| commit | 307485737f46a76c97aefb51b0fc3cd264c2bb94 | [log] [tgz] |
|---|---|---|
| author | Deven Desai <deven.desai.amd@gmail.com> | Thu Jun 04 18:03:57 2020 +0000 |
| committer | Deven Desai <deven.desai.amd@gmail.com> | Thu Jun 04 18:03:57 2020 +0000 |
| tree | 6d7b30c76a7c075c84c558deaa46f890d7d36762 | |
| parent | 0ca0c442c0c9a17aa5326ccfdd7b24b86fc49853 [diff] |
[ROCm] Fix for the build error in ROCm CSB - 200604 The folllowing commit introduced build errors on the ROCm platform https://github.com/tensorflow/tensorflow/commit/cf30d41ded3b5adae5c3bf3c7e05ce8bb7066a9c Those initial build errors are addressed by the following commit, but the build is still broken https://github.com/tensorflow/tensorflow/commit/a1e26e4298aac041e56c856c40bcb0c29fbc3f83 Error we get after the second commit ``` In file included from tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc:24: In file included from ./tensorflow/core/util/gpu_kernel_helper.h:25: ./tensorflow/core/util/gpu_device_functions.h:754:10: error: no matching function for call to 'atomicMax' return atomicMax(ptr, value); ^~~~~~~~~ tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc:61:5: note: in instantiation of function template specialization 'tensorflow::GpuAtomicMax<long long, long long>' requested here GpuAtomicMax(out, val); ^ tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc:118:9: note: in instantiation of member function 'tensorflow::(anonymous namespace)::LeftUpdate<long long, tensorflow::scatter_nd_op::UpdateOp::MAX>::operator()' requested here update(out + i + si, ldg(updates + (index * slice_size + si))); ^ tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc:156:33: note: in instantiation of function template specialization 'tensorflow::ScatterNdOpKernel<long long, int, tensorflow::scatter_nd_op::UpdateOp::MAX, 1>' requested here TF_CHECK_OK(GpuLaunchKernel(ScatterNdOpKernel<T, Index, op, IXDIM>, ^ /opt/rocm-3.3.0/hip/include/hip/hcc_detail/hip_atomic.h:178:5: note: candidate function not viable: no known conversion from 'long long *' to 'int *' for 1st argument int atomicMax(int* address, int val) ^ /opt/rocm-3.3.0/hip/include/hip/hcc_detail/hip_atomic.h:184:14: note: candidate function not viable: no known conversion from 'long long *' to 'unsigned int *' for 1st argument unsigned int atomicMax(unsigned int* address, unsigned int val) ^ /opt/rocm-3.3.0/hip/include/hip/hcc_detail/hip_atomic.h:190:20: note: candidate function not viable: no known conversion from 'long long *' to 'unsigned long long *' for 1st argument unsigned long long atomicMax( ^ ``` This PR fixes the build by adding the following type specializations for the `long logn` type * `GpuAtomicCasHelper` * `GpuAtomicMax` * `GpuAtomicMin`
Documentation |
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
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A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add --upgrade flag to the above commands.
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| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU |  | PyPI |
| Linux GPU |  | PyPI |
| Linux XLA |  | TBA |
| macOS |  | PyPI |
| Windows CPU |  | PyPI |
| Windows GPU |  | PyPI |
| Android |  |  |
| Raspberry Pi 0 and 1 |  | Py3 |
| Raspberry Pi 2 and 3 |  | Py3 |
| Build Type | Status | Artifacts |
|---|---|---|
| Linux AMD ROCm GPU Nightly | Nightly | |
| Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
Learn more about the TensorFlow community and how to contribute.