pytorch - 💡(How to fix) Fix [pipelining] Unable to use PP with HF transformers models due to non-float inputs

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Error Message

(pt) fyguan@wolf:~/beast/disk20$ CUDA_VISIBLE_DEVICES=1,2 torchrun --nproc-per-node 2 pipelining_model_backward.py W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] ***************************************** W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] ***************************************** [Rank 1] Starting model init on cuda:1 [Rank 0] Starting model init on cuda:0 Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 9823.66it/s] GPT2LMHeadModel( (transformer): GPT2Model( (wte): Embedding(50257, 768) (wpe): Embedding(1024, 768) (drop): Dropout(p=0.1, inplace=False) (h): ModuleList( (0-11): 12 x GPT2Block( (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): GPT2Attention( (c_attn): Conv1D(nf=2304, nx=768) (c_proj): Conv1D(nf=768, nx=768) (attn_dropout): Dropout(p=0.1, inplace=False) (resid_dropout): Dropout(p=0.1, inplace=False) ) (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (mlp): GPT2MLP( (c_fc): Conv1D(nf=3072, nx=768) (c_proj): Conv1D(nf=768, nx=3072) (act): NewGELUActivation() (dropout): Dropout(p=0.1, inplace=False) ) ) ) (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=768, out_features=50257, bias=False) ) Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 8754.15it/s] GPT2LMHeadModel( (transformer): GPT2Model( (wte): Embedding(50257, 768) (wpe): Embedding(1024, 768) (drop): Dropout(p=0.1, inplace=False) (h): ModuleList( (0-11): 12 x GPT2Block( (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): GPT2Attention( (c_attn): Conv1D(nf=2304, nx=768) (c_proj): Conv1D(nf=768, nx=768) (attn_dropout): Dropout(p=0.1, inplace=False) (resid_dropout): Dropout(p=0.1, inplace=False) ) (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (mlp): GPT2MLP( (c_fc): Conv1D(nf=3072, nx=768) (c_proj): Conv1D(nf=768, nx=3072) (act): NewGELUActivation() (dropout): Dropout(p=0.1, inplace=False) ) ) ) (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=768, out_features=50257, bias=False) ) /home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: isinstance(treespec, LeafSpec) is deprecated, use isinstance(treespec, TreeSpec) and treespec.is_leaf() instead. return cls.new(cls, *args) /home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: isinstance(treespec, LeafSpec) is deprecated, use isinstance(treespec, TreeSpec) and treespec.is_leaf() instead. return cls.new(cls, *args) [rank1]: Traceback (most recent call last): [rank1]: File "/home/fyguan/beast/disk20/pipelining_model_backward.py", line 67, in <module> [rank1]: output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 604, in step [rank1]: self._step_microbatches( [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 851, in _step_microbatches [rank1]: self._initialize_stage(arg_mbs[0], kwarg_mbs[0]) [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 555, in _initialize_stage [rank1]: self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs) [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1291, in _prepare_forward_infra [rank1]: self.args_recv_info[chunk] = self._create_act_recv_info() [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1447, in create_act_recv_info [rank1]: args_recv_info.append(create_recv_tensor(placeholder, arg_node)) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1429, in create_recv_tensor [rank1]: buffer.requires_grad(True) [rank1]: RuntimeError: only Tensors of floating point dtype can require gradients W0509 02:41:13.106000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:1012] Sending process 4192332 closing signal SIGTERM E0509 02:41:13.272000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:986] failed (exitcode: 1) local_rank: 1 (pid: 4192333) of binary: /home/fyguan/disk10/miniconda3/envs/pt/bin/python3.12 Traceback (most recent call last): File "/home/fyguan/disk10/miniconda3/envs/pt/bin/torchrun", line 6, in <module> sys.exit(main()) ^^^^^^ File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/init.py", line 362, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 990, in main run(args) File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 981, in run elastic_launch( File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 170, in call return launch_agent(self._config, self._entrypoint, list(args)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

pipelining_model_backward.py FAILED

Failures: [1]: time : 2026-05-09_02:41:13 host : wolf rank : 0 (local_rank: 0) exitcode : -15 (pid: 4192332) error_file: <N/A> traceback : Signal 15 (SIGTERM) received by PID 4192332

Root Cause (first observed failure): [0]: time : 2026-05-09_02:41:13 host : wolf rank : 1 (local_rank: 1) exitcode : 1 (pid: 4192333) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html

Root Cause

(pt) fyguan@wolf:~/beast/disk20$ CUDA_VISIBLE_DEVICES=1,2 torchrun --nproc-per-node 2 pipelining_model_backward.py 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
[Rank 1] Starting model init on cuda:1
[Rank 0] Starting model init on cuda:0
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 9823.66it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 8754.15it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
[rank1]: Traceback (most recent call last):
[rank1]:   File "/home/fyguan/beast/disk20/pipelining_model_backward.py", line 67, in <module>
[rank1]:     output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses)
[rank1]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 604, in step
[rank1]:     self._step_microbatches(
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 851, in _step_microbatches
[rank1]:     self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 555, in _initialize_stage
[rank1]:     self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs)
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1291, in _prepare_forward_infra
[rank1]:     self.args_recv_info[chunk] = self._create_act_recv_info()
[rank1]:                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1447, in _create_act_recv_info
[rank1]:     args_recv_info.append(create_recv_tensor(placeholder, arg_node))
[rank1]:                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1429, in create_recv_tensor
[rank1]:     buffer.requires_grad_(True)
[rank1]: RuntimeError: only Tensors of floating point dtype can require gradients
W0509 02:41:13.106000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:1012] Sending process 4192332 closing signal SIGTERM
E0509 02:41:13.272000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:986] failed (exitcode: 1) local_rank: 1 (pid: 4192333) of binary: /home/fyguan/disk10/miniconda3/envs/pt/bin/python3.12
Traceback (most recent call last):
  File "/home/fyguan/disk10/miniconda3/envs/pt/bin/torchrun", line 6, in <module>
    sys.exit(main())
             ^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 362, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 990, in main
    run(args)
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 981, in run
    elastic_launch(
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
pipelining_model_backward.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 0 (local_rank: 0)
  exitcode  : -15 (pid: 4192332)
  error_file: <N/A>
  traceback : Signal 15 (SIGTERM) received by PID 4192332
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 4192333)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Fix Action

Fix / Workaround

Attempting to pipeline a HF transformers model results in RuntimeError: only Tensors of floating point dtype can require gradients. This is not unique to the GPT2 implementation, as it is the same behavior with the llama implementation and likely others due to their similarity. I suspect that this is due to non-float intermediates used by the model, though I am not sure what the exact cause is. Applying the patches from #182182 and #182644 over 2.11 don't resolve the issue. I would expect something like this to simply work and not have this error, since this should be a common use case for pipelining. I'm not sure if there is some other way of getting these models running successfully using PP or if this is indeed a current bug/limitation of PP in pytorch. The error message is also not particularly helpful for debugging.

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 144 On-line CPU(s) list: 0-143 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8452Y CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 36 Socket(s): 2 Stepping: 8 CPU(s) scaling MHz: 34% CPU max MHz: 3200.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 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 smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities ibpb_exit_to_user Virtualization: VT-x L1d cache: 3.4 MiB (72 instances) L1i cache: 2.3 MiB (72 instances) L2 cache: 144 MiB (72 instances) L3 cache: 135 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143 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 Reg file data sampling: 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; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Code Example

import torch
import torch.distributed as dist
from torch.distributed.pipelining import pipeline, SplitPoint, PipelineStage, Schedule1F1B
from transformers import AutoModelForCausalLM
import os

# Initialize torchrun's distributed environment
pp_group = dist.init_process_group(backend="nccl")
rank = dist.get_rank()
world_size = dist.get_world_size()
local_rank = int(os.environ["LOCAL_RANK"])

# Assign this specific process to its designated GPU
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)

print(f"[Rank {rank}] Starting model init on {device}")

# GPT2
model_id = "openai-community/gpt2"

device_count = torch.cuda.device_count()
model = AutoModelForCausalLM.from_pretrained(model_id)
print(model)

# kv cache is enabled by default and causes other issues with dynamo tracing/pipelining, disabling
model.config.use_cache = False

# run on 2 or more gpus
split_spec = {
    f"transformer.h.5": SplitPoint.END,
} 
model = model.to(device)

pipe_module = pipeline(
    module=model,
    mb_args=(),
    mb_kwargs={
        'input_ids': torch.zeros((1, 512), dtype=torch.long).to(device),
        'attention_mask': torch.ones((1, 512), dtype=torch.bool).to(device)
    },
    split_spec=split_spec,
)

stage = pipe_module.build_stage(rank, device, pp_group)


# placeholder loss function
def loss_fn(outputs, other):
    logits = outputs['logits']
    loss = logits.sum() - other.sum()
    return loss

schedule = Schedule1F1B(stage, n_microbatches=4, loss_fn=loss_fn)

if rank == 0:
    inputs = {
        'input_ids': torch.zeros((4, 512), dtype=torch.long).to(device),
        'attention_mask': torch.ones((4, 512), dtype=torch.bool).to(device),
    }

    schedule.step(
        **inputs
    )
elif rank == world_size - 1:
    losses = []
    output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses)
    print(f"losses: {losses}")
else:
    schedule.step()

---

(pt) fyguan@wolf:~/beast/disk20$ CUDA_VISIBLE_DEVICES=1,2 torchrun --nproc-per-node 2 pipelining_model_backward.py 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
[Rank 1] Starting model init on cuda:1
[Rank 0] Starting model init on cuda:0
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 9823.66it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 8754.15it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
[rank1]: Traceback (most recent call last):
[rank1]:   File "/home/fyguan/beast/disk20/pipelining_model_backward.py", line 67, in <module>
[rank1]:     output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses)
[rank1]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 604, in step
[rank1]:     self._step_microbatches(
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 851, in _step_microbatches
[rank1]:     self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 555, in _initialize_stage
[rank1]:     self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs)
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1291, in _prepare_forward_infra
[rank1]:     self.args_recv_info[chunk] = self._create_act_recv_info()
[rank1]:                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1447, in _create_act_recv_info
[rank1]:     args_recv_info.append(create_recv_tensor(placeholder, arg_node))
[rank1]:                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1429, in create_recv_tensor
[rank1]:     buffer.requires_grad_(True)
[rank1]: RuntimeError: only Tensors of floating point dtype can require gradients
W0509 02:41:13.106000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:1012] Sending process 4192332 closing signal SIGTERM
E0509 02:41:13.272000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:986] failed (exitcode: 1) local_rank: 1 (pid: 4192333) of binary: /home/fyguan/disk10/miniconda3/envs/pt/bin/python3.12
Traceback (most recent call last):
  File "/home/fyguan/disk10/miniconda3/envs/pt/bin/torchrun", line 6, in <module>
    sys.exit(main())
             ^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 362, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 990, in main
    run(args)
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 981, in run
    elastic_launch(
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
pipelining_model_backward.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 0 (local_rank: 0)
  exitcode  : -15 (pid: 4192332)
  error_file: <N/A>
  traceback : Signal 15 (SIGTERM) received by PID 4192332
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 4192333)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

---

(pt) fyguan@wolf:~/beast/disk20$ curl -sL https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py | python
Collecting environment information...
PyTorch version: 2.11.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39

Python version: 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-90-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S

Nvidia driver version: 560.35.03
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        46 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               144
On-line CPU(s) list:                  0-143
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8452Y
CPU family:                           6
Model:                                143
Thread(s) per core:                   2
Core(s) per socket:                   36
Socket(s):                            2
Stepping:                             8
CPU(s) scaling MHz:                   34%
CPU max MHz:                          3200.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4000.00
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 smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities ibpb_exit_to_user
Virtualization:                       VT-x
L1d cache:                            3.4 MiB (72 instances)
L1i cache:                            2.3 MiB (72 instances)
L2 cache:                             144 MiB (72 instances)
L3 cache:                             135 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142
NUMA node1 CPU(s):                    1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143
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 Reg file data sampling: 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; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] numpy==2.4.3
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-nvrtc-cu12==12.6.85
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] torch==2.11.0+cu126
[pip3] torchvision==0.26.0+cu126
[pip3] triton==3.6.0
[conda] numpy                       2.4.3            pypi_0                pypi
[conda] nvidia-cublas               13.1.0.3         pypi_0                pypi
[conda] nvidia-cublas-cu12          12.6.4.1         pypi_0                pypi
[conda] nvidia-cuda-cupti           13.0.85          pypi_0                pypi
[conda] nvidia-cuda-cupti-cu12      12.6.80          pypi_0                pypi
[conda] nvidia-cuda-nvrtc           13.0.88          pypi_0                pypi
[conda] nvidia-cuda-nvrtc-cu12      12.6.85          pypi_0                pypi
[conda] nvidia-cuda-runtime         13.0.96          pypi_0                pypi
[conda] nvidia-cuda-runtime-cu12    12.6.77          pypi_0                pypi
[conda] nvidia-cudnn-cu12           9.10.2.21        pypi_0                pypi
[conda] nvidia-cudnn-cu13           9.19.0.56        pypi_0                pypi
[conda] nvidia-cufft                12.0.0.61        pypi_0                pypi
[conda] nvidia-cufft-cu12           11.3.0.4         pypi_0                pypi
[conda] nvidia-curand               10.4.0.35        pypi_0                pypi
[conda] nvidia-curand-cu12          10.3.7.77        pypi_0                pypi
[conda] nvidia-cusolver             12.0.4.66        pypi_0                pypi
[conda] nvidia-cusolver-cu12        11.7.1.2         pypi_0                pypi
[conda] nvidia-cusparse             12.6.3.3         pypi_0                pypi
[conda] nvidia-cusparse-cu12        12.5.4.2         pypi_0                pypi
[conda] nvidia-cusparselt-cu12      0.7.1            pypi_0                pypi
[conda] nvidia-cusparselt-cu13      0.8.0            pypi_0                pypi
[conda] nvidia-nccl-cu12            2.28.9           pypi_0                pypi
[conda] nvidia-nccl-cu13            2.28.9           pypi_0                pypi
[conda] nvidia-nvjitlink            13.0.88          pypi_0                pypi
[conda] nvidia-nvjitlink-cu12       12.6.85          pypi_0                pypi
[conda] nvidia-nvtx                 13.0.85          pypi_0                pypi
[conda] nvidia-nvtx-cu12            12.6.77          pypi_0                pypi
[conda] torch                       2.11.0+cu126     pypi_0                pypi
[conda] torchvision                 0.26.0+cu126     pypi_0                pypi
[conda] triton                      3.6.0            pypi_0                pypi
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

Attempting to pipeline a HF transformers model results in RuntimeError: only Tensors of floating point dtype can require gradients. This is not unique to the GPT2 implementation, as it is the same behavior with the llama implementation and likely others due to their similarity. I suspect that this is due to non-float intermediates used by the model, though I am not sure what the exact cause is. Applying the patches from #182182 and #182644 over 2.11 don't resolve the issue. I would expect something like this to simply work and not have this error, since this should be a common use case for pipelining. I'm not sure if there is some other way of getting these models running successfully using PP or if this is indeed a current bug/limitation of PP in pytorch. The error message is also not particularly helpful for debugging.

<details> <summary> Repro script, run on 2 gpus </summary>
import torch
import torch.distributed as dist
from torch.distributed.pipelining import pipeline, SplitPoint, PipelineStage, Schedule1F1B
from transformers import AutoModelForCausalLM
import os

# Initialize torchrun's distributed environment
pp_group = dist.init_process_group(backend="nccl")
rank = dist.get_rank()
world_size = dist.get_world_size()
local_rank = int(os.environ["LOCAL_RANK"])

# Assign this specific process to its designated GPU
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)

print(f"[Rank {rank}] Starting model init on {device}")

# GPT2
model_id = "openai-community/gpt2"

device_count = torch.cuda.device_count()
model = AutoModelForCausalLM.from_pretrained(model_id)
print(model)

# kv cache is enabled by default and causes other issues with dynamo tracing/pipelining, disabling
model.config.use_cache = False

# run on 2 or more gpus
split_spec = {
    f"transformer.h.5": SplitPoint.END,
} 
model = model.to(device)

pipe_module = pipeline(
    module=model,
    mb_args=(),
    mb_kwargs={
        'input_ids': torch.zeros((1, 512), dtype=torch.long).to(device),
        'attention_mask': torch.ones((1, 512), dtype=torch.bool).to(device)
    },
    split_spec=split_spec,
)

stage = pipe_module.build_stage(rank, device, pp_group)


# placeholder loss function
def loss_fn(outputs, other):
    logits = outputs['logits']
    loss = logits.sum() - other.sum()
    return loss

schedule = Schedule1F1B(stage, n_microbatches=4, loss_fn=loss_fn)

if rank == 0:
    inputs = {
        'input_ids': torch.zeros((4, 512), dtype=torch.long).to(device),
        'attention_mask': torch.ones((4, 512), dtype=torch.bool).to(device),
    }

    schedule.step(
        **inputs
    )
elif rank == world_size - 1:
    losses = []
    output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses)
    print(f"losses: {losses}")
else:
    schedule.step()
</details> <details> <summary> Full traceback </summary>
(pt) fyguan@wolf:~/beast/disk20$ CUDA_VISIBLE_DEVICES=1,2 torchrun --nproc-per-node 2 pipelining_model_backward.py 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0509 02:41:04.614000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py:851] *****************************************
[Rank 1] Starting model init on cuda:1
[Rank 0] Starting model init on cuda:0
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 9823.66it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 148/148 [00:00<00:00, 8754.15it/s]
GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.1, inplace=False)
    (h): ModuleList(
      (0-11): 12 x GPT2Block(
        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (attn): GPT2Attention(
          (c_attn): Conv1D(nf=2304, nx=768)
          (c_proj): Conv1D(nf=768, nx=768)
          (attn_dropout): Dropout(p=0.1, inplace=False)
          (resid_dropout): Dropout(p=0.1, inplace=False)
        )
        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (mlp): GPT2MLP(
          (c_fc): Conv1D(nf=3072, nx=768)
          (c_proj): Conv1D(nf=768, nx=3072)
          (act): NewGELUActivation()
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/copyreg.py:99: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
  return cls.__new__(cls, *args)
[rank1]: Traceback (most recent call last):
[rank1]:   File "/home/fyguan/beast/disk20/pipelining_model_backward.py", line 67, in <module>
[rank1]:     output = schedule.step(target=torch.randn(4, 512).to(device), losses=losses)
[rank1]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 604, in step
[rank1]:     self._step_microbatches(
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 851, in _step_microbatches
[rank1]:     self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/schedules.py", line 555, in _initialize_stage
[rank1]:     self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs)
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1291, in _prepare_forward_infra
[rank1]:     self.args_recv_info[chunk] = self._create_act_recv_info()
[rank1]:                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1447, in _create_act_recv_info
[rank1]:     args_recv_info.append(create_recv_tensor(placeholder, arg_node))
[rank1]:                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/pipelining/stage.py", line 1429, in create_recv_tensor
[rank1]:     buffer.requires_grad_(True)
[rank1]: RuntimeError: only Tensors of floating point dtype can require gradients
W0509 02:41:13.106000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:1012] Sending process 4192332 closing signal SIGTERM
E0509 02:41:13.272000 4192266 /mnt/disk10/user/fyguan/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py:986] failed (exitcode: 1) local_rank: 1 (pid: 4192333) of binary: /home/fyguan/disk10/miniconda3/envs/pt/bin/python3.12
Traceback (most recent call last):
  File "/home/fyguan/disk10/miniconda3/envs/pt/bin/torchrun", line 6, in <module>
    sys.exit(main())
             ^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 362, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 990, in main
    run(args)
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/run.py", line 981, in run
    elastic_launch(
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/fyguan/disk10/miniconda3/envs/pt/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
pipelining_model_backward.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 0 (local_rank: 0)
  exitcode  : -15 (pid: 4192332)
  error_file: <N/A>
  traceback : Signal 15 (SIGTERM) received by PID 4192332
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-05-09_02:41:13
  host      : wolf
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 4192333)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
</details> <details> <summary> Versions </summary>
(pt) fyguan@wolf:~/beast/disk20$ curl -sL https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py | python
Collecting environment information...
PyTorch version: 2.11.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39

Python version: 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-90-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S

Nvidia driver version: 560.35.03
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        46 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               144
On-line CPU(s) list:                  0-143
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8452Y
CPU family:                           6
Model:                                143
Thread(s) per core:                   2
Core(s) per socket:                   36
Socket(s):                            2
Stepping:                             8
CPU(s) scaling MHz:                   34%
CPU max MHz:                          3200.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4000.00
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 smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities ibpb_exit_to_user
Virtualization:                       VT-x
L1d cache:                            3.4 MiB (72 instances)
L1i cache:                            2.3 MiB (72 instances)
L2 cache:                             144 MiB (72 instances)
L3 cache:                             135 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142
NUMA node1 CPU(s):                    1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143
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 Reg file data sampling: 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; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] numpy==2.4.3
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-nvrtc-cu12==12.6.85
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] torch==2.11.0+cu126
[pip3] torchvision==0.26.0+cu126
[pip3] triton==3.6.0
[conda] numpy                       2.4.3            pypi_0                pypi
[conda] nvidia-cublas               13.1.0.3         pypi_0                pypi
[conda] nvidia-cublas-cu12          12.6.4.1         pypi_0                pypi
[conda] nvidia-cuda-cupti           13.0.85          pypi_0                pypi
[conda] nvidia-cuda-cupti-cu12      12.6.80          pypi_0                pypi
[conda] nvidia-cuda-nvrtc           13.0.88          pypi_0                pypi
[conda] nvidia-cuda-nvrtc-cu12      12.6.85          pypi_0                pypi
[conda] nvidia-cuda-runtime         13.0.96          pypi_0                pypi
[conda] nvidia-cuda-runtime-cu12    12.6.77          pypi_0                pypi
[conda] nvidia-cudnn-cu12           9.10.2.21        pypi_0                pypi
[conda] nvidia-cudnn-cu13           9.19.0.56        pypi_0                pypi
[conda] nvidia-cufft                12.0.0.61        pypi_0                pypi
[conda] nvidia-cufft-cu12           11.3.0.4         pypi_0                pypi
[conda] nvidia-curand               10.4.0.35        pypi_0                pypi
[conda] nvidia-curand-cu12          10.3.7.77        pypi_0                pypi
[conda] nvidia-cusolver             12.0.4.66        pypi_0                pypi
[conda] nvidia-cusolver-cu12        11.7.1.2         pypi_0                pypi
[conda] nvidia-cusparse             12.6.3.3         pypi_0                pypi
[conda] nvidia-cusparse-cu12        12.5.4.2         pypi_0                pypi
[conda] nvidia-cusparselt-cu12      0.7.1            pypi_0                pypi
[conda] nvidia-cusparselt-cu13      0.8.0            pypi_0                pypi
[conda] nvidia-nccl-cu12            2.28.9           pypi_0                pypi
[conda] nvidia-nccl-cu13            2.28.9           pypi_0                pypi
[conda] nvidia-nvjitlink            13.0.88          pypi_0                pypi
[conda] nvidia-nvjitlink-cu12       12.6.85          pypi_0                pypi
[conda] nvidia-nvtx                 13.0.85          pypi_0                pypi
[conda] nvidia-nvtx-cu12            12.6.77          pypi_0                pypi
[conda] torch                       2.11.0+cu126     pypi_0                pypi
[conda] torchvision                 0.26.0+cu126     pypi_0                pypi
[conda] triton                      3.6.0            pypi_0                pypi
</details>

cc @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @pragupta @msaroufim @dcci @aditvenk @weifengpy

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