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未验证 提交 695dd371 编写于 作者: L lilong12 提交者： GitHub
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Adjust pipeline optimizer for 3d parallelism (#31939) 

* update, test=develop
上级 6f85e241
隐藏空白更改
内联 并排
Showing with 168 addition and 161 deletion
paddle/fluid/framework/pipeline_trainer.cc
浏览文件 @ 695dd371
......@@ -71,37 +71,16 @@ void PipelineTrainer::CopyParameters(int microbatch_id,
const ProgramDesc& program,
const platform::Place& place) {
auto& global_block = program.Block(0);
std::map<std::string, int> param_map;
for (auto& var : global_block.AllVars()) {
if (var->Persistable()) {
param_map[var->Name()] = 1;
}
}
for (auto& var : global_block.AllVars()) {
bool is_param_grad = false;
size_t pos = 0;
// A magic suffix to indicate the merged gradient
std::string magicSuffix = std::string(kGradVarSuffix) + "@MERGED";
if ((pos = 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;
}
}
if (var->Persistable() && microbatch_id == 0) {
auto* ptr = root_scope_->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create persistable var: " << var->Name()
<< ", which pointer is " << ptr;
} else if (is_param_grad && microbatch_id == 0) {
auto* ptr = minibatch_scope_->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create grad for persistable var: " << var->Name()
VLOG(5) << "Create persistable var: " << var->Name()
<< ", which pointer is " << ptr;
} else if (!var->Persistable() && !is_param_grad) {
} else if (!var->Persistable()) {
auto* ptr = microbatch_scopes_[microbatch_id]->Var(var->Name());
VLOG(3) << "Create variable " << var->Name() << " for microbatch "
VLOG(5) << "Create variable " << var->Name() << " for microbatch "
<< microbatch_id << ", which pointer is " << ptr;
InitializeVariable(ptr, var->GetType());
}
......
python/paddle/distributed/fleet/meta_optimizers/common.py
浏览文件 @ 695dd371
......@@ -106,6 +106,11 @@ class CollectiveHelper(object):
'use_calc_stream': True,
OP_ROLE_KEY: OpRole.Forward
})
block.append_op(
type='c_sync_calc_stream',
inputs={'X': sync_var},
outputs={'Out': sync_var},
attrs={OP_ROLE_KEY: OpRole.Forward})
block = program.global_block()
if core.is_compiled_with_cuda():
......
python/paddle/distributed/fleet/meta_optimizers/pipeline_optimizer.py
浏览文件 @ 695dd371
......@@ -171,6 +171,7 @@ class PipelineOptimizer(MetaOptimizerBase):
program._pipeline_opt['ring_id'] = self.start_pipeline_ring_id
program._pipeline_opt['micro_batch_size'] = self.micro_batch_size
program._pipeline_opt['schedule_mode'] = self.schedule_mode
program._pipeline_opt['use_sharding'] = False
optimize_ops, params_grads, prog_list, pp_pair, ring_map = self.wrapped_opt.minimize(
loss, startup_program, parameter_list, no_grad_set)
self.startup_program = orig_startup_program._pipeline_opt[
......@@ -218,7 +219,6 @@ class PipelineOptimizer(MetaOptimizerBase):
grad = None
processed_param_name = set()
first_optimize_op_idx = None
add_sync_calc_stream = False
for idx, op in reversed(list(enumerate(block.ops))):
if is_backward_op(op) and not first_optimize_op_idx:
first_optimize_op_idx = idx + 1
......@@ -242,15 +242,6 @@ class PipelineOptimizer(MetaOptimizerBase):
origin_param = origin_block.vars[op_role_var[i]]
if origin_param.is_distributed:
continue
if not add_sync_calc_stream:
add_sync_calc_stream = True
block._insert_op(
first_optimize_op_idx + offset,
type='c_sync_calc_stream',
inputs={'X': grad},
outputs={'Out': grad},
attrs={OP_ROLE_KEY: OpRole.Optimize})
offset += 1
block._insert_op(
first_optimize_op_idx + offset,
......
python/paddle/fluid/optimizer.py
浏览文件 @ 695dd371
......@@ -3805,7 +3805,6 @@ class PipelineOptimizer(object):
self._param_device_map = None
self._pipeline_pair = []
self._pp_ring_map = dict()
self._global_ring_id = None
# insert allreduce op to sync global information for global
# gradient clip and amp
......@@ -3841,7 +3840,7 @@ class PipelineOptimizer(object):
inputs={'X': temp_var if op.type == "reduce_any" else out_var},
outputs={'Out': temp_var if op.type == "reduce_any" else out_var},
attrs={
'ring_id': self._global_ring_id,
'ring_id': self.global_ring_id,
self._op_role_key: self._op_role.Optimize,
'use_calc_stream': True
})
......@@ -3887,6 +3886,16 @@ class PipelineOptimizer(object):
reserved_x.append(input_name)
op.desc.set_input('X', reserved_x)
op.desc.set_output('Out', reserved_x)
elif op.type == 'check_finite_and_unscale':
for input_name in op.desc.input("X"):
if block._find_var_recursive(input_name):
reserved_x.append(input_name)
op.desc.set_input('X', reserved_x)
op.desc.set_output('Out', reserved_x)
if len(reserved_x) == 0:
block._remove_op(op_idx)
op_size -= 1
continue
elif op.type == 'sum' and self._is_gradient_clip_op(op):
for input_name in op.desc.input("X"):
if block._find_var_recursive(input_name):
......@@ -4020,63 +4029,32 @@ class PipelineOptimizer(object):
self._create_vars(new_startup_program.global_block(), block)
return new_startup_program
def _find_post_op(self, ops, cur_op, var_name):
def _find_post_op(self, index, var_name):
"""
Find the real post op that has variable named var_name as input.
Args:
ops (list): A list of ops.
cur_op (Operator): Current operator which has variable named
var_name as output.
var_name (string): Variable name.
Find the post op that has variable named var_name as input.
"""
# To skip the cast op added by amp which has no op_device set
if '.cast_fp32' in var_name:
var_name = var_name.replace('.cast_fp32', '')
elif '.cast_fp16' in var_name:
var_name = var_name.replace('.cast_fp16', '')
post_op = []
before = True
for op in ops:
if op == cur_op:
before = False
continue
if before:
continue
for in_var_name in op.input_arg_names:
if in_var_name == var_name:
post_op.append(op)
break
if post_op:
return post_op[0]
return None
post_ops = self.input_var_to_op[var_name]
if post_ops == None: return None
result_op = None
for post_op, post_idx in reversed(post_ops):
if post_idx > index:
result_op = post_op
break
return result_op
def _find_real_prev_op(self, ops, cur_op, var_name):
def _find_prev_op(self, index, var_name):
"""
Find the real previous op that outputs variable named var_name.
Args:
ops (list): A list of ops.
cur_op (Operator): Current operator which has variable named
var_name as input.
var_name (string): Variable name.
Find the previous op of op with index that outputs
variable named var_name.
"""
prev_op = []
for op in ops:
if op.type == 'send_v2' or op.type == 'recv_v2' \
or op.type == 'c_broadcast':
continue
if op == cur_op:
prev_ops = self.output_var_to_op[var_name]
if prev_ops == None: return None
result_op = None
for prev_op, prev_idx in reversed(prev_ops):
if prev_idx < index:
result_op = prev_op
break
for out_var_name in op.output_arg_names:
if out_var_name == var_name:
prev_op.append(op)
if prev_op:
# A op may have more than one prev op,
# e.g., for 'learning_rate', there may be multiple ops have it as
# output.
return prev_op[-1]
return None
return result_op
def _rename_arg(self, op, old_name, new_name):
op._rename_input(old_name, new_name)
......@@ -4136,23 +4114,21 @@ class PipelineOptimizer(object):
# For LRSched ops, we should put them on all sub-programs to
# make sure each sub-program update the lr correctly
op._set_attr(self._op_device_key, "gpu:all")
elif (op.type == "cast" or
op.type == "scale") and self._is_backward_op(op):
prev_op = self._find_real_prev_op(block.ops, op,
op.desc.input("X")[0])
elif op.type == "scale" and self._is_backward_op(op):
prev_op = self._find_prev_op(idx, op.desc.input("X")[0])
op._set_attr(self._op_device_key, prev_op.attr(self._op_device_key))
elif op.type == "memcpy" and not self._is_optimize_op(op):
# for checkpoint offloading
assert len(op.input_arg_names) == 1 and len(
op.output_arg_names) == 1
input_name = op.input_arg_names[0]
output_name = op.output_arg_names[0]
if '@Fetch' in output_name:
post_op = self._find_post_op(block.ops, op, output_name)
post_op = self._find_post_op(idx, output_name)
op._set_attr(self._op_device_key,
post_op.attr(self._op_device_key))
else:
prev_op = self._find_real_prev_op(block.ops, op,
op.desc.input("X")[0])
prev_op = self._find_prev_op(idx, op.desc.input("X")[0])
op._set_attr(self._op_device_key,
prev_op.attr(self._op_device_key))
elif self._is_loss_op(op):
......@@ -4165,16 +4141,11 @@ class PipelineOptimizer(object):
assert device, "Please put you program within device_guard scope."
for i in range(offset):
block.ops[idx + i]._set_attr(self._op_device_key, device)
elif self._is_optimize_op(op) and op.type == "check_finite_and_unscale":
op_role_var = op.attr(self._op_role_var_key)
param_name = op_role_var[0]
device = self._param_device_map[param_name]
op._set_attr(self._op_device_key, device)
elif self._is_optimize_op(op) and op.type == "cast":
# For fp16-->fp32 cast added by AMP
grad_name = op.output('Out')
assert len(grad_name) == 1
param_name = grad_name[0].strip(core.grad_var_suffix())
param_name = self._strip_grad_suffix(grad_name[0])
device = self._param_device_map[param_name]
op._set_attr(self._op_device_key, device)
elif self._is_gradient_clip_op(op) or self._is_regularization_op(op):
......@@ -4197,7 +4168,11 @@ class PipelineOptimizer(object):
op._set_attr(self._op_device_key, device)
else:
other_known_ops = [
'update_loss_scaling', 'reduce_any', 'concat', 'sum'
'update_loss_scaling',
'reduce_any',
'concat',
'sum',
'check_finite_and_unscale',
]
assert op.type in other_known_ops, "For other ops without " \
"op_device set, they must be one of {}, but it " \
......@@ -4274,41 +4249,70 @@ class PipelineOptimizer(object):
Insert a pair of send and recv ops for every two
consecutive ops on different devices.
"""
extra_index = 0
extra_index_info = {'index': 0}
# A map from var to device where op takes it as input,
# avoiding multiple send and recv ops.
var_dev_map = dict()
input_var_to_device = dict()
for index, op in enumerate(list(block.ops)):
cur_device = op.attr(self._op_device_key)
if cur_device == "gpu:all": continue
for var_name in op.input_arg_names:
# i.e., lod_tensor_blocking_queue created by DataLoader,
# which only exists in startup program.
var = block.var(var_name)
# skip data, because we will process it later
# skip data var
if var.is_data: continue
prev_device = None
if var_name in self._param_device_map:
generate_ops = self.output_var_to_op.get(var_name)
if generate_ops is None:
if var_name not in self._param_device_map:
continue
prev_device = self._param_device_map[var_name]
prev_op = self._find_real_prev_op(block.ops, op, var_name)
prev_op = self._find_prev_op(index, var_name)
if not prev_device:
prev_device = prev_op.attr(self._op_device_key) \
if prev_op else None
if not prev_device or prev_device == 'gpu:all': continue
if prev_device != cur_device:
if var_name not in var_dev_map: var_dev_map[var_name] = []
if cur_device in var_dev_map[var_name]: continue
var_dev_map[var_name].append(cur_device)
if prev_device is None or prev_device == "gpu:all": continue
if prev_device == cur_device: continue
op_role = op.all_attrs()[self._op_role_key]
if var_name not in input_var_to_device:
input_var_to_device[var_name] = []
if (cur_device, prev_device) in input_var_to_device[var_name]:
continue
device_type = cur_device.split(':')[0] + ':'
def _insert_send_recv(cur_id, prev_id):
cur_dev = device_type + str(cur_id)
prev_dev = device_type + str(prev_id)
if (cur_dev, prev_dev) in input_var_to_device[var_name]:
return
if cur_id - prev_id > 1:
_insert_send_recv(cur_id - 1, prev_id)
_insert_send_recv(cur_id, cur_id - 1)
input_var_to_device[var_name].append(
(cur_dev, prev_dev))
return
elif cur_id - prev_id < -1:
_insert_send_recv(cur_id + 1, prev_id)
_insert_send_recv(cur_id, cur_id + 1)
input_var_to_device[var_name].append(
(cur_dev, prev_dev))
return
assert abs(cur_id - prev_id) == 1
input_var_to_device[var_name].append((cur_dev, prev_dev))
op_role = op.attr(self._op_role_key)
var = block.vars[var_name]
prev_device_index = int(prev_device.split(':')[1])
cur_device_index = int(cur_device.split(':')[1])
pair = (prev_device_index, cur_device_index)
pair_key = prev_device_index * 1000 + cur_device_index
pair = (prev_id, cur_id)
# 1000 is just a magic number
pair_key = prev_id * 1000 + cur_id
if pair not in self._pipeline_pair:
self._pipeline_pair.append(pair)
self._pp_ring_map[pair_key] = self.ring_id
......@@ -4316,89 +4320,95 @@ class PipelineOptimizer(object):
self.ring_id += 1
else:
ring_id = self._pp_ring_map[pair_key]
if self.schedule_mode == 'F-then-B': # F-then-B
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='send_v2',
inputs={'X': var},
attrs={
self._op_device_key: prev_device,
self._op_device_key: prev_dev,
self._op_role_key: op_role,
'use_calc_stream': True,
'peer': 1,
'ring_id': ring_id
})
extra_index += 1
extra_index_info['index'] += 1
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='recv_v2',
outputs={'Out': [var]},
attrs={
'out_shape': var.shape,
'dtype': var.dtype,
self._op_device_key: cur_device,
self._op_device_key: cur_dev,
self._op_role_key: op_role,
'use_calc_stream': True,
'peer': 0,
'ring_id': ring_id
})
extra_index += 1
extra_index_info['index'] += 1
elif self.schedule_mode == '1F1B': # 1F1B
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='c_sync_calc_stream',
inputs={'X': [var]},
outputs={'Out': [var]},
attrs={
self._op_device_key: prev_device,
self._op_device_key: prev_dev,
self._op_role_key: op_role,
})
extra_index += 1
extra_index_info['index'] += 1
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='send_v2',
inputs={'X': var},
attrs={
self._op_device_key: prev_device,
self._op_device_key: prev_dev,
self._op_role_key: op_role,
'use_calc_stream': False,
'ring_id': ring_id,
'peer': 1,
})
extra_index += 1
extra_index_info['index'] += 1
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='c_sync_comm_stream',
inputs={'X': [var]},
outputs={'Out': [var]},
attrs={
self._op_device_key: prev_device,
self._op_device_key: prev_dev,
self._op_role_key: self._op_role.Backward,
'ring_id': ring_id,
})
extra_index += 1
extra_index_info['index'] += 1
var_shape = list(var.shape)
var_shape[0] = self.micro_batch_size if var_shape[
0] < 0 else var_shape[0]
block._insert_op(
index=index + extra_index,
index=index + extra_index_info['index'],
type='recv_v2',
outputs={'Out': [var]},
attrs={
'out_shape': var_shape,
'dtype': var.dtype,
self._op_device_key: cur_device,
self._op_device_key: cur_dev,
self._op_role_key: op_role,
'use_calc_stream': True,
'peer': 0,
'ring_id': ring_id
})
extra_index += 1
extra_index_info['index'] += 1
else:
raise ValueError(
"Now only 'F-then-B' and '1F1B' are supported."
"The given value is {}.".format(self.schedule_mode))
_insert_send_recv(
int(cur_device.split(':')[1]),
int(prev_device.split(':')[1]))
block._sync_with_cpp()
def _insert_loss_scale(self, block):
"""
Scale the loss corresponding to number of micro-batches.
......@@ -4675,6 +4685,23 @@ class PipelineOptimizer(object):
return op.desc.has_attr("op_namescope") \
and op.desc.attr("op_namescope").startswith("/regularization")
def _get_input_output_info(self, block):
'''
Get info of op input and output.
'''
# A map from output var to op which generate it.
self.output_var_to_op = dict()
# A map from var to op which takes it as input.
self.input_var_to_op = dict()
for index, op in enumerate(list(block.ops)):
for var_name in op.input_arg_names:
ops = self.input_var_to_op.setdefault(var_name, [])
ops.append([op, index])
for var_name in op.output_arg_names:
ops = self.output_var_to_op.setdefault(var_name, [])
ops.append([op, index])
def minimize(self,
loss,
startup_program=None,
......@@ -4682,30 +4709,35 @@ class PipelineOptimizer(object):
no_grad_set=None):
main_block = loss.block
self.origin_main_block = main_block
main_program = main_block.program
if startup_program is None:
startup_program = default_startup_program()
optimize_ops, params_grads = self._optimizer.minimize(
loss, startup_program, parameter_list, no_grad_set)
self._param_device_map = self._origin_optimizer._param_device_map
assert main_block.program._pipeline_opt \
and 'local_rank' in main_block.program._pipeline_opt, \
'Please use pipeline with fleet.'
local_rank = main_block.program._pipeline_opt['local_rank']
self._global_ring_id = main_block.program._pipeline_opt[
'global_ring_id']
schedule_mode = 0
if 'schedule_mode' in main_block.program._pipeline_opt:
schedule_mode = main_block.program._pipeline_opt['schedule_mode']
self.schedule_mode = schedule_mode
# micro batch size
assert main_program._pipeline_opt, 'Please use pipeline with fleet.'
required_keys = [
'local_rank',
'schedule_mode',
'micro_batch_size',
'ring_id',
'global_ring_id',
'use_sharding',
]
for key in required_keys:
assert key in main_program._pipeline_opt, \
'Please use pipeline with fleet to use {}.'.format(key)
self.local_rank = main_block.program._pipeline_opt['local_rank']
self.schedule_mode = main_block.program._pipeline_opt['schedule_mode']
self.micro_batch_size = main_block.program._pipeline_opt[
'micro_batch_size']
self.use_sharding = False
if 'use_sharding' in main_block.program._pipeline_opt:
self.use_sharding = main_block.program._pipeline_opt['use_sharding']
self.use_sharding = main_block.program._pipeline_opt['use_sharding']
self.ring_id = main_block.program._pipeline_opt['ring_id']
self.global_ring_id = main_block.program._pipeline_opt['global_ring_id']
optimize_ops, params_grads = self._optimizer.minimize(
loss, startup_program, parameter_list, no_grad_set)
self._param_device_map = self._origin_optimizer._param_device_map
self._get_input_output_info(main_block)
# Step1: add default op_device attribute for ops.
self._add_op_device_attr(main_block)
device_list = self._check_validation(main_block)
......@@ -4742,20 +4774,20 @@ class PipelineOptimizer(object):
# Step5: Add sub blocks for section programs
self._add_sub_blocks(main_block, program_list)
local_rank = main_program._pipeline_opt['local_rank'] % len(device_list)
self.local_rank %= len(device_list)
place_list = []
for dev in device_list:
dev_index = int(dev.split(":")[1])
place_list.append(core.CUDAPlace(dev_index % 8))
place_list.append(core.CUDAPlace(0))
# Step6: Split startup program
new_startup_program = self._split_startup_program(startup_program,
local_rank)
self.local_rank)
startup_program._pipeline_opt = {
"startup_program": new_startup_program,
}
real_block = program_list[local_rank].global_block()
real_block = program_list[self.local_rank].global_block()
self._insert_loss_scale(real_block)
if not self.use_sharding:
# Step7: clear gradients before each mini-batch and
......@@ -4769,12 +4801,12 @@ class PipelineOptimizer(object):
main_program._pipeline_opt = {
"trainer": "PipelineTrainer",
"device_worker": "Section",
"pipeline_stage": local_rank,
"pipeline_stage": self.local_rank,
"num_pipeline_stages": len(device_list),
"schedule_mode": self.schedule_mode,
"inner_parallelism": len(device_list),
"section_program": program_list[local_rank],
"place": place_list[local_rank],
"section_program": program_list[self.local_rank],
"place": place_list[self.local_rank],
"place_id": place_id,
"sync_steps": -1,
"num_microbatches": self._num_microbatches,
......
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