[Quality] Add code formatter and linter (#326)

.pylintrc

+# This Pylint rcfile contains a best-effort configuration to uphold the
+# best-practices and style described in the Google Python style guide:
+#   https://google.github.io/styleguide/pyguide.html
+#
+# Its canonical open-source location is:
+#   https://google.github.io/styleguide/pylintrc
+
+[MASTER]
+
+# Files or directories to be skipped. They should be base names, not paths.
+ignore=docs,parallel_utils
+
+# Files or directories matching the regex patterns are skipped. The regex
+# matches against base names, not paths.
+ignore-patterns=
+
+# Pickle collected data for later comparisons.
+persistent=no
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Use multiple processes to speed up Pylint.
+jobs=4
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
+confidence=
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+#enable=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+disable=abstract-method,
+        apply-builtin,
+        arguments-differ,
+        attribute-defined-outside-init,
+        backtick,
+        bad-option-value,
+        basestring-builtin,
+        buffer-builtin,
+        c-extension-no-member,
+        consider-using-enumerate,
+        cmp-builtin,
+        cmp-method,
+        coerce-builtin,
+        coerce-method,
+        delslice-method,
+        div-method,
+        duplicate-code,
+        eq-without-hash,
+        execfile-builtin,
+        file-builtin,
+        filter-builtin-not-iterating,
+        fixme,
+        getslice-method,
+        global-statement,
+        hex-method,
+        idiv-method,
+        implicit-str-concat-in-sequence,
+        import-error,
+        import-self,
+        import-star-module-level,
+        inconsistent-return-statements,
+        input-builtin,
+        intern-builtin,
+        invalid-str-codec,
+        locally-disabled,
+        logging-fstring-interpolation,  # added by vLLM
+        logging-not-lazy,  # added by vLLM
+        long-builtin,
+        long-suffix,
+        map-builtin-not-iterating,
+        misplaced-comparison-constant,
+        missing-class-docstring,  # TODO (vLLM): enable
+        missing-function-docstring,
+        missing-module-docstring,  # TODO (vLLM): enable
+        metaclass-assignment,
+        next-method-called,
+        next-method-defined,
+        no-absolute-import,
+        no-else-break,
+        no-else-continue,
+        no-else-raise,
+        no-else-return,
+        no-init,  # added
+        no-member,
+        no-name-in-module,
+        no-self-use,
+        nonzero-method,
+        oct-method,
+        old-division,
+        old-ne-operator,
+        old-octal-literal,
+        old-raise-syntax,
+        parameter-unpacking,
+        print-statement,
+        raising-string,
+        range-builtin-not-iterating,
+        raw_input-builtin,
+        rdiv-method,
+        reduce-builtin,
+        relative-import,
+        reload-builtin,
+        round-builtin,
+        setslice-method,
+        signature-differs,
+        standarderror-builtin,
+        suppressed-message,
+        sys-max-int,
+        too-few-public-methods,
+        too-many-ancestors,
+        too-many-arguments,
+        too-many-boolean-expressions,
+        too-many-branches,
+        too-many-instance-attributes,
+        too-many-locals,
+        too-many-nested-blocks,
+        too-many-public-methods,
+        too-many-return-statements,
+        too-many-statements,
+        trailing-newlines,
+        unichr-builtin,
+        unicode-builtin,
+        unnecessary-pass,
+        unpacking-in-except,
+        unspecified-encoding,
+        useless-else-on-loop,
+        useless-object-inheritance,
+        useless-suppression,
+        using-cmp-argument,
+        wrong-import-order,
+        xrange-builtin,
+        zip-builtin-not-iterating,
+
+
+[REPORTS]
+
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Tells whether to display a full report or only the messages
+reports=no
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details
+#msg-template=
+
+
+[BASIC]
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=main,_
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Include a hint for the correct naming format with invalid-name
+include-naming-hint=no
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+property-classes=abc.abstractproperty,cached_property.cached_property,cached_property.threaded_cached_property,cached_property.cached_property_with_ttl,cached_property.threaded_cached_property_with_ttl
+
+# Regular expression matching correct function names
+function-rgx=^(?:(?P<exempt>setUp|tearDown|setUpModule|tearDownModule)|(?P<camel_case>_?[A-Z][a-zA-Z0-9]*)|(?P<snake_case>_?[a-z][a-z0-9_]*))$
+
+# Regular expression matching correct variable names
+variable-rgx=^[a-z][a-z0-9_]*$
+
+# Regular expression matching correct constant names
+const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$
+
+# Regular expression matching correct attribute names
+attr-rgx=^_{0,2}[a-z][a-z0-9_]*$
+
+# Regular expression matching correct argument names
+argument-rgx=^[a-z][a-z0-9_]*$
+
+# Regular expression matching correct class attribute names
+class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$
+
+# Regular expression matching correct inline iteration names
+inlinevar-rgx=^[a-z][a-z0-9_]*$
+
+# Regular expression matching correct class names
+class-rgx=^_?[A-Z][a-zA-Z0-9]*$
+
+# Regular expression matching correct module names
+module-rgx=^(_?[a-z][a-z0-9_]*|__init__)$
+
+# Regular expression matching correct method names
+method-rgx=(?x)^(?:(?P<exempt>_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P<camel_case>_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P<snake_case>_{0,2}[a-z][a-z0-9_]*))$
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=(__.*__|main|test.*|.*test|.*Test)$
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=10
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+
+[FORMAT]
+
+# Maximum number of characters on a single line.
+max-line-length=80
+
+# TODO(https://github.com/PyCQA/pylint/issues/3352): Direct pylint to exempt
+# lines made too long by directives to pytype.
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=(?x)(
+  ^\s*(\#\ )?<?https?://\S+>?$|
+  ^\s*(from\s+\S+\s+)?import\s+.+$)
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=yes
+
+# Maximum number of lines in a module
+max-module-lines=99999
+
+# String used as indentation unit.  The internal Google style guide mandates 2
+# spaces.  Google's externaly-published style guide says 4, consistent with
+# PEP 8.  Here, we use 2 spaces, for conformity with many open-sourced Google
+# projects (like TensorFlow).
+indent-string='    '
+
+# Number of spaces of indent required inside a hanging  or continued line.
+indent-after-paren=4
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=TODO
+
+
+[STRING]
+
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency=yes
+
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the name of dummy variables (i.e. expectedly
+# not used).
+dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_)
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,_cb
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six,six.moves,past.builtins,future.builtins,functools
+
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format
+logging-modules=logging,absl.logging,tensorflow.io.logging
+
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+
+[SPELLING]
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package.
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=regsub,
+                   TERMIOS,
+                   Bastion,
+                   rexec,
+                   sets
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant, absl
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls,
+                            class_
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=StandardError,
+                       Exception,
+                       BaseException

CONTRIBUTING.md

@@ -49,12 +49,15 @@ If not, please file a new issue, providing as much relevant information as possi
 
 In general, we adhere to [Google Python style guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ style guide](https://google.github.io/styleguide/cppguide.html).
 
+We include a formatting script [`format.sh`](./format.sh) to format the code.
+
 ### Pull Requests
 
 When submitting a pull request:
 
 1. Make sure your code has been rebased on top of the latest commit on the main branch.
-2. Include a detailed description of the changes in the pull request.
+2. Ensure code is properly formatted by running [`format.sh`](./format.sh).
+3. Include a detailed description of the changes in the pull request.
 Explain why you made the changes you did.
 If your pull request fixes an open issue, please include a reference to it in the description.
 

examples/api_client.py

@@ -14,7 +14,9 @@ def clear_line(n: int = 1) -> None:
         print(LINE_UP, end=LINE_CLEAR, flush=True)
 
 
-def post_http_request(prompt: str, api_url: str, n: int = 1,
+def post_http_request(prompt: str,
+                      api_url: str,
+                      n: int = 1,
                       stream: bool = False) -> requests.Response:
     headers = {"User-Agent": "Test Client"}
     pload = {
@@ -30,7 +32,8 @@ def post_http_request(prompt: str, api_url: str, n: int = 1,
 
 
 def get_streaming_response(response: requests.Response) -> Iterable[List[str]]:
-    for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False,
+    for chunk in response.iter_lines(chunk_size=8192,
+                                     decode_unicode=False,
                                      delimiter=b"\0"):
         if chunk:
             data = json.loads(chunk.decode("utf-8"))

examples/gradio_webserver.py

@@ -12,9 +12,14 @@ def http_bot(prompt):
         "stream": True,
         "max_tokens": 128,
     }
-    response = requests.post(args.model_url, headers=headers, json=pload, stream=True)
-
-    for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"):
+    response = requests.post(args.model_url,
+                             headers=headers,
+                             json=pload,
+                             stream=True)
+
+    for chunk in response.iter_lines(chunk_size=8192,
+                                     decode_unicode=False,
+                                     delimiter=b"\0"):
         if chunk:
             data = json.loads(chunk.decode("utf-8"))
             output = data["text"][0]
@@ -23,11 +28,11 @@ def http_bot(prompt):
 
 def build_demo():
     with gr.Blocks() as demo:
-        gr.Markdown(
-            "# 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")
+        gr.Markdown("# 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")
         inputbox.submit(http_bot, [inputbox], [outputbox])
     return demo
 
@@ -36,7 +41,9 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8001)
-    parser.add_argument("--model-url", type=str, default="http://localhost:8000/generate")
+    parser.add_argument("--model-url",
+                        type=str,
+                        default="http://localhost:8000/generate")
     args = parser.parse_args()
 
     demo = build_demo()

examples/llm_engine_example.py

@@ -14,9 +14,14 @@ def main(args: argparse.Namespace):
         ("To be or not to be,",
          SamplingParams(temperature=0.8, top_k=5, presence_penalty=0.2)),
         ("What is the meaning of life?",
-         SamplingParams(n=2, best_of=5, temperature=0.8, top_p=0.95, frequency_penalty=0.1)),
+         SamplingParams(n=2,
+                        best_of=5,
+                        temperature=0.8,
+                        top_p=0.95,
+                        frequency_penalty=0.1)),
         ("It is only with the heart that one can see rightly",
-         SamplingParams(n=3, best_of=3, use_beam_search=True, temperature=0.0)),
+         SamplingParams(n=3, best_of=3, use_beam_search=True,
+                        temperature=0.0)),
     ]
 
     # Run the engine by calling `engine.step()` manually.

examples/offline_inference.py

 from vllm import LLM, SamplingParams
 
-
 # Sample prompts.
 prompts = [
     "Hello, my name is",

examples/openai_client.py

@@ -12,8 +12,13 @@ print("Models:", models)
 # Test completion API
 stream = True
 completion = openai.Completion.create(
-    model=model, prompt="A robot may not injure a human being", echo=False, n=2,
-    best_of=3, stream=stream, logprobs=3)
+    model=model,
+    prompt="A robot may not injure a human being",
+    echo=False,
+    n=2,
+    best_of=3,
+    stream=stream,
+    logprobs=3)
 
 # print the completion
 if stream:

format.sh

+#!/usr/bin/env bash
+# YAPF formatter, adapted from ray and skypilot.
+#
+# Usage:
+#    # Do work and commit your work.
+
+#    # Format files that differ from origin/main.
+#    bash format.sh
+
+#    # Commit changed files with message 'Run yapf and pylint'
+#
+#
+# YAPF + Clang formatter (if installed). This script formats all changed files from the last mergebase.
+# You are encouraged to run this locally before pushing changes for review.
+
+# Cause the script to exit if a single command fails
+set -eo pipefail
+
+# this stops git rev-parse from failing if we run this from the .git directory
+builtin cd "$(dirname "${BASH_SOURCE:-$0}")"
+ROOT="$(git rev-parse --show-toplevel)"
+builtin cd "$ROOT" || exit 1
+
+YAPF_VERSION=$(yapf --version | awk '{print $2}')
+PYLINT_VERSION=$(pylint --version | head -n 1 | awk '{print $2}')
+MYPY_VERSION=$(mypy --version | awk '{print $2}')
+
+# # params: tool name, tool version, required version
+tool_version_check() {
+    if [[ $2 != $3 ]]; then
+        echo "Wrong $1 version installed: $3 is required, not $2."
+        exit 1
+    fi
+}
+
+tool_version_check "yapf" $YAPF_VERSION "$(grep yapf requirements-dev.txt | cut -d'=' -f3)"
+tool_version_check "pylint" $PYLINT_VERSION "$(grep "pylint==" requirements-dev.txt | cut -d'=' -f3)"
+tool_version_check "mypy" "$MYPY_VERSION" "$(grep mypy requirements-dev.txt | cut -d'=' -f3)"
+
+YAPF_FLAGS=(
+    '--recursive'
+    '--parallel'
+)
+
+YAPF_EXCLUDES=(
+    '--exclude' 'build/**'
+    '--exclude' 'vllm/model_executor/parallel_utils/**'
+)
+
+# Format specified files
+format() {
+    yapf --in-place "${YAPF_FLAGS[@]}" "$@"
+}
+
+# Format files that differ from main branch. Ignores dirs that are not slated
+# for autoformat yet.
+format_changed() {
+    # The `if` guard ensures that the list of filenames is not empty, which
+    # could cause yapf to receive 0 positional arguments, making it hang
+    # waiting for STDIN.
+    #
+    # `diff-filter=ACM` and $MERGEBASE is to ensure we only format files that
+    # exist on both branches.
+    MERGEBASE="$(git merge-base origin/main HEAD)"
+
+    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
+        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs -P 5 \
+             yapf --in-place "${YAPF_EXCLUDES[@]}" "${YAPF_FLAGS[@]}"
+    fi
+
+}
+
+# Format all files
+format_all() {
+    yapf --in-place "${YAPF_FLAGS[@]}" "${YAPF_EXCLUDES[@]}" vllm
+}
+
+## This flag formats individual files. --files *must* be the first command line
+## arg to use this option.
+if [[ "$1" == '--files' ]]; then
+   format "${@:2}"
+   # If `--all` is passed, then any further arguments are ignored and the
+   # entire python directory is formatted.
+elif [[ "$1" == '--all' ]]; then
+   format_all
+else
+   # Format only the files that changed in last commit.
+   format_changed
+fi
+echo 'vLLM yapf: Done'
+
+# Run mypy
+# TODO(zhuohan): Enable mypy
+# echo 'vLLM mypy:'
+# mypy
+
+# Run Pylint
+echo 'vLLM Pylint:'
+pylint vllm
+
+if ! git diff --quiet &>/dev/null; then
+    echo 'Reformatted files. Please review and stage the changes.'
+    echo 'Changes not staged for commit:'
+    echo
+    git --no-pager diff --name-only
+
+    exit 1
+fi

requirements-dev.txt

-mypy
+# formatting
+yapf==0.32.0
+pylint==2.8.2
+
+# type checking
+mypy==0.991
+types-PyYAML
+types-requests
+types-setuptools
+
+# testing
 pytest

tests/kernels/test_attention.py

@@ -60,7 +60,7 @@ def ref_single_query_cached_kv_attention(
         keys = torch.stack(keys, dim=0)
         values = torch.stack(values, dim=0)
 
-        scale = 1.0 / (head_size ** 0.5)
+        scale = 1.0 / (head_size**0.5)
         out = ref_masked_attention(q, keys, values, scale)
         out = out.view(num_heads, head_size)
         output[i].copy_(out, non_blocking=True)
@@ -74,7 +74,7 @@ def ref_multi_query_kv_attention(
     dtype: torch.dtype,
 ) -> torch.Tensor:
     head_size = query.shape[-1]
-    scale = 1.0 / (head_size ** 0.5)
+    scale = 1.0 / (head_size**0.5)
 
     num_seqs = len(cu_seq_lens) - 1
     ref_outputs = []
@@ -84,8 +84,8 @@ def ref_multi_query_kv_attention(
         seq_len = end_idx - start_idx
 
         # Create attention mask.
-        attn_mask = torch.triu(
-            torch.ones(seq_len, seq_len, dtype=dtype), diagonal=1)
+        attn_mask = torch.triu(torch.ones(seq_len, seq_len, dtype=dtype),
+                               diagonal=1)
         attn_mask = attn_mask * torch.finfo(dtype).min
         attn_mask = attn_mask.to(dtype=dtype, device='cuda')
 
@@ -113,7 +113,7 @@ def ref_multi_query_cached_kv_attention(
     num_heads = value_cache.shape[1]
     head_size = value_cache.shape[2]
     block_size = value_cache.shape[3]
-    scale = 1.0 / (head_size ** 0.5)
+    scale = 1.0 / (head_size**0.5)
 
     num_queries = len(cu_query_lens) - 1
     ref_outputs = []
@@ -125,8 +125,8 @@ def ref_multi_query_cached_kv_attention(
         block_table = block_tables[i]
 
         # Create attention mask
-        attn_mask = torch.triu(
-            torch.ones(query_len, context_len), diagonal=context_len - query_len + 1) * -1e5
+        attn_mask = torch.triu(torch.ones(query_len, context_len),
+                               diagonal=context_len - query_len + 1) * -1e5
         attn_mask = attn_mask.to(dtype=dtype, device='cuda')
 
         keys = []
@@ -165,19 +165,25 @@ def run_single_query_cached_kv_attention(
     num_blocks: int,
     dtype: torch.dtype,
 ) -> None:
-    qkv = torch.empty(
-        num_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
+    qkv = torch.empty(num_tokens,
+                      3,
+                      num_heads,
+                      head_size,
+                      dtype=dtype,
+                      device='cuda')
     qkv.uniform_(-1e-3, 1e-3)
     query, _, _ = qkv.unbind(dim=1)
 
     x = 16 // torch.tensor([], dtype=dtype).element_size()
     key_block_shape = (num_heads, head_size // x, block_size, x)
-    key_cache = torch.empty(
-        size=(num_blocks, *key_block_shape), dtype=dtype, device='cuda')
+    key_cache = torch.empty(size=(num_blocks, *key_block_shape),
+                            dtype=dtype,
+                            device='cuda')
     key_cache.uniform_(-1e-3, 1e-3)
     value_block_shape = (num_heads, head_size, block_size)
-    value_cache = torch.empty(
-        size=(num_blocks, *value_block_shape), dtype=dtype, device='cuda')
+    value_cache = torch.empty(size=(num_blocks, *value_block_shape),
+                              dtype=dtype,
+                              device='cuda')
     value_cache.uniform_(-1e-3, 1e-3)
 
     context_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_tokens)]
@@ -194,9 +200,12 @@ def run_single_query_cached_kv_attention(
         block_tables.append(block_table)
     block_tables = torch.tensor(block_tables, dtype=torch.int, device='cuda')
 
-    scale = float(1.0 / (head_size ** 0.5))
-    output = torch.empty(
-        num_tokens, num_heads, head_size, dtype=dtype, device='cuda')
+    scale = float(1.0 / (head_size**0.5))
+    output = torch.empty(num_tokens,
+                         num_heads,
+                         head_size,
+                         dtype=dtype,
+                         device='cuda')
     attention_ops.single_query_cached_kv_attention(
         output,
         query,
@@ -235,9 +244,13 @@ def run_multi_query_kv_attention(
     seq_lens = random.sample(range(1, MAX_SEQ_LEN), num_seqs)
     num_tokens = sum(seq_lens)
 
-    scale = float(1.0 / (head_size ** 0.5))
-    qkv = torch.empty(
-        num_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
+    scale = float(1.0 / (head_size**0.5))
+    qkv = torch.empty(num_tokens,
+                      3,
+                      num_heads,
+                      head_size,
+                      dtype=dtype,
+                      device='cuda')
     qkv.uniform_(-1e-3, 1e-3)
     query, key, value = qkv.unbind(dim=1)
 

tests/kernels/test_cache.py

@@ -26,8 +26,9 @@ def run_copy_blocks(
     key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
     key_caches = []
     for _ in range(num_layers):
-        key_cache = torch.randn(
-            size=key_cache_shape, dtype=dtype, device='cuda')
+        key_cache = torch.randn(size=key_cache_shape,
+                                dtype=dtype,
+                                device='cuda')
         key_caches.append(key_cache)
     cloned_key_caches = []
     for key_cache in key_caches:
@@ -36,8 +37,9 @@ def run_copy_blocks(
     value_cache_shape = (num_blocks, num_heads, head_size, block_size)
     value_caches = []
     for _ in range(num_layers):
-        value_cache = torch.randn(
-            size=value_cache_shape, dtype=dtype, device='cuda')
+        value_cache = torch.randn(size=value_cache_shape,
+                                  dtype=dtype,
+                                  device='cuda')
         value_caches.append(value_cache)
     cloned_value_caches = []
     for value_cache in value_caches:
@@ -49,15 +51,18 @@ def run_copy_blocks(
     # Reference implementation.
     for src, dsts in block_mapping.items():
         for dst in dsts:
-            for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
+            for key_cache, cloned_key_cache in zip(key_caches,
+                                                   cloned_key_caches):
                 cloned_key_cache[dst] = cloned_key_cache[src]
-            for value_cache, cloned_value_cache in zip(value_caches, cloned_value_caches):
+            for value_cache, cloned_value_cache in zip(value_caches,
+                                                       cloned_value_caches):
                 cloned_value_cache[dst] = cloned_value_cache[src]
 
     # Compare the results.
     for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
         assert torch.allclose(key_cache, cloned_key_cache)
-    for value_cache, cloned_value_cache in zip(value_caches, cloned_value_caches):
+    for value_cache, cloned_value_cache in zip(value_caches,
+                                               cloned_value_caches):
         assert torch.allclose(value_cache, cloned_value_cache)
 
 
@@ -74,8 +79,12 @@ def run_reshape_and_cache(
     slot_mapping = random.sample(range(num_slots), num_tokens)
     slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
 
-    qkv = torch.randn(
-        num_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
+    qkv = torch.randn(num_tokens,
+                      3,
+                      num_heads,
+                      head_size,
+                      dtype=dtype,
+                      device='cuda')
     _, key, value = qkv.unbind(dim=1)
 
     x = 16 // torch.tensor([], dtype=dtype).element_size()
@@ -84,15 +93,19 @@ def run_reshape_and_cache(
     cloned_key_cache = key_cache.clone()
 
     value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_cache = torch.randn(
-        size=value_cache_shape, dtype=dtype, device='cuda')
+    value_cache = torch.randn(size=value_cache_shape,
+                              dtype=dtype,
+                              device='cuda')
     cloned_value_cache = value_cache.clone()
 
-    cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slot_mapping)
+    cache_ops.reshape_and_cache(key, value, key_cache, value_cache,
+                                slot_mapping)
 
     for i in range(num_tokens):
         reshaped_key = key.reshape(num_tokens, num_heads, head_size // x, x)
-        block_idx = torch.div(slot_mapping[i], block_size, rounding_mode='floor')
+        block_idx = torch.div(slot_mapping[i],
+                              block_size,
+                              rounding_mode='floor')
         block_offset = slot_mapping[i] % block_size
         cloned_key_cache[block_idx, :, :, block_offset, :] = reshaped_key[i]
         cloned_value_cache[block_idx, :, :, block_offset] = value[i]
@@ -114,8 +127,12 @@ def run_gather_cached_kv(
     slot_mapping = random.sample(range(num_slots), num_tokens)
     slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
 
-    qkv = torch.randn(
-        num_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
+    qkv = torch.randn(num_tokens,
+                      3,
+                      num_heads,
+                      head_size,
+                      dtype=dtype,
+                      device='cuda')
     _, key, value = qkv.unbind(dim=1)
 
     qkv_clone = qkv.clone()
@@ -126,15 +143,20 @@ def run_gather_cached_kv(
     key_cache = torch.randn(size=key_cache_shape, dtype=dtype, device='cuda')
 
     value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_cache = torch.randn(
-        size=value_cache_shape, dtype=dtype, device='cuda')
+    value_cache = torch.randn(size=value_cache_shape,
+                              dtype=dtype,
+                              device='cuda')
 
-    cache_ops.gather_cached_kv(key, value, key_cache, value_cache, slot_mapping)
+    cache_ops.gather_cached_kv(key, value, key_cache, value_cache,
+                               slot_mapping)
 
     # Reference implementation.
     for i in range(num_tokens):
-        reshaped_key = cloned_key.reshape(num_tokens, num_heads, head_size // x, x)
-        block_idx = torch.div(slot_mapping[i], block_size, rounding_mode='floor')
+        reshaped_key = cloned_key.reshape(num_tokens, num_heads,
+                                          head_size // x, x)
+        block_idx = torch.div(slot_mapping[i],
+                              block_size,
+                              rounding_mode='floor')
         block_offset = slot_mapping[i] % block_size
         reshaped_key[i] = key_cache[block_idx, :, :, block_offset, :]
         cloned_value[i] = value_cache[block_idx, :, :, block_offset]
@@ -145,20 +167,30 @@ def run_gather_cached_kv(
 
 def test_copy_blocks() -> None:
     for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_copy_blocks(
-            num_mappings=23, num_layers=7, num_heads=17, head_size=16,
-            block_size=8, num_blocks=1024, dtype=dtype)
+        run_copy_blocks(num_mappings=23,
+                        num_layers=7,
+                        num_heads=17,
+                        head_size=16,
+                        block_size=8,
+                        num_blocks=1024,
+                        dtype=dtype)
 
 
 def test_reshape_and_cache() -> None:
     for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_reshape_and_cache(
-            num_tokens=3, num_heads=2, head_size=16, block_size=8, num_blocks=2,
+        run_reshape_and_cache(num_tokens=3,
+                              num_heads=2,
+                              head_size=16,
+                              block_size=8,
+                              num_blocks=2,
                               dtype=dtype)
 
 
 def test_gather_cached_kv() -> None:
     for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_gather_cached_kv(
-            num_tokens=3, num_heads=2, head_size=16, block_size=8, num_blocks=2,
+        run_gather_cached_kv(num_tokens=3,
+                             num_heads=2,
+                             head_size=16,
+                             block_size=8,
+                             num_blocks=2,
                              dtype=dtype)

tests/kernels/test_layernorm.py

@@ -14,8 +14,10 @@ class RefRMSNorm(nn.Module):
         self.variance_epsilon = eps
 
     def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1,
+                                                               keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance +
+                                                    self.variance_epsilon)
         if self.weight.dtype in [torch.half, torch.float16, torch.bfloat16]:
             hidden_states = hidden_states.to(self.weight.dtype)
         return self.weight * hidden_states

tests/kernels/test_pos_encoding.py

@@ -8,8 +8,8 @@ from vllm import pos_encoding_ops
 
 
 def rotate_half(x: torch.Tensor) -> torch.Tensor:
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
+    x1 = x[..., :x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
     return torch.cat((-x2, x1), dim=-1)
 
 
@@ -38,7 +38,7 @@ class RefRotaryEmbeddingNeox(nn.Module):
         self.max_position_embeddings = max_position_embeddings
 
         # Create cos and sin embeddings.
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2) / dim))
+        inv_freq = 1.0 / (base**(torch.arange(0, dim, 2) / dim))
         t = torch.arange(max_position_embeddings).float()
         freqs = torch.einsum("i,j->ij", t, inv_freq.float())
         emb = torch.cat((freqs, freqs), dim=-1)
@@ -54,11 +54,10 @@ class RefRotaryEmbeddingNeox(nn.Module):
         key: torch.Tensor,  # [num_tokens, num_heads, head_size]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
 
-        query_rot = query[..., : self.rotary_dim]
-        query_pass = query[..., self.rotary_dim :]
-        key_rot = key[..., : self.rotary_dim]
-        key_pass = key[..., self.rotary_dim :]
-
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
 
         query_rot = query_rot.transpose(0, 1)
         key_rot = key_rot.transpose(0, 1)
@@ -85,12 +84,18 @@ def run_rotary_embedding_neox(
     dtype: torch.dtype,
     base: int = 10000,
 ) -> None:
-    positions = torch.randint(0, max_position, (num_tokens,), device='cuda')
-    query = torch.randn(num_tokens, num_heads * head_size, dtype=dtype, device='cuda')
-    key = torch.randn(num_tokens, num_heads * head_size, dtype=dtype, device='cuda')
+    positions = torch.randint(0, max_position, (num_tokens, ), device='cuda')
+    query = torch.randn(num_tokens,
+                        num_heads * head_size,
+                        dtype=dtype,
+                        device='cuda')
+    key = torch.randn(num_tokens,
+                      num_heads * head_size,
+                      dtype=dtype,
+                      device='cuda')
 
     # Create the rotary embedding.
-    inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2) / rotary_dim))
+    inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
     t = torch.arange(max_position).float()
     freqs = torch.einsum('i,j -> ij', t, inv_freq.float())
     cos = freqs.cos()

vllm/__init__.py

+"""vLLM: a high-throughput and memory-efficient inference engine for LLMs"""
+
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.engine.llm_engine import LLMEngine

vllm/block.py

@@ -35,7 +35,8 @@ class LogicalTokenBlock:
 
     def append_tokens(self, token_ids: List[int]) -> None:
         assert len(token_ids) <= self.get_num_empty_slots()
-        self.token_ids[self.num_tokens:self.num_tokens + len(token_ids)] = token_ids
+        curr_idx = self.num_tokens
+        self.token_ids[curr_idx:curr_idx + len(token_ids)] = token_ids
         self.num_tokens += len(token_ids)
 
     def get_token_ids(self) -> List[int]:

vllm/config.py

@@ -8,7 +8,7 @@ from vllm.utils import get_cpu_memory
 
 logger = init_logger(__name__)
 
-_GiB = 1 << 30
+_GB = 1 << 30
 
 
 class ModelConfig:
@@ -106,6 +106,7 @@ class CacheConfig:
             vLLM execution.
         swap_space: Size of the CPU swap space per GPU (in GiB).
     """
+
     def __init__(
         self,
         block_size: int,
@@ -114,7 +115,7 @@ class CacheConfig:
     ) -> None:
         self.block_size = block_size
         self.gpu_memory_utilization = gpu_memory_utilization
-        self.swap_space_bytes = swap_space * _GiB
+        self.swap_space_bytes = swap_space * _GB
         self._verify_args()
 
         # Will be set after profiling.
@@ -137,14 +138,13 @@ class CacheConfig:
         num_gpus_per_node = parallel_config.tensor_parallel_size
         cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node
 
-        msg = (
-            f"{cpu_memory_usage / _GiB:.2f} GiB out of "
-            f"the {total_cpu_memory / _GiB:.2f} GiB total CPU memory is "
+        msg = (f"{cpu_memory_usage / _GB:.2f} GiB out of "
+               f"the {total_cpu_memory / _GB:.2f} GiB total CPU memory is "
                "allocated for the swap space.")
         if cpu_memory_usage > 0.7 * total_cpu_memory:
             raise ValueError("Too large swap space. " + msg)
         elif cpu_memory_usage > 0.4 * total_cpu_memory:
-            logger.warn("Possibly too large swap space. " + msg)
+            logger.warning("Possibly too large swap space. " + msg)
 
 
 class ParallelConfig:
@@ -157,6 +157,7 @@ class ParallelConfig:
             True if either pipeline_parallel_size or tensor_parallel_size is
             greater than 1.
     """
+
     def __init__(
         self,
         pipeline_parallel_size: int,
@@ -189,12 +190,9 @@ class SchedulerConfig:
         max_seq_len: Maximum length of a sequence (including prompt
             and generated text).
     """
-    def __init__(
-        self,
-        max_num_batched_tokens: int,
-        max_num_seqs: int,
-        max_seq_len: int
-    ) -> None:
+
+    def __init__(self, max_num_batched_tokens: int, max_num_seqs: int,
+                 max_seq_len: int) -> None:
         self.max_num_batched_tokens = max_num_batched_tokens
         self.max_num_seqs = max_num_seqs
         self.max_seq_len = max_seq_len
@@ -241,7 +239,7 @@ def _get_and_verify_dtype(
             pass
         else:
             # Casting between float16 and bfloat16 is allowed with a warning.
-            logger.warn(f"Casting {config_dtype} to {torch_dtype}.")
+            logger.warning(f"Casting {config_dtype} to {torch_dtype}.")
 
     # Check if the GPU supports the dtype.
     if torch_dtype == torch.bfloat16:

vllm/core/block_manager.py

@@ -27,8 +27,9 @@ class BlockAllocator:
         # Initialize the free blocks.
         self.free_blocks: List[PhysicalTokenBlock] = []
         for i in range(num_blocks):
-            block = PhysicalTokenBlock(
-                device=device, block_number=i, block_size=block_size)
+            block = PhysicalTokenBlock(device=device,
+                                       block_number=i,
+                                       block_size=block_size)
             self.free_blocks.append(block)
 
     def allocate(self) -> PhysicalTokenBlock:
@@ -84,10 +85,12 @@ class BlockSpaceManager:
         num_required_blocks = len(seq.logical_token_blocks)
         num_free_gpu_blocks = self.gpu_allocator.get_num_free_blocks()
         # Use watermark to avoid frequent cache eviction.
-        return num_free_gpu_blocks - num_required_blocks >= self.watermark_blocks
+        return (num_free_gpu_blocks - num_required_blocks >=
+                self.watermark_blocks)
 
     def allocate(self, seq_group: SequenceGroup) -> None:
-        # NOTE: Here we assume that all sequences in the group have the same prompt.
+        # NOTE: Here we assume that all sequences in the group have the same
+        # prompt.
         seq = seq_group.get_seqs()[0]
 
         # Allocate new physical token blocks that will store the prompt tokens.
@@ -143,7 +146,8 @@ class BlockSpaceManager:
         for block in src_block_table:
             block.ref_count += 1
 
-    def _get_physical_blocks(self, seq_group: SequenceGroup) -> List[PhysicalTokenBlock]:
+    def _get_physical_blocks(
+            self, seq_group: SequenceGroup) -> List[PhysicalTokenBlock]:
         # NOTE: Here, we assume that the physical blocks are only shared by
         # the sequences in the same group.
         blocks: Set[PhysicalTokenBlock] = set()

vllm/core/scheduler.py

@@ -43,8 +43,7 @@ class SchedulerOutputs:
         assert not (blocks_to_swap_in and blocks_to_swap_out)
 
     def is_empty(self) -> bool:
-        return (not self.blocks_to_swap_in
-                and not self.blocks_to_swap_out
+        return (not self.blocks_to_swap_in and not self.blocks_to_swap_out
                 and not self.blocks_to_copy)
 
 
@@ -61,7 +60,7 @@ class Scheduler:
         self.log_stats = log_stats
 
         # Instantiate the scheduling policy.
-        self.policy = PolicyFactory.get_policy(policy_name='fcfs')
+        self.policy = PolicyFactory.get_policy(policy_name="fcfs")
         # Create the block space manager.
         self.block_manager = BlockSpaceManager(
             block_size=self.cache_config.block_size,
@@ -102,7 +101,8 @@ class Scheduler:
     def get_num_unfinished_seq_groups(self) -> int:
         return len(self.waiting) + len(self.running) + len(self.swapped)
 
-    def _schedule(self) -> Tuple[SchedulerOutputs, List[str], List[SequenceGroup]]:
+    def _schedule(
+            self) -> Tuple[SchedulerOutputs, List[str], List[SequenceGroup]]:
         # Blocks that need to be swaped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}
         blocks_to_swap_out: Dict[int, int] = {}
@@ -160,7 +160,8 @@ class Scheduler:
             num_curr_seqs = sum(
                 seq_group.num_seqs(status=SequenceStatus.RUNNING)
                 for seq_group in self.running)
-            if num_curr_seqs + num_new_seqs > self.scheduler_config.max_num_seqs:
+            if (num_curr_seqs + num_new_seqs >
+                    self.scheduler_config.max_num_seqs):
                 break
 
             seq_group = self.swapped.pop(0)
@@ -170,8 +171,7 @@ class Scheduler:
 
         num_batched_tokens = sum(
             seq_group.num_seqs(status=SequenceStatus.RUNNING)
-            for seq_group in self.running
-        )
+            for seq_group in self.running)
 
         # Join waiting sequences if possible.
         prompt_group_ids: List[str] = []
@@ -191,7 +191,7 @@ class Scheduler:
 
                 num_prompt_tokens = seq_group.get_seqs()[0].get_len()
                 if num_prompt_tokens >= self.scheduler_config.max_seq_len:
-                    logger.warn(
+                    logger.warning(
                         f"Input prompt ({num_prompt_tokens} tokens) is too long"
                         " and exceeds limit of "
                         f"{self.scheduler_config.max_seq_len}")
@@ -206,17 +206,19 @@ class Scheduler:
                     break
 
                 # If the number of batched tokens exceeds the limit, stop.
-                if (num_batched_tokens + num_prompt_tokens
-                    > self.scheduler_config.max_num_batched_tokens):
+                if (num_batched_tokens + num_prompt_tokens >
+                        self.scheduler_config.max_num_batched_tokens):
                     break
 
                 # The total number of sequences in the RUNNING state should not
                 # exceed the maximum number of sequences.
-                num_new_seqs = seq_group.num_seqs(status=SequenceStatus.WAITING)
+                num_new_seqs = seq_group.num_seqs(
+                    status=SequenceStatus.WAITING)
                 num_curr_seqs = sum(
                     seq_group.num_seqs(status=SequenceStatus.RUNNING)
                     for seq_group in self.running)
-                if num_curr_seqs + num_new_seqs > self.scheduler_config.max_num_seqs:
+                if (num_curr_seqs + num_new_seqs >
+                        self.scheduler_config.max_num_seqs):
                     break
 
                 seq_group = self.waiting.pop(0)
@@ -240,12 +242,11 @@ class Scheduler:
         elapsed_time = now - self.last_logging_time
         if elapsed_time > _LOGGING_INTERVAL_SEC:
             self.last_logging_time = now
-            self.num_input_tokens = [
-                (t, n) for t, n in self.num_input_tokens
-                if now - t < _LOGGING_INTERVAL_SEC
-            ]
+            self.num_input_tokens = [(t, n) for t, n in self.num_input_tokens
+                                     if now - t < _LOGGING_INTERVAL_SEC]
             if len(self.num_input_tokens) > 1:
-                total_num_tokens = sum(n for _, n in self.num_input_tokens[:-1])
+                total_num_tokens = sum(n
+                                       for _, n in self.num_input_tokens[:-1])
                 window = now - self.num_input_tokens[0][0]
                 avg_throughput = total_num_tokens / window
             else:
@@ -258,14 +259,14 @@ class Scheduler:
 
             total_num_cpu_blocks = self.cache_config.num_cpu_blocks
             if total_num_cpu_blocks > 0:
-                num_free_cpu_blocks = self.block_manager.get_num_free_cpu_blocks()
+                num_free_cpu_blocks = (
+                    self.block_manager.get_num_free_cpu_blocks())
                 num_used_cpu_blocks = total_num_cpu_blocks - num_free_cpu_blocks
                 cpu_cache_usage = num_used_cpu_blocks / total_num_cpu_blocks
             else:
                 cpu_cache_usage = 0.0
 
-            logger.info(
-                f"Throughput: {avg_throughput:.1f} tokens/s, "
+            logger.info(f"Throughput: {avg_throughput:.1f} tokens/s, "
                         f"Running: {len(self.running)} reqs, "
                         f"Swapped: {len(self.swapped)} reqs, "
                         f"Pending: {len(self.waiting)} reqs, "
@@ -273,11 +274,15 @@ class Scheduler:
                         f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
         return scheduler_outputs, prompt_group_ids, ignored_seq_groups
 
-    def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs, List[SequenceGroup]]:
+    def schedule(
+        self
+    ) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs,
+               List[SequenceGroup]]:
         # Schedule sequence groups.
         # This function call changes the internal states of the scheduler
         # such as self.running, self.swapped, and self.waiting.
-        scheduler_outputs, prompt_group_ids, ignored_seq_groups = self._schedule()
+        (scheduler_outputs, prompt_group_ids,
+         ignored_seq_groups) = self._schedule()
 
         # Create input data structures.
         seq_group_metadata_list: List[SequenceGroupMetadata] = []
@@ -311,8 +316,8 @@ class Scheduler:
             for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
                 output = seq_outputs[seq.seq_id]
                 if seq.seq_id != output.parent_seq_id:
-                    # The sequence is a fork of the parent sequence (beam search).
-                    # Free the current sequence.
+                    # The sequence is a fork of the parent sequence (beam
+                    # search). Free the current sequence.
                     self.block_manager.free(seq)
                     # Fork the parent sequence.
                     parent_seq = seq_group.find(output.parent_seq_id)
@@ -385,7 +390,7 @@ class Scheduler:
         elif preemption_mode == PreemptionMode.SWAP:
             self._preempt_by_swap(seq_group, blocks_to_swap_out)
         else:
-            assert False, 'Invalid preemption mode.'
+            assert False, "Invalid preemption mode."
 
     def _preempt_by_recompute(
         self,

vllm/engine/arg_utils.py

@@ -12,11 +12,11 @@ class EngineArgs:
     """Arguments for vLLM engine."""
     model: str
     tokenizer: Optional[str] = None
-    tokenizer_mode: str = "auto"
+    tokenizer_mode: str = 'auto'
     download_dir: Optional[str] = None
     use_np_weights: bool = False
     use_dummy_weights: bool = False
-    dtype: str = "auto"
+    dtype: str = 'auto'
     seed: int = 0
     worker_use_ray: bool = False
     pipeline_parallel_size: int = 1
@@ -35,76 +35,101 @@ class EngineArgs:
 
     @staticmethod
     def add_cli_args(
-        parser: argparse.ArgumentParser,
-    ) -> argparse.ArgumentParser:
+            parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
         """Shared CLI arguments for vLLM engine."""
         # Model arguments
-        parser.add_argument('--model', type=str, default='facebook/opt-125m',
+        parser.add_argument(
+            '--model',
+            type=str,
+            default='facebook/opt-125m',
             help='name or path of the huggingface model to use')
-        parser.add_argument('--tokenizer', type=str, default=EngineArgs.tokenizer,
+        parser.add_argument(
+            '--tokenizer',
+            type=str,
+            default=EngineArgs.tokenizer,
             help='name or path of the huggingface tokenizer to use')
-        parser.add_argument('--tokenizer-mode', type=str,
+        parser.add_argument('--tokenizer-mode',
+                            type=str,
                             default=EngineArgs.tokenizer_mode,
                             choices=['auto', 'slow'],
                             help='tokenizer mode. "auto" will use the fast '
                             'tokenizer if available, and "slow" will '
                             'always use the slow tokenizer.')
-        parser.add_argument('--download-dir', type=str,
+        parser.add_argument('--download-dir',
+                            type=str,
                             default=EngineArgs.download_dir,
                             help='directory to download and load the weights, '
                             'default to the default cache dir of '
                             'huggingface')
-        parser.add_argument('--use-np-weights', action='store_true',
+        parser.add_argument('--use-np-weights',
+                            action='store_true',
                             help='save a numpy copy of model weights for '
                             'faster loading. This can increase the disk '
                             'usage by up to 2x.')
-        parser.add_argument('--use-dummy-weights', action='store_true',
+        parser.add_argument('--use-dummy-weights',
+                            action='store_true',
                             help='use dummy values for model weights')
         # TODO(woosuk): Support FP32.
-        parser.add_argument('--dtype', type=str, default=EngineArgs.dtype,
+        parser.add_argument(
+            '--dtype',
+            type=str,
+            default=EngineArgs.dtype,
             choices=['auto', 'half', 'bfloat16', 'float'],
             help='data type for model weights and activations. '
             'The "auto" option will use FP16 precision '
             'for FP32 and FP16 models, and BF16 precision '
             'for BF16 models.')
         # Parallel arguments
-        parser.add_argument('--worker-use-ray', action='store_true',
+        parser.add_argument('--worker-use-ray',
+                            action='store_true',
                             help='use Ray for distributed serving, will be '
                             'automatically set when using more than 1 GPU')
-        parser.add_argument('--pipeline-parallel-size', '-pp', type=int,
+        parser.add_argument('--pipeline-parallel-size',
+                            '-pp',
+                            type=int,
                             default=EngineArgs.pipeline_parallel_size,
                             help='number of pipeline stages')
-        parser.add_argument('--tensor-parallel-size', '-tp', type=int,
+        parser.add_argument('--tensor-parallel-size',
+                            '-tp',
+                            type=int,
                             default=EngineArgs.tensor_parallel_size,
                             help='number of tensor parallel replicas')
         # KV cache arguments
-        parser.add_argument('--block-size', type=int,
+        parser.add_argument('--block-size',
+                            type=int,
                             default=EngineArgs.block_size,
                             choices=[8, 16, 32],
                             help='token block size')
         # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
-        parser.add_argument('--seed', type=int, default=EngineArgs.seed,
+        parser.add_argument('--seed',
+                            type=int,
+                            default=EngineArgs.seed,
                             help='random seed')
-        parser.add_argument('--swap-space', type=int,
+        parser.add_argument('--swap-space',
+                            type=int,
                             default=EngineArgs.swap_space,
                             help='CPU swap space size (GiB) per GPU')
-        parser.add_argument('--gpu-memory-utilization', type=float,
+        parser.add_argument('--gpu-memory-utilization',
+                            type=float,
                             default=EngineArgs.gpu_memory_utilization,
                             help='the percentage of GPU memory to be used for'
                             'the model executor')
-        parser.add_argument('--max-num-batched-tokens', type=int,
+        parser.add_argument('--max-num-batched-tokens',
+                            type=int,
                             default=EngineArgs.max_num_batched_tokens,
                             help='maximum number of batched tokens per '
                             'iteration')
-        parser.add_argument('--max-num-seqs', type=int,
+        parser.add_argument('--max-num-seqs',
+                            type=int,
                             default=EngineArgs.max_num_seqs,
                             help='maximum number of sequences per iteration')
-        parser.add_argument('--disable-log-stats', action='store_true',
+        parser.add_argument('--disable-log-stats',
+                            action='store_true',
                             help='disable logging statistics')
         return parser
 
     @classmethod
-    def from_cli_args(cls, args: argparse.Namespace) -> "EngineArgs":
+    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
         # Get the list of attributes of this dataclass.
         attrs = [attr.name for attr in dataclasses.fields(cls)]
         # Set the attributes from the parsed arguments.
@@ -115,18 +140,19 @@ class EngineArgs:
         self,
     ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig]:
         # Initialize the configs.
-        model_config = ModelConfig(
-            self.model, self.tokenizer, self.tokenizer_mode, self.download_dir,
-            self.use_np_weights, self.use_dummy_weights, self.dtype, self.seed)
-        cache_config = CacheConfig(self.block_size, self.gpu_memory_utilization,
+        model_config = ModelConfig(self.model, self.tokenizer,
+                                   self.tokenizer_mode, self.download_dir,
+                                   self.use_np_weights, self.use_dummy_weights,
+                                   self.dtype, self.seed)
+        cache_config = CacheConfig(self.block_size,
+                                   self.gpu_memory_utilization,
                                    self.swap_space)
         parallel_config = ParallelConfig(self.pipeline_parallel_size,
                                          self.tensor_parallel_size,
                                          self.worker_use_ray)
-        max_seq_len = min(
-            self.max_num_batched_tokens, 
-            getattr(model_config.hf_config, "max_position_embeddings", 
-                    float("inf")))
+        model_max_len = getattr(model_config.hf_config,
+                                'max_position_embeddings', float('inf'))
+        max_seq_len = min(self.max_num_batched_tokens, model_max_len)
         scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
                                            self.max_num_seqs, max_seq_len)
         return model_config, cache_config, parallel_config, scheduler_config
@@ -140,12 +166,13 @@ class AsyncEngineArgs(EngineArgs):
 
     @staticmethod
     def add_cli_args(
-        parser: argparse.ArgumentParser,
-    ) -> argparse.ArgumentParser:
+            parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
         parser = EngineArgs.add_cli_args(parser)
-        parser.add_argument('--engine-use-ray', action='store_true',
+        parser.add_argument('--engine-use-ray',
+                            action='store_true',
                             help='use Ray to start the LLM engine in a '
                             'separate process as the server process.')
-        parser.add_argument('--disable-log-requests', action='store_true',
+        parser.add_argument('--disable-log-requests',
+                            action='store_true',
                             help='disable logging requests')
         return parser

vllm/engine/async_llm_engine.py

@@ -35,8 +35,13 @@ class AsyncLLMEngine:
         log_requests: Whether to log the requests.
         *args, *kwargs: Arguments for LLMEngine.
     """
-    def __init__(self, worker_use_ray: bool, engine_use_ray: bool,
-                 log_requests: bool = True, *args, **kwargs) -> None:
+
+    def __init__(self,
+                 worker_use_ray: bool,
+                 engine_use_ray: bool,
+                 *args,
+                 log_requests: bool = True,
+                 **kwargs) -> None:
         self.worker_use_ray = worker_use_ray
         self.engine_use_ray = engine_use_ray
         self.log_requests = log_requests
@@ -80,8 +85,7 @@ class AsyncLLMEngine:
             prompt: Optional[str],
             sampling_params: SamplingParams,
             request_id: str,
-        prompt_token_ids: Optional[List[int]] = None
-    ) -> RequestOutput:
+            prompt_token_ids: Optional[List[int]] = None) -> RequestOutput:
         """Generate outputs for a request.
 
         Generate outputs for a request. This method is a coroutine. It adds the
@@ -117,12 +121,15 @@ class AsyncLLMEngine:
         # 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,
+                request_id,
+                prompt,
+                sampling_params,
                 prompt_token_ids=prompt_token_ids,
                 arrival_time=arrival_time)
         else:
-            self.engine.add_request(
-                request_id, prompt, sampling_params,
+            self.engine.add_request(request_id,
+                                    prompt,
+                                    sampling_params,
                                     prompt_token_ids=prompt_token_ids,
                                     arrival_time=arrival_time)
 
@@ -200,7 +207,8 @@ class AsyncLLMEngine:
             self.kicking_request_id = None
 
     @classmethod
-    def from_engine_args(cls, engine_args: AsyncEngineArgs) -> "AsyncLLMEngine":
+    def from_engine_args(cls,
+                         engine_args: AsyncEngineArgs) -> "AsyncLLMEngine":
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
         engine_configs = engine_args.create_engine_configs()
@@ -211,8 +219,9 @@ class AsyncLLMEngine:
         # Create the async LLM engine.
         engine = cls(engine_args.worker_use_ray,
                      engine_args.engine_use_ray,
-                     not engine_args.disable_log_requests,
                      *engine_configs,
-                     distributed_init_method, devices,
+                     distributed_init_method,
+                     devices,
+                     log_requests=not engine_args.disable_log_requests,
                      log_stats=not engine_args.disable_log_stats)
         return engine