[Sparse-Gluon] embedding with sparse grad

python/mxnet/gluon/nn/basic_layers.py

@@ -381,7 +381,8 @@ class Embedding(HybridBlock):
         Data type of output embeddings.
     weight_initializer : Initializer
         Initializer for the `embeddings` matrix.
-
+    sparse_grad: bool
+        If True, gradient w.r.t. weight will be a 'row_sparse' NDArray.
 
     Inputs:
         - **data**: (N-1)-D tensor with shape: `(x1, x2, ..., xN-1)`.
@@ -390,13 +391,14 @@ class Embedding(HybridBlock):
         - **out**: N-D tensor with shape: `(x1, x2, ..., xN-1, output_dim)`.
     """
     def __init__(self, input_dim, output_dim, dtype='float32',
-                 weight_initializer=None, **kwargs):
+                 weight_initializer=None, sparse_grad=False, **kwargs):
         super(Embedding, self).__init__(**kwargs)
+        grad_stype = 'row_sparse' if sparse_grad else 'default'
         self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim,
-                        'dtype': dtype}
+                        'dtype': dtype, 'sparse_grad': sparse_grad}
         self.weight = self.params.get('weight', shape=(input_dim, output_dim),
                                       init=weight_initializer, dtype=dtype,
-                                      allow_deferred_init=True)
+                                      allow_deferred_init=True, grad_stype=grad_stype)
 
     def hybrid_forward(self, F, x, weight):
         return F.Embedding(x, weight, name='fwd', **self._kwargs)

python/mxnet/gluon/parameter.py

@@ -81,6 +81,8 @@ class Parameter(object):
         Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.
     init : Initializer, default None
         Initializer of this parameter. Will use the global initializer by default.
+    grad_stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.
+        The storage type of the parameter's gradient.
 
     Attributes
     ----------
@@ -97,7 +99,7 @@ class Parameter(object):
     """
     def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t,
                  lr_mult=1.0, wd_mult=1.0, init=None, allow_deferred_init=False,
-                 differentiable=True):
+                 differentiable=True, grad_stype='default'):
         self._var = None
         self._data = None
         self._grad = None
@@ -114,6 +116,11 @@ def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t,
         self.wd_mult = wd_mult
         self.grad_req = grad_req
         self.init = init
+        assert grad_stype in ['default', 'row_sparse', 'csr'], \
+            "grad_stype for Parameter '%s' must be one of 'default', 'row_sparse', or 'csr'," \
+            " but got '%s'" % (name, grad_stype)
+        self._grad_stype = grad_stype
+
 
     def __repr__(self):
         s = 'Parameter {name} (shape={shape}, dtype={dtype})'
@@ -261,7 +268,9 @@ def _init_grad(self):
             self._grad = None
             return
 
-        self._grad = [ndarray.zeros_like(i) for i in self._data]
+        self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
+                                    stype=self._grad_stype) for i in self._data]
+
         autograd.mark_variables(self.list_data(), self.list_grad(), self.grad_req)
 
     def _reduce(self):
@@ -431,7 +440,7 @@ def zero_grad(self):
         if self._grad is None:
             return
         for i in self._grad:
-            i[:] = 0
+            ndarray.zeros_like(i, out=i)
 
     def var(self):
         """Returns a symbol representing this parameter."""

python/mxnet/gluon/trainer.py

@@ -110,16 +110,30 @@ def _init_optimizer(self, optimizer, optimizer_params):
                             for _ in self._contexts]
 
     def _init_kvstore(self):
-        arg_arrays = {param.name: param.data(self._contexts[0]) for param in self._params}
+        arg_arrays = {}
+        contains_sparse = False
+        for param in self._params:
+            arg_arrays[param.name] = param.data(self._contexts[0])
+            if param._grad_stype != 'default':
+                contains_sparse = True
+                # update_on_kvstore is set to False by the user
+                if self._update_on_kvstore is False:
+                    raise RuntimeError("Cannot set update_on_kvstore to False when sparse "
+                                       "gradients and/or sparse weights are present for "
+                                       "Parameter %s." % param.name)
         kvstore, update_on_kvstore = _create_kvstore(self._kvstore, len(self._contexts),
                                                      arg_arrays)
         update_on_kvstore = self._update_on_kvstore if self._update_on_kvstore is not None \
                             else update_on_kvstore
         if kvstore:
             if self._compression_params:
                 kvstore.set_gradient_compression(self._compression_params)
-            if 'dist' in kvstore.type:
-                update_on_kvstore = False
+            # kv.pull(row_sparse_grad) is not supported
+            if contains_sparse:
+                update_on_kvstore = True
+            else:
+                if 'dist' in kvstore.type:
+                    update_on_kvstore = False
             if update_on_kvstore:
                 kvstore.set_optimizer(self._optimizer)
             # optimizer preferably needs to be set before init for multiprecision

src/kvstore/kvstore_local.h

@@ -276,7 +276,7 @@ class KVStoreLocal : public KVStore {
       // invalid, print warning messages once
       if (this->warnings_printed_.find(key) == this->warnings_printed_.end()) {
         LOG(INFO) << "Warning: non-default weights detected during kvstore pull. "
-                     "This call has been ignored. Please make sure to use"
+                     "This call has been ignored. Please make sure to use "
                      "kv.row_sparse_pull() or module.prepare() with row_ids.";
         this->warnings_printed_.insert(key);
       }

src/operator/tensor/init_op.h

@@ -383,8 +383,8 @@ void FillComputeZerosEx(const nnvm::NodeAttrs& attrs,
   Stream<xpu> *s = ctx.get_stream<xpu>();
   CHECK_EQ(outputs.size(), 1);
   auto stype = outputs[0].storage_type();
-  if (req[0] == kNullOp) return;
-  CHECK_EQ(req[0], kWriteTo) << "kWriteTo is expected for FillComputeZerosEx";
+  // x + 0 == x
+  if (req[0] == kNullOp || req[0] == kAddTo) return;
   if (stype == kRowSparseStorage) {
     FillZerosRspImpl(s, outputs[0]);
   } else if (stype == kCSRStorage) {

tests/python/unittest/test_gluon.py

@@ -38,6 +38,21 @@ def test_parameter():
     assert p.data(mx.cpu(1)).context == mx.cpu(1)
     assert p.data(mx.cpu(0)).shape == (10, 10)
     assert p.var().name == 'weight'
+    assert p.grad(mx.cpu(0)).stype == 'default'
+
+    p.reset_ctx(ctx=[mx.cpu(1), mx.cpu(2)])
+    assert p.list_ctx() == [mx.cpu(1), mx.cpu(2)]
+
+@with_seed()
+def test_sparse_parameter():
+    p = gluon.Parameter('weight', shape=(10, 10), grad_stype='row_sparse')
+    p.initialize(init='xavier', ctx=[mx.cpu(0), mx.cpu(1)])
+    assert len(p.list_data()) == 2
+    assert len(p.list_grad()) == 2
+    assert p.data(mx.cpu(1)).context == mx.cpu(1)
+    assert p.data(mx.cpu(0)).shape == (10, 10)
+    assert p.var().name == 'weight'
+    assert p.grad(mx.cpu(0)).stype == 'row_sparse'
 
     p.reset_ctx(ctx=[mx.cpu(1), mx.cpu(2)])
     assert p.list_ctx() == [mx.cpu(1), mx.cpu(2)]
@@ -676,15 +691,17 @@ def test_global_norm_clip():
 
 @with_seed()
 def test_embedding():
-    layer = gluon.nn.Embedding(10, 100)
-    layer.initialize()
-    x = mx.nd.array([3,4,2,0,1])
-    with mx.autograd.record():
-        y = layer(x)
-        y.backward()
-    assert (layer.weight.grad()[:5] == 1).asnumpy().all()
-    assert (layer.weight.grad()[5:] == 0).asnumpy().all()
-
+    def check_embedding(sparse_grad):
+        layer = gluon.nn.Embedding(10, 100, sparse_grad=sparse_grad)
+        layer.initialize()
+        x = mx.nd.array([3,4,2,0,1])
+        with mx.autograd.record():
+            y = layer(x)
+            y.backward()
+        assert (layer.weight.grad().asnumpy()[:5] == 1).all()
+        assert (layer.weight.grad().asnumpy()[5:] == 0).all()
+    check_embedding(True)
+    check_embedding(False)
 
 @with_seed()
 def test_export():
@@ -977,6 +994,7 @@ def test_req():
     assert_almost_equal(grad * 2, grad_double)
 
 
+@with_seed()
 def test_save_load():
     net = mx.gluon.model_zoo.vision.get_resnet(1, 18, pretrained=True)
     net.save_params('test.params')
@@ -987,13 +1005,27 @@ def test_save_load():
     net.load_params('test.params')
 
 
+@with_seed()
 def test_hybrid_multi_context():
     net = mx.gluon.model_zoo.vision.get_resnet(1, 18)
     net.initialize(ctx=[mx.cpu(0), mx.cpu(1)])
     net.hybridize()
     net(mx.nd.zeros((1, 3, 32, 32), ctx=mx.cpu(0))).asnumpy()
 
 
+@with_seed()
+def test_zero_grad():
+    data = mx.nd.random.uniform(shape=(3,3))
+    net = nn.Embedding(3, 4, sparse_grad=True, prefix='test_zero_grad_')
+    net.initialize()
+    with mx.autograd.record():
+        l = net(data)
+        l.backward()
+    net.collect_params().zero_grad()
+    grad = net.collect_params()['test_zero_grad_weight'].grad()
+    assert_almost_equal(grad.asnumpy(), grad.asnumpy() * 0)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()