load inference model from memory buffer, test=release/1.7 (#22562)

* 1. load model from memory

2. scale is no longer added when saving inference model

test=develop

* raise ci coverage, test=develop

* supports saving weights to memory. test=develop

* raise ci coverage, test=develop

* fix PADDLE_ENFORCE messages, test=develop

paddle/fluid/operators/save_combine_op.cc

@@ -71,6 +71,23 @@ to a file on disk.
         "The \"file_path\" where the LoDTensor variables will be saved.")
         .AddCustomChecker(
             [](const std::string& path) { return !path.empty(); });
+    AddAttr<bool>("save_to_memory",
+                  "(boolean, default false)"
+                  "If true, the variables will be saved to binary strings.")
+        .SetDefault(false);
+    AddOutput("Y",
+              "(RAW, default empty)."
+              "This output is used when saving variables to binary strings.")
+        .AsDispensable();
+  }
+};
+
+class SaveCombineOpInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(framework::InferVarTypeContext* ctx) const override {
+    for (auto& o : ctx->Output("Y")) {
+      ctx->SetType(o, framework::proto::VarType::RAW);
+    }
   }
 };
 
@@ -80,7 +97,7 @@ to a file on disk.
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
-                  ops::SaveCombineOpProtoMaker);
+                  ops::SaveCombineOpProtoMaker, ops::SaveCombineOpInferVarType);
 
 REGISTER_OP_CPU_KERNEL(
     save_combine,

paddle/fluid/operators/save_combine_op.h

@@ -38,6 +38,8 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
     auto filename = ctx.Attr<std::string>("file_path");
     auto overwrite = ctx.Attr<bool>("overwrite");
     auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
+    auto save_to_memory = ctx.Attr<bool>("save_to_memory");
+    auto output = ctx.Output<std::string>("Y");
 
     bool is_present = FileExists(filename);
     if (is_present && !overwrite) {
@@ -45,11 +47,7 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
                    filename, overwrite);
     }
 
-    MkDirRecursively(DirName(filename).c_str());
-    std::ofstream fout(filename, std::ios::binary);
-    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
-                   filename);
-
+    std::ostringstream ss;
     auto inp_var_names = ctx.InputNames("X");
     auto &inp_vars = ctx.MultiInputVar("X");
     PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
@@ -82,12 +80,25 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
         // copy LoD info to the new tensor
         out.set_lod(tensor.lod());
         framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
-        framework::SerializeToStream(fout, out, dev_ctx);
+        framework::SerializeToStream(ss, out, dev_ctx);
       } else {
-        framework::SerializeToStream(fout, tensor, dev_ctx);
+        framework::SerializeToStream(ss, tensor, dev_ctx);
       }
     }
-    fout.close();
+    if (save_to_memory) {
+      PADDLE_ENFORCE_NE(output, nullptr,
+                        platform::errors::InvalidArgument(
+                            "Cannot find variable Y for save_combine_op"));
+      *output = ss.str();
+    } else {
+      MkDirRecursively(DirName(filename).c_str());
+      std::ofstream fout(filename, std::ios::binary);
+      PADDLE_ENFORCE_EQ(
+          static_cast<bool>(fout), true,
+          platform::errors::NotFound("Cannot open %s to write", filename));
+      fout << ss.str();
+      fout.close();
+    }
   }
 };
 

paddle/fluid/pybind/pybind.cc

@@ -915,6 +915,10 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<LoDTensor>();
            },
            py::return_value_policy::reference)
+      .def("get_bytes",
+           [](Variable &self) {
+             return py::bytes(*self.GetMutable<std::string>());
+           })
       .def("get_lod_rank_table",
            [](Variable &self) { return self.GetMutable<LoDRankTable>(); },
            py::return_value_policy::reference)

python/paddle/fluid/io.py

@@ -35,6 +35,7 @@ from paddle.fluid.framework import Program, Parameter, default_main_program, def
 from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.log_helper import get_logger
 from . import reader
+from . import unique_name
 from .reader import *
 from . import core
 from .. import compat as cpt
@@ -213,7 +214,8 @@ def save_vars(executor,
 
     Args:
         executor(Executor): The executor to run for saving variables.
-        dirname(str): The folder where to save variables.
+        dirname(str, optional): The folder where to save variables.
+                            When you need to save the parameter to the memory, set it to None.
         main_program(Program, optional): The program whose variables will be saved.
                                     If it is None, the default main program will
                                     be used automatically.
@@ -228,7 +230,8 @@ def save_vars(executor,
                                  Default: None
 
     Returns:
-        None
+        str: When saving parameters to a file, returns None.
+             When saving parameters to memory, returns a binary string containing parameters.
 
     Raises:
         TypeError: If `main_program` is not an instance of Program nor None.
@@ -265,17 +268,21 @@ def save_vars(executor,
             fluid.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc)
             # all variables whose names contain "fc " are saved.
     """
-    save_dirname = os.path.normpath(dirname)
+    save_to_memory = False
+    if dirname is None and filename is None:
+        save_to_memory = True
+
     main_program = _get_valid_program(main_program)
 
     if vars is None:
-        save_vars(
+        return save_vars(
             executor,
             main_program=main_program,
-            dirname=save_dirname,
+            dirname=dirname,
             vars=list(filter(predicate, main_program.list_vars())),
             filename=filename)
     else:
+        params_var_name = unique_name.generate("saved_params")
         # give warning when there is no var in model
         if len(list(vars)) == 0:
             warnings.warn(
@@ -292,33 +299,45 @@ def save_vars(executor,
             if each_var.type == core.VarDesc.VarType.RAW:
                 continue
             new_var = _clone_var_in_block_(save_block, each_var)
-            if filename is None:
-                save_file_path = os.path.join(save_dirname, new_var.name)
-                save_file_path = os.path.normpath(save_file_path)
+            if filename is None and save_to_memory is False:
+                save_file_path = os.path.join(
+                    os.path.normpath(dirname), new_var.name)
                 save_block.append_op(
                     type='save',
                     inputs={'X': [new_var]},
                     outputs={},
-                    attrs={'file_path': save_file_path})
+                    attrs={'file_path': os.path.normpath(save_file_path)})
             else:
                 save_var_map[new_var.name] = new_var
 
-        if filename is not None:
+        if filename is not None or save_to_memory:
             save_var_list = []
             for name in sorted(save_var_map.keys()):
                 save_var_list.append(save_var_map[name])
 
+            save_path = str()
+            if save_to_memory is False:
+                save_path = os.path.join(os.path.normpath(dirname), filename)
+
+            saved_params = save_block.create_var(
+                type=core.VarDesc.VarType.RAW, name=params_var_name)
+            saved_params.desc.set_persistable(True)
             save_block.append_op(
                 type='save_combine',
                 inputs={'X': save_var_list},
-                outputs={},
-                attrs={'file_path': os.path.join(save_dirname, filename)})
+                outputs={'Y': saved_params},
+                attrs={
+                    'file_path': save_path,
+                    'save_to_memory': save_to_memory
+                })
 
         #NOTE(zhiqiu): save op will add variable kLookupTablePath in save_program.desc,
         # which leads to diff on save_program and its desc. Call _sync_with_cpp
         # to keep consistency.
         save_program._sync_with_cpp()
         executor.run(save_program)
+        if save_to_memory:
+            return global_scope().find_var(params_var_name).get_bytes()
 
 
 def save_params(executor, dirname, main_program=None, filename=None):
@@ -346,7 +365,8 @@ def save_params(executor, dirname, main_program=None, filename=None):
     Args:
         executor(Executor): The executor to run for saving parameters, You can 
                             refer to :ref:`api_guide_executor_en`.
-        dirname(str): The saving directory path.
+        dirname(str, optional): The saving directory path.
+                            When you need to save the parameter to the memory, set it to None.
         main_program(Program, optional): The program whose parameters will be
                                          saved. You can refer to 
                                          :ref:`api_guide_Program_en` for more 
@@ -359,7 +379,8 @@ def save_params(executor, dirname, main_program=None, filename=None):
                                  Default: None
 
     Returns:
-        None
+        str: When saving parameters to a file, returns None.
+             When saving parameters to memory, returns a binary string containing parameters.
 
     Examples:
         .. code-block:: python
@@ -381,7 +402,7 @@ def save_params(executor, dirname, main_program=None, filename=None):
             # The parameters weights and bias of the fc layer in the network are going to 
             # be saved in different files in the path "./my_paddle_model" 
     """
-    save_vars(
+    return save_vars(
         executor,
         dirname=dirname,
         main_program=main_program,
@@ -558,7 +579,8 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
         executor(Executor): The executor to run for saving persistable variables.
                             You can refer to :ref:`api_guide_executor_en` for 
                             more details.
-        dirname(str): The saving directory path.
+        dirname(str, optional): The saving directory path.
+                            When you need to save the parameter to the memory, set it to None.
         main_program(Program, optional): The program whose persistbale variables will
                                          be saved. You can refer to 
                                          :ref:`api_guide_Program_en` for more details.
@@ -570,7 +592,8 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
                                  Default: None.
 
     Returns:
-        None
+        str: When saving parameters to a file, returns None.
+             When saving parameters to memory, returns a binary string containing parameters.
 
     Examples:
         .. code-block:: python
@@ -594,10 +617,10 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
             # "./my_paddle_model"
     """
     if main_program and main_program._is_distributed:
-        _save_distributed_persistables(
+        return _save_distributed_persistables(
             executor, dirname=dirname, main_program=main_program)
     else:
-        save_vars(
+        return save_vars(
             executor,
             dirname=dirname,
             main_program=main_program,
@@ -687,7 +710,11 @@ def load_vars(executor,
             # And all the variables are supposed to be saved in separate files.
 
     """
-    load_dirname = os.path.normpath(dirname)
+    vars_from_memory = False
+    if dirname is not None:
+        dirname = os.path.normpath(dirname)
+    else:
+        vars_from_memory = True
 
     if vars is None:
         if main_program is None:
@@ -697,7 +724,7 @@ def load_vars(executor,
 
         load_vars(
             executor,
-            dirname=load_dirname,
+            dirname=dirname,
             main_program=main_program,
             vars=list(filter(predicate, main_program.list_vars())),
             filename=filename)
@@ -724,13 +751,15 @@ def load_vars(executor,
                 ))
             new_var = _clone_var_in_block_(load_block, each_var)
             if filename is None:
+                if dirname is None:
+                    raise ValueError(
+                        "The directory path and params cannot be None at the same time."
+                    )
                 load_block.append_op(
                     type='load',
                     inputs={},
                     outputs={'Out': [new_var]},
-                    attrs={
-                        'file_path': os.path.join(load_dirname, new_var.name)
-                    })
+                    attrs={'file_path': os.path.join(dirname, new_var.name)})
             else:
                 load_var_map[new_var.name] = new_var
 
@@ -739,11 +768,17 @@ def load_vars(executor,
             for name in sorted(load_var_map.keys()):
                 load_var_list.append(load_var_map[name])
 
+            if vars_from_memory is False:
+                filename = os.path.join(dirname, filename)
+
             load_block.append_op(
                 type='load_combine',
                 inputs={},
                 outputs={"Out": load_var_list},
-                attrs={'file_path': os.path.join(load_dirname, filename)})
+                attrs={
+                    'file_path': filename,
+                    'model_from_memory': vars_from_memory
+                })
         executor.run(load_prog)
 
         # check var shape
@@ -1129,17 +1164,6 @@ def save_inference_model(dirname,
             )
             break
 
-    # fix the bug that the activation op's output as target will be pruned.
-    # will affect the inference performance.
-    # TODO(Superjomn) add an IR pass to remove 1-scale op.
-    with program_guard(main_program):
-        uniq_target_vars = []
-        for i, var in enumerate(target_vars):
-            if isinstance(var, Variable):
-                var = layers.scale(
-                    var, 1., name="save_infer_model/scale_{}".format(i))
-            uniq_target_vars.append(var)
-        target_vars = uniq_target_vars
     target_var_name_list = [var.name for var in target_vars]
 
     # when a pserver and a trainer running on the same machine, mkdir may conflict
@@ -1223,19 +1247,22 @@ def load_inference_model(dirname,
     You can refer to :ref:`api_guide_model_save_reader_en` for more details.
 
     Args:
-        dirname(str): The given directory path.
+        dirname(str): One of the following:
+          - The given directory path.
+          - Set to None when reading the model from memory.
         executor(Executor): The executor to run for loading inference model.
                             See :ref:`api_guide_executor_en` for more details about it.
-        model_filename(str, optional): The name of file to load the inference program.
-                                  If it is None, the default filename
-                                  ``__model__`` will be used.
-                                  Default: ``None``.
-        params_filename(str, optional): The name of file to load all parameters.
-                                   It is only used for the case that all
-                                   parameters were saved in a single binary
-                                   file. If parameters were saved in separate
-                                   files, set it as ``None``.
-                                   Default: ``None``.
+        model_filename(str, optional): One of the following:
+          - The name of file to load the inference program.
+          - If it is None, the default filename ``__model__`` will be used.
+          - When ``dirname`` is ``None``, it must be set to a string containing model.
+          Default: ``None``.
+        params_filename(str, optional): It is only used for the case that all
+            parameters were saved in a single binary file. One of the following:
+          - The name of file to load all parameters.  
+          - When ``dirname`` is ``None``, it must be set to a string containing all the parameters.
+          - If parameters were saved in separate files, set it as ``None``.
+            Default: ``None``.
 
         pserver_endpoints(list, optional): It is only needed by the distributed inference.
                                     If using a distributed look up table during the training,
@@ -1303,21 +1330,32 @@ def load_inference_model(dirname,
             # fetch_targets, we can use an executor to run the inference
             # program for getting the inference result.
     """
-    load_dirname = os.path.normpath(dirname)
-    if not os.path.isdir(load_dirname):
-        raise ValueError("There is no directory named '%s'", dirname)
+    load_from_memory = False
+    if dirname is not None:
+        load_dirname = os.path.normpath(dirname)
+        if not os.path.isdir(load_dirname):
+            raise ValueError("There is no directory named '%s'", dirname)
 
-    if model_filename is not None:
-        model_filename = os.path.basename(model_filename)
-    else:
-        model_filename = "__model__"
-    model_filename = os.path.join(load_dirname, model_filename)
+        if model_filename is None:
+            model_filename = '__model__'
 
-    if params_filename is not None:
-        params_filename = os.path.basename(params_filename)
+        model_filename = os.path.join(load_dirname,
+                                      os.path.basename(model_filename))
+
+        if params_filename is not None:
+            params_filename = os.path.basename(params_filename)
 
-    with open(model_filename, "rb") as f:
-        program_desc_str = f.read()
+        with open(model_filename, "rb") as f:
+            program_desc_str = f.read()
+    else:
+        load_from_memory = True
+        if params_filename is None:
+            raise ValueError(
+                "The path of params cannot be None when the directory path is None."
+            )
+        load_dirname = dirname
+        program_desc_str = model_filename
+        params_filename = params_filename
 
     program = Program.parse_from_string(program_desc_str)
     if not core._is_program_version_supported(program._version()):

python/paddle/fluid/tests/unittests/test_inference_model_io.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import unittest
 
+import os
 import six
 import numpy as np
 import paddle.fluid.core as core
@@ -27,13 +28,20 @@ import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
 from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.framework import Program, program_guard
-from paddle.fluid.io import save_inference_model, load_inference_model
+from paddle.fluid.io import save_inference_model, load_inference_model, save_persistables
 from paddle.fluid.transpiler import memory_optimize
 
 
 class TestBook(unittest.TestCase):
+    class InferModel(object):
+        def __init__(self, list):
+            self.program = list[0]
+            self.feed_var_names = list[1]
+            self.fetch_vars = list[2]
+
     def test_fit_line_inference_model(self):
         MODEL_DIR = "./tmp/inference_model"
+        UNI_MODEL_DIR = "./tmp/inference_model1"
 
         init_program = Program()
         program = Program()
@@ -65,30 +73,43 @@ class TestBook(unittest.TestCase):
                           'y': tensor_y},
                     fetch_list=[avg_cost])
 
+        # Separated model and unified model
         save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
+        save_inference_model(UNI_MODEL_DIR, ["x", "y"], [avg_cost], exe,
+                             program, 'model', 'params')
+        main_program = program.clone()._prune_with_input(
+            feeded_var_names=["x", "y"], targets=[avg_cost])
+        params_str = save_persistables(exe, None, main_program, None)
+
         expected = exe.run(program,
                            feed={'x': tensor_x,
                                  'y': tensor_y},
                            fetch_list=[avg_cost])[0]
 
         six.moves.reload_module(executor)  # reload to build a new scope
-        exe = executor.Executor(place)
 
-        [infer_prog, feed_var_names, fetch_vars] = load_inference_model(
-            MODEL_DIR, exe)
-
-        outs = exe.run(
-            infer_prog,
-            feed={feed_var_names[0]: tensor_x,
-                  feed_var_names[1]: tensor_y},
-            fetch_list=fetch_vars)
-        actual = outs[0]
-
-        self.assertEqual(feed_var_names, ["x", "y"])
-        self.assertEqual(len(fetch_vars), 1)
-        print("fetch %s" % str(fetch_vars[0]))
-        self.assertTrue("scale" in str(fetch_vars[0]))
-        self.assertEqual(expected, actual)
+        model_0 = self.InferModel(load_inference_model(MODEL_DIR, exe))
+        with open(os.path.join(UNI_MODEL_DIR, 'model'), "rb") as f:
+            model_str = f.read()
+        model_1 = self.InferModel(
+            load_inference_model(None, exe, model_str, params_str))
+
+        for model in [model_0, model_1]:
+            outs = exe.run(model.program,
+                           feed={
+                               model.feed_var_names[0]: tensor_x,
+                               model.feed_var_names[1]: tensor_y
+                           },
+                           fetch_list=model.fetch_vars)
+            actual = outs[0]
+
+            self.assertEqual(model.feed_var_names, ["x", "y"])
+            self.assertEqual(len(model.fetch_vars), 1)
+            print("fetch %s" % str(model.fetch_vars[0]))
+            self.assertEqual(expected, actual)
+
+        self.assertRaises(ValueError, fluid.io.load_inference_model, None, exe,
+                          model_str, None)
 
 
 class TestSaveInferenceModel(unittest.TestCase):