Add support of passive kwargs hook: they are forwarded as is

This is sufficient for python numbers, str, tuple, etc.

syft/frameworks/torch/hook.py

@@ -463,7 +463,7 @@ def overloaded_pointer_method(self, *args, **kwargs):
                         raise TensorsNotCollocatedException(pointer, args[0], attr)
 
             # Send the command
-            command = (attr, self, args)
+            command = (attr, self, args, kwargs)
             response = owner.send_command(location, command)
 
             return response
@@ -484,11 +484,15 @@ def overloaded_attr(self, *args, **kwargs):
             """
 
             # Replace all syft tensor with their child attribute
-            new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(attr, self, args)
+            new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+                attr, self, args, kwargs
+            )
 
             results = list()
             for k, v in new_self.items():
-                results.append(v.__getattribute__(attr)(*map(lambda x: x[k], new_args), **kwargs))
+                results.append(
+                    v.__getattribute__(attr)(*map(lambda x: x[k], new_args), **new_kwargs)
+                )
 
             return MultiPointerTensor(children=results)
 
@@ -510,10 +514,12 @@ def overloaded_syft_method(self, *args, **kwargs):
             """
             # TODO: I can't manage the import issue, can you?
             # Replace all syft tensor with their child attribute
-            new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(attr, self, args)
+            new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+                attr, self, args, kwargs
+            )
 
             # Send it to the appropriate class and get the response
-            response = getattr(new_self, attr)(*new_args, **kwargs)
+            response = getattr(new_self, attr)(*new_args, **new_kwargs)
 
             # Put back SyftTensor on the tensors found in the response
             response = syft.frameworks.torch.hook_args.hook_response(
@@ -547,9 +553,9 @@ def overloaded_native_method(self, *args, **kwargs):
 
                 try:
                     if isinstance(args, tuple):
-                        response = method(*args)
+                        response = method(*args, **kwargs)
                     else:
-                        response = method(args)
+                        response = method(args, **kwargs)
 
                 except BaseException as e:
                     # we can make some errors more descriptive with this method
@@ -558,16 +564,16 @@ def overloaded_native_method(self, *args, **kwargs):
             else:  # means that there is a wrapper to remove
                 try:
                     # Replace all torch tensor with their child attribute
-                    new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(
-                        method_name, self, args
+                    new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+                        method_name, self, args, kwargs
                     )
                 except BaseException as e:
                     # we can make some errors more descriptive with this method
                     raise route_method_exception(e, self, args, kwargs)
 
                 # Send the new command to the appropriate class and get the response
                 method = getattr(new_self, method_name)
-                response = method(*new_args, **kwargs)
+                response = method(*new_args, **new_kwargs)
 
                 # For inplace methods, just directly return self
                 if syft.torch.is_inplace_method(method_name):
@@ -604,7 +610,7 @@ def overloaded_func(*args, **kwargs):
             Operate the hooking
             """
             cmd_name = f"{attr.__module__}.{attr.__name__}"
-            command = (cmd_name, None, args)  # TODO add kwargs
+            command = (cmd_name, None, args, kwargs)
             response = TorchTensor.handle_func_command(command)
             return response
 

syft/frameworks/torch/hook_args.py

@@ -57,7 +57,7 @@
 }
 
 
-def hook_method_args(attr, method_self, args):
+def hook_method_args(attr, method_self, args, kwargs):
     """Method arguments are sometimes simple types (such as strings or ints) but
     sometimes they are custom Syft tensors such as wrappers (torch.Tensor) or LoggingTensor
     or some other tensor type. Complex types (which have a .child attribute) need to
@@ -92,10 +92,10 @@ def hook_method_args(attr, method_self, args):
         # Run it
         new_self, new_args = args_hook_function((method_self, args))
 
-    return (new_self, new_args)
+    return (new_self, new_args, kwargs)
 
 
-def hook_function_args(attr, args, return_args_type=False):
+def hook_function_args(attr, args, kwargs, return_args_type=False):
     """See hook_method_args for details
 
     Args:
@@ -130,12 +130,16 @@ def hook_function_args(attr, args, return_args_type=False):
     new_type = get_tensor_type_function(new_args)
     if return_args_type:
         args_type = get_tensor_type_function(args)
-        return new_args, new_type, args_type
+        return new_args, kwargs, new_type, args_type
     else:
-        return new_args, new_type
+        return new_args, kwargs, new_type
 
 
 def build_hook_args_function(args, return_tuple=False):
+    """
+    Build the function f that hook the arguments:
+    f(args) = new_args
+    """
     # Inspect the call to find tensor arguments and return a rule whose
     # structure is the same as the args object, with 1 where there was
     # (torch or syft) tensors and 0 when not (ex: number, str, ...)
@@ -206,6 +210,14 @@ def hook_response(attr, response, wrap_type, wrap_args={}, new_self=None):
 
 
 def build_hook_response_function(response, wrap_type, wrap_args):
+    """
+    Build the function that hook the response.
+
+    Example:
+        p is of type Pointer
+        f is the hook_response_function
+        then f(p) = (Wrapper)>Pointer
+    """
     # Inspect the call to find tensor arguments and return a rule whose
     # structure is the same as the response object, with 1 where there was
     # (torch or syft) tensors and 0 when not (ex: number, str, ...)

syft/frameworks/torch/tensors/decorators/logging.py

@@ -37,11 +37,13 @@ def add(self, _self, *args, **kwargs):
 
     def manual_add(self, *args, **kwargs):
         # Replace all syft tensor with their child attribute
-        new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args("add", self, args)
+        new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+            "add", self, args, kwargs
+        )
 
         print("Log add")
         # Send it to the appropriate class and get the response
-        response = getattr(new_self, "add")(*new_args, **kwargs)
+        response = getattr(new_self, "add")(*new_args, **new_kwargs)
 
         # Put back SyftTensor on the tensors found in the response
         response = syft.frameworks.torch.hook_args.hook_response(
@@ -63,18 +65,19 @@ def handle_func_command(cls, command):
         <no self>, arguments[, kwargs])
         :return: the response of the function command
         """
-        # TODO: add kwargs in command
-        cmd, _, args = command
+        cmd, _, args, kwargs = command
 
         # Do what you have to
         print("Logtensor logging function", cmd)
 
         # TODO: I can't manage the import issue, can you?
         # Replace all LoggingTensor with their child attribute
-        new_args, new_type = syft.frameworks.torch.hook_args.hook_function_args(cmd, args)
+        new_args, new_kwargs, new_type = syft.frameworks.torch.hook_args.hook_function_args(
+            cmd, args, kwargs
+        )
 
         # build the new command
-        new_command = (cmd, None, new_args)
+        new_command = (cmd, None, new_args, new_kwargs)
 
         # Send it to the appropriate class and get the response
         response = new_type.handle_func_command(new_command)

syft/frameworks/torch/tensors/interpreters/native.py

@@ -179,22 +179,21 @@ def handle_func_command(cls, command):
         <no self>, arguments[, kwargs])
         :return: the response of the function command
         """
-        # TODO: add kwargs
-        cmd, _, args = command
+        cmd, _, args, kwargs = command
 
         try:  # will work if tensors are wrappers
             # Replace all torch tensor with their child attribute
             # Note that we return also args_type which helps handling case 3 in the docstring
-            new_args, new_type, args_type = syft.frameworks.torch.hook_args.hook_function_args(
-                cmd, args, return_args_type=True
+            new_args, new_kwargs, new_type, args_type = syft.frameworks.torch.hook_args.hook_function_args(
+                cmd, args, kwargs, return_args_type=True
             )
             # This handles case 3: it redirects the command to the appropriate class depending
             # of the syft type of the arguments and returns
             if args_type not in (torch.Tensor, torch.nn.Parameter):
                 return args_type.handle_func_command(command)
 
             # build the new command
-            new_command = (cmd, None, new_args)
+            new_command = (cmd, None, new_args, new_kwargs)
             # Send it to the appropriate class and get the response
             response = new_type.handle_func_command(new_command)
             # Put back the wrappers where needed
@@ -213,9 +212,9 @@ def handle_func_command(cls, command):
             # Note the the cmd should already be checked upon reception by the worker
             # in the execute_command function
             if isinstance(args, tuple):
-                response = eval(cmd)(*args)
+                response = eval(cmd)(*args, **kwargs)
             else:
-                response = eval(cmd)(args)
+                response = eval(cmd)(args, **kwargs)
 
         return response
 
@@ -438,7 +437,7 @@ def remote_get(self):
         """
 
         location = self.child.location
-        self.owner.send_command(message=("mid_get", self.child, ()), recipient=location)
+        self.owner.send_command(message=("mid_get", self.child, (), {}), recipient=location)
 
         return self
 

syft/frameworks/torch/tensors/interpreters/pointer.py

@@ -116,8 +116,8 @@ def find_a_pointer(cls, command):
         with the raising error RemoteTensorFoundError
         """
         try:
-            cmd, _, args = command
-            _ = syft.frameworks.torch.hook_args.hook_function_args(cmd, args)
+            cmd, _, args, kwargs = command
+            _ = syft.frameworks.torch.hook_args.hook_function_args(cmd, args, kwargs)
         except RemoteTensorFoundError as err:
             pointer = err.pointer
             return pointer

syft/frameworks/torch/tensors/interpreters/precision.py

@@ -125,10 +125,12 @@ def mul(self, *args, **kwargs):
         in the fixed precision setting
         """
         # Replace all syft tensor with their child attribute
-        new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args("mul", self, args)
+        new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+            "mul", self, args, kwargs
+        )
 
         # Send it to the appropriate class and get the response
-        response = getattr(new_self, "mul")(*new_args, **kwargs)
+        response = getattr(new_self, "mul")(*new_args, **new_kwargs)
 
         # Put back SyftTensor on the tensors found in the response
         response = syft.frameworks.torch.hook_args.hook_response(
@@ -151,10 +153,12 @@ def matmul(self, *args, **kwargs):
         in the fixed precision setting
         """
         # Replace all syft tensor with their child attribute
-        new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args("matmul", self, args)
+        new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+            "matmul", self, args, kwargs
+        )
 
         # Send it to the appropriate class and get the response
-        response = getattr(new_self, "matmul")(*new_args, **kwargs)
+        response = getattr(new_self, "matmul")(*new_args, **new_kwargs)
 
         # Put back SyftTensor on the tensors found in the response
         response = syft.frameworks.torch.hook_args.hook_response(
@@ -184,7 +188,7 @@ def handle_func_command(cls, command):
         :return: the response of the function command
         """
         # TODO: add kwargs in command
-        cmd, _, args = command
+        cmd, _, args, kwargs = command
 
         # unhook
         if cmd == "torch.nn.functional.linear":
@@ -199,10 +203,12 @@ def handle_func_command(cls, command):
 
         # TODO: I can't manage the import issue, can you?
         # Replace all FixedPrecisionTensor with their child attribute
-        new_args, new_type = syft.frameworks.torch.hook_args.hook_function_args(cmd, args)
+        new_args, new_kwargs, new_type = syft.frameworks.torch.hook_args.hook_function_args(
+            cmd, args, kwargs
+        )
 
         # build the new command
-        new_command = (cmd, None, new_args)
+        new_command = (cmd, None, new_args, new_kwargs)
 
         # Send it to the appropriate class and get the response
         response = new_type.handle_func_command(new_command)

syft/frameworks/torch/tensors/interpreters/utils.py

@@ -8,12 +8,12 @@ def hook(attr):
 
     def hook_args(self, *args, **kwargs):
         # Replace all syft tensor with their child attribute
-        new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(
-            attr.__name__, self, args
+        new_self, new_args, new_kwargs = syft.frameworks.torch.hook_args.hook_method_args(
+            attr.__name__, self, args, kwargs
         )
 
         # Send it to the appropriate class and get the response
-        response = attr(self, new_self, *new_args, **kwargs)
+        response = attr(self, new_self, *new_args, **new_kwargs)
 
         # Put back SyftTensor on the tensors found in the response
         response = syft.frameworks.torch.hook_args.hook_response(

syft/workers/base.py

@@ -284,7 +284,7 @@ def execute_command(self, message):
         :return: a pointer to the result
         """
 
-        command, _self, args = message
+        command, _self, args, kwargs = message
 
         # TODO add kwargs
         kwargs = {}