| /external/executorch/backends/vulkan/runtime/graph/ops/impl/ |
| D | BatchNorm.cpp | 29 "native_batch_norm requires ", in check_and_prepack_arg()
101 void native_batch_norm(ComputeGraph& graph, const std::vector<ValueRef>& args) { in native_batch_norm() function
109 aten._native_batch_norm_legit_no_training.default, native_batch_norm);
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| /external/executorch/exir/tests/ |
| D | dynamic_shape_models.py | 14 batch_norm_op = torch.ops.aten.native_batch_norm.default
40 if nd.target == torch.ops.aten.native_batch_norm.out
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| /external/executorch/backends/xnnpack/_passes/ |
| D | fuse_batch_norm_with_conv.py | 45 bn.target != exir_ops.edge.aten.native_batch_norm.default
64 bn.target == exir_ops.edge.aten.native_batch_norm.default
80 # args[7] for native_batch_norm, but args[6] for
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| /external/executorch/backends/transforms/ |
| D | fuse_batch_norm_with_conv.py | 47 bn.target != exir_ops.edge.aten.native_batch_norm.default
66 bn.target == exir_ops.edge.aten.native_batch_norm.default
82 # args[7] for native_batch_norm, but args[6] for
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| /external/pytorch/test/lazy/ |
| D | test_reuse_ir.py | 116 # so we call `torch.ops.aten.native_batch_norm` to bypass the checks.
117 z, _, _ = torch.ops.aten.native_batch_norm(
129 z_lazy, _, _ = torch.ops.aten.native_batch_norm(
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| /external/pytorch/aten/src/ATen/functorch/ |
| D | BatchRulesNorm.cpp | 848 …OT good. In the ideal world, we do NOT want to convert the new legit op back into native_batch_norm
849 // as native_batch_norm has a problematic schema--it promises it is functional when it is not. Howe…
851 // make native_batch_norm composite implicit within a few weeks and we can fix this before vmap wor…
855 …return at::native_batch_norm(self, weight_opt, bias_opt, running_mean, running_var, train, momentu… in _native_batch_norm_legit_batch()
861 …return at::native_batch_norm(self, weight_opt, bias_opt, Tensor(), Tensor(), train, momentum, eps); in _native_batch_norm_legit_no_stats_batch()
865 VMAP_SUPPORT(native_batch_norm, NATIVE_BATCH_NORM_BATCH_RULE(native_batch_norm)); in TORCH_LIBRARY_IMPL()
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| /external/pytorch/aten/src/ATen/native/ |
| D | group_norm.cpp | 15 #include <ATen/ops/native_batch_norm.h>
230 auto outputs = at::native_batch_norm( in math_group_norm()
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| D | ts_native_functions.yaml | 87 - native_batch_norm
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| D | layer_norm.cpp | 18 #include <ATen/ops/native_batch_norm.h>
240 auto outputs = at::native_batch_norm( in math_native_layer_norm()
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| /external/pytorch/functorch/op_analysis/ |
| D | public_api | 437 native_batch_norm
|
| /external/executorch/kernels/aten/ |
| D | functions.yaml | 274 - op: native_batch_norm.out
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| /external/pytorch/test/ |
| D | test_decomp.py | 194 (torch.bfloat16, torch.ops.aten.native_batch_norm.default): 1e-5,
195 (torch.float16, torch.ops.aten.native_batch_norm.default): 1e-5,
411 # native_batch_norm is only implicit when python dispatcher is on (and noncomposite otherwise)
412 (None, None, "native_batch_norm"),
716 res = torch._decomp.decompositions.native_batch_norm(
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| D | test_meta.py | 738 torch.native_batch_norm: {bf16, f16},
755 torch.native_batch_norm: {f32, f64},
881 aten.native_batch_norm.default: {bf16, f16},
908 aten.native_batch_norm.default: {f32, f64},
916 aten.native_batch_norm.out: {bf16, f16, f32, f64}
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| /external/pytorch/torch/jit/ |
| D | _shape_functions.py | 1135 def native_batch_norm( function
1435 …"aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor?…
1436 native_batch_norm,
1440 native_batch_norm,
1444 native_batch_norm,
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| /external/pytorch/benchmarks/dynamo/microbenchmarks/operator_inp_logs/timm_train/ |
| D | convmixer_768_32_training.txt | 32 Operator: aten.native_batch_norm.default
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| /external/pytorch/test/quantization/eager/ |
| D | test_fuse_eager.py | 92 …with self.assertRaisesRegex(RuntimeError, "Could not run 'aten::native_batch_norm' with arguments …
101 …with self.assertRaisesRegex(RuntimeError, "Could not run 'aten::native_batch_norm' with arguments …
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| /external/pytorch/torch/csrc/utils/ |
| D | schema_info.cpp | 286 …{"aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor… in getTrainingOps()
288 …{"aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Te… in getTrainingOps()
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| /external/pytorch/test/edge/ |
| D | selected_operators.yaml | 266 aten::native_batch_norm.out:
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| /external/pytorch/benchmarks/dynamo/microbenchmarks/operator_inp_logs/torchbench_train/ |
| D | dcgan_training.txt | 29 Operator: aten.native_batch_norm.default
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| D | maml_omniglot_training.txt | 28 Operator: aten.native_batch_norm.default
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| D | pytorch_CycleGAN_and_pix2pix_training.txt | 31 Operator: aten.native_batch_norm.default
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| /external/pytorch/test/functorch/ |
| D | test_ops.py | 563 "native_batch_norm"
1037 xfail("native_batch_norm"),
1152 skip("native_batch_norm"),
1314 xfail("native_batch_norm"),
1561 xfail("native_batch_norm"),
1676 xfail("native_batch_norm"),
2060 xfail("native_batch_norm"),
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| /external/pytorch/torchgen/ |
| D | model.py | 2503 # (native_batch_norm is a good example, although this isn't the case today).
2510 # native_batch_norm (mutable variant)
2511 # native_batch_norm.functional (functional variant)
2516 # native_batch_norm.mutable (mutable variant)
2517 # native_batch_norm (functional variant)
2558 # for now on ops that have a functional + mutable variant (like native_batch_norm).
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| /external/pytorch/aten/src/ATen/native/cudnn/ |
| D | BatchNorm.cpp | 241 // This keeps a consistent output with native_batch_norm in cudnn_batch_norm()
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| /external/pytorch/test/expect/ |
| D | HasDecompTest.test_aten_core_operators.expect | 365 aten::native_batch_norm.out
|