use new rotary two in chatglm4 (#11312)

* use new rotary two in chatglm4

* rempve

python/llm/src/ipex_llm/transformers/models/chatglm4.py

@@ -47,10 +47,6 @@ def chatglm4_model_forward(
 ) -> Union[Tuple, BaseModelOutputWithPast]:
     from ipex_llm.transformers.kv import DynamicFp8Cache
     use_cache = use_cache if use_cache is not None else self.config.use_cache
-    # if use_cache and use_quantize_kv_cache(
-    #         self.encoder.layers[0].self_attention.query_key_value, input_ids):
-    #     if not isinstance(past_key_values, DynamicFp8Cache):
-    #         past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
     return chatglm4_model_forward_internal(
         self=self,
         input_ids=input_ids,
@@ -108,25 +104,17 @@ def chatglm4_model_forward_internal(
                                         dtype=torch.int64, device=inputs_embeds.device)
         position_ids = position_ids.repeat(batch_size, 1)
 
-    use_fuse_rope = input_ids.device.type == "xpu"
-    use_fuse_rope = use_fuse_rope and not self.training
-
-    # Rotary positional embeddings
-    rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
-    if position_ids is not None:
-        rotary_pos_emb = rotary_pos_emb[position_ids]
-    else:
-        rotary_pos_emb = rotary_pos_emb[None, :seq_length]
-    if use_fuse_rope:
-        # Repeat cos sin here, call only once for each token.
-        # Chatglm2's rotary embedding is similar to gptj's, is rotate_every_two.
-        # If put this to attension forward, it will generate too many times.
-        cos, sin = rotary_pos_emb.split(rotary_pos_emb.shape[-1] // 2, dim=-1)
-        cos = cos.squeeze(-1)
-        sin = sin.squeeze(-1)
-        cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
-        sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
-        rotary_pos_emb = (cos, sin)
+    if getattr(self.rotary_pos_emb, "cached_dtype", None) != inputs_embeds.dtype:
+        rot_dim = self.rotary_pos_emb.dim
+        base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
+        # We should generate float inv_freq to avoid overflow, as base is too large.
+        inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
+                                                dtype=torch.float,
+                                                device=inputs_embeds.device) / rot_dim))
+        self.rotary_pos_emb.register_buffer("inv_freq",
+                                            inv_freq.to(inputs_embeds.dtype),
+                                            persistent=False)
+        self.rotary_pos_emb.cached = True
 
     # `full_attention_mask` is not None only when
     #  `past_key_values` is not None and `seq_length` > 1
@@ -148,7 +136,7 @@ def chatglm4_model_forward_internal(
 
     hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
         inputs_embeds, causal_mask,
-        rotary_pos_emb=rotary_pos_emb,
+        rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids),
         kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
     )
     # ipex-llm changes end
@@ -172,26 +160,6 @@ def chatglm4_model_forward_internal(
     )
 
 
-def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
-    # x: [b, np, sq, hn]
-    b, np, sq, hn = x.size(0), x.size(1), x.size(2), x.size(3)
-    rot_dim = rope_cache.shape[-2] * 2
-    x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
-    # truncate to support variable sizes
-    rope_cache = rope_cache[:, :sq]
-    xshaped = x.reshape(b, np, sq, rot_dim // 2, 2)
-    rope_cache = rope_cache.view(-1, 1, sq, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
-            xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
-        ],
-        -1,
-    )
-    x_out2 = x_out2.flatten(3)
-    return torch.cat((x_out2, x_pass), dim=-1)
-
-
 def chatglm4_attention_forward(
     self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
 ):
@@ -209,34 +177,33 @@ def chatglm4_attention_forward(
     qkv = self.query_key_value(hidden_states)
     # [bs, q_len, np * 3 * hn] -> [bsz, n_head, seq_len, head_dim]
     qkv = qkv.view(bsz, q_len, n_head + 2 * n_kv_head, head_dim)
+    qkv = qkv.transpose(1, 2)
 
     query_states, key_states, value_states = qkv.split([n_head,
                                                         n_kv_head,
-                                                        n_kv_head], dim=2)
+                                                        n_kv_head], dim=1)
 
-    kv_seq_len = key_states.shape[1]
+    kv_seq_len = key_states.shape[2]
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[2]
 
-    if isinstance(rotary_pos_emb, tuple) and len(rotary_pos_emb) == 2:
-        # use_fuse_rope, see chatglm4_model_forward
-        cos, sin = rotary_pos_emb
-        rot_dim = cos.shape[-1]
-        query_layer_cur = query_states[..., :rot_dim]
-        key_layer_cur = key_states[..., :rot_dim]
-        # ipex_llm's apply_rotary_embedding can change the origin storage,
-        # so query_layer will get the result directly.
-        torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
-        torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-    elif rotary_pos_emb is not None:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        query_states = apply_rotary_pos_emb(query_states, rotary_pos_emb)
-        key_states = apply_rotary_pos_emb(key_states, rotary_pos_emb)
+    # IPEX-LLM OPT: fuse rope
+    inv_freq, position_ids = rotary_pos_emb
+    rot_dim = inv_freq.size(-1) * 2
+    if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training):
+        import xe_addons
+        xe_addons.rotary_two_inplaced(inv_freq, position_ids,
+                                      query_states[..., :rot_dim], key_states[..., :rot_dim])
+    else:
+        idx_theta = torch.outer(position_ids[0].float(),
+                                inv_freq.float()).to(hidden_states.dtype)
+        idx_theta = idx_theta.unsqueeze(0).unsqueeze(0)
+        cos = torch.cos(idx_theta).repeat_interleave(2, -1)
+        sin = torch.sin(idx_theta).repeat_interleave(2, -1)
+        q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim],
+                                            cos, sin, position_ids, "chatglm")
+        query_states[..., :rot_dim] = q_rot[...]
+        key_states[..., :rot_dim] = k_rot[...]
 
     # IPEX-LLM OPT: kv cache and quantize kv
     use_quantize_kv = use_quantize_kv_cache(self.query_key_value, hidden_states)