support qwen1.5-1.8b

bunny/model/__init__.py

@@ -1,2 +1,3 @@
 from .language_model.bunny_phi import BunnyPhiForCausalLM, BunnyPhiConfig
 from .language_model.bunny_stablelm import BunnyStableLMForCausalLM, BunnyStableLMConfig
+from .language_model.bunny_qwen import BunnyQwenForCausalLM, BunnyQwenConfig

bunny/model/builder.py

@@ -11,7 +11,7 @@
 
 def load_pretrained_model(model_path, model_base, model_name, model_type, load_8bit=False, load_4bit=False,
                           device_map="auto", device="cuda", **kwargs):
-    if model_type not in {'phi-1.5', 'phi-2', 'stablelm-2'}:
+    if model_type not in {'phi-1.5', 'phi-2', 'stablelm-2', 'qwen1.5-1.8b'}:
         raise ValueError(f"Unknown Model Type {model_type}")
 
     kwargs = {"device_map": device_map, **kwargs}
@@ -48,6 +48,10 @@ def load_pretrained_model(model_path, model_base, model_name, model_type, load_8
             tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True, trust_remote_code=True)
             model = BunnyStableLMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
                                                              config=lora_cfg_pretrained, **kwargs)
+        elif model_type == 'qwen1.5-1.8b':
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
+            model = BunnyQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained,
+                                                         **kwargs)
 
         token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
         if model.lm_head.weight.shape[0] != token_num:
@@ -97,6 +101,10 @@ def load_from_hf(repo_id, filename, subfolder=None):
             tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True, trust_remote_code=True)
             model = BunnyStableLMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
                                                              config=cfg_pretrained, **kwargs)
+        elif model_type == 'qwen1.5-1.8b':
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
+            model = BunnyQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained,
+                                                         **kwargs)
 
         mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
         mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
@@ -108,6 +116,9 @@ def load_from_hf(repo_id, filename, subfolder=None):
         elif model_type == 'stablelm-2':
             tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, trust_remote_code=True)
             model = BunnyStableLMForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+        elif model_type == 'qwen1.5-1.8b':
+            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+            model = BunnyQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
 
     model.resize_token_embeddings(len(tokenizer))
 

bunny/model/language_model/bunny_qwen.py

@@ -0,0 +1,100 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from transformers import AutoConfig, AutoModelForCausalLM
+
+from .qwen2 import Qwen2Model, Qwen2Config, Qwen2ForCausalLM
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
+
+
+class BunnyQwenConfig(Qwen2Config):
+    model_type = "bunny-qwen"
+
+
+class BunnyQwenModel(BunnyMetaModel, Qwen2Model):
+    config_class = BunnyQwenConfig
+
+    def __init__(self, config: Qwen2Config):
+        super(BunnyQwenModel, self).__init__(config)
+
+
+class BunnyQwenForCausalLM(Qwen2ForCausalLM, BunnyMetaForCausalLM):
+    config_class = BunnyQwenConfig
+
+    def __init__(self, config):
+        super(Qwen2ForCausalLM, self).__init__(config)
+        self.model = BunnyQwenModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            images: Optional[torch.FloatTensor] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        if inputs_embeds is None:
+            (
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                inputs_embeds,
+                labels
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                labels,
+                images
+            )
+
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
+                                      **kwargs):
+        images = kwargs.pop("images", None)
+
+        _inputs = super().prepare_inputs_for_generation(
+            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
+            **kwargs
+        )
+
+        if images is not None:
+            _inputs['images'] = images
+        return _inputs
+
+
+AutoConfig.register("bunny-qwen", BunnyQwenConfig)
+AutoModelForCausalLM.register(BunnyQwenConfig, BunnyQwenForCausalLM)

bunny/model/language_model/qwen2/__init__.py

@@ -0,0 +1,80 @@
+# Copyright 2024 The Qwen Team and The 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.
+from typing import TYPE_CHECKING
+
+from transformers.utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_qwen2": ["QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Qwen2Config"],
+    "tokenization_qwen2": ["Qwen2Tokenizer"],
+}
+
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_qwen2_fast"] = ["Qwen2TokenizerFast"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_qwen2"] = [
+        "Qwen2ForCausalLM",
+        "Qwen2Model",
+        "Qwen2PreTrainedModel",
+        "Qwen2ForSequenceClassification",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_qwen2 import QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP, Qwen2Config
+    from .tokenization_qwen2 import Qwen2Tokenizer
+
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_qwen2_fast import Qwen2TokenizerFast
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_qwen2 import (
+            Qwen2ForCausalLM,
+            Qwen2ForSequenceClassification,
+            Qwen2Model,
+            Qwen2PreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

bunny/model/language_model/qwen2/configuration_qwen2.py

@@ -0,0 +1,144 @@
+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and the 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.
+""" Qwen2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json",
+}
+
+
+class Qwen2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
+    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen2Model, Qwen2Config
+
+    >>> # Initializing a Qwen2 style configuration
+    >>> configuration = Qwen2Config()
+
+    >>> # Initializing a model from the Qwen2-7B style configuration
+    >>> model = Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    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,
+        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,
+        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
+        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.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.attention_dropout = attention_dropout
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )