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/external/tensorflow/tensorflow/core/kernels/
Dstrided_slice_op_impl.h39 template <typename Device, typename T, int NDIM>
47 template <typename Device, typename T, int NDIM>
55 template <typename Device, typename T, int NDIM>
77 template <typename Device, typename T, int NDIM>
88 Eigen::DSizes<Eigen::DenseIndex, NDIM> begin_di; in HandleStridedSliceCase()
89 Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes_di; in HandleStridedSliceCase()
90 for (int i = 0; i < NDIM; ++i) { in HandleStridedSliceCase()
94 functor::Slice<Device, Proxy, NDIM>()( in HandleStridedSliceCase()
96 result->bit_casted_shaped<Proxy, NDIM>(processing_dims), in HandleStridedSliceCase()
97 context->input(0).bit_casted_tensor<Proxy, NDIM>(), begin_di, sizes_di); in HandleStridedSliceCase()
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Dbetainc_op.cc91 #define CASE(NDIM) \ in Compute() argument
92 case NDIM: { \ in Compute()
93 functor::Betainc<Device, T, NDIM> functor; \ in Compute()
94 auto a_value = a.shaped<T, NDIM>(a_shaper.x_reshape()); \ in Compute()
95 auto b_value = b.shaped<T, NDIM>(b_shaper.x_reshape()); \ in Compute()
96 auto x_value = x.shaped<T, NDIM>(x_shaper.x_reshape()); \ in Compute()
98 BCast::ToIndexArray<NDIM>(a_shaper.x_bcast()), b_value, \ in Compute()
99 BCast::ToIndexArray<NDIM>(b_shaper.x_bcast()), x_value, \ in Compute()
100 BCast::ToIndexArray<NDIM>(x_shaper.x_bcast()), \ in Compute()
101 output->shaped<T, NDIM>(a_shaper.y_reshape())); \ in Compute()
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Dtile_ops_gpu_impl.h24 // DEFINE_TILE_OPS(NDIM)
27 // where NDIM is an integer.
41 #define DEFINE_DIM(T, NDIM) \ argument
42 template struct TileGrad<Eigen::GpuDevice, T, NDIM>; \
43 template struct ReduceAndReshape<Eigen::GpuDevice, T, NDIM, 1>;
45 #define DEFINE_TILE_OPS(NDIM) \ argument
48 DEFINE_DIM(int16, NDIM) \
49 DEFINE_DIM(int32, NDIM) \
50 DEFINE_DIM(int64, NDIM) \
51 DEFINE_DIM(Eigen::half, NDIM) \
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Dtile_ops.cc53 template <typename Device, typename T, int NDIM>
55 void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
56 typename TTypes<T, NDIM>::ConstTensor in,
57 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& indices,
58 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& sizes,
70 template <typename Device, typename T, int NDIM, int REDUCEDNDIM>
73 const Device& d, typename TTypes<T, NDIM>::Tensor out,
74 typename TTypes<T, NDIM>::ConstTensor in,
76 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& reshape_dim) const;
101 #define DECLARE_CUDA_DIM(T, NDIM) \ argument
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Dbetainc_op.h27 template <typename Device, typename T, int NDIM>
29 void operator()(const Device& d, typename TTypes<T, NDIM>::ConstTensor a, in operator()
30 typename TTypes<T, NDIM>::ConstTensor b, in operator()
31 typename TTypes<T, NDIM>::ConstTensor x, in operator()
32 typename TTypes<T, NDIM>::Tensor output) { in operator()
36 void BCast(const Device& d, typename TTypes<T, NDIM>::ConstTensor a, in BCast()
37 const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_a, in BCast()
38 typename TTypes<T, NDIM>::ConstTensor b, in BCast()
39 const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_b, in BCast()
40 typename TTypes<T, NDIM>::ConstTensor x, in BCast()
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Dslice_op.cc195 #define HANDLE_DIM(NDIM) \ in Compute() argument
196 if (input_dims == NDIM) { \ in Compute()
197 HandleCase<NDIM>(context, begin, size, input, result); \ in Compute()
219 template <int NDIM>
223 Eigen::DSizes<Eigen::DenseIndex, NDIM> indices; in HandleCase()
224 Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes; in HandleCase()
225 for (int i = 0; i < NDIM; ++i) { in HandleCase()
230 functor::Slice<Device, T, NDIM>()(context->eigen_device<Device>(), in HandleCase()
231 result->tensor<T, NDIM>(), in HandleCase()
232 input.tensor<T, NDIM>(), indices, sizes); in HandleCase()
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Dwhere_op_gpu.cu.h39 template <int NDIM, typename TIndex>
41 const TIndex output_rows, const typename Eigen::array<TIndex, NDIM> strides, in PropagateWhereIndicesKernel()
47 TIndex index_value = ldg(output + NDIM * i); in PropagateWhereIndicesKernel()
49 for (int c = 0; c < NDIM; ++c) { in PropagateWhereIndicesKernel()
50 *(output + NDIM * i + c) = index_value / strides[c]; in PropagateWhereIndicesKernel()
200 template <int NDIM>
233 return *(ptr_ + (valid ? (NDIM * n) : 0));
241 template <typename TIndex, typename T, int NDIM>
242 Eigen::array<TIndex, NDIM> CalculateStrides(
243 typename TTypes<T, NDIM>::ConstTensor input) {
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Dtile_ops_impl.h27 template <typename Device, typename T, int NDIM>
29 void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out, in operator()
30 typename TTypes<T, NDIM>::ConstTensor in, in operator()
31 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& indices, in operator()
32 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& sizes, in operator()
57 template <typename Device, typename T, int NDIM, int REDUCEDNDIM>
60 const Device& d, typename TTypes<T, NDIM>::Tensor out,
61 typename TTypes<T, NDIM>::ConstTensor in,
63 const Eigen::DSizes<Eigen::DenseIndex, NDIM>& reshape_dim) const {
/external/pytorch/aten/src/ATen/native/cpu/
DPaddingKernel.cpp16 int ndim; member
30 ndim = padding.size() / 2; in PaddingParams()
32 bool is_batch = input.dim() == ndim + 2; in PaddingParams()
40 for (const auto d : c10::irange(ndim)) { in PaddingParams()
51 if (ndim == 1) { in PaddingParams()
53 } else if (ndim == 2) { in PaddingParams()
61 for (const auto d : c10::irange(ndim)) { in PaddingParams()
145 int ndim = p.ndim; in cpu_padding() local
146 int64_t input_depth = ndim == 3 ? p.ishape[ndim - 3] : 1; in cpu_padding()
147 int64_t input_height = ndim >=2 ? p.ishape[ndim - 2] : 1; in cpu_padding()
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DUpSampleMoreKernel.cpp106 auto ndim = input_sizes.size(); in cpu_upsample_nearest_backward() local
110 int64_t input_depth = (ndim == 5) ? input_sizes[2] : 1; in cpu_upsample_nearest_backward()
111 int64_t output_depth = (ndim == 5) ? output_sizes[2] : 1; in cpu_upsample_nearest_backward()
112 int64_t input_height = (ndim >= 4) ? input_sizes[ndim - 2] : 1; in cpu_upsample_nearest_backward()
113 int64_t output_height = (ndim >= 4) ? output_sizes[ndim - 2] : 1; in cpu_upsample_nearest_backward()
114 int64_t input_width = input_sizes[ndim - 1]; in cpu_upsample_nearest_backward()
115 int64_t output_width = output_sizes[ndim - 1]; in cpu_upsample_nearest_backward()
207 if (ndim == 3) { in cpu_upsample_nearest_backward()
210 } else if (ndim == 4) { in cpu_upsample_nearest_backward()
215 TORCH_INTERNAL_ASSERT(ndim == 5); in cpu_upsample_nearest_backward()
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/external/pytorch/aten/src/ATen/native/
DPool.h128 const int64_t ndim = input.ndimension(); in pool2d_shape_check() local
146 TORCH_CHECK((ndim == 4 && valid_dims && input.size(3) != 0), in pool2d_shape_check()
150 TORCH_CHECK((ndim == 3 && input.size(0) != 0 && valid_dims) || in pool2d_shape_check()
151 (ndim == 4 && valid_dims && input.size(3) != 0), in pool2d_shape_check()
184 const int64_t ndim = input.ndimension(); in max_pool2d_backward_shape_check() local
187 check_dim_size(gradOutput, ndim, ndim-3, nOutputPlane); in max_pool2d_backward_shape_check()
188 check_dim_size(gradOutput, ndim, ndim-2, outputHeight); in max_pool2d_backward_shape_check()
189 check_dim_size(gradOutput, ndim, ndim-1, outputWidth); in max_pool2d_backward_shape_check()
191 check_dim_size(indices, ndim, ndim-3, nOutputPlane); in max_pool2d_backward_shape_check()
192 check_dim_size(indices, ndim, ndim-2, outputHeight); in max_pool2d_backward_shape_check()
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/external/tensorflow/tensorflow/python/keras/engine/
Dinput_spec.py46 ndim: Integer, expected rank of the input.
74 ndim=None, argument
87 self.ndim = len(shape)
90 self.ndim = ndim
102 if self.axes and (self.ndim is not None or self.max_ndim is not None):
103 max_dim = (self.ndim if self.ndim else self.max_ndim) - 1
112 ('ndim=' + str(self.ndim)) if self.ndim else '',
122 'ndim': self.ndim,
135 If the InputSpec's shape or ndim is defined, this method will return a fully
144 if spec.ndim is None and spec.shape is None:
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/external/pytorch/torch/distributed/tensor/_ops/
D_view_ops.py171 def dim_pad_left(ndim: int, min_dims: int) -> DimMap:
172 return (Singleton(),) * max(0, min_dims - ndim) + tuple(
173 InputDim(i) for i in range(ndim)
177 def dim_atleast_3d(ndim: int) -> DimMap:
178 if ndim == 0:
180 elif ndim == 1:
182 elif ndim == 2:
185 return tuple(InputDim(i) for i in range(ndim))
221 def dim_flatten(ndim: int, start_dim=0, end_dim=-1) -> DimMap:
222 if ndim == 0:
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/external/executorch/exir/
Ddim_order_utils.py16 def _get_contiguous_dim_order(ndim: int) -> List[int]:
17 if ndim < 0:
19 …pported rank for contiguous dim order. Only supports ndim greater than or equal to 0, but got {ndi…
22 return list(range(ndim))
25 def _get_channels_last_dim_order(ndim: int) -> List[int]:
26 if ndim == 4:
30 … f"Unsupported rank for channels last dim order. Only support ndim equal to 4, but got {ndim}"
53 memory_format: Optional[torch.memory_format], ndim: int
61 return _get_contiguous_dim_order(ndim)
63 return _get_channels_last_dim_order(ndim)
/external/pytorch/aten/src/ATen/native/cuda/
DSortImpl.cu9 int64_t ndim = self.dim(); in infer_dense_strides_dim_last() local
14 std::vector<int64_t> original_dim(ndim); in infer_dense_strides_dim_last()
15 for (int64_t i = 0; i < ndim; i++) { in infer_dense_strides_dim_last()
19 thrust::host, strides.data(), strides.data() + ndim, original_dim.data(), in infer_dense_strides_dim_last()
23 std::vector<int64_t> new_strides(ndim); in infer_dense_strides_dim_last()
24 std::vector<int64_t> new_strides_unsort(ndim); in infer_dense_strides_dim_last()
26 for (int64_t i = 0; i < ndim; i++) { in infer_dense_strides_dim_last()
27 new_strides[ndim - 1 - i] = cumprod; in infer_dense_strides_dim_last()
28 cumprod *= self.sizes()[original_dim[ndim - 1 - i]]; in infer_dense_strides_dim_last()
31 for (int64_t i = 0; i < ndim; i++) { in infer_dense_strides_dim_last()
/external/tensorflow/tensorflow/python/framework/
Dfast_tensor_util.pyx10 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument
22 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument
30 tensor_proto, np.ndarray[np.float32_t, ndim=1] nparray): argument
38 tensor_proto, np.ndarray[np.float64_t, ndim=1] nparray): argument
46 tensor_proto, np.ndarray[np.int32_t, ndim=1] nparray): argument
53 tensor_proto, np.ndarray[np.uint32_t, ndim=1] nparray): argument
60 tensor_proto, np.ndarray[np.int64_t, ndim=1] nparray): argument
67 tensor_proto, np.ndarray[np.uint64_t, ndim=1] nparray): argument
74 tensor_proto, np.ndarray[np.uint8_t, ndim=1] nparray): argument
82 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument
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/external/executorch/kernels/portable/cpu/
Dop_tril.cpp66 int64_t ndim = self.dim(); in tril_kernel() local
70 ndim < kTensorDimensionLimit, in tril_kernel()
73 "ndim %" PRId64 " >= %zu", in tril_kernel()
74 ndim, in tril_kernel()
80 for (size_t i = 0; i < ndim; ++i) { in tril_kernel()
85 IntArrayRef sizes_ref(sizes, ndim); in tril_kernel()
86 IntArrayRef strides_ref(strides, ndim); in tril_kernel()
88 int64_t num_rows = sizes_ref[ndim - 2]; in tril_kernel()
89 int64_t num_cols = sizes_ref[ndim - 1]; in tril_kernel()
95 int64_t batch_size = getLeadingDims(self, ndim - 2); in tril_kernel()
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Dop_constant_pad_nd.cpp32 size_t ndim, in apply_padding_to_dim() argument
43 if (dim >= ndim) { in apply_padding_to_dim()
47 size_t pad_i = ndim - 1 - dim; in apply_padding_to_dim()
80 ndim, in apply_padding_to_dim()
112 size_t ndim = self.dim(); in constant_pad_nd_out_impl() local
114 if (ndim == 0) { in constant_pad_nd_out_impl()
127 for (size_t i = 0; i < ndim; ++i) { in constant_pad_nd_out_impl()
133 size_t pad_i = ndim - 1 - i; in constant_pad_nd_out_impl()
141 IntArrayRef self_sizes_ref(self_sizes, ndim); in constant_pad_nd_out_impl()
142 IntArrayRef self_strides_ref(self_strides, ndim); in constant_pad_nd_out_impl()
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/external/pytorch/aten/src/ATen/
DExpandUtils.cpp21 auto ndim = dimsA > dimsB ? dimsA : dimsB; in infer_size_impl() local
22 Container expandedSizes(ndim); in infer_size_impl()
24 for (ptrdiff_t i = ndim - 1; i >= 0; --i) { in infer_size_impl()
25 ptrdiff_t offset = ndim - 1 - i; in infer_size_impl()
66 int64_t ndim = static_cast<int64_t>(sizes.size()); in inferExpandGeometryImpl() local
70 return InferExpandGeometryResult<Container>(sizes, ndim); in inferExpandGeometryImpl()
73 InferExpandGeometryResult<Container> result(ndim); in inferExpandGeometryImpl()
78 for (int64_t i = ndim - 1; i >= 0; --i) { in inferExpandGeometryImpl()
79 int64_t offset = ndim - 1 - i; in inferExpandGeometryImpl()
153 size_t ndim = tensor_sizes.size(); in infer_dense_strides() local
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/external/tensorflow/tensorflow/core/util/sparse/
Dsparse_tensor_test.cc36 GetSimpleIndexTensor(int N, const int NDIM) { in GetSimpleIndexTensor() argument
37 Eigen::Tensor<int64_t, 2, Eigen::RowMajor, Eigen::DenseIndex> ix(N, NDIM); in GetSimpleIndexTensor()
62 const int NDIM = 3; in TEST() local
63 auto ix = GetSimpleIndexTensor(N, NDIM); in TEST()
64 TTypes<int64_t>::Matrix map(ix.data(), N, NDIM); in TEST()
95 const int NDIM = 3; in TEST() local
96 Tensor ix(DT_INT32, TensorShape({N, NDIM})); in TEST()
108 const int NDIM = 3; in TEST() local
109 Tensor ix(DT_INT64, TensorShape({N, NDIM, 1})); in TEST()
121 const int NDIM = 3; in TEST() local
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/external/pytorch/torch/_numpy/
D_util.py47 def normalize_axis_index(ax, ndim, argname=None): argument
48 if not (-ndim <= ax < ndim):
49 raise AxisError(f"axis {ax} is out of bounds for array of dimension {ndim}")
51 ax += ndim
56 def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False): argument
71 ndim : int
83 The normalized axis index, such that `0 <= normalized_axis < ndim`
92 axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])
120 def apply_keepdims(tensor, axis, ndim): argument
123 shape = (1,) * ndim
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/external/pytorch/aten/src/ATen/native/mkldnn/xpu/detail/
DConv.cpp19 int64_t ndim, in conv_dst_size() argument
27 dnnl::memory::dims dst_size(ndim); in conv_dst_size()
30 for (int d = 2; d < ndim; ++d) { in conv_dst_size()
52 const int64_t ndim, in conv_src_fmt() argument
55 return (ndim == 3) in conv_src_fmt()
57 : ((ndim == 4) ? dnnl::memory::format_tag::nchw in conv_src_fmt()
58 : ((ndim == 5) ? dnnl::memory::format_tag::ncdhw in conv_src_fmt()
61 return (ndim == 3) in conv_src_fmt()
63 : ((ndim == 4) ? dnnl::memory::format_tag::nhwc in conv_src_fmt()
64 : ((ndim == 5) ? dnnl::memory::format_tag::ndhwc in conv_src_fmt()
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/external/python/cpython3/Modules/
D_testbuffer.c52 #define ND_SCALAR 0x008 /* scalar: ndim = 0 */
151 base->ndim = 1; in ndbuf_new()
264 if (ndbuf->base.ndim == 0) in init_flags()
470 copy_rec(const Py_ssize_t *shape, Py_ssize_t ndim, Py_ssize_t itemsize, in copy_rec() argument
477 assert(ndim >= 1); in copy_rec()
479 if (ndim == 1) { in copy_rec()
503 copy_rec(shape+1, ndim-1, itemsize, in copy_rec()
517 dest->ndim != src->ndim) in cmp_structure()
520 for (i = 0; i < dest->ndim; i++) { in cmp_structure()
531 ndim and shape. Copying is atomic, the function never fails with
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/external/pytorch/aten/src/ATen/cuda/
DCUDASparseDescriptors.cpp63 auto ndim = input.dim(); in createRawDnMatDescriptor() local
64 TORCH_INTERNAL_ASSERT_DEBUG_ONLY(ndim >= 2); in createRawDnMatDescriptor()
65 auto rows = input_sizes[ndim - 2]; in createRawDnMatDescriptor()
66 auto cols = input_sizes[ndim - 1]; in createRawDnMatDescriptor()
76 is_row_major ? input_strides[ndim - 2] : input_strides[ndim - 1]; in createRawDnMatDescriptor()
85 auto batch_stride = ndim > 2 && batch_offset >= 0 ? input_strides[ndim - 3] : 0; in createRawDnMatDescriptor()
106 if (ndim >= 3 && batch_offset == -1) { in createRawDnMatDescriptor()
110 raw_descriptor, batch_count, input_strides[ndim - 3])); in createRawDnMatDescriptor()
146 auto ndim = input.dim(); in CuSparseSpMatCsrDescriptor() local
147 auto rows = input_sizes[ndim - 2]; in CuSparseSpMatCsrDescriptor()
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/external/python/cpython3/Objects/
Dmemoryobject.c239 /* Fast contiguity test. Caller must ensure suboffsets==NULL and ndim==1. */
259 with the same logical structure: format, itemsize, ndim and shape
260 are identical, with ndim > 0.
266 /* Assumptions: ndim >= 1. The macro tests for a corner case that should
269 (view->suboffsets && view->suboffsets[view->ndim-1] >= 0)
274 assert(dest->ndim > 0 && src->ndim > 0); in last_dim_is_contiguous()
277 dest->strides[dest->ndim-1] == dest->itemsize && in last_dim_is_contiguous()
278 src->strides[src->ndim-1] == src->itemsize); in last_dim_is_contiguous()
312 if (dest->ndim != src->ndim) in equiv_shape()
315 for (i = 0; i < dest->ndim; i++) { in equiv_shape()
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