[pipeline] refactor the pipeline module (#1087)

* [pipeline] refactor the pipeline module

* polish code

colossalai/pipeline/pipelinable.py

@@ -0,0 +1,260 @@
+import torch
+import inspect
+from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses
+from .utils import partition_uniform, partition_balanced, build_kwargs_for_function, build_kwargs_for_module, exec_func_with_kwargs, exec_funcs_with_kwargs
+from colossalai.nn.layer.utils import CheckpointModule
+from colossalai.tensor import ColoParameter
+from .layer_sepc import LayerSpec
+
+
+class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
+    """
+    A context manager to split the model into pipeline stages.
+    """
+
+    def __init__(self, policy: str="balanced"):
+        super().__init__()
+        self._layer_spec_dict = {}
+        self._root_children = None
+        self._model = None
+        self._layer_spec_list = []
+        self._func_dict = {}
+        self._policy = policy
+
+    @property
+    def policy(self):
+        return self._policy
+
+    @policy.setter
+    def policy(self, policy: str):
+        self._policy = policy
+
+    @property
+    def layers_count(self):
+        return len(self._layer_spec_list)
+
+    @property
+    def funcs_count(self):
+        return len(self._func_dict)
+
+    def _pre_context_exec(self):
+        """
+        The Callback function when entering the context
+        """
+        # reserve rng states
+        self.cpu_rng_state = torch.get_rng_state()
+        self.cuda_rng_state = torch.cuda.get_rng_state()
+
+    def _post_context_exec(self):
+        """
+        The callback function when exiting context.
+        """
+
+        # reset rng states
+        torch.set_rng_state(self.cpu_rng_state)
+        torch.cuda.set_rng_state(self.cuda_rng_state)
+
+    def _post_init_method(self, module: torch.nn.Module, *args, **kwargs):
+        """
+        The function to call at the end of the constructor of each module.
+        NOTE() The module may be passed to this function multiple times.
+        """
+        # iterate over the positional arguments
+        # to check if an argument is a torch Module
+        # if found any torch Module, replace it with its layer spec 
+        # for storage purpose
+        modified_args = []
+        for arg in args:
+            if isinstance(arg, torch.nn.Module):
+                arg = self._layer_spec_dict[id(arg)]
+            modified_args.append(arg)
+
+        # to the same for the keyword arguments
+        modified_kwargs = {}
+        for k, v in kwargs.items():
+            if isinstance(v, torch.nn.Module):
+                v = self._layer_spec_dict[id(v)]
+            # (lyl)TODO: analyse ColoTensor as well
+            modified_kwargs[k] = v
+
+        # keep track of the module children
+        # as torch.nn.Module.__init__ is called from inner module to outer module,
+        # the final value of self._model will be the outermost model
+        # e.g. if the model is torchvision.models.resnet18, then the final value of self._model
+        # will be the ``ResNet`` object.
+        self._root_children = list(module.children())
+        self._model = module
+
+        # store the children to keep the module hierarchy
+        layer_spec = LayerSpec(module.__class__, *modified_args, **modified_kwargs)
+        layer_spec.set_children(module.children())
+
+        # store the layer spec in this context
+        module_id = id(module)
+        self._layer_spec_dict[module_id] = layer_spec
+
+        # convert all torch.nn.Parameter to colossalai.tensor.ColoParameter
+        name_list = []
+        for name, param in module.named_parameters():
+            if isinstance(param, ColoParameter):
+                continue
+            name_list.append((name, param))
+
+        for name, param in name_list:
+            delattr(module, name)
+            setattr(module, name, ColoParameter.from_torch_tensor(tensor=param.data, requires_grad=param.requires_grad))
+
+    def to_layer_list(self, exec_seq=None):
+        """
+        Create a layer spec list and func list with execution sequence given by user.
+        If exec_seq is None, we will take the module initizing order as execution order.
+        """
+        if exec_seq is None:
+            # if user do not provide the model executing sequence, we use the initialization order as the executing order.
+            children_name = []
+            for child in self._root_children:
+                layer_spec = self._layer_spec_dict[id(child)]
+                if layer_spec.typename in (torch.nn.modules.container.ModuleList,
+                                           torch.nn.modules.container.Sequential):
+                    for child_in_container in layer_spec.children:
+                        self._layer_spec_list.append(self._layer_spec_dict[id(child_in_container)])
+                        for name, module in self._model.named_modules():
+                            if id(module) == id(child_in_container):
+                                children_name.append(name)
+                                break
+                else:
+                    self._layer_spec_list.append(layer_spec)
+                    for name, module in self._model.named_modules():
+                        if id(module) == id(child):
+                            children_name.append(name)
+                            break
+
+        else:
+            front_funcs_list = []
+            named_modules = dict(self._model.named_modules())
+            for index, element in enumerate(exec_seq):
+                if isinstance(element, str):
+                    assert element in named_modules, f'Found invalid module name {element}, please check if you spell the module name correctly.'
+
+                    # get the layer spec based on the module ID
+                    module = named_modules[element]
+                    layer_spec = self._layer_spec_dict[id(module)]
+
+                    # check whether there are functions which should be executed before this module
+                    if len(front_funcs_list) != 0:
+                        func_key = (layer_spec, "front")
+                        if func_key not in self._func_dict:
+                            self._func_dict[func_key] = []
+                        for f in front_funcs_list:
+                            self._func_dict[func_key].append(f)
+                        front_funcs_list = []
+
+                    func_key = (layer_spec, "behind")
+                    self._layer_spec_list.append(layer_spec)
+                elif isinstance(element, tuple) and element[1] == "front":
+                    front_funcs_list.append(element[0])
+                else:
+                    if func_key not in self._func_dict:
+                        self._func_dict[func_key] = []
+                    if isinstance(element, tuple):
+                        self._func_dict[func_key].append(element[0])
+                    else:
+                        self._func_dict[func_key].append(element)
+
+    def partition(self, num_chunks, pipeline_size, rank):
+        """
+        Partitioned model will be built respect to partion policy.
+        The real module instance will be built in this method.
+        """
+        if isinstance(self._policy, str):
+            if self._policy == "uniform":
+                parts = partition_uniform(len(self._layer_spec_list), pipeline_size, num_chunks)[rank]
+            elif self._policy == "balanced":
+                param_counts = []
+                for layer_spec in self._layer_spec_list:
+                    param_counts.append(layer_spec.count_params())
+                parts = partition_balanced(param_counts, pipeline_size, num_chunks)[rank]
+            else:
+                raise ValueError("A string partition policy should be one of ['uniform', 'balanced'].")
+        elif isinstance(self._policy, dict):
+            parts = self._policy[rank]
+        else:
+            raise ValueError("A partition policy should be either a string or a dictionary.")
+
+        layers_to_build = []
+        for start, end in parts:
+            layers_to_build += self._layer_spec_list[start:end]
+        behind_func_dict_in_partition = {}
+        front_func_dict_in_partition = {}
+        module_list_in_partition = []
+        for layer in layers_to_build:
+            module = layer.build()
+            module_list_in_partition.append(module)
+            if (layer, "front") in self._func_dict:
+                front_func_dict_in_partition[id(module)] = self._func_dict[(layer, "front")]
+            elif (layer, "behind") in self._func_dict:
+                behind_func_dict_in_partition[id(module)] = self._func_dict[(layer, "behind")]
+        module_list_in_partition = torch.nn.ModuleList(module_list_in_partition)
+        pipeline_model = PipelinableModel(module_list_in_partition, front_func_dict_in_partition,
+                                          behind_func_dict_in_partition)
+
+        return pipeline_model
+
+
+class PipelinableModel(torch.nn.Module):
+
+    def __init__(self, module_list, front_func_dict, behind_func_dict):
+        super().__init__()
+        self._module_list = module_list
+        self._front_func_dict = front_func_dict
+        self._behind_func_dict = behind_func_dict
+
+    def forward(self, input_tensor, **kwargs):
+
+        for module in self._module_list:
+
+            if id(module) in self._front_func_dict:
+                input_tensor = exec_funcs_with_kwargs(self._front_func_dict, id(module), input_tensor, kwargs)
+
+            if isinstance(module, CheckpointModule):
+                forward_func = module._forward
+            else:
+                forward_func = module.forward
+            if input_tensor is None:
+                module_kwargs = build_kwargs_for_function(forward_func, kwargs)
+            else:
+                module_kwargs = build_kwargs_for_module(forward_func, kwargs)
+            if module_kwargs is not None and input_tensor is not None:
+                if isinstance(module, CheckpointModule):
+                    convert_kwargs_to_args = []
+                    for v in module_kwargs.values():
+                        convert_kwargs_to_args.append(v)
+                    rst = module(input_tensor, *convert_kwargs_to_args)
+                else:
+                    rst = module(input_tensor, **module_kwargs)
+                if isinstance(rst, tuple):
+                    input_tensor = rst[0]
+                else:
+                    input_tensor = rst
+            elif module_kwargs is not None and input_tensor is None:
+                if isinstance(module, CheckpointModule):
+                    convert_kwargs_to_args = []
+                    for v in module_kwargs.values():
+                        convert_kwargs_to_args.append(v)
+                    rst = module(input_tensor, *convert_kwargs_to_args)
+                else:
+                    rst = module(**module_kwargs)
+                if isinstance(rst, tuple):
+                    input_tensor = rst[0]
+                else:
+                    input_tensor = rst
+            else:
+                input_tensor = module(input_tensor)
+
+            if id(module) in self._behind_func_dict:
+                input_tensor = exec_funcs_with_kwargs(self._behind_func_dict, id(module), input_tensor, kwargs)
+
+        return input_tensor
+
+
+