def test_push_gradient_async_update(self):
self.create_default_server_and_stub()
self.push_gradient_test_setup()
# Test applying gradients to embedding and non-embedding parameters
req = elasticdl_pb2.PushGradientsRequest()
for g, name in zip(self.grad_values0, self.var_names):
serialize_ndarray(g, req.gradients.dense_parameters[name])
serialize_indexed_slices(
self.embedding_grads0,
req.gradients.embedding_tables[self._embedding_info.name],
)
res = self._stub.push_gradients(req)
self.assertEqual(res.accepted, True)
self.assertEqual(res.version, 1)
expected_values = [
v - self._lr * g
for v, g in zip(self.var_values, self.grad_values0)
]
for name, expected_value in zip(self.var_names, expected_values):
self.assertTrue(
np.allclose(
expected_value,
self._parameters.non_embedding_params[name].numpy(),
)
)
expected_embed_table = np.copy(self.embedding_table)
for gv, gi in zip(
self.embedding_grads0.values, self.embedding_grads0.indices
):
expected_embed_table[gi] -= self._lr * gv
actual_embed_table = self._parameters.get_embedding_param(
self._embedding_info.name, range(len(expected_embed_table))
)
self.assertTrue(np.allclose(expected_embed_table, actual_embed_table))
# Test applying gradients with same name
for name, var in zip(self.var_names, self.var_values):
self._parameters.non_embedding_params[name] = tf.Variable(var)
req = elasticdl_pb2.PushGradientsRequest()
serialize_ndarray(
self.grad_values1[1],
req.gradients.dense_parameters[self.var_names[0]],
)
res = self._stub.push_gradients(req)
self.assertEqual(res.accepted, True)
self.assertEqual(res.version, 2)
expected_values = [
self.var_values[0] - self._lr * self.grad_values1[1],
self.var_values[1],
]
for expected_value, name in zip(expected_values, self.var_names):
self.assertTrue(
np.allclose(
expected_value,
self._parameters.non_embedding_params[name].numpy(),
)
)
def setUp(self):
self.params = Parameters()
self.model_pb = Model()
self.infos_pb = self.model_pb.embedding_table_infos
self.tensors_pb = self.model_pb.dense_parameters
self.embedding_tables_pb = self.model_pb.embedding_tables
self.embedding_table_name = "embedding_1"
self.embedding_dim = 10
embedding_pb = self.infos_pb.add()
embedding_pb.name = self.embedding_table_name
embedding_pb.dim = self.embedding_dim
embedding_pb.initializer = "uniform"
arr1 = np.random.uniform(size=(3, 4))
serialize_ndarray(arr1, self.tensors_pb["x"])
arr2 = np.random.uniform(size=(4, 5))
serialize_ndarray(arr2, self.tensors_pb["y"])
embedding_vectors = np.random.uniform(size=(2, 10))
embedding_indices = np.array([0, 8])
serialize_indexed_slices(
Tensor(None, embedding_vectors, embedding_indices),
self.embedding_tables_pb[self.embedding_table_name],
)
def to_model_pb(self):
""" Convert all parameters including embedding and non-embedding
parameters to `elasticdl_pb2.Model` which can be serialized.
"""
model_pb = elasticdl_pb2.Model()
model_pb.version = self.version
for name, var in self.non_embedding_params.items():
serialize_ndarray(var.numpy(), model_pb.dense_parameters[name])
for name, embedding_table in self.embedding_params.items():
# Slot embedding table is not weights in the model, so we don't
# save it to checkpoint.
if not embedding_table.is_slot:
serialize_indexed_slices(
embedding_table.to_indexed_slices(),
model_pb.embedding_tables[name],
)
embedding_info = embedding_table.to_embedding_table_info_pb()
model_pb.embedding_table_infos.append(embedding_info)
return model_pb
def report_gradient_to_ps(self, grads):
self._timing.start_record_time("report_gradient")
reqs = [
elasticdl_pb2.PushGradientsRequest() for i in range(self._ps_num)
]
ps_grads = {}
non_embed_vars_n = len(self._non_embed_vars)
for g, v in zip(
grads[:non_embed_vars_n], self._non_embed_vars.values()
):
ps_id = self._var_to_ps[v.name]
if ps_id not in ps_grads:
ps_grads[ps_id] = {v.name: g}
else:
if v.name not in ps_grads[ps_id]:
ps_grads[ps_id][v.name] = g
else:
if isinstance(g, tf.IndexedSlices):
ps_grads[ps_id][v.name] = merge_indexed_slices(
ps_grads[ps_id][v.name], g
)
else:
ps_grads[ps_id][v.name] += g
for ps_id, pair in ps_grads.items():
for name, g in pair.items():
if isinstance(g, tf.IndexedSlices):
v, i = deduplicate_indexed_slices(g.values, g.indices)
ps_grads[ps_id][name] = tf.IndexedSlices(v, i)
for ps_id in ps_grads:
req = reqs[ps_id]
for name, g in ps_grads[ps_id].items():
# Keras embedding layer has a dense parameter,
# but an indexed slices type gradient
if isinstance(g, tf.IndexedSlices):
serialize_indexed_slices(
Tensor(None, g.values.numpy(), g.indices.numpy()),
req.gradients.embedding_tables[name],
)
else:
serialize_ndarray(
g.numpy(), req.gradients.dense_parameters[name]
)
edl_embedding_name_values = self._collect_edl_embedding_name_values()
if edl_embedding_name_values:
edl_embedding_grads = grads[non_embed_vars_n:]
bet_number = 0
for name, embedding_and_ids in edl_embedding_name_values:
bet_number += len(embedding_and_ids)
if len(edl_embedding_grads) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d."
% (len(edl_embedding_grads), bet_number)
)
grad_accum_iter = 0
for name, embedding_and_ids in edl_embedding_name_values:
g_values = None
g_indices = None
for _, ids in embedding_and_ids:
grad = edl_embedding_grads[grad_accum_iter]
grad_accum_iter += 1
# ElasticDL embedding layer with Sparse Gradients
if isinstance(grad, tf.IndexedSlices):
grad = grad.values
if g_values is not None:
g_values = tf.concat([g_values, grad], axis=0)
g_indices = tf.concat([g_indices, ids], axis=0)
else:
g_values = grad
g_indices = ids
# Sum up the values of the duplicated indices in the
# gradients. It can reduce the gradient payload of the
# dense embedding.
g_values, g_indices = deduplicate_indexed_slices(
values=g_values, indices=g_indices
)
results = scatter_embedding_vector(
g_values.numpy(), g_indices.numpy(), self._ps_num
)
for ps_id in results:
req = reqs[ps_id]
gv, gi = results[ps_id]
serialize_indexed_slices(
Tensor(None, gv, gi),
req.gradients.embedding_tables[name],
)
report_futures = []
for ps_id in range(self._ps_num):
req = reqs[ps_id]
req.gradients.version = self._model_versions_from_ps[ps_id]
req.learning_rate = K.get_value(self._model.optimizer.lr)
report_future = self._ps_stubs[ps_id].push_gradients.future(req)
report_futures.append(report_future)
accepted = False
max_version = -1
for report_future in report_futures:
res = report_future.result()
if res.accepted:
accepted = True
if res.version > max_version:
max_version = res.version
self._timing.end_record_time("report_gradient")
return accepted, max_version
def test_push_gradient_sync_update(self):
self.create_server_and_stub(
grads_to_wait=2, lr_staleness_modulation=False, use_async=False
)
self.push_gradient_test_setup()
req = elasticdl_pb2.PushGradientsRequest()
req.gradients.version = 0
for g, name in zip(self.grad_values0, self.var_names):
serialize_ndarray(g, req.gradients.dense_parameters[name])
serialize_indexed_slices(
self.embedding_grads0,
req.gradients.embedding_tables[self._embedding_info.name],
)
res = self._stub.push_gradients(req)
self.assertEqual(res.accepted, True)
self.assertEqual(res.version, 0)
req = elasticdl_pb2.PushGradientsRequest()
req.gradients.version = 0
for g, name in zip(self.grad_values1, self.var_names):
serialize_ndarray(g, req.gradients.dense_parameters[name])
serialize_indexed_slices(
self.embedding_grads1,
req.gradients.embedding_tables[self._embedding_info.name],
)
res = self._stub.push_gradients(req)
self.assertEqual(res.accepted, True)
self.assertEqual(res.version, 1)
req = elasticdl_pb2.PushGradientsRequest()
req.gradients.version = 0
for g, name in zip(self.grad_values1, self.var_names):
serialize_ndarray(g, req.gradients.dense_parameters[name])
res = self._stub.push_gradients(req)
self.assertEqual(res.accepted, False)
self.assertEqual(res.version, 1)
expected_values = [
self.var_values[0]
- self._lr * (self.grad_values0[0] + self.grad_values1[0]) / 2,
self.var_values[1]
- self._lr * (self.grad_values0[1] + self.grad_values1[1]) / 2,
]
for expected_value, name in zip(expected_values, self.var_names):
self.assertTrue(
np.allclose(
expected_value,
self._parameters.non_embedding_params[name].numpy(),
)
)
expected_embed_table = np.copy(self.embedding_table)
for gv, gi in zip(
self.embedding_grads0.values, self.embedding_grads0.indices
):
expected_embed_table[gi] -= self._lr * gv
for gv, gi in zip(
self.embedding_grads1.values, self.embedding_grads1.indices
):
expected_embed_table[gi] -= self._lr * gv
actual_embed_table = self._parameters.get_embedding_param(
self._embedding_info.name, range(len(expected_embed_table))
)
self.assertTrue(np.allclose(expected_embed_table, actual_embed_table))
def push_gradients(
self, grads, edl_grads, learning_rate, model_versions,
):
"""
Push gradients to PS. There two kinds of gradients:
- gradients of normal layers
- sparse gradients of ElasticDL embedding layers
"""
reqs = [
elasticdl_pb2.PushGradientsRequest() for i in range(self.ps_num)
]
ps_grads = {}
# 1. handle grads
for grad in grads:
ps_id = self.parameter_to_ps[grad.name]
if ps_id not in ps_grads:
ps_grads[ps_id] = {grad.name: grad}
else:
if grad.name not in ps_grads[ps_id]:
ps_grads[ps_id][grad.name] = grad
else:
if grad.indices is not None:
ps_grads[ps_id][grad.name] = merge_indexed_slices(
ps_grads[ps_id][grad.name], grad
)
else:
ps_grads[ps_id][grad.name].values += grad.values
for ps_id, pair in ps_grads.items():
for name, grad in pair.items():
if grad.indices is not None:
v, i = deduplicate_indexed_slices(
grad.values, grad.indices
)
ps_grads[ps_id][name] = Tensor(None, v, i)
for ps_id in ps_grads:
req = reqs[ps_id]
for name, grad in ps_grads[ps_id].items():
# Keras embedding layer has a dense parameter,
# but an indexed slices type gradient
if grad.indices is not None:
serialize_indexed_slices(
Tensor(None, grad.values, grad.indices),
req.gradients.embedding_tables[name],
)
else:
serialize_ndarray(
grad.values, req.gradients.dense_parameters[name]
)
# 2. handle sparse grads of elasticdl embedding layers
groups = {}
for grad in edl_grads:
if grad.name not in groups:
groups[grad.name] = grad
else:
groups[grad.name] = merge_indexed_slices(
groups[grad.name], grad
)
# Sum up the values of the duplicated indices in the
# gradients. It can reduce the gradient payload of the
# dense embedding.
for name, grad in groups.items():
v, i = deduplicate_indexed_slices(grad.values, grad.indices)
groups[name] = Tensor(None, v, i)
results = scatter_embedding_vector(
groups[name].values, groups[name].indices, self.ps_num
)
for ps_id in results:
req = reqs[ps_id]
gv, gi = results[ps_id]
serialize_indexed_slices(
Tensor(None, gv, gi), req.gradients.embedding_tables[name],
)
# 3. push gradients to PS
report_futures = []
for ps_id in range(self.ps_num):
req = reqs[ps_id]
req.gradients.version = model_versions[ps_id]
req.learning_rate = learning_rate
report_future = self.ps_stubs[ps_id].push_gradients.future(req)
report_futures.append(report_future)
accepted = False
max_version = -1
for report_future in report_futures:
res = report_future.result()
if res.accepted:
accepted = True
if res.version > max_version:
max_version = res.version
return accepted, max_version
