Add dcap proof system

.gitignore

@@ -195,3 +195,4 @@ competition_circuit/**
 
 # intellij files
 .idea/
+**.onnx

neurons/constants.py

@@ -162,6 +162,23 @@ class Roles:
 )
 # EZKL path
 LOCAL_EZKL_PATH = os.path.join(os.path.expanduser("~"), ".ezkl", "ezkl")
+# TEEONNX path
+LOCAL_TEEONNX_PATH = os.path.join(os.path.expanduser("~"), ".teeonnx", "teeonnx")
+# SNARKJS paths
+LOCAL_SNARKJS_INSTALL_DIR = os.path.join(os.path.expanduser("~"), ".snarkjs")
+LOCAL_SNARKJS_PATH = os.path.join(
+    LOCAL_SNARKJS_INSTALL_DIR, "node_modules", ".bin", "snarkjs"
+)
+# External files required by miners and validators
+MINER_EXTERNAL_FILES = [
+    "circuit.zkey",
+    "pk.key",
+    "network.onnx",
+]
+VALIDATOR_EXTERNAL_FILES = [
+    "circuit.zkey",
+    "network.onnx",
+]
 # GitHub repository URL
 REPO_URL = "https://github.com/inference-labs-inc/omron-subnet"
 # Various time constants in seconds

neurons/deployment_layer/model_815d49886a610c17e34204b1c544c363bb06328a6cb126181bd0b6e77164cd4b/input.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+from pydantic import BaseModel
+from execution_layer.base_input import BaseInput
+from execution_layer.input_registry import InputRegistry
+from _validator.models.request_type import RequestType
+import numpy as np
+
+BATCH_SIZE = 1
+SEQUENCE_LENGTH = 64
+
+
+class CircuitInputSchema(BaseModel):
+    input_ids: list[list[int]]
+    shapes: list[list[int]]
+    variables: list[list[object]]
+
+
+@InputRegistry.register(
+    "815d49886a610c17e34204b1c544c363bb06328a6cb126181bd0b6e77164cd4b"
+)
+class CircuitInput(BaseInput):
+
+    schema = CircuitInputSchema
+
+    def __init__(
+        self, request_type: RequestType, data: dict[str, object] | None = None
+    ):
+        super().__init__(request_type, data)
+
+    @staticmethod
+    def generate() -> dict[str, object]:
+        input_ids = np.random.randint(
+            0, 256, size=(BATCH_SIZE, SEQUENCE_LENGTH)
+        ).tolist()
+        return {
+            "input_ids": input_ids,
+            "shapes": [[BATCH_SIZE, SEQUENCE_LENGTH]],
+            "variables": [["batch_size", BATCH_SIZE], ["sequence", SEQUENCE_LENGTH]],
+        }
+
+    @staticmethod
+    def validate(data: dict[str, object]) -> None:
+        return CircuitInputSchema(**data)
+
+    @staticmethod
+    def process(data: dict[str, object]) -> dict[str, object]:
+        return data

neurons/deployment_layer/model_815d49886a610c17e34204b1c544c363bb06328a6cb126181bd0b6e77164cd4b/metadata.toml

@@ -0,0 +1,11 @@
+name = "Dishonest Advisor"
+description = "A dishonest advisor which provides generic cryptocurrency advise (NFA)"
+author = "Inference Labs"
+version = "0.0.1"
+proof_system = "DCAP"
+type = "proof_of_computation"
+benchmark_choice_weight = 0.2
+network_weight = 0.1
+
+[external_files]
+network.onnx = "https://storage.omron.ai/815d49886a610c17e34204b1c544c363bb06328a6cb126181bd0b6e77164cd4b/network.onnx"

neurons/deployment_layer/model_c2493244e153e8dcb2bfa6e0ebb411b76069074041d95718c47a36f4ec637237/input.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+from pydantic import BaseModel
+from execution_layer.base_input import BaseInput
+from execution_layer.input_registry import InputRegistry
+from _validator.models.request_type import RequestType
+import numpy as np
+
+BATCH_SIZE = 1
+SEQUENCE_LENGTH = 64
+
+
+class CircuitInputSchema(BaseModel):
+    input_ids: list[list[int]]
+    shapes: list[list[int]]
+    variables: list[list[object]]
+
+
+@InputRegistry.register(
+    "c2493244e153e8dcb2bfa6e0ebb411b76069074041d95718c47a36f4ec637237"
+)
+class CircuitInput(BaseInput):
+
+    schema = CircuitInputSchema
+
+    def __init__(
+        self, request_type: RequestType, data: dict[str, object] | None = None
+    ):
+        super().__init__(request_type, data)
+
+    @staticmethod
+    def generate() -> dict[str, object]:
+        input_ids = np.random.randint(
+            0, 256, size=(BATCH_SIZE, SEQUENCE_LENGTH)
+        ).tolist()
+        return {
+            "input_ids": input_ids,
+            "shapes": [[BATCH_SIZE, SEQUENCE_LENGTH]],
+            "variables": [["batch_size", BATCH_SIZE], ["sequence", SEQUENCE_LENGTH]],
+        }
+
+    @staticmethod
+    def validate(data: dict[str, object]) -> None:
+        return CircuitInputSchema(**data)
+
+    @staticmethod
+    def process(data: dict[str, object]) -> dict[str, object]:
+        return data

neurons/deployment_layer/model_c2493244e153e8dcb2bfa6e0ebb411b76069074041d95718c47a36f4ec637237/metadata.toml

@@ -0,0 +1,11 @@
+name = "Honest Advisor"
+description = "An honest advisor which provides generic cryptocurrency advise (NFA)"
+author = "Inference Labs"
+version = "0.0.1"
+proof_system = "DCAP"
+type = "proof_of_computation"
+benchmark_choice_weight = 0.2
+network_weight = 0.1
+
+[external_files]
+network.onnx = "https://storage.omron.ai/c2493244e153e8dcb2bfa6e0ebb411b76069074041d95718c47a36f4ec637237/network.onnx"

neurons/execution_layer/circuit.py

@@ -19,6 +19,7 @@
 from utils import with_rate_limit
 import time
 import re
+import toml
 
 # trunk-ignore(pylint/E0611)
 from bittensor import logging, subtensor, Wallet
@@ -38,11 +39,11 @@ class ProofSystem(str, Enum):
     Enum representing supported proof systems.
     """
 
-    # ZK Proof Systems
+    # Supported provers
     ZKML = "ZKML"
     CIRCOM = "CIRCOM"
-    JOLT = "JOLT"
     EZKL = "EZKL"
+    DCAP = "DCAP"
 
     def __str__(self):
         return self.value
@@ -101,7 +102,10 @@ def __post_init__(self):
                 f"model_{self.model_id}",
             )
         self.input = os.path.join(self.base_path, "input.json")
-        self.metadata = os.path.join(self.base_path, "metadata.json")
+        if os.path.exists(os.path.join(self.base_path, "metadata.toml")):
+            self.metadata = os.path.join(self.base_path, "metadata.toml")
+        else:
+            self.metadata = os.path.join(self.base_path, "metadata.json")
         self.compiled_model = os.path.join(self.base_path, "model.compiled")
         self.settings = os.path.join(self.base_path, "settings.json")
         self.witness = os.path.join(self.base_path, "witness.json")
@@ -121,14 +125,12 @@ def set_proof_system_paths(self, proof_system: ProofSystem):
             self.pk = os.path.join(self.external_base_path, "circuit.zkey")
             self.vk = os.path.join(self.base_path, "verification_key.json")
             self.compiled_model = os.path.join(self.base_path, "circuit.wasm")
-        elif proof_system == ProofSystem.JOLT:
-            self.compiled_model = os.path.join(
-                self.base_path, "target", "release", "circuit"
-            )
         elif proof_system == ProofSystem.EZKL:
             self.pk = os.path.join(self.external_base_path, "pk.key")
             self.vk = os.path.join(self.base_path, "vk.key")
             self.compiled_model = os.path.join(self.base_path, "model.compiled")
+        elif proof_system == ProofSystem.DCAP:
+            self.compiled_model = os.path.join(self.external_base_path, "network.onnx")
         else:
             raise ValueError(f"Proof system {proof_system} not supported")
 
@@ -143,7 +145,7 @@ class CircuitMetadata:
     description: str
     author: str
     version: str
-    proof_system: str
+    proof_system: ProofSystem
     type: CircuitType
     external_files: dict[str, str]
     netuid: int | None = None
@@ -163,7 +165,18 @@ def from_file(cls, metadata_path: str) -> CircuitMetadata:
             ModelMetadata: An instance of ModelMetadata.
         """
         with open(metadata_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
+            if metadata_path.endswith(".json"):
+                metadata = json.load(f)
+            elif metadata_path.endswith(".toml"):
+                metadata = toml.load(f)
+            else:
+                metadata = {}
+
+        if "proof_system" in metadata:
+            metadata["proof_system"] = ProofSystem(metadata["proof_system"])
+        if "type" in metadata:
+            metadata["type"] = CircuitType(metadata["type"])
+
         return cls(**metadata)
 
 

neurons/execution_layer/proof_handlers/base_handler.py

@@ -62,15 +62,3 @@ def generate_witness(
             session (VerifiedModelSession): The current handler session.
             return_content (bool): Whether to return the witness content.
         """
-
-    @abstractmethod
-    def aggregate_proofs(
-        self, session: VerifiedModelSession, proofs: list[str]
-    ) -> tuple[str, float]:
-        """
-        Aggregate multiple proofs into a single proof for the given session.
-
-        Returns:
-            tuple[str, float]: A tuple containing the aggregated proof content (str)
-            and the time taken to aggregate the proofs (float).
-        """

neurons/execution_layer/proof_handlers/circom_handler.py

@@ -201,11 +201,3 @@ def proof_worker(
             bt.logging.error(f"Proof generation stdout: {e.stdout}")
             bt.logging.error(f"Proof generation stderr: {e.stderr}")
             raise
-
-    @staticmethod
-    def aggregate_proofs(
-        session: "VerifiedModelSession", proofs: list[str]
-    ) -> tuple[str, float]:
-        raise NotImplementedError(
-            "Aggregation of proofs is not implemented for CircomHandler"
-        )