Sampling from adapters different than sampling from saved merged model #1462
Description
Hello, this is actually a follow up to this issue #1415 but is a bit of a different problem. I verified essentially that the backpropogation works, however, essentially the last piece of the puzzle is when sampling from the adapters, I noticed that the original weights do not change and therefore the generations do not change. When generating from the adapters. However, when generating from the merged16bit model that I uploaded, it does in fact does generate better responses after many online DPO updates. I know for lora W = Wo + A@B and in the Peft layers for the unsloth model (I am using llama 3.2 1b parameters):
PeftModelForCausalLM(
(base_model): LoraModel(
(model): LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(128256, 2048)
(layers): ModuleList(
(0-15): 16 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=2048, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(k_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=512, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=512, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(v_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=512, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=512, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(o_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=2048, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(rotary_emb): LlamaExtendedRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=8192, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(up_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=8192, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(down_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=8192, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=2048, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(act_fn): SiLU()
)
(input_layernorm): LlamaRMSNorm((2048,), eps=1e-05)
(post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-05)
)
)
(norm): LlamaRMSNorm((2048,), eps=1e-05)
(rotary_emb): LlamaRotaryEmbedding()
)
(lm_head): Linear(in_features=2048, out_features=128256, bias=False)
)
)
)
The Q projection for example has the lora_A and lora_B model dict and the base_layer, I want to know how with the parameters below I would perform W = Wo + A@B in order to get the updated weights or is there code for it already somewhere in the unsloth repo? Essentially, I need to add the changes that were updated in the A and B matrices into the initial weights in order for the actual weights for the models to be updated. Thank you.
(q_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=2048, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=2048, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)