Skip to content

P
Paddle

wk1234123 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 4
Star 0
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 58c3cc8c 编写于 作者: M mapingshuo 提交者： GitHub
浏览文件
操作

Revert "fix strategy example (#26856)" 

This reverts commit 9e4fe923.
上级 9e4fe923
隐藏空白更改
内联 并排
Showing with 44 addition and 68 deletion
python/paddle/distributed/fleet/base/distributed_strategy.py
浏览文件 @ 58c3cc8c
......@@ -118,7 +118,7 @@ class DistributedStrategy(object):
strategy = fleet.DistributedStrategy()
strategy.dgc = True
strategy.recompute = True
strategy.recompute_configs = {"checkpoints": ["x"]}
strategy.recompute_configs = {"checkpoint": ["x"]}
strategy.save_to_prototxt("dist_strategy.prototxt")
"""
with open(output, "w") as fout:
......@@ -133,7 +133,7 @@ class DistributedStrategy(object):
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.load_from_prototxt("dist_strategy.prototxt")
strategy.load_from_prototxt("dist_strategy.protoxt")
"""
with open(pb_file, 'r') as f:
self.strategy = google.protobuf.text_format.Merge(
......@@ -147,7 +147,6 @@ class DistributedStrategy(object):
Examples:
.. code-block:: python
import paddle
exe_strategy = paddle.fluid.ExecutionStrategy()
exe_strategy.num_threads = 10
exe_strategy.num_iteration_per_drop_scope = 10
......@@ -180,7 +179,6 @@ class DistributedStrategy(object):
Examples:
.. code-block:: python
import paddle
build_strategy = paddle.fluid.BuildStrategy()
build_strategy.enable_sequential_execution = True
build_strategy.fuse_elewise_add_act_ops = True
......@@ -254,19 +252,14 @@ class DistributedStrategy(object):
a dict.
**Notes**:
k_step(int): number of local optimization updates before communication
max_merge_var_num(int): maximum number of merged gradients before communication
send_queue_size(int): a buffer size of worker communication
independent_recv_thread(bool): if we are using independent recv thread for communication
thread_pool_size(int): number of thread pool
send_wait_times(int): waiting time for sending gradients
runtime_split_send_recv(bool): if we are using Tensor split for send and recv during runtime
**Detailed arguments for a_sync_configs**
**k_step**: number of local optimization updates before communication
**max_merge_var_num**: maximum number of merged gradients before communication
**send_queue_size**: a buffer size of worker communication
**independent_recv_thread**: if we are using independent recv thread for communication
**thread_pool_size**: number of thread pool
**send_wait_times**: waiting time for sending gradients
**runtime_split_send_recv**: if we are using Tensor split for send and recv during runtime
Examples:
.. code-block:: python
......@@ -277,12 +270,11 @@ class DistributedStrategy(object):
strategy = fleet.DistributedStrategy()
strategy.a_sync = True # by default this is True
configs = {"k_steps": 1024, "send_queue_size": 32}
configs = {"k_step": 10000, "send_queue_size": 32}
strategy.a_sync_configs = configs
# code block for defining loss and local optimizer
# sgd = fleet.distributed_optimizer(optimizer, strategy)
"""
return get_msg_dict(self.strategy.a_sync_configs)
......@@ -322,21 +314,14 @@ class DistributedStrategy(object):
settings that can be configured through a dict.
**Notes**:
init_loss_scaling(float): The initial loss scaling factor. Default 32768.
use_dynamic_loss_scaling(bool): Whether to use dynamic loss scaling. Default True.
incr_every_n_steps(int): Increases loss scaling every n consecutive steps with finite gradients. Default 1000.
decr_every_n_nan_or_inf(int): Decreases loss scaling every n accumulated steps with nan or inf gradients. Default 2.
incr_ratio(float): The multiplier to use when increasing the loss scaling. Default 2.0.
decr_ratio(float): The less-than-one-multiplier to use when decreasing the loss scaling. Default 0.5.
custom_white_list(list[str]): Users' custom white list which always execution fp16.
custom_black_list(list[str]): Users' custom black list which forbidden execution fp16.
**init_loss_scaling(float)**: The initial loss scaling factor. Default 32768.
**use_dynamic_loss_scaling(bool)**: Whether to use dynamic loss scaling. Default True.
**incr_every_n_steps(int)**: Increases loss scaling every n consecutive steps with finite gradients. Default 1000.
**decr_every_n_nan_or_inf(int)**: Decreases loss scaling every n accumulated steps with nan or inf gradients. Default 2.
**incr_ratio(float)**: The multiplier to use when increasing the loss scaling. Default 2.0.
**decr_ratio(float)**: The less-than-one-multiplier to use when decreasing the loss scaling. Default 0.5.
**custom_white_list(list[str])**: Users' custom white list which always execution fp16.
**custom_black_list(list[str])**: Users' custom black list which forbidden execution fp16.
Examples:
.. code-block:: python
......@@ -568,7 +553,7 @@ class DistributedStrategy(object):
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.recompute = True
strategy.recompute_configs = {"checkpoints": ["x", "y"]}
strategy.recompute_configs = {"checkpionts": ["x", "y"]}
"""
return get_msg_dict(self.strategy.recompute_configs)
......@@ -618,7 +603,6 @@ class DistributedStrategy(object):
**Notes**:
**Detailed arguments for pipeline_configs**
**micro_batch**: the number of small batches in each user defined batch
Examples:
......@@ -642,10 +626,10 @@ class DistributedStrategy(object):
@property
def localsgd(self):
"""
Indicating whether we are using Local SGD training. Default Value: False
For more details, please refer to
`Don't Use Large Mini-Batches, Use Local SGD <https://arxiv.org/pdf/1808.07217.pdf>`_.
Indicating whether we are using Local SGD training. For more details, please refer to
[Don't Use Large Mini-Batches, Use Local SGD](https://arxiv.org/pdf/1808.07217.pdf),
Default Value: False
Examples:
.. code-block:: python
......@@ -671,12 +655,13 @@ class DistributedStrategy(object):
setting that can be configured through a dict.
**Notes**:
k_steps(int) The local steps for training before parameter synchronization. Default 1.
If strategy.auto is set True, the local steps will be calculated automatically during training.
The algorithm is referenced in this paper:
`Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD <https://arxiv.org/pdf/1810.08313.pdf>`_.
In this case, k_steps indicates the first local steps which is suggested setting to 1.
**k_steps(int)**: The local steps for training before parameter
synchronization. Default 1. If strategy.auto is set True, the
local steps will be calculated automatically during training.
The algorithm is referenced in this paper:
[Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD](https://arxiv.org/pdf/1810.08313.pdf).
In this case, k_steps indicates the first local steps which
is suggested setting to 1.
Examples:
.. code-block:: python
......@@ -727,16 +712,14 @@ class DistributedStrategy(object):
settings that can be configured through a dict.
**Notes**:
rampup_begin_step(int): The beginning step from which gradient compression is implemented. Default 0.
rampup_step(int): Time steps used in sparsity warm-up periods. Default is 1. \
For example, if the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 100, \
it will use 0.75 at 0~19 steps, and 0.9375 at 20~39 steps, and so on. And when reach sparsity array \
ends, it will use 0.999 then and after.
sparsity(list[float]): Get top important element from gradient tensor, the ratio is (1 - sparsity). \
Default is [0.999]. For example, if the sparsity is [0.99, 0.999], the top [1%, 0.1%] important \
element will be transmitted.
**rampup_begin_step(int)**: The beginning step from which gradient compression is implemented. Default 0.
**rampup_step(int)**: Time steps used in sparsity warm-up periods. Default is 1.
For example, if the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 100,
it will use 0.75 at 0~19 steps, and 0.9375 at 20~39 steps, and so on. And when reach sparsity array
ends, it will use 0.999 then and after.
**sparsity(list[float])**: Get top important element from gradient tensor, the ratio is (1 - sparsity).
Default is [0.999]. For example, if the sparsity is [0.99, 0.999], the top [1%, 0.1%] important
element will be transmitted.
Examples:
.. code-block:: python
......@@ -766,8 +749,7 @@ class DistributedStrategy(object):
to model parameters.
Examples:
.. code-block:: python
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.gradient_merge = True
......@@ -786,15 +768,11 @@ class DistributedStrategy(object):
def gradient_merge_configs(self):
"""
the key-value configs of distribute_strategy
**Note**:
k_steps(int): the update period of the parameters.
avg(bool): whether to average the gradients of each mini-batch, the default value is `True`
Examples:
.. code-block:: python
Keys:
k_steps (int): the update period of the parameters
avg (bool): whether to average the gradients of each mini-batch,
the default value is `True`
Example:
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.gradient_merge = True
......@@ -848,7 +826,6 @@ class DistributedStrategy(object):
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.lars = True
......@@ -905,7 +882,6 @@ class DistributedStrategy(object):
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.lamb = True
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或