Revert "Fix: Dynamic RoPE Cache Expansion to Prevent Position-ID Out-of-Bounds in EAGLE + Long-Sequence Workloads" (#11827)

python/sglang/srt/environ.py

@@ -222,11 +222,6 @@ class Envs:
     SGLANG_TRITON_PREFILL_TRUNCATION_ALIGN_SIZE = EnvInt(4096)
     SGLANG_TRITON_DECODE_SPLIT_TILE_SIZE = EnvInt(256)
 
-    # RoPE cache configuration
-    SGLANG_SPEC_EXPANSION_SAFETY_FACTOR = EnvInt(2)
-    SGLANG_ROPE_CACHE_SAFETY_MARGIN = EnvInt(256)
-    SGLANG_ROPE_CACHE_ALIGN = EnvInt(128)
-
     # Overlap Spec V2
     SGLANG_ENABLE_OVERLAP_PLAN_STREAM = EnvBool(False)
 

python/sglang/srt/layers/rotary_embedding.py

@@ -147,36 +147,6 @@ class RotaryEmbedding(CustomOp):
         cache = torch.cat((cos, sin), dim=-1)
         return cache
 
-    def _ensure_cos_sin_cache_length(self, needed_max_pos: int):
-        """Ensure cos_sin_cache length > needed_max_pos."""
-        cur_len = int(self.cos_sin_cache.shape[0])
-        if needed_max_pos < cur_len:
-            return
-
-        # Align to 128 to reduce realloc frequency
-        new_len = ((needed_max_pos + 128) // 128) * 128
-        device = self.cos_sin_cache.device
-        dtype = self.cos_sin_cache.dtype
-
-        # Compute inv_freq on same device
-        inv_freq = self._compute_inv_freq(self.base).to(device=device)
-
-        # Incremental computation for new positions only
-        start = cur_len
-        t_new = torch.arange(start, new_len, dtype=inv_freq.dtype, device=device)
-        if t_new.numel() == 0:
-            return
-
-        freqs_new = torch.einsum("i,j->ij", t_new, inv_freq)
-        cos_new = freqs_new.cos()
-        sin_new = freqs_new.sin()
-        new_rows = torch.cat((cos_new, sin_new), dim=-1).to(dtype=dtype)
-
-        # Update cache with new rows
-        self.cos_sin_cache = torch.cat((self.cos_sin_cache, new_rows), dim=0).to(
-            device=device, dtype=dtype
-        )
-
     def forward_native(
         self,
         positions: torch.Tensor,

python/sglang/srt/model_executor/model_runner.py

@@ -140,7 +140,6 @@ from sglang.srt.utils import (
     log_info_on_rank0,
     monkey_patch_p2p_access_check,
     monkey_patch_vllm_gguf_config,
-    reserve_rope_cache_for_long_sequences,
     set_cuda_arch,
     slow_rank_detector,
 )
@@ -899,15 +898,6 @@ class ModelRunner:
             f"mem usage={self.weight_load_mem_usage:.2f} GB."
         )
 
-        # Pre-expand RoPE cache before CUDA Graph capture
-        reserve_rope_cache_for_long_sequences(
-            self.model,
-            self.server_args,
-            self.model_config,
-            self.req_to_token_pool,
-            logger,
-        )
-
         if self.server_args.elastic_ep_backend == "mooncake":
             # Mooncake does not support `monitored_barrier`
             dist.barrier(group=get_tp_group().cpu_group)

python/sglang/srt/utils/common.py

@@ -3460,61 +3460,3 @@ def cached_triton_kernel(key_fn=None):
         return CachedKernel(fn, key_fn)
 
     return decorator
-
-
-def reserve_rope_cache_for_long_sequences(
-    model, server_args, model_config, req_to_token_pool=None, logger=None
-):
-    """Pre-expand RoPE cache for long sequences and speculative decoding."""
-    from sglang.srt.environ import envs
-
-    if logger is None:
-        import logging
-
-        logger = logging.getLogger(__name__)
-
-    SAFETY_FACTOR = envs.SGLANG_SPEC_EXPANSION_SAFETY_FACTOR.value
-    MARGIN = envs.SGLANG_ROPE_CACHE_SAFETY_MARGIN.value
-    ALIGN = envs.SGLANG_ROPE_CACHE_ALIGN.value
-
-    # 1) Estimate base context upper bound
-    base_ctx = (
-        getattr(server_args, "context_length", None)
-        or getattr(model_config, "context_len", None)
-        or getattr(model_config, "max_model_len", None)
-        or getattr(model_config.hf_text_config, "max_position_embeddings", None)
-        or 2048
-    )
-
-    # 2) Runtime input capacity (including extra_len from req_to_token_pool)
-    inferred_cap = getattr(req_to_token_pool, "max_context_len", None) or base_ctx
-
-    # 3) Speculative decoding expansion
-    steps = int(getattr(server_args, "speculative_num_steps", 0) or 0)
-    draft = int(getattr(server_args, "speculative_num_draft_tokens", 0) or 0)
-    reserve = inferred_cap + steps * draft * SAFETY_FACTOR + MARGIN
-
-    # 4) Align to reduce reallocation frequency
-    reserve = (reserve + ALIGN - 1) // ALIGN * ALIGN
-
-    logger.info(
-        f"RoPE cache reserve={reserve} (base={base_ctx}, cap={inferred_cap}, steps={steps}, draft={draft}, k={SAFETY_FACTOR}, margin={MARGIN})"
-    )
-
-    # Recursively expand all RoPE layers
-    def reserve_rope_cache_recursive(module):
-        for child in module.children():
-            if hasattr(child, "_ensure_cos_sin_cache_length") and hasattr(
-                child, "cos_sin_cache"
-            ):
-                old_len = child.cos_sin_cache.shape[0]
-                child._ensure_cos_sin_cache_length(reserve - 1)
-                new_len = child.cos_sin_cache.shape[0]
-                if new_len > old_len:
-                    logger.info(
-                        f"Expanded RoPE cache from {old_len} to {new_len} positions"
-                    )
-            else:
-                reserve_rope_cache_recursive(child)
-
-    reserve_rope_cache_recursive(model)