mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-08 19:44:57 -05:00
feat: add convolution extension
extend the current tracing and compilation with convolution, which should compile to the FHELinalg.conv2d operation from the compiler
This commit is contained in:
youben11
@@ -15,6 +15,7 @@ from ..representation.intermediate import (
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ALL_IR_NODES,
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Add,
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Constant,
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Conv2D,
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Dot,
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GenericFunction,
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IndexConstant,
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@@ -27,6 +28,7 @@ from ..representation.intermediate import (
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IR_NODE_COLOR_MAPPING = {
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Input: "blue",
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Constant: "cyan",
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Conv2D: "brown",
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Add: "red",
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Sub: "yellow",
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Mul: "green",
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@@ -1,2 +1,2 @@
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"""Extensions module to provide additional functionality to our users."""
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from . import multi_table, table
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from . import convolution, multi_table, table
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161
concrete/common/extensions/convolution.py
Normal file
161
concrete/common/extensions/convolution.py
Normal file
@@ -0,0 +1,161 @@
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"""This file contains tracers for convolution operations."""
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from typing import List, Optional, Tuple, Union, cast
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import numpy as np
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from ...numpy.tracing import NPConstant, NPTracer
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from ..representation.intermediate import Conv2D
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from ..tracing.base_tracer import BaseTracer
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SUPPORTED_AUTO_PAD = [
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"NOTSET",
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]
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def conv2d(
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x: Union[np.ndarray, BaseTracer],
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weight: Union[np.ndarray, BaseTracer],
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bias: Optional[Union[np.ndarray, BaseTracer]] = None,
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pads: Union[Tuple[int, int, int, int], List[int]] = (0, 0, 0, 0),
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strides: Union[Tuple[int, int], List[int]] = (1, 1),
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dilations: Union[Tuple[int, int], List[int]] = (1, 1),
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auto_pad: str = "NOTSET",
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) -> Union[np.ndarray, NPTracer]:
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"""Trace or evaluate 2D convolution.
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Args:
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x (Union[np.ndarray, BaseTracer]): Input of shape (NxCxHxW)
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weight (Union[np.ndarray, BaseTracer]): Weight (kernel) of shape (FxCxHxW)
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bias (Optional[Union[np.ndarray, BaseTracer]], optional): Bias vector of size (F).
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Defaults to None.
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pads (Union[Tuple[int, int, int, int], List[int]], optional): Padding over each axis
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(H_beg, W_beg, H_end, W_end). Defaults to (0, 0, 0, 0).
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strides (Union[Tuple[int, int], List[int]], optional): Stride over each axis
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(height and width). Defaults to (1, 1).
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dilations (Union[Tuple[int, int], List[int]], optional): Dilation over each axis
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(height and width). Defaults to (1, 1).
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auto_pad (str, optional): Padding strategy. Defaults to "NOTSET".
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Raises:
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ValueError: If one argument isn't in the range of expected values.
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TypeError: If one argument isn't of the appropriate type.
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Returns:
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Union[np.ndarray, BaseTracer]: Evaluation result, or traced computation
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"""
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if auto_pad not in SUPPORTED_AUTO_PAD:
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raise ValueError("invalid auto_pad is specified")
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if not isinstance(x, (np.ndarray, BaseTracer)):
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raise TypeError(f"input x must be an ndarray, or a BaseTracer, not a {type(x)}")
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if not isinstance(weight, (np.ndarray, BaseTracer)):
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raise TypeError(f"weight must be an ndarray, or a BaseTracer, not a {type(weight)}")
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if not isinstance(bias, (np.ndarray, BaseTracer, type(None))):
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raise TypeError(f"bias must be an ndarray, a BaseTracer, or None, not a {type(bias)}")
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if not isinstance(pads, (tuple, list)):
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raise TypeError(f"padding must be a tuple, or list, not a {type(pads)}")
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if not isinstance(strides, (tuple, list)):
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raise TypeError(f"strides must be a tuple, or list, not a {type(strides)}")
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if not isinstance(dilations, (tuple, list)):
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raise TypeError(f"dilations must be a tuple, or list, not a {type(dilations)}")
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if len(pads) != 4:
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raise ValueError(
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f"padding should be of the form (pad_height_begin, pad_width_begin, pad_height_end, "
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f" pad_width_end), but got {type(pads)} of length {len(pads)}"
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)
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if len(strides) != 2:
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raise ValueError(
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f"strides should be of the form (stride_height, stride_width), but got {type(strides)}"
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f" of length {len(strides)}"
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)
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if len(dilations) != 2:
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raise ValueError(
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f"dilations should be of the form (dilation_height, dilation_width), but got"
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f" {type(dilations)} of length {len(dilations)}"
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)
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assert len(x.shape) == 4, f"input x should have size (N x C x H x W), not {x.shape}"
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assert len(weight.shape) == 4, f"weight should have size (F x C x H x W), not {weight.shape}"
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if bias is not None:
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assert len(bias.shape) == 1, f"bias should have size (F), not {bias.shape}"
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if isinstance(x, BaseTracer):
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return _trace_conv2d(x, weight, bias, pads, strides, dilations)
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# X is an ndarray
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bias = np.zeros(weight.shape[0]) if bias is None else bias
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# For mypy
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weight = cast(np.ndarray, weight)
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bias = cast(np.ndarray, bias)
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return _evaluate_conv2d(x, weight, bias, pads, strides, dilations)
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def _trace_conv2d(
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x: BaseTracer,
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weight: Union[np.ndarray, BaseTracer],
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bias: Optional[Union[np.ndarray, BaseTracer]],
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pads: Union[Tuple[int, int, int, int], List[int]],
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strides: Union[Tuple[int, int], List[int]],
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dilations: Union[Tuple[int, int], List[int]],
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) -> NPTracer:
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"""Trace 2D convolution.
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Args:
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x (BaseTracer): Input of shape (NxCxHxW)
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weight (Union[np.ndarray, BaseTracer]): Weight (kernel) of shape (FxCxHxW)
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bias (Optional[Union[np.ndarray, BaseTracer]]): Bias vector of size (F)
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pads (Union[Tuple[int, int, int, int], List[int]]): Padding over each
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axis (H_beg, W_beg, H_end, W_end)
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strides (Union[Tuple[int, int], List[int]]): Stride over each
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axis (height and width)
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dilations (Union[Tuple[int, int], List[int]]): Dilation over each
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axis (height and width)
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Returns:
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BaseTracer: Traced computation
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"""
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weight_tracer = (
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weight if isinstance(weight, BaseTracer) else NPTracer([], NPConstant(weight), 0)
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)
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inputs = [x.output, weight_tracer.output]
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output_tracer_inputs = [x, weight_tracer]
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if bias is not None:
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bias_tracer = bias if isinstance(bias, BaseTracer) else NPTracer([], NPConstant(bias), 0)
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inputs.append(bias_tracer.output)
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# For mypy
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bias = cast(BaseTracer, bias_tracer)
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output_tracer_inputs.append(bias)
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traced_computation = Conv2D(inputs, x.output.dtype, pads, strides, dilations)
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output_tracer = x.__class__(
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output_tracer_inputs, traced_computation=traced_computation, output_idx=0
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)
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# For mypy
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assert isinstance(output_tracer, NPTracer)
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return output_tracer
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def _evaluate_conv2d(
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x: np.ndarray,
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weight: np.ndarray,
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bias: np.ndarray,
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pads: Union[Tuple[int, int, int, int], List[int]],
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strides: Union[Tuple[int, int], List[int]],
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dilations: Union[Tuple[int, int], List[int]],
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) -> np.ndarray:
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"""Evaluate 2D convolution.
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Args:
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x (np.ndarray): Input of shape (NxCxHxW)
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weight (np.ndarray): Weight (kernel) of shape (FxCxHxW)
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bias (np.ndarray): Bias vector of size (F)
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pads (Union[Tuple[int, int, int, int], List[int]]): Padding over each
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axis (H_beg, W_beg, H_end, W_end)
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strides (Union[Tuple[int, int], List[int]]): Stride over each axis (height and width)
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dilations (Union[Tuple[int, int], List[int]]): Dilation over each axis (height and width)
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Returns:
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np.ndarray: Result of the convolution of shape (NxCxHxW)
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"""
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return Conv2D.evaluate_conv2d(x, weight, bias, pads, strides, dilations)
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@@ -29,6 +29,7 @@ from ..operator_graph import OPGraph
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from ..representation.intermediate import (
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Add,
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Constant,
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Conv2D,
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Dot,
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GenericFunction,
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IndexConstant,
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@@ -140,6 +141,9 @@ class IntermediateNodeConverter:
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elif isinstance(self.node, Sub):
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result = self.convert_sub()
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elif isinstance(self.node, Conv2D):
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result = self.convert_conv2d()
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else: # pragma: no cover
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# this branch is not covered as unsupported opeations fail on check mlir compatibility
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raise NotImplementedError(f"{type(self.node)} nodes cannot be converted to MLIR yet")
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@@ -282,6 +286,51 @@ class IntermediateNodeConverter:
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return arith.ConstantOp(resulting_type, attr).result
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def convert_conv2d(self) -> OpResult:
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"""Convert a Conv2D node to its corresponding MLIR representation.
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Returns:
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str: textual MLIR representation corresponding to self.node
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"""
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assert_true(len(self.node.inputs) == 2 or len(self.node.inputs) == 3)
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assert_true(len(self.node.outputs) == 1)
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has_bias = len(self.node.inputs) == 3
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x = self.node.inputs[0]
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weight = self.node.inputs[1]
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if not (x.is_encrypted and weight.is_clear): # pragma: no cover
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raise NotImplementedError(
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f"Conv2D with input {x} and weight {weight} cannot be converted to MLIR yet",
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)
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resulting_type = value_to_mlir_type(self.ctx, self.node.outputs[0])
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preds = self.preds
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node = cast(Conv2D, self.node)
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integer_type = IntegerType.get_signless(64, context=self.ctx)
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strides = DenseElementsAttr.get(
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numpy.array(list(node.strides), dtype=numpy.uint64),
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context=self.ctx,
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type=integer_type,
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)
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dilations = DenseElementsAttr.get(
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numpy.array(list(node.dilations), dtype=numpy.uint64),
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context=self.ctx,
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type=integer_type,
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)
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pads = DenseElementsAttr.get(
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numpy.array(list(node.pads), dtype=numpy.uint64), context=self.ctx, type=integer_type
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)
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if has_bias:
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result = fhelinalg.Conv2dOp(resulting_type, *preds, pads, strides, dilations).result
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else:
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result = fhelinalg.Conv2dOp(
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resulting_type, *preds, None, pads, strides, dilations
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).result
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return result
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def convert_dot(self) -> OpResult:
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"""Convert a Dot node to its corresponding MLIR representation.
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@@ -15,7 +15,7 @@ from ..debugging import format_operation_graph
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from ..debugging.custom_assert import assert_not_reached, assert_true
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from ..operator_graph import OPGraph
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from ..representation import intermediate
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from ..representation.intermediate import IntermediateNode
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from ..representation.intermediate import Conv2D, IntermediateNode
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# TODO: should come from compiler, through an API, #402
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ACCEPTABLE_MAXIMAL_BITWIDTH_FROM_CONCRETE_LIB = 7
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@@ -103,6 +103,9 @@ def check_node_compatibility_with_mlir(
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elif isinstance(node, intermediate.MatMul): # constraints for matrix multiplication
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assert_true(len(inputs) == 2)
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elif isinstance(node, Conv2D):
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assert_true(len(inputs) in [2, 3])
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else: # pragma: no cover
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assert_not_reached("Non IntermediateNode object in the OPGraph")
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@@ -4,8 +4,11 @@ from abc import ABC, abstractmethod
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from collections import deque
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from copy import deepcopy
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from enum import Enum, unique
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from math import floor
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from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, cast
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import numpy as np
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import torch
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from loguru import logger
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from ..data_types.base import BaseDataType
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@@ -221,6 +224,109 @@ class Constant(IntermediateNode):
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return self._constant_data
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class Conv2D(IntermediateNode):
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"""Return the node representing a 2d-convolution."""
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def __init__(
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self,
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inputs: Iterable[BaseValue],
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output_dtype: BaseDataType,
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pads: Union[List[int], Tuple[int, int, int, int]],
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strides: Union[List[int], Tuple[int, int]],
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dilations: Union[List[int], Tuple[int, int]],
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) -> None:
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# TODO: remove this when padding is supported (#427)
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assert all(pad == 0 for pad in pads), "conv2d doesn't support padding yet"
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super().__init__(inputs)
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self.pads = pads
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self.strides = strides
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self.dilations = dilations
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self._n_in = len(self.inputs)
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assert_true(len(self.inputs) == 2 or len(self.inputs) == 3)
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assert_true(
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all(
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isinstance(input_value, TensorValue) and input_value.ndim == 4
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for input_value in self.inputs[:2]
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),
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f"Conv2D only supports input and weight tensors of 4 dimensions"
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f"({TensorValue.__name__} with ndim == 4)",
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)
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bias = cast(TensorValue, self.inputs[2]) if len(self.inputs) == 3 else None
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if bias is not None:
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assert_true(
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isinstance(bias, TensorValue) and bias.ndim == 1,
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f"Conv2D only supports bias 1 dimension ({TensorValue.__name__} with ndim == 1)",
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)
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x = cast(TensorValue, self.inputs[0])
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weight = cast(TensorValue, self.inputs[1])
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# Compute output shape
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input_n, _, input_h, input_w = x.shape
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weight_f, _, weight_h, weight_w = weight.shape
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pads_h = pads[0] + pads[2]
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pads_w = pads[1] + pads[3]
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output_h = floor((input_h + pads_h - dilations[0] * (weight_h - 1) - 1) / strides[0]) + 1
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output_w = floor((input_w + pads_w - dilations[1] * (weight_w - 1) - 1) / strides[1]) + 1
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output_shape = (input_n, weight_f, output_h, output_w)
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output_value = EncryptedTensor(dtype=output_dtype, shape=output_shape)
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self.outputs = [output_value]
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def text_for_drawing(self) -> str:
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return "conv2d"
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def evaluate(self, inputs: Dict[int, Any]) -> Any:
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assert_true(
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len(inputs) == self._n_in, f"expected {self.n_in} inputs, but got {len(inputs)}"
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)
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x, weight = inputs[0], inputs[1]
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bias = inputs[2] if len(inputs) == 3 else np.zeros(weight.shape[0])
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return self.evaluate_conv2d(x, weight, bias, self.pads, self.strides, self.dilations)
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@staticmethod
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def evaluate_conv2d(
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x: np.ndarray,
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weight: np.ndarray,
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bias: np.ndarray,
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# TODO: use padding when supported (#427)
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_: Union[Tuple[int, int, int, int], List[int]],
|
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strides: Union[Tuple[int, int], List[int]],
|
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dilations: Union[Tuple[int, int], List[int]],
|
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):
|
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"""Evaluate 2D convolution.
|
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|
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Args:
|
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x (np.ndarray): Input of shape (NxCxHxW)
|
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weight (np.ndarray): Weight (kernel) of shape (FxCxHxW)
|
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bias (np.ndarray): Bias vector of size (F)
|
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pads (Union[Tuple[int, int, int, int], List[int]]): Padding over each
|
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axis (H_beg, W_beg, H_end, W_end)
|
||||
strides (Union[Tuple[int, int], List[int]]): Stride over each
|
||||
axis (height and width)
|
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dilations (Union[Tuple[int, int], List[int]]): Dilation over each
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axis (height and width)
|
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|
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Returns:
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np.ndarray: Result of the convolution of shape (NxCxHxW)
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"""
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# pylint: disable=no-member
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return torch.conv2d(
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torch.tensor(x, dtype=torch.long),
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torch.tensor(weight, dtype=torch.long),
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torch.tensor(bias, dtype=torch.long),
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stride=strides,
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dilation=dilations,
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).numpy()
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# pylint: enable=no-member
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|
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|
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class IndexConstant(IntermediateNode):
|
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"""Node representing a constant indexing in the program.
|
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|
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|
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@@ -1,5 +1,13 @@
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"""Module for compiling numpy functions to homomorphic equivalents."""
|
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# Import differently to put at the top, and avoid circular import issues
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from concrete.numpy.compile import (
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compile_numpy_function,
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compile_numpy_function_into_op_graph_and_measure_bounds,
|
||||
)
|
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from concrete.numpy.np_fhe_compiler import NPFHECompiler
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from concrete.numpy.tracing import trace_numpy_function
|
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|
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from ..common.compilation import CompilationArtifacts, CompilationConfiguration
|
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from ..common.data_types import (
|
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Float,
|
||||
@@ -11,9 +19,7 @@ from ..common.data_types import (
|
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UnsignedInteger,
|
||||
)
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from ..common.debugging import draw_graph, format_operation_graph
|
||||
from ..common.extensions.convolution import conv2d
|
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from ..common.extensions.multi_table import MultiLookupTable
|
||||
from ..common.extensions.table import LookupTable
|
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from ..common.values import ClearScalar, ClearTensor, EncryptedScalar, EncryptedTensor, TensorValue
|
||||
from .compile import compile_numpy_function, compile_numpy_function_into_op_graph_and_measure_bounds
|
||||
from .np_fhe_compiler import NPFHECompiler
|
||||
from .tracing import trace_numpy_function
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
Name Version License
|
||||
Pillow 9.0.1 Historical Permission Notice and Disclaimer (HPND)
|
||||
PyYAML 6.0 MIT License
|
||||
concrete-compiler 0.2.0 BSD-3
|
||||
concrete-compiler 0.3.1 BSD-3
|
||||
cycler 0.11.0 BSD License
|
||||
fonttools 4.29.1 MIT License
|
||||
kiwisolver 1.3.2 BSD License
|
||||
@@ -16,3 +16,5 @@
|
||||
setuptools-scm 6.4.2 MIT License
|
||||
six 1.16.0 MIT License
|
||||
tomli 1.2.3 MIT License
|
||||
torch 1.10.2 BSD License
|
||||
typing-extensions 4.1.1 Python Software Foundation License
|
||||
|
||||
133
poetry.lock
generated
133
poetry.lock
generated
@@ -243,7 +243,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
|
||||
[[package]]
|
||||
name = "concrete-compiler"
|
||||
version = "0.2.0"
|
||||
version = "0.3.1"
|
||||
description = "Concrete Compiler"
|
||||
category = "main"
|
||||
optional = false
|
||||
@@ -255,7 +255,7 @@ PyYAML = "*"
|
||||
|
||||
[[package]]
|
||||
name = "coverage"
|
||||
version = "6.3.1"
|
||||
version = "6.3.2"
|
||||
description = "Code coverage measurement for Python"
|
||||
category = "dev"
|
||||
optional = false
|
||||
@@ -2111,6 +2111,17 @@ category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "1.10.2"
|
||||
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6.2"
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "tornado"
|
||||
version = "6.1"
|
||||
@@ -2186,7 +2197,7 @@ python-versions = "*"
|
||||
name = "typing-extensions"
|
||||
version = "4.1.1"
|
||||
description = "Backported and Experimental Type Hints for Python 3.6+"
|
||||
category = "dev"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
@@ -2268,7 +2279,7 @@ full = ["pygraphviz"]
|
||||
[metadata]
|
||||
lock-version = "1.1"
|
||||
python-versions = ">=3.8,<3.10"
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||||
content-hash = "c46630b3a44a45815631ebc5f42ef77b14b4648cccce3405231597465b316884"
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||||
content-hash = "89f3c912cef146d06a3da96e36347367e1703fa5670f3153637e1822c5e81897"
|
||||
|
||||
[metadata.files]
|
||||
alabaster = [
|
||||
@@ -2419,55 +2430,54 @@ colorama = [
|
||||
{file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
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]
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||||
concrete-compiler = [
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||||
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coverage = [
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|
||||
{file = "coverage-6.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c6dbb42f3ad25760010c45191e9757e7dce981cbfb90e42feef301d71540059"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c76aeef1b95aff3905fb2ae2d96e319caca5b76fa41d3470b19d4e4a3a313512"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8cf5cfcb1521dc3255d845d9dca3ff204b3229401994ef8d1984b32746bb45ca"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8fbbdc8d55990eac1b0919ca69eb5a988a802b854488c34b8f37f3e2025fa90d"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ec6bc7fe73a938933d4178c9b23c4e0568e43e220aef9472c4f6044bfc6dd0f0"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:9baff2a45ae1f17c8078452e9e5962e518eab705e50a0aa8083733ea7d45f3a6"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fd9e830e9d8d89b20ab1e5af09b32d33e1a08ef4c4e14411e559556fd788e6b2"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-win32.whl", hash = "sha256:f7331dbf301b7289013175087636bbaf5b2405e57259dd2c42fdcc9fcc47325e"},
|
||||
{file = "coverage-6.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:68353fe7cdf91f109fc7d474461b46e7f1f14e533e911a2a2cbb8b0fc8613cf1"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b78e5afb39941572209f71866aa0b206c12f0109835aa0d601e41552f9b3e620"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4e21876082ed887baed0146fe222f861b5815455ada3b33b890f4105d806128d"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:34626a7eee2a3da12af0507780bb51eb52dca0e1751fd1471d0810539cefb536"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1ebf730d2381158ecf3dfd4453fbca0613e16eaa547b4170e2450c9707665ce7"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd6fe30bd519694b356cbfcaca9bd5c1737cddd20778c6a581ae20dc8c04def2"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:96f8a1cb43ca1422f36492bebe63312d396491a9165ed3b9231e778d43a7fca4"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:dd035edafefee4d573140a76fdc785dc38829fe5a455c4bb12bac8c20cfc3d69"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5ca5aeb4344b30d0bec47481536b8ba1181d50dbe783b0e4ad03c95dc1296684"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-win32.whl", hash = "sha256:f5fa5803f47e095d7ad8443d28b01d48c0359484fec1b9d8606d0e3282084bc4"},
|
||||
{file = "coverage-6.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:9548f10d8be799551eb3a9c74bbf2b4934ddb330e08a73320123c07f95cc2d92"},
|
||||
{file = "coverage-6.3.2-pp36.pp37.pp38-none-any.whl", hash = "sha256:18d520c6860515a771708937d2f78f63cc47ab3b80cb78e86573b0a760161faf"},
|
||||
{file = "coverage-6.3.2.tar.gz", hash = "sha256:03e2a7826086b91ef345ff18742ee9fc47a6839ccd517061ef8fa1976e652ce9"},
|
||||
]
|
||||
cryptography = [
|
||||
{file = "cryptography-36.0.1-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:73bc2d3f2444bcfeac67dd130ff2ea598ea5f20b40e36d19821b4df8c9c5037b"},
|
||||
@@ -3533,6 +3543,27 @@ tomlkit = [
|
||||
{file = "tomlkit-0.7.0-py2.py3-none-any.whl", hash = "sha256:6babbd33b17d5c9691896b0e68159215a9387ebfa938aa3ac42f4a4beeb2b831"},
|
||||
{file = "tomlkit-0.7.0.tar.gz", hash = "sha256:ac57f29693fab3e309ea789252fcce3061e19110085aa31af5446ca749325618"},
|
||||
]
|
||||
torch = [
|
||||
{file = "torch-1.10.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:8f3fd2e3ffc3bb867133fdf7fbcc8a0bb2e62a5c0696396f51856f5abf9045a8"},
|
||||
{file = "torch-1.10.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:258a0729fb77a3457d5822d84b536057cd119b08049a8d3c41dc3dcdeb48d56e"},
|
||||
{file = "torch-1.10.2-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:935e5ac804c5093c79f23a7e6ca5b912c166071aa9d8b4a0a3d6a85126d6a47b"},
|
||||
{file = "torch-1.10.2-cp36-cp36m-win_amd64.whl", hash = "sha256:65fd02ed889c63fd82bf1a440c5a94c1310c29f3e6f9f62add416d34da355d97"},
|
||||
{file = "torch-1.10.2-cp36-none-macosx_10_9_x86_64.whl", hash = "sha256:6a81f886823bbd15edc2dc0908fa214070df61c9f7ab8831f0a03630275cca5a"},
|
||||
{file = "torch-1.10.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:3eee3cf53c1f8fb3f1fe107a22025a8501fc6440d14e09599ba7153002531f84"},
|
||||
{file = "torch-1.10.2-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:ef99b8cca5f9358119b07956915faf6e7906f433ab4a603c160ae9de88918371"},
|
||||
{file = "torch-1.10.2-cp37-cp37m-win_amd64.whl", hash = "sha256:d43bc3f3a2d89ae185ef96d903c935c335219231e57685658648396984e2a67a"},
|
||||
{file = "torch-1.10.2-cp37-none-macosx_10_9_x86_64.whl", hash = "sha256:6da1b877880435440a5aa9678ef0f01986d4886416844db1d97ebfb7fd1778d0"},
|
||||
{file = "torch-1.10.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:ab77a9f838874f295ed5410c0686fa22547456e0116efb281c66ef5f9d46fe28"},
|
||||
{file = "torch-1.10.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:9ef4c004f9e5168bd1c1930c6aff25fed5b097de81db6271ffbb2e4fb8b89319"},
|
||||
{file = "torch-1.10.2-cp38-cp38-win_amd64.whl", hash = "sha256:376fc18407add20daa6bbaaffc5a5e06d733abe53bcbd60ef2532bfed34bc091"},
|
||||
{file = "torch-1.10.2-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:f281438ee99bd72ad65c0bba1026a32e45c3b636bc067fc145ad291e9ea2faab"},
|
||||
{file = "torch-1.10.2-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:3592d3dd62b32760c82624e7586222747fe2281240e8653970b35f1d6d4a434c"},
|
||||
{file = "torch-1.10.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:fbaf18c1b3e0b31af194a9d853e3739464cf982d279df9d34dd18f1c2a471878"},
|
||||
{file = "torch-1.10.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:97b7b0c667e8b0dd1fc70137a36e0a4841ec10ef850bda60500ad066bef3e2de"},
|
||||
{file = "torch-1.10.2-cp39-cp39-win_amd64.whl", hash = "sha256:901b52787baeb2e9e1357ca7037da0028bc6ad743f530e0040ae96ef8e27156c"},
|
||||
{file = "torch-1.10.2-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:5b68e9108bd7ebd99eee941686046c517cfaac5331f757bcf440fe02f2e3ced1"},
|
||||
{file = "torch-1.10.2-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:b07ef01e36b716d0d65ca60c4db0ac9d094a0e797d9b55290da4dcda91463b6c"},
|
||||
]
|
||||
tornado = [
|
||||
{file = "tornado-6.1-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:d371e811d6b156d82aa5f9a4e08b58debf97c302a35714f6f45e35139c332e32"},
|
||||
{file = "tornado-6.1-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:0d321a39c36e5f2c4ff12b4ed58d41390460f798422c4504e09eb5678e09998c"},
|
||||
|
||||
@@ -45,7 +45,8 @@ pygraphviz = { version = "^1.7", optional = true }
|
||||
Pillow = "^9.0.0"
|
||||
loguru = "^0.5.3"
|
||||
setuptools = "*"
|
||||
concrete-compiler = "^0.2.0"
|
||||
concrete-compiler = "^0.3.1"
|
||||
torch = "^1.10.2"
|
||||
|
||||
[tool.poetry.extras]
|
||||
full = ["pygraphviz"]
|
||||
|
||||
193
tests/common/extensions/test_convolution.py
Normal file
193
tests/common/extensions/test_convolution.py
Normal file
@@ -0,0 +1,193 @@
|
||||
"""Test file for convolution"""
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from concrete.common.extensions import convolution
|
||||
from concrete.common.representation.intermediate import Conv2D
|
||||
from concrete.common.tracing.base_tracer import BaseTracer
|
||||
from concrete.common.values.tensors import TensorValue
|
||||
from concrete.numpy.tracing import NPConstant, NPTracer
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"kwargs, error_msg",
|
||||
[
|
||||
pytest.param(
|
||||
{"x": None, "weight": np.zeros(1)},
|
||||
"input x must be an ndarray, or a BaseTracer, not a",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": None},
|
||||
"weight must be an ndarray, or a BaseTracer, not a",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "bias": 0},
|
||||
"bias must be an ndarray, a BaseTracer, or None, not a",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "strides": None},
|
||||
"strides must be a tuple, or list, not a",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "dilations": None},
|
||||
"dilations must be a tuple, or list, not a",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "pads": None},
|
||||
"padding must be a tuple, or list, not a",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_invalid_arg_types(kwargs, error_msg):
|
||||
"""Test function to make sure convolution doesn't accept invalid types"""
|
||||
|
||||
with pytest.raises(TypeError) as err:
|
||||
convolution.conv2d(**kwargs)
|
||||
|
||||
assert error_msg in str(err)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"kwargs, error_msg",
|
||||
[
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1)},
|
||||
"input x should have size (N x C x H x W), not",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros((1, 2, 3, 4)), "weight": np.zeros(1)},
|
||||
"weight should have size (F x C x H x W), not",
|
||||
),
|
||||
pytest.param(
|
||||
{
|
||||
"x": np.zeros((1, 2, 3, 4)),
|
||||
"weight": np.zeros((1, 2, 3, 4)),
|
||||
"bias": np.zeros((1, 2)),
|
||||
},
|
||||
"bias should have size (F), not",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "strides": (1,)},
|
||||
"strides should be of the form",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "dilations": (1,)},
|
||||
"dilations should be of the form",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "pads": (1,)},
|
||||
"padding should be of the form",
|
||||
),
|
||||
pytest.param(
|
||||
{"x": np.zeros(1), "weight": np.zeros(1), "auto_pad": None},
|
||||
"invalid auto_pad is specified",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_invalid_input_shape(kwargs, error_msg):
|
||||
"""Test function to make sure convolution doesn't accept invalid shapes"""
|
||||
|
||||
with pytest.raises((ValueError, AssertionError)) as err:
|
||||
convolution.conv2d(**kwargs)
|
||||
|
||||
assert error_msg in str(err)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"input_shape, weight_shape",
|
||||
[
|
||||
pytest.param((1, 1, 4, 4), (1, 1, 2, 2)),
|
||||
pytest.param((3, 1, 4, 4), (1, 1, 2, 2)),
|
||||
pytest.param((1, 1, 4, 4), (3, 1, 2, 2)),
|
||||
pytest.param((1, 3, 4, 4), (1, 3, 2, 2)),
|
||||
pytest.param((4, 3, 4, 4), (3, 3, 2, 2)),
|
||||
pytest.param((4, 3, 16, 16), (3, 3, 2, 2)),
|
||||
pytest.param((4, 3, 16, 16), (3, 3, 3, 3)),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("strides", [(1, 1), (1, 2), (2, 1), (2, 2)])
|
||||
@pytest.mark.parametrize("dilations", [(1, 1), (1, 2), (2, 1), (2, 2)])
|
||||
@pytest.mark.parametrize("has_bias", [True, False])
|
||||
@pytest.mark.parametrize("use_ndarray", [True, False])
|
||||
def test_tracing(input_shape, weight_shape, strides, dilations, has_bias, use_ndarray):
|
||||
"""Test function to make sure tracong of conv2d works properly"""
|
||||
if has_bias:
|
||||
bias = np.random.randint(0, 4, size=(weight_shape[0],))
|
||||
if not use_ndarray:
|
||||
bias = NPTracer([], NPConstant(bias), 0)
|
||||
else:
|
||||
bias = None
|
||||
|
||||
x = NPTracer([], NPConstant(np.random.randint(0, 4, size=input_shape)), 0)
|
||||
weight = np.random.randint(0, 4, size=weight_shape)
|
||||
if not use_ndarray:
|
||||
weight = NPTracer([], NPConstant(weight), 0)
|
||||
|
||||
output_tracer = convolution.conv2d(x, weight, bias, strides=strides, dilations=dilations)
|
||||
traced_computation = output_tracer.traced_computation
|
||||
assert isinstance(traced_computation, Conv2D)
|
||||
|
||||
if has_bias:
|
||||
assert len(output_tracer.inputs) == 3
|
||||
else:
|
||||
assert len(output_tracer.inputs) == 2
|
||||
|
||||
assert all(
|
||||
isinstance(input_, BaseTracer) for input_ in output_tracer.inputs
|
||||
), f"{output_tracer.inputs}"
|
||||
|
||||
assert len(traced_computation.outputs) == 1
|
||||
output_value = traced_computation.outputs[0]
|
||||
assert isinstance(output_value, TensorValue) and output_value.is_encrypted
|
||||
# pylint: disable=no-member
|
||||
expected_shape = torch.conv2d(
|
||||
torch.randn(input_shape),
|
||||
torch.randn(weight_shape),
|
||||
torch.randn((weight_shape[0])),
|
||||
stride=strides,
|
||||
dilation=dilations,
|
||||
).shape
|
||||
# pylint: enable=no-member
|
||||
|
||||
assert output_value.shape == expected_shape
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"input_shape, weight_shape",
|
||||
[
|
||||
pytest.param((1, 1, 4, 4), (1, 1, 2, 2)),
|
||||
pytest.param((3, 1, 4, 4), (1, 1, 2, 2)),
|
||||
pytest.param((1, 1, 4, 4), (3, 1, 2, 2)),
|
||||
pytest.param((1, 3, 4, 4), (1, 3, 2, 2)),
|
||||
pytest.param((4, 3, 4, 4), (3, 3, 2, 2)),
|
||||
pytest.param((4, 3, 16, 16), (3, 3, 2, 2)),
|
||||
pytest.param((4, 3, 16, 16), (3, 3, 3, 3)),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("strides", [(1, 1), (1, 2), (2, 1), (2, 2)])
|
||||
@pytest.mark.parametrize("dilations", [(1, 1), (1, 2), (2, 1), (2, 2)])
|
||||
@pytest.mark.parametrize("has_bias", [True, False])
|
||||
def test_evaluation(input_shape, weight_shape, strides, dilations, has_bias):
|
||||
"""Test function to make sure evaluation of conv2d on plain data works properly"""
|
||||
if has_bias:
|
||||
bias = np.random.randint(0, 4, size=(weight_shape[0],))
|
||||
else:
|
||||
bias = np.zeros((weight_shape[0],))
|
||||
x = np.random.randint(0, 4, size=input_shape)
|
||||
weight = np.random.randint(0, 4, size=weight_shape)
|
||||
# pylint: disable=no-member
|
||||
expected = torch.conv2d(
|
||||
torch.tensor(x, dtype=torch.long),
|
||||
torch.tensor(weight, dtype=torch.long),
|
||||
torch.tensor(bias, dtype=torch.long),
|
||||
stride=strides,
|
||||
dilation=dilations,
|
||||
).numpy()
|
||||
# pylint: enable=no-member
|
||||
# conv2d should handle None biases
|
||||
if not has_bias:
|
||||
bias = None
|
||||
result = convolution.conv2d(x, weight, bias, strides=strides, dilations=dilations)
|
||||
assert (result == expected).all()
|
||||
@@ -21,6 +21,7 @@ from concrete.common.representation.intermediate import (
|
||||
ALL_IR_NODES,
|
||||
Add,
|
||||
Constant,
|
||||
Conv2D,
|
||||
Dot,
|
||||
GenericFunction,
|
||||
IndexConstant,
|
||||
@@ -261,6 +262,11 @@ def is_equivalent_matmul(lhs: MatMul, rhs: object) -> bool:
|
||||
return isinstance(rhs, MatMul) and is_equivalent_intermediate_node(lhs, rhs)
|
||||
|
||||
|
||||
def is_equivalent_conv2d(lhs: Conv2D, rhs: object) -> bool:
|
||||
"""Helper function to check if a Conv2D node is equivalent to an other object."""
|
||||
return isinstance(rhs, Conv2D) and is_equivalent_intermediate_node(lhs, rhs)
|
||||
|
||||
|
||||
def is_equivalent_intermediate_node(lhs: IntermediateNode, rhs: object) -> bool:
|
||||
"""Helper function to check if an IntermediateNode node is equivalent to an other object."""
|
||||
return (
|
||||
@@ -274,6 +280,7 @@ EQUIVALENT_TEST_FUNC: Dict[Type, Callable[..., bool]] = {
|
||||
Add: is_equivalent_add,
|
||||
GenericFunction: is_equivalent_arbitrary_function,
|
||||
Constant: is_equivalent_constant,
|
||||
Conv2D: is_equivalent_conv2d,
|
||||
Dot: is_equivalent_dot,
|
||||
IndexConstant: is_equivalent_index_constant,
|
||||
Input: is_equivalent_input,
|
||||
|
||||
44
tests/numpy/test_compile_conv.py
Normal file
44
tests/numpy/test_compile_conv.py
Normal file
@@ -0,0 +1,44 @@
|
||||
"""Test module for convolution compilation and execution."""
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import concrete.numpy as hnp
|
||||
from concrete.common.data_types.integers import Integer
|
||||
from concrete.common.values.tensors import EncryptedTensor
|
||||
from concrete.numpy.compile import compile_numpy_function
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"input_shape, weight_shape",
|
||||
[
|
||||
pytest.param((1, 1, 4, 4), (1, 1, 2, 2)),
|
||||
pytest.param((4, 3, 4, 4), (2, 3, 2, 2)),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("strides", [(2, 2)])
|
||||
@pytest.mark.parametrize("dilations", [(1, 1)])
|
||||
@pytest.mark.parametrize("has_bias", [True, False])
|
||||
def test_compile_and_run(
|
||||
input_shape, weight_shape, strides, dilations, has_bias, default_compilation_configuration
|
||||
):
|
||||
"""Test function to make sure compilation and execution of conv2d works properly"""
|
||||
if has_bias:
|
||||
bias = np.random.randint(0, 4, size=(weight_shape[0],))
|
||||
else:
|
||||
bias = None
|
||||
weight = np.random.randint(0, 4, size=weight_shape)
|
||||
|
||||
def conv(x):
|
||||
return hnp.conv2d(x, weight, bias, strides=strides, dilations=dilations)
|
||||
|
||||
compiler_engine = compile_numpy_function(
|
||||
conv,
|
||||
{"x": EncryptedTensor(Integer(64, False), input_shape)},
|
||||
[np.random.randint(0, 4, size=input_shape) for i in range(20)],
|
||||
default_compilation_configuration,
|
||||
)
|
||||
x = np.random.randint(0, 4, size=input_shape, dtype=np.uint8)
|
||||
expected = conv(x)
|
||||
result = compiler_engine.run(x)
|
||||
assert (expected == result).all()
|
||||
Reference in New Issue
Block a user