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compiler.py
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compiler.py
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# Copyright (c) 2018 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.
import multiprocessing
import os
import sys
import warnings
from . import framework
from .framework import _get_paddle_place, _get_paddle_place_list
from .framework import cuda_places, cpu_places, xpu_places
from . import core
__all__ = [
'CompiledProgram',
'ExecutionStrategy',
'BuildStrategy',
'IpuCompiledProgram',
'IpuStrategy',
]
ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
InferNativeConfig = core.NativeConfig
InferAnalysisConfig = core.AnalysisConfig
DeviceType = core.DeviceType
def _place_obj(place):
p = core.Place()
p.set_place(place)
return p
def _is_pserver_mode(main_program):
main = main_program if main_program else framework.default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
def _has_backward_op(graph):
for node in graph.nodes():
if (
node.is_op()
and node.op() is not None
and node.op().type().endswith("_grad")
):
return True
return False
def _prune_feed_ops(program):
# prune the feed ops in the program.
pop_idx = []
for i, op in enumerate(program.global_block().ops):
if op.type == "feed":
pop_idx.append(i)
for index in pop_idx[::-1]:
program.global_block()._remove_op(index)
def _has_optimize_op(block):
for op in block.ops:
op_maker = core.op_proto_and_checker_maker
optimize = core.op_proto_and_checker_maker.OpRole.Optimize
if op_maker.kOpRoleVarAttrName() in op.attr_names and int(
op.all_attrs()[op_maker.kOpRoleAttrName()]
) == int(optimize):
return True
return False
def _should_broadcast_or_not_exists(program, var_name):
block = program.global_block()
var = block.vars.get(var_name, None)
if var is None:
return True
is_distributed = getattr(var, '_is_distributed', False) or getattr(
var, 'is_distributed', False
)
return not is_distributed
class CompiledProgram:
"""
:api_attr: Static Graph
The CompiledProgram is used to transform a program or graph for
various optimizations according to the configuration of build_strategy,
for example, the operators' fusion in the computation graph, memory
optimization during the execution of the computation graph, etc.
For more information about build_strategy, please refer to
:code:`paddle.static.BuildStrategy`.
Args:
program_or_graph (Graph|Program): This argument is the Program or Graph
being executed.
build_strategy(BuildStrategy): This argument is used to compile the
program or graph with the specified options, such as operators' fusion
in the computational graph and memory optimization during the execution
of the computational graph. For more information about build_strategy,
please refer to :code:`paddle.static.BuildStrategy`. The default is None.
Returns:
CompiledProgram
Example:
.. code-block:: python
import numpy
import paddle
import paddle.static as static
paddle.enable_static()
place = paddle.CUDAPlace(0) # paddle.CPUPlace()
exe = static.Executor(place)
data = static.data(name='X', shape=[None, 1], dtype='float32')
hidden = static.nn.fc(x=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(static.default_startup_program())
compiled_prog = static.CompiledProgram(
static.default_main_program())
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = exe.run(compiled_prog,
feed={"X": x},
fetch_list=[loss.name])
"""
def __init__(self, program_or_graph, build_strategy=None):
if isinstance(program_or_graph, core.Graph):
self._graph = program_or_graph
# don't not create a new program here.
self._program = None
elif isinstance(program_or_graph, framework.Program):
_prune_feed_ops(program_or_graph)
self._graph = core.Graph(program_or_graph.desc)
self._program = program_or_graph
else:
raise TypeError(
"The type of program_to_graph parameter is wrong, expected Graph or Program, but received %s"
% type(program_or_graph)
)
self._scope = None
self._place = None
self._executor = None
self._compiled = False
self._is_inference = False
self._share_vars_from = None
self._places = None
self._build_strategy = build_strategy
self._exec_strategy = None
def _with_inference_optimize(self, config):
"""Add inference optimize
Args:
config: instance of `NativeConfig` or `AnalysisConfig` to create predictor
Returns:
self
"""
assert (
not self._is_inference
), "Already compiled with inference, cannot be recompiled."
assert any(
[
isinstance(config, InferNativeConfig),
isinstance(config, InferAnalysisConfig),
]
)
self._is_inference = True
self._infer_config = config
return self
def _with_distributed(self):
raise NotImplementedError(
"Subclass of CompiledProgram should implement _with_distributed method."
)
def _compile_data_parallel(self, places, use_device, scope=None):
if self._share_vars_from:
if scope:
sys.stderr.write("share_vars_from is set, scope is ignored.\n")
if self._share_vars_from._executor is None:
raise ValueError(
"The shared Program is not compiled and executed, so there is no "
"variables to share."
)
self._local_scopes = self._share_vars_from._executor.local_scopes()
else:
assert scope is not None, ""
self._local_scopes = []
assert isinstance(places, tuple) or isinstance(
places, list
), "Currently , The places type can only be list or tuple, but the input type is {}.".format(
type(places)
)
if self._build_strategy is None:
self._build_strategy = BuildStrategy()
self._build_strategy.is_distribution = _is_pserver_mode(self._program)
if self._exec_strategy is None:
self._exec_strategy = ExecutionStrategy()
self._exec_strategy._use_device = use_device
if self._exec_strategy.num_threads == 0:
if self._exec_strategy._use_device == DeviceType.CUDA:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
self._exec_strategy.num_threads = len(places) * 4
elif self._exec_strategy._use_device == DeviceType.XPU:
# Currently only single thread is supported in Kunlun XPU.
self._exec_strategy.num_threads = 1
else:
self._exec_strategy.num_threads = len(places) * 2
if (
"FLAGS_use_cinn" in core.globals()
and core.globals()["FLAGS_use_cinn"]
and self._exec_strategy.num_threads != 1
):
warnings.warn(
"At present, when CINN is turned on, each process can "
"only contain one thread, so reset the number of threads to 1 here."
)
self._exec_strategy.num_threads = 1
# TODO(wuyi): trainer endpoints should be passed in through
# build_strategy, not program.xxx.
# TODO(gongwb): let user to set them once.
if (
self._program
and self._build_strategy.num_trainers > 1
and self._program._trainers_endpoints
):
tps = self._program._trainers_endpoints
assert self._build_strategy.num_trainers == len(
tps
), "The trainer numbers is not equal to endpoint numbers."
self._build_strategy.trainers_endpoints = tps
if self._program:
self._build_strategy.nccl_comm_num = self._program._nccl_comm_num
self._build_strategy.use_hierarchical_allreduce = (
self._program._use_hierarchical_allreduce
)
self._build_strategy.hierarchical_allreduce_inter_nranks = (
self._program._hierarchical_allreduce_inter_nranks
)
if self._build_strategy.sync_batch_norm:
self._build_strategy.enable_sequential_execution = True
if self._program is not None and self._program._enable_dgc:
assert (
self._exec_strategy._use_device == DeviceType.CUDA
), "DGC only used under CUDA environment."
assert (
self._build_strategy.num_trainers * len(places) > 1
), "DGC is not avaliable for single card training."
assert (
self._build_strategy.reduce_strategy
== BuildStrategy.ReduceStrategy.AllReduce
), "DGC \
only can be used for AllReduce BuildStrategy."
# DGC doesn't support fuse for now, close fuse.
self._build_strategy.fuse_all_reduce_ops = False
self._persistable_vars = []
for node in self._graph.nodes():
if (
node.is_var()
and node.var() is not None
and node.var().persistable()
and node.var().type() != core.VarDesc.VarType.RAW
):
name = node.name()
if (
self._program is not None
and _should_broadcast_or_not_exists(self._program, name)
):
self._persistable_vars.append(node.name())
places = list(map(_place_obj, places))
# ParallelExecutor would broadcast all the parameters during initializing.
# The parameters of each process should be in the same ordered for the data-parallelism
# distributed training to keep the broadcast correct.
self._persistable_vars = list(set(self._persistable_vars))
self._persistable_vars.sort()
if core.is_cuda_graph_capturing():
raise RuntimeError(
"CUDA Graph is not allowed to capture when running the first batch."
)
return core.ParallelExecutor(
places,
self._persistable_vars,
'',
self._scope,
self._local_scopes,
self._exec_strategy,
self._build_strategy,
self._graph,
)
def _compile_inference(self):
return core.create_paddle_predictor(self._infer_config)
def _compile(self, scope, place):
"""Compile the program based on the configs.
Args:
scope: The variables (resources) that are associated with
this compiled program.
place: The location that the compiled program will be run on.
Returns:
self
"""
if self._compiled:
if scope and self._scope != scope:
raise ValueError("Cannot compile program with different scope.")
if place and not self._place._equals(place):
raise ValueError("Cannot compile program with different place.")
return self
self._compiled = True
self._scope = scope
self._place = place
if self._is_inference:
self._executor = self._compile_inference()
else:
self._places = [self._place]
if isinstance(self._place, core.CUDAPlace):
use_device = DeviceType.CUDA
elif isinstance(self._place, core.XPUPlace):
use_device = DeviceType.XPU
else:
use_device = DeviceType.CPU
self._executor = self._compile_data_parallel(
use_device=use_device, scope=self._scope, places=self._places
)
return self
def _get_places(self, place, place_list):
has_set_place = place_list is not None
if has_set_place:
for p in place_list:
assert (
p._type() == place._type()
), "Place type not match. You may set wrong type of places."
else:
if isinstance(place, core.CUDAPlace):
place_list = cuda_places()
elif isinstance(place, core.XPUPlace):
place_list = xpu_places()
else:
place_list = cpu_places()
assert place_list, "No places for execution."
return place_list
class IpuDynamicPatcher:
"""
Patcher for IPU dynamic2static support.
"""
patcher_cache = []
def __init__(self):
pass
@staticmethod
def convert_concrete_program(
ipu_strategy, concrete_program, class_instance=None
):
"""
Convert the ConcreteProgram to IPUConcreteProgram.
"""
from ..fluid.dygraph.base import switch_to_static_graph
from ..fluid import backward
from ..fluid.framework import device_guard
import paddle
inputs = concrete_program.inputs
outputs = concrete_program.outputs
startup_program = concrete_program.startup_program
scope = paddle.static.global_scope()
@switch_to_static_graph
def append_backward_desc():
program = concrete_program.main_program
# backward with optimizer to add backward graph to program
backward.gradients_with_optimizer(program, ipu_strategy._optimizer)
# initialize backward parameters
exe = paddle.static.Executor(paddle.CPUPlace())
startup_program = paddle.static.default_startup_program()
exe.run(startup_program)
return program
if ipu_strategy.enable_fp16:
class_instance.to(dtype="float16")
# copy the bias and filters
for param_or_buffer in concrete_program.parameters:
param_or_buffer_tensor = scope.var(
param_or_buffer.name
).get_tensor()
src_tensor = param_or_buffer.value().get_tensor()
param_or_buffer_tensor._share_data_with(src_tensor)
# TODO(czr): feed and fetch list needs to consider more type
if class_instance:
feed_list = [elem.name for elem in inputs[1:] if elem is not None]
else:
feed_list = [elem.name for elem in inputs if elem is not None]
fetch_list = [elem.name for elem in outputs]
if ipu_strategy.is_training:
concrete_program.main_program = append_backward_desc()
# copy optimizer parameters
optimizer = ipu_strategy._optimizer
for k, v in optimizer._accumulators.items():
for param_name, var_tmp in v.items():
var = optimizer.helper.create_global_variable(
name=var_tmp.name,
persistable=True,
dtype=var_tmp.dtype,
type=var_tmp.type,
shape=var_tmp.shape,
belong_to_optimizer=True,
)
device = optimizer._get_device_for_param(param_name)
with device_guard(device):
optimizer.helper.set_variable_initializer(
var,
initializer=paddle.nn.initializer.Constant(
value=0.0
),
)
param_or_lr_tensor = scope.find_var(
var_tmp.name
).get_tensor()
optim_tensor = var.value().get_tensor()
param_or_lr_tensor._share_data_with(optim_tensor)
optimizer._accumulators[k][param_name] = var
@switch_to_static_graph
def func_compile():
if ipu_strategy.enable_fp16:
amp_list = paddle.static.amp.CustomOpLists()
amp_list.unsupported_list = {"cumsum"}
to_fp16_var_names = paddle.static.amp.cast_model_to_fp16(
concrete_program.main_program,
amp_list,
use_fp16_guard=False,
)
paddle.static.amp.cast_parameters_to_fp16(
paddle.CPUPlace(),
concrete_program.main_program,
to_fp16_var_names=to_fp16_var_names,
)
program = IpuCompiledProgram(
concrete_program.main_program,
ipu_strategy=ipu_strategy,
scope=scope,
).compile(feed_list, fetch_list)
return program
main_program = func_compile()
concrete_program.main_program = main_program
return concrete_program
@staticmethod
def patch_program_cache(ipu_strategy):
"""Monkey patch ProgramCache discriptor to support dynamic2static in IPU.
Args:
ipu_strategy: The ipu_strategy used in dynamic graph.
Returns:
None
"""
from paddle.jit.dy2static.program_translator import (
CacheKey,
ProgramCache,
MAX_TRACED_PROGRAM_COUNT,
)
from paddle.jit.dy2static import logging_utils
from paddle.jit.dy2static.partial_program import (
partial_program_from,
)
old_getter = ProgramCache.__getitem__
def patch_getter(self, item):
if not isinstance(item, CacheKey):
raise ValueError(
'type(item) should be CacheKey, but received %s'
% type(item).__name__
)
item_id = hash(item)
self._recent_key = item_id
if item_id not in self._caches or ipu_strategy.need_compile:
if item_id in self._caches:
logging_utils.warn(
"ipu_strategy chances detected. Please sync weights."
)
if self._caches and not ipu_strategy.need_compile:
logging_utils.warn(
"dynamic2static on IPU doesn't support mutiple caches. Please make sure"
"dynamic inputs is not used."
)
concrete_program, _ = self._build_once(item)
concrete_program = IpuDynamicPatcher.convert_concrete_program(
ipu_strategy, concrete_program, item.class_instance
)
self._caches[item_id] = (
concrete_program,
partial_program_from(
concrete_program, item.class_instance is not None
),
)
# Note: raise warnings if number of traced program is more than `max_tracing_count`
current_tracing_count = len(self._caches)
if current_tracing_count > MAX_TRACED_PROGRAM_COUNT:
logging_utils.warn(
"Current traced program number: {} > `max_tracing_count`:{}. Too much cached programs will bring expensive overhead. "
"The reason may be: (1) passing tensors with different shapes, (2) passing python objects instead of tensors.".format(
current_tracing_count, MAX_TRACED_PROGRAM_COUNT
)
)
return self._caches[item_id]
setattr(ProgramCache, '__getitem__', patch_getter)
IpuDynamicPatcher.patcher_cache.append(
[ProgramCache, '__getitem__', old_getter]
)
@staticmethod
def patch_lr_scheduler(ipu_strategy):
from paddle.optimizer.lr import LRScheduler
# For IPU dynamic graph usage, lr_var is not synced in executor as static graph mode do.
# Manually set lr to ipu_strategy to update the lr.
old_step = LRScheduler.step
def patch_step(self, epoch=None):
old_step(self, epoch)
ipu_strategy.set_options({"lr": self.last_lr})
setattr(LRScheduler, 'step', patch_step)
IpuDynamicPatcher.patcher_cache.append([LRScheduler, 'step', old_step])
@staticmethod
def register_patch(ipu_strategy):
IpuDynamicPatcher.patch_program_cache(ipu_strategy)
IpuDynamicPatcher.patch_lr_scheduler(ipu_strategy)
@staticmethod
def release_patch():
for module, key, attr in IpuDynamicPatcher.patcher_cache:
setattr(module, key, attr)
class IpuStrategy:
"""
Help users precisely control the graph building in :code:`paddle.static.IpuCompiledProgram` .
Returns:
The IpuStrategy instance.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
"""
def __init__(self):
if core.is_compiled_with_ipu():
self._ipu_strategy = core.IpuStrategy()
default_options = {
'location_optimizer': {
'on_chip': 0,
'use_replicated_tensor_sharding': 1,
}, # set optimizer location
'accumulation_and_replication_reduction_type': 1, # popart::ReductionType::Mean
'mean_accumulation_and_replication_reduction_strategy': 1, # popart::MeanReductionStrategy::Post
}
self._ipu_strategy.set_options(default_options)
self.has_custom_ops = False
self.custom_op_names = []
self.need_compile = True
else:
raise RuntimeError(
"Can not use IpuStrategy in non IPU compiled environment, please re-compile with WITH_IPU=ON."
)
from paddle import in_dynamic_mode
if in_dynamic_mode():
self.register_patch()
def register_patch(self):
"""
Register patchs function to support dynamic to static on IPU. This operation would break the dy2static functionality on CPU.
Use `release_patch` to release the patch.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
ipu_strategy = static.IpuStrategy()
ipu_strategy.register_patch()
"""
IpuDynamicPatcher.register_patch(self)
def release_patch(self):
"""
Release the registered IPU functions.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
ipu_strategy = static.IpuStrategy()
ipu_strategy.release_patch()
"""
IpuDynamicPatcher.release_patch()
def set_optimizer(self, optimizer):
"""
Set optimizer to ipu_strategy in dynamic mode.
Args:
optimizer (Optimizer): Optimizer to be used in training.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
linear = paddle.nn.Linear(10, 10)
optimizer = paddle.optimizer.SGD(learning_rate=0.01,
parameters=linear.parameters())
ipu_strategy = static.IpuStrategy()
ipu_strategy.set_optimizer(optimizer)
"""
from paddle import in_dynamic_mode
if in_dynamic_mode():
self._optimizer = optimizer
optimizer_attrs = self.parse_optimizer(optimizer)
self._ipu_strategy.set_options(optimizer_attrs)
else:
raise RuntimeError("Only needs to set optimizer in dynamic mode.")
def parse_optimizer(self, optimizer):
"""
Parse optimizer attributes for IPU dynamic to static support. Currently only support parse lr.
Args:
optimizer (Optimizer): Optimizer to be parsed.
Returns:
Dict.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
linear = paddle.nn.Linear(10, 10)
optimizer = paddle.optimizer.SGD(learning_rate=0.01,
parameters=linear.parameters())
ipu_strategy = static.IpuStrategy()
attrs = ipu_strategy.parse_optimizer(optimizer)
"""
def get_lr():
from paddle.optimizer.lr import LRScheduler
if isinstance(optimizer._learning_rate, float):
return {"lr": optimizer._learning_rate}
elif isinstance(optimizer._learning_rate, LRScheduler):
return {"lr": optimizer._learning_rate()}
attr_fn = [get_lr]
optimizer_attrs = {"is_dynamic": True}
for fn in attr_fn:
optimizer_attrs.update(fn())
return optimizer_attrs
def set_graph_config(
self,
num_ipus=1,
is_training=True,
micro_batch_size=1,
enable_manual_shard=False,
):
"""
Set graph configuration to the IpuStrategy instance.
Args:
num_ipus (int, optional): Number of IPU devices. Default 1, which means only use 1 IPU.
is_training (bool, optional): True is training graph, False is inference graph. Default True, which means is training mode.
batch_size (int, optional): The batch-size in the graph. Used to make the graph batch-size fixed,
if the batch-size in the graph is dynamic. Default 1, which means the batch-size would be set 1, if the batch-size is dynamice.
enable_manual_shard (bool, optional): Enable graph sharding or not. Only if num_ipus > 1, enable_manual_shard is able to be set True.
Default False, which means disabled.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
ipu_strategy.set_graph_config(num_ipus=1,
is_training=True,
micro_batch_size=1,
enable_manual_shard=False)
"""
if num_ipus == 1 and enable_manual_shard:
raise RuntimeError(
"Only if num_ipus > 1, enable_manual_shard is able to be set True."
)
options = {
'num_ipus': num_ipus,
'is_training': is_training,
'micro_batch_size': micro_batch_size,
'enable_manual_shard': enable_manual_shard,
}
self.set_options(options)
def set_pipelining_config(
self,
enable_pipelining=False,
batches_per_step=1,
enable_gradient_accumulation=False,
accumulation_factor=1,
):
"""
Set pipelining configuration to the IpuStrategy instance. Used to optimize the throughput performance.
Args:
enable_pipelining (bool, optional): Enable data pipelining between subgraphs. Only if enable_manual_shard=True, enable_pipelining is able to be set True.
Default False, which means disabled.
batches_per_step (int, optional): Set the batches per run in data pipelining mode. Only if enable_pipelining=True, batches_per_step is able to be set > 1.
Default 1, which means no data pipelining.
enable_gradient_accumulation (bool, optional): Enable to accumulate gradients before updating the weights in training mode. Only if enable_pipelining=True,
enable_gradient_accumulation is able to be set True. Default False, which means no gradient accumulation.
accumulation_factor (int, optional): Specify the number of micro-batches to accumulate
before applying the varUpdate. Default 1, which means disable the accumulation.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
ipu_strategy.set_pipelining_config(enable_pipelining=False,
batches_per_step=1,
enable_gradient_accumulation=False,
accumulation_factor=1)
"""
enable_manual_shard = self.get_option('enable_manual_shard')
if not enable_manual_shard and enable_pipelining:
raise RuntimeError(
"Only if enable_manual_shard=True, enable_pipelining is able to be set True."
)
options = {
'enable_pipelining': enable_pipelining,
'batches_per_step': batches_per_step,
'enable_gradient_accumulation': enable_gradient_accumulation,
'accumulation_factor': accumulation_factor,
}
self.set_options(options)
def set_precision_config(self, enable_fp16=False):
"""
Set half computation configuration to the IpuStrategy instance. Used to optimize the performance.
Args:
enable_fp16 (bool, optional): Enable FLOAT16 mode and transform FLOAT32 to FLOAT16. Default False, which means disable FLOAT16 mode.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
ipu_strategy.set_precision_config(enable_fp16=False)
"""
options = {
'enable_fp16': enable_fp16,
}
self.set_options(options)
def add_custom_op(
self, paddle_op, popart_op=None, domain='custom.ops', version=1
):
"""
Add a mapping to use popart custom ops running on the IPU.
Args:
paddle_op(str): the name of custom op in paddle.
popart_op(str): the name of custom op in popart.
domain(str): domain name of custom op in popart.
version(int): version of custom op in popart.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
ipu_strategy.add_custom_op('paddle_relu', 'popart_relu')
"""
if popart_op is None:
popart_op = paddle_op
custom_op = {
'paddle_op': paddle_op,
'popart_op': popart_op,
'domain': domain,
'version': version,
}
self.set_options({'custom_op': custom_op})
self.custom_op_names.append(paddle_op)
if not self.has_custom_ops:
self.has_custom_ops = True
def set_options(self, options):
"""
Set options from dict.
Args:
options(dict): dict of options.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
options = {'num_ipus':1, 'enable_fp16': True}
ipu_strategy.set_options(options)
"""
self._ipu_strategy.set_options(options)
# check whether to recompile program with updated ipu options.
recompile_white_list = {'lr'}
if options.keys() - recompile_white_list:
self.need_compile = True
def get_option(self, option):
"""
Get option.
Args:
option(str): name of option.
Returns:
option value.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
num_ipus = ipu_strategy.get_option('num_ipus')
"""
return self._ipu_strategy.get_option(option)['value']
def enable_pattern(self, pattern):
"""
Enable PopART pattern to optimize the graph.
Args:
pattern(string): the name of the pattern.
Returns:
None.
Examples:
.. code-block:: python
# required: ipu
import paddle
import paddle.static as static
paddle.enable_static()
ipu_strategy = static.IpuStrategy()
ipu_strategy.enable_pattern("ViewSimplifyPattern")