/aosp_15_r20/external/tensorflow/tensorflow/python/ops/ragged/ |
H A D | ragged_gather_ops.py | 35 indices: ragged_tensor.RaggedOrDense, 40 """Gathers ragged slices from `params` axis `0` according to `indices`. 43 as `tf.gather`, but supports ragged `params` and `indices`.) 48 >>> indices = tf.constant([3, 1, 2, 1, 0]) 55 >>> tf.gather(ragged_params, indices) 64 indices: The potentially ragged tensor indicating which values to gather. 68 axis: The axis in `params` to gather `indices` from. 74 `output.shape=indices.shape + params.shape[1:]` and 75 `output.ragged_rank=indices.shape.ndims + params.ragged_rank`. 78 ValueError: If indices.shape.ndims is not known statically. [all …]
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H A D | ragged_batch_gather_op_test.py | 44 indices=ragged_factory_ops.constant_value([[1, 2, 0], [], [], [0, 52 descr='params: [P1], indices: [I], result: [I]', 54 indices=[3, 2], 57 descr='params: [P1, (P2)], indices: [I], result: [I, (P2)]', 60 indices=[3, 2], 66 descr='params: [B1, P1], indices: [B1, I], result: [B1, I]', 68 indices=[[2, 0], [0, 1], [1, 0]], 71 descr='params: [B1, (P1)], indices: [B1, I], result: [B1, I]', 74 indices=[[2, 0], [0, 1], [0, 0]], 77 descr='params: [B1, P1], indices: [B1, (I)], result: [B1, (I)]', [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/python/ops/ |
H A D | state_ops.py | 383 def scatter_update(ref, indices, updates, use_locking=True, name=None): argument 390 # Scalar indices 391 ref[indices, ...] = updates[...] 393 # Vector indices (for each i) 394 ref[indices[i], ...] = updates[i, ...] 396 # High rank indices (for each i, ..., j) 397 ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] 404 duplicate entries in `indices`, the order at which the updates happen 407 Requires `updates.shape = indices.shape + ref.shape[1:]`. 415 indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/array_ops/ |
H A D | scatter_nd_ops_test.py | 59 def _NumpyScatterNd(ref, indices, updates, op): argument 60 ixdim = indices.shape[-1] 61 num_updates = indices.size // ixdim 66 flat_indices = _FlatInnerDims(indices) 76 def _NumpyUpdate(ref, indices, updates): argument 77 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u) 80 def _NumpyAdd(ref, indices, updates): argument 81 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p + u) 84 def _NumpySub(ref, indices, updates): argument 85 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p - u) [all …]
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H A D | gather_op_test.py | 66 for indices in 4, [1, 2, 2, 4, 5]: 67 with self.subTest(dtype=dtype, indices=indices): 70 indices_tf = constant_op.constant(indices) 73 np_val = params_np[indices] 86 indices = constant_op.constant(2) 87 gather_t = array_ops.gather(params, indices, axis=axis) 102 # The indices must be in bounds for any axis. 103 indices = constant_op.constant([0, 1, 0, 2]) 104 gather_t = array_ops.gather(params, indices, axis=axis) 113 # We check that scalar and empty indices shapes work as well [all …]
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H A D | one_hot_op_test.py | 55 indices = np.asarray([0, 2, -1, 1], dtype=np.int64) 67 indices=indices, 76 indices=indices, 85 indices = np.asarray([0, 2, -1, 1], dtype=np.int64) 93 self._testBothOneHot(indices=indices, depth=depth, dtype=dtype, truth=truth) 97 indices=indices, depth=depth, axis=0, dtype=dtype, 128 indices = np.asarray([[0, 2, -1, 1], [1, 0, 1, -1]], dtype=np.int64) 141 indices=indices, 150 indices=indices, 159 indices = np.asarray([[0, 2, -1, 1], [1, 0, 1, -1]], dtype=np.int64) [all …]
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H A D | scatter_ops_test.py | 32 def _NumpyAdd(ref, indices, updates): argument 33 # Since numpy advanced assignment does not support repeated indices, 35 for i, indx in np.ndenumerate(indices): 39 def _NumpyAddScalar(ref, indices, update): argument 40 for _, indx in np.ndenumerate(indices): 44 def _NumpySub(ref, indices, updates): argument 45 for i, indx in np.ndenumerate(indices): 49 def _NumpySubScalar(ref, indices, update): argument 50 for _, indx in np.ndenumerate(indices): 54 def _NumpyMul(ref, indices, updates): argument [all …]
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/aosp_15_r20/external/perfetto/src/trace_processor/db/column/ |
H A D | numeric_storage_unittest.cc | 45 using Indices = DataLayerChain::Indices; typedef 255 Indices common_indices = Indices::CreateWithIndexPayloadForTesting( in TEST() 256 {0, 4, 4, 5, 1, 6}, Indices::State::kNonmonotonic); in TEST() 259 auto indices = common_indices; in TEST() local 260 chain->IndexSearch(FilterOp::kEq, val, indices); in TEST() 261 ASSERT_THAT(utils::ExtractPayloadForTesting(indices), ElementsAre(3)); in TEST() 263 indices = common_indices; in TEST() 264 chain->IndexSearch(FilterOp::kNe, val, indices); in TEST() 265 ASSERT_THAT(utils::ExtractPayloadForTesting(indices), in TEST() 268 indices = common_indices; in TEST() [all …]
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H A D | string_storage_unittest.cc | 38 using Indices = DataLayerChain::Indices; typedef 167 Indices common_indices = Indices::CreateWithIndexPayloadForTesting( in TEST() 168 {6, 5, 4, 3, 2, 1, 0}, Indices::State::kNonmonotonic); in TEST() 170 auto indices = common_indices; in TEST() local 171 chain->IndexSearch(FilterOp::kEq, val, indices); in TEST() 172 ASSERT_THAT(utils::ExtractPayloadForTesting(indices), ElementsAre(2)); in TEST() 174 indices = common_indices; in TEST() 175 chain->IndexSearch(FilterOp::kNe, val, indices); in TEST() 176 ASSERT_THAT(utils::ExtractPayloadForTesting(indices), in TEST() 179 indices = common_indices; in TEST() [all …]
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H A D | null_overlay.cc | 41 using Indices = DataLayerChain::Indices; typedef 43 std::optional<Token> UpdateIndicesForInner(Indices& indices, in UpdateIndicesForInner() argument 47 indices.tokens.begin(), indices.tokens.end(), in UpdateIndicesForInner() 52 if (first_null_it != indices.tokens.end()) { in UpdateIndicesForInner() 57 indices.tokens.erase(std::remove_if(first_null_it, indices.tokens.end(), in UpdateIndicesForInner() 61 indices.tokens.end()); in UpdateIndicesForInner() 64 for (auto& token : indices.tokens) { in UpdateIndicesForInner() 77 // Reconcile the results of the Search operation with the non-null indices in ReconcileStorageResult() 99 // For the IS NULL constraint, we also need to include all the null indices in ReconcileStorageResult() 199 // Figure out the bounds of the indices in the underlying storage and search in SearchValidated() [all …]
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H A D | dense_null_overlay.cc | 39 using Indices = DataLayerChain::Indices; typedef 42 Indices& indices, in RemoveAllNullsAndReturnTheFirstOne() argument 46 indices.tokens.begin(), indices.tokens.end(), in RemoveAllNullsAndReturnTheFirstOne() 51 if (first_null_it != indices.tokens.end()) { in RemoveAllNullsAndReturnTheFirstOne() 56 indices.tokens.erase(std::remove_if(first_null_it, indices.tokens.end(), in RemoveAllNullsAndReturnTheFirstOne() 60 indices.tokens.end()); in RemoveAllNullsAndReturnTheFirstOne() 181 Indices& indices) const { in IndexSearchValidated() 186 // Partition the vector into all the null indices followed by all the in IndexSearchValidated() 187 // non-null indices. in IndexSearchValidated() 189 indices.tokens.begin(), indices.tokens.end(), in IndexSearchValidated() [all …]
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/quantized/cpu/ |
H A D | qembeddingbag.cpp | 39 const at::Tensor& indices, in embedding_lookup_fallback_impl() argument 50 const auto indices_data = indices.data_ptr<IndexType>(); in embedding_lookup_fallback_impl() 55 const int index_size = indices.numel(); in embedding_lookup_fallback_impl() 66 lengths_data.push_back(indices.numel() - lower); in embedding_lookup_fallback_impl() 79 "Expect the lengths data to be less than indices size"); in embedding_lookup_fallback_impl() 86 TORCH_CHECK((idx >= 0 && idx < N), "Invalid indices data"); in embedding_lookup_fallback_impl() 94 "Invalid indices data for Sparse Op.") in embedding_lookup_fallback_impl() 200 const IndexType* indices) { in fbgemm_spmdm_report_error_() argument 204 IndexType idx = indices[i]; in fbgemm_spmdm_report_error_() 218 "the size of the indices tensor, but it appears not."); in fbgemm_spmdm_report_error_() [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/spmd/ |
H A D | gather_scatter_handler.cc | 65 // Return an update sharding that is compatible with the indices sharding for 68 const PartitionedHlo& updates, const PartitionedHlo& indices, in ComputeUpdateShardingFromIndices() argument 71 std::vector<int64_t> index_dim_to_update_dim(indices.base_shape().rank(), -1); in ComputeUpdateShardingFromIndices() 79 indices.sharding(), index_dim_to_update_dim, update_dim_to_index_dim); in ComputeUpdateShardingFromIndices() 83 // Returns the min and max for the indices (replicated) in a scatter/gather 121 // Broadcast the index bounds to the same shape as the indices. in IndexBoundsForGatherScatterOperandPartitionedOnTrivialSliceDims() 161 PartitionedHlo& indices, const Shape& output_shape, 165 // Perform partitioning of Gather when the indices are partitioned on the 170 PartitionedHlo& indices, absl::Span<const int64_t> batch_dims, in PartitionGatherIndexPassthroughPartition() argument 173 if (!indices.sharding().IsTileMaximal() && in PartitionGatherIndexPassthroughPartition() [all …]
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/aosp_15_r20/external/eigen/Eigen/src/plugins/ |
H A D | IndexedViewMethods.h | 27 template<typename Indices> 28 struct IvcRowType : public internal::IndexedViewCompatibleType<Indices,RowsAtCompileTime> {}; 30 template<typename Indices> 31 struct IvcColType : public internal::IndexedViewCompatibleType<Indices,ColsAtCompileTime> {}; 33 template<typename Indices> 34 struct IvcType : public internal::IndexedViewCompatibleType<Indices,SizeAtCompileTime> {}; 38 template<typename Indices> 39 typename IvcRowType<Indices>::type 40 ivcRow(const Indices& indices) const { in ivcRow() argument 41 …return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,RowsAtComp… in ivcRow() [all …]
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/aosp_15_r20/external/executorch/kernels/portable/cpu/util/ |
H A D | advanced_index_util.cpp | 20 bool check_indices_dtypes(TensorOptList indices) { in check_indices_dtypes() argument 21 for (auto i = 0; i < indices.size(); i++) { in check_indices_dtypes() 22 if (indices[i].has_value()) { in check_indices_dtypes() 23 const Tensor& index = indices[i].value(); in check_indices_dtypes() 42 bool check_mask_indices(const Tensor& in, TensorOptList indices) { in check_mask_indices() argument 44 for (auto i = 0; i < indices.size(); i++) { in check_mask_indices() 45 if (indices[i].has_value()) { in check_mask_indices() 46 const Tensor& index = indices[i].value(); in check_mask_indices() 141 bool check_index_args(const Tensor& in, TensorOptList indices, Tensor& out) { in check_index_args() argument 143 ET_LOG_AND_RETURN_IF_FALSE(check_indices_dtypes(indices)); in check_index_args() [all …]
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/aosp_15_r20/external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
H A D | TensorRef.h | 212 const array<Index, num_indices> indices{{firstIndex, otherIndices...}}; in operator() 213 return coeff(indices); in operator() 219 const array<Index, num_indices> indices{{firstIndex, otherIndices...}}; in coeffRef() 220 return coeffRef(indices); in coeffRef() 227 array<Index, 2> indices; in operator() local 228 indices[0] = i0; in operator() 229 indices[1] = i1; in operator() 230 return coeff(indices); in operator() 235 array<Index, 3> indices; in operator() local 236 indices[0] = i0; in operator() [all …]
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/aosp_15_r20/external/rust/android-crates-io/crates/itertools/src/ |
D | combinations.rs | 14 indices: Vec<usize>, field 24 clone_fields!(indices, pool, first); 32 debug_fmt_fields!(Combinations, indices, pool, first); 41 indices: (0..k).collect(), in combinations() 51 self.indices.len() in k() 74 if k < self.indices.len() { in reset() 75 self.indices.truncate(k); in reset() 77 self.indices[i] = i; in reset() 80 for i in 0..self.indices.len() { in reset() 81 self.indices[i] = i; in reset() [all …]
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/aosp_15_r20/external/pytorch/aten/src/ATen/functorch/ |
H A D | BatchRulesScatterOps.cpp | 29 static int64_t get_num_leading_nones(ArrayRef<std::optional<Tensor>> indices) { in get_num_leading_nones() argument 31 for (const auto& idx : indices) { in get_num_leading_nones() 42 ArrayRef<std::optional<Tensor>> indices, in get_max_index_logical_dim() argument 45 TORCH_INTERNAL_ASSERT(indices.size() == indices_bdims.size()); in get_max_index_logical_dim() 46 TORCH_INTERNAL_ASSERT(!indices.empty()); in get_max_index_logical_dim() 47 for (const auto i : c10::irange(0, indices.size())) { in get_max_index_logical_dim() 48 const auto& maybe_tensor = indices[i]; in get_max_index_logical_dim() 59 ArrayRef<std::optional<Tensor>> indices, in batchIndices() argument 65 // 1. self is batched, indices/values are not batched in batchIndices() 66 // In this case, we just need to augment indices with a None at the front to in batchIndices() [all …]
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/quantized/cuda/ |
H A D | EmbeddingBag.cu | 89 const PackedTensorAccessor32<index_t, 1, RestrictPtrTraits> indices, in embedding_bag_nbits_rowwise_offsets_kernel() argument 119 : indices.size(0); in embedding_bag_nbits_rowwise_offsets_kernel() 140 int64_t idx = indices[l]; in embedding_bag_nbits_rowwise_offsets_kernel() 192 const at::Tensor& indices, in embedding_bag_byte_impl() argument 200 TORCH_CHECK(indices.is_cuda()); in embedding_bag_byte_impl() 202 TORCH_CHECK(indices.device() == weight.device()) in embedding_bag_byte_impl() 229 "Compressed indices mapping not yet implemented for embedding_bag_byte_rowwise_offsets_cuda"); in embedding_bag_byte_impl() 245 indices.scalar_type(), "embedding_bag_byte_rowwise_offsets_kernel", ([&] { in embedding_bag_byte_impl() 252 indices.packed_accessor32<index_t, 1, RestrictPtrTraits>(), in embedding_bag_byte_impl() 269 const Tensor& indices, in embedding_bag_byte_rowwise_offsets() argument [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/math_ops/ |
H A D | segment_reduction_ops_test.py | 48 def _segmentReduce(self, indices, x, op1, op2=None, num_segments=None, argument 52 indices = np.asarray(indices) 54 num_segments = indices[-1] + 1 56 slice_shape = x.shape[indices.ndim:] 57 x_flat = x.reshape((indices.size,) + slice_shape) 58 for i, index in enumerate(indices.ravel()): 109 indices = [i // 3 for i in range(n)] 121 indices, np_x, np_op1, np_op2, initial_value=initial_value) 122 s = tf_op(data=tf_x, segment_ids=indices) 135 indices = constant_op.constant([0, 1, 2, 2], shape=[2, 2]) [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/tests/ |
H A D | scatter_nd_op_test.py | 46 def _NumpyScatterNd(ref, indices, updates, op): argument 47 ixdim = indices.shape[-1] 48 num_updates = indices.size // ixdim 53 flat_indices = _FlatInnerDims(indices) 63 def _NumpyUpdate(indices, updates, shape): argument 65 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u) 91 indices = np.array(all_indices[:num_updates]) 94 indices = indices[:num_updates // 2] 96 indices = np.append( 97 indices, [indices[np.random.randint(num_updates // 2)]], axis=0) [all …]
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/aosp_15_r20/external/mesa3d/src/compiler/nir/ |
H A D | nir_intrinsics.py | 46 indices, flags, sysval, bit_sizes): argument 56 - indices: list of constant indicies 66 assert isinstance(indices, list) 67 if indices: 68 assert isinstance(indices[0], Index) 85 self.num_indices = len(indices) 86 self.indices = indices 118 def intrinsic(name, src_comp=[], dest_comp=-1, indices=[], argument 122 indices, flags, sysval, bit_sizes) 125 # Possible indices: [all …]
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/aosp_15_r20/external/angle/util/ |
H A D | geometry_utils.cpp | 47 result->indices.clear(); in CreateSphereGeometry() 48 result->indices.reserve(indexCount); in CreateSphereGeometry() 53 result->indices.push_back(static_cast<unsigned short>(i * (sliceCount + 1) + j)); in CreateSphereGeometry() 54 result->indices.push_back(static_cast<unsigned short>((i + 1) * (sliceCount + 1) + j)); in CreateSphereGeometry() 55 result->indices.push_back( in CreateSphereGeometry() 58 result->indices.push_back(static_cast<unsigned short>(i * (sliceCount + 1) + j)); in CreateSphereGeometry() 59 result->indices.push_back( in CreateSphereGeometry() 61 result->indices.push_back(static_cast<unsigned short>(i * (sliceCount + 1) + (j + 1))); in CreateSphereGeometry() 146 result->indices.resize(36); in GenerateCubeGeometry() 147 result->indices[0] = 0; in GenerateCubeGeometry() [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/sparse_ops/ |
H A D | sparse_cross_op_test.py | 46 indices = [] 51 indices.append([batch_ix, column_ix]) 59 constant_op.constant(indices, dtypes.int64, [len(indices), 2]), 60 constant_op.constant(values, value_type, [len(indices)]), 64 self.assertAllEqual(sp1.indices, sp2.indices) 69 self.assertEqual(0, sp.indices.size) 411 self.assertAllEqual([[0, i] for i in range(6)], out.indices) 417 self.assertEqual(0, sp.indices.size) 423 self.assertAllEqual(sp1.indices, sp2.indices) 440 indices = [] [all …]
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/aosp_15_r20/external/rust/android-crates-io/crates/indexmap/src/map/ |
D | core.rs | 26 /// indices mapping from the entry hash to its index. 27 indices: RawTable<usize>, field 65 let indices = self.indices.clone(); in clone() localVariable 66 let mut entries = Vec::with_capacity(indices.capacity()); in clone() 68 IndexMapCore { indices, entries } in clone() 73 self.indices.clone_from_with_hasher(&other.indices, hasher); in clone_from() 75 // If we must resize, match the indices capacity in clone_from() 89 .field("indices", &raw::DebugIndices(&self.indices)) in fmt() 126 indices: RawTable::new(), in new() 134 indices: RawTable::with_capacity(n), in with_capacity() [all …]
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