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545d2126f6
Summary:
This change makes two major improvements to PyTorch Vulkan's shader authoring workflow.
## Review Guide
There are a lot of changed files because every GLSL shader had to be touched. The majority of changes is changing
```
#define PRECISION $precision
#define FORMAT $format
```
to
```
#define PRECISION ${PRECISION}
#define FORMAT ${FORMAT}
```
due to changes in how shader templates are processed.
For reviewers, the primary functional changes to review are:
* `gen_vulkan_spv.py`
* Majority of functional changes are in this file, which controls how shader templates are processed.
* `shader_params.yaml`
* controls how shader variants are generated
## Python Codeblocks in Shader Templates
From now on, every compute shader (i.e. `.glsl`) is treated as a shader template. To this effect, the `templates/` folder has been removed and there is now a global `shader_params.yaml` file to describe the shader variants that should be generated for all shader templates.
**Taking inspiration from XNNPACK's [`xngen` tool](https://github.com/google/XNNPACK/blob/master/tools/xngen.py), shader templates can now use Python codeblocks**. One example is:
```
$if not INPLACE:
layout(set = 0, binding = 0, FORMAT) uniform PRECISION restrict writeonly image3D uOutput;
layout(set = 0, binding = 1) uniform PRECISION sampler3D uInput;
layout(set = 0, binding = 2) uniform PRECISION sampler3D uOther;
layout(set = 0, binding = 3) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 input_sizes;
ivec4 other_sizes;
float alpha;
}
uArgs;
$else:
layout(set = 0, binding = 0, FORMAT) uniform PRECISION restrict image3D uOutput;
layout(set = 0, binding = 1) uniform PRECISION sampler3D uOther;
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
uArgs;
```
Another is:
```
// PYTHON CODEBLOCK
$if not IS_DIV:
const int c_index = (pos.z % ((uArgs.output_sizes.z + 3) / 4)) * 4;
if (uArgs.other_sizes.z != 1 && c_index + 3 >= uArgs.output_sizes.z) {
ivec4 c_ind = ivec4(c_index) + ivec4(0, 1, 2, 3);
vec4 mask = vec4(lessThan(c_ind, ivec4(uArgs.output_sizes.z)));
other_texel = other_texel * mask + vec4(1, 1, 1, 1) - mask;
}
// PYTHON CODEBLOCK
$if not INPLACE:
ivec3 input_pos =
map_output_pos_to_input_pos(pos, uArgs.output_sizes, uArgs.input_sizes);
const vec4 in_texel =
load_texel(input_pos, uArgs.output_sizes, uArgs.input_sizes, uInput);
imageStore(uOutput, pos, OP(in_texel, other_texel, uArgs.alpha));
$else:
const vec4 in_texel = imageLoad(uOutput, pos);
imageStore(uOutput, pos, OP(in_texel, other_texel, uArgs.alpha));
```
In addition to making it easier and clearer to write shader templates, this enables shaders that were previously unable to be consolidated into a single template to now be represented using a single template, such as non inplace and inplace variants of the same shader.
## `generate_variant_forall` in shader variant YAML configuration
YAML files that describe how shader variants should be generated can now use a `generate_variant_forall` field to iterate over various settings for a specific parameter for each variant defined. Example:
```
unary_op:
parameter_names_with_default_values:
OPERATOR: exp(X)
INPLACE: 0
generate_variant_forall:
INPLACE:
- VALUE: 0
SUFFIX: ""
- VALUE: 1
SUFFIX: "inplace"
shader_variants:
- NAME: exp
OPERATOR: exp(X)
- NAME: sqrt
OPERATOR: sqrt(X)
- NAME: log
OPERATOR: log(X)
```
Previously, the `inplace` variants would need to have separate `shader_variants` entries. If there are multiple variables that need to be iterated across, then all possible combinations will be generated. Would be good to take a look to see how the new YAML configuration works.
Test Plan:
There is no functional change to this diff; we only need to make sure that the generated shaders are still correct. Therefore, we only need to run `vulkan_api_test`.
```
# On Mac Laptop
buck run --target-platforms ovr_config//platform/macos:arm64-fbsource //xplat/caffe2:pt_vulkan_api_test_binAppleMac\#macosx-arm64 -c pt.vulkan_full_precision=1 -- --gtest_filter="*"
```
Reviewed By: digantdesai
Differential Revision: D52087084
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115948
Approved by: https://github.com/manuelcandales
|
||
|---|---|---|
| .. | ||
| alerts | ||
| amd_build | ||
| autograd | ||
| bazel_tools | ||
| build/bazel | ||
| build_defs | ||
| code_analyzer | ||
| code_coverage | ||
| config | ||
| coverage_plugins_package | ||
| dynamo | ||
| gdb | ||
| github | ||
| iwyu | ||
| jit | ||
| linter | ||
| lite_interpreter | ||
| lldb | ||
| onnx | ||
| pyi | ||
| rules | ||
| rules_cc | ||
| setup_helpers | ||
| shared | ||
| stats | ||
| test | ||
| testing | ||
| __init__.py | ||
| bazel.bzl | ||
| BUCK.bzl | ||
| BUCK.oss | ||
| build_libtorch.py | ||
| build_pytorch_libs.py | ||
| download_mnist.py | ||
| extract_scripts.py | ||
| gen_flatbuffers.sh | ||
| gen_vulkan_spv.py | ||
| generate_torch_version.py | ||
| generated_dirs.txt | ||
| git_add_generated_dirs.sh | ||
| git_reset_generated_dirs.sh | ||
| nightly.py | ||
| nvcc_fix_deps.py | ||
| pytorch.version | ||
| README.md | ||
| render_junit.py | ||
| substitute.py | ||
| update_masked_docs.py | ||
| vscode_settings.py | ||
This folder contains a number of scripts which are used as
part of the PyTorch build process. This directory also doubles
as a Python module hierarchy (thus the __init__.py).
Overview
Modern infrastructure:
- autograd - Code generation for autograd. This includes definitions of all our derivatives.
- jit - Code generation for JIT
- shared - Generic infrastructure that scripts in
tools may find useful.
- module_loader.py - Makes it easier to import arbitrary Python files in a script, without having to add them to the PYTHONPATH first.
Build system pieces:
- setup_helpers - Helper code for searching for third-party dependencies on the user system.
- build_pytorch_libs.py - cross-platform script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself.
- build_libtorch.py - Script for building libtorch, a standalone C++ library without Python support. This build script is tested in CI.
Developer tools which you might find useful:
- git_add_generated_dirs.sh and git_reset_generated_dirs.sh - Use this to force add generated files to your Git index, so that you can conveniently run diffs on them when working on code-generation. (See also generated_dirs.txt which specifies the list of directories with generated files.)
Important if you want to run on AMD GPU:
- amd_build - HIPify scripts, for transpiling CUDA
into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
do this transpilation, but have separate entry-points for transpiling
either PyTorch or Caffe2 code.
- build_amd.py - Top-level entry point for HIPifying our codebase.
Tools which are only situationally useful:
- docker - Dockerfile for running (but not developing) PyTorch, using the official conda binary distribution. Context: https://github.com/pytorch/pytorch/issues/1619
- download_mnist.py - Download the MNIST dataset; this is necessary if you want to run the C++ API tests.