peft-internal-testing/mixtral-pre-v5-lora

In transformers v5, Mixtral uses a different architecture with fused layers compared to pre-v5. This LoRA checkpoint is based on the old architecture and we want to ensure that it can still be correctly loaded post-v5.

Old architecture:

MixtralForCausalLM(
  (model): MixtralModel(
    (embed_tokens): Embedding(32000, 1024)
    (layers): ModuleList(
      (0-1): 2 x MixtralDecoderLayer(
        (self_attn): MixtralAttention(
          (q_proj): Linear(in_features=1024, out_features=1024, bias=False)
          (k_proj): Linear(in_features=1024, out_features=256, bias=False)
          (v_proj): Linear(in_features=1024, out_features=256, bias=False)
          (o_proj): Linear(in_features=1024, out_features=1024, bias=False)
        )
        (block_sparse_moe): MixtralSparseMoeBlock(
          (gate): Linear(in_features=1024, out_features=8, bias=False)
          (experts): ModuleList(
            (0-7): 8 x MixtralBlockSparseTop2MLP(
              (w1): Linear(in_features=1024, out_features=3584, bias=False)
              (w2): Linear(in_features=3584, out_features=1024, bias=False)
              (w3): Linear(in_features=1024, out_features=3584, bias=False)
              (act_fn): SiLUActivation()
            )
          )
        )
        (input_layernorm): MixtralRMSNorm((1024,), eps=1e-05)
        (post_attention_layernorm): MixtralRMSNorm((1024,), eps=1e-05)
      )
    )
    (norm): MixtralRMSNorm((1024,), eps=1e-05)
    (rotary_emb): MixtralRotaryEmbedding()
  )
  (lm_head): Linear(in_features=1024, out_features=32000, bias=False)
)

New architecture:

MixtralForCausalLM(
  (model): MixtralModel(
    (embed_tokens): Embedding(32000, 1024)
    (layers): ModuleList(
      (0-1): 2 x MixtralDecoderLayer(
        (self_attn): MixtralAttention(
          (q_proj): Linear(in_features=1024, out_features=1024, bias=False)
          (k_proj): Linear(in_features=1024, out_features=256, bias=False)
          (v_proj): Linear(in_features=1024, out_features=256, bias=False)
          (o_proj): Linear(in_features=1024, out_features=1024, bias=False)
        )
        (mlp): MixtralSparseMoeBlock(
          (gate): MixtralTopKRouter()
          (experts): MixtralExperts(
            (act_fn): SiLUActivation()
          )
        )
        (input_layernorm): MixtralRMSNorm((1024,), eps=1e-05)
        (post_attention_layernorm): MixtralRMSNorm((1024,), eps=1e-05)
      )
    )
    (norm): MixtralRMSNorm((1024,), eps=1e-05)
    (rotary_emb): MixtralRotaryEmbedding()
  )
  (lm_head): Linear(in_features=1024, out_features=32000, bias=False)
)

The weight conversion spec is:

        "mixtral": [
            WeightRenaming(".block_sparse_moe.gate", ".mlp.gate"),
            WeightConverter(
                source_patterns=[
                    "block_sparse_moe.experts.*.w1.weight",
                    "block_sparse_moe.experts.*.w3.weight",
                ],  # you give me a list of 2 keys, I collect a list of a list of tensors
                target_patterns="mlp.experts.gate_up_proj",  # target key gets the list of two tensors
                operations=[
                    MergeModulelist(
                        dim=0
                    ),  # each process has two lists of tensors, we cat each list. -> we end up with 2 tensors
                    Concatenate(dim=1),  # each process has 2 tensors, gate and up, we concat them into gate_up
                ],  # we want the loading to add this shard operation here. Though we can't shard after concats and merge, needs to be first
            ),
            WeightConverter(
                source_patterns=[
                    "block_sparse_moe.experts.*.w2.weight",
                ],
                target_patterns="mlp.experts.down_proj",  # target key gets the list of two tensors
                operations=[
                    MergeModulelist(
                        dim=0
                    ),  # each process has two lists of tensors, we cat each list. -> we end up with 2 tensors
                ],  # we want the loading to add this shard operation here. Though we can't shard after concats and merge, needs to be first
            ),
        ],