Spaces:
Build error
Build error
File size: 7,685 Bytes
51ff9e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import copy
import time
from pydantic import BaseModel, Field
class Cost(BaseModel):
model: str
cost: float
timestamp: float = Field(default_factory=time.time)
class ResponseLatency(BaseModel):
"""Metric tracking the round-trip time per completion call."""
model: str
latency: float
response_id: str
class TokenUsage(BaseModel):
"""Metric tracking detailed token usage per completion call."""
model: str = Field(default='')
prompt_tokens: int = Field(default=0)
completion_tokens: int = Field(default=0)
cache_read_tokens: int = Field(default=0)
cache_write_tokens: int = Field(default=0)
context_window: int = Field(default=0)
per_turn_token: int = Field(default=0)
response_id: str = Field(default='')
def __add__(self, other: 'TokenUsage') -> 'TokenUsage':
"""Add two TokenUsage instances together."""
return TokenUsage(
model=self.model,
prompt_tokens=self.prompt_tokens + other.prompt_tokens,
completion_tokens=self.completion_tokens + other.completion_tokens,
cache_read_tokens=self.cache_read_tokens + other.cache_read_tokens,
cache_write_tokens=self.cache_write_tokens + other.cache_write_tokens,
context_window=max(self.context_window, other.context_window),
per_turn_token=other.per_turn_token,
response_id=self.response_id,
)
class Metrics:
"""Metrics class can record various metrics during running and evaluation.
We track:
- accumulated_cost and costs
- A list of ResponseLatency
- A list of TokenUsage (one per call).
"""
def __init__(self, model_name: str = 'default') -> None:
self._accumulated_cost: float = 0.0
self._costs: list[Cost] = []
self._response_latencies: list[ResponseLatency] = []
self.model_name = model_name
self._token_usages: list[TokenUsage] = []
self._accumulated_token_usage: TokenUsage = TokenUsage(
model=model_name,
prompt_tokens=0,
completion_tokens=0,
cache_read_tokens=0,
cache_write_tokens=0,
context_window=0,
response_id='',
)
@property
def accumulated_cost(self) -> float:
return self._accumulated_cost
@accumulated_cost.setter
def accumulated_cost(self, value: float) -> None:
if value < 0:
raise ValueError('Total cost cannot be negative.')
self._accumulated_cost = value
@property
def costs(self) -> list[Cost]:
return self._costs
@property
def response_latencies(self) -> list[ResponseLatency]:
if not hasattr(self, '_response_latencies'):
self._response_latencies = []
return self._response_latencies
@response_latencies.setter
def response_latencies(self, value: list[ResponseLatency]) -> None:
self._response_latencies = value
@property
def token_usages(self) -> list[TokenUsage]:
if not hasattr(self, '_token_usages'):
self._token_usages = []
return self._token_usages
@token_usages.setter
def token_usages(self, value: list[TokenUsage]) -> None:
self._token_usages = value
@property
def accumulated_token_usage(self) -> TokenUsage:
"""Get the accumulated token usage, initializing it if it doesn't exist."""
if not hasattr(self, '_accumulated_token_usage'):
self._accumulated_token_usage = TokenUsage(
model=self.model_name,
prompt_tokens=0,
completion_tokens=0,
cache_read_tokens=0,
cache_write_tokens=0,
context_window=0,
response_id='',
)
return self._accumulated_token_usage
def add_cost(self, value: float) -> None:
if value < 0:
raise ValueError('Added cost cannot be negative.')
self._accumulated_cost += value
self._costs.append(Cost(cost=value, model=self.model_name))
def add_response_latency(self, value: float, response_id: str) -> None:
self._response_latencies.append(
ResponseLatency(
latency=max(0.0, value), model=self.model_name, response_id=response_id
)
)
def add_token_usage(
self,
prompt_tokens: int,
completion_tokens: int,
cache_read_tokens: int,
cache_write_tokens: int,
context_window: int,
response_id: str,
) -> None:
"""Add a single usage record."""
# Token each turn for calculating context usage.
per_turn_token = prompt_tokens + completion_tokens
usage = TokenUsage(
model=self.model_name,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
context_window=context_window,
per_turn_token=per_turn_token,
response_id=response_id,
)
self._token_usages.append(usage)
# Update accumulated token usage using the __add__ operator
self._accumulated_token_usage = self.accumulated_token_usage + TokenUsage(
model=self.model_name,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
context_window=context_window,
per_turn_token=per_turn_token,
response_id='',
)
def merge(self, other: 'Metrics') -> None:
"""Merge 'other' metrics into this one."""
self._accumulated_cost += other.accumulated_cost
self._costs += other._costs
# use the property so older picked objects that lack the field won't crash
self.token_usages += other.token_usages
self.response_latencies += other.response_latencies
# Merge accumulated token usage using the __add__ operator
self._accumulated_token_usage = (
self.accumulated_token_usage + other.accumulated_token_usage
)
def get(self) -> dict:
"""Return the metrics in a dictionary."""
return {
'accumulated_cost': self._accumulated_cost,
'accumulated_token_usage': self.accumulated_token_usage.model_dump(),
'costs': [cost.model_dump() for cost in self._costs],
'response_latencies': [
latency.model_dump() for latency in self._response_latencies
],
'token_usages': [usage.model_dump() for usage in self._token_usages],
}
def reset(self) -> None:
self._accumulated_cost = 0.0
self._costs = []
self._response_latencies = []
self._token_usages = []
# Reset accumulated token usage with a new instance
self._accumulated_token_usage = TokenUsage(
model=self.model_name,
prompt_tokens=0,
completion_tokens=0,
cache_read_tokens=0,
cache_write_tokens=0,
context_window=0,
response_id='',
)
def log(self) -> str:
"""Log the metrics."""
metrics = self.get()
logs = ''
for key, value in metrics.items():
logs += f'{key}: {value}\n'
return logs
def copy(self) -> 'Metrics':
"""Create a deep copy of the Metrics object."""
return copy.deepcopy(self)
def __repr__(self) -> str:
return f'Metrics({self.get()}'
|