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[1/2] Support deterministic inference with flashinfer attention backend (#10645)

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Co-authored-by: hebiao064 <hebiaobuaa@gmail.com>
Co-authored-by: Qiaolin-Yu <liin1211@outlook.com>
This commit is contained in:
Baizhou Zhang
2025-09-19 23:34:29 -07:00
committed by GitHub
parent 1d1ce62495
commit 8ecef73f12
10 changed files with 427 additions and 6 deletions
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5
python/sglang/environ.py
View File

@@ -197,6 +197,11 @@ class Envs:
SGLANG_SYNC_TOKEN_IDS_ACROSS_TP = EnvBool(False)
SGLANG_ENABLE_COLOCATED_BATCH_GEN = EnvBool(False)
# Deterministic inference
SGLANG_ENABLE_DETERMINISTIC_INFERENCE = EnvBool(False)
SGLANG_FLASHINFER_PREFILL_SPLIT_TILE_SIZE = EnvInt(4096)
SGLANG_FLASHINFER_DECODE_SPLIT_TILE_SIZE = EnvInt(2048)
# fmt: on

66
python/sglang/srt/layers/attention/flashinfer_backend.py
View File

@@ -31,6 +31,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMo
from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
from sglang.srt.speculative.lookahead_utils import LookaheadVerifyInput
from sglang.srt.utils import (
get_int_env_var,
is_flashinfer_available,
is_sm100_supported,
next_power_of_2,
@@ -40,6 +41,7 @@ if TYPE_CHECKING:
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import ModelRunner
if is_flashinfer_available():
from flashinfer import (
BatchDecodeWithPagedKVCacheWrapper,
@@ -123,12 +125,33 @@ class FlashInferAttnBackend(AttentionBackend):
):
global_config.flashinfer_workspace_size = 512 * 1024 * 1024
# When deterministic inference is enabled, tensor cores should be used for decode
# Also set split tile sizes for prefill and decode from environment variables, and disable kv split for cuda graph
# More information can be found here: https://github.com/flashinfer-ai/flashinfer/pull/1675
self.enable_deterministic = (
model_runner.server_args.enable_deterministic_inference
)
self.prefill_split_tile_size = None
self.decode_split_tile_size = None
self.disable_cuda_graph_kv_split = False
if self.enable_deterministic:
self.decode_use_tensor_cores = True
self.prefill_split_tile_size = get_int_env_var(
"SGLANG_FLASHINFER_PREFILL_SPLIT_TILE_SIZE", 4096
)
self.decode_split_tile_size = get_int_env_var(
"SGLANG_FLASHINFER_DECODE_SPLIT_TILE_SIZE", 2048
)
self.disable_cuda_graph_kv_split = True
global_config.flashinfer_workspace_size = 2048 * 1024 * 1024
# Allocate buffers
global global_workspace_buffer
if global_workspace_buffer is None:
# different from flashinfer zero_init_global_workspace_buffer
global_workspace_size = global_config.flashinfer_workspace_size
global_workspace_buffer = torch.empty(
global_config.flashinfer_workspace_size,
global_workspace_size,
dtype=torch.uint8,
device=model_runner.device,
)
@@ -219,6 +242,8 @@ class FlashInferAttnBackend(AttentionBackend):
decode_wrappers=self.decode_wrappers,
encoder_lens=forward_batch.encoder_lens,
spec_info=forward_batch.spec_info,
fixed_split_size=self.decode_split_tile_size,
disable_split_kv=False,
)
self.forward_metadata = DecodeMetadata(self.decode_wrappers)
elif forward_batch.forward_mode.is_draft_extend():
@@ -258,7 +283,7 @@ class FlashInferAttnBackend(AttentionBackend):
use_ragged = False
extend_no_prefix = False
else:
use_ragged = True
use_ragged = not self.enable_deterministic
extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
self.indices_updater_prefill.update(
@@ -271,6 +296,7 @@ class FlashInferAttnBackend(AttentionBackend):
use_ragged=use_ragged,
encoder_lens=forward_batch.encoder_lens,
spec_info=None,
fixed_split_size=self.prefill_split_tile_size,
)
self.forward_metadata = PrefillMetadata(
self.prefill_wrappers_paged, use_ragged, extend_no_prefix
@@ -347,6 +373,8 @@ class FlashInferAttnBackend(AttentionBackend):
decode_wrappers=decode_wrappers,
encoder_lens=encoder_lens,
spec_info=spec_info,
fixed_split_size=None,
disable_split_kv=self.disable_cuda_graph_kv_split,
)
self.decode_cuda_graph_metadata[bs] = decode_wrappers
self.forward_metadata = DecodeMetadata(decode_wrappers)
@@ -439,6 +467,8 @@ class FlashInferAttnBackend(AttentionBackend):
decode_wrappers=self.decode_cuda_graph_metadata[bs],
encoder_lens=encoder_lens[:bs] if encoder_lens is not None else None,
spec_info=spec_info,
fixed_split_size=None,
disable_split_kv=self.disable_cuda_graph_kv_split,
)
elif forward_mode.is_target_verify():
self.indices_updater_prefill.update(
@@ -646,6 +676,8 @@ class FlashInferIndicesUpdaterDecode:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
disable_split_kv: Optional[bool] = None,
):
# Keep the signature for type checking. It will be assigned during runtime.
raise NotImplementedError()
@@ -661,6 +693,8 @@ class FlashInferIndicesUpdaterDecode:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
disable_split_kv: Optional[bool] = None,
):
decode_wrappers = decode_wrappers or self.decode_wrappers
self.call_begin_forward(
@@ -672,6 +706,8 @@ class FlashInferIndicesUpdaterDecode:
None,
spec_info,
seq_lens_cpu,
fixed_split_size=fixed_split_size,
disable_split_kv=disable_split_kv,
)
def update_sliding_window(
@@ -685,6 +721,8 @@ class FlashInferIndicesUpdaterDecode:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
disable_split_kv: Optional[bool] = None,
):
assert self.sliding_window_size is not None
for wrapper_id in range(2):
@@ -735,6 +773,8 @@ class FlashInferIndicesUpdaterDecode:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
disable_split_kv: Optional[bool] = None,
):
for wrapper_id in range(2):
if wrapper_id == 0:
@@ -771,6 +811,8 @@ class FlashInferIndicesUpdaterDecode:
],
seq_lens_cpu: Optional[torch.Tensor],
use_sliding_window_kv_pool: bool = False,
fixed_split_size: Optional[int] = None,
disable_split_kv: Optional[bool] = None,
):
if spec_info is None:
bs = len(req_pool_indices)
@@ -825,6 +867,10 @@ class FlashInferIndicesUpdaterDecode:
data_type=self.data_type,
q_data_type=self.q_data_type,
non_blocking=True,
fixed_split_size=fixed_split_size,
disable_split_kv=(
disable_split_kv if disable_split_kv is not None else False
),
)
if locally_override:
@@ -876,6 +922,7 @@ class FlashInferIndicesUpdaterPrefill:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
):
# Keep the signature for type checking. It will be assigned during runtime.
raise NotImplementedError()
@@ -893,6 +940,7 @@ class FlashInferIndicesUpdaterPrefill:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
):
if use_ragged:
# TODO: remove this device sync, we can use forward_batch.extend_prefix_lens_cpu
@@ -916,6 +964,7 @@ class FlashInferIndicesUpdaterPrefill:
self.qo_indptr[0],
use_ragged,
spec_info,
fixed_split_size=fixed_split_size,
)
def update_sliding_window(
@@ -931,6 +980,7 @@ class FlashInferIndicesUpdaterPrefill:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
):
for wrapper_id in range(2):
if wrapper_id == 0:
@@ -979,6 +1029,7 @@ class FlashInferIndicesUpdaterPrefill:
spec_info: Optional[
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
fixed_split_size: Optional[int] = None,
):
for wrapper_id in range(2):
if wrapper_id == 0:
@@ -1024,6 +1075,7 @@ class FlashInferIndicesUpdaterPrefill:
Union[EagleDraftInput, EagleVerifyInput, LookaheadVerifyInput]
],
use_sliding_window_kv_pool: bool = False,
fixed_split_size: Optional[int] = None,
):
bs = len(seq_lens)
if spec_info is None:
@@ -1094,6 +1146,7 @@ class FlashInferIndicesUpdaterPrefill:
kv_data_type=self.data_type,
custom_mask=custom_mask,
non_blocking=True,
fixed_split_size=fixed_split_size,
)
@@ -1327,6 +1380,8 @@ def fast_decode_plan(
rope_scale: Optional[float] = None,
rope_theta: Optional[float] = None,
non_blocking: bool = True,
fixed_split_size: Optional[int] = None,
disable_split_kv: bool = False,
) -> None:
"""
A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for FlashInferMultiStepDraftBackend.
@@ -1352,6 +1407,9 @@ def fast_decode_plan(
if self.use_tensor_cores:
qo_indptr_host = _get_range_buf(batch_size + 1, "cpu")
# Here we set fixed_split_size to -1 to avoid the assertion error in flashinfer's plan function
if fixed_split_size is None:
fixed_split_size = -1
if self.is_cuda_graph_enabled:
if batch_size != self._fixed_batch_size:
@@ -1433,8 +1491,8 @@ def fast_decode_plan(
head_dim,
False, # causal
window_left,
-1,
False,
fixed_split_size,
disable_split_kv,
)
except Exception as e:
raise RuntimeError(f"Error in standard plan: {e}")

3
python/sglang/srt/layers/layernorm.py
View File

@@ -14,6 +14,7 @@
"""Fused operators for normalization layers."""
import logging
import os
from typing import Optional, Tuple, Union
import torch
@@ -80,6 +81,8 @@ class RMSNorm(CustomOp):
)
if _use_aiter:
self._forward_method = self.forward_aiter
if os.environ["SGLANG_ENABLE_DETERMINISTIC_INFERENCE"] == "1":
self._forward_method = self.forward_native
def forward_cuda(
self,

1
python/sglang/srt/managers/schedule_batch.py
View File

@@ -111,6 +111,7 @@ GLOBAL_SERVER_ARGS_KEYS = [
"enable_symm_mem",
"enable_custom_logit_processor",
"disaggregation_mode",
"enable_deterministic_inference",
]
# Put some global args for easy access

15
python/sglang/srt/managers/schedule_policy.py
View File

@@ -541,7 +541,9 @@ class PrefillAdder:
return self.budget_state()
def add_one_req(self, req: Req, has_chunked_req: bool):
def add_one_req(
self, req: Req, has_chunked_req: bool, truncation_align_size: Optional[int]
):
if req.sampling_params.ignore_eos and getattr(self.tree_cache, "disable", True):
return self.add_one_req_ignore_eos(req, has_chunked_req)
@@ -600,6 +602,17 @@ class PrefillAdder:
if trunc_len <= 0:
return AddReqResult.OTHER
# When truncation align size is set, we want to assert that the prefill prefix length is multiple of truncation align size
# A typical use case is when deterministic inference is enabled with flashinfer attention backend,
# we need the prefill prefix length to be multiple of attention split size
if truncation_align_size is not None:
if trunc_len < truncation_align_size:
return AddReqResult.OTHER
else:
trunc_len = truncation_align_size * (
trunc_len // truncation_align_size
)
# Chunked prefill
req.extend_input_len = trunc_len
req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]

18
python/sglang/srt/managers/scheduler.py
View File

@@ -172,6 +172,7 @@ from sglang.srt.utils import (
freeze_gc,
get_available_gpu_memory,
get_bool_env_var,
get_int_env_var,
get_zmq_socket,
is_cpu,
kill_itself_when_parent_died,
@@ -565,6 +566,17 @@ class Scheduler(
if get_bool_env_var("SGLANG_GC_LOG"):
configure_gc_logger()
# Init prefill kv split size when deterministic inference is enabled with flashinfer attention backend
if (
self.server_args.enable_deterministic_inference
and self.server_args.attention_backend == "flashinfer"
):
self.truncation_align_size = get_int_env_var(
"SGLANG_FLASHINFER_PREFILL_SPLIT_TILE_SIZE", 4096
)
else:
self.truncation_align_size = None
# Init request dispatcher
self._request_dispatcher = TypeBasedDispatcher(
[
@@ -1846,7 +1858,11 @@ class Scheduler(
continue
req.init_next_round_input(self.tree_cache)
res = adder.add_one_req(req, has_chunked_req=(self.chunked_req is not None))
res = adder.add_one_req(
req,
has_chunked_req=(self.chunked_req is not None),
truncation_align_size=self.truncation_align_size,
)
if res != AddReqResult.CONTINUE:
if res == AddReqResult.NO_TOKEN:

6
python/sglang/srt/model_executor/model_runner.py
View File

@@ -406,6 +406,12 @@ class ModelRunner:
)
self.init_double_sparsity_channel_config(server_args.ds_heavy_channel_type)
# Enable batch invariant mode
if server_args.enable_deterministic_inference:
from batch_invariant_ops import enable_batch_invariant_mode
enable_batch_invariant_mode()
# Init memory pool and attention backends
self.init_memory_pool(
min_per_gpu_memory,

1
python/sglang/srt/sampling/sampling_batch_info.py
View File

@@ -75,6 +75,7 @@ class SamplingBatchInfo:
@classmethod
def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
global_server_args_dict = cls._get_global_server_args_dict()
enable_deterministic = global_server_args_dict["enable_deterministic_inference"]
reqs = batch.reqs
device = batch.device

35
python/sglang/srt/server_args.py
View File

@@ -118,6 +118,8 @@ DISAGG_TRANSFER_BACKEND_CHOICES = ["mooncake", "nixl", "ascend", "fake"]
GRAMMAR_BACKEND_CHOICES = ["xgrammar", "outlines", "llguidance", "none"]
DETERMINISTIC_ATTENTION_BACKEND_CHOICES = ["flashinfer"]
# Allow external code to add more choices
def add_load_format_choices(choices):
@@ -437,6 +439,9 @@ class ServerArgs:
max_mamba_cache_size: Optional[int] = None
mamba_ssm_dtype: str = "float32"
# For deterministic inference
enable_deterministic_inference: bool = False
# Deprecated arguments
enable_ep_moe: bool = False
enable_deepep_moe: bool = False
@@ -980,6 +985,29 @@ class ServerArgs:
"Please set --tokenizer-metrics-custom-labels-header when setting --tokenizer-metrics-allowed-customer-labels."
)
# Deterministic inference
os.environ["SGLANG_ENABLE_DETERMINISTIC_INFERENCE"] = (
"1" if self.enable_deterministic_inference else "0"
)
if self.enable_deterministic_inference:
# Check batch_invariant_ops dependency
import importlib
if not importlib.util.find_spec("batch_invariant_ops"):
raise ValueError(
"batch_invariant_ops is not installed. Please install it from https://github.com/thinking-machines-lab/batch_invariant_ops/."
)
# Check some settings
self.disable_radix_cache = True
logger.warning(
"Currently radix cache is disabled for deterministic inference. It will be supported in the future."
)
if self.attention_backend not in DETERMINISTIC_ATTENTION_BACKEND_CHOICES:
raise ValueError(
f"Currently only {DETERMINISTIC_ATTENTION_BACKEND_CHOICES} attention backends are supported for deterministic inference."
)
@staticmethod
def add_cli_args(parser: argparse.ArgumentParser):
# Model and tokenizer
@@ -2470,6 +2498,13 @@ class ServerArgs:
help="Number of sm partition groups.",
)
# For deterministic inference
parser.add_argument(
"--enable-deterministic-inference",
action="store_true",
help="Enable deterministic inference mode with batch invariant ops.",
)
# Deprecated arguments
parser.add_argument(
"--enable-ep-moe",

283
python/sglang/test/test_deterministic.py Normal file
View File

@@ -0,0 +1,283 @@
"""
Batch the same prompt in random batch sizes, and test if the results are consistent across different trials.
Usage:
python3 -m sglang.test.test_deterministic --n-trials <numer_of_trials> --test-mode <single|mixed|prefix> --profile
"""
import argparse
import dataclasses
import json
import os
import random
from typing import List
import requests
from sglang.profiler import run_profile
PROMPT_1 = "Tell me about Richard Feynman: "
PROMPT_2 = "Generate 1000 random numbers. Go directly into it, don't say Sure and don't say here are numbers. Just start with a number."
dirpath = os.path.dirname(__file__)
with open("python/sglang/test/long_prompt.txt", "r") as f:
LONG_PROMPT = f.read()
@dataclasses.dataclass
class BenchArgs:
host: str = "localhost"
port: int = 30000
batch_size: int = 1
temperature: float = 0.0
max_new_tokens: int = 100
frequency_penalty: float = 0.0
presence_penalty: float = 0.0
return_logprob: bool = False
stream: bool = False
profile: bool = False
profile_steps: int = 3
profile_by_stage: bool = False
test_mode: str = "single"
@staticmethod
def add_cli_args(parser: argparse.ArgumentParser):
parser.add_argument("--host", type=str, default=BenchArgs.host)
parser.add_argument("--port", type=int, default=BenchArgs.port)
parser.add_argument("--n-trials", type=int, default=50)
parser.add_argument("--temperature", type=float, default=BenchArgs.temperature)
parser.add_argument(
"--max-new-tokens", type=int, default=BenchArgs.max_new_tokens
)
parser.add_argument(
"--frequency-penalty", type=float, default=BenchArgs.frequency_penalty
)
parser.add_argument(
"--presence-penalty", type=float, default=BenchArgs.presence_penalty
)
parser.add_argument("--return-logprob", action="store_true")
parser.add_argument("--stream", action="store_true")
parser.add_argument(
"--test-mode",
type=str,
default=BenchArgs.test_mode,
choices=["single", "mixed", "prefix"],
)
parser.add_argument("--profile", action="store_true")
parser.add_argument(
"--profile-steps", type=int, default=BenchArgs.profile_steps
)
parser.add_argument("--profile-by-stage", action="store_true")
@classmethod
def from_cli_args(cls, args: argparse.Namespace):
attrs = [attr.name for attr in dataclasses.fields(cls)]
return cls(**{attr: getattr(args, attr) for attr in attrs})
def send_single(
args,
batch_size: int,
profile: bool = False,
profile_steps: int = 3,
profile_by_stage: bool = False,
):
base_url = f"http://{args.host}:{args.port}"
prompt = [PROMPT_1] * batch_size
json_data = {
"text": prompt,
"sampling_params": {
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"frequency_penalty": args.frequency_penalty,
"presence_penalty": args.presence_penalty,
},
"return_logprob": args.return_logprob,
"stream": args.stream,
}
if profile:
run_profile(
base_url, profile_steps, ["CPU", "GPU"], None, None, profile_by_stage
)
response = requests.post(
f"{base_url}/generate",
json=json_data,
stream=args.stream,
)
if args.stream:
for chunk in response.iter_lines(decode_unicode=False):
chunk = chunk.decode("utf-8")
if chunk and chunk.startswith("data:"):
if chunk == "data: [DONE]":
break
ret = json.loads(chunk[5:].strip("\n"))
else:
ret = response.json()
ret = ret[0]
if response.status_code != 200:
print(ret)
return -1
return ret["text"]
def send_mixed(args, batch_size: int):
num_long_prompt = 0 if batch_size <= 10 else random.randint(1, 10)
num_prompt_1 = random.randint(1, batch_size - num_long_prompt)
num_prompt_2 = batch_size - num_prompt_1 - num_long_prompt
json_data = {
"text": [PROMPT_1] * num_prompt_1
+ [PROMPT_2] * num_prompt_2
+ [LONG_PROMPT] * num_long_prompt,
"sampling_params": {
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"frequency_penalty": args.frequency_penalty,
"presence_penalty": args.presence_penalty,
},
"return_logprob": args.return_logprob,
"stream": args.stream,
}
response = requests.post(
f"http://{args.host}:{args.port}/generate",
json=json_data,
stream=args.stream,
)
ret = response.json()
if response.status_code != 200:
print(ret)
return -1, -1, -1
prompt_1_ret = [ret[i]["text"] for i in range(num_prompt_1)]
prompt_2_ret = [
ret[i]["text"] for i in range(num_prompt_1, num_prompt_1 + num_prompt_2)
]
long_prompt_ret = [
ret[i]["text"]
for i in range(
num_prompt_1 + num_prompt_2, num_prompt_1 + num_prompt_2 + num_long_prompt
)
]
return prompt_1_ret, prompt_2_ret, long_prompt_ret
def send_prefix(args, batch_size: int, prompts: List[str]):
requests.post(f"http://{args.host}:{args.port}/flush_cache")
batch_data = []
sampled_indices = []
for _ in range(batch_size):
sampled_index = random.randint(0, len(prompts) - 1)
sampled_indices.append(sampled_index)
batch_data.append(prompts[sampled_index])
json_data = {
"text": batch_data,
"sampling_params": {
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"frequency_penalty": args.frequency_penalty,
"presence_penalty": args.presence_penalty,
},
"return_logprob": args.return_logprob,
"stream": args.stream,
}
response = requests.post(
f"http://{args.host}:{args.port}/generate",
json=json_data,
stream=args.stream,
)
ret = response.json()
if response.status_code != 200:
print(ret)
return -1, -1, -1
ret_dict = {i: [] for i in range(len(prompts))}
for i in range(batch_size):
ret_dict[sampled_indices[i]].append(ret[i]["text"])
return ret_dict
def test_deterministic(args):
# First do some warmups
for i in range(3):
send_single(args, 16, args.profile)
if args.test_mode == "single":
# In single mode, we test the deterministic behavior by sending the same prompt in batch sizes ranging from 1 to n_trials.
texts = []
for i in range(1, args.n_trials + 1):
batch_size = i
text = send_single(args, batch_size, args.profile)
text = text.replace("\n", " ")
print(f"Trial {i} with batch size {batch_size}: {text}")
texts.append(text)
print(f"Total samples: {len(texts)}, Unique samples: {len(set(texts))}")
elif args.test_mode == "mixed":
# In mixed mode, we send a mixture of two short prompts and one long prompt in the same batch with batch size ranging from 1 to n_trials.
output_prompt_1 = []
output_prompt_2 = []
output_long_prompt = []
for i in range(1, args.n_trials + 1):
batch_size = i
ret_prompt_1, ret_prompt_2, ret_long_prompt = send_mixed(args, batch_size)
output_prompt_1.extend(ret_prompt_1)
output_prompt_2.extend(ret_prompt_2)
output_long_prompt.extend(ret_long_prompt)
print(
f"Testing Trial {i} with batch size {batch_size}, number of prompt 1: {len(ret_prompt_1)}, number of prompt 2: {len(ret_prompt_2)}, number of long prompt: {len(ret_long_prompt)}"
)
print(
f"Prompt 1: total samples: {len(output_prompt_1)}, Unique samples: {len(set(output_prompt_1))}"
)
print(
f"Prompt 2: total samples: {len(output_prompt_2)}, Unique samples: {len(set(output_prompt_2))}"
)
print(
f"Long prompt: total samples: {len(output_long_prompt)}, Unique samples: {len(set(output_long_prompt))}"
)
elif args.test_mode == "prefix":
# In prefix mode, we create prompts from the same long prompt, with different lengths of common prefix.
len_prefix = [1, 511, 2048, 4097]
num_prompts = len(len_prefix)
outputs = {i: [] for i in range(4)}
prompts = [LONG_PROMPT[: len_prefix[i]] for i in range(4)]
for i in range(1, args.n_trials + 1):
batch_size = i
ret_dict = send_prefix(args, batch_size, prompts)
msg = f"Testing Trial {i} with batch size {batch_size},"
for i in range(num_prompts):
msg += f" # prefix length {len_prefix[i]}: {len(ret_dict[i])},"
print(msg)
for i in range(num_prompts):
outputs[i].extend(ret_dict[i])
for i in range(num_prompts):
print(
f"Prompt {i} with prefix length {len_prefix[i]}: total samples: {len(outputs[i])}, Unique samples: {len(set(outputs[i]))}"
)
else:
raise ValueError(f"Invalid test mode: {args.test_mode}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
BenchArgs.add_cli_args(parser)
args = parser.parse_args()
test_deterministic(args)