wehub-resource-sync
347 lines
12 KiB
Python
347 lines
12 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from __future__ import annotations
|
|
|
|
import paddle
|
|
from paddle.distributed.auto_parallel.static.process_group import (
|
|
get_world_process_group,
|
|
)
|
|
from paddle.distributed.fleet.meta_optimizers.common import (
|
|
OpRole,
|
|
)
|
|
from paddle.framework import (
|
|
_current_expected_place_ as _get_device,
|
|
)
|
|
|
|
from .pass_base import PassBase, PassType, register_pass
|
|
|
|
world_process_group = get_world_process_group()
|
|
|
|
|
|
def _move_used_grad_op(used_grad_op, grad):
|
|
move_to_opt_block_flag = True
|
|
move_to_opt_ops = []
|
|
cannot_move_op = ["pd_op.send_v2", "pd_op.send"]
|
|
|
|
def find_move_op(backward_op):
|
|
nonlocal move_to_opt_block_flag
|
|
if not move_to_opt_block_flag or backward_op in move_to_opt_ops:
|
|
return
|
|
if backward_op.name() in cannot_move_op:
|
|
move_to_opt_block_flag = False
|
|
return
|
|
if backward_op.num_operands() == 1:
|
|
move_to_opt_block_flag = True
|
|
move_to_opt_ops.append(backward_op)
|
|
elif backward_op.name() == "pd_op.slice":
|
|
move_to_opt_ops.append(backward_op)
|
|
for i in range(0, backward_op.num_operands()):
|
|
if not grad.is_same(backward_op.operand_source(i)):
|
|
move_to_opt_ops.append(
|
|
backward_op.operand_source(i).get_defining_op()
|
|
)
|
|
move_to_opt_block_flag = True
|
|
else:
|
|
# NOTE(zhangwl):temp only consider one operand op
|
|
move_to_opt_block_flag = False
|
|
return
|
|
for op_result in backward_op.results():
|
|
for next_op in op_result.all_used_ops():
|
|
if next_op.op_role != int(OpRole.Optimize):
|
|
find_move_op(next_op)
|
|
|
|
find_move_op(used_grad_op)
|
|
if move_to_opt_block_flag:
|
|
for move_op in move_to_opt_ops:
|
|
move_op.op_role = int(OpRole.Optimize)
|
|
|
|
|
|
def _pir_append_gradient_merge_backward_op(
|
|
main_program,
|
|
startup_program,
|
|
params_grads,
|
|
):
|
|
main_block = main_program.global_block()
|
|
startup_block = startup_program.global_block()
|
|
|
|
# {param: gradient_merge_var} to insert scale op and fill_constant op
|
|
new_params_grads = []
|
|
place = _get_device()
|
|
if isinstance(place, paddle.framework.CUDAPlace):
|
|
place = paddle.framework.CUDAPlace(
|
|
paddle.distributed.ParallelEnv().dev_id
|
|
)
|
|
cur_place = paddle.base.libpaddle.Place()
|
|
cur_place.set_place(place)
|
|
|
|
for param, grad in params_grads:
|
|
if grad is None:
|
|
continue
|
|
|
|
assert not param.is_selected_row_type(), (
|
|
"SELECTED_ROWS is not supported in GradientMergeOptimizer for now"
|
|
)
|
|
|
|
grad_dtype = grad.dtype
|
|
grad_type = grad.type()
|
|
|
|
for op in grad.all_used_ops():
|
|
if op.has_attr("master_grad_cast"):
|
|
grad_dtype = op.result(0).dtype
|
|
grad_type = op.result(0).type()
|
|
|
|
# step1: create gradient_merge var and init with 0
|
|
# Add persistable gradient variables in startup_program
|
|
paddle.pir.set_insertion_point_to_block_end(startup_block)
|
|
gradient_merge_var = paddle.full(
|
|
shape=grad._local_shape, fill_value=0.0, dtype=grad_dtype
|
|
)
|
|
gradient_merge_var.persistable = True
|
|
paddle.pir.set_insertion_point_after(
|
|
gradient_merge_var.get_defining_op()
|
|
)
|
|
paddle._C_ops.set_persistable_value(
|
|
gradient_merge_var, param.name + "@GRAD@MERGE"
|
|
)
|
|
|
|
# step2: Accumulate persistable gradient variables in main_program
|
|
# NOTE(zhaoyingli): inplace operation must be 'a = a + b', cannot be 'a = b + a'
|
|
grad_defining_op = grad.get_defining_op()
|
|
paddle.pir.set_insertion_point_after(grad_defining_op)
|
|
|
|
new_gradient_merge_var = main_block.add_kwarg(
|
|
param.name + "@GRAD@MERGE", grad_type
|
|
)
|
|
new_gradient_merge_var.persistable = True
|
|
new_gradient_merge_var.place_attr = cur_place
|
|
new_gradient_merge_var_add = paddle._C_ops.add_(
|
|
new_gradient_merge_var, grad
|
|
)
|
|
new_gradient_merge_var_add_op = (
|
|
new_gradient_merge_var_add.get_defining_op()
|
|
)
|
|
new_gradient_merge_var_add_op.op_role = grad_defining_op.op_role
|
|
|
|
new_gradient_merge_var_add_op.dist_attr = (
|
|
paddle.base.libpaddle.pir.create_op_dist_attribute(
|
|
grad_defining_op.dist_attr.process_mesh,
|
|
grad_defining_op.dist_attr.operands(),
|
|
grad_defining_op.dist_attr.results(),
|
|
grad_defining_op.dist_attr.chunk_id,
|
|
)
|
|
)
|
|
new_gradient_merge_var_add_op.set_bool_attr("grad_merge_add", True)
|
|
|
|
# NOTE(zhangweilong): grad may in different device in auto_parallel, so need consider all_gather/all_reduce/split/... op
|
|
for used_grad_op in grad.all_used_ops():
|
|
_move_used_grad_op(used_grad_op, grad)
|
|
|
|
opt_ops_use_grad = [
|
|
op
|
|
for op in grad.all_used_ops()
|
|
if op.op_role == int(OpRole.Optimize)
|
|
]
|
|
|
|
grad.replace_grad_users_with(
|
|
new_gradient_merge_var, set(opt_ops_use_grad)
|
|
)
|
|
|
|
# reset gradient merge var to zero after finishing optimization
|
|
paddle.pir.set_insertion_point_to_block_end(main_block)
|
|
set_value = paddle.full(
|
|
shape=[1], fill_value=float(0), dtype=grad_dtype
|
|
)
|
|
new_gradient_merge_var_zero = paddle._C_ops.set_value_with_tensor_(
|
|
new_gradient_merge_var, set_value, [], [], [], [], [], []
|
|
)
|
|
|
|
set_value_op = new_gradient_merge_var_zero.get_defining_op()
|
|
set_value_op.op_role = int(OpRole.Optimize)
|
|
for id in range(1, set_value_op.num_operands()):
|
|
op_input = set_value_op.operand_source(id)
|
|
op_input.get_defining_op().op_role = int(OpRole.Optimize)
|
|
|
|
# step3: Construct new_params_grads and grad_to_gradient_merge
|
|
new_params_grads.append((param, new_gradient_merge_var))
|
|
|
|
return new_params_grads
|
|
|
|
|
|
def _pir_move_reduce_to_backward_stage(main_program):
|
|
pass
|
|
|
|
|
|
def _pir_remove_cast_for_master_grad(main_program, params_grads):
|
|
for op in main_program.global_block().ops:
|
|
if op.has_attr("master_grad_cast"):
|
|
op.result(0).replace_all_uses_with(op.operand_source(0))
|
|
op.erase()
|
|
|
|
|
|
def _find_trivial_optimizer_ops(block):
|
|
optimizer_ops = []
|
|
for op in block.ops:
|
|
if "adam" in op.name() or "sgd" in op.name():
|
|
optimizer_ops.append(op)
|
|
return optimizer_ops
|
|
|
|
|
|
def _get_prev_op(block, optimizer_op):
|
|
found = False
|
|
for op in reversed(block.ops):
|
|
if found:
|
|
return op
|
|
if op.id == optimizer_op.id:
|
|
found = True
|
|
return None
|
|
|
|
|
|
def _insert_scale_op_after(target_value, optimizer_op, scale, bias=0.0):
|
|
scaled_grad = paddle._C_ops.scale_(target_value, scale, bias, False)
|
|
|
|
scale_op = scaled_grad.get_defining_op()
|
|
scale_op.op_role = int(OpRole.Optimize)
|
|
|
|
full_op = scale_op.operand_source(1).get_defining_op()
|
|
assert full_op.name() == "pd_op.full", (
|
|
f"The defining op of the scale value should be `pd_op.full`, but got {full_op.name()}"
|
|
)
|
|
full_op.op_role = int(OpRole.Optimize)
|
|
|
|
if "adam" in optimizer_op.name():
|
|
optimizer_op.operand(1).set_source(scaled_grad)
|
|
elif "sgd" in optimizer_op.name():
|
|
optimizer_op.operand(2).set_source(scaled_grad)
|
|
|
|
|
|
def _append_scale_op_before_comm(block, new_params_to_grads, k_steps):
|
|
for op in reversed(block.ops):
|
|
if op.op_role == int(OpRole.Backward):
|
|
paddle.pir.set_insertion_point_after(op)
|
|
break
|
|
for _, new_grad in new_params_to_grads:
|
|
new_grad = paddle._C_ops.scale_(new_grad, 1.0 / k_steps, 0.0, False)
|
|
|
|
scale_op = new_grad.get_defining_op()
|
|
scale_op.op_role = int(OpRole.Optimize)
|
|
|
|
full_op = scale_op.operand_source(1).get_defining_op()
|
|
assert full_op.name() == "pd_op.full", (
|
|
f"The defining op of the scale value should be `pd_op.full`, but got {full_op.name()}"
|
|
)
|
|
full_op.op_role = int(OpRole.Optimize)
|
|
paddle.pir.set_insertion_point_to_block_end(block)
|
|
|
|
|
|
def _append_scale_op_after_comm(block, optimizer_ops, k_steps):
|
|
for optimizer_op in optimizer_ops:
|
|
target_value = None
|
|
if "adam" in optimizer_op.name(): # adam and adamw are included
|
|
target_value = optimizer_op.operand_source(1)
|
|
elif "sgd" in optimizer_op.name():
|
|
target_value = optimizer_op.operand_source(2)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"We yet support adamw, adam and sgd, but got {optimizer_op.name()}"
|
|
)
|
|
assert target_value is not None, (
|
|
"target_value is not expected to be None"
|
|
)
|
|
insertion_point = target_value.get_defining_op()
|
|
if insertion_point is None:
|
|
# target_value is a gradient_merge_var, which hasn't defining_op
|
|
# so we find the prev op of optimizer_op, inserting a scale op behind.
|
|
insertion_point = _get_prev_op(block, optimizer_op)
|
|
paddle.pir.set_insertion_point_after(insertion_point)
|
|
_insert_scale_op_after(target_value, optimizer_op, 1.0 / k_steps)
|
|
paddle.pir.set_insertion_point_to_block_end(block)
|
|
|
|
|
|
def _pir_append_scale_op(program, new_params_to_grads, k_steps):
|
|
block = program.global_block()
|
|
optimizer_ops = _find_trivial_optimizer_ops(block)
|
|
if len(optimizer_ops) > 0:
|
|
_append_scale_op_after_comm(block, optimizer_ops, k_steps)
|
|
else:
|
|
_append_scale_op_before_comm(block, new_params_to_grads, k_steps)
|
|
|
|
|
|
def _pir_parse_program(
|
|
main_program,
|
|
startup_program,
|
|
params_grads,
|
|
k_steps,
|
|
avg,
|
|
gradient_sync_after_accumulate,
|
|
):
|
|
# step1: append gradient merge backward op to main_program
|
|
new_params_to_grads = _pir_append_gradient_merge_backward_op(
|
|
main_program, startup_program, params_grads
|
|
)
|
|
|
|
# step2: move back reduce op to backward stage
|
|
if not gradient_sync_after_accumulate:
|
|
_pir_move_reduce_to_backward_stage(main_program, params_grads)
|
|
|
|
# _pir_remove_cast_for_master_grad(main_program, params_grads)
|
|
|
|
# step3: append scale op
|
|
if avg:
|
|
_pir_append_scale_op(main_program, new_params_to_grads, k_steps)
|
|
|
|
|
|
@register_pass("auto_parallel_gradient_merge_pass")
|
|
class GradientMergePass(PassBase):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.set_attr("k_steps", -1)
|
|
self.set_attr("avg", True)
|
|
self._in_pir_mode = paddle.base.framework.get_flags(
|
|
"FLAGS_enable_pir_api"
|
|
)["FLAGS_enable_pir_api"]
|
|
|
|
def _check_self(self):
|
|
if self.get_attr("k_steps") < 1:
|
|
return False
|
|
return True
|
|
|
|
def _check_conflict(self, other_pass):
|
|
return True
|
|
|
|
def _type(self):
|
|
return PassType.COMM_OPT
|
|
|
|
def _apply_single_impl(self, main_program, startup_program, context):
|
|
k_steps = self.get_attr("k_steps", -1)
|
|
avg = self.get_attr("avg", False)
|
|
params_grads = self.get_attr("params_grads")
|
|
gradient_sync_after_accumulate = self.get_attr(
|
|
"gradient_sync_after_accumulate", False
|
|
)
|
|
|
|
if self._in_pir_mode:
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
_pir_parse_program(
|
|
main_program,
|
|
startup_program,
|
|
params_grads,
|
|
k_steps,
|
|
avg,
|
|
gradient_sync_after_accumulate,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"auto_parallel_gradient_merge_pass() only support PIR now."
|
|
)
|