You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
386 lines
14 KiB
386 lines
14 KiB
# Copyright (c) 2020 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.
|
|
"""Fleet Metrics"""
|
|
|
|
import paddle.fluid as fluid
|
|
import math
|
|
import numpy as np
|
|
from paddle.fluid.framework import Variable
|
|
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
|
|
|
|
|
|
def sum(input, scope=None):
|
|
"""
|
|
distributed sum in fleet
|
|
|
|
Args:
|
|
input(numpy.array|Variable|string): output of a layer
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
global_metric(numpy.array): sum array
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
input = fluid.layers.cast(some_input, dtype='float32')
|
|
cnt = fluid.layers.reduce_sum(input)
|
|
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
|
|
tmp = fluid.layers.elementwise_add(cnt, global_cnt)
|
|
fluid.layers.assign(tmp, global_cnt)
|
|
|
|
# in train.py, after train or infer
|
|
res = np.array(scope.find_var(global_cnt.name).get_tensor())
|
|
print("sum array: ", paddle.fleet.sum(res))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(input, Variable):
|
|
input = np.array(scope.find_var(input.name).get_tensor())
|
|
elif isinstance(input, str):
|
|
input = np.array(scope.find_var(input).get_tensor())
|
|
old_shape = np.array(input.shape)
|
|
output = np.copy(input) * 0
|
|
fleet._role_maker._all_reduce(input, output, mode="sum")
|
|
output = output.reshape(old_shape)
|
|
return output
|
|
|
|
|
|
def max(input, scope=None):
|
|
"""
|
|
distributed max in fleet
|
|
|
|
Args:
|
|
input(numpy.array|Variable|string): output of a layer
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
global_metric(numpy.array): max array
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
input = fluid.layers.cast(some_input, dtype='float32')
|
|
cnt = fluid.layers.reduce_sum(input)
|
|
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
|
|
tmp = fluid.layers.elementwise_max(cnt, global_cnt)
|
|
fluid.layers.assign(tmp, global_cnt)
|
|
|
|
# in train.py, after train or infer
|
|
res = np.array(scope.find_var(global_cnt.name).get_tensor())
|
|
print("max array: ", paddle.fleet.max(res))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(input, Variable):
|
|
input = np.array(scope.find_var(input.name).get_tensor())
|
|
elif isinstance(input, str):
|
|
input = np.array(scope.find_var(input).get_tensor())
|
|
old_shape = np.array(input.shape)
|
|
output = np.copy(input) * 0
|
|
fleet._role_maker._all_reduce(input, output, mode="max")
|
|
output = output.reshape(old_shape)
|
|
return output
|
|
|
|
|
|
def min(input, scope=None):
|
|
"""
|
|
distributed min in fleet
|
|
|
|
Args:
|
|
input(numpy.array|Variable|string): output of a layer
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
global_metric(numpy.array): min array
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
input = fluid.layers.cast(some_input, dtype='float32')
|
|
cnt = fluid.layers.reduce_sum(input)
|
|
global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
|
|
tmp = fluid.layers.elementwise_min(cnt, global_cnt)
|
|
fluid.layers.assign(tmp, global_cnt)
|
|
|
|
# in train.py, after train or infer
|
|
res = np.array(scope.find_var(global_cnt.name).get_tensor())
|
|
print("min array: ", paddle.fleet.min(res))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(input, Variable):
|
|
input = np.array(scope.find_var(input.name).get_tensor())
|
|
elif isinstance(input, str):
|
|
input = np.array(scope.find_var(input).get_tensor())
|
|
old_shape = np.array(input.shape)
|
|
output = np.copy(input) * 0
|
|
fleet._role_maker._all_reduce(input, output, mode="min")
|
|
output = output.reshape(old_shape)
|
|
return output
|
|
|
|
|
|
def auc(stat_pos, stat_neg, scope=None):
|
|
"""
|
|
distributed auc in fleet
|
|
|
|
Args:
|
|
stat_pos(numpy.array|Variable|string): stat_pos in output of fluid.layers.auc
|
|
stat_neg(numpy.array|Variable|string): stat_neg in output of fluid.layers.auc
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
auc_value(float): auc value
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
similarity_norm = fluid.layers.sigmoid(fluid.layers.clip(output, min=-15.0, max=15.0))
|
|
binary_predict = fluid.layers.concat(
|
|
input=[fluid.layers.elementwise_sub(fluid.layers.ceil(similarity_norm), similarity_norm), similarity_norm], axis=1)
|
|
self.auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg] =
|
|
fluid.layers.auc(input=binary_predict, label=label, curve='ROC', num_thresholds=4096)
|
|
|
|
# in train.py, after train or infer
|
|
pos = np.array(scope.find_var(stat_pos.name).get_tensor())
|
|
neg = np.array(scope.find_var(stat_neg.name).get_tensor())
|
|
print("auc: ", paddle.fleet.auc(pos, neg))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(stat_pos, Variable):
|
|
stat_pos = np.array(scope.find_var(stat_pos.name).get_tensor())
|
|
elif isinstance(stat_pos, str):
|
|
stat_pos = np.array(scope.find_var(stat_pos).get_tensor())
|
|
if isinstance(stat_neg, Variable):
|
|
stat_neg = np.array(scope.find_var(stat_neg.name).get_tensor())
|
|
elif isinstance(stat_neg, str):
|
|
stat_neg = np.array(scope.find_var(stat_neg).get_tensor())
|
|
# auc pos bucket shape
|
|
old_pos_shape = np.array(stat_pos.shape)
|
|
# reshape to one dim
|
|
stat_pos = stat_pos.reshape(-1)
|
|
global_pos = np.copy(stat_pos) * 0
|
|
# mpi allreduce
|
|
fleet._role_maker._all_reduce(stat_pos, global_pos)
|
|
# reshape to its original shape
|
|
global_pos = global_pos.reshape(old_pos_shape)
|
|
|
|
# auc neg bucket
|
|
old_neg_shape = np.array(stat_neg.shape)
|
|
stat_neg = stat_neg.reshape(-1)
|
|
global_neg = np.copy(stat_neg) * 0
|
|
fleet._role_maker._all_reduce(stat_neg, global_neg)
|
|
global_neg = global_neg.reshape(old_neg_shape)
|
|
|
|
# calculate auc
|
|
num_bucket = len(global_pos[0])
|
|
area = 0.0
|
|
pos = 0.0
|
|
neg = 0.0
|
|
new_pos = 0.0
|
|
new_neg = 0.0
|
|
total_ins_num = 0
|
|
for i in range(num_bucket):
|
|
index = num_bucket - 1 - i
|
|
new_pos = pos + global_pos[0][index]
|
|
total_ins_num += global_pos[0][index]
|
|
new_neg = neg + global_neg[0][index]
|
|
total_ins_num += global_neg[0][index]
|
|
area += (new_neg - neg) * (pos + new_pos) / 2
|
|
pos = new_pos
|
|
neg = new_neg
|
|
|
|
auc_value = None
|
|
if pos * neg == 0 or total_ins_num == 0:
|
|
auc_value = 0.5
|
|
else:
|
|
auc_value = area / (pos * neg)
|
|
|
|
fleet._role_maker._barrier_worker()
|
|
return auc_value
|
|
|
|
|
|
def mae(abserr, total_ins_num, scope=None):
|
|
"""
|
|
distributed mae in fleet
|
|
|
|
Args:
|
|
abserr(numpy.array|Variable|string): abserr in output of fluid.contrib.layers.ctr_metric_bundle
|
|
total_ins_num(int|float): total train/infer instance count
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
mae(float): mae value
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
|
|
|
|
# in train.py, after train or infer
|
|
res = np.array(scope.find_var(abserr.name).get_tensor())
|
|
print("mae: ", paddle.fleet.mae(res, total_ins_num))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(abserr, Variable):
|
|
abserr = np.array(scope.find_var(abserr.name).get_tensor())
|
|
elif isinstance(abserr, str):
|
|
abserr = np.array(scope.find_var(abserr).get_tensor())
|
|
old_metric_shape = np.array(abserr.shape)
|
|
abserr = abserr.reshape(-1)
|
|
global_metric = np.copy(abserr) * 0
|
|
fleet._role_maker._all_reduce(abserr, global_metric)
|
|
global_metric = global_metric.reshape(old_metric_shape)
|
|
mae_value = global_metric[0] / total_ins_num
|
|
return mae_value
|
|
|
|
|
|
def rmse(sqrerr, total_ins_num, scope=None):
|
|
"""
|
|
distributed rmse in fleet
|
|
|
|
Args:
|
|
sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
|
|
total_ins_num(int|float): total train/infer instance count
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
rmse(float): rmse value
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
|
|
|
|
# in train.py, after train or infer
|
|
res = np.array(scope.find_var(sqrerr.name).get_tensor())
|
|
print("rmse: ", paddle.fleet.rmse(res, total_ins_num))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(sqrerr, Variable):
|
|
sqrerr = np.array(scope.find_var(sqrerr.name).get_tensor())
|
|
elif isinstance(sqrerr, str):
|
|
sqrerr = np.array(scope.find_var(sqrerr).get_tensor())
|
|
old_metric_shape = np.array(sqrerr.shape)
|
|
sqrerr = sqrerr.reshape(-1)
|
|
global_metric = np.copy(sqrerr) * 0
|
|
fleet._role_maker._all_reduce(sqrerr, global_metric)
|
|
global_metric = global_metric.reshape(old_metric_shape)
|
|
rmse_value = math.sqrt(global_metric[0] / total_ins_num)
|
|
return rmse_value
|
|
|
|
|
|
def mse(sqrerr, total_ins_num, scope=None):
|
|
"""
|
|
distributed mse in fleet
|
|
|
|
Args:
|
|
sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
|
|
total_ins_num(int|float): total train/infer instance count
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
mse(float): mse value
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
|
|
|
|
# in train.py, after train or infer
|
|
metric = np.array(scope.find_var(sqrerr.name).get_tensor())
|
|
print("mse: ", paddle.fleet.mse(metric, total_ins_num))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(sqrerr, Variable):
|
|
sqrerr = np.array(scope.find_var(sqrerr.name).get_tensor())
|
|
elif isinstance(sqrerr, str):
|
|
sqrerr = np.array(scope.find_var(sqrerr).get_tensor())
|
|
old_metric_shape = np.array(sqrerr.shape)
|
|
sqrerr = sqrerr.reshape(-1)
|
|
global_metric = np.copy(sqrerr) * 0
|
|
fleet._role_maker._all_reduce(sqrerr, global_metric)
|
|
global_metric = global_metric.reshape(old_metric_shape)
|
|
mse_value = global_metric[0] / total_ins_num
|
|
return mse_value
|
|
|
|
|
|
def acc(correct, total, scope=None):
|
|
"""
|
|
distributed accuracy in fleet
|
|
|
|
Args:
|
|
correct(numpy.array|Variable|string): correct Variable
|
|
total(numpy.array|Variable): total Variable
|
|
scope(Scope): specific scope
|
|
|
|
Returns:
|
|
acc(float): accuracy value
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
# in model.py
|
|
correct = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
|
|
total = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
|
|
acc = fluid.layers.acc(predict, label, k=1, correct=correct, total=total)
|
|
|
|
global_correct = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
|
|
tmp1 = fluid.layers.elementwise_min(correct, global_correct)
|
|
fluid.layers.assign(tmp1, global_correct)
|
|
|
|
global_total = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
|
|
tmp2 = fluid.layers.elementwise_min(total, global_total)
|
|
fluid.layers.assign(tmp2, global_total)
|
|
|
|
# in train.py, after train or infer
|
|
correct_num = np.array(scope.find_var(correct.name).get_tensor())
|
|
total_num = np.array(scope.find_var(total.name).get_tensor())
|
|
print("accuracy: ", paddle.fleet.acc(correct_num, total_num))
|
|
"""
|
|
fleet._role_maker._barrier_worker()
|
|
if scope is None:
|
|
scope = fluid.global_scope()
|
|
if isinstance(correct, Variable):
|
|
correct = np.array(scope.find_var(correct.name).get_tensor())
|
|
elif isinstance(correct, str):
|
|
correct = np.array(scope.find_var(correct).get_tensor())
|
|
if isinstance(total, Variable):
|
|
total = np.array(scope.find_var(total.name).get_tensor())
|
|
elif isinstance(total, str):
|
|
total = np.array(scope.find_var(total).get_tensor())
|
|
global_correct_num = np.copy(correct) * 0
|
|
global_total_num = np.copy(total) * 0
|
|
fleet._role_maker._all_reduce(correct, global_correct_num)
|
|
fleet._role_maker._all_reduce(total, global_total_num)
|
|
return float(global_correct_num[0]) / float(global_total_num[0])
|