| /third_party/mindspore/mindspore/core/ops/ |
| D | masked_fill.cc | 63 …return CheckAndConvertUtils::CheckTensorTypeSame(types, {kFloat16, kFloat32, kInt8, kInt32}, op_na… in InferType()
67 {kFloat16, kFloat32, kInt8, kInt32}, op_name); in InferType()
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| D | reluv2.cc | 38 if (x_dtype == kUInt8 || x_dtype == kInt8) { in GetOutputMaskShape()
49 if (x_dtype == kUInt8 || x_dtype == kInt8) { in GetOutputMaskShape()
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| D | sigmoid_cross_entropy_with_logits.cc | 43 …const std::set<TypePtr> valid_types = {kBool, kInt, kInt8, kInt16, kInt32, kInt64, kUIn… in SigmoidCrossEntropyWithLogitsInfer()
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| D | hsigmoid.cc | 38 const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64, kFloat16, kFloat32}; in InferType()
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| D | hashtable_lookup.cc | 43 auto hits = std::make_shared<abstract::AbstractTensor>(kInt8, hits_shape); in HashtableLookupInfer()
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| D | roll.cc | 45 const std::set<TypePtr> valid_types = {kFloat32, kFloat16, kInt32, kUInt32, kInt8, kUInt8}; in InferType()
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| D | reverse_v2.cc | 35 const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64, kUInt8, kUInt16, in InferType()
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| D | broadcast.cc | 57 const std::set<TypePtr> valid_types = {kInt8, kInt32, kFloat16, kFloat32}; in BroadcastInfer()
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| D | zeros.cc | 48 const std::set<TypePtr> valid_types = {kBool, kInt8, kInt16, kInt32, kInt64, kUInt8, in ZerosInferType()
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| D | ones.cc | 46 const std::set<TypePtr> valid_types = {kBool, kInt8, kInt16, kInt32, kInt64, kUInt8, in OnesInferType()
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| D | op_utils.h | 281 const std::set<TypePtr> common_valid_types = {kInt8, kInt16, kInt32, kInt64, kUInt8, kUInt1…
285 kBool, kInt, kInt8, kInt16, kInt32, kInt64, kUInt, kUInt8,
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| D | gather_nd.cc | 53 const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64}; in InferType()
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| D | embedding_lookup.cc | 49 const std::set<TypePtr> int_valid_types = {kInt8, kInt16, kInt32, kInt64}; in EmbeddingLookupInfer()
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| D | index_add.cc | 66 …const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kUInt8, kFloat16, kFloat32, kFloat64… in IndexAddInferType()
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| D | one_hot.cc | 80 {kInt8, kInt16, kInt32, kInt64}, op_name); in OneHotInferType()
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| D | split_v.cc | 88 const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64, kUInt8, in InferType()
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| D | gather.cc | 42 std::set<TypePtr> int_types = {kInt8, kInt16, kInt32, kInt64}; in GatherInfer()
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| D | mat_mul.cc | 95 …const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64, kFloat16, kFloat32, kFloat64… in MatMulInferType()
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| D | conv2d.cc | 242 const std::set<TypePtr> valid_types = {kInt8, kInt32, kInt64, kFloat16, kFloat32}; in Conv2dInferType()
363 const std::set<TypePtr> valid_types = {kInt8, kInt32, kInt64, kFloat16, kFloat32}; in Conv2dInfer()
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| /third_party/mindspore/mindspore/lite/src/runtime/kernel/ascend310/src/ |
| D | custom_kernel.cc | 157 const auto kInt8 = DataType::kNumberTypeInt8; variable
161 REGISTER_CUSTOM_KERNEL(ASCEND310, ACL, kInt8, ACL, kernel::acl::CustomCreateKernel)
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| /third_party/mindspore/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/ |
| D | rec_tensor.h | 24 enum TensorType { kInt8, kFloat16, kFloat32, kDouble64 }; enumerator
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| /third_party/mindspore/mindspore/core/ops/grad/ |
| D | sigmoid_cross_entropy_with_logits_grad.cc | 46 …const std::set<TypePtr> valid_types = {kBool, kInt, kInt8, kInt16, kInt32, kInt64, kUIn… in SigmoidCrossEntropyWithLogitsGradInfer()
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| D | hsigmoid_grad.cc | 52 const std::set<TypePtr> valid_types = {kInt8, kInt16, kInt32, kInt64, kFloat16, kFloat32}; in InferType()
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| D | smooth_l1_loss_grad.cc | 53 …const std::set<TypePtr> valid_types = {kBool, kInt, kInt8, kInt16, kInt32, kInt64, kUIn… in SmoothL1LossGradInfer()
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| /third_party/mindspore/mindspore/core/ir/ |
| D | scalar.h | 94 Int8Imm() : IntergerImm(kInt8), v_(0) {} in Int8Imm()
95 …explicit Int8Imm(int8_t v) : IntergerImm(kInt8), v_(v) { hash_ = hash_combine({tid(), std::hash<in… in Int8Imm()
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