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b3bda94393
Summary: Had a bunch of merged commits that shouldn't have been there, reverted them to prevent conflicts. Lots of new features, highlights listed below. **Overall:** - Enables pointwise fusion, single (but N-D) broadcast -- pointwise fusion, single (but N-D) broadcast -- pointwise -- single (but N-D) reduction fusion. **Integration:** - Separate "magic scheduler" logic that takes a fusion and generates code generator schedule - Reduction fusion scheduling with heuristics closely matching eagermode (unrolling supported, but no vectorize support) - 2-Stage caching mechanism, one on contiguity, device, type, and operations, the other one is input size->reduction heuristic **Code Generation:** - More generic support in code generation for computeAt - Full rework of loop nest generation and Indexing to more generically handle broadcast operations - Code generator has automatic kernel launch configuration (including automatic allocation of grid reduction buffers) - Symbolic (runtime) tilling on grid/block dimensions is supported - Simplified index generation based on user-defined input contiguity - Automatic broadcast support (similar to numpy/pytorch semantics) - Support for compile time constant shared memory buffers - Parallelized broadcast support (i.e. block reduction -> block broadcast support) Pull Request resolved: https://github.com/pytorch/pytorch/pull/43129 Reviewed By: mrshenli Differential Revision: D23162207 Pulled By: soumith fbshipit-source-id: 16deee4074c64de877eed7c271d6a359927111b2
13 lines
195 B
C++
13 lines
195 B
C++
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#include <torch/csrc/jit/codegen/cuda/kernel.h>
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namespace torch {
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namespace jit {
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namespace fuser {
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void Kernel::print() const {}
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} // namespace fuser
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} // namespace jit
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} // namespace torch
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