/third_party/mindspore/tests/ut/cpp/parallel/ops_info/ |
D | reduce_method_test.cc | 31 ReduceSumInfoPtr reduce_sum; variable 66 reduce_sum = std::make_shared<ReduceSumInfo>("sum_info", inputs_shape, outputs_shape, attr); in SetUp() 67 reduce_sum->set_input_value(val); in SetUp() 74 reduce_sum->Init(strategy); in TEST_F() 75 Shape dev_matrix_shape = reduce_sum->dev_matrix_shape(); in TEST_F() 85 reduce_sum->Init(strategy); in TEST_F() 86 std::vector<TensorInfo> inputs = reduce_sum->inputs_tensor_info(); in TEST_F() 87 std::vector<TensorInfo> outputs = reduce_sum->outputs_tensor_info(); in TEST_F() 106 reduce_sum->Init(strategy); in TEST_F() 107 std::vector<TensorInfo> inputs = reduce_sum->inputs_tensor_info(); in TEST_F() [all …]
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/third_party/mindspore/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/ |
D | confusion_mul_grad_fusion.cc | 33 CNodePtr CreateFusionNode(const FuncGraphPtr &graph, const CNodePtr &reduce_sum, const AnfNodePtr &… in CreateFusionNode() argument 36 MS_EXCEPTION_IF_NULL(reduce_sum); in CreateFusionNode() 46 fusion_node->set_scope(reduce_sum->scope()); in CreateFusionNode() 47 AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node); in CreateFusionNode() 48 AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node); in CreateFusionNode() 49 …types = {AnfAlgo::GetOutputInferDataType(mul0, 0), AnfAlgo::GetOutputInferDataType(reduce_sum, 0)}; in CreateFusionNode() 50 …auto shapes = {AnfAlgo::GetOutputInferShape(mul0, 0), AnfAlgo::GetOutputInferShape(reduce_sum, 0)}; in CreateFusionNode() 79 const AnfNodePtr &reduce_sum, const AnfNodePtr &input2) { in QuitFusion() argument 82 MS_EXCEPTION_IF_NULL(reduce_sum); in QuitFusion() 108 if (IsDepend(*graph, mul0->input(1), {reduce_sum})) { in QuitFusion() [all …]
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/third_party/mindspore/tests/st/model_zoo_tests/yolov3_darknet53/src/ |
D | loss.py | 25 self.reduce_sum = P.ReduceSum() 29 xy_loss = self.reduce_sum(xy_loss, ()) 38 self.reduce_sum = P.ReduceSum() 42 wh_loss = self.reduce_sum(wh_loss, ()) 51 self.reduce_sum = P.ReduceSum() 56 confidence_loss = self.reduce_sum(confidence_loss, ()) 65 self.reduce_sum = P.ReduceSum() 69 class_loss = self.reduce_sum(class_loss, ())
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/third_party/mindspore/tests/ut/cpp/python_input/gtest_input/pre_activate/ |
D | softmax_grad_ext_fusion.py | 46 reduce_sum = ReduceSum(mul, axes) 47 sub = Sub(input0, reduce_sum) 66 reduce_sum = ReduceSum(mul, axes) 67 sub = Sub(input0, reduce_sum) 86 reduce_sum = ReduceSum(mul, axes) 87 sub = Sub(input0, reduce_sum)
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D | square_sum_fusion.py | 22 reduce_sum = P.ReduceSum() variable 46 res = reduce_sum(square_output, axis) 57 sum_output = reduce_sum(square_output, axis)
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D | confusion_softmax_grad_rule.py | 20 reduce_sum = P.ReduceSum(keep_dims=True) variable 47 res = reduce_sum(res, axis)
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D | confusion_mul_grad_fusion.py | 21 reduce_sum = P.ReduceSum() variable 49 output2 = reduce_sum(mul1, axis)
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/third_party/mindspore/tests/ut/python/parallel/ |
D | test_different_type_for_div_op.py | 49 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 53 out = self.reduce_sum(out, (0, 1)) 72 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 76 out = self.reduce_sum(out, (0, 1)) 95 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 99 out = self.reduce_sum(out, (0, 1))
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D | test_auto_parallel_reduce_method.py | 62 self.reduce_sum = P.ReduceSum(keep_dims=False) 67 out = self.reduce_sum(out, (0,)) 86 self.reduce_sum = P.ReduceSum(keep_dims=False) 91 out = self.reduce_sum(out, (0, 1)) 110 self.reduce_sum = P.ReduceSum(keep_dims=False) 115 out = self.reduce_sum(out, -1)
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D | test_reduce_method_info.py | 88 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2) 93 out = self.reduce_sum(out, (1,)) 115 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2) 120 out = self.reduce_sum(out, (0, 1)) 142 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2) 147 out = self.reduce_sum(out, -1) 169 self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2) 174 out = self.reduce_sum(out, -1) 196 self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2) 200 out = self.reduce_sum(out, 0) [all …]
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D | test_sum_as_loss.py | 49 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 53 out = self.reduce_sum(out, (0, 1)) 72 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 76 out = self.reduce_sum(out, (0, 1))
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D | test_scalar_loss.py | 44 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1) 49 out = self.reduce_sum(out, (0, 1))
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D | test_cus_matmul_dds.py | 52 self.reduce_sum = P.ReduceSum() 76 local_prob_reduce = self.reduce_sum(local_prob, 2) 77 global_prob_reduce = self.reduce_sum(global_prob, 2)
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/third_party/mindspore/mindspore/nn/probability/zhusuan/framework/ |
D | bn.py | 35 self.reduce_sum = P.ReduceSum(keep_dims=True) 61 log_prob = self.reduce_sum(self.normal_dist( 82 log_prob = self.reduce_sum(
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/third_party/mindspore/tests/st/control/ |
D | test_ascend_control_sink.py | 161 self.reduce_sum = op.ReduceSum() 164 x_sum = self.reduce_sum(x) 165 y_sum = self.reduce_sum(y) 173 self.reduce_sum = op.ReduceSum() 176 x_sum = self.reduce_sum(x) 177 y_sum = self.reduce_sum(y) 185 self.reduce_sum = op.ReduceSum() 188 x_sum = self.reduce_sum(x)
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/third_party/mindspore/tests/st/networks/models/deeplabv3/src/ |
D | losses.py | 38 self.reduce_sum = P.ReduceSum(keep_dims=False) 53 loss = self.reduce_sum(weighted_losses, (0,)) 57 present = self.reduce_sum(present, (0,))
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/third_party/mindspore/mindspore/nn/loss/ |
D | loss.py | 65 self.reduce_sum = P.ReduceSum() 99 x = self.reduce_sum(x, self.get_axis(x)) 640 intersection = self.reduce_sum(self.mul(logits.view(-1), label.view(-1))) 641 unionset = self.reduce_sum(self.mul(logits.view(-1), logits.view(-1))) + \ 642 self.reduce_sum(self.mul(label.view(-1), label.view(-1))) 856 self.reduce_sum = P.ReduceSum() 888 return -self.reduce_sum(targets * self.log(pred + 1.0e-20), 1) 960 true_logits = self.reshape(self.reduce_sum(dots_as_matrix, 1), (-1, num_true)) 1115 self.reduce_sum = P.ReduceSum() 1128 prod_sum = self.reduce_sum(logits_x1 * logits_x2, (1,)) [all …]
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/third_party/mindspore/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/ |
D | dynamic_rnn_grad_fission_v2.cc | 457 auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs); in CreateDwReduceSum() local 460 … {AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 0)}, reduce_sum.get()); in CreateDwReduceSum() 462 AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int64_t>{0}), reduce_sum); in CreateDwReduceSum() 463 AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); in CreateDwReduceSum() 464 AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); in CreateDwReduceSum() 465 return reduce_sum; in CreateDwReduceSum() 507 auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs); in CreateDbReduceSum() local 510 AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reduce_sum.get()); in CreateDbReduceSum() 512 AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int64_t>{0}), reduce_sum); in CreateDbReduceSum() 513 AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); in CreateDbReduceSum() [all …]
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/third_party/mindspore/tests/ut/python/dtype/ |
D | test_list.py | 223 self.reduce_sum = P.ReduceSum() 231 ret_sum = self.reduce_sum(x, self.axis) 242 self.reduce_sum = P.ReduceSum() 246 return self.reduce_sum(x, self.axis) 252 self.reduce_sum = P.ReduceSum() 255 return self.reduce_sum(x)
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/third_party/mindspore/mindspore/nn/probability/distribution/ |
D | categorical.py | 153 self.reduce_sum = P.ReduceSum(keep_dims=True) 199 return self.reduce_sum(index * probs, -1) 215 return self.reduce_sum(self.square(index) * probs, -1) -\ 216 self.square(self.reduce_sum(index * probs, -1)) 227 return self.squeeze(-self.reduce_sum(logits * probs, -1)) 244 return self.squeeze(self.reduce_sum(
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/third_party/mindspore/tests/st/networks/models/bert/src/ |
D | bert_for_pre_training.py | 192 self.reduce_sum = P.ReduceSum() 206 … per_example_loss = self.neg(self.reduce_sum(prediction_scores * one_hot_labels, self.last_idx)) 207 numerator = self.reduce_sum(label_weights * per_example_loss, ()) 208 …denominator = self.reduce_sum(label_weights, ()) + self.cast(F.tuple_to_array((1e-5,)), mstype.flo… 214 per_example_loss = self.neg(self.reduce_sum( 371 self.reduce_sum = P.ReduceSum(keep_dims=False) 424 flag_sum = self.reduce_sum(init, (0,))
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/third_party/mindspore/tests/ut/python/pipeline/parse/ |
D | test_ms_function_pass_non_tensor_inputs.py | 50 reduce_sum = P.ReduceSum() 51 ret = reduce_sum(tensor_x, axis) + tensor_y
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/third_party/mindspore/tests/st/networks/models/resnet50/src_thor/ |
D | metric.py | 51 self.reduce_sum = P.ReduceSum() 60 y_correct = self.reduce_sum(y_correct)
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/third_party/mindspore/tests/st/networks/models/resnet50/src/ |
D | metric.py | 51 self.reduce_sum = P.ReduceSum() 60 y_correct = self.reduce_sum(y_correct)
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/third_party/mindspore/tests/st/auto_monad/ |
D | test_float_overflow.py | 41 self.reduce_sum = P.ReduceSum(keep_dims=True) 54 flag_sum = self.reduce_sum(init, (0,))
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