How to use this frame work to train a LLM with multi-GPUs?

[zhhvvv]

Is the frame work support multi-gpu training?
I want to use the frame work to train a 70B model, however, I did not find the parameter settings or methods for multi-gpus training.

[yezhem]

The parallel functionality is currently under development and testing, and is expected to be available by the end of the month.

[zhhvvv]

Hi, thx for replying!

Will your team will test the frame work on the llama2-70B model? As, I also find that the frame work can support llama2-7b and llama2-13b, but it does not support the llama2-70B model.

Maybe this problem caused by inconsistent output dimensions of the qkv layers of the 70b model? （7b and 13b model have the same output dimensions at qkv layers‘ output)

I hope you can teach me how to fix the code to address this problem! thx

And here is the traceback of the error:

Traceback (most recent call last):
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora.py", line 199, in <module>
    mlora.train(mlora.Dispatcher(config, tokenizer), model,
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/train.py", line 69, in train
    output, router_outputs = llm_model.forward(input)
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/model_llama.py", line 292, in forward
    data = seq_layer.forward(data)
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/model_llama.py", line 223, in forward
    output = CheckpointRecomputeFunction.apply(
  File "/home/miniconda3/envs/mlora_env/lib/python3.10/site-packages/torch/autograd/function.py", line 506, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/checkpoint.py", line 99, in forward
    outputs = run_function(*args)
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/model_llama.py", line 158, in forward
    xv = self.wv_.forward(attention_norm_data, input_args)
  File "/mnt/project/longcontext/multi-lora-fine-tune/mlora/lora_liner.py", line 110, in forward
    result[start_idx: end_idx] += self.loras_[
RuntimeError: The size of tensor a (1024) must match the size of tensor b (8192) at non-singleton dimension 2

I print the tensors' shape with:(in the lora_liner.py Linear.forward() )

print('==============================')
            print(result[start_idx: end_idx].shape)
            #print(self.loras_.shape)
            print(self.loras_[adapter_name].forward(data[start_idx:end_idx]).shape)
            print('++++++++++++++++++++++++++++++')

I got

==============================
torch.Size([1, 104, 8192])
torch.Size([1, 104, 8192])
++++++++++++++++++++++++++++++
==============================
torch.Size([1, 104, 1024])
torch.Size([1, 104, 8192])
++++++++++++++++++++++++++++++

Before the error of the execution, it printed twice.

[yezhem]

seem the wrong lora dimension.
can you check the in_dim and out_dim value in the init_lora_weight function(mlora/lora_liner.py:58)
the lora's dimension be inited in this function, maybe you can fix the problem in this function.

[zhhvvv]

in (model_llama.py:301)
I print the layers info in the mlora.model_llama.LlamaModel object

for idx, layer in enumerate(llama_model.model.layers):
            model.layers_[idx].wq_ = Linear(
                layer.self_attn.q_proj, device=device)
            model.layers_[idx].wk_ = Linear(
                layer.self_attn.k_proj, device=device)
            model.layers_[idx].wv_ = Linear(
                layer.self_attn.v_proj, device=device)
            print('==============================')
            print(idx)
            print(layer.self_attn.k_proj)
            print(model.layers_[idx].wk_)
            print('++++++++++++++++++++++++++++++')
            model.layers_[idx].wo_ = Linear(
                layer.self_attn.o_proj, device=device)
            model.layers_[idx].ffn_ = FeedForward(
                norm=RMSNorm(layer.post_attention_layernorm.weight.to(
                    device=device).detach(), model.norm_eps_),
                w1=Linear(layer.mlp.gate_proj, device=device),
                w2=Linear(layer.mlp.down_proj, device=device),
                w3=Linear(layer.mlp.up_proj, device=device),
                device=device
            )
            model.layers_[idx].attention_norm_ = RMSNorm(
                layer.input_layernorm.weight.to(device=device).detach(), model.norm_eps_)
        return model

And I got this result:

Linear4bit(in_features=8192, out_features=8192, bias=False)
Linear(
  (weight_): Linear4bit(in_features=8192, out_features=8192, bias=False)

layer.self_attn.k_proj of each layers is different from the llama2-70b model's config(Linear(in_features=8192, out_features=1024, bias=False))

I think there are bugs in the LlamaModel Class and LLMModelArgs Class.

[yezhem]

the raw liners are created from the transformers.LlamaForCausalLM, and we get the in_features and out_features from the Linear.
can you check the llama_model's structure in mlora/model_llama.py:332.

[zhhvvv]

I sove the problem by updating the transformers

I found transformers==4.30.2
It can not load a llama2-70b model

size mismatch for model.layers.79.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 8192]) from checkpoint, the shape in current model is torch.Size([8192, 8192]).
size mismatch for model.layers.79.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 8192]) from checkpoint, the shape in current model is torch.Size([8192, 8192]).
	You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.

I recommend that you upgrade the dependencies of transformers during subsequent updates.

[yezhem]

thks, we will evaluate this.