fix qwen-14b model (#1173)

vllm/model_executor/models/qwen.py

@@ -141,17 +141,17 @@ class QWenBlock(nn.Module):
 
     def __init__(self, config: QWenConfig):
         super().__init__()
-        self.ln_1 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
         rope_theta = getattr(config, "rope_theta", 10000)
-        self.attn = QWenAttention(config.n_embd,
+        self.attn = QWenAttention(config.hidden_size,
                                   config.num_attention_heads,
                                   config.max_position_embeddings,
                                   rope_theta=rope_theta)
 
-        self.ln_2 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-        self.mlp = QWenMLP(config.n_embd, config.ffn_hidden_size // 2)
+        self.mlp = QWenMLP(config.hidden_size, config.intermediate_size // 2)
 
     def forward(
         self,
@@ -190,11 +190,11 @@ class QWenModel(nn.Module):
 
         vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.wte = VocabParallelEmbedding(vocab_size,
-                                          config.n_embd,
+                                          config.hidden_size,
                                           perform_initialization=False)
         self.h = nn.ModuleList(
             [QWenBlock(config) for _ in range(config.num_hidden_layers)])
-        self.ln_f = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
     def forward(
         self,
@@ -230,7 +230,7 @@ class QWenLMHeadModel(nn.Module):
         self.transformer = QWenModel(config)
         vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.lm_head = ColumnParallelLinear(
-            config.n_embd,
+            config.hidden_size,
             vocab_size,
             bias=False,
             gather_output=False,

vllm/transformers_utils/configs/qwen.py

@@ -7,65 +7,54 @@ from transformers import PretrainedConfig
 class QWenConfig(PretrainedConfig):
     model_type = "qwen"
     keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "max_position_embeddings": "n_positions",
-        "num_hidden_layers": "n_layer",
-    }
 
     def __init__(
         self,
-        vocab_size=151851,
-        n_embd=4096,
-        n_layer=32,
-        n_head=32,
-        n_inner=None,
-        embd_pdrop=0.0,
-        attn_pdrop=0.0,
-        layer_norm_epsilon=1e-5,
+        vocab_size=151936,
+        hidden_size=4096,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        emb_dropout_prob=0.0,
+        attn_dropout_prob=0.0,
+        layer_norm_epsilon=1e-6,
         initializer_range=0.02,
+        max_position_embeddings=8192,
         scale_attn_weights=True,
         use_cache=True,
-        eos_token_id=151643,
-        apply_residual_connection_post_layernorm=False,
-        bf16=True,
+        bf16=False,
+        fp16=False,
+        fp32=False,
         kv_channels=128,
         rotary_pct=1.0,
         rotary_emb_base=10000,
-        use_dynamic_ntk=False,
-        use_logn_attn=False,
-        use_flash_attn=True,
-        ffn_hidden_size=22016,
+        use_dynamic_ntk=True,
+        use_logn_attn=True,
+        use_flash_attn="auto",
+        intermediate_size=22016,
         no_bias=True,
         tie_word_embeddings=False,
         **kwargs,
     ):
-        self.eos_token_id = eos_token_id
-        super().__init__(eos_token_id=eos_token_id,
-                         tie_word_embeddings=tie_word_embeddings,
-                         **kwargs)
-
         self.vocab_size = vocab_size
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.n_inner = n_inner
-        self.embd_pdrop = embd_pdrop
-        self.attn_pdrop = attn_pdrop
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.emb_dropout_prob = emb_dropout_prob
+        self.attn_dropout_prob = attn_dropout_prob
         self.layer_norm_epsilon = layer_norm_epsilon
         self.initializer_range = initializer_range
         self.scale_attn_weights = scale_attn_weights
         self.use_cache = use_cache
-        self.apply_residual_connection_post_layernorm = (
-            apply_residual_connection_post_layernorm)
+        self.max_position_embeddings = max_position_embeddings
         self.bf16 = bf16
+        self.fp16 = fp16
+        self.fp32 = fp32
         self.kv_channels = kv_channels
         self.rotary_pct = rotary_pct
         self.rotary_emb_base = rotary_emb_base
         self.use_dynamic_ntk = use_dynamic_ntk
         self.use_logn_attn = use_logn_attn
         self.use_flash_attn = use_flash_attn
-        self.ffn_hidden_size = ffn_hidden_size
         self.no_bias = no_bias
-        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)