as titled, this PR improves the device selection logic when user did not
set the device before calling the DeviceMesh constructor, as a device
manager, DeviceMesh should try to set the device for users in a good
way.
The behavior of set_device before:
* If user call init_process_group to init a world process group, we assume user already called set_device and we don't set the device for the user
* If user does not init a world process group by themselves, we init a world process group for the user and follow a heuristic to set the device.
This is ok but sometimes the set_device heuristic wouldn't work well (i.e. if user use TORCH_CUDA_VISBILE_DEVICES
So this PR improves the device selection logic to:
* If the default cuda context is initialized by the time we init DeviceMesh, then we assume user must called some cuda operation before therefore must have selected the device by themselves
* If not the above, then we check if envvars have "LOCAL_RANK" and "WORLD_SIZE" from the launcher (i.e. torchrun), if so, we use "LOCAL_RANK" to set the device for the current process, which is a very standard practice. (This solves the TORCH_CUDA_VISBILE_DEVICES issue)
* If not above, then we throw warning to users about situation, and fallback to the old heuristic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150897
Approved by: https://github.com/tianyu-l
ghstack dependencies: #150898
This PR adds a tensor LR variant for the CPU Adagrad(fused=True).
I copied the behavior from the tensor LR variant of CPU Adam(fused=True), where the `lr.item()` is cast to a double and passed in the default function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153078
Approved by: https://github.com/janeyx99
In #117066, shutdown of the rendezvous was added if a worker shuts down. This is incorrect, because the rendezvous is actually shutdown in [this file](fa6f9eb2be/torch/distributed/launcher/api.py (L290)) but should not be shutdown if a signal is received. See also [this pull request](https://github.com/pytorch/pytorch/pull/67749).
#124819 then tried to remediate the situation by fixing the faulty shutdown for the restart case. But this is only triggered if the agent restarts the training, but not if the shutdown of the rendezvous happened before.
Removing both these changes restores the original behavior. The rendezvous should only be shutdown if a run completes or fails, not for a single worker leaving.
Fixes#150916Fixes#147064
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152525
Approved by: https://github.com/kiukchung
Summary: I forgot to remove this unused field in D73809989.
Test Plan: `buck test 'fbcode//mode/opt' fbcode//caffe2/test:fbonly -- --exact 'caffe2/test:fbonly - test_compilation_metrics_logger_in_sync (caffe2.test.fb.test_fb.TestFBOnly)'`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153413
Approved by: https://github.com/c00w
Adds create_graph support if you don't compile or compile only with torch.compile(backend="eager").
Using a backend that uses AOTDispatch produces a post-dispatch AOT backward, where its double backward will be silently incorrect if the forward trace involved any ops that are not composite implicit.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153222
Approved by: https://github.com/jansel
ghstack dependencies: #153193
Toggling on `torch._dynamo.config.compiled_autograd = True` was erroring export (optimize_assert didn't have `rebuild_ctx` defined). Separately add a way to `rebuild_ctx` for `optimize_assert` since it is a public API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153193
Approved by: https://github.com/jansel
Support xpu tf32 matmul using torch.bachend.mkldnn.allow_tf32, we will discuss in future if we need a new api to control matmul only
~~Support xpu tf32 matmul using torch.set_float32_matmul_precision. For conv, check https://github.com/pytorch/pytorch/pull/137570
We decide not following torch.backends.cuda.matmul.allow_tf32 because this API actually calls setAllowTF32CuBLAS to set matmul_precison to high. We also avoid other related tf32 changes (i.e. in inductor) by not introducing new API.~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144240
Approved by: https://github.com/EikanWang
By invalidating all variable created during the loop except for the context of iterator_cache, as storage can be done inside reduction loop and clear `IteratorRangeEntry` codegen cache.
Which results in the following kernel for `x / x.sum()` if x size is 2048 and max thread group size is 1024
```metal
[[max_total_threads_per_threadgroup(1024)]]
kernel void generated_kernel(
device half* out_ptr1,
constant half* in_ptr0,
uint2 thread_pos [[thread_position_in_grid]],
uint2 group_pos [[thread_position_in_threadgroup]]
) {
auto xindex = thread_pos.x;
auto r0_index = thread_pos.y;
threadgroup float tmp_acc_0[32];
float tmp_acc_1 = 0;
for(auto r0_0_cnt = 0; r0_0_cnt < 2; ++r0_0_cnt) {
int r0_0 = 2 * r0_index + r0_0_cnt;
auto tmp0 = static_cast<float>(in_ptr0[r0_0]);
tmp_acc_1 += tmp0;
}
auto tmp1 = c10:🤘:threadgroup_sum(tmp_acc_0, tmp_acc_1, r0_index * 1, 1024);
for(auto r0_0_cnt = 0; r0_0_cnt < 2; ++r0_0_cnt) {
int r0_0 = 2 * r0_index + r0_0_cnt;
auto tmp2 = static_cast<float>(in_ptr0[r0_0]);
auto tmp3 = tmp2 / tmp1;
out_ptr1[r0_0] = static_cast<half>(tmp3);
}
}
```
Fixes compilation report reported while running `GPUTests.test_pattern_matcher_multi_user_mps` and `GPUTests.test_weight_norm_bwd_mps`
Fixes https://github.com/pytorch/pytorch/issues/152155
Though inductor tests are still failing, need to keep refining the variable invalidation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153362
Approved by: https://github.com/manuelcandales, https://github.com/dcci, https://github.com/jansel
Summary:
To add PT2 information to memory snapshot we piggyback off of the Kineto implementation using record_function similar to adding the user annotations. To do this we add the following:
1. Stack implementation that we instantiate to keep track of which compile context stack we are currently in (top element of the stack). The stack will be per device and thread-local since different threads of a process can be in different compile contexts at a given time. For this reason, we do not need to add mutexes to our stack impl since no two threads will touch a given stack
2. RecordFunction hooks to properly pipe the correct events to the compile context stack. These hooks are similar to the annotation ones in the fact that we just register them lazily and DO NOT unregister them. This is done out of convenience. In the future, we should save the handles and unregister them to minimize overhead after profiling is finished. As of now, we are registering this at the FUNCTION scope which is wide; however, we treat any function that does not start with "Torch-Compiled Region" as a no-op so we anticipate the difference in performance to be negligible during and after profiling. We also hide this feature behind a flag set to off on default so existing jobs will be unaffected
3. Piping for compile context to pickle output
Test Plan:
In D74039793, we add CompileContext to the visualizer and we see the following {F1977654658}
Differential Revision: D74028214
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152707
Approved by: https://github.com/eqy
Summary:
Previously, we only support non-scaling quantization, which may lead to overflow, here we support scaling quantization, and set it as the default version.
Here, we quantize activation nodes based on the size_in_mb, the default value is 100, i.e., as long as the node has at least 100MB size, we will quantize it.
Test Plan:
### how to enable
```
torch._inductor.config.post_grad_fusion_options = {
"activation_quantization_aten_pass": {
"quant_type": "torch.float8_e5m2", -> default is this type to quantize, you can change the type
"use_scaling": False, -> default is False, if you want to use scaling verison, set it to True
"size_in_mb": 0.0, -> default is 100, you can tune the value.
"exclude_primals": False, -> whether want to exclude quantize parameters, default is False
"allowed_dtypes": "torch.float16;torch.bfloat16;torch.float32", -> dtype you consider to quant, use ";" to separate, default is torch.bfloat16
},
}
```
### toy model
```
buck2 run mode/opt //scripts/qyz/autoac:quantization
```
```
Epoch [80/200], Loss: 19227.2109
Epoch [100/200], Loss: 1353.5272
Epoch [120/200], Loss: 38630.6758
Epoch [140/200], Loss: 6239.9155
Epoch [160/200], Loss: 6039.1567
Epoch [180/200], Loss: 3994.3569
Epoch [200/200], Loss: 146.3966
```
Differential Revision: D73015996
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151770
Approved by: https://github.com/Mingming-Ding
The function signature of fused_scaled_matmul_reduce_scatter was changed. This PR fixes the function signature. However when scatter_dim is 1, the two outputs are not close. We need a followup on this.
Another followup is to change fused_scaled_matmul_reduce_scatter to make those newly added arguments optional. Users shouldn't need to these arguments if they don't flatten the inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153286
Approved by: https://github.com/kwen2501
