[fx]add gpt2 passes for pipeline performance test (#1366)

* [CLI] add CLI launcher

* Revert "[CLI] add CLI launcher"

This reverts commit df7e6506d4.

* [fx]add gpt2 passes for pipeline performance test

colossalai/fx/passes/meta_info_prop.py

@@ -4,6 +4,7 @@ from torch.fx.node import Node, map_aggregate
 from typing import Any, Tuple, NamedTuple, Optional, Dict
 from functools import reduce
 from torch.fx._compatibility import compatibility
+from torch.fx.immutable_collections import immutable_dict, immutable_list
 
 
 @compatibility(is_backward_compatible=True)
@@ -16,6 +17,7 @@ class TensorMetadata(NamedTuple):
     requires_grad: bool
     stride: Tuple[int]
     numel: int
+    is_tensor: bool
     # TODO: we can add a list of sharding spec here, and record the sharding
     # behaviour by appending sharding spec into list.
 
@@ -29,8 +31,9 @@ def _extract_tensor_metadata(result: torch.Tensor) -> TensorMetadata:
     requires_grad = result.requires_grad
     stride = result.stride()
     numel = result.numel()
+    is_tensor = True
 
-    return TensorMetadata(shape, dtype, requires_grad, stride, numel)
+    return TensorMetadata(shape, dtype, requires_grad, stride, numel, is_tensor)
 
 
 def _compute_node_numel(node_metadata: any) -> int:
@@ -51,6 +54,24 @@ def _compute_node_numel(node_metadata: any) -> int:
     return node_numel
 
 
+def _map_aggregate(arg, fn):
+    """
+    Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.
+    """
+    if isinstance(arg, torch.Size):
+        return fn(arg)
+    if isinstance(arg, tuple):
+        return tuple(map_aggregate(elem, fn) for elem in arg)
+    elif isinstance(arg, list):
+        return immutable_list(map_aggregate(elem, fn) for elem in arg)
+    elif isinstance(arg, dict):
+        return immutable_dict((k, map_aggregate(v, fn)) for k, v in arg.items())
+    elif isinstance(arg, slice):
+        return slice(map_aggregate(arg.start, fn), map_aggregate(arg.stop, fn), map_aggregate(arg.step, fn))
+    else:
+        return fn(arg)
+
+
 @compatibility(is_backward_compatible=True)
 class MetaInfoProp(torch.fx.Interpreter):
     """
@@ -85,23 +106,16 @@ class MetaInfoProp(torch.fx.Interpreter):
 
     def run_node(self, n: Node) -> Any:
         result = super().run_node(n)
-        found_tensor = False
 
         def extract_tensor_meta(obj):
             if isinstance(obj, torch.Tensor):
-                nonlocal found_tensor
-                found_tensor = True
                 return _extract_tensor_metadata(obj)
             else:
-                return obj
+                return TensorMetadata(None, None, False, None, 0, False)
 
-        meta = map_aggregate(result, extract_tensor_meta)
+        meta = _map_aggregate(result, extract_tensor_meta)
 
-        if found_tensor:
-            n.meta['tensor_meta'] = meta
-        else:
-            n.meta['tensor_meta'] = TensorMetadata(None, None, False, None, 0)
-        # counting the total size of node outputs
+        n.meta['tensor_meta'] = meta
         total_node_size = _compute_node_numel(n.meta['tensor_meta'])
         # counting the total size of parameters
         total_param_size = 0

colossalai/fx/passes/passes_for_gpt2_test.py

@@ -0,0 +1,288 @@
+import torch
+from torch.fx.graph_module import GraphModule
+from typing import Callable, List, Dict, Any, Optional
+from torch.fx._compatibility import compatibility
+from packaging import version
+from colossalai.fx.passes.meta_info_prop import TensorMetadata
+import inspect
+from colossalai.fx.passes.split_module import Partition
+from colossalai.fx.passes.adding_split_node_pass import pipe_split
+from torch.fx.node import Node
+
+
+def split_with_split_nodes_pass_for_gp2_test(annotated_gm: torch.fx.GraphModule):
+    '''
+    This pass will be used in gpt2 test, only a part of changes may be added into
+    split_with_split_nodes_pass, and it will be deprecated in future.
+    '''
+    part_idx = 0
+
+    def eliminate_unused_placeholders(gm):
+        for node in gm.graph.nodes:
+            if node.op == 'placeholder':
+                if not len(node.users):
+                    gm.graph.erase_node(node)
+        gm.recompile()
+        return gm
+
+    def eliminate_unused_outputs(gm, next_partition_placeholders):
+        '''
+        This method is used to eliminate the outputs in previous partition which is unused in next partition.
+        '''
+        for node in gm.graph.nodes:
+            if node.op == 'output':
+                output_type = node.args[0].__class__
+                output_args = list(node.args[0])
+                for n in node.args[0]:
+                    if n.name not in next_partition_placeholders:
+                        output_args.remove(n)
+                gm.graph.erase_node(node)
+        gm.graph.output(output_type(output_args))
+        gm.recompile()
+        return gm
+
+    def split_callback(n: torch.fx.Node):
+        nonlocal part_idx
+        if (n.op, n.target) == ('call_function', pipe_split):
+            part_idx += 1
+        return part_idx
+
+    split_mod = split_module_for_gpt2_test(annotated_gm, None, split_callback)
+    split_submodules = []
+    for name, submodule in split_mod.named_modules():
+        if isinstance(submodule, torch.fx.GraphModule):
+            for node in submodule.graph.nodes:
+                if (node.op, node.target) == ('call_function', pipe_split):
+                    submodule.graph.erase_node(node)
+            submodule.recompile()
+            split_submodules.append(submodule)
+
+    submodules = list(split_mod.children())
+    placeholder_dict = {}
+    for submodule in submodules:
+        submodule = eliminate_unused_placeholders(submodule)
+        placeholder_dict[submodule] = []
+        for node in submodule.graph.nodes:
+            if node.op == 'placeholder':
+                placeholder_dict[submodule].append(node.name)
+
+    for index, submodule in enumerate(submodules):
+        if index >= len(submodules) - 1:
+            break
+        submodule = eliminate_unused_outputs(submodule, placeholder_dict[submodules[index + 1]])
+        submodule.recompile()
+    split_mod.recompile()
+
+    return split_mod, split_submodules
+
+
+@compatibility(is_backward_compatible=True)
+def split_module_for_gpt2_test(
+    m: GraphModule,
+    root_m: torch.nn.Module,
+    split_callback: Callable[[torch.fx.node.Node], int],
+):
+    """
+    This pass will be used in gpt2 pp performance test, only a part of changes may be added into
+    split_module, and it will be deprecated in future.
+    """
+    partitions: Dict[str, Partition] = {}
+    orig_nodes: Dict[str, torch.fx.node.Node] = {}
+
+    def _node_with_all_tensor_element(node_metadata: Any) -> int:
+        """
+        return whether node contains non-tensor element.
+        """
+        all_tensor_node = True
+
+        if isinstance(node_metadata, TensorMetadata):
+            all_tensor_node = node_metadata.is_tensor and all_tensor_node
+        elif isinstance(node_metadata, dict):
+            value_list = [v for _, v in node_metadata.items()]
+            all_tensor_node += _node_with_all_tensor_element(value_list)
+        else:
+            for element in node_metadata:
+                all_tensor_node += _node_with_all_tensor_element(element)
+
+        return all_tensor_node
+
+    def _move_all_ancestors_into_partition(node, partition_name):
+        all_ancestors = set()
+
+        def _gen_all_ancestors_set(node):
+            all_ancestors.add(node)
+            for n in node.all_input_nodes:
+                if n in all_ancestors:
+                    continue
+                _gen_all_ancestors_set(n)
+
+        _gen_all_ancestors_set(node)
+        for n in list(all_ancestors):
+            if n.op != 'placeholder':
+                n._fx_partition = partition_name
+
+    def record_cross_partition_use(def_node: torch.fx.node.Node,
+                                   use_node: Optional[torch.fx.node.Node]):    # noqa: B950
+        def_partition_name = getattr(def_node, '_fx_partition', None)
+        use_partition_name = getattr(use_node, '_fx_partition', None)
+        if def_partition_name != use_partition_name:
+            if 'tensor_meta' in def_node.meta:
+                if not _node_with_all_tensor_element(def_node.meta['tensor_meta']):
+                    _move_all_ancestors_into_partition(use_node, def_partition_name)
+                    node_process_list.extend(use_node.all_input_nodes)
+                    node_process_list.extend(list(use_node.users))
+                    node_process_list.append(use_node)
+
+                    return
+
+            if def_partition_name is not None:
+                def_partition = partitions[def_partition_name]
+                def_partition.outputs.setdefault(def_node.name)
+                if use_partition_name is not None:
+                    def_partition.partition_dependents.setdefault(use_partition_name)
+
+            if use_partition_name is not None:
+                use_partition = partitions[use_partition_name]
+                use_partition.inputs.setdefault(def_node.name)
+                if def_partition_name is not None:
+                    use_partition.partitions_dependent_on.setdefault(def_partition_name)
+
+    node_process_list = list(m.graph.nodes)
+    # split nodes into parititons
+    while node_process_list:
+        node = node_process_list.pop(0)
+        orig_nodes[node.name] = node
+
+        if node.op in ["placeholder"]:
+            continue
+        if node.op == 'output':
+            # partition_name = str(split_callback(node))
+            # def _set_output_args_partition(n, partition_name):
+            #     n._fx_partition = partition_name
+            # torch.fx.graph.map_arg(node.args[0], lambda n: _set_output_args_partition(n, partition_name))
+            torch.fx.graph.map_arg(node.args[0], lambda n: record_cross_partition_use(n, None))
+            continue
+        partition_name = str(split_callback(node))
+
+        # add node to partitions
+        partition = partitions.get(partition_name)
+        if partition is None:
+            partitions[partition_name] = partition = Partition(partition_name)
+
+        partition.node_names.append(node.name)
+        origin_partition_name = getattr(node, '_fx_partition', None)
+        if origin_partition_name is None:
+            node._fx_partition = partition_name
+
+        torch.fx.graph.map_arg(node.args, lambda def_node: record_cross_partition_use(def_node, node))
+        torch.fx.graph.map_arg(node.kwargs, lambda def_node: record_cross_partition_use(def_node, node))    # noqa: B950
+
+    # find partitions with no dependencies
+    root_partitions: List[str] = []
+    for partition_name, partition in partitions.items():
+        if not len(partition.partitions_dependent_on):
+            root_partitions.append(partition_name)
+
+    # check partitions for circular dependencies and create topological partition ordering
+    sorted_partitions: List[str] = []
+    while root_partitions:
+        root_partition = root_partitions.pop()
+        sorted_partitions.append(root_partition)
+        for dependent in partitions[root_partition].partition_dependents:
+            partitions[dependent].partitions_dependent_on.pop(root_partition)
+            if not partitions[dependent].partitions_dependent_on:
+                root_partitions.append(dependent)
+    if len(sorted_partitions) != len(partitions):
+        raise RuntimeError("cycle exists between partitions!")
+
+    # add placeholders to parititons
+    for partition_name in sorted_partitions:
+        partition = partitions[partition_name]
+        for input in partition.inputs:
+            placeholder = partition.graph.placeholder(input)
+            placeholder.meta = orig_nodes[input].meta.copy()
+            partition.environment[orig_nodes[input]] = placeholder
+
+    # Transform nodes and collect targets for partition's submodule
+    for node in m.graph.nodes:
+        if hasattr(node, '_fx_partition'):
+            partition = partitions[node._fx_partition]
+
+            # swap out old graph nodes in kw/args with references to new nodes in this submodule
+            environment = partition.environment
+            gathered_args = torch.fx.graph.map_arg(node.args, lambda n: environment[n])
+            gathered_kwargs = torch.fx.graph.map_arg(node.kwargs, lambda n: environment[n])
+
+            if node.op not in ['call_module', 'get_attr']:
+                target = node.target
+            else:
+                target_atoms = node.target.split('.')
+                target_attr = m
+                for atom in target_atoms:
+                    if not hasattr(target_attr, atom):
+                        raise RuntimeError(f'Operator target {node.target} not found!')
+                    target_attr = getattr(target_attr, atom)
+                # target = target_atoms[-1]
+                target = '_'.join(target_atoms)
+                partition.targets[target] = target_attr
+
+            assert isinstance(gathered_args, tuple)
+            assert isinstance(gathered_kwargs, dict)
+            new_node = partition.graph.create_node(op=node.op,
+                                                   target=target,
+                                                   args=gathered_args,
+                                                   kwargs=gathered_kwargs)
+            new_node.meta = node.meta.copy()
+            partition.environment[node] = new_node
+
+    # Set up values to construct base module
+    base_mod_env: Dict[str, torch.fx.node.Node] = {}
+    base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
+    base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule] = {}
+    for node in m.graph.nodes:
+        if node.op == 'placeholder':
+            if version.parse(torch.__version__) < version.parse('1.11.0'):
+                base_mod_env[node.name] = base_mod_graph.placeholder(node.name, type_expr=node.type)
+            else:
+                default_value = node.args[0] if len(node.args) > 0 else inspect.Signature.empty
+                base_mod_env[node.name] = base_mod_graph.placeholder(node.name,
+                                                                     type_expr=node.type,
+                                                                     default_value=default_value)
+            base_mod_env[node.name].meta = node.meta.copy()
+
+    # Do some things iterating over the partitions in topological order again:
+    # 1) Finish off submodule Graphs by setting corresponding outputs
+    # 2) Construct GraphModules for each submodule
+    # 3) Construct the base graph by emitting calls to those submodules in
+    #    topological order
+
+    for partition_name in sorted_partitions:
+        partition = partitions[partition_name]
+
+        # Set correct output values
+        output_vals = tuple(partition.environment[orig_nodes[name]] for name in partition.outputs)
+        output_vals = output_vals[0] if len(output_vals) == 1 else output_vals    # type: ignore[assignment]
+        partition.graph.output(output_vals)
+
+        # Construct GraphModule for this partition
+        submod_name = f'submod_{partition_name}'
+        base_mod_attrs[submod_name] = torch.fx.graph_module.GraphModule(partition.targets,
+                                                                        partition.graph)    # noqa: B950
+
+        # Emit call in base graph to this submodule
+        output_val = base_mod_graph.call_module(submod_name, tuple(base_mod_env[name] for name in partition.inputs))
+        if len(partition.outputs) > 1:
+            # Unpack multiple return values from submodule
+            output_val_proxy = torch.fx.proxy.Proxy(output_val)
+            for i, output_name in enumerate(partition.outputs):
+                base_mod_env[output_name] = output_val_proxy[i].node    # type: ignore[index]
+        else:
+            if not partition.outputs:
+                continue
+            base_mod_env[list(partition.outputs)[0]] = output_val
+
+    for node in m.graph.nodes:
+        if node.op == 'output':
+            base_mod_graph.output(torch.fx.graph.map_arg(node.args[0], lambda n: base_mod_env[n.name]))    # noqa: B950
+
+    return torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph)