fix: 修复random类算子example的编译和运行问题-ops-math-AtomGit

Mrandom/drop_out_do_mask_v3/examples/test_geir_drop_out_do_mask_v3.cpp+25-7

	@@ -31,7 +31,6 @@
31	#include "array_ops.h"	31	#include "array_ops.h"
32	#include "ge_ir_build.h"	32	#include "ge_ir_build.h"
33		33
34	#include "experiment_ops.h"
35	#include "nn_other.h"	34	#include "nn_other.h"
36	#include "../op_graph/drop_out_do_mask_v3_proto.h"	35	#include "../op_graph/drop_out_do_mask_v3_proto.h"
37		36
	@@ -127,6 +126,7 @@ int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor& input_tensor, TensorD
127	*(pData + i) = value;	126	*(pData + i) = value;
128	}	127	}
129	input_tensor = Tensor(input_tensor_desc, (uint8_t*)pData, data_len);	128	input_tensor = Tensor(input_tensor_desc, (uint8_t*)pData, data_len);
		129	delete[] pData;
130	return SUCCESS;	130	return SUCCESS;
131	}	131	}
132		132
	@@ -139,11 +139,12 @@ int32_t GenOnesData(
139	size *= shapes[i];	139	size *= shapes[i];
140	}	140	}
141	uint32_t data_len = size * GetDataTypeSize(data_type);	141	uint32_t data_len = size * GetDataTypeSize(data_type);
142	int32_t* pData = new (std::nothrow) int32_t[data_len];	142	int64_t *pData = new (std::nothrow) int64_t[size];
		D D DaiHuina16月8日分配内存后未delete回收，确定是否存在内存泄漏问题 X xuejinghui 6月9日评论： 6月9日评论：
143	for (uint32_t i = 0; i < size; ++i) {	143	for (uint32_t i = 0; i < size; ++i) {
144	*(pData + i) = value;	144	pData[i] = static_cast<int64_t>(value);
145	}	145	}
146	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t*>(pData), data_len);	146	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);
		147	delete[] pData;
147	return SUCCESS;	148	return SUCCESS;
148	}	149	}
149		150
	@@ -165,9 +166,26 @@ int CreateOppInGraph(
165	std::vector<int64_t> xShape = {32};	166	std::vector<int64_t> xShape = {32};
166	std::vector<int64_t> maskShape = {128};	167	std::vector<int64_t> maskShape = {128};
167	std::vector<int64_t> keep_prob_shape = {1};	168	std::vector<int64_t> keep_prob_shape = {1};
168	ADD_INPUT(1, x, inDtype, xShape);	169	ADD_INPUT(1, x, ge::DT_FLOAT, xShape);
169	ADD_INPUT(2, mask, inDtype, maskShape);	170	{
170	ADD_INPUT(3, keep_prob, inDtype, keep_prob_shape);	171	auto placeholder2 = op::Data("placeholder2").set_attr_index(0);
		172	TensorDesc desc2(ge::Shape(maskShape), FORMAT_ND, ge::DT_UINT8);
		173	desc2.SetPlacement(ge::kPlacementHost);
		174	desc2.SetFormat(FORMAT_ND);
		175	uint8_t *mask_data = new (std::nothrow) uint8_t[128];
		D D DaiHuina16月8日分配内存后未释放，内存溢出风险 X xuejinghui 6月9日评论： 6月9日评论：
		176	memset(mask_data, 1, 128);
		177	Tensor tensor2(desc2, mask_data, 128);
		178	delete[] mask_data;
		179	placeholder2.update_input_desc_x(desc2);
		180	placeholder2.update_output_desc_y(desc2);
		181	input.push_back(tensor2);
		182	graph.AddOp(placeholder2);
		183	dropoutdomaskv3.set_input_mask(placeholder2);
		184	inputs.push_back(placeholder2);
		185	}
		186	ADD_INPUT(3, keep_prob, ge::DT_FLOAT, keep_prob_shape);
		187	float keep_prob_val = 0.5f;
		188	memcpy(input.back().GetData(), &keep_prob_val, sizeof(float));
171	outputs.push_back(dropoutdomaskv3);	189	outputs.push_back(dropoutdomaskv3);
172	// 添加完毕	190	// 添加完毕
173	return SUCCESS;	191	return SUCCESS;

Mrandom/random_standard_normal_v2/examples/test_geir_random_standard_normal_v2.cpp+4-3

	@@ -26,7 +26,6 @@
26	#include "array_ops.h"	26	#include "array_ops.h"
27	#include "ge_ir_build.h"	27	#include "ge_ir_build.h"
28		28
29	#include "experiment_ops.h"
30	#include "nn_other.h"	29	#include "nn_other.h"
31	#include "../op_graph/random_standard_normal_v2_proto.h"	30	#include "../op_graph/random_standard_normal_v2_proto.h"
32		31
	@@ -175,6 +174,7 @@ int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor &input_tensor, TensorD
175	*(pData + i) = value;	174	*(pData + i) = value;
176	}	175	}
177	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);	176	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);
		177	delete[] pData;
178	return SUCCESS;	178	return SUCCESS;
179	}	179	}
180		180
	@@ -187,11 +187,12 @@ int32_t GenOnesData(
187	size *= shapes[i];	187	size *= shapes[i];
188	}	188	}
189	uint32_t data_len = size * GetDataTypeSize(data_type);	189	uint32_t data_len = size * GetDataTypeSize(data_type);
190	int32_t *pData = new (std::nothrow) int32_t[data_len];	190	int64_t *pData = new (std::nothrow) int64_t[size];
191	for (uint32_t i = 0; i < size; ++i) {	191	for (uint32_t i = 0; i < size; ++i) {
192	*(pData + i) = value;	192	pData[i] = static_cast<int64_t>(value);
193	}	193	}
194	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);	194	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);
		195	delete[] pData;
195	return SUCCESS;	196	return SUCCESS;
196	}	197	}
197		198

Mrandom/random_uniform_int_v2/examples/test_geir_random_uniform_int_v2.cpp+4-3

	@@ -26,7 +26,6 @@
26	#include "array_ops.h"	26	#include "array_ops.h"
27	#include "ge_ir_build.h"	27	#include "ge_ir_build.h"
28		28
29	#include "experiment_ops.h"
30	#include "nn_other.h"	29	#include "nn_other.h"
31	#include "../op_graph/random_uniform_int_v2_proto.h"	30	#include "../op_graph/random_uniform_int_v2_proto.h"
32		31
	@@ -169,6 +168,7 @@ int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor &input_tensor, TensorD
169	*(pData + i) = value;	168	*(pData + i) = value;
170	}	169	}
171	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);	170	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);
		171	delete[] pData;
172	return SUCCESS;	172	return SUCCESS;
173	}	173	}
174		174
	@@ -181,11 +181,12 @@ int32_t GenOnesData(
181	size *= shapes[i];	181	size *= shapes[i];
182	}	182	}
183	uint32_t data_len = size * GetDataTypeSize(data_type);	183	uint32_t data_len = size * GetDataTypeSize(data_type);
184	int32_t *pData = new (std::nothrow) int32_t[data_len];	184	int64_t *pData = new (std::nothrow) int64_t[size];
185	for (uint32_t i = 0; i < size; ++i) {	185	for (uint32_t i = 0; i < size; ++i) {
186	*(pData + i) = value;	186	pData[i] = static_cast<int64_t>(value);
187	}	187	}
188	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);	188	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);
		189	delete[] pData;
189	return SUCCESS;	190	return SUCCESS;
190	}	191	}
191		192

Mrandom/truncated_normal_v2/examples/test_geir_truncated_normal_v2.cpp+4-3

	@@ -26,7 +26,6 @@
26	#include "array_ops.h"	26	#include "array_ops.h"
27	#include "ge_ir_build.h"	27	#include "ge_ir_build.h"
28		28
29	#include "experiment_ops.h"
30	#include "nn_other.h"	29	#include "nn_other.h"
31	#include "../op_graph/truncated_normal_v2_proto.h"	30	#include "../op_graph/truncated_normal_v2_proto.h"
32		31
	@@ -175,6 +174,7 @@ int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor &input_tensor, TensorD
175	*(pData + i) = value;	174	*(pData + i) = value;
176	}	175	}
177	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);	176	input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);
		177	delete[] pData;
178	return SUCCESS;	178	return SUCCESS;
179	}	179	}
180		180
	@@ -187,11 +187,12 @@ int32_t GenOnesData(
187	size *= shapes[i];	187	size *= shapes[i];
188	}	188	}
189	uint32_t data_len = size * GetDataTypeSize(data_type);	189	uint32_t data_len = size * GetDataTypeSize(data_type);
190	int32_t *pData = new (std::nothrow) int32_t[data_len];	190	int64_t *pData = new (std::nothrow) int64_t[size];
191	for (uint32_t i = 0; i < size; ++i) {	191	for (uint32_t i = 0; i < size; ++i) {
192	*(pData + i) = value;	192	pData[i] = static_cast<int64_t>(value);
193	}	193	}
194	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);	194	input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);
		195	delete[] pData;
195	return SUCCESS;	196	return SUCCESS;
196	}	197	}
197		198