我正在开发一个 PyTorch 项目，其中有一个名为的自定义模块，它将轴向旋转位置嵌入应用于张量。我使用 torch.compile 实现了该模块的常规版本和编译版本。当我尝试多次反向传播编译版本时，遇到以下错误：
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
但是，该模块的常规（未编译）版本可以很好地处理多个反向传播调用。
相关代码如下：
import math
from functools import reduce
import torch
from einops import rearrange
from torch import nn
def bounding_box(
h: int, w: int, pixel_ar: float = 1.0
) -> tuple[float, float, float, float]:
# Compute the adjusted aspect ratio based on the pixel aspect ratio
ar = w / (h * pixel_ar)
# Compute the bounding box
h_bounds = (-1.0 / ar, 1.0 / ar) if ar > 1.0 else (-1.0, 1.0)
w_bounds = (-ar, ar) if ar < 1.0 else (-1.0, 1.0)
return h_bounds + w_bounds
def centered_linspace(
start: float,
end: float,
steps: int,
*,
dtype: torch.dtype = None,
device: torch.device = None,
) -> torch.Tensor:
edges = torch.linspace(start, end, steps + 1, dtype=dtype, device=device)
# Compute the midpoint between each pair of edges
return (edges[:-1] + edges[1:]) / 2
def make_axial_positions(
h: int,
w: int,
pixel_ar: float = 1.0,
align_corners: bool = False,
dtype: torch.dtype = None,
device: torch.device = None,
) -> torch.Tensor:
h_min, h_max, w_min, w_max = bounding_box(h, w, pixel_ar)
# If align_corners is set to True, the grid will include the corners of the bounding box
# Otherwise, the grid boundaries will include the centers of the pixels
linspace_fn = torch.linspace if align_corners else centered_linspace
h_grid = linspace_fn(h_min, h_max, h, dtype=dtype, device=device)
w_grid = linspace_fn(w_min, w_max, w, dtype=dtype, device=device)
# Create a grid of positions
h_positions, w_positions = torch.meshgrid(h_grid, w_grid, indexing="ij")
return torch.stack((h_positions, w_positions), dim=-1)
def apply_axial_rope(
x: torch.Tensor, theta: torch.Tensor, conjugate: bool = False
) -> None:
# Ensure the operations are performed in float32
dtype = reduce(torch.promote_types, (x.dtype, theta.dtype, torch.float32))
# Ensure that the dimensions of x and theta are compatible
dim = theta.shape[-1]
assert dim * 2 <= x.shape[-1], f"x must have at least {2 * dim} channels"
# Extract tensor components and ensure they have the correct dtype
x_1, x_2, x_3 = x[..., :dim], x[..., dim : dim * 2], x[..., dim * 2 :]
x_1, x_2, theta = map(lambda t: t.to(dtype), (x_1, x_2, theta))
# Compute the rotation angles
cos, sin = theta.cos(), theta.sin()
sin = -sin if conjugate else sin
# Rotate the tensors
x_1 = (cos * x_1 - sin * x_2).to(x.dtype)
x_2 = (sin * x_1 + cos * x_2).to(x.dtype)
return torch.cat((x_1, x_2, x_3), dim=-1)
class AxialRoPE(nn.Module):
def __init__(self, dim: int, n_heads: int) -> None:
super().__init__()
freqs_min, freqs_max = math.log(math.pi), math.log(10.0 * math.pi)
freqs = torch.linspace(freqs_min, freqs_max, n_heads * dim // 4 + 1)[:-1].exp()
freqs = freqs.view(n_heads, dim // 4).contiguous()
self.register_buffer("freqs", freqs)
def forward(
self, q: torch.Tensor, k: torch.Tensor, positions: torch.Tensor
) -> torch.Tensor:
# Compute the rotation angles
freqs = self.freqs.to(positions.dtype)
h_theta = positions[..., None, 0:1] * freqs
w_theta = positions[..., None, 1:2] * freqs
theta = torch.cat((h_theta, w_theta), dim=-1)
theta = rearrange(theta, "... x y h d -> ... h x y d")
# Apply the RoPE to the queries and keys
q = apply_axial_rope(q, theta)
k = apply_axial_rope(k, theta)
return q, k
if __name__ == "__main__":
with torch.device("cuda:0" if torch.cuda.is_available() else "cpu"):
positions = make_axial_positions(32, 32)
q = torch.randn(1, 8, 32, 32, 64, requires_grad=True)
k = torch.randn(1, 8, 32, 32, 64, requires_grad=True)
axial_rope = AxialRoPE(64, 8)
q, k = axial_rope(q, k, positions)
# Test the backward pass
q.sum().backward()
k.sum().backward()
positions = make_axial_positions(32, 32)
q = torch.randn(1, 8, 32, 32, 64, requires_grad=True)
k = torch.randn(1, 8, 32, 32, 64, requires_grad=True)
axial_rope_compiled = torch.compile(AxialRoPE(64, 8))
q, k = axial_rope_compiled(q, k, positions)
# Test the backward pass for the compiled version
q.sum().backward()
k.sum().backward()
这是我的环境详细信息：
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 23.10 (x86_64)
GCC version: (Ubuntu 13.2.0-4ubuntu3) 13.2.0
Clang version: Could not collect
CMake version: version 3.20.3
Libc version: glibc-2.38
Python version: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:53:32) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.38
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 550.54.15
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: GenuineIntel
Model name: Intel(R) Core(TM) i9-14900KF
CPU family: 6
Model: 183
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
CPU(s) scaling MHz: 20%
CPU max MHz: 6000.0000
CPU min MHz: 800.0000
BogoMIPS: 6374.40
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 896 KiB (24 instances)
L1i cache: 1.3 MiB (24 instances)
L2 cache: 32 MiB (12 instances)
L3 cache: 36 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] clip-anytorch==2.6.0
[pip3] dctorch==0.1.2
[pip3] mypy-extensions==1.0.0
[pip3] natten==0.15.1+torch220cu121
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==1.9.5
[pip3] rotary-embedding-torch==0.5.3
[pip3] torch==2.2.1
[pip3] torchaudio==2.2.1
[pip3] torchdiffeq==0.2.3
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.3.2
[pip3] torchsde==0.2.6
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] clip-anytorch 2.6.0 pypi_0 pypi
[conda] dctorch 0.1.2 pypi_0 pypi
[conda] natten 0.15.1+torch220cu121 pypi_0 pypi
[conda] numpy 1.26.4 pypi_0 pypi
[conda] pytorch-lightning 1.9.5 pypi_0 pypi
[conda] rotary-embedding-torch 0.5.3 pypi_0 pypi
[conda] torch 2.2.1 pypi_0 pypi
[conda] torchaudio 2.2.1 pypi_0 pypi
[conda] torchdiffeq 0.2.3 pypi_0 pypi
[conda] torchinfo 1.8.0 pypi_0 pypi
[conda] torchmetrics 1.3.2 pypi_0 pypi
[conda] torchsde 0.2.6 pypi_0 pypi
[conda] torchvision 0.17.1 pypi_0 pypi
[conda] triton 2.2.0 pypi_0 pypi
为什么我在多次通过已编译的 PyTorch 模块反向传播时收到错误？
正如错误所暗示的，您收到此错误是因为您正在反向传播多个！
q.sum().backward()
k.sum().backward()
反向传播时，默认情况下会释放计算，除非
retain_graphTruedq/dθdk/dθd(q+k)/dθ(q+k).sum().backward()