[Model] add a bunch of supported lora modules for mixtral (#9008)

Signed-off-by: Prashant Gupta <prashantgupta@us.ibm.com>

Author: prashantgupta24

tests/lora/conftest.py

@@ -173,6 +173,11 @@ def mixtral_lora_files():
     return snapshot_download(repo_id="SangBinCho/mixtral-lora")
 
 
+@pytest.fixture(scope="session")
+def mixtral_lora_files_all_target_modules():
+    return snapshot_download(repo_id="dyang415/mixtral-lora-v0")
+
+
 @pytest.fixture(scope="session")
 def gemma_lora_files():
     return snapshot_download(repo_id="wskwon/gemma-7b-test-lora")

tests/lora/test_mixtral.py

@@ -9,12 +9,9 @@
 MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 
 
-def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
-    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]",  # noqa: E501
-        "[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]",  # noqa: E501
-        "[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]",  # noqa: E501
-    ]
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int,
+              prompts: List[str]) -> List[str]:
+
     sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
     outputs = llm.generate(
         prompts,
@@ -33,22 +30,71 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
 
 @pytest.mark.parametrize("tp_size", [4])
 def test_mixtral_lora(mixtral_lora_files, tp_size):
+    """Original test, the LoRA model has the common target modules, not all"""
     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,
-                   distributed_executor_backend="ray",
-                   tensor_parallel_size=tp_size)
+    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]",  # noqa: E501
+        "[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]",  # noqa: E501
+        "[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]",  # noqa: E501
+    ]
+
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        distributed_executor_backend="ray",
+        tensor_parallel_size=tp_size,
+    )
 
     expected_lora_output = [
         "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",  # noqa: E501
         "give_opinion(name[SpellForce 3], developer[Grimlore Games], release_year[2017], rating[poor])",  # noqa: E501
         "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])",  # noqa: E501
     ]
-    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
+    assert do_sample(llm, mixtral_lora_files, lora_id=1,
+                     prompts=prompts) == expected_lora_output
+    assert do_sample(llm, mixtral_lora_files, lora_id=2,
+                     prompts=prompts) == expected_lora_output
+
+
+@pytest.mark.parametrize("tp_size", [4])
+def test_mixtral_lora_all_target_modules(mixtral_lora_files_all_target_modules,
+                                         tp_size):
+    """This LoRA model has all supported Mixtral target modules"""
+
+    if torch.cuda.device_count() < tp_size:
+        pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
+
+    prompts = [
+        "Question: What happens if you touch the eyes of a blind man?\nChoices:\nB: The man's sight will be restored.\nA: Nothing happens if you touch the eyes of a blind man.\nC: The man will get his eyesight back.\nD: The man will no longer be blind.\nAnswer:",  # noqa: E501
+        "Question: Which will increase the energy of motion of water molecules?\nChoices:\nA: add heat\nB: add ice\nC: remove some of the water\nD: place the water in the freezer\nAnswer:",  # noqa: E501
+        "Since Craig threw aluminum cans in the trash and Benjamin recycled, _ was environmentally irresponsible.\nChoices:\n1: Craig\n2: Benjamin\nAnswer:",  # noqa: E501
+    ]
+
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        distributed_executor_backend="ray",
+        tensor_parallel_size=tp_size,
+        max_lora_rank=32,
+    )
+
+    expected_lora_output = [
+        "A: Nothing happens if you touch the eyes of a blind man.",
+        "A: add heat",
+        "1: Craig",
+    ]
+
+    assert do_sample(llm,
+                     mixtral_lora_files_all_target_modules,
+                     lora_id=1,
+                     prompts=prompts) == expected_lora_output
+    assert do_sample(llm,
+                     mixtral_lora_files_all_target_modules,
+                     lora_id=2,
+                     prompts=prompts) == expected_lora_output

vllm/model_executor/models/mixtral.py

@@ -322,10 +322,8 @@ class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     # LoRA specific attributes
     supported_lora_modules = [
-        "qkv_proj",
-        "o_proj",
-        "embed_tokens",
-        "lm_head",
+        "qkv_proj", "o_proj", "embed_tokens", "lm_head", "w1", "w2", "w3",
+        "gate"
     ]
     embedding_modules = {
         "embed_tokens": "input_embeddings",