[Core][LoRA][1/N] Add LoRA for EncoderDecoderModelRunner (#15990)

Signed-off-by: Jee Jee Li <[email protected]>

Author: jeejeelee

vllm/lora/layers.py

@@ -866,6 +866,11 @@ def can_replace_layer(
                 and len(packed_modules_list) == 3)
 
 
+#TODO: Implement this
+class QKVCrossParallelLinearWithLoRA(BaseLayerWithLoRA):
+    pass
+
+
 class RowParallelLinearWithLoRA(BaseLinearLayerWithLoRA):
 
     def __init__(self, base_layer: RowParallelLinear) -> None:

vllm/model_executor/models/mllama.py

@@ -52,6 +52,7 @@
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalEncDecInputs,
@@ -1181,6 +1182,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: MllamaConfig = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
+        self.config = config
         self.quant_config = quant_config
         self.vocab_size = config.text_config.vocab_size
         self.hidden_size = config.text_config.hidden_size
@@ -1517,6 +1519,15 @@ def load_weights(self, weights: Iterable[Tuple[str,
                 updated_params.add(name)
         return updated_params
 
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector",
+            tower_model="vision_model")
+
 
 def skip_attention_mask(sparse_mask: List[List[int]]) -> bool:
     for mask in sparse_mask:

vllm/worker/enc_dec_model_runner.py

@@ -16,6 +16,7 @@
 from vllm.forward_context import set_forward_context
 from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
@@ -34,6 +35,7 @@
 from vllm.worker.utils import assert_enc_dec_mr_supported_scenario
 
 logger = init_logger(__name__)
+LORA_WARMUP_RANK = 8
 
 
 @dataclasses.dataclass(frozen=True)
@@ -160,7 +162,11 @@ def execute_model(
         if num_steps > 1:
             raise ValueError("num_steps > 1 is not supported in "
                              "EncoderDecoderModelRunner")
-
+        if self.lora_config:
+            assert model_input.lora_requests is not None
+            assert model_input.lora_mapping is not None
+            self.set_active_loras(model_input.lora_requests,
+                                  model_input.lora_mapping)
         if (model_input.attn_metadata is not None
                 and model_input.attn_metadata.prefill_metadata is None
                 and model_input.attn_metadata.decode_metadata.use_cuda_graph):
@@ -268,6 +274,22 @@ def profile_run(self) -> None:
         max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
         max_num_seqs = self.scheduler_config.max_num_seqs
 
+        # This represents the maximum number of different requests
+        # that will have unique loras, and therefore the max amount of
+        # memory consumption. Create dummy lora request copies from the
+        # lora request passed in, which contains a lora from the lora
+        # warmup path.
+        dummy_lora_requests: List[LoRARequest] = []
+        dummy_lora_requests_per_seq: List[LoRARequest] = []
+        if self.lora_config:
+            dummy_lora_requests = self._add_dummy_loras(
+                self.lora_config.max_loras)
+            assert len(dummy_lora_requests) == self.lora_config.max_loras
+            dummy_lora_requests_per_seq = [
+                dummy_lora_requests[idx % len(dummy_lora_requests)]
+                for idx in range(max_num_seqs)
+            ]
+
         # Profile memory usage with max_num_sequences sequences and the total
         # number of tokens equal to max_num_batched_tokens.
         seqs: List[SequenceGroupMetadata] = []
@@ -315,6 +337,8 @@ def profile_run(self) -> None:
                 block_tables=None,
                 encoder_seq_data=encoder_dummy_data.seq_data,
                 cross_block_table=None,
+                lora_request=dummy_lora_requests_per_seq[group_id]
+                if dummy_lora_requests_per_seq else None,
                 multi_modal_data=decoder_dummy_data.multi_modal_data
                 or encoder_dummy_data.multi_modal_data,
                 multi_modal_placeholders=decoder_dummy_data.