I am splitting caching the loading of modules from the caching the codegen since its trivial and much easier.
Module loading is 50% of the cost, and codegen is 50% of maybe_append choice on full graph model. which is 40% of total compile time.
<img width="434" alt="Screenshot 2025-03-24 at 4 35 12 PM" src="https://github.com/user-attachments/assets/aa851c6a-bde9-43f8-b12d-e439504ef62c" />
running mm_loop benchmark,
before this change:
67947323682
after this change:
25845073249
2.6X faster.
it seems that the cache was there then got dropped. I added benchmark so it wont be dropped again by mistake.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149910
Approved by: https://github.com/eellison, https://github.com/aorenste
ghstack dependencies: #149932
Summary:
We need to properly fakify torchbind objects, including the ones in graph module attributes, so the resgitered fake implementation works properly.
- _fakify_script_objects in `compile_fx`
- Allow fake torchbind objects in `torchbind_constants`
Remove `node.meta["unbacked_bindings"]` for `aot_compile` in `compile_fx`. Otherwise `ShapeProp` will fail when trying to resolve the `unbacked_bindings` of `with_effect` tokens.
Update `sigrid_transforms_test` to use the latest `torch._inductor.aot_compile` API.
Add a test for `Fakify torchbind objects in compile_fx and add tests for SigridTransformsInstanceTorchBind` in `e2e_test`.
Test Plan:
```
buck run //caffe2/torch/fb/sparsenn:sigrid_test -- -r test_transform_torch_bind
buck run //sigmoid/inference/test:e2e_test_cpu -- -r SigridTransforms
buck2 run mode/dev-nosan sigmoid/inference/ts_migration:pt2i_readiness_main -- --model_id 545017754 --test_suite ads_all --mode test_preproc
```
Differential Revision: D70013257
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149529
Approved by: https://github.com/angelayi
Summary: The parallel compile workers are holding on to more memory than they need to because they're loading the compiled modules into memory. Update the post-fork initializer to record when in a subprocess and skip some of the unnecessary overhead.
Test Plan: Ran a test script to compile 15k Triton kernels and used tracemalloc in the subprocs to investigate the overhead. On my devgpu:
* After importing torch in a subproc: 371M
* Without this PR, after compiling 15k kernels: 825M
* With this PR, after compiling 15k kernels: 531M
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149168
Approved by: https://github.com/jansel
Summary: as title.
See internal Diff summary for more context.
Test Plan: buck run @fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r config_not_generated
Differential Revision: D71241676
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149246
Approved by: https://github.com/houseroad
Co-authored-by: Huamin Li <huaminli@meta.com>
Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Differential [disconnected] Revision: D70471332
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148305
Approved by: https://github.com/shunting314, https://github.com/eellison
Summary:
**Codegen**
- Skip some codegen parts for torchbind (such as arg decleration) because they are loaded in proxy executor, so we do not need to declare torchbind args in cpp code
- Added a helper method to get the schema of CallTorchBind HOP. The returned schema is only the schema of `obj.method()`.
**Serialization**
Add support for torchbind object in serialization
- For CallTorchBind HOP, we need to handle it specially because of it's schema. The output serialized args is in the format of `(obj, method, *args, **kwargs)`.
- it.TorchBindObject inputs are serialized to `as_custom_obj` Argument.
**Packaging**
Add torchbind objects file and `custom_objs_config.json` file to generated files output of `aot_compile`.
The json file is stored in the `data/aotinductor/<model_name>` folder in pt2 archive.
The torchbind objects are stored in data/constants/ folder in pt2 archive.
The format of torchbind objects are `f"{CUSTOM_OBJ_FILENAME_PREFIX}{custom_obj_idx}"`. e.g. `custom_obj_0`.
CustomClassHolder objects implement their own pickle methods.
Note that this `custom_objs_config.json` file is different from the `model_constants_config.json` file produced in package_sigmoid(). The keys in `custom_objs_config` directly correspond to the arg name in extern nodes json.
The key in `model_constants_config.json` produced by `package_sigmoid` is the attribute name in the user mode code.
This is required for both internal and OSS torchbind support.
For OSS torchbind support, we also need to package torchbind_constants into the .pt2 output.
**Work Left**
We still need to add torchbind support in ProxyExecutor for inductor.aoti_load_package to work. See other diffs in the stack.
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r schema
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r aot_compile
```
Differential Revision: D69490718
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148506
Approved by: https://github.com/angelayi
Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Differential Revision: D70471332
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148305
Approved by: https://github.com/shunting314, https://github.com/eellison
Add a mode to fx_codegen_and_compile() to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Fixed the test which caused the previous version (#146134) to be reverted:
```
$ PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TEST_WITH_SLOW=1 PYTORCH_TEST_SKIP_FAST=1 python test/inductor/test_compile_subprocess.py CpuTests.test_conv_bn_fuse_cpu
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148635
Approved by: https://github.com/jamesjwu
Add a mode to `fx_codegen_and_compile()` to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146134
Approved by: https://github.com/jamesjwu
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Note the attached diff contains some minor fbcode-only changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147583
Approved by: https://github.com/eellison, https://github.com/shunting314
This PR intends to fix the cache related issues from https://github.com/pytorch/pytorch/issues/147405.
It does *not* handle the dynamo recompile case in process, because it does not introduce any extra guards. For FXGraphCache and AOTAutogradCache, we simply have to have the device context in the cache key.
Note that for any function that accepts tensor inputs, the device context is naturally already included in the cache key by the metadata of example inputs. However, for functions that return constants or have no arguments, the device context still needs to be in the cache key.
A more robust fix for this would be to have inductor generate device guards that are dynamic, instead of specialized. This would also help us share more cache artifacts.
I've added unit tests for FXGraphCache and AOTAutogradCache, both of which would fail without this change.
Differential Revision: [D69875939](https://our.internmc.facebook.com/intern/diff/D69875939)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147464
Approved by: https://github.com/bdhirsh, https://github.com/anijain2305
Summary: _compile_file in codecache.py only handles specific cpp compilation in fbcode. The next step is to consolidate it with cpp_builder.
Test Plan: CI
Differential Revision: D69592025
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147106
Approved by: https://github.com/yushangdi
### Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor codegen. Basically instead of calling async_compile.triton for the first time only after the entire code has been generated, we start compiling as soon as we know we'll need to compile the kernel. Then, when loading the generated inductor code, we can simply read from our in memory future cache, considerably increasing the parallelism.
### Implementation Overview
In total, the diff does the following:
- Converts TritonFuture to LambdaFuture, only calling triton.compile on worker processes
- Now that triton.compile() isn't called on the main process, we call TritonBundler on all compiled kernels when we get them back from workers
- Extend @eellison's future cache to a class, mostly as a refactor
- Finally, call async_compile.triton ahead of time in Scheduler.codegen if workers are warmed up. This causes the subsequent
async_compile.triton call that occurs after codegen to cache hit on cold start.
In the diffs after this, I will add more to CompiledTritonKernels so that TritonBundler, on a warm start, automatically populates the in memory cache on warm start with the existing triton kernels, avoiding calling triton altogether on warm starts.
Because LambdaFutures are much faster to kick off than TritonFutures, due to not needing to load from TritonCodeCache at all, the time spent kicking off these worker jobs is pretty minimal for inductor codegen.
Differential Revision: [D69123174](https://our.internmc.facebook.com/intern/diff/D69123174/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146417
Approved by: https://github.com/jansel
I think its good to have everything in the .cu file. Especially the nvcc compile command.
Technically, the configuration name can be found in the template already. So let me know if you think its not needed.
Differential Revision: D69281295
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146668
Approved by: https://github.com/chenyang78
Summary: The current implementation introduces a compile-time regression due to overhead hashing large constants. To support freezing+caching, we consider only the tensor metadata of frozen params, but we neglect to do the same for any constants created as a result of folding frozen params. This PR Explicitly marks the constants created during freezing (and constant folding during freezing) and uses that info in the inductor cache to determine when to hash a tensor value+metadata vs. metadata only.
Test Plan: `python benchmarks/dynamo/torchbench.py --backend inductor --device cuda --only alexnet --bfloat16 --cold-start-latency --print-compilation-time --inference --performance --freezing`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145868
Approved by: https://github.com/eellison
Summary: In https://github.com/pytorch/pytorch/pull/143563 we have a report of a problem with the treatment of frozen params in the inductor cache implementation. There seems to be a path where new constants are added in the `GraphLowering`. On a cache hit when we try to find those constant names in the `torch.fx.GraphModule`, they do not exist. The current approach treats all constants differently if the GM has any frozen params. This PR changes the approach to only treat the _frozen_ params specially, but store all other constants in the cache entry (as we do without freezing):
1) When creating a cache entry, store the names of any frozen params, but the values of any other constants.
2) On a cache hit, restore the values of the frozen params by looking up in the current GM.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143808
Approved by: https://github.com/leslie-fang-intel, https://github.com/eellison
