Pikaliov
1b3206b6a7
- Add enhanced README with project structure and quick start guide - Initialize repository with DCNv4 CUDA extension (PyTorch module) - Include classification, detection, and segmentation subdirectories - Reference upstream OpenGVLab DCNv4 implementation Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
201 lines
7.0 KiB
Python
201 lines
7.0 KiB
Python
# --------------------------------------------------------
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# DCNv4
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import time
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import torch
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import torch.nn as nn
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import math
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from torch.autograd import gradcheck
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import pandas as pd
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from easydict import EasyDict as edict
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import argparse
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from torch.cuda import Event
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from functions import DCNv4Function, DCNv3Function
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torch.set_printoptions(threshold=10000)
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torch.manual_seed(3)
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def speed_test_backward(func, args, inputs, name='Unknown'):
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# warmup
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# for i in range(args.warmup_num):
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# o = func(*inputs)
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# o.sum().backward()
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total_time = 0
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len_input = len(inputs)
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for i in range(args.warmup_num + args.test_num):
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tic = Event(enable_timing=True)
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toc = Event(enable_timing=True)
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inputs[0] = inputs[0].detach()
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inputs[0].requires_grad = True
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if len_input > 1 and isinstance(inputs[1], torch.Tensor):
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inputs[1] = inputs[1].detach()
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inputs[1].requires_grad = True
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if len_input > 2 and isinstance(inputs[2], torch.Tensor):
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inputs[2] = inputs[2].detach()
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inputs[2].requires_grad = True
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o = func(*inputs)
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torch.cuda.synchronize()
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tic.record()
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o.sum().backward()
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toc.record()
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torch.cuda.synchronize()
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_time = tic.elapsed_time(toc)
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if i >= args.warmup_num:
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total_time += _time
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o = o.detach()
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# toc.record()
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# torch.cuda.synchronize()
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avg_time = total_time / args.test_num
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#print(
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# f'>>> {name: <10} finished {args.test_num} running, avg_time: {avg_time:.6f} ms')
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return avg_time
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# @torch.no_grad()
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def test(N=64, H_in=32, W_in=32, M=4, D=16, spec=None):
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"""
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64x56x56x128(G=4)
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2 64: 3.66
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- offset_mask collection write 3.4022
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- offset_mask collection 3.1968
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"""
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Kh, Kw = 3, 3
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remove_center = False
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P = Kh * Kw - remove_center
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offset_scale = 2.0
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pad = 1
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dilation = 1
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stride = 1
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H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
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W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1
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additions = [None, None, spec[0], spec[1], False]
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input = torch.rand(N, H_in, W_in, M*D).cuda() * 10
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#offset = torch.rand(N, H_out, W_out, M*P*2).cuda() * 0
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offset = (torch.rand(N, H_out, W_out, M*P*2).cuda() * 2 - 1)*2
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mask_origin = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
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mask_origin = mask_origin.half()
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mask_origin.requires_grad = True
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# offset_mask = torch.cat([offset.unflatten(-1, (M, P, 2)), mask_origin.detach().unsqueeze(-1)], dim=-1).flatten(-3)
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# mask /= mask.sum(-1, keepdim=True)
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# mask = torch.nn.functional.softmax(mask_origin, dim=-1, dtype=torch.float32)
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mask = mask_origin
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# mask = mask.reshape(N, H_out, W_out, M*P)
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# offset_mask = torch.cat([offset.unflatten(-1, (M, P, 2)), mask.detach().unsqueeze(-1)], dim=-1).flatten(-3)
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offset_mask = torch.cat([offset.detach().unflatten(-1, (M, P * 2)), mask_origin.detach()], dim=-1).flatten(-2)
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im2col_step = 128
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input = input.half()
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offset = offset.half()
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mask = mask.half()
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input.requires_grad = True
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offset.requires_grad = True
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# mask.requires_grad = True
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output_pytorch = DCNv3Function.apply(
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input,
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offset,
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mask,
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Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
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im2col_step, remove_center)#.detach().cpu()
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(output_pytorch.sum()/10).backward()
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def pad(om):
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padded_zero = int(math.ceil(om.shape[3]/8)*8) - om.shape[3]
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padded = torch.zeros(om.shape[0], om.shape[1], om.shape[2], padded_zero).to(om)
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return torch.cat([om, padded], dim=-1)
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# value_offset_mask = input.detach()
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input1 = input.detach()
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input1.requires_grad = True
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offset_mask = offset_mask.half()
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offset_mask.requires_grad = True
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# offset_mask1.requires_grad = True
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torch.cuda.profiler.cudart().cudaProfilerStart()
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output_flash_cuda = DCNv4Function.apply(
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input1, offset_mask,
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Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
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im2col_step, remove_center, *additions)#.detach().cpu()
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(output_flash_cuda.sum()/10).backward()
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torch.cuda.profiler.cudart().cudaProfilerStop()
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input_grad = input.grad
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input2_grad = input1.grad
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bwdok = torch.allclose(input_grad.float(), input2_grad.float(), rtol=1e-2, atol=1e-3)
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rel_err = (input_grad.abs() - input2_grad.abs())/(input_grad.abs()+1e-3)
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offset_grad1 = offset.grad
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offset_grad2 = offset_mask.grad.reshape(N, H_out, W_out, M, P*3)[..., :P*2].reshape(N, H_out, W_out, M*P*2)
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bwdok2 = torch.allclose(offset_grad1.float(), offset_grad2.float(), rtol=1e-2, atol=1e-3)
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rel_err = (offset_grad1 - offset_grad2).abs() / (offset_grad1.abs()+1e-3)
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mask_grad1 = mask_origin.grad
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mask_grad2 = offset_mask.grad.reshape(N, H_out, W_out, M, P*3)[..., P*2:].reshape(N, H_out, W_out, M, P)
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bwdok3 = torch.allclose(mask_grad1, mask_grad2, rtol=1e-2, atol=1e-3)
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rel_err = (mask_grad1 - mask_grad2).abs() / (mask_grad1.abs()+1e-3)
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fwdok = torch.allclose(output_flash_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
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max_abs_err = (output_flash_cuda - output_pytorch).abs().max()
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max_rel_err = ((output_flash_cuda - output_pytorch).abs() /
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(output_pytorch.abs()+ 1e-3)).max()
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if not (bwdok and bwdok2 and bwdok3):
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print(f"Wrong: {N}x{H_in}x{W_in}x{M}x{D} \t {spec[0]}/{spec[1]}({spec[2]})")
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return
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# fn_args = [
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# input,
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# offset,
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# mask,
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# Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
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# im2col_step, remove_center
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# ]
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flash_dcn_fn_args = [
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input1,
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offset_mask,
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Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
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im2col_step, remove_center, *additions
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]
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test_args = edict({'warmup_num': 1000, 'test_num': 1000})
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try:
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exp_time = speed_test_backward(DCNv4Function.apply, test_args, flash_dcn_fn_args, name='exp')
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except:
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print(f"Wrong: {N}x{H_in}x{W_in}x{M}x{D} \t {spec[0]}/{spec[1]}({spec[2]})")
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return
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torch.cuda.synchronize()
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print(f"{N}x{H_in}x{W_in}x{M}x{D} \t {spec[0]}/{spec[1]}({spec[2]}): {exp_time}")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--n", type=int)
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parser.add_argument("--h", type=int)
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parser.add_argument("--w", type=int)
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parser.add_argument("--g", type=int)
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parser.add_argument("--c", type=int)
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parser.add_argument("--dstride", type=int)
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parser.add_argument("--blockthread", type=int)
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parser.add_argument("--multiplier", type=int)
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args = parser.parse_args()
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test(args.n, args.h, args.w, args.g, args.c, (args.dstride, args.blockthread, args.multiplier))
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