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fix bug when dump op with trans_flag true

This commit is contained in:
laiyongqiang 2020-04-01 14:51:43 +08:00
parent 22a9c00bcd
commit bb421b70db
6 changed files with 35 additions and 30 deletions
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12
config/e2e_dump_config.json
View File

@ -6,17 +6,17 @@
"net_name": "ResNet50", "net_name": "ResNet50",
"mode": 0, "mode": 0,
"iteration": 0, "iteration": 0,
"kernels": ["TensorAdd"] "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
}, },
"DumpSettingsSpec": { "DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable", "enable": "true: dump enable, false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format", "trans_flag": "true: trans to host format, false: not trans format",
"path": "the dump file folder", "path": "the dump file folder",
"net_name": "net name eg:ResNet50", "net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list", "mode": "0: dump all kernels, 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ", "iteration": "0: all iteration, others: specified iteration ",
"kernels": "kernel name list need to be dump" "kernels": "op's full scope name which need to be dump"
}, },
"other": {} "other": {}
} }

14
config/e2e_dump_config_0.json
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@ -6,17 +6,17 @@
"net_name": "ResNet50", "net_name": "ResNet50",
"mode": 0, "mode": 0,
"iteration": 0, "iteration": 0,
"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"] "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
}, },
"DumpSettingsSpec": { "DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable", "enable": "true: dump enable, false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format", "trans_flag": "true: trans to host format, false: not trans format",
"path": "the dump file folder", "path": "the dump file folder",
"net_name": "net name eg:ResNet50", "net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list", "mode": "0: dump all kernels, 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ", "iteration": "0: all iteration, others: specified iteration ",
"kernels": "kernel name list need to be dump" "kernels": "op's full scope name which need to be dump"
}, },
"other": {} "other": {}
} }

14
config/e2e_dump_config_1.json
View File

@ -6,17 +6,17 @@
"net_name": "ResNet50", "net_name": "ResNet50",
"mode": 0, "mode": 0,
"iteration": 0, "iteration": 0,
"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"] "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
}, },
"DumpSettingsSpec": { "DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable", "enable": "true: dump enable, false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format", "trans_flag": "true: trans to host format, false: not trans format",
"path": "the dump file folder", "path": "the dump file folder",
"net_name": "net name eg:ResNet50", "net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list", "mode": "0: dump all kernels, 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ", "iteration": "0: all iteration, others: specified iteration ",
"kernels": "kernel name list need to be dump" "kernels": "op's full scope name which need to be dump"
}, },
"other": {} "other": {}
} }

7
mindspore/ccsrc/common/trans.cc
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@ -53,6 +53,7 @@ enum DataTypeTransMode {
FROM_INT8_TO_FLOAT, FROM_INT8_TO_FLOAT,
FROM_INT8_TO_INT32, FROM_INT8_TO_INT32,
FROM_INT64_TO_INT32, FROM_INT64_TO_INT32,
FROM_UINT16_TO_INT32,
}; };
const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{ const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
@ -68,7 +69,8 @@ const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
{std::pair<TypeId, TypeId>(kNumberTypeUInt8, kNumberTypeInt32), FROM_UINT8_TO_INT32}, {std::pair<TypeId, TypeId>(kNumberTypeUInt8, kNumberTypeInt32), FROM_UINT8_TO_INT32},
{std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeFloat32), FROM_INT8_TO_FLOAT}, {std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeFloat32), FROM_INT8_TO_FLOAT},
{std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeInt32), FROM_INT8_TO_INT32}, {std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeInt32), FROM_INT8_TO_INT32},
{std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32}}; {std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32},
{std::pair<TypeId, TypeId>(kNumberTypeUInt16, kNumberTypeInt32), FROM_UINT16_TO_INT32}};
template <typename SrcT, typename DstT> template <typename SrcT, typename DstT>
void TransDataSrc2Dst(const TypeIdArgs &args, void *dst, const size_t data_size) { void TransDataSrc2Dst(const TypeIdArgs &args, void *dst, const size_t data_size) {
@ -116,6 +118,9 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const
case FROM_INT64_TO_INT32: case FROM_INT64_TO_INT32:
TransDataSrc2Dst<int64_t, int32_t>(args, dst, data_size); TransDataSrc2Dst<int64_t, int32_t>(args, dst, data_size);
break; break;
case FROM_UINT16_TO_INT32:
TransDataSrc2Dst<uint16_t, int32_t>(args, dst, data_size);
break;
default: default:
MS_LOG(ERROR) << "unsupported datatype trans"; MS_LOG(ERROR) << "unsupported datatype trans";
return false; return false;

6
mindspore/ccsrc/device/ascend/ascend_device_address.cc
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@ -106,13 +106,13 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector<int> &shape, size_t
} else { } else {
auto shape_size = trans::ShapeSize(host_shape); auto shape_size = trans::ShapeSize(host_shape);
auto host = std::vector<uint8_t>(size_); auto host = std::vector<uint8_t>(size_);
const trans::TypeIdArgs type_args{ptr_, shape_size, type_id_, type}; SyncMemory(host.data(), ptr_, size_, RT_MEMCPY_DEVICE_TO_HOST);
sync_ok = trans::TransDataType(type_args, host.data()); const trans::TypeIdArgs type_args{host.data(), shape_size, type_id_, type};
sync_ok = trans::TransDataType(type_args, host_ptr);
if (!sync_ok) { if (!sync_ok) {
MS_LOG(ERROR) << "trans data type failed."; MS_LOG(ERROR) << "trans data type failed.";
return false; return false;
} }
SyncMemory(host_ptr, host.data(), size, RT_MEMCPY_DEVICE_TO_HOST);
} }
} else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) { } else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) {
sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr); sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr);

12
mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc
View File

@ -150,9 +150,9 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path,
auto output_size = AnfAlgo::GetOutputTensorNum(node); auto output_size = AnfAlgo::GetOutputTensorNum(node);
for (size_t j = 0; j < output_size; ++j) { for (size_t j = 0; j < output_size; ++j) {
auto addr = AnfAlgo::GetOutputAddr(node, j); auto addr = AnfAlgo::GetOutputAddr(node, j);
auto shape = AnfAlgo::GetOutputDeviceShape(node, j); auto shape = AnfAlgo::GetOutputInferShape(node, j);
auto type = AnfAlgo::GetOutputDeviceDataType(node, j); auto type = AnfAlgo::GetOutputInferDataType(node, j);
auto format = AnfAlgo::GetOutputFormat(node, j); auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j); string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr); auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
std::vector<int> int_shapes; std::vector<int> int_shapes;
@ -181,9 +181,9 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p
continue; continue;
} }
auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX); auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX);
auto shape = AnfAlgo::GetOutputDeviceShape(item, PRAMATER_OUTPUT_INDEX); auto shape = AnfAlgo::GetOutputInferShape(item, PRAMATER_OUTPUT_INDEX);
auto type = AnfAlgo::GetOutputDeviceDataType(item, PRAMATER_OUTPUT_INDEX); auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX);
auto format = AnfAlgo::GetOutputFormat(item, PRAMATER_OUTPUT_INDEX); auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + parameter_name + '_' + "output_0"; string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr); auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
std::vector<int> int_shapes; std::vector<int> int_shapes;