| /external/tensorflow/tensorflow/python/kernel_tests/array_ops/ |
| D | scatter_nd_ops_test.py | 59 def _NumpyScatterNd(ref, indices, updates, op): argument
67 flat_updates = updates.reshape((num_updates, slice_size))
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)
88 def _NumpyMul(ref, indices, updates): argument
89 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p * u)
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| D | scatter_ops_test.py | 32 def _NumpyAdd(ref, indices, updates): argument
36 ref[indx] += updates[i]
44 def _NumpySub(ref, indices, updates): argument
46 ref[indx] -= updates[i]
54 def _NumpyMul(ref, indices, updates): argument
56 ref[indx] *= updates[i]
64 def _NumpyDiv(ref, indices, updates): argument
66 ref[indx] /= updates[i]
74 def _NumpyMin(ref, indices, updates): argument
76 ref[indx] = np.minimum(ref[indx], updates[i])
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| /external/tensorflow/tensorflow/lite/kernels/ |
| D | scatter_nd.cc | 51 const RuntimeShape& updates, in CheckShapes() argument
55 (updates.DimensionsCount() >= 1) && in CheckShapes()
60 TF_LITE_ENSURE_EQ(context, indices.Dims(i), updates.Dims(i)); in CheckShapes()
64 TF_LITE_ENSURE_EQ(context, updates.DimensionsCount() - outer_dims, in CheckShapes()
66 for (int i = 0; i + outer_dims < updates.DimensionsCount(); ++i) { in CheckShapes()
67 TF_LITE_ENSURE_EQ(context, updates.Dims(i + outer_dims), in CheckShapes()
79 const TfLiteTensor* updates; in Prepare() local
80 TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kUpdates, &updates)); in Prepare()
84 switch (updates->type) { in Prepare()
94 context, "Updates of type '%s' are not supported by scatter_nd.", in Prepare()
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| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| D | scatter_test.cc | 28 Literal* scatter_indices, Literal* updates) { in RunTest() argument
29 RunTest(hlo_text, {operand, scatter_indices, updates}); in RunTest()
53 updates = s32[2,3] parameter(2) in XLA_TEST_F()
54 ROOT scatter = s32[3,3] scatter(operand, indices, updates), in XLA_TEST_F()
65 Literal updates = in XLA_TEST_F() local
67 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
85 updates = s32[2,3] add(p2, p2) in XLA_TEST_F()
86 ROOT scatter = s32[3,3] scatter(operand, indices, updates), in XLA_TEST_F()
97 Literal updates = in XLA_TEST_F() local
99 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
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| /external/tensorflow/tensorflow/python/ops/ |
| D | state_ops.py | 383 def scatter_update(ref, indices, updates, use_locking=True, name=None): argument
385 r"""Applies sparse updates to a variable reference.
391 ref[indices, ...] = updates[...]
394 ref[indices[i], ...] = updates[i, ...]
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:]`.
417 updates: A `Tensor`. Must have the same type as `ref`.
429 return gen_state_ops.scatter_update(ref, indices, updates,
432 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | scatter_nd_op_test.py | 46 def _NumpyScatterNd(ref, indices, updates, op): argument
54 flat_updates = updates.reshape((num_updates, slice_size))
63 def _NumpyUpdate(indices, updates, shape): argument
64 ref = np.zeros(shape, dtype=updates.dtype)
65 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
104 updates = _AsType(np.random.randn(*(updates_shape)), vtype)
107 np_out = np_scatter(indices, updates, ref_shape)
109 tf_out = tf_scatter(indices, updates, ref_shape)
118 def _runScatterNd(self, indices, updates, shape): argument
120 updates_placeholder = array_ops.placeholder(updates.dtype)
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| /external/swiftshader/third_party/llvm-16.0/llvm/include/llvm/Analysis/ |
| D | DomTreeUpdater.h | 74 /// as having no pending updates. This function does not check
79 /// Returns true if there are DominatorTree updates queued.
83 /// Returns true if there are PostDominatorTree updates queued.
90 /// These methods provide APIs for submitting updates to the DominatorTree and
95 /// 1. Eager UpdateStrategy: Updates are submitted and then flushed
97 /// 2. Lazy UpdateStrategy: Updates are submitted but only flushed when you
99 /// when you submit a bunch of updates multiple times which can then
100 /// add up to a large number of updates between two queries on the
101 /// DominatorTree. The incremental updater can reschedule the updates or
103 /// process depending on the number of updates.
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| /external/swiftshader/third_party/llvm-10.0/llvm/include/llvm/Analysis/ |
| D | DomTreeUpdater.h | 72 /// as having no pending updates. This function does not check
77 /// Returns true if there are DominatorTree updates queued.
81 /// Returns true if there are PostDominatorTree updates queued.
88 /// These methods provide APIs for submitting updates to the DominatorTree and
93 /// 1. Eager UpdateStrategy: Updates are submitted and then flushed
95 /// 2. Lazy UpdateStrategy: Updates are submitted but only flushed when you
97 /// when you submit a bunch of updates multiple times which can then
98 /// add up to a large number of updates between two queries on the
99 /// DominatorTree. The incremental updater can reschedule the updates or
101 /// process depending on the number of updates.
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | scatter_nd_op.cc | 49 // If shape_input has 0 elements, then we need to have indices and updates with
51 // then updates should also have 0 elements, otherwise we should error.
72 const Tensor& updates = c->input(1); in Compute() local
79 OP_REQUIRES(c, updates.shape().dims() >= 1, in Compute()
81 "Updates shape must have rank at least one. Found:", in Compute()
82 updates.shape().DebugString())); in Compute()
92 updates.shape().num_elements()), in Compute()
94 "Indices and updates specified for empty output shape")); in Compute()
100 c, indices.shape().dim_size(i) == updates.shape().dim_size(i), in Compute()
105 ") of updates[shape=", updates.shape().DebugString(), "]")); in Compute()
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| D | scatter_op.cc | 33 // Check whether updates.shape = indices.shape + params.shape[1:]
34 static bool ValidShapes(const Tensor& params, const Tensor& updates, in ValidShapes() argument
36 if (updates.dims() == 0) return true; in ValidShapes()
37 if (updates.dims() != indices.dims() + params.dims() - 1) return false; in ValidShapes()
39 if (updates.dim_size(d) != indices.dim_size(d)) { in ValidShapes()
44 if (params.dim_size(d) != updates.dim_size(d - 1 + indices.dims())) { in ValidShapes()
52 const Tensor& indices, const Tensor& updates) { in DoValidationChecking() argument
59 c, ValidShapes(params, updates, indices), in DoValidationChecking()
60 errors::InvalidArgument("Must have updates.shape = indices.shape + " in DoValidationChecking()
61 "params.shape[1:] or updates.shape = [], got ", in DoValidationChecking()
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| /external/tensorflow/tensorflow/compiler/tf2xla/lib/ |
| D | scatter.cc | 35 const xla::XlaOp& buffer, const xla::XlaOp& updates, in XlaScatter() argument
41 TF_ASSIGN_OR_RETURN(xla::Shape updates_shape, builder->GetShape(updates)); in XlaScatter()
72 // succeed since it updates a slice of size 1. in XlaScatter()
81 // Example of a 1-D scatter that updates two [3,1] tensors in a tensor of in XlaScatter()
87 // updates = s32[3,2] parameter(2) in XlaScatter()
88 // scatter = s32[3,3] scatter(operand, indices, updates), in XlaScatter()
96 // Example of a 1-D scatter that updates two [1,3] tensors in a tensor of in XlaScatter()
101 // updates = s32[2,3] parameter(2) in XlaScatter()
102 // scatter = s32[3,3] scatter(operand, indices, updates), in XlaScatter()
115 // updates = s32[2,2] parameter(2) in XlaScatter()
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|
| /external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/ |
| D | verifier_scatter_op.mlir | 5 %scatter_indices: tensor<10x2xi32>, %updates: tensor<10x300xf32>) ->
7 %0 = "mhlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
27 %scatter_indices: tensor<*xi32>, %updates: tensor<*xf32>) ->
29 %0 = "mhlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
50 %scatter_indices: tensor<10x2xf32>, %updates: tensor<10x300xf32>) ->
53 %0 = "mhlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
74 %scatter_indices: tensor<10x2xi32>, %updates: tensor<*xf32>) ->
77 %0 = "mhlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
98 %scatter_indices: tensor<10x2xi32>, %updates: tensor<10x300xf32>) ->
101 %0 = "mhlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
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|
| /external/tensorflow/tensorflow/compiler/xla/mlir_hlo/stablehlo/tests/ |
| D | verify_scatter.mlir | 5 %scatter_indices: tensor<10x2xi32>, %updates: tensor<10x300xf32>) ->
7 %0 = "stablehlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
27 %scatter_indices: tensor<*xi32>, %updates: tensor<*xf32>) ->
29 %0 = "stablehlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
50 %scatter_indices: tensor<10x2xf32>, %updates: tensor<10x300xf32>) ->
53 %0 = "stablehlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
74 %scatter_indices: tensor<10x2xi32>, %updates: tensor<*xf32>) ->
77 %0 = "stablehlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
98 %scatter_indices: tensor<10x2xi32>, %updates: tensor<10x300xf32>) ->
101 %0 = "stablehlo.scatter" (%input_tensor, %scatter_indices, %updates) ({
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_TensorScatterUpdate.pbtxt | 16 name: "updates"
18 Updates to scatter into output.
24 A new tensor with the given shape and updates applied according
28 summary: "Scatter `updates` into an existing tensor according to `indices`."
30 This operation creates a new tensor by applying sparse `updates` to the passed
32 This operation is very similar to `tf.scatter_nd`, except that the updates are
42 - The order in which updates are applied is nondeterministic, so the output
52 if `indices.shape[-1] = tensor.rank` this Op indexes and updates scalar elements.
53 if `indices.shape[-1] < tensor.rank` it indexes and updates slices of the input
57 The overall shape of `updates` is:
|
| D | api_def_ScatterNd.pbtxt | 13 name: "updates"
27 A new tensor with the given shape and updates applied according
31 summary: "Scatters `updates` into a tensor of shape `shape` according to `indices`."
33 Scatter sparse `updates` according to individual values at the specified
39 is zero-initialized. Calling `tf.scatter_nd(indices, updates, shape)`
41 `tf.tensor_scatter_nd_add(tf.zeros(shape, updates.dtype), indices, updates)`
43 If `indices` contains duplicates, the associated `updates` are accumulated
47 This is because the order in which the updates are applied is nondeterministic
63 `updates` is a tensor with shape:
79 updates = tf.constant([9, 10, 11, 12])
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| D | api_def_ScatterNdNonAliasingAdd.pbtxt | 17 name: "updates"
27 updated with `updates`.
32 from `updates` according to indices `indices`. The updates are non-aliasing:
35 respect to both `input` and `updates`.
46 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
55 updates = tf.constant([9, 10, 11, 12])
56 output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
64 See `tf.scatter_nd` for more details about how to make updates to slices.
|
| D | api_def_TensorScatterSub.pbtxt | 16 name: "updates"
18 Updates to scatter into output.
24 A new tensor copied from tensor and updates subtracted according to the indices.
27 summary: "Subtracts sparse `updates` from an existing tensor according to `indices`."
29 This operation creates a new tensor by subtracting sparse `updates` from the
31 This operation is very similar to `tf.scatter_nd_sub`, except that the updates
43 `shape`. `updates` is a tensor with shape
55 updates = tf.constant([9, 10, 11, 12])
57 updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
73 updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
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| D | api_def_ScatterUpdate.pbtxt | 16 name: "updates"
35 summary: "Applies sparse updates to a variable reference."
41 ref[indices, ...] = updates[...]
44 ref[indices[i], ...] = updates[i, ...]
47 ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
54 duplicate entries in `indices`, the order at which the updates happen
57 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| /external/tensorflow/tensorflow/python/keras/ |
| D | optimizer_v1.py | 58 self.updates = []
193 self.updates = [state_ops.assign_add(self.iterations, 1)]
206 self.updates.append(state_ops.assign(m, v))
217 self.updates.append(state_ops.assign(p, new_p))
218 return self.updates
268 self.updates = [state_ops.assign_add(self.iterations, 1)]
281 self.updates.append(state_ops.assign(a, new_a))
288 self.updates.append(state_ops.assign(p, new_p))
289 return self.updates
307 updated during training. The more updates a parameter receives,
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|
| /external/flatbuffers/go/ |
| D | table.go | 297 // MutateBool updates a bool at the given offset.
303 // MutateByte updates a Byte at the given offset.
309 // MutateUint8 updates a Uint8 at the given offset.
315 // MutateUint16 updates a Uint16 at the given offset.
321 // MutateUint32 updates a Uint32 at the given offset.
327 // MutateUint64 updates a Uint64 at the given offset.
333 // MutateInt8 updates a Int8 at the given offset.
339 // MutateInt16 updates a Int16 at the given offset.
345 // MutateInt32 updates a Int32 at the given offset.
351 // MutateInt64 updates a Int64 at the given offset.
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| /external/google-cloud-java/java-asset/proto-google-cloud-asset-v1p2beta1/src/main/java/com/google/cloud/asset/v1p2beta1/ |
| D | FeedOrBuilder.java | 65 * A list of the full names of the assets to receive updates. You must specify
66 * either or both of asset_names and asset_types. Only asset updates matching
84 * A list of the full names of the assets to receive updates. You must specify
85 * either or both of asset_names and asset_types. Only asset updates matching
103 * A list of the full names of the assets to receive updates. You must specify
104 * either or both of asset_names and asset_types. Only asset updates matching
123 * A list of the full names of the assets to receive updates. You must specify
124 * either or both of asset_names and asset_types. Only asset updates matching
144 * A list of types of the assets to receive updates. You must specify either
145 * or both of asset_names and asset_types. Only asset updates matching
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|
| /external/mesa3d/docs/relnotes/ |
| D | 6.4 | 36 Glide (3dfx Voodoo1/2) requires updates
37 SVGA requires updates
38 DJGPP requires updates
39 GGI requires updates
40 BeOS requires updates
41 Allegro requires updates
42 D3D requires updates
44 The drivers which require updates mostly need to be updated to work
|
| /external/coreboot/Documentation/releases/ |
| D | coreboot-4.3-relnotes.md | 69 * intel/strago: GPIO, DDR, & SD config, FSP updates, Clock fixes
73 ### Continued updates for the Intel Skylake platform
75 * google/chell, glados, & lars: FSP & Memory updates, Add Fan & NHLT
77 * intel/kunimitsu: FSP & GPIO updates, Add Fan & NHLT (audio) support
88 * Updates to get the ADA compiler to work correctly for coreboot
98 * Toolchain updates: new versions of GMP & MPFR. Add ADA.
99 * Updates for building on NetBSD & OS X
105 * libpayload: updates for cbfs, XHCI and DesignWare HCD controllers
124 Areas with significant work on updates and fixes
133 * soc/intel/braswell: FSP & ACPI updates, GPIO & clock Fixes
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| /external/apache-commons-math/src/main/java/org/apache/commons/math3/ode/events/ |
| D | EventFilter.java | 57 /** Number of past transformers updates stored. */
70 private final double[] updates; field in EventFilter
86 this.updates = new double[HISTORY_SIZE]; in EventFilter()
99 Arrays.fill(updates, extremeT); in init()
124 System.arraycopy(updates, 1, updates, 0, last); in g()
126 updates[last] = extremeT; in g()
140 if (updates[i] <= t) { in g()
163 System.arraycopy(updates, 0, updates, 1, updates.length - 1); in g()
165 updates[0] = extremeT; in g()
178 for (int i = 0; i < updates.length - 1; ++i) { in g()
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| /external/grpc-grpc/test/core/security/ |
| D | grpc_tls_certificate_distributor_test.cc | 64 // if the status updates are correct.
78 // and check in each test if the status updates are correct.
159 // updates are correct.
283 // Push credential updates to kRootCert1Name and check if the status works as in TEST_F()
287 // Check the updates are delivered to watcher 1. in TEST_F()
290 // Push credential updates to kRootCert2Name. in TEST_F()
293 // Check the updates are delivered to watcher 2. in TEST_F()
296 // Push credential updates to kIdentityCert1Name and check if the status works in TEST_F()
301 // Check the updates are delivered to watcher 1 and watcher 2. in TEST_F()
337 // Push credential updates to kRootCert1Name and check if the status works as in TEST_F()
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