Searched refs:shape_min (Results 1 – 9 of 9) sorted by relevance
/third_party/mindspore/mindspore/ops/_op_impl/_custom_op/ |
D | minmax_update_perlayer.py | 54 shape_min = te.lang.cce.util.shape_to_list(min_val.shape) 55 min_val = te.lang.cce.broadcast(min_val, shape_min, x.dtype) 56 max_val = te.lang.cce.broadcast(max_val, shape_min, x.dtype) 64 x_min = te.lang.cce.broadcast(x_min, shape_min) 65 x_max = te.lang.cce.broadcast(x_max, shape_min) 106 shape_min, _, _ = util.produce_shapes(min_shape, input_shape) 109 min_data = tvm.placeholder(shape_min, name="min_data", dtype=min_dtype) 110 max_data = tvm.placeholder(shape_min, name="max_data", dtype=max_dtype)
|
D | fake_quant_perlayer.py | 57 shape_min = te.lang.cce.util.shape_to_list(min_val.shape) 58 quant_min = te.lang.cce.broadcast(quant_min, shape_min, x.dtype) 59 quant_max = te.lang.cce.broadcast(quant_max, shape_min, x.dtype) 120 shape_min, _, _ = util.produce_shapes(min_shape, input_shape) 128 min_data = tvm.placeholder(shape_min, name="min_data", dtype=min_dtype) 129 max_data = tvm.placeholder(shape_min, name="max_data", dtype=max_dtype)
|
D | fake_quant_perlayer_grad.py | 85 shape_min = te.lang.cce.util.shape_to_list(min_val.shape) 88 quant_min = te.lang.cce.broadcast(quant_min, shape_min) 89 quant_max = te.lang.cce.broadcast(quant_max, shape_min) 146 shape_min, _, _ = util.produce_shapes(min_shape, input_shape) 155 min_data = tvm.placeholder(shape_min, name="min_data", dtype=min_dtype) 156 max_data = tvm.placeholder(shape_min, name="max_data", dtype=max_dtype)
|
D | minmax_update_perchannel.py | 54 shape_min = te.lang.cce.util.shape_to_list(min_val.shape) 67 x_min = te.lang.cce.broadcast(x_min, shape_min) 68 x_max = te.lang.cce.broadcast(x_max, shape_min)
|
/third_party/mindspore/mindspore/core/ops/ |
D | dropout_gen_mask.cc | 126 auto shape_min = shape_abstract->get_min_value(); in InferShape() local 127 MS_EXCEPTION_IF_NULL(shape_min); in InferShape() 128 auto min_value = shape_min->isa<ValueList>() ? shape_min->cast<ValueListPtr>()->value() in InferShape() 129 : shape_min->cast<ValueTuplePtr>()->value(); in InferShape()
|
D | reduce_sum.cc | 169 ShapeVector shape_min = {}; in InferShape() local 171 InferImplReduceFuncCalShape(&shape_min, input_min_shape, axis_value, keep_dims); in InferShape() 173 return std::make_shared<abstract::Shape>(out_shape, shape_min, shape_max); in InferShape()
|
/third_party/mindspore/mindspore/core/ops/grad/ |
D | conv2d_backprop_filter.cc | 84 auto shape_min = GetValue<std::vector<int64_t>>(shape_min_value); in Conv2DBackpropFilterInferShape() local 87 if (shape_max.size() != filter_len || shape_min.size() != filter_len) { in Conv2DBackpropFilterInferShape() 92 if (shape_min[i] == shape_max[i]) { in Conv2DBackpropFilterInferShape() 93 out_shape.push_back(shape_min[i]); in Conv2DBackpropFilterInferShape() 98 ret_shape = std::make_shared<abstract::Shape>(out_shape, shape_min, shape_max); in Conv2DBackpropFilterInferShape()
|
D | conv2d_backprop_input.cc | 113 auto shape_min = GetValue<std::vector<int64_t>>(shape_min_value); in Conv2DBackpropInputInferShape() local 116 if (shape_max.size() != x_size_len || shape_min.size() != x_size_len) { in Conv2DBackpropInputInferShape() 121 if (shape_min[i] == shape_max[i]) { in Conv2DBackpropInputInferShape() 122 out_shape.push_back(shape_min[i]); in Conv2DBackpropInputInferShape() 127 ret_shape = std::make_shared<abstract::Shape>(out_shape, shape_min, shape_max); in Conv2DBackpropInputInferShape()
|
/third_party/mindspore/mindspore/core/abstract/ |
D | prim_maths.cc | 151 ShapeVector shape_min = {}; in InferImplReduceFunc() local 155 InferImplReduceFuncCalShape(&shape_min, x_shape_min, axis, keep_dims_value); in InferImplReduceFunc() 157 …e_shared<AbstractTensor>(input_x->element(), std::make_shared<Shape>(shape, shape_min, shape_max)); in InferImplReduceFunc()
|