Merge branch 'main' of https://github.com/vllm-project/vllm into svij…

…-ultravox-lora-dec-16

benchmarks/backend_request_func.py

@@ -22,6 +22,7 @@ class RequestFuncInput:
     prompt_len: int
     output_len: int
     model: str
+    model_name: Optional[str] = None
     best_of: int = 1
     logprobs: Optional[int] = None
     extra_body: Optional[dict] = None
@@ -78,7 +79,7 @@ async def async_request_tgi(
                             continue
                         chunk_bytes = chunk_bytes.decode("utf-8")
 
-                        #NOTE: Sometimes TGI returns a ping response without
+                        # NOTE: Sometimes TGI returns a ping response without
                         # any data, we should skip it.
                         if chunk_bytes.startswith(":"):
                             continue
@@ -235,7 +236,8 @@ async def async_request_openai_completions(
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
         payload = {
-            "model": request_func_input.model,
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
             "prompt": request_func_input.prompt,
             "temperature": 0.0,
             "best_of": request_func_input.best_of,
@@ -328,7 +330,8 @@ async def async_request_openai_chat_completions(
         if request_func_input.multi_modal_content:
             content.append(request_func_input.multi_modal_content)
         payload = {
-            "model": request_func_input.model,
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
             "messages": [
                 {
                     "role": "user",

benchmarks/benchmark_serving.py

@@ -525,6 +525,7 @@ async def benchmark(
     api_url: str,
     base_url: str,
     model_id: str,
+    model_name: str,
     tokenizer: PreTrainedTokenizerBase,
     input_requests: List[Tuple[str, int, int]],
     logprobs: Optional[int],
@@ -553,6 +554,7 @@ async def benchmark(
             "Multi-modal content is only supported on 'openai-chat' backend.")
     test_input = RequestFuncInput(
         model=model_id,
+        model_name=model_name,
         prompt=test_prompt,
         api_url=api_url,
         prompt_len=test_prompt_len,
@@ -573,6 +575,7 @@ async def benchmark(
     if profile:
         print("Starting profiler...")
         profile_input = RequestFuncInput(model=model_id,
+                                         model_name=model_name,
                                          prompt=test_prompt,
                                          api_url=base_url + "/start_profile",
                                          prompt_len=test_prompt_len,
@@ -616,6 +619,7 @@ async def limited_request_func(request_func_input, pbar):
     async for request in get_request(input_requests, request_rate, burstiness):
         prompt, prompt_len, output_len, mm_content = request
         request_func_input = RequestFuncInput(model=model_id,
+                                              model_name=model_name,
                                               prompt=prompt,
                                               api_url=api_url,
                                               prompt_len=prompt_len,
@@ -780,6 +784,7 @@ def main(args: argparse.Namespace):
 
     backend = args.backend
     model_id = args.model
+    model_name = args.served_model_name
     tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
     tokenizer_mode = args.tokenizer_mode
 
@@ -877,6 +882,7 @@ def main(args: argparse.Namespace):
             api_url=api_url,
             base_url=base_url,
             model_id=model_id,
+            model_name=model_name,
             tokenizer=tokenizer,
             input_requests=input_requests,
             logprobs=args.logprobs,
@@ -1222,5 +1228,12 @@ def main(args: argparse.Namespace):
         'always use the slow tokenizer. \n* '
         '"mistral" will always use the `mistral_common` tokenizer.')
 
+    parser.add_argument("--served-model-name",
+                        type=str,
+                        default=None,
+                        help="The model name used in the API. "
+                        "If not specified, the model name will be the "
+                        "same as the ``--model`` argument. ")
+
     args = parser.parse_args()
     main(args)

docs/source/models/supported_models.md

@@ -754,7 +754,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc.
   - ✅︎
   - ✅︎
-  -
+  - ✅︎
 * - `UltravoxModel`
   - Ultravox
   - T + A<sup>E+</sup>

tests/models/decoder_only/vision_language/test_qwen2_vl.py

@@ -105,7 +105,7 @@ def batch_make_image_embeddings(
     pixel_values = preprocess_result["pixel_values"]
     image_grid_thw = preprocess_result["image_grid_thw"]
 
-    # pixel values to embeddinds & grid_thws
+    # pixel values to embeddings & grid_thws
     with torch.no_grad():
         visual = llm.llm_engine.model_executor.driver_worker. \
             model_runner.model.visual
@@ -124,11 +124,10 @@ def batch_make_image_embeddings(
     for image_batch in image_batches_:
         cur_batch_image_count = len(image_batch)
         merge_size = image_processor.merge_size
-        cur_batch_embed_len = sum([
-            grid_thw.prod() // merge_size // merge_size
+        cur_batch_embed_len = sum(
+            grid_thw.prod(-1) // merge_size // merge_size
             for grid_thw in image_grid_thw[image_counter:image_counter +
-                                           cur_batch_image_count]
-        ])
+                                           cur_batch_image_count])
 
         result.append({
             "image_embeds":
@@ -187,7 +186,7 @@ def batch_make_video_embeddings(
     pixel_values = preprocess_result["pixel_values_videos"]
     video_grid_thw = preprocess_result["video_grid_thw"]
 
-    # pixel values to embeddinds & grid_thws
+    # pixel values to embeddings & grid_thws
     with torch.no_grad():
         visual = llm.llm_engine.model_executor.driver_worker.\
             model_runner.model.visual
@@ -206,11 +205,10 @@ def batch_make_video_embeddings(
     for video_batch in video_batches_:
         cur_batch_video_count = len(video_batch)
         merge_size = image_processor.merge_size
-        cur_batch_embed_len = sum([
-            grid_thw.prod() // merge_size // merge_size
+        cur_batch_embed_len = sum(
+            grid_thw.prod(-1) // merge_size // merge_size
             for grid_thw in video_grid_thw[video_counter:video_counter +
-                                           cur_batch_video_count]
-        ])
+                                           cur_batch_video_count])
 
         result.append({
             "video_embeds":

tests/models/registry.py

@@ -69,6 +69,7 @@ class _HfExamplesInfo:
     "DeepseekV3ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3",  # noqa: E501
                                          trust_remote_code=True),
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
+    "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),

tests/weight_loading/models.txt

@@ -30,4 +30,5 @@ marlin, nm-testing/zephyr-beta-7b-marlin-g128, main
 marlin, robertgshaw2/zephyr-7b-beta-channelwise-marlin, main
 qqq, HandH1998/QQQ-Llama-3-8b-g128, main
 qqq, HandH1998/QQQ-Llama-3-8b, main
-hqq, nm-testing/Llama-3.2-1B-Instruct-HQQ, main
+hqq, nm-testing/Llama-3.2-1B-Instruct-HQQ, main
+None, mgleize/fairseq2-dummy-Llama-3.2-1B, main

tests/weight_loading/test_weight_loading.py

@@ -20,12 +20,13 @@ def test_weight_loading(vllm_runner):
     """
     Test parameter weight loading with tp>1.
     """
-    with vllm_runner(model_name=MODEL_NAME,
-                     revision=REVISION,
-                     dtype=torch.half if QUANTIZATION == "gptq" else "auto",
-                     quantization=QUANTIZATION,
-                     max_model_len=MAX_MODEL_LEN,
-                     tensor_parallel_size=2) as model:
+    with vllm_runner(
+            model_name=MODEL_NAME,
+            revision=REVISION,
+            dtype=torch.half if QUANTIZATION == "gptq" else "auto",
+            quantization=None if QUANTIZATION == "None" else QUANTIZATION,
+            max_model_len=MAX_MODEL_LEN,
+            tensor_parallel_size=2) as model:
 
         output = model.generate_greedy("Hello world!", max_tokens=20)
         print(output)

vllm/compilation/backends.py

@@ -25,23 +25,30 @@
 logger = init_logger(__name__)
 
 
+@dataclasses.dataclass
+class InductorArtifact:
+    hash_str: str = ""
+    file_path: str = ""
+
+
 class InductorHashCache:
     """
     Disk format: a Python list of tuples, each tuple is
-    (runtime_shape, graph_index, hash_str)
+    (runtime_shape, graph_index, hash_str, file_path)
     We use list of tuple for readability.
 
     In-memory format: a defaultdict of dict, where the key is
     runtime_shape, and the value is a dict of graph_index to hash_str.
 
-    The data is essentially `Dict[Optional[int], Dict[int, str]]`,
+    The data is essentially `Dict[Optional[int], Dict[int, InductorArtifact]]`,
     we don't use json here because json doesn't support int as key.
 
     TODO: better off-the-shelf solution to serialize the data?
     """
 
     def __init__(self, cache_dir: str, disabled: bool = False):
-        self.cache: defaultdict = defaultdict(dict)
+        self.cache: Dict[Optional[int],
+                         Dict[int, InductorArtifact]] = defaultdict(dict)
         self.disabled = disabled
         self.cache_dir = cache_dir
         self.cache_file_path = os.path.join(cache_dir,
@@ -66,14 +73,25 @@ def deserialize(self, data: str):
         # because it is a safe way to parse Python literals.
         # do not use eval(), it is unsafe.
         list_data = ast.literal_eval(data)
-        for runtime_shape, graph_index, hash_str in list_data:
-            self.cache[runtime_shape][graph_index] = hash_str
+        for item in list_data:
+            runtime_shape = item[0]
+            graph_index = item[1]
+            hash_str = item[2]
+            # for compatibility of old version,
+            # where we don't have file_path.
+            # NOTE: after running the new code, the file_path
+            # will be updated.
+            file_path = "" if len(item) == 3 else item[3]
+            self.cache[runtime_shape][graph_index] = InductorArtifact(
+                hash_str=hash_str, file_path=file_path)
 
     def serialize(self) -> str:
         data = []
-        for runtime_shape, graph_index_to_hash_str in self.cache.items():
-            for graph_index, hash_str in graph_index_to_hash_str.items():
-                data.append((runtime_shape, graph_index, hash_str))
+        for runtime_shape, value in self.cache.items():
+            for graph_index, inductor_artifact in value.items():
+                data.append(
+                    (runtime_shape, graph_index, inductor_artifact.hash_str,
+                     inductor_artifact.file_path))
         printer = pprint.PrettyPrinter(indent=4)
         return printer.pformat(data)
 
@@ -90,13 +108,14 @@ def __contains__(self, key: Tuple[Optional[int], int]) -> bool:
         return runtime_shape in self.cache and graph_index in self.cache[
             runtime_shape]
 
-    def __getitem__(self, key: Tuple[Optional[int], int]) -> str:
+    def __getitem__(self, key: Tuple[Optional[int], int]) -> InductorArtifact:
         if self.disabled:
             raise KeyError("cannot read from disabled cache")
         runtime_shape, graph_index = key
         return self.cache[runtime_shape][graph_index]
 
-    def __setitem__(self, key: Tuple[Optional[int], int], value: str):
+    def __setitem__(self, key: Tuple[Optional[int], int],
+                    value: InductorArtifact):
         # setitem for disabled cache is fine, because we
         # don't actually write to the disk
         runtime_shape, graph_index = key
@@ -181,7 +200,8 @@ def wrap_inductor(graph: fx.GraphModule,
     if (runtime_shape, graph_index) in cache_data:
         # we compiled this graph before
         # so we can directly lookup the compiled graph via hash
-        hash_str = cache_data[(runtime_shape, graph_index)]
+        inductor_artifact = cache_data[(runtime_shape, graph_index)]
+        hash_str = inductor_artifact.hash_str
         if graph_index == 0:
             # adds some info logging for the first graph
             logger.info(
@@ -199,6 +219,7 @@ def wrap_inductor(graph: fx.GraphModule,
                 "Inductor cache lookup failed. Please remove"
                 f"the cache file {cache_data.cache_file_path} and try again."  # noqa
             )
+            inductor_artifact.file_path = inductor_compiled_graph.current_callable.__code__.co_filename  # noqa
 
         # Inductor calling convention (function signature):
         # f(list) -> tuple
@@ -224,19 +245,20 @@ def compiled_graph(*args):
         # the assumption is that we don't have nested Inductor compilation.
         # compiled_fx_graph_hash will only be called once, and we can hook
         # it to get the hash of the compiled graph directly.
-        from torch._inductor.codecache import compiled_fx_graph_hash
+
+        inductor_artifact = InductorArtifact()
+        from torch._inductor.codecache import (FxGraphCache,
+                                               compiled_fx_graph_hash)
+        original_load = FxGraphCache.load
+
+        def hijack_load(*args, **kwargs):
+            inductor_compiled_graph = original_load(*args, **kwargs)
+            inductor_artifact.file_path = inductor_compiled_graph.current_callable.__code__.co_filename  # noqa
+            return inductor_compiled_graph
 
         def hijack_compiled_fx_graph_hash(*args, **kwargs):
             out = compiled_fx_graph_hash(*args, **kwargs)
-            # store the hash in the cache
-            nonlocal cache_data
-            cache_data[(runtime_shape, graph_index)] = out[0]
-            if graph_index == 0:
-                # adds some info logging for the first graph
-                logger.info("Cache the graph of shape %s for later use",
-                            str(runtime_shape))
-            logger.debug("store the %s-th graph for shape %s via hash %s",
-                         graph_index, str(runtime_shape), out[0])
+            inductor_artifact.hash_str = out[0]
             return out
 
         def _check_can_cache(*args, **kwargs):
@@ -255,6 +277,11 @@ def _get_shape_env() -> AlwaysHitShapeEnv:
             if not cache_data.disabled:
                 # compilation cache is enabled, patch several functions
 
+                # hijack to get the compiled graph itself
+                stack.enter_context(
+                    patch("torch._inductor.codecache.FxGraphCache.load",
+                          hijack_load))
+
                 # for hijacking the hash of the compiled graph
                 stack.enter_context(
                     patch("torch._inductor.codecache.compiled_fx_graph_hash",
@@ -275,7 +302,16 @@ def _get_shape_env() -> AlwaysHitShapeEnv:
             compiled_graph = compile_fx(graph,
                                         example_inputs,
                                         config_patches=current_config)
-
+        # store the inductor_artifact in the cache
+        cache_data[(runtime_shape, graph_index)] = inductor_artifact
+        if graph_index == 0:
+            # adds some info logging for the first graph
+            logger.info("Cache the graph of shape %s for later use",
+                        str(runtime_shape))
+        logger.debug(
+            "store the %s-th graph for shape %s via hash %s from file %s",
+            graph_index, str(runtime_shape), inductor_artifact.hash_str,
+            inductor_artifact.file_path)
     # after compiling the last graph, record the end time
     if graph_index == num_graphs - 1:
         now = time.time()

vllm/compilation/decorators.py

@@ -76,8 +76,8 @@ def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
     During runtime, when we actually mark dimensions of tensors,
      it depends on the value of arguments:
 
-    - if it is a single integer, the corresponding dimension of the argument
-        will be marked as dynamic.
+    - if it is a single integer (can be negative), the corresponding dimension 
+        of the argument will be marked as dynamic.
     - if it is `None`, ignored.
     - if it is `IntermediateTensors`, all the tensors in the intermediate
         tensors will be marked as dynamic.
@@ -177,10 +177,20 @@ def __call__(self, *args, **kwargs):
             for k, dims in dynamic_arg_dims.items():
                 arg = bound_args.arguments.get(k)
                 if arg is not None:
+                    dims = [dims] if isinstance(dims, int) else dims
                     if isinstance(arg, torch.Tensor):
+                        # In case dims is specified with negative indexing
+                        dims = [
+                            arg.ndim + dim if dim < 0 else dim for dim in dims
+                        ]
                         torch._dynamo.mark_dynamic(arg, dims)
                     elif isinstance(arg, IntermediateTensors):
                         for tensor in arg.tensors.values():
+                            # In case dims is specified with negative indexing
+                            dims = [
+                                tensor.ndim + dim if dim < 0 else dim
+                                for dim in dims
+                            ]
                             torch._dynamo.mark_dynamic(tensor, dims)
                     else:
                         raise ValueError(