Use simulate acc len from sglang.environ (#10771)

python/sglang/environ.py

@@ -124,6 +124,8 @@ class Envs:
     SGLANG_TEST_REQUEST_TIME_STATS = EnvBool(False)
     SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK = EnvBool(False)
     SGLANG_DISABLE_REQUEST_LOGGING = EnvBool(False)
+    SGLANG_SIMULATE_ACC_LEN = EnvFloat(-1)
+    SGLANG_SIMULATE_ACC_METHOD = EnvStr("multinomial")
 
     # Model Parallel
     SGLANG_USE_MESSAGE_QUEUE_BROADCASTER = EnvBool(True)

python/sglang/launch_server.py

@@ -7,9 +7,23 @@ from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import prepare_server_args
 from sglang.srt.utils import kill_process_tree
 
+MOVE_ENVS_WARN = """
+########################################################################
+# For contributors and developers:                                    #
+# Please move environment variable definitions to 'sglang/environ.py' #
+# using the following pattern:                                        #
+#     SGLANG_XXX = EnvBool(False)                                     #
+#                                                                     #
+########################################################################
+"""
+
 if __name__ == "__main__":
     server_args = prepare_server_args(sys.argv[1:])
 
+    from sglang.srt.server_args import print_deprecated_warning
+
+    print_deprecated_warning(MOVE_ENVS_WARN)
+
     try:
         launch_server(server_args)
     finally:

python/sglang/srt/speculative/eagle_utils.py

@@ -12,6 +12,7 @@ import torch.nn.functional as F
 import triton
 import triton.language as tl
 
+from sglang.environ import envs
 from sglang.srt.constrained.base_grammar_backend import BaseGrammarObject
 from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
@@ -23,7 +24,7 @@ from sglang.srt.managers.schedule_batch import (
     global_server_args_dict,
 )
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
-from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardMode
+from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode
 from sglang.srt.utils import is_cuda, is_hip, next_power_of_2
 
 if is_cuda():
@@ -42,8 +43,8 @@ logger = logging.getLogger(__name__)
 
 
 # Simulate acceptance length for benchmarking purposes
-SIMULATE_ACC_LEN = os.environ.get("SIMULATE_ACC_LEN")
+SIMULATE_ACC_LEN = envs.SGLANG_SIMULATE_ACC_LEN.get()  # turn off if < 0
-SIMULATE_ACC_METHOD = os.environ.get("SIMULATE_ACC_METHOD", "multinomial")
+SIMULATE_ACC_METHOD = envs.SGLANG_SIMULATE_ACC_METHOD.get()
 
 TREE_TRAVERSE_TIME_THRESHOLD = 1  # TODO: set this properly
 
@@ -500,13 +501,12 @@ class EagleVerifyInput:
                 deterministic=True,
             )
 
-        if SIMULATE_ACC_LEN:
+        if SIMULATE_ACC_LEN > 0.0:
             # Do simulation
             accept_index = _generate_simulated_accept_index(
                 accept_index=accept_index,
                 predict=predict,  # mutable
                 accept_length=accept_length,  # mutable
-                simulate_acc_len=SIMULATE_ACC_LEN,
                 bs=bs,
                 spec_steps=self.spec_steps,
             )
@@ -1131,14 +1131,16 @@ def _generate_simulated_accept_index(
     accept_index,
     predict,
     accept_length,
-    simulate_acc_len,
     bs,
     spec_steps,
+    simulate_acc_len: float = SIMULATE_ACC_LEN,
+    simulate_acc_method: str = SIMULATE_ACC_METHOD,
 ):
-    simulate_acc_len_float = float(simulate_acc_len)
+    assert simulate_acc_len > 0.0
-    if SIMULATE_ACC_METHOD == "multinomial":
+
+    if simulate_acc_method == "multinomial":
         simulated_values = torch.normal(
-            mean=simulate_acc_len_float,
+            mean=simulate_acc_len,
             std=1.0,
             size=(1,),
             device="cpu",
@@ -1146,19 +1148,19 @@ def _generate_simulated_accept_index(
         # clamp simulated values to be between 1 and self.spec_steps
         simulated_values = torch.clamp(simulated_values, min=1.0, max=spec_steps + 1)
         simulate_acc_len = int(simulated_values.round().item())
-    elif SIMULATE_ACC_METHOD == "match-expected":
+    elif simulate_acc_method == "match-expected":
         # multinomial sampling does not match the expected length
         # we keep it for the sake of compatibility of existing tests
         # but it's better to use "match-expected" for the cases that need to
         # match the expected length, One caveat is that this will only sample
         # either round down or round up of the expected length
-        simulate_acc_len_float = max(1.0, min(spec_steps + 1, simulate_acc_len_float))
+        simulate_acc_len = max(1.0, min(spec_steps + 1, simulate_acc_len))
-        lower = int(simulate_acc_len_float // 1)
+        lower = int(simulate_acc_len // 1)
         upper = lower + 1 if lower < spec_steps + 1 else lower
         if lower == upper:
             simulate_acc_len = lower
         else:
-            weight_upper = simulate_acc_len_float - lower
+            weight_upper = simulate_acc_len - lower
             weight_lower = 1.0 - weight_upper
             probs = torch.tensor([weight_lower, weight_upper], device="cpu")
             sampled_index = torch.multinomial(probs, num_samples=1)