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
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
|
import asyncio
import json
import time
from typing import Callable, List, Union
import litellm
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
_get_httpx_client,
get_async_httpx_client,
)
from litellm.types.llms.openai import AllMessageValues
from litellm.utils import CustomStreamWrapper, ModelResponse
from ..common_utils import ReplicateError
from .transformation import ReplicateConfig
replicate_config = ReplicateConfig()
# Function to handle prediction response (streaming)
def handle_prediction_response_streaming(
prediction_url, api_token, print_verbose, headers: dict, http_client: HTTPHandler
):
previous_output = ""
output_string = ""
status = ""
while True and (status not in ["succeeded", "failed", "canceled"]):
time.sleep(0.5) # prevent being rate limited by replicate
print_verbose(f"replicate: polling endpoint: {prediction_url}")
response = http_client.get(prediction_url, headers=headers)
if response.status_code == 200:
response_data = response.json()
status = response_data["status"]
if "output" in response_data:
try:
output_string = "".join(response_data["output"])
except Exception:
raise ReplicateError(
status_code=422,
message="Unable to parse response. Got={}".format(
response_data["output"]
),
headers=response.headers,
)
new_output = output_string[len(previous_output) :]
print_verbose(f"New chunk: {new_output}")
yield {"output": new_output, "status": status}
previous_output = output_string
status = response_data["status"]
if status == "failed":
replicate_error = response_data.get("error", "")
raise ReplicateError(
status_code=400,
message=f"Error: {replicate_error}",
headers=response.headers,
)
else:
# this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
print_verbose(
f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}"
)
# Function to handle prediction response (streaming)
async def async_handle_prediction_response_streaming(
prediction_url,
api_token,
print_verbose,
headers: dict,
http_client: AsyncHTTPHandler,
):
previous_output = ""
output_string = ""
status = ""
while True and (status not in ["succeeded", "failed", "canceled"]):
await asyncio.sleep(0.5) # prevent being rate limited by replicate
print_verbose(f"replicate: polling endpoint: {prediction_url}")
response = await http_client.get(prediction_url, headers=headers)
if response.status_code == 200:
response_data = response.json()
status = response_data["status"]
if "output" in response_data:
try:
output_string = "".join(response_data["output"])
except Exception:
raise ReplicateError(
status_code=422,
message="Unable to parse response. Got={}".format(
response_data["output"]
),
headers=response.headers,
)
new_output = output_string[len(previous_output) :]
print_verbose(f"New chunk: {new_output}")
yield {"output": new_output, "status": status}
previous_output = output_string
status = response_data["status"]
if status == "failed":
replicate_error = response_data.get("error", "")
raise ReplicateError(
status_code=400,
message=f"Error: {replicate_error}",
headers=response.headers,
)
else:
# this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
print_verbose(
f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}"
)
# Main function for prediction completion
def completion(
model: str,
messages: list,
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
optional_params: dict,
litellm_params: dict,
logging_obj,
api_key,
encoding,
custom_prompt_dict={},
logger_fn=None,
acompletion=None,
headers={},
) -> Union[ModelResponse, CustomStreamWrapper]:
headers = replicate_config.validate_environment(
api_key=api_key,
headers=headers,
model=model,
messages=messages,
optional_params=optional_params,
)
# Start a prediction and get the prediction URL
version_id = replicate_config.model_to_version_id(model)
input_data = replicate_config.transform_request(
model=model,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
headers=headers,
)
if acompletion is not None and acompletion is True:
return async_completion(
model_response=model_response,
model=model,
encoding=encoding,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
version_id=version_id,
input_data=input_data,
api_key=api_key,
api_base=api_base,
logging_obj=logging_obj,
print_verbose=print_verbose,
headers=headers,
) # type: ignore
## COMPLETION CALL
model_response.created = int(
time.time()
) # for pricing this must remain right before calling api
prediction_url = replicate_config.get_complete_url(
api_base=api_base,
model=model,
optional_params=optional_params,
litellm_params=litellm_params,
)
## COMPLETION CALL
httpx_client = _get_httpx_client(
params={"timeout": 600.0},
)
response = httpx_client.post(
url=prediction_url,
headers=headers,
data=json.dumps(input_data),
)
prediction_url = replicate_config.get_prediction_url(response)
# Handle the prediction response (streaming or non-streaming)
if "stream" in optional_params and optional_params["stream"] is True:
print_verbose("streaming request")
_response = handle_prediction_response_streaming(
prediction_url,
api_key,
print_verbose,
headers=headers,
http_client=httpx_client,
)
return CustomStreamWrapper(_response, model, logging_obj=logging_obj, custom_llm_provider="replicate") # type: ignore
else:
for retry in range(litellm.DEFAULT_REPLICATE_POLLING_RETRIES):
time.sleep(
litellm.DEFAULT_REPLICATE_POLLING_DELAY_SECONDS + 2 * retry
) # wait to allow response to be generated by replicate - else partial output is generated with status=="processing"
response = httpx_client.get(url=prediction_url, headers=headers)
if (
response.status_code == 200
and response.json().get("status") == "processing"
):
continue
return litellm.ReplicateConfig().transform_response(
model=model,
raw_response=response,
model_response=model_response,
logging_obj=logging_obj,
api_key=api_key,
request_data=input_data,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=encoding,
)
raise ReplicateError(
status_code=500,
message="No response received from Replicate API after max retries",
headers=None,
)
async def async_completion(
model_response: ModelResponse,
model: str,
messages: List[AllMessageValues],
encoding,
optional_params: dict,
litellm_params: dict,
version_id,
input_data,
api_key,
api_base,
logging_obj,
print_verbose,
headers: dict,
) -> Union[ModelResponse, CustomStreamWrapper]:
prediction_url = replicate_config.get_complete_url(
api_base=api_base,
model=model,
optional_params=optional_params,
litellm_params=litellm_params,
)
async_handler = get_async_httpx_client(
llm_provider=litellm.LlmProviders.REPLICATE,
params={"timeout": 600.0},
)
response = await async_handler.post(
url=prediction_url, headers=headers, data=json.dumps(input_data)
)
prediction_url = replicate_config.get_prediction_url(response)
if "stream" in optional_params and optional_params["stream"] is True:
_response = async_handle_prediction_response_streaming(
prediction_url,
api_key,
print_verbose,
headers=headers,
http_client=async_handler,
)
return CustomStreamWrapper(_response, model, logging_obj=logging_obj, custom_llm_provider="replicate") # type: ignore
for retry in range(litellm.DEFAULT_REPLICATE_POLLING_RETRIES):
await asyncio.sleep(
litellm.DEFAULT_REPLICATE_POLLING_DELAY_SECONDS + 2 * retry
) # wait to allow response to be generated by replicate - else partial output is generated with status=="processing"
response = await async_handler.get(url=prediction_url, headers=headers)
if (
response.status_code == 200
and response.json().get("status") == "processing"
):
continue
return litellm.ReplicateConfig().transform_response(
model=model,
raw_response=response,
model_response=model_response,
logging_obj=logging_obj,
api_key=api_key,
request_data=input_data,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=encoding,
)
# Add a fallback return if no response is received after max retries
raise ReplicateError(
status_code=500,
message="No response received from Replicate API after max retries",
headers=None,
)
|
