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fix(anthropic/chat/transformation.py): normalize max_tokens if decimal
This commit is contained in:
Krrish Dholakia
parent
cc1e196167
commit
a019f434a7
2 changed files with 94 additions and 37 deletions
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@ -46,6 +46,7 @@ from litellm.types.llms.openai import (
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ChatCompletionToolCallChunk,
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ChatCompletionToolCallFunctionChunk,
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ChatCompletionToolParam,
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OpenAIChatCompletionFinishReason,
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OpenAIMcpServerTool,
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OpenAIWebSearchOptions,
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)
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@ -54,10 +55,7 @@ from litellm.types.utils import (
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CompletionTokensDetailsWrapper,
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)
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from litellm.types.utils import Message as LitellmMessage
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from litellm.types.utils import (
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PromptTokensDetailsWrapper,
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ServerToolUse,
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)
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from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
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from litellm.utils import (
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ModelResponse,
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Usage,
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@ -251,10 +249,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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# All numeric/string/array constraints not supported by Anthropic
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unsupported_fields = {
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"maxItems", "minItems", # array constraints
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"minimum", "maximum", # numeric constraints
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"exclusiveMinimum", "exclusiveMaximum", # numeric constraints
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"minLength", "maxLength", # string constraints
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"maxItems",
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"minItems", # array constraints
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"minimum",
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"maximum", # numeric constraints
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"exclusiveMinimum",
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"exclusiveMaximum", # numeric constraints
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"minLength",
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"maxLength", # string constraints
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}
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# Build description additions from removed constraints
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@ -844,7 +846,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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@staticmethod
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def map_openai_context_management_to_anthropic(
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context_management: Union[List[Dict[str, Any]], Dict[str, Any]]
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context_management: Union[List[Dict[str, Any]], Dict[str, Any]],
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) -> Optional[Dict[str, Any]]:
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"""
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OpenAI format: [{"type": "compaction", "compact_threshold": 200000}]
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@ -876,19 +878,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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entry_type = entry.get("type")
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if entry_type == "compaction":
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anthropic_edit: Dict[str, Any] = {
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"type": "compact_20260112"
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}
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anthropic_edit: Dict[str, Any] = {"type": "compact_20260112"}
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compact_threshold = entry.get("compact_threshold")
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# Rewrite to 'trigger' with correct nesting if threshold exists
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if compact_threshold is not None and isinstance(compact_threshold, (int, float)):
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if compact_threshold is not None and isinstance(
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compact_threshold, (int, float)
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):
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anthropic_edit["trigger"] = {
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"type": "input_tokens",
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"value": int(compact_threshold)
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"value": int(compact_threshold),
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}
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# Map any other keys by passthrough except handled ones
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for k in entry:
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if k not in {"type", "compact_threshold"}: # only passthrough other keys
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if k not in {
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"type",
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"compact_threshold",
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}: # only passthrough other keys
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anthropic_edit[k] = entry[k]
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anthropic_edits.append(anthropic_edit)
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@ -911,10 +916,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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for param, value in non_default_params.items():
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if param == "max_tokens":
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optional_params["max_tokens"] = value
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if param == "max_completion_tokens":
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optional_params["max_tokens"] = value
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if param == "tools":
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optional_params["max_tokens"] = (
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value if isinstance(value, int) else max(1, int(round(value)))
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)
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elif param == "max_completion_tokens":
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optional_params["max_tokens"] = (
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value if isinstance(value, int) else max(1, int(round(value)))
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)
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elif param == "tools":
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# check if optional params already has tools
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anthropic_tools, mcp_servers = self._map_tools(value)
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optional_params = self._add_tools_to_optional_params(
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@ -922,7 +931,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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)
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if mcp_servers:
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optional_params["mcp_servers"] = mcp_servers
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if param == "tool_choice" or param == "parallel_tool_calls":
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elif param == "tool_choice" or param == "parallel_tool_calls":
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_tool_choice: Optional[AnthropicMessagesToolChoice] = (
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self._map_tool_choice(
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tool_choice=non_default_params.get("tool_choice"),
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@ -932,17 +941,19 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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if _tool_choice is not None:
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optional_params["tool_choice"] = _tool_choice
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if param == "stream" and value is True:
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elif param == "stream" and value is True:
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optional_params["stream"] = value
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if param == "stop" and (isinstance(value, str) or isinstance(value, list)):
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elif param == "stop" and (
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isinstance(value, str) or isinstance(value, list)
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):
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_value = self._map_stop_sequences(value)
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if _value is not None:
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optional_params["stop_sequences"] = _value
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if param == "temperature":
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elif param == "temperature":
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optional_params["temperature"] = value
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if param == "top_p":
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elif param == "top_p":
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optional_params["top_p"] = value
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if param == "response_format" and isinstance(value, dict):
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elif param == "response_format" and isinstance(value, dict):
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if any(
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substring in model
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for substring in {
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@ -982,14 +993,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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optional_params=optional_params, tools=[_tool]
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)
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optional_params["json_mode"] = True
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if (
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elif (
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param == "user"
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and value is not None
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and isinstance(value, str)
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and _valid_user_id(value) # anthropic fails on emails
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):
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optional_params["metadata"] = {"user_id": value}
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if param == "thinking":
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elif param == "thinking":
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optional_params["thinking"] = value
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elif param == "reasoning_effort" and isinstance(value, str):
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optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
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@ -1007,9 +1018,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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elif param == "context_management":
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# Supports both OpenAI list format and Anthropic dict format
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if isinstance(value, (list, dict)):
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anthropic_context_management = self.map_openai_context_management_to_anthropic(value)
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anthropic_context_management = (
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self.map_openai_context_management_to_anthropic(value)
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)
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if anthropic_context_management is not None:
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optional_params["context_management"] = anthropic_context_management
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optional_params["context_management"] = (
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anthropic_context_management
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)
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elif param == "speed" and isinstance(value, str):
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# Pass through Anthropic-specific speed parameter for fast mode
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optional_params["speed"] = value
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@ -1071,7 +1086,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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if not system_message_block["content"]:
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continue
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# Skip system messages containing x-anthropic-billing-header metadata
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if system_message_block["content"].startswith("x-anthropic-billing-header:"):
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if system_message_block["content"].startswith(
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"x-anthropic-billing-header:"
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):
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continue
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anthropic_system_message_content = AnthropicSystemMessageContent(
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type="text",
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@ -1091,7 +1108,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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if _content.get("type") == "text" and not text_value:
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continue
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# Skip system messages containing x-anthropic-billing-header metadata
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if _content.get("type") == "text" and text_value and text_value.startswith("x-anthropic-billing-header:"):
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if (
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_content.get("type") == "text"
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and text_value
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and text_value.startswith("x-anthropic-billing-header:")
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):
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continue
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anthropic_system_message_content = (
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AnthropicSystemMessageContent(
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@ -1201,7 +1222,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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# Add context management header if any other edits/entries exist
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if has_other:
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self._ensure_beta_header(
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headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
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headers,
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ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value,
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)
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def update_headers_with_optional_anthropic_beta(
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@ -1227,7 +1249,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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ANTHROPIC_HOSTED_TOOLS.MEMORY.value
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):
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self._ensure_beta_header(
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headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
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headers,
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ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value,
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)
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if optional_params.get("context_management") is not None:
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self._ensure_context_management_beta_header(
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@ -1491,7 +1514,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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if thinking_content is not None:
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reasoning_content += thinking_content
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return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results, compaction_blocks
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return (
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text_content,
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citations,
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thinking_blocks,
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reasoning_content,
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tool_calls,
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web_search_results,
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tool_results,
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compaction_blocks,
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)
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def calculate_usage(
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self,
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@ -1576,7 +1608,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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)
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completion_token_details = CompletionTokensDetailsWrapper(
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reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0,
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text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens,
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text_tokens=(
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completion_tokens - reasoning_tokens
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if reasoning_tokens > 0
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else completion_tokens
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),
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)
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total_tokens = prompt_tokens + completion_tokens
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@ -1696,8 +1732,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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"content"
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] # allow user to access raw anthropic tool calling response
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model_response.choices[0].finish_reason = map_finish_reason(
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completion_response["stop_reason"]
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model_response.choices[0].finish_reason = cast(
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OpenAIChatCompletionFinishReason,
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map_finish_reason(completion_response["stop_reason"]),
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)
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## CALCULATING USAGE
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@ -2924,3 +2924,23 @@ def test_fast_mode_parameter_mapping():
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assert "speed" in result
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assert result["speed"] == "fast"
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def test_map_openai_params_max_tokens_normalized_to_int():
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"""
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Test that map_openai_params normalizes max_tokens to an integer (e.g. 0.7 -> 1).
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"""
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config = AnthropicConfig()
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non_default_params = {"max_tokens": 0.7}
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optional_params = {}
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result = config.map_openai_params(
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non_default_params=non_default_params,
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optional_params=optional_params,
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model="claude-3-5-sonnet-20241022",
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drop_params=False,
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)
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assert "max_tokens" in result
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assert result["max_tokens"] == 1
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