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329 lines
13 KiB
329 lines
13 KiB
import copy
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from typing import Any, Callable, Dict, Iterable, List, Tuple
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import torch
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from torch.fx.node import Node, map_arg
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from colossalai.fx.profiler import activation_size, parameter_size
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from .utils import (
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delete_free_var_from_last_use,
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find_idx_by_name,
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get_node_shape,
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is_non_compute_node_except_placeholder,
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)
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class EstimateMemory(object):
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"""
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Estimate memory with chunk
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"""
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def __init__(self) -> None:
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pass
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def _get_meta_node_size(self, x):
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x = x.meta["tensor_meta"]
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x = x.numel * torch.tensor([], dtype=x.dtype).element_size()
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return x
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def _get_output_node(self, n):
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out_size = activation_size(n.meta["fwd_out"])
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out_node = [n.name] if out_size > 0 else []
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return out_size, out_node
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def _get_output_node_size(self, n):
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return self._get_output_node(n)[0]
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def _add_active_node(self, n, active_list):
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new_active = self._get_output_node(n)[1]
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if n.op == "placeholder":
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new_active.append(n.name)
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for i in new_active:
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if i not in active_list:
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active_list.append(i)
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def _get_delete_node(self, user, user_to_last_uses, to_keep=None):
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delete_size = 0
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delete_node = []
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if user.op not in ("output",):
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nodes_to_delete = user_to_last_uses.get(user, [])
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if to_keep is not None:
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keep_list = []
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for n in nodes_to_delete:
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if n.name in to_keep:
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keep_list.append(n)
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for n in keep_list:
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if n in nodes_to_delete:
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nodes_to_delete.remove(n)
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if len(nodes_to_delete):
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out_node = [self._get_output_node(i) for i in nodes_to_delete]
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delete_size = sum([i[0] for i in out_node])
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for i in range(len(out_node)):
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if out_node[i][0] > 0:
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delete_node.append(out_node[i][1][0])
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elif nodes_to_delete[i].op == "placeholder":
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delete_node.append(nodes_to_delete[i].name)
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# elif any(j in nodes_to_delete[i].name for j in ['transpose', 'permute', 'view']):
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# delete_node.append(nodes_to_delete[i].name)
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return delete_size, delete_node
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def _get_delete_node_size(self, user, user_to_last_uses, to_keep):
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return self._get_delete_node(user, user_to_last_uses, to_keep)[0]
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def _remove_deactive_node(self, user, user_to_last_uses, active_list):
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delete_node = self._get_delete_node(user, user_to_last_uses)[1]
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for i in delete_node:
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if i in active_list:
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active_list.remove(i)
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def _get_chunk_inputs_size(
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self, chunk_inputs, chunk_inputs_non_chunk, node_list, chunk_end_idx
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):
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nodes_to_delete = []
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for chunk_input in chunk_inputs + chunk_inputs_non_chunk:
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chunk_input_users = chunk_input.users.keys()
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chunk_input_users_idx = [
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find_idx_by_name(i.name, node_list) for i in chunk_input_users
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]
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if all(i <= chunk_end_idx for i in chunk_input_users_idx):
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if chunk_input not in nodes_to_delete:
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nodes_to_delete.append(chunk_input)
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out_node = [self._get_output_node(i) for i in nodes_to_delete]
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delete_size = sum([i[0] for i in out_node])
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return delete_size
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def _get_last_usr(self, nodes):
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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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return user_to_last_uses
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def _get_contiguous_memory(self, node, not_contiguous_list, delete=False):
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mem = 0
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not_contiguous_ops = ["permute"]
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inherit_contiguous_ops = ["transpose", "view"]
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if node.op == "call_function" and any(
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n in node.name for n in ["matmul", "reshape"]
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):
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for n in node.args:
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if n in not_contiguous_list:
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# matmul won't change origin tensor, but create a tmp copy
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mem += self._get_output_node_size(n)
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elif node.op == "call_module":
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for n in node.args:
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if n in not_contiguous_list:
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# module will just make origin tensor to contiguous
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if delete:
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not_contiguous_list.remove(n)
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elif node.op == "call_method" and any(
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i in node.name for i in not_contiguous_ops
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):
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if node not in not_contiguous_list:
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not_contiguous_list.append(node)
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return mem
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def _get_chunk_ratio(self, node, chunk_node_dim, chunk_size):
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if node not in chunk_node_dim:
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return 1.0
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node_shape = get_node_shape(node)
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chunk_dim = chunk_node_dim[node]["chunk_dim"]
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if chunk_dim is None:
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return 1.0
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else:
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return float(chunk_size) / node_shape[chunk_dim]
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def _get_chunk_delete_node_size(
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self, user, user_to_last_uses, chunk_ratio, chunk_inputs_names
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):
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# if any(j in user.name for j in ['transpose', 'permute', 'view']):
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# return 0
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if user.op in ("placeholder", "output"):
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return 0
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nodes_to_delete = user_to_last_uses.get(user, [])
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delete_size = 0
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for n in nodes_to_delete:
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if n.name in chunk_inputs_names:
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continue
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delete_size += self._get_output_node_size(n) * chunk_ratio
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return delete_size
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def _print_mem_log(self, log, nodes, title=None):
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if title:
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print(title)
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for idx, (l, n) in enumerate(zip(log, nodes)):
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print("%s:%.2f \t" % (n.name, l), end="")
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if (idx + 1) % 3 == 0:
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print("")
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print("\n")
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def _print_compute_op_mem_log(self, log, nodes, title=None):
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if title:
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print(title)
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for idx, (l, n) in enumerate(zip(log, nodes)):
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if n.op in ["placeholder", "get_attr", "output"]:
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continue
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if any(i in n.name for i in ["getitem", "getattr"]):
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continue
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print("%s:%.2f \t" % (n.name, l), end="")
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if (idx + 1) % 3 == 0:
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print("")
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print("\n")
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def estimate_chunk_inference_mem(
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self,
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node_list: List,
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chunk_infos=None,
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print_mem=False,
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):
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"""
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Estimate inference memory with chunk
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Args:
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node_list (List): _description_
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chunk_infos (Dict): Chunk information. Defaults to None.
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print_mem (bool): Wether to print peak memory of every node. Defaults to False.
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Returns:
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act_memory_peak_log (List): peak memory of every node
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act_memory_after_node_log (List): memory after excuting every node
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active_node_list_log (List): active nodes of every node. active nodes refer to
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nodes generated but not deleted.
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"""
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act_memory = 0.0
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act_memory_peak_log = []
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act_memory_after_node_log = []
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active_node_list = []
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active_node_list_log = []
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not_contiguous_list = []
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user_to_last_uses = self._get_last_usr(node_list)
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user_to_last_uses_no_free_var = self._get_last_usr(node_list)
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delete_free_var_from_last_use(user_to_last_uses_no_free_var)
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use_chunk = True if chunk_infos is not None else False
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chunk_within = False
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chunk_region_idx = None
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chunk_ratio = 1 # use it to estimate chunk mem
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chunk_inputs_names = []
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if use_chunk:
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chunk_regions = [i["region"] for i in chunk_infos]
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chunk_starts = [i[0] for i in chunk_regions]
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chunk_ends = [i[1] for i in chunk_regions]
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chunk_inputs = [i["inputs"] for i in chunk_infos]
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chunk_inputs_non_chunk = [i["inputs_non_chunk"] for i in chunk_infos]
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chunk_inputs_names = [j.name for i in chunk_inputs for j in i] + [
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j.name for i in chunk_inputs_non_chunk for j in i
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]
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chunk_outputs = [i["outputs"][0] for i in chunk_infos]
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chunk_node_dim = [i["node_chunk_dim"] for i in chunk_infos]
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chunk_sizes = [
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i["chunk_size"] if "chunk_size" in i else 1 for i in chunk_infos
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]
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for idx, node in enumerate(node_list):
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# if node in chunk start nodes, change chunk ratio and add chunk_tensor
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if use_chunk and idx in chunk_starts:
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chunk_within = True
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chunk_region_idx = chunk_starts.index(idx)
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act_memory += self._get_output_node_size(
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chunk_outputs[chunk_region_idx]
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) / (1024**2)
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# determine chunk ratio for current node
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if chunk_within:
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chunk_ratio = self._get_chunk_ratio(
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node,
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chunk_node_dim[chunk_region_idx],
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chunk_sizes[chunk_region_idx],
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)
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# if node is placeholder, just add the size of the node
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if node.op == "placeholder":
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act_memory += self._get_meta_node_size(node) * chunk_ratio / (1024**2)
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act_memory_peak_log.append(act_memory)
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# skip output
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elif node.op == "output":
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continue
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# no change for non compute node
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elif is_non_compute_node_except_placeholder(node):
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act_memory_peak_log.append(act_memory)
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# node is a compute op
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# calculate tmp, output node and delete node memory
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else:
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# forward memory
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# TODO: contiguous_memory still not accurate for matmul, view, reshape and transpose
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act_memory += (
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self._get_contiguous_memory(node, not_contiguous_list)
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* chunk_ratio
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/ (1024**2)
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)
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act_memory += (
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self._get_output_node_size(node) * chunk_ratio / (1024**2)
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)
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# record max act memory
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act_memory_peak_log.append(act_memory)
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# delete useless memory
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act_memory -= (
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self._get_contiguous_memory(node, not_contiguous_list, delete=True)
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* chunk_ratio
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/ (1024**2)
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)
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# delete unused vars not in chunk_input_list
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# we can't delete input nodes until chunk ends
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if chunk_within:
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act_memory -= self._get_chunk_delete_node_size(
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node,
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user_to_last_uses_no_free_var,
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chunk_ratio,
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chunk_inputs_names,
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) / (1024**2)
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else:
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act_memory -= self._get_delete_node_size(
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node, user_to_last_uses_no_free_var, chunk_inputs_names
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) / (1024**2)
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# log active node, only effective without chunk
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self._add_active_node(node, active_node_list)
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self._remove_deactive_node(node, user_to_last_uses, active_node_list)
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# if node in chunk end nodes, restore chunk settings
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if use_chunk and idx in chunk_ends:
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act_memory -= (
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self._get_output_node_size(node) * chunk_ratio / (1024**2)
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)
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act_memory -= self._get_chunk_inputs_size(
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chunk_inputs[chunk_region_idx],
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chunk_inputs_non_chunk[chunk_region_idx],
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node_list,
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chunk_regions[chunk_region_idx][1],
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) / (1024**2)
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chunk_within = False
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chunk_ratio = 1
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chunk_region_idx = None
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act_memory_after_node_log.append(act_memory)
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active_node_list_log.append(copy.deepcopy(active_node_list))
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if print_mem:
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print("with chunk" if use_chunk else "without chunk")
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# self._print_mem_log(act_memory_peak_log, node_list, "peak")
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# self._print_mem_log(act_memory_after_node_log, node_list, "after")
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self._print_compute_op_mem_log(act_memory_peak_log, node_list, "peak")
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# self._print_compute_op_mem_log(
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# act_memory_after_node_log, node_list, "after"
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# )
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# param_memory = parameter_size(gm)
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# all_memory = act_memory + param_memory
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return act_memory_peak_log, act_memory_after_node_log, active_node_list_log
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