Merge tag 'v0.5.3.post1' into v0.5.3.post1-dtk24.04.1

examples/api_client.py

-"""Example Python client for vllm.entrypoints.api_server
+"""Example Python client for `vllm.entrypoints.api_server`
 NOTE: The API server is used only for demonstration and simple performance
 benchmarks. It is not intended for production use.
-For production use, we recommend vllm.entrypoints.openai.api_server
-and the OpenAI client API
+For production use, we recommend `vllm serve` and the OpenAI client API.
 """
 
 import argparse

examples/cpu_offload.py

+from vllm import LLM, SamplingParams
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Create an LLM.
+llm = LLM(model="meta-llama/Llama-2-13b-chat-hf", cpu_offload_gb=10)
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

examples/llava_example.py

-import os
-import subprocess
-
-from PIL import Image
-
 from vllm import LLM
-
-# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
-# You can use `.buildkite/download-images.sh` to download them
+from vllm.assets.image import ImageAsset
 
 
 def run_llava():
@@ -14,7 +7,7 @@ def run_llava():
 
     prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
 
-    image = Image.open("images/stop_sign.jpg")
+    image = ImageAsset("stop_sign").pil_image
 
     outputs = llm.generate({
         "prompt": prompt,
@@ -28,25 +21,5 @@ def run_llava():
         print(generated_text)
 
 
-def main():
-    run_llava()
-
-
 if __name__ == "__main__":
-    # Download from s3
-    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
-    local_directory = "images"
-
-    # Make sure the local directory exists or create it
-    os.makedirs(local_directory, exist_ok=True)
-
-    # Use AWS CLI to sync the directory, assume anonymous access
-    subprocess.check_call([
-        "aws",
-        "s3",
-        "sync",
-        s3_bucket_path,
-        local_directory,
-        "--no-sign-request",
-    ])
-    main()
+    run_llava()

examples/logging_configuration.md

@@ -95,9 +95,7 @@ to the path of the custom logging configuration JSON file:
 
 ```bash
 VLLM_LOGGING_CONFIG_PATH=/path/to/logging_config.json \
-    python3 -m vllm.entrypoints.openai.api_server \
-    --max-model-len 2048 \
-    --model mistralai/Mistral-7B-v0.1
+    vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
 ```
 
 
@@ -152,9 +150,7 @@ to the path of the custom logging configuration JSON file:
 
 ```bash
 VLLM_LOGGING_CONFIG_PATH=/path/to/logging_config.json \
-    python3 -m vllm.entrypoints.openai.api_server \
-    --max-model-len 2048 \
-    --model mistralai/Mistral-7B-v0.1
+    vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
 ```
 
 
@@ -167,9 +163,7 @@ loggers.
 
 ```bash
 VLLM_CONFIGURE_LOGGING=0 \
-    python3 -m vllm.entrypoints.openai.api_server \
-    --max-model-len 2048 \
-    --model mistralai/Mistral-7B-v0.1
+    vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
 ```
 
 

examples/offline_inference_tpu.py

+from vllm import LLM, SamplingParams
+
+prompts = [
+    "A robot may not injure a human being",
+    "It is only with the heart that one can see rightly;",
+    "The greatest glory in living lies not in never falling,",
+]
+answers = [
+    " or, through inaction, allow a human being to come to harm.",
+    " what is essential is invisible to the eye.",
+    " but in rising every time we fall.",
+]
+N = 1
+# Currently, top-p sampling is disabled. `top_p` should be 1.0.
+sampling_params = SamplingParams(temperature=0.7,
+                                 top_p=1.0,
+                                 n=N,
+                                 max_tokens=16)
+
+# Set `enforce_eager=True` to avoid ahead-of-time compilation.
+# In real workloads, `enforace_eager` should be `False`.
+llm = LLM(model="google/gemma-2b", enforce_eager=True)
+outputs = llm.generate(prompts, sampling_params)
+for output, answer in zip(outputs, answers):
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    assert generated_text.startswith(answer)

examples/openai_vision_api_client.py

 """An example showing how to use vLLM to serve VLMs.
 
 Launch the vLLM server with the following command:
-python -m vllm.entrypoints.openai.api_server \
-    --model llava-hf/llava-1.5-7b-hf \
-    --chat-template template_llava.jinja
+vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
 """
 import base64
 

examples/paligemma_example.py

-import os
-import subprocess
-
-from PIL import Image
-
 from vllm import LLM
-
-# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
-# You can use `.buildkite/download-images.sh` to download them
+from vllm.assets.image import ImageAsset
 
 
 def run_paligemma():
@@ -14,7 +7,7 @@ def run_paligemma():
 
     prompt = "caption es"
 
-    image = Image.open("images/stop_sign.jpg")
+    image = ImageAsset("stop_sign").pil_image
 
     outputs = llm.generate({
         "prompt": prompt,
@@ -28,25 +21,5 @@ def run_paligemma():
         print(generated_text)
 
 
-def main():
-    run_paligemma()
-
-
 if __name__ == "__main__":
-    # Download from s3
-    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
-    local_directory = "images"
-
-    # Make sure the local directory exists or create it
-    os.makedirs(local_directory, exist_ok=True)
-
-    # Use AWS CLI to sync the directory, assume anonymous access
-    subprocess.check_call([
-        "aws",
-        "s3",
-        "sync",
-        s3_bucket_path,
-        local_directory,
-        "--no-sign-request",
-    ])
-    main()
+    run_paligemma()

examples/phi3v_example.py

-import os
-import subprocess
-
-from PIL import Image
-
 from vllm import LLM, SamplingParams
-
-# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
-# You can use `.buildkite/download-images.sh` to download them
+from vllm.assets.image import ImageAsset
 
 
 def run_phi3v():
@@ -24,7 +17,7 @@ def run_phi3v():
         max_num_seqs=5,
     )
 
-    image = Image.open("images/cherry_blossom.jpg")
+    image = ImageAsset("cherry_blossom").pil_image
 
     # single-image prompt
     prompt = "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n"  # noqa: E501
@@ -44,19 +37,4 @@ def run_phi3v():
 
 
 if __name__ == "__main__":
-    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
-    local_directory = "images"
-
-    # Make sure the local directory exists or create it
-    os.makedirs(local_directory, exist_ok=True)
-
-    # Use AWS CLI to sync the directory, assume anonymous access
-    subprocess.check_call([
-        "aws",
-        "s3",
-        "sync",
-        s3_bucket_path,
-        local_directory,
-        "--no-sign-request",
-    ])
     run_phi3v()

examples/production_monitoring/Otel.md

@@ -36,7 +36,7 @@
     ```
     export OTEL_SERVICE_NAME="vllm-server"
     export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
-    python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+    vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
     ```
 
 1. In a new shell, send requests with trace context from a dummy client
@@ -62,7 +62,7 @@ By default, `grpc` is used. To set `http/protobuf` as the protocol, configure th
 ```
 export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
 export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
-python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
 ```
 
 ## Instrumentation of FastAPI
@@ -74,7 +74,7 @@ OpenTelemetry allows automatic instrumentation of FastAPI.
 
 1. Run vLLM with `opentelemetry-instrument`
     ```
-    opentelemetry-instrument python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" 
+    opentelemetry-instrument vllm serve facebook/opt-125m
     ```
 
 1. Send a request to vLLM and find its trace in Jaeger. It should contain spans from FastAPI.

examples/production_monitoring/README.md

@@ -10,8 +10,7 @@ Install:
 
 Prometheus metric logging is enabled by default in the OpenAI-compatible server. Launch via the entrypoint:
 ```bash
-python3 -m vllm.entrypoints.openai.api_server \
-    --model mistralai/Mistral-7B-v0.1 \
+vllm serve mistralai/Mistral-7B-v0.1 \
     --max-model-len 2048 \
     --disable-log-requests
 ```

examples/run_cluster.sh

+#!/bin/bash
+
+# Check for minimum number of required arguments
+if [ $# -lt 4 ]; then
+    echo "Usage: $0 docker_image head_node_address --head|--worker path_to_hf_home [additional_args...]"
+    exit 1
+fi
+
+# Assign the first three arguments and shift them away
+DOCKER_IMAGE="$1"
+HEAD_NODE_ADDRESS="$2"
+NODE_TYPE="$3"  # Should be --head or --worker
+PATH_TO_HF_HOME="$4"
+shift 4
+
+# Additional arguments are passed directly to the Docker command
+ADDITIONAL_ARGS="$@"
+
+# Validate node type
+if [ "${NODE_TYPE}" != "--head" ] && [ "${NODE_TYPE}" != "--worker" ]; then
+    echo "Error: Node type must be --head or --worker"
+    exit 1
+fi
+
+# Define a function to cleanup on EXIT signal
+cleanup() {
+    docker stop node
+    docker rm node
+}
+trap cleanup EXIT
+
+# Command setup for head or worker node
+RAY_START_CMD="ray start --block"
+if [ "${NODE_TYPE}" == "--head" ]; then
+    RAY_START_CMD+=" --head --port=6379"
+else
+    RAY_START_CMD+=" --address=${HEAD_NODE_ADDRESS}:6379"
+fi
+
+# Run the docker command with the user specified parameters and additional arguments
+docker run \
+    --entrypoint /bin/bash \
+    --network host \
+    --name node \
+    --shm-size 10.24g \
+    --gpus all \
+    -v "${PATH_TO_HF_HOME}:/root/.cache/huggingface" \
+    ${ADDITIONAL_ARGS} \
+    "${DOCKER_IMAGE}" -c "${RAY_START_CMD}"

requirements-rocm.txt

@@ -2,5 +2,9 @@
 -r requirements-common.txt
 
 # Dependencies for AMD GPUs
+awscli
+boto3
+botocore
 ray >= 2.10.0
+peft
 pytest-asyncio

rocm_patch/rocm_bf16.patch

---- amd_hip_bf16.h	2024-02-06 18:28:58.268699142 +0000
-+++ amd_hip_bf16.h.new	2024-02-06 18:28:31.988647133 +0000
-@@ -90,10 +90,10 @@
- #include "math_fwd.h"              // ocml device functions
- 
- #if defined(__HIPCC_RTC__)
--#define __HOST_DEVICE__ __device__
-+#define __HOST_DEVICE__ __device__ static
- #else
- #include <climits>
--#define __HOST_DEVICE__ __host__ __device__
-+#define __HOST_DEVICE__ __host__ __device__ static inline
- #endif
- 
- // Since we are using unsigned short to represent data in bfloat16, it can be of different sizes on

tests/async_engine/test_chat_template.py

 import os
 import pathlib
-from dataclasses import dataclass
 
 import pytest
 
+from vllm.entrypoints.chat_utils import load_chat_template
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest
-from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
 chatml_jinja_path = pathlib.Path(os.path.dirname(os.path.abspath(
@@ -50,24 +49,9 @@ TEST_MESSAGES = [
 ]
 
 
-@dataclass
-class MockTokenizer:
-    chat_template = None
-
-
-@dataclass
-class MockServingChat:
-    tokenizer: MockTokenizer
-
-
 def test_load_chat_template():
     # Testing chatml template
-    tokenizer = MockTokenizer()
-    mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=chatml_jinja_path)
-
-    template_content = tokenizer.chat_template
+    template_content = load_chat_template(chat_template=chatml_jinja_path)
 
     # Test assertions
     assert template_content is not None
@@ -79,24 +63,16 @@ def test_load_chat_template():
 def test_no_load_chat_template_filelike():
     # Testing chatml template
     template = "../../examples/does_not_exist"
-    tokenizer = MockTokenizer()
-
-    mock_serving_chat = MockServingChat(tokenizer)
 
     with pytest.raises(ValueError, match="looks like a file path"):
-        OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                              chat_template=template)
+        load_chat_template(chat_template=template)
 
 
 def test_no_load_chat_template_literallike():
     # Testing chatml template
     template = "{{ messages }}"
-    tokenizer = MockTokenizer()
 
-    mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=template)
-    template_content = tokenizer.chat_template
+    template_content = load_chat_template(chat_template=template)
 
     assert template_content == template
 
@@ -108,9 +84,7 @@ def test_get_gen_prompt(model, template, add_generation_prompt,
                         expected_output):
     # Initialize the tokenizer
     tokenizer = get_tokenizer(tokenizer_name=model)
-    mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=template)
+    template_content = load_chat_template(chat_template=template)
 
     # Create a mock request object using keyword arguments
     mock_request = ChatCompletionRequest(
@@ -122,7 +96,8 @@ def test_get_gen_prompt(model, template, add_generation_prompt,
     result = tokenizer.apply_chat_template(
         conversation=mock_request.messages,
         tokenize=False,
-        add_generation_prompt=mock_request.add_generation_prompt)
+        add_generation_prompt=mock_request.add_generation_prompt,
+        chat_template=mock_request.chat_template or template_content)
 
     # Test assertion
     assert result == expected_output, (

tests/async_engine/test_openapi_server_ray.py

@@ -9,9 +9,7 @@ MODEL_NAME = "facebook/opt-125m"
 
 @pytest.fixture(scope="module")
 def server():
-    with RemoteOpenAIServer([
-            "--model",
-            MODEL_NAME,
+    args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
         "float16",
@@ -19,7 +17,9 @@ def server():
         "2048",
         "--enforce-eager",
         "--engine-use-ray"
-    ]) as remote_server:
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
         yield remote_server
 
 

tests/basic_correctness/test_cpu_offload.py

+from vllm.utils import is_hip
+
+from ..utils import compare_two_settings
+
+
+def test_cpu_offload():
+    compare_two_settings("meta-llama/Llama-2-7b-hf", [],
+                         ["--cpu-offload-gb", "4"])
+    if not is_hip():
+        # compressed-tensors quantization is currently not supported in ROCm.
+        compare_two_settings(
+            "nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t", [],
+            ["--cpu-offload-gb", "1"])

tests/conftest.py

@@ -3,11 +3,7 @@ import gc
 import os
 import sys
 from collections import UserList
-from dataclasses import dataclass
-from functools import cached_property
-from pathlib import Path
-from typing import (Any, Dict, List, Literal, Optional, Tuple, TypedDict,
-                    TypeVar)
+from typing import Any, Dict, List, Optional, Tuple, TypedDict, TypeVar
 
 import pytest
 import torch
@@ -18,14 +14,16 @@ from transformers import (AutoModelForCausalLM, AutoModelForVision2Seq,
                           AutoTokenizer, BatchEncoding)
 
 from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
 from vllm.config import TokenizerPoolConfig
+from vllm.connections import global_http_connection
 from vllm.distributed import (destroy_distributed_environment,
                               destroy_model_parallel)
 from vllm.inputs import TextPrompt
 from vllm.logger import init_logger
-from vllm.multimodal.utils import fetch_image
 from vllm.sequence import SampleLogprobs
-from vllm.utils import cuda_device_count_stateless, is_cpu
+from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, cuda_device_count_stateless,
+                        is_cpu)
 
 logger = init_logger(__name__)
 
@@ -33,9 +31,6 @@ _TEST_DIR = os.path.dirname(__file__)
 _TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
 _LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
 
-_IMAGE_DIR = Path(_TEST_DIR) / "images"
-"""You can use `.buildkite/download-images.sh` to download the assets."""
-
 
 def _read_prompts(filename: str) -> List[str]:
     with open(filename, "r") as f:
@@ -43,24 +38,9 @@ def _read_prompts(filename: str) -> List[str]:
         return prompts
 
 
-@dataclass(frozen=True)
-class ImageAsset:
-    name: Literal["stop_sign", "cherry_blossom", "boardwalk"]
-
-    @cached_property
-    def pil_image(self) -> Image.Image:
-        if self.name == "boardwalk":
-            return fetch_image(
-                "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
-            )
-
-        return Image.open(_IMAGE_DIR / f"{self.name}.jpg")
-
-
 class _ImageAssetPrompts(TypedDict):
     stop_sign: str
     cherry_blossom: str
-    boardwalk: str
 
 
 if sys.version_info < (3, 9):
@@ -79,7 +59,6 @@ class _ImageAssets(_ImageAssetsBase):
         super().__init__([
             ImageAsset("stop_sign"),
             ImageAsset("cherry_blossom"),
-            ImageAsset("boardwalk")
         ])
 
     def prompts(self, prompts: _ImageAssetPrompts) -> List[str]:
@@ -89,16 +68,20 @@ class _ImageAssets(_ImageAssetsBase):
         The order of the returned prompts matches the order of the
         assets when iterating through this object.
         """
-        return [
-            prompts["stop_sign"], prompts["cherry_blossom"],
-            prompts["boardwalk"]
-        ]
+        return [prompts["stop_sign"], prompts["cherry_blossom"]]
 
 
 IMAGE_ASSETS = _ImageAssets()
 """Singleton instance of :class:`_ImageAssets`."""
 
 
+@pytest.fixture(autouse=True)
+def init_test_http_connection():
+    # pytest_asyncio may use a different event loop per test
+    # so we need to make sure the async client is created anew
+    global_http_connection.reuse_client = False
+
+
 def cleanup():
     destroy_model_parallel()
     destroy_distributed_environment()
@@ -150,12 +133,6 @@ def image_assets() -> _ImageAssets:
     return IMAGE_ASSETS
 
 
-_STR_DTYPE_TO_TORCH_DTYPE = {
-    "half": torch.half,
-    "bfloat16": torch.bfloat16,
-    "float": torch.float,
-}
-
 _T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding)
 
 
@@ -177,8 +154,7 @@ class HfRunner:
         is_vision_model: bool = False,
         is_sparseml_model: bool = False,
     ) -> None:
-        assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
-        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
+        torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
 
         self.model_name = model_name
 
@@ -590,6 +566,10 @@ def get_tokenizer_pool_config(tokenizer_group_type):
         return TokenizerPoolConfig(pool_size=1,
                                    pool_type="ray",
                                    extra_config={})
+    if isinstance(tokenizer_group_type, type):
+        return TokenizerPoolConfig(pool_size=1,
+                                   pool_type=tokenizer_group_type,
+                                   extra_config={})
     raise ValueError(f"Unknown tokenizer_group_type: {tokenizer_group_type}")
 
 

tests/core/block/test_block_manager_v2.py

@@ -249,10 +249,13 @@ def test_append_slots(block_size, prompt_len, num_slots_to_append,
 
     # Expect consumed blocks to be new blocks required to support the new slots.
     expected_consumed_blocks = len(
+        list(
             chunk_list(
                 list(
-                range(prompt_len + num_slots_to_append + num_lookahead_slots)),
-            block_size)) - len(chunk_list(list(range(prompt_len)), block_size))
+                    range(prompt_len + num_slots_to_append +
+                          num_lookahead_slots)),
+                block_size))) - len(
+                    list(chunk_list(list(range(prompt_len)), block_size)))
     assert num_consumed_blocks == expected_consumed_blocks
 
 

tests/core/block/test_cpu_gpu_block_allocator.py

@@ -58,10 +58,10 @@ def test_allocate_immutable_block(num_cpu_blocks: int, num_gpu_blocks: int,
 
     unique_token_ids = list(
         range((num_cpu_blocks + num_gpu_blocks) * block_size))
-    gpu_token_ids = chunk_list(unique_token_ids[:num_gpu_blocks * block_size],
-                               block_size)
-    cpu_token_ids = chunk_list(unique_token_ids[num_gpu_blocks * block_size:],
-                               block_size)
+    gpu_token_ids = list(
+        chunk_list(unique_token_ids[:num_gpu_blocks * block_size], block_size))
+    cpu_token_ids = list(
+        chunk_list(unique_token_ids[num_gpu_blocks * block_size:], block_size))
 
     assert allocator.get_num_free_blocks(Device.CPU) == num_cpu_blocks
     assert allocator.get_num_free_blocks(Device.GPU) == num_gpu_blocks

tests/core/test_scheduler.py

@@ -462,7 +462,7 @@ def test_prefill_schedule_max_lora():
                                            lora_request=LoRARequest(
                                                lora_name=str(i),
                                                lora_int_id=i + 1,
-                                               lora_local_path="abc"))
+                                               lora_path="abc"))
         waiting.append(seq_group)
     # Add two more requests to verify lora is prioritized.
     # 0: Lora, 1: Lora, 2: regular, 3: regular
@@ -760,7 +760,7 @@ def test_schedule_swapped_max_loras():
                                            lora_request=LoRARequest(
                                                lora_name=str(i),
                                                lora_int_id=i + 1,
-                                               lora_local_path="abc"))
+                                               lora_path="abc"))
         scheduler._allocate_and_set_running(seq_group)
         append_new_token_seq_group(60, seq_group, 1)
         scheduler._swap_out(seq_group, blocks_to_swap_out)