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Fix token usage accounting for openai-style llms #36

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naftali-g merged 1 commit into from
Sep 1, 2026
Merged

Fix token usage accounting for openai-style llms #36

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58 changes: 32 additions & 26 deletions graphcore/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,20 +13,15 @@
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.

from typing import TypedDict, Literal, List, Sequence
import logging
from typing import TypedDict, List, Sequence

from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, AnyMessage, BaseMessage
from langchain_core.runnables import Runnable

logger = logging.getLogger(__name__)

type TokenUsageKeysT = Literal[
"input_tokens", "output_tokens", "cache_read_input_tokens", "cache_creation_input_tokens"
]

_token_usage_keys : list[TokenUsageKeysT] = [
"input_tokens", "output_tokens", "cache_read_input_tokens", "cache_creation_input_tokens"
]

class TokenUsageDict(TypedDict):
"""Dictionary for accumulating token usage across LLM calls."""
Expand All @@ -37,25 +32,36 @@ class TokenUsageDict(TypedDict):
model_name: str | None

def get_token_usage(m: AIMessage) -> TokenUsageDict:
to_ret : TokenUsageDict = {
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"input_tokens": 0,
"output_tokens": 0,
"model_name": None
"""Per-bucket raw counts for one response, read from ``usage_metadata`` — the
one field every provider and request surface populates.

The three input buckets are disjoint, and each bills at its own rate.
``usage_metadata`` reports an inclusive input total, so the cache buckets are
subtracted back out to leave the fresh input ``input_tokens`` means here."""
normalized = get_normalized_token_usage(m)
cache_read = normalized["cache_read_tokens"]
cache_write = normalized["cache_write_tokens"]

# A gateway normalizing usage from an arbitrary upstream can report a bucket
# bigger than the total it belongs to. Unclamped that lands as a negative count,
# which subtracts from the run's totals and bills below zero instead of failing.
fresh_input = normalized["total_input_tokens"] - cache_read - cache_write
if fresh_input < 0:
logger.warning(
"%s reported cache buckets (read %d, write %d) exceeding its %d-token "
"input total; counting fresh input as 0.",
normalized["model_name"], cache_read, cache_write,
normalized["total_input_tokens"],
)
fresh_input = 0

return {
"input_tokens": fresh_input,
"output_tokens": normalized["total_output_tokens"],
"cache_read_input_tokens": cache_read,
"cache_creation_input_tokens": cache_write,
"model_name": normalized["model_name"],
}
rm = m.response_metadata
if "model_name" in rm and isinstance(rm['model_name'], str):
to_ret["model_name"] = rm['model_name']
if "usage" not in rm or not isinstance(rm["usage"], dict):
return to_ret
usage_meta = m.response_metadata["usage"]
for k in _token_usage_keys:
tok = usage_meta.get(k, 0)
if not isinstance(tok, int):
continue # be cool
to_ret[k] = to_ret[k] + tok
return to_ret

class NormalizedTokenUsage(TypedDict):
total_input_tokens: int
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