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ColossalAI/colossalai/autochunk/trace_flow.py

421 lines
17 KiB

from .trace_indice import TraceIndice
from .utils import (
find_chunk_all_input_nodes,
find_chunk_compute_input_and_output_nodes,
find_idx_by_name,
get_node_shape,
is_non_compute_node,
is_non_compute_node_except_placeholder,
)
class TraceFlow(object):
def __init__(self, trace_indice: TraceIndice) -> None:
self.trace_indice = trace_indice
def check_index_source(self, start_dim, start_node, start_idx, end_dim, end_node):
"""
Check 2 given index: one index should be source of the other
Args:
start_idx(int): start node chunk dim
start_node(node): start node
end_idx(int): end node chunk dim
end_node(node): end node
Returns:
bool: True if check pass
"""
start_node_idx = find_idx_by_name(start_node.name, self.trace_indice.node_list)
end_node_trace = self.trace_indice._find_trace_from_node(end_node)
end_node_trace_source = end_node_trace["source"][end_dim]
sorted_source = sorted(
end_node_trace_source.items(), key=lambda d: d[0], reverse=True
)
for node_idx, node_dim in sorted_source:
if node_idx == start_node_idx and start_dim in node_dim:
return True
# it means we meet a node outside the loop, and the node is not input node
if node_idx < start_idx:
return False
return False
def check_index_compute(self, start_idx, end_dim, end_node, end_idx):
"""
Check 2 given index: check they haven't been computed in the source trace.
Args:
start_idx(int): start node chunk dim
start_node(node): start node
end_idx(int): end node chunk dim
end_node(node): end node
Returns:
bool: True if check pass
"""
end_node_trace = self.trace_indice._find_trace_from_node(end_node)
end_node_compute = end_node_trace["compute"][end_dim]
if any(start_idx <= i <= end_idx for i in end_node_compute):
return False
return True
def get_node_chunk_dim(self, node_from, node_from_dim, node_to):
node_from_source = self.trace_indice._find_source_trace_from_node(node_from)
dim_source = node_from_source[node_from_dim]
node_to_idx = find_idx_by_name(node_to.name, self.trace_indice.node_list)
for k, v in dim_source.items():
if k == node_to_idx:
return v
return None
def _find_inherit_dim(self, input_node, input_dim, node):
input_node_idx = find_idx_by_name(input_node.name, self.trace_indice.node_list)
node_trace_source = self.trace_indice._find_source_trace_from_node(node)
for node_dim in range(len(get_node_shape(node))):
if (
input_node_idx in node_trace_source[node_dim]
and input_dim[0] in node_trace_source[node_dim][input_node_idx]
):
return node_dim
return None
def check_index_duplicate(self, chunk_infos, return_dim=False):
input_dim_after_node = {}
for input_node_idx, input_node in enumerate(chunk_infos["inputs"]):
for k, v in chunk_infos["inputs_dim"][input_node_idx].items():
inherit_dim = self._find_inherit_dim(
input_node, v, self.trace_indice.node_list[k]
)
if inherit_dim:
input_dim_after_node[k] = inherit_dim
for node in self.trace_indice.node_list[
chunk_infos["region"][0] : chunk_infos["region"][1] + 1
]:
if is_non_compute_node_except_placeholder(node):
continue
count = 0
duplicate_dims = []
node_trace_source = self.trace_indice._find_source_trace_from_node(node)
for node_dim in range(len(get_node_shape(node))):
duplicate_dim = []
duplicate_flag = False
dim_source = node_trace_source[node_dim]
for k, v in dim_source.items():
if chunk_infos["region"][0] <= k <= chunk_infos["region"][1]:
if k in input_dim_after_node and input_dim_after_node[k] in v:
duplicate_flag = True
duplicate_dim.append((k, v))
duplicate_dims.append(duplicate_dim)
if duplicate_flag:
count += 1
if count > 1:
if return_dim:
return False, duplicate_dims
else:
return False
if return_dim:
return True, None
else:
return True
def _assgin_single_node_flow(
self,
arg_node,
start_idx,
end_idx,
cur_node_dim,
cur_node_compute,
cur_node_source,
cur_node_fix_dim,
all_node_info,
next_node_list,
):
arg_idx = find_idx_by_name(arg_node.name, self.trace_indice.node_list)
# arg in chunk range or be inputs
if not (start_idx <= arg_idx < end_idx):
return True
# find arg dim
if cur_node_dim is not None:
# dim is computed
if arg_idx in cur_node_compute[cur_node_dim]:
return False
if arg_idx not in cur_node_source[cur_node_dim]:
arg_dim = None
else:
arg_dim = cur_node_source[cur_node_dim][arg_idx][0]
else:
arg_dim = None
# get fix dim
arg_fix_dim = []
if cur_node_dim is not None:
for i in cur_node_fix_dim:
fix_dim_source = cur_node_source[i]
if arg_idx in fix_dim_source:
arg_fix_dim.append(fix_dim_source[arg_idx][0])
# if already in node_info, arg dim must be same
if arg_node in all_node_info:
if all_node_info[arg_node]["chunk_dim"] != arg_dim:
return False
all_node_info[arg_node]["fix_dim"] = list(
set(all_node_info[arg_node]["fix_dim"] + arg_fix_dim)
)
# else add it to list
else:
all_node_info[arg_node] = {"chunk_dim": arg_dim, "fix_dim": arg_fix_dim}
next_node_list.append(arg_node)
return True
def _get_all_node_info(self, end_dim, start_idx, end_idx):
cur_node_list = [
self.trace_indice.node_list[end_idx]
] # start from the last node
all_node_info = {cur_node_list[0]: {"chunk_dim": end_dim, "fix_dim": []}}
while len(cur_node_list) > 0:
next_node_list = []
for cur_node in cur_node_list:
# get cur node info
cur_node_chunk_dim = all_node_info[cur_node]["chunk_dim"]
cur_node_fix_dim = all_node_info[cur_node]["fix_dim"]
if cur_node_chunk_dim:
cur_node_compute = self.trace_indice._find_compute_trace_from_node(
cur_node
)
cur_node_source = self.trace_indice._find_source_trace_from_node(
cur_node
)
else:
cur_node_compute = cur_node_source = None
# get all valid args
arg_list = []
for arg in cur_node.args:
if type(arg) != type(cur_node):
continue
if is_non_compute_node(arg):
continue
arg_list.append(arg)
flow_flag = self._assgin_single_node_flow(
arg,
start_idx,
end_idx,
cur_node_chunk_dim,
cur_node_compute,
cur_node_source,
cur_node_fix_dim,
all_node_info,
next_node_list,
)
if flow_flag == False:
return None
if len(arg_list) == 2:
if any(i in cur_node.name for i in ["add", "mul"]):
for arg in arg_list:
if not (
start_idx
<= find_idx_by_name(
arg.name, self.trace_indice.node_list
)
< end_idx
):
continue
arg_chunk_dim = all_node_info[arg]["chunk_dim"]
arg_fix_dim = all_node_info[arg]["fix_dim"]
arg_shape = get_node_shape(arg)
# add all dim as fix dim except chunk dim
for i, shape in enumerate(arg_shape):
if shape != 1 and i != cur_node_chunk_dim:
if i == arg_chunk_dim:
return None
if i not in arg_fix_dim:
arg_fix_dim.append(i)
elif "einsum" in cur_node.name:
pass
elif "matmul" in cur_node.name:
pass
else:
raise NotImplementedError()
cur_node_list = next_node_list
return all_node_info
def _get_input_nodes_dim(self, inputs, start_idx, end_idx, all_node_info):
inputs_dim = []
remove_inputs = []
for input_node in inputs:
input_dict = {}
input_node_idx = find_idx_by_name(
input_node.name, self.trace_indice.node_list
)
for user in input_node.users.keys():
if is_non_compute_node(user):
continue
user_idx = find_idx_by_name(user.name, self.trace_indice.node_list)
if start_idx <= user_idx <= end_idx:
chunk_dim = all_node_info[user]["chunk_dim"]
if chunk_dim is not None:
user_source = self.trace_indice._find_source_trace_from_node(
user
)[chunk_dim]
if input_node_idx in user_source:
input_dict[user_idx] = user_source[input_node_idx]
else:
return None, None
if len(input_dict) == 0:
remove_inputs.append(input_node)
else:
inputs_dim.append(input_dict)
for i in remove_inputs:
if i in inputs:
inputs.remove(i)
return inputs, inputs_dim
def _get_prepose_nodes(self, all_node_info, start_idx, end_idx):
# get all possible prepose nodes
maybe_prepose_nodes = []
for node, node_info in all_node_info.items():
if node_info["chunk_dim"] is None:
maybe_prepose_nodes.append(node)
maybe_prepose_nodes.sort(
key=lambda x: find_idx_by_name(x.name, self.trace_indice.node_list),
reverse=True,
) # from last node to first node
prepose_nodes = []
# set every node as root, search its args, if all legal, turn root and args as prepose nodes
while len(maybe_prepose_nodes) > 0:
tmp_cur_prepose_nodes = [maybe_prepose_nodes[0]]
tmp_cur_related_prepose_nodes = []
prepose_flag = True
# loop cur node's all arg until out of chunk
while len(tmp_cur_prepose_nodes) > 0:
if prepose_flag == False:
break
tmp_next_prepose_nodes = []
tmp_cur_related_prepose_nodes.extend(tmp_cur_prepose_nodes)
for cur_prepose_node in tmp_cur_prepose_nodes:
if prepose_flag == False:
break
for cur_prepose_node_arg in cur_prepose_node.args:
if type(cur_prepose_node_arg) != type(cur_prepose_node):
continue
# out of loop
if not (
start_idx
<= find_idx_by_name(
cur_prepose_node_arg.name, self.trace_indice.node_list
)
< end_idx
):
continue
# compute op in loop
elif cur_prepose_node_arg in all_node_info:
if all_node_info[cur_prepose_node_arg]["chunk_dim"] is None:
tmp_next_prepose_nodes.append(cur_prepose_node_arg)
else:
prepose_flag = False
break
# non compute op
else:
tmp_next_prepose_nodes.append(cur_prepose_node_arg)
tmp_cur_prepose_nodes = tmp_next_prepose_nodes
if prepose_flag == False:
maybe_prepose_nodes.remove(maybe_prepose_nodes[0])
continue
else:
for n in tmp_cur_related_prepose_nodes:
if n not in prepose_nodes:
prepose_nodes.append(n)
if n in maybe_prepose_nodes:
maybe_prepose_nodes.remove(n)
# sort by index
prepose_nodes.sort(
key=lambda x: find_idx_by_name(x.name, self.trace_indice.node_list)
)
return prepose_nodes
def _get_non_chunk_inputs(self, chunk_info, start_idx, end_idx):
# we need to log input nodes to avoid deleteing them in the loop
chunk_node_list = self.trace_indice.node_list[start_idx : end_idx + 1]
# also need to get some prepose node's arg out of non_chunk_inputs
for n in chunk_info["args"]["prepose_nodes"]:
chunk_node_list.remove(n)
non_chunk_inputs = find_chunk_all_input_nodes(chunk_node_list)
for i in non_chunk_inputs:
if i not in chunk_info["inputs"]:
chunk_info["inputs_non_chunk"].append(i)
return chunk_info
def flow_search(self, start_idx, start_dim, end_idx, end_dim):
inputs, outputs = find_chunk_compute_input_and_output_nodes(
self.trace_indice.node_list[start_idx : end_idx + 1]
)
# only single ouput
if len(outputs) > 1:
return None
# get every node's chunk dim and fix dim
all_node_info = self._get_all_node_info(end_dim, start_idx, end_idx)
if all_node_info is None:
return None
# get input nodes' chunk dim
inputs, inputs_dim = self._get_input_nodes_dim(
inputs, start_idx, end_idx, all_node_info
)
if inputs is None:
return None
chunk_info = {
"region": (start_idx, end_idx),
"inputs": inputs,
"inputs_non_chunk": [],
"inputs_dim": inputs_dim,
"outputs": outputs,
"outputs_dim": end_dim,
"node_chunk_dim": all_node_info,
"args": {},
}
# move useless nodes ahead of loop
chunk_info["args"]["prepose_nodes"] = self._get_prepose_nodes(
all_node_info, start_idx, end_idx
)
# find non chunk inputs
chunk_info = self._get_non_chunk_inputs(chunk_info, start_idx, end_idx)
# reassgin reshape size, some size may have changed due to chunk
chunk_info = self._reassgin_reshape_size(chunk_info)
return chunk_info
def _reassgin_reshape_size(self, chunk_info):
chunk_region = chunk_info["region"]
reshape_size = {}
chunk_shape = get_node_shape(chunk_info["outputs"][0])[
chunk_info["outputs_dim"]
]
for node in self.trace_indice.node_list[chunk_region[0] : chunk_region[1] + 1]:
if any(i in node.name for i in ["reshape", "view"]):
reshape_args = node.args[1:]
reshape_log = self.trace_indice.indice_view_list[node]
chunk_dim = chunk_info["node_chunk_dim"][node]["chunk_dim"]
reshape_size[node.name] = {}
for reshape_arg_dim, reshape_arg in enumerate(reshape_args):
if reshape_arg_dim in reshape_log["dim_to"]:
continue
if reshape_arg_dim == chunk_dim:
reshape_size[node.name][reshape_arg.name] = (
"min(chunk_size, %d - chunk_idx)" % chunk_shape
)
chunk_info["reshape_size"] = reshape_size
return chunk_info