forked from mindspore-Ecosystem/mindspore
fix bug when dump op with trans_flag true
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parent
22a9c00bcd
commit
bb421b70db
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@ -6,17 +6,17 @@
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"net_name": "ResNet50",
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"mode": 0,
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"iteration": 0,
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"kernels": ["TensorAdd"]
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"kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
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},
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"DumpSettingsSpec": {
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"enable": "true: dump enable false: dump disable",
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"trans_flag": "true: trans to host format,false: not trans format",
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"enable": "true: dump enable, false: dump disable",
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"trans_flag": "true: trans to host format, false: not trans format",
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"path": "the dump file folder",
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"net_name": "net name eg:ResNet50",
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"mode": "0: dump all kernels 1: dump kernels in kernels list",
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"iteration": "0: all iteration others: specified iteration ",
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"kernels": "kernel name list need to be dump"
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"mode": "0: dump all kernels, 1: dump kernels in kernels list",
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"iteration": "0: all iteration, others: specified iteration ",
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"kernels": "op's full scope name which need to be dump"
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},
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"other": {}
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}
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@ -6,17 +6,17 @@
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"net_name": "ResNet50",
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"mode": 0,
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"iteration": 0,
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"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
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"kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
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},
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"DumpSettingsSpec": {
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"enable": "true: dump enable false: dump disable",
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"trans_flag": "true: trans to host format,false: not trans format",
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"enable": "true: dump enable, false: dump disable",
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"trans_flag": "true: trans to host format, false: not trans format",
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"path": "the dump file folder",
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"net_name": "net name eg:ResNet50",
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"mode": "0: dump all kernels 1: dump kernels in kernels list",
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"iteration": "0: all iteration others: specified iteration ",
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"kernels": "kernel name list need to be dump"
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"mode": "0: dump all kernels, 1: dump kernels in kernels list",
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"iteration": "0: all iteration, others: specified iteration ",
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"kernels": "op's full scope name which need to be dump"
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},
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"other": {}
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}
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}
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@ -6,17 +6,17 @@
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"net_name": "ResNet50",
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"mode": 0,
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"iteration": 0,
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"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
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"kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"]
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},
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"DumpSettingsSpec": {
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"enable": "true: dump enable false: dump disable",
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"trans_flag": "true: trans to host format,false: not trans format",
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"enable": "true: dump enable, false: dump disable",
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"trans_flag": "true: trans to host format, false: not trans format",
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"path": "the dump file folder",
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"net_name": "net name eg:ResNet50",
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"mode": "0: dump all kernels 1: dump kernels in kernels list",
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"iteration": "0: all iteration others: specified iteration ",
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"kernels": "kernel name list need to be dump"
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"mode": "0: dump all kernels, 1: dump kernels in kernels list",
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"iteration": "0: all iteration, others: specified iteration ",
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"kernels": "op's full scope name which need to be dump"
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},
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"other": {}
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}
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}
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@ -53,6 +53,7 @@ enum DataTypeTransMode {
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FROM_INT8_TO_FLOAT,
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FROM_INT8_TO_INT32,
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FROM_INT64_TO_INT32,
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FROM_UINT16_TO_INT32,
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};
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const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
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@ -68,7 +69,8 @@ const std::map<std::pair<TypeId, TypeId>, DataTypeTransMode> mode_map{
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{std::pair<TypeId, TypeId>(kNumberTypeUInt8, kNumberTypeInt32), FROM_UINT8_TO_INT32},
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{std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeFloat32), FROM_INT8_TO_FLOAT},
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{std::pair<TypeId, TypeId>(kNumberTypeInt8, kNumberTypeInt32), FROM_INT8_TO_INT32},
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{std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32}};
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{std::pair<TypeId, TypeId>(kNumberTypeInt64, kNumberTypeInt32), FROM_INT64_TO_INT32},
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{std::pair<TypeId, TypeId>(kNumberTypeUInt16, kNumberTypeInt32), FROM_UINT16_TO_INT32}};
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template <typename SrcT, typename DstT>
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void TransDataSrc2Dst(const TypeIdArgs &args, void *dst, const size_t data_size) {
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@ -116,6 +118,9 @@ bool CastKernel(const TypeIdArgs &args, void *dst, const size_t data_size, const
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case FROM_INT64_TO_INT32:
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TransDataSrc2Dst<int64_t, int32_t>(args, dst, data_size);
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break;
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case FROM_UINT16_TO_INT32:
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TransDataSrc2Dst<uint16_t, int32_t>(args, dst, data_size);
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break;
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default:
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MS_LOG(ERROR) << "unsupported datatype trans";
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return false;
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@ -106,13 +106,13 @@ bool AscendDeviceAddress::SyncDeviceToHost(const std::vector<int> &shape, size_t
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} else {
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auto shape_size = trans::ShapeSize(host_shape);
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auto host = std::vector<uint8_t>(size_);
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const trans::TypeIdArgs type_args{ptr_, shape_size, type_id_, type};
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sync_ok = trans::TransDataType(type_args, host.data());
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SyncMemory(host.data(), ptr_, size_, RT_MEMCPY_DEVICE_TO_HOST);
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const trans::TypeIdArgs type_args{host.data(), shape_size, type_id_, type};
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sync_ok = trans::TransDataType(type_args, host_ptr);
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if (!sync_ok) {
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MS_LOG(ERROR) << "trans data type failed.";
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return false;
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}
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SyncMemory(host_ptr, host.data(), size, RT_MEMCPY_DEVICE_TO_HOST);
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}
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} else if (format_ == kOpFormat_NC1HWC0 || format_ == kOpFormat_FRAC_Z || format_ == kOpFormat_FRAC_NZ) {
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sync_ok = SyncDeviceToHostAndConvertFormat(shape, size, type, host_ptr);
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@ -150,9 +150,9 @@ void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path,
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auto output_size = AnfAlgo::GetOutputTensorNum(node);
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for (size_t j = 0; j < output_size; ++j) {
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auto addr = AnfAlgo::GetOutputAddr(node, j);
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auto shape = AnfAlgo::GetOutputDeviceShape(node, j);
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auto type = AnfAlgo::GetOutputDeviceDataType(node, j);
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auto format = AnfAlgo::GetOutputFormat(node, j);
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auto shape = AnfAlgo::GetOutputInferShape(node, j);
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auto type = AnfAlgo::GetOutputInferDataType(node, j);
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auto format = kOpFormat_DEFAULT;
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string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
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auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
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std::vector<int> int_shapes;
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@ -181,9 +181,9 @@ void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_p
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continue;
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}
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auto addr = AnfAlgo::GetOutputAddr(item, PRAMATER_OUTPUT_INDEX);
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auto shape = AnfAlgo::GetOutputDeviceShape(item, PRAMATER_OUTPUT_INDEX);
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auto type = AnfAlgo::GetOutputDeviceDataType(item, PRAMATER_OUTPUT_INDEX);
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auto format = AnfAlgo::GetOutputFormat(item, PRAMATER_OUTPUT_INDEX);
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auto shape = AnfAlgo::GetOutputInferShape(item, PRAMATER_OUTPUT_INDEX);
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auto type = AnfAlgo::GetOutputInferDataType(item, PRAMATER_OUTPUT_INDEX);
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auto format = kOpFormat_DEFAULT;
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string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
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auto ascend_addr = dynamic_cast<const mindspore::device::ascend::AscendDeviceAddress *>(addr);
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std::vector<int> int_shapes;
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