src/dinov3/eval/segmentation/models/utils/ops/test.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.

# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import torch
from torch.autograd import gradcheck

from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch


N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros((1,)), shapes.prod(1).cumsum(0)[:-1]))
S = sum([(H * W).item() for H, W in shapes])


torch.manual_seed(3)


@torch.no_grad()
def check_forward_equal_with_pytorch_double():
    value = torch.rand(N, S, M, D).cuda() * 0.01
    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
    im2col_step = 2
    output_pytorch = (
        ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double())
        .detach()
        .cpu()
    )
    output_cuda = (
        MSDeformAttnFunction.apply(
            value.double(),
            shapes,
            level_start_index,
            sampling_locations.double(),
            attention_weights.double(),
            im2col_step,
        )
        .detach()
        .cpu()
    )
    fwdok = torch.allclose(output_cuda, output_pytorch)
    max_abs_err = (output_cuda - output_pytorch).abs().max()
    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()

    print(
        f"* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}"
    )


@torch.no_grad()
def check_forward_equal_with_pytorch_float():
    value = torch.rand(N, S, M, D).cuda() * 0.01
    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
    im2col_step = 2
    output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
    output_cuda = (
        MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step)
        .detach()
        .cpu()
    )
    fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
    max_abs_err = (output_cuda - output_pytorch).abs().max()
    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()

    print(
        f"* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}"
    )


def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
    value = torch.rand(N, S, M, channels).cuda() * 0.01
    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
    im2col_step = 2
    func = MSDeformAttnFunction.apply

    value.requires_grad = grad_value
    sampling_locations.requires_grad = grad_sampling_loc
    attention_weights.requires_grad = grad_attn_weight

    gradok = gradcheck(
        func,
        (
            value.double(),
            shapes,
            level_start_index,
            sampling_locations.double(),
            attention_weights.double(),
            im2col_step,
        ),
    )

    print(f"* {gradok} check_gradient_numerical(D={channels})")


if __name__ == "__main__":
    check_forward_equal_with_pytorch_double()
    check_forward_equal_with_pytorch_float()

    for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
        check_gradient_numerical(channels, True, True, True)