chore: import upstream snapshot with attribution

python/sglang/lang/backend/openai.py

@@ -0,0 +1,475 @@
+import dataclasses
+import logging
+import time
+import warnings
+from typing import List, Optional, Union
+
+import numpy as np
+
+from sglang.lang.backend.base_backend import BaseBackend
+from sglang.lang.chat_template import ChatTemplate, get_chat_template_by_model_path
+from sglang.lang.choices import ChoicesDecision, ChoicesSamplingMethod
+from sglang.lang.interpreter import StreamExecutor
+from sglang.lang.ir import SglSamplingParams
+
+try:
+    import openai
+    import tiktoken
+except ImportError as e:
+    openai = tiktoken = e
+
+
+logger = logging.getLogger(__name__)
+
+
+def create_logit_bias_int(tokenizer):
+    """Get logit bias for integer numbers."""
+    int_token_ids = []
+
+    tokens = tokenizer._mergeable_ranks
+    for token, token_id in tokens.items():
+        s = tokenizer.decode([token_id])
+        if all([c.isdigit() for c in s]) or s in [" "]:
+            int_token_ids.append(token_id)
+            if len(int_token_ids) >= 300:  # OpenAI API limit
+                break
+    special_tokens = tokenizer._special_tokens
+    mask = {t: 100 for t in int_token_ids[:299]}
+    mask[special_tokens["<|endoftext|>"]] = 100
+    return mask
+
+
+INSTRUCT_MODEL_NAMES = [
+    "gpt-3.5-turbo-instruct",
+]
+
+
+@dataclasses.dataclass
+class TokenUsage:
+    prompt_tokens: int
+    completion_tokens: int
+
+    def reset(self):
+        self.prompt_tokens = self.completion_tokens = 0
+
+
+class OpenAI(BaseBackend):
+    def __init__(
+        self,
+        model_name: str,
+        is_chat_model: Optional[bool] = None,
+        chat_template: Optional[ChatTemplate] = None,
+        is_azure: bool = False,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+
+        if isinstance(openai, Exception):
+            raise openai
+
+        if is_azure:
+            self.client = openai.AzureOpenAI(*args, **kwargs)
+        else:
+            self.client = openai.OpenAI(*args, **kwargs)
+
+        self.model_name = model_name
+        try:
+            self.tokenizer = tiktoken.encoding_for_model(model_name)
+        except KeyError:
+            self.tokenizer = tiktoken.get_encoding("cl100k_base")
+        self.logit_bias_int = create_logit_bias_int(self.tokenizer)
+
+        self.chat_template = chat_template or get_chat_template_by_model_path(
+            model_name
+        )
+
+        if is_chat_model is not None:
+            self.is_chat_model = is_chat_model
+        else:
+            if model_name in INSTRUCT_MODEL_NAMES:
+                self.is_chat_model = False
+            else:
+                self.is_chat_model = True
+
+        self.chat_prefix = self.chat_template.role_prefix_and_suffix["assistant"][0]
+
+        # Usage
+        self.token_usage = TokenUsage(0, 0)
+
+        # API speculative execution
+        # TODO(ying): This does not support multi-threading (run_batch)
+        self.spec_kwargs = {}
+        self.spec_format = []
+        self.spec_max_num_tries = 3
+
+    def get_chat_template(self):
+        return self.chat_template
+
+    def _prepare_spec_execution(
+        self,
+        sampling_params: SglSamplingParams,
+        num_api_spec_tokens: int,
+        spec_var_name: str,
+    ):
+        if "max_tokens" not in self.spec_kwargs:
+            self.spec_kwargs["max_tokens"] = num_api_spec_tokens
+        else:
+            assert self.spec_kwargs["max_tokens"] == num_api_spec_tokens
+
+        params = sampling_params.to_openai_kwargs()
+        for key, value in params.items():
+            if key in ["stop"]:
+                continue
+            if key in ["max_tokens"]:
+                warnings.warn(
+                    "The parameter max_tokens will be overwritten by speculated number of tokens."
+                )
+                continue
+            if key not in self.spec_kwargs:
+                self.spec_kwargs[key] = value
+            else:
+                assert (
+                    value == self.spec_kwargs[key]
+                ), "sampling parameters should be consistent if turn on api speculative execution."
+        self.spec_format.append(
+            {"text": "", "stop": params["stop"], "name": spec_var_name}
+        )
+        return "", {}
+
+    def generate(
+        self,
+        s: StreamExecutor,
+        sampling_params: SglSamplingParams,
+        spec_var_name: str = None,
+    ):
+        if sampling_params.dtype is None:
+            if self.is_chat_model:
+                if s.num_api_spec_tokens is None:
+                    if not s.text_.endswith(self.chat_prefix):
+                        raise RuntimeError(
+                            "This use case is not supported if api speculative execution is off. "
+                            "For OpenAI chat models, sgl.gen must be right after sgl.assistant. "
+                            "Example of adding api speculative execution: @function(num_api_spec_tokens=128)."
+                        )
+                    prompt = s.messages_
+                else:
+                    return self._prepare_spec_execution(
+                        sampling_params, s.num_api_spec_tokens, spec_var_name
+                    )
+            else:
+                prompt = s.text_
+
+            kwargs = sampling_params.to_openai_kwargs()
+            if (
+                self.model_name.startswith("o1")
+                or self.model_name.startswith("o3")
+                or "o1" in self.model_name
+            ):
+                kwargs.pop("max_tokens", None)
+            else:
+                kwargs.pop("max_completion_tokens", None)
+
+            comp = openai_completion(
+                client=self.client,
+                token_usage=self.token_usage,
+                is_chat=self.is_chat_model,
+                model=self.model_name,
+                prompt=prompt,
+                **kwargs,
+            )
+            # Keep the returned list (or string) as is.
+        elif sampling_params.dtype in [str, "str", "string"]:
+            assert (
+                not self.is_chat_model
+            ), "constrained type not supported on chat model"
+            kwargs = sampling_params.to_openai_kwargs()
+            kwargs.pop("stop")
+            comp = openai_completion(
+                client=self.client,
+                token_usage=self.token_usage,
+                is_chat=self.is_chat_model,
+                model=self.model_name,
+                prompt=s.text_ + '"',
+                stop='"',
+                **kwargs,
+            )
+            # Wrap each element in quotes if we have a list.
+            if isinstance(comp, list):
+                comp = ['"' + x + '"' for x in comp]
+            else:
+                comp = '"' + comp + '"'
+        elif sampling_params.dtype in [int, "int"]:
+            assert (
+                not self.is_chat_model
+            ), "constrained type not supported on chat model"
+            kwargs = sampling_params.to_openai_kwargs()
+            kwargs.pop("stop")
+            comp = openai_completion(
+                client=self.client,
+                token_usage=self.token_usage,
+                is_chat=self.is_chat_model,
+                model=self.model_name,
+                prompt=s.text_,
+                logit_bias=self.logit_bias_int,
+                stop=[" "],
+                **kwargs,
+            )
+            # Leave as a list if that's what is returned.
+        else:
+            raise ValueError(f"Unknown dtype: {sampling_params.dtype}")
+
+        return comp, {}
+
+    def spec_fill(self, value: str):
+        assert self.is_chat_model
+        self.spec_format.append({"text": value, "stop": None, "name": None})
+
+    def spec_pattern_match(self, comp):
+        for i, term in enumerate(self.spec_format):
+            text = term["text"]
+            if text != "":
+                if comp.startswith(text):
+                    comp = comp[len(text) :]
+                else:
+                    return False
+            else:
+                pos = comp.find(term["stop"])
+                if pos != -1:
+                    term["text"] = comp[:pos]
+                    comp = comp[pos:]
+                else:
+                    if i == len(self.spec_format) - 1:
+                        term["text"] = comp
+                    else:
+                        return False
+        return True
+
+    def role_end_generate(
+        self,
+        s: StreamExecutor,
+    ):
+        if s.num_api_spec_tokens is None or not s.text_.endswith(self.chat_prefix):
+            return
+
+        comp = ""
+        if not all(x["name"] is None for x in self.spec_format):
+            # TODO(ying): throw errors or warnings
+            for i in range(self.spec_max_num_tries):
+                comp = openai_completion(
+                    client=self.client,
+                    token_usage=self.token_usage,
+                    is_chat=self.is_chat_model,
+                    model=self.model_name,
+                    prompt=s.messages_,
+                    **self.spec_kwargs,
+                )
+                # Use a string for pattern matching.
+                comp_for_match = comp[0] if isinstance(comp, list) else comp
+                if self.spec_pattern_match(comp_for_match):
+                    break
+
+        for term in self.spec_format:
+            s.text_ += term["text"]
+            name = term["name"]
+            if name is not None:
+                s.variables[name] = term["text"]
+                s.meta_info[name] = {}
+                s.variable_event[name].set()
+
+        self.spec_kwargs = {}
+        self.spec_format = []
+
+    def generate_stream(
+        self,
+        s: StreamExecutor,
+        sampling_params: SglSamplingParams,
+    ):
+        if sampling_params.dtype is None:
+            if self.is_chat_model:
+                if not s.text_.endswith(self.chat_prefix):
+                    raise RuntimeError(
+                        "This use case is not supported. "
+                        "For OpenAI chat models, sgl.gen must be right after sgl.assistant"
+                    )
+                prompt = s.messages_
+            else:
+                prompt = s.text_
+
+            kwargs = sampling_params.to_openai_kwargs()
+            generator = openai_completion_stream(
+                client=self.client,
+                token_usage=self.token_usage,
+                is_chat=self.is_chat_model,
+                model=self.model_name,
+                prompt=prompt,
+                **kwargs,
+            )
+            return generator
+        else:
+            raise ValueError(f"Unknown dtype: {sampling_params.dtype}")
+
+    def select(
+        self,
+        s: StreamExecutor,
+        choices: List[str],
+        temperature: float,
+        choices_method: ChoicesSamplingMethod,
+    ) -> ChoicesDecision:
+        """Note: `choices_method` is not used by the OpenAI backend."""
+        if self.is_chat_model:
+            raise NotImplementedError(
+                "select/choices is not supported for chat models. "
+                "Please try to use a non-chat model such as gpt-3.5-turbo-instruct"
+            )
+
+        n_choices = len(choices)
+        token_ids = [self.tokenizer.encode(x) for x in choices]
+        scores = [0] * n_choices
+        valid = [len(x) > 0 for x in token_ids]
+        prompt_tokens = self.tokenizer.encode(s.text_)
+
+        max_len = max([len(x) for x in token_ids])
+        for step in range(max_len):
+            # Build logit bias
+            logit_bias = {}
+            for i in range(n_choices):
+                if valid[i]:
+                    logit_bias[token_ids[i][step]] = 100
+
+            # Call API
+            ret = self.client.completions.create(
+                model=self.model_name,
+                prompt=prompt_tokens,
+                logit_bias=logit_bias,
+                max_tokens=1,
+                temperature=temperature,
+            )
+            ret_str = ret.choices[0].text
+            ret_token = self.tokenizer.encode(ret_str)[0]
+            self.token_usage.prompt_tokens += ret.usage.prompt_tokens
+            self.token_usage.completion_tokens = ret.usage.completion_tokens
+
+            # TODO:
+            # 1. return logits as the scores
+            # 2. compute logits of the full choice
+            # 3. consider chunk-based decoding
+
+            # Update valid
+            hit = False
+            for i in range(n_choices):
+                if valid[i]:
+                    if step == len(token_ids[i]) - 1:
+                        valid[i] = False
+
+                    if ret_token == token_ids[i][step]:
+                        scores[i] += 1
+                        hit = True
+                    else:
+                        valid[i] = False
+            assert hit
+
+            if np.sum(valid) <= 1:
+                break
+
+            prompt_tokens.append(ret_token)
+
+        return ChoicesDecision(
+            decision=choices[np.argmax(scores)],
+            meta_info={"scores": scores},
+        )
+
+
+def openai_completion(
+    client, token_usage, is_chat=None, retries=3, prompt=None, **kwargs
+) -> Union[str, List[str]]:
+    # if "ebnf" is in kwargs, warn and remove
+    if "ebnf" in kwargs:
+        warnings.warn("EBNF is not officially supported by OpenAI endpoints. Ignoring.")
+        del kwargs["ebnf"]
+
+    for attempt in range(retries):
+        try:
+            if is_chat:
+                if "stop" in kwargs and kwargs["stop"] is None:
+                    kwargs.pop("stop")
+                ret = client.chat.completions.create(messages=prompt, **kwargs)
+                if len(ret.choices) == 1:
+                    comp = ret.choices[0].message.content
+                else:
+                    comp = [c.message.content for c in ret.choices]
+            else:
+                ret = client.completions.create(prompt=prompt, **kwargs)
+                if isinstance(prompt, (list, tuple)):
+                    comp = [c.text for c in ret.choices]
+                else:
+                    comp = ret.choices[0].text
+                    if len(ret.choices) > 1:
+                        comp = [c.text for c in ret.choices]
+
+            token_usage.prompt_tokens += ret.usage.prompt_tokens
+            token_usage.completion_tokens += ret.usage.completion_tokens
+            break
+        except (openai.APIError, openai.APIConnectionError, openai.RateLimitError) as e:
+            logger.error(f"OpenAI Error: {e}. Waiting 5 seconds...")
+            time.sleep(5)
+            if attempt == retries - 1:
+                raise e
+        except Exception as e:
+            logger.error(f"RuntimeError {e}.")
+            raise e
+
+    return comp
+
+
+def openai_completion_stream(
+    client, token_usage, is_chat=None, retries=3, prompt=None, **kwargs
+):
+    # if "ebnf" is in kwargs, warn and remove
+    if "ebnf" in kwargs:
+        warnings.warn("EBNF is not officially supported by OpenAI endpoints. Ignoring.")
+        del kwargs["ebnf"]
+
+    for attempt in range(retries):
+        try:
+            if is_chat:
+                if "stop" in kwargs and kwargs["stop"] is None:
+                    kwargs.pop("stop")
+                generator = client.chat.completions.create(
+                    messages=prompt,
+                    stream=True,
+                    stream_options={"include_usage": True},
+                    **kwargs,
+                )
+                for ret in generator:
+                    if len(ret.choices) == 0:
+                        continue
+                    try:
+                        content = ret.choices[0].delta.content
+                    except IndexError:
+                        content = None
+                    yield content or "", {}
+            else:
+                generator = client.completions.create(
+                    prompt=prompt,
+                    stream=True,
+                    stream_options={"include_usage": True},
+                    **kwargs,
+                )
+                for ret in generator:
+                    if len(ret.choices) == 0:
+                        continue
+                    content = ret.choices[0].text
+                    yield content or "", {}
+
+            token_usage.prompt_tokens += ret.usage.prompt_tokens
+            token_usage.completion_tokens += ret.usage.completion_tokens
+            break
+        except (openai.APIError, openai.APIConnectionError, openai.RateLimitError) as e:
+            logger.error(f"OpenAI Error: {e}. Waiting 5 seconds...")
+            time.sleep(5)
+            if attempt == retries - 1:
+                raise e
+        except Exception as e:
+            logger.error(f"RuntimeError {e}.")
+            raise e