[Model] Add Phi-2 LoRA support (#4886)

docs/source/models/supported_models.rst

@@ -118,7 +118,7 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`PhiForCausalLM`
     - Phi
     - :code:`microsoft/phi-1_5`, :code:`microsoft/phi-2`, etc.
-    -
+    - ✅︎
   * - :code:`Phi3ForCausalLM`
     - Phi-3
     - :code:`microsoft/Phi-3-mini-4k-instruct`, :code:`microsoft/Phi-3-mini-128k-instruct`, etc.

tests/lora/conftest.py

@@ -165,6 +165,11 @@ def tinyllama_lora_files():
     return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")
 
 
+@pytest.fixture(scope="session")
+def phi2_lora_files():
+    return snapshot_download(repo_id="isotr0py/phi-2-test-sql-lora")
+
+
 @pytest.fixture(scope="session")
 def long_context_lora_files_16k_1():
     return snapshot_download(repo_id="SangBinCho/long_context_16k_testing_1")

tests/lora/test_phi.py

@@ -0,0 +1,67 @@
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "microsoft/phi-2"
+
+PROMPT_TEMPLATE = "### Instruct: {sql_prompt}\n\n### Context: {context}\n\n### Output:"  # noqa: E501
+
+
+def do_sample(llm, lora_path: str, lora_id: int) -> str:
+    prompts = [
+        PROMPT_TEMPLATE.format(
+            sql_prompt=
+            "Which catalog publisher has published the most catalogs?",
+            context="CREATE TABLE catalogs (catalog_publisher VARCHAR);"),
+        PROMPT_TEMPLATE.format(
+            sql_prompt=
+            "Which trip started from the station with the largest dock count? Give me the trip id.",  # noqa: E501
+            context=
+            "CREATE TABLE trip (id VARCHAR, start_station_id VARCHAR); CREATE TABLE station (id VARCHAR, dock_count VARCHAR);"  # noqa: E501
+        ),
+        PROMPT_TEMPLATE.format(
+            sql_prompt=
+            "How many marine species are found in the Southern Ocean?",  # noqa: E501
+            context=
+            "CREATE TABLE marine_species (name VARCHAR(50), common_name VARCHAR(50), location VARCHAR(50));"  # noqa: E501
+        ),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0,
+                                          max_tokens=64,
+                                          stop="### End")
+    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_phi2_lora(phi2_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(MODEL_PATH,
+                   max_model_len=1024,
+                   enable_lora=True,
+                   max_loras=2,
+                   enforce_eager=True)
+
+    expected_lora_output = [
+        "SELECT catalog_publisher, COUNT(*) as num_catalogs FROM catalogs GROUP BY catalog_publisher ORDER BY num_catalogs DESC LIMIT 1;",  # noqa: E501
+        "SELECT trip.id FROM trip JOIN station ON trip.start_station_id = station.id WHERE station.dock_count = (SELECT MAX(dock_count) FROM station);",  # noqa: E501
+        "SELECT COUNT(*) FROM marine_species WHERE location = 'Southern Ocean';",  # noqa: E501
+    ]
+
+    output1 = do_sample(llm, phi2_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, phi2_lora_files, lora_id=2)
+    for i in range(len(expected_lora_output)):
+        assert output2[i].startswith(expected_lora_output[i])

vllm/model_executor/models/phi.py

@@ -42,7 +42,7 @@ from torch import nn
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig
+from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -229,11 +229,32 @@ class PhiModel(nn.Module):
 
 
 class PhiForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ]
+    }
 
-    def __init__(self,
+    # LoRA specific attributes
-                 config: PretrainedConfig,
+    supported_lora_modules = [
-                 cache_config: Optional[CacheConfig] = None,
+        "qkv_proj",
-                 quant_config: Optional[QuantizationConfig] = None):
+        "dense",
+        "fc1",
+        "fc2",
+    ]
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ):
+        del lora_config  # Unused.
         super().__init__()
         self.config = config
         self.quant_config = quant_config