[Model] PP support for embedding models and update docs (vllm-project#9090)

Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
Signed-off-by: LeiWang1999 <leiwang1999@outlook.com>

Author: 2 people

docs/source/models/supported_models.rst

@@ -7,10 +7,12 @@ vLLM supports a variety of generative Transformer models in `HuggingFace Transfo
 The following is the list of model architectures that are currently supported by vLLM.
 Alongside each architecture, we include some popular models that use it.
 
-----
+Text-only Language Models
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Text Generation
+---------------
 
-Decoder-only Language Models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. list-table::
   :widths: 25 25 50 5 5
   :header-rows: 1
@@ -40,6 +42,11 @@ Decoder-only Language Models
     - :code:`bigscience/bloom`, :code:`bigscience/bloomz`, etc.
     -
     - ✅︎
+  * - :code:`BartForConditionalGeneration`
+    - BART
+    - :code:`facebook/bart-base`, :code:`facebook/bart-large-cnn`, etc.
+    - 
+    - 
   * - :code:`ChatGLMModel`
     - ChatGLM
     - :code:`THUDM/chatglm2-6b`, :code:`THUDM/chatglm3-6b`, etc.
@@ -259,11 +266,55 @@ Decoder-only Language Models
 .. note::
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
 
-.. _supported_vlms:
+Text Embedding
+--------------
+
+.. list-table::
+  :widths: 25 25 50 5 5
+  :header-rows: 1
+
+  * - Architecture
+    - Models
+    - Example HuggingFace Models
+    - :ref:`LoRA <lora>`
+    - :ref:`PP <distributed_serving>`
+  * - :code:`Gemma2Model`
+    - Gemma2-based
+    - :code:`BAAI/bge-multilingual-gemma2`, etc.
+    - 
+    - ✅︎
+  * - :code:`MistralModel`
+    - Mistral-based
+    - :code:`intfloat/e5-mistral-7b-instruct`, etc.
+    - 
+    - ✅︎
+
+Reward Modeling
+---------------
+
+.. list-table::
+  :widths: 25 25 50 5 5
+  :header-rows: 1
+
+  * - Architecture
+    - Models
+    - Example HuggingFace Models
+    - :ref:`LoRA <lora>`
+    - :ref:`PP <distributed_serving>`
+  * - :code:`Qwen2ForRewardModel`
+    - Qwen2-based
+    - :code:`Qwen/Qwen2.5-Math-RM-72B`, etc.
+    - 
+    - ✅︎
+
+.. note::
+    As an interim measure, these models are supported via Embeddings API. See `this RFC <https://github.com/vllm-project/vllm/issues/8967>`_ for upcoming changes.
 
 Multimodal Language Models
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+.. _supported_vlms:
+
 .. list-table::
   :widths: 25 25 25 25 5 5
   :header-rows: 1
@@ -378,6 +429,7 @@ Multimodal Language Models
   For :code:`openbmb/MiniCPM-V-2`, the official repo doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
   For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
 
+----
 
 If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
 Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` and :ref:`Enabling Multimodal Inputs <enabling_multimodal_inputs>` 

docs/source/models/vlm.rst

@@ -6,10 +6,9 @@ Using VLMs
 vLLM provides experimental support for Vision Language Models (VLMs). See the :ref:`list of supported VLMs here <supported_vlms>`.
 This document shows you how to run and serve these models using vLLM.
 
-.. important::
-    We are actively iterating on VLM support. Expect breaking changes to VLM usage and development in upcoming releases without prior deprecation.
-
-    We are continuously improving user & developer experience for VLMs. Please `open an issue on GitHub <https://github.com/vllm-project/vllm/issues/new/choose>`_ if you have any feedback or feature requests.
+.. note::
+    We are actively iterating on VLM support. See `this RFC <https://github.com/vllm-project/vllm/issues/4194>`_ for upcoming changes,
+    and `open an issue on GitHub <https://github.com/vllm-project/vllm/issues/new/choose>`_ if you have any feedback or feature requests.
 
 Offline Inference
 -----------------

tests/distributed/test_pipeline_parallel.py

@@ -7,7 +7,7 @@
 """
 import os
 from dataclasses import dataclass
-from typing import List, NamedTuple, Optional
+from typing import List, Literal, NamedTuple, Optional
 
 import pytest
 
@@ -97,22 +97,23 @@ def iter_params(self, model_name: str):
                        self.trust_remote_code, self.tokenizer_mode)
 
 
+# NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU
+# The values displayed here are only a rough indicator of the size of the model
+
 # yapf: disable
 GENERATION_MODEL_SETTINGS = {
     # [DETAILED TESTS]
     "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(),
     # [FAST TESTS]
     # Uses Llama
     # "BAAI/AquilaChat-7B": PPTestSettings.fast(),
-    # TODO: Test on larger GPU
-    # "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
+    "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(tp_base=8, trust_remote_code=True),  # noqa: E501
     "baichuan-inc/Baichuan-7B": PPTestSettings.fast(trust_remote_code=True),
     "baichuan-inc/Baichuan2-13B-Chat": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
     "bigscience/bloomz-1b1": PPTestSettings.fast(),
     "THUDM/chatglm3-6b": PPTestSettings.fast(trust_remote_code=True),
     "CohereForAI/c4ai-command-r-v01": PPTestSettings.fast(tp_base=2, trust_remote_code=True),  # noqa: E501
-    # TODO: Test on larger GPU
-    # "databricks/dbrx-instruct": PPTestSettings.fast(),
+    "databricks/dbrx-instruct": PPTestSettings.fast(tp_base=8),
     "Deci/DeciLM-7B-instruct": PPTestSettings.fast(trust_remote_code=True),
     "deepseek-ai/deepseek-llm-7b-chat": PPTestSettings.fast(),
     "deepseek-ai/DeepSeek-V2-Lite-Chat": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
@@ -161,8 +162,9 @@ def iter_params(self, model_name: str):
 
 EMBEDDING_MODEL_SETTINGS = {  # type: ignore[var-annotated]
     # [FAST TESTS]
-    # Uses Llama
-    # "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(),
+    "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(),
+    "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(),
+    "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(tp_base=4, trust_remote_code=True),  # noqa: E501
 }
 
 MULTIMODAL_MODEL_SETTINGS = {
@@ -192,40 +194,35 @@ def iter_params(self, model_name: str):
 }
 # yapf: enable
 
-MODEL_SETTINGS = {
-    **GENERATION_MODEL_SETTINGS,
-    **EMBEDDING_MODEL_SETTINGS,
-    **MULTIMODAL_MODEL_SETTINGS,
-}
-
-# You can update this on your local machine to run specific tests
+# NOTE: You can update this on your local machine to run specific tests
 TEST_MODELS = [
+    # [LANGUAGE GENERATION]
     "meta-llama/Meta-Llama-3-8B",
-    "facebook/chameleon-7b",
+    "ibm/PowerLM-3b",
+    # [LANGUAGE EMBEDDING]
+    "intfloat/e5-mistral-7b-instruct",
+    "BAAI/bge-multilingual-gemma2",
+    # [MULTIMODAL GENERATION]
     "OpenGVLab/InternVL2-1B",
     "microsoft/Phi-3-vision-128k-instruct",
-    "mistralai/Pixtral-12B-2409",
     "fixie-ai/ultravox-v0_3",
 ]
 
 
-@pytest.mark.parametrize(
-    ("model_name", "parallel_setup", "distributed_backend",
-     "trust_remote_code", "tokenizer_mode"),
-    [
-        params for model_name, settings in MODEL_SETTINGS.items()
-        for params in settings.iter_params(model_name)
-        if model_name in TEST_MODELS
-    ],
-)
-@fork_new_process_for_each_test
-def test_compare_tp(model_name: str, parallel_setup: ParallelSetup,
-                    distributed_backend: str, trust_remote_code: bool,
-                    tokenizer_mode: Optional[str], num_gpus_available):
+def _compare_tp(
+    model_name: str,
+    parallel_setup: ParallelSetup,
+    distributed_backend: str,
+    trust_remote_code: bool,
+    tokenizer_mode: Optional[str],
+    num_gpus_available: int,
+    *,
+    method: Literal["generate", "encode"] = "encode",
+):
     tp_size, pp_size, eager_mode, chunked_prefill = parallel_setup
 
-    if num_gpus_available < tp_size:
-        pytest.skip(f"Need at least {tp_size} GPUs to run the test")
+    if num_gpus_available < tp_size * pp_size:
+        pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
     if VLLM_MULTI_NODE and distributed_backend == "mp":
         pytest.skip("Skipping multi-node pipeline parallel test for "
                     "multiprocessing distributed backend")
@@ -286,10 +283,95 @@ def test_compare_tp(model_name: str, parallel_setup: ParallelSetup,
     ]
 
     try:
-        compare_two_settings(model_name, pp_args, tp_args, pp_env)
+        compare_two_settings(model_name,
+                             pp_args,
+                             tp_args,
+                             pp_env,
+                             method=method)
     except Exception:
         if pp_env is None:
             raise
         else:
             # Ray ADAG tests are flaky, so we don't want to fail the test
             logger.exception("Ray ADAG tests failed")
+
+
+@pytest.mark.parametrize(
+    ("model_name", "parallel_setup", "distributed_backend",
+     "trust_remote_code", "tokenizer_mode"),
+    [
+        params for model_name, settings in GENERATION_MODEL_SETTINGS.items()
+        for params in settings.iter_params(model_name)
+        if model_name in TEST_MODELS
+    ],
+)
+@fork_new_process_for_each_test
+def test_tp_language_generation(
+    model_name: str,
+    parallel_setup: ParallelSetup,
+    distributed_backend: str,
+    trust_remote_code: bool,
+    tokenizer_mode: Optional[str],
+    num_gpus_available,
+):
+    _compare_tp(model_name,
+                parallel_setup,
+                distributed_backend,
+                trust_remote_code,
+                tokenizer_mode,
+                num_gpus_available,
+                method="generate")
+
+
+@pytest.mark.parametrize(
+    ("model_name", "parallel_setup", "distributed_backend",
+     "trust_remote_code", "tokenizer_mode"),
+    [
+        params for model_name, settings in EMBEDDING_MODEL_SETTINGS.items()
+        for params in settings.iter_params(model_name)
+        if model_name in TEST_MODELS
+    ],
+)
+@fork_new_process_for_each_test
+def test_tp_language_embedding(
+    model_name: str,
+    parallel_setup: ParallelSetup,
+    distributed_backend: str,
+    trust_remote_code: bool,
+    tokenizer_mode: Optional[str],
+    num_gpus_available,
+):
+    _compare_tp(model_name,
+                parallel_setup,
+                distributed_backend,
+                trust_remote_code,
+                tokenizer_mode,
+                num_gpus_available,
+                method="encode")
+
+
+@pytest.mark.parametrize(
+    ("model_name", "parallel_setup", "distributed_backend",
+     "trust_remote_code", "tokenizer_mode"),
+    [
+        params for model_name, settings in MULTIMODAL_MODEL_SETTINGS.items()
+        for params in settings.iter_params(model_name)
+        if model_name in TEST_MODELS
+    ],
+)
+@fork_new_process_for_each_test
+def test_tp_multimodal_generation(
+    model_name: str,
+    parallel_setup: ParallelSetup,
+    distributed_backend: str,
+    trust_remote_code: bool,
+    tokenizer_mode: Optional[str],
+    num_gpus_available,
+):
+    _compare_tp(model_name,
+                parallel_setup,
+                distributed_backend,
+                trust_remote_code,
+                tokenizer_mode,
+                num_gpus_available,
+                method="generate")