Upload folder using huggingface_hub

.ipynb_checkpoints/config-checkpoint.json

@@ -4,28 +4,7 @@
     "LlavaQwenForCausalLM"
   ],
   "attention_dropout": 0.0,
-  "beacon_accum": true,
-  "beacon_attend_prev": true,
-  "beacon_attn": "full-coverage",
-  "beacon_embed_init": "eos",
-  "beacon_parallel_window": 1,
-  "beacon_param": [
-    "q",
-    "k",
-    "v"
-  ],
-  "beacon_pos": "interleave",
-  "beacon_ratio": [
-    2,
-    4,
-    8
-  ],
-  "beacon_ratio_mix": "step-random",
-  "beacon_sink_size": 0,
-  "beacon_stride": 1440,
-  "beacon_window": 1440,
   "bos_token_id": 151643,
-  "enable_beacon": true,
   "eos_token_id": 151645,
   "freeze_mm_mlp_adapter": false,
   "freeze_mm_vision_resampler": false,

.ipynb_checkpoints/configuration_videoxlpro_llavaqwen-checkpoint.py

@@ -0,0 +1,157 @@
+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,
+        rope_scaling=None,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        beacon_window=1024,
+        beacon_stride=1024,
+        beacon_attn="full-coverage",
+        beacon_ratio=[2,4,8,16,32],
+        beacon_ratio_mix="step-random",
+        beacon_param=[],
+        beacon_embed_init="eos",
+        beacon_sink_size=0,
+        beacon_attend_prev=True,
+        beacon_pos="interleave",
+        beacon_parallel_window=1,
+        **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
+        self.rope_scaling = rope_scaling
+
+        # 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
+
+        self.beacon_window = beacon_window
+        self.beacon_stride = beacon_stride
+        self.beacon_attn = beacon_attn
+        self.beacon_ratio = beacon_ratio
+        self.beacon_ratio_mix = beacon_ratio_mix
+        self.beacon_param = beacon_param
+        self.beacon_embed_init = beacon_embed_init
+        self.beacon_sink_size = beacon_sink_size
+        self.beacon_attend_prev = beacon_attend_prev
+        self.beacon_pos = beacon_pos
+        self.beacon_parallel_window = beacon_parallel_window
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class LlavaQwenConfig(Qwen2Config):
+    model_type = "videoxlpro_llavaqwen" 

.ipynb_checkpoints/modeling_videoxlpro_llavaqwen-checkpoint.py

@@ -0,0 +1,156 @@
+#    Copyright 2024 Hao Zhang
+#
+#    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 List, Optional, Tuple, Union, Dict
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+import transformers
+from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.generation.utils import GenerateOutput
+
+# from ...constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from videoxlpro.videoxlpro.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
+from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM
+
+# from .qwen.modeling_qwen import QWenLMHeadModel, QWenModel
+# from .qwen.configuration_qwen import QWenConfig
+
+
+class LlavaQwenConfig(Qwen2Config):
+    model_type = "llava_qwen"
+
+
+class LlavaQwenModel(LlavaMetaModel, Qwen2Model):
+    config_class = LlavaQwenConfig
+
+    def __init__(self, config: Qwen2Config):
+        super(LlavaQwenModel, self).__init__(config)
+
+
+class LlavaQwenForCausalLM(Qwen2ForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaQwenConfig
+
+    def __init__(self, config):
+        # super(Qwen2ForCausalLM, self).__init__(config)
+        Qwen2ForCausalLM.__init__(self, config)
+        config.model_type = "llava_qwen"
+        config.rope_scaling = None
+
+        self.model = LlavaQwenModel(config)
+        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,
+        image_sizes: Optional[List[List[int]]] = None,
+        return_dict: Optional[bool] = None,
+        modalities: Optional[List[str]] = ["image"],
+        dpo_forward: Optional[bool] = False,
+        cache_position=None,
+        time_embedding=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, modalities, image_sizes,time_embedding)
+        
+        # print("input_ids",input_ids)
+        # print("input_embeds",inputs_embeds.shape)
+        # print("labels",labels)
+        # print("mask",attention_mask)
+
+        if dpo_forward:
+            outputs = self.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            hidden_states = outputs[0]
+            logits = self.lm_head(hidden_states)
+            return logits, labels
+
+        else:
+            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,
+            )
+
+    @torch.no_grad()
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        images: Optional[torch.Tensor] = None,
+        image_sizes: Optional[torch.Tensor] = None,
+        modalities: Optional[List[str]] = ["image"],
+        time_embedding=None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        position_ids = kwargs.pop("position_ids", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+        if "inputs_embeds" in kwargs:
+            raise NotImplementedError("`inputs_embeds` is not supported")
+
+        if images is not None:
+            (inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, modalities, image_sizes=image_sizes,time_embedding=time_embedding)
+        
+        else:
+            inputs_embeds = self.get_model().embed_tokens(inputs)
+
+        return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        images = kwargs.pop("images", None)
+        image_sizes = kwargs.pop("image_sizes", None)
+        inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
+        if images is not None:
+            inputs["images"] = images
+        if image_sizes is not None:
+            inputs["image_sizes"] = image_sizes
+        return inputs
+
+AutoConfig.register("llava_qwen", LlavaQwenConfig)
+AutoModelForCausalLM.register(LlavaQwenConfig, LlavaQwenForCausalLM)

config.json

@@ -3,6 +3,10 @@
   "architectures": [
     "LlavaQwenForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_videoxlpro_llavaqwen.LlavaQwenConfig",
+    "AutoModelForCausalLM": "modeling_videoxlpro_llavaqwen.LlavaQwenForCausalLM"
+  },
   "attention_dropout": 0.0,
   "bos_token_id": 151643,
   "eos_token_id": 151645,
@@ -833,7 +837,7 @@
   "mm_vision_select_layer": -1,
   "mm_vision_tower": "/share/LXRlxr0_0/code/videoxlturbo2.0/videoxl/google/siglip-so400m-patch14-384",
   "mm_vision_tower_lr": null,
-  "model_type": "qwen2",
+  "model_type": "videoxlpro_llavaqwen",
   "num_attention_heads": 16,
   "num_hidden_layers": 36,
   "num_key_value_heads": 2,