Change the name to vLLM (#150)

CONTRIBUTING.md

@@ -1,6 +1,6 @@
-# Contributing to CacheFlow
+# Contributing to vLLM
 
-Thank you for your interest in contributing to CacheFlow!
+Thank you for your interest in contributing to vLLM!
 Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large.
 There are several ways you can contribute to the project:
 
@@ -11,9 +11,9 @@ There are several ways you can contribute to the project:
 However, remember that contributions aren't just about code.
 We believe in the power of community support; thus, answering queries, assisting others, and enhancing the documentation are highly regarded and beneficial contributions.
 
-Finally, one of the most impactful ways to support us is by raising awareness about CacheFlow.
+Finally, one of the most impactful ways to support us is by raising awareness about vLLM.
 Talk about it in your blog posts, highlighting how it's driving your incredible projects.
-Express your support on Twitter if CacheFlow aids you, or simply offer your appreciation by starring our repository.
+Express your support on Twitter if vLLM aids you, or simply offer your appreciation by starring our repository.
 
 
 ## Setup for development
@@ -70,5 +70,5 @@ If a comment isn't clear or you disagree with a suggestion, feel free to ask for
 
 ### Thank You
 
-Finally, thank you for taking the time to read these guidelines and for your interest in contributing to CacheFlow.
-Your contributions make CacheFlow a great tool for everyone!
+Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
+Your contributions make vLLM a great tool for everyone!

README.md

@@ -1,4 +1,4 @@
-# CacheFlow
+# vLLM
 
 ## Build from source
 
@@ -28,7 +28,7 @@ python examples/simple_server.py --help
 To start the server:
 ```bash
 ray start --head
-python -m cacheflow.entrypoints.fastapi_server # --model <your_model>
+python -m vllm.entrypoints.fastapi_server # --model <your_model>
 ```
 
 To test the server:
@@ -45,9 +45,9 @@ pip install gradio
 
 Start the server:
 ```bash
-python -m cacheflow.http_frontend.fastapi_frontend
+python -m vllm.http_frontend.fastapi_frontend
 # At another terminal
-python -m cacheflow.http_frontend.gradio_webserver
+python -m vllm.http_frontend.gradio_webserver
 ```
 
 ## Load LLaMA weights
@@ -62,5 +62,5 @@ Since LLaMA weight is not fully public, we cannot directly download the LLaMA we
 2. For all the commands above, specify the model with `--model /output/path/llama-7b` to load the model. For example:
     ```bash
     python simple_server.py --model /output/path/llama-7b
-    python -m cacheflow.http_frontend.fastapi_frontend --model /output/path/llama-7b
+    python -m vllm.http_frontend.fastapi_frontend --model /output/path/llama-7b
     ```

benchmarks/README.md

@@ -1,4 +1,4 @@
-# Benchmarking CacheFlow
+# Benchmarking vLLM
 
 ## Downloading the ShareGPT dataset
 

benchmarks/benchmark_async_llm_server.py

@@ -11,7 +11,7 @@ def main(args: argparse.Namespace):
               for i in range(args.n_threads)]
 
     api_url = f"http://{args.host}:{args.port}/generate"
-    headers = {"User-Agent": "CacheFlow Benchmark Client"}
+    headers = {"User-Agent": "vLLM Benchmark Client"}
     ploads = [{
         "prompt": p,
         "max_tokens": args.max_tokens,

benchmarks/benchmark_latency.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from tqdm import tqdm
 
-from cacheflow import LLM, SamplingParams
+from vllm import LLM, SamplingParams
 
 
 def main(args: argparse.Namespace):

benchmarks/benchmark_serving.py

@@ -1,8 +1,8 @@
 """Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
-    (CacheFlow backend)
-    python -m cacheflow.entrypoints.api_server \
+    (vLLM backend)
+    python -m vllm.entrypoints.api_server \
         --disable-log-requests --model <your_model>
 
     (TGI backend)
@@ -114,7 +114,7 @@ async def send_request(
     request_start_time = time.time()
 
     headers = {"User-Agent": "Benchmark Client"}
-    if backend == "cacheflow":
+    if backend == "vllm":
         pload = {
             "prompt": prompt,
             "n": 1,
@@ -213,8 +213,8 @@ def main(args: argparse.Namespace):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Benchmark the online serving throughput.")
-    parser.add_argument("--backend", type=str, default="cacheflow",
-                        choices=["cacheflow", "tgi"])
+    parser.add_argument("--backend", type=str, default="vllm",
+                        choices=["vllm", "tgi"])
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8001)
     parser.add_argument("--dataset", type=str, required=True,

benchmarks/benchmark_throughput.py

@@ -5,12 +5,13 @@ import random
 import time
 from typing import List, Tuple
 
-from cacheflow import LLM, SamplingParams
 import torch
 from transformers import (AutoConfig, AutoTokenizer, AutoModelForCausalLM,
                           PreTrainedTokenizerBase)
 from tqdm import tqdm
 
+from vllm import LLM, SamplingParams
+
 
 def get_tokenizer(model_name: str) -> PreTrainedTokenizerBase:
     config = AutoConfig.from_pretrained(model_name)
@@ -70,7 +71,7 @@ def sample_requests(
     return sampled_requests
 
 
-def run_cacheflow(
+def run_vllm(
     requests: List[Tuple[str, int, int]],
     model: str,
     tensor_parallel_size: int,
@@ -172,8 +173,8 @@ def main(args: argparse.Namespace):
     tokenizer = get_tokenizer(args.model)
     requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
 
-    if args.backend == "cacheflow":
-        elapsed_time = run_cacheflow(
+    if args.backend == "vllm":
+        elapsed_time = run_vllm(
             requests, args.model, args.tensor_parallel_size, args.seed, args.n,
             args.use_beam_search)
     elif args.backend == "hf":
@@ -192,8 +193,8 @@ def main(args: argparse.Namespace):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Benchmark the throughput.")
-    parser.add_argument("--backend", type=str, choices=["cacheflow", "hf"],
-                        default="cacheflow")
+    parser.add_argument("--backend", type=str, choices=["vllm", "hf"],
+                        default="vllm")
     parser.add_argument("--dataset", type=str, required=True,
                         help="Path to the dataset.")
     parser.add_argument("--model", type=str, default="facebook/opt-125m")
@@ -207,7 +208,7 @@ if __name__ == "__main__":
     parser.add_argument("--hf-max-batch-size", type=int, default=None,
                         help="Maximum batch size for HF backend.")
     args = parser.parse_args()
-    if args.backend == "cacheflow":
+    if args.backend == "vllm":
         if args.hf_max_batch_size is not None:
             raise ValueError("HF max batch size is only for HF backend.")
     elif args.backend == "hf":

cacheflow/__init__.py

@@ -1,18 +0,0 @@
-from cacheflow.engine.arg_utils import EngineArgs
-from cacheflow.engine.llm_engine import LLMEngine
-from cacheflow.engine.ray_utils import initialize_cluster
-from cacheflow.entrypoints.llm import LLM
-from cacheflow.outputs import CompletionOutput, RequestOutput
-from cacheflow.sampling_params import SamplingParams
-
-__version__ = "0.1.0"
-
-__all__ = [
-    "LLM",
-    "SamplingParams",
-    "RequestOutput",
-    "CompletionOutput",
-    "LLMEngine",
-    "EngineArgs",
-    "initialize_cluster",
-]

cacheflow/model_executor/__init__.py

@@ -1,10 +0,0 @@
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.model_loader import get_model
-from cacheflow.model_executor.utils import set_random_seed
-
-
-__all__ = [
-    "InputMetadata",
-    "get_model",
-    "set_random_seed",
-]

cacheflow/model_executor/models/__init__.py

@@ -1,12 +0,0 @@
-from cacheflow.model_executor.models.gpt_neox import GPTNeoXForCausalLM
-from cacheflow.model_executor.models.gpt2 import GPT2LMHeadModel
-from cacheflow.model_executor.models.llama import LlamaForCausalLM
-from cacheflow.model_executor.models.opt import OPTForCausalLM
-
-
-__all__ = [
-    "GPT2LMHeadModel",
-    "GPTNeoXForCausalLM",
-    "LlamaForCausalLM",
-    "OPTForCausalLM",
-]

csrc/activation_kernels.cu

@@ -1,7 +1,7 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
-namespace cacheflow {
+namespace vllm {
 
 template<typename T>
 __device__ __forceinline__ T silu(const T& x) {
@@ -22,7 +22,7 @@ __global__ void silu_and_mul_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void silu_and_mul(
   torch::Tensor& out,      // [num_tokens, d]
@@ -40,7 +40,7 @@ void silu_and_mul(
     input.scalar_type(),
     "silu_and_mul_kernel",
     [&] {
-      cacheflow::silu_and_mul_kernel<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::silu_and_mul_kernel<scalar_t><<<grid, block, 0, stream>>>(
         out.data_ptr<scalar_t>(),
         input.data_ptr<scalar_t>(),
         d);

csrc/attention/attention_generic.cuh

@@ -1,6 +1,6 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,7 +19,7 @@
 
 #include <stdint.h>
 
-namespace cacheflow {
+namespace vllm {
 
 // A vector type to store Q, K, V elements.
 template<typename T, int VEC_SIZE>
@@ -61,4 +61,4 @@ inline __device__ void zero(T& dst) {
   dst = tmp.raw;
 }
 
-} // namespace cacheflow
+} // namespace vllm

csrc/attention/attention_kernels.cu

@@ -1,6 +1,6 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -27,7 +27,7 @@
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 
-namespace cacheflow {
+namespace vllm {
 
 // Utility function for attention softmax.
 template<int NUM_WARPS>
@@ -315,10 +315,10 @@ __global__ void single_query_cached_kv_attention_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 #define LAUNCH_ATTENTION_KERNEL(T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS)                        \
-  cacheflow::single_query_cached_kv_attention_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>   \
+  vllm::single_query_cached_kv_attention_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>        \
   <<<grid, block, shared_mem_size, stream>>>(                                                 \
     out_ptr,                                                                                  \
     query_ptr,                                                                                \

csrc/attention/attention_utils.cuh

@@ -1,6 +1,6 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -22,7 +22,7 @@
 #include <float.h>
 #include <type_traits>
 
-namespace cacheflow {
+namespace vllm {
 
 // Q*K^T operation.
 template<int THREAD_GROUP_SIZE, typename Vec, int N>
@@ -52,4 +52,4 @@ struct Qk_dot {
   }
 };
 
-} // namespace cacheflow
+} // namespace vllm

csrc/attention/dtype_bfloat16.cuh

@@ -1,7 +1,7 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
  * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,7 +25,7 @@
 #include <cuda_fp16.h>
 #include <stdint.h>
 
-namespace cacheflow {
+namespace vllm {
 
 // Define custom BF16 vector data types.
 struct bf16_4_t {
@@ -420,4 +420,4 @@ inline __device__ void from_float(bf16_8_t& dst, Float8_ src) {
 #endif
 }
 
-} // namespace cacheflow
+} // namespace vllm

csrc/attention/dtype_float16.cuh

@@ -1,7 +1,7 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
  * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -23,7 +23,7 @@
 
 #include <stdint.h>
 
-namespace cacheflow {
+namespace vllm {
 
 // FP16 vector types for Q, K, V.
 template<>
@@ -441,4 +441,4 @@ inline __device__ Float8_ to_float(uint4 u) {
   return tmp;
 }
 
-} // namespace cacheflow
+} // namespace vllm

csrc/attention/dtype_float32.cuh

@@ -1,7 +1,7 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
  * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -22,7 +22,7 @@
 
 #include <stdint.h>
 
-namespace cacheflow {
+namespace vllm {
 
 // Define custom FP32 vector data types.
 struct Float4_ {
@@ -265,4 +265,4 @@ inline __device__ Float8_ to_float(Float8_ u) {
   return u;
 }
 
-} // namespace cacheflow
+} // namespace vllm

csrc/cache_kernels.cu

@@ -46,7 +46,7 @@ void swap_blocks(
   }
 }
 
-namespace cacheflow {
+namespace vllm {
 
 // Grid: (num_layers, num_pairs)
 template<typename scalar_t>
@@ -77,7 +77,7 @@ __global__ void copy_blocks_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void copy_blocks(
   std::vector<torch::Tensor>& key_caches,
@@ -129,7 +129,7 @@ void copy_blocks(
     at::ScalarType::Half,
     at::ScalarType::BFloat16,
     key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
-      cacheflow::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key_cache_ptrs_tensor.data_ptr<int64_t>(),
         value_cache_ptrs_tensor.data_ptr<int64_t>(),
         block_mapping_tensor.data_ptr<int>(),
@@ -137,7 +137,7 @@ void copy_blocks(
     }));
 }
 
-namespace cacheflow {
+namespace vllm {
 
 template<typename scalar_t>
 __global__ void reshape_and_cache_kernel(
@@ -181,7 +181,7 @@ __global__ void reshape_and_cache_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void reshape_and_cache(
   torch::Tensor& key,           // [num_tokens, num_heads, head_size]
@@ -208,7 +208,7 @@ void reshape_and_cache(
     key.scalar_type(),
     "reshape_and_cache_kernel",
     [&] {
-      cacheflow::reshape_and_cache_kernel<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::reshape_and_cache_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key.data_ptr<scalar_t>(),
         value.data_ptr<scalar_t>(),
         key_cache.data_ptr<scalar_t>(),
@@ -223,7 +223,7 @@ void reshape_and_cache(
     });
 }
 
-namespace cacheflow {
+namespace vllm {
 
 // Grid: (num_blocks, block_size).
 template<typename scalar_t>
@@ -343,7 +343,7 @@ __global__ void gather_cached_kv_kernel_optimized(
     }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void gather_cached_kv(
   torch::Tensor& key,           // [out] [num_tokens, num_heads, head_size]
@@ -370,7 +370,7 @@ void gather_cached_kv(
     key.scalar_type(),
     "gather_cached_kv_kernel_optimized",
     [&] {
-      cacheflow::gather_cached_kv_kernel_optimized<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::gather_cached_kv_kernel_optimized<scalar_t><<<grid, block, 0, stream>>>(
         key.data_ptr<scalar_t>(),
         value.data_ptr<scalar_t>(),
         key_cache.data_ptr<scalar_t>(),

csrc/layernorm_kernels.cu

@@ -3,7 +3,7 @@
 
 #include "reduction_utils.cuh"
 
-namespace cacheflow {
+namespace vllm {
 
 // TODO(woosuk): Further optimize this kernel.
 template<typename scalar_t>
@@ -33,7 +33,7 @@ __global__ void rms_norm_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void rms_norm(
   torch::Tensor& out,      // [num_tokens, hidden_size]
@@ -52,7 +52,7 @@ void rms_norm(
     input.scalar_type(),
     "rms_norm_kernel",
     [&] {
-      cacheflow::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
         out.data_ptr<scalar_t>(),
         input.data_ptr<scalar_t>(),
         weight.data_ptr<scalar_t>(),

csrc/pos_encoding_kernels.cu

@@ -1,7 +1,7 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
-namespace cacheflow {
+namespace vllm {
 
 template<typename scalar_t>
 __global__ void rotary_embedding_neox_kernel(
@@ -46,7 +46,7 @@ __global__ void rotary_embedding_neox_kernel(
   }
 }
 
-} // namespace cacheflow
+} // namespace vllm
 
 void rotary_embedding_neox(
   torch::Tensor& positions,         // [num_tokens]
@@ -70,7 +70,7 @@ void rotary_embedding_neox(
     query.scalar_type(),
     "rotary_embedding_neox",
     [&] {
-      cacheflow::rotary_embedding_neox_kernel<scalar_t><<<grid, block, 0, stream>>>(
+      vllm::rotary_embedding_neox_kernel<scalar_t><<<grid, block, 0, stream>>>(
         positions.data_ptr<int64_t>(),
         query.data_ptr<scalar_t>(),
         key.data_ptr<scalar_t>(),

csrc/reduction_utils.cuh

@@ -1,6 +1,6 @@
 /*
  * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/reduce_kernel_utils.cuh
- * Copyright (c) 2023, The CacheFlow team.
+ * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,7 +17,7 @@
  */
 #pragma once
 
-namespace cacheflow {
+namespace vllm {
 
 template<typename T>
 __inline__ __device__ T warpReduceSum(T val) {
@@ -48,4 +48,4 @@ __inline__ __device__ T blockReduceSum(T val) {
   return val;
 }
 
-} // namespace cacheflow
+} // namespace vllm

docs/README.md

@@ -1,4 +1,4 @@
-# CacheFlow documents
+# vLLM documents
 
 ## Build the docs
 

docs/source/conf.py

@@ -17,9 +17,9 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'CacheFlow'
-copyright = '2023, CacheFlow Team'
-author = 'the CacheFlow Team'
+project = 'vLLM'
+copyright = '2023, vLLM Team'
+author = 'the vLLM Team'
 
 
 # -- General configuration ---------------------------------------------------
@@ -55,7 +55,7 @@ html_title = project
 html_theme = 'sphinx_book_theme'
 html_theme_options = {
     'path_to_docs': 'docs/source',
-    'repository_url': 'https://github.com/WoosukKwon/cacheflow',
+    'repository_url': 'https://github.com/WoosukKwon/vllm',
     'use_repository_button': True,
 }
 

docs/source/getting_started/installation.rst

@@ -1,8 +1,8 @@
 Installation
 ============
 
-CacheFlow is a Python library that includes some C++ and CUDA code.
-CacheFlow can run on systems that meet the following requirements:
+vLLM is a Python library that includes some C++ and CUDA code.
+vLLM can run on systems that meet the following requirements:
 
 * OS: Linux
 * Python: 3.8 or higher
@@ -10,23 +10,23 @@ CacheFlow can run on systems that meet the following requirements:
 * GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, etc.)
 
 .. note::
-    As of now, CacheFlow does not support CUDA 12.
+    As of now, vLLM does not support CUDA 12.
     If you are using Hopper or Lovelace GPUs, please use CUDA 11.8.
 
 .. tip::
-    If you have trouble installing CacheFlow, we recommend using the NVIDIA PyTorch Docker image.
+    If you have trouble installing vLLM, we recommend using the NVIDIA PyTorch Docker image.
 
     .. code-block:: console
 
         $ # Pull the Docker image with CUDA 11.8.
         $ docker run --gpus all -it --rm --shm-size=8g nvcr.io/nvidia/pytorch:22.12-py3
 
-    Inside the Docker container, please execute :code:`pip uninstall torch` before installing CacheFlow.
+    Inside the Docker container, please execute :code:`pip uninstall torch` before installing vLLM.
 
 Install with pip
 ----------------
 
-You can install CacheFlow using pip:
+You can install vLLM using pip:
 
 .. code-block:: console
 
@@ -34,8 +34,8 @@ You can install CacheFlow using pip:
     $ conda create -n myenv python=3.8 -y
     $ conda activate myenv
 
-    $ # Install CacheFlow.
-    $ pip install cacheflow  # This may take 5-10 minutes.
+    $ # Install vLLM.
+    $ pip install vllm  # This may take 5-10 minutes.
 
 
 .. _build_from_source:
@@ -43,10 +43,10 @@ You can install CacheFlow using pip:
 Build from source
 -----------------
 
-You can also build and install CacheFlow from source.
+You can also build and install vLLM from source.
 
 .. code-block:: console
 
-    $ git clone https://github.com/WoosukKwon/cacheflow.git
-    $ cd cacheflow
+    $ git clone https://github.com/WoosukKwon/vllm.git
+    $ cd vllm
     $ pip install -e .  # This may take 5-10 minutes.

docs/source/getting_started/quickstart.rst

@@ -8,7 +8,7 @@ Placeholder.
 
 .. code-block:: python
 
-    from cacheflow import LLM, SamplingParams
+    from vllm import LLM, SamplingParams
 
     # Sample prompts.
     prompts = [

docs/source/index.rst

@@ -1,5 +1,5 @@
-Welcome to CacheFlow!
-=====================
+Welcome to vLLM!
+================
 
 Documentation
 -------------

docs/source/models/adding_model.rst

@@ -3,30 +3,30 @@
 Adding a New Model
 ==================
 
-This document provides a high-level guide on integrating a `HuggingFace Transformers <https://github.com/huggingface/transformers>`_ model into CacheFlow.
+This document provides a high-level guide on integrating a `HuggingFace Transformers <https://github.com/huggingface/transformers>`_ model into vLLM.
 
 .. note::
     The complexity of adding a new model depends heavily on the model's architecture.
-    The process is considerably straightforward if the model shares a similar architecture with an existing model in CacheFlow.
+    The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
     However, for models that include new operators (e.g., a new attention mechanism), the process can be a bit more complex.
 
 .. tip::
-    If you are encountering issues while integrating your model into CacheFlow, feel free to open an issue on our `GitHub <https://github.com/WoosukKwon/cacheflow/issues>`_ repository.
+    If you are encountering issues while integrating your model into vLLM, feel free to open an issue on our `GitHub <https://github.com/WoosukKwon/vllm/issues>`_ repository.
     We will be happy to help you out!
 
 
-0. Fork the CacheFlow repository
+0. Fork the vLLM repository
 --------------------------------
 
-Start by forking our `GitHub <https://github.com/WoosukKwon/cacheflow/issues>`_ repository and then :ref:`build it from source <build_from_source>`.
+Start by forking our `GitHub <https://github.com/WoosukKwon/vllm/issues>`_ repository and then :ref:`build it from source <build_from_source>`.
 This gives you the ability to modify the codebase and test your model.
 
 
 1. Bring your model code
 ------------------------
 
-Clone the PyTorch model code from the HuggingFace Transformers repository and put it into the `cacheflow/model_executor/models <https://github.com/WoosukKwon/cacheflow/tree/main/cacheflow/model_executor/models>`_ directory.
-For instance, CacheFlow's `OPT model <https://github.com/WoosukKwon/cacheflow/blob/main/cacheflow/model_executor/models/opt.py>`_ was adpated from the HuggingFace's `modeling_opt.py <https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py>`_ file.
+Clone the PyTorch model code from the HuggingFace Transformers repository and put it into the `vllm/model_executor/models <https://github.com/WoosukKwon/vllm/tree/main/vllm/model_executor/models>`_ directory.
+For instance, vLLM's `OPT model <https://github.com/WoosukKwon/vllm/blob/main/vllm/model_executor/models/opt.py>`_ was adpated from the HuggingFace's `modeling_opt.py <https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py>`_ file.
 
 .. warning::
     When copying the model code, make sure to review and adhere to the code's copyright and licensing terms.
@@ -62,11 +62,11 @@ Next, you need to rewrite the :code:`forward` methods of your model by following
     +) -> Dict[int, SequenceOutputs]:
 
 3. Update the code by considering that :code:`input_ids` and :code:`positions` are now flattened tensors.
-4. Replace the attention operation with either :code:`GPTCacheFlowAttention` or :code:`GPTNeoXCacheFlowAttention`, depending on the model's architecture.
+4. Replace the attention operation with either :code:`GPTPagedAttention` or :code:`GPTNeoXPagedAttention`, depending on the model's architecture.
 
 .. note::
-    Currently, CacheFlow supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
-    If your model employs a different attention mechanism, you will need to implement a new attention layer in CacheFlow.
+    Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
+    If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
 
 
 3. (Optional) Implement tensor parallelism support
@@ -91,4 +91,4 @@ While the process is straightforward for most layers, the tensor-parallel layers
 5. Register your model
 ----------------------
 
-Finally, include your :code:`*ForCausalLM` class in `cacheflow/model_executor/models/__init__.py <https://github.com/WoosukKwon/cacheflow/blob/main/cacheflow/model_executor/models/__init__.py>`_ and register it to the :code:`_MODEL_REGISTRY` in `cacheflow/model_executor/model_loader.py <https://github.com/WoosukKwon/cacheflow/blob/main/cacheflow/model_executor/model_loader.py>`_.
+Finally, include your :code:`*ForCausalLM` class in `vllm/model_executor/models/__init__.py <https://github.com/WoosukKwon/vllm/blob/main/vllm/model_executor/models/__init__.py>`_ and register it to the :code:`_MODEL_REGISTRY` in `vllm/model_executor/model_loader.py <https://github.com/WoosukKwon/vllm/blob/main/vllm/model_executor/model_loader.py>`_.

docs/source/models/supported_models.rst

@@ -3,8 +3,8 @@
 Supported Models
 ================
 
-CacheFlow supports a variety of generative Transformer models in `HuggingFace Transformers <https://github.com/huggingface/transformers>`_.
-The following is the list of model architectures that are currently supported by CacheFlow.
+vLLM supports a variety of generative Transformer models in `HuggingFace Transformers <https://github.com/huggingface/transformers>`_.
+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.
 
 .. list-table::
@@ -22,19 +22,19 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`OPTForCausalLM`
     - OPT, OPT-IML
 
-If your model uses one of the above model architectures, you can seamlessly run your model with CacheFlow.
+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>` for instructions on how to implement support for your model.
-Alternatively, you can raise an issue on our `GitHub <https://github.com/WoosukKwon/cacheflow/issues>`_ project.
+Alternatively, you can raise an issue on our `GitHub <https://github.com/WoosukKwon/vllm/issues>`_ project.
 
 .. tip::
     The easiest way to check if your model is supported is to run the program below:
 
     .. code-block:: python
 
-        from cacheflow import LLM
+        from vllm import LLM
 
         llm = LLM(model=...)  # Name or path of your model
         output = llm.generate("Hello, my name is")
         print(output)
 
-    If CacheFlow successfully generates text, it indicates that your model is supported.
+    If vLLM successfully generates text, it indicates that your model is supported.

examples/api_client.py

@@ -1,4 +1,4 @@
-"""Example Python client for cacheflow.entrypoints.api_server"""
+"""Example Python client for vllm.entrypoints.api_server"""
 
 import argparse
 import json

examples/gradio_webserver.py

@@ -6,7 +6,7 @@ import requests
 
 
 def http_bot(prompt):
-    headers = {"User-Agent": "Cacheflow Client"}
+    headers = {"User-Agent": "vLLM Client"}
     pload = {
         "prompt": prompt,
         "stream": True,
@@ -24,7 +24,7 @@ def http_bot(prompt):
 def build_demo():
     with gr.Blocks() as demo:
         gr.Markdown(
-            "# Cacheflow text completion demo\n"
+            "# vLLM text completion demo\n"
         )
         inputbox = gr.Textbox(label="Input", placeholder="Enter text and press ENTER")
         outputbox = gr.Textbox(label="Output", placeholder="Generated result from the model")

examples/llm_engine_example.py

@@ -1,6 +1,6 @@
 import argparse
 
-from cacheflow import EngineArgs, LLMEngine, SamplingParams
+from vllm import EngineArgs, LLMEngine, SamplingParams
 
 
 def main(args: argparse.Namespace):

examples/offline_inference.py

@@ -1,4 +1,4 @@
-from cacheflow import LLM, SamplingParams
+from vllm import LLM, SamplingParams
 
 
 # Sample prompts.

examples/openai_client.py

@@ -1,6 +1,6 @@
 import openai
 
-# Modify OpenAI's API key and API base to use CacheFlow's API server.
+# Modify OpenAI's API key and API base to use vLLM's API server.
 openai.api_key = "EMPTY"
 openai.api_base = "http://localhost:8000/v1"
 model = "facebook/opt-125m"

mypy.ini

@@ -3,6 +3,6 @@ python_version = 3.8
 
 ignore_missing_imports = True
 
-files = cacheflow
+files = vllm
 # TODO(woosuk): Include the code from Megatron and HuggingFace.
-exclude = cacheflow/model_executor/parallel_utils/|cacheflow/model_executor/models/
+exclude = vllm/model_executor/parallel_utils/|vllm/model_executor/models/

setup.py

@@ -75,7 +75,7 @@ ext_modules = []
 
 # Cache operations.
 cache_extension = CUDAExtension(
-    name="cacheflow.cache_ops",
+    name="vllm.cache_ops",
     sources=["csrc/cache.cpp", "csrc/cache_kernels.cu"],
     extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
 )
@@ -83,7 +83,7 @@ ext_modules.append(cache_extension)
 
 # Attention kernels.
 attention_extension = CUDAExtension(
-    name="cacheflow.attention_ops",
+    name="vllm.attention_ops",
     sources=["csrc/attention.cpp", "csrc/attention/attention_kernels.cu"],
     extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
 )
@@ -91,7 +91,7 @@ ext_modules.append(attention_extension)
 
 # Positional encoding kernels.
 positional_encoding_extension = CUDAExtension(
-    name="cacheflow.pos_encoding_ops",
+    name="vllm.pos_encoding_ops",
     sources=["csrc/pos_encoding.cpp", "csrc/pos_encoding_kernels.cu"],
     extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
 )
@@ -99,7 +99,7 @@ ext_modules.append(positional_encoding_extension)
 
 # Layer normalization kernels.
 layernorm_extension = CUDAExtension(
-    name="cacheflow.layernorm_ops",
+    name="vllm.layernorm_ops",
     sources=["csrc/layernorm.cpp", "csrc/layernorm_kernels.cu"],
     extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
 )
@@ -107,7 +107,7 @@ ext_modules.append(layernorm_extension)
 
 # Activation kernels.
 activation_extension = CUDAExtension(
-    name="cacheflow.activation_ops",
+    name="vllm.activation_ops",
     sources=["csrc/activation.cpp", "csrc/activation_kernels.cu"],
     extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
 )
@@ -144,18 +144,18 @@ def get_requirements() -> List[str]:
 
 
 setuptools.setup(
-    name="cacheflow",
-    version=find_version(get_path("cacheflow", "__init__.py")),
-    author="CacheFlow Team",
-    author_email="cacheflow@gmail.com",
+    name="vllm",
+    version=find_version(get_path("vllm", "__init__.py")),
+    author="vLLM Team",
+    author_email="vllm@gmail.com",  # FIXME
     license="Apache 2.0",
-    description="CacheFlow: A high-performance LLM Serving System",
+    description="vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention",  # FIXME
     long_description=read_readme(),
     long_description_content_type="text/markdown",
-    url="https://github.com/WoosukKwon/cacheflow",
+    url="https://github.com/WoosukKwon/vllm",
     project_urls={
-        "Homepage": "https://github.com/WoosukKwon/cacheflow",
-        "Documentation": "https://cacheflow.readthedocs.io/en/latest/",
+        "Homepage": "https://github.com/WoosukKwon/vllm",
+        "Documentation": "https://vllm.readthedocs.io/en/latest/",  # FIXME
     },
     classifiers=[
         "Programming Language :: Python :: 3.8",

tests/kernels/test_activation.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
 
-from cacheflow import activation_ops
+from vllm import activation_ops
 
 
 def ref_silu_and_mul(x: torch.Tensor) -> torch.Tensor:

tests/kernels/test_attention.py

@@ -5,7 +5,7 @@ import torch
 from xformers import ops as xops
 from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
 
-from cacheflow import attention_ops
+from vllm import attention_ops
 
 MAX_SEQ_LEN = 4096
 TEST_SEED = 0

tests/kernels/test_cache.py

@@ -2,7 +2,7 @@ import random
 
 import torch
 
-from cacheflow import cache_ops
+from vllm import cache_ops
 
 
 @torch.inference_mode()

tests/kernels/test_layernorm.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 
-from cacheflow import layernorm_ops
+from vllm import layernorm_ops
 
 
 class RefRMSNorm(nn.Module):

tests/kernels/test_pos_encoding.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from cacheflow import pos_encoding_ops
+from vllm import pos_encoding_ops
 
 
 def rotate_half(x: torch.Tensor) -> torch.Tensor:

vllm/__init__.py

@@ -0,0 +1,21 @@
+from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.llm_engine import LLMEngine
+from vllm.engine.ray_utils import initialize_cluster
+from vllm.entrypoints.llm import LLM
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.sampling_params import SamplingParams
+
+__version__ = "0.1.0"
+
+__all__ = [
+    "LLM",
+    "SamplingParams",
+    "RequestOutput",
+    "CompletionOutput",
+    "LLMEngine",
+    "EngineArgs",
+    "AsyncLLMEngine",
+    "AsyncEngineArgs",
+    "initialize_cluster",
+]

vllm/block.py

@@ -1,7 +1,7 @@
 """Token blocks."""
 from typing import List
 
-from cacheflow.utils import Device
+from vllm.utils import Device
 
 _BLANK_TOKEN_ID = -1
 

vllm/config.py

@@ -3,8 +3,8 @@ from typing import Optional
 import torch
 from transformers import AutoConfig, PretrainedConfig
 
-from cacheflow.logger import init_logger
-from cacheflow.utils import get_cpu_memory
+from vllm.logger import init_logger
+from vllm.utils import get_cpu_memory
 
 logger = init_logger(__name__)
 
@@ -87,7 +87,7 @@ class CacheConfig:
     Args:
         block_size: Size of a cache block in number of tokens.
         gpu_memory_utilization: Fraction of GPU memory to use for the
-            CacheFlow execution.
+            vLLM execution.
         swap_space: Size of the CPU swap space per GPU (in GiB).
     """
     def __init__(

vllm/core/block_manager.py

@@ -1,9 +1,9 @@
 """A block manager that manages token blocks."""
 from typing import Dict, List, Optional, Set, Tuple
 
-from cacheflow.block import PhysicalTokenBlock
-from cacheflow.sequence import Sequence, SequenceGroup, SequenceStatus
-from cacheflow.utils import Device
+from vllm.block import PhysicalTokenBlock
+from vllm.sequence import Sequence, SequenceGroup, SequenceStatus
+from vllm.utils import Device
 
 
 class BlockAllocator:

vllm/core/policy.py

@@ -1,6 +1,6 @@
 from typing import List
 
-from cacheflow.sequence import SequenceGroup
+from vllm.sequence import SequenceGroup
 
 
 class Policy:

vllm/core/scheduler.py

@@ -2,13 +2,13 @@ import enum
 import time
 from typing import Dict, List, Optional, Tuple
 
-from cacheflow.config import CacheConfig, SchedulerConfig
-from cacheflow.core.block_manager import BlockSpaceManager
-from cacheflow.core.policy import PolicyFactory
-from cacheflow.logger import init_logger
-from cacheflow.sequence import (Sequence, SequenceData, SequenceGroup,
-                                SequenceGroupMetadata, SequenceOutputs,
-                                SequenceStatus)
+from vllm.config import CacheConfig, SchedulerConfig
+from vllm.core.block_manager import BlockSpaceManager
+from vllm.core.policy import PolicyFactory
+from vllm.logger import init_logger
+from vllm.sequence import (Sequence, SequenceData, SequenceGroup,
+                           SequenceGroupMetadata, SequenceOutputs,
+                           SequenceStatus)
 
 logger = init_logger(__name__)
 

vllm/engine/arg_utils.py

@@ -3,13 +3,13 @@ import dataclasses
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
-from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
-                              SchedulerConfig)
+from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig)
 
 
 @dataclass
 class EngineArgs:
-    """Arguments for CacheFlow engine."""
+    """Arguments for vLLM engine."""
     model: str
     download_dir: Optional[str] = None
     use_np_weights: bool = False
@@ -33,7 +33,7 @@ class EngineArgs:
     def add_cli_args(
         parser: argparse.ArgumentParser,
     ) -> argparse.ArgumentParser:
-        """Shared CLI arguments for CacheFlow engine."""
+        """Shared CLI arguments for vLLM engine."""
         # Model arguments
         parser.add_argument('--model', type=str, default='facebook/opt-125m',
                             help='name or path of the huggingface model to use')
@@ -118,7 +118,7 @@ class EngineArgs:
 
 @dataclass
 class AsyncEngineArgs(EngineArgs):
-    """Arguments for asynchronous CacheFlow engine."""
+    """Arguments for asynchronous vLLM engine."""
     engine_use_ray: bool = False
     disable_log_requests: bool = False
 

vllm/engine/async_llm_engine.py

@@ -2,12 +2,12 @@ import asyncio
 import time
 from typing import Dict, List, Optional
 
-from cacheflow.engine.arg_utils import AsyncEngineArgs
-from cacheflow.engine.llm_engine import LLMEngine
-from cacheflow.engine.ray_utils import initialize_cluster, ray
-from cacheflow.logger import init_logger
-from cacheflow.outputs import RequestOutput
-from cacheflow.sampling_params import SamplingParams
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.llm_engine import LLMEngine
+from vllm.engine.ray_utils import initialize_cluster, ray
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
 
 logger = init_logger(__name__)
 
@@ -104,7 +104,7 @@ class AsyncLLMEngine:
         arrival_time = time.time()
 
         # Create an event to notify us that there is new output from the
-        # cacheflow engine.
+        # vLLM engine.
         request_event = asyncio.Event()
         self.request_events[request_id] = request_event
 
@@ -114,7 +114,7 @@ class AsyncLLMEngine:
                         f"sampling params: {sampling_params}, "
                         f"prompt token ids: {prompt_token_ids}.")
 
-        # Add the request into the cacheflow engine's waiting queue.
+        # Add the request into the vLLM engine's waiting queue.
         if self.engine_use_ray:
             await self.engine.add_request.remote(
                 request_id, prompt, sampling_params,
@@ -126,7 +126,7 @@ class AsyncLLMEngine:
                 prompt_token_ids=prompt_token_ids,
                 arrival_time=arrival_time)
 
-        # The cacheflow engine does not have a background loop that keeps
+        # The vLLM engine does not have a background loop that keeps
         # processing incoming requests. Therefore, we need to keep kicking
         # the engine to process the requests.
         while True:

vllm/engine/llm_engine.py

@@ -1,19 +1,18 @@
 import time
 from typing import Any, List, Optional
 
-from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
-                              SchedulerConfig)
-from cacheflow.core.scheduler import Scheduler
-from cacheflow.engine.arg_utils import EngineArgs
-from cacheflow.engine.ray_utils import DeviceID, initialize_cluster, ray
-from cacheflow.engine.tokenizer_utils import (detokenize_incrementally,
-                                              get_tokenizer)
-from cacheflow.logger import init_logger
-from cacheflow.outputs import RequestOutput
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.sequence import Sequence, SequenceGroup, SequenceStatus
-from cacheflow.utils import Counter
-from cacheflow.worker.worker import Worker
+from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig)
+from vllm.core.scheduler import Scheduler
+from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.ray_utils import DeviceID, initialize_cluster, ray
+from vllm.engine.tokenizer_utils import detokenize_incrementally, get_tokenizer
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import Sequence, SequenceGroup, SequenceStatus
+from vllm.utils import Counter
+from vllm.worker.worker import Worker
 
 logger = init_logger(__name__)
 
@@ -21,7 +20,7 @@ logger = init_logger(__name__)
 class LLMEngine:
     """An LLM engine that receives requests and generates texts.
 
-    This is the main class for the CacheFlow LLM engine. It receives requests
+    This is the main class for the vLLM engine. It receives requests
     from clients and generates texts from the LLM. It includes a tokenizer, a
     language model (possibly distributed across multiple GPUs), and GPU memory
     space allocated for intermediate states (aka KV cache). This class utilizes

vllm/engine/ray_utils.py

@@ -6,7 +6,7 @@ try:
 except ImportError:
     ray = None
 
-from cacheflow.config import ParallelConfig
+from vllm.config import ParallelConfig
 
 DeviceID = Tuple[int, Optional[str], int]  # rank, node resource (node IP), device id
 

vllm/engine/tokenizer_utils.py

@@ -3,7 +3,7 @@ from typing import List, Tuple, Union
 from transformers import (AutoConfig, AutoTokenizer, PreTrainedTokenizer,
                           PreTrainedTokenizerFast)
 
-from cacheflow.logger import init_logger
+from vllm.logger import init_logger
 
 logger = init_logger(__name__)
 

vllm/entrypoints/api_server.py

@@ -6,10 +6,10 @@ from fastapi import BackgroundTasks, FastAPI, Request
 from fastapi.responses import Response, StreamingResponse
 import uvicorn
 
-from cacheflow.engine.arg_utils import AsyncEngineArgs
-from cacheflow.engine.async_llm_engine import AsyncLLMEngine
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.utils import random_uuid
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.sampling_params import SamplingParams
+from vllm.utils import random_uuid
 
 TIMEOUT_KEEP_ALIVE = 5 # seconds.
 TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds

vllm/entrypoints/llm.py

@@ -3,11 +3,11 @@ from typing import List, Optional, Union
 from tqdm import tqdm
 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 
-from cacheflow.engine.arg_utils import EngineArgs
-from cacheflow.engine.llm_engine import LLMEngine
-from cacheflow.outputs import RequestOutput
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.utils import Counter
+from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.llm_engine import LLMEngine
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.utils import Counter
 
 
 class LLM:

vllm/entrypoints/openai/api_server.py

@@ -13,17 +13,17 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
 import uvicorn
 
-from cacheflow.engine.arg_utils import AsyncEngineArgs
-from cacheflow.engine.async_llm_engine import AsyncLLMEngine
-from cacheflow.engine.tokenizer_utils import get_tokenizer
-from cacheflow.entrypoints.openai.protocol import (
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.tokenizer_utils import get_tokenizer
+from vllm.entrypoints.openai.protocol import (
     CompletionRequest, CompletionResponse, CompletionResponseChoice,
     CompletionResponseStreamChoice, CompletionStreamResponse, ErrorResponse,
     LogProbs, ModelCard, ModelList, ModelPermission, UsageInfo)
-from cacheflow.logger import init_logger
-from cacheflow.outputs import RequestOutput
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.utils import random_uuid
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.utils import random_uuid
 
 TIMEOUT_KEEP_ALIVE = 5 # seconds
 
@@ -93,11 +93,11 @@ async def create_completion(raw_request: Request):
     for the API specification. This API mimics the OpenAI Completion API.
 
     NOTE: Currently we do not support the following features:
-        - echo (since the cacheflow engine does not currently support
+        - echo (since the vLLM engine does not currently support
           getting the logprobs of prompt tokens)
         - suffix (the language models we currently support do not support
           suffix)
-        - logit_bias (to be supported in cacheflow engine)
+        - logit_bias (to be supported by vLLM engine)
     """
     request = CompletionRequest(**await raw_request.json())
     logger.info(f"Received completion request: {request}")
@@ -107,7 +107,7 @@ async def create_completion(raw_request: Request):
         return error_check_ret
 
     if request.echo:
-        # We do not support echo since the cacheflow engine does not
+        # We do not support echo since the vLLM engine does not
         # currently support getting the logprobs of prompt tokens.
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "echo is not currently supported")
@@ -118,7 +118,7 @@ async def create_completion(raw_request: Request):
                                     "suffix is not currently supported")
 
     if request.logit_bias is not None:
-        # TODO: support logit_bias in cacheflow engine.
+        # TODO: support logit_bias in vLLM engine.
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "logit_bias is not currently supported")
 
@@ -274,7 +274,7 @@ async def create_completion(raw_request: Request):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="CacheFlow OpenAI-Compatible RESTful API server."
+        description="vLLM OpenAI-Compatible RESTful API server."
     )
     parser.add_argument("--host", type=str, default="localhost", help="host name")
     parser.add_argument("--port", type=int, default=8000, help="port number")

vllm/entrypoints/openai/protocol.py

@@ -4,7 +4,7 @@ from typing import Dict, List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field
 
-from cacheflow.utils import random_uuid
+from vllm.utils import random_uuid
 
 
 class ErrorResponse(BaseModel):
@@ -34,7 +34,7 @@ class ModelCard(BaseModel):
     id: str
     object: str = "model"
     created: int = Field(default_factory=lambda: int(time.time()))
-    owned_by: str = "cacheflow"
+    owned_by: str = "vllm"
     root: Optional[str] = None
     parent: Optional[str] = None
     permission: List[ModelPermission] = Field(default_factory=list)
@@ -82,7 +82,7 @@ class CompletionRequest(BaseModel):
     best_of: Optional[int] = None
     logit_bias: Optional[Dict[str, float]] = None
     user: Optional[str] = None
-    # Additional parameters supported by cacheflow
+    # Additional parameters supported by vLLM
     top_k: Optional[int] = -1
     ignore_eos: Optional[bool] = False
     use_beam_search: Optional[bool] = False

vllm/logger.py

@@ -22,7 +22,7 @@ class NewLineFormatter(logging.Formatter):
         return msg
 
 
-_root_logger = logging.getLogger("cacheflow")
+_root_logger = logging.getLogger("vllm")
 _default_handler = None
 
 

vllm/model_executor/__init__.py

@@ -0,0 +1,10 @@
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.model_loader import get_model
+from vllm.model_executor.utils import set_random_seed
+
+
+__all__ = [
+    "InputMetadata",
+    "get_model",
+    "set_random_seed",
+]

vllm/model_executor/input_metadata.py

@@ -3,8 +3,8 @@ from typing import Dict, List, Tuple
 import torch
 from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
 
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.sequence import SequenceData
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import SequenceData
 
 
 class InputMetadata:

vllm/model_executor/layers/activation.py

@@ -2,7 +2,7 @@
 import torch
 import torch.nn as nn
 
-from cacheflow import activation_ops
+from vllm import activation_ops
 
 _ACTIVATION_REGISTRY = {
     "gelu": nn.GELU(),

vllm/model_executor/layers/attention.py

@@ -5,16 +5,16 @@ import torch
 import torch.nn as nn
 from xformers import ops as xops
 
-from cacheflow import attention_ops
-from cacheflow import cache_ops
-from cacheflow import pos_encoding_ops
-from cacheflow.model_executor.input_metadata import InputMetadata
+from vllm import attention_ops
+from vllm import cache_ops
+from vllm import pos_encoding_ops
+from vllm.model_executor.input_metadata import InputMetadata
 
 _SUPPORTED_HEAD_SIZES = [64, 80, 96, 128]
 
 
-class GPTCacheFlowAttention(nn.Module):
-    """GPT-style multi-head attention.
+class PagedAttention(nn.Module):
+    """GPT-style multi-head PagedAttention.
 
     This class takes flattened 1D query, key, and value tensors as input. The
     input 1D tensors can be split into three parts: the prompt tokens, the
@@ -164,8 +164,8 @@ class GPTCacheFlowAttention(nn.Module):
         return output.view(-1, self.num_heads * self.head_size)
 
 
-class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
-    """Attention with GPT-NeoX style rotary embedding."""
+class PagedAttentionWithRoPE(PagedAttention):
+    """PagedAttention with GPT-NeoX style rotary embedding."""
 
     def __init__(
         self,

vllm/model_executor/layers/layernorm.py

@@ -2,7 +2,7 @@
 import torch
 import torch.nn as nn
 
-from cacheflow import layernorm_ops
+from vllm import layernorm_ops
 
 
 class RMSNorm(nn.Module):

vllm/model_executor/layers/sampler.py

@@ -5,11 +5,11 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.parallel_utils.tensor_parallel import (
     gather_from_tensor_model_parallel_region)
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.sequence import SequenceOutputs
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import SequenceOutputs
 
 
 class Sampler(nn.Module):

vllm/model_executor/model_loader.py

@@ -5,10 +5,10 @@ import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
 
-from cacheflow.config import ModelConfig
-from cacheflow.model_executor.models import (
-    GPT2LMHeadModel, GPTNeoXForCausalLM, LlamaForCausalLM, OPTForCausalLM)
-from cacheflow.model_executor.weight_utils import initialize_dummy_weights
+from vllm.config import ModelConfig
+from vllm.model_executor.models import (GPT2LMHeadModel, GPTNeoXForCausalLM,
+                                        LlamaForCausalLM, OPTForCausalLM)
+from vllm.model_executor.weight_utils import initialize_dummy_weights
 
 # TODO(woosuk): Lazy-load the model classes.
 _MODEL_REGISTRY = {

vllm/model_executor/models/__init__.py

@@ -0,0 +1,12 @@
+from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
+from vllm.model_executor.models.gpt2 import GPT2LMHeadModel
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.models.opt import OPTForCausalLM
+
+
+__all__ = [
+    "GPT2LMHeadModel",
+    "GPTNeoXForCausalLM",
+    "LlamaForCausalLM",
+    "OPTForCausalLM",
+]

vllm/model_executor/models/gpt2.py

@@ -1,6 +1,6 @@
 # coding=utf-8
 # Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -26,17 +26,17 @@ import torch
 from torch import nn
 from transformers import GPT2Config
 
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.layers.activation import get_act_fn
-from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
-from cacheflow.model_executor.layers.sampler import Sampler
-from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
-                                                   load_tensor_parallel_weights)
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from cacheflow.sequence import SequenceOutputs
+from vllm.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -53,14 +53,14 @@ class GPT2Attention(nn.Module):
         self.head_dim = self.hidden_size // total_num_heads
         self.scale = self.head_dim ** -0.5
 
-        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size, bias=True,
-                                           gather_output=False,
+        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size,
+                                           bias=True, gather_output=False,
                                            perform_initialization=False)
-        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size, bias=True,
-                                        input_is_parallel=True,
+        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size,
+                                        bias=True, input_is_parallel=True,
                                         perform_initialization=False)
-        self.attn = GPTCacheFlowAttention(self.num_heads, self.head_dim,
-                                          scale=self.scale)
+        self.attn = PagedAttention(self.num_heads, self.head_dim,
+                                   scale=self.scale)
 
     def forward(
         self,

vllm/model_executor/models/gpt_neox.py

@@ -1,6 +1,6 @@
 # coding=utf-8
 # Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,17 +25,17 @@ import torch
 from torch import nn
 from transformers import GPTNeoXConfig
 
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.layers.activation import get_act_fn
-from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
-from cacheflow.model_executor.layers.sampler import Sampler
-from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
-                                                   load_tensor_parallel_weights)
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from cacheflow.sequence import SequenceOutputs
+from vllm.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -63,8 +63,8 @@ class GPTNeoXAttention(nn.Module):
         scaling = self.head_size ** -0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
         assert rotary_dim % 2 == 0
-        self.attn = GPTNeoXCacheFlowAttention(self.num_heads, self.head_size,
-                                              scaling, rotary_dim)
+        self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_size,
+                                           scaling, rotary_dim)
 
     def forward(
         self,
@@ -149,6 +149,7 @@ class GPTNeoXLayer(nn.Module):
 
 
 class GPTNeoXModel(nn.Module):
+
     def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.config = config

vllm/model_executor/models/llama.py

@@ -1,6 +1,6 @@
 # coding=utf-8
 # Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
 #
 # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
@@ -30,19 +30,19 @@ import torch
 from torch import nn
 from transformers import LlamaConfig
 
-from cacheflow.sequence import SequenceOutputs
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.layers.activation import SiluAndMul
-from cacheflow.model_executor.layers.layernorm import RMSNorm
-from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
-from cacheflow.model_executor.layers.sampler import Sampler
-from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
-                                                   load_tensor_parallel_weights)
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.sequence import SequenceOutputs
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from cacheflow.sequence import SequenceOutputs
+from vllm.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -104,8 +104,8 @@ class LlamaAttention(nn.Module):
             input_is_parallel=True,
             perform_initialization=False,
         )
-        self.attn = GPTNeoXCacheFlowAttention(self.num_heads, self.head_dim,
-                                              self.scaling, rotary_dim=self.head_dim)
+        self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_dim,
+                                           self.scaling, rotary_dim=self.head_dim)
 
     def forward(
         self,

vllm/model_executor/models/opt.py

@@ -1,6 +1,6 @@
 # coding=utf-8
 # Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,17 +25,17 @@ import torch
 from torch import nn
 from transformers import OPTConfig
 
-from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.layers.activation import get_act_fn
-from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
-from cacheflow.model_executor.layers.sampler import Sampler
-from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
-                                                   load_tensor_parallel_weights)
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from cacheflow.sequence import SequenceOutputs
+from vllm.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -75,8 +75,8 @@ class OPTAttention(nn.Module):
         self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias,
                                           input_is_parallel=True,
                                           perform_initialization=False)
-        self.attn = GPTCacheFlowAttention(self.num_heads, self.head_dim,
-                                          scale=self.scaling)
+        self.attn = PagedAttention(self.num_heads, self.head_dim,
+                                   scale=self.scaling)
 
     def forward(
         self,

vllm/model_executor/parallel_utils/__init__.py

@@ -1,5 +1,5 @@
-import cacheflow.model_executor.parallel_utils.parallel_state
-import cacheflow.model_executor.parallel_utils.tensor_parallel
+import vllm.model_executor.parallel_utils.parallel_state
+import vllm.model_executor.parallel_utils.tensor_parallel
 
 # Alias parallel_state as mpu, its legacy name
 mpu = parallel_state

vllm/model_executor/parallel_utils/parallel_state.py

@@ -1,4 +1,4 @@
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 

vllm/model_executor/parallel_utils/tensor_parallel/layers.py

@@ -1,4 +1,4 @@
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/layers.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
@@ -11,7 +11,7 @@ import torch.nn.functional as F
 import torch.nn.init as init
 from torch.nn.parameter import Parameter
 
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     get_all_reduce_launcher,

vllm/model_executor/parallel_utils/tensor_parallel/mappings.py

@@ -1,10 +1,10 @@
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/mappings.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 import torch
 
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     get_tensor_model_parallel_group,

vllm/model_executor/parallel_utils/tensor_parallel/random.py

@@ -1,4 +1,4 @@
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/random.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
@@ -11,7 +11,7 @@ import torch
 from torch import _C
 from torch.cuda import _lazy_call, device as device_ctx_manager
 
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
 )
 

vllm/model_executor/parallel_utils/tensor_parallel/utils.py

@@ -1,4 +1,4 @@
-# Copyright 2023 The CacheFlow team.
+# Copyright 2023 The vLLM team.
 # Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/utils.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 

vllm/model_executor/utils.py

@@ -4,8 +4,8 @@ import random
 import numpy as np
 import torch
 
-from cacheflow.model_executor.parallel_utils.parallel_state import model_parallel_is_initialized
-from cacheflow.model_executor.parallel_utils.tensor_parallel import model_parallel_cuda_manual_seed
+from vllm.model_executor.parallel_utils.parallel_state import model_parallel_is_initialized
+from vllm.model_executor.parallel_utils.tensor_parallel import model_parallel_cuda_manual_seed
 
 
 def set_random_seed(seed: int) -> None:

vllm/outputs.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional
 
-from cacheflow.sequence import SequenceGroup, SequenceStatus
+from vllm.sequence import SequenceGroup, SequenceStatus
 
 
 class CompletionOutput:

vllm/sequence.py

@@ -2,8 +2,8 @@ import copy
 import enum
 from typing import Dict, List, Optional, Union
 
-from cacheflow.block import LogicalTokenBlock
-from cacheflow.sampling_params import SamplingParams
+from vllm.block import LogicalTokenBlock
+from vllm.sampling_params import SamplingParams
 
 
 class SequenceStatus(enum.Enum):

vllm/worker/cache_engine.py

@@ -3,8 +3,8 @@ from typing import Dict, List, Tuple
 
 import torch
 
-from cacheflow import cache_ops
-from cacheflow.config import CacheConfig, ModelConfig, ParallelConfig
+from vllm import cache_ops
+from vllm.config import CacheConfig, ModelConfig, ParallelConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 

vllm/worker/worker.py

@@ -3,16 +3,15 @@ from typing import Dict, List, Tuple
 
 import torch
 
-from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig,
-                              SchedulerConfig)
-from cacheflow.model_executor import get_model, InputMetadata, set_random_seed
-from cacheflow.model_executor.parallel_utils.parallel_state import (
+from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig)
+from vllm.model_executor import get_model, InputMetadata, set_random_seed
+from vllm.model_executor.parallel_utils.parallel_state import (
     initialize_model_parallel, initialize_all_reduce_launcher)
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.sequence import (SequenceData, SequenceGroupMetadata,
-                                SequenceOutputs)
-from cacheflow.worker.cache_engine import CacheEngine
-from cacheflow.utils import get_gpu_memory
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import SequenceData, SequenceGroupMetadata, SequenceOutputs
+from vllm.worker.cache_engine import CacheEngine
+from vllm.utils import get_gpu_memory
 
 
 class Worker: