[Model] Fused rope implementation for DeepSeek-v2 (#3105)

This PR fuses the rope operation with the matrix transposes which
we were doing before the rope operation. Have tested the
model (after compiling) post the changes.

python/mlc_llm/model/deepseek_v2/deepseek_v2_model.py

@@ -137,38 +137,44 @@ def forward(
         k: Tensor,
         positions: Tensor,
     ):
-        def _rope(x: te.Tensor, positions: te.Tensor):
+        def _rope_fused(x: te.Tensor, positions: te.Tensor):
+            _, _, _, d_dim = x.shape
+            d_dim_half = d_dim // 2
             dtype = x.dtype
 
             def compute(b: tir.Var, s: tir.Var, h: tir.Var, d: tir.Var):
+                d1 = d // d_dim_half
+                d2 = d % d_dim_half
+
                 cos_freq, sin_freq, var_map = self.rope_fn(
                     positions[s], d, self.rotary_dim, self.theta, dtype
                 )
-                cos = cos_freq * x[b, s, h, d]
-                sin = sin_freq * tir.if_then_else(
+                cos = x[b, s, h, d2 * 2 + d1] * cos_freq
+
+                partner_d = tir.if_then_else(
                     d < self.rotary_dim // 2,
-                    -x[b, s, h, d + self.rotary_dim // 2],
-                    x[b, s, h, d - self.rotary_dim // 2],
+                    d + self.rotary_dim // 2,
+                    d - self.rotary_dim // 2,
+                )
+
+                partner_d1 = partner_d // d_dim_half
+                partner_d2 = partner_d % d_dim_half
+                sin = (
+                    x[b, s, h, partner_d2 * 2 + partner_d1]
+                    * sin_freq
+                    * tir.if_then_else(
+                        d < self.rotary_dim // 2, tir.const(-1, dtype), tir.const(1, dtype)
+                    )
                 )
                 expr = cos + sin
-                for var, value in var_map.items():
-                    expr = tir.Let(var, value, expr)
+                for var, val in var_map.items():
+                    expr = tir.Let(var, val, expr)
                 return expr
 
             return te.compute(x.shape, compute, name="yarn_rope")
 
-        b, s, h, d = q.shape
-        q = op.reshape(
-            op.permute_dims(op.reshape(q, (b, s, h, d // 2, 2)), [0, 1, 2, 4, 3]), (b, s, h, d)
-        )
-
-        b, s, h, d = k.shape
-        k = op.reshape(
-            op.permute_dims(op.reshape(k, (b, s, h, d // 2, 2)), [0, 1, 2, 4, 3]), (b, s, h, d)
-        )
-
-        q_embed = op.tensor_expr_op(_rope, "rope", [q, positions])
-        k_embed = op.tensor_expr_op(_rope, "rope", [k, positions])
+        q_embed = op.tensor_expr_op(_rope_fused, "rope", [q, positions])
+        k_embed = op.tensor_expr_op(_rope_fused, "rope", [k, positions])
         return q_embed, k_embed