Add back compatibility with torch

README.md

@@ -221,6 +221,11 @@ source ~/.bashrc  # Reload Bash configuration to take effect
 # The Bittensor Package
 The bittensor package contains data structures for interacting with the bittensor ecosystem, writing miners, validators and querying the network. Additionally, it provides many utilities for efficient serialization of Tensors over the wire, performing data analysis of the network, and other useful utilities.
 
+In the 7.0.0 release, we have removed `torch` by default. However, you can still use `torch` by setting the environment variable
+`USE_TORCH=1` and making sure that you have installed the `torch` library. 
+You can install `torch` by running `pip install bittensor[torch]` (if installing via PyPI), or by running `pip install -e ".[torch]"` (if installing from source). 
+We will not be adding any new functionality based on torch.
+
 Wallet: Interface over locally stored bittensor hot + coldkey styled wallets. 
 ```python
 import bittensor

bittensor/chain_data.py

@@ -15,7 +15,6 @@
 # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 # DEALINGS IN THE SOFTWARE.
 import bittensor
-
 import json
 from enum import Enum
 from dataclasses import dataclass, asdict
@@ -27,6 +26,7 @@
 
 from .utils import networking as net, U16_MAX, U16_NORMALIZED_FLOAT
 from .utils.balance import Balance
+from .utils.registration import torch, use_torch
 
 custom_rpc_type_registry = {
     "types": {
@@ -264,14 +264,43 @@ def from_neuron_info(cls, neuron_info: dict) -> "AxonInfo":
             coldkey=neuron_info["coldkey"],
         )
 
-    def to_parameter_dict(self) -> dict[str, Union[int, str]]:
-        r"""Returns a dict of the subnet info."""
-        return self.__dict__
+    def _to_parameter_dict(
+        self, return_type: str
+    ) -> Union[dict[str, Union[int, str]], "torch.nn.ParameterDict"]:
+        if return_type == "torch":
+            return torch.nn.ParameterDict(self.__dict__)
+        else:
+            return self.__dict__
+
+    def to_parameter_dict(
+        self,
+    ) -> Union[dict[str, Union[int, str]], "torch.nn.ParameterDict"]:
+        """Returns a torch tensor or dict of the subnet info, depending on the USE_TORCH flag set"""
+        if use_torch():
+            return self._to_parameter_dict("torch")
+        else:
+            return self._to_parameter_dict("numpy")
 
     @classmethod
-    def from_parameter_dict(cls, parameter_dict: dict[str, Any]) -> "AxonInfo":
-        r"""Returns an axon_info object from a parameter_dict."""
-        return cls(**parameter_dict)
+    def _from_parameter_dict(
+        cls,
+        parameter_dict: Union[dict[str, Any], "torch.nn.ParameterDict"],
+        return_type: str,
+    ) -> "AxonInfo":
+        if return_type == "torch":
+            return cls(**dict(parameter_dict))
+        else:
+            return cls(**parameter_dict)
+
+    @classmethod
+    def from_parameter_dict(
+        cls, parameter_dict: Union[dict[str, Any], "torch.nn.ParameterDict"]
+    ) -> "AxonInfo":
+        """Returns an axon_info object from a torch parameter_dict or a parameter dict."""
+        if use_torch():
+            return cls._from_parameter_dict(parameter_dict, "torch")
+        else:
+            return cls._from_parameter_dict(parameter_dict, "numpy")
 
 
 class ChainDataType(Enum):
@@ -980,15 +1009,42 @@ def fix_decoded_values(cls, decoded: Dict) -> "SubnetInfo":
             owner_ss58=ss58_encode(decoded["owner"], bittensor.__ss58_format__),
         )
 
-    def to_parameter_dict(self) -> dict[str, Any]:
-        r"""Returns a dict of the subnet info."""
-        return self.__dict__
+    def _to_parameter_dict(
+        self, return_type: str
+    ) -> Union[dict[str, Any], "torch.nn.ParameterDict"]:
+        if return_type == "torch":
+            return torch.nn.ParameterDict(self.__dict__)
+        else:
+            return self.__dict__
+
+    def to_parameter_dict(self) -> Union[dict[str, Any], "torch.nn.ParameterDict"]:
+        """Returns a torch tensor or dict of the subnet info."""
+        if use_torch():
+            return self._to_parameter_dict("torch")
+        else:
+            return self._to_parameter_dict("numpy")
+
+    @classmethod
+    def _from_parameter_dict_torch(
+        cls, parameter_dict: "torch.nn.ParameterDict"
+    ) -> "SubnetInfo":
+        """Returns a SubnetInfo object from a torch parameter_dict."""
+        return cls(**dict(parameter_dict))
 
     @classmethod
-    def from_parameter_dict(cls, parameter_dict: dict[str, Any]) -> "SubnetInfo":
+    def _from_parameter_dict_numpy(cls, parameter_dict: dict[str, Any]) -> "SubnetInfo":
         r"""Returns a SubnetInfo object from a parameter_dict."""
         return cls(**parameter_dict)
 
+    @classmethod
+    def from_parameter_dict(
+        cls, parameter_dict: Union[dict[str, Any], "torch.nn.ParameterDict"]
+    ) -> "SubnetInfo":
+        if use_torch():
+            return cls._from_parameter_dict_torch(parameter_dict)
+        else:
+            return cls._from_parameter_dict_numpy(parameter_dict)
+
 
 @dataclass
 class SubnetHyperparameters:
@@ -1074,15 +1130,46 @@ def fix_decoded_values(cls, decoded: Dict) -> "SubnetHyperparameters":
             difficulty=decoded["difficulty"],
         )
 
-    def to_parameter_dict(self) -> dict[str, Union[int, float, bool]]:
-        r"""Returns a dict of the subnet hyperparameters."""
-        return self.__dict__
+    def _to_parameter_dict_torch(
+        self, return_type: str
+    ) -> Union[dict[str, Union[int, float, bool]], "torch.nn.ParameterDict"]:
+        if return_type == "torch":
+            return torch.nn.ParameterDict(self.__dict__)
+        else:
+            return self.__dict__
+
+    def to_parameter_dict(
+        self,
+    ) -> Union[dict[str, Union[int, float, bool]], "torch.nn.ParameterDict"]:
+        """Returns a torch tensor or dict of the subnet hyperparameters."""
+        if use_torch():
+            return self._to_parameter_dict_torch("torch")
+        else:
+            return self._to_parameter_dict_torch("numpy")
+
+    @classmethod
+    def _from_parameter_dict_torch(
+        cls, parameter_dict: "torch.nn.ParameterDict"
+    ) -> "SubnetHyperparameters":
+        """Returns a SubnetHyperparameters object from a torch parameter_dict."""
+        return cls(**dict(parameter_dict))
 
     @classmethod
-    def from_parameter_dict(cls, parameter_dict: dict[str, Any]) -> "SubnetInfo":
-        r"""Returns a SubnetHyperparameters object from a parameter_dict."""
+    def _from_parameter_dict_numpy(
+        cls, parameter_dict: dict[str, Any]
+    ) -> "SubnetHyperparameters":
+        """Returns a SubnetHyperparameters object from a parameter_dict."""
         return cls(**parameter_dict)
 
+    @classmethod
+    def from_parameter_dict(
+        cls, parameter_dict: Union[dict[str, Any], "torch.nn.ParameterDict"]
+    ) -> "SubnetHyperparameters":
+        if use_torch():
+            return cls._from_parameter_dict_torch(parameter_dict)
+        else:
+            return cls._from_parameter_dict_numpy(parameter_dict)
+
 
 @dataclass
 class IPInfo:
@@ -1137,15 +1224,45 @@ def fix_decoded_values(cls, decoded: Dict) -> "IPInfo":
             protocol=decoded["ip_type_and_protocol"] & 0xF,
         )
 
-    def to_parameter_dict(self) -> dict[str, Union[str, int]]:
-        r"""Returns a dict of the subnet ip info."""
-        return self.__dict__
+    def _to_parameter_dict(
+        self, return_type: str
+    ) -> Union[dict[str, Union[str, int]], "torch.nn.ParameterDict"]:
+        """Returns a torch tensor of the subnet info."""
+        if return_type == "torch":
+            return torch.nn.ParameterDict(self.__dict__)
+        else:
+            return self.__dict__
+
+    def to_parameter_dict(
+        self,
+    ) -> Union[dict[str, Union[str, int]], "torch.nn.ParameterDict"]:
+        """Returns a torch tensor or dict of the subnet IP info."""
+        if use_torch():
+            return self._to_parameter_dict("torch")
+        else:
+            return self._to_parameter_dict("numpy")
+
+    @classmethod
+    def _from_parameter_dict_torch(
+        cls, parameter_dict: "torch.nn.ParameterDict"
+    ) -> "IPInfo":
+        """Returns a IPInfo object from a torch parameter_dict."""
+        return cls(**dict(parameter_dict))
 
     @classmethod
-    def from_parameter_dict(cls, parameter_dict: dict[str, Any]) -> "IPInfo":
-        r"""Returns a IPInfo object from a parameter_dict."""
+    def _from_parameter_dict_numpy(cls, parameter_dict: dict[str, Any]) -> "IPInfo":
+        """Returns a IPInfo object from a parameter_dict."""
         return cls(**parameter_dict)
 
+    @classmethod
+    def from_parameter_dict(
+        cls, parameter_dict: Union[dict[str, Any], "torch.nn.ParameterDict"]
+    ) -> "IPInfo":
+        if use_torch():
+            return cls._from_parameter_dict_torch(parameter_dict)
+        else:
+            return cls._from_parameter_dict_numpy(parameter_dict)
+
 
 # Senate / Proposal data
 

bittensor/commands/root.py

@@ -16,7 +16,6 @@
 # DEALINGS IN THE SOFTWARE.
 
 import re
-import numpy as np
 import typing
 import argparse
 import numpy as np
@@ -25,6 +24,7 @@
 from rich.prompt import Prompt
 from rich.table import Table
 from .utils import get_delegates_details, DelegatesDetails
+from bittensor.utils.registration import torch, use_torch
 
 from . import defaults
 
@@ -301,7 +301,11 @@ def _run(cli: "bittensor.cli", subtensor: "bittensor.subtensor"):
             f"Boosting weight for netuid {cli.config.netuid} from {prev_weight} -> {new_weight}"
         )
         my_weights[cli.config.netuid] = new_weight
-        all_netuids = np.arange(len(my_weights))
+        all_netuids = (
+            torch.tensor(list(range(len(my_weights))))
+            if use_torch()
+            else np.arange(len(my_weights))
+        )
 
         bittensor.__console__.print("Setting root weights...")
         subtensor.root_set_weights(
@@ -419,7 +423,11 @@ def _run(cli: "bittensor.cli", subtensor: "bittensor.subtensor"):
         my_weights = root.weights[my_uid]
         my_weights[cli.config.netuid] -= cli.config.amount
         my_weights[my_weights < 0] = 0  # Ensure weights don't go negative
-        all_netuids = np.arange(len(my_weights))
+        all_netuids = (
+            torch.tensor(list(range(len(my_weights))))
+            if use_torch()
+            else np.arange(len(my_weights))
+        )
 
         subtensor.root_set_weights(
             wallet=wallet,
@@ -520,12 +528,23 @@ def _run(cli: "bittensor.cli", subtensor: "bittensor.subtensor"):
             cli.config.weights = Prompt.ask(f"Enter weights (e.g. {example})")
 
         # Parse from string
-        netuids = np.array(
-            list(map(int, re.split(r"[ ,]+", cli.config.netuids))), dtype=np.int64
+        matched_netuids = list(map(int, re.split(r"[ ,]+", cli.config.netuids)))
+        netuids = (
+            torch.tensor(matched_netuids, dtype=torch.long)
+            if use_torch()
+            else np.array(matched_netuids, dtype=np.int64)
         )
-        weights = np.array(
-            list(map(float, re.split(r"[ ,]+", cli.config.weights))),
-            dtype=np.float32,
+
+        matched_weights = [
+            float(weight) for weight in re.split(r"[ ,]+", cli.config.weights)
+        ]
+        weights = (
+            torch.tensor(matched_weights, dtype=torch.float32)
+            if use_torch()
+            else np.array(
+                matched_weights,
+                dtype=np.float32,
+            )
         )
 
         # Run the set weights operation.

bittensor/commands/utils.py

@@ -19,7 +19,7 @@
 import os
 import bittensor
 import requests
-from bittensor.utils.registration import maybe_get_torch
+from bittensor.utils.registration import torch
 from typing import List, Dict, Any, Optional
 from rich.prompt import Confirm, PromptBase
 from dataclasses import dataclass
@@ -78,11 +78,9 @@ def check_netuid_set(
 
 def check_for_cuda_reg_config(config: "bittensor.config") -> None:
     """Checks, when CUDA is available, if the user would like to register with their CUDA device."""
-
-    torch = maybe_get_torch()
-    if torch is not None and torch.cuda.is_available():
+    if torch and torch.cuda.is_available():
         if not config.no_prompt:
-            if config.pow_register.cuda.get("use_cuda") == None:  # flag not set
+            if config.pow_register.cuda.get("use_cuda") is None:  # flag not set
                 # Ask about cuda registration only if a CUDA device is available.
                 cuda = Confirm.ask("Detected CUDA device, use CUDA for registration?\n")
                 config.pow_register.cuda.use_cuda = cuda

bittensor/dendrite.py

@@ -24,10 +24,11 @@
 import time
 import aiohttp
 import bittensor
-from typing import Union, Optional, List, Union, AsyncGenerator, Any
+from typing import Optional, List, Union, AsyncGenerator, Any
+from bittensor.utils.registration import torch, use_torch
 
 
-class dendrite:
+class DendriteMixin:
     """
     The Dendrite class represents the abstracted implementation of a network client module.
 
@@ -104,7 +105,7 @@ def __init__(
                 The user's wallet or keypair used for signing messages. Defaults to ``None``, in which case a new :func:`bittensor.wallet().hotkey` is generated and used.
         """
         # Initialize the parent class
-        super(dendrite, self).__init__()
+        super(DendriteMixin, self).__init__()
 
         # Unique identifier for the instance
         self.uuid = str(uuid.uuid1())
@@ -121,9 +122,6 @@ def __init__(
 
         self._session: Optional[aiohttp.ClientSession] = None
 
-    async def __call__(self, *args, **kwargs):
-        return await self.forward(*args, **kwargs)
-
     @property
     async def session(self) -> aiohttp.ClientSession:
         """
@@ -808,3 +806,24 @@ def __del__(self):
             del dendrite  # This will implicitly invoke the __del__ method and close the session.
         """
         self.close_session()
+
+
+# For back-compatibility with torch
+BaseModel: Union["torch.nn.Module", object] = torch.nn.Module if use_torch() else object
+
+
+class dendrite(DendriteMixin, BaseModel):  # type: ignore
+    def __init__(
+        self, wallet: Optional[Union[bittensor.wallet, bittensor.Keypair]] = None
+    ):
+        if use_torch():
+            torch.nn.Module.__init__(self)
+        DendriteMixin.__init__(self, wallet)
+
+
+if not use_torch():
+
+    async def call(self, *args, **kwargs):
+        return await self.forward(*args, **kwargs)
+
+    dendrite.__call__ = call