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/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/sparse_ops/
H A Dsparse_cross_op_test.py82 ['batch2-FC1-F1', 'batch2-FC1-F2']]),
84 ['batch2-FC2-F1', 'batch2-FC2-F2']])
87 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',
88 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'
98 ['batch2-FC1-F1', 'batch2-FC1-F2']],
101 ['batch2-FC2-F1', 'batch2-FC2-F2']],
108 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',
109 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'
120 ['batch2-FC2-F1', 'batch2-FC2-F2']])
135 ['batch2-FC2-F1', 'batch2-FC2-F2']],
[all …]
/aosp_15_r20/external/pytorch/aten/src/ATen/native/mkldnn/xpu/
H A DBlas.cpp171 // result = beta * input + alpha * (batch1 @ batch2)
175 const Tensor& batch2, in baddbmm_out() argument
179 checkBackend("baddbmm_out", {input, batch1, batch2}, Backend::XPU); in baddbmm_out()
181 TORCH_CHECK(batch2.dim() == 3, "expected 3D tensor"); in baddbmm_out()
184 batch1.size(0), batch1.size(1), batch2.size(2)}; in baddbmm_out()
205 if (batch1.is_complex() || batch2.scalar_type() == ScalarType::Double) { in baddbmm_out()
229 onednn::matmul(result, batch1, batch2, at::Tensor(), true, attr); in baddbmm_out()
236 const Tensor& batch2, in baddbmm_() argument
239 … the same, got: input ", self.dtype(), ", batch1: ", batch1.dtype(), ", batch2: ", batch2.dtype()); in baddbmm_()
241 self, batch1, batch2, beta, alpha, self); in baddbmm_()
[all …]
/aosp_15_r20/external/pytorch/aten/src/ATen/native/mps/operations/
H A DLinearAlgebra.mm304 const Tensor& batch2,
313 TORCH_CHECK(batch2.is_mps());
320 TORCH_CHECK(batch2.dim() == 3, "batch2 must be a 3D tensor");
321 TORCH_CHECK(batch1.size(0) == batch2.size(0),
322 "batch1 and batch2 must have same number of batches, got ",
325 batch2.size(0));
326 TORCH_CHECK(batch1.size(2) == batch2.size(1),
332 batch2.size(1),
334 batch2.size(2),
360 …key += getTensorsStringKey({batch1, batch2, input}) + ":" + std::to_string(beta.toDouble()) + ":" +
[all …]
/aosp_15_r20/external/pytorch/tools/autograd/
H A Ddeprecated.yaml13 - name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor
14 aten: addbmm(self, batch1, batch2, beta, alpha)
16 - name: addbmm_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor…
17 aten: addbmm_(self, batch1, batch2, beta, alpha)
19 - name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) …
20 aten: addbmm_out(out, self, batch1, batch2, beta, alpha)
22 - name: addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2) -> Tensor
23 aten: addbmm(self, batch1, batch2, beta, 1)
25 - name: addbmm_(Scalar beta, Tensor(a!) self, Tensor batch1, Tensor batch2) -> Tensor(a!)
26 aten: addbmm_(self, batch1, batch2, beta, 1)
[all …]
H A Dderivatives.yaml238 - name: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tens…
240 …(0).expand_symint({ batch1.sym_size(0), batch1.sym_size(1), batch2.sym_size(2) }).bmm(batch2.trans…
241 …batch2: maybe_multiply(batch1.transpose(1, 2).conj().bmm(grad.unsqueeze(0).expand_symint({ batch1.…
358 - name: baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Ten…
360 batch1: maybe_multiply(grad.bmm(batch2.transpose(1, 2).conj()), alpha.conj())
361 batch2: maybe_multiply(batch1.transpose(1, 2).conj().bmm(grad), alpha.conj())
/aosp_15_r20/external/pytorch/aten/src/ATen/native/
H A DLinearAlgebra.cpp284 void common_checks_baddbmm_bmm(Meta& meta, const Tensor& batch1, const Tensor& batch2, const Scalar… in common_checks_baddbmm_bmm() argument
286 TORCH_CHECK(batch2.dim() == 3, "batch2 must be a 3D tensor"); in common_checks_baddbmm_bmm()
289 const auto batch2_sizes = batch2.sizes(); in common_checks_baddbmm_bmm()
298 "Expected size for first two dimensions of batch2 tensor to be: [", in common_checks_baddbmm_bmm()
303 meta.set_output_raw_strided(0, output_size, {}, batch2.options()); in common_checks_baddbmm_bmm()
318 outnames = namedinference::compute_baddbmm_outnames(result, batch1, batch2, self); in common_checks_baddbmm_bmm()
321 outnames = namedinference::compute_bmm_outnames(result, batch1, batch2); in common_checks_baddbmm_bmm()
334 TORCH_META_FUNC(baddbmm)(const Tensor& self, const Tensor& batch1, const Tensor& batch2, const Scal… in TORCH_META_FUNC()
335 auto self_ = expand_size(self, {batch1.size(0), batch1.size(1), batch2.size(2)}, "baddbmm"); in TORCH_META_FUNC()
336 … the same, got: input ", self.dtype(), ", batch1: ", batch1.dtype(), ", batch2: ", batch2.dtype()); in TORCH_META_FUNC()
[all …]
/aosp_15_r20/external/sdk-platform-java/gax-java/gax/src/test/java/com/google/api/gax/rpc/
H A DBatchTest.java68 Batch<LabeledIntList, List<Integer>> batch2 = createBatch(request2); in testMerge() local
70 batch1.merge(batch2); in testMerge()
82 Batch<LabeledIntList, List<Integer>> batch2 = createBatch(request2); in testMergeStartEmpty() local
84 batch1.merge(batch2); in testMergeStartEmpty()
88 Truth.assertThat(batch1.getCallable()).isSameInstanceAs(batch2.getCallable()); in testMergeStartEmpty()
116 Batch<LabeledIntList, List<Integer>> batch2 = createBatch(request2); in testBatchMergerImpl() local
119 batchMerger.merge(batch1, batch2); in testBatchMergerImpl()
/aosp_15_r20/packages/modules/Bluetooth/android/app/tests/unit/src/com/android/bluetooth/opp/
DBluetoothOppServiceTest.java171 BluetoothOppBatch batch2 = new BluetoothOppBatch(mService, shareInfo2); in deleteShare_deleteShareAndCorrespondingBatch() local
172 batch2.mStatus = Constants.BATCH_STATUS_FINISHED; in deleteShare_deleteShareAndCorrespondingBatch()
175 mService.mBatches.add(batch2); in deleteShare_deleteShareAndCorrespondingBatch()
180 assertThat(mService.mBatches).containsExactly(batch2); in deleteShare_deleteShareAndCorrespondingBatch()
/aosp_15_r20/external/pytorch/benchmarks/operator_benchmark/pt/
H A Dbmm_test.py15 "batch2": torch.rand(
28 def forward(self, batch1, batch2): argument
29 return self.op(batch1, batch2)
H A Dadd_test.py122 "batch2": torch.rand(
134 def forward(self, input_one, batch1, batch2): argument
135 return torch.addbmm(input_one, batch1, batch2)
/aosp_15_r20/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/
H A Dmake_batched_features_dataset_test.py130 batch2 = self._run_actual_batch(outputs2)
132 self.assertAllEqual(batch1[i], batch2[i])
159 batch2 = self._run_actual_batch(outputs2)
161 all_equal = all_equal and np.array_equal(batch1[i], batch2[i])
H A Dmake_csv_dataset_test.py729 batch2 = nest.flatten(self.evaluate(next2()))
731 self.assertAllEqual(batch1[i], batch2[i])
759 batch2 = nest.flatten(self.evaluate(next2()))
761 all_equal = all_equal and np.array_equal(batch1[i], batch2[i])
/aosp_15_r20/external/pytorch/test/fx/
H A Dtest_shape_inference.py47 …"Expected size for first two dimensions of batch2 tensor to be: [s0, (s2//2) + 12] but got: [s0, 1…
57 … "Expected size for first two dimensions of batch2 tensor to be: [s0, 40] but got: [s0, s5]."
66 … "Expected size for first two dimensions of batch2 tensor to be: [s0, 47*s6] but got: [s0*s6, 47]."
/aosp_15_r20/external/pytorch/torch/
H A D_meta_registrations.py2020 def meta_baddbmm(self, batch1, batch2, *, beta=1, alpha=1): argument
2023 dim3 = batch2.size(2)
2026 torch._check(batch2.dim() == 3, lambda: "batch2 must be a 3D tensor")
2028 self.dtype == batch1.dtype == batch2.dtype,
2029 …dtypes must be the same, got: input: {self.dtype}, batch1: {batch1.dtype}, batch2: {batch2.dtype}",
2032 batch2_sizes = batch2.shape
2038 f"Expected size for first two dimensions of batch2 tensor to be: "
3129 def meta_addbmm(self, batch1, batch2, *, beta=1, alpha=1): argument
3131 dim2 = batch2.size(2)
3134 torch._check(batch2.dim() == 3, lambda: "batch2 must be a 3D tensor")
[all …]
/aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/
H A DBlas.cpp511 … Tensor& result, const Tensor& self, const Tensor& batch1, const Tensor& batch2, const Scalar& bet… in baddbmm_out_cuda_impl() argument
543 int64_t k = (transpose_result ? batch2 : batch1).sizes()[leading_dim]; in baddbmm_out_cuda_impl()
547 …auto batch1_ = prepare_batch_matrix_for_cublas(transpose_result ? batch2 : batch1, transpose_batch… in baddbmm_out_cuda_impl()
548 …auto batch2_ = prepare_batch_matrix_for_cublas(transpose_result ? batch1 : batch2, transpose_batch… in baddbmm_out_cuda_impl()
607 TORCH_IMPL_FUNC(baddbmm_out_cuda)(const Tensor& self, const Tensor& batch1, const Tensor& batch2, c… in TORCH_IMPL_FUNC()
610 baddbmm_out_cuda_impl(result, self, batch1, batch2, beta, alpha); in TORCH_IMPL_FUNC()
614 TORCH_IMPL_FUNC(bmm_out_cuda)(const Tensor& batch1, const Tensor& batch2, const Tensor &result) { in TORCH_IMPL_FUNC()
619 baddbmm_out_cuda_impl(result, result, batch1, batch2, beta, alpha); in TORCH_IMPL_FUNC()
/aosp_15_r20/external/pytorch/torch/_inductor/
H A Ddecomposition.py232 batch2: torch.Tensor,
236 batch2.shape[2] == 1
238 out = (self.unsqueeze(-1) * batch2.unsqueeze(1)).sum(dim=2)
242 batch2.size(-1) == 1
246 self.squeeze(1) * batch2.squeeze(-1), dim=1, keepdim=True
/aosp_15_r20/external/pytorch/test/distributed/fsdp/
H A Dtest_fsdp_grad_acc.py154 for batch1, batch2 in itertools.combinations(batches, r=2):
155 for t1, t2 in zip(batch1, batch2):
/aosp_15_r20/external/pytorch/aten/src/ATen/functorch/
H A DBatchRulesLinearAlgebra.cpp126 …const Tensor& input, const Tensor& batch1, const Tensor& batch2, const Scalar& beta, const Scalar&… in addbmm_decomp() argument
127 Tensor out = at::bmm(batch1, batch2).sum(0); in addbmm_decomp()
138 …const Tensor& input, const Tensor& batch1, const Tensor& batch2, const Scalar& beta, const Scalar&… in baddbmm_decomp() argument
139 Tensor out = at::bmm(batch1, batch2); in baddbmm_decomp()
/aosp_15_r20/external/pytorch/aten/src/ATen/native/nested/
H A DNestedTensorMatmul.cpp22 TORCH_CHECK(mat2.dim() == 3, "batch2 must be a 3D tensor"); in bmm_nested()
28 "Expected size for the 1st dimension of batch2 tensor to be: ", ntensors, in bmm_nested()
/aosp_15_r20/external/pytorch/aten/src/ATen/native/nested/cuda/
H A DNestedTensorMatmul.cu291 TORCH_CHECK(mat2_ptr->dim() == 3, "batch2 must be a 3D tensor"); in bmm_nested_cuda()
295 "Expected size for the 1st dimension of batch2 tensor to be: ", in bmm_nested_cuda()
/aosp_15_r20/external/tensorflow/tensorflow/compiler/tests/
H A Dimage_ops_test.py56 batch2 = image_ops.hsv_to_rgb(batch1)
63 batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2],
68 self.assertAllCloseAccordingToType(batch2, join2, half_rtol=0.000002)
70 batch2, inp, bfloat16_atol=0.03, half_rtol=0.02)
/aosp_15_r20/external/perfetto/ui/src/trace_processor/
H A Dquery_result_unittest.ts251 const batch2 = protos.QueryResult.create({ constant
265 qr.appendResultBatch(protos.QueryResult.encode(batch2).finish());
/aosp_15_r20/external/tensorflow/tensorflow/python/ops/
H A Dimage_ops_test.py74 batch2 = image_ops.hsv_to_rgb(batch1)
80 batch1, batch2, join1, join2 = self.evaluate(
81 [batch1, batch2, join1, join2])
85 self.assertAllClose(batch2, join2)
86 self.assertAllClose(batch2, inp)
130 batch2 = image_ops.yiq_to_rgb(batch1)
136 batch1, batch2, join1, join2 = self.evaluate(
137 [batch1, batch2, join1, join2])
141 self.assertAllClose(batch2, join2, rtol=1e-4, atol=1e-4)
142 self.assertAllClose(batch2, inp, rtol=1e-4, atol=1e-4)
[all …]
/aosp_15_r20/external/pytorch/test/xpu/
H A Dtest_gemm.py752 batch2 = torch.rand((1, 2, 2), dtype=torch.float32, device=device)
756 y = torch.baddbmm(input_tensor, batch1, batch2, beta=0.0)
759 y_ref = torch.bmm(batch1, batch2)
760 y = torch.baddbmm(input_tensor, batch1, batch2, beta=0.0, out=out)
/aosp_15_r20/external/pytorch/torch/fx/experimental/shape_inference/
H A Dinfer_symbol_values.py26 elif constraint.find("first two dimensions of batch2 tensor to be") != -1:

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