[gpt-oss] Add gpt-oss bf16 support

vllm/transformers_utils/configs/telechat2.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://www.modelscope.cn/models/TeleAI/TeleChat2-3B/resolve/master/configuration_telechat2.py
+""" Telechat configuration compatible with LlamaConfig. """
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Telechat2Config(PretrainedConfig):
+
+    model_type = "telechat"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "n_layer",
+        "num_attention_heads": "n_head",
+        "intermediate_size": "ffn_hidden_size",
+        "rms_norm_eps": "layer_norm_epsilon"
+    }
+
+    def __init__(
+        self,
+        vocab_size=160256,
+        hidden_size=4096,
+        n_layer=30,
+        n_head=32,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        apply_residual_connection_post_layernorm=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        ffn_hidden_size=12288,
+        training_seqlen=8192,
+        logn=True,
+        embed_layernorm=False,
+        hidden_act="silu",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.apply_residual_connection_post_layernorm = (
+            apply_residual_connection_post_layernorm)
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.logn = logn
+        self.training_seqlen = training_seqlen
+        self.embed_layernorm = embed_layernorm
+        self.num_key_value_heads = kwargs.pop("num_key_value_heads", None)
+        self.ffn_hidden_size = ffn_hidden_size
+        self.hidden_act = hidden_act
+        super().__init__(bos_token_id=bos_token_id,
+                         eos_token_id=eos_token_id,
+                         **kwargs)