[NPU] Support qwen models with cos_sin_input=True (#12788)

python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py

@@ -98,6 +98,8 @@ class LowBitQwenMultiDecoderlayer(LLMBaseNNFactory):
         n_splits_linear: int = 1,
         n_splits_down_proj: int = 1,
         group_size: int = 0,
+        cos_len: int = 1,
+        keep_position_ids=True,
         asym: bool = False,
     ):
         super().__init__(max_seq_len=max_seq_len,
@@ -114,18 +116,13 @@ class LowBitQwenMultiDecoderlayer(LLMBaseNNFactory):
         self.dtype = dtype
         self.cached_cos = cached_cos
         self.cached_sin = cached_sin
+        self.cos_len = cos_len
         self.batch_size, self.seq_len, self.hidden_size = hidden_shape
         self.mode = mode
         self.rms_norm_eps = rms_norm_eps
         self.transpose_value = transpose_value
         self.num_layers = num_layers
 
-        cos = self.constant(self.cached_cos)
-        self.cos = self.unsqueeze(cos, axis=0)
-
-        sin = self.constant(self.cached_sin)
-        self.sin = self.unsqueeze(sin, axis=0)
-
         if mode == "decode":
             self.kv_seq_len = self.max_seq_len + 1
         else:
@@ -148,7 +145,21 @@ class LowBitQwenMultiDecoderlayer(LLMBaseNNFactory):
             attention_mask = self.create_input_op(
                 (self.batch_size, 1, self.seq_len, self.seq_len), dtype=np.float16)
 
+        if self.cached_cos is None:
+            if mode == "prefill" and keep_position_ids:
                 position_ids = self.create_input_op((self.batch_size, self.seq_len), dtype=np.int64)
+            cos = self.create_input_op((self.batch_size, self.cos_len, self.head_dim),
+                                       dtype=np.float32)
+            self.cos = self.convert_to_fp16(cos)
+            sin = self.create_input_op((self.batch_size, self.cos_len, self.head_dim),
+                                       dtype=np.float32)
+            self.sin = self.convert_to_fp16(sin)
+        else:
+            position_ids = self.create_input_op((self.batch_size, self.seq_len), dtype=np.int64)
+            cos = self.constant(self.cached_cos)
+            self.cos = self.unsqueeze(cos, axis=0)
+            sin = self.constant(self.cached_sin)
+            self.sin = self.unsqueeze(sin, axis=0)
 
         if input_layernorm_weights is None:
             input_layernorm_weights = []
@@ -211,11 +222,12 @@ class LowBitQwenMultiDecoderlayer(LLMBaseNNFactory):
         hidden_states = input
 
         curr_key_values = []
+        cos_condition = cached_cos is not None or (mode == "prefill" and keep_position_ids)
         for i in range(num_layers):
             hidden_states, new_key_states, new_value_states = self.build_decoder(
                 hidden_states=hidden_states,
                 attention_mask=attention_mask,
-                position_ids=position_ids,
+                position_ids=position_ids if cos_condition else None,
                 input_layernorm_weight=input_layernorm_weights[i],
                 post_attention_layernorm_weight=post_attn_layernorm_weights[i],
                 q_bias=q_biases[i],

python/llm/src/ipex_llm/transformers/npu_pipeline_model/common.py

@@ -173,6 +173,105 @@ class LLMEmbedding(NNFactory):
         self.compile()
 
 
+class Llama32Embedding(NNFactory):
+    def __init__(
+        self,
+        vocab_size,
+        embedding_dim,
+        embedding_weight,
+        padding_idx,
+        inv_freq,
+        attention_scaling,
+        dtype,  # fp16
+        device: str = "NPU",
+    ):
+        super().__init__(False, device)
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.attention_scaling = attention_scaling
+        self.dtype = dtype
+
+        # define input
+        weight = self.constant(embedding_weight)
+        input = self.parameter((1, 1), dtype=np.int32)
+        position_ids = self.parameter((1, 1), dtype=np.int64)
+        inv_freq = self.constant(inv_freq)
+
+        # embed_tokens module
+        if padding_idx == -1:
+            padding_idx += vocab_size
+
+        axis_node = self.constant(np.array([0], dtype=np.int64))
+        if padding_idx is not None:
+            masked_embeddings = np.ones(weight.shape, dtype=np.float16)
+            masked_embeddings[padding_idx, :] = 0.0  # mask
+
+            node_mask = self.constant(masked_embeddings)
+            node_masked_w = self.eltwise_mul(weight, node_mask)
+            res = self.gather(node_masked_w, input, axis_node, 0)
+        else:
+            res = self.gather(weight, input, axis_node, 0)
+
+        # rotary_emb module
+        inv_freq = self.reshape(inv_freq, (1, inv_freq.shape[0], 1))
+        position_ids = self.reshape(position_ids, (1, 1, 1))
+        freqs = self.eltwise_mul(self.convert_to_fp32(inv_freq),
+                                 self.convert_to_fp32(position_ids))
+        freqs = self.transpose(freqs, [0, 2, 1])
+        emb = self.concat(freqs, freqs, axis=2)
+        cos = self.cos(emb)
+        sin = self.sin(emb)
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        # define outputs
+        res = self.convert_to_fp16(res)
+        cos = self.convert_to_fp32(cos)
+        sin = self.convert_to_fp32(sin)
+
+        print("start compiling")
+        self.compile()
+
+
+class Llama32PostEmbedding(NNFactory):
+    def __init__(
+        self,
+        inv_freq,
+        attention_scaling,
+        input_len: int = 1,
+        device: str = "NPU",
+    ):
+        super().__init__(False, device)
+        self.attention_scaling = attention_scaling
+
+        # define input
+        position_ids = self.parameter((1, input_len), dtype=np.int64)
+        inv_freq = self.constant(inv_freq)
+
+        # rotary_emb module
+        inv_freq = self.reshape(inv_freq, (1, inv_freq.shape[0], 1))
+        position_ids = self.reshape(position_ids, (1, 1, input_len))
+        freqs = self.eltwise_mul(self.convert_to_fp32(inv_freq),
+                                 self.convert_to_fp32(position_ids))
+        freqs = self.transpose(freqs, [0, 2, 1])
+        emb = self.concat(freqs, freqs, axis=2)
+        cos = self.cos(emb)
+        sin = self.sin(emb)
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+        if input_len > 1:
+            cos = self.unsqueeze(cos, [1])
+            sin = self.unsqueeze(sin, [1])
+
+        # define outputs
+        cos = self.convert_to_fp32(cos)
+        sin = self.convert_to_fp32(sin)
+
+        print("start compiling")
+        self.compile()
+
+
 def obtain_weight_from_single_layer(attn_layer, mlp_layer):
     weights = []
     if hasattr(attn_layer, "q_proj_dq_list"):
@@ -216,3 +315,65 @@ def obtain_qkv_bias_from_single_layer(attn_layer):
         k_bias = attn_layer.k_proj.bias.to(torch.float16)
         v_bias = attn_layer.v_proj.bias.to(torch.float16)
     return q_bias, k_bias, v_bias
+
+
+def obtain_embedding_from_model(model, convert_model, temp_dir, weight_dir,
+                                max_prompt_len, keep_ir, compile_blob):
+    if hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
+        # llama-2-7B & llama-3-8B
+        embedding_layer = model.model.embed_tokens
+        new_embedding = LLMEmbedding(
+            vocab_size=model.config.vocab_size,
+            embedding_dim=model.config.hidden_size,
+            embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
+            padding_idx=model.config.pad_token_id,
+            dtype=np.float16,
+        )
+        if convert_model:
+            bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
+            embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
+            first_blob_path = None
+        else:
+            first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
+                                                                 temp_dir, keep_ir=keep_ir,
+                                                                 compile_blob=compile_blob)
+            os.remove(os.path.join(temp_dir, "embedding.bin"))
+    else:
+        # llama-3.2-3B & llama-3.2-1B
+        # for transformers >= 4.45.0
+        embedding_layer = model.model.embed_tokens
+        new_embedding = Llama32Embedding(
+            vocab_size=model.config.vocab_size,
+            embedding_dim=model.config.hidden_size,
+            embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
+            padding_idx=model.config.pad_token_id,
+            inv_freq=model.model.rotary_emb.inv_freq.to(torch.float16),
+            attention_scaling=model.model.rotary_emb.attention_scaling,
+            dtype=np.float16,
+        )
+        if convert_model:
+            bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
+            embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
+            first_blob_path = None
+            # save embedding post module
+            inv_freq = model.model.rotary_emb.inv_freq.to(torch.float16)
+            attention_scaling = model.model.rotary_emb.attention_scaling
+            embedding_post = Llama32PostEmbedding(inv_freq=inv_freq,
+                                                  attention_scaling=attention_scaling,
+                                                  input_len=1)
+            update_names_of_IR_and_export_blob(embedding_post, "embedding_post",
+                                               temp_dir, keep_ir=keep_ir, compile_blob=compile_blob)
+            embedding_post_prefill = Llama32PostEmbedding(inv_freq=inv_freq,
+                                                          attention_scaling=attention_scaling,
+                                                          input_len=max_prompt_len)
+            update_names_of_IR_and_export_blob(embedding_post_prefill,
+                                               "embedding_post_prefill",
+                                               temp_dir, keep_ir=keep_ir, compile_blob=compile_blob)
+            os.remove(os.path.join(temp_dir, "embedding_post.bin"))
+            os.remove(os.path.join(temp_dir, "embedding_post_prefill.bin"))
+        else:
+            first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
+                                                                 temp_dir, keep_ir=keep_ir,
+                                                                 compile_blob=compile_blob)
+            os.remove(os.path.join(temp_dir, "embedding.bin"))
+    return first_blob_path

python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py

@@ -31,6 +31,7 @@ import tempfile
 import numpy as np
 from ipex_llm.transformers.npu_models.lm_head import SlicedLMHead
 from multiprocessing import Pool
+import transformers
 
 
 def generate(
@@ -456,6 +457,8 @@ def convert_llm_for_deploy(model: torch.nn.Module,
         custom_object_save(model, save_directory, config=model.config)
 
     if model.config.model_type == "qwen2":
+        cos_sin_input = not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached")
+        embedding_post = not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached")
         if group_size == 0:
             if model.config.hidden_size == 1536:
                 # Qwen2-1.5B-Instruct
@@ -476,6 +479,8 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                        "use_prefill_sdp": False,
                        "weight_num": 7,
                        "weight_idx": 8,
+                       "embedding_post": embedding_post,
+                       "cos_sin_input": cos_sin_input,
                        "n_splits_linear": n_splits_linear,
                        "n_splits_down_proj": n_splits_down_proj,
                        "lm_head_low_bit": lm_head_low_bit}
@@ -493,8 +498,8 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                            group_size, layernorm_const, "prefill",
                            keep_ir=keep_ir, compile_blob=compile_blob)
         # save blob of lmhead and bin of embedding
-        convert_lm_head_and_embedding(model, save_directory, weight_dir,
+        convert_lm_head_and_embedding(model, save_directory, weight_dir, convert_model=True,
-                                      convert_model=True, group_size=group_size,
+                                      group_size=group_size, max_prompt_len=max_prompt_len,
                                       keep_ir=keep_ir, compile_blob=compile_blob)
     elif model.config.model_type == "llama":
         embedding_post = False

python/llm/src/ipex_llm/transformers/npu_pipeline_model/llama.py

@@ -18,108 +18,8 @@
 import torch
 import numpy as np
 import os
-from .common import update_names_of_IR_and_export_blob, LLMEmbedding, LowBitLLMLMHead, \
+from .common import update_names_of_IR_and_export_blob, LowBitLLMLMHead, \
-    obtain_weight_from_single_layer
+    obtain_weight_from_single_layer, obtain_embedding_from_model
-from intel_npu_acceleration_library.backend.factory import NNFactory
-
-
-class Llama32Embedding(NNFactory):
-    def __init__(
-        self,
-        vocab_size,
-        embedding_dim,
-        embedding_weight,
-        padding_idx,
-        inv_freq,
-        attention_scaling,
-        dtype,  # fp16
-        device: str = "NPU",
-    ):
-        super().__init__(False, device)
-        self.vocab_size = vocab_size
-        self.embedding_dim = embedding_dim
-        self.padding_idx = padding_idx
-        self.attention_scaling = attention_scaling
-        self.dtype = dtype
-
-        # define input
-        weight = self.constant(embedding_weight)
-        input = self.parameter((1, 1), dtype=np.int32)
-        position_ids = self.parameter((1, 1), dtype=np.int64)
-        inv_freq = self.constant(inv_freq)
-
-        # embed_tokens module
-        if padding_idx == -1:
-            padding_idx += vocab_size
-
-        axis_node = self.constant(np.array([0], dtype=np.int64))
-        if padding_idx is not None:
-            masked_embeddings = np.ones(weight.shape, dtype=np.float16)
-            masked_embeddings[padding_idx, :] = 0.0  # mask
-
-            node_mask = self.constant(masked_embeddings)
-            node_masked_w = self.eltwise_mul(weight, node_mask)
-            res = self.gather(node_masked_w, input, axis_node, 0)
-        else:
-            res = self.gather(weight, input, axis_node, 0)
-
-        # rotary_emb module
-        inv_freq = self.reshape(inv_freq, (1, inv_freq.shape[0], 1))
-        position_ids = self.reshape(position_ids, (1, 1, 1))
-        freqs = self.eltwise_mul(self.convert_to_fp32(inv_freq),
-                                 self.convert_to_fp32(position_ids))
-        freqs = self.transpose(freqs, [0, 2, 1])
-        emb = self.concat(freqs, freqs, axis=2)
-        cos = self.cos(emb)
-        sin = self.sin(emb)
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
-
-        # define outputs
-        res = self.convert_to_fp16(res)
-        cos = self.convert_to_fp32(cos)
-        sin = self.convert_to_fp32(sin)
-
-        print("start compiling")
-        self.compile()
-
-
-class Llama32PostEmbedding(NNFactory):
-    def __init__(
-        self,
-        inv_freq,
-        attention_scaling,
-        input_len: int = 1,
-        device: str = "NPU",
-    ):
-        super().__init__(False, device)
-        self.attention_scaling = attention_scaling
-
-        # define input
-        position_ids = self.parameter((1, input_len), dtype=np.int64)
-        inv_freq = self.constant(inv_freq)
-
-        # rotary_emb module
-        inv_freq = self.reshape(inv_freq, (1, inv_freq.shape[0], 1))
-        position_ids = self.reshape(position_ids, (1, 1, input_len))
-        freqs = self.eltwise_mul(self.convert_to_fp32(inv_freq),
-                                 self.convert_to_fp32(position_ids))
-        freqs = self.transpose(freqs, [0, 2, 1])
-        emb = self.concat(freqs, freqs, axis=2)
-        cos = self.cos(emb)
-        sin = self.sin(emb)
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
-        if input_len > 1:
-            cos = self.unsqueeze(cos, [1])
-            sin = self.unsqueeze(sin, [1])
-
-        # define outputs
-        cos = self.convert_to_fp32(cos)
-        sin = self.convert_to_fp32(sin)
-
-        print("start compiling")
-        self.compile()
 
 
 def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
@@ -197,62 +97,10 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
         bin_file = os.path.join(weight_dir, f"model_lm_head_input_{1+idx}.bin")
         weight.tofile(bin_file)
 
-    if hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
+    first_blob_path = obtain_embedding_from_model(model, convert_model,
-        # llama-2-7B & llama-3-8B
+                                                  temp_dir, weight_dir,
-        embedding_layer = model.model.embed_tokens
+                                                  max_prompt_len,
-        new_embedding = LLMEmbedding(
+                                                  keep_ir, compile_blob)
-            vocab_size=model.config.vocab_size,
-            embedding_dim=model.config.hidden_size,
-            embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
-            padding_idx=model.config.pad_token_id,
-            dtype=np.float16,
-        )
-        if convert_model:
-            bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
-            embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
-            first_blob_path = None
-        else:
-            first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
-                                                                 temp_dir, keep_ir=keep_ir,
-                                                                 compile_blob=compile_blob)
-            os.remove(os.path.join(temp_dir, "embedding.bin"))
-    else:
-        # llama-3.2-3B & llama-3.2-1B
-        embedding_layer = model.model.embed_tokens
-        new_embedding = Llama32Embedding(
-            vocab_size=model.config.vocab_size,
-            embedding_dim=model.config.hidden_size,
-            embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
-            padding_idx=model.config.pad_token_id,
-            inv_freq=model.model.rotary_emb.inv_freq.to(torch.float16),
-            attention_scaling=model.model.rotary_emb.attention_scaling,
-            dtype=np.float16,
-        )
-        if convert_model:
-            bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
-            embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
-            first_blob_path = None
-            # save embedding post module
-            inv_freq = model.model.rotary_emb.inv_freq.to(torch.float16)
-            attention_scaling = model.model.rotary_emb.attention_scaling
-            embedding_post = Llama32PostEmbedding(inv_freq=inv_freq,
-                                                  attention_scaling=attention_scaling,
-                                                  input_len=1)
-            update_names_of_IR_and_export_blob(embedding_post, "embedding_post",
-                                               temp_dir, keep_ir=keep_ir, compile_blob=compile_blob)
-            embedding_post_prefill = Llama32PostEmbedding(inv_freq=inv_freq,
-                                                          attention_scaling=attention_scaling,
-                                                          input_len=max_prompt_len)
-            update_names_of_IR_and_export_blob(embedding_post_prefill,
-                                               "embedding_post_prefill",
-                                               temp_dir, keep_ir=keep_ir, compile_blob=compile_blob)
-            os.remove(os.path.join(temp_dir, "embedding_post.bin"))
-            os.remove(os.path.join(temp_dir, "embedding_post_prefill.bin"))
-        else:
-            first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
-                                                                 temp_dir, keep_ir=keep_ir,
-                                                                 compile_blob=compile_blob)
-            os.remove(os.path.join(temp_dir, "embedding.bin"))
 
     return first_blob_path, last_blob_path
 

python/llm/src/ipex_llm/transformers/npu_pipeline_model/qwen.py

@@ -18,13 +18,14 @@
 import torch
 import numpy as np
 import os
-from .common import update_names_of_IR_and_export_blob, LLMEmbedding, LowBitLLMLMHead, \
+from .common import update_names_of_IR_and_export_blob, LowBitLLMLMHead, \
-    obtain_weight_from_single_layer, obtain_qkv_bias_from_single_layer
+    obtain_weight_from_single_layer, obtain_qkv_bias_from_single_layer, \
+    obtain_embedding_from_model
 from ipex_llm.transformers.npu_models.lm_head import SlicedLMHead
 
 
 def convert_lm_head_and_embedding(model, temp_dir, weight_dir,
-                                  convert_model=False, group_size=0,
+                                  convert_model=False, group_size=0, max_prompt_len=1,
                                   keep_ir=False, compile_blob=True):
     num_heads = model.model.layers[0].self_attn.num_heads
     head_dim = model.model.layers[0].self_attn.head_dim
@@ -107,24 +108,10 @@ def convert_lm_head_and_embedding(model, temp_dir, weight_dir,
         bin_file = os.path.join(weight_dir, f"model_lm_head_input_{1+idx}.bin")
         weight.tofile(bin_file)
 
-    embedding_layer = model.model.embed_tokens
+    first_blob_path = obtain_embedding_from_model(model, convert_model,
-    new_embedding = LLMEmbedding(
+                                                  temp_dir, weight_dir,
-        vocab_size=model.config.vocab_size,
+                                                  max_prompt_len,
-        embedding_dim=model.config.hidden_size,
+                                                  keep_ir, compile_blob)
-        embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
-        padding_idx=model.config.pad_token_id,
-        dtype=np.float16,
-        input_length=1,
-    )
-    if convert_model:
-        bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
-        embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
-        first_blob_path = True
-    else:
-        first_blob_path = update_names_of_IR_and_export_blob(new_embedding, f"embedding",
-                                                             temp_dir, keep_ir=keep_ir,
-                                                             compile_blob=compile_blob)
-        os.remove(os.path.join(temp_dir, "embedding.bin"))
     return first_blob_path, last_blob_path
 
 
@@ -145,8 +132,13 @@ def convert_qwen_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
     mlp_layer = curr_layer.mlp
     weights = obtain_weight_from_single_layer(attn_layer, mlp_layer)
     q_bias, k_bias, v_bias = obtain_qkv_bias_from_single_layer(attn_layer)
+    if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
         cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
         cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
+    else:
+        # transformers >= 4.45.0
+        cached_cos = None
+        cached_sin = None
     layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
     layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)
 
@@ -158,10 +150,12 @@ def convert_qwen_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
     if mode == "decode":
         input_len = 1
         decoder_name = f"decoder_layer_{layer_idx}"
+        keep_position_ids = True
         npu_dpu_groups = None
     else:
         input_len = kv_len
         decoder_name = "decoder_layer_prefill"
+        keep_position_ids = False
         npu_dpu_groups = 6
 
     single_decoder = LowBitQwenMultiDecoderlayer(
@@ -185,6 +179,8 @@ def convert_qwen_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
         n_splits_linear=n_splits_linear,
         n_splits_down_proj=n_splits_down_proj,
         group_size=group_size,
+        cos_len=input_len,
+        keep_position_ids=keep_position_ids,
         asym=asym
     )
     rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
@@ -196,6 +192,7 @@ def convert_qwen_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
 
     # 0, 1, 2 are input_embed/attention_mask/position_id
     if mode == "decode":
+        if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
             if layernorm_const:
                 st_idx = 3
             else:
@@ -204,6 +201,16 @@ def convert_qwen_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
                 layer_norm_0.data.numpy().tofile(input_lm_bin_file)
                 layer_norm_1.data.numpy().tofile(post_lm_bin_file)
                 st_idx = 5
+        else:
+            # transformers >= 4.45.0
+            if layernorm_const:
+                st_idx = 4
+            else:
+                input_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_4.bin")
+                post_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_5.bin")
+                layer_norm_0.data.numpy().tofile(input_lm_bin_file)
+                layer_norm_1.data.numpy().tofile(post_lm_bin_file)
+                st_idx = 6
         q_bias_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx}.bin")
         k_bias_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+1}.bin")
         v_bias_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+2}.bin")
@@ -261,8 +268,13 @@ def convert_fused_qwen_layer(model, fused_layers, n_splits_linear, n_splits_down
             attn_layer = curr_layer.self_attn
             mlp_layer = curr_layer.mlp
             weights = obtain_weight_from_single_layer(attn_layer, mlp_layer)
+            if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
                 cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
                 cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
+            else:
+                # transformers >= 4.45.0
+                cached_cos = None
+                cached_sin = None
             layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
             layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)