Update configuration_telechat3.py

configuration_telechat3.py

@@ -1,106 +1,106 @@
-# coding=utf-8
-# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" Telechat configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class Telechat3Config(PretrainedConfig):
-    model_type = "telechat3"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-
-    def __init__(
-            self,
-            attention_bias=False,
-            attention_dropout=0.0,
-            bos_token_id=1,
-            eos_token_id=2,
-            head_dim=128,
-            hidden_act="silu",
-            hidden_size=6144,
-            initializer_range=0.0048,
-            intermediate_size=24576,
-            max_position_embeddings=2048,
-            mlp_bias=False,
-            model_type="telechat3",
-            num_attention_heads=48,
-            num_hidden_layers=64,
-            num_key_value_heads=None,
-            original_max_position_embeddings=8192,
-            pad_token_id=None,
-            pretraining_tp=1,
-            rms_norm_eps=1e-5,
-            rope_scaling=None,
-            rope_theta=1000000.0,
-            tie_word_embeddings=False,
-            use_cache=True,
-            vocab_size=131072,
-            **kwargs,
-    ):
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.hidden_size = hidden_size
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.mlp_bias = mlp_bias
-        self.max_position_embeddings = max_position_embeddings
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # 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.initializer_range = initializer_range
-
-        self.pretraining_tp = pretraining_tp
-        self.rms_norm_eps = rms_norm_eps
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.use_cache = use_cache
-        self.vocab_size = vocab_size
-
-        if head_dim is not None and head_dim != self.hidden_size // self.num_attention_heads:
-            raise ValueError("head_dim != hidden_size//num_attention_head.Please check the config.")
-        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
-
-        # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, copy it 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__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
+# coding=utf-8
+# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Telechat configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class TeleChat3Config(PretrainedConfig):
+    model_type = "telechat3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+            self,
+            attention_bias=False,
+            attention_dropout=0.0,
+            bos_token_id=1,
+            eos_token_id=2,
+            head_dim=128,
+            hidden_act="silu",
+            hidden_size=6144,
+            initializer_range=0.0048,
+            intermediate_size=24576,
+            max_position_embeddings=2048,
+            mlp_bias=False,
+            model_type="telechat3",
+            num_attention_heads=48,
+            num_hidden_layers=64,
+            num_key_value_heads=None,
+            original_max_position_embeddings=8192,
+            pad_token_id=None,
+            pretraining_tp=1,
+            rms_norm_eps=1e-5,
+            rope_scaling=None,
+            rope_theta=1000000.0,
+            tie_word_embeddings=False,
+            use_cache=True,
+            vocab_size=131072,
+            **kwargs,
+    ):
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.mlp_bias = mlp_bias
+        self.max_position_embeddings = max_position_embeddings
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # 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.initializer_range = initializer_range
+
+        self.pretraining_tp = pretraining_tp
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.use_cache = use_cache
+        self.vocab_size = vocab_size
+
+        if head_dim is not None and head_dim != self.hidden_size // self.num_attention_heads:
+            raise ValueError("head_dim != hidden_size//num_attention_head.Please check the config.")
+        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, copy it 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__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )