/aosp_15_r20/external/tensorflow/tensorflow/python/ops/ragged/ |
H A D | ragged_where_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 34 dict( # shape=[D1, (D2)] 38 dict( # shape=[D1, (D2)] 47 dict( # shape=[D1, (D2)] 54 # Coordinate-retrieval mode 56 dict( # shape=[D1] 59 dict( # shape=[D1, D2] 62 dict( # shape=[D1, (D2)] 66 dict( # shape=[D1, (D2), (D3)] 73 dict( # shape=[D1, (D2), D3] [all …]
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H A D | ragged_boolean_mask_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 61 descr='data.shape=[7]; mask.shape=[7]', 66 descr='data.shape=[5, 3]; mask.shape=[5]', 71 descr='data.shape=[5, 3]; mask.shape=[5, 3]', 77 descr='data.shape=[3, 2, 2]; mask.shape=[3]', 82 descr='data.shape=[3, 2, 2]; mask.shape=[3]', 87 descr='data.shape=[3, 2, 2]; mask.shape=[3, 2]', 94 descr='data.shape=[3, 2, 2]; mask.shape=[3, 2, 2]', 100 descr='data.shape=mask.shape=[2, 2, 2, 2]', 112 descr='data.shape=[5, (D2)]; mask.shape=[5, (D2)]', [all …]
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H A D | ragged_array_ops.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 64 mask: A potentially ragged boolean tensor. `mask`'s shape must be a prefix 65 of `data`'s shape. `rank(mask)` must be known statically. 73 * `output.ragged_rank = max(data.ragged_rank, rank(mask) - 1)`. 76 ValueError: if `rank(mask)` is not known statically; or if `mask.shape` is 77 not a prefix of `data.shape`. 108 if mask.shape.ndims is None: 109 raise ValueError('mask.shape.ndims must be known statically.') 110 elif mask.shape.ndims == 0: 113 # If mask is ragged, then recurse with a non-ragged mask. [all …]
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H A D | ragged_reverse_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 40 descr='data.shape=[5, (D2)]; axis=[0]', 45 descr='data.shape=[5, (D2)]; axis=[1]', 50 descr='data.shape=[5, (D2), (D3)]; axis=[0, -1]', 52 axis=[0, -1], 55 descr='data.shape=[2, (D2), 2]; axis=[2]', 61 descr='data.shape=[2, (D2), (D3)]; axis=[-1]', 63 axis=[-1], 66 descr='data.shape=[2, (D2), (D3)]; axis=[]',
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H A D | ragged_expand_dims_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 29 # An example 4-d ragged tensor with shape [3, (D2), (D3), 2], and the 40 4: [[[[[d3] for d3 in d2] for d2 in d1] for d1 in d0] for d0 in EXAMPLE4D] 78 # 4D Ragged Inputs: [3, (D2), (D3), 2] 114 self.assertEqual(expanded.shape.ndims, rt.shape.ndims + 1) 116 self.assertEqual(expanded.shape.as_list(), expected_shape)
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H A D | ragged_map_fn_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 41 # transformation that returns with shape: 42 # [d1, (d2)] -> [d1] 57 # [d1, (d2)] -> [d1, 2] 67 # [d1, (d2)] -> [d1, (d2)] 76 # [d1, (d2), d3] -> [d1, (d2), d3] 86 # [d1, (d2)] -> [d1, (d2), (d3)] 94 # [d1, (d2), (d3)] -> [d1, (d2), (d3)] 102 # [d1, (d2), (d3)] -> [d1, (d2)] 110 # [d1, (d2), (d3)] -> [d1, (d3)] [all …]
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H A D | ragged_batch_gather_op_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0 154 indices=[[1, 2, -1], [], [], [0, 10]], 163 indices=[1, 100, 0, -1], 165 'Yahoo', '!', 'rejected', 'a', '47.5', '-', 'billion', '-', 172 'Yahoo', '!', 'rejected', 'a', '47.5', '-', 'billion', 173 '-', 'dollar', 'takeover', 'offer', 'from', 'Microsoft', 186 indices=[-1, 0, 1000], 193 descr='Test underbound indices of shape [1, (d2)]', 196 '!', 'rejected', 'a', '47.5', '-', 'billion', '-', 'dollar', 199 indices=[[8, -1]], [all …]
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/aosp_15_r20/external/aws-sdk-java-v2/services/snowball/src/main/resources/codegen-resources/ |
H A D | service-2.json | 4 "apiVersion":"2016-06-30", 13 "uid":"snowball-2016-06-30" 22 "input":{"shape":"CancelClusterRequest"}, string 23 "output":{"shape":"CancelClusterResult"}, string 25 {"shape":"KMSRequestFailedException"}, string 26 {"shape":"InvalidJobStateException"}, string 27 {"shape":"InvalidResourceException"} string 37 "input":{"shape":"CancelJobRequest"}, string 38 "output":{"shape":"CancelJobResult"}, string 40 {"shape":"InvalidResourceException"}, string [all …]
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/aosp_15_r20/external/pytorch/test/fx/ |
H A D | test_z3_gradual_types.py | 69 always have a fixed shape regardless of the input 164 self.assertEqual(s.model()[output].arg(0).arg(1), b.shape[0]) 165 self.assertEqual(s.model()[output].arg(1).arg(1), b.shape[1]) 166 self.assertEqual(s.model()[output].arg(2).arg(1), b.shape[2]) 183 self.assertEqual(s.model()[output].arg(0).arg(1), b.shape[0]) 185 self.assertEqual(s.model()[output].arg(2).arg(1), b.shape[2]) 215 self.assertEqual(s.model()[output].arg(0).arg(1), b.shape[0]) 216 self.assertEqual(s.model()[output].arg(1).arg(1), b.shape[1]) 217 self.assertEqual(s.model()[output].arg(2).arg(1), b.shape[2]) 218 self.assertEqual(s.model()[output].arg(3).arg(1), b.shape[3]) [all …]
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/aosp_15_r20/external/pytorch/torch/fx/experimental/migrate_gradual_types/ |
H A D | constraint_generator.py | 1 # mypy: allow-untyped-decorators 2 # mypy: allow-untyped-defs 38 start_dim = n if start_dim == -1 else abs(start_dim) 48 If the attribute is "device" then the tensor shape is preserved 110 The output replaces the input with the shape of the vector 287 # will treat this as a static shape. So we will not use matching. 298 The output shape differs from the input shape in the last dimension 353 t2 = [symbols[elem] if isinstance(elem, Node) else elem for elem in n.args[1:]] # target shape 358 if t == -1: 521 # TODO: we should figure out why there is a key-error here. [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/gml_st/ |
H A D | fusion.mlir | 1 // RUN: mlir-hlo-opt %s --split-input-file --gml-fusion | FileCheck %s 3 // CHECK-LABEL: @dynamic_broadcast_in_dim_at_tile 4 // CHECK-SAME: %[[ARG:.*]]: tensor<?x?xf32>, %[[SHAPE:.*]]: tensor<3xindex>, %[[TILE:.*]]: !gml_st… 6 %shape : tensor<3xindex>, %tile : !gml_st.tile<3x4x?>) 7 -> tensor<3x4x?xf32> { 8 // CHECK-DAG: %[[C0:.*]] = arith.constant 0 9 // CHECK-DAG: %[[C1:.*]] = arith.constant 1 10 // CHECK-DAG: %[[C2:.*]] = arith.constant 2 11 // CHECK-DAG: %[[S0:.*]] = tensor.extract %[[SHAPE]][%[[C0]]] 12 // CHECK-DAG: %[[S1:.*]] = tensor.extract %[[SHAPE]][%[[C1]]] [all …]
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/aosp_15_r20/external/perfetto/infra/perfetto.dev/ |
H A D | pnpm-lock.yaml | 25 fs-extra: 34 node-watch: 43 /@braintree/sanitize-[email protected]: 44 …resolution: {integrity: sha512-s3jaWicZd0pkP0jf5ysyHUI/RE7MHos6qlToFcGWXVp+ykHOy77OUMrfbgJ9it2C5bo… 48 …resolution: {integrity: sha512-j+gKExEuLmKwvz3OgROXtrJ2UG2x8Ch2YZUxahh+s1F2HZ+wAceUNLkvy6zKCPVRkU+… 52 …resolution: {integrity: sha512-AZkcAA5vnN/v4PDqKyMR5lx7hZttPDgClv83E//FMNhR2TMcLUhfRUBHCmSl0oi9zMg… 56 …resolution: {integrity: sha512-YyFaikqM5sH0ziFZCN3xDC7zeGaB/d0IUb9CATugHWbd1FRFwWwt4ld4OYMPWu5a3Xe… 60 …resolution: {integrity: sha512-j9ednRT81vYJ9OfVuXG6ERSTdEL1xVsNgqpkxMsbIabzSo3goCjDIveeGv5d03om39M… 64 …resolution: {integrity: sha512-lljVXpqXebpsijW71PZaCYeIcE5on1w5DlQy5WH6GLbFryLUrBD4932W/E2BSpfRJWs… 71 …resolution: {integrity: sha512-Ddb+kVXlXst9d+R9PfTIxh1EdNkgoRe5tOX6t01f1lYWOvJnSPDBlG241QLzcyPdoNT… [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/lite/tools/ |
H A D | visualize.py | 8 # http://www.apache.org/licenses/LICENSE-2.0 29 # pylint: disable=g-import-not-at-top 43 body {font-family: sans-serif; background-color: #fa0;} 44 table {background-color: #eca;} 45 th {background-color: black; color: white;} 47 background-color: ffaa00; 55 border-style: solid; 56 border-color: black; 61 border-radius: 5px; 62 background-color: #fec; [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/lite/kernels/ |
H A D | kernel_util.cc | 7 http://www.apache.org/licenses/LICENSE-2.0 45 if (context->tensors != nullptr) { in GetTensorAtIndex() 46 return &context->tensors[tensor_index]; in GetTensorAtIndex() 48 return context->GetTensor(context, tensor_index); in GetTensorAtIndex() 74 // Same as above but returns -1 for invalid inputs instead of status + logging 84 return -1; in ValidateTensorIndexing() 90 context, index, node->inputs->size, node->inputs->data); in GetMutableInput() 102 context, ValidateTensorIndexingSafe(context, index, node->inputs->size, in GetMutableInputSafe() 103 node->inputs->data, &tensor_index)); in GetMutableInputSafe() 124 return tensor->is_variable ? tensor : nullptr; in GetVariableInput() [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/linalg/ |
H A D | banded_triangular_solve_op.cc | 7 http://www.apache.org/licenses/LICENSE-2.0 58 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2), in TensorSliceToEigenMatrix() 59 t->dim_size(1), t->dim_size(2)); in TensorSliceToEigenMatrix() 80 // x_i = (b_i - sum a_ij * x_j) / a_ii, where the sum is from in Run() 81 // j = 0 to i - 1. in Run() 84 // then the sum goes from j = i - band_size to i - 1, since the other in Run() 95 (rhs.row(i) - matrix.block(1, i, i, 1).reverse().transpose() * in Run() 100 (rhs.row(i) - in Run() 101 matrix.block(1, i, num_bands - 1, 1).reverse().transpose() * in Run() 102 output.middleRows(i - (num_bands - 1), num_bands - 1)) / in Run() [all …]
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H A D | matrix_triangular_solve_op_impl.h | 7 http://www.apache.org/licenses/LICENSE-2.0 75 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2), in TensorSliceToEigenMatrix() 76 t->dim_size(1), t->dim_size(2)); in TensorSliceToEigenMatrix() 122 auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); 143 OP_REQUIRES_OK(context, context->GetAttr("lower", &lower_)); 144 OP_REQUIRES_OK(context, context->GetAttr("adjoint", &adjoint_)); 150 const Tensor& in0 = ctx->input(0); 151 const Tensor& in1 = ctx->input(1); 154 if (!ctx->status().ok()) { 158 MatMulBCast bcast(in0.shape().dim_sizes(), in1.shape().dim_sizes()); [all …]
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/aosp_15_r20/cts/tests/tests/graphics/src/android/graphics/drawable/cts/ |
H A D | GradientDrawableTest.java | 8 * http://www.apache.org/licenses/LICENSE-2.0 158 gradientDrawable.setCornerRadius(-2.5f); in testSetCornerRadius() 178 helpTestSetStroke(-2, Color.TRANSPARENT); in testSetStroke() 191 verifySetStroke_WidthGap(-2, Color.TRANSPARENT, -3.4f, -5.5f); in testSetStroke_WidthGap() 205 verifySetStrokeList(-2, ColorStateList.valueOf(Color.TRANSPARENT)); in testSetStrokeList() 219 verifySetStrokeList_WidthGap(-2, ColorStateList.valueOf(Color.TRANSPARENT), -3.4f, -5.5f); in testSetStrokeList_WidthGap() 233 verifySetSize(-30, -40); in testSetSize() 248 int shape; in testSetShape() local 250 shape = GradientDrawable.OVAL; in testSetShape() 251 gradientDrawable.setShape(shape); in testSetShape() [all …]
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/aosp_15_r20/external/ruy/ruy/ |
H A D | kernel_arm32.cc | 7 http://www.apache.org/licenses/LICENSE-2.0 77 // Float kernel for ARM32 out-of-order cores. 79 // use 16 128-bit NEON registers. This is a "first pass" kernel and not 80 // tuned. It is meant to run on out-of-order CPUs like the Krait 400 or A9. 87 // In ARM32 NEON, there are 16 128-bit "q" registers. These registers are in KernelFloat32Neon() 88 // each composed of two 64-bit "d" registers. The asm kernel below has the in KernelFloat32Neon() 90 // Registers q3 -- q10 are accumulators. During accumulation, in KernelFloat32Neon() 91 // q0 -- q2 (d0 -- d5) are used to load data from LHS and RHS. q0 and q1 in KernelFloat32Neon() 97 // /--------------------------| in KernelFloat32Neon() 99 // \--------------------------/ in KernelFloat32Neon() [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/ |
H A D | hlo-legalize-to-linalg.mlir | 1 // RUN: mlir-hlo-opt %s -hlo-legalize-to-linalg -split-input-file --canonicalize | FILECHECK_OPTS="… 3 // CHECK: #map = affine_map<(d0, d1) -> (d0, d1)> 4 // CHECK-LABEL: func @float_add 6 %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> { 8 // CHECK-SAME: {someattr} 9 // CHECK: ^{{[a-z0-9_]*}} 10 // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: f32 11 // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: f32 12 // CHECK: %[[RESULT:[a-zA-Z0-9_]*]] = arith.addf %[[ARG0]], %[[ARG1]] 15 : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> [all …]
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/aosp_15_r20/external/pytorch/test/functorch/ |
H A D | test_ops.py | 6 # This source code is licensed under the BSD-style license found in the 69 # - pytree inputs is allowed (but leaves of the pytree have to all 71 # - if an input is not used as part of derivatives, we will return a 72 # zero-filled tensor for the result 127 # - f' takes only positional arguments 128 # - All arguments to f' are floating-point Tensors 129 # - All outputs of f' are floating-point Tensors 289 # We want this higher-order variant of jvp, so that it can 320 # We want this higher-order variant of jvp, so that it can 423 # Non-contiguous Bugs [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/ |
H A D | matmul_op_impl.h | 7 http://www.apache.org/licenses/LICENSE-2.0 76 // Parallel batch matmul kernel based on the multi-threaded tensor contraction 81 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device(); in Conjugate() 82 auto z = out->tensor<Scalar, 3>(); in Conjugate() 93 auto Tz = out->tensor<Scalar, 3>(); in Run() 101 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device(); in Run() 136 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device(); 142 auto Tz = out->flat_inner_dims<Scalar, 2>(); 147 auto Tz = out->tensor<Scalar, 3>(); 166 // better on vector-matrix and matrix-vector products. [all …]
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/aosp_15_r20/external/libopus/dnn/training_tf2/ |
H A D | rdovae.py | 8 - Redistributions of source code must retain the above copyright 11 - Redistributions in binary form must reproduce the above copyright 52 #return K.clip(p, -self.c, self.c) 62 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) 63 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) 64 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) 68 return soft_quantize(x + (K.random_uniform((128, 16, 80))-.5) ) 73 return x + tf.stop_gradient(quantized - x) 78 y = x - d*tf.math.tanh(x/(.1+d)) 83 n = y_pred.shape[-1] [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/core/common_runtime/eager/ |
H A D | tensor_handle_test.cc | 7 http://www.apache.org/licenses/LICENSE-2.0 30 EXPECT_TRUE(t.shape().IsSameSize(TensorShape({2, 2}))); in TEST() 31 for (int64_t a = 0; a < t.shape().dim_size(0); a++) { in TEST() 32 for (int64_t b = 0; b < t.shape().dim_size(1); b++) { in TEST() 48 EXPECT_TRUE(async_th->CopyInferenceShape(sync_th).ok()); in TEST() 52 EXPECT_TRUE(sync_th->Shape(&sync_shape).ok()); in TEST() 53 EXPECT_TRUE(async_th->Shape(&async_shape).ok()); in TEST() 56 int num_dims = -1; in TEST() 57 EXPECT_TRUE(async_th->NumDims(&num_dims).ok()); in TEST() 60 int64_t num_elements = -1; in TEST() [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/ |
H A D | literal_util.h | 7 http://www.apache.org/licenses/LICENSE-2.0 68 // and the compiler) these CreateR[0-2] methods should explicitly specify the 115 // primitive type. For floating-point types, returns -inf. 118 // primitive type. For floating-point types, returns inf. 121 // primitive type. Fail for non-inexact types. For complex types, returns a 124 // Creates a literal of the given shape where each element is `value`. 156 // Creates a linspace-populated literal with the given number of rows and 238 // Creates a literal with a new shape with the given new dimensions using the 246 // Creates a literal with the supplied shape, and uses the provided value 253 const Shape& shape, [all …]
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/aosp_15_r20/external/pytorch/benchmarks/tensorexpr/ |
H A D | elementwise.py | 32 self.d3 = self.rand( 38 self.inputs = [self.d1, self.d2, self.d3, self.d4] 41 def _eval(self, d1, d2, d3, d4, binary_op, unary_op): argument 55 d3 = unary_op(d3) 59 d3 = unary_op(d1 + 0.002) 63 b = binary_op(d3, d4) 67 def forward(self, d1, d2, d3, d4): argument 70 return self._eval(d1, d2, d3, d4, binary_op, unary_op) 75 [d1, d2, d3, d4] = [self.numpy(d) for d in [self.d1, self.d2, self.d3, self.d4]] 76 return self._eval(d1, d2, d3, d4, binary_op, unary_op) [all …]
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