Summary:
This PR added more supported operations in CUDA fuser. We are covering major point-wise operations supported in legacy fuser.
In an attempt to adapt to legacy executor:
1. added an naive shape propagation pass on pytorch JIT IR;
2. small refactor on graph partitioning;
3. fallback interpreter execution of fusion group;
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37849
Reviewed By: yf225
Differential Revision: D21444320
Pulled By: soumith
fbshipit-source-id: 712e18ab8497f8d58a07e6f8d200cdab52cf0d74
Summary:
Unrolling support has been added in a way that we get good performing code on GPUs. Not sure how long this link will last but an example of a generated unrolled kernel is:
https://godbolt.org/z/i0uAv3
What can be seen from there is multiple calls of "ld.global.f32" without "ld.store.f32" in between them (and vice versa). This means that we are launching multiple loads that can be run in parallel, as well as multiple stores that can be run in parallel. This can be a crucial optimization for memory bound kernels. This was generally a point of concern in TVM as an attempt of a similar kernel from TVM produces: https://godbolt.org/z/Vu97vG which surrounds load - store pairs in conditional branches preventing the benefits of unrolling.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36435
Reviewed By: ZolotukhinM
Differential Revision: D21024011
Pulled By: soumith
fbshipit-source-id: e852e282fa7a304aba962e1926f756098c011fe0
Summary:
This PR completely refactors the code lowering process from our IR to CUDA. Before we had one giant step that would go from a relatively high level IR straight to CUDA, now we're lowering this first into concepts like ForLoop, IfThenElse, TensorIndex, Allocate. This lowering will allow us to do more complex code lowering like reductions and unrolling. Unrolling will quickly follow this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36199
Reviewed By: dzhulgakov
Differential Revision: D20925220
Pulled By: soumith
fbshipit-source-id: 8f621c694c68a1aad8653e625d7287fe2d8b35dc
Summary:
**Summary:** This PR contains the infrastructure of a new CUDA fuser. This CUDA fuser is based on many of the same principles of TensorExpressions and Halide, however the implementation is ground up. The fusion pass itself is similar to the default CUDA fuser, however, it has undergone some refactoring and is using the new code generation infrastructure. For those who are interested in how the code generation in this PR works, I would recommend reviewing _test/cpp/jit/test_gpu_fusion.cpp_ as well as the long comment section at the beginning of _torch/csrc/jit/codegen/cuda/transform_replay.h_ One of the largest differences between our approach and that of TVM/Halide, is the concept of "TensorView". TensorView from a high level should be thought of similarly to how we think of working with Tensors in PyTorch. It's an N-D object which can undergo transformations that change its dimensionality. Dimensionality changes are done through the operations split/merge/reorder/computeAt. These transformations are similar to split/fuse/reorder/compute_at of TVM, they modify how a tensor is iterated over to generate GPU code. Interestingly, in our scheme these transformations are applied to tensors and only impact how that tensor is generated.
**Warning:** This PR is purposefully not feature complete with the current fuser. We wanted to separate out the infrastructure from the fusion capabilities. Once in, smaller incremental PRs will be submitted to expand capabilities of the fuser.
**Short term goals:**
Parity with current CUDA fuser (including performance):
- Dynamic shapes (no recompilation)
- Implicit handling of braodcast (broadcasted tensors are treated as tensors of the braodcasted size in the generated code)
- Dropout
**Mid-term goals:**
- Transposes fused with pointwise operations where transpose involves only 2 axes (across the fused operation).
- 1-D reductions fused with pointwise operations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34785
Reviewed By: ZolotukhinM
Differential Revision: D20650977
Pulled By: soumith
fbshipit-source-id: ee39c95a880e1b9822e874ed4cc180971572bf63
