I’m trying to build a recommender model in Gluon. The model learns user and item embeddings from a co-occurrence matrix, similar to the example here: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_applying_machine_learning/gluon_recommender_system/recommender.py The training data is in the format <UserID: int32, ItemID: int32, Score: float32>
My user embedding matrix is too large to fit into the memory of a single GPU so my plan was to partition co-occurrences by UserID and only store embeddings for a given user on a subset of the GPUs (probably just 1). My hope was to use sparse weights indexed by the original UserID to avoid having to manage a separate mapping from UserID to local GPU index which would be required if I were to use dense embeddings on each GPU, and I think this would become even harder to manage if I wanted to assign a user to more than 1 GPU.
I have some proof of concept code which tries to use
mxnet.gluon.contrib.nn.SparseEmbedding for this but I can’t get it to work, or figure out if it’s even possible. If someone could review what I’m trying to do and provide guidance that would be much appreciated!
import mxnet as mx from mxnet.gluon.contrib.nn import SparseEmbedding # Weight initializer from a pre-determined NDArray class NDArrayWeightInitializer(mx.init.Initializer): def __init__(self, nd_array): super(NDArrayWeightInitializer, self).__init__() self._nd_array = nd_array def _init_weight(self, _, arr): self._nd_array.copyto(arr) # Total number of users across all contexts. User IDs range from [0, total_users - 1] total_users = 100000000 embedding_dimensionality = 100 # User IDs that are assigned to this context # (I do not want their embeddings to be replicated to all contexts) user_ids_in_context = mx.nd.array([0,2,4,6,8,10]) # Create initial sparse weights (ones for users embedded in this context) dense_weights_for_users_in_context = mx.nd.ones((user_ids_in_context.shape, embedding_dimensionality)) sparse_weights_for_users_in_context = mx.nd.sparse.row_sparse_array((dense_weights_for_users_in_context, user_ids_in_context), shape=(total_users, embedding_dimensionality)) # Create sparse embedding layer and initialize weights for users in this context layer = SparseEmbedding(total_users, embedding_dimensionality) layer.initialize(init=NDArrayWeightInitializer(sparse_weights_for_users_in_context)) trainer = mx.gluon.Trainer(layer.collect_params(), 'sgd') print(layer.weight.row_sparse_data(row_id=user_ids_in_context).indices) print(layer.weight.row_sparse_data(row_id=user_ids_in_context).data) # Minibatch of training data contains a subset of the users that are embedded in this context user_ids_in_minibatch = mx.nd.array([0,4,8]) with mx.autograd.record(): y = layer(user_ids_in_minibatch) y.backward() trainer.step(user_ids_in_minibatch.shape) # Check gradients are properly set for users in minibatch print(layer.weight.grad().indices) print(layer.weight.grad().data) # Check weights are updated for users in minibatch print(layer.weight.row_sparse_data(row_id=user_ids_in_context).data)
When I run this, the last line fails with an error related to an implementation gap in SGDUpdate:
print(layer.weight.row_sparse_data(row_id=user_ids_in_context).data) Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/usr/local/lib/python3.6/dist-packages/mxnet/ndarray/sparse.py", line 730, in data return self._data() File "/usr/local/lib/python3.6/dist-packages/mxnet/ndarray/sparse.py", line 268, in _data self.wait_to_read() File "/usr/local/lib/python3.6/dist-packages/mxnet/ndarray/ndarray.py", line 1806, in wait_to_read check_call(_LIB.MXNDArrayWaitToRead(self.handle)) File "/usr/local/lib/python3.6/dist-packages/mxnet/base.py", line 252, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: [16:50:32] src/operator/contrib/.././operator_common.h:477: Check failed: arr.storage_shape() == arr.shape() SGDUpdate for RowSparse weights is only implemented for RowSparse weights with all rows containing non-zeros. Expects weights.data.shape (6) == weights.shape (100000000).