[auto-chunk] support extramsa (#3) (#2504)

pull/2506/head^2
oahzxl 2023-01-20 10:13:03 +08:00 committed by GitHub
parent 0f02b8c6e6
commit 72341e65f4
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8 changed files with 283 additions and 54 deletions

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@ -6,12 +6,7 @@ from torch.fx.node import Node, map_arg
from colossalai.fx.profiler import activation_size, parameter_size
from .utils import (
delete_free_var_from_last_use,
find_idx_by_name,
get_node_shape,
is_non_compute_node_except_placeholder,
)
from .utils import delete_free_var_from_last_use, find_idx_by_name, get_node_shape, is_non_memory_node
class EstimateMemory(object):
@ -240,7 +235,7 @@ class EstimateMemory(object):
elif node.op == "output":
continue
# no change for non compute node
elif is_non_compute_node_except_placeholder(node):
elif is_non_memory_node(node):
act_memory_peak_log.append(act_memory)
# node is a compute op
# calculate tmp, output node and delete node memory

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@ -118,16 +118,34 @@ class TraceFlow(object):
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_node: Node,
start_idx: int,
end_idx: int,
cur_node_dim: int,
cur_node_compute: Dict,
cur_node_source: Dict,
cur_node_fix_dim: List,
all_node_info: Dict,
next_node_list: List,
) -> bool:
"""
Given the current node and one of its arg node,
this function finds out arg node's chunk dim and fix dim
Args:
arg_node (Node): input node
start_idx (int): chunk region start
end_idx (int): chunk region end
cur_node_dim (int): current node chunk dim
cur_node_compute (Dict): current node compute dict
cur_node_source (Dict): current node source dict
cur_node_fix_dim (List): current node fix dim
all_node_info (Dict): all node chunk info in the chunk region
next_node_list (List)
Returns:
bool: True if this node can be added to the flow, vice versa.
"""
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):
@ -142,6 +160,9 @@ class TraceFlow(object):
arg_dim = None
else:
arg_dim = cur_node_source[cur_node_dim][arg_idx][0]
# chunk dim should be None if shape size is 1
if get_node_shape(arg_node)[arg_dim] == 1:
arg_dim = None
else:
arg_dim = None
@ -184,7 +205,7 @@ class TraceFlow(object):
# get all valid args
arg_list = []
for arg in cur_node.args:
for arg in cur_node.all_input_nodes:
if type(arg) != type(cur_node):
continue
if is_non_compute_node(arg):

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@ -432,6 +432,38 @@ class TraceIndice(object):
"""
self._assign_all_indice(node, node_idx)
def _assign_cat_indice(self, node: Node, node_idx: int):
"""
Assign indice for cat op.
Args:
node (node)
node_idx (int)
"""
nodes_in = flat_list(node.args[0])
self._assign_indice_as_input(node, node_idx, input_node=nodes_in[0])
for n in nodes_in[1:]:
self._mark_computation_from_node(n, node)
cat_dim = node.kwargs["dim"]
self._del_dim(node_idx, cat_dim)
self._add_dim(node_idx, cat_dim)
def _assign_sum_indice(self, node: Node, node_idx: int):
"""
Assign indice for sum op.
Args:
node (node)
node_idx (int)
"""
nodes_in = flat_list(node.args[0])
self._add_dim(node_idx, 0)
self._assign_indice_as_input(node, node_idx, input_node=nodes_in[0])
for n in nodes_in[1:]:
self._mark_computation_from_node(n, node)
cat_dim = node.kwargs["dim"]
self._del_dim(node_idx, cat_dim)
def _assign_getitem_indice(self, node: Node, node_idx: int):
"""
Assign indice for getitem.
@ -442,7 +474,16 @@ class TraceIndice(object):
node_idx (int)
"""
node_args = flat_list(node.args[1:])
if not any(i == str(node_arg) for i in ["None", "Ellipsis"] for node_arg in node_args):
flag = False
for node_arg in node_args:
node_arg_str = str(node_arg)
if any(i == node_arg_str for i in ["None", "Ellipsis"]):
flag = True
break
if "slice" in node_arg_str:
flag = True
break
if flag == False:
return
# node args should be like [Ellipsis, slice(start, step, end), None]
@ -461,8 +502,11 @@ class TraceIndice(object):
shape_gap = len(node_shape) - len(node_args) + 1
origin_idx_count += shape_gap
new_idx_count += shape_gap
# slice(None, None, None) means all indexes, doesn't support other slice
elif "slice(None, None, None)" == node_arg_str:
# slice(None, None, None) means all indexes
elif "slice" in node_arg_str:
if "slice(None, None, None)" != node_arg_str:
self._del_dim(node_idx, new_idx_count)
self._add_dim(node_idx, new_idx_count)
origin_idx_count += 1
new_idx_count += 1
# None means a new dim
@ -565,7 +609,7 @@ class TraceIndice(object):
self._assign_view_reshape_indice(node, idx)
elif "unsqueeze" in node.name:
self._assign_unsqueeze_indice(node, idx)
elif any(i in node.name for i in ["to", "contiguous"]):
elif any(i in node.name for i in ["to", "contiguous", "clone"]):
self._assgin_no_change_indice(node, idx)
elif "new_ones" in node.name:
self._assign_ones_like_indice(node, idx)
@ -574,6 +618,8 @@ class TraceIndice(object):
elif node.op == "call_function":
if "linear" in node.name:
self._assign_linear_indice(node, idx)
elif "cat" in node.name:
self._assign_cat_indice(node, idx)
elif "matmul" in node.name:
self._assign_matmul_indice(node, idx)
elif "softmax" in node.name:
@ -586,6 +632,8 @@ class TraceIndice(object):
self._assign_dropout_indice(node, idx)
elif "einsum" in node.name:
self._assign_einsum_indice(node, idx)
elif "sum" in node.name:
self._assign_sum_indice(node, idx)
elif "layer_norm" in node.name:
self._assign_layernorm_indice(node, idx)
elif "getitem" in node.name:

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@ -3,10 +3,12 @@ from typing import Any, Callable, Dict, Iterable, List, Tuple
from torch.fx.node import Node
def flat_list(inputs):
def flat_list(inputs: Any) -> List:
"""
flat a list by recursion
"""
if not (isinstance(inputs, list) or isinstance(inputs, set) or isinstance(inputs, tuple)):
return [inputs]
res = []
for i in inputs:
if isinstance(i, list) or isinstance(i, set) or isinstance(i, tuple):
@ -16,7 +18,7 @@ def flat_list(inputs):
return res
def find_first_tensor_arg(node):
def find_first_tensor_arg(node: Node) -> Node:
"""
Find the first input tensor arg for a node
"""
@ -26,7 +28,7 @@ def find_first_tensor_arg(node):
raise RuntimeError()
def is_non_compute_node(node):
def is_non_compute_node(node: Node) -> bool:
if any(i in node.op for i in ["placeholder", "get_attr", "output"]) or any(i in node.name for i in ["getattr"]):
return True
if "getitem" in node.name:
@ -34,16 +36,26 @@ def is_non_compute_node(node):
for node_arg in node_args:
if any(i == str(node_arg) for i in ["None", "Ellipsis"]):
return False
if "slice" in str(node_arg):
return False
return True
return False
def get_node_shape(node):
def get_node_shape(node: Node) -> List:
if hasattr(node.meta["tensor_meta"], "shape"):
return node.meta["tensor_meta"].shape
return None
def is_non_memory_node(node: Node) -> bool:
if "getitem" in node.name:
return True
if "output" in node.op:
return True
return is_non_compute_node(node)
def is_non_compute_node_except_placeholder(node):
if "placeholder" in node.op:
return False

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@ -130,7 +130,7 @@ def _test_evoformer_codegen(rank, msa_len, pair_len, max_memory):
},
)
graph.set_codegen(codegen)
gm = ColoGraphModule(model, graph)
gm = ColoGraphModule(model, graph, ckpt_codegen=False)
gm.recompile()
# assert we have inserted chunk

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@ -0,0 +1,164 @@
from functools import partial
import pytest
import torch
import torch.fx
import torch.multiprocessing as mp
try:
from fastfold.model.nn.evoformer import ExtraMSABlock
HAS_REPO = True
except:
HAS_REPO = False
import colossalai
from colossalai.core import global_context as gpc
from colossalai.fx._compatibility import is_compatible_with_meta
from colossalai.fx.codegen.activation_checkpoint_codegen import CODEGEN_AVAILABLE
from colossalai.fx.graph_module import ColoGraphModule
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from colossalai.utils import free_port
if CODEGEN_AVAILABLE and is_compatible_with_meta():
from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen
from colossalai.fx.profiler import MetaTensor
from colossalai.fx.tracer.experimental import ColoTracer, symbolic_trace
def _test_fwd(model: torch.nn.Module, gm: ColoGraphModule, node, pair, node_mask, pair_mask):
# for memory test
# model = model.cuda()
# torch.cuda.reset_peak_memory_stats()
# now_mem = torch.cuda.memory_allocated() / 1024**2
# with torch.no_grad():
# node1 = node.clone()
# pair1 = pair.clone()
# node_mask1 = node_mask.clone()
# pair_mask1 = pair_mask.clone()
# gm(node1, pair1, node_mask1, pair_mask1)
# new_max_mem = torch.cuda.max_memory_allocated() / 1024**2
# print("autochunk max mem:%.2f"% (new_max_mem - now_mem))
# test forward
model = model.cuda()
with torch.no_grad():
non_fx_out = model(node, pair, node_mask, pair_mask)
fx_out = gm(node, pair, node_mask, pair_mask)
assert torch.allclose(non_fx_out[0], fx_out[0],
atol=1e-4), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(
torch.abs(non_fx_out[0] - fx_out[0]))
assert torch.allclose(non_fx_out[1], fx_out[1],
atol=1e-4), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(
torch.abs(non_fx_out[1] - fx_out[1]))
def _build_openfold():
model = ExtraMSABlock(
c_m=256,
c_z=128,
c_hidden_msa_att=32,
c_hidden_opm=32,
c_hidden_mul=128,
c_hidden_pair_att=32,
no_heads_msa=8,
no_heads_pair=4,
transition_n=4,
msa_dropout=0.15,
pair_dropout=0.15,
inf=1e4,
eps=1e-4,
ckpt=False,
is_multimer=False,
).eval().cuda()
return model
def _test_extramsa_codegen(rank, msa_len, pair_len, max_memory):
# launch colossalai
colossalai.launch(
config={},
rank=rank,
world_size=1,
host="localhost",
port=free_port(),
backend="nccl",
)
# build model and input
model = _build_openfold()
node = torch.randn(1, msa_len, pair_len, 256).cuda()
node_mask = torch.randn(1, msa_len, pair_len).cuda()
pair = torch.randn(1, pair_len, pair_len, 128).cuda()
pair_mask = torch.randn(1, pair_len, pair_len).cuda()
# trace the meta graph and setup codegen
meta_graph = symbolic_trace(
model,
meta_args={
"m": node.to(torch.device("meta")),
"z": pair.to(torch.device("meta")),
"msa_mask": node_mask.to(torch.device("meta")),
"pair_mask": pair_mask.to(torch.device("meta")),
},
concrete_args={
"chunk_size": None,
"_chunk_logits": 1024,
},
)
interp = MetaInfoProp(meta_graph)
interp.propagate(
MetaTensor(node, fake_device="cuda:0"),
MetaTensor(pair, fake_device="cuda:0"),
MetaTensor(node_mask, fake_device="cuda:0"),
MetaTensor(pair_mask, fake_device="cuda:0"),
)
codegen = AutoChunkCodeGen(meta_graph, max_memory=max_memory, print_mem=False)
# trace and recompile
# MetaInfoProp requires symbolic_trace but CodeGen requires ColoTracer
graph = ColoTracer().trace(
model,
meta_args={
"m": node.to(torch.device("meta")),
"z": pair.to(torch.device("meta")),
"msa_mask": node_mask.to(torch.device("meta")),
"pair_mask": pair_mask.to(torch.device("meta")),
},
concrete_args={
"chunk_size": None,
"_chunk_logits": 1024,
},
)
graph.set_codegen(codegen)
gm = ColoGraphModule(model, graph, ckpt_codegen=False)
gm.recompile()
# assert we have inserted chunk
code = graph.python_code("self").src
# print(code)
assert "chunk_result = None; chunk_size = None;" in code
_test_fwd(model, gm, node, pair, node_mask, pair_mask)
gpc.destroy()
@pytest.mark.skipif(
not (CODEGEN_AVAILABLE and is_compatible_with_meta() and HAS_REPO),
reason="torch version is lower than 1.12.0",
)
@pytest.mark.parametrize("max_memory", [None, 24, 28, 32])
@pytest.mark.parametrize("msa_len", [32])
@pytest.mark.parametrize("pair_len", [64])
def test_extramsa_codegen(msa_len, pair_len, max_memory):
run_func = partial(
_test_extramsa_codegen,
msa_len=msa_len,
pair_len=pair_len,
max_memory=max_memory,
)
mp.spawn(run_func, nprocs=1)
if __name__ == "__main__":
_test_extramsa_codegen(0, 32, 64, None)

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@ -73,7 +73,7 @@ def _test_simple_evoformer_codegen(rank, msa_len, pair_len, max_memory):
},
)
graph.set_codegen(codegen)
gm = ColoGraphModule(model, graph)
gm = ColoGraphModule(model, graph, ckpt_codegen=False)
gm.recompile()
# assert we have inserted chunk

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@ -13,6 +13,7 @@ except:
import colossalai
from colossalai.core import global_context as gpc
from colossalai.fx import symbolic_trace
from colossalai.fx._compatibility import is_compatible_with_meta
from colossalai.fx.codegen.activation_checkpoint_codegen import CODEGEN_AVAILABLE
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
@ -28,10 +29,10 @@ def assert_chunk_infos(chunk_infos, max_memory, msa_len, pair_len):
if msa_len == 32 and pair_len == 64:
if max_memory is None:
target_regions = [(142, 154), (366, 373), (233, 283), (301, 351), (127, 134), (204, 228), (167, 191),
(161, 166), (198, 203), (6, 69)]
target_regions = [(142, 154), (366, 373), (234, 283), (302, 351), (127, 134), (211, 228), (174, 191),
(161, 166), (198, 203), (7, 57)]
elif max_memory == 20:
target_regions = [(142, 154), (369, 373), (233, 269), (301, 351)]
target_regions = [(142, 154), (369, 373), (235, 269), (303, 351), (130, 131)]
elif max_memory == 25:
target_regions = [(144, 154), (369, 370)]
elif max_memory == 30:
@ -41,25 +42,10 @@ def assert_chunk_infos(chunk_infos, max_memory, msa_len, pair_len):
else:
raise NotImplementedError()
assert len(found_regions) == len(
target_regions), "len of found regions %s doesn't equal len of target regions %s" % (
str(found_regions),
str(target_regions),
)
for region in target_regions:
assert (region in found_regions), "region:%s not in found regions for msa:%d, pair:%d, maxmem:%s" % (
str(region),
msa_len,
pair_len,
str(max_memory),
)
for region in found_regions:
assert (region in target_regions), "region:%s should not be found for msa:%d, pair:%d, maxmem:%d" % (
str(region),
msa_len,
pair_len,
str(max_memory),
)
assert found_regions == target_regions, "found regions %s doesn't equal target regions %s" % (
str(found_regions),
str(target_regions),
)
def _test_simple_evoformer_search(rank, msa_len, pair_len, max_memory):
@ -78,11 +64,14 @@ def _test_simple_evoformer_search(rank, msa_len, pair_len, max_memory):
node = torch.randn(1, msa_len, pair_len, 256).cuda()
pair = torch.randn(1, pair_len, pair_len, 128).cuda()
gm_prop = torch.fx.symbolic_trace(model) # must use symbolic_trace
interp = MetaInfoProp(gm_prop)
meta_graph = symbolic_trace(model,
meta_args={
"node": node.to(torch.device("meta")),
"pair": pair.to(torch.device("meta")),
}) # must use symbolic_trace
interp = MetaInfoProp(meta_graph)
interp.propagate(MetaTensor(node, fake_device="cuda:0"), MetaTensor(pair, fake_device="cuda:0"))
codegen = AutoChunkCodeGen(gm_prop, max_memory=max_memory)
codegen = AutoChunkCodeGen(meta_graph, max_memory=max_memory)
chunk_infos = codegen.chunk_infos
assert_chunk_infos(chunk_infos, max_memory, msa_len, pair_len)