Fix group offloading synchronization bug for parameter-only GroupModule's

[a-r-r-o-w]

Fixes #11981.

Requires #11990 to be merged first.

code

import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.hooks import apply_group_offloading, ModelHook, HookRegistry
from diffusers.models import ModelMixin
from diffusers.utils.logging import set_verbosity_debug
from torch.profiler import profile, record_function, ProfilerActivity

set_verbosity_debug()

class LayerOutputTrackerHook(ModelHook):
    def __init__(self):
        super().__init__()
        self.outputs = []
    
    def post_forward(self, module, output):
        self.outputs.append(output)
        return output


class Model(ModelMixin):
    def __init__(self, d_model=1024, num_layers=1):
        super().__init__()
        self.d_model = d_model
        
        self.input_proj = nn.Linear(1024, d_model)
        # self.norm = nn.LayerNorm(d_model, elementwise_affine=True)
        self.blocks = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(num_layers)])
        
        # This is problematic
        self.norm = nn.LayerNorm(d_model, elementwise_affine=True)
        # This works
        # self.norm = nn.LayerNorm(d_model, elementwise_affine=False)
        
        self.output_proj = nn.Linear(d_model, 1024)
    
    def forward(self, x):
        x = self.input_proj(x)
        # x = self.norm(x)
        for block in self.blocks:
            x = block(x)
            x = F.relu(x)
        x = self.norm(x)
        x = self.output_proj(x)
        return x


def apply_layer_output_tracker_hook(model: Model):
    for name, module in model.named_modules():
        if not isinstance(module, (torch.nn.Linear, torch.nn.LayerNorm)):
            continue
        registry = HookRegistry.check_if_exists_or_initialize(module)
        hook = LayerOutputTrackerHook()
        registry.register_hook(hook, "layer_output_tracker")


def print_output_diffs(ref_model: Model, model: Model):
    for (ref_name, ref_module), (name, module) in zip(ref_model.named_modules(), model.named_modules()):
        assert ref_name == name
        if not isinstance(ref_module, (torch.nn.Linear, torch.nn.LayerNorm)):
            continue
        ref_outputs = HookRegistry.check_if_exists_or_initialize(ref_module).get_hook("layer_output_tracker").outputs
        outputs = HookRegistry.check_if_exists_or_initialize(module).get_hook("layer_output_tracker").outputs
        cumulated_absmax = 0.0
        for i in range(len(outputs)):
            diff = ref_outputs[0] - outputs[i]
            absdiff = diff.abs()
            absmax = absdiff.max().item()
            cumulated_absmax += absmax
            if ref_name == "output_proj":
                print(f"{ref_name} absmax {i}: {absmax}")
        print(f"{name}: cumulated_absmax={cumulated_absmax:.5f}, num_outputs={len(outputs)}")


torch.manual_seed(42)
model_ref = Model()
model1 = Model()
model2 = Model()

model1.load_state_dict(model_ref.state_dict())
model2.load_state_dict(model_ref.state_dict())

model_ref.eval()
model1.eval()
model2.eval()

onload_device = torch.device("cuda:0")
offload_device = torch.device("cpu")

model_ref = model_ref.to(onload_device)
apply_group_offloading(
    model1,
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="block_level",
    num_blocks_per_group=1,
    use_stream=True,
)
apply_group_offloading(
    model2,
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="leaf_level",
    use_stream=True,
)

apply_layer_output_tracker_hook(model_ref)
apply_layer_output_tracker_hook(model1)
apply_layer_output_tracker_hook(model2)

x = torch.randn(1, 512, 1024).to("cuda")
out_ref = model_ref(x)

def compare_outputs(out1, out2):
    diff = out1 - out2
    absdiff = diff.abs()
    absmax = absdiff.max()
    mae = absdiff.mean()
    mse = (absdiff ** 2).mean()
    cossim = F.cosine_similarity(out1.flatten(), out2.flatten(), dim=0)
    print(f"{absmax=:.5f}, {mae=:.5f}, {mse=:.5f}, {cossim=:.5f}")

for _ in range(2):
    model1(x)
    print("=" * 80)
    model2(x)

do_profile = False
activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
# context = profile(
#     activities=activities,
#     record_shapes=True,
#     profile_memory=True,
#     with_stack=True,
# ) if do_profile else contextlib.nullcontext()
context = contextlib.nullcontext()

with context as prof:
    with torch.inference_mode():
        for i in range(10):
            with record_function(f"model_1_run_{i}"):
                output1 = model1(x)
            print(i)
            compare_outputs(out_ref, output1)
            print()

        print("=" * 80)
        
        for i in range(10):
            with record_function(f"model_2_run_{i}"):
                output2 = model2(x)

            print(i)
            compare_outputs(out_ref, output2)
            print()


print_output_diffs(model_ref, model1)
print()
print_output_diffs(model_ref, model2)

# prof.export_chrome_trace("dump_trace.json")
# print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=1000))

Tested for 100 rounds with:

seq 100 | xargs -Iz python3 dump12.py

Testing with profiling is not helpful because the problem never shows up. See heisenbug thread: https://huggingface.slack.com/archives/C065E480NN9/p1754035222558869

[HuggingFaceDocBuilderDev]

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[a-r-r-o-w]

cc @seed93, this seems to resolve many different tests I tried that were previously causing outputs to be different. Could you verify on your end if everything works well? Thanks 🤗

[sayakpaul]

Let's quickly merge the cleaning PR so that it's easier to review this one :)

[seed93]

Sorry, I am on a long vacation. Maybe I will try at night.-------- 原始邮件 --------发件人： Aryan ***@***.***>日期： 2025年8月6日周三 06:43收件人： huggingface/diffusers ***@***.***>抄送： seed93 ***@***.***>, Mention ***@***.***>主 题： Re: [huggingface/diffusers] Fix group offloading synchronization bug for parameter-only GroupModule's (PR #12077)a-r-r-o-w left a comment (huggingface/diffusers#12077) cc @seed93, this seems to resolve many different tests I tried that were previously causing outputs to be different. Could you verify on your end if everything works well? Thanks 🤗 —Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: ***@***.***>