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internal review #6

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internal review #6

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Chao1Han
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Fixes #ISSUE_NUMBER

auto currentTimepoint = std::chrono::steady_clock::now();
auto timeElapsed = std::chrono::duration_cast<std::chrono::milliseconds>(
currentTimepoint - workStartTime_);
std::chrono::milliseconds opTimeout = std::chrono::milliseconds(60000);
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where do you use it?

@@ -67,17 +67,15 @@ ccl::reduction getXcclReduceOp(const ReduceOp& reduceOp, at::Tensor& input) {
return xcclOps.at(reduceOp);
} catch (const std::out_of_range&) {
switch (reduceOp) {
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No need to switch

: Work(rank, opType, "profilingTitle", inputs),
device_(device),
workStartTime_(std::chrono::steady_clock::now()) {
unsigned char enable_timing = 0;
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If you always set it as 0, then we don't need to keep it, right?

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Yes, Defining this variable serves as a form of annotation, informing reviewers and users that 0 represents the state of enable_timing, which is meaningful.

"Work ran for ",
timeElapsed.count(),
" milliseconds before timing out.");
TORCH_CHECK(false, exceptionMsg)
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TORCH_CHECK(false, exceptionMsg);
abort();

} // namespace

static std::mutex xcclCommDevIdxMapMutex;
static std::unordered_map<std::shared_ptr<xcclComm_t>, int> xcclCommDevIdxMap;
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Those static variables are not used in your code. Please check.

blockingWait_ = getCvarBool(TORCH_XCCL_BLOCKING_WAIT, false);
init();

// Intel oneCCL requires passing CCL_LOCAL_RANK and CCL_LOCAL_SIZE for non-MPI
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More comment for why we use LOCAL_RANK and LOCAL_WORLD_SIZE.

std::shared_ptr<xcclComm_t> ProcessGroupXCCL::getXCCLComm(
const std::string& deviceKey,
at::Device& device) {
if (deviceKey.empty()) {
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C10_THROW_ERROR_WITH

devXCCLCommMap_.emplace(deviceKey, XCCLComm);
}

xcclStreamsMap_.emplace(deviceKey, std::move(stream));
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so xcclEventsMap does not needed?

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restore it

PreProcess pre,
PostProcess post,
OpType opType) {
using traits = function_traits<Fn>;
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which collective need attribute as a must?

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Yes, allgather meet build error

for (const auto i : c10::irange(inputs.size())) {
c10::xpu::XPUCachingAllocator::recordStream(
inputs[i].storage().data_ptr(), stream);
fn(inputs[i], outputs[i], attr, *comm, stream);
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add comment for output record stream.

false, "ProcessGroupXCCL::WorkXCCL::isSuccess not implemented");
}

void abort() override {
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abort here?


bool isCompleted() override;

bool isSuccess() const override {
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remove

case ReduceOp::BXOR:
C10_THROW_ERROR(ValueError, "Cannot use ReduceOp.BXOR with NCCL");
C10_THROW_ERROR(
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don't change NCCL now.


c10::impl::VirtualGuardImpl impl(device.type());
c10::Stream stream = impl.getStream(device);
sycl::queue& q = c10::xpu::XPUStream(stream).queue();
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It's a big bug to use current stream as communication stream.

int rank,
OpType opType,
const std::optional<std::vector<at::Tensor>>& inputs)
: Work(rank, opType, "profilingTitle", inputs),
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Need change

@@ -126,6 +131,13 @@ class TORCH_API ProcessGroup : public torch::CustomClassHolder {
return backendType_;
};

inline bool backendSupportsSequenceNumbers(BackendType backendType) {
if (backendType == BackendType::GLOO || backendType == BackendType::NCCL ||
backendType == BackendType::XCCL || backendType == BackendType::UCC)
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Do you make sure that we need to support this sequence number?

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Sequence number used by RECORD_PARAM_COMMS. so we need it

@@ -180,7 +181,8 @@ def skip_if_lt_x_gpu(x):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
if torch.cuda.is_available() and torch.cuda.device_count() >= x:
if (torch.cuda.is_available() and torch.cuda.device_count() >= x) or \
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Don't use if for accelerator related check

Chao1Han pushed a commit that referenced this pull request Dec 16, 2024
See pytorch#140725 (comment)
Running `torch.mps.synchronize()` after metal kernel resulted in infinite wait inside `[_MTLCommandBuffer waitUntilCompleted]`
```
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGSTOP
  * frame #0: 0x00000001aa919084 Metal`pthread_cond_wait + 12
    frame #1: 0x00000001aa78b1b4 Metal`-[_MTLCommandBuffer waitUntilCompleted] + 84
    frame #2: 0x00000001032bf358 libtorch_python.dylib`torch::mps::MPSModule_deviceSynchronize(_object*, _object*) + 40
    frame #3: 0x0000000100e94c20 Python`cfunction_vectorcall_NOARGS + 100
    frame #4: 0x0000000100e389b8 Python`PyObject_Vectorcall + 92
    frame #5: 0x0000000100f61e38 Python`_PyEval_EvalFrameDefault + 19040
    frame #6: 0x0000000100f5d180 Python`PyEval_EvalCode + 200
    frame #7: 0x0000000100fcd1a4 Python`run_eval_code_obj + 104
    frame #8: 0x0000000100fccbe4 Python`run_mod + 168
    frame #9: 0x0000000100fcb518 Python`pyrun_file + 164
    frame #10: 0x0000000100fca854 Python`_PyRun_SimpleFileObject + 256
    frame pytorch#11: 0x0000000100fca4e8 Python`_PyRun_AnyFileObject + 80
    frame pytorch#12: 0x0000000100ff2028 Python`pymain_run_file_obj + 164
    frame pytorch#13: 0x0000000100ff1ce4 Python`pymain_run_file + 72
    frame pytorch#14: 0x0000000100ff0f74 Python`Py_RunMain + 988
    frame pytorch#15: 0x0000000100ff1564 Python`pymain_main + 304
    frame pytorch#16: 0x0000000100ff1604 Python`Py_BytesMain + 40
    frame pytorch#17: 0x000000019f630274 dyld`start + 2840
```

Pull Request resolved: pytorch#141296
Approved by: https://github.com/huydhn
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2 participants