Add LoRA support for Mixtral

tests/lora/conftest.py

@@ -121,6 +121,11 @@ def sql_lora_files():
     return snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
 
 
+@pytest.fixture(scope="session")
+def mixtral_lora_files():
+    return snapshot_download(repo_id="terrysun/mixtral-lora-adapter")
+
+
 @pytest.fixture
 def llama_2_7b_engine_extra_embeddings() -> nn.Module:
     cleanup()

tests/lora/test_lora_manager.py

@@ -11,25 +11,35 @@
                               RowParallelLinearWithLoRA,
                               MergedColumnParallelLinearWithLoRA)
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
-from vllm.lora.models import (EMBEDDING_MODULES, LoRAModel, LoRAModelManager,
+from vllm.lora.models import (LoRAModel, LoRAModelManager,
                               LRUCacheLoRAModelManager, LoRAMapping)
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager,
                                       WorkerLoRAManager)
 from vllm.model_executor.layers.linear import RowParallelLinear
 
+EMBEDDING_MODULES = {
+    "embed_tokens": "input_embeddings",
+    "lm_head": "output_embeddings",
+}
+
+EMBEDDING_PADDING_MODULES = ["lm_head"]
+
 
 def test_from_lora_tensors(sql_lora_files):
     tensors = load_file(
         os.path.join(sql_lora_files, "adapter_model.safetensors"))
     new_embeddings = load_file(
         os.path.join(sql_lora_files, "new_embeddings.safetensors"))
-    lora_model = LoRAModel.from_lora_tensors(1,
-                                             8,
-                                             16,
-                                             tensors,
-                                             "cuda",
-                                             embeddings=new_embeddings)
+    lora_model = LoRAModel.from_lora_tensors(
+        1,
+        8,
+        16,
+        tensors,
+        "cuda",
+        embeddings=new_embeddings,
+        embedding_modules=EMBEDDING_MODULES,
+        embedding_padding_modules=EMBEDDING_PADDING_MODULES)
     for module_name, lora in lora_model.loras.items():
         assert lora.module_name == module_name
         assert lora.rank == 8
@@ -90,14 +100,11 @@ def create_packed_lora(
 
 def test_replace_submodules(dist_init, dummy_model):
     model = dummy_model
-    manager = LoRAModelManager(model,
-                               1,
-                               1,
-                               1,
-                               LoRAConfig(max_lora_rank=8,
-                                          max_cpu_loras=8,
-                                          max_loras=8),
-                               lora_target_modules=["dense1", "layer1.dense2"])
+    model.supported_lora_modules = ["dense1", "layer1.dense2"]
+    model.packed_modules_mapping = {}
+    manager = LoRAModelManager(
+        model, 1, 1, 1,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=8, max_loras=8))
     model = manager.model
 
     assert isinstance(model.get_submodule("dense1"),
@@ -111,16 +118,14 @@ def test_replace_submodules(dist_init, dummy_model):
 
 def test_lora_model_manager(dist_init, dummy_model):
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     manager = LoRAModelManager(
-        model,
-        2,
-        2,
-        2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2),
-        lora_target_modules=["dense1", "dense2", "lm_head"])
+        model, 2, 2, 2,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2))
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_lora(model_lora1)
     assert manager.activate_lora(1)
@@ -159,16 +164,14 @@ def test_lora_model_manager(dist_init, dummy_model):
 
 def test_lora_lru_cache_model_manager(dist_init, dummy_model):
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     manager = LRUCacheLoRAModelManager(
-        model,
-        2,
-        2,
-        2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2),
-        lora_target_modules=["dense1", "dense2", "lm_head"])
+        model, 2, 2, 2,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2))
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_lora(model_lora1)
     assert manager.activate_lora(1)
@@ -212,14 +215,15 @@ def test_lru_lora_model_manager(dist_init, dummy_model):
     # This tests just the LRU cache functionality, everything else is
     # tested in test_lora_model_manager
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     model_lora4 = create_lora(4, model, ["dense1", "dense2", "lm_head"])
     manager = LRUCacheLoRAModelManager(
         model, 2, 2, 2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2),
-        ["dense1", "dense2", "lm_head"])
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2))
 
     assert all(x is None for x in manager.lora_index_to_id)
 
@@ -289,8 +293,9 @@ def test_lru_cache_worker_lora_manager(llama_2_7b_model_extra_embeddings,
                                        sql_lora_files):
     lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
     worker_lora_manager = LRUCacheWorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.config.vocab_size, lora_config,
-        torch.device("cuda"))
+        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
+        lora_config.lora_extra_vocab_size, lora_config, torch.device("cuda"),
+        EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
     worker_lora_manager.create_lora_manager(llama_2_7b_model_extra_embeddings)
 
     mapping = LoRAMapping([], [])
@@ -362,8 +367,9 @@ def test_worker_lora_manager(llama_2_7b_model_extra_embeddings,
     # Should remove every LoRA not specified in the request.
     lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
     worker_lora_manager = WorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.config.vocab_size, lora_config,
-        torch.device("cuda"))
+        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
+        lora_config.lora_extra_vocab_size, lora_config, torch.device("cuda"),
+        EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
     worker_lora_manager.create_lora_manager(llama_2_7b_model_extra_embeddings)
 
     mapping = LoRAMapping([], [])
@@ -428,6 +434,13 @@ def test_worker_lora_manager(llama_2_7b_model_extra_embeddings,
 
 def test_packed_loras(dist_init, dummy_model_gate_up):
     model = dummy_model_gate_up
+    model.supported_lora_modules = ["gate_up_proj"]
+    model.packed_modules_mapping = {
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
     model_lora = create_packed_lora(
         1,
         model,
@@ -443,8 +456,7 @@ def test_packed_loras(dist_init, dummy_model_gate_up):
 
     manager = LoRAModelManager(
         model, 2, 2, 2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2),
-        ["gate_up_proj"])
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2))
     model = manager.model
 
     assert isinstance(model.get_submodule("gate_up_proj"),

tests/lora/test_mixtral.py

@@ -0,0 +1,53 @@
+import pytest
+import torch
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+
+def do_sample(llm, lora_path: str, lora_id: int):
+    prompts = [
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nSpellForce 3 is a pretty bad game. The developer Grimlore Games is clearly a bunch of no-talent hacks, and 2017 was a terrible year for games anyway. [/user] [assistant]",
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nI wanted to like Grimlore Games' 2017 entry, but in SpellForce 3 they just didn't get anything right. [/user] [assistant]",
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nBioShock is a good role-playing, action-adventure, shooter that released for PlayStation, Xbox, and PC in 2007. It is available on Steam, and it has a Mac release but not a Linux release. [/user] [assistant]",
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
+    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
+
+
+@pytest.mark.parametrize("tp_size", [4])
+def test_mixtral_lora(mixtral_lora_files, tp_size):
+    if torch.cuda.device_count() < tp_size:
+        pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
+
+    llm = vllm.LLM(MODEL_PATH,
+                   enable_lora=True,
+                   max_num_seqs=16,
+                   max_loras=4,
+                   tensor_parallel_size=tp_size,
+                   worker_use_ray=True)
+
+    expected_lora_output = [
+        "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",
+        "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",
+        "inform(name[BioShock], release_year[2007], rating[good], genres[action-adventure, role-playing, shooter], platforms[PlayStation, Xbox, PC], available_on_steam[yes], has_linux_release[no], has_mac_release[yes])",
+    ]
+
+    assert do_sample(llm, mixtral_lora_files,
+                     lora_id=1) == expected_lora_output
+    assert do_sample(llm, mixtral_lora_files,
+                     lora_id=2) == expected_lora_output