jiebi/CodeConvo huggingface dataset:
int collapseIndicesDim = indicesInfo.collapseDims(dim);                 int64_t inputSlices = 1;                                               <del> int64_t topKSlices = 1;                                                for (int i = 0; i < numDims; ++i) {                                    </del> <ins> for (int i = 0; i < inputInfo.dims; ++i) {                             </ins> inputSlices *= inputInfo.sizes[i];                                   <ins> }                                                                      int64_t topKSlices = 1;                                                for (int i = 0; i < topKInfo.dims; ++i) {                              </ins> topKSlices *= topKInfo.sizes[i];                                     }
] if WITH_CUDNN: main_libraries += ['cudnn'] <del> library_dirs.append(CUDNN_LIB_DIR) </del> <ins> library_dirs.insert(0, CUDNN_LIB_DIR) </ins> # NOTE: these are at the front, in case there's another cuDNN in CUDA path include_dirs.insert(0, CUDNN_INCLUDE_DIR) if not IS_WINDOWS:
int batch_size = THTensor_(size)(input, 0); THTensor_(resize1d)(output, batch_size); <ins> int invalid_target = -1;  // We cannot throw an exception inside omp parallel </ins> int i; #pragma omp parallel for private(i) for (i = 0; i < batch_size; i++) { int cur_target = THTensor_fastGet1d(target, i) - TH_INDEX_BASE; <del> if (cur_target == ignore_index) { THTensor_fastSet1d(output, i, 0.0f); continue; </del> <ins> if (cur_target >= 0 && cur_target < n_classes) { if (cur_target == ignore_index) { THTensor_fastSet1d(output, i, 0.0f); continue; } real cur_weight = weights ? THTensor_fastGet1d(weights, cur_target) : 1.0f; THTensor_fastSet1d(output, i, -THTensor_fastGet2d(input, i, cur_target) * cur_weight); } else { THAtomicCompareAndSwap(&invalid_target, -1, cur_target); </ins> } <del> real cur_weight = weights ? THTensor_fastGet1d(weights, cur_target) : 1.0f; THTensor_fastSet1d(output, i, -THTensor_fastGet2d(input, i, cur_target) * cur_weight); </del> <ins> } if (invalid_target >= 0) { THError("Target %d out of bounds", invalid_target); </ins> } return;
res2[i] = max(min_val, min(max_val, res2[i])) self.assertEqual(res1, res2) <ins> out = m1.clone() torch.clamp(m1, min=min_val, max=max_val, out=out) self.assertEqual(out, res1) </ins> res1 = torch.clamp(m1, min=min_val) res2 = m1.clone() for i in iter_indices(res2): res2[i] = max(min_val, res2[i]) self.assertEqual(res1, res2) <ins> torch.clamp(m1, min=min_val, out=out) self.assertEqual(out, res1) </ins> res1 = torch.clamp(m1, max=max_val) res2 = m1.clone() for i in iter_indices(res2): res2[i] = min(max_val, res2[i]) self.assertEqual(res1, res2) <ins> torch.clamp(m1, max=max_val, out=out) self.assertEqual(out, res1) </ins> def test_pow(self): # [res] torch.pow([res,] x)
} } <del> static Tensor dispatch_clamp(const Tensor & self, Scalar min, Scalar max) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp(min, max); } static Tensor dispatch_clamp_min(const Tensor & self, Scalar min) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp_min(min); } static Tensor dispatch_clamp_max(const Tensor & self, Scalar max) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp_max(max); } </del> // The Python clamp() syntax has to be mapped to one of three C++ functions static PyObject * THPVariable_clamp(PyObject* module, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ <del> "clamp(Tensor input, Scalar min=None, Scalar max=None)", </del> <ins> "clamp(Tensor input, Scalar min=None, Scalar max=None, *, Tensor out=None)", </ins> }); <del> ParsedArgs<3> parsed_args; </del> <ins> ParsedArgs<4> parsed_args; </ins> auto r = parser.parse(args, kwargs, parsed_args); if (!r.isNone(1) && !r.isNone(2)) { <del> return THPVariable_Wrap(dispatch_clamp(r.tensor(0), r.scalar(1), r.scalar(2))); </del> <ins> if (!r.isNone(3)) { return wrap(dispatch_clamp(r.tensor(0), r.scalar(1), r.scalar(2), r.tensor(3))); } else { return wrap(dispatch_clamp(r.tensor(0), r.scalar(1), r.scalar(2))); } </ins> } else if (!r.isNone(1)) { <del> return THPVariable_Wrap(dispatch_clamp_min(r.tensor(0), r.scalar(1))); </del> <ins> if (!r.isNone(3)) { return wrap(dispatch_clamp_min(r.tensor(0), r.scalar(1), r.tensor(3))); } else { return wrap(dispatch_clamp_min(r.tensor(0), r.scalar(1))); } </ins> } else if (!r.isNone(2)) { <del> return THPVariable_Wrap(dispatch_clamp_max(r.tensor(0), r.scalar(2))); </del> <ins> if (!r.isNone(3)) { return wrap(dispatch_clamp_max(r.tensor(0), r.scalar(2), r.tensor(3))); } else { return wrap(dispatch_clamp_max(r.tensor(0), r.scalar(2))); } </ins> } else { throw std::runtime_error("At least one of 'min' or 'max' must not be None"); } <ins> Py_RETURN_NONE; </ins> END_HANDLE_TH_ERRORS }
} } <ins> // manual dispatch code for clamp inline Tensor dispatch_clamp(const Tensor & self, Scalar min, Scalar max) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp(min, max); } inline Tensor dispatch_clamp_min(const Tensor & self, Scalar min) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp_min(min); } inline Tensor dispatch_clamp_max(const Tensor & self, Scalar max) { AutoNoGIL no_gil; AutoGPU auto_gpu(self); return self.clamp_max(max); } inline Tensor & dispatch_clamp(const Tensor & self, Scalar min, Scalar max, Tensor result) { AutoNoGIL no_gil; AutoGPU auto_gpu(result); return at::clamp_out(result, self, min, max); } inline Tensor & dispatch_clamp_min(const Tensor & self, Scalar min, Tensor result) { AutoNoGIL no_gil; AutoGPU auto_gpu(result); return at::clamp_min_out(result, self, min); } inline Tensor & dispatch_clamp_max(const Tensor & self, Scalar max, Tensor result) { AutoNoGIL no_gil; AutoGPU auto_gpu(result); return at::clamp_max_out(result, self, max); } </ins> ${py_method_dispatch} }} // namespace torch::autograd
See :func:`torch.ormqr` """) <ins> add_docstr_all('permute', r""" permute(*dims) -> Tensor Permute the dimensions of this tensor. Args: *dims (int...): The desired ordering of dimensions Example: >>> x = torch.randn(2, 3, 5) >>> x.size() torch.Size([2, 3, 5]) >>> x.permute(2, 0, 1).size() torch.Size([5, 2, 3]) """) </ins> add_docstr_all('potrf', r""" potrf(upper=True) -> Tensor
endforeach(flag_var) endif() <del> set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-omit-frame-pointer") set (CMAKE_LINKER_FLAGS_DEBUG "${CMAKE_STATIC_LINKER_FLAGS_DEBUG} -fno-omit-frame-pointer") </del> <ins> set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-omit-frame-pointer -O0") set (CMAKE_LINKER_FLAGS_DEBUG "${CMAKE_STATIC_LINKER_FLAGS_DEBUG} -fno-omit-frame-pointer -O0") </ins> if (USE_ASAN) set (CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fsanitize=address") set (CMAKE_LINKER_FLAGS_DEBUG "${CMAKE_STATIC_LINKER_FLAGS_DEBUG} -fsanitize=address")
def forward(ctx, input, grid, padding_mode='zeros'): ctx.save_for_backward(input, grid) <ins> if input.device != grid.device: raise RuntimeError(("input (device {}) and grid (device {}) must be on the same device" + "for grid_sampler").format(input.device, grid.device)) </ins> if padding_mode == 'zeros': ctx.padding_mode = MODE_ZEROS elif padding_mode == 'border':
lib_paths = list(filter(bool, [ os.getenv('CUDNN_LIB_DIR'), os.path.join(CUDA_HOME, 'lib'), <del> os.path.join(CUDA_HOME, 'lib64') </del> <ins> os.path.join(CUDA_HOME, 'lib64'), '/usr/lib/x86_64-linux-gnu/', </ins> ])) include_paths = list(filter(bool, [ os.getenv('CUDNN_INCLUDE_DIR'), os.path.join(CUDA_HOME, 'include'), <ins> '/usr/include/' </ins> ])) for path in lib_paths: if path is None or not os.path.exists(path):
thisdir = path.dirname(__file__) libpaths = ['', path.join(thisdir, '../../lib')] if sys.platform.startswith('linux'): <del> libnames = ['libcudnn.so.5.1.5', 'libcudnn.so.5.1.3', 'libcudnn.so.5.0.5'] </del> <ins> libnames = ['libcudnn.so.5.1.5', 'libcudnn.so.5.1.3', 'libcudnn.so.5.0.5', 'libcudnn.so.5.1.10'] </ins> elif sys.platform == 'darwin': libnames = ['libcudnn.5.dylib'] else:
#  binary_linux_libtorch_3.6m_cpu_test: #    environment: #      BUILD_ENVIRONMENT: "libtorch 3.6m cpu" <del> #    resource_class: gpu.medium </del> <ins> #    resource_class: gpu.nvidia.small </ins> #    <<: *binary_linux_test # #  binary_linux_libtorch_3.6m_cu90_test: #    environment: #      BUILD_ENVIRONMENT: "libtorch 3.6m cu90" <del> #    resource_class: gpu.medium </del> <ins> #    resource_class: gpu.nvidia.small </ins> #    <<: *binary_linux_test # docker_build_job:
name: binary_linux_manywheel_3_7m_cu102_devtoolset7_test requires: - binary_linux_manywheel_3_7m_cu102_devtoolset7_build <del> resource_class: gpu.medium </del> <ins> resource_class: gpu.nvidia.small </ins> use_cuda_docker_runtime: "1" - binary_linux_test: build_environment: libtorch 3.7m cpu devtoolset7
#  binary_linux_libtorch_3.6m_cpu_test: #    environment: #      BUILD_ENVIRONMENT: "libtorch 3.6m cpu" <del> #    resource_class: gpu.medium </del> <ins> #    resource_class: gpu.nvidia.small </ins> #    <<: *binary_linux_test # #  binary_linux_libtorch_3.6m_cu90_test: #    environment: #      BUILD_ENVIRONMENT: "libtorch 3.6m cu90" <del> #    resource_class: gpu.medium </del> <ins> #    resource_class: gpu.nvidia.small </ins> #    <<: *binary_linux_test #
meant to be installed as pip packages) (default: False). relative_to (str, optional): path of the build file. Required when ``package is True``. It's best to use ``__file__`` for this argument. <del> kwargs: additional arguments that are passed to ffi to declar the </del> <ins> kwargs: additional arguments that are passed to ffi to declare the </ins> extension. See `Extension API reference`_ for details. .. _`Extension API reference`: https://docs.python.org/3/distutils/apiref.html#distutils.core.Extension
# test is no more than 4 higher than the 10th available at the # start. This attempts to catch file descriptor leaks, but allows # one-off initialization that may use up a file descriptor <del> available_fds = self._get_next_fds(10) self.test_case.assertLessEqual( available_fds[-1] - self.next_fds[-1], 5) </del> <ins> # TODO: Disabled because this check is too flaky # available_fds = self._get_next_fds(10) # self.test_case.assertLessEqual( #     available_fds[-1] - self.next_fds[-1], 5) </ins> self.test_case.assertFalse(self.has_shm_files()) return False