Rohan Mukherjee
65cd19facf
* added test infrastructure for frozen TF2 models * linting with black * removing some comments * change in comment in sequential test * addressed the comments * refactored to place vmobj_to_list in a common file * Added helper function in python/tvm/relay/testing/tf.py Co-authored-by: David Huang <davhuan@amazon.com> Co-authored-by: Rohan Mukherjee <mukrohan@amazon.com> Co-authored-by: Xiao <weix@amazon.com> * Refactor tf according to CI error Co-authored-by: David Huang <davhuan@amazon.com> Co-authored-by: Rohan Mukherjee <mukrohan@amazon.com> Co-authored-by: Xiao <weix@amazon.com> * Added docstring Co-authored-by: David Huang <davhuan@amazon.com> Co-authored-by: Rohan Mukherjee <mukrohan@amazon.com> Co-authored-by: Xiao <weix@amazon.com> * removing print Co-authored-by: David Huang <davhuan@amazon.com> Co-authored-by: Xiao <weix@amazon.com>
Open Deep Learning Compiler Stack
Documentation | Contributors | Community | Release Notes
Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.
License
© Contributors Licensed under an Apache-2.0 license.
Contribute to TVM
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
Acknowledgement
We learned a lot from the following projects when building TVM.
- Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
- Loopy: use of integer set analysis and its loop transformation primitives.
- Theano: the design inspiration of symbolic scan operator for recurrence.