From 2b944dce9aa74713a11fa484bffa5b5c5a18f4b1 Mon Sep 17 00:00:00 2001 From: mhchia Date: Fri, 13 Sep 2024 01:55:59 +0800 Subject: [PATCH 1/2] detect selected columns in computation_to_model --- README.md | 10 ++-- poetry.lock | 4 ++ pyproject.toml | 1 + tests/helpers.py | 45 +++-------------- tests/test_computation.py | 103 +++++++++++++++++++++++++++++++++----- tests/test_core.py | 37 +++++++------- tests/test_ops.py | 14 +++--- zkstats/computation.py | 93 ++++++++++++++++++++++++++-------- 8 files changed, 208 insertions(+), 99 deletions(-) diff --git a/README.md b/README.md index 5a31a97..2f70e04 100644 --- a/README.md +++ b/README.md @@ -30,15 +30,15 @@ poetry install ### Define Your Computation -User computation must be defined as **a function** using ZKStats operations and PyTorch functions. The function signature must be `Callable[[State, list[torch.Tensor]], torch.Tensor]`: +User computation must be defined as **a function** using ZKStats operations and PyTorch functions. The function signature must be `Callable[[State, dict[str, torch.Tensor]], torch.Tensor]`: ```python import torch -from zkstats.computation import State +from zkstats.computation import State, Args # User-defined computation -def user_computation(s: State, data: list[torch.Tensor]) -> torch.Tensor: +def user_computation(state: State, args: Args) -> torch.Tensor: # Define your computation here ... @@ -116,9 +116,9 @@ Note here, that we can also just let prover generate model, and then send that m ```python from zkstats.core import computation_to_model # For prover: generate prover_model, and write to precal_witness file -_, prover_model = computation_to_model(user_computation, precal_witness_path, True, selected_columns, error) +selected_columns, _, prover_model = computation_to_model(user_computation, precal_witness_path, data_shape, True, error) # For verifier, generate verifier model (which is same as prover_model) by reading precal_witness file -_, verifier_model = computation_to_model(user_computation, precal_witness_path, False, selected_columns, error) +selected_columns, _, verifier_model = computation_to_model(user_computation, precal_witness_path, data_shape, False, error) ``` #### Data Provider: generate settings diff --git a/poetry.lock b/poetry.lock index d71e85f..b75acb3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2014,4 +2014,8 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.9" +<<<<<<< Updated upstream content-hash = "3b9e31329875d4c44356a5b2ec0c2d5b95b82b31d99a91e70054cc7728266e52" +======= +content-hash = "d110ae744ffbe6fa855ed3d352e0da87f4fcd306245ea27189b86417f3b32f26" +>>>>>>> Stashed changes diff --git a/pyproject.toml b/pyproject.toml index 49be20c..09da955 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ matplotlib = "^3.8.2" statistics = "^1.0.3" onnx = "^1.15.0" click = "^8.1.7" +six = "^1.16.0" [tool.poetry.dev-dependencies] ipykernel = "^6.27.1" diff --git a/tests/helpers.py b/tests/helpers.py index 5ed4455..966dc02 100644 --- a/tests/helpers.py +++ b/tests/helpers.py @@ -16,23 +16,21 @@ ERROR_CIRCUIT_RELAXED = 0.1 -def data_to_json_file(data_path: Path, data: list[torch.Tensor]) -> dict[str, list]: - column_names = [f"columns_{i}" for i in range(len(data))] +def data_to_json_file(data_path: Path, data: dict[str, torch.Tensor]) -> dict[str, list]: column_to_data = { column: d.tolist() - for column, d in zip(column_names, data) + for column, d in data.items() } - print('columnnnn: ', column_to_data) with open(data_path, "w") as f: json.dump(column_to_data, f) return column_to_data - -def compute_model( +def compute( basepath: Path, - data: list[torch.Tensor], - model: IModel, + data: dict[str, torch.Tensor], + model: Type[IModel], + # computation: TComputation, scales_params: Optional[Sequence[int]] = None, selected_columns_params: Optional[list[str]] = None, ): @@ -47,10 +45,10 @@ def compute_model( data_path = basepath / "data.json" data_commitment_path = basepath / "commitments.json" - column_to_data = data_to_json_file(data_path, data) + data_to_json_file(data_path, data) # If selected_columns_params is None, select all columns if selected_columns_params is None: - selected_columns = list(column_to_data.keys()) + selected_columns = list(data.keys()) else: selected_columns = selected_columns_params @@ -62,44 +60,17 @@ def compute_model( else: scales = scales_params scales_for_commitments = scales_params - # create_dummy((data_path), (dummy_data_path)) generate_data_commitment((data_path), scales_for_commitments, (data_commitment_path)) - # _, prover_model = computation_to_model(computation, (precal_witness_path), True, selected_columns, error) prover_gen_settings((data_path), selected_columns, (sel_data_path), model, (model_path), scales, "resources", (settings_path)) # No need, since verifier & prover share the same onnx - # _, verifier_model = computation_to_model(computation, (precal_witness_path), False, selected_columns, error) - # verifier_define_calculation((dummy_data_path), selected_columns, (sel_dummy_data_path),verifier_model, (verifier_model_path)) - setup((model_path), (compiled_model_path), (settings_path),(vk_path), (pk_path )) prover_gen_proof((model_path), (sel_data_path), (witness_path), (compiled_model_path), (settings_path), (proof_path), (pk_path)) - # print('slett col: ', selected_columns) verifier_verify((proof_path), (settings_path), (vk_path), selected_columns, (data_commitment_path)) -def compute( - basepath: Path, - data: list[torch.Tensor], - computation: TComputation, - scales_params: Optional[Sequence[int]] = None, - selected_columns_params: Optional[list[str]] = None, -) -> State: - data_path = basepath / "data.json" - precal_witness_path = basepath / "precal_witness_path.json" - - column_to_data = data_to_json_file(data_path, data) - # If selected_columns_params is None, select all columns - if selected_columns_params is None: - selected_columns = list(column_to_data.keys()) - else: - selected_columns = selected_columns_params - - state, model = computation_to_model(computation, precal_witness_path, True, selected_columns) - compute_model(basepath, data, model, scales_params, selected_columns_params) - return state - # Error tolerance between zkstats python implementation and python statistics module diff --git a/tests/test_computation.py b/tests/test_computation.py index 4b2e18c..980dd19 100644 --- a/tests/test_computation.py +++ b/tests/test_computation.py @@ -4,7 +4,7 @@ import pytest -from zkstats.computation import State, Args, computation_to_model +from zkstats.computation import State, computation_to_model, analyze_computation, TComputation, Args from zkstats.ops import ( Mean, Median, @@ -25,9 +25,9 @@ def nested_computation(state: State, args: Args): - x = args['columns_0'] - y = args['columns_1'] - z = args['columns_2'] + x = args["x"] + y = args["y"] + z = args["z"] out_0 = state.median(x) out_1 = state.geometric_mean(y) out_2 = state.harmonic_mean(x) @@ -63,8 +63,14 @@ def nested_computation(state: State, args: Args): [ERROR_CIRCUIT_DEFAULT], ) def test_nested_computation(tmp_path, column_0: torch.Tensor, column_1: torch.Tensor, column_2: torch.Tensor, error, scales): + precal_witness_path = tmp_path / "precal_witness_path.json" x, y, z = column_0, column_1, column_2 - state = compute(tmp_path, [x, y, z], nested_computation, scales) + data_shape = {"x": len(x), "y": len(y), "z": len(z)} + data = {"x": x, "y": y, "z": z} + selected_columns, state, model = computation_to_model(nested_computation, precal_witness_path, data_shape, True, error) + compute(tmp_path, data, model, scales, selected_columns) + # There are 11 ops in the computation + assert state.current_op_index == 12 ops = state.ops @@ -152,10 +158,14 @@ def test_computation_with_where_1d(tmp_path, error, column_0, op_type: Callable[ def condition(_x: torch.Tensor): return _x < 4 - def where_and_op(state: State, args: Args): - x = args['columns_0'] + column_name = "x" + + def where_and_op(state, args): + x = args[column_name] return op_type(state, state.where(condition(x), x)) - state = compute(tmp_path, [column], where_and_op, scales) + precal_witness_path = tmp_path / "precal_witness_path.json" + _, state, model = computation_to_model(where_and_op, precal_witness_path, {column_name: column.shape}, True, error) + compute(tmp_path, {column_name: column}, model, scales) res_op = state.ops[-1] filtered = column[condition(column)] @@ -174,14 +184,18 @@ def test_computation_with_where_2d(tmp_path, error, column_0, column_1, op_type: def condition_0(_x: torch.Tensor): return _x > 4 - def where_and_op(state: State, args: Args): - x = args['columns_0'] - y = args['columns_1'] + def where_and_op(state: State, args: list[torch.Tensor]): + x = args["x"] + y = args["y"] condition_x = condition_0(x) filtered_x = state.where(condition_x, x) filtered_y = state.where(condition_x, y) return op_type(state, filtered_x, filtered_y) - state = compute(tmp_path, [column_0, column_1], where_and_op, scales) + precal_witness_path = tmp_path / "precal_witness_path.json" + data_shape = {"x": len(column_0), "y": len(column_1)} + data = {"x": column_0, "y": column_1} + selected_columns, state, model = computation_to_model(where_and_op, precal_witness_path, data_shape, True ,error) + compute(tmp_path, data, model, scales, selected_columns) res_op = state.ops[-1] condition_x = condition_0(column_0) @@ -189,3 +203,68 @@ def where_and_op(state: State, args: Args): filtered_y = column_1[condition_x] expected_res = expected_func(filtered_x.tolist(), filtered_y.tolist()) assert_result(res_op.result.data, expected_res) + + +def test_analyze_computation_success(): + def valid_computation(state, args): + x = args["column1"] + y = args["column2"] + return state.mean(x) + state.median(y) + + result = analyze_computation(valid_computation) + assert set(result) == {"column1", "column2"} + +def test_analyze_computation_no_columns(): + def no_columns_computation(state, args): + return state.mean(state.median([1, 2, 3])) + + result = analyze_computation(no_columns_computation) + assert result == [] + +def test_analyze_computation_multiple_uses(): + def multiple_uses_computation(state, args): + x = args["column1"] + y = args["column2"] + z = args["column1"] # Using column1 twice + return state.mean(x) + state.median(y) + state.sum(z) + + result = analyze_computation(multiple_uses_computation) + assert set(result) == {"column1", "column2"} + +def test_analyze_computation_nested_args(): + def nested_args_computation(state, args): + x = args["column1"]["nested"] + y = args["column2"] + return state.mean(x) + state.median(y) + + result = analyze_computation(nested_args_computation) + assert set(result) == {"column1", "column2"} + +def test_analyze_computation_invalid_params(): + def invalid_params_computation(invalid1, invalid2): + return invalid1.mean(invalid2["column"]) + + with pytest.raises(ValueError, match="The computation function must have two parameters named 'state' and 'args'"): + analyze_computation(invalid_params_computation) + +def test_analyze_computation_wrong_param_names(): + def wrong_param_names(state, wrong_name): + return state.mean(wrong_name["column"]) + + with pytest.raises(ValueError, match="The computation function must have two parameters named 'state' and 'args'"): + analyze_computation(wrong_param_names) + +def test_analyze_computation_dynamic_column_access(): + def dynamic_column_access(state, args): + columns = ["column1", "column2"] + return sum(state.mean(args[col]) for col in columns) + + # This won't catch dynamically accessed columns + result = analyze_computation(dynamic_column_access) + assert result == [] + +def test_analyze_computation_lambda(): + lambda_computation = lambda state, args: state.mean(args["column"]) + + with pytest.raises(ValueError, match="Lambda functions are not supported in analyze_computation"): + analyze_computation(lambda_computation) diff --git a/tests/test_core.py b/tests/test_core.py index 2b09a13..71cc4ba 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -16,7 +16,8 @@ def test_get_data_commitment_maps(tmp_path, column_0, column_1, scales): # "columns_0": [1, 2, 3, 4, 5], # "columns_1": [6, 7, 8, 9, 10], # } - data_json = data_to_json_file(data_path, [column_0, column_1]) + data_json = {"columns_0": column_0, "columns_1": column_1} + data_to_json_file(data_path, data_json) # data_commitment is a mapping[scale -> mapping[column_name, commitment_hex]] # { # scale_0: { @@ -51,7 +52,8 @@ def test_get_data_commitment_maps_hardcoded(tmp_path): data_commitment_path = tmp_path / "commitments.json" column_0 = torch.tensor([3.0, 4.5, 1.0, 2.0, 7.5, 6.4, 5.5]) column_1 = torch.tensor([2.7, 3.3, 1.1, 2.2, 3.8, 8.2, 4.4]) - data_to_json_file(data_path, [column_0, column_1]) + data_json = {"columns_0": column_0, "columns_1": column_1} + data_to_json_file(data_path, data_json) scales = [2, 3] generate_data_commitment(data_path, scales, data_commitment_path) with open(data_commitment_path, "r") as f: @@ -63,30 +65,28 @@ def test_get_data_commitment_maps_hardcoded(tmp_path): def test_integration_select_partial_columns(tmp_path, column_0, column_1, error, scales): data_path = tmp_path / "data.json" - data_json = data_to_json_file(data_path, [column_0, column_1]) - columns = list(data_json.keys()) - assert len(columns) == 2 - # Select only the first column from two columns - selected_columns = [columns[0]] + data_json = {"columns_0": column_0, "columns_1": column_1} + data_shape = {"columns_0": len(column_0), "columns_1": len(column_1)} + data_to_json_file(data_path, data_json) def simple_computation(state, args): - x = args['columns_0'] - return state.mean(x) + return state.mean(args["columns_0"]) + precal_witness_path = tmp_path / "precal_witness_path.json" + selected_columns, _, model = computation_to_model(simple_computation, precal_witness_path, data_shape, True, error) # gen settings, setup, prove, verify - compute(tmp_path, [column_0, column_1], simple_computation, scales, selected_columns) + compute(tmp_path, data_json, model, scales, selected_columns) def test_csv_data(tmp_path, column_0, column_1, error, scales): data_json_path = tmp_path / "data.json" data_csv_path = tmp_path / "data.csv" - data_json = data_to_json_file(data_json_path, [column_0, column_1]) + data_json = {"columns_0": column_0, "columns_1": column_1} + data_shape = {"columns_0": len(column_0), "columns_1": len(column_1)} + data_to_json_file(data_json_path, data_json) json_file_to_csv(data_json_path, data_csv_path) - selected_columns = list(data_json.keys()) - def simple_computation(state, args): - x = args['columns_0'] - return state.mean(x) + return state.mean(args["columns_0"]) sel_data_path = tmp_path / "comb_data.json" model_path = tmp_path / "model.onnx" @@ -98,7 +98,7 @@ def simple_computation(state, args): generate_data_commitment(data_csv_path, scales, data_commitment_path) # Test: `prover_gen_settings` works with csv - _, model_for_proving = computation_to_model(simple_computation, precal_witness_path, True, selected_columns, error) + selected_columns, _, model_for_proving = computation_to_model(simple_computation, precal_witness_path, data_shape, True, error) prover_gen_settings( data_path=data_csv_path, selected_columns=selected_columns, @@ -112,7 +112,7 @@ def simple_computation(state, args): # Test: `prover_gen_settings` works with csv # Instantiate the model for verification since the state of `model_for_proving` is changed after `prover_gen_settings` - _, model_for_verification = computation_to_model(simple_computation, precal_witness_path, False, selected_columns, error) + selected_columns, _, model_for_verification = computation_to_model(simple_computation, precal_witness_path, data_shape, False, error) verifier_define_calculation(data_csv_path, selected_columns, str(sel_data_path), model_for_verification, str(model_path)) def json_file_to_csv(data_json_path, data_csv_path): @@ -135,7 +135,8 @@ def json_file_to_csv(data_json_path, data_csv_path): def test__preprocess_data_file_to_json(tmp_path, column_0, column_1): data_json_path = tmp_path / "data.json" - data_from_json = data_to_json_file(data_json_path, [column_0, column_1]) + data_json = {"columns_0": column_0, "columns_1": column_1} + data_from_json = data_to_json_file(data_json_path, data_json) # Test: csv can be converted to json # 1. Generate a csv file from json diff --git a/tests/test_ops.py b/tests/test_ops.py index af64bd5..9a4e7ac 100644 --- a/tests/test_ops.py +++ b/tests/test_ops.py @@ -5,9 +5,9 @@ import torch from zkstats.ops import Mean, Median, GeometricMean, HarmonicMean, Mode, PStdev, PVariance, Stdev, Variance, Covariance, Correlation, Operation, Regression -from zkstats.computation import IModel, IsResultPrecise, State, computation_to_model +from zkstats.computation import IModel, IsResultPrecise -from .helpers import compute_model, assert_result, ERROR_CIRCUIT_DEFAULT, ERROR_CIRCUIT_STRICT, ERROR_CIRCUIT_RELAXED +from .helpers import compute, assert_result, ERROR_CIRCUIT_DEFAULT, ERROR_CIRCUIT_STRICT, ERROR_CIRCUIT_RELAXED @pytest.mark.parametrize( @@ -49,8 +49,8 @@ def test_ops_2_parameters(tmp_path, column_0: torch.Tensor, column_1: torch.Tens ) def test_linear_regression(tmp_path, column_0: torch.Tensor, column_1: torch.Tensor, error: float, scales: list[float]): expected_res = statistics.linear_regression(column_0.tolist(), column_1.tolist()) - columns = [column_0, column_1] - regression = Regression.create(columns, error) + columns = {"columns_0": column_0, "columns_1": column_1} + regression = Regression.create(list(columns.values()), error) # shape = [2, 1] actual_res = regression.result assert_result(expected_res.slope, actual_res[0][0]) @@ -58,8 +58,7 @@ def test_linear_regression(tmp_path, column_0: torch.Tensor, column_1: torch.Ten class Model(IModel): def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor]: return regression.ezkl(x), regression.result - compute_model(tmp_path, columns, Model, scales) - + compute(tmp_path, columns, Model, scales) def run_test_ops(tmp_path, op_type: Type[Operation], expected_func: Callable[[list[float]], float], error: float, scales: list[float], columns: list[torch.Tensor]): @@ -70,4 +69,5 @@ def run_test_ops(tmp_path, op_type: Type[Operation], expected_func: Callable[[li class Model(IModel): def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor]: return op.ezkl(x), op.result - compute_model(tmp_path, columns, Model, scales) + data = {f"columns_{i}": column for i, column in enumerate(columns)} + compute(tmp_path, data, Model, scales) diff --git a/zkstats/computation.py b/zkstats/computation.py index 2153ade..f320199 100644 --- a/zkstats/computation.py +++ b/zkstats/computation.py @@ -264,30 +264,81 @@ def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor # An computation function. Example: -# def computation(state: State, x: list[torch.Tensor]): -# out_0 = state.median(x[0]) -# out_1 = state.median(x[1]) +# def computation(state: State, args: dict[str, torch.Tensor]): +# out_0 = state.median(args["x"]) +# out_1 = state.median(args["y"]) # return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1)) -TComputation = Callable[[State, list[torch.Tensor]], torch.Tensor] +TComputation = Callable[[State, dict[str, torch.Tensor]], torch.Tensor] + + +import ast +import inspect + + +def analyze_computation(computation: TComputation): + source = inspect.getsource(computation) + + # Check if it's a lambda function + if source.strip().startswith('lambda'): + raise ValueError("Lambda functions are not supported in analyze_computation. Please use a regular function definition instead.") + + # Existing code for regular functions + # Correct indentation + lines = source.splitlines() + min_indent = min(len(line) - len(line.lstrip()) for line in lines if line.strip()) + corrected_source = '\n'.join(line[min_indent:] for line in lines) + + tree = ast.parse(corrected_source) + column_names = set() + + class ComputationVisitor(ast.NodeVisitor): + def __init__(self): + self.valid_params = False + + def visit_FunctionDef(self, node): + if len(node.args.args) == 2: + if node.args.args[0].arg == 'state' and node.args.args[1].arg == 'args': + self.valid_params = True + self.generic_visit(node) + + def visit_Subscript(self, node): + if isinstance(node.value, ast.Name) and node.value.id == 'args': + if isinstance(node.slice, ast.Constant): + column_names.add(node.slice.value) + self.generic_visit(node) + + visitor = ComputationVisitor() + visitor.visit(tree) + + if not visitor.valid_params: + raise ValueError("The computation function must have two parameters named 'state' and 'args'") + + return list(column_names) + class Args: def __init__( self, - columns: list[str], + data_shape: dict[str, int], data: list[torch.Tensor], ): - if len(columns) != len(data): - raise ValueError("columns and data must have the same length") + column_names = list(data_shape.keys())[:len(data)] self.data_dict = { column_name: d - for column_name, d in zip(columns, data) + for column_name, d in zip(column_names, data) } def __getitem__(self, key: str) -> torch.Tensor: return self.data_dict[key] -def computation_to_model(computation: TComputation, precal_witness_path: str, isProver:bool, selected_columns: list[str], error: float = DEFAULT_ERROR ) -> tuple[State, Type[IModel]]: +def computation_to_model( + computation: TComputation, + precal_witness_path:str, + data_shape: dict[str, int], + isProver: bool, + error: float = DEFAULT_ERROR, +) -> tuple[list[str], State, Type[IModel]]: """ Create a torch model from a `computation` function defined by user :param computation: A function that takes a State and a list of torch.Tensor, and returns a torch.Tensor @@ -295,6 +346,16 @@ def computation_to_model(computation: TComputation, precal_witness_path: str, is :return: A tuple of State and Model. The Model is a torch model that can be used for exporting to onnx. State is a container for intermediate results of computation, which can be useful when debugging. """ + + selected_columns_unordered = analyze_computation(computation) + # Preserve the order from data_shape + selected_columns = [col for col in data_shape.keys() if col in selected_columns_unordered] + assert len(selected_columns) == len(selected_columns_unordered), "Selected columns must match" + + invalid_columns = set(selected_columns) - set(data_shape.keys()) + if invalid_columns: + raise ValueError(f"Computation uses columns not present in data: {invalid_columns}") + state = State(error) state.precal_witness_path = precal_witness_path @@ -302,25 +363,17 @@ def computation_to_model(computation: TComputation, precal_witness_path: str, is class Model(IModel): def preprocess(self, x: list[torch.Tensor]) -> None: - """ - Calculate the witnesses of the computation and store them in the state. - """ - # In the preprocess step, the operations are calculated and the results are stored in the state. - # So we don't need to get the returned result - args = Args(selected_columns, x) + args = Args(data_shape, x) computation(state, args) state.set_ready_for_exporting_onnx() def forward(self, *x: list[torch.Tensor]) -> tuple[IsResultPrecise, torch.Tensor]: - """ - Called by torch.onnx.export. - """ - args = Args(selected_columns, x) + args = Args(data_shape, x) result = computation(state, args) is_computation_result_accurate = state.bools[0]() for op_precise_check in state.bools[1:]: is_op_result_accurate = op_precise_check() is_computation_result_accurate = torch.logical_and(is_computation_result_accurate, is_op_result_accurate) return is_computation_result_accurate, result - return state, Model + return selected_columns, state, Model From 94fcdfb78b10e729d70dc2abb2d25ede2e82ee45 Mon Sep 17 00:00:00 2001 From: mhchia Date: Fri, 13 Sep 2024 16:13:25 +0800 Subject: [PATCH 2/2] fix: poetry file and readme --- README.md | 17 +- poetry.lock | 1557 ++++++++++++++++++++++++++---------------------- pyproject.toml | 4 +- 3 files changed, 836 insertions(+), 742 deletions(-) diff --git a/README.md b/README.md index 2f70e04..2da3600 100644 --- a/README.md +++ b/README.md @@ -30,22 +30,9 @@ poetry install ### Define Your Computation -User computation must be defined as **a function** using ZKStats operations and PyTorch functions. The function signature must be `Callable[[State, dict[str, torch.Tensor]], torch.Tensor]`: - -```python -import torch - -from zkstats.computation import State, Args - -# User-defined computation -def user_computation(state: State, args: Args) -> torch.Tensor: - # Define your computation here - ... - -``` - +User computation must be defined as **a function** using ZKStats operations and PyTorch functions. The function signature must be `Callable[[State, Args], torch.Tensor]`: - first argument is a `State` object, which contains the statistical functions that ZKStats supports. -- second argument is a list of PyTorch tensors, the input data. `data[0]` is the first column, `data[1]` is the second column, and so on. +- second argument is a `Args` object, which is a dictionary of PyTorch tensors, the input data. `Args['column1']` is the first column, `Args['column2']` is the second column, and so on. For example, we have two columns of data and we want to compute the mean of the medians of the two columns: diff --git a/poetry.lock b/poetry.lock index b75acb3..be914f3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.8" files = [ - {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"}, - {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"}, + {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"}, + {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"}, ] [package.extras] @@ -1998,24 +2105,24 @@ files = [ [[package]] name = "zipp" -version = "3.17.0" +version = "3.20.1" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, - {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, + {file = "zipp-3.20.1-py3-none-any.whl", hash = "sha256:9960cd8967c8f85a56f920d5d507274e74f9ff813a0ab8889a5b5be2daf44064"}, + {file = "zipp-3.20.1.tar.gz", hash = "sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.9" -<<<<<<< Updated upstream -content-hash = "3b9e31329875d4c44356a5b2ec0c2d5b95b82b31d99a91e70054cc7728266e52" -======= -content-hash = "d110ae744ffbe6fa855ed3d352e0da87f4fcd306245ea27189b86417f3b32f26" ->>>>>>> Stashed changes +content-hash = "e88edfd192f02c737e3825cc61bddcfd70085ae2f136c6a4c651610f68a33e40" diff --git a/pyproject.toml b/pyproject.toml index 09da955..a051c75 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,8 @@ authors = ["Jern Kunpittaya", "Kevin Chia"] [tool.poetry.dependencies] python = "^3.9" ezkl = "9.1.0" -torch = "^2.1.1" +# fix torch version to 2.2.0 due to a weird issue when upgrading to 2.4.1 +torch = "2.2.0" requests = "^2.31.0" scipy = "^1.11.4" numpy = "^1.26.2" @@ -15,7 +16,6 @@ matplotlib = "^3.8.2" statistics = "^1.0.3" onnx = "^1.15.0" click = "^8.1.7" -six = "^1.16.0" [tool.poetry.dev-dependencies] ipykernel = "^6.27.1"