revert skip-merge-desc

Signed-off-by: Robert Shaw <robshaw@redhat.com>

benchmarks/benchmark_one_concurrent.py

@@ -0,0 +1,362 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import asyncio
+import logging
+import random
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import aiohttp  # Import aiohttp
+import numpy as np
+from tqdm import tqdm
+
+from backend_request_func import RequestFuncInput, RequestFuncOutput
+from benchmark_dataset import RandomDataset, SampleRequest
+
+try:
+    from vllm.transformers_utils.tokenizer import get_tokenizer
+except ImportError:
+    from backend_request_func import get_tokenizer
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class BenchmarkMetrics:
+    completed: int
+    total_input: int
+    total_output: int
+    mean_ttft_ms: float
+    median_ttft_ms: float
+    std_ttft_ms: float
+    percentiles_ttft_ms: list[tuple[float, float]]
+    mean_itl_ms: float
+    median_itl_ms: float
+    std_itl_ms: float
+    percentiles_itl_ms: list[tuple[float, float]]
+    mean_e2el_ms: float
+    median_e2el_ms: float
+    std_e2el_ms: float
+    percentiles_e2el_ms: list[tuple[float, float]]
+
+
+async def reset_cache(reset_url: str):
+    """Sends a POST request to reset the prefix cache."""
+    logger.debug("Resetting prefix cache at %s", reset_url)
+    try:
+        async with (
+            aiohttp.ClientSession() as session,
+            session.post(reset_url) as response,
+        ):
+            response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
+            logger.debug("Prefix cache reset successful: %s", response.status)
+    except aiohttp.ClientConnectorError as e:
+        logger.error("Failed to connect to cache reset endpoint %s: %s}", reset_url, e)
+    except aiohttp.ClientResponseError as e:
+        logger.error(
+            "Cache reset request failed with status %s: %s", e.status, e.message
+        )
+    except Exception as e:
+        logger.error("An unexpected error occurred during cache reset: %s", e)
+
+
+async def sequential_benchmark(
+    backend: str,
+    api_url: str,
+    model_id: str,
+    tokenizer,
+    input_requests: list[SampleRequest],
+    request_func,
+    selected_percentiles: list[float],
+    cache_reset_url: Optional[str] = None,
+):
+    """
+    Benchmark that processes requests sequentially, waiting for each to complete
+    before starting the next one. Resets prefix cache between requests.
+    """
+    outputs = []
+
+    pbar = tqdm(total=len(input_requests))
+
+    benchmark_start_time = time.perf_counter()
+
+    # Process requests sequentially
+    for request in input_requests:
+        prompt, prompt_len, output_len = (
+            request.prompt,
+            request.prompt_len,
+            request.expected_output_len,
+        )
+
+        logger.info("Sending request with len %s", request.prompt_len)
+        logger.debug('Request str: "%s"', request.prompt[:50])
+        request_start_time = time.perf_counter()
+
+        request_func_input = RequestFuncInput(
+            model=model_id,
+            prompt=prompt,
+            api_url=api_url,
+            prompt_len=prompt_len,
+            output_len=output_len,
+        )
+
+        output = await request_func(request_func_input=request_func_input)
+
+        request_end_time = time.perf_counter()
+        # Add timing information
+        if output.success and not hasattr(output, "latency"):
+            output.latency = request_end_time - request_start_time
+        logger.info("Finished request with latency %.4f s", output.latency)
+
+        outputs.append(output)
+        pbar.update(1)
+
+    pbar.close()
+
+    benchmark_duration = time.perf_counter() - benchmark_start_time
+
+    # Calculate metrics
+    metrics = calculate_metrics(
+        input_requests=input_requests,
+        outputs=outputs,
+        dur_s=benchmark_duration,
+        tokenizer=tokenizer,
+        selected_percentiles=selected_percentiles,
+    )
+
+    print_results(metrics, benchmark_duration)
+
+    result = {
+        "duration": benchmark_duration,
+        "completed": metrics.completed,
+        "total_input_tokens": metrics.total_input,
+        "total_output_tokens": metrics.total_output,
+        "input_lens": [request.prompt_len for request in input_requests],
+        "output_lens": [
+            output.output_tokens if output.success else 0 for output in outputs
+        ],
+        "ttfts": [output.ttft for output in outputs if output.success],
+        "itls": [output.itl for output in outputs if output.success],
+        "generated_texts": [
+            output.generated_text for output in outputs if output.success
+        ],
+        "errors": [output.error for output in outputs if not output.success],
+    }
+
+    # Add summary statistics
+    for stat_name in ["ttft", "itl", "e2el"]:
+        for metric_name in ["mean", "median", "std"]:
+            result[f"{metric_name}_{stat_name}_ms"] = getattr(
+                metrics, f"{metric_name}_{stat_name}_ms"
+            )
+
+        for p, value in getattr(metrics, f"percentiles_{stat_name}_ms"):
+            p_word = str(int(p)) if int(p) == p else str(p)
+            result[f"p{p_word}_{stat_name}_ms"] = value
+
+    return result
+
+
+def calculate_metrics(
+    input_requests: list[SampleRequest],
+    outputs: list[RequestFuncOutput],
+    dur_s: float,
+    tokenizer,
+    selected_percentiles: list[float],
+) -> BenchmarkMetrics:
+    """Calculate benchmark metrics from results."""
+    total_input = 0
+    completed = 0
+    total_output = 0
+    ttfts = []
+    itls = []
+    e2els = []
+
+    for i, output in enumerate(outputs):
+        if output.success:
+            output_len = output.output_tokens
+
+            if not output_len:
+                # Use tokenizer to count output tokens if not provided
+                output_len = len(
+                    tokenizer(output.generated_text, add_special_tokens=False).input_ids
+                )
+
+            total_output += output_len
+            total_input += input_requests[i].prompt_len
+
+            if hasattr(output, "ttft") and output.ttft is not None:
+                ttfts.append(output.ttft)
+
+            if hasattr(output, "itl") and output.itl:
+                # Ensure itl is a list of floats
+                if isinstance(output.itl, list):
+                    itls.extend(output.itl)
+                else:
+                    logger.warning(
+                        "Expected list for ITL but got %s. Appending as is.",
+                        type(output.itl),
+                    )
+                    itls.append(output.itl)
+
+            if hasattr(output, "latency") and output.latency is not None:
+                e2els.append(output.latency)
+
+            completed += 1
+
+    return BenchmarkMetrics(
+        completed=completed,
+        total_input=total_input,
+        total_output=total_output,
+        mean_ttft_ms=np.mean(ttfts or [0]) * 1000,
+        median_ttft_ms=np.median(ttfts or [0]) * 1000,
+        std_ttft_ms=np.std(ttfts or [0]) * 1000,
+        percentiles_ttft_ms=[
+            (p, np.percentile(ttfts or [0], p) * 1000) for p in selected_percentiles
+        ],
+        mean_itl_ms=np.mean(itls or [0]) * 1000,
+        median_itl_ms=np.median(itls or [0]) * 1000,
+        std_itl_ms=np.std(itls or [0]) * 1000,
+        percentiles_itl_ms=[
+            (p, np.percentile(itls or [0], p) * 1000) for p in selected_percentiles
+        ],
+        mean_e2el_ms=np.mean(e2els or [0]) * 1000,
+        median_e2el_ms=np.median(e2els or [0]) * 1000,
+        std_e2el_ms=np.std(e2els or [0]) * 1000,
+        percentiles_e2el_ms=[
+            (p, np.percentile(e2els or [0], p) * 1000) for p in selected_percentiles
+        ],
+    )
+
+
+def print_results(metrics: BenchmarkMetrics, benchmark_duration: float):
+    """Print benchmark results in a formatted way."""
+    print("{s:{c}^{n}}".format(s=" Sequential Benchmark Result ", n=60, c="="))
+    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
+    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
+    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
+    print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
+
+    def print_metric_stats(metric_name, header):
+        print("{s:{c}^{n}}".format(s=header, n=60, c="-"))
+        print(
+            "{:<40} {:<10.2f}".format(
+                f"Mean {metric_name} (ms):",
+                getattr(metrics, f"mean_{metric_name.lower()}_ms"),
+            )
+        )
+        print(
+            "{:<40} {:<10.2f}".format(
+                f"Median {metric_name} (ms):",
+                getattr(metrics, f"median_{metric_name.lower()}_ms"),
+            )
+        )
+
+        for p, value in getattr(metrics, f"percentiles_{metric_name.lower()}_ms"):
+            p_word = str(int(p)) if int(p) == p else str(p)
+            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
+
+    print_metric_stats("TTFT", "Time to First Token")
+    print_metric_stats("ITL", "Inter-token Latency")
+    print_metric_stats("E2EL", "End-to-end Latency")
+    print("=" * 60)
+
+
+async def main_async(args):
+    # Import needed functions based on your setup
+    from backend_request_func import ASYNC_REQUEST_FUNCS
+
+    backend = args.backend
+    model_id = args.model
+    tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
+
+    # Set up API URL
+    if args.base_url is not None:
+        api_url = f"{args.base_url}{args.endpoint}"
+    else:
+        api_url = f"http://{args.host}:{args.port}{args.endpoint}"
+
+    # Set up Cache Reset URL
+    cache_reset_url = f"http://{args.host}:{args.port}/reset_prefix_cache"
+    logger.info("Prefix cache reset configured at: %s", cache_reset_url)
+
+    # Get tokenizer
+    tokenizer = get_tokenizer(tokenizer_id, trust_remote_code=args.trust_remote_code)
+
+    # Get request function
+    if backend in ASYNC_REQUEST_FUNCS:
+        request_func = ASYNC_REQUEST_FUNCS[backend]
+    else:
+        raise ValueError(f"Unknown backend: {backend}")
+
+    input_requests = RandomDataset().sample(
+        tokenizer=tokenizer,
+        num_requests=args.num_requests,
+        prefix_len=0,
+        input_len=args.input_len,
+        output_len=args.output_len,
+        range_ratio=0.0,
+    )
+
+    # Run benchmark
+    result = await sequential_benchmark(
+        backend=backend,
+        api_url=api_url,
+        model_id=model_id,
+        tokenizer=tokenizer,
+        input_requests=input_requests,
+        request_func=request_func,
+        selected_percentiles=[50, 90, 95, 99],
+        cache_reset_url=cache_reset_url,
+    )
+
+    return result
+
+
+def main(args):
+    print(args)
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+
+    asyncio.run(main_async(args))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Sequential benchmark for LLM serving")
+    parser.add_argument(
+        "--backend", type=str, default="vllm", help="Backend to use for requests"
+    )
+    parser.add_argument(
+        "--base-url",
+        type=str,
+        default=None,
+        help="Server base URL (overrides --host and --port)",
+    )
+    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument(
+        "--endpoint", type=str, default="/v1/completions", help="API endpoint"
+    )
+    parser.add_argument("--model", type=str, required=True, help="Name of the model")
+    parser.add_argument(
+        "--tokenizer", type=str, help="Name of the tokenizer (defaults to model name)"
+    )
+    parser.add_argument(
+        "--num-requests", type=int, default=100, help="Number of requests to process"
+    )
+    parser.add_argument(
+        "--input-len", type=int, default=128, help="Input len for generated prompts"
+    )
+    parser.add_argument(
+        "--output-len", type=int, default=None, help="Override output len for requests"
+    )
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument(
+        "--trust-remote-code",
+        action="store_true",
+        help="Trust remote code from HuggingFace",
+    )
+
+    args = parser.parse_args()
+    main(args)

tests/test_utils.py

@@ -20,11 +20,10 @@ from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.utils import (CacheInfo, FlexibleArgumentParser, LRUCache,
                         MemorySnapshot, PlaceholderModule, StoreBoolean,
                         bind_kv_cache, common_broadcastable_dtype,
-                        deprecate_kwargs, get_open_port, get_tcp_uri,
-                        is_lossless_cast, join_host_port, make_zmq_path,
-                        make_zmq_socket, memory_profiling,
-                        merge_async_iterators, sha256, split_host_port,
-                        split_zmq_path, supports_kw, swap_dict_values)
+                        deprecate_kwargs, get_open_port, is_lossless_cast,
+                        make_zmq_path, make_zmq_socket, memory_profiling,
+                        merge_async_iterators, sha256, split_zmq_path,
+                        supports_kw, swap_dict_values)
 
 from .utils import create_new_process_for_each_test, error_on_warning
 
@@ -877,44 +876,3 @@ def test_make_zmq_socket_ipv6():
 def test_make_zmq_path():
     assert make_zmq_path("tcp", "127.0.0.1", "5555") == "tcp://127.0.0.1:5555"
     assert make_zmq_path("tcp", "::1", "5555") == "tcp://[::1]:5555"
-
-
-def test_get_tcp_uri():
-    assert get_tcp_uri("127.0.0.1", 5555) == "tcp://127.0.0.1:5555"
-    assert get_tcp_uri("::1", 5555) == "tcp://[::1]:5555"
-
-
-def test_split_host_port():
-    # valid ipv4
-    assert split_host_port("127.0.0.1:5555") == ("127.0.0.1", 5555)
-    # invalid ipv4
-    with pytest.raises(ValueError):
-        # multi colon
-        assert split_host_port("127.0.0.1::5555")
-    with pytest.raises(ValueError):
-        # tailing colon
-        assert split_host_port("127.0.0.1:5555:")
-    with pytest.raises(ValueError):
-        # no colon
-        assert split_host_port("127.0.0.15555")
-    with pytest.raises(ValueError):
-        # none int port
-        assert split_host_port("127.0.0.1:5555a")
-
-    # valid ipv6
-    assert split_host_port("[::1]:5555") == ("::1", 5555)
-    # invalid ipv6
-    with pytest.raises(ValueError):
-        # multi colon
-        assert split_host_port("[::1]::5555")
-    with pytest.raises(IndexError):
-        # no colon
-        assert split_host_port("[::1]5555")
-    with pytest.raises(ValueError):
-        # none int port
-        assert split_host_port("[::1]:5555a")
-
-
-def test_join_host_port():
-    assert join_host_port("127.0.0.1", 5555) == "127.0.0.1:5555"
-    assert join_host_port("::1", 5555) == "[::1]:5555"

tools/pd_disagg/Justfile

@@ -0,0 +1,88 @@
+# Needed for the proxy server
+vllm-directory := "/home/rshaw/vllm/" 
+
+PREFILL_GPU := "0,1,2,3"
+DECODE_GPU := "4,5,6,7"
+
+PREFILL_TP := env("PREFILL_TP", "1")
+DECODE_TP := env("DECODE_TP", "1")
+
+BLOCK_SIZE := env("BLOCK_SIZE", "128")
+
+MODEL := "meta-llama/Llama-3.1-8B-Instruct"
+PROXY_PORT := "8192"
+PREFILL_PORT := "8100"
+DECODE_PORT := "8200"
+PREFILL_NIXL_SIDE_CHANNEL_PORT := "5557"
+DECODE_NIXL_SIDE_CHANNEL_PORT := "5568"
+
+prefill:
+    VLLM_NIXL_SIDE_CHANNEL_PORT={{PREFILL_NIXL_SIDE_CHANNEL_PORT}} \
+    CUDA_VISIBLE_DEVICES={{PREFILL_GPU}} \
+    vllm serve {{MODEL}} \
+      --port {{PREFILL_PORT}} \
+      --tensor-parallel-size {{PREFILL_TP}} \
+      --enforce-eager \
+      --disable-log-requests \
+      --block-size {{BLOCK_SIZE}} \
+      --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+
+decode:
+    VLLM_NIXL_SIDE_CHANNEL_PORT={{DECODE_NIXL_SIDE_CHANNEL_PORT}} \
+    CUDA_VISIBLE_DEVICES={{DECODE_GPU}} \
+    vllm serve {{MODEL}} \
+      --port {{DECODE_PORT}} \
+      --tensor-parallel-size {{DECODE_TP}} \
+      --enforce-eager \
+      --disable-log-requests \
+      --block-size {{BLOCK_SIZE}} \
+      --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+
+proxy:
+    python "{{vllm-directory}}tests/v1/kv_connector/nixl_integration/toy_proxy_server.py" \
+      --port {{PROXY_PORT}} \
+      --prefiller-port {{PREFILL_PORT}} \
+      --decoder-port {{DECODE_PORT}}
+
+send_request:
+  curl -X POST http://localhost:{{PROXY_PORT}}/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{ \
+      "model": "{{MODEL}}", \
+      "prompt": "Red Hat is the best open source company by far across Linux, K8s, and AI, and vLLM has the greatest community in open source AI software infrastructure. I love vLLM because", \
+      "max_tokens": 150, \
+      "temperature": 0.7 \
+    }'
+
+benchmark NUM_PROMPTS:
+  python {{vllm-directory}}/benchmarks/benchmark_serving.py \
+    --port {{PROXY_PORT}} \
+    --model {{MODEL}} \
+    --dataset-name random \
+    --random-input-len 30000 \
+    --random-output-len 10 \
+    --num-prompts {{NUM_PROMPTS}} \
+    --seed $(date +%s) \
+
+benchmark_one INPUT_LEN:
+  python {{vllm-directory}}benchmarks/benchmark_one_concurrent.py \
+    --port {{PROXY_PORT}} \
+    --model {{MODEL}} \
+    --input-len {{INPUT_LEN}} \
+    --output-len 1 \
+    --num-requests 10 \
+    --seed $(date +%s)
+
+benchmark_one_no_pd INPUT_LEN:
+  python {{vllm-directory}}benchmarks/benchmark_one_concurrent_req.py \
+    --port {{DECODE_PORT}} \
+    --model {{MODEL}} \
+    --input-len {{INPUT_LEN}} \
+    --output-len 1 \
+    --num-requests 10 \
+    --seed $(date +%s)
+
+eval:
+  lm_eval --model local-completions --tasks gsm8k \
+    --model_args model={{MODEL}},base_url=http://127.0.0.1:{{PROXY_PORT}}/v1/completions,num_concurrent=100,max_retries=3,tokenized_requests=False \
+    --limit 1000

vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py

@@ -16,7 +16,6 @@ from safetensors.torch import save as safetensors_save
 from vllm.config import KVTransferConfig
 from vllm.distributed.kv_transfer.kv_pipe.base import KVPipeBase
 from vllm.logger import init_logger
-from vllm.utils import join_host_port, make_zmq_path, split_host_port
 
 logger = init_logger(__name__)
 NONE_INT = -150886311
@@ -80,19 +79,18 @@ class MooncakeTransferEngine:
             logger.error(
                 "An error occurred while loading the configuration: %s", exc)
             raise
-        prefill_host, base_prefill_port = split_host_port(
-            self.config.prefill_url)
-        decode_host, base_decode_port = split_host_port(self.config.decode_url)
+        prefill_host, base_prefill_port = self.config.prefill_url.split(':')
+        decode_host, base_decode_port = self.config.decode_url.split(':')
 
         # Avoid ports conflict when running prefill and decode on the same node
         if prefill_host == decode_host and \
                 base_prefill_port == base_decode_port:
-            base_decode_port = base_decode_port + 100
+            base_decode_port = str(int(base_decode_port) + 100)
 
-        prefill_port = base_prefill_port + self.local_rank
-        decode_port = base_decode_port + self.local_rank
-        self.prefill_url = join_host_port(prefill_host, prefill_port)
-        self.decode_url = join_host_port(decode_host, decode_port)
+        prefill_port = int(base_prefill_port) + self.local_rank
+        decode_port = int(base_decode_port) + self.local_rank
+        self.prefill_url = ':'.join([prefill_host, str(prefill_port)])
+        self.decode_url = ':'.join([decode_host, str(decode_port)])
 
         self.initialize(self.prefill_url if kv_rank == 0 else self.decode_url,
                         self.config.metadata_server, self.config.protocol,
@@ -112,30 +110,22 @@ class MooncakeTransferEngine:
         self._setup_metadata_sockets(kv_rank, prefill_host, base_prefill_port,
                                      decode_host, base_decode_port)
 
-    def _setup_metadata_sockets(self, kv_rank: int, p_host: str, p_port: int,
-                                d_host: str, d_port: int) -> None:
+    def _setup_metadata_sockets(self, kv_rank: int, p_host: str, p_port: str,
+                                d_host: str, d_port: str) -> None:
         """Set up ZeroMQ sockets for sending and receiving data."""
         # Offsets < 8 are left for initialization in case tp and pp are enabled
-        p_rank_offset = p_port + 8 + self.local_rank * 2
-        d_rank_offset = d_port + 8 + self.local_rank * 2
+        p_rank_offset = int(p_port) + 8 + self.local_rank * 2
+        d_rank_offset = int(d_port) + 8 + self.local_rank * 2
         if kv_rank == 0:
-            self.sender_socket.bind(
-                make_zmq_path("tcp", p_host, p_rank_offset + 1))
-            self.receiver_socket.connect(
-                make_zmq_path("tcp", d_host, d_rank_offset + 1))
-            self.sender_ack.connect(
-                make_zmq_path("tcp", d_host, d_rank_offset + 2))
-            self.receiver_ack.bind(
-                make_zmq_path("tcp", p_host, p_rank_offset + 2))
+            self.sender_socket.bind(f"tcp://{p_host}:{p_rank_offset + 1}")
+            self.receiver_socket.connect(f"tcp://{d_host}:{d_rank_offset + 1}")
+            self.sender_ack.connect(f"tcp://{d_host}:{d_rank_offset + 2}")
+            self.receiver_ack.bind(f"tcp://{p_host}:{p_rank_offset + 2}")
         else:
-            self.receiver_socket.connect(
-                make_zmq_path("tcp", p_host, p_rank_offset + 1))
-            self.sender_socket.bind(
-                make_zmq_path("tcp", d_host, d_rank_offset + 1))
-            self.receiver_ack.bind(
-                make_zmq_path("tcp", d_host, d_rank_offset + 2))
-            self.sender_ack.connect(
-                make_zmq_path("tcp", p_host, p_rank_offset + 2))
+            self.receiver_socket.connect(f"tcp://{p_host}:{p_rank_offset + 1}")
+            self.sender_socket.bind(f"tcp://{d_host}:{d_rank_offset + 1}")
+            self.receiver_ack.bind(f"tcp://{d_host}:{d_rank_offset + 2}")
+            self.sender_ack.connect(f"tcp://{p_host}:{p_rank_offset + 2}")
 
     def initialize(self, local_hostname: str, metadata_server: str,
                    protocol: str, device_name: str,

vllm/model_executor/models/glm4_1v.py

@@ -55,6 +55,9 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -176,7 +179,6 @@ class Glm4vVisionMLP(nn.Module):
         hidden_features: int,
         bias: bool = False,
         quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
     ):
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
@@ -184,12 +186,13 @@ class Glm4vVisionMLP(nn.Module):
             output_sizes=[hidden_features] * 2,
             bias=bias,
             quant_config=quant_config,
-            prefix=f"{prefix}.gate_up_proj")
-        self.down_proj = RowParallelLinear(hidden_features,
-                                           in_features,
-                                           bias=bias,
-                                           quant_config=quant_config,
-                                           prefix=f"{prefix}.down_proj")
+        )
+        self.down_proj = RowParallelLinear(
+            hidden_features,
+            in_features,
+            bias=bias,
+            quant_config=quant_config,
+        )
         self.act_fn = SiluAndMul()
 
     def forward(self, x: torch.Tensor):
@@ -404,7 +407,6 @@ class Glm4vVisionBlock(nn.Module):
             mlp_hidden_dim,
             bias=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.mlp",
         )
 
     def forward(
@@ -1276,7 +1278,7 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.visual = Glm4vVisionTransformer(
             config.vision_config,
             norm_eps=getattr(config, "rms_norm_eps", 1e-5),
-            quant_config=quant_config,
+            quant_config=self._maybe_ignore_quant_config(quant_config),
             prefix=maybe_prefix(prefix, "visual"),
         )
 
@@ -1289,6 +1291,13 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid vision encoder sections for some models.
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
     def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                         name: str) -> torch.Tensor:
         if not isinstance(mm_input, (torch.Tensor, list)):

vllm/transformers_utils/config.py

@@ -33,8 +33,10 @@ from vllm.logger import init_logger
 from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
                                              DbrxConfig, DeepseekVLV2Config,
                                              EAGLEConfig, ExaoneConfig,
-                                             JAISConfig, KimiVLConfig,
-                                             MedusaConfig, MiniMaxText01Config,
+                                             H2OVLChatConfig,
+                                             InternVLChatConfig, JAISConfig,
+                                             KimiVLConfig, MedusaConfig,
+                                             MiniMaxText01Config,
                                              MiniMaxVL01Config, MllamaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, NVLM_D_Config,
@@ -88,6 +90,8 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
     "medusa": MedusaConfig,
     "eagle": EAGLEConfig,
     "exaone": ExaoneConfig,
+    "h2ovl_chat": H2OVLChatConfig,
+    "internvl_chat": InternVLChatConfig,
     "minimax_text_01": MiniMaxText01Config,
     "minimax_vl_01": MiniMaxVL01Config,
     "nemotron": NemotronConfig,
@@ -100,10 +104,6 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
     **_CONFIG_REGISTRY_OVERRIDE_HF
 }
 
-_CONFIG_ATTRS_MAPPING: dict[str, str] = {
-    "llm_config": "text_config",
-}
-
 
 class ConfigFormat(str, enum.Enum):
     AUTO = "auto"
@@ -286,18 +286,6 @@ def is_encoder_decoder(config: PretrainedConfig) -> bool:
     return getattr(config, "is_encoder_decoder", False)
 
 
-def _maybe_remap_hf_config_attrs(config: PretrainedConfig) -> PretrainedConfig:
-    """Remap config attributes to match the expected names."""
-    for old_attr, new_attr in _CONFIG_ATTRS_MAPPING.items():
-        if hasattr(config, old_attr):
-            if not hasattr(config, new_attr):
-                config.update({new_attr: getattr(config, old_attr)})
-            delattr(config, old_attr)
-            logger.debug("Remapped config attribute '%s' to '%s'", old_attr,
-                         new_attr)
-    return config
-
-
 def get_config(
     model: Union[str, Path],
     trust_remote_code: bool,
@@ -373,9 +361,6 @@ def get_config(
                     revision=revision,
                     code_revision=code_revision,
                     token=_get_hf_token(),
-                    # some old custom model's config needs
-                    # `has_no_defaults_at_init=True` to work.
-                    has_no_defaults_at_init=trust_remote_code,
                     **kwargs,
                 )
             except ValueError as e:
@@ -391,7 +376,6 @@ def get_config(
                     raise RuntimeError(err_msg) from e
                 else:
                     raise e
-        config = _maybe_remap_hf_config_attrs(config)
 
     elif config_format == ConfigFormat.MISTRAL:
         config = load_params_config(model, revision, **kwargs)

vllm/transformers_utils/configs/__init__.py

@@ -11,6 +11,8 @@ from vllm.transformers_utils.configs.exaone import ExaoneConfig
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
+from vllm.transformers_utils.configs.h2ovl import H2OVLChatConfig
+from vllm.transformers_utils.configs.internvl import InternVLChatConfig
 from vllm.transformers_utils.configs.jais import JAISConfig
 from vllm.transformers_utils.configs.kimi_vl import KimiVLConfig
 from vllm.transformers_utils.configs.medusa import MedusaConfig
@@ -36,6 +38,8 @@ __all__ = [
     "DeepseekVLV2Config",
     "MPTConfig",
     "RWConfig",
+    "H2OVLChatConfig",
+    "InternVLChatConfig",
     "JAISConfig",
     "MedusaConfig",
     "EAGLEConfig",

vllm/transformers_utils/configs/h2ovl.py

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/h2oai/h2ovl-mississippi-2b/blob/main/configuration_h2ovl_chat.py
+# --------------------------------------------------------
+# H2OVL-Mississippi
+# Copyright (c) 2024 H2O.AI
+# Licensed under Apache 2.0 License [see LICENSE for details]
+# --------------------------------------------------------
+
+from .internvl import InternVLChatConfig
+
+
+class H2OVLChatConfig(InternVLChatConfig):
+    model_type = "h2ovl_chat"

vllm/transformers_utils/configs/internvl.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/configuration_internvl_chat.py
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from transformers.configuration_utils import PretrainedConfig
+
+
+class InternVLChatConfig(PretrainedConfig):
+    model_type = 'internvl_chat'
+    is_composition = True
+
+    def __init__(self,
+                 vision_config=None,
+                 llm_config=None,
+                 use_backbone_lora=0,
+                 use_llm_lora=0,
+                 select_layer=-1,
+                 force_image_size=None,
+                 downsample_ratio=0.5,
+                 template=None,
+                 dynamic_image_size=False,
+                 use_thumbnail=False,
+                 ps_version='v1',
+                 min_dynamic_patch=1,
+                 max_dynamic_patch=6,
+                 **kwargs):
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {}
+
+        if llm_config is None:
+            llm_config = {}
+
+        self.vision_config = PretrainedConfig(**vision_config)
+        self.text_config = PretrainedConfig(**llm_config)
+
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch

vllm/transformers_utils/configs/nvlm_d.py

@@ -8,24 +8,8 @@
 # Copyright (c) 2024 NVIDIA
 # Licensed under Apache 2.0 License [see LICENSE for details]
 # --------------------------------------------------------
-from transformers import Qwen2Config
-from transformers.configuration_utils import PretrainedConfig
+from .internvl import InternVLChatConfig
 
 
-class NVLM_D_Config(PretrainedConfig):
+class NVLM_D_Config(InternVLChatConfig):
     model_type = 'NVLM_D'
-    is_composition = True
-
-    def __init__(self, vision_config=None, llm_config=None, **kwargs):
-        super().__init__(**kwargs)
-
-        # Handle vision_config initialization
-        if vision_config is None:
-            vision_config = {}
-
-        # Handle llm_config initialization
-        if llm_config is None:
-            llm_config = {}
-
-        self.vision_config = PretrainedConfig(**vision_config)
-        self.text_config = Qwen2Config(**llm_config)

vllm/utils/__init__.py

@@ -46,7 +46,7 @@ from dataclasses import dataclass, field
 from functools import cache, lru_cache, partial, wraps
 from types import MappingProxyType
 from typing import (TYPE_CHECKING, Any, Callable, Generic, Literal, NamedTuple,
-                    Optional, Tuple, TypeVar, Union, cast, overload)
+                    Optional, TypeVar, Union, cast, overload)
 from urllib.parse import urlparse
 from uuid import uuid4
 
@@ -628,34 +628,14 @@ def is_valid_ipv6_address(address: str) -> bool:
         return False
 
 
-def split_host_port(host_port: str) -> Tuple[str, int]:
-    # ipv6
-    if host_port.startswith('['):
-        host, port = host_port.rsplit(']', 1)
-        host = host[1:]
-        port = port.split(':')[1]
-        return host, int(port)
-    else:
-        host, port = host_port.split(':')
-        return host, int(port)
-
-
-def join_host_port(host: str, port: int) -> str:
-    if is_valid_ipv6_address(host):
-        return f"[{host}]:{port}"
-    else:
-        return f"{host}:{port}"
-
-
 def get_distributed_init_method(ip: str, port: int) -> str:
     return get_tcp_uri(ip, port)
 
 
 def get_tcp_uri(ip: str, port: int) -> str:
-    if is_valid_ipv6_address(ip):
-        return f"tcp://[{ip}]:{port}"
-    else:
-        return f"tcp://{ip}:{port}"
+    # Brackets are not permitted in ipv4 addresses,
+    # see https://github.com/python/cpython/issues/103848
+    return f"tcp://[{ip}]:{port}" if ":" in ip else f"tcp://{ip}:{port}"
 
 
 def get_open_zmq_ipc_path() -> str: