| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| D | scatter_test.cc | 30 Literal* scatter_indices, Literal* updates) { in RunTest() argument
31 RunTest(hlo_text, {operand, scatter_indices, updates}); in RunTest()
67 Literal updates = LiteralUtil::CreateR2<int32>({{10, 20, 30}, {70, 80, 90}}); in XLA_TEST_F() local
68 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
98 Literal updates = LiteralUtil::CreateR2<int32>({{10, 20, 30}, {70, 80, 90}}); in XLA_TEST_F() local
99 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
126 Literal updates = in XLA_TEST_F() local
128 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
192 Literal updates = in XLA_TEST_F() local
195 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/array_ops/ |
| D | scatter_nd_ops_test.py | 63 def _NumpyScatterNd(ref, indices, updates, op): argument
71 flat_updates = updates.reshape((num_updates, slice_size))
80 def _NumpyUpdate(ref, indices, updates): argument
81 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
84 def _NumpyAdd(ref, indices, updates): argument
85 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p + u)
88 def _NumpySub(ref, indices, updates): argument
89 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p - u)
92 def _NumpyMul(ref, indices, updates): argument
93 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p * u)
[all …]
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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 TfLiteTypeGetName(updates->type)); in Prepare()
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | scatter_nd_op_test.py | 50 def _NumpyScatterNd(ref, indices, updates, op): argument
58 flat_updates = updates.reshape((num_updates, slice_size))
67 def _NumpyUpdate(indices, updates, shape): argument
68 ref = np.zeros(shape, dtype=updates.dtype)
69 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
108 updates = _AsType(np.random.randn(*(updates_shape)), vtype)
111 np_out = np_scatter(indices, updates, ref_shape)
113 tf_out = tf_scatter(indices, updates, ref_shape)
122 def _runScatterNd(self, indices, updates, shape): argument
124 updates_placeholder = array_ops.placeholder(updates.dtype)
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| /external/tensorflow/tensorflow/python/ops/ |
| D | state_ops.py | 256 def scatter_update(ref, indices, updates, use_locking=True, name=None): argument
302 return gen_state_ops.scatter_update(ref, indices, updates,
305 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
310 def scatter_nd_update(ref, indices, updates, use_locking=True, name=None): argument
365 ref, indices, updates, use_locking, name)
367 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
372 def scatter_add(ref, indices, updates, use_locking=False, name=None): argument
416 return gen_state_ops.scatter_add(ref, indices, updates,
419 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
424 def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): argument
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | scatter_ops_test.py | 35 def _NumpyAdd(ref, indices, updates): argument
39 ref[indx] += updates[i]
47 def _NumpySub(ref, indices, updates): argument
49 ref[indx] -= updates[i]
57 def _NumpyMul(ref, indices, updates): argument
59 ref[indx] *= updates[i]
67 def _NumpyDiv(ref, indices, updates): argument
69 ref[indx] /= updates[i]
77 def _NumpyMin(ref, indices, updates): argument
79 ref[indx] = np.minimum(ref[indx], updates[i])
[all …]
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| D | batch_scatter_ops_test.py | 35 def _NumpyUpdate(ref, indices, updates): argument
38 ref[indx] = updates[i]
63 updates = _AsType(
71 np_scatter(new, indices, updates)
77 ref.batch_scatter_update(ops.IndexedSlices(indices, updates))
79 self.evaluate(tf_scatter(ref, indices, updates))
103 updates = np.array([-3, -4, -5]).astype(np.float32)
110 self.evaluate(state_ops.batch_scatter_update(ref, indices, updates))
116 self.evaluate(state_ops.batch_scatter_update(ref, indices, updates))
121 self.evaluate(state_ops.batch_scatter_update(ref, indices, updates))
|
| /external/tensorflow/tensorflow/core/kernels/ |
| D | scatter_op.cc | 33 static bool ValidShapes(const Tensor& params, const Tensor& updates, in ValidShapes() argument
35 if (updates.dims() == 0) return true; in ValidShapes()
36 if (updates.dims() != indices.dims() + params.dims() - 1) return false; in ValidShapes()
38 if (updates.dim_size(d) != indices.dim_size(d)) { in ValidShapes()
43 if (params.dim_size(d) != updates.dim_size(d - 1 + indices.dims())) { in ValidShapes()
51 const Tensor& indices, const Tensor& updates) { in DoValidationChecking() argument
58 c, ValidShapes(params, updates, indices), in DoValidationChecking()
61 "updates.shape ", updates.shape().DebugString(), in DoValidationChecking()
94 const Tensor& updates = c->input(2); in DoCompute() local
95 DoValidationChecking(c, params, indices, updates); in DoCompute()
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| D | scatter_nd_op.cc | 71 const Tensor& updates = c->input(1); in Compute() local
78 OP_REQUIRES(c, updates.shape().dims() >= 1, in Compute()
81 updates.shape().DebugString())); in Compute()
91 updates.shape().num_elements()), in Compute()
99 c, indices.shape().dim_size(i) == updates.shape().dim_size(i), in Compute()
104 ") of updates[shape=", updates.shape().DebugString(), "]")); in Compute()
108 OP_REQUIRES(c, updates.shape().dims() - outer_dims == shape.dims() - ix, in Compute()
112 outer_dims, ",", updates.shape().dims(), in Compute()
113 ") of updates[shape=", updates.shape().DebugString(), "]")); in Compute()
115 for (int i = 0; i + outer_dims < updates.shape().dims(); ++i) { in Compute()
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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
|
| D | 6.4.2.rst | 53 Glide (3dfx Voodoo1/2) requires updates
54 SVGA requires updates
55 DJGPP requires updates
56 GGI requires updates
57 BeOS requires updates
58 Allegro requires updates
59 D3D requires updates
|
| /external/tensorflow/tensorflow/python/kernel_tests/v1_compat_tests/ |
| D | scatter_nd_ops_test.py | 49 def _NumpyScatterNd(ref, indices, updates, op): argument
57 flat_updates = updates.reshape((num_updates, slice_size))
66 def _NumpyMin(ref, indices, updates): argument
67 return _NumpyScatterNd(ref, indices, updates, np.minimum)
70 def _NumpyMax(ref, indices, updates): argument
71 return _NumpyScatterNd(ref, indices, updates, np.maximum)
111 updates = _AsType(np.random.randn(*(updates_shape)), vtype)
116 np_scatter(new, indices, updates)
120 self.evaluate(tf_scatter(ref_var, indices, updates))
141 updates = np.array([-3, -4, -5]).astype(np.float32)
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_ResourceScatterAdd.pbtxt | 16 name: "updates"
21 summary: "Adds sparse updates to the variable referenced by `resource`."
26 ref[indices, ...] += updates[...]
29 ref[indices[i], ...] += updates[i, ...]
32 ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterDiv.pbtxt | 16 name: "updates"
21 summary: "Divides sparse updates into the variable referenced by `resource`."
26 ref[indices, ...] /= updates[...]
29 ref[indices[i], ...] /= updates[i, ...]
32 ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterMul.pbtxt | 16 name: "updates"
21 summary: "Multiplies sparse updates into the variable referenced by `resource`."
26 ref[indices, ...] *= updates[...]
29 ref[indices[i], ...] *= updates[i, ...]
32 ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterSub.pbtxt | 16 name: "updates"
21 summary: "Subtracts sparse updates from the variable referenced by `resource`."
26 ref[indices, ...] -= updates[...]
29 ref[indices[i], ...] -= updates[i, ...]
32 ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterMin.pbtxt | 16 name: "updates"
21 …summary: "Reduces sparse updates into the variable referenced by `resource` using the `min` operat…
26 ref[indices, ...] = min(ref[indices, ...], updates[...])
29 ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
32 ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterMax.pbtxt | 16 name: "updates"
21 …summary: "Reduces sparse updates into the variable referenced by `resource` using the `max` operat…
26 ref[indices, ...] = max(ref[indices, ...], updates[...])
29 ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
32 ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_TensorScatterUpdate.pbtxt | 16 name: "updates"
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_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_TensorScatterAdd.pbtxt | 16 name: "updates"
24 A new tensor copied from tensor and updates added according to the indices.
27 summary: "Adds sparse `updates` to an existing tensor according to `indices`."
29 This operation creates a new tensor by adding sparse `updates` to the passed
31 This operation is very similar to `tf.scatter_nd_add`, except that the updates
44 `indices.shape[-1]` of `tensor.shape`. `updates` is a tensor with shape
56 updates = tf.constant([9, 10, 11, 12])
58 updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
74 updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
79 updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
|
| D | api_def_TensorScatterSub.pbtxt | 16 name: "updates"
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],
78 updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
|
| 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 | 63 self.updates = []
198 self.updates = [state_ops.assign_add(self.iterations, 1)]
210 self.updates.append(state_ops.assign(m, v))
221 self.updates.append(state_ops.assign(p, new_p))
222 return self.updates
269 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
344 self.updates = [state_ops.assign_add(self.iterations, 1)]
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| /external/llvm-project/llvm/test/Transforms/Attributor/ |
| D | depgraph.ll | 71 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
72 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
73 ; GRAPH-NEXT: updates [AANoUnwind] for CtxI ' %6 = call i32* @checkAndAdvance(i32* %5)' at posit…
74 ; GRAPH-NEXT: updates [AANoUnwind] for CtxI ' %6 = call i32* @checkAndAdvance(i32* %5)' at posit…
75 ; GRAPH-NEXT: updates [AANoUnwind] for CtxI ' %6 = call i32* @checkAndAdvance(i32* %5)' at posit…
76 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
77 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
78 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
79 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
80 ; GRAPH-NEXT: updates [AANoCapture] for CtxI ' %2 = load i32, i32* %0, align 4' at position {arg…
[all …]
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