https://github.com/pytorch/pytorch/pull/152708 expanded support of `get_estimated_runtime` to many more types of `SchedulerNodes`. This caused an increase in compile time because we're always calling `get_estimated_runtime` to populate the metrics table. This PR adds a flag for this logging, which reduces the instruction count by 8%. Long term, we should probably merge metrics.py with TORCH_LOGS/tlparse (suggestion from @xmfan).
Update: added support for TORCH_LOGS for the metrics logging.
Test Plan:
mm_loop.py and many existing tests cover.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153506
Approved by: https://github.com/eellison
move benchmarking out of `torch._inductor.runtime.runtime_utils` and into `torch._inductor.runtime.benchmarking`, and prefer this path over directly accessing Triton's benchmarking
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132827
Approved by: https://github.com/eellison
Inductor currently materialize a large sparse matrix in the backward pass for CrossEntropyLoss and load that to compute gradients of Softmax input. If we could fuse the sparse matrix computation to the consumer sides, we gonna have both perf and memory usage wins.
The Fx graph snippets that construct this aforementioned sparse matrix looks like:
```
full_default_3: "bf16[32768, 50257]" = torch.ops.aten.full.default([32768, 50257], 0, dtype = torch.bfloat16, layout = torch.strided, device = device(type='cuda', index=0), pin_memory = False)
scatter: "bf16[32768, 50257]" = torch.ops.aten.scatter.value(full_default_3, 1, where_2, -1.0); full_default_3 = where_2 = None
```
Leveraging the following observations:
- the scatter is applied upon a all zero (or more generally a const tensor)
- the index tensor for the scatter has a single element on the scatter dimension. In this case it's the label tensor
allow us to lower this 'scatter_upon_const_tensor' pattern to a pointwise kernel that can be easily fused with downstream kernels:
```
def inner_fn(idx):
selector_idx = list(idx)
selector_idx[dim] = 0 # can do this since the index tensor has a single element on the scatter dimension
selector = selector_loader(selector_idx)
return ops.where(
selector == ops.index_expr(idx[dim], torch.int64),
ops.constant(val, dtype),
ops.constant(background_val, dtype),
)
```
## Test result on microbenchmark
For the microbenchmark added as `test_cross_entropy_loss`, we improve latency from 47.340ms to 42.768ms, memory footprint from 10.524GB to 7.227GB on A100. (on H100, we improve latency from 27.54ms to 23.51ms, memory footprint from 10.574GB to 7.354GB).
The saving matches the back-of-envelope calculation. We avoid storing a BF16 tensor with shape [30K, 50K] which is about 3GB in size. On A100, avoid loading and storing such a tensor can roughly save 3GB x 2 / 1.5TBGS = 4ms
## Test result on llm.c
We also test this on llm.c and the saving is much larger especially for memory footprint. The reason is due to autotuning that allocates extra memory for benchmarking. (Check https://github.com/pytorch/pytorch/issues/129258 and https://github.com/pytorch/pytorch/pull/129399 for more details).
For llm.c PyTorch implementation on A100, we improve from
171K tokens/s , 33.6G peak memory usage to
180K tokens/s, 18.6G peak memory usage. (A **45%** saving of peak memory)
## Test on PyTorch 2.0 Dashboard
The optimization is quite general especially for transformers. We tested this on PyTorch2.0 dashboard. Here is the [result](https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Mon%2C%2017%20Jun%202024%2018%3A07%3A51%20GMT&stopTime=Mon%2C%2024%20Jun%202024%2018%3A07%3A51%20GMT&granularity=hour&suite=torchbench&mode=training&dtype=amp&lBranch=gh/shunting314/158/head&lCommit=c62c55e29c65497d495217b6574bb36b0c4da7d4&rBranch=main&rCommit=0d25f096c1beaf8749932a3d6083ad653405ed71).
TLDR, for Huggingface benchmark suite, we get **6%** geomean perf improvement and **10%** geomean memory footprint improvement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129043
Approved by: https://github.com/jansel, https://github.com/Chillee
