ML-338 Tensor manipulation smart contract

examples.yaml

@@ -23,6 +23,18 @@ steps:
   - run_python_test: { "script": "ml_examples.01_smart_contract_model.submit_contract", "mode": "boston"}
 ...
 ---
+test_description: >
+  Tensor manipulation test
+setup_conditions:
+  test_name: "tensor_test"
+  number_of_nodes: 1
+  mining_nodes: [0]
+  max_test_time: 60
+steps:
+  - sleep: 5
+  - run_python_test: { "script": "tensor_manipulation.submit_contract"}
+...
+---
 test_description: >
   Basic contract submission test
 setup_conditions:

tensor_manipulation/submit_contract.py

@@ -0,0 +1,79 @@
+import os
+
+from fetchai.ledger.api import LedgerApi
+from fetchai.ledger.contract import Contract
+from fetchai.ledger.crypto import Entity, Address
+
+HERE = os.path.dirname(__file__)
+
+
+# generic contract setup
+def contract_setup(source, benefactor, options):
+    # Create keypair for the contract owner
+    entity1 = Entity()
+    Address(entity1)
+
+    host = options['host']
+    port = options['port']
+    # create the APIs
+    api = LedgerApi(host, port)
+
+    # Transfer tokens from benefactor
+    api.sync(api.tokens.transfer(benefactor, entity1, int(1e8), 1000))
+
+    # Create contract
+    contract = Contract(source, entity1)
+
+    # Deploy contract
+    api.sync(contract.create(api, entity1, api.tokens.balance(entity1)))
+
+    return api, contract, entity1
+
+
+# run function for running on end to end tests
+def run(options, benefactor):
+    source_file = os.path.join(HERE, "tensor_manipulation_contract.etch")
+
+    with open(source_file, "r") as fp:
+        source = fp.read()
+
+    print(options)
+
+    if benefactor is None or options is None:
+        raise Exception("Must give options and benefactor")
+
+    api, contract, entity = contract_setup(source, benefactor, options)
+
+    # add two tensors and get result
+    data_string = "2,3,4,5"
+    label_string = "1,2,3,4"
+    api.sync(contract.action(api, 'add', api.tokens.balance(entity),
+                             [entity], data_string, label_string))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)
+
+    # subtract two tensors and get result
+    api.sync(contract.action(api, 'subtract', api.tokens.balance(entity),
+                             [entity], data_string, label_string))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)
+
+    api.sync(contract.action(api, 'multiply', api.tokens.balance(entity),
+                             [entity], data_string, label_string))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)
+
+    api.sync(contract.action(api, 'divide', api.tokens.balance(entity),
+                             [entity], data_string, label_string))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)
+
+    api.sync(contract.action(api, 'copy', api.tokens.balance(entity),
+                             [entity], data_string))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)
+
+    api.sync(contract.action(api, 'at', api.tokens.balance(entity),
+                             [entity], data_string, 3))
+    prediction = contract.query(api, 'getOutput')
+    print("Ouput is: " + prediction)

tensor_manipulation/tensor_manipulation_contract.etch

@@ -0,0 +1,158 @@
+//------------------------------------------------------------------------------
+//
+//   Copyright 2019 Fetch.AI Limited
+//
+//   Licensed under the Apache License, Version 2.0 (the "License");
+//   you may not use this file except in compliance with the License.
+//   You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+//   Unless required by applicable law or agreed to in writing, software
+//   distributed under the License is distributed on an "AS IS" BASIS,
+//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//   See the License for the specific language governing permissions and
+//   limitations under the License.
+//
+//------------------------------------------------------------------------------
+
+persistent output_state : Tensor;
+
+// initial set up creates the model and persistent data
+@init
+function setup(owner : Address)
+  use output_state;
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 2u64;
+  tensor_shape[1] = 2u64;
+  var output = Tensor(tensor_shape);
+
+  output_state.set(output);
+endfunction
+
+// get the output state
+@query
+function getOutput() : String
+  use output_state;
+  var output = output_state.get();
+  return output.toString();
+endfunction
+
+
+// add two tensors and save to state
+@action
+function add(tensor1_string: String, tensor2_string: String)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  var tensor2 = Tensor(tensor_shape);
+  tensor2.fromString(tensor2_string);
+
+  use output_state;
+  var output = output_state.get();
+  output = tensor1 + tensor2;
+  output_state.set(output);
+endfunction
+
+
+// subtract two tensors and save to state
+@action
+function subtract(tensor1_string: String, tensor2_string: String)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  var tensor2 = Tensor(tensor_shape);
+  tensor2.fromString(tensor2_string);
+
+  use output_state;
+  var output = output_state.get();
+  output = tensor1 - tensor2;
+  output_state.set(output);
+endfunction
+
+@action
+function multiply(tensor1_string: String, tensor2_string: String)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  var tensor2 = Tensor(tensor_shape);
+  tensor2.fromString(tensor2_string);
+
+  use output_state;
+  var output = output_state.get();
+  output = tensor1 * tensor2;
+  output_state.set(output);
+endfunction
+
+@action
+function divide(tensor1_string: String, tensor2_string: String)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  var tensor2 = Tensor(tensor_shape);
+  tensor2.fromString(tensor2_string);
+
+  use output_state;
+  var output = output_state.get();
+  output = tensor1 / tensor2;
+  output_state.set(output);
+endfunction
+
+@action
+function copy(tensor1_string: String)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  use output_state;
+  var output = output_state.get();
+  output = tensor1.copy();
+  output_state.set(output);
+endfunction
+
+@action
+function at(tensor1_string: String, index: UInt64)
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 1u64;
+  tensor_shape[1] = 1u64;
+  var tensor1 = Tensor(tensor_shape);
+  tensor1.fromString(tensor1_string);
+
+  use output_state;
+  tensor_shape = Array<UInt64>(1);
+  tensor_shape[0] = 1u64;
+  var output = Tensor(tensor_shape);
+  output.setAt(0u64, tensor1.at(0u64, index));
+  output_state.set(output);
+endfunction
+
+function main()
+  use output_state;
+  var tensor_shape = Array<UInt64>(2);
+  tensor_shape[0] = 2u64;
+  tensor_shape[1] = 2u64;
+  var output = Tensor(tensor_shape);
+
+  output_state.set(output);
+
+  var tensor1_string = "2, 3, 4, 5";
+  var tensor2_string = "1, 2, 3, 4";
+  add(tensor1_string, tensor2_string);
+  subtract(tensor1_string, tensor2_string);
+
+  printLn(getOutput());
+endfunction