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提交 47042a97 编写于 作者: S sandyhouse
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上级 d1c428da
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Showing with 180 addition and 271 deletion
paddle/fluid/framework/pipeline_trainer.cc
浏览文件 @ 47042a97
......@@ -82,7 +82,10 @@ void PipelineTrainer::CopyParameters(int microbatch_id,
for (auto& var : global_block.AllVars()) {
bool is_param_grad = false;
size_t pos = 0;
if ((pos = var->Name().find(kGradVarSuffix)) != std::string::npos) {
// A magic suffix to indicated the merged gradient.
std::string magicSuffix = "MERGED";
if ((pos = var->Name().find(kGradVarSuffix)) != std::string::npos &&
var->Name().find(magicSuffix) != std::string::npos) {
auto prefix_name = var->Name().substr(0, pos);
if (param_map.find(prefix_name) != param_map.end()) {
is_param_grad = true;
......
python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py
浏览文件 @ 47042a97
......@@ -153,6 +153,9 @@ class ShardingOptimizer(MetaOptimizerBase):
if self.use_pipeline:
pp_optimizer._rename_gradient_var_name(main_block)
pp_optimizer._accumulate_gradients(main_block)
with open("main_%d" % self.role_maker._worker_index(), 'w') as f:
f.writelines(str(main_program))
# step1: set_up
self._set_up(params_grads)
......@@ -210,23 +213,6 @@ class ShardingOptimizer(MetaOptimizerBase):
# if self._shard.has_param(param_name):
# param_list.append(param_name)
#pp_optimizer._clear_gradients(main_block, param_list)
accumulated_grad_names = pp_optimizer._accumulate_gradients(
main_block,
pp_allreduce_in_optimize=self.pp_allreduce_in_optimize)
# accumulated_grad_names = sorted(accumulated_grad_names)
if self.pp_allreduce_in_optimize:
print("persistable FP32 grad: ")
print(accumulated_grad_names)
first_optimize_op_index = get_first_check_finite_and_unscale_op_idx(
main_block)
insert_reduce_ops(
main_block,
first_optimize_op_index,
self.sharding_ring_id,
accumulated_grad_names,
self._shard,
core.op_proto_and_checker_maker.OpRole.Optimize,
use_calc_stream=True)
#if not self._shard.has_param(param_name): continue
##if not main_block.has_var(grad_name): continue
#assert main_block.has_var(grad_name)
......@@ -246,131 +232,130 @@ class ShardingOptimizer(MetaOptimizerBase):
# 'op_role': core.op_proto_and_checker_maker.OpRole.LRSched,
# })
pass
#def _create_var(block, ref_var, name):
# """
# Create a new var for block, which has the same type,
# shape and dtype as ref_var, then rename it with the
# name `name`.
# """
# new_var = block.create_var(
# name=name,
# shape=ref_var.shape,
# dtype=ref_var.dtype,
# type=ref_var.type,
# lod_level=ref_var.lod_level,
# persistable=ref_var.persistable,
# is_data=ref_var.is_data,
# need_check_feed=ref_var.desc.need_check_feed())
# new_var.stop_gradient = ref_var.stop_gradient
# return new_var
#def _rename_arg(op, old_name, new_name):
# op_desc = op.desc
# if isinstance(op_desc, tuple):
# op_desc = op_desc[0]
# op_desc._rename_input(old_name, new_name)
# op_desc._rename_output(old_name, new_name)
#print("params_grads:", params_grads)
#for param_name, grad_name in params_grads:
# if not self._shard.has_param(param_name): continue
# #if not main_block.has_var(grad_name): continue
# assert main_block.has_var(grad_name)
# use_fp16 = False
# fp16_grad_name = param_name + '.cast_fp16@GRAD'
# if main_block.has_var(grad_name):
# fp16_grad_var = main_block.vars[fp16_grad_name]
# use_fp16 = True
# grad_var = main_block.vars[grad_name]
# if use_fp16:
# cast_grad_var_name = paddle.fluid.unique_name.generate(
# grad_name)
# cast_var = _create_var(main_block, fp16_grad_var,
# cast_grad_var_name)
# cast_var.persistable = False
# main_block.append_op(
# #index=offset + 1,
# type='cast',
# inputs={'X': grad_var},
# outputs={'Out': cast_var},
# attrs={
# 'in_dtype': grad_var.dtype,
# 'out_dtype': cast_var.dtype,
# 'op_role':
# core.op_proto_and_checker_maker.OpRole.Backward,
# })
# #offset += 1
# main_block.append_op(
# #index=offset + 1,
# type='sum',
# inputs={'X': [fp16_grad_var, cast_var]},
# outputs={'Out': fp16_grad_var},
# attrs={
# 'op_role':
# core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var
# })
# for index, op in reversed(tuple(enumerate(list(main_block.ops)))):
# offset = index
# if is_backward_op(op) and (
# 'op_role_var' in op.attr_names):
# op_role_var = op.all_attrs()['op_role_var']
# if len(op_role_var) == 0:
# continue
# assert len(op_role_var) % 2 == 0
# offset = index
# for i in range(0, len(op_role_var), 2):
# grad_name = op_role_var[i + 1]
# if not main_block.has_var(grad_name): continue
# grad_var = main_block.vars[grad_name]
# if not 'cast_fp16' in grad_name:
# new_grad_var_name = paddle.fluid.unique_name.generate(grad_name)
# new_var = _create_var(main_block, grad_var,
# new_grad_var_name)
# new_var.persistable = False
# _rename_arg(op, grad_name, new_grad_var_name)
# main_block._insert_op(
# index=offset + 1,
# type='sum',
# inputs={'X': [grad_var, new_var]},
# outputs={'Out': grad_var},
# attrs={
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var
# })
# offset += 1
# if 'cast_fp16' in grad_name:
# param_name = op_role_var[i]
# fp32_grad_var_name = param_name + "@GRAD"
# fp32_grad_var = main_block.vars[grad_name]
# cast_grad_var_name = paddle.fluid.unique_name.generate(
# fp32_grad_var_name)
# cast_var = _create_var(main_block, grad_var,
# cast_grad_var_name)
# cast_var.persistable = False
# main_block._insert_op(
# index=offset + 1,
# type='cast',
# inputs={'X': fp32_grad_var},
# outputs={'Out': cast_var},
# attrs={
# 'in_dtype': fp32_grad_var.dtype,
# 'out_dtype': cast_var.dtype,
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# # self._op_role_var_key: op_role_var
# })
# offset += 1
# main_block._insert_op(
# index=offset + 1,
# type='sum',
# inputs={'X': [grad_var, cast_var]},
# outputs={'Out': grad_var},
# attrs={
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var})
#def _create_var(block, ref_var, name):
# """
# Create a new var for block, which has the same type,
# shape and dtype as ref_var, then rename it with the
# name `name`.
# """
# new_var = block.create_var(
# name=name,
# shape=ref_var.shape,
# dtype=ref_var.dtype,
# type=ref_var.type,
# lod_level=ref_var.lod_level,
# persistable=ref_var.persistable,
# is_data=ref_var.is_data,
# need_check_feed=ref_var.desc.need_check_feed())
# new_var.stop_gradient = ref_var.stop_gradient
# return new_var
#def _rename_arg(op, old_name, new_name):
# op_desc = op.desc
# if isinstance(op_desc, tuple):
# op_desc = op_desc[0]
# op_desc._rename_input(old_name, new_name)
# op_desc._rename_output(old_name, new_name)
#print("params_grads:", params_grads)
#for param_name, grad_name in params_grads:
# if not self._shard.has_param(param_name): continue
# #if not main_block.has_var(grad_name): continue
# assert main_block.has_var(grad_name)
# use_fp16 = False
# fp16_grad_name = param_name + '.cast_fp16@GRAD'
# if main_block.has_var(grad_name):
# fp16_grad_var = main_block.vars[fp16_grad_name]
# use_fp16 = True
# grad_var = main_block.vars[grad_name]
# if use_fp16:
# cast_grad_var_name = paddle.fluid.unique_name.generate(
# grad_name)
# cast_var = _create_var(main_block, fp16_grad_var,
# cast_grad_var_name)
# cast_var.persistable = False
# main_block.append_op(
# #index=offset + 1,
# type='cast',
# inputs={'X': grad_var},
# outputs={'Out': cast_var},
# attrs={
# 'in_dtype': grad_var.dtype,
# 'out_dtype': cast_var.dtype,
# 'op_role':
# core.op_proto_and_checker_maker.OpRole.Backward,
# })
# #offset += 1
# main_block.append_op(
# #index=offset + 1,
# type='sum',
# inputs={'X': [fp16_grad_var, cast_var]},
# outputs={'Out': fp16_grad_var},
# attrs={
# 'op_role':
# core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var
# })
# for index, op in reversed(tuple(enumerate(list(main_block.ops)))):
# offset = index
# if is_backward_op(op) and (
# 'op_role_var' in op.attr_names):
# op_role_var = op.all_attrs()['op_role_var']
# if len(op_role_var) == 0:
# continue
# assert len(op_role_var) % 2 == 0
# offset = index
# for i in range(0, len(op_role_var), 2):
# grad_name = op_role_var[i + 1]
# if not main_block.has_var(grad_name): continue
# grad_var = main_block.vars[grad_name]
# if not 'cast_fp16' in grad_name:
# new_grad_var_name = paddle.fluid.unique_name.generate(grad_name)
# new_var = _create_var(main_block, grad_var,
# new_grad_var_name)
# new_var.persistable = False
# _rename_arg(op, grad_name, new_grad_var_name)
# main_block._insert_op(
# index=offset + 1,
# type='sum',
# inputs={'X': [grad_var, new_var]},
# outputs={'Out': grad_var},
# attrs={
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var
# })
# offset += 1
# if 'cast_fp16' in grad_name:
# param_name = op_role_var[i]
# fp32_grad_var_name = param_name + "@GRAD"
# fp32_grad_var = main_block.vars[grad_name]
# cast_grad_var_name = paddle.fluid.unique_name.generate(
# fp32_grad_var_name)
# cast_var = _create_var(main_block, grad_var,
# cast_grad_var_name)
# cast_var.persistable = False
# main_block._insert_op(
# index=offset + 1,
# type='cast',
# inputs={'X': fp32_grad_var},
# outputs={'Out': cast_var},
# attrs={
# 'in_dtype': fp32_grad_var.dtype,
# 'out_dtype': cast_var.dtype,
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# # self._op_role_var_key: op_role_var
# })
# offset += 1
# main_block._insert_op(
# index=offset + 1,
# type='sum',
# inputs={'X': [grad_var, cast_var]},
# outputs={'Out': grad_var},
# attrs={
# 'op_role': core.op_proto_and_checker_maker.OpRole.Backward,
# 'op_role_var': op_role_var})
main_block._sync_with_cpp()
# TODO(wangxi): add optimize offload
......
python/paddle/fluid/optimizer.py
浏览文件 @ 47042a97
......@@ -4064,11 +4064,8 @@ class PipelineOptimizer(object):
return None
def _rename_arg(self, op, old_name, new_name):
op_desc = op.desc
if isinstance(op_desc, tuple):
op_desc = op_desc[0]
op_desc._rename_input(old_name, new_name)
op_desc._rename_output(old_name, new_name)
op._rename_input(old_name, new_name)
op._rename_output(old_name, new_name)
def _create_var(self, block, ref_var, name):
"""
......@@ -4823,48 +4820,33 @@ class PipelineOptimizer(object):
def _rename_gradient_var_name(self, block):
for index, op in enumerate(block.ops):
if self._is_backward_op(op) and (
self._op_role_var_key in op.attr_names):
op_role_var = op.attr(self._op_role_var_key)
if len(op_role_var) == 0:
continue
for i in range(0, len(op_role_var), 2):
grad_name = op_role_var[i + 1]
grad_var = block.vars[grad_name]
new_grad_var_name = unique_name.generate(grad_name)
new_var = self._create_var(block, grad_var,
new_grad_var_name)
new_var.persistable = False
self._rename_arg(op, grad_name, new_grad_var_name)
if not self._is_optimize_op(op): continue
input_names = op.input_arg_names
output_names = op.output_arg_names
in_out_names = input_names + output_names
# append "MERGED" to the names of parameter gradients,
# and mofify the op_role_var attribute (by rename_arg func).
for name in in_out_names:
if not core.grad_var_suffix() in name: continue
param_name = name.strip(core.grad_var_suffix())
new_grad_name = name + "@MERGED"
self._rename_arg(op, name, new_grad_name)
def _accumulate_gradients(self, block, pp_allreduce_in_optimize=False):
"""
Accumulate the gradients generated in microbatch to the one in mini-batch.
Create a new merged gradient for each parameter and accumulate the
corresponding gradient to it.
"""
# the name of real grad vars that should be allreduce
# accumulated_gradient_names = []
first_optimize_op_index = None
accumulated_grad_names = []
for index, op in reversed(tuple(enumerate(list(block.ops)))):
# remove the cast op of fp16 grad to fp32 grad
if self._is_optimize_op(op) and op.type == 'cast':
in_name = op.input_arg_names[0]
out_name = op.output_arg_names[0]
if out_name.strip('@GRAD') in self._param_device_map:
if out_name.strip('@GRAD@MERGED') in self._param_device_map:
assert in_name.replace('.cast_fp16', '') == out_name
block._remove_op(index)
continue
if not self._is_optimize_op(op) and not first_optimize_op_index:
first_optimize_op_index = index + 1
if block.ops[
first_optimize_op_index].type == 'c_sync_comm_stream':
block.ops[first_optimize_op_index]._set_attr(
self._op_role_key, self._op_role.Backward)
first_optimize_op_index += 1
if self._is_backward_op(op) and (
self._op_role_var_key in op.attr_names):
op_role_var = op.attr(self._op_role_var_key)
......@@ -4872,143 +4854,80 @@ class PipelineOptimizer(object):
if len(op_role_var) == 0:
continue
assert len(op_role_var) % 2 == 0
op._remove_attr(self._op_role_var_key)
for i in range(0, len(op_role_var), 2):
offset = 0
offset = 1
param_name = op_role_var[i]
if not pp_allreduce_in_optimize:
if not block.has_var(param_name):
continue
if '@BroadCast' in param_name:
param_name = param_name[0:param_name.find('@BroadCast')]
assert block.has_var(param_name), (
"parameter {} not in "
"current block.".format(param_name))
# clear gradient
assert param_name in self.origin_main_block.vars, "[{}] not in original main block".format(
param_name)
param_grad_name = self._append_grad_suffix(param_name)
if not block.has_var(param_grad_name):
self._create_var(
block, self.origin_main_block.vars[param_name],
param_grad_name)
assert block.has_var(param_grad_name)
merged_param_grad_name = param_grad_name + '@MERGED'
if not block.has_var(merged_param_grad_name):
self._create_var(block, block.vars[param_name],
merged_param_grad_name)
assert block.has_var(merged_param_grad_name)
param_grad_var = block.var(param_grad_name)
param_grad_var.persistable = True
merged_param_grad_var = block.var(merged_param_grad_name)
merged_param_grad_var.persistable = True
block._insert_op(
index=first_optimize_op_index + offset,
index=index + offset,
type='fill_constant',
inputs={},
outputs={'Out': [param_grad_var]},
outputs={'Out': [merged_param_grad_var]},
attrs={
'shape': param_grad_var.shape,
'dtype': param_grad_var.dtype,
'shape': merged_param_grad_var.shape,
'dtype': merged_param_grad_var.dtype,
'value': float(0),
# self._op_device_key: device,
# a trick to run this op once per mini-batch
self._op_role_key: self._op_role.Optimize.LRSched,
})
#offset += 1
grad_name = op_role_var[i + 1] # with _0 suffix
offset += 1
grad_name = op_role_var[i + 1]
grad_var = block.vars[grad_name]
#real_grad_name = grad_name[0:grad_name.find(
# '@GRAD')] + '@GRAD' # without _0 suffix
#real_grad_var = block.vars[
# real_grad_name] # without _0 suffix
# new_grad_var_name = unique_name.generate(grad_name)
# new_var = self._create_var(block, grad_var,
# new_grad_var_name)
# new_var.persistable = False
# self._rename_arg(op, grad_name, new_grad_var_name)
if not 'cast_fp16' in grad_name:
block._insert_op(
index=index + 1,
index=index + offset,
type='sum',
inputs={'X': [grad_var, param_grad_var]},
outputs={'Out': param_grad_var},
inputs={'X': [grad_var, merged_param_grad_var]},
outputs={'Out': merged_param_grad_var},
attrs={
#self._op_device_key: device,
self._op_role_key: self._op_role.Backward,
#self._op_role_var_key: op_role_var
})
#offset += 1
accumulated_grad_names.append(param_grad_var.name)
offset += 1
else:
grad_name = op_role_var[i + 1] # with _0 suffix
grad_var = block.vars[grad_name]
#fp32_grad_var_name = param_name + core.grad_var_suffix(
#) # without _0 suffix
#fp32_grad_var = block.vars[fp32_grad_var_name]
#fp32_grad_var.persistable = True
cast_grad_var_name = unique_name.generate(
param_grad_name)
# cast gradient to fp32 to accumulate to merged gradient
cast_grad_var_name = param_grad_name + '@TMP'
cast_grad_var = self._create_var(block, param_grad_var,
cast_grad_var_name)
cast_grad_var.persistable = False
block._insert_op(
index=index + 1,
index=index + offset,
type='cast',
inputs={'X': grad_var},
outputs={'Out': cast_grad_var},
attrs={
'in_dtype': grad_var.dtype,
'out_dtype': cast_grad_var.dtype,
# self._op_device_key: device,
self._op_role_key: self._op_role.Backward,
# self._op_role_var_key: op_role_var
})
offset += 1
block._insert_op(
index=index + 2,
index=index + offset,
type='sum',
inputs={'X': [param_grad_var, cast_grad_var]},
outputs={'Out': param_grad_var},
inputs={
'X': [merged_param_grad_var, cast_grad_var]
},
outputs={'Out': merged_param_grad_var},
attrs={
# self._op_device_key: device,
self._op_role_key: self._op_role.Backward,
# self._op_role_var_key: op_role_var
self._op_role_var_key: op_role_var
})
offset += 1
accumulated_grad_names.append(param_grad_var.name)
#real_grad_name = grad_name[0:grad_name.find(
# '@GRAD')] + '@GRAD'
#real_grad_var = block.vars[
# real_grad_name] # without _0 suffix
#block._insert_op(
# index=first_optimize_op_index + offset,
# type='cast',
# inputs={'X': fp32_grad_var},
# outputs={'Out': cast_var},
# attrs={
# 'in_dtype': fp32_grad_var.dtype,
# 'out_dtype': cast_var.dtype,
# # self._op_device_key: device,
# self._op_role_key: self._op_role.Backward,
# # self._op_role_var_key: op_role_var
# })
#offset += 1
#block._insert_op(
# index=first_optimize_op_index + offset,
# type='sum',
# inputs={'X': [grad_var, cast_var]},
# outputs={'Out': real_grad_var},
# attrs={
# # self._op_device_key: device,
# self._op_role_key: self._op_role.Backward,
# # self._op_role_var_key: op_role_var
# })
#offset += 1
#block._insert_op(
# index=first_optimize_op_index + offset,
# type='cast',
# inputs={'X': real_grad_var},
# outputs={'Out': fp32_grad_var},
# attrs={
# 'in_dtype': real_grad_var.dtype,
# 'out_dtype': fp32_grad_var.dtype,
# # self._op_device_key: device,
# self._op_role_key: self._op_role.Backward,
# # self._op_role_var_key: op_role_var
# })
return accumulated_grad_names
def _add_sub_blocks(self, main_block, program_list):
main_program = main_block.program
......@@ -5351,7 +5270,9 @@ class PipelineOptimizer(object):
if real_block.has_var(param): param_list.append(param)
#self._clear_gradients(real_block, param_list)
self._rename_gradient_var_name(real_block)
real_block._sync_with_cpp()
self._accumulate_gradients(real_block)
real_block._sync_with_cpp()
place_id = int(os.getenv("FLAGS_selected_gpus", "0"))
main_program._pipeline_opt = {
......
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