[pipelinable]use pipelinable to support GPT model. (#903)

* [CLI] add CLI launcher

* Revert "[CLI] add CLI launcher"

This reverts commit df7e6506d4500af6a9220ef7fe4d3c7b1daebd4c.

* [pipelinable]use pipelinable to support GPT model.

* fix a bug caused by ShardedModel

* polish

* fix front func list

colossalai/engine/schedule/_pipeline_schedule.py

@@ -119,9 +119,12 @@ class PipelineSchedule(BaseSchedule):
     def pre_processing(self, engine):
         # TODO: remove this after testing new zero with pipeline parallelism
         model = engine.model
-        if isinstance(model, (NaiveAMPModel, ShardedModelV2)):
+        if isinstance(model, NaiveAMPModel):
             self.dtype = torch.half
             model = model.model
+        if isinstance(model, ShardedModelV2):
+            self.dtype = torch.half
+            model = model.module
         sig = inspect.signature(model.forward)
         for p in sig.parameters.values():
             assert p.kind != inspect.Parameter.VAR_POSITIONAL, '*args is not supported'
@@ -135,6 +138,12 @@ class PipelineSchedule(BaseSchedule):
         else:
             sig = inspect.signature(model.forward)
         if isinstance(batch_data, torch.Tensor):
+            for p in sig.parameters.values():
+                if p.kind == inspect.Parameter.VAR_KEYWORD:
+                    if input_tensor is None:
+                        return model(batch_data)
+                    else:
+                        return model(input_tensor)
             if input_tensor is None:
                 return model(batch_data)
             elif len(sig.parameters) > 1:
@@ -148,7 +157,7 @@ class PipelineSchedule(BaseSchedule):
                     filter_batch = False
             if filter_batch:
                 batch_data = {k: v for k, v in batch_data.items() if k in sig.parameters}
-            if input_tensor is None:
+            if input_tensor is None and filter_batch:
                 return model(**batch_data)
             else:
                 return model(input_tensor, **batch_data)

colossalai/utils/model/pipelinable.py

 import torch
 import functools
+import inspect
+from colossalai.amp.naive_amp import NaiveAMPModel
 from colossalai.utils.model.utils import _substitute_init_recursively, InsertPostInitMethodToModuleSubClasses, call_to_str
 from colossalai.builder.pipeline import partition_uniform, partition_balanced
 from colossalai.core import global_context as gpc
+from colossalai.nn.layer.utils import CheckpointModule
 from colossalai.tensor import ColoTensor
 
 
@@ -58,11 +61,18 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
             if issubclass(obj.__class__, torch.nn.modules.module.Module):
                 obj = self._layer_spec_dict[id(obj)]
             modified_args.append(obj)
-        # (lyl)TODO: analyse kwargs as well
+
+        modified_kwargs = {}
+        for k, v in kwargs.items():
+            if issubclass(v.__class__, torch.nn.modules.module.Module):
+                v = self._layer_spec_dict[id(v)]
+            # (lyl)TODO: analyse ColoTensor as well
+            modified_kwargs[k] = v
+
         modified_args = tuple(modified_args)
         self._root_children = list(module.children())
         self._model = module
-        layer_spec = LayerSpec(module.__class__, *modified_args, **kwargs)
+        layer_spec = LayerSpec(module.__class__, *modified_args, **modified_kwargs)
         layer_spec.set_children(module.children())
         self._layer_spec_dict[module_id] = layer_spec
         name_list = []
@@ -82,28 +92,49 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
         """
         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:
-            func_key = "first"
+            front_funcs_list = []
             for index, element in enumerate(exec_seq):
                 if isinstance(element, str):
                     module = dict(self._model.named_modules())[element]
                     layer_spec = self._layer_spec_dict[id(module)]
-                    func_key = layer_spec
+                    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] = []
-                    self._func_dict[func_key].append(element)
+                    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):
         """
@@ -128,17 +159,19 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
         layers_to_build = []
         for start, end in parts:
             layers_to_build += self._layer_spec_list[start:end]
-        func_dict_in_partition = {}
+        behind_func_dict_in_partition = {}
+        front_func_dict_in_partition = {}
         module_list_in_partition = []
-        if rank == 0 and "first" in self._func_dict:
-            func_dict_in_partition["first"] = self._func_dict["first"]
         for layer in layers_to_build:
             module = layer.build()
             module_list_in_partition.append(module)
-            if layer in self._func_dict:
-                func_dict_in_partition[id(module)] = self._func_dict[layer]
+            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, func_dict_in_partition)
+        pipeline_model = PipelinableModel(module_list_in_partition, front_func_dict_in_partition,
+                                          behind_func_dict_in_partition)
 
         return pipeline_model
 
@@ -146,31 +179,119 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
         self._policy = policy
 
 
+def _build_kwargs_for_module(function, kw_dict):
+    """
+    Generally, the first argument of module.forward is an input tensor come from the previous layer.
+    Therefore, we just filter the kwargs from second element of the dictionary.
+    """
+    sig = inspect.signature(function)
+    if len(sig.parameters) <= 1:
+        return None
+    args_name_list = list(sig.parameters.keys())
+    kw_dict = {k: v for k, v in kw_dict.items() if k in args_name_list[1:]}
+    return kw_dict
+
+
+def _build_kwargs_for_function(function, kw_dict):
+    sig = inspect.signature(function)
+    kw_dict = {k: v for k, v in kw_dict.items() if k in sig.parameters}
+    if len(kw_dict) == 0:
+        return None
+    return kw_dict
+
+
+def _exec_func_with_kwargs(func, kw_dict, input_tensor, kwargs):
+    """
+    We suppose the callable object passed to to_layer_list method in two purpose:
+        a. use the callable object to modify input tensor, such as \
+            lambda x: torch.flatten(x, 1)
+        b. use the callable object to modify kwargs value, such as \
+            def foo(attention_mask=None):
+                if attention_mask is not None:
+                    batch_size = input_ids.shape[0]
+                    attention_mask = attention_mask.view(batch_size, -1)
+                return attention_mask
+    """
+
+    if kw_dict is not None:
+        rst = func(**kw_dict)
+        if isinstance(rst, tuple):
+            for i, k in enumerate(kw_dict.keys()):
+                kwargs[k] = rst[i]
+        else:
+            for k in kw_dict.keys():
+                kwargs[k] = rst
+        return input_tensor
+    return func(input_tensor)
+
+
+def _exec_funcs_with_kwargs(func_dict, func_key, input_tensor, kwargs):
+
+    assert func_key in func_dict, f"{func_key} is not in the function_dict."
+    funcs_to_exec = func_dict[func_key]
+    if isinstance(funcs_to_exec, list):
+        for f in funcs_to_exec:
+            f_kwargs = _build_kwargs_for_function(f, kwargs)
+            input_tensor = _exec_func_with_kwargs(f, f_kwargs, input_tensor, kwargs)
+    else:
+        f_kwargs = _build_kwargs_for_function(funcs_to_exec, kwargs)
+        input_tensor = _exec_func_with_kwargs(funcs_to_exec, f_kwargs, input_tensor, kwargs)
+
+    return input_tensor
+
+
 class PipelinableModel(torch.nn.Module):
 
-    def __init__(self, module_list, func_dict):
+    def __init__(self, module_list, front_func_dict, behind_func_dict):
         super().__init__()
         self._module_list = module_list
-        self._func_dict = func_dict
-
-    def forward(self, input_tensor):
-        if "first" in self._func_dict:
-            funcs = self._func_dict["first"]
-            if isinstance(funcs, list):
-                for f in funcs:
-                    input_tensor = f(input_tensor)
-            else:
-                input_tensor = funcs(input_tensor)
+        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:
-            input_tensor = module(input_tensor)
-            if id(module) in self._func_dict:
-                funcs = self._func_dict[id(module)]
-                if isinstance(funcs, list):
-                    for f in funcs:
-                        input_tensor = f(input_tensor)
+
+            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:
-                    input_tensor = funcs(input_tensor)
+                    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
 
@@ -203,7 +324,14 @@ class LayerSpec:
                 obj = obj.build()
             recovered_args.append(obj)
         recovered_args = tuple(recovered_args)
-        return self.typename(*recovered_args, **self.module_kwargs)
+
+        recovered_kwargs = {}
+        for k, v in self.module_kwargs.items():
+            if isinstance(v, LayerSpec):
+                v = v.build()
+            recovered_kwargs[k] = v
+
+        return self.typename(*recovered_args, **recovered_kwargs)
 
     def set_children(self, children):
         self.children = children