mirror of https://github.com/hpcaitech/ColossalAI
473 lines
19 KiB
Python
473 lines
19 KiB
Python
from typing import Any, Dict, List, Tuple
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import torch
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try:
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from torch.fx.graph import CodeGen
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except:
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pass
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from torch.fx.graph import (
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PythonCode,
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_custom_builtins,
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_format_target,
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_is_from_torch,
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_Namespace,
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_origin_type_map,
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_register_custom_builtin,
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inplace_methods,
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magic_methods,
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)
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from torch.fx.node import Argument, Node, _get_qualified_name, _type_repr, map_arg
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import colossalai
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from colossalai.fx._compatibility import compatibility
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_register_custom_builtin("colossalai", "import colossalai", colossalai)
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def _gen_ckpt_fn_def(label, free_vars: List[str]) -> str:
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"""
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Generate the checkpoint function definition
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"""
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return f"def checkpoint_{label}({', '.join(['self'] + free_vars)}):"
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def _gen_ckpt_output(output_vars: List[str]) -> str:
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"""
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Generate the return statement for checkpoint region
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"""
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return f"return {', '.join(output_vars)}"
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def _gen_ckpt_usage(label, input_vars, output_vars, use_reentrant=True):
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"""
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Generate the checkpoint function call code text
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"""
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outputs = ", ".join(output_vars)
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inputs = ", ".join(input_vars)
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return f"{outputs} = torch.utils.checkpoint.checkpoint(self.checkpoint_{label}, {inputs}, use_reentrant={use_reentrant})"
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def _end_of_ckpt(node: Node, ckpt_level: int) -> bool:
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"""
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Check if the node could end the ckpt region at `ckpt_level`
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"""
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if len(node.meta["info"].activation_checkpoint) > ckpt_level:
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return node.meta["info"].activation_checkpoint[ckpt_level] is not None
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return True
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def _find_input_and_output_nodes(nodes: List[Node]):
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"""
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Find the input and output node names which are not found in the given list of nodes.
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"""
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input_nodes = []
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output_nodes = []
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# if a node has an input node which is not in the node list
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# we treat that input node as the input of the checkpoint function
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for node in nodes:
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for input_node in node._input_nodes.keys():
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node_repr = repr(input_node)
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if input_node not in nodes and node_repr not in input_nodes:
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input_nodes.append(node_repr)
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# if a node has a user node which is not in the node list
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# we treat that user node as the node receiving the current node output
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for node in nodes:
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for output_node in node.users.keys():
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node_repr = repr(node)
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if output_node not in nodes and node_repr not in output_nodes:
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output_nodes.append(node_repr)
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return input_nodes, output_nodes
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def _find_nested_ckpt_regions(node_list: List[Node], ckpt_level: int = 0):
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"""
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Find the nested checkpoint regions given a list of consecutive nodes. The outputs
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will be list of tuples, each tuple is in the form of (start_index, end_index).
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"""
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ckpt_regions = []
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start = -1
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end = -1
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current_region = None
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for idx, node in enumerate(node_list):
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if len(node.meta["info"].activation_checkpoint) > ckpt_level:
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act_ckpt_label = node.meta["info"].activation_checkpoint[ckpt_level]
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# this activation checkpoint label is not set yet
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# meaning this is the first node of the activation ckpt region
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if current_region is None:
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current_region = act_ckpt_label
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start = idx
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# if activation checkpoint has changed
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# we restart the tracking
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# e.g. node ckpt states = [ckpt1, ckpt2, ckpt2, ckpt2]
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if act_ckpt_label != current_region:
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assert start != -1
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ckpt_regions.append((start, idx - 1))
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current_region = act_ckpt_label
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start = idx
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end = -1
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elif current_region is not None and _end_of_ckpt(node, ckpt_level):
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# used to check the case below
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# node ckpt states = [ckpt, ckpt, non-ckpt]
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end = idx - 1
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assert start != -1 and end != -1
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ckpt_regions.append((start, end))
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start = end = -1
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current_region = None
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else:
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pass
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if current_region is not None:
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end = len(node_list) - 1
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ckpt_regions.append((start, end))
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return ckpt_regions
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def emit_ckpt_func(
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body, ckpt_func, node_list: List[Node], emit_node_func, delete_unused_value_func, ckpt_level=0, in_ckpt=False
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):
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"""Emit ckpt function in nested way
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Args:
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body: forward code - in recursive calls, this part will be checkpoint
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functions code
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ckpt_func: checkpoint functions code - in recursive calls, this part
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will be a buffer
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node_list (List[Node]): list of torch.fx.Node
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emit_node_func: function to emit a node
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delete_unused_value_func: function to delete unused value
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level (int, optional): checkpoint level. Defaults to 0.
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in_ckpt (bool, optional): indicates wether the func is in recursive
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call. Defaults to False.
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"""
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inputs, outputs = _find_input_and_output_nodes(node_list)
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# label given by each layer, e.g. if you are currently at level (0, 1, 1)
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# the label will be '0_1_1'
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label = "_".join([str(idx) for idx in node_list[0].meta["info"].activation_checkpoint[: ckpt_level + 1]])
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ckpt_fn_def = _gen_ckpt_fn_def(label, inputs)
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ckpt_func.append(f"{ckpt_fn_def}\n")
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# if there is more level to fetch
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if ckpt_level + 1 < max(map(lambda node: len(node.meta["info"].activation_checkpoint), node_list)):
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ckpt_regions = _find_nested_ckpt_regions(node_list, ckpt_level + 1)
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start_idx = [item[0] for item in ckpt_regions]
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end_idx = [item[1] for item in ckpt_regions]
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# use ckpt_func_buffer to store nested checkpoint functions
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ckpt_func_buffer = []
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node_idx = 0
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while 1:
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if node_idx >= len(node_list):
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break
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if node_idx in start_idx:
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ckpt_node_list = node_list[node_idx : end_idx[start_idx.index(node_idx)] + 1]
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emit_ckpt_func(
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ckpt_func,
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ckpt_func_buffer,
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ckpt_node_list,
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emit_node_func,
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delete_unused_value_func,
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ckpt_level + 1,
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True,
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)
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node_idx += len(ckpt_node_list)
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else:
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node = node_list[node_idx]
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emit_node_func(node, ckpt_func)
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ckpt_func[-1] = " " + ckpt_func[-1]
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delete_unused_value_func(node, ckpt_func)
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node_idx += 1
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ckpt_func.append(" " + _gen_ckpt_output(outputs) + "\n\n")
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ckpt_func += ckpt_func_buffer
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# last level
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else:
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for node in node_list:
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emit_node_func(node, ckpt_func)
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ckpt_func[-1] = " " + ckpt_func[-1]
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delete_unused_value_func(node, ckpt_func)
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ckpt_func.append(" " + _gen_ckpt_output(outputs) + "\n\n")
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usage = _gen_ckpt_usage(label, inputs, outputs, False) + "\n"
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if in_ckpt:
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usage = " " + usage
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body.append(usage)
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def emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node_func, delete_unused_value_func):
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"""Emit code with nested activation checkpoint
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When we detect some of the annotation is a , we will use
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this function to emit the activation checkpoint codes.
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Args:
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body: forward code
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ckpt_func: checkpoint functions code
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nodes: graph.nodes
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emit_node_func: function to emit node
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delete_unused_value_func: function to remove the unused value
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"""
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ckpt_regions = _find_nested_ckpt_regions(nodes, 0)
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start_idx = [item[0] for item in ckpt_regions]
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end_idx = [item[1] for item in ckpt_regions]
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node_list = list(nodes)
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node_idx = 0
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while 1:
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# break if we finish the processing all the nodes
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if node_idx >= len(node_list):
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break
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# process ckpt_regions
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if node_idx in start_idx:
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ckpt_node_list = node_list[node_idx : end_idx[start_idx.index(node_idx)] + 1]
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emit_ckpt_func(body, ckpt_func, ckpt_node_list, emit_node_func, delete_unused_value_func)
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node_idx += len(ckpt_node_list)
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# process node in forward function
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else:
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node = node_list[node_idx]
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emit_node_func(node, body)
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delete_unused_value_func(node, body)
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node_idx += 1
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@compatibility(is_backward_compatible=True)
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class ActivationCheckpointCodeGen(CodeGen):
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def _gen_python_code(self, nodes, root_module: str, namespace: _Namespace, verbose=None) -> PythonCode:
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free_vars: List[str] = []
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body: List[str] = []
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globals_: Dict[str, Any] = {}
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wrapped_fns: Dict[str, None] = {}
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# Wrap string in list to pass by reference
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maybe_return_annotation: List[str] = [""]
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def add_global(name_hint: str, obj: Any):
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"""Add an obj to be tracked as a global.
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We call this for names that reference objects external to the
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Graph, like functions or types.
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Returns: the global name that should be used to reference 'obj' in generated source.
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"""
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if _is_from_torch(obj) and obj != torch.device: # to support registering torch.device
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# HACK: workaround for how torch custom ops are registered. We
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# can't import them like normal modules so they must retain their
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# fully qualified name.
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return _get_qualified_name(obj)
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# normalize the name hint to get a proper identifier
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global_name = namespace.create_name(name_hint, obj)
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if global_name in globals_:
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assert globals_[global_name] is obj
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return global_name
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globals_[global_name] = obj
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return global_name
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# Pre-fill the globals table with registered builtins.
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for name, (_, obj) in _custom_builtins.items():
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add_global(name, obj)
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def type_repr(o: Any):
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if o == ():
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# Empty tuple is used for empty tuple type annotation Tuple[()]
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return "()"
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typename = _type_repr(o)
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if hasattr(o, "__origin__"):
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# This is a generic type, e.g. typing.List[torch.Tensor]
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origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
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origin_typename = add_global(_type_repr(origin_type), origin_type)
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if hasattr(o, "__args__"):
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# Assign global names for each of the inner type variables.
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args = [type_repr(arg) for arg in o.__args__]
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if len(args) == 0:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python < 3.9
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return origin_typename
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return f'{origin_typename}[{",".join(args)}]'
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else:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python 3.9+
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return origin_typename
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# Common case: this is a regular module name like 'foo.bar.baz'
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return add_global(typename, o)
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def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
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def _get_repr(arg):
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# Handle NamedTuples (if it has `_fields`) via add_global.
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if isinstance(arg, tuple) and hasattr(arg, "_fields"):
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qualified_name = _get_qualified_name(type(arg))
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global_name = add_global(qualified_name, type(arg))
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return f"{global_name}{repr(tuple(arg))}"
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return repr(arg)
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args_s = ", ".join(_get_repr(a) for a in args)
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kwargs_s = ", ".join(f"{k} = {_get_repr(v)}" for k, v in kwargs.items())
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if args_s and kwargs_s:
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return f"{args_s}, {kwargs_s}"
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return args_s or kwargs_s
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# Run through reverse nodes and record the first instance of a use
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# of a given node. This represents the *last* use of the node in the
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# execution order of the program, which we will use to free unused
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# values
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node_to_last_use: Dict[Node, Node] = {}
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user_to_last_uses: Dict[Node, List[Node]] = {}
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def register_last_uses(n: Node, user: Node):
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if n not in node_to_last_use:
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node_to_last_use[n] = user
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user_to_last_uses.setdefault(user, []).append(n)
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for node in reversed(nodes):
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map_arg(node.args, lambda n: register_last_uses(n, node))
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map_arg(node.kwargs, lambda n: register_last_uses(n, node))
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# NOTE: we add a variable to distinguish body and ckpt_func
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def delete_unused_values(user: Node, body):
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"""
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Delete values after their last use. This ensures that values that are
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not used in the remainder of the code are freed and the memory usage
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of the code is optimal.
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"""
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if user.op == "placeholder":
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return
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if user.op == "output":
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body.append("\n")
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return
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nodes_to_delete = user_to_last_uses.get(user, [])
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if len(nodes_to_delete):
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to_delete_str = " = ".join([repr(n) for n in nodes_to_delete] + ["None"])
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body.append(f"; {to_delete_str}\n")
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else:
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body.append("\n")
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# NOTE: we add a variable to distinguish body and ckpt_func
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def emit_node(node: Node, body):
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maybe_type_annotation = "" if node.type is None else f" : {type_repr(node.type)}"
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if node.op == "placeholder":
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assert isinstance(node.target, str)
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maybe_default_arg = "" if not node.args else f" = {repr(node.args[0])}"
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free_vars.append(f"{node.target}{maybe_type_annotation}{maybe_default_arg}")
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raw_name = node.target.replace("*", "")
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if raw_name != repr(node):
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body.append(f"{repr(node)} = {raw_name}\n")
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return
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elif node.op == "call_method":
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assert isinstance(node.target, str)
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body.append(
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f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}"
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f"({_format_args(node.args[1:], node.kwargs)})"
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)
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return
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elif node.op == "call_function":
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assert callable(node.target)
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# pretty print operators
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if node.target.__module__ == "_operator" and node.target.__name__ in magic_methods:
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assert isinstance(node.args, tuple)
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body.append(
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f"{repr(node)}{maybe_type_annotation} = "
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f"{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}"
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)
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return
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# pretty print inplace operators; required for jit.script to work properly
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# not currently supported in normal FX graphs, but generated by torchdynamo
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if node.target.__module__ == "_operator" and node.target.__name__ in inplace_methods:
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body.append(
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f"{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; "
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f"{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}"
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)
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return
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qualified_name = _get_qualified_name(node.target)
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global_name = add_global(qualified_name, node.target)
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# special case for getattr: node.args could be 2-argument or 3-argument
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# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
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if (
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global_name == "getattr"
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and isinstance(node.args, tuple)
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and isinstance(node.args[1], str)
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and node.args[1].isidentifier()
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and len(node.args) == 2
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):
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body.append(
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f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}"
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)
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return
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body.append(
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f"{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})"
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)
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if node.meta.get("is_wrapped", False):
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wrapped_fns.setdefault(global_name)
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return
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elif node.op == "call_module":
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assert isinstance(node.target, str)
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body.append(
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f"{repr(node)}{maybe_type_annotation} = "
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f"{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})"
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)
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return
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elif node.op == "get_attr":
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assert isinstance(node.target, str)
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body.append(f"{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}")
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return
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elif node.op == "output":
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if node.type is not None:
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maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
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body.append(self.generate_output(node.args[0]))
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return
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raise NotImplementedError(f"node: {node.op} {node.target}")
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# Modified for activation checkpointing
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ckpt_func = []
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emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node, delete_unused_values)
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if len(body) == 0:
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# If the Graph has no non-placeholder nodes, no lines for the body
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# have been emitted. To continue to have valid Python code, emit a
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# single pass statement
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body.append("pass\n")
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if len(wrapped_fns) > 0:
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wrap_name = add_global("wrap", torch.fx.wrap)
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wrap_stmts = "\n".join([f'{wrap_name}("{name}")' for name in wrapped_fns])
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else:
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wrap_stmts = ""
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if self._body_transformer:
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body = self._body_transformer(body)
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for name, value in self.additional_globals():
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add_global(name, value)
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prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
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prologue = "".join(ckpt_func) + prologue
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prologue = prologue
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code = "".join(body)
|
|
code = "\n".join(" " + line for line in code.split("\n"))
|
|
fn_code = f"""
|
|
{wrap_stmts}
|
|
{prologue}
|
|
{code}"""
|
|
return PythonCode(fn_code, globals_)
|