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Unverified Commit fe63c17a authored by Alec's avatar Alec Committed by GitHub
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fix: Revert "feat: add vLLM v1 multi-modal example. Add llama4 Maverick ex… (#2017)

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examples/multimodal_v1/utils/logging.py deleted 100644 → 0
View file @ bf1998f0
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import logging
from dynamo._core import Client
logger = logging.getLogger(__name__)
async def check_required_workers(
workers_client: Client, required_workers: int, on_change=True, poll_interval=0.5
):
"""Wait until the minimum number of workers are ready."""
worker_ids = workers_client.instance_ids()
num_workers = len(worker_ids)
while num_workers < required_workers:
await asyncio.sleep(poll_interval)
worker_ids = workers_client.instance_ids()
new_count = len(worker_ids)
if (not on_change) or new_count != num_workers:
logger.info(
f"Waiting for more workers to be ready.\n"
f" Current: {new_count},"
f" Required: {required_workers}"
)
num_workers = new_count
print(f"Workers ready: {worker_ids}")
return worker_ids
examples/multimodal_v1/utils/model.py deleted 100644 → 0
View file @ bf1998f0
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Any, Dict, Tuple
import torch
from transformers import AutoConfig
from utils.protocol import EncodeResponse
from vllm import AsyncEngineArgs
from vllm.utils import get_distributed_init_method, get_ip, get_open_port
from vllm.worker.worker import Worker
# from transformers import AutoImageProcessor, LlavaForConditionalGeneration
# from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
logger = logging.getLogger(__name__)
def load_vision_model(model_id: str) -> torch.nn.Module:
"""
Load a vision model from a HuggingFace model ID.
"""
engine_args = AsyncEngineArgs(model=model_id, trust_remote_code=True)
engine_config = engine_args.create_engine_config()
distributed_init_method = get_distributed_init_method(get_ip(), get_open_port())
worker = Worker(
vllm_config=engine_config,
local_rank=0,
rank=0,
distributed_init_method=distributed_init_method,
is_driver_worker=True,
)
# Initialize the worker.
worker.init_device()
worker.load_model()
return worker.model_runner.model
# model = LlavaForConditionalGeneration.from_pretrained(
# model_id, device_map="auto", torch_dtype=torch.float16
# ).eval()
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# model_id, torch_dtype="auto", device_map="auto"
# ).eval()
# return model
def get_vision_embeddings_info(
model_id: str, num_patches: int
) -> Tuple[Tuple[int, int, int], torch.dtype]:
"""Calculate vision embeddings size and dtype using model config
Returns a tuple of (batch_size, num_patches, hidden_dim), dtype.
"""
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
assert num_patches > 0, "Number of patches must be positive"
if not hasattr(config, "torch_dtype"):
raise ValueError("Model config missing required 'torch_dtype' attribute")
if not hasattr(config, "hidden_size"):
logger.warning(
"Model config missing required 'hidden_size' attribute, using 4096"
)
hidden_size = 4096
else:
hidden_size = config.hidden_size
return (1, num_patches, hidden_size), config.torch_dtype
def construct_mm_data(
model: str,
encode_output: EncodeResponse,
image_embeds: torch.Tensor,
embeddings_dtype: torch.dtype,
) -> Dict[str, torch.Tensor | Dict[str, Any]]:
"""Construct multimodal data for a vLLM request for models that require additional parameters alongside the embeddings"""
image_embeds = image_embeds.to(embeddings_dtype)
if "Qwen2" in model:
return {
"image": {
"image_embeds": image_embeds.squeeze(0),
"image_grid_thw": torch.tensor(encode_output.image_grid_thw).squeeze(0),
}
}
elif "MiniCPM-V" in model:
return {
"image": {
"image_embeds": image_embeds,
"image_sizes": encode_output.image_sizes,
}
}
else:
return {"image": image_embeds}
examples/multimodal_v1/utils/protocol.py deleted 100644 → 0
View file @ bf1998f0
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import Any, List, Literal, Optional, Union
import connect
import msgspec
from pydantic import BaseModel, ConfigDict, field_validator
from pydantic_core import core_schema
from typing_extensions import NotRequired
from vllm.inputs.data import TokensPrompt
from vllm.outputs import CompletionOutput
from vllm.sampling_params import SamplingParams
from vllm.sequence import PromptLogprobs, RequestMetrics
class Request(BaseModel):
prompt: str
sampling_params: dict
class Tokens(BaseModel):
tokens: list[int]
class PrefillRequest(Request):
request_id: str
class Response(BaseModel):
text: str
class PrefillResponse(BaseModel):
prefilled: bool
# Hack to override the type of multi_modal_data in TokensPrompt
# as pydantic doesn't understand generic types
# TokensPrompt is defined here: https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/inputs/data.py#L38
# multi_modal_data is defined here: https://github.com/vllm-project/vllm/blob/main/vllm/multimodal/inputs.py#L103
# ModalityData is defined here: https://github.com/vllm-project/vllm/blob/main/vllm/multimodal/inputs.py#L80
class PatchedTokensPrompt(TokensPrompt):
multi_modal_data: NotRequired[Optional[Any]] # type: ignore
# Monkey-patch the SamplingParams type to add a dummy core schema so pydantic can validate it
# Sampling params is a mspspec struct
# SamplingParams is defined here: https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/sampling_params.py#L88
SamplingParams.__get_pydantic_core_schema__ = classmethod(
lambda cls, source, handler: core_schema.any_schema()
)
class vLLMGenerateRequest(BaseModel):
"""
Serializable class of all the fields vLLM engine requires for inference
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
engine_prompt: PatchedTokensPrompt
sampling_params: SamplingParams
request_id: str
prefix_hit_rate: Optional[float] = 0.0
@field_validator("sampling_params", mode="before")
@classmethod
def parse_sampling_params(cls, v: Any) -> SamplingParams:
if isinstance(v, str):
v = json.loads(v)
if isinstance(v, dict):
return SamplingParams(**v)
return v
model_config = ConfigDict(
json_encoders={SamplingParams: lambda v: msgspec.json.encode(v)}
)
class TextContent(BaseModel):
type: Literal["text"]
text: str
class ImageURLDetail(BaseModel):
url: str
class ImageContent(BaseModel):
type: Literal["image_url"]
image_url: ImageURLDetail
MessageContent = Union[TextContent, ImageContent]
class ChatMessage(BaseModel):
role: Literal["user", "system", "assistant"]
content: List[MessageContent]
class MultiModalRequest(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
model: str
messages: List[ChatMessage]
max_tokens: Optional[int] = None
temperature: Optional[float] = None
stream: Optional[bool] = True
class vLLMMultimodalRequest(vLLMGenerateRequest):
model_config = ConfigDict(arbitrary_types_allowed=True)
image_url: Optional[str] = None
# image_features: Optional[List[List[List[float]]]] = None # Remove once have NIXL support
serialized_request: Optional[connect.SerializedRequest] = None
class EncodeRequest(BaseModel):
"""
Serializable class of all the fields vLLM engine requires for inference
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
image_url: str
request_id: str
serialized_request: Optional[connect.SerializedRequest] = None
class EncodeResponse(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
request_id: str
image_grid_thw: Optional[List[Any]] = None
image_sizes: Optional[List[Any]] = None
serialized_request: Optional[connect.SerializedRequest] = None
image_features: List[List[List[float]]] # Remove once have NIXL support
class MyRequestOutput(BaseModel):
"""
RequestOutput from vLLM is not serializable by default
https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/outputs.py#L85
This class is used to serialize the RequestOutput and any recursively defined types
We can do this because PromptLogprobs, RequestMetrics, and CompletionOutput are all serializable dataclasses
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
request_id: str
prompt: Optional[str] = None
prompt_token_ids: Optional[List[int]] = None
prompt_logprobs: Optional[PromptLogprobs] = None
outputs: List[CompletionOutput]
finished: bool
metrics: Optional[RequestMetrics] = None
kv_transfer_params: Optional[dict[str, Any]] = None
# lora_request: Optional[LoRARequest] = None
# encoder_prompt: Optional[str] = None
# encoder_prompt_token_ids: Optional[List[int]] = None
# num_cached_tokens: Optional[int] = None
# multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None
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