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pipeline_parallel.py
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pipeline_parallel.py
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# 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
import paddle
import paddle.fluid as fluid
from .meta_parallel_base import MetaParallelBase
from .parallel_layers.pp_layers import PipelineLayer
from ..utils.hybrid_parallel_util import broadcast_mp_parameters
from ..utils.hybrid_parallel_util import broadcast_dp_parameters
from ..utils.hybrid_parallel_util import broadcast_sharding_parameters
from ..utils.log_util import logger
from ..meta_optimizers.dygraph_optimizer import HybridParallelOptimizer
import paddle.fluid.framework as framework
from .pp_utils import p2p_communication as p2p
import paddle.fluid.core as core
__all__ = []
class PipelineParallel(MetaParallelBase):
def __init__(self, layers, hcg, strategy):
if not isinstance(layers, PipelineLayer):
raise TypeError(
"The Layer should be a derived class of PipelineLayer."
)
super(PipelineParallel, self).__init__(layers, hcg, strategy)
self.use_data_parallel = self._hcg.get_data_parallel_world_size() > 1
self.use_model_parallel = self._hcg.get_model_parallel_world_size() > 1
self.use_sharding_parallel = (
self._hcg.get_sharding_parallel_world_size() > 1
)
self.total_loss = None
self.micro_batch_size = self._strategy.pipeline_configs[
'micro_batch_size'
]
self.accumulate_steps = self._strategy.pipeline_configs[
'accumulate_steps'
]
# If sent tensor are not the same from different hosts,
# they shouldn't been sent partially and then concated as a whole tensor.
self._enable_partial_send_recv = self._strategy.pipeline_configs[
'enable_partial_send_recv'
]
self._using_cache = self._strategy.pipeline_configs['p2p_cache_shape']
self.num_stages = self._hcg.get_pipe_parallel_world_size()
self.stage_id = self._hcg.get_stage_id()
self.pp_group = self._hcg.get_pipe_parallel_group()
self._virtual_pp_world_size = None
self._virtual_pp_rank = None
self._real_pp_world_size = self.num_stages
self._real_pp_rank = self.stage_id
p2p.initialize_p2p_groups(
hcg, self._using_cache, self._enable_partial_send_recv
)
self.global_rank = self._hcg.get_global_rank()
self.micro_batch_id = 0
self._compute_loss = True
logger.info(
"Pipeline Info -- num_stages: {}, stage_id: {}".format(
self.num_stages, self.stage_id
)
)
if self.use_model_parallel:
logger.info("start broadcast mp parameters")
broadcast_mp_parameters(self._layers, self._hcg)
if self.use_sharding_parallel:
logger.info("start broadcast sharding parameters")
broadcast_sharding_parameters(self._layers, self._hcg)
if self.use_data_parallel:
logger.info("start broadcast dp parameters")
broadcast_dp_parameters(self._layers, self._hcg)
def is_pipeline_first_stage(self, ignore_virtual=False):
if not ignore_virtual:
if self._virtual_pp_world_size is not None:
assert self._virtual_pp_rank is not None
if self._virtual_pp_rank != 0:
return False
assert self._real_pp_rank is not None
return self._real_pp_rank == 0
def is_pipeline_last_stage(self, ignore_virtual=False):
if not ignore_virtual:
if self._virtual_pp_world_size is not None:
assert self._virtual_pp_rank is not None
if self._virtual_pp_rank != (self._virtual_pp_world_size - 1):
return False
assert self._real_pp_rank is not None
assert self._real_pp_world_size is not None
return self._real_pp_rank == (self._real_pp_world_size - 1)
def set_virtual_pipeline_rank(self, rank):
self._virtual_pp_rank = rank
def forward_backward_pipeline(self, data, scaler=None):
# use the 1f1b scheduling strategy.
# this strategy is inspired by:
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/schedules.py
self.scaler = scaler
# store data for train
self.data = data
# store total loss of entire batch
self.total_loss = None
# store data id for micro_batch
self.micro_batch_id = 0
startup_steps = self.num_stages - self.stage_id - 1
startup_steps = min(startup_steps, self.accumulate_steps)
steady_steps = self.accumulate_steps - startup_steps
input_buffers = []
output_buffers = []
for step_id in range(startup_steps):
input_tensor = p2p.recv_forward(self.is_pipeline_first_stage())
output_tensor = self._forward_step(input_tensor)
p2p.send_forward(output_tensor, self.is_pipeline_last_stage())
input_buffers.append(input_tensor)
output_buffers.append(output_tensor)
if steady_steps > 0:
input_tensor = p2p.recv_forward(self.is_pipeline_first_stage())
for i in range(steady_steps):
last_iter = i == (steady_steps - 1)
output_tensor = self._forward_step(input_tensor)
output_tensor_grad = p2p.send_forward_recv_backward(
output_tensor, self.is_pipeline_last_stage()
)
input_buffers.append(input_tensor)
output_buffers.append(output_tensor)
input_tensor, output_tensor = input_buffers.pop(
0
), output_buffers.pop(0)
input_tensor_grad = self._backward_step(
input_tensor, output_tensor, output_tensor_grad
)
if last_iter:
input_tensor = None
p2p.send_backward(
input_tensor_grad, self.is_pipeline_first_stage()
)
else:
input_tensor = p2p.send_backward_recv_forward(
input_tensor_grad, self.is_pipeline_first_stage()
)
for i in range(startup_steps):
input_tensor = input_buffers.pop(0)
output_tensor = output_buffers.pop(0)
output_tensor_grad = p2p.recv_backward(
self.is_pipeline_last_stage()
)
input_tensor_grad = self._backward_step(
input_tensor, output_tensor, output_tensor_grad
)
p2p.send_backward(input_tensor_grad, self.is_pipeline_first_stage())
self._layers.allreduce_shared_weight_gradients()
with paddle.amp.auto_cast(enable=False):
train_loss = self._broadcast_final_loss()
return train_loss
def _prepare_training(self, data, optimizer, lr_scheduler):
# reset the virtual pp rank for each run
self.set_virtual_pipeline_rank(0)
assert isinstance(
optimizer, HybridParallelOptimizer
), 'optimizer should be HybridParallelOptimizer subclass.'
assert (
fluid.framework._dygraph_tracer()._has_grad
), 'Please enable the generation of gradients.'
if self.is_pipeline_first_stage(
ignore_virtual=True
) or self.is_pipeline_last_stage(ignore_virtual=True):
assert (
data is not None
), "For the first and the last stage, the data must be set."
else:
data = None
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self._layers.train()
return data
def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
data = self._prepare_training(data, optimizer, lr_scheduler)
# 1f1b scheduler for pipeline parallel
train_loss = self.forward_backward_pipeline(data, scaler)
# optimizer
with paddle.amp.auto_cast(enable=False):
self._optimizer_step()
return train_loss
def eval_batch(self, data, compute_loss=False):
# reset the virtual pp rank for each run
self.set_virtual_pipeline_rank(0)
self._layers.eval()
self._compute_loss = compute_loss
# save data for eval
self.data = data
# store data id for micro_batch
self.micro_batch_id = 0
# store total loss of entire batch
self.total_loss = None
startup_steps = self.num_stages - self.stage_id - 1
startup_steps = min(startup_steps, self.accumulate_steps)
steady_steps = self.accumulate_steps - startup_steps
input_buffers = []
output_buffers = []
for step_id in range(startup_steps):
input_tensor = p2p.recv_forward(self.is_pipeline_first_stage())
output_tensor = self._forward_step(input_tensor)
p2p.send_forward(output_tensor, self.is_pipeline_last_stage())
input_buffers.append(input_tensor)
output_buffers.append(output_tensor)
if steady_steps > 0:
input_tensor = p2p.recv_forward(self.is_pipeline_first_stage())
for i in range(steady_steps):
last_iter = i == (steady_steps - 1)
output_tensor = self._forward_step(input_tensor)
p2p.send_forward(output_tensor, self.is_pipeline_last_stage())
input_buffers.append(input_tensor)
output_buffers.append(output_tensor)
if not last_iter:
input_tensor = p2p.recv_forward(self.is_pipeline_first_stage())
if self._compute_loss:
self.train_loss = self._broadcast_final_loss()
else:
self.train_loss = output_buffers
return self.train_loss
def _forward_step(self, input_tensor, chunk_id=None):
if self.is_pipeline_first_stage():
input_tensor = self._load_micro_batch(self.micro_batch_id)
assert chunk_id is None or isinstance(chunk_id, int)
output_tensor = self._layers.forward(input_tensor, chunk_id=chunk_id)
if self.is_pipeline_last_stage():
# train calculate loss for train
if self._compute_loss:
assert (
self._layers._loss_fn is not None
), "loss function should exist to compute loss"
labels = self._load_micro_batch(self.micro_batch_id)
output_tensor = self._layers._loss_fn(output_tensor, labels)
assert isinstance(
output_tensor, (paddle.Tensor, core.eager.Tensor)
), "Currently, loss_fn should obtain Paddle.Tensor dtype"
with paddle.amp.auto_cast(enable=False):
if self.accumulate_steps > 1:
output_tensor = output_tensor / self.accumulate_steps
if self.total_loss is None:
self.total_loss = paddle.zeros_like(output_tensor)
self.total_loss += output_tensor.detach()
if self.is_pipeline_first_stage() or self.is_pipeline_last_stage():
# Only increase micro batch id at virtual first/last pp stage.
# The micro batch id is used to load data, therefore, only increase it when load data.
self.micro_batch_id += 1
return output_tensor
def _backward_step(self, input_tensor, output_tensor, output_tensor_grad):
with paddle.amp.auto_cast(enable=False):
if self.is_pipeline_last_stage():
assert output_tensor_grad is None
if self.scaler:
paddle.autograd.backward(self.scaler.scale(output_tensor))
else:
paddle.autograd.backward(output_tensor)
else:
if isinstance(output_tensor, tuple):
outputs = [t for t in output_tensor if not t.stop_gradient]
assert len(outputs) == len(output_tensor_grad)
paddle.autograd.backward(
tensors=outputs,
grad_tensors=[t for t in output_tensor_grad],
)
else:
paddle.autograd.backward(
tensors=[output_tensor],
grad_tensors=[output_tensor_grad],
)
input_tensor_grad = None
if input_tensor is not None:
if isinstance(input_tensor, tuple):
input_tensor_grad = tuple(
[t.grad for t in input_tensor if not t.stop_gradient]
)
else:
input_tensor_grad = input_tensor.grad
return input_tensor_grad
def _check_data_vaild(self, data):
batch_size = data.shape[0]
assert self.micro_batch_size * self.accumulate_steps == batch_size, (
"batch_size needs to be divisible by micro_batch_size. Currently, "
"batch_size = %d, micro_batch_size = %d, accumulate_steps = %d."
% (batch_size, self.micro_batch_size, self.accumulate_steps)
)
def _load_micro_batch_impl(self, inputs, cache_id):
begin = cache_id * self.micro_batch_size
end = begin + self.micro_batch_size
if isinstance(inputs, tuple):
output = []
for data in inputs:
if isinstance(data, list):
assert (
len(data) == self.accumulate_steps
), "length of data should be %d, but it is %d" % (
self.accumulate_steps,
len(data),
)
output.append(data[cache_id].detach())
else:
self._check_data_vaild(data)
output.append(data[begin:end, :].detach())
return tuple(output)
elif isinstance(inputs, list):
assert (
len(inputs) == self.accumulate_steps
), "length of data should be %d, but it is %d" % (
self.accumulate_steps,
len(inputs),
)
return inputs[cache_id].detach()
else:
self._check_data_vaild(inputs)
return inputs[begin:end, :].detach()
def _load_micro_batch(self, cache_id):
inputs = self.data
if self.is_pipeline_first_stage():
assert len(inputs) == 2, "length of input should be 2"
return self._load_micro_batch_impl(inputs[0], cache_id)
elif self.is_pipeline_last_stage():
assert len(inputs) == 2, "length of input should be 2"
return self._load_micro_batch_impl(inputs[1], cache_id)
else:
inputs = None
def _broadcast_final_loss(self):
# Since the last backward run in interleave will set the virtual rank to 0,
# here we need to check last stage ignoring virtual stage.
if self.is_pipeline_last_stage(ignore_virtual=True):
assert (
self.total_loss is not None
), "train_batch() in last stage should obtain vaild loss"
loss = self.total_loss.detach()
is_fp32 = (
paddle.to_tensor(1)
if loss.dtype == paddle.float32
else paddle.to_tensor(0)
)
paddle.distributed.broadcast(
is_fp32, src=self.global_rank, sync_op=True, group=self.pp_group
)
paddle.distributed.broadcast(
loss, src=self.global_rank, sync_op=True, group=self.pp_group
)
else:
is_fp32 = paddle.to_tensor(1)
paddle.distributed.broadcast(
is_fp32,
src=self._hcg.get_rank_from_stage(self.num_stages - 1),
sync_op=True,
group=self.pp_group,
)
loss = (
paddle.zeros(shape=[1], dtype="float32")
if is_fp32.numpy()[0]
else paddle.zeros(shape=[1], dtype="float16")
)
paddle.distributed.broadcast(
loss,
src=self._hcg.get_rank_from_stage(self.num_stages - 1),
sync_op=True,
group=self.pp_group,
)
return loss
def _optimizer_step(self):
if self.scaler:
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.optimizer.step()
self.optimizer.clear_grad()
if self.lr_scheduler:
self.lr_scheduler.step()
class PipelineParallelWithInterleave(PipelineParallel):
# pipeline parallel with interleave scheduler
def __init__(self, layers, hcg, strategy):
super(PipelineParallelWithInterleave, self).__init__(
layers=layers, hcg=hcg, strategy=strategy
)
assert layers.get_num_virtual_stages() > 1
assert (
framework.in_dygraph_mode()
), "virtual pipeline stage with interleave only support eager dygraph mode"
# setup for interleave scheduler
self.num_model_chunks = layers.get_num_virtual_stages()
self.model_chunks = layers.get_model_chunks()
assert self.model_chunks is not None
assert len(self.model_chunks) == self.num_model_chunks
self._virtual_pp_world_size = self.num_model_chunks
self._virtual_pp_rank = 0
def _get_virtual_pp_rank(self, micro_step, forward):
virtual_pp_stage = micro_step % (
self.num_stages * self.num_model_chunks
)
virtual_pp_stage = virtual_pp_stage // self.num_stages
if not forward:
virtual_pp_stage = self.num_model_chunks - virtual_pp_stage - 1
return virtual_pp_stage
def _forward_step_helper(self, micro_step):
virtual_pp_rank = self._get_virtual_pp_rank(micro_step, forward=True)
self.set_virtual_pipeline_rank(virtual_pp_rank)
# some checkers
assert hasattr(self, 'input_tensors')
assert hasattr(self, 'output_tensors')
if not self._forward_only:
assert hasattr(self, 'output_tensor_grads')
if self.is_pipeline_first_stage():
if len(self.input_tensors[virtual_pp_rank]) == len(
self.output_tensors[virtual_pp_rank]
):
self.input_tensors[virtual_pp_rank].append(None)
input_tensor = self.input_tensors[virtual_pp_rank][-1]
output_tensor = self._forward_step(input_tensor, virtual_pp_rank)
self.output_tensors[virtual_pp_rank].append(output_tensor)
if self._forward_only:
# no need to store tensor for backward
self.input_tensors[virtual_pp_rank].pop()
self.output_tensors[virtual_pp_rank].pop()
return output_tensor
def _backward_step_helper(self, micro_step):
virtual_pp_rank = self._get_virtual_pp_rank(micro_step, forward=False)
self.set_virtual_pipeline_rank(virtual_pp_rank)
# some checkers
assert hasattr(self, 'input_tensors')
assert hasattr(self, 'output_tensors')
assert hasattr(self, 'output_tensor_grads')
if self.is_pipeline_last_stage():
if len(self.output_tensor_grads[virtual_pp_rank]) == 0:
self.output_tensor_grads[virtual_pp_rank].append(None)
input_tensor = self.input_tensors[virtual_pp_rank].pop(0)
output_tensor = self.output_tensors[virtual_pp_rank].pop(0)
output_tensor_grad = self.output_tensor_grads[virtual_pp_rank].pop(0)
input_tensor_grad = self._backward_step(
input_tensor, output_tensor, output_tensor_grad
)
return input_tensor_grad
def forward_backward_pipeline(
self, data, scaler, forward_only=False, compute_loss=True
):
# use interleave scheduling strategy.
# this strategy is inspired by:
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/schedules.py
if not compute_loss:
assert (
not forward_only
), "compute_loss can only be set to False when forward_only is set to True"
# init some attributes for this batch run
self.scaler = scaler
self.data = data
self.total_loss = None
self.micro_batch_id = 0
self._forward_only = forward_only
# init some data buffers for interleave scheduler
self.input_tensors = [[] for _ in range(self.num_model_chunks)]
self.output_tensors = [[] for _ in range(self.num_model_chunks)]
self.output_tensor_grads = [[] for _ in range(self.num_model_chunks)]
num_steps = self.accumulate_steps * self.num_model_chunks
all_startup_steps = False
if forward_only:
# If only forward, since there is no backward during running, all steps are startup steps
startup_steps = num_steps
else:
if self.accumulate_steps == self.num_stages:
startup_steps = num_steps
all_startup_steps = True
else:
startup_steps = (self.num_stages - self.stage_id - 1) * 2
startup_steps += (self.num_model_chunks - 1) * self.num_stages
startup_steps = min(startup_steps, num_steps)
steady_steps = num_steps - startup_steps
self.set_virtual_pipeline_rank(0)
self.input_tensors[0].append(
p2p.recv_forward(self.is_pipeline_first_stage(), sync_recv=False)
)
# run startup steps
for micro_step in range(startup_steps):
output_tensor = self._forward_step_helper(micro_step)
# determine whether recv forward tensor or not
next_virtual_pp_rank = self._get_virtual_pp_rank(
micro_step + 1, forward=True
)
recv_prev = True
if self.is_pipeline_first_stage(ignore_virtual=True):
if next_virtual_pp_rank == 0:
# next chunk is the first chunk, not need to pre recv an input tensor
recv_prev = False
# last micro step, no next run
if micro_step == (num_steps - 1):
recv_prev = False
# last stage shouldn't send tensor to downstream
if self.is_pipeline_last_stage():
output_tensor = None
if (
micro_step == (startup_steps - 1)
and not forward_only
and not all_startup_steps
):
input_tensor_grad = None
recv_next = True
if self.is_pipeline_last_stage(ignore_virtual=True):
recv_next = False
# the last startup step needs on four direction comm to set up for steady 1f1b
(
input_tensor,
output_tensor_grad,
) = p2p.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
)
self.output_tensor_grads[self.num_model_chunks - 1].append(
output_tensor_grad
)
else:
input_tensor = p2p.send_forward_recv_forward(
output_tensor, recv_prev=recv_prev
)
self.input_tensors[next_virtual_pp_rank].append(input_tensor)
# run 1f1b steady steps
for micro_step in range(steady_steps):
# forward
forward_micro_step_id = micro_step + startup_steps
output_tensor = self._forward_step_helper(forward_micro_step_id)
# backward
backward_micro_step_id = micro_step
input_tensor_grad = self._backward_step_helper(
backward_micro_step_id
)
# four directions comm
# send output tensor to downstream
# send input tensor grad to upstream
# recv input tensor from upstream
# recv output tensor grad from downstream
# last stage doesn't send rst to downstream
forward_virtual_pp_rank = self._get_virtual_pp_rank(
forward_micro_step_id, forward=True
)
self.set_virtual_pipeline_rank(forward_virtual_pp_rank)
if self.is_pipeline_last_stage():
output_tensor = None
# first stage doesn't send grad to upstream
backward_virtual_pp_rank = self._get_virtual_pp_rank(
backward_micro_step_id, forward=False
)
self.set_virtual_pipeline_rank(backward_virtual_pp_rank)
if self.is_pipeline_first_stage():
input_tensor_grad = None
# determine whether to recv input tensor from upstream
recv_prev = True
if self.is_pipeline_first_stage(ignore_virtual=True):
next_forward_virtual_pp_rank = self._get_virtual_pp_rank(
forward_micro_step_id - (self.num_stages - 1), forward=True
)
if next_forward_virtual_pp_rank == (self.num_model_chunks - 1):
# first pp stage and first virtual stage
recv_prev = False
next_forward_virtual_pp_rank += 1
else:
next_forward_virtual_pp_rank = self._get_virtual_pp_rank(
forward_micro_step_id + 1, forward=True
)
# last iteration doesn't need recv from upstream
if micro_step == (steady_steps - 1):
recv_prev = False
# determine whether to recv grad from downstream
recv_next = True
if self.is_pipeline_last_stage(ignore_virtual=True):
next_backward_virtual_pp_rank = self._get_virtual_pp_rank(
backward_micro_step_id - (self.num_stages - 1),
forward=False,
)
if next_backward_virtual_pp_rank == 0:
# last pp stage and last virtual stage
recv_next = False
next_backward_virtual_pp_rank -= 1
else:
next_backward_virtual_pp_rank = self._get_virtual_pp_rank(
backward_micro_step_id + 1, forward=False
)
(
input_tensor,
output_tensor_grad,
) = p2p.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
)
if recv_prev:
self.input_tensors[next_forward_virtual_pp_rank].append(
input_tensor
)
if recv_next:
self.output_tensor_grads[next_backward_virtual_pp_rank].append(
output_tensor_grad
)
# remaining backward steps
if not forward_only:
if all_startup_steps:
self.output_tensor_grads[self.num_model_chunks - 1].append(
p2p.recv_backward(
self.is_pipeline_last_stage(), sync_recv=False
)
)
for micro_step in range(steady_steps, num_steps):
# cooldown loop
input_tensor_grad = self._backward_step_helper(micro_step)
next_backward_virtual_pp_rank = self._get_virtual_pp_rank(
micro_step + 1, forward=False
)
recv_next = True
if self.is_pipeline_last_stage(ignore_virtual=True):
if next_backward_virtual_pp_rank == (
self.num_model_chunks - 1
):
recv_next = False
if micro_step == (num_steps - 1):
recv_next = False
self.output_tensor_grads[next_backward_virtual_pp_rank].append(
p2p.send_backward_recv_backward(
input_tensor_grad, recv_next=recv_next
)
)
self._layers.allreduce_shared_weight_gradients()
if compute_loss:
# return loss if compute loss
with paddle.amp.auto_cast(enable=False):
train_loss = self._broadcast_final_loss()
else:
# else just return all intermediate output tensor for all micro steps
train_loss = self.output_tensors
return train_loss
def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
data = self._prepare_training(data, optimizer, lr_scheduler)
# interleave scheduler for pipeline parallel
train_loss = self.forward_backward_pipeline(data, scaler)
# optimizer
with paddle.amp.auto_cast(enable=False):
self._optimizer_step()
return train_loss
def eval_batch(self, data, compute_loss=False):
# reset the virtual pp rank for each run
self.set_virtual_pipeline_rank(0)
self._layers.eval()
self._compute_loss = compute_loss
return self.forward_backward_pipeline(data, None, forward_only=True)