add dolly configuration

LLM_code/configuration_dolly.py

@@ -0,0 +1,87 @@
+from transformers import PretrainedConfig
+import logging
+
+logger = logging.getLogger(__name__)
+
+class DollyConfig(PretrainedConfig):
+    model_type = "dolly"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # 添加张量并行配置
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",  # self-attention 的查询投影使用列并行
+        "layers.*.self_attn.k_proj": "colwise",  # self-attention 的键投影使用列并行
+        "layers.*.self_attn.v_proj": "colwise",  # self-attention 的值投影使用列并行
+        "layers.*.self_attn.o_proj": "rowwise",  # self-attention 的输出投影使用行并行
+        "layers.*.mlp.gate_proj": "colwise",    # MLP 的门投影使用列并行
+        "layers.*.mlp.up_proj": "colwise",      # MLP 的上升投影使用列并行
+        "layers.*.mlp.down_proj": "rowwise",    # MLP 的下降投影使用行并行
+    }
+
+    # 添加模型并行配置
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),  # 输入 ID 使用 embed_tokens 层生成嵌入
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),  # layers 层的输入输出
+        "norm": (["hidden_states"], ["hidden_states"]),  # 标准化层的输入输出
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        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.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window  # we check `use_sliding_window` in the modeling code
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["DollyConfig"]