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/aosp_15_r20/test/mlts/models/assets/models_list/
Dmobilenet_topk_aosp.json8 "className": "TopK",
25 "className": "TopK",
44 "className": "TopK",
61 "className": "TopK",
80 "className": "TopK",
97 "className": "TopK",
116 "className": "TopK",
133 "className": "TopK",
152 "className": "TopK",
169 "className": "TopK",
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/aosp_15_r20/external/tensorflow/tensorflow/core/tpu/kernels/
H A Dtopk_ops.cc123 // Returns the TopK unique values in the array in sorted order and the
133 // returned. If a TopK element never appears in the input due to
146 // TopK with reasonable semantics.
219 auto topk = CreateTopKUnique(builder, input, input_shape, k_, false); in Compile() local
220 ctx->SetOutput(0, topk.first); in Compile()
221 ctx->SetOutput(1, topk.second); in Compile()
234 // suppressing identical elements. For most TopK users, the indices of
235 // the TopK elements are important but the relative order of the TopK
237 // the indices of the TopK elements of the output of MakeUnique are
238 // the same as the indices of the TopK elements of the inputs.
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/spmd/
H A Dcustom_call_handler.cc123 // Each partition needs to do TopK separately, thus the base shape in HandleCustomCallTopK()
133 auto topk = b_.AddInstruction( in HandleCustomCallTopK() local
135 topk->set_sharding(custom_call_sharding); in HandleCustomCallTopK()
137 PartitionedHlo partitioned_topk(topk, replicated_shape, in HandleCustomCallTopK()
139 topk = partitioned_topk.hlo(); in HandleCustomCallTopK()
141 // Get value from TopK. in HandleCustomCallTopK()
144 topk->shape().tuple_shapes(0), topk, 0)); in HandleCustomCallTopK()
154 // Get index from TopK. in HandleCustomCallTopK()
157 topk->shape().tuple_shapes(1), topk, 1)); in HandleCustomCallTopK()
181 // Creates replicated sort to do TopK, the input is value and index pairs in HandleCustomCallTopK()
[all …]
/aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/
H A DTensorTopK.cu48 at::cuda::detail::TensorInfo<T, IndexType> topK, in gatherTopK() argument
70 at::cuda::detail::IndexToOffset<T, IndexType, Dim>::get(slice, topK); in gatherTopK()
75 T* topKSliceStart = &topK.data[topKSliceStartIndex]; in gatherTopK()
190 at::cuda::detail::TensorInfo<T, IndexType> topK, in launch() argument
197 TORCH_INTERNAL_ASSERT(getGridFromTiles(numInputSlices, grid), "Too many slices for topk"); in launch()
207 topK, in launch()
246 // for largest topk, k_to_find = slice_size - k + 1
504 at::cuda::detail::TensorInfo<T, IndexType> topK, in C10_LAUNCH_BOUNDS_1()
542 at::cuda::detail::IndexToOffset<T, IndexType, Dim>::get(slice_idx, topK); in C10_LAUNCH_BOUNDS_1()
547 T* topKSliceStart = &topK.data[topKSliceStartIndex]; in C10_LAUNCH_BOUNDS_1()
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/aosp_15_r20/external/guava/android/guava-tests/test/com/google/common/collect/
H A DTopKSelectorTest.java66 assertThat(top.topK()).isEmpty(); in testZeroK()
71 assertThat(top.topK()).isEmpty(); in testNoElementsOffered()
79 assertThat(top.topK()).containsExactly(2, 3, 5).inOrder(); in testOfferedFewerThanK()
86 assertThat(top.topK()).containsExactly(1, 2, 3, 4).inOrder(); in testOfferedKPlusOne()
94 assertThat(top.topK()).containsExactly(1, 2).inOrder(); in testOfferedThreeK()
101 assertThat(top.topK()).containsExactly("a", "B", "c").inOrder(); in testDifferentComparator()
121 assertThat(top.topK()).containsExactlyElementsIn(Collections.nCopies(k, 0)); in testWorstCase()
131 assertThat(top.topK()).isEqualTo(Ints.asList(0, 0, 0, 0, 0, 0, 0)); in testExceedMaxIteration()
/aosp_15_r20/external/guava/guava-tests/test/com/google/common/collect/
H A DTopKSelectorTest.java66 assertThat(top.topK()).isEmpty(); in testZeroK()
71 assertThat(top.topK()).isEmpty(); in testNoElementsOffered()
79 assertThat(top.topK()).containsExactly(2, 3, 5).inOrder(); in testOfferedFewerThanK()
86 assertThat(top.topK()).containsExactly(1, 2, 3, 4).inOrder(); in testOfferedKPlusOne()
94 assertThat(top.topK()).containsExactly(1, 2).inOrder(); in testOfferedThreeK()
101 assertThat(top.topK()).containsExactly("a", "B", "c").inOrder(); in testDifferentComparator()
121 assertThat(top.topK()).containsExactlyElementsIn(Collections.nCopies(k, 0)); in testWorstCase()
131 assertThat(top.topK()).isEqualTo(Ints.asList(0, 0, 0, 0, 0, 0, 0)); in testExceedMaxIteration()
/aosp_15_r20/external/executorch/exir/tests/
H A Dtest_op_convert.py21 "aten::topk",
27 op_overload = torch.ops.aten.topk.values
32 op_overload = torch.ops.aten.topk.default
38 self.assertTrue(out_var_op is not torch.ops.aten.topk.default)
39 self.assertTrue(out_var_op is torch.ops.aten.topk.values)
62 aten.topk.default: (aten.topk.values, ("values", "indices")),
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/client/lib/
H A Dsorting.cc28 XlaOp TopK(XlaOp input, int64_t k) { in TopK() function
115 // The k in TopK is static so we shouldn't generate a dynamic dimension in TopK()
141 // The k in TopK is static so we shouldn't generate a dynamic dimension in TopK()
163 // Do normal TopK when per partition size is smaller than or equal to k. in TopKWithPartitions()
165 return TopK(input, k); in TopKWithPartitions()
206 // Slice topk. in TopKWithPartitions()
216 // Get the values and indices for the first topk so that they can in TopKWithPartitions()
234 // Slice topk. in TopKWithPartitions()
242 // Pass the result of the first TopK to the while loop and do in TopKWithPartitions()
H A Dsorting_test.cc35 xla::GetTupleElement(xla::TopK(x, 3), 0); in XLA_TEST_F()
43 xla::GetTupleElement(xla::TopK(x_rev, 3), 1); in XLA_TEST_F()
52 xla::GetTupleElement(xla::TopK(x_rev, 3), 1); in XLA_TEST_F()
65 xla::GetTupleElement(xla::TopK(x, kSize), 0); in XLA_TEST_F()
75 xla::GetTupleElement(xla::TopK(a, 5), 1); in XLA_TEST_F()
129 xla::GetTupleElement(xla::TopK(x, 1000), 0); in XLA_TEST_F()
/aosp_15_r20/external/pytorch/test/
H A Dtest_sort_and_select.py321 torch.topk(tensor, 6, sorted=sorted, out=(values, indices))
322 values_cont, indices_cont = tensor.topk(6, sorted=sorted)
461 # gather from the input using the topk indices and compare against
467 topKVal, topKInd = t.topk(k, dim, dir, True)
496 # This tests the code path where on CUDA, topk is implemented with sort.
501 # This tests the code path where on CUDA, topk is implemented with multiblock
507 # Calling topk on a quantized scalar input used to segfault,
510 x.topk(1)
515 self.assertRaises(TypeError, lambda: q.topk(4, True))
803 # test different topk paths on cuda
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/aosp_15_r20/test/mlts/benchmark/src/com/android/nn/benchmark/evaluators/
DTopK.java35 public class TopK implements EvaluatorInterface { class
50 int[] topk = new int[K_TOP]; in EvaluateAccuracy() local
54 throw new IllegalArgumentException("Needs mInferenceOutput for TopK"); in EvaluateAccuracy()
59 "supported by TopK evaluator"); in EvaluateAccuracy()
90 topk[k]++; in EvaluateAccuracy()
96 outValues.add(new Float((float) topk[i] / (float) total)); in EvaluateAccuracy()
100 float top1 = ((float) topk[0] / (float) total); in EvaluateAccuracy()
/aosp_15_r20/test/mlts/benchmark/results/
Dgenerate_result.py248 """Are these evaluator keys from TopK evaluator?"""
296 topk = [TOPK_BASELINE_TEMPLATE.format(val=x) for x in val]
298 top1=topk[0], top2=topk[1], top3=topk[2], top4=topk[3],
299 top5=topk[4]
304 topk = [TOPK_DIFF_TEMPLATE.format(
308 top1=topk[0], top2=topk[1], top3=topk[2], top4=topk[3],
309 top5=topk[4]
/aosp_15_r20/external/tensorflow/tensorflow/lite/kernels/
H A Dtopk_v2.cc51 "TopK k input must have 1 or more dimensions."); in ResizeOutput()
54 "TopK k is higher than the internal dimension."); in ResizeOutput()
177 void TopK(int32 row_size, int32 num_rows, const T* data, int32 k, in TopK() function
257 TopK(row_size, num_rows, GetTensorData<float>(input), k, in Eval()
261 TopK(row_size, num_rows, input->data.uint8, k, output_indexes->data.i32, in Eval()
265 TopK(row_size, num_rows, input->data.int8, k, output_indexes->data.i32, in Eval()
269 TopK(row_size, num_rows, input->data.i32, k, output_indexes->data.i32, in Eval()
273 TopK(row_size, num_rows, input->data.i64, k, output_indexes->data.i32, in Eval()
277 TF_LITE_KERNEL_LOG(context, "Type %s is currently not supported by TopK.", in Eval()
/aosp_15_r20/external/pytorch/android/test_app/app/src/main/java/org/pytorch/testapp/
H A DUtils.java7 public static int[] topK(float[] a, final int topk) { in topK() method in Utils
8 float values[] = new float[topk]; in topK()
10 int ixs[] = new int[topk]; in topK()
14 for (int j = 0; j < topk; j++) { in topK()
16 for (int k = topk - 1; k >= j + 1; k--) { in topK()
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/
H A Dtopk_rewriter.cc155 // Non-slice user means we are not doing a TopK in SortIsInTopK()
191 // Check if sort is in TopK. in TransformToCustomCall()
233 HloInstruction* topk = comp->AddInstruction( in TransformToCustomCall() local
234 HloInstruction::CreateCustomCall(topk_shape, {input}, "TopK")); in TransformToCustomCall()
237 topk->shape().tuple_shapes(0), topk, 0)); in TransformToCustomCall()
240 topk->shape().tuple_shapes(1), topk, 1)); in TransformToCustomCall()
261 "topk rewriter"; in TransformToCustomCall()
H A Dtopk_rewriter.h25 // This pass pattern-matches soups of HLOs executing a TopK operation and
26 // replaces them with a TopK CustomCall when the given values are supported by
34 absl::string_view name() const override { return "topk-rewriter"; } in name()
42 // Check if the sort instruction is in TopK.
51 // Check if the sort instruction is in TopK.
/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/
H A Dtopk_op.cc40 class TopK : public OpKernel { class
42 explicit TopK(OpKernelConstruction* context) : OpKernel(context) { in TopK() function in tensorflow::TopK
44 if (num_inputs() < 2) { // k is an attr (TopK). in TopK()
244 TopK<CPUDevice, type>)
247 REGISTER_KERNELS_NAME(TopK, type); \
275 Name("TopK").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
276 TopK<GPUDevice, type>) \
281 TopK<GPUDevice, type>)
/aosp_15_r20/external/guava/guava/src/com/google/common/collect/
H A DTopKSelector.java46 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
63 * relative to the natural ordering of the elements, and returns them via {@link #topK} in
74 * relative to the specified comparator, and returns them via {@link #topK} in ascending order.
85 * relative to the natural ordering of the elements, and returns them via {@link #topK} in
96 * relative to the specified comparator, and returns them via {@link #topK} in descending order.
276 public List<T> topK() { in topK() method in TopKSelector
286 T[] topK = Arrays.copyOf(castBuffer, bufferSize); in topK() local
288 return Collections.unmodifiableList(Arrays.asList(topK)); in topK()
/aosp_15_r20/external/guava/android/guava/src/com/google/common/collect/
H A DTopKSelector.java45 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
62 * relative to the natural ordering of the elements, and returns them via {@link #topK} in
73 * relative to the specified comparator, and returns them via {@link #topK} in ascending order.
84 * relative to the natural ordering of the elements, and returns them via {@link #topK} in
95 * relative to the specified comparator, and returns them via {@link #topK} in descending order.
275 public List<T> topK() { in topK() method in TopKSelector
285 T[] topK = Arrays.copyOf(castBuffer, bufferSize); in topK() local
287 return Collections.unmodifiableList(Arrays.asList(topK)); in topK()
/aosp_15_r20/external/tensorflow/tensorflow/compiler/tests/
H A Dsort_ops_test.py254 def topk(v, k=k): function
258 topk, [x.astype(dtype)],
290 def topk(v, k=k): function
294 topk, [x.astype(dtype)],
305 topk = nn_ops.top_k(p, k=4)
307 topk,
320 topk = nn_ops.top_k(p, k=6)
321 results = sess.run(topk, {
/aosp_15_r20/external/executorch/backends/qualcomm/builders/
H A Dop_topk.py20 class TopK(NodeVisitor): class
21 target = ["aten.topk.default"]
52 … "[QNN Delegate Op Builder]: QNN currently only supports channel as dimension for topK.",
62 # QNN constraint, topk output_0 requires having the same quant config as input
72 # topk output_1 is index, do not quantize it.
/aosp_15_r20/external/tensorflow/tensorflow/python/compiler/tensorrt/test/
H A Dtopk_test.py31 values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
43 return {"TRTEngineOp_000": ["Const", "TopK"]}
52 values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
53 # Reshape will act as a layer between the TopK output and the engine
68 return {"TRTEngineOp_000": ["Const", "TopK", "Reshape", "Reshape/shape"]}
/aosp_15_r20/external/pytorch/test/quantization/core/experimental/
H A Dquantization_util.py55 def accuracy(output, target, topk=(1,)): argument
58 maxk = max(topk)
61 _, pred = output.topk(maxk, 1, True, True)
66 for k in topk:
80 acc1, acc5 = accuracy(output, target, topk=(1, 5))
/aosp_15_r20/external/pytorch/torch/nn/utils/
H A Dprune.py467 topk = torch.topk(prob.view(-1), k=nparams_toprune)
468 mask.view(-1)[topk.indices] = 0
525 topk = torch.topk(torch.abs(t).view(-1), k=nparams_toprune, largest=False)
526 # topk will have .indices and .values
527 mask.view(-1)[topk.indices] = 0
616 # 1s wherever topk.indices indicates, along self.dim.
724 # L_n norm along each channel to then find the topk based on this
729 topk = torch.topk(norm, k=nparams_tokeep, largest=True)
730 # topk will have .indices and .values
733 # 1s wherever topk.indices indicates, along self.dim.
[all …]
/aosp_15_r20/external/pytorch/torch/utils/bottleneck/
H A D__main__.py88 def print_cprofile_summary(prof, sortby='tottime', topk=15): argument
91 cprofile_stats.print_stats(topk)
120 def print_autograd_prof_summary(prof, mode, sortby='cpu_time', topk=15): argument
138 topk_events = sorted_events[:topk]
142 'description': f'top {topk} events sorted by {sortby}',

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