From dfddae19f68689a28ff5c0fcffdab3edd4f73ad8 Mon Sep 17 00:00:00 2001 From: Stephanie Jingyi Yuan Date: Mon, 27 Aug 2018 16:45:37 -0400 Subject: [PATCH] Implemented a python SVRGModule for performing SVRG Optimization Logic. This version supports single machine SVRG with single cpu, gpu and multi-gpus. --- docs/api/python/contrib/svrg_optimization.md | 86 +++ docs/api/python/index.md | 3 +- docs/api/python/module/module.md | 2 +- example/svrg_module/README.md | 33 + .../api_usage_example/example_api_train.py | 124 ++++ .../api_usage_example/example_inference.py | 106 ++++ example/svrg_module/benchmarks/benchmark1.png | Bin 0 -> 272381 bytes example/svrg_module/benchmarks/benchmark2.png | Bin 0 -> 355487 bytes .../svrg_module/linear_regression/common.py | 118 ++++ .../linear_regression/data_reader.py | 45 ++ .../svrg_module/linear_regression/train.py | 45 ++ .../contrib/svrg_optimization/__init__.py | 22 + .../contrib/svrg_optimization/svrg_module.py | 590 ++++++++++++++++++ .../svrg_optimization/svrg_optimizer.py | 171 +++++ .../unittest/test_contrib_svrg_module.py | 301 +++++++++ .../unittest/test_contrib_svrg_optimizer.py | 101 +++ 16 files changed, 1745 insertions(+), 2 deletions(-) create mode 100644 docs/api/python/contrib/svrg_optimization.md create mode 100644 example/svrg_module/README.md create mode 100644 example/svrg_module/api_usage_example/example_api_train.py create mode 100644 example/svrg_module/api_usage_example/example_inference.py create mode 100644 example/svrg_module/benchmarks/benchmark1.png create mode 100644 example/svrg_module/benchmarks/benchmark2.png create mode 100644 example/svrg_module/linear_regression/common.py create mode 100644 example/svrg_module/linear_regression/data_reader.py create mode 100644 example/svrg_module/linear_regression/train.py create mode 100644 python/mxnet/contrib/svrg_optimization/__init__.py create mode 100644 python/mxnet/contrib/svrg_optimization/svrg_module.py create mode 100644 python/mxnet/contrib/svrg_optimization/svrg_optimizer.py create mode 100644 tests/python/unittest/test_contrib_svrg_module.py create mode 100644 tests/python/unittest/test_contrib_svrg_optimizer.py diff --git a/docs/api/python/contrib/svrg_optimization.md b/docs/api/python/contrib/svrg_optimization.md new file mode 100644 index 000000000000..e6e1c3e23ee3 --- /dev/null +++ b/docs/api/python/contrib/svrg_optimization.md @@ -0,0 +1,86 @@ +# SVRG Optimization in Python Module API + +## Overview +SVRG which stands for Stochastic Variance Reduced Gradients, is an optimization technique that was first introduced in +paper _Accelerating Stochastic Gradient Descent using Predictive Variance Reduction_ in 2013. It is complement to SGD +(Stochastic Gradient Descent), which is known for large scale optimization but suffers from slow convergence +asymptotically due to its inherent variance. SGD approximates the full gradients using a small batch of data or +a single data sample, which will introduce variance and thus requires to start with a small learning rate in order to +ensure convergence. SVRG remedies the problem by keeping track of a version of estimated weights that close to the +optimal parameter values and maintaining an average of full gradients over a full pass of data. The average of full +gradients is calculated with respect to the weights from the last m-th epochs in the training. SVRG uses a different +update rule: gradients w.r.t current parameter values minus gradients w.r.t to parameters from the last m-th epochs +plus the average of full gradients over all data. + +Key Characteristics of SVRG: +* Employs explicit variance reduction by using a different update rule compared to SGD. +* Ability to use relatively large learning rate, which leads to faster convergence compared to SGD. +* Guarantees for fast convergence for smooth and strongly convex functions. + +SVRG optimization is implemented as a SVRGModule in `mxnet.contrib.svrg_optimization`, which is an extension of the +existing `mxnet.module.Module` APIs and encapsulates SVRG optimization logic within several new functions. SVRGModule +API changes compared to Module API to end users are minimal. + +In distributed training, each worker gets the same special weights from the last m-th epoch and calculates the full +gradients with respect to its own shard of data. The standard SVRG optimization requires building a global full +gradients, which is calculated by aggregating the full gradients from each worker and averaging over the number of +workers. The workaround is to keep an additional set of keys in the KVStore that maps to full gradients. +The `_SVRGOptimizer` is designed to wrap two optimizers, an `_AssignmentOptimizer` which is used for full gradients +accumulation in the KVStore and a regular optimizer that performs actual update rule to the parameters. +The `_SVRGOptimizer` and `_AssignmentOptimizer` are designed to be used in `SVRGModule` only. + +```eval_rst +.. warning:: This package contains experimental APIs and may change in the near future. +``` + +This document lists the SVRGModule APIs in MXNet/Contrib package: + +```eval_rst +.. autosummary:: + :nosignatures: + + mxnet.contrib.svrg_optimization.svrg_module +``` + +### Intermediate Level API for SVRGModule + +The only extra step to use a SVRGModule compared to use a Module is to check if the current epoch should update the +full gradients over all data. Code snippets below demonstrate the suggested usage of SVRGModule using intermediate +level APIs. + +```python +>>> mod = SVRGModule(symbol=model, update_freq=2, data_names=['data'], label_names=['lin_reg_label']) +>>> mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) +>>> mod.init_params() +>>> mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.01), ), kvstore='local') +>>> for epoch in range(num_epochs): +... if epoch % mod.update_freq == 0: +... mod.update_full_grads(di) +... di.reset() +... for batch in di: +... mod.forward_backward(data_batch=batch) +... mod.update() +``` + +### High Level API for SVRGModule + +The high level API usage of SVRGModule remains exactly the same as Module API. Code snippets below gives an example of +suggested usage of high level API. + +```python +>>> mod = SVRGModule(symbol=model, update_freq=2, data_names=['data'], label_names=['lin_reg_label']) +>>> mod.fit(di, num_epochs=100, optimizer='sgd', optimizer_params=(('learning_rate', 0.01), )) +``` + +## API reference + + + +```eval_rst + +.. automodule:: mxnet.contrib.svrg_optimization.svrg_module +.. autoclass:: mxnet.contrib.svrg_optimization.svrg_module.SVRGModule + :members: init_optimizer, bind, forward, backward, reshape, update, update_full_grads, fit, prepare + +``` + \ No newline at end of file diff --git a/docs/api/python/index.md b/docs/api/python/index.md index 42c4af9e46b5..15d1045a93e4 100644 --- a/docs/api/python/index.md +++ b/docs/api/python/index.md @@ -52,6 +52,7 @@ Code examples are placed throughout the API documentation and these can be run a contrib/contrib.md contrib/text.md contrib/onnx.md + contrib/svrg_optimization.md ``` ## Gluon API @@ -176,4 +177,4 @@ Code examples are placed throughout the API documentation and these can be run a :maxdepth: 1 symbol_in_pictures/symbol_in_pictures.md -``` +``` \ No newline at end of file diff --git a/docs/api/python/module/module.md b/docs/api/python/module/module.md index 86ed74db6c19..5a874ac6df02 100644 --- a/docs/api/python/module/module.md +++ b/docs/api/python/module/module.md @@ -207,4 +207,4 @@ additional functionality. We summarize them in this section. :members: ``` - + \ No newline at end of file diff --git a/example/svrg_module/README.md b/example/svrg_module/README.md new file mode 100644 index 000000000000..7edce14fa103 --- /dev/null +++ b/example/svrg_module/README.md @@ -0,0 +1,33 @@ +## SVRGModule Example +SVRGModule is an extension to the Module API that implements SVRG optimization, which stands for Stochastic +Variance Reduced Gradient. SVRG is an optimization technique that complements SGD and has several key +properties: +* Employs explicit variance reduction by using a different update rule compared to SGD. +* Ability to use relatively large learning rate, which leads to faster convergence compared to SGD. +* Guarantees for fast convergence for smooth and strongly convex functions. + +#### API Usage Example +SVRGModule provides both high-level and intermediate-level APIs while minimizing the changes with Module API. +example_api_train.py: provides suggested usage of SVRGModule high-level and intermediate-level API. +example_inference.py: provides example usage of SVRGModule inference. + +#### Linear Regression +This example trains a linear regression model using SVRGModule on a real dataset, YearPredictionMSD. +Logs of the training results can be found in experiments.log which will automatically generated when running the +training script. + +##### Dataset +YearPredictionMSD: contains predictions of the release year of a song from audio features. It has over +400,000 samples with 90 features. Please uncomment data downloading script from data_reader.py to download the data. + +#### Benchmarks: +An initial set of benchmarks has been performed on YearPredictionDatasetMSD with linear regression model. + +* benchmark1.py: A lr_scheduler returns a new learning rate based on the number of updates that have been performed. +The training loss of SVRG is less than SGD with lr_scheduler over all of the 100 epochs. + +* benchmark2.py: One drawback for SGD is that in order to converge faster, the learning rate has to decay to zero, +thus SGD needs to start with a small learning rate. The learning rate does not need to decay to zero for SVRG, +therefore we can use a relatively larger learning rate. SGD with learning rate of (0.001, 0.0025) and SVRG with +learning rate of (0.025) are benchmarked. Even though SVRG starts with a relatively large learning rate, it converges +much faster than SGD in both cases. diff --git a/example/svrg_module/api_usage_example/example_api_train.py b/example/svrg_module/api_usage_example/example_api_train.py new file mode 100644 index 000000000000..f6cd1b2e592c --- /dev/null +++ b/example/svrg_module/api_usage_example/example_api_train.py @@ -0,0 +1,124 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 mxnet as mx +import numpy as np +from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule + + +def test_svrg_intermediate_level_api(args): + """Demonstrates intermediate level SVRGModule API where the training process + need to be explicitly defined. KVstore is not explicitly created. + + Parameters + ---------- + args: args + Command line arguments + """ + num_epoch = args.epochs + batch_size = args.batch_size + update_freq = args.update_freq + + di, mod = create_network(batch_size, update_freq) + + mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + mod.init_params(initializer=mx.init.Uniform(0.01), allow_missing=False, force_init=False, allow_extra=False) + kv = mx.kv.create("local") + mod.init_optimizer(kvstore=kv, optimizer='sgd', optimizer_params=(('learning_rate', 0.025),)) + metrics = mx.metric.create("mse") + for e in range(num_epoch): + metrics.reset() + if e % mod.update_freq == 0: + mod.update_full_grads(di) + di.reset() + for batch in di: + mod.forward_backward(data_batch=batch) + mod.update() + mod.update_metric(metrics, batch.label) + mod.logger.info('Epoch[%d] Train cost=%f', e, metrics.get()[1]) + + +def test_svrg_high_level_api(args): + """Demonstrates suggested usage of high level SVRGModule API. KVStore is explicitly created. + + Parameters + ---------- + args: args + Command line arguments + """ + num_epoch = args.epochs + batch_size = args.batch_size + update_freq = args.update_freq + + di, mod = create_network(batch_size, update_freq) + mod.fit(di, eval_metric='mse', optimizer='sgd', optimizer_params=(('learning_rate', 0.025),), num_epoch=num_epoch, + kvstore='local') + + +def create_network(batch_size, update_freq): + """Create a linear regression network for performing SVRG optimization. + Parameters + ---------- + batch_size: int + Size of data split + update_freq: int + Update Frequency for calculating full gradients + + Returns + ---------- + di: mx.io.NDArrayIter + Data iterator + update_freq: SVRGModule + An instance of SVRGModule for performing SVRG optimization + """ + import logging + head = '%(asctime)-15s %(message)s' + logging.basicConfig(level=logging.INFO, format=head) + + train_data = np.random.randint(1, 5, [1000, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=batch_size, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=update_freq, logger=logging + ) + + return di, mod + +# run as a script +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument('-e', dest='epochs', default=100, type=int) + parser.add_argument('-bs', dest='batch_size', default=32, type=int) + parser.add_argument('-f', dest="update_freq", default=2, type=int) + args = parser.parse_args() + + print("========================== Intermediate Level API ==========================") + test_svrg_intermediate_level_api(args) + print("========================== High Level API ==========================") + test_svrg_high_level_api(args) diff --git a/example/svrg_module/api_usage_example/example_inference.py b/example/svrg_module/api_usage_example/example_inference.py new file mode 100644 index 000000000000..312f9796074d --- /dev/null +++ b/example/svrg_module/api_usage_example/example_inference.py @@ -0,0 +1,106 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 mxnet as mx +import numpy as np +import logging +from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule + + +def test_svrg_inference(args): + epoch = args.epochs + batch_size = args.batch_size + update_freq = args.update_freq + + train_iter, val_iter, mod = create_network(batch_size, update_freq) + mod.fit(train_iter, eval_data=val_iter, eval_metric='mse', optimizer='sgd', + optimizer_params=(('learning_rate', 0.025),), + num_epoch=epoch) + + +def get_validation_score(args): + epoch = args.epochs + batch_size = args.batch_size + update_freq = args.update_freq + + train_iter, val_iter, mod = create_network(batch_size, update_freq) + mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label) + mod.init_params(initializer=mx.init.Uniform(0.01), allow_missing=False, force_init=False, allow_extra=False) + mod.init_optimizer(kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.025),)) + metrics = mx.metric.create("mse") + for e in range(epoch): + metrics.reset() + if e % mod.update_freq == 0: + mod.update_full_grads(train_iter) + train_iter.reset() + for batch in train_iter: + mod.forward_backward(data_batch=batch) + mod.update() + mod.update_metric(metrics, batch.label) + + y = mod.predict(val_iter) + + # test-train data split, 20% test data out of 1000 data samples + assert y.shape == (200, 1) + score = mod.score(val_iter, ['mse']) + print("Training Loss on Validation Set is {}".format(score[0][1])) + + +def create_network(batch_size, update_freq): + """Create a linear regression network for performing SVRG optimization. + :return: an instance of mx.io.NDArrayIter + :return: an instance of mx.mod.svrgmodule for performing SVRG optimization + """ + head = '%(asctime)-15s %(message)s' + logging.basicConfig(level=logging.INFO, format=head) + data = np.random.randint(1, 5, [1000, 2]) + + #Test_Train data split + n_train = int(data.shape[0] * 0.8) + weights = np.array([1.0, 2.0]) + label = data.dot(weights) + + di = mx.io.NDArrayIter(data[:n_train, :], label[:n_train], batch_size=batch_size, shuffle=True, label_name='lin_reg_label') + val_iter = mx.io.NDArrayIter(data[n_train:, :], label[n_train:], batch_size=batch_size) + + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=update_freq, logger=logging) + + return di, val_iter, mod + + +# run as a script +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('-e', dest='epochs', default=100, type=int) + parser.add_argument('-bs', dest='batch_size', default=32, type=int) + parser.add_argument('-f', dest="update_freq", default=2, type=int) + args = parser.parse_args() + + print("========================== SVRG Module Inference ==========================") + test_svrg_inference(args) + print("========================SVRG Module Score ============================") + get_validation_score(args) diff --git a/example/svrg_module/benchmarks/benchmark1.png b/example/svrg_module/benchmarks/benchmark1.png new file mode 100644 index 0000000000000000000000000000000000000000..4217db5c93db10f7db71670a75ef529446b58601 GIT binary patch literal 272381 zcma%j2{@E*+dd+h5~XQTmQpGcT2S__(q@Y>j6Go(J7eD?ZI%|1!brr7nIXnLM1>N@ zGKR5dH^y!(WB5OQ@Atj$+wc7!eQ(FnEYCd8{XF+_UDtV?*Lizvpr^wR5(aT_aPZ%} zarG_-2QQ6-gB!hTCvc>MtUb-au?Os=t!;2qTU*?~%iY1r#h!!X#^Xd7ukp+82h*xQ z2);1j+Ly3DQ0IwHZU%^}t>*d-uBiRTk3TXsJOs9=FwK9D6$jyY60TkQ+Wg*zAh28N zhtxrroXVS=me$I%X_vNw%e-e(XZwHte8Z{nE$!vox3L(m1c{%AuJN)8_rH8$_8}&G zR}m*)7srVhNb~a3Cm>#fhpg&G7M1?R5oS-jn*;RNL|B#v{scwbO&& 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zDoK&&pD^`&=Hm`(n7O3cLmJNu1)AD38LAn(UCG#EosESm9DL}jbz-RoB%|a(r1Jno zuu4;9JqW6z&eF#phf1jnY@%$23Gtapx(}Gfed%5si|*#9OZg;FCL^mQo&}z!xegMA zne2Dwat;gwF*y`IN|*tZHM&|h#J42Oys3kSR=)rntca1My-KFoAoRqJ095>b&yM)8 zqCHna7hJL0DbfdAYx(+$*+mq}b0TE7Lxk^G)uUzHVU zw^9M=ZjIlj0;%MhuQzL;oi8bbr2)ly37#d)S$a@X`P?+Bd3`~34*Q8jE)g=J7A>LG zY8$_U^vQeh7rIw(XItC5IJ#^|*?_;IdP%co6SO@Y_D@NxYJxyB{B*ZDi$Ve~onIDODK9zvVRB-+J=xF9)qeZF@Syh|XD@ z8n*iiGu`Ip8Y()i^%130k1I|6R7?~CH>`qW-N$XY-&5K|7ZS)kI5RZAgq5Jk6`OLX zWzfz-26XZM3aFDZ)nO)G=3kW}Z3Yr)Q*=ubt1@1psRjusrc6@^r7YDyvSE!vQ~id4 zRX9s@fq*XH_ekMi{CSxgiK*4peoA&W#=t-3s4|IUp$aQClUZ361d%O8Dk@L|0v>6S z=3NeR^cCaldvN2ALX3>%gtpyH3+4Gx=3`QA`nE2gSEZ7ZYmQk!v%Oal(KmHfMBv~) zP6l0mRyJ-&A21(e)W(@!E$(pxA!YX2{4L&s^S0G8RM{_>Ar!)(Ld0e(bsV5C+B1X{ zBT#a|(np`_x1d%Po{~7<0`NeRsDf=H6kjJ%mg0V^p2)$|Qs9m+!R4Cv%E`7Gluwoz&OjC-cYA+5YcJP8i9XN>0{;ZU`fVYf`JI{d<`y_Z@ z0hbbZHOkIwSt)$5F~qWpoTDm7$JOR~s&Tfw^`m)%22}4k4n* zMMu=&V=fjP_5o9l6W1;+1uqmYRPqAm?`FJ9wH>> # An example of declaring and using SVRGModule. + >>> mod = SVRGModule(symbol=lro, data_names=['data'], label_names=['lin_reg_label'], update_freq=2) + >>> mod.fit(di, eval_metric='mse', optimizer='sgd', optimizer_params=(('learning_rate', 0.025),), + >>> num_epoch=num_epoch, kvstore='local') + """ + + def __init__(self, symbol, data_names=('data',), label_names=('softmax_label',), + logger=logging, context=mx.cpu(), work_load_list=None, + fixed_param_names=None, state_names=None, group2ctxs=None, + compression_params=None, update_freq=None): + super(SVRGModule, self).__init__(symbol, data_names=data_names, label_names=label_names, logger=logger, + context=context, work_load_list=work_load_list, + fixed_param_names=fixed_param_names, state_names=state_names, + group2ctxs=group2ctxs, compression_params=compression_params) + + # Type check update_frequency + if isinstance(update_freq, int): + if update_freq <= 0: + raise ValueError("update_freq in SVRGModule must be a positive integer to represent the frequency for " + "calculating full gradients") + self.update_freq = update_freq + else: + raise TypeError("update_freq in SVRGModule must be a positive integer to represent the frequency for " + "calculating full gradients") + + self._mod_aux = mx.mod.Module(symbol, data_names, label_names, logger, context, work_load_list, + fixed_param_names, state_names, group2ctxs, compression_params) + + self._param_dict = None + self._ctx_len = len(self._context) + + def _reset_bind(self): + """Internal function to reset binded state for both modules.""" + super(SVRGModule, self)._reset_bind() + self._mod_aux._reset_bind() + + def reshape(self, data_shapes, label_shapes=None): + """Reshapes both modules for new input shapes. + + Parameters + ---------- + data_shapes : list of (str, tuple) + Typically is ``data_iter.provide_data``. + label_shapes : list of (str, tuple) + Typically is ``data_iter.provide_label``. + """ + super(SVRGModule, self).reshape(data_shapes, label_shapes=label_shapes) + self._mod_aux.reshape(data_shapes, label_shapes=label_shapes) + + def init_optimizer(self, kvstore='local', optimizer='sgd', + optimizer_params=(('learning_rate', 0.01),), force_init=False): + """Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is + passed in and a special AssignmentOptimizer to accumulate the full gradients. If KVStore is 'local' or None, + the full gradients will be accumulated locally without pushing to the KVStore. Otherwise, additional keys will + be pushed to accumulate the full gradients in the KVStore. + + Parameters + ---------- + kvstore : str or KVStore + Default `'local'`. + optimizer : str or Optimizer + Default `'sgd'` + optimizer_params : dict + Default `(('learning_rate', 0.01),)`. The default value is not a dictionary, + just to avoid pylint warning of dangerous default values. + force_init : bool + Default ``False``, indicating whether we should force re-initializing the + optimizer in the case an optimizer is already installed. + """ + # Init dict for storing average of full gradients for each device + + self._param_dict = [{key: mx.nd.zeros(shape=value.shape, ctx=self._context[i]) + for key, value in self.get_params()[0].items()} for i in range(self._ctx_len)] + + svrg_optimizer = self._create_optimizer(_SVRGOptimizer.__name__, default_opt=optimizer, + kvstore=kvstore, optimizer_params=optimizer_params) + + super(SVRGModule, self).init_optimizer(kvstore=kvstore, optimizer=svrg_optimizer, + optimizer_params=optimizer_params, force_init=force_init) + + # Init additional keys for accumulating full grads in KVStore + if self._kvstore: + for idx, param_on_devs in enumerate(self._exec_group.param_arrays): + name = self._exec_group.param_names[idx] + self._kvstore.init(name + "_full", mx.nd.zeros(shape=self._arg_params[name].shape)) + if self._update_on_kvstore: + self._kvstore.pull(name + "_full", param_on_devs, priority=-idx) + + def _create_optimizer(self, optimizer, default_opt, kvstore, optimizer_params): + """Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and + will redirect update() to the correct optimizer. + + Parameters + ---------- + kvstore : str or KVStore + Default `'local'`. + optimizer: str + Name for SVRGOptimizer + default_opt : str or Optimizer that was passed in. + optimizer_params : dict + optimizer params that was passed in. + """ + + # code partially copied from mxnet module.init_optimizer() to accomodate svrg_optimizer + batch_size = self._exec_group.batch_size + + (kv_store, update_on_kvstore) = mx.model._create_kvstore(kvstore, self._ctx_len, self._arg_params) + if kv_store and 'dist' in kv_store.type and '_sync' in kv_store.type: + batch_size *= kv_store.num_workers + rescale_grad = 1.0 / batch_size + + idx2name = {} + if update_on_kvstore: + idx2name.update(enumerate(self._exec_group.param_names)) + else: + for k in range(self._ctx_len): + idx2name.update({i * self._ctx_len + k: n + for i, n in enumerate(self._exec_group.param_names)}) + + # update idx2name to include new keys + for key in self._param_dict[0].keys(): + max_key = max(list(idx2name.keys())) + max_key += 1 + idx2name[max_key] = key + "_full" + + optimizer_params = dict(optimizer_params) + if 'rescale_grad' not in optimizer_params: + optimizer_params['rescale_grad'] = rescale_grad + optimizer_params["default_optimizer"] = default_opt + optimizer_params["param_idx2name"] = idx2name + optimizer = mx.optimizer.create(optimizer, **optimizer_params) + + return optimizer + + def bind(self, data_shapes, label_shapes=None, for_training=True, + inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): + """Binds the symbols to construct executors for both two modules. This is necessary before one + can perform computation with the SVRGModule. + + Parameters + ---------- + data_shapes : list of (str, tuple) + Typically is ``data_iter.provide_data``. + label_shapes : list of (str, tuple) + Typically is ``data_iter.provide_label``. + for_training : bool + Default is ``True``. Whether the executors should be bound for training. + inputs_need_grad : bool + Default is ``False``. Whether the gradients to the input data need to be computed. + Typically this is not needed. But this might be needed when implementing composition + of modules. + force_rebind : bool + Default is ``False``. This function does nothing if the executors are already + bound. But with this ``True``, the executors will be forced to rebind. + shared_module : Module + Default is ``None``. This is used in bucketing. When not ``None``, the shared module + essentially corresponds to a different bucket -- a module with different symbol + but with the same sets of parameters (e.g. unrolled RNNs with different lengths). + """ + # force rebinding is typically used when one want to switch from + # training to prediction phase. + + super(SVRGModule, self).bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind, + shared_module, grad_req) + + if for_training: + self._mod_aux.bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind, shared_module, + grad_req) + + def forward(self, data_batch, is_train=None): + """Forward computation for both two modules. It supports data batches with different shapes, such as + different batch sizes or different image sizes. + If reshaping of data batch relates to modification of symbol or module, such as + changing image layout ordering or switching from training to predicting, module + rebinding is required. + + See Also + ---------- + :meth:`BaseModule.forward`. + + Parameters + ---------- + data_batch : DataBatch + Could be anything with similar API implemented. + is_train : bool + Default is ``None``, which means ``is_train`` takes the value of ``self.for_training``. + """ + + super(SVRGModule, self).forward(data_batch, is_train) + + if is_train: + self._mod_aux.forward(data_batch, is_train) + + def backward(self, out_grads=None): + """Backward computation. + + See Also + ---------- + :meth:`BaseModule.backward`. + + Parameters + ---------- + out_grads : NDArray or list of NDArray, optional + Gradient on the outputs to be propagated back. + This parameter is only needed when bind is called + on outputs that are not a loss function. + """ + + super(SVRGModule, self).backward(out_grads) + + if self._mod_aux.binded: + self._mod_aux.backward(out_grads) + + def update(self): + """Updates parameters according to the installed optimizer and the gradients computed + in the previous forward-backward batch. The gradients in the _exec_group will be overwritten + using the gradients calculated by the SVRG update rule. + + When KVStore is used to update parameters for multi-device or multi-machine training, + a copy of the parameters is stored in KVStore. Note that for `row_sparse` parameters, + this function does update the copy of parameters in KVStore, but doesn't broadcast the + updated parameters to all devices / machines. Please call `prepare` to broadcast + `row_sparse` parameters with the next batch of data. + + See Also + ---------- + :meth:`BaseModule.update`. + """ + + self._update_svrg_gradients() + super(SVRGModule, self).update() + + def update_full_grads(self, train_data): + """Computes the gradients over all data w.r.t weights of past + m epochs. For distributed env, it will accumulate full grads in the kvstore. + + Parameters + ---------- + train_data: DataIter + Train data iterator + + """ + param_names = self._exec_group.param_names + arg, aux = self.get_params() + self._mod_aux.set_params(arg_params=arg, aux_params=aux) + train_data.reset() + nbatch = 0 + padding = 0 + for batch in train_data: + self._mod_aux.forward(batch, is_train=True) + self._mod_aux.backward() + nbatch += 1 + + for ctx in range(self._ctx_len): + for index, name in enumerate(param_names): + grads = self._mod_aux._exec_group.grad_arrays[index][ctx] + self._param_dict[ctx][name] = mx.nd.broadcast_add(self._param_dict[ctx][name], grads, axis=0) + padding = batch.pad + + # Average full gradients over number of batches, accumulate in the kvstore if kvstore is set + for i in range(self._ctx_len): + for name in param_names: + self._param_dict[i][name] /= (nbatch - padding / train_data.batch_size) + + if self._kvstore: + # Push a list of gradients from each device in the KVStore + for name in param_names: + grad_list = list(self._param_dict[i][name] for i in range(self._ctx_len)) + self._accumulate_kvstore(name, grad_list) + + def _accumulate_kvstore(self, key, value): + """Accumulate gradients over all data in the KVStore. In distributed setting, each worker sees a portion of + data. The full gradients will be aggregated from each worker in the KVStore. + + Parameters + ---------- + + key: int or str + Key in the KVStore. + value: NDArray, RowSparseNDArray + Average of the full gradients. + """ + + # Accumulate full gradients for current epochs + self._kvstore.push(key + "_full", value) + self._kvstore._barrier() + self._kvstore.pull(key + "_full", value) + + self._allocate_gradients(key, value) + + def _allocate_gradients(self, key, value): + """Allocate average of full gradients accumulated in the KVStore to each device. + + Parameters + ---------- + + key: int or str + Key in the kvstore. + value: List of NDArray, List of RowSparseNDArray + A list of average of the full gradients in the KVStore. + """ + + for i in range(self._ctx_len): + self._param_dict[i][key] = value[i] / self._ctx_len + + def _svrg_grads_update_rule(self, g_curr_batch_curr_weight, g_curr_batch_special_weight, + g_special_weight_all_batch): + """Calculates the gradient based on the SVRG update rule. + Parameters + ---------- + g_curr_batch_curr_weight : NDArray + gradients of current weight of self.mod w.r.t current batch of data + g_curr_batch_special_weight: NDArray + gradients of the weight of past m epochs of self._mod_special w.r.t current batch of data + g_special_weight_all_batch: NDArray + average of full gradients over full pass of data + + Returns + ---------- + Gradients calculated using SVRG update rule: + grads = g_curr_batch_curr_weight - g_curr_batch_special_weight + g_special_weight_all_batch + """ + + for index, grad in enumerate(g_curr_batch_curr_weight): + grad -= g_curr_batch_special_weight[index] + grad += g_special_weight_all_batch[index] + return g_curr_batch_curr_weight + + def _update_svrg_gradients(self): + """Calculates gradients based on the SVRG update rule. + """ + param_names = self._exec_group.param_names + for ctx in range(self._ctx_len): + for index, name in enumerate(param_names): + g_curr_batch_reg = self._exec_group.grad_arrays[index][ctx] + g_curr_batch_special = self._mod_aux._exec_group.grad_arrays[index][ctx] + g_special_weight_all_batch = self._param_dict[ctx][name] + g_svrg = self._svrg_grads_update_rule(g_curr_batch_reg, g_curr_batch_special, + g_special_weight_all_batch) + self._exec_group.grad_arrays[index][ctx] = g_svrg + + def fit(self, train_data, eval_data=None, eval_metric='acc', + epoch_end_callback=None, batch_end_callback=None, kvstore='local', + optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), + eval_end_callback=None, + eval_batch_end_callback=None, initializer=mx.init.Uniform(0.01), + arg_params=None, aux_params=None, allow_missing=False, + force_rebind=False, force_init=False, begin_epoch=0, num_epoch=None, + validation_metric=None, monitor=None, sparse_row_id_fn=None): + """Trains the module parameters. + Parameters + ---------- + train_data : DataIter + Train DataIter. + eval_data : DataIter + If not ``None``, will be used as validation set and the performance + after each epoch will be evaluated. + eval_metric : str or EvalMetric + Defaults to 'accuracy'. The performance measure used to display during training. + Other possible predefined metrics are: + 'ce' (CrossEntropy), 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy'. + epoch_end_callback : function or list of functions + Each callback will be called with the current `epoch`, `symbol`, `arg_params` + and `aux_params`. + batch_end_callback : function or list of function + Each callback will be called with a `BatchEndParam`. + kvstore : str or KVStore + Defaults to 'local'. + optimizer : str or Optimizer + Defaults to 'sgd'. + optimizer_params : dict + Defaults to ``(('learning_rate', 0.01),)``. The parameters for + the optimizer constructor. + The default value is not a dict, just to avoid pylint warning on dangerous + default values. + eval_end_callback : function or list of function + These will be called at the end of each full evaluation, with the metrics over + the entire evaluation set. + eval_batch_end_callback : function or list of function + These will be called at the end of each mini-batch during evaluation. + initializer : Initializer + The initializer is called to initialize the module parameters when they are + not already initialized. + arg_params : dict + Defaults to ``None``, if not ``None``, should be existing parameters from a trained + model or loaded from a checkpoint (previously saved model). In this case, + the value here will be used to initialize the module parameters, unless they + are already initialized by the user via a call to `init_params` or `fit`. + `arg_params` has a higher priority than `initializer`. + aux_params : dict + Defaults to ``None``. Similar to `arg_params`, except for auxiliary states. + allow_missing : bool + Defaults to ``False``. Indicates whether to allow missing parameters when `arg_params` + and `aux_params` are not ``None``. If this is ``True``, then the missing parameters + will be initialized via the `initializer`. + force_rebind : bool + Defaults to ``False``. Whether to force rebinding the executors if already bound. + force_init : bool + Defaults to ``False``. Indicates whether to force initialization even if the + parameters are already initialized. + begin_epoch : int + Defaults to 0. Indicates the starting epoch. Usually, if resumed from a + checkpoint saved at a previous training phase at epoch N, then this value should be + N+1. + num_epoch : int + Number of epochs for training. + sparse_row_id_fn : A callback function + The function takes `data_batch` as an input and returns a dict of + str -> NDArray. The resulting dict is used for pulling row_sparse + parameters from the kvstore, where the str key is the name of the param, + and the value is the row id of the param to pull. + validation_metric: + """ + + assert num_epoch is not None, 'please specify number of epochs' + + self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label, + for_training=True, force_rebind=force_rebind) + if monitor is not None: + self.install_monitor(monitor) + self.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params, + allow_missing=allow_missing, force_init=force_init) + self.init_optimizer(kvstore=kvstore, optimizer=optimizer, optimizer_params=optimizer_params) + + if validation_metric is None: + validation_metric = eval_metric + if not isinstance(eval_metric, mx.metric.EvalMetric): + eval_metric = mx.metric.create(eval_metric) + + ################################################################################ + # training loop + ################################################################################ + for epoch in range(begin_epoch, num_epoch): + eval_metric.reset() + tic = time.time() + if epoch % self.update_freq == 0: + self.update_full_grads(train_data) + + train_data.reset() + data_iter = iter(train_data) + end_of_batch = False + nbatch = 0 + next_data_batch = next(data_iter) + + while not end_of_batch: + data_batch = next_data_batch + if monitor is not None: + monitor.tic() + + self.forward_backward(data_batch) + self.update() + + if isinstance(data_batch, list): + self.update_metric(eval_metric, [db.label for db in data_batch], pre_sliced=True) + else: + self.update_metric(eval_metric, data_batch.label) + + try: + # pre fetch next batch + next_data_batch = next(data_iter) + self.prepare(next_data_batch, sparse_row_id_fn=sparse_row_id_fn) + except StopIteration: + end_of_batch = True + + if monitor is not None: + monitor.toc_print() + + if end_of_batch: + eval_name_vals = eval_metric.get_name_value() + + if batch_end_callback is not None: + batch_end_params = mx.model.BatchEndParam(epoch=epoch, nbatch=nbatch, + eval_metric=eval_metric, locals=locals()) + for callback in mx.base._as_list(batch_end_callback): + callback(batch_end_params) + + nbatch += 1 + for name, val in eval_name_vals: + self.logger.info('Epoch[%d] Train-%s=%f', epoch, name, val) + toc = time.time() + self.logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc - tic)) + + # sync aux params across devices + arg_params, aux_params = self.get_params() + self.set_params(arg_params, aux_params) + + if epoch_end_callback is not None: + for callback in mx.base._as_list(epoch_end_callback): + callback(epoch, self.symbol, arg_params, aux_params) + + # ---------------------------------------- + # evaluation on validation set + if eval_data: + res = self.score(eval_data, validation_metric, + score_end_callback=eval_end_callback, + batch_end_callback=eval_batch_end_callback, epoch=epoch) + for name, val in res: + self.logger.info('Epoch[%d] Validation-%s=%f', epoch, name, val) + + def prepare(self, data_batch, sparse_row_id_fn=None): + """Prepares two modules for processing a data batch. + + Usually involves switching bucket and reshaping. + For modules that contain `row_sparse` parameters in KVStore, + it prepares the `row_sparse` parameters based on the sparse_row_id_fn. + + When KVStore is used to update parameters for multi-device or multi-machine training, + a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters, + the `update()` updates the copy of parameters in KVStore, but doesn't broadcast + the updated parameters to all devices / machines. The `prepare` function is used to + broadcast `row_sparse` parameters with the next batch of data. + + Parameters + ---------- + data_batch : DataBatch + The current batch of data for forward computation. + + sparse_row_id_fn : A callback function + The function takes `data_batch` as an input and returns a dict of + str -> NDArray. The resulting dict is used for pulling row_sparse + parameters from the kvstore, where the str key is the name of the param, + and the value is the row id of the param to pull. + """ + super(SVRGModule, self).prepare(data_batch, sparse_row_id_fn) + self._mod_aux.prepare(data_batch, sparse_row_id_fn) diff --git a/python/mxnet/contrib/svrg_optimization/svrg_optimizer.py b/python/mxnet/contrib/svrg_optimization/svrg_optimizer.py new file mode 100644 index 000000000000..0f695a1b2ff0 --- /dev/null +++ b/python/mxnet/contrib/svrg_optimization/svrg_optimizer.py @@ -0,0 +1,171 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. +"""A `_SVRGOptimizer` encapsulates two optimizers to support SVRGModule in single machine and distributed settings. +Both `_AssignmentOptimizer` and `_SVRGOptimizer` are designed to be used with SVRGModule only. +""" + + +import mxnet as mx + + +@mx.optimizer.register +class _AssignmentOptimizer(mx.optimizer.Optimizer): + """_AssignmentOptimizer assigns gradients to weights for SVRGModule's full gradients + accumulation in the KVStore. It is a helper optimizer that is designed to be used with SVRGModule only. + """ + def update(self, index, weight, grad, state): + """Assign the gradients to weight for accumulating full gradients in the KVStore across all devices and workers. + + Parameters + ---------- + index : int + The unique index of the parameter into the individual learning + rates and weight decays. Learning rates and weight decay + may be set via `set_lr_mult()` and `set_wd_mult()`, respectively. + weight : NDArray + The parameter to be updated. + grad : NDArray + The gradient of the objective with respect to this parameter. + state: any obj + AssignmentOptimizer will not need to be associated with state. + """ + + weight[:] = grad + + +@mx.optimizer.register +class _SVRGOptimizer(mx.optimizer.Optimizer): + """_SVRGOptimizer is a wrapper class for two optimizers: _AssignmentOptimizer for accumulating full gradients in the + KVStore and a default optimizer that is passed in as a parameter in `mod.init_optimizer()` + The _SVRGOptimizer is designed to be used with SVRGModule only. + + This optimizer accepts the following parameters in addition to those accepted by :class:`.Optimizer`. + + Parameters + ---------- + default_optimizer: str or Optimizer + Optimizer passed-in when invoke on mx.mod.init_optimizer in SVRGModule + """ + + def __init__(self, default_optimizer, **kwargs): + # Reconstruct kwargs to identify additional params for default optimizer + base_param = self._check_params(**kwargs) + super(_SVRGOptimizer, self).__init__(**base_param) + if isinstance(default_optimizer, str): + self.default_opt = mx.optimizer.create(default_optimizer, **kwargs) + else: + self.default_opt = default_optimizer + self.aux_opt = mx.optimizer.create(_AssignmentOptimizer.__name__) + + @staticmethod + def _check_params(**kwargs): + """ Reassemble kwargs to identify additional optimizer params for default optimizers. base_params contains + all the param names in base class Optimizer. + + Parameters + ---------- + kwargs: dict + Parameters for the default optimizer + + Returns + ---------- + default_params: dict + Optimizer parameters that are defined in base class Optimizer + """ + + optimizer_param = dict(kwargs) + base_params = ['rescale_grad', 'param_idx2name', 'wd', 'clip_gradient', 'learning_rate', 'lr_scheduler', 'sym', + 'begin_num_update', 'multi_precision', 'param_dict'] + + default_params = {} + for key, _ in optimizer_param.items(): + if key in base_params: + default_params[key] = optimizer_param[key] + + return default_params + + def update(self, index, weight, grad, state): + """Updates the given parameter using the corresponding gradient and state. If key contains 'full', update with + `_AssignmentOptimizer` otherwise will use default optimizer. + + Parameters + ---------- + index : int + The unique index of the parameter into the individual learning + rates and weight decays. Learning rates and weight decay + may be set via `set_lr_mult()` and `set_wd_mult()`, respectively. + weight : NDArray + The parameter to be updated. + grad : NDArray + The gradient of the objective with respect to this parameter. + state : any obj + The state returned by `create_state()`. + """ + + name = self._check_index(index) + + if "full" in name: + self.aux_opt.update(index, weight, grad, state) + else: + # use the default optimizer + self.default_opt.update(index, weight, grad, state) + + def create_state(self, index, weight): + """Creates auxiliary state for a given weight. + Some optimizers require additional states, e.g. as momentum, in addition + to gradients in order to update weights. This function creates state + for a given weight which will be used in `update`. This function is + called only once for each weight. + + Parameters + ---------- + index : int + An unique index to identify the weight. + weight : NDArray + The weight. + Returns + ------- + state : any obj + The state associated with the weight. + """ + + name = self._check_index(index) + if "full" in name: + return self.aux_opt.create_state(index, weight) + else: + # + return self.default_opt.create_state(index, weight) + + def _check_index(self, index): + """Check index in idx2name to get corresponding param_name + Parameters + ---------- + index : int or str + An unique index to identify the weight. + Returns + ------- + name : str + Name of the Module parameter + """ + + if index in self.idx2name.values(): + # index is a str + name = index + else: + # index is an int + name = self.idx2name[index] + return name diff --git a/tests/python/unittest/test_contrib_svrg_module.py b/tests/python/unittest/test_contrib_svrg_module.py new file mode 100644 index 000000000000..2a73f1cc8e79 --- /dev/null +++ b/tests/python/unittest/test_contrib_svrg_module.py @@ -0,0 +1,301 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 mxnet as mx +import numpy as np +from common import with_seed, assertRaises +from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule +from mxnet.test_utils import * + + +def setup(): + train_data = np.random.randint(1, 5, [1000, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=32, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=2) + mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + mod.init_params(initializer=mx.init.Uniform(0.01), allow_missing=False, force_init=False, allow_extra=False) + + return di, mod + + +def test_bind_module(): + _, mod = setup() + assert mod.binded == True + assert mod._mod_aux.binded == True + + +def test_module_init(): + _, mod = setup() + assert mod._mod_aux is not None + + +def test_module_initializer(): + def regression_model(m): + x = mx.symbol.var("data", stype='csr') + v = mx.symbol.var("v", shape=(m, 1), init=mx.init.Uniform(scale=.1), + stype='row_sparse') + model = mx.symbol.dot(lhs=x, rhs=v) + y = mx.symbol.Variable("label") + model = mx.symbol.LinearRegressionOutput(data=model, label=y, name="out") + return model + + #shape of the data + n, m = 128, 100 + model = regression_model(m) + + data = mx.nd.zeros(shape=(n, m), stype='csr') + label = mx.nd.zeros((n, 1)) + iterator = mx.io.NDArrayIter(data=data, label={'label': label}, + batch_size=n, last_batch_handle='discard') + + # create module + mod = SVRGModule(symbol=model, data_names=['data'], label_names=['label'], update_freq=2) + mod.bind(data_shapes=iterator.provide_data, label_shapes=iterator.provide_label) + mod.init_params() + v = mod._arg_params['v'] + assert v.stype == 'row_sparse' + assert np.sum(v.asnumpy()) != 0 + + +def test_module_bind(): + x = mx.sym.Variable("data") + net = mx.sym.FullyConnected(x, num_hidden=1) + + mod = SVRGModule(symbol=net, data_names=['data'], label_names=None, update_freq=2) + assertRaises(TypeError, mod.bind, data_shapes=['data', mx.nd.zeros(shape=(2, 1))]) + + mod.bind(data_shapes=[('data', (2, 1))]) + assert mod.binded == True + assert mod._mod_aux.binded == True + +@with_seed() +def test_module_save_load(): + import tempfile + import os + + x = mx.sym.Variable("data") + y = mx.sym.Variable("softmax_label") + net = mx.sym.FullyConnected(x, y, num_hidden=1) + + mod = SVRGModule(symbol=net, data_names=['data'], label_names=['softmax_label'], update_freq=2) + mod.bind(data_shapes=[('data', (1, 1))]) + mod.init_params() + mod.init_optimizer(optimizer='sgd', optimizer_params={'learning_rate': 0.1}) + mod.update() + + #create tempfile + tmp = tempfile.mkdtemp() + tmp_file = os.path.join(tmp, 'svrg_test_output') + mod.save_checkpoint(tmp_file, 0, save_optimizer_states=True) + + mod2 = SVRGModule.load(tmp_file, 0, load_optimizer_states=True, data_names=('data', )) + mod2.bind(data_shapes=[('data', (1, 1))]) + mod2.init_optimizer(optimizer_params={'learning_rate': 0.1}) + assert mod._symbol.tojson() == mod2._symbol.tojson() + + # Multi-device + mod3 = SVRGModule(symbol=net, data_names=['data'], label_names=['softmax_label'], update_freq=3, + context=[mx.cpu(0), mx.cpu(1)]) + mod3.bind(data_shapes=[('data', (10, 10))]) + mod3.init_params() + mod3.init_optimizer(optimizer_params={'learning_rate': 1.0}) + mod3.update() + mod3.save_checkpoint(tmp_file, 0, save_optimizer_states=True) + + mod4 = SVRGModule.load(tmp_file, 0, load_optimizer_states=True, data_names=('data', )) + mod4.bind(data_shapes=[('data', (10, 10))]) + mod4.init_optimizer(optimizer_params={'learning_rate': 1.0}) + assert mod3._symbol.tojson() == mod4._symbol.tojson() + +@with_seed() +def test_svrgmodule_reshape(): + data = mx.sym.Variable("data") + sym = mx.sym.FullyConnected(data=data, num_hidden=4, name='fc') + + dshape=(3, 4) + mod = SVRGModule(sym, data_names=["data"], label_names=None, context=[mx.cpu(0), mx.cpu(1)], update_freq=1) + mod.bind(data_shapes=[('data', dshape)]) + mod.init_params() + mod._mod_aux.init_params() + mod.init_optimizer(optimizer_params={"learning_rate": 1.0}) + + data_batch = mx.io.DataBatch(data=[mx.nd.ones(dshape)], label=None) + mod.forward(data_batch) + mod.backward([mx.nd.ones(dshape)]) + mod.update() + assert mod.get_outputs()[0].shape == dshape + + dshape = (2, 4) + mod.reshape(data_shapes=[('data', dshape)]) + mod.forward(mx.io.DataBatch(data=[mx.nd.ones(dshape)], + label=None)) + mod.backward([mx.nd.ones(dshape)]) + mod.update() + assert mod.get_outputs()[0].shape == dshape + + +def test_update_full_grad(): + def create_network(): + train_data = np.random.randint(1, 5, [10, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=5, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=2) + mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + mod.init_params(initializer=mx.init.One(), allow_missing=False, force_init=False, allow_extra=False) + mod.init_optimizer(kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), + force_init=False) + return di, mod + + di, svrg_mod = create_network() + + # Calculates the average of full gradients over number batches + full_grads_weights = mx.nd.zeros(shape=svrg_mod.get_params()[0]['fc1_weight'].shape) + arg, aux = svrg_mod.get_params() + svrg_mod._mod_aux.set_params(arg_params=arg, aux_params=aux) + num_batch = 2 + + for batch in di: + svrg_mod.forward(batch) + svrg_mod.backward() + full_grads_weights = mx.nd.broadcast_add(svrg_mod._exec_group.grad_arrays[0][0], full_grads_weights, axis=0) + full_grads_weights /= num_batch + + di.reset() + svrg_mod.update_full_grads(di) + assert same(full_grads_weights, svrg_mod._param_dict[0]['fc1_weight']) + + +@with_seed() +def test_svrg_with_sgd(): + def create_module_with_sgd(): + train_data = np.random.randint(1, 5, [100, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=10, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + reg_mod = mx.mod.Module( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label']) + reg_mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + reg_mod.init_params(initializer=mx.init.One(), allow_missing=False, force_init=False, allow_extra=False) + reg_mod.init_optimizer(kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),)) + + svrg_mod = SVRGModule(symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], + update_freq=2) + svrg_mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + svrg_mod.init_params(initializer=mx.init.One(), allow_missing=False, force_init=False, allow_extra=False) + svrg_mod.init_optimizer(kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),)) + + return di,reg_mod, svrg_mod + + di, reg_mod, svrg_mod = create_module_with_sgd() + num_epoch = 10 + + # Use metric MSE + metrics = mx.metric.create("mse") + + # Train with SVRGModule + for e in range(num_epoch): + metrics.reset() + if e % svrg_mod.update_freq == 0: + svrg_mod.update_full_grads(di) + di.reset() + for batch in di: + svrg_mod.forward_backward(data_batch=batch) + svrg_mod.update() + svrg_mod.update_metric(metrics, batch.label) + svrg_mse = metrics.get()[1] + + # Train with SGD standard Module + di.reset() + for e in range(num_epoch): + metrics.reset() + di.reset() + for batch in di: + reg_mod.forward_backward(data_batch=batch) + reg_mod.update() + reg_mod.update_metric(metrics, batch.label) + sgd_mse = metrics.get()[1] + + assert svrg_mse < sgd_mse + + +def test_accumulate_kvstore(): + # Test KVStore behavior when push a list of values + kv = mx.kv.create('local') + kv.init("fc1_weight", mx.nd.zeros(shape=(1, 2))) + kv.init("fc1_weight_full", mx.nd.zeros(shape=(1, 2))) + b = [mx.nd.ones(shape=(1, 2)) for i in range(4)] + a = mx.nd.zeros(shape=(1, 2)) + kv.push("fc1_weight_full", b) + kv.pull("fc1_weight_full", out=a) + assert same(a, [mx.nd.array([4, 4])]) + assert kv.num_workers == 1 + + # Test accumulate in KVStore and allocate gradients + kv_test = mx.kv.create('local') + _, svrg_mod = setup() + svrg_mod.init_optimizer(kvstore=kv_test, optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False) + svrg_mod._accumulate_kvstore("fc1_weight", b) + assert len(svrg_mod._param_dict) == svrg_mod._ctx_len + assert same(svrg_mod._param_dict[0]["fc1_weight"], b[0]) + + +def test_fit(): + di, mod = setup() + num_epoch = 100 + metric = mx.metric.create("mse") + mod.fit(di, eval_metric=metric, optimizer='sgd', optimizer_params=(('learning_rate', 0.025),), num_epoch=num_epoch, + kvstore='local') + + # Estimated MSE for using SGD optimizer of lr = 0.025, SVRG MSE should be smaller + estimated_mse = 1e-6 + assert metric.get()[1] < estimated_mse + + +if __name__ == "__main__": + import nose + nose.runmodule() diff --git a/tests/python/unittest/test_contrib_svrg_optimizer.py b/tests/python/unittest/test_contrib_svrg_optimizer.py new file mode 100644 index 000000000000..a1898479ff1f --- /dev/null +++ b/tests/python/unittest/test_contrib_svrg_optimizer.py @@ -0,0 +1,101 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 numpy as np +import mxnet as mx +from mxnet.test_utils import same +from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule +from mxnet.contrib.svrg_optimization.svrg_optimizer import _SVRGOptimizer + + +def create_network(): + + train_data = np.random.randint(1, 5, [1000, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + batch_size = 32 + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=batch_size, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=2 + ) + + mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + mod.init_params(initializer=mx.init.Uniform(0.01), allow_missing=False, + force_init=False, allow_extra=False) + + return di, mod + + +def test_init_svrg_optimizer(): + _, mod = create_network() + + kv = mx.kv.create('local') + mod.init_optimizer(kvstore=kv, optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), + force_init=False) + + assert type(mod._optimizer).__name__ == _SVRGOptimizer.__name__ + + +def test_svrg_optimizer_constructor(): + kv = mx.kv.create('local') + svrg_optimizer = _SVRGOptimizer(default_optimizer='sgd', learning_rate=-1.0) + kv.set_optimizer(svrg_optimizer) + + assert svrg_optimizer.default_opt.lr == -1.0 + + +def test_kvstore_init_aux_keys(): + param_idx2name = {0: "weight", 1: "weight_full"} + + svrg_optimizer = _SVRGOptimizer(default_optimizer='sgd', param_idx2name= param_idx2name, learning_rate=1.0) + kv = mx.kv.create('local') + kv.set_optimizer(svrg_optimizer) + + # Use default sgd optimizer + param_weight_init = mx.nd.array([0, 0, 0]) + param_weight_update = mx.nd.array([1, 1, 1]) + + kv.init(0, param_weight_init) + kv.push(0, param_weight_update) + kv.pull(0, param_weight_init) + + param_weight_full_init = mx.nd.array([1, 1, 1]) + param_weight_full_update = mx.nd.array([2, 2, 2]) + + # Use AssignmentOptimizer + kv.init(1, param_weight_full_init) + kv.push(1, param_weight_full_update) + kv.pull(1, param_weight_full_init) + + # updated weights using default sgd optimizer + same(param_weight_init.asnumpy(), np.array([-1, -1, -1])) + # updated with AssignmentOptimizer + same(param_weight_full_init.asnumpy(), np.array([2, 2, 2])) + + +if __name__ == "__main__": + import nose + nose.runmodule()

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