[Model] Support Tele-FLM Model (#15023)

Signed-off-by: Naitong Yu <ntyu@baai.ac.cn>
Signed-off-by: jiangxin <horizon94@outlook.com>
Co-authored-by: Jason Fang <jasonfang3900@gmail.com>
Co-authored-by: jiangxin <horizon94@outlook.com>

docs/source/models/supported_models.md

@@ -472,6 +472,11 @@ See [this page](#generative-models) for more information on how to use generativ
   * `Tele-AI/TeleChat2-3B`, `Tele-AI/TeleChat2-7B`, `Tele-AI/TeleChat2-35B`, etc.
   * ✅︎
   * ✅︎
+- * `TeleFLMForCausalLM`
+  * TeleFLM
+  * `CofeAI/FLM-2-52B-Instruct-2407`, `CofeAI/Tele-FLM`, etc.
+  * ✅︎
+  * ✅︎
 - * `XverseForCausalLM`
   * XVERSE
   * `xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.

examples/template_teleflm.jinja

@@ -0,0 +1,12 @@
+{%- for message in messages %}
+    {%- if message['role'] == 'user' %}
+        {{- '<_user>' + message['content']|trim }}
+    {%- elif message['role'] == 'system' %}
+        {{- '<_system>' + message['content']|trim }}
+    {%- elif message['role'] == 'assistant' %}
+        {{- '<_bot>' + message['content'] }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<_bot>' }}
+{%- endif %}

tests/models/registry.py

@@ -192,6 +192,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "SolarForCausalLM": _HfExamplesInfo("upstage/solar-pro-preview-instruct"),
     "TeleChat2ForCausalLM": _HfExamplesInfo("Tele-AI/TeleChat2-3B",
                                             trust_remote_code=True),
+    "TeleFLMForCausalLM": _HfExamplesInfo("CofeAI/FLM-2-52B-Instruct-2407",
+                                            trust_remote_code=True),
     "XverseForCausalLM": _HfExamplesInfo("xverse/XVERSE-7B-Chat",
                                          is_available_online=False,
                                          trust_remote_code=True),

vllm/model_executor/models/registry.py

@@ -104,6 +104,7 @@ _TEXT_GENERATION_MODELS = {
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
     "SolarForCausalLM": ("solar", "SolarForCausalLM"),
     "TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
+    "TeleFLMForCausalLM": ("teleflm", "TeleFLMForCausalLM"),
     "XverseForCausalLM": ("llama", "LlamaForCausalLM"),
     "Zamba2ForCausalLM": ("zamba2", "Zamba2ForCausalLM"),
     # [Encoder-decoder]

vllm/model_executor/models/teleflm.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+from typing import Type
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.models.llama import (LlamaDecoderLayer,
+                                              LlamaForCausalLM, LlamaModel)
+
+
+class TeleFLMModel(LlamaModel):
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: Type[LlamaDecoderLayer] = LlamaDecoderLayer,
+    ):
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         layer_type=layer_type)
+        """
+        This implementation is based on the µScaling paper presented at  
+        the ICLR 2025 Workshop:  
+        NanoLM: An Affordable LLM Study Benchmark \
+        via Accurate Loss Prediction across Scales
+        by Yiqun Yao et al.  
+        Available at: https://openreview.net/forum?id=IwaPYg1SCA  
+        arXiv preprint: https://arxiv.org/abs/2304.06875
+        """
+        self.use_mup = self.config.use_mup
+        if self.use_mup:
+            self.input_mult = self.config.input_mult
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        embedding = self.embed_tokens(input_ids)
+        if self.use_mup:
+            embedding = embedding * self.input_mult
+        return embedding
+
+
+class TeleFLMForCausalLM(LlamaForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        # mup
+        self.use_mup = self.config.use_mup
+        if self.use_mup:
+            self.mup_scale_factor = self.config.mup_scale_factor
+            self.output_mult = self.config.output_mult / self.mup_scale_factor
+            logit_scale = self.output_mult
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    self.config.vocab_size,
+                                                    logit_scale)