Initial commit

vary/data/conversation_dataset_qwen.py

+
+import io
+import os
+import copy
+import json
+import logging
+import torch
+import random
+
+from typing import List, Optional, Tuple, Union, Dict, Sequence
+from PIL import Image, ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from vary.data.base_dataset import BaseDataset
+from vary.utils.constants import *
+from vary.utils import conversation as conversation_lib
+
+class ConversationDataset(BaseDataset):
+    """Conversation format dataset stage2 fine-tuning."""
+
+    def __init__(self, datasets, tokenizer, multimodal_cfg):
+        super(ConversationDataset, self).__init__(datasets, tokenizer, multimodal_cfg)
+        # v0 version format conversation
+        conversation_lib.default_conversation = conversation_lib.conv_templates["mpt"]
+        logging.warning("Formatting inputs into conversation type: mpt-fixed")
+        logging.warning("Loading data...")
+
+        list_data_dict = []
+        list_image_path = []
+
+
+        for name in datasets.split("+"):
+            # for name in vary_data_dict[name_all]:
+            dataset = CONVERSATION_DATA[name]
+
+            data_path = dataset['annotations']
+            data = json.load(open(data_path, "r"))
+
+            list_data_dict.extend(data)
+
+            image_path = dataset['images']
+
+            list_image_path.extend([image_path] * len(data))
+
+            logging.warning(f"Data from {data_path} provide {len(data)} conversations.")
+
+        assert len(list_data_dict) == len(list_image_path)
+        logging.warning(f"{len(list_data_dict)} conversations in total.")
+        a_new_list = list(zip(list_data_dict, list_image_path))
+        random.shuffle(a_new_list)
+        list_data_dict_new, list_image_path_new = zip(*a_new_list)
+        self.list_data_dict = list_data_dict_new
+        self.list_image_path = list_image_path_new
+
+        self.im_patch_token = 151859
+
+        self.im_start_token = 151857
+
+        self.im_end_token = 151858
+    
+    def multimodal_processor(self, sources):
+        for source in sources:
+            if self.multimodal_cfg['sep_image_conv_front']:
+                assert DEFAULT_IMAGE_TOKEN in source[0]['value']
+                source[0]['value'] = source[0]['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
+                source[0]['value'] = DEFAULT_IMAGE_TOKEN + conversation_lib.default_conversation.sep + conversation_lib.default_conversation.roles[0] + ": " + source[0]['value']
+            for sentence in source:
+                replace_token = DEFAULT_IMAGE_PATCH_TOKEN * self.multimodal_cfg['image_token_len']
+                # if self.multimodal_cfg['use_im_start_end']:
+                replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
+                sentence["value"] = str(sentence["value"]).replace(DEFAULT_IMAGE_TOKEN, replace_token)
+        return sources
+
+    def _tokenize_fn(self, strings):
+        """Tokenize a list of strings."""
+        tokenized_list = [
+            self.tokenizer(
+                text,
+                return_tensors="pt",
+                padding="longest",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+            ) for text in strings
+        ]
+        input_ids = labels = [
+            tokenized.input_ids[0] for tokenized in tokenized_list
+        ]
+        input_ids_lens = labels_lens = [
+            tokenized.input_ids.ne(self.tokenizer.pad_token_id).sum().item()
+            for tokenized in tokenized_list
+        ]
+        return dict(
+            input_ids=input_ids,
+            labels=labels,
+            input_ids_lens=input_ids_lens,
+            labels_lens=labels_lens,
+        )
+
+    def _mask_targets(self, target, tokenized_lens, speakers):
+        # cur_idx = 0
+        cur_idx = tokenized_lens[0]
+        tokenized_lens = tokenized_lens[1:]
+        target[:cur_idx] = IGNORE_INDEX
+        for tokenized_len, speaker in zip(tokenized_lens, speakers):
+            if speaker.lower() == "human":
+                target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
+            cur_idx += tokenized_len
+
+    def token_processor(self, sources):
+        conv = conversation_lib.default_conversation.copy()
+        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+        # Apply prompt templates
+        conversations = []
+        for i, source in enumerate(sources):
+            if roles[source[0]["from"]] != conv.roles[0]:
+                # Skip the first one if it is not from human
+                source = source[1:]
+
+            conv.messages = []
+            for j, sentence in enumerate(source):
+                role = roles[sentence["from"]]
+                assert role == conv.roles[j % 2], f"{i}"
+                conv.append_message(role, sentence["value"])
+            conversations.append(conv.get_prompt())
+
+        # Tokenize conversations
+
+
+        input_ids = self.tokenizer(
+            conversations,
+            return_tensors="pt",
+            padding="longest",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+        ).input_ids
+
+        # input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
+        targets = input_ids.clone()
+        assert conv.sep_style == conversation_lib.SeparatorStyle.MPT
+
+        # Mask targets
+        sep = conv.sep + conv.roles[1]
+        for conversation, target in zip(conversations, targets):
+            total_len = int(target.ne(self.tokenizer.pad_token_id).sum())
+
+            rounds = conversation.split(conv.sep)
+            re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
+            for conv_idx in range(3, len(rounds), 2):
+                re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2]))    # user + gpt
+            cur_len = 0
+            target[:cur_len] = IGNORE_INDEX
+            for i, rou in enumerate(re_rounds):
+                if rou == "":
+                    break
+
+                parts = rou.split(sep)
+                if len(parts) != 2:
+                    break
+                parts[0] += sep
+                round_len = len(self.tokenizer(rou).input_ids) + len(self.tokenizer(conv.sep).input_ids)
+
+                instruction_len = len(self.tokenizer(parts[0]).input_ids)
+                target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+                cur_len += round_len
+            target[cur_len:] = IGNORE_INDEX
+
+            if cur_len < self.tokenizer.model_max_length:
+                if cur_len != total_len:
+                    target[:] = IGNORE_INDEX
+                    print(
+                        f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                        f" (ignored)"
+                    )
+
+        return dict(
+            input_ids=input_ids,
+            labels=targets,
+        )
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        # data = self.list_data_dict[i]
+        data = copy.deepcopy(self.list_data_dict[i])
+
+        if isinstance(data, dict):
+            if 'image' in data:
+                image_path = self.list_image_path[i]
+                image_file = data['image']
+
+                try:
+                    image = Image.open(image_path + image_file).convert('RGB')
+                except:
+                    print(f'cannot identify image file {image_path + image_file}.')
+                    return self.__getitem__(0)
+
+                try:
+                    image, image_1 = self.image_processor(image)
+                except:
+                    print(f'image {image_file} are broken or grayscale! we thus select 0-th sample instead!')
+                    return self.__getitem__(0)
+
+            conversations = self.multimodal_processor([data["conversations"]])
+
+        else:
+            conversations = [data]
+
+        # align with fastchat & llava here, put the conversation into a list for tokenization
+        data_dict = self.token_processor(conversations)
+        data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
+        
+        if isinstance(data, dict) and 'image' in data:
+            data_dict['image'] = [image]
+            data_dict['image_high'] = [image_1]
+        else:
+            crop_size = self.multimodal_cfg['image_processor'].crop_size
+            data_dict['image'] = [torch.zeros(3, crop_size['height'], crop_size['width'])]
+            data_dict['image_high'] = [torch.zeros(3, 1024, 1024)]
+        return data_dict
+

vary/demo/run_opt.py

+import argparse
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+from vary.utils.conversation import conv_templates, SeparatorStyle
+from vary.utils.utils import disable_torch_init
+from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
+from vary.model import *
+from vary.utils.utils import KeywordsStoppingCriteria
+
+from PIL import Image
+
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+
+from transformers import TextStreamer
+
+
+from vary.model.plug.blip_process import BlipImageEvalProcessor
+
+from vary.model.vision_encoder.sam import build_sam_vit_b
+from vary.model.plug.transforms import train_transform, test_transform
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = '<imgpad>'
+DEFAULT_IM_START_TOKEN = '<img>'
+DEFAULT_IM_END_TOKEN = '</img>'
+
+
+
+def load_image(image_file):
+    if image_file.startswith('http') or image_file.startswith('https'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_name = os.path.expanduser(args.model_name)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+
+    model = varyOPTForCausalLM.from_pretrained(model_name)
+
+
+
+    model.to(device='cuda',  dtype=torch.bfloat16)
+
+    image_processor_high =  test_transform
+
+
+    image_token_len = 256
+
+
+    prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + DEFAULT_IM_END_TOKEN
+    inputs = tokenizer([prompt])
+
+
+    image = load_image(args.image_file)
+    image_1 = image.copy()
+
+    image_tensor_1 = image_processor_high(image_1).to(torch.bfloat16)
+
+
+    input_ids = torch.as_tensor(inputs.input_ids).cuda()
+
+    stop_str = '</s>'
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+
+
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    with torch.autocast("cuda", dtype=torch.bfloat16):
+        output_ids = model.generate(
+            input_ids,
+            images=[(image_tensor_1.unsqueeze(0).half().cuda(), image_tensor_1.unsqueeze(0).cuda())],
+            do_sample=True,
+            num_beams = 1,
+            streamer=streamer,
+            max_new_tokens=4096,
+            stopping_criteria=[stopping_criteria]
+            )
+        
+
+
+
+        # input_token_len = input_ids.shape[1]
+        # outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+
+        # if outputs.endswith(stop_str):
+        #     outputs = outputs[:-len(stop_str)]
+        # outputs = outputs.strip()
+
+        # print(outputs)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
+    parser.add_argument("--image-file", type=str, required=True)
+    # parser.add_argument("--query", type=str, required=True)
+    parser.add_argument("--conv-mode", type=str, default=None)
+    args = parser.parse_args()
+
+    eval_model(args)

vary/demo/run_qwen_vary.py

+import argparse
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+from vary.utils.conversation import conv_templates, SeparatorStyle
+from vary.utils.utils import disable_torch_init
+from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
+from vary.model import *
+from vary.utils.utils import KeywordsStoppingCriteria
+
+from PIL import Image
+
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+from vary.model.plug.blip_process import BlipImageEvalProcessor
+from transformers import TextStreamer
+from vary.model.plug.transforms import train_transform, test_transform
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = '<imgpad>'
+DEFAULT_IM_START_TOKEN = '<img>'
+DEFAULT_IM_END_TOKEN = '</img>'
+
+
+def load_image(image_file):
+    if image_file.startswith('http') or image_file.startswith('https'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_name = os.path.expanduser(args.model_name)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+    model = varyQwenForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, device_map='cuda', trust_remote_code=True)
+
+
+    model.to(device='cuda',  dtype=torch.bfloat16)
+
+
+    image_processor = CLIPImageProcessor.from_pretrained("/data/hypertext/ucaswei/cache/vit-large-patch14/vit-large-patch14/", torch_dtype=torch.float16)
+
+    image_processor_high = BlipImageEvalProcessor(image_size=1024)
+
+    use_im_start_end = True
+
+    image_token_len = 256
+
+    qs = 'Provide the ocr results of this image.'
+
+    if use_im_start_end:
+        qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN*image_token_len + DEFAULT_IM_END_TOKEN  + '\n' + qs
+    else:
+        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+
+
+    
+
+    conv_mode = "mpt"
+    args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+
+    inputs = tokenizer([prompt])
+
+
+    image = load_image(args.image_file)
+    image_1 = image.copy()
+    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+
+    image_tensor_1 = image_processor_high(image_1)
+
+    input_ids = torch.as_tensor(inputs.input_ids).cuda()
+
+    # stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+
+    with torch.autocast("cuda", dtype=torch.bfloat16):
+        output_ids = model.generate(
+            input_ids,
+            images=[(image_tensor.unsqueeze(0).half().cuda(), image_tensor_1.unsqueeze(0).half().cuda())],
+            do_sample=True,
+            num_beams = 1,
+            # temperature=0.2,
+            streamer=streamer,
+            max_new_tokens=2048,
+            stopping_criteria=[stopping_criteria]
+            )
+        
+        # print(output_ids)
+
+        # outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+        
+        # # conv.messages[-1][-1] = outputs
+        # if outputs.endswith(stop_str):
+        #     outputs = outputs[:-len(stop_str)]
+        # outputs = outputs.strip()
+
+        # print(outputs)
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
+    parser.add_argument("--image-file", type=str, required=True)
+    parser.add_argument("--conv-mode", type=str, default=None)
+    args = parser.parse_args()
+
+    eval_model(args)

vary/model/__init__.py

+from .vary_opt import varyOPTModel, varyOPTForCausalLM
+# from .vary_qwen_vary import varyQwenModel, varyQwenForCausalLM, varyConfig
+from .vary_toy_qwen1_8 import varyQwenModel, varyQwenForCausalLM, varyConfig
+

vary/model/llm/opt/modeling_opt.py

+# coding=utf-8
+# Copyright 2022 The Fairseq Authors 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.
+""" PyTorch OPT model."""
+import random
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.models.opt.configuration_opt import OPTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
+_CONFIG_FOR_DOC = "OPTConfig"
+_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
+
+# Base model docstring
+_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
+
+# SequenceClassification docstring
+_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
+_SEQ_CLASS_EXPECTED_LOSS = 1.71
+_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
+
+# QuestionAnswering docstring
+_QA_EXPECTED_OUTPUT = "'a nice puppet'"
+_QA_EXPECTED_LOSS = 7.41
+_QA_TARGET_START_INDEX = 14
+_QA_TARGET_END_INDEX = 15
+
+OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "facebook/opt-125m",
+    "facebook/opt-350m",
+    "facebook/opt-1.3b",
+    "facebook/opt-2.7b",
+    "facebook/opt-6.7b",
+    "facebook/opt-13b",
+    "facebook/opt-30b",
+    # See all OPT models at https://huggingface.co/models?filter=opt
+]
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
+    mask_cond = torch.arange(mask.size(-1))
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+class OPTLearnedPositionalEmbedding(nn.Embedding):
+    """
+    This module learns positional embeddings up to a fixed maximum size.
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
+        # and adjust num_embeddings appropriately. Other models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(
+        self, attention_mask: torch.LongTensor, past_key_values_length: int = 0
+    ):
+        """`input_ids_shape` is expected to be [bsz x seqlen]."""
+        attention_mask = attention_mask.long()
+
+        # create positions depending on attention_mask
+        positions = (
+            torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask
+        ).long() - 1
+
+        # cut positions if `past_key_values_length` is > 0
+        positions = positions[:, past_key_values_length:]
+
+        return super().forward(positions + self.offset)
+
+
+class OPTAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+
+        if (self.head_dim * num_heads) != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+                f" and `num_heads`: {num_heads})."
+            )
+        self.scaling = self.head_dim**-0.5
+        self.is_decoder = is_decoder
+
+        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        layer_head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+
+        bsz, tgt_len, _ = hidden_states.size()
+
+        # get query proj
+        query_states = self.q_proj(hidden_states) * self.scaling
+        # get key, value proj
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_states = past_key_value[0]
+            value_states = past_key_value[1]
+        elif is_cross_attention:
+            # cross_attentions
+            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+        elif past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+        else:
+            # self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_states, value_states)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_states = value_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = (
+                attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+                + attention_mask
+            )
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
+        if attn_weights.dtype == torch.float16:
+            attn_weights = nn.functional.softmax(
+                attn_weights, dim=-1, dtype=torch.float32
+            ).to(torch.float16)
+        else:
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        if layer_head_mask is not None:
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
+                    f" {layer_head_mask.size()}"
+                )
+            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if output_attentions:
+            # this operation is a bit awkward, but it's required to
+            # make sure that attn_weights keeps its gradient.
+            # In order to do so, attn_weights have to be reshaped
+            # twice and have to be reused in the following
+            attn_weights_reshaped = attn_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            attn_weights = attn_weights_reshaped.view(
+                bsz * self.num_heads, tgt_len, src_len
+            )
+        else:
+            attn_weights_reshaped = None
+
+        attn_probs = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )
+
+        attn_output = torch.bmm(attn_probs, value_states)
+
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+
+        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
+        # partitioned aross GPUs when using tensor-parallelism.
+        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights_reshaped, past_key_value
+
+
+class OPTDecoderLayer(nn.Module):
+    def __init__(self, config: OPTConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = OPTAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.num_attention_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+        )
+        self.do_layer_norm_before = config.do_layer_norm_before
+        self.dropout = config.dropout
+        self.activation_fn = ACT2FN[config.activation_function]
+
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
+        self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        layer_head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
+                `(encoder_attention_heads,)`.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            past_key_value=past_key_value,
+            attention_mask=attention_mask,
+            layer_head_mask=layer_head_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = nn.functional.dropout(
+            hidden_states, p=self.dropout, training=self.training
+        )
+        hidden_states = residual + hidden_states
+
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Fully Connected
+        hidden_states_shape = hidden_states.shape
+        hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
+        residual = hidden_states
+
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = nn.functional.dropout(
+            hidden_states, p=self.dropout, training=self.training
+        )
+
+        hidden_states = (residual + hidden_states).view(hidden_states_shape)
+
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+OPT_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`OPTConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare OPT Model outputting raw hidden-states without any specific head on top.",
+    OPT_START_DOCSTRING,
+)
+class OPTPreTrainedModel(PreTrainedModel):
+
+    config_class = OPTConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["OPTDecoderLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.init_std
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (OPTDecoder)):
+            module.gradient_checkpointing = value
+
+
+OPT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class OPTDecoder(OPTPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
+
+    Args:
+        config: OPTConfig
+    """
+
+    def __init__(self, config: OPTConfig):
+        super().__init__(config)
+        self.dropout = config.dropout
+        self.layerdrop = config.layerdrop
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_position_embeddings
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.word_embed_proj_dim, self.padding_idx
+        )
+        self.embed_positions = OPTLearnedPositionalEmbedding(
+            config.max_position_embeddings, config.hidden_size
+        )
+
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_out = nn.Linear(
+                config.hidden_size, config.word_embed_proj_dim, bias=False
+            )
+        else:
+            self.project_out = None
+
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_in = nn.Linear(
+                config.word_embed_proj_dim, config.hidden_size, bias=False
+            )
+        else:
+            self.project_in = None
+
+        # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
+        # with checkpoints that have been fine-tuned before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(config.hidden_size)
+        else:
+            self.final_layer_norm = None
+
+        self.layers = nn.ModuleList(
+            [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                past_key_values_length=past_key_values_length,
+            ).to(inputs_embeds.device)
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        query_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if query_embeds is not None:
+            inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1)
+            input_shape = inputs_embeds.size()[:-1]
+
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device
+            )
+        pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
+
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, input_shape, inputs_embeds, past_key_values_length
+        )
+
+        if self.project_in is not None:
+            inputs_embeds = self.project_in(inputs_embeds)
+
+        hidden_states = inputs_embeds + pos_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        # check if head_mask has a correct number of layers specified if desired
+        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
+            if attn_mask is not None:
+                if attn_mask.size()[0] != (len(self.layers)):
+                    raise ValueError(
+                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+                        f" {head_mask.size()[0]}."
+                    )
+
+        for idx, decoder_layer in enumerate(self.layers):
+            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            dropout_probability = random.uniform(0, 1)
+            if self.training and (dropout_probability < self.layerdrop):
+                continue
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warning(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    head_mask[idx] if head_mask is not None else None,
+                    None,
+                )
+            else:
+
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        if self.project_out is not None:
+            hidden_states = self.project_out(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+@add_start_docstrings(
+    "The bare OPT Model outputting raw hidden-states without any specific head on top.",
+    OPT_START_DOCSTRING,
+)
+class OPTModel(OPTPreTrainedModel):
+    def __init__(self, config: OPTConfig):
+        super().__init__(config)
+        self.decoder = OPTDecoder(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.decoder.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.decoder.embed_tokens = value
+
+    def get_decoder(self):
+        return self.decoder
+
+    @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_EXPECTED_OUTPUT_SHAPE,
+    )
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        query_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
+        decoder_outputs = self.decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            query_embeds=query_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if not return_dict:
+            return decoder_outputs
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            hidden_states=decoder_outputs.hidden_states,
+            attentions=decoder_outputs.attentions,
+        )
+
+
+class OPTForCausalLM(OPTPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = OPTModel(config)
+
+        # the lm_head weight is automatically tied to the embed tokens weight
+        self.lm_head = nn.Linear(
+            config.word_embed_proj_dim, config.vocab_size, bias=False
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.decoder.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.decoder.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model.decoder = decoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        query_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,
+        return_dict: Optional[bool] = None,
+        reduction: Optional[str] = "mean",
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
+                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import GPT2Tokenizer, OPTForCausalLM
+
+        >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
+        >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            query_embeds=query_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        logits = self.lm_head(outputs[0]).contiguous()
+
+        loss = None
+        if labels is not None:
+            logits = logits[:, -labels.size(1) :, :]
+
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(reduction=reduction)
+            loss = loss_fct(
+                shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)
+            )
+            if reduction == "none":
+                loss = loss.view(shift_logits.size(0), -1).sum(1)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids=None,
+        query_embeds=None,
+        past=None,
+        attention_mask=None,
+        use_cache=None,
+        **kwargs,
+    ):
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            if input_ids is not None:
+                attention_mask = input_ids.new_ones(input_ids.shape)
+        if past:
+            input_ids = input_ids[:, -1:]
+            query_embeds = None
+        # first step, decoder_cached_states are empty
+        return {
+            "input_ids": input_ids,
+            "query_embeds": query_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past,
+            "use_cache": use_cache,
+        }
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx) for past_state in layer_past
+                ),
+            )
+        return reordered_past

vary/model/llm/qwen/configuration_qwen.py

+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from transformers import PretrainedConfig
+
+
+class QWenConfig(PretrainedConfig):
+    model_type = "qwen"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        emb_dropout_prob=0.0,
+        attn_dropout_prob=0.0,
+        layer_norm_epsilon=1e-6,
+        initializer_range=0.02,
+        max_position_embeddings=8192,
+        scale_attn_weights=True,
+        use_cache=True,
+        bf16=False,
+        fp16=False,
+        fp32=False,
+        kv_channels=128,
+        rotary_pct=1.0,
+        rotary_emb_base=10000,
+        use_dynamic_ntk=True,
+        use_logn_attn=True,
+        use_flash_attn="auto",
+        intermediate_size=22016,
+        no_bias=True,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        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.emb_dropout_prob = emb_dropout_prob
+        self.attn_dropout_prob = attn_dropout_prob
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+        self.max_position_embeddings = max_position_embeddings
+        self.bf16 = bf16
+        self.fp16 = fp16
+        self.fp32 = fp32
+        self.kv_channels = kv_channels
+        self.rotary_pct = rotary_pct
+        self.rotary_emb_base = rotary_emb_base
+        self.use_dynamic_ntk = use_dynamic_ntk
+        self.use_logn_attn = use_logn_attn
+        self.use_flash_attn = use_flash_attn
+        self.no_bias = no_bias
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )

vary/model/llm/qwen/modeling_qwen.py

+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import importlib
+import math
+from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.cuda.amp import autocast
+
+from torch.nn import CrossEntropyLoss
+from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
+from transformers.generation.logits_process import LogitsProcessorList
+
+if TYPE_CHECKING:
+    from transformers.generation.streamers import BaseStreamer
+from transformers.generation.utils import GenerateOutput
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+try:
+    from einops import rearrange
+except ImportError:
+    rearrange = None
+from torch import nn
+
+SUPPORT_CUDA = torch.cuda.is_available()
+SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
+SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
+
+from .configuration_qwen import QWenConfig
+from .qwen_generation_utils import (
+    HistoryType,
+    make_context,
+    decode_tokens,
+    get_stop_words_ids,
+    StopWordsLogitsProcessor,
+)
+# from .visual import VisionTransformer
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "qwen"
+_CONFIG_FOR_DOC = "QWenConfig"
+
+QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]
+
+_ERROR_BAD_CHAT_FORMAT = """\
+We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
+If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
+我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
+如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
+"""
+
+_SENTINEL = object()
+_ERROR_STREAM_IN_CHAT = """\
+Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
+向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
+"""
+
+apply_rotary_emb_func = None
+rms_norm = None
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class QWenAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        max_positions = config.max_position_embeddings
+        self.register_buffer(
+            "bias",
+            torch.tril(
+                torch.ones((max_positions, max_positions), dtype=torch.bool)
+            ).view(1, 1, max_positions, max_positions),
+            persistent=False,
+        )
+        self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
+        self.seq_length = config.seq_length
+
+        self.hidden_size = config.hidden_size
+        self.split_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+
+        self.scale_attn_weights = True
+
+        self.projection_size = config.kv_channels * config.num_attention_heads
+
+        assert self.projection_size % config.num_attention_heads == 0
+        self.hidden_size_per_attention_head = (
+            self.projection_size // config.num_attention_heads
+        )
+
+        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
+
+        self.c_proj = nn.Linear(
+            config.hidden_size, self.projection_size, bias=not config.no_bias
+        )
+
+        self.is_fp32 = not (config.bf16 or config.fp16)
+        self.bf16 = config.bf16
+
+        if config.rotary_pct == 1.0:
+            self.rotary_ndims = None
+        else:
+            assert config.rotary_pct < 1
+            self.rotary_ndims = int(
+                self.hidden_size_per_attention_head * config.rotary_pct
+            )
+        dim = (
+            self.rotary_ndims
+            if self.rotary_ndims is not None
+            else self.hidden_size_per_attention_head
+        )
+        self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
+
+        self.use_dynamic_ntk = config.use_dynamic_ntk
+        self.use_logn_attn = config.use_logn_attn
+
+        logn_list = [
+            math.log(i, self.seq_length) if i > self.seq_length else 1
+            for i in range(1, 32768)
+        ]
+        self.logn_tensor = torch.tensor(logn_list)[None, :, None, None]
+        self._ntk_cached = 1.0
+
+        self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
+
+    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
+        attn_weights = torch.matmul(query, key.transpose(-1, -2))
+
+        if self.scale_attn_weights:
+            attn_weights = attn_weights / torch.full(
+                [],
+                value.size(-1) ** 0.5,
+                dtype=attn_weights.dtype,
+                device=attn_weights.device,
+            )
+
+        query_length, key_length = query.size(-2), key.size(-2)
+        # causal_mask = self.bias[
+        #     :, :, key_length - query_length : key_length, :key_length
+        # ]
+        # mask_value = torch.finfo(attn_weights.dtype).min
+        # mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(
+        #     attn_weights.device
+        # )
+        # attn_weights = torch.where(
+        #     causal_mask, attn_weights.to(attn_weights.dtype), mask_value
+        # )
+        attn_weights = attn_weights + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        attn_weights = attn_weights.type(value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = torch.matmul(attn_weights, value)
+        attn_output = attn_output.transpose(1, 2)
+
+        return attn_output, attn_weights
+
+    def _upcast_and_reordered_attn(
+        self, query, key, value, attention_mask=None, head_mask=None
+    ):
+        bsz, num_heads, q_seq_len, dk = query.size()
+        _, _, k_seq_len, _ = key.size()
+
+        attn_weights = torch.empty(
+            bsz * num_heads,
+            q_seq_len,
+            k_seq_len,
+            dtype=torch.float32,
+            device=query.device,
+        )
+
+        scale_factor = 1.0
+        if self.scale_attn_weights:
+            scale_factor /= float(value.size(-1)) ** 0.5
+
+        with autocast(enabled=False):
+            q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
+                -1, dk, k_seq_len
+            )
+            attn_weights = torch.baddbmm(
+                attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
+            )
+            attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
+
+        query_length, key_length = query.size(-2), key.size(-2)
+        causal_mask = self.bias[
+            :, :, key_length - query_length : key_length, :key_length
+        ]
+        mask_value = torch.finfo(attn_weights.dtype).min
+        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(
+            attn_weights.device
+        )
+        attn_weights = torch.where(causal_mask, attn_weights, mask_value)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        if attn_weights.dtype != torch.float32:
+            raise RuntimeError(
+                "Error with upcasting, attn_weights does not have dtype torch.float32"
+            )
+        attn_weights = attn_weights.type(value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = torch.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def _split_heads(self, tensor, num_heads, attn_head_size):
+        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+        tensor = tensor.view(new_shape)
+        return tensor
+
+    def _merge_heads(self, tensor, num_heads, attn_head_size):
+        tensor = tensor.contiguous()
+        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+        return tensor.view(new_shape)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+
+        mixed_x_layer = self.c_attn(hidden_states)
+        query, key, value = mixed_x_layer.split(self.split_size, dim=2)
+
+        query = self._split_heads(query, self.num_heads, self.head_dim)
+        key = self._split_heads(key, self.num_heads, self.head_dim)
+        value = self._split_heads(value, self.num_heads, self.head_dim)
+
+        kv_seq_len = hidden_states.size()[1]
+        if layer_past:
+            # layer past[0] shape: bs * seq_len * head_num * dim
+            kv_seq_len += layer_past[0].shape[1]
+        if (
+            self.use_dynamic_ntk
+            and kv_seq_len == hidden_states.size()[1]
+            and not self.training
+        ):
+            context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
+            ntk_alpha = 2 ** math.ceil(context_value) - 1
+            ntk_alpha = max(ntk_alpha, 1)
+            self._ntk_cached = ntk_alpha
+        else:
+            ntk_alpha = self._ntk_cached
+        rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha).to(
+            hidden_states.device
+        )
+
+        if rotary_pos_emb is not None:
+            if isinstance(rotary_pos_emb, tuple):
+                rotary_pos_emb = rotary_pos_emb
+            else:
+                rotary_pos_emb = (rotary_pos_emb,) * 2
+
+        if rotary_pos_emb is not None:
+            q_pos_emb, k_pos_emb = rotary_pos_emb
+            # Slice the pos emb for current inference
+            cur_len = query.shape[1]
+            q_pos_emb = q_pos_emb[:, -cur_len:, :, :]
+            k_pos_emb = k_pos_emb[:, -cur_len:, :, :]
+            query = apply_rotary_pos_emb(query, q_pos_emb)
+            key = apply_rotary_pos_emb(key, k_pos_emb)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past[0], layer_past[1]
+            key = torch.cat((past_key, key), dim=1)
+            value = torch.cat((past_value, value), dim=1)
+
+        if use_cache:
+            present = (key, value)
+        else:
+            present = None
+
+        if self.use_logn_attn and not self.training:
+            if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
+                self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
+            seq_start = key.size(1) - query.size(1)
+            seq_end = key.size(1)
+            logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
+            query = query * logn_tensor.expand_as(query)
+
+        query = query.permute(0, 2, 1, 3)
+        key = key.permute(0, 2, 1, 3)
+        value = value.permute(0, 2, 1, 3)
+        attn_output, attn_weight = self._attn(
+            query, key, value, attention_mask, head_mask
+        )
+        context_layer = self._merge_heads(
+            attn_output, self.num_heads, self.head_dim
+        )
+
+        attn_output = self.c_proj(context_layer)
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weight,)
+
+        return outputs
+
+
+class QWenMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w1 = nn.Linear(
+            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
+        )
+        self.w2 = nn.Linear(
+            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
+        )
+        ff_dim_in = config.intermediate_size // 2
+        self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
+
+    def forward(self, hidden_states):
+        a1 = self.w1(hidden_states)
+        a2 = self.w2(hidden_states)
+        intermediate_parallel = a1 * F.silu(a2)
+        output = self.c_proj(intermediate_parallel)
+        return output
+
+
+class QWenBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.bf16 = config.bf16
+
+        self.ln_1 = RMSNorm(
+            hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+        self.attn = QWenAttention(config)
+        self.ln_2 = RMSNorm(
+            hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+
+        self.mlp = QWenMLP(config)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ):
+        layernorm_output = self.ln_1(hidden_states)
+
+        attn_outputs = self.attn(
+            layernorm_output,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0]
+
+        outputs = attn_outputs[1:]
+
+        residual = hidden_states
+        layernorm_input = attn_output + residual
+
+        layernorm_output = self.ln_2(layernorm_input)
+
+        residual = layernorm_input
+        mlp_output = self.mlp(layernorm_output)
+        hidden_states = residual + mlp_output
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs
+
+
+class QWenPreTrainedModel(PreTrainedModel):
+    config_class = QWenConfig
+    base_model_prefix = "transformer"
+    is_parallelizable = False
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["QWenBlock"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, RMSNorm):
+            module.weight.data.fill_(1.0)
+
+        for name, p in module.named_parameters():
+            if name == "c_proj.weight":
+                p.data.normal_(
+                    mean=0.0,
+                    std=(
+                        self.config.initializer_range
+                        / math.sqrt(2 * self.config.num_hidden_layers)
+                    ),
+                )
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, QWenModel):
+            module.gradient_checkpointing = value
+
+
+class QWenModel(QWenPreTrainedModel):
+    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vocab_size = config.vocab_size
+        self.num_hidden_layers = config.num_hidden_layers
+        self.embed_dim = config.hidden_size
+
+        self.gradient_checkpointing = False
+
+        self.wte = nn.Embedding(self.vocab_size, self.embed_dim)
+
+        self.drop = nn.Dropout(config.emb_dropout_prob)
+        self.h = nn.ModuleList(
+            [
+                QWenBlock(
+                    config,
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.ln_f = RMSNorm(
+            self.embed_dim,
+            eps=config.layer_norm_epsilon,
+        )
+
+        # self.visual = VisionTransformer(**config.visual)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+    
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        # if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
+        #     bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
+        #     eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
+        #     assert (bos_pos[0] == eos_pos[0]).all()
+        #     img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
+        #     images = []
+        #     for i, a, b in img_pos:
+        #         image = input_ids[i][a + 1 : b - 1].tolist()
+        #         image = image[ : image.index(self.config.visual['image_start_id'] + 2)]
+        #         images.append(bytes(image).decode('utf-8'))
+
+        #     images = self.visual.encode(images)
+        #     assert images.shape[0] == len(images)
+        # else:
+        #     images = None
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size = inputs_embeds.shape[0]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, input_shape[-1])
+        if position_ids is not None:
+            position_ids = position_ids.view(-1, input_shape[-1])
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * len(self.h))
+        else:
+            past_length = past_key_values[0][0].size(-2)
+
+        if position_ids is None:
+            position_ids = torch.arange(
+                past_length,
+                input_shape[-1] + past_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+        encoder_attention_mask = None
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+
+        if batch_size <= 0:
+            raise ValueError("batch_size has to be defined and > 0")
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, input_shape, inputs_embeds, past_length
+        )
+
+        hidden_states = inputs_embeds
+
+        hidden_states = self.drop(hidden_states)
+        # if images is not None:
+        #     for idx, (i, a, b) in enumerate(img_pos):
+        #         hidden_states[i][a + 1 : b] = images[idx]
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache, output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    None,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[1],)
+
+        hidden_states = self.ln_f(hidden_states)
+        hidden_states = hidden_states.view(output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, presents, all_hidden_states] if v is not None
+            )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class QWenLMHeadModel(QWenPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
+    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        assert (
+            config.bf16 + config.fp16 + config.fp32 <= 1
+        ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"
+
+        autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
+
+        if autoset_precision:
+            if SUPPORT_BF16:
+                logger.warn(
+                    "The model is automatically converting to bf16 for faster inference. "
+                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
+                )
+                config.bf16 = True
+            elif SUPPORT_FP16:
+                logger.warn(
+                    "The model is automatically converting to fp16 for faster inference. "
+                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
+                )
+                config.fp16 = True
+            else:
+                config.fp32 = True
+
+        if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
+            logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
+        if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
+            logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
+        if config.fp32:
+            if SUPPORT_BF16:
+                logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
+            elif SUPPORT_FP16:
+                logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
+
+        self.transformer = QWenModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        if config.bf16:
+            self.transformer.bfloat16()
+            self.lm_head.bfloat16()
+        if config.fp16:
+            self.transformer.half()
+            self.lm_head.half()
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
+    ):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+                "attention_mask": attention_mask,
+                "token_type_ids": token_type_ids,
+            }
+        )
+        return model_inputs
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
+
+        return tuple(
+            tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past
+            )
+            for layer_past in past_key_values
+        )
+
+    def chat(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        query: str,
+        history: Optional[HistoryType],
+        system: str = "You are a helpful assistant.",
+        append_history: bool = True,
+        stream: Optional[bool] = _SENTINEL,
+        stop_words_ids: Optional[List[List[int]]] = None,
+        **kwargs,
+    ) -> Tuple[str, HistoryType]:
+        assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
+        assert self.generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
+        if history is None:
+            history = []
+        if stop_words_ids is None:
+            stop_words_ids = []
+
+        max_window_size = kwargs.get('max_window_size', None)
+        if max_window_size is None:
+            max_window_size = self.generation_config.max_window_size
+        raw_text, context_tokens = make_context(
+            tokenizer,
+            query,
+            history=history,
+            system=system,
+            max_window_size=max_window_size,
+            chat_format=self.generation_config.chat_format,
+        )
+
+        stop_words_ids.extend(get_stop_words_ids(
+            self.generation_config.chat_format, tokenizer
+        ))
+        input_ids = torch.tensor([context_tokens]).to(self.device)
+        outputs = self.generate(
+                    input_ids,
+                    stop_words_ids = stop_words_ids,
+                    return_dict_in_generate = False,
+                    **kwargs,
+                )
+
+        response = decode_tokens(
+            outputs[0],
+            tokenizer,
+            raw_text_len=len(raw_text),
+            context_length=len(context_tokens),
+            chat_format=self.generation_config.chat_format,
+            verbose=False,
+            errors='replace'
+        )
+
+        if append_history:
+            history.append((query, response))
+
+        return response, history
+
+    def chat_stream(
+            self,
+            tokenizer: PreTrainedTokenizer,
+            query: str,
+            history: Optional[HistoryType],
+            system: str = "You are a helpful assistant.",
+            stop_words_ids: Optional[List[List[int]]] = None,
+            logits_processor: Optional[LogitsProcessorList] = None,
+            **kwargs,
+    ) -> Generator[str, Any, None]:
+        assert self.generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
+        if history is None:
+            history = []
+        if stop_words_ids is None:
+            stop_words_ids = []
+
+        max_window_size = kwargs.get('max_window_size', None)
+        if max_window_size is None:
+            max_window_size = self.generation_config.max_window_size
+        raw_text, context_tokens = make_context(
+            tokenizer,
+            query,
+            history=history,
+            system=system,
+            max_window_size=max_window_size,
+            chat_format=self.generation_config.chat_format,
+        )
+
+        stop_words_ids.extend(get_stop_words_ids(
+            self.generation_config.chat_format, tokenizer
+        ))
+        if stop_words_ids is not None:
+            stop_words_logits_processor = StopWordsLogitsProcessor(
+                stop_words_ids=stop_words_ids,
+                eos_token_id=self.generation_config.eos_token_id,
+            )
+            if logits_processor is None:
+                logits_processor = LogitsProcessorList([stop_words_logits_processor])
+            else:
+                logits_processor.append(stop_words_logits_processor)
+        input_ids = torch.tensor([context_tokens]).to(self.device)
+
+        from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
+        self.__class__.generate_stream = NewGenerationMixin.generate
+        self.__class__.sample_stream = NewGenerationMixin.sample_stream
+        stream_config = StreamGenerationConfig(**self.generation_config.to_dict(), do_stream=True)
+        def stream_generator():
+            outputs = []
+            for token in self.generate_stream(
+                    input_ids,
+                    return_dict_in_generate=False,
+                    generation_config=stream_config,
+                    logits_processor=logits_processor,
+                    seed=-1,
+                    **kwargs):
+                outputs.append(token.item())
+                yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore')
+
+        return stream_generator()
+
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        prefix_allowed_tokens_fn: Optional[
+            Callable[[int, torch.Tensor], List[int]]
+        ] = None,
+        synced_gpus: Optional[bool] = None,
+        assistant_model: Optional["PreTrainedModel"] = None,
+        streamer: Optional["BaseStreamer"] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        # Process stop_words_ids.
+        stop_words_ids = kwargs.pop("stop_words_ids", None)
+        if stop_words_ids is None and generation_config is not None:
+            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
+        if stop_words_ids is None:
+            stop_words_ids = getattr(self.generation_config, "stop_words_ids", None)
+
+        if stop_words_ids is not None:
+            stop_words_logits_processor = StopWordsLogitsProcessor(
+                stop_words_ids=stop_words_ids,
+                eos_token_id=self.generation_config.eos_token_id,
+            )
+            if logits_processor is None:
+                logits_processor = LogitsProcessorList([stop_words_logits_processor])
+            else:
+                logits_processor.append(stop_words_logits_processor)
+
+        return super().generate(
+            inputs,
+            generation_config=generation_config,
+            logits_processor=logits_processor,
+            stopping_criteria=stopping_criteria,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            synced_gpus=synced_gpus,
+            assistant_model=assistant_model,
+            streamer=streamer,
+            **kwargs,
+        )
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, base=10000):
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        if importlib.util.find_spec("einops") is None:
+            raise RuntimeError("einops is required for Rotary Embedding")
+
+        self._rotary_pos_emb_cache = None
+        self._seq_len_cached = 0
+        self._ntk_alpha_cached = 1.0
+
+    def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0):
+        seqlen = max_seq_len + offset
+        if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
+            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
+            self.inv_freq = 1.0 / (
+                base
+                ** (
+                    torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
+                    / self.dim
+                )
+            )
+            self._seq_len_cached = max(2 * seqlen, 16)
+            self._ntk_alpha_cached = ntk_alpha
+            seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
+            freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            from einops import rearrange
+
+            self._rotary_pos_emb_cache = rearrange(emb, "n d -> 1 n 1 d")
+
+    def forward(self, max_seq_len, offset=0, ntk_alpha=1.0):
+        self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha)
+        return self._rotary_pos_emb_cache[:, offset : offset + max_seq_len]
+
+
+def _rotate_half(x):
+    from einops import rearrange
+
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t, freqs):
+    if apply_rotary_emb_func is not None and t.is_cuda:
+        t_ = t.float()
+        freqs = freqs.squeeze(0).squeeze(1)
+        cos = freqs[:, : freqs.shape[-1] // 2].cos()
+        sin = freqs[:, : freqs.shape[-1] // 2].sin()
+        output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
+        return output
+    else:
+        rot_dim = freqs.shape[-1]
+        t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
+        t_ = t_.float()
+        t_pass_ = t_pass_.float()
+        t_ = (t_ * freqs.cos()) + (_rotate_half(t_) * freqs.sin())
+        return torch.cat((t_, t_pass_), dim=-1).type_as(t)
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        if rms_norm is not None and x.is_cuda:
+            return rms_norm(x, self.weight, self.eps)
+        else:
+            output = self._norm(x.float()).type_as(x)
+            return output * self.weight

vary/model/llm/qwen/qwen_generation_utils.py

+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Generation support."""
+
+from typing import Tuple, List, Union, Iterable
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from transformers import PreTrainedTokenizer
+from transformers import logging
+from transformers.generation import LogitsProcessor
+
+logger = logging.get_logger(__name__)
+
+# Types.
+HistoryType = List[Tuple[str, str]]
+TokensType = List[int]
+BatchTokensType = List[List[int]]
+
+
+def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
+    for tokens in batch:
+        context_length = len(tokens)
+        if context_length < seq_length:
+            tokens.extend([pad_id] * (seq_length - context_length))
+    return batch
+
+
+def get_ltor_masks_and_position_ids(
+    data,
+    eod_token,
+    reset_position_ids,
+    reset_attention_mask,
+    eod_mask_loss,
+):
+    """Build masks and position id for left to right model."""
+
+    # Extract batch size and sequence length.
+    micro_batch_size, seq_length = data.size()
+
+    # Attention mask (lower triangular).
+    if reset_attention_mask:
+        att_mask_batch = micro_batch_size
+    else:
+        att_mask_batch = 1
+    attention_mask = torch.tril(
+        torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
+    ).view(att_mask_batch, 1, seq_length, seq_length)
+
+    # Loss mask.
+    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
+    if eod_mask_loss:
+        loss_mask[data == eod_token] = 0.0
+
+    # Position ids.
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(data)
+    # We need to clone as the ids will be modifed based on batch index.
+    if reset_position_ids:
+        position_ids = position_ids.clone()
+
+    if reset_position_ids or reset_attention_mask:
+        # Loop through the batches:
+        for b in range(micro_batch_size):
+
+            # Find indecies where EOD token is.
+            eod_index = position_ids[b, data[b] == eod_token]
+            # Detach indecies from positions if going to modify positions.
+            if reset_position_ids:
+                eod_index = eod_index.clone()
+
+            # Loop through EOD indecies:
+            prev_index = 0
+            for j in range(eod_index.size()[0]):
+                i = eod_index[j]
+                # Mask attention loss.
+                if reset_attention_mask:
+                    attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
+                # Reset positions.
+                if reset_position_ids:
+                    position_ids[b, (i + 1) :] -= i + 1 - prev_index
+                    prev_index = i + 1
+
+    # Convert attention mask to binary:
+    attention_mask = attention_mask < 0.5
+
+    return attention_mask, loss_mask, position_ids
+
+
+def get_batch(context_tokens: torch.LongTensor, eod_id: int):
+    """Generate batch from context tokens."""
+    # Move to GPU.
+    tokens = context_tokens.contiguous().to(context_tokens.device)
+    # Get the attention mask and postition ids.
+    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
+        tokens,
+        eod_id,
+        reset_position_ids=False,
+        reset_attention_mask=False,
+        eod_mask_loss=False,
+    )
+    return tokens, attention_mask, position_ids
+
+
+def get_stop_words_ids(chat_format, tokenizer):
+    if chat_format == "raw":
+        stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
+    elif chat_format == "chatml":
+        stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+    return stop_words_ids
+
+
+def make_context(
+    tokenizer: PreTrainedTokenizer,
+    query: str,
+    history: List[Tuple[str, str]] = None,
+    system: str = "",
+    max_window_size: int = 6144,
+    chat_format: str = "chatml",
+):
+    if history is None:
+        history = []
+
+    if chat_format == "chatml":
+        im_start, im_end = "<|im_start|>", "<|im_end|>"
+        im_start_tokens = [tokenizer.im_start_id]
+        im_end_tokens = [tokenizer.im_end_id]
+        nl_tokens = tokenizer.encode("\n")
+
+        def _tokenize_str(role, content):
+            return f"{role}\n{content}", tokenizer.encode(
+                role, allowed_special=set(tokenizer.IMAGE_ST)
+            ) + nl_tokens + tokenizer.encode(content, allowed_special=set(tokenizer.IMAGE_ST))
+
+        system_text, system_tokens_part = _tokenize_str("system", system)
+        system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
+
+        raw_text = ""
+        context_tokens = []
+
+        for turn_query, turn_response in reversed(history):
+            query_text, query_tokens_part = _tokenize_str("user", turn_query)
+            query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
+            if turn_response is not None:
+                response_text, response_tokens_part = _tokenize_str(
+                    "assistant", turn_response
+                )
+                response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
+
+                next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
+                prev_chat = (
+                    f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
+                )
+            else:
+                next_context_tokens = nl_tokens + query_tokens + nl_tokens
+                prev_chat = f"\n{im_start}{query_text}{im_end}\n"
+
+            current_context_size = (
+                len(system_tokens) + len(next_context_tokens) + len(context_tokens)
+            )
+            if current_context_size < max_window_size:
+                context_tokens = next_context_tokens + context_tokens
+                raw_text = prev_chat + raw_text
+            else:
+                break
+
+        context_tokens = system_tokens + context_tokens
+        raw_text = f"{im_start}{system_text}{im_end}" + raw_text
+        context_tokens += (
+            nl_tokens
+            + im_start_tokens
+            + _tokenize_str("user", query)[1]
+            + im_end_tokens
+            + nl_tokens
+            + im_start_tokens
+            + tokenizer.encode("assistant")
+            + nl_tokens
+        )
+        raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
+
+    elif chat_format == "raw":
+        raw_text = query
+        context_tokens = tokenizer.encode(raw_text)
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+
+    return raw_text, context_tokens
+
+
+def _decode_default(
+    tokens: List[int],
+    *,
+    stop_words: List[str],
+    eod_words: List[str],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str='replace',
+):
+    trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
+    if verbose:
+        print("\nRaw Generate: ", trim_decode_tokens)
+
+    end_reason = f"Gen length {len(tokens)}"
+    for stop_word in stop_words:
+        trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
+    for eod_word in eod_words:
+        if eod_word in trim_decode_tokens:
+            end_reason = f"Gen {eod_word!r}"
+        trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
+    trim_decode_tokens = trim_decode_tokens.strip()
+    if verbose:
+        print("\nEnd Reason:", end_reason)
+        print("\nGenerate: ", trim_decode_tokens)
+
+    if return_end_reason:
+        return trim_decode_tokens, end_reason
+    else:
+        return trim_decode_tokens
+
+
+def _decode_chatml(
+    tokens: List[int],
+    *,
+    stop_words: List[str],
+    eod_token_ids: List[int],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    context_length: int,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str='replace'
+):
+    end_reason = f"Gen length {len(tokens)}"
+    eod_token_idx = context_length
+    for eod_token_idx in range(context_length, len(tokens)):
+        if tokens[eod_token_idx] in eod_token_ids:
+            end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
+            break
+
+    trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
+    if verbose:
+        print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
+        print("\nRaw Generate:", trim_decode_tokens)
+        print("\nEnd Reason:", end_reason)
+    for stop_word in stop_words:
+        trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
+    trim_decode_tokens = trim_decode_tokens.strip()
+    if verbose:
+        print("\nGenerate:", trim_decode_tokens)
+
+    if return_end_reason:
+        return trim_decode_tokens, end_reason
+    else:
+        return trim_decode_tokens
+
+
+def decode_tokens(
+    tokens: Union[torch.LongTensor, TokensType],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    context_length: int,
+    chat_format: str,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str="replace",
+) -> str:
+    if torch.is_tensor(tokens):
+        tokens = tokens.cpu().numpy().tolist()
+
+    if chat_format == "chatml":
+        return _decode_chatml(
+            tokens,
+            stop_words=[],
+            eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
+            tokenizer=tokenizer,
+            raw_text_len=raw_text_len,
+            context_length=context_length,
+            verbose=verbose,
+            return_end_reason=return_end_reason,
+            errors=errors,
+        )
+    elif chat_format == "raw":
+        return _decode_default(
+            tokens,
+            stop_words=["<|endoftext|>"],
+            eod_words=["<|endoftext|>"],
+            tokenizer=tokenizer,
+            raw_text_len=raw_text_len,
+            verbose=verbose,
+            return_end_reason=return_end_reason,
+            errors=errors,
+        )
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+
+
+class StopWordsLogitsProcessor(LogitsProcessor):
+    """
+    :class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
+
+    Args:
+        stop_words_ids (:obj:`List[List[int]]`):
+            List of list of token ids of stop ids. In order to get the tokens of the words
+            that should not appear in the generated text, use :obj:`tokenizer(bad_word,
+            add_prefix_space=True).input_ids`.
+        eos_token_id (:obj:`int`):
+            The id of the `end-of-sequence` token.
+    """
+
+    def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
+
+        if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
+            raise ValueError(
+                f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
+            )
+        if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
+            raise ValueError(
+                f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
+            )
+        if any(
+            any(
+                (not isinstance(token_id, (int, np.integer)) or token_id < 0)
+                for token_id in stop_word_ids
+            )
+            for stop_word_ids in stop_words_ids
+        ):
+            raise ValueError(
+                f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
+            )
+
+        self.stop_words_ids = list(
+            filter(
+                lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
+            )
+        )
+        self.eos_token_id = eos_token_id
+        for stop_token_seq in self.stop_words_ids:
+            assert (
+                len(stop_token_seq) > 0
+            ), "Stop words token sequences {} cannot have an empty list".format(
+                stop_words_ids
+            )
+
+    def __call__(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        stopped_samples = self._calc_stopped_samples(input_ids)
+        for i, should_stop in enumerate(stopped_samples):
+            if should_stop:
+                scores[i, self.eos_token_id] = float(2**15)
+        return scores
+
+    def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
+        if len(tokens) == 0:
+            # if bad word tokens is just one token always ban it
+            return True
+        elif len(tokens) > len(prev_tokens):
+            # if bad word tokens are longer then prev input_ids they can't be equal
+            return False
+        elif prev_tokens[-len(tokens) :].tolist() == tokens:
+            # if tokens match
+            return True
+        else:
+            return False
+
+    def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
+        stopped_samples = []
+        for prev_input_ids_slice in prev_input_ids:
+            match = False
+            for stop_token_seq in self.stop_words_ids:
+                if self._tokens_match(prev_input_ids_slice, stop_token_seq):
+                    # if tokens do not match continue
+                    match = True
+                    break
+            stopped_samples.append(match)
+
+        return stopped_samples
+
+
+def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
+    """This function has been mostly taken from huggingface conversational
+    ai code at
+        https://medium.com/huggingface/how-to-build-a-state-of-the-art-
+             conversational-ai-with-transfer-learning-2d818ac26313"""
+
+    if top_k > 0:
+        # Remove all tokens with a probability less than the
+        # last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p > 0.0:
+        # Cconvert to 1D
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold
+        sorted_indices_to_remove = cumulative_probs > top_p
+        # Shift the indices to the right to keep also the first token
+        # above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+        for i in range(sorted_indices.size(0)):
+            indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
+            logits[i][indices_to_remove] = filter_value
+
+    return logits
+
+
+def switch(val1, val2, boolean):
+    boolean = boolean.type_as(val1)
+    return (1 - boolean) * val1 + boolean * val2

vary/model/llm/qwen/tokenization_qwen.py

+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tokenization classes for QWen."""
+
+import base64
+import logging
+import os
+import unicodedata
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+import tiktoken
+from transformers import PreTrainedTokenizer, AddedToken
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
+
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+ENDOFTEXT = "<|endoftext|>"
+IMSTART = "<|im_start|>"
+IMEND = "<|im_end|>"
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
+SPECIAL_TOKENS = (
+    ENDOFTEXT,
+    IMSTART,
+    IMEND,
+) + EXTRAS
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+class QWenTokenizer(PreTrainedTokenizer):
+    """QWen tokenizer."""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        errors="replace",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.errors = errors  # how to handle errors in decoding
+
+        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: dict[bytes, int]
+        self.special_tokens = {
+            token: index
+            for index, token in enumerate(
+                SPECIAL_TOKENS, start=len(self.mergeable_ranks)
+            )
+        }
+
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        assert (
+            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+        ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+        self.decoder = {
+            v: k for k, v in self.mergeable_ranks.items()
+        }  # type: dict[int, bytes|str]
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+        self.tokenizer = enc  # type: tiktoken.Encoding
+
+        self.eod_id = self.tokenizer.eot_token
+        self.im_start_id = self.special_tokens[IMSTART]
+        self.im_end_id = self.special_tokens[IMEND]
+
+    def __len__(self) -> int:
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        return self.mergeable_ranks
+
+    def convert_tokens_to_ids(
+        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.mergeable_ranks.get(token))
+        return ids
+
+    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError('Adding regular tokens is not supported')
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS:
+                raise ValueError('Adding unknown special tokens is not supported')
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "qwen.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)

vary/model/plug/blip_process.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import cv2
+import numpy as np
+
+import torch
+
+# from omegaconf import OmegaConf
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+from PIL import Image
+
+class BaseProcessor:
+    def __init__(self):
+        self.transform = lambda x: x
+        return
+
+    def __call__(self, item):
+        return self.transform(item)
+
+    # @classmethod
+    # def from_config(cls, cfg=None):
+    #     return cls()
+
+    # def build(self, **kwargs):
+    #     cfg = OmegaConf.create(kwargs)
+
+    #     return self.from_config(cfg)
+
+class BlipImageBaseProcessor(BaseProcessor):
+    def __init__(self, mean=None, std=None):
+        if mean is None:
+            mean = (0.48145466, 0.4578275, 0.40821073)
+        if std is None:
+            std = (0.26862954, 0.26130258, 0.27577711)
+
+        self.normalize = transforms.Normalize(mean, std)
+
+
+## aug functions
+def identity_func(img):
+    return img
+
+
+def autocontrast_func(img, cutoff=0):
+    """
+    same output as PIL.ImageOps.autocontrast
+    """
+    n_bins = 256
+
+    def tune_channel(ch):
+        n = ch.size
+        cut = cutoff * n // 100
+        if cut == 0:
+            high, low = ch.max(), ch.min()
+        else:
+            hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+            low = np.argwhere(np.cumsum(hist) > cut)
+            low = 0 if low.shape[0] == 0 else low[0]
+            high = np.argwhere(np.cumsum(hist[::-1]) > cut)
+            high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
+        if high <= low:
+            table = np.arange(n_bins)
+        else:
+            scale = (n_bins - 1) / (high - low)
+            offset = -low * scale
+            table = np.arange(n_bins) * scale + offset
+            table[table < 0] = 0
+            table[table > n_bins - 1] = n_bins - 1
+        table = table.clip(0, 255).astype(np.uint8)
+        return table[ch]
+
+    channels = [tune_channel(ch) for ch in cv2.split(img)]
+    out = cv2.merge(channels)
+    return out
+
+
+def equalize_func(img):
+    """
+    same output as PIL.ImageOps.equalize
+    PIL's implementation is different from cv2.equalize
+    """
+    n_bins = 256
+
+    def tune_channel(ch):
+        hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+        non_zero_hist = hist[hist != 0].reshape(-1)
+        step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
+        if step == 0:
+            return ch
+        n = np.empty_like(hist)
+        n[0] = step // 2
+        n[1:] = hist[:-1]
+        table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
+        return table[ch]
+
+    channels = [tune_channel(ch) for ch in cv2.split(img)]
+    out = cv2.merge(channels)
+    return out
+
+
+def rotate_func(img, degree, fill=(0, 0, 0)):
+    """
+    like PIL, rotate by degree, not radians
+    """
+    H, W = img.shape[0], img.shape[1]
+    center = W / 2, H / 2
+    M = cv2.getRotationMatrix2D(center, degree, 1)
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
+    return out
+
+
+def solarize_func(img, thresh=128):
+    """
+    same output as PIL.ImageOps.posterize
+    """
+    table = np.array([el if el < thresh else 255 - el for el in range(256)])
+    table = table.clip(0, 255).astype(np.uint8)
+    out = table[img]
+    return out
+
+
+def color_func(img, factor):
+    """
+    same output as PIL.ImageEnhance.Color
+    """
+    ## implementation according to PIL definition, quite slow
+    #  degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
+    #  out = blend(degenerate, img, factor)
+    #  M = (
+    #      np.eye(3) * factor
+    #      + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
+    #  )[np.newaxis, np.newaxis, :]
+    M = np.float32(
+        [[0.886, -0.114, -0.114], [-0.587, 0.413, -0.587], [-0.299, -0.299, 0.701]]
+    ) * factor + np.float32([[0.114], [0.587], [0.299]])
+    out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
+    return out
+
+
+def contrast_func(img, factor):
+    """
+    same output as PIL.ImageEnhance.Contrast
+    """
+    mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
+    table = (
+        np.array([(el - mean) * factor + mean for el in range(256)])
+        .clip(0, 255)
+        .astype(np.uint8)
+    )
+    out = table[img]
+    return out
+
+
+def brightness_func(img, factor):
+    """
+    same output as PIL.ImageEnhance.Contrast
+    """
+    table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
+    out = table[img]
+    return out
+
+
+def sharpness_func(img, factor):
+    """
+    The differences the this result and PIL are all on the 4 boundaries, the center
+    areas are same
+    """
+    kernel = np.ones((3, 3), dtype=np.float32)
+    kernel[1][1] = 5
+    kernel /= 13
+    degenerate = cv2.filter2D(img, -1, kernel)
+    if factor == 0.0:
+        out = degenerate
+    elif factor == 1.0:
+        out = img
+    else:
+        out = img.astype(np.float32)
+        degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
+        out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
+        out = out.astype(np.uint8)
+    return out
+
+
+def shear_x_func(img, factor, fill=(0, 0, 0)):
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, factor, 0], [0, 1, 0]])
+    out = cv2.warpAffine(
+        img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+    ).astype(np.uint8)
+    return out
+
+
+def translate_x_func(img, offset, fill=(0, 0, 0)):
+    """
+    same output as PIL.Image.transform
+    """
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, -offset], [0, 1, 0]])
+    out = cv2.warpAffine(
+        img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+    ).astype(np.uint8)
+    return out
+
+
+def translate_y_func(img, offset, fill=(0, 0, 0)):
+    """
+    same output as PIL.Image.transform
+    """
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, 0], [0, 1, -offset]])
+    out = cv2.warpAffine(
+        img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+    ).astype(np.uint8)
+    return out
+
+
+def posterize_func(img, bits):
+    """
+    same output as PIL.ImageOps.posterize
+    """
+    out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
+    return out
+
+
+def shear_y_func(img, factor, fill=(0, 0, 0)):
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, 0], [factor, 1, 0]])
+    out = cv2.warpAffine(
+        img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+    ).astype(np.uint8)
+    return out
+
+
+def cutout_func(img, pad_size, replace=(0, 0, 0)):
+    replace = np.array(replace, dtype=np.uint8)
+    H, W = img.shape[0], img.shape[1]
+    rh, rw = np.random.random(2)
+    pad_size = pad_size // 2
+    ch, cw = int(rh * H), int(rw * W)
+    x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
+    y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
+    out = img.copy()
+    out[x1:x2, y1:y2, :] = replace
+    return out
+
+
+### level to args
+def enhance_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        return ((level / MAX_LEVEL) * 1.8 + 0.1,)
+
+    return level_to_args
+
+
+def shear_level_to_args(MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * 0.3
+        if np.random.random() > 0.5:
+            level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * float(translate_const)
+        if np.random.random() > 0.5:
+            level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * cutout_const)
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def solarize_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * 256)
+        return (level,)
+
+    return level_to_args
+
+
+def none_level_to_args(level):
+    return ()
+
+
+def posterize_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * 4)
+        return (level,)
+
+    return level_to_args
+
+
+def rotate_level_to_args(MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * 30
+        if np.random.random() < 0.5:
+            level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+func_dict = {
+    "Identity": identity_func,
+    "AutoContrast": autocontrast_func,
+    "Equalize": equalize_func,
+    "Rotate": rotate_func,
+    "Solarize": solarize_func,
+    "Color": color_func,
+    "Contrast": contrast_func,
+    "Brightness": brightness_func,
+    "Sharpness": sharpness_func,
+    "ShearX": shear_x_func,
+    "TranslateX": translate_x_func,
+    "TranslateY": translate_y_func,
+    "Posterize": posterize_func,
+    "ShearY": shear_y_func,
+}
+
+translate_const = 10
+MAX_LEVEL = 10
+replace_value = (128, 128, 128)
+arg_dict = {
+    "Identity": none_level_to_args,
+    "AutoContrast": none_level_to_args,
+    "Equalize": none_level_to_args,
+    "Rotate": rotate_level_to_args(MAX_LEVEL, replace_value),
+    "Solarize": solarize_level_to_args(MAX_LEVEL),
+    "Color": enhance_level_to_args(MAX_LEVEL),
+    "Contrast": enhance_level_to_args(MAX_LEVEL),
+    "Brightness": enhance_level_to_args(MAX_LEVEL),
+    "Sharpness": enhance_level_to_args(MAX_LEVEL),
+    "ShearX": shear_level_to_args(MAX_LEVEL, replace_value),
+    "TranslateX": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
+    "TranslateY": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
+    "Posterize": posterize_level_to_args(MAX_LEVEL),
+    "ShearY": shear_level_to_args(MAX_LEVEL, replace_value),
+}
+
+
+class RandomAugment(object):
+    def __init__(self, N=2, M=10, isPIL=False, augs=[]):
+        self.N = N
+        self.M = M
+        self.isPIL = isPIL
+        if augs:
+            self.augs = augs
+        else:
+            self.augs = list(arg_dict.keys())
+
+    def get_random_ops(self):
+        sampled_ops = np.random.choice(self.augs, self.N)
+        return [(op, 0.5, self.M) for op in sampled_ops]
+
+    def __call__(self, img):
+        if self.isPIL:
+            img = np.array(img)
+        ops = self.get_random_ops()
+        for name, prob, level in ops:
+            if np.random.random() > prob:
+                continue
+            args = arg_dict[name](level)
+            img = func_dict[name](img, *args)
+        return img
+
+
+class VideoRandomAugment(object):
+    def __init__(self, N=2, M=10, p=0.0, tensor_in_tensor_out=True, augs=[]):
+        self.N = N
+        self.M = M
+        self.p = p
+        self.tensor_in_tensor_out = tensor_in_tensor_out
+        if augs:
+            self.augs = augs
+        else:
+            self.augs = list(arg_dict.keys())
+
+    def get_random_ops(self):
+        sampled_ops = np.random.choice(self.augs, self.N, replace=False)
+        return [(op, self.M) for op in sampled_ops]
+
+    def __call__(self, frames):
+        assert (
+            frames.shape[-1] == 3
+        ), "Expecting last dimension for 3-channels RGB (b, h, w, c)."
+
+        if self.tensor_in_tensor_out:
+            frames = frames.numpy().astype(np.uint8)
+
+        num_frames = frames.shape[0]
+
+        ops = num_frames * [self.get_random_ops()]
+        apply_or_not = num_frames * [np.random.random(size=self.N) > self.p]
+
+        frames = torch.stack(
+            list(map(self._aug, frames, ops, apply_or_not)), dim=0
+        ).float()
+
+        return frames
+
+    def _aug(self, img, ops, apply_or_not):
+        for i, (name, level) in enumerate(ops):
+            if not apply_or_not[i]:
+                continue
+            args = arg_dict[name](level)
+            img = func_dict[name](img, *args)
+        return torch.from_numpy(img)
+
+
+# if __name__ == "__main__":
+#     a = RandomAugment()
+#     img = np.random.randn(32, 32, 3)
+#     a(img)
+
+
+
+
+
+
+class BlipImageTrainProcessor(BlipImageBaseProcessor):
+    def __init__(
+        self, image_size=384, mean=None, std=None, min_scale=0.5, max_scale=1.0
+    ):
+        super().__init__(mean=mean, std=std)
+
+        self.transform = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(
+                    image_size,
+                    scale=(min_scale, max_scale),
+                    interpolation=InterpolationMode.BICUBIC,
+                ),
+                # transforms.RandomHorizontalFlip(),
+                RandomAugment(
+                    2,
+                    5,
+                    isPIL=True,
+                    augs=[
+                        "Identity",
+                        # "AutoContrast",
+                        "Brightness",
+                        "Sharpness",
+                        "Equalize",
+                        # "ShearX",
+                        # "ShearY",
+                        # "TranslateX",
+                        # "TranslateY",
+                        # "Rotate",
+                    ],
+                ),
+                transforms.ToTensor(),
+                self.normalize,
+            ]
+        )
+
+    def __call__(self, item):
+        return self.transform(item)
+
+
+class BlipImageEvalProcessor(BlipImageBaseProcessor):
+    def __init__(self, image_size=384, mean=None, std=None):
+        super().__init__(mean=mean, std=std)
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(
+                    (image_size, image_size), interpolation=InterpolationMode.BICUBIC
+                ),
+                transforms.ToTensor(),
+                self.normalize,
+            ]
+        )
+
+    def __call__(self, item):
+        return self.transform(item)
+
+
+# if __name__ == "__main__":
+#     a = BlipImageTrainProcessor(image_size=1024)
+#     # img = np.random.randn(1024, 1024, 3)
+#     # x = torch.zeros(1024, 1024, 3)
+#     x = Image.open("/data/codes/vary-main/log/serve_images/2023-05-23/a2a783d89ede819cdeae943a2199ad3d.jpg").convert("RGB")
+#     print(x.size)
+#     y = a(x)
+
+#     print(y.size())

vary/model/plug/transforms.py

+"""
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+This source code is licensed under the MIT license found in the
+LICENSE file in the root directory of this source tree.
+"""
+# Implements image augmentation
+
+import albumentations as alb
+from albumentations.pytorch import ToTensorV2
+import cv2
+import numpy as np
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+
+def alb_wrapper(transform):
+    def f(im):
+        return transform(image=np.asarray(im))["image"]
+
+    return f
+
+
+class Erosion(alb.ImageOnlyTransform):
+    """
+    Apply erosion operation to an image.
+
+    Erosion is a morphological operation that shrinks the white regions in a binary image.
+
+    Args:
+        scale (int or tuple/list of int): The scale or range for the size of the erosion kernel.
+            If an integer is provided, a square kernel of that size will be used.
+            If a tuple or list is provided, it should contain two integers representing the minimum
+            and maximum sizes for the erosion kernel.
+        always_apply (bool, optional): Whether to always apply this transformation. Default is False.
+        p (float, optional): The probability of applying this transformation. Default is 0.5.
+
+    Returns:
+        numpy.ndarray: The transformed image.
+    """
+
+    def __init__(self, scale, always_apply=False, p=0.5):
+        super().__init__(always_apply=always_apply, p=p)
+        if type(scale) is tuple or type(scale) is list:
+            assert len(scale) == 2
+            self.scale = scale
+        else:
+            self.scale = (scale, scale)
+
+    def apply(self, img, **params):
+        kernel = cv2.getStructuringElement(
+            cv2.MORPH_ELLIPSE, tuple(np.random.randint(self.scale[0], self.scale[1], 2))
+        )
+        img = cv2.erode(img, kernel, iterations=1)
+        return img
+
+
+class Dilation(alb.ImageOnlyTransform):
+    """
+    Apply dilation operation to an image.
+
+    Dilation is a morphological operation that expands the white regions in a binary image.
+
+    Args:
+        scale (int or tuple/list of int): The scale or range for the size of the dilation kernel.
+            If an integer is provided, a square kernel of that size will be used.
+            If a tuple or list is provided, it should contain two integers representing the minimum
+            and maximum sizes for the dilation kernel.
+        always_apply (bool, optional): Whether to always apply this transformation. Default is False.
+        p (float, optional): The probability of applying this transformation. Default is 0.5.
+
+    Returns:
+        numpy.ndarray: The transformed image.
+    """
+
+    def __init__(self, scale, always_apply=False, p=0.5):
+        super().__init__(always_apply=always_apply, p=p)
+        if type(scale) is tuple or type(scale) is list:
+            assert len(scale) == 2
+            self.scale = scale
+        else:
+            self.scale = (scale, scale)
+
+    def apply(self, img, **params):
+        kernel = cv2.getStructuringElement(
+            cv2.MORPH_ELLIPSE, tuple(np.random.randint(self.scale[0], self.scale[1], 2))
+        )
+        img = cv2.dilate(img, kernel, iterations=1)
+        return img
+
+
+class Bitmap(alb.ImageOnlyTransform):
+    """
+    Apply a bitmap-style transformation to an image.
+
+    This transformation replaces all pixel values below a certain threshold with a specified value.
+
+    Args:
+        value (int, optional): The value to replace pixels below the threshold with. Default is 0.
+        lower (int, optional): The threshold value below which pixels will be replaced. Default is 200.
+        always_apply (bool, optional): Whether to always apply this transformation. Default is False.
+        p (float, optional): The probability of applying this transformation. Default is 0.5.
+
+    Returns:
+        numpy.ndarray: The transformed image.
+    """
+
+    def __init__(self, value=0, lower=200, always_apply=False, p=0.5):
+        super().__init__(always_apply=always_apply, p=p)
+        self.lower = lower
+        self.value = value
+
+    def apply(self, img, **params):
+        img = img.copy()
+        img[img < self.lower] = self.value
+        return img
+
+
+train_transform = alb_wrapper(
+    alb.Compose(
+        [
+            Bitmap(p=0),
+            alb.OneOf([Erosion((2, 3)), Dilation((2, 3))], p=0.02),
+            alb.Affine(shear={"x": (0, 3), "y": (-3, 0)}, cval=(255, 255, 255), p=0.03),
+            alb.ShiftScaleRotate(
+                shift_limit_x=(0, 0.04),
+                shift_limit_y=(0, 0.03),
+                scale_limit=(-0.15, 0.03),
+                rotate_limit=2,
+                border_mode=0,
+                interpolation=2,
+                value=(255, 255, 255),
+                p=0.03,
+            ),
+            alb.GridDistortion(
+                distort_limit=0.05,
+                border_mode=0,
+                interpolation=2,
+                value=(255, 255, 255),
+                p=0.04,
+            ),
+            alb.Compose(
+                [
+                    alb.Affine(
+                        translate_px=(0, 5), always_apply=True, cval=(255, 255, 255)
+                    ),
+                    alb.ElasticTransform(
+                        p=1,
+                        alpha=50,
+                        sigma=120 * 0.1,
+                        alpha_affine=120 * 0.01,
+                        border_mode=0,
+                        value=(255, 255, 255),
+                    ),
+                ],
+                p=0.04,
+            ),
+            alb.RandomBrightnessContrast(0.1, 0.1, True, p=0.03),
+            alb.ImageCompression(95, p=0.07),
+            alb.GaussNoise(20, p=0.08),
+            alb.GaussianBlur((3, 3), p=0.03),
+            alb.Resize(1024, 1024),
+            alb.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ]
+    )
+)
+test_transform = alb_wrapper(
+    alb.Compose(
+        [
+            alb.Resize(1024, 1024),
+            alb.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ]
+    )
+)
+
+
+
+
+# if __name__ == '__main__':
+#     from PIL import Image
+#     image = Image.open('/data/hypertext/ucaswei/codes_new/show/49.jpg').convert('RGB')
+#     # image = np.array(image)
+#     for i in range(100):
+#         image1 = train_transform(image)
+#         image1 = Image.fromarray(np.uint8(image1))
+#         image1.save('/data/hypertext/ucaswei/codes_new/aug/' + str(i) + '.jpg')
+    # mm_projector_1 = nn.Linear(1024, 256)
+
+    # x = torch.zeros(2, 3, 1024, 1024)
+    
+    # with torch.no_grad():
+    #     y = model(x)
+        # print(y.shape)
+        # y = mm_projector_1(y.permute(0,2,1))
+    # print(y.shape)
+    # print(y.permute(0,2,1).shape)
\ No newline at end of file

vary/model/vary_opt.py

+#    Copyright 2023 Haotian Liu
+#
+#    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
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+
+from transformers import AutoConfig, AutoModelForCausalLM, \
+                         LlamaConfig, LlamaModel, LlamaForCausalLM, \
+                         CLIPVisionModel, CLIPImageProcessor
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+
+from vary.utils.constants import *
+
+from vary.model.plug.blip_process import BlipImageEvalProcessor
+
+from vary.model.vision_encoder.sam import build_sam_vit_b
+
+
+from transformers import OPTConfig, OPTModel, OPTForCausalLM
+
+from vary.model.plug.transforms import train_transform, test_transform
+
+
+
+class varyConfig(OPTConfig):
+    model_type = "vary"
+
+
+class varyOPTModel(OPTModel):
+    config_class = varyConfig
+
+    def __init__(self, config: OPTConfig):
+        super(varyOPTModel, self).__init__(config)
+
+
+        self.vision_tower = build_sam_vit_b()
+
+        self.mm_projector = nn.Linear(1024, 768)
+
+    def initialize_vision_modules(
+        self, 
+        vision_tower,
+        pretrained_stage1_model=None,
+        freeze_vision_tower=False,
+        use_im_start_end=False,
+        vision_select_layer=-1,
+        dtype=torch.float16,
+        device="cuda"
+    ):
+
+        # 224*224
+        # image_processor do not used in opt
+        image_processor = CLIPImageProcessor.from_pretrained('/cache/vit-large-patch14')
+        # 1024*1024
+
+        image_processor_high = train_transform
+
+        image_token_len = 256
+
+        self.config.vision_tower = vision_tower
+        self.config.image_token_len = image_token_len
+        self.config.use_im_start_end = True
+
+        self.config.vision_select_layer = vision_select_layer
+        self.config.freeze_vision_tower = freeze_vision_tower
+        
+        return dict(
+            image_processor=image_processor,
+            image_processor_high=image_processor_high,
+            image_token_len=image_token_len,
+        )
+
+    def embed_tokens(self, x):
+        return self.get_input_embeddings()(x)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = 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, BaseModelOutputWithPast]:
+
+        # HACK: replace back original embeddings for LLaVA pretraining
+        # orig_embeds_params = getattr(self, 'orig_embeds_params', None)
+        # if orig_embeds_params is not None:
+        #     with torch.no_grad():
+        #         self.get_input_embeddings().weight[:-self.num_new_tokens] = orig_embeds_params[:-self.num_new_tokens].data
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            # inputs_embeds = self.wte(input_ids)
+
+
+        vision_tower = getattr(self, 'vision_tower', None)
+
+
+        if vision_tower is not None and (input_ids.shape[1] != 1 or self.training) and images is not None:
+
+            use_im_start_end = getattr(self.config, "use_im_start_end", -1)
+
+            vision_select_layer = getattr(self.config, "vision_select_layer", -1)
+            im_patch_token = getattr(self.config, "im_patch_token", -1)
+            im_start_token = getattr(self.config, "im_start_token", -1)
+            im_end_token = getattr(self.config, "im_end_token", -1)
+            freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False)
+
+
+            image_features = []
+            for image in images:
+
+                with torch.set_grad_enabled(True):
+                    cnn_feature = vision_tower(image[1])
+                    cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1)
+                    image_feature_final = cnn_feature
+
+                image_features.append(image_feature_final)
+
+            if type(images) is list:
+                image_features = [self.mm_projector(image_feature) for image_feature in image_features]
+            else:
+                # image_features = self.mm_projector(image_features)
+                raise NotImplementedError
+            
+            # dummy_image_features = torch.zeros(1024, 1664, device=inputs_embeds.device, dtype=inputs_embeds.dtype).permute(0, 2, 1).reshape(dummy_image_features.shape[0], -1, 32, 32)
+            # VIT 1024; CNN:1024
+            dummy_image_features = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features = self.mm_projector(dummy_image_features)
+
+            use_im_start_end = True
+            new_input_embeds = []
+            for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features):
+                if (cur_input_ids == im_patch_token).sum() == 0:
+                    # multimodal LLM, but the current sample is not multimodal
+                    cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum()
+                    new_input_embeds.append(cur_input_embeds)
+                    continue
+
+                if use_im_start_end:
+                    if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum():
+                        raise ValueError("The number of image start tokens and image end tokens should be the same.")
+                    
+                    image_start_tokens = torch.where(cur_input_ids == im_start_token)[0]
+                    for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features):
+                        per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device)
+                        num_patches = per_cur_image_features.shape[0]
+                        # print(cur_input_ids)
+                        if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token:
+                            raise ValueError("The image end token should follow the image start token.")
+
+                        cur_input_embeds = torch.cat(
+                            (
+                                cur_input_embeds[:image_start_token_pos+1], 
+                                per_cur_image_features, 
+                                cur_input_embeds[image_start_token_pos + num_patches + 1:]
+                            ), 
+                            dim=0
+                        )
+
+
+                    new_input_embeds.append(cur_input_embeds)
+                else:
+                    raise NotImplementedError
+
+            inputs_embeds = torch.stack(new_input_embeds, dim=0)
+
+
+        return super(varyOPTModel, self).forward(
+            input_ids=None, attention_mask=attention_mask, 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
+        )
+
+
+class varyOPTForCausalLM(OPTForCausalLM):
+    config_class = varyConfig
+    # supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(OPTForCausalLM, self).__init__(config)
+        self.model = varyOPTModel(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 _set_gradient_checkpointing(self, module, value=False):
+    #     if isinstance(module, varyQwenModel):
+    #         module.gradient_checkpointing = value
+
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        # print(input_ids)
+        # print(len(images))
+
+        outputs = self.model(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            images=images,
+            return_dict=return_dict
+            
+        )
+
+
+
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states).contiguous()
+
+        # logits
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
+    ):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+                "attention_mask": attention_mask,
+                "token_type_ids": token_type_ids,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+    def initialize_vision_tokenizer(
+        self, 
+        tokenizer, 
+        freeze_lm_model=False, 
+        pretrained_stage1_model=None,
+        device="cuda"
+    ):
+        config = self.get_model().config
+
+        # add image patch token <image>
+        tokenizer.add_tokens("</s>", special_tokens=True)
+        self.resize_token_embeddings(len(tokenizer))
+
+        tokenizer.add_tokens(DEFAULT_IMAGE_PATCH_TOKEN, special_tokens=True)
+        self.resize_token_embeddings(len(tokenizer))
+        config.im_patch_token = tokenizer.convert_tokens_to_ids(DEFAULT_IMAGE_PATCH_TOKEN)
+
+
+        config.use_im_start_end = True
+
+        # add image start token <im_start> and end token <im_end>
+        if config.use_im_start_end:
+            num_new_tokens = 2
+            tokenizer.add_tokens(DEFAULT_IM_START_TOKEN , special_tokens=True)
+            tokenizer.add_tokens(DEFAULT_IM_END_TOKEN , special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+            config.im_start_token = tokenizer.convert_tokens_to_ids(DEFAULT_IM_START_TOKEN)
+            config.im_end_token =  tokenizer.convert_tokens_to_ids(DEFAULT_IM_END_TOKEN)
+
+            # config.im_start_token, config.im_end_token = 151857, 151858
+
+            if num_new_tokens > 0:
+                input_embeddings = self.get_input_embeddings().weight.data
+                output_embeddings = self.get_output_embeddings().weight.data
+
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
+
+                input_embeddings[-num_new_tokens:] = input_embeddings_avg
+                output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+
+
+AutoConfig.register("vary", varyConfig)
+AutoModelForCausalLM.register(varyConfig, varyOPTForCausalLM)

vary/model/vary_qwen_vary.py

+#    Copyright 2023 Haotian Liu
+#
+#    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
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+
+from transformers import AutoConfig, AutoModelForCausalLM, \
+                         CLIPVisionModel, CLIPImageProcessor
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+
+from vary.utils.constants import *
+
+from vary.model.plug.blip_process import BlipImageEvalProcessor
+
+from vary.model.llm.qwen.modeling_qwen import QWenLMHeadModel, QWenModel
+
+from vary.model.llm.qwen.configuration_qwen import QWenConfig
+from vary.model.vision_encoder.sam import build_sam_vit_b
+from vary.model.plug.transforms import train_transform, test_transform
+
+
+class varyConfig(QWenConfig):
+    model_type = "vary"
+
+
+class varyQwenModel(QWenModel):
+    config_class = varyConfig
+
+    def __init__(self, config: QWenConfig):
+        super(varyQwenModel, self).__init__(config)
+        # TODO download the clip-vit in huggingface
+        self.vision_tower = CLIPVisionModel.from_pretrained('/cache/vit-large-patch14/')
+
+        self.vision_tower_high = build_sam_vit_b()    #  build_sam_vit_b(checkpoint = 'xxxx') for train
+
+        self.mm_projector =  nn.Linear(1024, 2048)
+        self.mm_projector_vary =  nn.Linear(1024, 2048)
+
+    def initialize_vision_modules(
+        self, 
+        vision_tower,
+        pretrained_stage1_model=None,
+        freeze_vision_tower=False,
+        use_im_start_end=False,
+        vision_select_layer=-1,
+        dtype=torch.float16,
+        device="cuda"
+    ):
+
+        # 224*224
+        # TODO download the clip-vit in huggingface
+        image_processor = CLIPImageProcessor.from_pretrained('/cache/vit-large-patch14/')
+        # 1024*1024
+        image_processor_high = train_transform
+
+        self.vision_tower = self.vision_tower.to(dtype=dtype, device=device)
+
+        self.vision_tower_high = self.vision_tower_high.to(dtype=dtype, device=device)
+
+        self.mm_projector = self.mm_projector.to(dtype=dtype, device=device)
+        self.mm_projector_vary = self.mm_projector_vary.to(dtype=dtype, device=device)
+
+        image_token_len = 256
+
+        self.config.vision_tower = vision_tower
+        self.config.image_token_len = image_token_len
+
+        self.config.use_im_start_end = True
+
+        self.config.vision_select_layer = vision_select_layer
+        self.config.freeze_vision_tower = freeze_vision_tower
+        
+        return dict(
+            image_processor=image_processor,
+            image_processor_high=image_processor_high,
+            image_token_len=image_token_len,
+
+        )
+
+    def embed_tokens(self, x):
+        return self.wte(x)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = 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, BaseModelOutputWithPast]:
+
+        # HACK: replace back original embeddings for LLaVA pretraining
+        # orig_embeds_params = getattr(self, 'orig_embeds_params', None)
+        # if orig_embeds_params is not None:
+        #     with torch.no_grad():
+        #         self.get_input_embeddings().weight[:-self.num_new_tokens] = orig_embeds_params[:-self.num_new_tokens].data
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            # inputs_embeds = self.wte(input_ids)
+
+
+        vision_tower = getattr(self, 'vision_tower', None)
+        vision_tower_high = getattr(self, 'vision_tower_high', None)
+
+
+        if vision_tower is not None and (input_ids.shape[1] != 1 or self.training) and images is not None:
+
+            use_im_start_end = getattr(self.config, "use_im_start_end", -1)
+
+            vision_select_layer = getattr(self.config, "vision_select_layer", -1)
+            # im_patch_token = getattr(self.config, "im_patch_token", -1)
+            # im_start_token = getattr(self.config, "im_start_token", -1)
+            # im_end_token = getattr(self.config, "im_end_token", -1)
+            # freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False)
+
+            im_patch_token = 151859
+
+            im_start_token = 151857
+
+            im_end_token = 151858
+
+
+            image_features_1 = []
+            image_features_2 = []
+            for image in images:
+
+                with torch.set_grad_enabled(False):
+                    image_forward_out = vision_tower(image[0], output_hidden_states=True)
+                    select_hidden_state = image_forward_out.hidden_states[vision_select_layer]
+                    image_feature = select_hidden_state[:, 1:]  # 256*1024
+                with torch.set_grad_enabled(False):
+                    cnn_feature = vision_tower_high(image[1])
+                    cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256*1024
+
+                image_features_1.append(image_feature)
+                image_features_2.append(cnn_feature)
+
+
+            if type(images) is list:
+                image_features_1 = [self.mm_projector(image_feature) for image_feature in image_features_1]
+                image_features_2 = [self.mm_projector_vary(image_feature) for image_feature in image_features_2]
+                image_features = [torch.cat((image_feature[0], image_feature[1]), dim=-1) for image_feature in zip(image_features_1, image_features_2)]
+            else:
+
+                raise NotImplementedError
+
+
+            # dummy_image_features = torch.zeros(256, 4096, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features_1 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features_2 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features_1 = self.mm_projector(dummy_image_features_1)
+            dummy_image_features_2 = self.mm_projector_vary(dummy_image_features_2)
+            dummy_image_features = torch.cat((dummy_image_features_1, dummy_image_features_2), dim=-1)
+            use_im_start_end = True
+            new_input_embeds = []
+            for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features):
+                if (cur_input_ids == im_patch_token).sum() == 0:
+                    # multimodal LLM, but the current sample is not multimodal
+                    cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum()
+                    new_input_embeds.append(cur_input_embeds)
+                    continue
+
+                if use_im_start_end:
+                    if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum():
+                        raise ValueError("The number of image start tokens and image end tokens should be the same.")
+                    
+                    image_start_tokens = torch.where(cur_input_ids == im_start_token)[0]
+                    for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features):
+                        per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device)
+                        num_patches = per_cur_image_features.shape[0]
+
+                        if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token:
+                            raise ValueError("The image end token should follow the image start token.")
+                        
+                        # if orig_embeds_params is not None:
+                        #     cur_new_input_embeds = torch.cat(
+                        #         (
+                        #             cur_input_embeds[:image_start_token_pos].detach(), 
+                        #             cur_input_embeds[image_start_token_pos:image_start_token_pos+1], 
+                        #             per_cur_image_features, 
+                        #             cur_input_embeds[image_start_token_pos + num_patches + 1:image_start_token_pos + num_patches + 2], 
+                        #             cur_input_embeds[image_start_token_pos + num_patches + 2:].detach()
+                        #         ), 
+                        #         dim=0
+                        #     )
+                        # else:
+                        cur_input_embeds = torch.cat(
+                            (
+                                cur_input_embeds[:image_start_token_pos+1], 
+                                per_cur_image_features, 
+                                cur_input_embeds[image_start_token_pos + num_patches + 1:]
+                            ), 
+                            dim=0
+                        )
+
+
+                    new_input_embeds.append(cur_input_embeds)
+                else:
+                    raise NotImplementedError
+
+            inputs_embeds = torch.stack(new_input_embeds, dim=0)
+
+        return super(varyQwenModel, self).forward(
+            input_ids=None, attention_mask=attention_mask, 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
+        )
+
+
+class varyQwenForCausalLM(QWenLMHeadModel):
+    config_class = varyConfig
+    # supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(QWenLMHeadModel, self).__init__(config)
+        self.transformer = varyQwenModel(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.transformer
+
+    # def _set_gradient_checkpointing(self, module, value=False):
+    #     if isinstance(module, varyQwenModel):
+    #         module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            images=images,
+            return_dict=return_dict
+            
+        )
+
+
+
+        hidden_states = transformer_outputs[0]
+        lm_logits = self.lm_head(hidden_states)
+
+        # logits
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+        
+        # print(loss)
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
+    ):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+                "attention_mask": attention_mask,
+                "token_type_ids": token_type_ids,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+    def initialize_vision_tokenizer(
+        self, 
+        tokenizer, 
+        freeze_lm_model=False, 
+        pretrained_stage1_model=None,
+        device="cuda"
+    ):
+        config = self.get_model().config
+
+
+        self.resize_token_embeddings(len(tokenizer))
+
+
+        config.im_patch_token = 151859
+
+        config.use_im_start_end = True
+
+        # add image start token <im_start> and end token <im_end>
+        if config.use_im_start_end:
+            # num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+            # config.im_start_token, config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
+
+            config.im_start_token, config.im_end_token = 151857, 151858
+
+
+AutoConfig.register("vary", varyConfig)
+AutoModelForCausalLM.register(varyConfig, varyQwenForCausalLM)

vary/model/vary_toy_qwen1_8.py

+from typing import List, Optional, Tuple, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+from transformers import AutoConfig, AutoModelForCausalLM, \
+                         CLIPVisionModel, CLIPImageProcessor
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from vary.utils.constants import *
+from vary.model.plug.blip_process import BlipImageEvalProcessor
+from vary.model.llm.qwen.modeling_qwen import QWenLMHeadModel, QWenModel
+from vary.model.llm.qwen.configuration_qwen import QWenConfig
+from vary.model.vision_encoder.sam import build_sam_vit_b
+
+
+class varyConfig(QWenConfig):
+    model_type = "vary"
+
+
+class varyQwenModel(QWenModel):
+    config_class = varyConfig
+
+    def __init__(self, config: QWenConfig):
+        super(varyQwenModel, self).__init__(config)
+
+        self.vision_tower = CLIPVisionModel.from_pretrained('/data/hypertext/ucaswei/cache/vit-large-patch14/vit-large-patch14/')
+        self.vision_tower_high = build_sam_vit_b()
+
+        self.mm_projector =  nn.Linear(1024, 1024)
+        self.mm_projector_vary =  nn.Linear(1024, 1024)
+
+    def initialize_vision_modules(
+        self, 
+        vision_tower,
+        pretrained_stage1_model=None,
+        freeze_vision_tower=False,
+        use_im_start_end=False,
+        vision_select_layer=-1,
+        dtype=torch.float16,
+        device="cuda"
+    ):
+
+        # 224*224
+        image_processor = CLIPImageProcessor.from_pretrained('/data/hypertext/ucaswei/cache/vit-large-patch14/vit-large-patch14/') 
+        # 1024*1024
+        image_processor_high = BlipImageEvalProcessor(image_size=1024)
+      
+        self.vision_tower = self.vision_tower.to(dtype=dtype, device=device)
+        self.vision_tower_high = self.vision_tower_high.to(dtype=dtype, device=device)
+
+        self.mm_projector = self.mm_projector.to(dtype=dtype, device=device)
+        self.mm_projector_vary = self.mm_projector_vary.to(dtype=dtype, device=device)
+   
+        image_token_len = 256
+
+        self.config.vision_tower = vision_tower
+        self.config.image_token_len = image_token_len
+        self.config.use_im_start_end = True
+        self.config.vision_select_layer = vision_select_layer
+        self.config.freeze_vision_tower = freeze_vision_tower
+        
+        return dict(
+            image_processor=image_processor,
+            image_processor_high=image_processor_high,
+            image_token_len=image_token_len,
+            # vision_config=vision_config
+        )
+
+    def embed_tokens(self, x):
+        return self.wte(x)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = 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, BaseModelOutputWithPast]:
+
+
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+
+        vision_tower = getattr(self, 'vision_tower', None)
+        vision_tower_high = getattr(self, 'vision_tower_high', None)
+
+
+        if vision_tower is not None and (input_ids.shape[1] != 1 or self.training) and images is not None:
+
+            use_im_start_end = getattr(self.config, "use_im_start_end", -1)
+
+            vision_select_layer = getattr(self.config, "vision_select_layer", -1)
+            im_patch_token = getattr(self.config, "im_patch_token", -1)
+            im_start_token = getattr(self.config, "im_start_token", -1)
+            im_end_token = getattr(self.config, "im_end_token", -1)
+            freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False)
+
+            im_patch_token = 151859
+            im_start_token = 151857
+            im_end_token = 151858
+
+            image_features = []
+            image_features_1 = []
+            image_features_2 = []
+            for image in images:
+                with torch.set_grad_enabled(False):
+                    image_forward_out = vision_tower(image[0], output_hidden_states=True)
+                    select_hidden_state = image_forward_out.hidden_states[vision_select_layer]
+                    image_feature = select_hidden_state[:, 1:]  # 256*1024
+                with torch.set_grad_enabled(False):
+                    cnn_feature = vision_tower_high(image[1])
+                    cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256*1024
+                image_features_1.append(image_feature)
+                image_features_2.append(cnn_feature)
+
+
+            if type(images) is list:
+                image_features_1 = [self.mm_projector(image_feature) for image_feature in image_features_1]
+                image_features_2 = [self.mm_projector_vary(image_feature) for image_feature in image_features_2]
+                image_features = [torch.cat((image_feature[0], image_feature[1]), dim=-1) for image_feature in zip(image_features_1, image_features_2)]
+            else:
+                raise NotImplementedError
+            
+            dummy_image_features_1 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features_2 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            dummy_image_features_1 = self.mm_projector(dummy_image_features_1)
+            dummy_image_features_2 = self.mm_projector_vary(dummy_image_features_2)
+            dummy_image_features = torch.cat((dummy_image_features_1, dummy_image_features_2), dim=-1)
+            use_im_start_end = True
+            new_input_embeds = []
+            for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features):
+                if (cur_input_ids == im_patch_token).sum() == 0:
+                    # multimodal LLM, but the current sample is not multimodal
+                    cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum()
+                    new_input_embeds.append(cur_input_embeds)
+                    continue
+
+                if use_im_start_end:
+                    if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum():
+                        raise ValueError("The number of image start tokens and image end tokens should be the same.")
+                    
+                    image_start_tokens = torch.where(cur_input_ids == im_start_token)[0]
+                    for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features):
+                        per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device)
+                        num_patches = per_cur_image_features.shape[0]
+
+                        if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token:
+                            raise ValueError("The image end token should follow the image start token.")
+                        
+                        cur_input_embeds = torch.cat(
+                            (
+                                cur_input_embeds[:image_start_token_pos+1], 
+                                per_cur_image_features, 
+                                cur_input_embeds[image_start_token_pos + num_patches + 1:]
+                            ), 
+                            dim=0
+                        )
+
+
+                    new_input_embeds.append(cur_input_embeds)
+                else:
+                    raise NotImplementedError
+
+            inputs_embeds = torch.stack(new_input_embeds, dim=0)
+
+        return super(varyQwenModel, self).forward(
+            input_ids=None, attention_mask=attention_mask, 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
+        )
+
+
+class varyQwenForCausalLM(QWenLMHeadModel):
+    config_class = varyConfig
+    # supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(QWenLMHeadModel, self).__init__(config)
+        self.transformer = varyQwenModel(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.transformer
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            images=images,
+            return_dict=return_dict
+            
+        )
+
+        hidden_states = transformer_outputs[0]
+        lm_logits = self.lm_head(hidden_states)
+
+        # logits
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+        
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
+    ):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+                "attention_mask": attention_mask,
+                "token_type_ids": token_type_ids,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+    def initialize_vision_tokenizer(
+        self, 
+        tokenizer, 
+        freeze_lm_model=False, 
+        pretrained_stage1_model=None,
+        device="cuda"
+    ):
+        config = self.get_model().config
+
+        self.resize_token_embeddings(len(tokenizer))
+
+        config.im_patch_token = 151859
+
+        config.use_im_start_end = True
+
+        if config.use_im_start_end:
+            self.resize_token_embeddings(len(tokenizer))
+
+            config.im_start_token, config.im_end_token = 151857, 151858
+
+
+
+AutoConfig.register("vary", varyConfig)
+AutoModelForCausalLM.register(varyConfig, varyQwenForCausalLM)

vary/model/vision_encoder/__init__.py

+

vary/model/vision_encoder/sam.py

+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Type
+
+from functools import partial
+
+import torch
+import torch.nn as nn
+
+from typing import Type
+
+import math
+
+
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+        
+        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
+        self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+        x = self.net_2(x)
+        x = self.net_3(x)
+
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+
+def build_sam_vit_b(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    image_encoder=ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        )
+    
+    if checkpoint is not None:
+        # with open(checkpoint, "rb") as f:
+        state_dict = torch.load(checkpoint)
+
+        
+        image_encoder.load_state_dict(state_dict, strict=True)
+        # image_encoder.load_state_dict({k[19:]: v for k, v in state_dict.items() if 'vision_tower' in k}, strict=True)
+        print(checkpoint)
+    return image_encoder
+
+
+
+
+if __name__ == '__main__':
+
+    x = torch.zeros(2, 3, 1024, 1024)
+
+    # x.permute(0, 3, 1, 2)
+
+    net = build_sam_vit_b(checkpoint ='')
+

vary/train/train_flash_attn.py

+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
+# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn.
+
+# Need to call this before importing transformers.
+from vary.utils.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
+
+replace_llama_attn_with_flash_attn()
+
+from vary.train.train import train
+
+if __name__ == "__main__":
+    train()

vary/train/train_lora_flash_attn.py

+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
+# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn.
+
+# Need to call this before importing transformers.
+from vary.utils.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
+
+replace_llama_attn_with_flash_attn()
+
+# from vary.train.train import train
+from vary.train.train_lora import train
+
+if __name__ == "__main__":
+    train()

vary/train/train_opt.py

+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
+#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
+#
+#    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.
+
+import logging
+import pathlib
+import torch
+import transformers
+
+from vary.train.trainer_vit_fixlr import varyTrainer
+from vary.model import *
+from vary.data import make_supervised_data_module
+from vary.utils.arguments import *
+from vary.utils.constants import *
+from vary.model.vision_encoder.sam import build_sam_vit_b
+
+def train():
+    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_args.model_name_or_path, use_fast=False, padding_side="right", model_max_length=training_args.model_max_length)
+
+
+    model = varyOPTForCausalLM.from_pretrained(model_args.model_name_or_path)
+
+
+
+    dtype = torch.float32
+    if training_args.fp16:
+        dtype = torch.float16
+    if training_args.bf16:
+        dtype = torch.bfloat16
+
+    vision_tower_dict = model.get_model().initialize_vision_modules(
+        vision_tower=model_args.vision_tower,
+        pretrained_stage1_model=model_args.pretrained_stage1_model,
+        freeze_vision_tower=model_args.freeze_vision_tower,
+        use_im_start_end=model_args.use_im_start_end,
+        vision_select_layer=model_args.vision_select_layer,
+        dtype=dtype,
+        device=training_args.device
+    )
+
+    model.initialize_vision_tokenizer(
+        tokenizer=tokenizer, 
+        freeze_lm_model=model_args.freeze_lm_model, 
+        pretrained_stage1_model=model_args.pretrained_stage1_model,
+        device=training_args.device,
+    )
+
+
+
+    model.to(dtype=dtype, device=training_args.device)
+
+    data_args.image_token_len = 256
+    data_args.image_processor = vision_tower_dict['image_processor']
+    data_args.image_processor_high = vision_tower_dict['image_processor_high']
+    data_args.use_im_start_end = model_args.use_im_start_end
+
+    # mixed relation, to be fixed
+    if model_args.freeze_lm_model:
+        model.requires_grad_(False)
+        for p in model.get_model().mm_projector.parameters():
+            p.requires_grad = True
+
+        for p in model.get_input_embeddings().parameters():
+            p.requires_grad = True
+
+
+        if not model_args.freeze_vision_tower:
+
+            model.get_model().vision_tower.requires_grad_(True)
+
+                
+    params_grad = [p.numel() for n, p in model.named_parameters() if p.requires_grad]
+    print(f"Number of Mapping Trainable Parameters: {sum(params_grad) / (1 << 20):.2f} M")
+
+    # params_no_grad = [n for n, p in model.named_parameters() if not p.requires_grad]
+    # if len(params_no_grad) > 0:
+    #     if training_args.fsdp is not None and len(training_args.fsdp) > 0:
+    #         if len(params_no_grad) < 10:
+    #             print('[WARNING] Attempting to use FSDP while {} parameters do not require gradients: {}'. format(len(params_no_grad), params_no_grad))
+    #         else:
+    #             print('[WARNING] Attempting to use FSDP while {} parameters do not require gradients: {}...(omitted)'. format(len(params_no_grad), ', '.join(params_no_grad[:10])))
+    #         print("[WARNING] Attempting to use FSDP with partially frozen paramters, this is experimental.")
+    #         print("[WARNING] As of 4/30/23, this feature requires PyTorch-nightly build.  See here for details: https://github.com/haotian-liu/LLaVA#experimental-use-fsdp-to-save-memory-in-pretraining")
+
+    #         from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
+    #         def patch_FSDP_use_orig_params(func):
+    #             def wrap_func(*args, **kwargs):
+    #                 use_orig_params = kwargs.pop('use_orig_params', True)
+    #                 return func(*args, **kwargs, use_orig_params=use_orig_params)
+    #             return wrap_func
+
+    #         FSDP.__init__ = patch_FSDP_use_orig_params(FSDP.__init__)
+
+    # interleave = True
+    data_module = make_supervised_data_module(
+        interleave=training_args.interleave, 
+        with_box=training_args.with_box, 
+        tokenizer=tokenizer, 
+        data_args=data_args
+    )
+
+    trainer = varyTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        args=training_args,
+        **data_module)
+
+    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
+        trainer.train(resume_from_checkpoint=True)
+    else:
+        trainer.train()
+    trainer.save_state()
+    trainer._safe_save(output_dir=training_args.output_dir)
+
+
+if __name__ == "__main__":
+    train()
+