chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,673 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import inspect
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import logging
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from collections import defaultdict
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import paddle
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from paddle import core
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from paddle.jit import not_to_static, to_static
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from paddle.jit.dy2static.program_translator import (
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ProgramTranslator,
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StaticFunction,
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)
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from paddle.jit.dy2static.utils import as_not_paddle_func
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from paddle.nn import Layer
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from paddle.static import Parameter, global_scope, program_guard
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from paddle.static.amp.fp16_utils import (
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DEFAULT_AMP_OPTIONS,
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prepare_op_amp_options,
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)
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from .converter import Converter
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from .dist_attribute import TensorDistAttr
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from .process_group import get_world_process_group
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from .utils import get_logger, to_list
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class ProxyLayer(Layer):
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"""
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ProxyLayer implements all logic for converting dygraph model into
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static Program IR. Meanwhile, it provides conventional interfaces for
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auto parallel to visit feed/fetch/loss/metric variables.
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"""
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def __init__(self, layer, loss_func, metrics):
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super().__init__()
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# NOTE: All verify logics are finished in Engine.Prepare
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self.inner_layer = layer
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self.loss_func = loss_func
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self.metrics = metrics
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# train / eval / predict
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self.mode = None
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# generated program vars
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self._input_vars = defaultdict(list)
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self._label_vars = defaultdict(list)
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self._output_vars = defaultdict(list)
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self._loss_vars = defaultdict(list)
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self._loss_names = defaultdict(list)
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self._metric_vars = defaultdict(list)
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# Consider ProxyLayer as not Paddle inner function because it contains
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# user-defined layer.
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for fn_name in [
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"_train",
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"_eval",
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"_predict",
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"call_loss",
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"call_metrics",
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]:
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as_not_paddle_func(
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f"{inspect.getmodule(ProxyLayer).__name__}.ProxyLayer.{fn_name}"
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)
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@paddle.jit.not_to_static
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def append_loss_to_shadow_output(self, mode):
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name = paddle.utils.unique_name.generate('loss')
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paddle._C_ops.set_persistable_value(self._loss_vars[mode], name)
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self._loss_names[mode] = name
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def _train(self, inputs, labels):
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"""
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Train process of inner_layer with forward/loss/metric logic.
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"""
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# step 1. save feed variables of Program
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mode = 'train'
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self._input_vars[mode] = inputs
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self._label_vars[mode] = labels
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# step 2. call inner_layer.forward
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self._output_vars[mode] = self.inner_layer(*inputs)
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# step 3. calculate loss if needed
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new_inputs = self._prepare(self.output_vars, labels)
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self._loss_vars[mode] = self.call_loss(new_inputs)
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if paddle.base.framework.get_flags("FLAGS_enable_pir_api")[
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"FLAGS_enable_pir_api"
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]:
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self.append_loss_to_shadow_output(mode)
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# step 4. calculate metrics if needed
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self._metric_vars[mode] = self.call_metrics(new_inputs)
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def _eval(self, inputs, labels):
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"""
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Evaluate process of inner_layer with forward/loss/metric logic.
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"""
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# TODO(dev): we can reuse codes with self._train after making
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# sure if they can.
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# step 1. save feed variables of Program
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mode = 'eval'
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self._input_vars[mode] = inputs
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self._label_vars[mode] = labels
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# step 2. call inner_layer.forward
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self._output_vars[mode] = self.inner_layer(*inputs)
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# step 3. calculate loss if needed
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new_inputs = self._prepare(self.output_vars, labels)
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self._loss_vars[mode] = self.call_loss(new_inputs)
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if paddle.base.framework.get_flags("FLAGS_enable_pir_api")[
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"FLAGS_enable_pir_api"
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]:
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self.append_loss_to_shadow_output(mode)
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# step 4. calculate metrics if needed
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self._metric_vars[mode] = self.call_metrics(new_inputs)
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def _predict(self, inputs, labels):
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"""
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Predict process of inner_layer with forward logic.
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"""
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# step 1. save feed variables of Program
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mode = 'predict'
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self._input_vars[mode] = inputs
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self._label_vars[mode] = labels
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# step 2. call inner_layer.forward
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self._output_vars[mode] = self.inner_layer(*inputs)
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@not_to_static
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def _prepare(self, outputs, labels):
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"""
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Concat outputs and labels as a single list
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NOTE(dev): We use @not_to_static to avoid AST Analysis.
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"""
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return to_list(outputs) + to_list(labels)
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def call_loss(self, inputs):
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"""
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Apply Loss Function on outputs and labels.
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Args:
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inputs: List[Variable]
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Returns: List[Variable]
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"""
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res = []
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if self.loss_func is not None:
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res = self.loss_func(*inputs)
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return res
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def call_metrics(self, inputs):
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"""
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Apply Metrics Function on outputs and labels.
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Args:
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inputs: List[Variable]
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Returns: List[Variable]
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"""
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outs = []
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for metric in self.metrics:
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outs.append(to_list(metric.compute(*inputs)))
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return outs
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def set_mode(self, mode):
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self.mode = mode
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self.training = mode == 'train'
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def clone(self):
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return ProxyLayer(self.inner_layer, self.loss_func, self.metrics)
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@property
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def input_vars(self):
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return self._input_vars[self.mode]
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@property
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def label_vars(self):
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return self._label_vars[self.mode]
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@property
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def output_vars(self):
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return self._output_vars[self.mode]
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@property
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def loss_vars(self):
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return self._loss_vars[self.mode]
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@property
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def loss_names(self):
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return self._loss_names[self.mode]
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@property
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def metric_vars(self):
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return self._metric_vars[self.mode]
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@property
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def startup_program(self):
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return self.inner_layer._startup_program()
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class BuildInfo:
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def __init__(self):
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self.clear()
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def has_cache(self, mode, update=False):
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is_cache = self.states[mode]
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if update:
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self.cache(mode)
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return is_cache
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def cache(self, mode):
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self.states[mode] = True
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def clear(self):
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self.states = defaultdict(bool)
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class ProgramHelper:
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"""
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A Helper class for Engine to provides different Program IR according specified 'mode'.
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"""
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def __init__(self, layer, loss_func, metrics, inputs_spec, labels_spec):
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# original model config information
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# TODO(Aurelius84): Implement append_backward and optimizer in ProxyLayer
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# after distribute engine satisfy basic condition.
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self.proxy_layer = ProxyLayer(layer, loss_func, metrics)
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self.inputs_spec = inputs_spec
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self.labels_spec = labels_spec
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self.build_info = BuildInfo()
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self._logger = get_logger(logging.INFO)
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self.lazy_init = False
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self._all_params_dist_attr = {}
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def reset(self):
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"""
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Reset all state of current Object.
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"""
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self.build_info.clear()
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self.proxy_layer = self.proxy_layer.clone()
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def build_program(self, mode):
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"""
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Convert dygraph model into static Program IR.
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"""
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assert mode in ['train', 'eval', 'predict']
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self.proxy_layer.set_mode(mode)
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# skip if we has already built program.
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if self.build_info.has_cache(mode, True):
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self._logger.info(
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f"Already build program with mode = {mode}, use cached program."
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)
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return
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self._logger.info(f"start to build program for mode = {mode}.")
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input_spec = [self.inputs_spec, self.labels_spec]
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static_func = to_static(
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self.static_func(), input_spec=input_spec, full_graph=True
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)
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func_name = '_' + mode
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setattr(self.proxy_layer, func_name, static_func)
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# NOTE(dev): Because @to_static is a Lazy mechanism, so we explicitly call this to trigger
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# generating Program IR immediately.
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concrete_program = getattr(self.proxy_layer, func_name).concrete_program
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# TODO(zhiqiu): prepare_op_amp_options is not supported for PIR program
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# It will to use dynamic-static unified amp in pir program, and there is
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# no need to fit for prepare_op_amp_options
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if not paddle.base.framework.get_flags("FLAGS_enable_pir_api")[
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"FLAGS_enable_pir_api"
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]:
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prepare_op_amp_options(
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concrete_program.main_program,
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ProgramTranslator.get_instance()._amp_records,
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DEFAULT_AMP_OPTIONS,
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)
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self._build_startup_program()
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def _build_startup_program(self):
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"""
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Create and Sync parameters into startup program.
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"""
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startup_program = self.startup_program
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if len(startup_program.global_block().ops) > 1:
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self.lazy_init = True
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return
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for param in self.concrete_program.parameters:
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Parameter(
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name=param.name,
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desc=param,
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type=param.type,
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shape=param.shape,
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dtype=param.dtype,
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stop_gradient=param.stop_gradient,
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block=startup_program.global_block(),
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)
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def apply_optimizer(self, optimizer):
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"""
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Append backward and generate optimizer operations.
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"""
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self._verify_optimizer(optimizer)
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self._logger.info(
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"start to apply optimizer: %s ", type(optimizer).__name__
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)
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# clear optimizer parameters
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original_params = optimizer._parameter_list
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optimizer._parameter_list = None
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with program_guard(self.main_program, self.startup_program):
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res = optimizer.minimize(self.loss_vars[0])
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# restore optimizer parameters
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optimizer._parameter_list = original_params
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return res
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def _verify_optimizer(self, optimizer):
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assert optimizer is not None
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assert hasattr(optimizer, "minimize"), (
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"Optimizer must have minimize() method."
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)
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assert self.proxy_layer.mode == 'train', (
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f"Required mode == 'train', but received '{self.proxy_layer.mode}'"
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)
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assert len(self.loss_vars) == 1, (
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f"Required len(loss_vars) == 1, but received len(loss_vars) = {len(self.loss_vars)}"
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)
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def to(self, mode):
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"""
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Switch underly proxy layer mode into target mode.
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"""
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assert mode in ['train', 'eval', 'predict']
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func = getattr(self.proxy_layer, '_' + mode)
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assert isinstance(func, StaticFunction), (
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"Please call build_program(mode) firstly."
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)
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self.proxy_layer.set_mode(mode)
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def static_func(self):
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"""
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Return StaticFunction instance with underly target mode.
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"""
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assert self.proxy_layer.mode in [
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'train',
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'eval',
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'predict',
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], "Please call build_program(mode) firstly."
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func_name = '_' + self.proxy_layer.mode
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return getattr(self.proxy_layer, func_name)
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def init_pir(self, main_program, place):
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# collect all params in current dist program
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param_values = main_program.global_block().all_parameters()
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value_name_to_value = {}
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dy_param_name_to_pir_param_name = {}
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for value in param_values:
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value_name_to_value[value.name] = value
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dy_params = self.concrete_program.parameters[0]
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pir_param = self.concrete_program.parameters[1]
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for i in range(len(pir_param)):
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if pir_param[i].name in value_name_to_value:
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dy_param_name_to_pir_param_name[dy_params[i].name] = pir_param[
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i
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].name
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is_comm = False
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for param in dy_params:
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if param.is_dist():
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process_mesh, dims_mapping = self._all_params_dist_attr[
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param.name
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]
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var_dist_attr = TensorDistAttr()
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var_dist_attr.process_mesh = process_mesh
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var_dist_attr.dims_mapping = dims_mapping
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is_comm = True
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with paddle.no_grad():
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tmp = paddle.base.core.reshard(param, var_dist_attr)
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if tmp._is_initialized():
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param.get_tensor()._share_data_with(tmp.get_tensor())
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else:
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# Only setting the "param" to "None" can't release the memory
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param.get_tensor()._clear()
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param = None
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# create var in scope and share parameters to scope
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if param is None:
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continue
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if param.name not in dy_param_name_to_pir_param_name:
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# Release the redundant params
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param.get_tensor()._clear()
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continue
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if not param._is_initialized():
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continue
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if param.is_dense():
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value_name = dy_param_name_to_pir_param_name[param.name]
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value = value_name_to_value[value_name]
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# get param_var's dist_attr
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assert value.is_dist_dense_tensor_type(), (
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f"param [{value.name}] is not dist tensor type"
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)
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dist_attr = {
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"dims_mapping": value.dist_attr().dims_mapping,
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"process_shape": value.dist_attr().process_mesh.shape,
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"process_group": value.dist_attr().process_mesh.process_ids,
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}
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# slice param_value with dist_attr
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# share sliced_param_value with param_tensor in global_scope
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pir_scope_param = global_scope().var(value_name).get_tensor()
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sliced_param = Converter.slice_with_dist_attr(
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param.numpy(), dist_attr
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)
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pir_scope_param.set(sliced_param, place)
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param.get_tensor()._clear()
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elif param.is_dist():
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value_name = dy_param_name_to_pir_param_name[param.name]
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value = value_name_to_value[value_name]
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# assert value.is_dist_dense_tensor_type(), "param [{}] is not dist tensor type".format(value.name)
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pir_scope_param = global_scope().var(value_name).get_tensor()
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pir_scope_param._share_data_with(
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param.get_tensor().get_tensor()
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)
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param.get_tensor()._clear()
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world_group = get_world_process_group()
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if (
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is_comm
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and world_group.nranks > 1
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and paddle.distributed.get_world_size() > 1
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):
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paddle.disable_static()
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barrier_tensor = paddle.full([1], 1, dtype="int32")
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# barrier is not available in xpu for now
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if not paddle.framework.core.is_compiled_with_xpu():
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paddle._legacy_C_ops.barrier(
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barrier_tensor, barrier_tensor, 'ring_id', 0
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)
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paddle.enable_static()
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def init(self, main_program, place, dist_context):
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if self.lazy_init:
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return
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amp_strategy = dist_context.strategy.amp
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amp_config = copy.deepcopy(amp_strategy.to_dict())
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need_cast_parameter = amp_strategy.enable and amp_config["level"] in [
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"o2",
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"o3",
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]
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is_comm = False
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for param in self.concrete_program.parameters:
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if param.is_dist():
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serial_main_program = self.concrete_program.main_program
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var = serial_main_program.global_block().vars[param.name]
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var_dist_attr = dist_context.get_tensor_dist_attr_for_program(
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var
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)
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is_comm = True
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# No need to construct backward.
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||||
with paddle.no_grad():
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tmp = paddle.base.core.reshard(param, var_dist_attr)
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||||
if tmp._is_initialized():
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||||
param.get_tensor()._share_data_with(tmp.get_tensor())
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||||
else:
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||||
# Only setting the "param" to "None" can't release the memory
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||||
param.get_tensor()._clear()
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param = None
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paddle.device.synchronize()
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||||
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||||
# create var in scope and share parameters to scope
|
||||
if param is None:
|
||||
continue
|
||||
if param.name not in main_program.global_block().vars:
|
||||
# Release the redundant params
|
||||
param.get_tensor()._clear()
|
||||
continue
|
||||
if not param._is_initialized():
|
||||
continue
|
||||
if param.is_dense():
|
||||
# get param_var's dist_attr
|
||||
var = main_program.global_block().vars[param.name]
|
||||
var_dist_attr = dist_context.get_tensor_dist_attr_for_program(
|
||||
var
|
||||
)
|
||||
dist_attr = {
|
||||
"dims_mapping": var_dist_attr.dims_mapping,
|
||||
"process_shape": var_dist_attr.process_mesh.shape,
|
||||
"process_group": var_dist_attr.process_mesh.process_ids,
|
||||
}
|
||||
# slice param_value with dist_attr
|
||||
# share sliced_param_value with param_tensor in global_scope
|
||||
param_tensor = global_scope().var(param.name).get_tensor()
|
||||
sliced_param = Converter.slice_with_dist_attr(
|
||||
param.numpy(), dist_attr
|
||||
)
|
||||
param_tensor.set(sliced_param, place)
|
||||
if not need_cast_parameter:
|
||||
param.get_tensor()._clear()
|
||||
elif param.is_dist():
|
||||
dense_tensor = global_scope().var(param.name).get_tensor()
|
||||
dense_tensor._share_data_with(param.get_tensor().get_tensor())
|
||||
|
||||
# transform the parameter in eager mode for amp.
|
||||
if need_cast_parameter:
|
||||
for param in self.concrete_program.parameters:
|
||||
amp_dtype = amp_config["dtype"]
|
||||
scope_var = global_scope().find_var(param.name)
|
||||
# The parameter is not in this rank.
|
||||
if not scope_var:
|
||||
continue
|
||||
# The parameter do not need to transform
|
||||
if param.dtype in [paddle.float16, paddle.bfloat16]:
|
||||
continue
|
||||
scope_tensor = global_scope().var(param.name).get_tensor()
|
||||
assert scope_var and scope_tensor._is_initialized(), (
|
||||
f"Parameter: {param.name} is not put into global_scope or not initialized."
|
||||
)
|
||||
param_used = param
|
||||
# For the params without dist_attr.
|
||||
# NOTE(lizhiyu): In principle, each param should have dist_attr.
|
||||
if param.is_dense():
|
||||
# get param_var's dist_attr
|
||||
var = main_program.global_block().vars[param.name]
|
||||
var_dist_attr = (
|
||||
dist_context.get_tensor_dist_attr_for_program(var)
|
||||
)
|
||||
dist_attr = {
|
||||
"dims_mapping": var_dist_attr.dims_mapping,
|
||||
"process_shape": var_dist_attr.process_mesh.shape,
|
||||
"process_group": var_dist_attr.process_mesh.process_ids,
|
||||
}
|
||||
# slice param_value with dist_attr
|
||||
sliced_param = Converter.slice_with_dist_attr(
|
||||
param.numpy(), dist_attr
|
||||
)
|
||||
with paddle.base.dygraph.guard():
|
||||
param_used = paddle.to_tensor(
|
||||
sliced_param, place=param.place
|
||||
)
|
||||
param.get_tensor()._clear()
|
||||
with paddle.base.dygraph.guard():
|
||||
if amp_dtype == "float16":
|
||||
with (
|
||||
paddle.no_grad(),
|
||||
paddle.base.framework._dygraph_place_guard(
|
||||
place=place
|
||||
),
|
||||
):
|
||||
t_casted = param_used.cast(
|
||||
dtype=core.VarDesc.VarType.FP16
|
||||
)
|
||||
elif amp_dtype == "bfloat16":
|
||||
with (
|
||||
paddle.no_grad(),
|
||||
paddle.base.framework._dygraph_place_guard(
|
||||
place=place
|
||||
),
|
||||
):
|
||||
t_casted = param_used.cast(
|
||||
dtype=core.VarDesc.VarType.BF16
|
||||
)
|
||||
# NOTE(lizhiyu): Clear the origin param. Don't use `param_used.get_tensor().get_tensor()._clear()` to
|
||||
# clear the `DistTensor`, because it can't clear the `_holder`,
|
||||
# which `param_used.get_tensor().get_tensor()` will copy one `DenseTensor`.
|
||||
param_used.get_tensor()._clear()
|
||||
if t_casted.is_dist():
|
||||
scope_tensor._share_data_with(
|
||||
t_casted.get_tensor().get_tensor()
|
||||
)
|
||||
else:
|
||||
scope_tensor._share_data_with(t_casted.get_tensor())
|
||||
|
||||
world_group = get_world_process_group()
|
||||
if (
|
||||
is_comm
|
||||
and world_group.nranks > 1
|
||||
and paddle.distributed.get_world_size() > 1
|
||||
):
|
||||
paddle.disable_static()
|
||||
barrier_tensor = paddle.full([1], 1, dtype="int32")
|
||||
# barrier is not available in xpu for now
|
||||
if not paddle.framework.core.is_compiled_with_xpu():
|
||||
paddle._legacy_C_ops.barrier(
|
||||
barrier_tensor, barrier_tensor, 'ring_id', 0
|
||||
)
|
||||
paddle.enable_static()
|
||||
|
||||
def cache_whole_graph_dist_attr(self, all_params):
|
||||
for param_value in all_params:
|
||||
dist_attr = param_value.dist_attr()
|
||||
if dist_attr:
|
||||
process_mesh = dist_attr.process_mesh
|
||||
dims_mapping = dist_attr.dims_mapping
|
||||
self._all_params_dist_attr[param_value.name] = [
|
||||
process_mesh,
|
||||
dims_mapping,
|
||||
]
|
||||
|
||||
@property
|
||||
def concrete_program(self):
|
||||
return self.static_func().concrete_program
|
||||
|
||||
@property
|
||||
def main_program(self):
|
||||
return self.concrete_program.main_program
|
||||
|
||||
@property
|
||||
def startup_program(self):
|
||||
try:
|
||||
return self.proxy_layer.startup_program
|
||||
except Exception as err:
|
||||
self._logger.warning(
|
||||
"The startup_program is not built by `lazy init`."
|
||||
)
|
||||
if isinstance(err, AssertionError):
|
||||
return self.concrete_program.startup_program
|
||||
raise err
|
||||
|
||||
@property
|
||||
def input_vars(self):
|
||||
return to_list(self.proxy_layer.input_vars)
|
||||
|
||||
@property
|
||||
def output_vars(self):
|
||||
return to_list(self.proxy_layer.output_vars)
|
||||
|
||||
@property
|
||||
def label_vars(self):
|
||||
return to_list(self.proxy_layer.label_vars)
|
||||
|
||||
@property
|
||||
def loss_vars(self):
|
||||
return to_list(self.proxy_layer.loss_vars)
|
||||
|
||||
@property
|
||||
def loss_names(self):
|
||||
return to_list(self.proxy_layer.loss_names)
|
||||
|
||||
@property
|
||||
def metric_vars(self):
|
||||
return to_list(self.proxy_layer.metric_vars)
|
||||
|
||||
def named_parameters(self):
|
||||
static_func = self.static_func()
|
||||
partial_program = static_func.get_concrete_program(
|
||||
self.inputs_spec, self.labels_spec
|
||||
)[-1]
|
||||
# TODO(xiongkun): support pir in the feature.
|
||||
return {param.name: param for param in partial_program._params}
|
||||
Reference in New Issue
Block a user