[Refactor] Remove deepseek dead code (#48780)
Signed-off-by: yewentao256 <zhyanwentao@126.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
This commit is contained in:
Wentao Ye
co-authored by
mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
parent
c4dd6d78fd
commit
877dae9c68
@@ -12,7 +12,6 @@ from transformers import DeepseekV2Config, DeepseekV3Config
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.logger import init_logger
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ReplicatedLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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@@ -36,8 +35,6 @@ from .utils import (
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process_eagle_weight,
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)
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logger = init_logger(__name__)
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class DeepseekV2Eagle3DecoderLayer(nn.Module):
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"""
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@@ -11,7 +11,6 @@ from vllm._aiter_ops import rocm_aiter_ops
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.distributed import tensor_model_parallel_all_gather
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import (
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fused_moe_make_expert_params_mapping,
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)
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@@ -38,8 +37,6 @@ from .deepseek_v2 import (
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)
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from .utils import get_pp_missing_layer_names, maybe_prefix
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logger = init_logger(__name__)
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def _restore_full_token_layout_if_needed(
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hidden_states: torch.Tensor,
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@@ -15,7 +15,6 @@ import torch
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from vllm.logger import init_logger
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from vllm.tracing import instrument
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from vllm.utils.math_utils import cdiv
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logger = init_logger(__name__)
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@@ -39,23 +38,6 @@ _DEFAULT_TOKEN_SIZE_CANDIDATES = (
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)
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def _compute_mhc_pre_num_split(
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*,
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num_tokens: int,
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hidden_size: int,
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hc_mult: int,
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num_sms: int,
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) -> int:
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block_k = 64
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block_m = 64
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k = hc_mult * hidden_size
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grid_size = cdiv(num_tokens, block_m)
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split_k = num_sms // grid_size
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num_block_k = cdiv(k, block_k)
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split_k = min(split_k, num_block_k // 4)
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return max(split_k, 1)
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def _normalize_token_sizes(
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token_sizes: Iterable[int],
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*,
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@@ -8,7 +8,6 @@ from vllm.entrypoints.chat_utils import (
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parse_chat_messages,
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parse_chat_messages_async,
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)
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from vllm.logger import init_logger
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from vllm.tokenizers.deepseek_v32 import DeepseekV32Tokenizer
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from vllm.utils.async_utils import make_async
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@@ -17,8 +16,6 @@ from .inputs import DictPrompt
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from .inputs.preprocess import parse_dec_only_prompt
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from .params import ChatParams
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logger = init_logger(__name__)
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class DeepseekV32Renderer(BaseRenderer[DeepseekV32Tokenizer]):
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def __init__(
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@@ -8,7 +8,6 @@ from vllm.entrypoints.chat_utils import (
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parse_chat_messages,
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parse_chat_messages_async,
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)
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from vllm.logger import init_logger
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from vllm.tokenizers.deepseek_v4 import DeepseekV4Tokenizer
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from vllm.utils.async_utils import make_async
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@@ -17,8 +16,6 @@ from .inputs import DictPrompt
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from .inputs.preprocess import parse_dec_only_prompt
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from .params import ChatParams
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logger = init_logger(__name__)
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class DeepseekV4Renderer(BaseRenderer[DeepseekV4Tokenizer]):
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def __init__(
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@@ -7,8 +7,6 @@ import copy
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import json
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from typing import Any
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import regex as re
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# flake8: noqa: E501
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TOOLS_SYSTEM_TEMPLATE = """## Tools
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You have access to a set of tools you can use to answer the user's question.
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@@ -79,19 +77,6 @@ def tool_calls_from_openai_format(tool_calls):
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]
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def tool_calls_to_openai_format(tool_calls):
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return [
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{
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"type": "function",
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"function": {
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"name": tool_call["name"],
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"arguments": tool_call["arguments"],
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},
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}
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for tool_call in tool_calls
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]
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def encode_arguments_to_dsml(tool_call: dict[str, str]) -> str:
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p_dsml_template = """<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>"""
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P_dsml_strs = []
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@@ -113,24 +98,6 @@ def encode_arguments_to_dsml(tool_call: dict[str, str]) -> str:
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return "\n".join(P_dsml_strs)
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def decode_dsml_to_arguments(
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tool_name: str, tool_args: dict[str, tuple[str, str]]
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) -> dict[str, str]:
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def _decode_value(key: str, value: str, string: str):
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if string == "true":
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value = to_json(value)
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return f"{to_json(key)}: {value}"
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tool_args_json = (
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"{"
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+ ", ".join(
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[_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]
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)
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+ "}"
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)
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return dict(name=tool_name, arguments=tool_args_json)
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def render_tools(tools: list[dict[str, str | dict[str, Any]]]) -> str:
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tools_json = [to_json(t) for t in tools]
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@@ -333,139 +300,3 @@ def encode_messages(
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)
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return prompt
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def _read_until_stop(
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index: int, text: str, stop: list[str]
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) -> tuple[int, str, None | str]:
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min_pos = len(text)
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matched_stop = None
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for s in stop:
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pos = text.find(s, index)
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if pos != -1 and pos < min_pos:
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min_pos = pos
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matched_stop = s
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if matched_stop:
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content = text[index:min_pos]
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return min_pos + len(matched_stop), content, matched_stop
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else:
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content = text[index:]
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return len(text), content, None
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def parse_tool_calls(index: int, text: str):
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tool_calls: list[dict[str, Any]] = []
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stop_token = None
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tool_calls_end_token = f"</{dsml_token}function_calls>"
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while index < len(text):
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index, _, stop_token = _read_until_stop(
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index, text, [f"<{dsml_token}invoke", tool_calls_end_token]
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)
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if _ != ">\n":
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raise RuntimeError("Tool call format error")
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if stop_token == tool_calls_end_token:
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break
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if stop_token is None:
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raise RuntimeError("Missing special token")
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index, tool_name_content, stop_token = _read_until_stop(
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index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"]
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)
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p_tool_name = re.findall(
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r'^\s*name="(.*?)">\n$', tool_name_content, flags=re.DOTALL
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)
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if len(p_tool_name) != 1:
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raise RuntimeError("Tool name format error")
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tool_name = p_tool_name[0]
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tool_args: dict[str, tuple[str, str]] = {}
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while stop_token == f"<{dsml_token}parameter":
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index, param_content, stop_token = _read_until_stop(
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index, text, [f"/{dsml_token}parameter"]
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)
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param_kv = re.findall(
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r'^ name="(.*?)" string="(true|false)">(.*?)<$',
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param_content,
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flags=re.DOTALL,
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)
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if len(param_kv) != 1:
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raise RuntimeError("Parameter format error")
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param_name, string, param_value = param_kv[0]
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if param_name in tool_args:
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raise RuntimeError("Duplicate parameter name")
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tool_args[param_name] = (param_value, string)
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index, content, stop_token = _read_until_stop(
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index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"]
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)
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if content != ">\n":
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raise RuntimeError("Parameter format error")
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tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args)
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tool_calls.append(tool_call)
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return index, stop_token, tool_calls
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# NOTE: This function is designed to parse only correctly
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# formatted string and will not attempt to correct malformed output
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# that may be generated by the model.
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def parse_message_from_completion_text(text: str, thinking_mode: str):
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summary_content, reasoning, tool_calls = "", "", []
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index, stop_token = 0, None
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tool_calls_start_token = f"\n\n<{dsml_token}function_calls"
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is_thinking, is_tool_calling = thinking_mode == "thinking", False
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if is_thinking:
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index, content_delta, stop_token = _read_until_stop(
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index, text, [thinking_end_token, tool_calls_start_token]
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)
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reasoning = content_delta
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if stop_token != thinking_end_token:
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raise RuntimeError("Invalid thinking format")
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index, content_delta, stop_token = _read_until_stop(
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index, text, [eos_token, tool_calls_start_token]
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)
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summary_content = content_delta
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if stop_token == tool_calls_start_token:
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is_tool_calling = True
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else:
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if stop_token != eos_token:
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raise RuntimeError("Invalid summary format")
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if is_tool_calling:
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index, stop_token, tool_calls = parse_tool_calls(index, text)
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index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token])
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if tool_ends_text:
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raise RuntimeError("Unexpected content after tool calls")
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if not (len(text) == index and stop_token in [eos_token, None]):
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raise RuntimeError("Unexpected content at end")
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for sp_token in [
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bos_token,
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eos_token,
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thinking_start_token,
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thinking_end_token,
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dsml_token,
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]:
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if sp_token in summary_content or sp_token in reasoning:
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raise RuntimeError("Unexpected special token in content")
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return {
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"role": "assistant",
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"content": summary_content,
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"reasoning": reasoning,
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"tool_calls": tool_calls_to_openai_format(tool_calls),
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}
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@@ -6,16 +6,14 @@
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"""
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DeepSeek-V4 Encoding
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A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages
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with tool calling, thinking mode, and quick instruction task support.
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A self-contained implementation for encoding DeepSeek-V4 chat messages with tool
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calling, thinking mode, and quick instruction task support.
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"""
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from typing import Any, Dict, List, Union, Optional, Tuple
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from typing import Any, Dict, List, Union, Optional
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import copy
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import json
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import regex as re
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# ============================================================
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# Special Tokens
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# ============================================================
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@@ -128,20 +126,6 @@ def tool_calls_from_openai_format(tool_calls):
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]
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def tool_calls_to_openai_format(tool_calls):
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"""Convert internal tool calls to OpenAI format."""
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return [
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{
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"type": "function",
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"function": {
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"name": tool_call["name"],
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"arguments": tool_call["arguments"],
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}
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}
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for tool_call in tool_calls
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]
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def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
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"""
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Encode tool call arguments into DSML parameter format.
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@@ -172,26 +156,6 @@ def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
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return "\n".join(P_dsml_strs)
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def decode_dsml_to_arguments(tool_name: str, tool_args: Dict[str, Tuple[str, str]]) -> Dict[str, str]:
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"""
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Decode DSML parameters back to a tool call dict.
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Args:
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tool_name: Name of the tool.
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tool_args: Dict mapping param_name -> (value, is_string_flag).
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Returns:
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Dict with "name" and "arguments" (JSON string) keys.
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"""
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def _decode_value(key: str, value: str, string: str):
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if string == "true":
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value = to_json(value)
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return f"{to_json(key)}: {value}"
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tool_args_json = "{" + ", ".join([_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]) + "}"
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return dict(name=tool_name, arguments=tool_args_json)
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def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
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"""
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Render tool schemas into the system prompt format.
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@@ -605,153 +569,4 @@ def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, An
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return result
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# ============================================================
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# Parsing (Decoding model output)
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# ============================================================
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def _read_until_stop(index: int, text: str, stop: List[str]) -> Tuple[int, str, Optional[str]]:
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"""
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Read text from index until one of the stop strings is found.
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Returns:
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Tuple of (new_index, content_before_stop, matched_stop_string_or_None).
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"""
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min_pos = len(text)
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matched_stop = None
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for s in stop:
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pos = text.find(s, index)
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if pos != -1 and pos < min_pos:
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min_pos = pos
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matched_stop = s
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if matched_stop:
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content = text[index:min_pos]
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return min_pos + len(matched_stop), content, matched_stop
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else:
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content = text[index:]
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return len(text), content, None
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def parse_tool_calls(index: int, text: str) -> Tuple[int, Optional[str], List[Dict[str, str]]]:
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"""
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Parse DSML tool calls from text starting at the given index.
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Args:
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index: Starting position in text.
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text: The full text to parse.
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Returns:
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Tuple of (new_index, last_stop_token, list_of_tool_call_dicts).
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Each tool call dict has "name" and "arguments" keys.
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"""
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tool_calls: List[Dict[str, Any]] = []
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stop_token = None
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tool_calls_end_token = f"</{dsml_token}{tool_calls_block_name}>"
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while index < len(text):
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index, content_before, stop_token = _read_until_stop(index, text, [f"<{dsml_token}invoke", tool_calls_end_token])
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if content_before != ">\n":
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raise ValueError(f"Tool call format error: expected '>\\n' but got '{content_before}'")
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if stop_token == tool_calls_end_token:
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break
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if stop_token is None:
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raise ValueError("Missing special token in tool calls")
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index, tool_name_content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
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p_tool_name = re.findall(r'^\s*name="(.*?)">\n$', tool_name_content, flags=re.DOTALL)
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if len(p_tool_name) != 1:
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raise ValueError(f"Tool name format error: '{tool_name_content}'")
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tool_name = p_tool_name[0]
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tool_args: Dict[str, Tuple[str, str]] = {}
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while stop_token == f"<{dsml_token}parameter":
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index, param_content, stop_token = _read_until_stop(index, text, [f"/{dsml_token}parameter"])
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param_kv = re.findall(r'^ name="(.*?)" string="(true|false)">(.*?)<$', param_content, flags=re.DOTALL)
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if len(param_kv) != 1:
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raise ValueError(f"Parameter format error: '{param_content}'")
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param_name, string, param_value = param_kv[0]
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if param_name in tool_args:
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raise ValueError(f"Duplicate parameter name: '{param_name}'")
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tool_args[param_name] = (param_value, string)
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index, content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
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if content != ">\n":
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raise ValueError(f"Parameter format error: expected '>\\n' but got '{content}'")
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tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args)
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tool_calls.append(tool_call)
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return index, stop_token, tool_calls
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def parse_message_from_completion_text(text: str, thinking_mode: str) -> Dict[str, Any]:
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"""
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Parse a model completion text into a structured assistant message.
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||||
|
||||
This function takes the raw text output from the model (a single assistant turn)
|
||||
and extracts:
|
||||
- reasoning (thinking block)
|
||||
- content (summary/response)
|
||||
- tool_calls (if any)
|
||||
|
||||
NOTE: This function is designed to parse only correctly formatted strings and
|
||||
will raise ValueError for malformed output.
|
||||
|
||||
Args:
|
||||
text: The raw completion text (including EOS token).
|
||||
thinking_mode: Either "chat" or "thinking".
|
||||
|
||||
Returns:
|
||||
Dict with keys: "role", "content", "reasoning", "tool_calls".
|
||||
tool_calls are in OpenAI format.
|
||||
"""
|
||||
summary_content, reasoning = "", ""
|
||||
tool_calls: List[Dict[str, str]] = []
|
||||
index, stop_token = 0, None
|
||||
tool_calls_start_token = f"\n\n<{dsml_token}{tool_calls_block_name}"
|
||||
|
||||
is_thinking = thinking_mode == "thinking"
|
||||
is_tool_calling = False
|
||||
|
||||
if is_thinking:
|
||||
index, content_delta, stop_token = _read_until_stop(index, text, [thinking_end_token, tool_calls_start_token])
|
||||
reasoning = content_delta
|
||||
if stop_token != thinking_end_token:
|
||||
raise ValueError("Invalid thinking format: missing </think>")
|
||||
|
||||
index, content_delta, stop_token = _read_until_stop(index, text, [eos_token, tool_calls_start_token])
|
||||
summary_content = content_delta
|
||||
if stop_token == tool_calls_start_token:
|
||||
is_tool_calling = True
|
||||
else:
|
||||
if stop_token != eos_token:
|
||||
raise ValueError("Invalid format: missing EOS token")
|
||||
|
||||
if is_tool_calling:
|
||||
index, stop_token, tool_calls = parse_tool_calls(index, text)
|
||||
|
||||
index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token])
|
||||
if tool_ends_text:
|
||||
raise ValueError("Unexpected content after tool calls")
|
||||
|
||||
if len(text) != index or stop_token not in [eos_token, None]:
|
||||
raise ValueError("Unexpected content at end")
|
||||
|
||||
for sp_token in [bos_token, eos_token, thinking_start_token, thinking_end_token, dsml_token]:
|
||||
if sp_token in summary_content or sp_token in reasoning:
|
||||
raise ValueError(f"Unexpected special token '{sp_token}' in content")
|
||||
|
||||
return {
|
||||
"role": "assistant",
|
||||
"content": summary_content,
|
||||
"reasoning": reasoning,
|
||||
"tool_calls": tool_calls_to_openai_format(tool_calls)
|
||||
}
|
||||
|
||||
# fmt: on
|
||||
|
||||
Reference in New Issue
Block a user