def test_backward_response_success_text_priority():
def priority(pubkey: str, request_type: str, inputs_x):
return 100
axon = bittensor.axon(wallet=wallet, priority=priority)
def backward(inputs_x: torch.FloatTensor, grads_dy: torch.FloatTensor):
return torch.zeros([1, 1])
axon.attach_backward_callback(backward,
modality=bittensor.proto.Modality.TEXT)
inputs_raw = torch.ones((1, 1))
grads_raw = torch.zeros((1, 1, bittensor.__network_dim__))
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TEXT,
from_type=bittensor.proto.TensorType.TORCH)
grads_serialized = serializer.serialize(
grads_raw,
modality=bittensor.proto.Modality.TEXT,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized, grads_serialized])
response, code, call_time, message = axon._backward(request)
assert code == bittensor.proto.ReturnCode.Success
def test_receptor_neuron_server_response_with_nans_backward():
import numpy as np
y = torch.rand(3, 3, bittensor.__network_dim__)
y[0][0][0] = np.nan
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
y_serialized = serializer.serialize(
y,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
mock_return_val = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=wallet.hotkey.public_key,
return_code=bittensor.proto.ReturnCode.Success,
tensors=[y_serialized])
future = asyncio.Future()
future.set_result(mock_return_val)
stub.Backward.future = MagicMock(return_value=future)
receptor.stub = stub
x = torch.rand(3, 3, bittensor.__network_dim__)
out, ops, time = receptor.backward(x,
x,
bittensor.proto.Modality.TENSOR,
timeout=1)
assert ops == bittensor.proto.ReturnCode.Success
assert out[0][0][0] == 0
def test_receptor_neuron_mock_server_shape_error_backward():
y = torch.rand(1, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
y_serialized = serializer.serialize(
y,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
mock_return_val = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=wallet.hotkey.public_key,
return_code=bittensor.proto.ReturnCode.Success,
tensors=[y_serialized])
future = asyncio.Future()
future.set_result(mock_return_val)
stub.Backward.future = MagicMock(return_value=future)
receptor.stub = stub
x = torch.rand(3, 3, bittensor.__network_dim__)
out, ops, time = receptor.backward(x,
x,
bittensor.proto.Modality.TENSOR,
timeout=1)
assert ops == bittensor.proto.ReturnCode.ResponseShapeException
assert list(out.shape) == [3, 3, bittensor.__network_dim__]
def test_receptor_backward_exception():
y = torch.zeros(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
y_serialized = serializer.serialize(
y,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
mock_return_val = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=wallet.hotkey.ss58_address,
return_code=bittensor.proto.ReturnCode.UnknownException,
tensors=[y_serialized])
future = asyncio.Future()
future.set_result(mock_return_val)
stub.Backward.future = MagicMock(return_value=future)
receptor.stub = stub
x = torch.rand(3, 3, bittensor.__network_dim__)
out, ops, time = receptor.backward(x,
x,
bittensor.proto.Modality.TENSOR,
timeout=1)
assert ops == bittensor.proto.ReturnCode.UnknownException
def test_serialize_deserialize_tensor(self):
data = torch.rand([12, 23])
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_tensor_message = serializer.serialize(
data,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
assert data.requires_grad == serialized_tensor_message.requires_grad
assert list(data.shape) == serialized_tensor_message.shape
assert serialized_tensor_message.modality == bittensor.proto.Modality.TENSOR
assert serialized_tensor_message.dtype == bittensor.proto.DataType.FLOAT32
deserialized_tensor_message = serializer.deserialize(
serialized_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_tensor_message.requires_grad == deserialized_tensor_message.requires_grad
assert serialized_tensor_message.shape == list(
deserialized_tensor_message.shape)
assert bittensor.serializer.torch_dtype_to_bittensor_dtype(
deserialized_tensor_message.dtype
) == bittensor.proto.DataType.FLOAT32
assert torch.all(torch.eq(deserialized_tensor_message, data))
def test_serialize_deserialize_image(self):
# Let's grab some image data
# Let's grab a random image, and give it a crazy type to break the system
image = torch.ones([1, 28, 28])
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_image_tensor_message = serializer.serialize(
image,
modality=bittensor.proto.Modality.IMAGE,
from_type=bittensor.proto.TensorType.TORCH)
assert image.requires_grad == serialized_image_tensor_message.requires_grad
assert list(image.shape) == serialized_image_tensor_message.shape
assert serialized_image_tensor_message.modality == bittensor.proto.Modality.IMAGE
assert serialized_image_tensor_message.dtype != bittensor.proto.DataType.UNKNOWN
deserialized_image_tensor_message = serializer.deserialize(
serialized_image_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_image_tensor_message.requires_grad == deserialized_image_tensor_message.requires_grad
assert serialized_image_tensor_message.shape == list(
deserialized_image_tensor_message.shape)
assert bittensor.serializer.torch_dtype_to_bittensor_dtype(
deserialized_image_tensor_message.dtype
) != bittensor.proto.DataType.UNKNOWN
assert torch.all(torch.eq(deserialized_image_tensor_message, image))
def test_backward_response_exception():
def backward(inputs_x: torch.FloatTensor, grads_dy: torch.FloatTensor):
if inputs_x.size() == (1, 1, 1):
return None
else:
raise Exception('Timeout')
axon.attach_backward_callback(backward,
modality=bittensor.proto.Modality.TENSOR)
inputs_raw = torch.rand(2, 2, 2)
grads_raw = torch.rand(2, 2, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
grads_serialized = serializer.serialize(
grads_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized, grads_serialized])
response, code, call_time, message = axon._backward(request)
assert code == bittensor.proto.ReturnCode.UnknownException
def test_receptor_pool_forward_hang():
endpoints = [neuron_obj, neuron_obj]
x = torch.ones((2, 2, 2))
y = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
y_serialized = serializer.serialize(
y,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
mock_return_val = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=wallet.hotkey.ss58_address,
return_code=bittensor.proto.ReturnCode.Timeout,
tensors=[])
future = asyncio.Future()
future.set_result(mock_return_val)
receptor_pool._get_or_create_receptor_for_endpoint(neuron_obj)
receptor_pool.receptors[neuron_obj.hotkey].stub.Forward.future = MagicMock(
return_value=future)
resp1, codes, _ = receptor_pool.forward(endpoints,
x,
bittensor.proto.Modality.TENSOR,
timeout=1)
assert codes == [
bittensor.proto.ReturnCode.Timeout, bittensor.proto.ReturnCode.Timeout
]
def deserialize_backward_response(self, request):
r"""Deserialization for the backward request.
The result would update request.output.
Args:
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
Returns:
success: (:type:`bool`, `required`):
True if the deserialization is successful.
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
"""
# ---- Post-process request ----
try:
outputs = request.response.tensors[0]
deserializer = bittensor.serializer(outputs.serializer)
outputs = deserializer.deserialize(
outputs, to_type=bittensor.proto.TensorType.TORCH)
except Exception as e:
request.code = bittensor.proto.ReturnCode.ResponseDeserializationException
request.message = 'deserialization exception with error:{}'.format(
e)
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape))
return False, request
try:
# ---- Check response shape is same as inputs ----
if outputs.size() != request.inputs.size():
request.code = bittensor.proto.ReturnCode.ResponseShapeException
request.message = 'output shape does not match inputs shape'
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape))
return False, request
except Exception as e:
request.code = bittensor.proto.ReturnCode.UnknownException
request.message = 'Size Error: {}'.format(e)
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape))
return False, request
# ---- Safe catch NaNs and replace with 0.0 ----
request.outputs = torch.where(torch.isnan(outputs),
torch.zeros_like(outputs), outputs)
# ---- Return ----
request.code = bittensor.proto.ReturnCode.Success
request.message = 'Success'
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape))
self.stats.codes[request.code] += 1
return False, request
def deserialize_forward_response(self, request):
r"""Deserialization for the forward request.
The result would update request.output.
Args:
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
Returns:
success: (:type:`bool`, `required`):
True if the deserialization is successful.
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
"""
# ---- Deserialize response ----
try:
outputs = request.response.tensors[0]
deserializer = bittensor.serializer(outputs.serializer)
outputs = deserializer.deserialize(
outputs, to_type=bittensor.proto.TensorType.TORCH)
except Exception as e:
request.code = bittensor.proto.ReturnCode.ResponseDeserializationException
request.message = 'Deserialziation exception with error:{}'.format(
str(e))
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape))
return False, request
# ---- Check response shape ----
if (outputs.size(0) != request.inputs.size(0)
or outputs.size(1) != request.inputs.size(1)
or outputs.size(2) != bittensor.__network_dim__):
request.code = bittensor.proto.ReturnCode.ResponseShapeException
request.message = "output.shape:{} does not match inputs:{}".format(
outputs.shape, request.inputs.shape)
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape),
outputs=list(outputs.shape))
return False, request
# ---- Safe catch NaNs and replace with 0.0 ----
request.outputs = torch.where(torch.isnan(outputs),
torch.zeros_like(outputs), outputs)
# ---- Return ----
request.code = request.response.return_code
self.request_log(request=request,
is_response=True,
inputs=list(request.inputs.shape),
outputs=list(outputs.shape))
self.stats.codes[request.code] += 1
return True, request
def test_serialize_object_type_exception(self):
# Let's grab a random image, and try and de-serialize it incorrectly.
image = torch.ones([1, 28, 28])
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
with pytest.raises(
bittensor.serializer.SerializationTypeNotImplementedException):
serializer.serialize(image,
modality=bittensor.proto.Modality.IMAGE,
from_type=11)
def test_serialize(self):
for _ in range(10):
tensor_a = torch.rand([12, 23])
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
content = serializer.serialize(
tensor_a,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
tensor_b = serializer.deserialize(
content, to_type=bittensor.proto.TensorType.TORCH)
torch.all(torch.eq(tensor_a, tensor_b))
def test_deserialization_object_type_exception(self):
data = torch.rand([12, 23])
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
tensor_message = serializer.serialize(
data,
modality=bittensor.proto.Modality.TEXT,
from_type=bittensor.proto.TensorType.TORCH)
with pytest.raises(
bittensor.serializer.SerializationTypeNotImplementedException):
serializer.deserialize(tensor_message, to_type=11)
def test_forward_wandb():
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__, tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
axon.update_stats_for_request(request, response, call_time, code)
print(axon.to_wandb())
def test_forward_not_implemented():
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__, tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.NotImplemented
def test_forward_tensor_shape_error():
inputs_raw = torch.rand(1, 1, 1, 1)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.RequestShapeException
def test_backward_invalid_request():
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized])
response, code, call_time, message = axon._backward(request)
assert code == bittensor.proto.ReturnCode.InvalidRequest
def test_grpc_backward_works():
def backward(inputs_x: torch.FloatTensor, grads_dy: torch.FloatTensor):
return torch.zeros([1, 1, 1])
axon = bittensor.axon(
port=7081,
ip='127.0.0.1',
wallet=wallet,
)
axon.attach_backward_callback(backward,
modality=bittensor.proto.Modality.TENSOR)
axon.start()
channel = grpc.insecure_channel('127.0.0.1:7081',
options=[
('grpc.max_send_message_length', -1),
('grpc.max_receive_message_length', -1)
])
stub = bittensor.grpc.BittensorStub(channel)
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
grads_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
grads_serialized = serializer.serialize(
grads_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey='1092310312914',
tensors=[inputs_serialized, grads_serialized])
response = stub.Backward(request,
metadata=(
('rpc-auth-header', 'Bittensor'),
('bittensor-signature', sign(axon.wallet)),
('bittensor-version',
str(bittensor.__version_as_int__)),
))
outputs = serializer.deserialize(response.tensors[0],
to_type=bittensor.proto.TensorType.TORCH)
assert outputs.tolist() == [[[0]]]
axon.stop()
def test_forward_tensor_success_image():
def forward(inputs_x: torch.FloatTensor):
return torch.zeros(
[inputs_x.shape[0], inputs_x.shape[1], bittensor.__network_dim__])
axon.attach_forward_callback(forward, modality=1)
inputs_raw = torch.rand(1, 1, 1, 1, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.IMAGE,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__, tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.Success
def test_forward_unknown_error():
def forward(inputs_x: torch.FloatTensor, modality):
raise Exception('Unknown')
with mock.patch.object(axon, '_call_forward', new=forward):
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
tensors=[inputs_serialized],
hotkey='123')
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.UnknownException
def test_forward_response_deserialization_error():
def forward(inputs_x: torch.Tensor):
return dict()
axon.attach_forward_callback(forward,
modality=bittensor.proto.Modality.TENSOR)
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.ResponseDeserializationException
def test_forward_tensor_exception():
def forward(inputs_x: torch.FloatTensor):
if inputs_x.size() == (1, 1, 1):
return None
else:
raise Exception('Mock')
axon.attach_forward_callback(forward, modality=2)
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
tensors=[inputs_serialized],
hotkey='123')
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.UnknownException
def test_grpc_backward_fails():
def backward(inputs_x: torch.FloatTensor, grads_dy: torch.FloatTensor):
return torch.zeros([1, 1, 1])
axon = bittensor.axon(port=7081, ip='127.0.0.1', wallet=wallet)
axon.attach_backward_callback(backward,
modality=bittensor.proto.Modality.TENSOR)
axon.start()
channel = grpc.insecure_channel('127.0.0.1:7081',
options=[
('grpc.max_send_message_length', -1),
('grpc.max_receive_message_length', -1)
])
stub = bittensor.grpc.BittensorStub(channel)
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
grads_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
grads_serialized = serializer.serialize(
grads_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey='1092310312914',
tensors=[inputs_serialized, grads_serialized])
try:
response = stub.Backward(request)
except grpc.RpcError as rpc_error_call:
grpc_code = rpc_error_call.code()
assert grpc_code == grpc.StatusCode.UNAUTHENTICATED
axon.stop()
def test_forward_tensor_success_priority():
def priority(pubkey: str, request_type: str, inputs_x):
return 100
axon = bittensor.axon(wallet=wallet, priority=priority)
def forward(inputs_x: torch.FloatTensor):
return torch.zeros(
[inputs_x.shape[0], inputs_x.shape[1], bittensor.__network_dim__])
axon.attach_forward_callback(forward, modality=2)
inputs_raw = torch.rand(3, 3, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__, tensors=[inputs_serialized])
response, code, call_time, message = axon._forward(request)
assert code == bittensor.proto.ReturnCode.Success
def serialization(self, request):
r""" Does the serialization to the request inputs and grads(backward request only).
The result would update request.serialized_inputs and request.serialized_grad.
Args:
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
Returns:
success: (:type:`bool`, `required`):
True if the serialization is successful.
request: (:obj:`Request`, required):
The request object holds all specifications and processing of the request.
"""
try:
serializer = bittensor.serializer(
bittensor.proto.Serializer.MSGPACK)
request.serialized_inputs = serializer.serialize(
request.inputs,
modality=request.modality,
from_type=bittensor.proto.TensorType.TORCH)
if request.backward:
request.serialized_grads = serializer.serialize(
request.grads_dy,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
except Exception as e:
request.code = bittensor.proto.ReturnCode.RequestSerializationException
request.message = 'Input serialization exception with error:{}'.format(
str(e))
self.request_log(request=request,
is_response=False,
inputs=list(request.inputs.shape))
return False, request
return True, request
def test_backward_response_success():
def backward(inputs_x: torch.FloatTensor, grads_dy: torch.FloatTensor):
return torch.zeros([1, 1, 1])
axon.attach_backward_callback(backward,
modality=bittensor.proto.Modality.TENSOR)
inputs_raw = torch.rand(1, 1, 1)
grads_raw = torch.rand(1, 1, bittensor.__network_dim__)
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
inputs_serialized = serializer.serialize(
inputs_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
grads_serialized = serializer.serialize(
grads_raw,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
request = bittensor.proto.TensorMessage(
version=bittensor.__version_as_int__,
hotkey=axon.wallet.hotkey.ss58_address,
tensors=[inputs_serialized, grads_serialized])
response, code, call_time, message = axon._backward(request)
assert code == bittensor.proto.ReturnCode.Success
def test_serialize_deserialize_text(self):
# Let's create some text data
words = ["This", "is", "a", "word", "list"]
max_l = 0
ts_list = []
for w in words:
ts_list.append(torch.ByteTensor(list(bytes(w, 'utf8'))))
max_l = max(ts_list[-1].size()[0], max_l)
data = torch.zeros((len(ts_list), max_l), dtype=torch.int64)
for i, ts in enumerate(ts_list):
data[i, 0:ts.size()[0]] = ts
serializer = bittensor.serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_data_tensor_message = serializer.serialize(
data,
modality=bittensor.proto.Modality.TEXT,
from_type=bittensor.proto.TensorType.TORCH)
assert data.requires_grad == serialized_data_tensor_message.requires_grad
assert list(data.shape) == serialized_data_tensor_message.shape
assert serialized_data_tensor_message.modality == bittensor.proto.Modality.TEXT
assert serialized_data_tensor_message.dtype != bittensor.proto.DataType.UNKNOWN
deserialized_data_tensor_message = serializer.deserialize(
serialized_data_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_data_tensor_message.requires_grad == deserialized_data_tensor_message.requires_grad
assert serialized_data_tensor_message.shape == list(
deserialized_data_tensor_message.shape)
assert bittensor.serializer.torch_dtype_to_bittensor_dtype(
deserialized_data_tensor_message.dtype
) != bittensor.proto.DataType.UNKNOWN
assert torch.all(torch.eq(deserialized_data_tensor_message, data))
