[WIP] Addition of Dora #936

Prakyathkantharaju · 2024-05-05T06:45:54Z

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add a new feature
fix a bug
update tests and/or documentation
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Please link to any issues this PR addresses.

Changelog

What are the changes made in this PR?
I have added DORA for the loralinear module. Request was proposed in the issue: #893

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add unit tests for any new functionality
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run unit tests via pytest tests
run recipe tests via pytest tests -m integration_test
manually run any new or modified recipes with sufficient proof of correctness
- include relevant commands and any other artifacts in this summary (pastes of loss curves, eval results, etc.)

pytorch-bot · 2024-05-05T06:45:58Z

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ebsmothers

Thanks for opening the PR! I am still not sure about the correctness of the implementation though. Can you run forward on the same input tensor and confirm you get the same results with a known correct implementation (e.g. the one from PEFT referenced in your L138 of lora.py).

There are some other forward-looking considerations as well: specifically how we expose DoRA in our higher-level model builders (could potentially be similar to what we do for QLoRA), how we will merge weights when DoRA is applied, determining to what extent we want to support enabling and disabling DoRA adapters (this functionality is used in e.g. our DPO recipe). But for now the main thing is to make sure the linear component itself is correct and well-tested.

ebsmothers · 2024-05-06T00:09:53Z

torchtune/modules/peft/lora.py

+                + (self.alpha / self.rank)
+                * (self.lora_a.weight.T @ self.lora_b.weight.T).T,
+                dim=1,
+            ).to(self.weight.dtype)


What about the detach used in the PEFT implementation referenced in your comment?

ebsmothers · 2024-05-06T00:10:56Z

torchtune/modules/peft/lora.py

+                dim=1,
+            ).to(self.weight.dtype)
+            mag_norm_scale = (self.m / weight_norm - 1).view(1, -1)
+            return mag_norm_scale * out + mag_norm_scale * lora_out


Sorry maybe I am still missing the point but I don't think this actually matches the version that was ultimately added to PEFT. Ref

ebsmothers · 2024-05-06T00:18:03Z

torchtune/modules/peft/lora.py

+            weight_norm = torch.linalg.norm(
+                self.weight
+                + (self.alpha / self.rank)
+                * (self.lora_a.weight.T @ self.lora_b.weight.T).T,


Do we need all the transposes here? Isn't this just self.lora_b.weight @ self.lora_a.weight?

ebsmothers · 2024-05-06T00:18:26Z

torchtune/modules/peft/lora.py

@@ -67,6 +72,7 @@ def __init__(
        self.dropout = nn.Dropout(p=dropout)
        self.lora_a = nn.Linear(in_features=in_dim, out_features=rank, bias=False)
        self.lora_b = nn.Linear(in_features=rank, out_features=out_dim, bias=False)
+        self.m = nn.Parameter(torch.ones(1, out_dim)) if self.use_dora else None


This may necessitate some extra logic for checkpoint loading

rohan-varma

In addition to @ebsmothers comments, let's have comprehensive testing to ensure we have implementation parity compared to a well-known implementation, such as the one offered by HF PEFT. This will ensure we have the appropriate correctness guarantees in place.

Thanks so much for working on this!

rohan-varma · 2024-05-06T08:02:53Z

tests/torchtune/modules/peft/test_lora.py

@@ -97,6 +110,12 @@ def test_forward(self, inputs, lora_linear, out_dim) -> None:
        assert actual.shape == (BSZ, SEQ_LEN, out_dim)
        torch.testing.assert_close(actual.mean(), expected, atol=1e-4, rtol=1e-6)

+    def test_dora_forward(self, inputs, dora_linear, out_dim) -> None:
+        expected = torch.tensor(EXPECTED_VAL)


Does this mean the expected val of DoRA is the same as LoRA? Why is this, and intuitively, I'm not sure if I understand if the result is the exact same, how DoRA results in different training than LoRA? Pretty sure I'm missing something basic here but would be good to clarify.

Here is the explanation:
$$DORA = W*x + (\frac{m}{weight norm} -1)*W *x + \frac{m}{weight norm} * {lora}_b({lora}_a(x)) + {scaling}$$

The m vector is initialized as self.m == weight norm. So the ratio $\frac{m}{weight norm}$ is 1 for the first iteration.
so LORA == DORA for the first pass.

Better explanation from the author: huggingface/peft#1474 (comment)

rohan-varma · 2024-05-06T08:03:06Z

torchtune/modules/peft/lora.py

@@ -32,6 +33,8 @@ class LoRALinear(nn.Module, AdapterModule):
        rank (int): rank of the low-rank approximation
        alpha (float): scaling factor for the low-rank approximation
        dropout (float): dropout probability. Default: 0.0
+        use_dora (bool): whether to use DORA (weight-Decomposed Low-Rank Adaptation).


nit: link to paper.

Updated in the new commit.

rohan-varma · 2024-05-06T08:03:50Z

torchtune/modules/peft/lora.py

@@ -32,6 +33,8 @@ class LoRALinear(nn.Module, AdapterModule):
        rank (int): rank of the low-rank approximation
        alpha (float): scaling factor for the low-rank approximation
        dropout (float): dropout probability. Default: 0.0
+        use_dora (bool): whether to use DORA (weight-Decomposed Low-Rank Adaptation).
+            Default: False


how do we want to expose DoRA? I see following the pattern of how we enabled QLoRA and just passing in a use_dora flag. Curious about the tradeoffs compared to a DoraLinear layer though. @ebsmothers any thoughts?

Yeah this is a good question. I do like the analogy with QLoRA and using a flag as in this PR matches what's done in PEFT too. But one place the analogy with QLoRA breaks down is that DoRA is actually introducing a new parameter, which QLoRA does not do. This can potentially make stuff like checkpointing a bit trickier. So we may need to think about this a bit more.

Prakyathkantharaju · 2024-05-07T04:38:35Z

torchtune/modules/peft/lora.py

@@ -111,6 +118,19 @@ def adapter_params(self) -> List[str]:
        adapter_params = ["lora_a.weight", "lora_b.weight"]
        return adapter_params

+    def dora_init(self) -> None:


Hello @rohan-varma and @ebsmothers, I am wondering if there is a more efficient approach to the problem I am facing. I have followed the PEFT (Parameter Efficient Fine-Tuning) method but I am encountering an issue with the initialization of the self.m (magnitude vector) in torch tune because I require the weights. Currently, I am using the flag (self.dora_initialized) to address this issue, but I believe this may not be the best solution. I would appreciate any suggestions you may have.

Prakyathkantharaju · 2024-05-07T04:44:57Z

Thanks for opening the PR! I am still not sure about the correctness of the implementation though. Can you run forward on the same input tensor and confirm you get the same results with a known correct implementation (e.g. the one from PEFT referenced in your L138 of lora.py).

There are some other forward-looking considerations as well: specifically how we expose DoRA in our higher-level model builders (could potentially be similar to what we do for QLoRA), how we will merge weights when DoRA is applied, determining to what extent we want to support enabling and disabling DoRA adapters (this functionality is used in e.g. our DPO recipe). But for now the main thing is to make sure the linear component itself is correct and well-tested.

Hello @ebsmothers , There were some bugs in my initial commit, I have fixed those bugs ( detach missing, too many transposes and -1 in the mag_norm_scale calculation). I also added a dora_init function, which will initialize the self.m vector, similar to PEFT, However, I think this method might not be the best approach. If you have a better idea for initialization, please let me know.

ebsmothers · 2024-05-22T23:49:04Z

Hi @Prakyathkantharaju sorry for the delay in responding here. I think beyond just exposing the DoRA logic in LoRALinear we probably want to think about the overall design, interaction with other parts of the library, and thorough testing (basically some of the points in my comment here and @rohan-varma's comment here). Since it's a fair amount of effort to do all of this, I am gonna tag in @calvinpelletier to help out here. Let me know if this works for you, we'd love to have your collaboration on design and code reviews.

Prakyathkantharaju · 2024-05-25T02:59:09Z

Hello and thank you for your response. I apologize for not updating you on this issue for a while. I am currently working on comparing the performance of the Dora implementation with the PEFT. This was an aspect that was missing from the request raised by @ebsmothers and @rohan-varma. As you suggested, I welcome input from @calvinpelletier and anyone else who is willing to help move this forward. Let me know if you have any other requests.

Here are the scripts I am using to generate the PEFT model: https://gist.github.com/Prakyathkantharaju/53777b5997b9fc14ba6f40c9b5788b6a

Here is the comparison between the PEFT Dora and the Torchtune Dora loss: https://api.wandb.ai/links/continuous-optimization/991283uj.

ebsmothers · 2024-05-28T13:46:24Z

recipes/configs/llama3/8B_Dora_single_device.yml

+  _component_: torchtune.models.llama3.lora_llama3_8b
+  lora_attn_modules: ['q_proj', 'v_proj', 'k_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: False
+  lora_rank: 8
+  lora_alpha: 16


This isn't using DoRA?

I have updated the configs and recipe. Now, there is no specific Dora recipe, and everything is done using the Lora recipe, where I check if use_dora is defined and perform a specific class.

ebsmothers · 2024-05-28T13:46:28Z

recipes/dora_finetune_single_device.py

+log = utils.get_logger("DEBUG")
+
+
+class LoRAFinetuneRecipeSingleDevice(FTRecipeInterface):


Why do we need an entirely new recipe for DoRA? In my mind this is analogous to QLoRA: an extension of LoRA that shouldn't require fundamental changes to the training loop. Am I missing something here?

Update. Now, everything is done through Lora recipe now.

ebsmothers · 2024-05-28T14:03:27Z

torchtune/modules/peft/lora.py

+    Initialize DORA m to ones.
+    """
+    nn.init.zeros_(x)


This isn't accurate?

Updated with ones. These weights are updated later in the init_dora code. I wanted to keep the initialization of the new weights very similar to Lora, so I followed this format.

ebsmothers · 2024-05-28T14:04:10Z

torchtune/modules/peft/lora.py

+    def _dora_weight_norm(self) -> Tensor:
+        if self._quantize_base:
+            # Convert NF4Tensor to regular Tensor for computation TODO(prakyath): Fix this.
+            weight = to_regular_tensor(self.weight)


This isn't defined?

Updated, now the weight norm is calculated by dequantizing the weights. Please let me know if you have a better/faster way to do this ?

Prakyathkantharaju · 2024-06-02T18:59:01Z

Hello everyone,

I apologize for the delayed response, and I appreciate your review of my changes.

I have addressed the comments made by @ebsmothers and updated the structure of how Dora is initialized. Here are the details on how Dora is initialized (I have kept the initialization as similar to QLora as possible):

I added a partial Dora class, similar to QLora, where Dora is initialized by the use_dora option. You can find the link to the change here.
I updated the Lora recipe with Dora initialization. You can find the link to the changes here.
I updated the llama-3 lora initialization with the Dora-specific option. The links to the changed files are here.

Additionally, I would like feedback from @rohan-varma @kostmo and @ebsmothers. I also added a clamp function to avoid making the denominator 0 (link to the code here). This is not done in the peft or other Dora implementations. If you feel this is not necessary, then I can remove it.

Please let me know if you need any changes. I am willing to work on them. Moreover, if you have any additional feedback, I am happy to incorporate it as well.

calvinpelletier

Hi @Prakyathkantharaju, thanks for contributing! I left some comments.

One thing that you are currently missing is updating the merging logic at torchtune/module/peft/peft_utils.py::get_merged_lora_ckpt.

I'll work on setting up a direct comparison to the huggingface implementation to verify that the loss graph, memory usage, and training speed are similar.

calvinpelletier · 2024-06-03T18:57:57Z

torchtune/_recipe_registry.py

@@ -106,6 +106,10 @@ class Recipe:
                name="gemma/2B_qlora_single_device",
                file_path="gemma/2B_qlora_single_device.yaml",
            ),
+            Config(
+                name="llama3/*B_dora_single_device",


calvinpelletier · 2024-06-05T23:59:54Z

torchtune/models/llama3/_model_builders.py

+
+dora_llama3_8b.__doc__ = """
+Builder for creating a Llama3 model with DORA enabled. Base model weights in linear layers
+that DORA is applied to are quantized per the Dora paper: https://arxiv.org/abs/2402.09353.


nit: proper capitalization of DoRA in comments and docstrings

calvinpelletier · 2024-06-06T03:57:02Z

recipes/configs/llama3/8B_dora_single_device.yaml

+# Config for single device QLoRA with lora_finetune_single_device.py
+# using a Llama3 8b Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3-8b-Instruct --output-dir /tmp/Meta-Llama-3-8b-Instruct --hf-token <HF_TOKEN>
+#
+# To launch on a single device, run the following command from root:
+#   tune run lora_finetune_single_device --config llama3/8b_qlora_single_device
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run lora_finetune_single_device --config llama3/8b_qlora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>


qlora -> dora

calvinpelletier · 2024-06-06T04:04:18Z

recipes/configs/llama3/8B_dora_single_device.yaml

+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /teamspace/studios/this_studio/models/Meta-Llama-3-8b-Instruct/original/tokenizer.model


no rush on this, but before merging make sure to change the paths to /tmp/, the metric logger to DiskLogger, etc.

calvinpelletier · 2024-06-06T04:57:46Z

torchtune/modules/peft/lora.py

+    @property
+    def _dora_weight_norm(self) -> Tensor:


this should be a regular function instead of a property IMO

calvinpelletier · 2024-06-06T05:12:16Z

torchtune/modules/peft/peft_utils.py

+def activate_dora_params(model: nn.Module) -> nn.Module:
+    for k, v in model.named_modules():
+        if hasattr(v, "adapter_params") and callable(v.adapter_params):
+            current_adapter_params = v.adapter_params()


this line doesn't do anything

calvinpelletier · 2024-06-06T05:30:57Z

torchtune/modules/peft/lora.py

+        norm = torch.linalg.norm(result, dim=1)
+
+        # Clamp the norm to avoid division by zero
+        # TODO(Prakyath): Check with torchtune team whether this should be a parameter ?


what is this question referring to?

Prakyathkantharaju added 4 commits May 1, 2024 02:58

update lora.py with dora parameters

5374cd8

updated dora parameter initialization

aefb8cb

updated ones initialization and test

dffb2a3

updated dora update based on author recommendataion

cbafe85

facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label May 5, 2024

Prakyathkantharaju mentioned this pull request May 5, 2024

[FR] (Q)DoRA #893

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ebsmothers reviewed May 6, 2024

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rohan-varma reviewed May 6, 2024

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fixed bugs and magnitude intialization

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Prakyathkantharaju commented May 7, 2024

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Prakyathkantharaju added 2 commits May 27, 2024 20:41

working dora training with - default true

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updated config with seed and the print for testing and comparision

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ebsmothers requested changes May 28, 2024

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Prakyathkantharaju added 5 commits May 31, 2024 22:53

update dora initialization and llama3 loading

f096e4b

removed dora specific recipe and merge recipes

a55a962

fixed model loading bugs and tested training

9110f0e

update the lora finetune recipe for lora init

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updated llama3 docstring with use_dora

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Prakyathkantharaju requested review from ebsmothers and rohan-varma June 2, 2024 18:59

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[WIP] Addition of Dora #936

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		log = utils.get_logger("DEBUG")


		class LoRAFinetuneRecipeSingleDevice(FTRecipeInterface):

[WIP] Addition of Dora #936

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Prakyathkantharaju commented May 5, 2024

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Test plan

pytorch-bot bot commented May 5, 2024 • edited

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/936

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