Add LoRA support for Gemma (#3050)

.buildkite/test-pipeline.yaml

@@ -50,7 +50,7 @@ steps:
   command: pytest -v -s worker
 
 - label: LoRA Test
-  command: pytest -v -s lora
+  command: pytest -v -s lora --forked
 
 - label: Metrics Test
   command: pytest -v -s metrics

csrc/punica/bgmv/bgmv_config.h

@@ -28,6 +28,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 5120) \
     f(in_T, out_T, W_T, narrow, 5504) \
     f(in_T, out_T, W_T, narrow, 5632) \
+    f(in_T, out_T, W_T, narrow, 6144) \
     f(in_T, out_T, W_T, narrow, 6912) \
     f(in_T, out_T, W_T, narrow, 7168) \
     f(in_T, out_T, W_T, narrow, 8192) \
@@ -39,6 +40,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 14336) \
     f(in_T, out_T, W_T, narrow, 16384) \
     f(in_T, out_T, W_T, narrow, 20480) \
+    f(in_T, out_T, W_T, narrow, 24576) \
     f(in_T, out_T, W_T, narrow, 28672) \
     f(in_T, out_T, W_T, narrow, 32000) \
     f(in_T, out_T, W_T, narrow, 32256) \

tests/lora/conftest.py

@@ -126,6 +126,11 @@ def mixtral_lora_files():
     return snapshot_download(repo_id="terrysun/mixtral-lora-adapter")
 
 
+@pytest.fixture(scope="session")
+def gemma_lora_files():
+    return snapshot_download(repo_id="wskwon/gemma-7b-test-lora")
+
+
 @pytest.fixture
 def llama_2_7b_engine_extra_embeddings() -> nn.Module:
     cleanup()

tests/lora/test_gemma.py

+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "google/gemma-7b"
+
+
+def do_sample(llm, lora_path: str, lora_id: int) -> str:
+    prompts = [
+        "Quote: Imagination is",
+        "Quote: Be yourself;",
+        "Quote: So many books,",
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None)
+    # Print the outputs.
+    generated_texts = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+def test_gemma_lora(gemma_lora_files):
+    llm = vllm.LLM(MODEL_PATH,
+                   max_model_len=1024,
+                   enable_lora=True,
+                   max_loras=4)
+
+    expected_lora_output = [
+        "more important than knowledge.\nAuthor: Albert Einstein\n",
+        "everyone else is already taken.\nAuthor: Oscar Wilde\n",
+        "so little time\nAuthor: Frank Zappa\n",
+    ]
+
+    output1 = do_sample(llm, gemma_lora_files, lora_id=1)
+    for i in range(len(expected_lora_output)):
+        assert output1[i].startswith(expected_lora_output[i])
+    output2 = do_sample(llm, gemma_lora_files, lora_id=2)
+    for i in range(len(expected_lora_output)):
+        assert output2[i].startswith(expected_lora_output[i])

tests/lora/test_punica.py

@@ -44,8 +44,8 @@ def _lora_ref_impl(
 
 H1 = H2 = [
     128, 256, 512, 1024, 1280, 2048, 2560, 2752, 3072, 3456, 3584, 4096, 5120,
-    5504, 5632, 6912, 7168, 8192, 9216, 10240, 11008, 13824, 14336, 32000,
-    32256, 32512, 32768, 33024
+    5504, 5632, 6144, 6912, 7168, 8192, 9216, 10240, 11008, 13824, 14336,
+    24576, 32000, 32256, 32512, 32768, 33024
 ]
 SEED = [0xabcdabcd987]
 

vllm/model_executor/models/gemma.py

@@ -20,6 +20,7 @@ import torch
 from torch import nn
 from transformers import GemmaConfig
 
+from vllm.config import LoRAConfig
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import GeluAndMul
 from vllm.model_executor.layers.attention import PagedAttention
@@ -246,12 +247,36 @@ class GemmaModel(nn.Module):
 
 
 class GemmaForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+    # Gemma does not apply LoRA to the embedding layer.
+    embedding_modules = {}
+    embedding_padding_modules = []
 
     def __init__(
         self,
         config: GemmaConfig,
         linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
     ) -> None:
+        del lora_config  # Unused.
         super().__init__()
         self.config = config
         self.linear_method = linear_method
@@ -305,9 +330,6 @@ class GemmaForCausalLM(nn.Module):
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
-                # Skip loading extra layer for lora models.
-                if "lm_head" in name:
-                    continue
                 # GemmaRMSNorm is different from Llama's in that it multiplies
                 # (1 + weight) to the output, instead of just weight.
                 if "norm.weight" in name:

vllm/model_executor/models/llama.py

@@ -27,6 +27,7 @@ import torch
 from torch import nn
 from transformers import LlamaConfig
 
+from vllm.config import LoRAConfig
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import PagedAttention
@@ -45,7 +46,6 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput
-from vllm.config import LoRAConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]