Merge branch 'habana_main' into private/jmaksymczuk/fake_hpu

HabanaAI · Sep 2, 2024 · 8c40e98 · 8c40e98
2 parents 33d2f54 + 2897805
commit 8c40e98
Show file tree

Hide file tree

Showing 34 changed files with 2,063 additions and 254 deletions.
diff --git a/README_GAUDI.md b/README_GAUDI.md
diff --git a/docs/source/getting_started/gaudi-installation.rst b/docs/source/getting_started/gaudi-installation.rst
diff --git a/docs/source/getting_started/quickstart.rst b/docs/source/getting_started/quickstart.rst
@@ -9,7 +9,7 @@ This guide shows how to use vLLM to:
 * build an API server for a large language model;
 * start an OpenAI-compatible API server.
 
-Be sure to complete the :ref:`installation instructions <installation>` before continuing with this guide.
+Be sure to complete the `Gaudi installation instructions <https://github.com/HabanaAI/vllm-fork/blob/habana_main/docs/source/getting_started/gaudi-installation.rst#run-docker-image>`_ before continuing with this guide.
 
 .. note::
 

diff --git a/examples/lora_inference_hpu.py b/examples/lora_inference_hpu.py
@@ -0,0 +1,47 @@
+from huggingface_hub import snapshot_download
+
+from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
+
+sql_lora_path = snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
+
+llm = LLM(model="meta-llama/Llama-2-7b-hf",
+          enable_lora=True,
+          max_num_seqs=2,
+          dtype='bfloat16')
+
+sampling_params = SamplingParams(temperature=0,
+                                 max_tokens=1024,
+                                 stop=["[/assistant]"])
+
+prompts = [
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]",  # noqa: E501
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? [/user] [assistant]",  # noqa: E501
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_95 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a low tone mora with a gloss of /˩okiru/ [òkìɽɯ́]? [/user] [assistant]",  # noqa: E501
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]",  # noqa: E501
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? [/user] [assistant]",  # noqa: E501
+    "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]"  # noqa: E501
+]
+
+expected_output = [
+    "  SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ",  # noqa: E501
+    "  SELECT nationality FROM table_name_11 WHERE elector = 'Anchero Pantaleone' ",  # noqa: E501
+    "  SELECT one_mora FROM table_name_95 WHERE gloss = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] AND accented_mora = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] ",  # noqa: E501
+    "  SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ",  # noqa: E501
+    "  SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ",  # noqa: E501
+    "  SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' "  # noqa: E501
+]
+
+outputs = llm.generate(prompts,
+                       sampling_params,
+                       lora_request=LoRARequest("sql_adapter", 1,
+                                                sql_lora_path))
+
+for i, output in enumerate(outputs):
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    match = expected_output[i] == generated_text
+    if not match:
+        print(
+            f"Comparison failed for request_id::{i}\n\t[PROMPT]{prompt!r}\n\t[GENERATED]{generated_text!r}\n\t[EXPECTED]{expected_output[i]!r}"  # noqa: E501
+        )
diff --git a/tests/conftest.py b/tests/conftest.py
@@ -590,9 +590,17 @@ def caplog_vllm(temporary_enable_log_propagate, caplog):
     yield caplog
 
 
+def is_hpu():
+    from importlib import util
+    return util.find_spec('habana_frameworks') is not None
+
+
 @pytest.fixture(scope="session")
 def num_gpus_available():
     """Get number of GPUs without initializing the CUDA context
     in current process."""
 
+    if is_hpu():
+        return torch.hpu.device_count()
+
     return cuda_device_count_stateless()
diff --git a/tests/lora/conftest.py b/tests/lora/conftest.py
@@ -48,13 +48,19 @@ class ContextInfo(TypedDict):
 }]
 
 
+def is_hpu():
+    from importlib import util
+    return util.find_spec('habana_frameworks') is not None
+
+
 def cleanup():
     destroy_model_parallel()
     destroy_distributed_environment()
     with contextlib.suppress(AssertionError):
         torch.distributed.destroy_process_group()
     gc.collect()
-    torch.cuda.empty_cache()
+    if not is_hpu():
+        torch.cuda.empty_cache()
     ray.shutdown()
 
 

diff --git a/tests/lora/test_llama_hpu.py b/tests/lora/test_llama_hpu.py
@@ -0,0 +1,100 @@
+from multiprocessing import Process
+from typing import List
+
+from conftest import cleanup
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "meta-llama/Llama-2-7b-hf"
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    prompts = [
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_95 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a low tone mora with a gloss of /˩okiru/ [òkìɽɯ́]? [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]"  # noqa: E501
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0,
+                                          max_tokens=256,
+                                          stop=["[/assistant]"])
+    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: List[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+def _test_llama_lora(sql_lora_files, tp_size):
+    llm = vllm.LLM(MODEL_PATH,
+                   enable_lora=True,
+                   max_num_seqs=16,
+                   max_loras=4,
+                   dtype='float32',
+                   tensor_parallel_size=tp_size)
+
+    expected_no_lora_output = [
+        "\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_75 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_76 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_77 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_78 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user]",  # noqa: E501
+        " Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? ",  # noqa: E501
+        "\n\n answer: 1\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_96 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_97 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_98 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one m",  # noqa: E501
+        " Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. ",  # noqa: E501
+        " Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? ",  # noqa: E501
+        "\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE",  # noqa: E501
+    ]
+    expected_lora_output = [
+        "  SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ",  # noqa: E501
+        "  SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ",  # noqa: E501
+        "  SELECT one_mora FROM table_name_95 WHERE gloss = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] AND accented_mora = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] ",  # noqa: E501
+        "  SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ",  # noqa: E501
+        "  SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ",  # noqa: E501
+        "  SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' "  # noqa: E501
+    ]
+
+    print("lora adapter created")
+    assert do_sample(llm, sql_lora_files, lora_id=0) == expected_no_lora_output
+
+    print("lora 1")
+    assert do_sample(llm, sql_lora_files, lora_id=1) == expected_lora_output
+
+    print("no lora")
+    assert do_sample(llm, sql_lora_files, lora_id=0) == expected_no_lora_output
+
+    print("lora 2")
+    assert do_sample(llm, sql_lora_files, lora_id=2) == expected_lora_output
+
+    print("removing lora")
+    cleanup()
+
+
+def test_llama_lora_1x(sql_lora_files):
+    p = Process(target=_test_llama_lora, args=(sql_lora_files, 1))
+    p.start()
+    p.join()
+    assert p.exitcode == 0
+
+
+def test_llama_lora_2x(sql_lora_files):
+    # Work-around to resolve stalling issue in multi-card scenario
+    p = Process(target=_test_llama_lora, args=(sql_lora_files, 2))
+    p.start()
+    p.join()
+    assert p.exitcode == 0
+
+
+def test_llama_lora_4x(sql_lora_files):
+    # Work-around to resolve stalling issue in multi-card scenario
+    p = Process(target=_test_llama_lora, args=(sql_lora_files, 4))
+    p.start()
+    p.join()
+    assert p.exitcode == 0