save print data to file

mindspore/ccsrc/CMakeLists.txt

@@ -80,6 +80,7 @@ if (ENABLE_DUMP_PROTO)
         "utils/anf_ir.proto"
         "utils/summary.proto"
         "utils/checkpoint.proto"
+        "utils/print.proto"
     )
     ms_protobuf_generate_py(PY_SRCS PY_HDRS PY_PYS ${PROTO_PY})
 

mindspore/ccsrc/pipeline/init.cc

@@ -146,7 +146,8 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("get_check_bprop_flag", &mindspore::MsContext::check_bprop_flag, "Get whether to check bprop.")
     .def("set_check_bprop_flag", &mindspore::MsContext::set_check_bprop_flag, "Set whether to check bprop.")
     .def("get_max_device_memory", &mindspore::MsContext::max_device_memory, "Get deivce memory max size.")
-    .def("set_max_device_memory", &mindspore::MsContext::set_max_device_memory, "Set deivce memory max size.");
+    .def("set_max_device_memory", &mindspore::MsContext::set_max_device_memory, "Set deivce memory max size.")
+    .def("set_print_file_path", &mindspore::MsContext::set_print_file_path, "Set path to print.");
 
   (void)py::class_<mindspore::MpiConfig, std::shared_ptr<mindspore::MpiConfig>>(m, "MpiConfig")
     .def_static("get_instance", &mindspore::MpiConfig::GetInstance, "Get mpi config instance.")

mindspore/ccsrc/utils/context/ms_context.cc

@@ -83,6 +83,7 @@ MsContext::MsContext(const std::string &policy, const std::string &target) {
   profiling_options_ = "training_trace";
   check_bprop_flag_ = false;
   max_device_memory_ = kDefaultMaxDeviceMemory;
+  print_file_path_ = "";
 }
 
 std::shared_ptr<MsContext> MsContext::GetInstance() {

mindspore/ccsrc/utils/context/ms_context.h

@@ -147,6 +147,8 @@ class MsContext {
   std::string profiling_options() const { return profiling_options_; }
   bool check_bprop_flag() const { return check_bprop_flag_; }
   void set_check_bprop_flag(bool check_bprop_flag) { check_bprop_flag_ = check_bprop_flag; }
+  void set_print_file_path(const std::string &file) { print_file_path_ = file; }
+  const std::string &print_file_path() const { return print_file_path_; }
 
   float max_device_memory() const { return max_device_memory_; }
   void set_max_device_memory(float max_device_memory) { max_device_memory_ = max_device_memory; }
@@ -192,6 +194,7 @@ class MsContext {
   std::string profiling_options_;
   bool check_bprop_flag_;
   float max_device_memory_;
+  std::string print_file_path_;
 };
 
 }  // namespace mindspore

mindspore/ccsrc/utils/print.proto

@@ -0,0 +1,39 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+syntax = "proto2";
+
+package mindspore.prntpb;
+
+message TensorProto {
+    // The shape of the tensor.
+    repeated int64 dims = 1;
+    // The type of the tensor.
+    required string tensor_type = 2;
+    // The data of the tensor.
+    required bytes tensor_content = 3;
+}
+
+
+message Print {
+    message Value {
+        oneof value {
+            string desc = 1;
+            TensorProto tensor = 2;
+        }
+    }
+    repeated Value value = 1;
+}

mindspore/ccsrc/utils/tensorprint_utils.cc

@@ -47,6 +47,18 @@ static std::map<std::string, size_t> type_size_map = {
   {"int64_t", sizeof(int64_t)},   {"uint64_t", sizeof(uint64_t)}, {"float16", sizeof(float) / 2},
   {"float", sizeof(float)},       {"double", sizeof(double)},     {"bool", sizeof(bool)}};
 
+std::string GetParseType(const std::string &tensorType_) {
+  static const std::map<std::string, std::string> print_parse_map = {
+    {"int8_t", "Int8"},     {"uint8_t", "Uint8"},   {"int16_t", "Int16"},  {"uint16_t", "Uint16"},
+    {"int32_t", "Int32"},   {"uint32_t", "Uint32"}, {"int64_t", "Int64"},  {"uint64_t", "Uint64"},
+    {"float16", "Float16"}, {"float", "Float32"},   {"double", "Float64"}, {"bool", "Bool"}};
+  auto type_iter = print_parse_map.find(tensorType_);
+  if (type_iter == print_parse_map.end()) {
+    MS_LOG(EXCEPTION) << "type of tensor need to print is not support " << tensorType_;
+  }
+  return type_iter->second;
+}
+
 bool ParseTensorShape(const std::string &input_shape_str, std::vector<int> *const tensor_shape, size_t *dims) {
   if (tensor_shape == nullptr) {
     return false;
@@ -141,7 +153,7 @@ void convertDataItem2Scalar(const char *str_data_ptr, const string &tensor_type,
   } else {
     MS_LOG(EXCEPTION) << "Cannot print scalar because of unsupport data type: " << tensor_type << ".";
   }
-}  // namespace mindspore
+}
 
 bool judgeLengthValid(const size_t str_len, const string &tensor_type) {
   auto type_iter = type_size_map.find(tensor_type);
@@ -200,14 +212,84 @@ bool ConvertDataItem2Tensor(const std::vector<tdt::DataItem> &items) {
   return ret_end_sequence;
 }
 
-void TensorPrint::operator()() {
+bool SaveDataItem2File(const std::vector<tdt::DataItem> &items, const std::string &print_file_path, prntpb::Print print,
-  while (true) {
+                       std::fstream *output) {
-    std::vector<tdt::DataItem> bundle;
+  bool ret_end_sequence = false;
-    if (tdt::TdtHostPopData("_npu_log", bundle) != 0) {
+  for (auto &item : items) {
+    if (item.dataType_ == tdt::TDT_END_OF_SEQUENCE) {
+      ret_end_sequence = true;
       break;
     }
-    if (ConvertDataItem2Tensor(bundle)) {
+    prntpb::Print_Value *value = print.add_value();
-      break;
+    std::shared_ptr<std::string> str_data_ptr = std::static_pointer_cast<std::string>(item.dataPtr_);
+    MS_EXCEPTION_IF_NULL(str_data_ptr);
+    if (item.tensorShape_ == kShapeScalar || item.tensorShape_ == kShapeNone) {
+      if (!judgeLengthValid(str_data_ptr->size(), item.tensorType_)) {
+        MS_LOG(EXCEPTION) << "Print op receive data length is invalid.";
+      }
+    }
+
+    std::vector<int> tensor_shape;
+    size_t totaldims = 1;
+    if (!ParseTensorShape(item.tensorShape_, &tensor_shape, &totaldims)) {
+      MS_LOG(EXCEPTION) << "Tensor print can not parse tensor shape, receive info" << item.tensorShape_;
+    }
+
+    if (item.tensorType_ == "string") {
+      std::string data(reinterpret_cast<const char *>(str_data_ptr->c_str()), item.dataLen_);
+      value->set_desc(data);
+    } else {
+      auto parse_type = GetParseType(item.tensorType_);
+      prntpb::TensorProto *tensor = value->mutable_tensor();
+      if (!(item.tensorShape_ == kShapeScalar) && !(item.tensorShape_ == kShapeNone)) {
+        for (const auto &dim : tensor_shape) {
+          tensor->add_dims(static_cast<::google::protobuf::int64>(dim));
+        }
+      }
+      tensor->set_tensor_type(parse_type);
+      std::string data(reinterpret_cast<const char *>(str_data_ptr->c_str()), item.dataLen_);
+      tensor->set_tensor_content(data);
+    }
+
+    if (!print.SerializeToOstream(output)) {
+      MS_LOG(EXCEPTION) << "Save print file:" << print_file_path << " fail.";
+    }
+    print.Clear();
+  }
+  return ret_end_sequence;
+}
+
+void TensorPrint::operator()() {
+  prntpb::Print print;
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
+  std::string print_file_path = ms_context->print_file_path();
+  if (print_file_path == "") {
+    while (true) {
+      std::vector<tdt::DataItem> bundle;
+      if (tdt::TdtHostPopData("_npu_log", bundle) != 0) {
+        break;
+      }
+      if (ConvertDataItem2Tensor(bundle)) {
+        break;
+      }
+    }
+  } else {
+    std::fstream output(print_file_path, std::ios::out | std::ios::trunc | std::ios::binary);
+    while (true) {
+      std::vector<tdt::DataItem> bundle;
+      if (tdt::TdtHostPopData("_npu_log", bundle) != 0) {
+        break;
+      }
+      if (SaveDataItem2File(bundle, print_file_path, print, &output)) {
+        break;
+      }
+    }
+    output.close();
+    std::string path_string = print_file_path;
+    if (chmod(common::SafeCStr(path_string), S_IRUSR) == -1) {
+      MS_LOG(ERROR) << "Modify file:" << print_file_path << " to r fail.";
+      return;
     }
   }
 }

mindspore/ccsrc/utils/tensorprint_utils.h

@@ -23,6 +23,8 @@
 #include "tdt/tsd_client.h"
 #include "tdt/tdt_host_interface.h"
 #include "tdt/data_common.h"
+#include "proto/print.pb.h"
+#include "utils/context/ms_context.h"
 #endif
 namespace mindspore {
 class TensorPrint {

mindspore/context.py

@@ -346,6 +346,15 @@ class _Context:
             raise ValueError("Context param max_device_memory should be in correct format! Such as \"3.5GB\"")
         self._context_handle.set_max_device_memory(max_device_memory_value)
 
+    @property
+    def print_file_path(self):
+        return None
+
+    @print_file_path.setter
+    def print_file_path(self, file):
+        self._context_handle.set_print_file_path(file)
+
+
 def check_input_format(x):
     import re
     pattern = r'[1-9][0-9]*(\.)?[0-9]*GB|0\.[0-9]*GB'
@@ -473,7 +482,7 @@ def reset_auto_parallel_context():
                  save_graphs_path=str, save_ms_model=bool, save_ms_model_path=str, enable_dump=bool,
                  save_dump_path=str, enable_reduce_precision=bool, variable_memory_max_size=str,
                  enable_profiling=bool, profiling_options=str, enable_auto_mixed_precision=bool,
-                 check_bprop=bool, max_device_memory=str)
+                 check_bprop=bool, max_device_memory=str, print_file_path=str)
 def set_context(**kwargs):
     """
     Sets context for running environment.

mindspore/train/serialization.py

@@ -21,6 +21,7 @@ import mindspore.nn as nn
 import mindspore.context as context
 from mindspore import log as logger
 from mindspore.train.checkpoint_pb2 import Checkpoint
+from mindspore.train.print_pb2 import Print
 from mindspore.common.tensor import Tensor
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
@@ -30,11 +31,15 @@ from mindspore._checkparam import check_input_data
 
 __all__ = ["save_checkpoint", "load_checkpoint", "load_param_into_net", "export"]
 
-tensor_to_ms_type = {"Int8": mstype.int8, "Int16": mstype.int16, "Int32": mstype.int32, "Int64": mstype.int64,
+tensor_to_ms_type = {"Int8": mstype.int8, "Uint8": mstype.uint8, "Int16": mstype.int16, "Uint16": mstype.uint16,
-                     "Float16": mstype.float16, "Float32": mstype.float32, "Float64": mstype.float64}
+                     "Int32": mstype.int32, "Uint32": mstype.uint32, "Int64": mstype.int64, "Uint64": mstype.uint64,
+                     "Float16": mstype.float16, "Float32": mstype.float32, "Float64": mstype.float64,
+                     "Bool": mstype.bool_}
+
+tensor_to_np_type = {"Int8": np.int8, "Uint8": np.uint8, "Int16": np.int16, "Uint16": np.uint16,
+                     "Int32": np.int32, "Uint32": np.uint32, "Int64": np.int64, "Uint64": np.uint64,
+                     "Float16": np.float16, "Float32": np.float32, "Float64": np.float64, "Bool": np.bool_}
 
-tensor_to_np_type = {"Int8": np.int8, "Int16": np.int16, "Int32": np.int32, "Int64": np.int64,
-                     "Float16": np.float16, "Float32": np.float32, "Float64": np.float64}
 
 def _special_process_par(par, new_par):
     """
@@ -442,3 +447,64 @@ def export(net, *inputs, file_name, file_format='GEIR'):
     # restore network training mode
     if is_training:
         net.set_train(mode=True)
+
+
+def parse_print(print_file_name):
+    """
+    Loads Print data from a specified file.
+
+    Args:
+        print_file_name (str): The file name of save print data.
+
+    Returns:
+        List, element of list is Tensor.
+
+    Raises:
+        ValueError: Print file is incorrect.
+    """
+    if not os.path.realpath(print_file_name):
+        raise ValueError("Please input the correct print file name.")
+
+    if os.path.getsize(print_file_name) == 0:
+        raise ValueError("The print file may be empty, please make sure enter the correct file name.")
+
+    logger.info("Execute load print process.")
+    print_list = Print()
+
+    try:
+        with open(print_file_name, "rb") as f:
+            pb_content = f.read()
+        print_list.ParseFromString(pb_content)
+    except BaseException as e:
+        logger.error("Failed to read the print file %s, please check the correct of the file.", print_file_name)
+        raise ValueError(e.__str__())
+
+    tensor_list = []
+
+    try:
+        for print_ in print_list.value:
+            # String type
+            if print_.HasField("desc"):
+                tensor_list.append(print_.desc)
+            elif print_.HasField("tensor"):
+                dims = print_.tensor.dims
+                data_type = print_.tensor.tensor_type
+                data = print_.tensor.tensor_content
+                np_type = tensor_to_np_type[data_type]
+                param_data = np.fromstring(data, np_type)
+                ms_type = tensor_to_ms_type[data_type]
+                param_dim = []
+                for dim in dims:
+                    param_dim.append(dim)
+                if param_dim:
+                    param_value = param_data.reshape(param_dim)
+                    tensor_list.append(Tensor(param_value, ms_type))
+                # Scale type
+                else:
+                    tensor_list.append(Tensor(param_data, ms_type))
+
+    except BaseException as e:
+        logger.error("Failed to load the print file %s.", print_list)
+        raise RuntimeError(e.__str__())
+
+    return tensor_list

tests/ut/python/utils/test_serialize.py

@@ -16,8 +16,9 @@
 import os
 import stat
 import time
-import pytest
+
 import numpy as np
+import pytest
 
 import mindspore.common.dtype as mstype
 import mindspore.nn as nn
@@ -33,7 +34,7 @@ from mindspore.train.serialization import save_checkpoint, load_checkpoint, load
     _exec_save_checkpoint, export, _save_graph
 from ..ut_filter import non_graph_engine
 
-context.set_context(mode=context.GRAPH_MODE)
+context.set_context(mode=context.GRAPH_MODE, print_file_path="print.pb")
 
 
 class Net(nn.Cell):
@@ -327,8 +328,52 @@ def test_binary_export():
     export(net, input_data, file_name="./me_binary_export.pb", file_format="BINARY")
 
 
+class PrintNet(nn.Cell):
+    def __init__(self):
+        super(PrintNet, self).__init__()
+        self.print = P.Print()
+
+    def construct(self, int8, uint8, int16, uint16, int32, uint32, int64, uint64, flt16, flt32, flt64, bool_,
+                  scale1, scale2):
+        self.print('============tensor int8:==============', int8)
+        self.print('============tensor uint8:==============', uint8)
+        self.print('============tensor int16:==============', int16)
+        self.print('============tensor uint16:==============', uint16)
+        self.print('============tensor int32:==============', int32)
+        self.print('============tensor uint32:==============', uint32)
+        self.print('============tensor int64:==============', int64)
+        self.print('============tensor uint64:==============', uint64)
+        self.print('============tensor float16:==============', flt16)
+        self.print('============tensor float32:==============', flt32)
+        self.print('============tensor float64:==============', flt64)
+        self.print('============tensor bool:==============', bool_)
+        self.print('============tensor scale1:==============', scale1)
+        self.print('============tensor scale2:==============', scale2)
+        return int8, uint8, int16, uint16, int32, uint32, int64, uint64, flt16, flt32, flt64, bool_, scale1, scale2
+
+
+def test_print():
+    print_net = PrintNet()
+    int8 = Tensor(np.random.randint(100, size=(10, 10), dtype="int8"))
+    uint8 = Tensor(np.random.randint(100, size=(10, 10), dtype="uint8"))
+    int16 = Tensor(np.random.randint(100, size=(10, 10), dtype="int16"))
+    uint16 = Tensor(np.random.randint(100, size=(10, 10), dtype="uint16"))
+    int32 = Tensor(np.random.randint(100, size=(10, 10), dtype="int32"))
+    uint32 = Tensor(np.random.randint(100, size=(10, 10), dtype="uint32"))
+    int64 = Tensor(np.random.randint(100, size=(10, 10), dtype="int64"))
+    uint64 = Tensor(np.random.randint(100, size=(10, 10), dtype="uint64"))
+    float16 = Tensor(np.random.rand(224, 224).astype(np.float16))
+    float32 = Tensor(np.random.rand(224, 224).astype(np.float32))
+    float64 = Tensor(np.random.rand(224, 224).astype(np.float64))
+    bool_ = Tensor(np.arange(-10, 10, 2).astype(np.bool_))
+    scale1 = Tensor(np.array(1))
+    scale2 = Tensor(np.array(0.1))
+    print_net(int8, uint8, int16, uint16, int32, uint32, int64, uint64, float16, float32, float64, bool_, scale1,
+              scale2)
+
+
 def teardown_module():
-    files = ['parameters.ckpt', 'new_ckpt.ckpt', 'empty.ckpt']
+    files = ['parameters.ckpt', 'new_ckpt.ckpt', 'empty.ckpt', 'print.pb']
     for item in files:
         file_name = './' + item
         if not os.path.exists(file_name):