Introduce CUDA OpenXLA fallback. #7318

ysiraichi · 2024-06-20T13:56:39Z

This PR introduces OpenXLA fallback on PyTorch GPU eager. Instead of running fallback operations (i.e. whenever a operation has no lowering implemented) on CPU, we now make it possible to run them on GPU. This makes sense specially when using XLA:CUDA devices.

In summary, this PR introduces the following changes:

Rename xla_cpu_fallback into xla_fallback
- Changes every call site that manually invokes the fallback
Implement cuda_fallback function
- A version of at::native::cpu_fallback, but with a few changes (called out before each function)
- Ideally, it would be better to generalize at::native::cpu_fallback implementation inside PyTorch, though
Add XLA_FALLBACK_CUDA flag for using this feature
Add tests for fallback operations that are found in torchbench

cc @miladm @JackCaoG @vanbasten23

ysiraichi · 2024-06-20T13:59:48Z

I'm still running torchbench. Will report back when it is over.

vanbasten23 · 2024-06-20T23:51:56Z

test/test_ops.py

+@dataclass
+class AllowedFallbackOpInfoEntry(AllowedOpInfoEntry):
+  fallback_ops: List[str] = field(default_factory=list)
+  allow_sample: Optional[Callable[[SampleInput], bool]] = None


What does allow_sample mean?

It filters the sample list, looking for a specific one. I will leave a comment there.

SampleInput is the sample list that you are referring to?

SampleInput is the class that represents one sample.

test/test_ops.py

vanbasten23 · 2024-06-21T00:09:11Z

torch_xla/csrc/BUILD

@@ -211,18 +213,6 @@ cc_library(
    ],
 )

-ptxla_cc_library(


What's this change for?

Now, aten_cpu_fallback.cpp needs functions from:

aten_xla_bridge.cpp

xla_graph_executor.cpp

dl_convertor.cpp

So, I thought it would be easier to merge it into the main library.

torch_xla/csrc/aten_cpu_fallback.cpp

vanbasten23 · 2024-06-21T00:20:05Z

torch_xla/csrc/aten_cpu_fallback.cpp

+  return runtime::sys_util::GetEnvBool("XLA_FALLBACK_CUDA", false);
+}
+
+// Change: use of std::any_of instead of iterating with a for-loop.


nit: the comment is stale now?

No. This is the change that I applied to that function.

nit: maybe rephrase Change: to Change made:? Change: sounds it is something we want to change next lol

vanbasten23 · 2024-06-21T00:28:11Z

torch_xla/csrc/aten_cpu_fallback.cpp

+}
+
+// Synchronizes the CUDA device being used by PyTorch.
+static void torch_cuda_synchronize(at::DeviceIndex common_device) {


What does the param common_device mean? The device to be synchronized?

vanbasten23 · 2024-06-21T00:34:52Z

torch_xla/csrc/aten_cpu_fallback.cpp

+//   1. Track the device index being used. Rationale: we synchronize the device
+//      before crossing device borders for correctness.
+//
+void cuda_fallback(const c10::OperatorHandle& op, torch::jit::Stack* stack,


I found similar implementations in PyTorch, one in pytorch/aten/src/ATen/native/CPUFallback.cpp and the other one in pytorch/torch/csrc/lazy/ts_backend/ts_eager_fallback.cpp. How is this implementation different from those?

The main difference is that we (a) are falling back to CUDA. Here are more details regarding these 3 implementations:

CPUFallback.cpp (b) looks more updated than ts_eager_fallback.cpp (c), e.g. handles mutation in tensor lists

Device support: even though (c) does support other devices, its conversion uses tensor.to(device), which copies the tensor. In contrast, (a) we simply share the storage of the tensors

Device synchronization: (a) needs further device synchronization, since we are not calling tensor.to method

With all that said, I do agree the functions are all similar to each other. A better approach would be to generalize (b), adding support for some customization.

ysiraichi · 2024-06-21T14:35:27Z

The CI error is a bit tricky to solve.

Problem: I'm using some CUDA functions defined inside PyTorch, which requires linking libc10_cuda.so to the test binaries. However, since (in CI) PyTorch isn't being compiled with CUDA support, that won't work.

While I could condition compilation of that code with C++ macros (e.g. using XLA_CUDA definition), that would mean that we never compile that code in CI, since PyTorch/XLA is compiled without that flag set + PyTorch is compiled without CUDA support (in that specific CI action).

Possible Solution: create a phony implementation for the CUDA functions I'm using, and compile it to another library. Then, if we don't find the library libc10_cuda.so, we link this other library.

Notice that this is only needed for the test binaries.

@JackCaoG @vanbasten23 @lezcano
What do you think?

lezcano · 2024-06-21T15:14:54Z

We could also always compile PyTorch with CUDA support in CI.

vanbasten23 · 2024-06-21T19:40:47Z

The CI error is a bit tricky to solve.

Problem: I'm using some CUDA functions defined inside PyTorch, which requires linking libc10_cuda.so to the test binaries. However, since (in CI) PyTorch isn't being compiled with CUDA support, that won't work.

While I could condition compilation of that code with C++ macros (e.g. using XLA_CUDA definition), that would mean that we never compile that code in CI, since PyTorch/XLA is compiled without that flag set + PyTorch is compiled without CUDA support (in that specific CI action).

Possible Solution: create a phony implementation for the CUDA functions I'm using, and compile it to another library. Then, if we don't find the library libc10_cuda.so, we link this other library.

Notice that this is only needed for the test binaries.

@JackCaoG @vanbasten23 @lezcano What do you think?

If it's only the test binary that requires pytorch built with CUDA, there is a way to achieve it. In our CI, there is a workflow that build pytorch with CUDA, build torch_xla with CUDA, and run only those tests that requires pytorch with CUDA:

You can add your tests to

xla/.github/workflows/_test_requiring_torch_cuda.yml

Line 104 in bb27cb2

PJRT_DEVICE=CUDA python pytorch/xla/test/dynamo/test_dynamo.py -v

.

miladm · 2024-06-24T16:30:02Z

torch_xla/core/xla_env_vars.py

@@ -31,3 +31,4 @@
 WORLD_SIZE = 'WORLD_SIZE'
 LOCAL_WORLD_SIZE = 'LOCAL_WORLD_SIZE'
 ZERO_COPY_ENABLED = 'ZERO_COPY_ENABLED'
+XLA_FALLBACK_CUDA = 'XLA_FALLBACK_CUDA'


I suggest we define a plan to make this feature default (e.g. for 2.5 release) and remove the env variable. Wdyt @ysiraichi?

Environment variables need a description here: https://github.com/pytorch/xla/blob/master/configuration.yaml

I think it makes sense to defaul XLA:CUDA executions on CUDA fallback, while XLA:CPU and XLA:TPU remain on CPU fallback.

As discussed offline, more specifically, after this PR, it would be good to:

Step1: Reversing the function of XLA_FALLBACK_CUDA so the default would be CUDA fallback enabled (e.g. define DISABLE_XLA_FALLBACK_CUDA).

Step2: define a mechanism to remove the env variable and have a native solution that just works, and avoids user experience hiccups.

vanbasten23 · 2024-06-24T17:09:42Z

For the problem 1 "Problem1: C++ test binaries need all references to be resolved", you mentioned the "Solution: Create a fallback implementation of the CUDA functions". Could you point to me where is the fallback implementation of the CUDA functions?

miladm · 2024-06-24T17:12:53Z

@zpcore to upgrade the XLA:GPU benchmarking to adopt CUDA fallback setting after this PR lands.

cc @will-cromar for viz re: comment #7318 (comment)

ysiraichi · 2024-06-24T17:23:42Z

torch_xla/csrc/aten_cuda_functions.cpp

+#include "torch_xla/csrc/aten_cuda_functions.h"
+
+#include <c10/util/Exception.h>
+
+static void fail(const char* name) {
+  TORCH_CHECK(false, "Could not call the CUDA function: ", name,
+              ". PyTorch was compiled without CUDA support.");
+}
+
+namespace c10::cuda {
+
+c10::DeviceIndex current_device() noexcept { return -1; }
+
+void set_device(c10::DeviceIndex) { fail("c10::cuda::set_device()"); }
+
+void device_synchronize() { fail("c10::cuda::device_synchronize()"); }
+
+}  // namespace c10::cuda


@vanbasten23 this is the fallback implementation.

ysiraichi · 2024-06-24T17:24:36Z

test/cpp/BUILD

@@ -137,6 +142,7 @@ ptxla_cc_test(
            ":torch_xla_test",
            "//torch_xla/csrc/runtime:metrics",
            "//torch_xla/csrc:tensor",
+            "//torch_xla/csrc:aten_cuda_functions",


I'm using the fallback implementation for solving the undefined references in C++ tests. I think this should be reasonable, since we don't test fallback on C++ tests.

ysiraichi · 2024-06-24T23:27:30Z

@will-cromar I'm having a hard time figuring out how to make this PR work with CI. Specifically: compile + run fallback operations test (at test_ops.py).

Context: I'm calling a few PyTorch CUDA functions inside a function in aten_cpu_fallback.cpp. The implementation of these functions live in libc10_cuda.so.

Problem: In the CI action we compile PyTorch/XLA, we actually compile PyTorch and PyTorch/XLA without CUDA support. In other words, libc10_cuda.so is not created.

When we try to import torch_xla in that same CI action, it fails because _XLAC has undefined references to the CUDA functions
We can't conditionally compile (i.e. #ifdef XLA_CUDA) the CUDA functions, since it would mean CUDA OpenXLA fallback never gets compiled

Proposed Solution: have 2 libraries: _XLAC_cpu (no CUDA OpenXLA fallback) and _XLAC_cuda.

Conditionally import either of them, depending on whether PyTorch was compiled with support for CUDA
Create an alias like so: import _XLAC_cpu as _XLAC for backwards compatibility

I know this is not a pretty solution, so do you have any suggestions?

will-cromar · 2024-06-25T00:16:40Z

Hey @ysiraichi, I'll spend some more time going over this PR tomorrow to try to understand it better.

We were just preparing to remove the separate GPU variant of the main torch_xla package by moving the GPU runtime implementation to a PJRT plugin. PyPI doesn't support any sort of platform tag that would let us release separate stable TPU and GPU variants of the main package. We need to figure out how to build one variant of the torch_xla package so everyone can just pip install torch_xla.

Most of the team that is building from source is doing so on TPUs realistically, so it is a nice convenience to not have to build the CUDA version of PyTorch first. Obviously adding the CUDA torch build to the critical path on the CI will be a significant overhead as well. But if we can use a pre-built PyTorch package somehow, I actually don't mind if we use the regular CUDA torch package as a build dependency, since my main concern is how slow that build is. cc @JackCaoG since we've talked about this possibility a few times but never had a good enough reason to add this option

I don't fully understand after skimming the PR why we need libc10_cuda at build time. Can that be dynamically loaded as needed?

ysiraichi · 2024-06-25T14:11:23Z

I don't fully understand after skimming the PR why we need libc10_cuda at build time. Can that be dynamically loaded as needed?

It can be loaded at runtime. However, it can't be loaded conditionally. At least, not like this.

Loading conditionally ("as needed") was, in fact, the solution that I was proposing. We could have a separate library with a phony implementation of these CUDA functions. Then, import it only if we are in an environment where PyTorch has no CUDA support.

Let me have a first implementation. We can remove it, if that's not what we want.

ysiraichi · 2024-06-27T13:59:18Z

I have worked on this for a while, now, trying a bunch of things. Unfortunately, none of them worked. Here's the current state of things:

What I tried:

Created a new Python library _XLAC_cuda_functions.so that holds the definition for the c10::cuda functions I need
- Idea: introduce a definition to those c10::cuda functions whenever PyTorch doesn't have CUDA support
Modified the torch_xla/__init__.py so that we import this library if not torch.cuda.available()
- If CUDA is available, we rely on import torch to load libc10_cuda.so, which brings definition to c10::cuda functions

What is happening:

Even though I'm able to import the new _XLAC_cuda_functions.so library, I'm still getting undefined reference for c10::cuda functions when import torch_xla is called

I'm not sure why this is not working given that:

$ nm -CD _XLAC.cpython-310-x86_64-linux-gnu.so | grep c10::cuda
                 U c10::cuda::set_device(signed char)
                 U c10::cuda::current_device()
                 U c10::cuda::device_synchronize()

$ nm -CD _XLAC_cuda_functions.cpython-310-x86_64-linux-gnu.so | grep c10::cuda
000000000002c0b1 T c10::cuda::set_device(signed char)
000000000002c09e T c10::cuda::current_device()
000000000002c0cd T c10::cuda::device_synchronize()

# This works!
$ LD_PRELOAD=./_XLAC_cuda_functions.cpython-310-x86_64-linux-gnu.so python -c "import torch_xla"

# This doesn't work...
$ python -c "import _XLAC_cuda_functions; import torch_xla"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "xla/torch_xla/__init__.py", line 11, in <module>
    import _XLAC
ImportError: xla/_XLAC.cpython-310-x86_64-linux-gnu.so: undefined symbol: _ZN3c104cuda14current_deviceEv

@JackCaoG @vanbasten23 @lezcano @will-cromar
Any thoughts?

isuruf · 2024-06-27T14:28:00Z

python imports the libraries with RTLD_LOCAL which means the symbols from _XLAC_cuda_functions are not added to the global symbol table. You need to set RTLD_GLOBAL before importing _XLAC_cuda_functions.

import sys, os
prev = sys.getdlopenflags()
sys.setdlopenflags(prev | os.RTLD_GLOBAL)
import _XLAC_cuda_functions
sys.setdlopenflags(prev)

import torch_xla

vanbasten23 · 2024-07-01T23:54:20Z

torch_xla/csrc/aten_cuda_functions.cpp

+
+namespace c10::cuda {
+
+c10::DeviceIndex current_device() { fail("c10::cuda::current_device()"); }


This mean, if pytorch is built without CUDA, then these phony definition will be used. Without it, doing import torch_xla will fail with something like ImportError: xla/_XLAC.cpython-310-x86_64-linux-gnu.so: undefined symbol: _ZN3c104cuda14current_deviceEv?

vanbasten23 · 2024-07-01T23:56:35Z

BUILD

+cc_binary(
+    name = "_XLAC_cuda_functions.so",
+    copts = [
+        "-fopenmp",


I wonder how the copts and linkopts are determined

To be honest, I just copied them from _XLAC. I guess I could get rid of them, though. What do you think?

Well, if it works now, feel free to keep it :p

vanbasten23 · 2024-07-01T23:58:12Z

torch_xla/__init__.py

+if not torch.cuda.is_available():
+  # Load _XLAC_cuda_functions to RTLD_GLOBAL, so that it can be used by _XLAC.
+  flags = sys.getdlopenflags()
+  sys.setdlopenflags(flags | os.RTLD_NOW | os.RTLD_GLOBAL)


why use os.RTLD_NOW to perform all necessary relocations when dlopen is called, as I don't see in isuruf's example?

Internally we discussed that it would be better, just to be safe. That's because dlopen needs one of RTLD_NOW or RTLD_LAZY.

vanbasten23 · 2024-07-01T23:59:43Z

torch_xla/__init__.py

 import tempfile
 import warnings

 import torch
+
+if not torch.cuda.is_available():
+  # Load _XLAC_cuda_functions to RTLD_GLOBAL, so that it can be used by _XLAC.


can you point me to the place where _XLAC_cuda_functions is used by _XLAC?

oh I guess you mean the phone definition will be made available when we do import _XLAC below

Yes. Exactly.

vanbasten23 · 2024-07-02T00:01:33Z

torch_xla/__init__.py

+  import _XLAC_cuda_functions
+
+  # Then, restore the original flags.
+  sys.setdlopenflags(flags)


Do you know why we need to restore the original flags?

I'd guess that, in general, we don't really want to load things and make them available globally (i.e. some encapsulation for loaded functions).

vanbasten23 · 2024-07-02T00:10:12Z

torch_xla/csrc/aten_cpu_fallback.cpp

+  // device.
+  //
+  // This variable is updated over the course of 'to_cuda' calls.
+  c10::DeviceIndex common_device = -1;


I'm confused about the common_device. Do we ever change the value after this line?

Would the original tgt_device be easier to understand?

We do change inside to_cuda function. There, we check the device of every XLA tensor, so that we are able to synchronize the computation later.

Would the original tgt_device be easier to understand?

Since they have different types, and are used in different ways, I thought that it would be a bit confusing to name it tgt_device.

I can see your point. Maybe add some comment such as: common_device refers to the device that all tensors should be on; Ideally, all the tensors should be on the same device. Wdyt?

Just did that.

// Common device for all XLA tensors. // // CUDA OpenXLA fallback is supported only when all XLA tensors live in // the same XLA device. This field should be updated and checked every // time we convert an XLA tensor argument into a CUDA tensor. c10::Device common_device;

vanbasten23 · 2024-07-02T02:39:21Z

torch_xla/csrc/aten_cpu_fallback.cpp

+        opt_tensors[idx] = cuda_tensors[i];
+      }
+      (*stack)[arguments_begin + idx] = c10::IValue(opt_tensors);
+    }


In the cpu implementation, there is a

else if (ivalue.isDevice()) { tgt_device = ivalue.toDevice(); (*stack)[arguments_begin + idx] = c10::IValue(c10::Device(kCPU)); }

why don't we need it?

Right. I thought we didn't need it, since we would always be on XLA. But, I guess it's important to have it just to be safe.

Done. Let me know what you think.

vanbasten23 · 2024-07-02T02:43:40Z

torch_xla/csrc/aten_cpu_fallback.cpp

+  // If any input tensors are mutable aliases, we need to
+  // directly copy the updated data on the CUDA tensors back to the original
+  // inputs.
+  for (const auto i : c10::irange(tensor_args_indices.size())) {


Does torch_xla has a concept of mutable aliases?
Also, do you know if there is a specific test cases for step3?

It doesn't. The functional layer abstracts it for PyTorch/XLA. That's why we have to propagate the results back to the input arguments that were mutated.

Also, do you know if there is a specific test cases for step3?

Not sure we have those in PyTorch/XLA. For that, we would need an operation that mutates at least one of the inputs, e.g. operations that have the out parameter.

vanbasten23 · 2024-07-02T02:47:13Z

Mostly LGTM with minor comments.

Amazing work!

ysiraichi · 2024-07-02T15:52:57Z

torch_xla/csrc/aten_cpu_fallback.cpp

+struct DeviceInfo {
+  DeviceInfo(c10::Device device, c10::DeviceIndex i = -1)
+      : common_device(device), index(i) {}
+
+  // Synchronizes the CUDA device being used by PyTorch.
+  void synchronize() {
+    TORCH_CHECK(index != -1, "No defined XLA tensors found for CUDA fallback: ",
+                op.operator_name());
+
+    // Save the current PyTorch device, in case it's not the same as the
+    // recorded tensor device.
+    c10::DeviceIndex current = c10::cuda::current_device();
+    c10::cuda::set_device(index);
+    c10::cuda::device_synchronize();
+    c10::cuda::set_device(current);
+  }
+
+  // Common device for all XLA tensors.
+  //
+  // CUDA OpenXLA fallback is supported only when all XLA tensors live in
+  // the same XLA device. This field should be updated and checked every
+  // time we convert an XLA tensor argument into a CUDA tensor.
+  c10::Device common_device;
+
+  // CUDA device index where the tensors live in.
+  //
+  // This is used for synchronizing the device where the fallback operation
+  // was called. This should ensure completion of the CUDA computation, in
+  // order to be used by another XLA computation.
+  c10::DeviceIndex index;
+};


This struct helps making sure only one device is used in the CUDA OpenXLA fallback.

XLA tensor devices are checked against common_device

CUDA device index is also checked just to be safe

ysiraichi · 2024-07-03T21:58:59Z

Running TorchBench with --verify flag, showed no new accuracy problems on inference (the flag doesn't work with training). Therefore, I will go on and merge this PR.

ysiraichi added the xla:gpu label Jun 20, 2024

ysiraichi requested review from vanbasten23, bdhirsh and JackCaoG June 20, 2024 13:56

vanbasten23 reviewed Jun 20, 2024

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ysiraichi mentioned this pull request Jun 24, 2024

Failing Torchbench Models: tracking issue #5932

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ysiraichi mentioned this pull request Jun 24, 2024

OpenXLA CUDA fallback: Tracking Issue #7342

Open

2 tasks

ysiraichi added 14 commits July 1, 2024 00:09

Fix lint.

1304dc7

Fix compilation.

e4642f9

Address reviews.

2f32ba8

Create fallback implementation for tests.

8c0dddd

Fix lint issue.

9c2e28b

Fix test builds and add libc10_cuda.so dependency.

ea33a74

Fix C++ test dependencies.

0ef4e1b

Build a second library for conditionally loading CUDA functions.

d035935

Add Python module initialization.

4129e86

Fix lib and dlopen options.

9a22be7

Fix lint issues.

7d5063a

Skip CUDA fallback tests on CPU envs.

75ffb01

Fix lint issues.

0dd29da

Clean up.

2c2b165

ysiraichi force-pushed the ysiraichi/cuda-fallback branch from 70b2146 to 2c2b165 Compare July 1, 2024 03:09

vanbasten23 reviewed Jul 1, 2024

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vanbasten23 reviewed Jul 2, 2024

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Make sure only one XLA device is used.

4686238

ysiraichi commented Jul 2, 2024

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Fix compilation issues.

8d5fe8f

vanbasten23 approved these changes Jul 3, 2024

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ysiraichi merged commit c782e0d into master Jul 3, 2024
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		namespace c10::cuda {

		c10::DeviceIndex current_device() { fail("c10::cuda::current_device()"); }

Introduce CUDA OpenXLA fallback. #7318

Introduce CUDA OpenXLA fallback. #7318

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ysiraichi commented Jun 20, 2024

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