[Bugfix] Fix the LoRA weight sharding in ColumnParallelLinearWithLoRA

.buildkite/test-pipeline.yaml

@@ -230,7 +230,7 @@ steps:
   source_file_dependencies:
   - vllm/lora
   - tests/lora
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py
+  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore lora/test_long_context.py lora/test_chatglm3_tp.py lora/test_llama_tp.py
   parallelism: 4
 
 - label: "PyTorch Fullgraph Smoke Test" # 9min
@@ -475,18 +475,23 @@ steps:
   - pytest -v -s distributed/test_pp_cudagraph.py
   - pytest -v -s distributed/test_pipeline_parallel.py
 
-- label: LoRA Long Context (Distributed) # 11min
-  # This test runs llama 13B, so it is required to run on 4 GPUs.
+- label: LoRA TP Test (Distributed)
   num_gpus: 4
   soft_fail: true
   source_file_dependencies:
   - vllm/lora
-  - tests/lora/test_long_context
+  - tests/lora
   commands:
     # FIXIT: find out which code initialize cuda before running the test
     # before the fix, we need to use spawn to test it
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    # This test runs llama 13B, so it is required to run on 4 GPUs.
     - pytest -v -s -x lora/test_long_context.py
+    # There is some Tensor Parallelism related processing logic in LoRA that 
+    # requires multi-GPU testing for validation.
+    - pytest -v -s -x lora/test_chatglm3_tp.py
+    - pytest -v -s -x lora/test_llama_tp.py
+
 
 - label: Weight Loading Multiple GPU Test  # 33min
   working_dir: "/vllm-workspace/tests"

tests/lora/test_chatglm3.py → tests/lora/test_chatglm3_tp.py

@@ -1,12 +1,21 @@
 from typing import List
 
 import vllm
+from tests.utils import fork_new_process_for_each_test
 from vllm.lora.request import LoRARequest
 
+from ..utils import multi_gpu_test
+
 MODEL_PATH = "THUDM/chatglm3-6b"
 
 PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501
 
+EXPECTED_LORA_OUTPUT = [
+    "SELECT count(*) FROM singer",
+    "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",  # noqa: E501
+    "SELECT name ,  country ,  age FROM singer ORDER BY age",
+]
+
 
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
     prompts = [
@@ -20,7 +29,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
             "Show name, country, age for all singers ordered by age from the oldest to the youngest."  # noqa: E501
         ),
     ]
-    print(prompts)
     sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
     outputs = llm.generate(
         prompts,
@@ -37,23 +45,58 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
     return generated_texts
 
 
+@fork_new_process_for_each_test
 def test_chatglm3_lora(chatglm3_lora_files):
     llm = vllm.LLM(MODEL_PATH,
                    max_model_len=1024,
                    enable_lora=True,
                    max_loras=4,
                    max_lora_rank=64,
+                   tensor_parallel_size=1,
                    trust_remote_code=True)
 
-    expected_lora_output = [
-        "SELECT count(*) FROM singer",
-        "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",  # noqa: E501
-        "SELECT name ,  country ,  age FROM singer ORDER BY age",
-    ]
+    output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, chatglm3_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
 
+@multi_gpu_test(num_gpus=4)
+@fork_new_process_for_each_test
+def test_chatglm3_lora_tp4(chatglm3_lora_files):
+    llm = vllm.LLM(MODEL_PATH,
+                   max_model_len=1024,
+                   enable_lora=True,
+                   max_loras=4,
+                   max_lora_rank=64,
+                   tensor_parallel_size=4,
+                   trust_remote_code=True,
+                   fully_sharded_loras=False)
+
+    output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, chatglm3_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
+
+@multi_gpu_test(num_gpus=4)
+@fork_new_process_for_each_test
+def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files):
+    llm = vllm.LLM(MODEL_PATH,
+                   max_model_len=1024,
+                   enable_lora=True,
+                   max_loras=4,
+                   max_lora_rank=64,
+                   tensor_parallel_size=4,
+                   trust_remote_code=True,
+                   fully_sharded_loras=True)
     output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
-    for i in range(len(expected_lora_output)):
-        assert output1[i] == expected_lora_output[i]
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
     output2 = do_sample(llm, chatglm3_lora_files, lora_id=2)
-    for i in range(len(expected_lora_output)):
-        assert output2[i] == expected_lora_output[i]
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]