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new 33226ab Remove kvstore calls from FM example (#10946)
33226ab is described below
commit 33226abe767b1311942b7e1805557838a2aa1d36
Author: Haibin Lin <linhaibin.e...@gmail.com>
AuthorDate: Thu May 17 09:45:26 2018 -0700
Remove kvstore calls from FM example (#10946)
* Remove kvstore calls from FM example
* fix typo
---
example/sparse/factorization_machine/train.py | 24 +++++++++++++-----------
example/sparse/linear_classification/train.py | 2 +-
2 files changed, 14 insertions(+), 12 deletions(-)
diff --git a/example/sparse/factorization_machine/train.py
b/example/sparse/factorization_machine/train.py
index 741cf95..af3d60b 100644
--- a/example/sparse/factorization_machine/train.py
+++ b/example/sparse/factorization_machine/train.py
@@ -58,6 +58,7 @@ parser.add_argument('--log-interval', type=int, default=100,
parser.add_argument('--kvstore', type=str, default='local',
help='what kvstore to use', choices=["dist_async",
"local"])
+
if __name__ == '__main__':
import logging
head = '%(asctime)-15s %(message)s'
@@ -75,6 +76,16 @@ if __name__ == '__main__':
assert(args.data_train is not None and args.data_test is not None), \
"dataset for training or test is missing"
+ def batch_row_ids(data_batch):
+ """ Generate row ids based on the current mini-batch """
+ idx = data_batch.data[0].indices
+ return {'w': idx, 'v': idx}
+
+ def all_row_ids(data_batch):
+ """ Generate row ids for all rows """
+ all_rows = mx.nd.arange(0, num_features, dtype='int64')
+ return {'w': all_rows, 'v': all_rows}
+
# create kvstore
kv = mx.kvstore.create(kvstore)
# data iterator
@@ -102,12 +113,6 @@ if __name__ == '__main__':
metric = mx.metric.create(['log_loss'])
speedometer = mx.callback.Speedometer(batch_size, log_interval)
- # get the sparse weight parameter
- w_index = mod._exec_group.param_names.index('w')
- w_param = mod._exec_group.param_arrays[w_index]
- v_index = mod._exec_group.param_names.index('v')
- v_param = mod._exec_group.param_arrays[v_index]
-
logging.info('Training started ...')
train_iter = iter(train_data)
eval_iter = iter(eval_data)
@@ -118,9 +123,7 @@ if __name__ == '__main__':
nbatch += 1
# manually pull sparse weights from kvstore so that _square_sum
# only computes the rows necessary
- row_ids = batch.data[0].indices
- kv.row_sparse_pull('w', w_param, row_ids=[row_ids],
priority=-w_index)
- kv.row_sparse_pull('v', v_param, row_ids=[row_ids],
priority=-v_index)
+ mod.prepare(batch, sparse_row_id_fn=batch_row_ids)
mod.forward_backward(batch)
# update all parameters (including the weight parameter)
mod.update()
@@ -131,8 +134,7 @@ if __name__ == '__main__':
speedometer(speedometer_param)
# pull all updated rows before validation
- kv.row_sparse_pull('w', w_param, row_ids=[row_ids], priority=-w_index)
- kv.row_sparse_pull('v', v_param, row_ids=[row_ids], priority=-v_index)
+ mod.prepare(None, all_row_ids)
# evaluate metric on validation dataset
score = mod.score(eval_iter, ['log_loss'])
logging.info("epoch %d, eval log loss = %s" % (epoch, score[0][1]))
diff --git a/example/sparse/linear_classification/train.py
b/example/sparse/linear_classification/train.py
index cde40dd..4d60efb 100644
--- a/example/sparse/linear_classification/train.py
+++ b/example/sparse/linear_classification/train.py
@@ -46,7 +46,7 @@ AVAZU = {
def batch_row_ids(data_batch):
""" Generate row ids based on the current mini-batch """
- return {'weight': batch.data[0].indices}
+ return {'weight': data_batch.data[0].indices}
def all_row_ids(data_batch):
""" Generate row ids for all rows """
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