Merge remote-tracking branch 'upstream/main' into chunked_prefill_fix

Author: santhnm2

megatron/core/extensions/transformer_engine.py

@@ -2575,11 +2575,26 @@ def fused_apply_rotary_pos_emb_thd(
         freqs: torch.Tensor,
         cp_size: int = 1,
         cp_rank: int = 0,
+        interleaved: bool = False,
     ) -> torch.Tensor:
         """
         Apply rotary positional embedding to input tensor T in `thd` format with CP support.
         """
-        if is_te_min_version("1.12.0", check_equality=True):
+        if interleaved:
+            assert is_te_min_version("2.3.0"), "Only TE >= 2.3.0 supports interleaved fused RoPE."
+
+        if is_te_min_version("2.3.0", check_equality=True):
+            return apply_rotary_pos_emb(
+                t,
+                freqs,
+                tensor_format="thd",
+                fused=True,
+                cu_seqlens=cu_seqlens,
+                cp_size=cp_size,
+                cp_rank=cp_rank,
+                interleaved=interleaved,
+            )
+        elif is_te_min_version("1.12.0", check_equality=True):
             return apply_rotary_pos_emb(
                 t,
                 freqs,

megatron/core/models/common/embeddings/rope_utils.py

@@ -288,7 +288,12 @@ def apply_rotary_pos_emb(
         else:
             assert fused_apply_rotary_pos_emb_thd is not None, "apply_rope_fusion is not available."
             return fused_apply_rotary_pos_emb_thd(
-                t, cu_seqlens, freqs, cp_size=cp_group.size(), cp_rank=cp_group.rank()
+                t,
+                cu_seqlens,
+                freqs,
+                cp_size=cp_group.size(),
+                cp_rank=cp_group.rank(),
+                interleaved=config.rotary_interleaved,
             )
     # use unfused implementation
     if cu_seqlens is None: