Merge tag 'v0.6.0' into v0.6.0-dev

vllm/entrypoints/openai/serving_embedding.py

@@ -31,7 +31,9 @@ def _get_embedding(
     if encoding_format == "float":
         return output.embedding
     elif encoding_format == "base64":
-        embedding_bytes = np.array(output.embedding).tobytes()
+        # Force to use float32 for base64 encoding
+        # to match the OpenAI python client behavior
+        embedding_bytes = np.array(output.embedding, dtype="float32").tobytes()
         return base64.b64encode(embedding_bytes).decode("utf-8")
 
     assert_never(encoding_format)

vllm/entrypoints/openai/serving_tokenization.py

@@ -4,7 +4,7 @@ from vllm.config import ModelConfig
 from vllm.engine.protocol import AsyncEngineClient
 from vllm.entrypoints.chat_utils import (apply_chat_template,
                                          load_chat_template,
-                                         parse_chat_messages)
+                                         parse_chat_messages_futures)
 from vllm.entrypoints.logger import RequestLogger
 # yapf conflicts with isort for this block
 # yapf: disable
@@ -43,7 +43,11 @@ class OpenAIServingTokenization(OpenAIServing):
                          request_logger=request_logger)
 
         # If this is None we use the tokenizer's default chat template
-        self.chat_template = load_chat_template(chat_template)
+        # the list of commonly-used chat template names for HF named templates
+        hf_chat_templates: List[str] = ['default', 'tool_use']
+        self.chat_template = chat_template \
+            if chat_template in hf_chat_templates \
+            else load_chat_template(chat_template)
 
     async def create_tokenize(
         self,
@@ -65,10 +69,11 @@ class OpenAIServingTokenization(OpenAIServing):
         if isinstance(request, TokenizeChatRequest):
             model_config = self.model_config
 
-            conversation, mm_futures = parse_chat_messages(
+            conversation, mm_data_future = parse_chat_messages_futures(
                 request.messages, model_config, tokenizer)
 
-            if mm_futures:
+            mm_data = await mm_data_future
+            if mm_data:
                 logger.warning(
                     "Multi-modal inputs are ignored during tokenization")
 

vllm/entrypoints/openai/tool_parsers/__init__.py

+from .abstract_tool_parser import ToolParser
+from .hermes_tool_parser import Hermes2ProToolParser
+from .mistral_tool_parser import MistralToolParser
+
+__all__ = ["ToolParser", "Hermes2ProToolParser", "MistralToolParser"]
\ No newline at end of file

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

+from typing import Dict, List, Sequence, Union
+
+from vllm.entrypoints.openai.protocol import (DeltaMessage,
+                                              ExtractedToolCallInformation)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+logger = init_logger(__name__)
+
+
+class ToolParser:
+    """
+    Abstract ToolParser class that should not be used directly. Provided
+    properties and methods should be used in
+    derived classes.
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        self.prev_tool_call_arr: List[Dict] = []
+        # the index of the tool call that is currently being parsed
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.current_tool_initial_sent: bool = False
+        self.streamed_args_for_tool: List[str] = []
+
+        self.model_tokenizer = tokenizer
+
+    def extract_tool_calls(self,
+                           model_output: str) -> ExtractedToolCallInformation:
+        """
+        Static method that should be implemented for extracting tool calls from
+        a complete model-generated string.
+        Used for non-streaming responses where we have the entire model response
+        available before sending to the client.
+        Static because it's stateless.
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls has not been implemented!")
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> Union[DeltaMessage, None]:
+        """
+        Instance method that should be implemented for extracting tool calls
+        from an incomplete response; for use when handling tool calls and
+        streaming. Has to be an instance method because  it requires state -
+        the current tokens/diffs, but also the information about what has
+        previously been parsed and extracted (see constructor)
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls_streaming has not been "
+            "implemented!")

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

+import json
+import re
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall,
+                                              InitialDeltaToolCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+
+logger = init_logger(__name__)
+
+
+class Hermes2ProToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if isinstance(self.model_tokenizer, MistralTokenizer):
+            logger.error(
+                "Detected Mistral tokenizer when using a Hermes model")
+            self.model_tokenizer = self.model_tokenizer.tokenizer
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent = False
+        self.current_tool_initial_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+
+        self.tool_call_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*)", re.DOTALL)
+        self.scratch_pad_regex = re.compile(
+            r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction.")
+        self.tool_call_start_token_id: int = self.model_tokenizer.vocab[
+            self.tool_call_start_token]
+        self.tool_call_end_token_id: int = self.model_tokenizer.vocab[
+            self.tool_call_end_token]
+        if not self.tool_call_start_token_id or not self.tool_call_end_token_id:
+            raise RuntimeError(
+                "Hermes 2 Pro Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!")
+
+    def extract_tool_calls(self,
+                           model_output: str) -> ExtractedToolCallInformation:
+
+        # sanity check; avoid unnecessary processing
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        else:
+
+            try:
+                # there are two possible captures - between tags, or between a
+                # tag and end-of-string so the result of
+                # findall is an array of tuples where one is a function call and
+                # the other is None
+                function_call_tuples = (
+                    self.tool_call_regex.findall(model_output))
+
+                # load the JSON, and then use it to build the Function and
+                # Tool Call
+                raw_function_calls = [
+                    json.loads(match[0] if match[0] else match[1])
+                    for match in function_call_tuples
+                ]
+                tool_calls = [
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=function_call["name"],
+                            # function call args are JSON but as a string
+                            arguments=json.dumps(function_call["arguments"])))
+                    for function_call in raw_function_calls
+                ]
+
+                content = model_output[:model_output.
+                                       find(self.tool_call_start_token)]
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None)
+
+            except Exception as e:
+                logger.error("Error in extracting tool call from response %s",
+                             e)
+                return ExtractedToolCallInformation(tools_called=False,
+                                                    tool_calls=[],
+                                                    content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> Union[DeltaMessage, None]:
+
+        logger.debug("delta_text: %s", delta_text)
+        logger.debug("delta_token_ids: %s", delta_token_ids)
+        # check to see if we should be streaming a tool call - is there a
+        if self.tool_call_start_token_id not in current_token_ids:
+            logger.debug("No tool call tokens found!")
+            return DeltaMessage(content=delta_text)
+
+        try:
+
+            # figure out where we are in the parsing by counting tool call
+            # start & end tags
+            prev_tool_start_count = previous_token_ids.count(
+                self.tool_call_start_token_id)
+            prev_tool_end_count = previous_token_ids.count(
+                self.tool_call_end_token_id)
+            cur_tool_start_count = current_token_ids.count(
+                self.tool_call_start_token_id)
+            cur_tool_end_count = current_token_ids.count(
+                self.tool_call_end_token_id)
+
+            # case: if we're generating text, OR rounding out a tool call
+            if (cur_tool_start_count == cur_tool_end_count
+                    and prev_tool_end_count == cur_tool_end_count):
+                logger.debug("Generating text content! skipping tool parsing.")
+                if delta_text != self.tool_call_end_token:
+                    return DeltaMessage(content=delta_text)
+
+            # case: if tool open & close tag counts don't match, we're doing
+            # imaginary "else" block here
+            # something with tools with this diff.
+            # flags for partial JSON parting. exported constants from
+            # "Allow" are handled via BIT MASK
+            flags = Allow.ALL if self.current_tool_name_sent \
+                else Allow.ALL & ~Allow.STR
+
+            # case -- we're starting a new tool call
+            if (cur_tool_start_count > cur_tool_end_count
+                    and cur_tool_start_count > prev_tool_start_count):
+                if len(delta_token_ids) > 1:
+                    tool_call_portion = current_text.split(
+                        self.tool_call_start_token)[-1]
+                else:
+                    tool_call_portion = None
+                    delta = None
+
+                text_portion = None
+
+                # set cursors and state appropriately
+                self.current_tool_id += 1
+                self.current_tool_name_sent = False
+                self.current_tool_initial_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("Starting on a new tool %s", self.current_tool_id)
+
+            # case -- we're updating an existing tool call
+            elif (cur_tool_start_count > cur_tool_end_count
+                  and cur_tool_start_count == prev_tool_start_count):
+
+                # get the portion of the text that's the tool call
+                tool_call_portion = current_text.split(
+                    self.tool_call_start_token)[-1]
+                text_portion = None
+
+            # case -- the current tool call is being closed.
+            elif (cur_tool_start_count == cur_tool_end_count
+                  and cur_tool_end_count > prev_tool_end_count):
+                diff = self.prev_tool_call_arr[self.current_tool_id].get(
+                    "arguments")
+                if diff:
+                    diff = json.dumps(diff).replace(
+                        self.streamed_args_for_tool[self.current_tool_id], "")
+                    logger.debug(
+                        "Finishing tool and found diff that had not "
+                        "been streamed yet: %s", diff)
+                    self.streamed_args_for_tool[self.current_tool_id] \
+                        += diff
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=diff).model_dump(
+                                              exclude_none=True))
+                    ])
+
+            # case -- otherwise we're just generating text
+            else:
+                text = delta_text.replace(self.tool_call_start_token, "")
+                text = text.replace(self.tool_call_end_token, "")
+                delta = DeltaMessage(tool_calls=[], content=text)
+                return delta
+
+            try:
+
+                current_tool_call = partial_json_parser.loads(
+                    tool_call_portion or "{}",
+                    flags) if tool_call_portion else None
+                logger.debug("Parsed tool call %s", current_tool_call)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # case - we haven't sent the initial delta with the tool call ID
+            #   (it will be sent)
+            if not self.current_tool_initial_sent:
+                self.current_tool_initial_sent = True
+                return DeltaMessage(tool_calls=[
+                    InitialDeltaToolCall(
+                        index=self.current_tool_id).model_dump(
+                            exclude_none=True)
+                ])
+
+            # case - we haven't sent the tool name yet. If it's available, send
+            #   it. otherwise, wait until it's available.
+            elif not self.current_tool_name_sent:
+                function_name: Union[str, None] = current_tool_call.get("name")
+                if function_name:
+                    self.current_tool_name_sent = True
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                else:
+                    return None
+            # case -- otherwise, send the tool call delta
+
+            # if the tool call portion is None, send the delta as text
+            if tool_call_portion is None:
+                # if there's text but not tool calls, send that -
+                # otherwise None to skip chunk
+                delta = DeltaMessage(content=delta_text) \
+                    if text_portion is not None else None
+                return delta
+
+            # now, the nitty-gritty of tool calls
+            # now we have the portion to parse as tool call.
+
+            logger.debug("Trying to parse current tool call with ID %s",
+                         self.current_tool_id)
+
+            # if we're starting a new tool call, push an empty object in as
+            #   a placeholder for the arguments
+            if len(self.prev_tool_call_arr) <= self.current_tool_id:
+                self.prev_tool_call_arr.append({})
+
+            # main logic for tool parsing here - compare prev. partially-parsed
+            #   JSON to the current partially-parsed JSON
+            prev_arguments = (
+                self.prev_tool_call_arr[self.current_tool_id].get("arguments"))
+            cur_arguments = current_tool_call.get("arguments")
+
+            logger.debug("diffing old arguments: %s", prev_arguments)
+            logger.debug("against new ones: %s", cur_arguments)
+
+            # case -- no arguments have been created yet. skip sending a delta.
+            if not cur_arguments and not prev_arguments:
+                logger.debug("Skipping text %s - no arguments", delta_text)
+                delta = None
+
+            # case -- prev arguments are defined, but non are now.
+            #   probably impossible, but not a fatal error - just keep going
+            elif not cur_arguments and prev_arguments:
+                logger.error("should be impossible to have arguments reset "
+                             "mid-call. skipping streaming anything.")
+                delta = None
+
+            # case -- we now have the first info about arguments available from
+            #   autocompleting the JSON
+            elif cur_arguments and not prev_arguments:
+
+                cur_arguments_json = json.dumps(cur_arguments)
+                logger.debug("finding %s in %s", delta_text,
+                             cur_arguments_json)
+
+                # get the location where previous args differ from current
+                args_delta_start_loc = cur_arguments_json.index(delta_text) \
+                                       + len(delta_text)
+
+                # use that to find the actual delta
+                arguments_delta = cur_arguments_json[:args_delta_start_loc]
+                logger.debug("First tokens in arguments received: %s",
+                             arguments_delta)
+
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=arguments_delta).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += arguments_delta
+
+            # last case -- we have an update to existing arguments.
+            elif cur_arguments and prev_arguments:
+
+                cur_args_json = json.dumps(cur_arguments)
+                prev_args_json = json.dumps(prev_arguments)
+                logger.debug("Searching for diff between\n%s", cur_args_json)
+                logger.debug("and\n%s", prev_args_json)
+                argument_diff = extract_intermediate_diff(
+                    cur_args_json, prev_args_json)
+                logger.debug("got argument diff %s", argument_diff)
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=argument_diff).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += argument_diff
+
+            # handle saving the state for the current tool into
+            # the "prev" list for use in diffing for the next iteration
+            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
+                self.prev_tool_call_arr[self.current_tool_id] = \
+                    current_tool_call
+            else:
+                self.prev_tool_call_arr.append(current_tool_call)
+
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            return None  # do not stream a delta. skip this token ID.

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

+import json
+import re
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall,
+                                              InitialDeltaToolCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+
+logger = init_logger(__name__)
+
+
+class MistralToolParser(ToolParser):
+    """
+    Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
+    examples/tool_chat_template_mistral.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser gmistral are all set
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if isinstance(self.model_tokenizer, MistralTokenizer):
+            self.model_tokenizer = self.model_tokenizer.tokenizer
+        else:
+            logger.info("Non-Mistral tokenizer detected when using a Mistral "
+                        "model...")
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.current_tool_initial_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token = "[TOOL_CALLS]"
+        self.bot_token_id = self.model_tokenizer.vocab[self.bot_token]
+        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
+
+    def extract_tool_calls(self,
+                           model_output: str) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response. Requires
+        find-and-replacing single quotes with double quotes for JSON parsing,
+        make sure your tool call arguments don't ever include quotes!
+        """
+
+        # case -- if a tool call token is not present, return a text response
+        if self.bot_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+        try:
+
+            # use a regex to find the tool call. remove the BOT token
+            #   and make sure to replace single quotes with double quotes
+            raw_tool_call = self.tool_call_regex.findall(
+                model_output.replace(self.bot_token, ""))[0]
+
+            # load the JSON, and then use it to build the Function and
+            # Tool Call
+            function_call_arr = json.loads(raw_tool_call)
+            tool_calls: List[ToolCall] = [
+                ToolCall(
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(raw_function_call["arguments"])))
+                for raw_function_call in function_call_arr
+            ]
+
+            # get any content before  the tool call
+            content = model_output.split(self.bot_token)[0]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=content if len(content) > 0 else None)
+
+        except Exception as e:
+            logger.error("Error in extracting tool call from response: %s", e)
+            print("ERROR", e)
+            # return information to just treat the tool call as regular JSON
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> Union[DeltaMessage, None]:
+
+        # if the tool call token is not in the tokens generated so far, append
+        # output to contents since it's not a tool
+        if self.bot_token_id not in current_token_ids:
+            return DeltaMessage(content=delta_text)
+
+        # if the tool call token ID IS in the tokens generated so far, that
+        # means we're parsing as tool calls now
+
+        # handle if we detected the BOT token which means the start of tool
+        # calling
+        if (self.bot_token_id in delta_token_ids
+                and len(delta_token_ids) == 1):
+            # if it's the only token, return None, so we don't send a chat
+            # completion any don't send a control token
+            return None
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+        try:
+
+            # replace BOT token with empty string, and convert single quotes
+            # to double to allow parsing as JSON since mistral uses single
+            # quotes instead of double for tool calls
+            parsable_arr = current_text.split(self.bot_token)[1]
+
+            # tool calls are generated in an array, so do partial JSON
+            # parsing on the entire array
+            try:
+                tool_call_arr: List[Dict] = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # select as the current tool call the one we're on the state at
+
+            current_tool_call: Dict = tool_call_arr[self.current_tool_id] \
+                if len(tool_call_arr) > 0 else {}
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return None
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (len(tool_call_arr) > 0
+                  and len(tool_call_arr) > self.current_tool_id + 1):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                if self.current_tool_id >= 0:
+                    diff: Union[str, None] = current_tool_call.get("arguments")
+
+                    if diff:
+                        diff = json.dumps(diff).replace(
+                            self.streamed_args_for_tool[self.current_tool_id],
+                            "")
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=diff).model_dump(
+                                                  exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += diff
+                    else:
+                        delta = None
+                else:
+                    delta = None
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.current_tool_initial_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("starting on new tool %d", self.current_tool_id)
+                return delta
+
+            # case: update an existing tool - this is handled below
+
+            # if the current tool initial data incl. the id, type=function
+            # and idx not sent, send that
+            if not self.current_tool_initial_sent:
+                self.current_tool_initial_sent = True
+                delta = DeltaMessage(tool_calls=[
+                    InitialDeltaToolCall(
+                        index=self.current_tool_id).model_dump(
+                            exclude_none=True)
+                ])
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            elif not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                else:
+                    delta = None
+
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+
+                prev_arguments = self.prev_tool_call_arr[
+                    self.current_tool_id].get("arguments")
+                cur_arguments = current_tool_call.get("arguments")
+
+                new_text = delta_text.replace("\'", "\"")
+
+                if not cur_arguments and not prev_arguments:
+
+                    delta = None
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+                    logger.debug("finding %s in %s", new_text,
+                                 cur_arguments_json)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(new_text) +
+                                                         len(new_text)]
+                    logger.debug("First tokens in arguments received: %s",
+                                 arguments_delta)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+                    logger.debug("Searching for diff between \n%s\n%s",
+                                 cur_args_json, prev_args_json)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+                    logger.debug("got arguments diff: %s", argument_diff)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+                else:
+                    # try parsing it with regular JSON - if it works we're
+                    # at the end, and we need to send the difference between
+                    # tokens streamed so far and the valid JSON
+                    delta = None
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            self.prev_tool_call_arr = tool_call_arr
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None

vllm/entrypoints/openai/tool_parsers/utils.py

+def find_common_prefix(s1: str, s2: str) -> str:
+    """
+    Finds a common prefix that is shared between two strings, if there is one.
+    Order of arguments is NOT important.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely.
+
+    e.g. find_common_prefix('{"fruit": "ap"}', '{"fruit": "apple"}') ->
+    '{"fruit": "ap'
+    """
+    prefix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(0, min_length):
+        if s1[i] == s2[i]:
+            prefix += s1[i]
+        else:
+            break
+    return prefix
+
+
+def find_common_suffix(s1: str, s2: str) -> str:
+    """
+    Finds a common suffix shared between two strings, if there is one. Order of
+    arguments is NOT important.
+    Stops when the suffix ends OR it hits an alphanumeric character
+
+    e.g. find_common_suffix('{"fruit": "ap"}', '{"fruit": "apple"}') -> '"}'
+    """
+    suffix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(1, min_length + 1):
+        if s1[-i] == s2[-i] and not s1[-i].isalnum():
+            suffix = s1[-i] + suffix
+        else:
+            break
+    return suffix
+
+
+def extract_intermediate_diff(curr: str, old: str) -> str:
+    """
+    Given two strings, extract the difference in the middle between two strings
+    that are known to have a common prefix and/or suffix.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely. The order of arguments IS
+    important - the new version of the partially-parsed JSON must be the first
+    argument, and the secnod argument must be from the previous generation.
+
+    What it returns, is tokens that should be streamed to the client.
+
+    e.g. extract_intermediate_diff('{"fruit": "apple"}', '{"fruit": "ap"}')
+        -> 'ple'
+
+    """
+    suffix = find_common_suffix(curr, old)
+
+    old = old[::-1].replace(suffix[::-1], '', 1)[::-1]
+    prefix = find_common_prefix(curr, old)
+    diff = curr
+    if len(suffix):
+        diff = diff[::-1].replace(suffix[::-1], '', 1)[::-1]
+
+    if len(prefix):
+        # replace the prefix only once in case it's mirrored
+        diff = diff.replace(prefix, '', 1)
+
+    return diff
+
+
+def find_all_indices(string, substring):
+    """
+    Find all (starting) indices of a substring in a given string. Useful for
+    tool call extraction
+    """
+    indices = []
+    index = -1
+    while True:
+        index = string.find(substring, index + 1)
+        if index == -1:
+            break
+        indices.append(index)
+    return indices

vllm/envs.py

@@ -35,6 +35,7 @@ if TYPE_CHECKING:
     VLLM_TRACE_FUNCTION: int = 0
     VLLM_ATTENTION_BACKEND: Optional[str] = None
     VLLM_USE_FLASHINFER_SAMPLER: bool = False
+    VLLM_USE_FLASHINFER_REJECTION_SAMPLER: bool = False
     VLLM_PP_LAYER_PARTITION: Optional[str] = None
     VLLM_CPU_KVCACHE_SPACE: int = 0
     VLLM_CPU_OMP_THREADS_BIND: str = ""
@@ -220,6 +221,10 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     # Internal flag to enable Dynamo graph capture
     "VLLM_TEST_DYNAMO_GRAPH_CAPTURE":
     lambda: int(os.environ.get("VLLM_TEST_DYNAMO_GRAPH_CAPTURE", "0")),
+    "VLLM_DYNAMO_USE_CUSTOM_DISPATCHER":
+    lambda:
+    (os.environ.get("VLLM_DYNAMO_USE_CUSTOM_DISPATCHER", "True").lower() in
+     ("true", "1")),
 
     # local rank of the process in the distributed setting, used to determine
     # the GPU device id
@@ -372,7 +377,7 @@ environment_variables: Dict[str, Callable[[], Any]] = {
             os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
         )),
     "VLLM_FUSED_MOE_CHUNK_SIZE":
-    lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "65536")),
+    lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
 
     # If set, vllm will skip the deprecation warnings.
     "VLLM_NO_DEPRECATION_WARNING":
@@ -424,6 +429,10 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     "VLLM_TORCH_PROFILER_DIR":
     lambda: (None if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None else os
              .path.expanduser(os.getenv("VLLM_TORCH_PROFILER_DIR", "."))),
+
+    # If set, vLLM will use Triton implementations of AWQ.
+    "VLLM_USE_TRITON_AWQ":
+    lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
 }
 
 # end-env-vars-definition

vllm/executor/cpu_executor.py

@@ -11,8 +11,9 @@ from vllm.executor.multiproc_worker_utils import (ProcessWorkerWrapper,
                                                   ResultHandler, WorkerMonitor)
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (GiB_bytes, get_distributed_init_method, get_open_port,
                         get_vllm_instance_id, make_async)
 from vllm.worker.worker_base import WorkerWrapperBase

vllm/executor/distributed_gpu_executor.py

@@ -6,7 +6,8 @@ from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 
 logger = init_logger(__name__)
 
@@ -64,8 +65,9 @@ class DistributedGPUExecutor(GPUExecutor):
                           num_cpu_blocks=num_cpu_blocks)
 
     def execute_model(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        self,
+        execute_model_req: ExecuteModelRequest,
+    ) -> List[SamplerOutput]:
         if self.parallel_worker_tasks is None:
             self.parallel_worker_tasks = self._run_workers(
                 "start_worker_execution_loop",
@@ -188,7 +190,7 @@ class DistributedGPUExecutorAsync(DistributedGPUExecutor, ExecutorAsyncBase):
     @abstractmethod
     async def _driver_execute_model_async(
         self,
-        execute_model_req: Optional[ExecuteModelRequest] = None
+        execute_model_req: Optional[ExecuteModelRequest] = None,
     ) -> List[SamplerOutput]:
         """Execute the model asynchronously in the driver worker.
 

vllm/executor/executor_base.py

@@ -6,8 +6,9 @@ from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          PromptAdapterConfig, SchedulerConfig,
                          SpeculativeConfig)
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 
 
 class ExecutorBase(ABC):

vllm/executor/gpu_executor.py

@@ -3,8 +3,9 @@ from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sequence import ExecuteModelRequest, PoolerOutput, SamplerOutput
+from vllm.sequence import ExecuteModelRequest, PoolerOutput
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         make_async)
 from vllm.worker.worker_base import WorkerBase, WorkerWrapperBase
@@ -176,5 +177,5 @@ class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):
         execute_model_req: ExecuteModelRequest,
     ) -> List[Union[SamplerOutput, PoolerOutput]]:
         output = await make_async(self.driver_worker.execute_model
-                                  )(execute_model_req=execute_model_req, )
+                                  )(execute_model_req=execute_model_req)
         return output

vllm/executor/multiproc_gpu_executor.py

@@ -14,7 +14,8 @@ from vllm.executor.gpu_executor import create_worker
 from vllm.executor.multiproc_worker_utils import (ProcessWorkerWrapper,
                                                   ResultHandler, WorkerMonitor)
 from vllm.logger import init_logger
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.triton_utils import maybe_set_triton_cache_manager
 from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless,
                         get_distributed_init_method, get_open_port,
@@ -30,16 +31,12 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
     uses_ray: bool = False
 
     def _init_executor(self) -> None:
+        self._check_executor_parameters()
+
         # Create the parallel GPU workers.
         world_size = self.parallel_config.world_size
         tensor_parallel_size = self.parallel_config.tensor_parallel_size
 
-        # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
-        if "CUDA_VISIBLE_DEVICES" not in os.environ:
-            update_environment_variables({
-                "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
-            })
-
         # Ensure that VLLM_INSTANCE_ID is set, to be inherited by workers
         os.environ["VLLM_INSTANCE_ID"] = get_vllm_instance_id()
 
@@ -68,16 +65,6 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
         if world_size > 1:
             maybe_set_triton_cache_manager()
 
-        cuda_device_count = cuda_device_count_stateless()
-        # Use confusing message for more common TP-only case.
-        assert tensor_parallel_size <= cuda_device_count, (
-            f"please set tensor_parallel_size ({tensor_parallel_size}) "
-            f"to less than max local gpu count ({cuda_device_count})")
-
-        assert world_size <= cuda_device_count, (
-            f"please ensure that world_size ({world_size}) "
-            f"is less than than max local gpu count ({cuda_device_count})")
-
         # Multiprocessing-based executor does not support multi-node setting.
         # Since it only works for single node, we can use the loopback address
         # 127.0.0.1 for communication.
@@ -139,6 +126,26 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
                           max_concurrent_workers=self.parallel_config.
                           max_parallel_loading_workers)
 
+    def _check_executor_parameters(self):
+        world_size = self.parallel_config.world_size
+        tensor_parallel_size = self.parallel_config.tensor_parallel_size
+
+        # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
+        if "CUDA_VISIBLE_DEVICES" not in os.environ:
+            update_environment_variables({
+                "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
+            })
+
+        cuda_device_count = cuda_device_count_stateless()
+        # Use confusing message for more common TP-only case.
+        assert tensor_parallel_size <= cuda_device_count, (
+            f"please set tensor_parallel_size ({tensor_parallel_size}) "
+            f"to less than max local gpu count ({cuda_device_count})")
+
+        assert world_size <= cuda_device_count, (
+            f"please ensure that world_size ({world_size}) "
+            f"is less than than max local gpu count ({cuda_device_count})")
+
     def shutdown(self):
         if (worker_monitor := getattr(self, "worker_monitor",
                                       None)) is not None:

vllm/executor/multiproc_xpu_executor.py

+import vllm.envs as envs
+from vllm.executor.multiproc_gpu_executor import (
+    MultiprocessingGPUExecutor, MultiprocessingGPUExecutorAsync)
+from vllm.executor.xpu_executor import XPUExecutor
+from vllm.logger import init_logger
+from vllm.utils import make_async
+
+logger = init_logger(__name__)
+
+
+class MultiprocessingXPUExecutor(MultiprocessingGPUExecutor, XPUExecutor):
+    """Python multiprocessing-based multi-XPU executor"""
+
+    def _check_executor_parameters(self):
+        mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
+        if mp_method != "spawn":
+            raise RuntimeError(
+                "XPU multiprocess executor only support spawn as mp method")
+
+
+class MultiprocessingXPUExecutorAsync(MultiprocessingXPUExecutor,
+                                      MultiprocessingGPUExecutorAsync):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.driver_exec_model = make_async(self.driver_worker.execute_model)

vllm/executor/neuron_executor.py

@@ -3,8 +3,10 @@ from typing import List, Set, Tuple
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
-from vllm.utils import make_async
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
+from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
+                        make_async)
 
 logger = init_logger(__name__)
 
@@ -24,14 +26,17 @@ class NeuronExecutor(ExecutorBase):
 
     def _init_worker(self):
         from vllm.worker.neuron_worker import NeuronWorker
-
+        distributed_init_method = get_distributed_init_method(
+            get_ip(), get_open_port())
         self.driver_worker = NeuronWorker(
-            self.model_config,
-            self.parallel_config,
-            self.scheduler_config,
-            self.device_config,
-            self.cache_config,
-        )
+            model_config=self.model_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config,
+            device_config=self.device_config,
+            cache_config=self.cache_config,
+            local_rank=0,
+            rank=0,
+            distributed_init_method=distributed_init_method)
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 

vllm/executor/openvino_executor.py

@@ -9,7 +9,8 @@ from vllm.config import CacheConfig, ModelConfig
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (GiB_bytes, get_distributed_init_method, get_ip,
                         get_open_port, make_async)
 

vllm/executor/ray_gpu_executor.py

@@ -12,7 +12,8 @@ from vllm.executor.distributed_gpu_executor import (  # yapf: disable
 from vllm.executor.msgspec_utils import encode_hook
 from vllm.executor.ray_utils import RayWorkerWrapper, ray
 from vllm.logger import init_logger
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (_run_task_with_lock, get_distributed_init_method,
                         get_ip, get_open_port, get_vllm_instance_id,
                         make_async)

vllm/executor/ray_tpu_executor.py

@@ -10,7 +10,8 @@ from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.ray_utils import RayWorkerWrapper, ray
 from vllm.executor.tpu_executor import TPUExecutor
 from vllm.logger import init_logger
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         get_vllm_instance_id, make_async)
 
@@ -70,6 +71,19 @@ class RayTPUExecutor(TPUExecutor):
             worker_module_name = "vllm.worker.tpu_worker"
             worker_class_name = "TPUWorker"
 
+            # GKE does not fetch environment information from metadata server
+            # and instead sets these from within the Ray process. Therefore we
+            # need to override the Ray environment variables manually.
+            override_env = {}
+            if "TPU_CHIPS_PER_HOST_BOUNDS" in os.environ:
+                override_env.update({
+                    "TPU_CHIPS_PER_HOST_BOUNDS":
+                    os.environ["TPU_CHIPS_PER_HOST_BOUNDS"]
+                })
+            if "TPU_HOST_BOUNDS" in os.environ:
+                override_env.update(
+                    {"TPU_HOST_BOUNDS": os.environ["TPU_HOST_BOUNDS"]})
+
             worker = ray.remote(
                 num_cpus=0,
                 resources={"TPU": 1},
@@ -80,6 +94,8 @@ class RayTPUExecutor(TPUExecutor):
                 worker_class_name=worker_class_name,
                 trust_remote_code=self.model_config.trust_remote_code,
             )
+            if override_env:
+                worker.override_env_vars.remote(override_env)
 
             worker_ip = ray.get(worker.get_node_ip.remote())
             if worker_ip == driver_ip and self.driver_dummy_worker is None:
@@ -95,12 +111,40 @@ class RayTPUExecutor(TPUExecutor):
                 # Else, added to the list of workers.
                 self.workers.append(worker)
 
+        logger.debug("workers: %s", self.workers)
+        logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
         if self.driver_dummy_worker is None:
             raise ValueError(
                 "Ray does not allocate any TPUs on the driver node. Consider "
                 "adjusting the Ray placement group or running the driver on a "
                 "TPU node.")
 
+        worker_ips = [
+            ray.get(worker.get_node_ip.remote())  # type: ignore[attr-defined]
+            for worker in self.workers
+        ]
+        ip_counts: Dict[str, int] = {}
+        for ip in worker_ips:
+            ip_counts[ip] = ip_counts.get(ip, 0) + 1
+
+        def sort_by_driver_then_worker_ip(worker):
+            """
+            Sort the workers based on 3 properties:
+            1. If the worker is on the same node as the driver (vllm engine),
+                it should be placed first.
+            2. Then, if the worker is on a node with fewer workers, it should
+                be placed first.
+            3. Finally, if the work is on a node with smaller IP address, it
+                should be placed first.
+            """
+            ip = ray.get(worker.get_node_ip.remote())
+            return (ip != driver_ip, ip_counts[ip], ip)
+
+        # After sorting, the workers on the same node will be
+        # close to each other, and the workers on the driver
+        # node will be placed first.
+        self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip)
+
         # Get the set of TPU IDs used on each node.
         worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids",
                                                     use_dummy_driver=True)

vllm/executor/ray_utils.py

+import os
 import time
 from collections import defaultdict
 from typing import Dict, List, Optional, Tuple, Union
@@ -84,6 +85,9 @@ try:
 
             return output
 
+        def override_env_vars(self, vars: Dict[str, str]):
+            os.environ.update(vars)
+
     ray_import_err = None
 
 except ImportError as e:
@@ -291,3 +295,28 @@ def initialize_ray_cluster(
     _verify_bundles(current_placement_group, parallel_config, device_str)
     # Set the placement group in the parallel config
     parallel_config.placement_group = current_placement_group
+
+
+def get_num_tpu_nodes() -> int:
+    from ray._private.accelerators import TPUAcceleratorManager
+    cluster_resources = ray.cluster_resources()
+    total_tpus = int(cluster_resources["TPU"])
+    tpus_per_node = TPUAcceleratorManager.get_current_node_num_accelerators()
+    assert total_tpus % tpus_per_node == 0
+    return total_tpus // tpus_per_node
+
+
+def get_num_nodes_in_placement_group() -> int:
+    pg_table = ray.util.placement_group_table()
+    current_pg = ray.util.get_current_placement_group()
+    num_nodes = 0
+
+    if current_pg:
+        nodes_in_pg = set()
+        for pg_key, pg in pg_table.items():
+            if pg_key == current_pg.id.hex():
+                for _, node in pg["bundles_to_node_id"].items():
+                    nodes_in_pg.add(node)
+        num_nodes = len(nodes_in_pg)
+
+    return num_nodes

vllm/executor/tpu_executor.py

@@ -5,7 +5,8 @@ import torch
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         make_async)