[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops #150

weifengpy · 2024-04-19T20:37:32Z

why FSDP needs those ops

torch.chunk / aten.split.Tensor: dim0 sharding on parameters torch.chunk(tensor, world_size, dim=0)
tensor.new_zeros / aten.new_zeros.default: allocate storage for padded params.
tensor[:end_idx] / aten.slice.Tensor and tensor.copy_: copy sharded params into padded params
tensor.view(-1) / aten.view.default: flatten ND tensors into 1D
torch.as_strided(tensor, orig_size) / aten.as_strided.default: restore 1D tensors to ND
tensor.pin_memory: move cpu tensor to pin memory for nonblocking D2H copy
tensor.cpu(): move gpu tensor to cpu

unit test: pytest test/dtypes/test_nf4.py

run fsdp in TorchTune

git clone https://github.com/weifengpy/torchtune.git
cd torchtune && pip install -e ".[dev]"
tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --hf-token <HF_TOKEN>
tune run --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config recipes/configs/llama2/7B_qlora_single_device.yaml max_steps_per_epoch=1

user flow and gaps

step 1: load llama2/3 from HF checkpoints. gap is memory spikes in NF4Tensor.from_tensor [NF4][FSDP2] DTensor + fused adam on cpu #205
step 2: training loop
- numerics [NF4][FSDP2]: enable multi-gpu CI #202
- perf and compare with answer.ai https://github.com/pytorch/ao/issues/203
- memory: 500 MB unexplained
- torch.compile(TransformerBlock)
- cpu offloading with DTensor + fused adam [NF4][FSDP2] DTensor + fused adam on cpu #205
step 3: save checkpoint: verify if DTensor(NF4Tensor).full_tensor + torch.save works for NF4Tensor
step 4: load checkpoint to resume finetuning: verify if torch.load + DTensor(NF4Tensor).distributed works

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msaroufim · 2024-04-29T18:55:25Z

Didn't forget to review, will give it a thorough read this afternoon

msaroufim

Thank you! Made a first pass and can do a second one tomorrow morning

msaroufim · 2024-04-29T23:18:33Z

test/dtypes/test_nf4.py

+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_to_cpu(self):
+        nf4_tensor = to_nf4(torch.randn(512 * 512, device='cuda'))
+        nf4_tensor.cpu()


so this is just testing against crashes or do also expect the nf4_tensor.device to be cpu?

good catch. this is testing against crashes but i will add assertion on nf4_tensor.device.type == 'cpu'

msaroufim · 2024-04-29T23:23:45Z

test/dtypes/test_nf4.py

+                torch.as_strided(nf4_tensor, nf4_tensor.size(), stride, nf4_tensor.storage_offset())
+
+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_pin_memory(self):


I think you mentioned this briefly last week but could you remind me how you figured out these would be the functions that needed to be tested. (I'm thinking ahead with a tutorial for someone who wants to upstream some new exotic dtpye and get it working with fsdp). That's probably a good candidate for what I mean by we should add another smoke test so we know for sure FSDP will work

So I ran the tests locally and they all worked and fast! So this gives me confidence the nf4 tensor now supports many new ops but it doesnt give me confidence that fsdp won't break in some way

I was hoping we could have a smoke test of the sort fsdp(torch.nn.Sequential(LinearNF4(64,64))) that would ensure nothing breaks and that fsdp doesn't silently drop the dtype since that functionality wasn't tested for fsdp 1 and we had to rely on twitter to get that signal

agree that we need a smoke test on fsdp(model). Not sure how to setup a multi-gpu test in torchao though. Is there some .ci files to change? Is there some example in torchAO? I am happy to fill in the actual logic into the template. As a reference, FSDP tests in pytorch are done like this pytorch/test/distributed/_composable/fsdp/test_fully_shard_training.py

Something identical should work the machines we have in CI, every commit is already running on 4 A10Gs linux.g5.12xlarge. No existing example since this is our first distributed test

Let's just do this, first thing we meet tomorrow

msaroufim · 2024-04-29T23:30:00Z

torchao/dtypes/nf4tensor.py

 def noop_detach(func, *args, **kwargs):
    return args[0][0]


+@implements(


more of a n00b q to @drisspg : what's up with all the args[0] I feel like there's some sort of contract I can't quite parse

EDIT: It's the NF4 tensor, could we add some comment somewhere to make this clearer?

I updated PR with nf4tensor = args[0] at the begining to make it clearer

msaroufim · 2024-04-29T23:33:37Z

torchao/dtypes/nf4tensor.py

+            self.scaler_block_size,
+            self.scaler_mean,
+            self.nf4,
+            mesh.get_group().size(),


n00b q: what is this doing?

Also more generally I don't follow what the 2 fsdp tests are trying to do. I think in fsdp_post_all_gather you are testing to make sure nf4 tensors are preserved and not silently casted to some other type

I don't follow what the 2 fsdp tests are trying to do

This is core logic in nf4tensor.py. unit tests happens in another file test_nf4.py

fsdp_pre_all_gather returns a tuple of two things

tuple[0] are quantized_scalers, quantization_factor and quantized_data. They are input for all-gather

tuple[1] are SubclassTensorArgs, block_size etc are metadata to reconstruct NF4Tensor. mesh.get_group().size() is the group size for all-gather (how many gpus). it's helpful to restore NF4Tensor.size. Eg for 2 gpus, all-gathering tensor(512) will return tensor(512 x 2)

msaroufim · 2024-04-29T23:42:14Z

torchao/dtypes/nf4tensor.py

+    scaler_mean = aten_op(args[0].scaler_mean, *args[1:], **kwargs)
+    nf4 = aten_op(args[0].nf4, *args[1:], **kwargs)
+    tensor_meta = SubclassTensorArgs(
+        args[0].size(),


+1 This also confused me. I think what driss means is just give a human readable name to args[0] so its easier to read the code

msaroufim

Thank you! Made a first pass and can do a second one tomorrow morning

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awgu · 2024-04-30T13:42:10Z

torchao/dtypes/nf4tensor.py

+        aten.detach.default,
+    ]
+)
+def nf4_detach(aten_op, args, kwargs=None):


If we make that assumption that requires_grad=False and detach is a no-op, can we add an assertion that checks for args[0].requires_grad?

Also, I am not sure that we need to detach all inner tensors. cc: @bdhirsh

awgu · 2024-04-30T13:44:34Z

torchao/dtypes/nf4tensor.py

+        raise NotImplementedError(f"aten.new_zeros(NF4Tensor) with new size {new_size}")
+    ratio = nf4_tensor.numel() // math.prod(new_size)
+
+    assert nf4_tensor.quantized_scalers.size(0) % ratio == 0, f"quantized_scalers.numel() must be divisible by {ratio}"


nit: These assertion messages preferably should include the values (i.e. both nf4_tensor.quantized_scalers.size(0) and ratio) so that they can be more actionable.

awgu · 2024-04-30T13:48:04Z

torchao/dtypes/nf4tensor.py

+    quantization_factor = aten_op(nf4_tensor.quantization_factor, *(args[1:]), **kwargs)
+    quantized_data = aten_op(nf4_tensor.quantized_data, *(args[1:]), **kwargs)
+    return NF4Tensor(
+        SubclassTensorArgs(


nit: I seem to see this pattern a lot where we construct SubclassTensorArgs directly from an existing nf4_tensor. Perhaps, consider making this into a helper to avoid the duplication.

haha. not nit at all. added util function to keep the code dry: NF4Tensor(*construct_nf4_args(nf4tensor, updated_attrs))

awgu · 2024-04-30T13:49:43Z

torchao/dtypes/nf4tensor.py

+            assert (
+                quantized_scalers.untyped_storage().data_ptr()
+                == out.quantized_scalers.untyped_storage().data_ptr() and
+                quantization_factor.untyped_storage().data_ptr()
+                == out.quantization_factor.untyped_storage().data_ptr() and
+                quantized_data.untyped_storage().data_ptr()
+                == out.quantized_data.untyped_storage().data_ptr()
+            ), f"Expects out's data to be the all-gather output"


We may consider removing these asserts (in the future) especially if tracing through this becomes an issue. In theory, NF4Tensor should not need to make this kind of assert, but for now, it might be helpful for debugging as the FSDP extension is still in its early stages.

awgu · 2024-04-30T13:52:23Z

torchao/dtypes/nf4tensor.py

+            )
+        )
+    ) and len(args) == 2:
+        # Tensor.to(device, non_blocking)


Does this mean that if we tried to use __torch_dispatch__, we would not be able to tell that it is simply .to(device, non_blocking=True) without a dtype argument/dtype change?

What is the story for dequantization? Namely, what is the outer NF4Tensor's dtype, and what happens when you call .to(dtype) with that same dtype? (e.g. if NF4Tensor.dtype == torch.bfloat16, what if you call NF4Tensor.to(torch.bfloat16)?)

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weifengpy · 2024-04-30T22:06:50Z

torchao/dtypes/nf4tensor.py


 NF4_OPS_TABLE: Dict[Any, Any] = {}

+INNER_TENSOR_NAMES_FOR_FSDP = ["quantized_scalers", "quantization_factor", "quantized_data"]


I exclude two tiny tensors: nf4 (numel=16) and scaler_mean (numel=1)
when GPU > numel, we need to implement padding for inner tensors. it's not worth the time in my opinion

This seems like it'd apply to more than just FSDP. Is that correct?

This seems like it'd apply to more than just FSDP. Is that correct?

it applies general distributed case when we shard a single tensor to N GPUs. I can change the name to INNER_TENSOR_NAMES_FOR_SHARDING if that's clearer

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cpuhrsch · 2024-05-01T16:17:16Z

torchao/dtypes/nf4tensor.py

+    assert nf4tensor.quantized_scalers.size(0) % ratio == 0, f"quantized_scalers.numel() must be divisible by {ratio}"
+    quantized_scalers = aten_op(nf4tensor.quantized_scalers, [nf4tensor.quantized_scalers.size(0) // ratio], **kwargs)
+
+    assert nf4tensor.quantization_factor.size(0) % ratio == 0, f"quantization_factor.size(0) must be divisible by {ratio}"


Maybe these asserts could be unified?

good suggestion. I removed duplicative asserts with for loop over inner tensors

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cpuhrsch · 2024-05-01T18:16:10Z

torchao/dtypes/nf4tensor.py


 NF4_OPS_TABLE: Dict[Any, Any] = {}

+INNER_TENSOR_NAMES_FOR_SHARDING = ["quantized_scalers", "quantization_factor", "quantized_data"]


So this is something FSDP2 requires any Tensor subclass to have defined?

for any Tensor subclass, we prefer reusing __tensor_flatten__ to lookup inner tensors. For NF4, we define INNER_TENSOR_NAMES_FOR_SHARDING as a subset of __tensor_flatten__ because scaler_mean and nf4 are too tiny to shard

Hm, isn't that something you could filter with a numel based heuristic within FSDP itself instead of requiring some tensor subclasses to communicate it?

I think the inner tensors that are sharded needs to match the torch.chunk implementation in the subclass, so FSDP cannot necessarily determine the tensors to shard itself. (E.g., if FSDP filtered by numel but the subclass implemented torch.chunk to still shard some tensor smaller than the numel threshold, then there would be a correctness issue.)

changed to private const with underscore _INNER_TENSOR_NAMES_FOR_SHARDING after discussion with @cpuhrsch

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msaroufim

Thank you for the heroic work. Let's open up an issue with known gaps

weifengpy · 2024-05-01T22:34:17Z

Thank you for the heroic work. Let's open up an issue with known gaps

yes, will open issue for the renaming work

weifengpy · 2024-05-02T02:32:29Z

Thank you for the heroic work. Let's open up an issue with known gaps

opened issues and linked them here

)

weifengpy and others added 26 commits April 3, 2024 18:18

proof of concept for FSDP2 + NF4Tensor

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Merge branch 'main' into main

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fsdp extention for tensor subclass

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support fp32

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UNIT TEST FOR STATE DICT

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implement to

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remove torch.override from torch function

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use dtype in compile unit test

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add dtype in all unit test

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keep original dtype

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fix linter

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use torch testing @parametrize

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remove unused import

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sm8 for fp16

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remove sm check for fp16

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skip 2.2.2 and below for tracing tensor subclass

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include kwargs

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raise unimplemented

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fsdp2 ops

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remove irrelevant changes

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add unit test

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assert cpu device

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name args[0] as nf4tensor

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utils for apply to inner tensors and constructor

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use original copy_

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decorator for args check

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weifengpy requested review from msaroufim and cpuhrsch May 1, 2024 01:39

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enable QLoRA + FSDP2 pytorch/torchtune#909

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INNER_TENSOR_NAMES_FOR_SHARDING and unify assert in split and new_zeros

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indicate private constant with _

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Merge branch 'main' into fsdp2ops

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do not land: Create test_distributed #190

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[Tracker] WIP Features for torchao v0.2 #132

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[Tracker] WIP features for torchao 0.3 #252

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dbyoung18 pushed a commit to dbyoung18/ao that referenced this pull request Jul 31, 2024

[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops (pytorch#150

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[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops #150

[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops #150

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		NF4_OPS_TABLE: Dict[Any, Any] = {}

		INNER_TENSOR_NAMES_FOR_FSDP = ["quantized_scalers", "quantization_factor", "quantized_data"]


		NF4_OPS_TABLE: Dict[Any, Any] = {}

		INNER_TENSOR_NAMES_FOR_SHARDING = ["quantized_scalers", "quantization_factor", "quantized_data"]

[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops #150

[FSDP2][NF4Tensor][2/n] implement torch.chunk and other ops #150

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