mirror of
https://github.com/zama-ai/concrete.git
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44016cc80c
- add op_attributes on ArbitraryFunction - this works if all constants are smaller than the input tensor - otherwise it requires more advanced code and a concatenate operator which currently does not exist
398 lines
13 KiB
Python
398 lines
13 KiB
Python
"""Test file for float subgraph fusing"""
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import random
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from inspect import signature
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import numpy
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import pytest
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from concrete.common.data_types.integers import Integer
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from concrete.common.debugging.custom_assert import assert_not_reached
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from concrete.common.optimization.topological import fuse_float_operations
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from concrete.common.values import EncryptedScalar, EncryptedTensor
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from concrete.numpy import tracing
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from concrete.numpy.tracing import trace_numpy_function
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def no_fuse(x):
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"""No fuse"""
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return x + 2
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def no_fuse_unhandled(x, y):
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"""No fuse unhandled"""
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x_1 = x + 0.7
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y_1 = y + 1.3
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intermediate = x_1 + y_1
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return intermediate.astype(numpy.int32)
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def simple_fuse_not_output(x):
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"""Simple fuse not output"""
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intermediate = x.astype(numpy.float64)
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intermediate = intermediate.astype(numpy.int32)
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return intermediate + 2
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def simple_fuse_output(x):
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"""Simple fuse output"""
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return x.astype(numpy.float64).astype(numpy.int32)
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def mix_x_and_y_intricately_and_call_f(function, x, y):
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"""Mix x and y in an intricated way, that can't be simplified by
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an optimizer eg, and then call function
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"""
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intermediate = x + y
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intermediate = intermediate + 2
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intermediate = intermediate.astype(numpy.float32)
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intermediate = intermediate.astype(numpy.int32)
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x_p_1 = intermediate + 1.5
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x_p_2 = intermediate + 2.7
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x_p_3 = function(x_p_1 + x_p_2)
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return (
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x_p_3.astype(numpy.int32),
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x_p_2.astype(numpy.int32),
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(x_p_2 + 3).astype(numpy.int32),
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x_p_3.astype(numpy.int32) + 67,
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y,
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(y + 4.7).astype(numpy.int32) + 3,
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)
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def mix_x_and_y_and_call_f(function, x, y):
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"""Mix x and y and then call function"""
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x_p_1 = x + 0.1
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x_p_2 = x + 0.2
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x_p_3 = function(x_p_1 + x_p_2)
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return (
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x_p_3.astype(numpy.int32),
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x_p_2.astype(numpy.int32),
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(x_p_2 + 3).astype(numpy.int32),
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x_p_3.astype(numpy.int32) + 67,
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y,
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(y + 4.7).astype(numpy.int32) + 3,
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)
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def mix_x_and_y_into_integer_and_call_f(function, x, y):
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"""Mix x and y but keep the entry to function as an integer"""
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x_p_1 = x + 1
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x_p_2 = x + 2
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x_p_3 = function(x_p_1 + x_p_2)
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return (
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x_p_3.astype(numpy.int32),
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x_p_2.astype(numpy.int32),
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(x_p_2 + 3).astype(numpy.int32),
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x_p_3.astype(numpy.int32) + 67,
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y,
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(y + 4.7).astype(numpy.int32) + 3,
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)
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@pytest.mark.parametrize(
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"function_to_trace,fused",
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[
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pytest.param(no_fuse, False, id="no_fuse"),
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pytest.param(no_fuse_unhandled, False, id="no_fuse_unhandled"),
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pytest.param(simple_fuse_not_output, True, id="no_fuse"),
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pytest.param(simple_fuse_output, True, id="no_fuse"),
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pytest.param(
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lambda x, y: mix_x_and_y_intricately_and_call_f(numpy.rint, x, y),
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True,
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id="mix_x_and_y_intricately_and_call_f_with_rint",
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),
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pytest.param(
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lambda x, y: mix_x_and_y_and_call_f(numpy.rint, x, y),
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True,
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id="mix_x_and_y_and_call_f_with_rint",
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),
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],
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)
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@pytest.mark.parametrize("input_", [0, 2, 42, 44])
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def test_fuse_float_operations(function_to_trace, fused, input_):
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"""Test function for fuse_float_operations"""
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params_names = signature(function_to_trace).parameters.keys()
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op_graph = trace_numpy_function(
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function_to_trace,
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{param_name: EncryptedScalar(Integer(32, True)) for param_name in params_names},
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)
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orig_num_nodes = len(op_graph.graph)
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fuse_float_operations(op_graph)
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fused_num_nodes = len(op_graph.graph)
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if fused:
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assert fused_num_nodes < orig_num_nodes
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else:
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assert fused_num_nodes == orig_num_nodes
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input_ = numpy.int32(input_)
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num_params = len(params_names)
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inputs = (input_,) * num_params
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assert function_to_trace(*inputs) == op_graph(*inputs)
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def subtest_tensor_no_fuse(fun, tensor_shape):
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"""Test case to verify float fusing is only applied on functions on scalars."""
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if tensor_shape == ():
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# We want tensors
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return
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if fun in LIST_OF_UFUNC_WHICH_HAVE_INTEGER_ONLY_SOURCES:
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# We need at least one input of the bivariate function to be float
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return
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# Float fusing currently cannot work if the constant in a bivariate operator is bigger than the
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# variable input.
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# Make a broadcastable shape but with the constant being bigger
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variable_tensor_shape = (1,) + tensor_shape
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constant_bigger_shape = (random.randint(2, 10),) + tensor_shape
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def tensor_no_fuse(x):
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intermediate = x.astype(numpy.float64)
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intermediate = fun(intermediate, numpy.ones(constant_bigger_shape))
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return intermediate.astype(numpy.int32)
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function_to_trace = tensor_no_fuse
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params_names = signature(function_to_trace).parameters.keys()
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op_graph = trace_numpy_function(
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function_to_trace,
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{
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param_name: EncryptedTensor(Integer(32, True), shape=variable_tensor_shape)
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for param_name in params_names
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},
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)
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orig_num_nodes = len(op_graph.graph)
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fuse_float_operations(op_graph)
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fused_num_nodes = len(op_graph.graph)
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assert orig_num_nodes == fused_num_nodes
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def check_results_are_equal(function_result, op_graph_result):
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"""Check the output of function execution and OPGraph evaluation are equal."""
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if isinstance(function_result, tuple) and isinstance(op_graph_result, tuple):
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assert len(function_result) == len(op_graph_result)
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are_equal = (
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function_output == op_graph_output
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for function_output, op_graph_output in zip(function_result, op_graph_result)
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)
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elif not isinstance(function_result, tuple) and not isinstance(op_graph_result, tuple):
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are_equal = (function_result == op_graph_result,)
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else:
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assert_not_reached(f"Incompatible outputs: {function_result}, {op_graph_result}")
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return all(value.all() if isinstance(value, numpy.ndarray) else value for value in are_equal)
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def subtest_fuse_float_unary_operations_correctness(fun, tensor_shape):
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"""Test a unary function with fuse_float_operations."""
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# Some manipulation to avoid issues with domain of definitions of functions
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if fun == numpy.arccosh:
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input_list = [1, 2, 42, 44]
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super_fun_list = [mix_x_and_y_and_call_f]
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elif fun in [numpy.arctanh, numpy.arccos, numpy.arcsin, numpy.arctan]:
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input_list = [0, 0.1, 0.2]
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super_fun_list = [mix_x_and_y_and_call_f]
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elif fun == numpy.invert:
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input_list = [1, 2, 42, 44]
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super_fun_list = [mix_x_and_y_into_integer_and_call_f]
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else:
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input_list = [0, 2, 42, 44]
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super_fun_list = [mix_x_and_y_and_call_f, mix_x_and_y_intricately_and_call_f]
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for super_fun in super_fun_list:
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for input_ in input_list:
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def get_function_to_trace():
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return lambda x, y: super_fun(fun, x, y)
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function_to_trace = get_function_to_trace()
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params_names = signature(function_to_trace).parameters.keys()
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op_graph = trace_numpy_function(
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function_to_trace,
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{
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param_name: EncryptedTensor(Integer(32, True), tensor_shape)
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for param_name in params_names
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},
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)
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orig_num_nodes = len(op_graph.graph)
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fuse_float_operations(op_graph)
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fused_num_nodes = len(op_graph.graph)
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assert fused_num_nodes < orig_num_nodes
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ones_input = (
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numpy.ones(tensor_shape, dtype=numpy.dtype(type(input_)))
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if tensor_shape != ()
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else 1
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)
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input_ = numpy.int32(input_ * ones_input)
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num_params = len(params_names)
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inputs = (input_,) * num_params
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function_result = function_to_trace(*inputs)
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op_graph_result = op_graph(*inputs)
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assert check_results_are_equal(function_result, op_graph_result)
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LIST_OF_UFUNC_WHICH_HAVE_INTEGER_ONLY_SOURCES = {
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numpy.bitwise_and,
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numpy.bitwise_or,
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numpy.bitwise_xor,
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numpy.gcd,
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numpy.invert,
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numpy.lcm,
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numpy.ldexp,
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numpy.left_shift,
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numpy.logical_and,
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numpy.logical_not,
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numpy.logical_or,
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numpy.logical_xor,
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numpy.remainder,
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numpy.right_shift,
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}
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def subtest_fuse_float_binary_operations_correctness(fun, tensor_shape):
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"""Test a binary functions with fuse_float_operations, with a constant as a source."""
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for i in range(4):
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# Know if the function is defined for integer inputs
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if fun in LIST_OF_UFUNC_WHICH_HAVE_INTEGER_ONLY_SOURCES:
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if i not in [0, 2]:
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continue
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# The .astype(numpy.float64) that we have in cases 0 and 2 is here to force
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# a float output even for functions which return an integer (eg, XOR), such
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# that our frontend always try to fuse them
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# The .astype(numpy.float64) that we have in cases 1 and 3 is here to force
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# a float output even for functions which return a bool (eg, EQUAL), such
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# that our frontend always try to fuse them
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# For bivariate functions: fix one of the inputs
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if i == 0:
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# With an integer in first position
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ones_0 = numpy.ones(tensor_shape, dtype=numpy.int64) if tensor_shape != () else 1
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def get_function_to_trace():
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return lambda x, y: fun(3 * ones_0, x + y).astype(numpy.float64).astype(numpy.int32)
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elif i == 1:
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# With a float in first position
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ones_1 = numpy.ones(tensor_shape, dtype=numpy.float64) if tensor_shape != () else 1
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def get_function_to_trace():
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return (
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lambda x, y: fun(2.3 * ones_1, x + y).astype(numpy.float64).astype(numpy.int32)
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)
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elif i == 2:
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# With an integer in second position
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ones_2 = numpy.ones(tensor_shape, dtype=numpy.int64) if tensor_shape != () else 1
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def get_function_to_trace():
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return lambda x, y: fun(x + y, 4 * ones_2).astype(numpy.float64).astype(numpy.int32)
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else:
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# With a float in second position
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ones_else = numpy.ones(tensor_shape, dtype=numpy.float64) if tensor_shape != () else 1
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def get_function_to_trace():
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return (
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lambda x, y: fun(x + y, 5.7 * ones_else)
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.astype(numpy.float64)
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.astype(numpy.int32)
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)
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input_list = [0, 2, 42, 44]
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# Domain of definition
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if fun in [numpy.true_divide, numpy.remainder, numpy.floor_divide, numpy.fmod]:
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input_list = [2, 42, 44]
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for input_ in input_list:
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ones_input = (
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numpy.ones(tensor_shape, dtype=numpy.dtype(type(input_)))
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if tensor_shape != ()
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else 1
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)
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input_ = input_ * ones_input
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function_to_trace = get_function_to_trace()
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params_names = signature(function_to_trace).parameters.keys()
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op_graph = trace_numpy_function(
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function_to_trace,
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{
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param_name: EncryptedTensor(Integer(32, True), tensor_shape)
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for param_name in params_names
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},
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)
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orig_num_nodes = len(op_graph.graph)
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fuse_float_operations(op_graph)
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fused_num_nodes = len(op_graph.graph)
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assert fused_num_nodes < orig_num_nodes
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input_ = numpy.int32(input_)
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num_params = len(params_names)
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inputs = (input_,) * num_params
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function_result = function_to_trace(*inputs)
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op_graph_result = op_graph(*inputs)
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assert check_results_are_equal(function_result, op_graph_result)
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def subtest_fuse_float_binary_operations_dont_support_two_variables(fun, tensor_shape):
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"""Test a binary function with fuse_float_operations, with no constant as
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a source."""
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def get_function_to_trace():
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return lambda x, y: fun(x, y).astype(numpy.int32)
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function_to_trace = get_function_to_trace()
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params_names = signature(function_to_trace).parameters.keys()
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with pytest.raises(NotImplementedError, match=r"Can't manage binary operator"):
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trace_numpy_function(
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function_to_trace,
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{
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param_name: EncryptedTensor(Integer(32, True), tensor_shape)
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for param_name in params_names
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},
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)
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@pytest.mark.parametrize("fun", tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC)
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@pytest.mark.parametrize("tensor_shape", [(), (3, 1, 2)])
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def test_ufunc_operations(fun, tensor_shape):
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"""Test functions which are in tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC."""
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if fun.nin == 1:
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subtest_fuse_float_unary_operations_correctness(fun, tensor_shape)
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elif fun.nin == 2:
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subtest_fuse_float_binary_operations_correctness(fun, tensor_shape)
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subtest_fuse_float_binary_operations_dont_support_two_variables(fun, tensor_shape)
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subtest_tensor_no_fuse(fun, tensor_shape)
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else:
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raise NotImplementedError("Only unary and binary functions are tested for now")
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