs in `export.capture_pre_autograd_graph` and how to make the model exportable. - -``` -# program capture -from torch._export import export - -m = export.capture_pre_autograd_graph(m, copy.deepcopy(example_inputs)) -``` -### Result -The result in this step will be a `fx.GraphModule` - -## 2. Quantization -### Process -Note: Before quantizing models, each backend need to implement their own `Quantizer` by following [this tutorial](https://pytorch.org/tutorials/prototype/pt2e_quantizer.html). - -Please take a look at the [pytorch 2.0 export post training static quantization tutorial](https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html) to learn about all the steps of quantization. Main APIs that's used to quantize the model would be: -* `prepare_pt2e`: used to insert observers to the model, it takes a backend specific `Quantizer` as argument, which will annotate the nodes with informations needed to quantize the model properly for the backend -* (not an api) calibration: run the model through some sample data -* `convert_pt2e`: convert a observed model to a quantized model. - - -### Result -The result after these steps will be a reference quantized model, with quantize/dequantize operators being further decomposed. Example: - -#### Q/DQ Representation (default) -We'll have (dq -> float32_op -> q) representation for all quantized operators - -``` -def quantized_linear(x_int8, x_scale, x_zero_point, weight_int8, weight_scale, weight_zero_point, bias_fp32, output_scale, output_zero_point): - x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor( - x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8) - weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor( - weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max, torch.int8) - weight_permuted = torch.ops.aten.permute_copy.default(weight_fp32, [1, 0]); - out_fp32 = torch.ops.aten.addmm.default(bias_fp32, x_fp32, weight_permuted) - out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor( - out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8) - return out_i8 -``` - - -#### Reference Quantized Model Representation -(WIP, expected to be ready at end of August): we have special representation for selected ops (e.g. quantized linear), other ops are represented as (dq -> float32_op -> q), and q/dq are decomposed into more primitive operators. - -You can get this representation by: -`convert_pt2e(..., use_reference_representation=True)` - -``` -# Reference Quantized Pattern for quantized linear -def quantized_linear(x_int8, x_scale, x_zero_point, weight_int8, weight_scale, weight_zero_point, bias_fp32, output_scale, output_zero_point): - x_int16 = x_int8.to(torch.int16) - weight_int16 = weight_int8.to(torch.int16) - acc_int32 = torch.ops.out_dtype(torch.mm, torch.int32, (x_int16 - x_zero_point), (weight_int16 - weight_zero_point)) - acc_rescaled_int32 = torch.ops.out_dtype(torch.ops.aten.mul.Scalar, torch.int32, acc_int32, x_scale * weight_scale / output_scale) - bias_scale = x_scale * weight_scale - bias_int32 = out_dtype(torch.ops.aten.mul.Tensor, torch.int32, bias_fp32, bias_scale / out_scale) - out_int8 = torch.ops.aten.clamp(acc_rescaled_int32 + bias_int32 + output_zero_point, qmin, qmax).to(torch.int8) - return out_int8 -``` - -See [here](https://docs.google.com/document/d/17h-OEtD4o_hoVuPqUFsdm5uo7psiNMY8ThN03F9ZZwg/edit#heading=h.ov8z39149wy8) for some operators that has integer operator representations. - -## 4. Lowering to Executorch -You can lower the quantized model to executorch by following [this tutorial](https://github.com/pytorch/executorch/blob/main/docs/website/docs/tutorials/exporting_to_executorch.md#12-lower-to-exir-edge-dialect). diff --git a/docs/website/docs/tutorials/short_term_quantization_flow.md b/docs/website/docs/tutorials/short_term_quantization_flow.md deleted file mode 100644 index dffe53696f..0000000000 --- a/docs/website/docs/tutorials/short_term_quantization_flow.md +++ /dev/null @@ -1,98 +0,0 @@ -# [Deprecated, Please Don't Use] Short Term Quantization Flow in Executorch - -Note: this is deprecated, pelase use [this](./quantization_flow.md) instead. - -High level flow for short term quantization flow in exeuctorch looks like the following: https://fburl.com/8pspa022 - -## 1. Make the model symbolically traceable with torch.fx -### Process -The flow uses [FX Graph Mode Quantization](https://pytorch.org/docs/stable/quantization.html#prototype-fx-graph-mode-quantization) to quantize the model, so we need to make the model symbolically traceable first. If the model is not symbolically traceable (failed when running step 2), please follow the [User Guide](https://pytorch.org/tutorials/prototype/fx_graph_mode_quant_guide.html) to make changes to model. -### Result -The result in this step will be a symbolically traceable model, or a refactored model with the non-traceable parts being factored out in a separate module or function so that we can skip tracing them in the next step. -## 2. Quantization -### Process -Please take a look at the [post training static quantization tutorial](https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html) or [post training dynamic quantization tutorial](https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_dynamic.html) to learn about all the steps of quantization (some of the apis will need to be updated). -[Here](https://www.internalfb.com/code/fbsource/[ea0e2ae0a4a88529f17342e656e820a528ed5bcd]/fbcode/executorch/exir/tests/test_quant_fusion_pass.py?lines=26) is the most up to date flow, main APIs that's used to quantize the model would be: -* [`prepare_fx`](https://pytorch.org/docs/master/generated/torch.quantization.quantize_fx.prepare_fx.html#torch.quantization.quantize_fx.prepare_fx): used to insert observers to the model - - * The main argument that should be configured by user is [QConfigMapping](https://pytorch.org/docs/master/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html#torch.ao.quantization.qconfig_mapping.QConfigMapping), which describes how a model should be quantized, e.g. quantize all linear modules with int8 static quantization/int8 dynamic quantization etc. - * Please use [PrepareCustomConfig](https://pytorch.org/docs/master/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html#torch.ao.quantization.fx.custom_config.PrepareCustomConfig) to skip the non traceable modules. - * Another important argument is [BackendConfig](https://pytorch.org/docs/master/generated/torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig), which is a config the encodes the quantization capabilities of a specific backend, we're using a [default one for executorch](https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/executorch.py) right now. -* (not an api) calibration: run the model through some sample data -* `_convert_to_reference_decomposed_fx`: short term private convert function to convert a observed model to a [reference quantized model](https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md#reference-quantized-model) -### Result -The result after these steps will be a reference quantized model, with quantize/dequantize operators that use decomposed Tensors. Example: - -``` -# Reference Quantized Pattern for quantized add -x = torch.ops.quantized_decomposed.dequantize_per_tensor(x, x_scale, x_zero_point, x_qmin, x_qmax, torch.uint8) -y = torch.ops.quantized_decomposed.dequantize_per_tensor(y, y_scale, y_zero_point, y_qmin, y_qmax, torch.uint8) -out = x + y -out = torch.ops.quantized_decomposed.quantize_per_tensor(out, out_scale, out_zero_point, out_qmin, out_qmax, torch.uint8) -``` - - -* What do we mean by decomposed quantized tensor? - * currently in PyTorch we have a quantized Tensor as a separate abstraction, see [this doc](https://github.com/pytorch/pytorch/wiki/Introducing-Quantized-Tensor) for more details, taking int8 quantized Tensor as an example, it stores int8 data, and some quantization parameters like scale and zero_point in the Tensor object - * Using decomposed quantized Tensor means instead of using quantized Tensor as a separate abstraction as input and output of quantized ops, we just use the decomposed int8 data Tensor, scale and zero_point to represent a quantized Tensor, and we will pass around int8 Tensor, and store scale and zero_point in the signature of quantized operator - -* Why using decomposed quantized tensor? - * Current quantized Tensor has some drawbacks, the main ones are - * coupling with core, which means whenever user wants to add new type of quantization, they will need to modify core - * more burden for downstream systems to support new Tensor types, e.g. executorch has a separate runtime, if we use the current quantized Tensor abastraction, executorch runtime (lean mode) will also need to support this abstraction - * For more informations about decomposed Tensor (prototypes and demos), please see https://fb.workplace.com/groups/2322282031156145/permalink/5674821579235490/ and https://fb.workplace.com/notes/1403734133487714 -Note: for dynamic quantized linear, the pattern will be: -``` -act_scale, act_zero_point = torch.ops.quantized_decomposed.choose_qparams(act, quant_min, quant_max, dtype) -act = torch.ops.quantized_decomposed.quantize_per_tensor(act, act_scale, act_zero_point, act_qmin, act_qmax, torch.uint8) -act = torch.ops.quantized_decomposed.dequantize_per_tensor(act, act_scale, act_zero_point, act_qmin, act_qmax, torch.uint8) -weight = torch.ops.quantized_decomposed.dequantize_per_tensor(y, y_scale, y_zero_point, y_qmin, y_qmax, torch.uint8) -out = torch.nn.functional.linear(act, weight, bias) -``` -Also here we have ops in torch API (e.g. `torch.nn.functional.linear`), but in the fusion passes, we'll be working with aten operators and all the torch ops, modules will be traced as aten ops. - -## 3. exir.capture -### Process -`m = exir.capture(m, example_inputs)` - -We'll call exir.capture to capture the graph to a representation using aten operators. -### Result -A model with aten operators (in [EXIR - ATen dialect](https://www.figma.com/file/l1f1UXfjofLT6D1HqDwp93/Executorch-Compilation-Flow?node-id=0%3A1&t=1c2UKQXUZsNeENDR-0)), reference quantized pattern will be expressed with aten operators as well. - -## 4. Lowering through Delegation -### Process -In this step we need to recognize reference quantized pattern for quantized operators, e.g. "dq - linear - q" and lower the pattern to delegation modules, since some quantized operators needs to run in special libraries (e.g. xnnpack) or runtime (e.g. GPU), so we have a delegation flow for them, see [this test](https://www.internalfb.com/code/fbsource/[ea0e2ae0a4a88529f17342e656e820a528ed5bcd]/fbcode/executorch/exir/tests/test_quant_lowering_custom_backend_pass.py?lines=404) for a end to end example with delegation. We are still working on how to do weight prepacking in delegation at the moment and will have an update for the a bit later. But [here](https://fb.workplace.com/notes/1307520240058002) is the delegation API. -Main things are (1) implement delegation module (2) implement lowering (partitioner) to delegation module. -Code (extracted from the [test](https://www.internalfb.com/code/fbsource/[ea0e2ae0a4a88529f17342e656e820a528ed5bcd]/fbcode/executorch/exir/tests/test_quant_lowering_custom_backend_pass.py?lines=404)): -``` -# duplicate dequant op -m = m.to_edge(exir.EdgeCompileConfig(passes=[DuplicateDequantNodePass()])) -m = to_backend(m, QuantizedConvAddOpPartitioner) -``` -### Result -A partially lowered model with some reference quantized patterns been replaced by calls to delegation modules (lowered_module.execute(...)) - -## 5. Lowering to quantized operators -### Process -Another path is to implement quantized operator and lower the reference quantized pattern to quantized operators. -See [this diff](https://www.internalfb.com/diff/D39974289) for an example implementation for quantized add. Basically we need to - -(1). Implement a functional quantized operator in quantized_decomposed namespace in [//exir/passes/_quant_patterns_and_replacements.py](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py). Notice that these operators are categorized as backend operators since they are meaningful to the target backends. - -(2). Implement the out variant quantized operator in quantized_decomposed namespace in //kernels/quantized/op_QOP.cpp and add test to //kernels/quantized/test/op_QOP_test.cpp -Also we need to make sure the operator here matches the operator in (1) in signature so that ToOutVar pass can establish the connection between these two ops. Example [here](https://github.com/pytorch/executorch/blob/main/kernels/quantized/cpu/op_add.cpp). - -(3). Implement lowering pass from reference quantized pattern to the functional quantized operator we write in (1), some examples for quantized add can be found in [//exir/passes/_quant_patterns_and_replacements.py](https://github.com/pytorch/executorch/blob/main/exir/passes/_quant_patterns_and_replacements.py) - -Example Code (extracted from [this test](https://github.com/pytorch/executorch/blob/main/exir/tests/test_quant_fusion_pass.py)): -``` -m = exir.capture(m, example_inputs).to_edge().to_executorch(ExecutorchBackendConfig(passes=[QuantFusionPass()])) -``` -### Result -A fully lowered quantized model, with both delegated quantized modules and functioanl quantized operators - -## 6. to_executorch -### Process -In this last step, we just call to_executorch to convert the functional variant operators to out variant operators. -### Result -A fully lowered quantized model, with both delegated quantized modules and out variant quantized operators. diff --git a/docs/website/docusaurus.config.js b/docs/website/docusaurus.config.js deleted file mode 100644 index 3e4a45c004..0000000000 --- a/docs/website/docusaurus.config.js +++ /dev/null @@ -1,123 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - * - * @format - */ - -const {fbContent} = require('docusaurus-plugin-internaldocs-fb/internal'); - -const lightCodeTheme = require('prism-react-renderer/themes/github'); -const darkCodeTheme = require('prism-react-renderer/themes/dracula'); - -// With JSDoc @type annotations, IDEs can provide config autocompletion -/** @type {import('@docusaurus/types').DocusaurusConfig} */ -(module.exports = { - title: 'Executorch', - tagline: 'A simple and portable executor of PyTorch programs.', - url: 'https://internalfb.com', // TODO: An external website - baseUrl: '/', - onBrokenLinks: 'log', - onBrokenMarkdownLinks: 'throw', - trailingSlash: true, - favicon: 'img/favicon.ico', - organizationName: 'facebook', - projectName: 'executorch', - - presets: [ - [ - require.resolve('docusaurus-plugin-internaldocs-fb/docusaurus-preset'), - { - docs: { - // Docs folder path relative to website dir - path: 'docs', - // Sidebars file relative to website dir - sidebarPath: require.resolve('./sidebars.js'), - // Where to point users when they click "Edit this page" - editUrl: fbContent({ - internal: - 'https://www.internalfb.com/code/fbsource/fbcode/executorch/docs/website', - external: - 'https://github.com/pytorch/executorch/', - }), - }, - theme: { - customCss: require.resolve('./src/css/custom.css'), - }, - staticDocsProject: 'executorch', - trackingFile: 'xplat/staticdocs/WATCHED_FILES', - 'remark-code-snippets': { - baseDir: '..', - }, - enableEditor: true, - }, - ], - ], - - themeConfig: - /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ - ({ - navbar: { - title: 'Executorch', - logo: { - alt: 'Executorch Logo', - src: 'img/logo.svg', - }, - items: [ - { - type: 'doc', - docId: 'tutorials/setting_up_executorch', - position: 'left', - label: 'Documentation', - }, - ...fbContent({ - internal: [ - { - label: 'Internal', - to: 'docs/fb/poc', - position: 'left', - }, - ], - external: [], - }), - { - label: 'API', - position: 'left', - items: [ - { - label: 'Python API', - to: 'py_api', - target: 'blank', - }, - { - label: 'C++ API', - to: 'cpp_api', - target: 'blank', - }, - ], - }, - ...fbContent({ - internal: [ - { - label: 'Code', - href: 'https://fburl.com/executorch', - position: 'right', - }, - ], - external: [], - }), - ], - }, - footer: { - style: 'dark', - copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc.`, - }, - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - }, - }), -}); diff --git a/docs/website/gen_docs.sh b/docs/website/gen_docs.sh deleted file mode 100755 index 52086c2bcc..0000000000 --- a/docs/website/gen_docs.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -set -ex - -# Build python docs and move to static/py_api -buck2 run //executorch/docs:sphinx-build -- -M html ../source_py/ sphinxbuild_py - -rm -r static/py_api || true -mv sphinxbuild_py/html/ static/py_api -rm -r sphinxbuild_py - -# Build C++ docs and move to static/cpp_api -buck2 run //executorch/docs:sphinx-build -- -M html ../source_cpp/ sphinxbuild_cpp - -rm -r static/cpp_api || true -mv sphinxbuild_cpp/html/ static/cpp_api -rm -r sphinxbuild_cpp diff --git a/docs/website/package.json b/docs/website/package.json deleted file mode 100644 index 7f18282cc9..0000000000 --- a/docs/website/package.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "name": "staticdocs-starter", - "version": "0.0.0", - "private": true, - "scripts": { - "docusaurus": "docusaurus", - "start": "docusaurus start", - "build": "./gen_docs.sh ; docusaurus build", - "swizzle": "docusaurus swizzle", - "deploy": "docusaurus deploy", - "clear": "docusaurus clear", - "clean": "docusaurus clear", - "serve": "docusaurus serve", - "write-translations": "docusaurus write-translations", - "write-heading-ids": "docusaurus write-heading-ids" - }, - "dependencies": { - "@docusaurus/core": "2.1.0", - "@docusaurus/preset-classic": "2.1.0", - "@mdx-js/react": "^1.6.21", - "clsx": "^1.1.1", - "docusaurus-plugin-internaldocs-fb": "1.8.0", - "prism-react-renderer": "^1.3.3", - "react": "^17.0.2", - "react-dom": "^17.0.2" - }, - "browserslist": { - "production": [ - ">0.5%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "engines": { - "node": ">=16", - "npm": "use yarn instead", - "yarn": "^1.16" - }, - "devDependencies": { - "yarn-audit-fix": "^9.3.12" - } -} diff --git a/docs/website/sidebars.js b/docs/website/sidebars.js deleted file mode 100644 index 429cf9faec..0000000000 --- a/docs/website/sidebars.js +++ /dev/null @@ -1,85 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -/** - * Creating a sidebar enables you to: - - create an ordered group of docs - - render a sidebar for each doc of that group - - provide next/previous navigation - - The sidebars can be generated from the filesystem, or explicitly defined here. - - Create as many sidebars as you want. - */ -const {fbContent, fbInternalOnly} = require("docusaurus-plugin-internaldocs-fb/internal"); - -module.exports = { - docSideBar: [ - { - type: "category", - label: "Basics", - items: [ - { - type: "autogenerated", - dirName: "basics", - }, - ] - }, - { - type: "category", - label: "Tutorials", - items: [ - { - type: "autogenerated", - dirName: "tutorials", - }, - ] - }, - { - type: "category", - label: "EXIR", - items: [ - { - type: "autogenerated", - dirName: "ir_spec", - }, - ] - }, - { - type: "category", - label: "SDK", - items: [ - { - type: "autogenerated", - dirName: "sdk", - }, - ] - }, - { - type: "category", - label: "Export", - items: [ - { - type: "autogenerated", - dirName: "export", - }, - ] - }, - { - type: "category", - label: "For Contributors", - items: [ - { - type: "autogenerated", - dirName: "contributors", - }, - ] - }, - ], - ...fbInternalOnly(() => require("./fb/sidebars.js")), -}; diff --git a/docs/website/src/css/custom.css b/docs/website/src/css/custom.css deleted file mode 100644 index 5a5d6a38b3..0000000000 --- a/docs/website/src/css/custom.css +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -/** - * (c) Meta Platforms, Inc. and affiliates. - */ - -:root { - --ifm-color-primary: #d35739; - --ifm-color-primary-dark: #c54a2c; - --ifm-color-primary-darker: #ba4629; - --ifm-color-primary-darkest: #993922; - --ifm-color-primary-light: #d86a4f; - --ifm-color-primary-lighter: #da735a; - --ifm-color-primary-lightest: #e28f7b; - --ifm-code-font-size: 95%; -} - -.docusaurus-highlight-code-line { - background-color: rgba(0, 0, 0, 0.1); - display: block; - margin: 0 calc(-1 * var(--ifm-pre-padding)); - padding: 0 var(--ifm-pre-padding); -} - -html[data-theme='dark'] .docusaurus-highlight-code-line { - background-color: rgba(0, 0, 0, 0.3); -} diff --git a/docs/website/src/pages/index.js b/docs/website/src/pages/index.js deleted file mode 100644 index 9cf65b1ac5..0000000000 --- a/docs/website/src/pages/index.js +++ /dev/null @@ -1,129 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -/** - * (c) Meta Platforms, Inc. and affiliates. - */ - -import React from 'react'; -import clsx from 'clsx'; -import Layout from '@theme/Layout'; -import Link from '@docusaurus/Link'; -import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; -import useBaseUrl from '@docusaurus/useBaseUrl'; -import styles from './index.module.css'; - -const FeatureList = [ - { - title: <>, - // imageUrl: 'img/undraw_docusaurus_react.svg', - description: ( - <> - Enable PyTorch to serve new requirements raised by Reality Labs use - cases while maintaining PyTorch’s ability to serve existing edge use cases. - - ), - }, - { - title: <>, - // imageUrl: 'img/undraw_docusaurus_react.svg', - description: ( - <> - Provide the best possible UX for end-users running PyTorch models on - edge devices. - - ), - }, - { - title: <>, - // imageUrl: 'img/undraw_docusaurus_react.svg', - description: ( - <> - Consistent story + messaging for how this toolchain fits together with - similar ones targeting specialized server use cases (e.g. server’s - Static runtime). - - ), - }, - { - title: <>, - // imageUrl: 'img/undraw_docusaurus_react.svg', - description: ( - <> - Achieve all these with a sustainable architecture that’s aligned across - all layers of the PyTorch stack. - - ), - }, -]; - -function Feature({imageUrl, title, description}) { - const imgUrl = useBaseUrl(imageUrl); - return ( -
- {imgUrl && ( -
- {title} -
- )} -

{title}

-

{description}

-
- ); -} - -function HomepageFeatures() { - return ( -
-
-
- {FeatureList.map(({title, description}) => ( - - ))} -
-
-
- ); -} - -function HomepageHeader() { - const {siteConfig} = useDocusaurusContext(); - return ( -
-
-

{siteConfig.title}

-

{siteConfig.tagline}

-
- - Documentation - -
-
-
- ); -} - -export default function Home() { - const {siteConfig} = useDocusaurusContext(); - return ( - - -
- -
- - ); -} diff --git a/docs/website/src/pages/index.module.css b/docs/website/src/pages/index.module.css deleted file mode 100644 index 988ad8e4fd..0000000000 --- a/docs/website/src/pages/index.module.css +++ /dev/null @@ -1,43 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -/** - * CSS files with the .module.css suffix will be treated as CSS modules - * and scoped locally. - */ - - .heroBanner { - padding: 4rem 0; - text-align: center; - position: relative; - overflow: hidden; -} - -@media screen and (max-width: 966px) { - .heroBanner { - padding: 2rem; - } -} - -.buttons { - display: flex; - align-items: center; - justify-content: center; -} - -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} - -.featureImage { - height: 200px; - width: 200px; -} diff --git a/docs/website/static/.nojekyll b/docs/website/static/.nojekyll deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/docs/website/static/img/favicon.ico b/docs/website/static/img/favicon.ico deleted file mode 100644 index 3082fb2d38..0000000000 Binary files a/docs/website/static/img/favicon.ico and /dev/null differ diff --git a/docs/website/static/img/logo.svg b/docs/website/static/img/logo.svg deleted file mode 100644 index 3d0c649165..0000000000 --- a/docs/website/static/img/logo.svg +++ /dev/null @@ -1,14 +0,0 @@ - - - - - - - - - diff --git a/docs/website/yarn.lock b/docs/website/yarn.lock deleted file mode 100644 index 03dd5e8f58..0000000000 --- a/docs/website/yarn.lock +++ /dev/null @@ -1,11749 +0,0 @@ -# THIS IS AN AUTOGENERATED FILE. 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