mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
d5a6762263
Replaces `view_func()` closures with a reified `ViewFunc` data structure. Codegen generates a `ViewFunc` subclass for each view op (e.g. `NarrowViewFunc`) containing state needed to reconstruct the view. The `ViewFunc` API allows for querying and hot-swapping any `SymInt`s or `Tensors` in the state through `get_symints()` / `get_tensors()` / `clone_and_set()`, which will be essential for fake-ification later on.
```cpp
/// Base class for view functions, providing reapplication of a view on a new base.
/// Each view op should get a codegenerated subclass of this class containing
/// any state needed to reconstruct the view. The class also provides convenience
/// accessors for saved SymInts / tensor state. This is useful for e.g. fake-ification,
/// where we want to use symbolic values or fake tensors instead.
struct TORCH_API ViewFunc {
virtual ~ViewFunc() {}
/// Returns any SymInts in the saved state.
virtual std::vector<c10::SymInt> get_symints() const { return {}; }
/// Returns the number of SymInts in the saved state.
virtual size_t num_symints() const { return 0; }
/// Returns any tensors in the saved state.
virtual std::vector<at::Tensor> get_tensors() const { return {}; }
/// Returns the number of tensors in the saved state.
virtual size_t num_tensors() const { return 0; }
/// Reapplies the view on the given base using the saved state.
virtual at::Tensor operator()(const at::Tensor&) const = 0;
/// Returns a clone of this ViewFunc, optionally with the specified saved state.
virtual std::unique_ptr<ViewFunc> clone_and_set(
std::optional<std::vector<c10::SymInt>> = c10::nullopt,
std::optional<std::vector<at::Tensor>> = c10::nullopt) const = 0;
protected:
/// Sets the values of any SymInts in the saved state. The input vector size must
/// match the number of SymInts in the saved state (i.e. the size of the list
/// returned by get_symints()).
virtual void set_symints(std::vector<c10::SymInt>) {}
/// Sets the values of any Tensors in the saved state. The input vector size must
/// match the number of Tensors in the saved state (i.e. the size of the list
/// returned by get_tensors()).
virtual void set_tensors(std::vector<at::Tensor>) {}
};
```
New codegen files:
* `torch/csrc/autograd/generated/ViewFunc.h`
* `torch/csrc/autograd/generated/ViewFuncs.cpp`
The templates for these also contains impls for `ChainedViewFunc` and `ErroringViewFunc` which are used in a few places within autograd.
Example codegen for `slice.Tensor`:
```cpp
// torch/csrc/autograd/generated/ViewFuncs.h
#define SLICE_TENSOR_VIEW_FUNC_AVAILABLE
struct SliceTensorViewFunc : public torch::autograd::ViewFunc {
SliceTensorViewFunc(int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) : dim(dim), start(start), end(end), step(step)
{};
virtual ~SliceTensorViewFunc() override {};
virtual std::vector<c10::SymInt> get_symints() const override;
virtual size_t num_symints() const override;
virtual std::vector<at::Tensor> get_tensors() const override;
virtual size_t num_tensors() const override;
virtual at::Tensor operator()(const at::Tensor&) const override;
virtual std::unique_ptr<ViewFunc> clone_and_set(
std::optional<std::vector<c10::SymInt>> = c10::nullopt,
std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
protected:
virtual void set_symints(std::vector<c10::SymInt>) override;
virtual void set_tensors(std::vector<at::Tensor>) override;
private:
int64_t dim;
c10::optional<c10::SymInt> start;
c10::optional<c10::SymInt> end;
c10::SymInt step;
};
...
// torch/csrc/autograd/generated/ViewFuncs.cpp
std::vector<c10::SymInt> SliceTensorViewFunc::get_symints() const {
::std::vector<c10::SymInt> symints;
symints.reserve((start.has_value() ? 1 : 0) + (end.has_value() ? 1 : 0) + 1);
if(start.has_value()) symints.insert(symints.end(), *(start));
if(end.has_value()) symints.insert(symints.end(), *(end));
symints.push_back(step);
return symints;
}
size_t SliceTensorViewFunc::num_symints() const {
return static_cast<size_t>((start.has_value() ? 1 : 0) + (end.has_value() ? 1 : 0) + 1);
}
void SliceTensorViewFunc::set_symints(std::vector<c10::SymInt> symints) {
TORCH_INTERNAL_ASSERT(symints.size() == num_symints());
auto i = 0;
if(start.has_value()) start = symints[i];
i += (start.has_value() ? 1 : 0);
if(end.has_value()) end = symints[i];
i += (end.has_value() ? 1 : 0);
step = symints[i];
}
std::vector<at::Tensor> SliceTensorViewFunc::get_tensors() const {
::std::vector<at::Tensor> tensors;
return tensors;
}
size_t SliceTensorViewFunc::num_tensors() const {
return static_cast<size_t>(0);
}
void SliceTensorViewFunc::set_tensors(std::vector<at::Tensor> tensors) {
TORCH_INTERNAL_ASSERT(tensors.size() == num_tensors());
}
at::Tensor SliceTensorViewFunc::operator()(const at::Tensor& input_base) const {
return at::_ops::slice_Tensor::call(input_base, dim, start, end, step);
}
std::unique_ptr<ViewFunc> SliceTensorViewFunc::clone_and_set(
std::optional<std::vector<c10::SymInt>> symints,
std::optional<std::vector<at::Tensor>> tensors) const {
auto output = std::make_unique<SliceTensorViewFunc>(dim, start, end, step);
if (symints.has_value()) {
output->set_symints(std::move(*(symints)));
}
if (tensors.has_value()) {
output->set_tensors(std::move(*(tensors)));
}
return output;
}
```
The `_view_func()` / `_view_func_unsafe()` methods now accept two additional (optional) args for `symint_visitor_fn` / `tensor_visitor_fn`. If these are defined, they are expected to be python callables that operate on a single SymInt / tensor and return a new one. This allows for the hot-swapping needed during fake-ification.
For testing, there are extensive pre-existing tests, and I added a test to ensure that hot-swapping functions correctly.
```sh
python test/test_autograd.py -k test_view_func_replay
python test/test_ops.py -k test_view_replay
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118404
Approved by: https://github.com/ezyang
247 lines
11 KiB
Python
247 lines
11 KiB
Python
# This file keeps a list of PyTorch source files that are used for templated selective build.
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# NB: as this is PyTorch Edge selective build, we assume only CPU targets are
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# being built
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load("@bazel_skylib//lib:paths.bzl", "paths")
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load("//tools/build_defs:fbsource_utils.bzl", "is_arvr_mode")
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load(":build_variables.bzl", "aten_native_source_list")
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load(
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":ufunc_defs.bzl",
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"aten_ufunc_generated_cpu_kernel_sources",
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"aten_ufunc_generated_cpu_sources",
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)
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# Files in this list are supposed to be built separately for each app,
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# for different operator allow lists.
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TEMPLATE_SOURCE_LIST = [
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"torch/csrc/jit/runtime/register_prim_ops.cpp",
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"torch/csrc/jit/runtime/register_special_ops.cpp",
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] + aten_native_source_list
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# For selective build, we can lump the CPU and CPU kernel sources altogether
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# because there is only ever one vectorization variant that is compiled
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def aten_ufunc_generated_all_cpu_sources(gencode_pattern = "{}"):
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return (
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aten_ufunc_generated_cpu_sources(gencode_pattern) +
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aten_ufunc_generated_cpu_kernel_sources(gencode_pattern)
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)
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TEMPLATE_MASKRCNN_SOURCE_LIST = [
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"register_maskrcnn_ops.cpp",
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]
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TEMPLATE_BATCH_BOX_COX_SOURCE_LIST = [
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"register_batch_box_cox_ops.cpp",
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]
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METAL_SOURCE_LIST = [
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"aten/src/ATen/native/metal/MetalAten.mm",
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"aten/src/ATen/native/metal/MetalGuardImpl.cpp",
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"aten/src/ATen/native/metal/MetalPrepackOpRegister.cpp",
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"aten/src/ATen/native/metal/MetalCommandBuffer.mm",
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"aten/src/ATen/native/metal/MetalContext.mm",
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"aten/src/ATen/native/metal/MetalConvParams.mm",
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"aten/src/ATen/native/metal/MetalTensorImplStorage.mm",
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"aten/src/ATen/native/metal/MetalTensorUtils.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSCNNClampOp.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSCNNConvOp.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSCNNFullyConnectedOp.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSCNNNeuronOp.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSCNNUtils.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSImage+Tensor.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSImageUtils.mm",
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"aten/src/ATen/native/metal/mpscnn/MPSImageWrapper.mm",
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"aten/src/ATen/native/metal/ops/MetalAddmm.mm",
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"aten/src/ATen/native/metal/ops/MetalBinaryElementwise.mm",
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"aten/src/ATen/native/metal/ops/MetalChunk.mm",
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"aten/src/ATen/native/metal/ops/MetalClamp.mm",
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"aten/src/ATen/native/metal/ops/MetalConcat.mm",
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"aten/src/ATen/native/metal/ops/MetalConvolution.mm",
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"aten/src/ATen/native/metal/ops/MetalCopy.mm",
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"aten/src/ATen/native/metal/ops/MetalHardswish.mm",
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"aten/src/ATen/native/metal/ops/MetalHardshrink.mm",
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"aten/src/ATen/native/metal/ops/MetalLeakyReLU.mm",
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"aten/src/ATen/native/metal/ops/MetalNeurons.mm",
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"aten/src/ATen/native/metal/ops/MetalPadding.mm",
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"aten/src/ATen/native/metal/ops/MetalPooling.mm",
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"aten/src/ATen/native/metal/ops/MetalReduce.mm",
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"aten/src/ATen/native/metal/ops/MetalReshape.mm",
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"aten/src/ATen/native/metal/ops/MetalSoftmax.mm",
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"aten/src/ATen/native/metal/ops/MetalTranspose.mm",
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"aten/src/ATen/native/metal/ops/MetalUpsamplingNearest.mm",
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]
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UNET_METAL_PREPACK_SOURCE_LIST = [
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"unet_metal_prepack.cpp",
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"unet_metal_prepack.mm",
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]
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METAL_MASKRCNN_SOURCE_LIST = [
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"maskrcnn/srcs/GenerateProposals.mm",
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"maskrcnn/srcs/RoIAlign.mm",
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]
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# The get_template_source_dict() returns a dict containing a path prefix
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# and a list of .cpp source files containing operator definitions and
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# registrations that should get selected via templated selective build.
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# The file selected_mobile_ops.h has the list of selected top level
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# operators.
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# NB: doesn't include generated files; copy_template_registration_files
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# handles those specially
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def get_template_source_dict():
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ret = {}
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for file_path in TEMPLATE_SOURCE_LIST:
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path_prefix = paths.dirname(file_path)
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if path_prefix not in ret:
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ret[path_prefix] = []
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ret[path_prefix].append(file_path)
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return ret
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def get_gen_oplist_outs():
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return {
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"SupportedMobileModelsRegistration.cpp": [
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"SupportedMobileModelsRegistration.cpp",
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],
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"selected_mobile_ops.h": [
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"selected_mobile_ops.h",
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],
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"selected_operators.yaml": [
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"selected_operators.yaml",
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],
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}
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def get_generate_code_bin_outs():
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outs = {
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"autograd/generated/ADInplaceOrViewTypeEverything.cpp": ["autograd/generated/ADInplaceOrViewTypeEverything.cpp"],
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"autograd/generated/ADInplaceOrViewType_0.cpp": ["autograd/generated/ADInplaceOrViewType_0.cpp"],
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"autograd/generated/ADInplaceOrViewType_1.cpp": ["autograd/generated/ADInplaceOrViewType_1.cpp"],
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"autograd/generated/Functions.cpp": ["autograd/generated/Functions.cpp"],
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"autograd/generated/Functions.h": ["autograd/generated/Functions.h"],
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"autograd/generated/TraceTypeEverything.cpp": ["autograd/generated/TraceTypeEverything.cpp"],
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"autograd/generated/TraceType_0.cpp": ["autograd/generated/TraceType_0.cpp"],
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"autograd/generated/TraceType_1.cpp": ["autograd/generated/TraceType_1.cpp"],
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"autograd/generated/TraceType_2.cpp": ["autograd/generated/TraceType_2.cpp"],
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"autograd/generated/TraceType_3.cpp": ["autograd/generated/TraceType_3.cpp"],
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"autograd/generated/TraceType_4.cpp": ["autograd/generated/TraceType_4.cpp"],
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"autograd/generated/VariableType.h": ["autograd/generated/VariableType.h"],
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"autograd/generated/VariableTypeEverything.cpp": ["autograd/generated/VariableTypeEverything.cpp"],
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"autograd/generated/VariableType_0.cpp": ["autograd/generated/VariableType_0.cpp"],
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"autograd/generated/VariableType_1.cpp": ["autograd/generated/VariableType_1.cpp"],
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"autograd/generated/VariableType_2.cpp": ["autograd/generated/VariableType_2.cpp"],
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"autograd/generated/VariableType_3.cpp": ["autograd/generated/VariableType_3.cpp"],
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"autograd/generated/VariableType_4.cpp": ["autograd/generated/VariableType_4.cpp"],
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"autograd/generated/variable_factories.h": ["autograd/generated/variable_factories.h"],
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"autograd/generated/ViewFuncs.cpp": ["autograd/generated/ViewFuncs.cpp"],
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"autograd/generated/ViewFuncs.h": ["autograd/generated/ViewFuncs.h"],
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}
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if is_arvr_mode():
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outs.update({
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"autograd/generated/python_enum_tag.cpp": ["autograd/generated/python_enum_tag.cpp"],
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"autograd/generated/python_fft_functions.cpp": ["autograd/generated/python_fft_functions.cpp"],
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"autograd/generated/python_functions.h": ["autograd/generated/python_functions.h"],
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"autograd/generated/python_functions_0.cpp": ["autograd/generated/python_functions_0.cpp"],
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"autograd/generated/python_functions_1.cpp": ["autograd/generated/python_functions_1.cpp"],
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"autograd/generated/python_functions_2.cpp": ["autograd/generated/python_functions_2.cpp"],
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"autograd/generated/python_functions_3.cpp": ["autograd/generated/python_functions_3.cpp"],
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"autograd/generated/python_functions_4.cpp": ["autograd/generated/python_functions_4.cpp"],
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"autograd/generated/python_linalg_functions.cpp": ["autograd/generated/python_linalg_functions.cpp"],
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"autograd/generated/python_nested_functions.cpp": ["autograd/generated/python_nested_functions.cpp"],
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"autograd/generated/python_nn_functions.cpp": ["autograd/generated/python_nn_functions.cpp"],
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"autograd/generated/python_return_types.h": ["autograd/generated/python_return_types.h"],
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"autograd/generated/python_return_types.cpp": ["autograd/generated/python_return_types.cpp"],
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"autograd/generated/python_sparse_functions.cpp": ["autograd/generated/python_sparse_functions.cpp"],
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"autograd/generated/python_special_functions.cpp": ["autograd/generated/python_special_functions.cpp"],
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"autograd/generated/python_torch_functions_0.cpp": ["autograd/generated/python_torch_functions_0.cpp"],
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"autograd/generated/python_torch_functions_1.cpp": ["autograd/generated/python_torch_functions_1.cpp"],
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"autograd/generated/python_torch_functions_2.cpp": ["autograd/generated/python_torch_functions_2.cpp"],
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"autograd/generated/python_variable_methods.cpp": ["autograd/generated/python_variable_methods.cpp"],
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})
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return outs
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def get_template_registration_files_outs(is_oss = False):
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outs = {}
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if not is_oss:
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for file_path in TEMPLATE_MASKRCNN_SOURCE_LIST:
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outs[file_path] = [file_path]
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for file_path in TEMPLATE_BATCH_BOX_COX_SOURCE_LIST:
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outs[file_path] = [file_path]
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for file_path in TEMPLATE_SOURCE_LIST:
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outs[file_path] = [file_path]
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for base_name in aten_ufunc_generated_all_cpu_sources():
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file_path = "aten/src/ATen/{}".format(base_name)
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outs[file_path] = [file_path]
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return outs
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def get_template_registration_file_rules(rule_name, is_oss = False):
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rules = []
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for file_path in TEMPLATE_SOURCE_LIST if is_oss else (TEMPLATE_SOURCE_LIST + TEMPLATE_MASKRCNN_SOURCE_LIST + TEMPLATE_BATCH_BOX_COX_SOURCE_LIST):
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rules.append(":{}[{}]".format(rule_name, file_path))
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for file_path in aten_ufunc_generated_all_cpu_sources():
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rules.append(":{}[aten/src/ATen/{}]".format(rule_name, file_path))
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return rules
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# ---------------------METAL RULES---------------------
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def get_metal_source_dict():
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ret = {}
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for file_path in METAL_SOURCE_LIST:
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path_prefix = paths.dirname(file_path)
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if path_prefix not in ret:
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ret[path_prefix] = []
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ret[path_prefix].append(file_path)
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return ret
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def get_metal_registration_files_outs():
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outs = {}
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for file_path in METAL_SOURCE_LIST:
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outs[file_path] = [file_path]
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for file_path in UNET_METAL_PREPACK_SOURCE_LIST:
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outs[file_path] = [file_path]
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for file_path in METAL_MASKRCNN_SOURCE_LIST:
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outs[file_path] = [file_path]
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return outs
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# There is a really weird issue with the arvr windows builds where
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# the custom op files are breaking them. See https://fburl.com/za87443c
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# The hack is just to not build them for that platform and pray they arent needed.
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def get_metal_registration_files_outs_windows():
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outs = {}
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for file_path in METAL_SOURCE_LIST:
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outs[file_path] = [file_path]
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return outs
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def get_metal_registration_files_rules(rule_name):
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ret = {}
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objc_rules = []
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cxx_rules = []
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for file_path in METAL_SOURCE_LIST + METAL_MASKRCNN_SOURCE_LIST + UNET_METAL_PREPACK_SOURCE_LIST:
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if ".cpp" not in file_path:
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objc_rules.append(":{}[{}]".format(rule_name, file_path))
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else:
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cxx_rules.append(":{}[{}]".format(rule_name, file_path))
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ret["objc"] = objc_rules
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ret["cxx"] = cxx_rules
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return ret
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def get_metal_registration_files_rules_windows(rule_name):
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ret = {}
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objc_rules = []
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cxx_rules = []
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for file_path in METAL_SOURCE_LIST:
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if ".cpp" not in file_path:
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objc_rules.append(":{}[{}]".format(rule_name, file_path))
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else:
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cxx_rules.append(":{}[{}]".format(rule_name, file_path))
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ret["objc"] = objc_rules
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ret["cxx"] = cxx_rules
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return ret
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