enable QLoRA + FSDP2 #909

weifengpy · 2024-05-01T06:44:34Z

this PR is built on top of

TorchAO nightly that contains NF4Tensor FSDP2 ops PR1 PR2
Pytorch nightly that contains meta init + cpu offloading PR

unit test: pytest -s tests/torchtune/utils/test_distributed.py -k test_qlora_state_dict

attaching snapshot on 2 runs

QLoRA + FSDP2 on 8 GPUs: tune run --nnodes 1 --nproc_per_node 8 lora_finetune_fsdp2 --config llama2/7B_qlora
QLoRA on 1 GPU: tune run lora_finetune_single_device --config llama2/7B_qlora_single_device

numerics

tokens_per_second_per_gpu: 1st is A100, 2nd is H100. H100 scales better with faster memory read/write

memory

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use torchao copy_

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enable saving checkpoint

pytorch-bot · 2024-05-01T06:44:37Z

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weifengpy · 2024-06-04T19:58:31Z

torchtune/modules/low_precision/_register_nf4_dispatch_ops.py

@@ -17,26 +17,3 @@ def clone(func, *args, **kwargs):
 in precision.
 """
 return to_nf4(args[0][0].get_original_weight())
-
-


starting from TorchAO==0.2.0, we implemented NF4.copy_. It's the superset of inplace_copy. We cover both NF4.copy_(bf16) and NF4.copy_(NF4)

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weifengpy · 2024-06-04T22:19:00Z

recipes/configs/dev/llama2/7B_qlora.yaml

+ _component_: torch.nn.CrossEntropyLoss
+
+fsdp:
+ cpu_offload: False


comparing with 7B_qlora_single_device.yaml, cpu_offload is the new feature

Strictly speaking you probably do not need to put cpu_offload config under fsdp (unless you think it helps for clarity, or anticipate other kwargs that we'd wanna pass through to fully_shard). No strong preference either way here though

I know Less has been working on cpu_offload for activations, which might be a root config. It will be complete different thing than cpu_offload for parameters in fsdp2

another things is, other fully_shard kwargs like reshard_after_forward=False can boost QPS

Does it make sense to you?

Ah yeah this is a good point, makes sense to me. Thanks for clarifying

weifengpy · 2024-06-04T22:25:29Z

recipes/configs/dev/llama2/7B_qlora.yaml

+# tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --hf-token <HF_TOKEN>
+#
+# To launch on a single device, run the following command from root:
+# tune run lora_finetune_single_device --config llama2/7B_qlora_single_device


will replace lora_finetune_single_device to lora_finetune_fsdp2 before landing. saving CI to publish PR earlier now

weifengpy · 2024-06-04T22:27:41Z

recipes/dev/lora_finetune_fsdp2.py

- # Before loading the state dict, ensure the state dict keys for the base
- # model and adapters (if available) match the keys in the full LoRA model
- # This is a good sanity check to prevent silent errors
- validate_state_dict_for_lora(


validate_state_dict_for_lora needs some refactor since FSDP2 use clean FQNs.
FSDP2 also check param on meta and throw Runtime error if any https://fburl.com/wrhxykn3

"FSDP parameters should be materialized from meta device before training, " f"but the following were still on meta device: {param_names_on_meta}\n" "For example, call module.to_empty(device) to materialize to device and " "call module.reset_parameters() on each module to initialize values."

Oh yeah this is what I was alluding to in the previous PR. You can try validate_missing_and_unexpected_for_lora as in the single-device recipe now that we have the correct FQN. If it doesn't work out of the box feel free to leave this as-is, we can come back to refactor after.

validate_missing_and_unexpected_for_lora works out of the box. added it in this PR. finger acrossed!

recipes/dev/lora_finetune_fsdp2.py

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weifengpy · 2024-06-04T23:27:14Z

recipes/configs/dev/llama2/70B_qlora.yaml

+#
+
+# Model Arguments
+model:


comparing with 70B_lora.yaml, added output_proj, apply_lora_to_mlp: True

the difference is similar to 7B_lora.yaml vs 7B_qlora_single_device.yaml

ebsmothers · 2024-06-04T22:45:43Z

recipes/configs/dev/llama2/7B_qlora.yaml

@@ -0,0 +1,91 @@
+# Config for single device QLoRA with lora_finetune_single_device.py


Should we rename this config as 7B_qlora_fsdp2.yaml to match the LoRA one?

good point! updating now

ebsmothers · 2024-06-04T22:54:44Z

recipes/dev/lora_finetune_fsdp2.py

- # Before loading the state dict, ensure the state dict keys for the base
- # model and adapters (if available) match the keys in the full LoRA model
- # This is a good sanity check to prevent silent errors
- validate_state_dict_for_lora(


Oh yeah this is what I was alluding to in the previous PR. You can try validate_missing_and_unexpected_for_lora as in the single-device recipe now that we have the correct FQN. If it doesn't work out of the box feel free to leave this as-is, we can come back to refactor after.

recipes/dev/lora_finetune_fsdp2.py

ebsmothers · 2024-06-04T23:06:56Z

recipes/configs/dev/llama2/7B_qlora.yaml

+ _component_: torch.nn.CrossEntropyLoss
+
+fsdp:
+ cpu_offload: False


Strictly speaking you probably do not need to put cpu_offload config under fsdp (unless you think it helps for clarity, or anticipate other kwargs that we'd wanna pass through to fully_shard). No strong preference either way here though

ebsmothers · 2024-06-04T23:09:24Z

torchtune/utils/_distributed.py

+ )
+ else:
+ sharded_tensor = distribute_tensor(
+ full_tensor,


No longer need to convert dtype as before?

it's moved to line 284 full_tensor = full_tensor.to(sharded_meta_param.dtype).to(device). The extra .to(device) is for NF4 since quantization on cpu is prohibitively slow

ebsmothers · 2024-06-04T23:28:00Z

torchtune/utils/_distributed.py

+ if isinstance(sharded_meta_param._local_tensor, NF4Tensor):
+ full_tensor = to_nf4(full_tensor)
+ # replicating logic from `_fsdp_param.py`` `_init_sharded_param`
+ # otherwise `distribute_tensor(DTensor(local=NF4))`
+ # requires dispatching `c10d.scatter_``
+ # long-term solution is `swap_tensor`
+ mesh = sharded_meta_param.device_mesh
+ if mesh.ndim > 1:
+ raise NotImplementedError(f"only support 1D FSDP but got {mesh.ndim=}")
+ shard_mesh_dim = 0
+ shard_world_size = mesh.size(shard_mesh_dim)
+ shard_rank = cast(
+ torch.distributed.ProcessGroup, mesh.get_group(shard_mesh_dim)
+ ).rank()
+ chunk = list(torch.chunk(full_tensor, shard_world_size, dim=0))[shard_rank]
+ sharded_param = full_tensor.new_zeros(chunk.size())
+ sharded_param[: chunk.size(0)].copy_(chunk)
+ sharded_tensor = DTensor(
+ sharded_param,
+ sharded_meta_param.device_mesh,
+ sharded_meta_param.placements,
+ shape=sharded_meta_param.size(),
+ dtype=sharded_meta_param.dtype,
+ requires_grad=sharded_meta_param.requires_grad,
+ stride=sharded_meta_param.stride(),
+ )


If it's not too much work, is it possible to extend the unit test you added in #855 to cover this case as well? As is I find it a bit hard to follow and want to make sure we have a reliable sanity check in case anything breaks in the future

you're right. will try to come up with a unit test to cover NF4

@ebsmothers I added pytest -s tests/torchtune/utils/test_distributed.py -k test_qlora_state_dict

Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:

ebsmothers

This is looking good! As discussed offline we can separate out the checkpoint save support into a follow-up PR to unblock other ongoing work. Once CI is green this one should be good to land. Thank you for enabling a huge huge feature for our users!

weifengpy · 2024-06-05T08:09:53Z

merged after confirming CI's all green. Will follow up on saving state dict

Co-authored-by: Kartikay Khandelwal <47255723+kartikayk@users.noreply.github.com> Co-authored-by: ebsmothers <ebs@meta.com> Co-authored-by: Rafi Ayub <33648637+RdoubleA@users.noreply.github.com> Co-authored-by: Joe Cummings <jrcummings27@gmail.com> Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com> Co-authored-by: Rohan Varma <rvarm1@fb.com> Co-authored-by: Optimox <sebastien.fischman@gmail.com> Co-authored-by: Tanish Ambulkar <tanish.ambulkar99@gmail.com> Co-authored-by: Botao Chen <markchen1015@meta.com> Co-authored-by: solitude-alive <44771751+solitude-alive@users.noreply.github.com> Co-authored-by: christobill <christobill@users.noreply.github.com> Co-authored-by: Philip Bontrager <pbontrager@gmail.com> Co-authored-by: Evan Smothers <ebs@fb.com>

weifengpy and others added 15 commits April 23, 2024 17:45

enable LoRA + FSDP2

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reset params for lora weights and rope

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support lora weights checkpoint and checkpoint utils

0cd21c6

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fix lora meta device bug

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save optim state dict

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mark TODO

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optimizer foreach=True for DTensor

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clip grad norm

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switch to ptd state dict api

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add profiler

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qlora 7b config

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use torchao copy_

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Merge pull request #1 from weifengpy/fsdp2

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use torchao copy_

enable saving checkpoint

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Merge pull request #2 from weifengpy/fsdp2

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enable saving checkpoint

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weifengpy marked this pull request as draft May 1, 2024 06:45

weifengpy mentioned this pull request May 1, 2024

[In Progress] FSDP2 + NF4Tensor #651

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weifengpy and others added 11 commits May 1, 2024 00:33

optimizer state dict: load on rank0 and broadcast

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import Optimizer

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resume training

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prepare for full test

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prepare for full test

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remove profiler

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passed integration test

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Merge branch 'main' into fsdp2

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bring back state dict validation

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align indent on comment

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weifengpy added 5 commits June 3, 2024 23:57

del logits to save memory

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fix linter

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gate NF4.copy_ on TorchAO==0.2.0

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improve torchao gating comment

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upgrade torchao to 0.2

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weifengpy commented Jun 4, 2024

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gate torchao 0.2

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weifengpy marked this pull request as ready for review June 4, 2024 22:32

weifengpy requested a review from ebsmothers June 4, 2024 22:33

weifengpy added 3 commits June 4, 2024 16:05

replace with lora_finetune_fsdp2

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add llama2-70B

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replace with qlora and lora_finetune_fsdp2 in yaml

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weifengpy added 3 commits June 4, 2024 17:05

rename yaml to _fsdp2.yaml

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add unit test for nf4 state dict

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python 3.8 style dict union

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weifengpy changed the title ~~[WIP] enable QLoRA + FSDP2~~ enable QLoRA + FSDP2 Jun 5, 2024

weifengpy added 2 commits June 4, 2024 20:26

validate lora sd missing

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skip test if <2 gpu

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[WIP] full finetune / qlora + ac/offload/optm in bwd drisspg/transformer_nuggets#21

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Multi-GPU QLoRA? #844

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enable QLoRA + FSDP2 #909

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		@@ -0,0 +1,91 @@
		# Config for single device QLoRA with lora_finetune_single_device.py

enable QLoRA + FSDP2 #909

enable QLoRA + FSDP2 #909

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/909

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