Multi-layer Decoder for Llama3 Draft Model

[xiaoxi-s]

Motivation

Support multiple Llama3DecoderLayers in LlamaForCausalLMEagle3.

Modifications

A LlamaForCausalLMEagle3 instance can now hold a arbitrary number (>=1) of Llama3DecoderLayerss configured via num_hidden_layers option of LlamaConfig. The test for the new config is also updated in tests/test_modeling/test_draft/test_llama3.py.

Related Issues

Sub-issue of #374

Accuracy Test

Tested under tests/test_modeling/test_draft/test_llama3.py.

Benchmark & Profiling

N/A

Checklist

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[gemini-code-assist]

Summary of Changes

Hello @xiaoxi-s, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the flexibility of the Llama3 draft decoder by enabling the configuration of multiple Multi-Layer Perceptrons (MLPs) within each decoder layer. Previously limited to a single MLP, the model can now be instantiated with a user-defined number of MLPs, allowing for more complex and potentially more powerful draft model architectures. The changes include adding a new command-line option for specifying the number of MLPs, updating the core model classes to handle this configuration, and ensuring comprehensive test coverage for the new functionality.

Highlights

• Multi-layer MLP Support: The Llama3 draft decoder now supports an arbitrary number of Multi-Layer Perceptrons (MLPs) within its LlamaDecoderLayer, configurable via a new parameter.
• New Command-Line Argument: A --num-draft-hidden-layers argument has been added to scripts/train_eagle3.py to specify the desired number of MLPs for the draft model decoder.
• Architectural Updates: The LlamaDecoderLayer and LlamaForCausalLMEagle3 classes have been modified to incorporate and utilize the num_draft_hidden_layers parameter, replacing a single MLP with a nn.Sequential module containing multiple MLPs.
• Test Coverage: Existing unit tests in tests/test_modeling/test_draft/test_llama3.py have been updated to validate the correct initialization and behavior of the model with the new multi-MLP configuration.

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[gemini-code-assist]

Code Review

This pull request successfully adds support for a multi-layer MLP in the draft decoder, with corresponding changes to the training script and tests. The implementation is consistent and the new functionality is well-tested. I have provided a couple of suggestions to improve the architecture of the multi-layer MLP for better training stability by incorporating residual connections for each MLP block. Additionally, I've pointed out a minor indentation issue in one of the test files.

I am having trouble creating individual review comments. Click here to see my feedback.

specforge/modeling/draft/llama3_eagle.py (1252)

Using nn.Sequential here creates a deep MLP stack without any intermediate normalization or residual connections. This can lead to training instability, especially if num_draft_hidden_layers is large. A more standard and stable approach is to use nn.ModuleList to treat each MLP as a separate block. I'll add a related suggestion on the forward method to complete this change.

        self.mlps = nn.ModuleList([LlamaMLP(config) for _ in range(num_draft_hidden_layers)])

specforge/modeling/draft/llama3_eagle.py (1308-1311)

To accompany the change to nn.ModuleList for self.mlps, the forward pass should be updated to loop through the MLPs. This applies a residual connection around each MLP block, which is a more standard and stable architecture than a single deep MLP. This implementation reuses the same post_attention_layernorm for each block.

        for mlp in self.mlps:
            residual = hidden_states
            hidden_states = self.post_attention_layernorm(hidden_states)
            hidden_states = mlp(hidden_states)
            hidden_states = residual + hidden_states

tests/test_modeling/test_draft/test_llama3.py (63)

This line has incorrect indentation. According to the PEP 8 style guide, indentation should be in multiples of 4 spaces. The line inside the for loop should be indented by 4 more spaces relative to the for statement.

            self.assertIsInstance(model.midlayer.mlps[i], LlamaMLP)

[Dogacel]

I guess for true multi-layer architecture, we should duplicate the decoder, not MLP. Why you wanted to duplicate the MLP instead?

[Dogacel]

This variable can be already set using the model config,

SpecForge/configs/llama3-8B-eagle3.json

Line 15 in 886ab9c

"num_hidden_layers": 1,

Which allows us to save & load models directly with the right number of hidden layers.

[sleepcoo]

@uygnef

[uygnef]

@sleepcoo LGTM

[justadogistaken]

hidden_states = torch.cat((input_emb, hidden_states), dim=-1)

Your implementation will do input_emb & hidden_states fusion every layer. May I ask why not just do fuse them in first layer. Them let others handle hidden_states only.

[xiaoxi-s]

Your implementation will do input_emb & hidden_states fusion every layer. May I ask why not just do fuse them in first layer. Them let others handle hidden_states only.

I believe the way of only iterating on hidden_states on later layers exists mostly in traditional transformer architectures. For the decoder layers used in draft model, this implementation keeps the initial token info available across all decoder layers for better speculative decoding instead of only next-token generation.

[Dogacel]

hidden_states = torch.cat((input_emb, hidden_states), dim=-1)

Your implementation will do input_emb & hidden_states fusion every layer. May I ask why not just do fuse them in first layer. Them let others handle hidden_states only.

I agree, I think input embedding should only be injected in the first layer. Original EAGLE only has 1 decoder layer therefore it didn't matter for them where they have done this injection. Injecting the same data to each layer would result in excessive computations.

However changing this means you have to update some code to make sure your input/output shapes match. For example attention takes 2 * hidden_size and outputs hidden_size.

I think it would be nice to configure if you want to map from (2 * hidden_size -> hidden_size) and save compute on deeper layers, or if you want to keep the full 2 * hidden_size shape and be more expressive.

SpecForge/specforge/modeling/draft/llama3_eagle.py

Lines 527 to 529 in e1e9695

	self.q_proj = nn.Linear(
	self.hidden_size * 2, self.num_heads * self.head_dim, bias=False
	)

[uygnef]

@xiaoxi-s Hi, xiaoxi.
A reviewer noted that input_emb is concatenated to hidden_states in every layer. Could you share the rationale behind this design?

It seems fusing them only in the first layer could be more efficient and less redundant. Have you experimented with or compared both approaches? If not, could we test the alternative to verify performance?

Appreciate your input.

[xiaoxi-s]

It seems fusing them only in the first layer could be more efficient and less redundant. Have you experimented with or compared both approaches? If not, could we test the alternative to verify performance?

Appreciate your input.

Working on the experiments. Will share after it's done.

#449 contains the implementation of the more traditional decoder layer as the additional layers. Will share their benchmarks on SharedGPT after the experiment is done.