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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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LICENSE

@@ -0,0 +1,22 @@
+Microsoft.
+Copyright (c) Microsoft Corporation.
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,76 @@
 ---
 license: mit
+tags:
+ - ONNX
+ - DML
+ - ONNXRuntime
+ - phi3
+ - custom_code
 ---
+
+# Phi-3.5 Vision Instruct ONNX models
+
+<!-- Provide a quick summary of what the model is/does. -->
+This repository hosts the optimized versions of [Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) to accelerate inference with ONNX Runtime for your CPU and GPU.
+
+Phi-3.5 Vision is a lightweight, state-of-the-art open multimodal model built upon datasets that include synthetic data and filtered publicly available web data with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3.5 model family, and the multimodal version supports up to 128K context length (in tokens). The base model has undergone a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization, to ensure precise instruction adherence and robust safety measures. 
+
+Optimized variants of the Phi-3.5 Vision models are published here in [ONNX](https://onnx.ai) format to run with [ONNX Runtime](https://onnxruntime.ai/) on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets.
+
+## ONNX Models 
+
+Here are some of the optimized configurations we have added:  
+
+1. ONNX model for INT4 CPU: ONNX model for CPUs using int4 quantization via RTN.
+2. ONNX model for INT4 GPU: ONNX model for GPUs using int4 quantization via RTN.
+
+## How to Get Started with the Model
+To support the Phi-3.5 models across a range of devices, platforms, and EP backends, we introduce a new API to wrap several aspects of generative AI inferencing. This API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps [here](https://aka.ms/run-phi3-v-onnx).
+
+## Hardware Supported
+
+The models are tested on:
+- Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz 
+- 1 A100 GPU, SKU: Standard_ND96amsr_A100_v4 (CUDA)
+- GPU SKU: RTX 4080 (DirectML)
+
+Minimum Configuration Required:
+- CPU machine with 16GB RAM
+- CUDA: NVIDIA GPU with [Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0
+- Windows: DirectX 12-capable GPU and a minimum of 10GB of combined RAM
+
+### Model Description
+
+- **Developed by:**  Microsoft
+- **Model type:** ONNX
+- **Language(s) (NLP):** Python, C, C++
+- **License:** MIT
+- **Model Description:** This is a conversion of the Phi-3.5 Vision Instruct model for ONNX Runtime inference.
+- **Disclaimer:** This model is only an optimization of the base model. Any risk associated with the model is the responsibility of the user of the model. Please verify and test for your scenarios. There may be a slight difference in output from the base model with the optimizations applied. We have conducted responsible AI evaluations and did not observe significant regressions compared to the base model.
+
+## Additional Details
+- [**Phi-3.5 Blog**](https://aka.ms/phi3_ONNXBuild24)
+- [**Phi-3.5 Model Blog Link**](https://aka.ms/phi3.5-techblog)
+- [**Phi-3.5 Model Card**](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
+- [**Phi-3.5 Technical Report**](https://aka.ms/phi3-tech-report)
+- [**Phi-3.5 on Azure AI Studio**](https://aka.ms/try-phi3.5vision)
+
+## Performance Metrics
+The performance of the ONNX vision model is similar to [Phi-3.5-mini-instruct-onnx](https://huggingface.co/microsoft/Phi-3.5-mini-instruct-onnx) during token generation.
+
+## Base Model Usage and Considerations
+For details and RAI considerations of the base model, please refer to [here](https://huggingface.co/microsoft/Phi-3.5-vision-instruct).
+
+Please note that ONNX model output may vary slightly from the base model. The users are responsible for verifying the output for their scenarios and own responsibility of the usage.
+
+## Appendix
+
+## Model Card Contact
+parinitarahi, kvaishnavi, natke, yunl, sunghcho
+
+## Contributors
+Kunal Vaishnavi, Sunghoon Choi, Yufeng Li, Baiju Meswani, Sheetal Arun Kadam, Rui Ren, Natalie Kershaw, Parinita Rahi, Patrice Vignola, Xiang Zhang, Chai Chaoweeraprasit, Logan Iyer, Vicente Rivera, Jacques Van Rhyn, Yun Liu
+
+## Trademarks 
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

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cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/genai_config.json

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+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|system|>",
+    "<|end|>",
+    "<|user|>",
+    "<|end|>"
+  ],
+  "auto_map": {
+    "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "legacy": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "processor_class": "Phi3VProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

onnx/builder.py

@@ -0,0 +1,232 @@
+import argparse
+import onnx
+import os
+import requests
+import shutil
+import subprocess
+import sys
+import torch
+
+from onnxruntime_genai.models.builder import create_model
+from PIL import Image
+from transformers import AutoConfig, AutoProcessor, AutoModelForCausalLM
+
+
+def build_vision(args):
+    # Many images:
+    prompt = f"{user_prompt}<|image_1|>\n <|image_2|>\n <|image_3|>\n <|image_4|>\n What is shown in these four images?{prompt_suffix}{assistant_prompt}"
+    url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    image_1 = Image.open(requests.get(url, stream=True).raw)
+    url = "https://img.freepik.com/free-photo/painting-mountain-lake-with-mountain-background_188544-9126.jpg?w=2000"
+    image_2 = Image.open(requests.get(url, stream=True).raw)
+    url = "https://th.bing.com/th/id/OIP.gCvQ1vmPVJmrq1nnzM3ZHQHaEo?rs=1&pid=ImgDetMain"
+    image_3 = Image.open(requests.get(url, stream=True).raw)
+    url = "https://wallpaper.dog/large/10809054.jpg"
+    image_4 = Image.open(requests.get(url, stream=True).raw)
+    images = [image_1, image_2, image_3, image_4]
+    inputs = processor(prompt, images, return_tensors="pt").to(args.execution_provider.replace("dml", "cuda"))
+    inputs["pixel_values"] = inputs["pixel_values"].to(args.precision)
+
+    # TorchScript export
+    dummy_inputs = (
+        inputs["pixel_values"],   # inputs_embeds: Optional[torch.FloatTensor] = None,
+        inputs["image_sizes"],    # image_sizes: Optional[torch.FloatTensor] = None,
+    )
+    dynamic_axes = {
+        "pixel_values": {0: "num_images", 1: "max_num_crops", 3: "height", 4: "width"},
+        "image_sizes": {0: "num_images"},
+        "image_features": {0: "num_image_tokens"},
+    }
+    filename = "phi-3.5-v-instruct-vision.onnx"
+
+    temp_folder_1 = os.path.join(args.output, "vision_init_export")
+    os.makedirs(temp_folder_1, exist_ok=True)
+
+    fpath_1 = os.path.join(temp_folder_1, filename)
+    torch.onnx.export(
+        model.model.vision_embed_tokens,
+        args=dummy_inputs,
+        f=fpath_1,
+        export_params=True,
+        input_names=["pixel_values", "image_sizes"],
+        output_names=["image_features"],
+        dynamic_axes=dynamic_axes,
+        opset_version=14,
+        do_constant_folding=True,
+    )
+
+    onnx.checker.check_model(fpath_1)
+    onnx.shape_inference.infer_shapes_path(fpath_1)
+    onnx_model = onnx.load_model(fpath_1, load_external_data=True)
+
+    temp_folder_2 = os.path.join(args.output, "vision_after_export")
+    os.makedirs(temp_folder_2, exist_ok=True)
+
+    fpath_2 = os.path.join(temp_folder_2, filename)
+    onnx.save_model(
+        onnx_model,
+        fpath_2,
+        save_as_external_data=True,
+        all_tensors_to_one_file=True,
+        location=f"{filename}.data",
+        size_threshold=0,
+        convert_attribute=False,
+    )
+    shutil.rmtree(temp_folder_1)
+
+    # ORT transformer optimizer
+    temp_folder_3 = os.path.join(args.output, "vision_after_opt")
+    fpath_3 = os.path.join(temp_folder_3, filename)
+    subprocess.run(
+        [
+            f"{sys.executable}", "-m", "onnxruntime.transformers.optimizer",
+            "--input", fpath_2,
+            "--output", fpath_3,
+            "--model_type", "clip",
+            "--num_heads", str(16),
+            "--hidden_size", str(1024),
+            "--use_external_data_format",
+            "--opt_level", str(0),
+            "--disable_shape_inference",
+        ]
+    )
+    shutil.rmtree(temp_folder_2)
+
+    # ORT 4-bits quantizer
+    fpath_4 = os.path.join(args.output, filename)
+    cmd = [
+        f"{sys.executable}", "-m", "onnxruntime.quantization.matmul_4bits_quantizer",
+        "--input_model", fpath_3,
+        "--output_model", fpath_4,
+        "--block_size", str(32),
+    ]
+    if args.precision == torch.float32: cmd.extend(["--accuracy_level", str(4)])
+    subprocess.run(cmd)
+    shutil.rmtree(temp_folder_3)
+
+
+def build_embedding(args):
+    # TorchScript export
+    batch_size, sequence_length, num_img_tokens = 2, 8, 2
+    inputs = {
+        "input_ids": torch.randint(low=0, high=config.vocab_size, size=(batch_size, sequence_length), device=args.execution_provider.replace("dml", "cuda"), dtype=torch.int64),
+        "image_features": torch.randn(num_img_tokens, config.hidden_size, device=args.execution_provider.replace("dml", "cuda"), dtype=args.precision),
+        "inputs_embeds": torch.randn(batch_size, sequence_length, config.hidden_size, device=args.execution_provider.replace("dml", "cuda"), dtype=args.precision),
+    }
+    inputs["input_ids"][0][0] = -1
+    inputs["input_ids"][0][1] = -1
+    dummy_inputs = (
+        inputs["input_ids"],      # input_ids: torch.LongTensor
+        inputs["image_features"], # image_features: Optional[torch.FloatTensor] = None,
+    )
+    dynamic_axes = {
+        "input_ids": {0: "batch_size", 1: "sequence_length"},
+        "image_features": {0: "num_image_tokens"},
+        "inputs_embeds": {0: "batch_size", 1: "sequence_length"},
+    }
+    filename = "phi-3.5-v-instruct-embedding.onnx"
+
+    temp_folder_1 = os.path.join(args.output, "embedding_init_export")
+    os.makedirs(temp_folder_1, exist_ok=True)
+
+    fpath_1 = os.path.join(temp_folder_1, filename)
+    torch.onnx.export(
+        model.model.combined_embed,
+        args=dummy_inputs,
+        f=fpath_1,
+        export_params=True,
+        input_names=["input_ids", "image_features"],
+        output_names=["inputs_embeds"],
+        dynamic_axes=dynamic_axes,
+        opset_version=14,
+        do_constant_folding=True,
+    )
+
+    onnx.checker.check_model(fpath_1)
+    onnx.shape_inference.infer_shapes_path(fpath_1)
+    onnx_model = onnx.load_model(fpath_1, load_external_data=True)
+
+    fpath_2 = os.path.join(args.output, filename)
+    onnx.save_model(
+        onnx_model,
+        fpath_2,
+        save_as_external_data=True,
+        all_tensors_to_one_file=True,
+        location=f"{filename}.data",
+        size_threshold=0,
+        convert_attribute=False,
+    )
+    shutil.rmtree(temp_folder_1)
+
+
+def build_text(args):
+    # Create ONNX model
+    model_name = None
+    precision = "int4"
+    extra_options = {
+        "exclude_embeds": "true",
+        "filename": "phi-3.5-v-instruct-text.onnx",
+    }
+    if args.precision == torch.float32: extra_options["int4_accuracy_level"] = 4
+    create_model(model_name, args.input, args.output, precision, args.execution_provider, args.cache_dir, **extra_options)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-i",
+        "--input",
+        required=True,
+        help="Path to folder on disk containing the Hugging Face config, model, tokenizer, etc.",
+    )
+
+    parser.add_argument(
+        "-o",
+        "--output",
+        required=True,
+        help="Path to folder to store ONNX model and additional files (e.g. GenAI config, external data files, etc.)",
+    )
+
+    parser.add_argument(
+        "-p",
+        "--precision",
+        required=True,
+        choices=["fp16", "fp32"],
+        help="Precision to export PyTorch components with",
+    )
+
+    parser.add_argument(
+        "-e",
+        "--execution_provider",
+        required=True,
+        choices=["cpu", "cuda", "dml"],
+        help="Execution provider for Phi-3.5 vision components",
+    )
+
+    parser.add_argument(
+        "-c",
+        "--cache_dir",
+        required=False,
+        default=os.path.join('.', 'cache_dir'),
+        help="Cache directory for Hugging Face files and temporary ONNX external data files",
+    )
+
+    args = parser.parse_args()
+    args.precision = torch.float16 if args.precision == "fp16" else torch.float32
+    return args
+
+if __name__ == "__main__":
+    user_prompt = '<|user|>\n'
+    assistant_prompt = '<|assistant|>\n'
+    prompt_suffix = "<|end|>\n"
+
+    args = get_args()
+    config = AutoConfig.from_pretrained(args.input, trust_remote_code=True)
+    processor = AutoProcessor.from_pretrained(args.input, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(args.input, trust_remote_code=True, torch_dtype=args.precision).to(args.execution_provider.replace("dml", "cuda"))
+
+    # Build model components
+    build_vision(args)
+    build_embedding(args)
+    build_text(args)

onnx/config.json

@@ -0,0 +1,151 @@
+{
+  "_name_or_path": "Phi-3.5-vision-instruct",
+  "architectures": [
+    "Phi3VForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3_v.Phi3VConfig",
+    "AutoModelForCausalLM": "modeling_phi3_v.Phi3VForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_layer": {
+    "embedding_cls": "image",
+    "hd_transform_order": "sub_glb",
+    "projection_cls": "mlp",
+    "use_hd_transform": true,
+    "with_learnable_separator": true
+  },
+  "embd_pdrop": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "img_processor": {
+    "image_dim_out": 1024,
+    "model_name": "openai/clip-vit-large-patch14-336",
+    "name": "clip_vision_model",
+    "num_img_tokens": 144
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "model_type": "phi3_v",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "long_factor": [
+      1.0800000429153442,
+      1.1100000143051147,
+      1.1399999856948853,
+      1.340000033378601,
+      1.5899999141693115,
+      1.600000023841858,
+      1.6200000047683716,
+      2.620000123977661,
+      3.2300000190734863,
+      3.2300000190734863,
+      4.789999961853027,
+      7.400000095367432,
+      7.700000286102295,
+      9.09000015258789,
+      12.199999809265137,
+      17.670000076293945,
+      24.46000099182129,
+      28.57000160217285,
+      30.420001983642578,
+      30.840002059936523,
+      32.590003967285156,
+      32.93000411987305,
+      42.320003509521484,
+      44.96000289916992,
+      50.340003967285156,
+      50.45000457763672,
+      57.55000305175781,
+      57.93000411987305,
+      58.21000289916992,
+      60.1400032043457,
+      62.61000442504883,
+      62.62000274658203,
+      62.71000289916992,
+      63.1400032043457,
+      63.1400032043457,
+      63.77000427246094,
+      63.93000411987305,
+      63.96000289916992,
+      63.970001220703125,
+      64.02999877929688,
+      64.06999969482422,
+      64.08000183105469,
+      64.12000274658203,
+      64.41000366210938,
+      64.4800033569336,
+      64.51000213623047,
+      64.52999877929688,
+      64.83999633789062
+    ],
+    "short_factor": [
+       1.08,
+      1.1,
+      1.1300000000000001,
+      1.2800000000000002,
+      1.3100000000000003,
+      1.4500000000000004,
+      1.4500000000000004,
+      1.9500000000000008,
+      2.030000000000001,
+      2.4299999999999926,
+      2.5699999999999896,
+      2.9499999999999815,
+      3.729999999999965,
+      3.869999999999962,
+      4.189999999999955,
+      4.43999999999995,
+      4.6399999999999455,
+      4.979999999999938,
+      5.159999999999934,
+      5.279999999999932,
+      5.759999999999922,
+      5.889999999999919,
+      5.889999999999919,
+      5.969999999999917,
+      6.089999999999915,
+      6.2799999999999105,
+      6.7699999999999,
+      6.8899999999998975,
+      7.109999999999893,
+      7.129999999999892,
+      7.179999999999891,
+      7.289999999999889,
+      7.339999999999888,
+      7.559999999999883,
+      7.619999999999882,
+      7.69999999999988,
+      7.879999999999876,
+      7.879999999999876,
+      7.879999999999876,
+      7.939999999999875,
+      7.949999999999875,
+      7.979999999999874,
+      8.19999999999987,
+      8.439999999999864,
+      8.469999999999864,
+      8.589999999999861,
+      8.809999999999857,
+      8.999999999999853
+    ],
+    "type": "su"
+  },
+  "rope_theta": 10000.0,
+  "sliding_window": 262144,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 32064,
+  "_attn_implementation": "eager"
+}

onnx/modeling_phi3_v.py

@@ -0,0 +1,2085 @@
+# coding=utf-8
+# Copyright 2024 Microsoft 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 Phi-3-V model."""
+
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_phi3_v import Phi3VConfig
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+except ImportError:
+    pass
+
+import torch
+from torch import nn
+from transformers import CLIPVisionConfig, CLIPVisionModel, PretrainedConfig
+from transformers.models.clip.modeling_clip import CLIPAttention
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+MAX_INPUT_ID = int(1e9)
+
+CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(
+  attention_dropout=0.0,
+  dropout=0.0,
+  hidden_act="quick_gelu",
+  hidden_size=1024,
+  image_size=336,
+  initializer_factor=1.0,
+  initializer_range=0.02,
+  intermediate_size=4096,
+  layer_norm_eps=1e-05,
+  num_attention_heads=16,
+  num_channels=3,
+  num_hidden_layers=24,
+  patch_size=14,
+  projection_dim=768,
+  attn_implementation="eager",
+)
+
+class CLIPAttentionFA2(CLIPAttention):
+    """Add flash attention 2 to CLIPAttention. (This is only used in the vision encoder)"""
+
+    def forward(self,
+        hidden_states,
+        attention_mask=None,
+        causal_attention_mask=None,
+        output_attentions=False,
+    ):
+        """Input shape: Batch x Time x Channel"""
+
+        assert attention_mask is None, "CLIPAttentionFA2 does not support attention_mask"
+        assert causal_attention_mask is None, "CLIPAttentionFA2 does not support causal_attention_mask"
+        assert output_attentions is False, "CLIPAttentionFA2 does not support output_attentions"
+
+        bsz, tgt_len, embed_dim = hidden_states.size()
+        query_states = self.q_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+        key_states = self.k_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+        value_states = self.v_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+
+        attn_output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=self.dropout if self.training else 0.0,
+            softmax_scale=self.scale,
+            causal=False,
+        ).reshape(bsz, tgt_len, embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+        return attn_output, None
+
+
+def reshape_hd_patches_2x2merge(image_features, h_crop, w_crop):
+    """
+    image_features: (num_images*num_crops, 24*24, 1024)
+    output: (num_images, h_crop*12, w_crop*12, 4096), h_crop*w_crop == num_crops
+    """
+    N, L, C = image_features.shape
+    assert L == 24 * 24 and C == 1024 and N % (h_crop * w_crop) == 0
+    num_images = torch.tensor(N // (h_crop * w_crop), dtype=torch.int64)
+    H = torch.tensor(int(L**0.5), dtype=torch.int64)
+    H_div_2 = torch.tensor(H // 2, dtype=torch.int64)
+
+    image_features_hd = (
+        image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
+        .reshape(N, H_div_2, 2, H_div_2, 2, C)  # N, 12, 2, 12, 2, 1024
+        .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
+        .reshape(N, -1, 4 * C)  # N, 144, 4096
+        .reshape(
+            num_images, h_crop, w_crop, H_div_2, H_div_2, -1
+        )  # n_img, h_crop, w_crop, 12, 12, 4096
+        .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
+        .reshape(
+            num_images, h_crop * H_div_2, w_crop * H_div_2, 4 * C
+        )  # n_img, h_crop*12, w_crop*12, 4096
+    )
+
+    return image_features_hd
+
+
+def add_image_newline(image_features_hd, sub_GN):
+    """
+    image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
+    output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
+    """
+    num_images, h, w, hid_dim = image_features_hd.shape
+    # add the newline token to the HD image feature patches
+    newline_embeddings = sub_GN.expand(num_images, h, -1, -1)  # (n_img, h, 1, hid_dim)
+    image_features_hd_newline = torch.cat(
+        [image_features_hd, newline_embeddings], dim=2
+    ).reshape(num_images, -1, hid_dim)
+    return image_features_hd_newline
+
+
+@torch.jit.script_if_tracing
+def get_image_embeddings(image_dim_out, image_sizes, image_features, global_image_features_hd_newline):
+    """
+    Get image embeddings for all images.
+    Need a for loop to process each image because of different image sizes
+    (patch arrangement is different for each image)
+    """
+    glb_GN = torch.zeros(1, 1, image_dim_out * 4).to(image_features.device)
+    sub_GN = torch.zeros(1, 1, 1, image_dim_out * 4).to(image_features.device)
+
+    all_image_embeddings = torch.empty(0, 4096).to(image_features.device)
+    for i, img_size in enumerate(image_sizes):
+        # h, w = img_size
+        h, w = img_size[0], img_size[1]
+        h_crop = torch.tensor(h // 336, dtype=torch.int64)
+        w_crop = torch.tensor(w // 336, dtype=torch.int64)
+        num_crops = h_crop * w_crop
+
+        # NOTE: real num_crops is padded
+        # (num_crops, 24*24, 1024)
+        sub_image_features = image_features[i, 1 : 1 + num_crops]
+        sub_image_features_hd = reshape_hd_patches_2x2merge(sub_image_features, h_crop, w_crop)
+        sub_image_features_hd_newline = add_image_newline(sub_image_features_hd, sub_GN)
+
+        # # [sub features, separator, global features]
+        # all_image_embeddings.extend(
+        #     [
+        #         sub_image_features_hd_newline.squeeze(0),  # (h_crop*12*(w_crop*12+1), 4096)
+        #         self.glb_GN.squeeze(0),
+        #         global_image_features_hd_newline[i],
+        #     ]
+        # )
+
+        # [sub features, separator, global features]
+        all_image_embeddings = torch.cat(
+            [
+                all_image_embeddings,
+                sub_image_features_hd_newline.view(-1, 4096),  # (h_crop*12*(w_crop*12+1), 4096)
+                glb_GN.view(-1, 4096),
+                global_image_features_hd_newline[i],
+            ]
+        )
+    
+    return all_image_embeddings
+
+
+@torch.jit.script_if_tracing
+def clamp_input_ids(input_ids: torch.LongTensor, image_features: torch.FloatTensor, vocab_size: int):
+    if image_features.numel():
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        # positions for image tokens
+        condition = (input_ids < 0) & (input_ids > -int(1e9))
+        positions = torch.where(condition)
+        # has_image = len(positions[0].tolist()) > 0
+        input_ids = input_ids.clamp_min(0).clamp_max(vocab_size).detach()
+
+        return input_ids, positions
+
+    return input_ids, torch.where(torch.zeros((1, 1), dtype=torch.bool))
+
+
+@torch.jit.script_if_tracing
+def select_logic(hidden_states: torch.FloatTensor, image_features: torch.FloatTensor, positions: List[torch.LongTensor]):
+    if image_features.numel():
+        # apply 'select' logic
+        hidden_states = hidden_states.index_put(
+            positions, image_features, accumulate=False
+        )
+
+    return hidden_states
+
+
+class Phi3Embedding(nn.Module):
+    """Phi3 embedding for text-only and vision + text."""
+    def __init__(self, wte, vocab_size):
+        super().__init__()
+        self.wte = wte
+        self.vocab_size = vocab_size
+
+    def forward(self, input_ids: torch.LongTensor, image_features: torch.FloatTensor) -> torch.FloatTensor:
+        input_ids, positions = clamp_input_ids(input_ids, image_features, self.vocab_size)
+        hidden_states = self.wte(input_ids)
+        hidden_states = select_logic(hidden_states, image_features, positions)
+        return hidden_states
+
+
+class Phi3ImageEmbedding(nn.Module):
+    """Phi3 Image embedding."""
+
+    def __init__(self, config: PretrainedConfig, wte=None, **kwargs) -> None:
+        super().__init__()
+
+        # n_embed or hidden_size
+        hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size
+        if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
+            embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop
+            self.drop = nn.Dropout(embd_drop)
+        else:
+            self.drop = None
+
+        self.wte = wte
+
+        if isinstance(config.img_processor, dict) and config.img_processor.get('name', None) == 'clip_vision_model':
+            assert 'model_name' in config.img_processor, 'model_name must be provided for CLIPVisionModel'
+            assert 'image_dim_out' in config.img_processor, 'image_dim_out must be provided for CLIPVisionModel'
+            assert 'num_img_tokens' in config.img_processor, 'num_img_tokens must be provided for CLIPVisionModel'
+            assert config.img_processor['model_name'] == 'openai/clip-vit-large-patch14-336'
+            clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
+            self.img_processor = CLIPVisionModel(clip_config)
+            image_dim_out = config.img_processor['image_dim_out']
+            self.num_img_tokens = config.img_processor['num_img_tokens']
+
+            # FA2 in CLIP
+            if config._attn_implementation == 'flash_attention_2':
+                for layer in self.img_processor.vision_model.encoder.layers:
+                    clip_fa2 = CLIPAttentionFA2(clip_config)
+                    del layer.self_attn
+                    layer.self_attn = clip_fa2
+        else:
+            raise NotImplementedError(f'img_processor = {config.img_processor}, not implemented')
+
+        self.image_dim_out = image_dim_out
+        self.img_sizes = None
+
+        # global_gn and sub_gn for hd transform, serves as line separator
+        self.use_hd_transform = kwargs.get('use_hd_transform', False)
+        self.with_learnable_separator = kwargs.get('with_learnable_separator', False)
+        self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub')
+        # with_hd_transform and with_learnable_separator should have same value
+        assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value'
+        if self.with_learnable_separator:
+            assert self.use_hd_transform, 'learnable separator is only for hd transform'
+            # 1024 * 4, merge spatial to channel dimension
+            self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * 4]))
+            self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * 4]))
+            logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}')
+
+        projection_cls = kwargs.get('projection_cls', 'linear')
+        if projection_cls == 'linear':
+            self.img_projection = nn.Linear(image_dim_out, hidden_size)
+        elif projection_cls == 'mlp' and self.use_hd_transform:
+            dim_projection = hidden_size
+            depth = 2
+            layers = [nn.Linear(image_dim_out * 4, dim_projection)]
+            for _ in range(1, depth):
+                layers.extend([nn.GELU(),
+                                nn.Linear(dim_projection, dim_projection)])
+            self.img_projection = nn.Sequential(*layers)
+        elif projection_cls == 'mlp':
+            dim_projection = hidden_size
+            depth = 2
+            layers = [nn.Linear(image_dim_out, dim_projection)]
+            for _ in range(1, depth):
+                layers.extend([nn.GELU(),
+                                nn.Linear(dim_projection, dim_projection)])
+            self.img_projection = nn.Sequential(*layers)
+        else:
+            raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented')
+
+        self.vocab_size = config.vocab_size
+        self.img_features = None
+
+        if isinstance(config.img_processor, dict):
+            self.layer_idx = config.img_processor.get('layer_idx', -2)
+            self.type_feature = config.img_processor.get('type_feature', 'patch')
+        else:
+            self.layer_idx = -2
+            self.type_feature = 'patch'
+
+
+    def set_img_features(self, img_features: torch.FloatTensor) -> None:
+        self.img_features = img_features
+
+    def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
+        self.img_sizes = img_sizes
+
+    def get_img_features(self, img_embeds: torch.FloatTensor) -> torch.FloatTensor:
+        LAYER_IDX = self.layer_idx
+        TYPE_FEATURE = self.type_feature
+
+        img_processor_output = self.img_processor(img_embeds, output_hidden_states=True)
+        img_feature = img_processor_output.hidden_states[LAYER_IDX]
+
+        if TYPE_FEATURE == "patch":
+            patch_feature = img_feature[:, 1:]
+            return patch_feature
+
+        raise NotImplementedError
+
+    # def forward(
+    #     self, input_ids: torch.LongTensor, pixel_values: torch.FloatTensor, image_sizes=None
+    # ) -> torch.FloatTensor:
+    #     input_shape = input_ids.size()
+    #     input_ids = input_ids.view(-1, input_shape[-1])
+
+    #     # positions for image tokens
+    #     positions = torch.nonzero((input_ids < 0) & (input_ids > -MAX_INPUT_ID), as_tuple=True)
+    #     has_image = len(positions[0].tolist()) > 0
+    #     input_ids = input_ids.clamp_min(0).clamp_max(self.vocab_size).detach()
+    #     hidden_states = self.wte(input_ids)
+
+    #     if has_image:
+    #         assert self.use_hd_transform
+    #         num_images, num_crops, c, h, w = pixel_values.shape
+    #         assert c == 3 and h == w == 336
+    #         img_features = self.get_img_features(pixel_values.flatten(0, 1)).reshape(
+    #             num_images, num_crops, -1, self.image_dim_out
+    #         )
+    #         image_features_proj = self.hd_feature_transform(img_features, image_sizes)
+    #         hidden_states = hidden_states.index_put(
+    #             positions, image_features_proj, accumulate=False
+    #         )
+
+    #     if self.drop is not None:
+    #         hidden_states = self.drop(hidden_states)
+
+    #     return hidden_states
+
+    def forward(self, pixel_values: torch.FloatTensor, image_sizes=None) -> torch.FloatTensor:
+        assert self.use_hd_transform
+        num_images, num_crops, c, h, w = pixel_values.shape
+        assert c == 3 and h == w == 336
+        img_features = self.get_img_features(pixel_values.flatten(0, 1)).reshape(
+            num_images, num_crops, -1, self.image_dim_out
+        )
+        image_features_proj = self.hd_feature_transform(img_features, image_sizes)
+
+        return image_features_proj
+
+    def hd_feature_transform(self, image_features, image_sizes):
+        """
+        image_features: (num_images, num_crops+1, 24*24, 1024)
+        """
+        assert (
+            self.hd_transform_order == 'sub_glb'
+        ), f'hd_transform_order `{self.hd_transform_order}` not implemented'
+        if isinstance(self.img_projection, nn.Sequential):
+            target_device = self.img_projection[0].bias.device
+            target_dtype = self.img_projection[0].bias.dtype
+        else:  # It's a single nn.Linear layer
+            target_device = self.img_projection.bias.device
+            target_dtype = self.img_projection.bias.dtype
+
+        global_image_features = image_features[:, 0]  # (num_images, 24*24, 1024)
+        # global feature can be viewed as a special HD case with num_crops 1x1
+        global_image_features_hd = self.reshape_hd_patches_2x2merge(global_image_features, 1, 1)
+        global_image_features_hd_newline = self.add_image_newline(global_image_features_hd)
+
+        # all_image_embeddings = []
+        # # need a for loop to process each image because of different image sizes
+        # # (patch arrangement is different for each image)
+        # for i, img_size in enumerate(image_sizes):
+        #     h, w = img_size
+        #     h_crop = h // 336
+        #     w_crop = w // 336
+        #     num_crops = h_crop * w_crop
+
+        #     # NOTE: real num_crops is padded
+        #     # (num_crops, 24*24, 1024)
+        #     sub_image_features = image_features[i, 1 : 1 + num_crops]
+        #     sub_image_features_hd = self.reshape_hd_patches_2x2merge(
+        #         sub_image_features, h_crop, w_crop
+        #     )
+        #     sub_image_features_hd_newline = self.add_image_newline(sub_image_features_hd)
+
+        #     # [sub features, separator, global features]
+        #     all_image_embeddings.extend(
+        #         [
+        #             sub_image_features_hd_newline.squeeze(0),  # (h_crop*12*(w_crop*12+1), 4096)
+        #             self.glb_GN.squeeze(0),
+        #             global_image_features_hd_newline[i],
+        #         ]
+        #     )
+
+        # image_features_proj = self.img_projection(
+        #     torch.cat(all_image_embeddings, dim=0).to(target_device).to(target_dtype)
+        # )
+
+        # return image_features_proj
+        
+        all_image_embeddings = get_image_embeddings(torch.tensor(self.image_dim_out), image_sizes, image_features, global_image_features_hd_newline)
+        image_features_proj = self.img_projection(
+            all_image_embeddings.unsqueeze(0).to(target_device).to(target_dtype)
+        )
+        return image_features_proj.squeeze()
+
+    def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
+        """
+        image_features: (num_images*num_crops, 24*24, 1024)
+        output: (num_images, h_crop*12, w_crop*12, 4096), h_crop*w_crop == num_crops
+        """
+        N, L, C = image_features.shape
+        assert L == 24 * 24 and C == 1024 and N % (h_crop * w_crop) == 0
+        num_images = N // (h_crop * w_crop)
+        H = int(L**0.5)
+        image_features_hd = (
+            image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
+            .reshape(N, H // 2, 2, H // 2, 2, C)  # N, 12, 2, 12, 2, 1024
+            .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
+            .reshape(N, -1, 4 * C)  # N, 144, 4096
+            .reshape(
+                num_images, h_crop, w_crop, H // 2, H // 2, -1
+            )  # n_img, h_crop, w_crop, 12, 12, 4096
+            .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
+            .reshape(
+                num_images, h_crop * H // 2, w_crop * H // 2, 4 * C
+            )  # n_img, h_crop*12, w_crop*12, 4096
+        )
+
+        # alternative implementation using einops
+        # from einops import rearrange
+        # image_features_nhwc = rearrange(
+        #     image_features,
+        #     'N (H W) c -> N H W c',
+        #     H=H,
+        #     W=H,
+        # )
+        # image_features_2x2merge = rearrange(
+        #     image_features_nhwc,
+        #     'N (h h_pool) (w w_pool) c -> N h w (h_pool w_pool c)',
+        #     h_pool=2,
+        #     w_pool=2,
+        # )
+        # image_features_hd = rearrange(
+        #     image_features_2x2merge,
+        #     '(n_img h_crop w_crop) h w C -> n_img (h_crop h) (w_crop w) C',
+        #     h_crop=h_crop,
+        #     w_crop=w_crop,
+        # )
+
+        return image_features_hd
+
+    def add_image_newline(self, image_features_hd):
+        """
+        image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
+        output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
+        """
+        num_images, h, w, hid_dim = image_features_hd.shape
+        # add the newline token to the HD image feature patches
+        newline_embeddings = self.sub_GN.expand(num_images, h, -1, -1)  # (n_img, h, 1, hid_dim)
+        image_features_hd_newline = torch.cat(
+            [image_features_hd, newline_embeddings], dim=2
+        ).reshape(num_images, -1, hid_dim)
+        return image_features_hd_newline
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-vision-128k-instruct"
+_CONFIG_FOR_DOC = "Phi3VConfig"
+
+PHI3V_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "microsoft/Phi-3-vision-128k-instruct",
+    # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
+]
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
+class Phi3RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Phi3RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3
+class Phi3RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        self.register_buffer("inv_freq", None, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.inv_freq is None:
+            self.inv_freq = 1.0 / (
+                self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
+            )
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Phi3SuScaledRotaryEmbedding(Phi3RotaryEmbedding):
+    def __init__(self, dim, config, device=None):
+        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
+
+        self.short_factor = config.rope_scaling["short_factor"]
+        self.long_factor = config.rope_scaling["long_factor"]
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.original_max_position_embeddings:
+            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
+        else:
+            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
+
+        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
+        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
+
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+
+            scale = self.max_position_embeddings / self.original_max_position_embeddings
+            if scale <= 1.0:
+                scaling_factor = 1.0
+            else:
+                scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))
+
+            cos = emb.cos() * scaling_factor
+            sin = emb.sin() * scaling_factor
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Phi3YarnScaledRotaryEmbedding(Phi3RotaryEmbedding):
+    def __init__(self, dim, config, device=None):
+        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
+
+        self.short_factor = config.rope_scaling["short_factor"]
+        self.long_factor = config.rope_scaling["long_factor"]
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.original_max_position_embeddings:
+            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
+        else:
+            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
+
+        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
+        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
+
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+
+            scale = self.max_position_embeddings / self.original_max_position_embeddings
+            if scale <= 1.0:
+                scaling_factor = 1.0
+            else:
+                scaling_factor = 0.1 * math.log(scale) + 1.0
+
+            cos = emb.cos() * scaling_factor
+            sin = emb.sin() * scaling_factor
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Phi3MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+        self.activation_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        up_states = self.gate_up_proj(hidden_states)
+
+        gate, up_states = up_states.chunk(2, dim=-1)
+        up_states = up_states * self.activation_fn(gate)
+
+        return self.down_proj(up_states)
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Phi3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Phi3VConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.rope_scaling = config.rope_scaling
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.rope_scaling is None:
+            self.rotary_emb = Phi3RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            if scaling_type == "su":
+                self.rotary_emb = Phi3SuScaledRotaryEmbedding(self.head_dim, self.config)
+            elif scaling_type == "yarn":
+                self.rotary_emb = Phi3YarnScaledRotaryEmbedding(self.head_dim, self.config)
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.")
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class Phi3FlashAttention2(Phi3Attention):
+    """
+    Phi-3 flash attention module. This module inherits from `Phi3Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # Phi3FlashAttention2 attention does not support output_attentions
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
+            )
+            raise ValueError("The current flash attention version does not support sliding window attention.")
+
+        output_attentions = False
+
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_dropout = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32.
+
+        if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.qkv_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=attn_dropout,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
+# TODO @Arthur no longer copied from LLama after static cache
+class Phi3SdpaAttention(Phi3Attention):
+    """
+    Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Phi3Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Phi3Model is using Phi3SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+PHI3_ATTENTION_CLASSES = {
+    "eager": Phi3Attention,
+    "flash_attention_2": Phi3FlashAttention2,
+    "sdpa": Phi3SdpaAttention,
+}
+
+
+class Phi3DecoderLayer(nn.Module):
+    def __init__(self, config: Phi3VConfig, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
+
+        self.mlp = Phi3MLP(config)
+        self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
+        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
+        self.post_attention_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        """
+        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.
+            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
+                `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+            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
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        attn_outputs, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        hidden_states = residual + self.resid_attn_dropout(attn_outputs)
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.resid_mlp_dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+PHI3V_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 ([`Phi3VConfig`]):
+            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 Phi-3-V model outputting raw hidden-states without any specific head on top.",
+    PHI3V_START_DOCSTRING,
+)
+class Phi3VPreTrainedModel(PreTrainedModel):
+    config_class = Phi3VConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Phi3DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = False
+    _supports_cache_class = True
+
+    _version = "0.0.5"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        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_()
+
+
+PHI3V_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 [`AutoTokenizer`]. 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 [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `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.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - 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)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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.
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
+            The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`].
+            See [`Phi3ImageProcessor.__call__`] for details.
+        image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
+            The sizes of the images in the batch, being (height, width) for each image.
+        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.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi-3-V model outputting raw hidden-states without any specific head on top.",
+    PHI3V_START_DOCSTRING,
+)
+class Phi3VModel(Phi3VPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
+
+    Args:
+        config: Phi3Config
+    """
+
+    def __init__(self, config: Phi3VConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.combined_embed = Phi3Embedding(self.embed_tokens, config.vocab_size)
+
+        self.vision_embed_tokens = None
+        if isinstance(config.embd_layer, dict):
+            # vision embedding layer
+            embedding_config = {
+                'embedding_cls': config.embd_layer['embedding_cls'],
+                **config.embd_layer
+            }
+            self.vision_embed_tokens = Phi3ImageEmbedding(config, wte=self.embed_tokens, **embedding_config)
+            # # set wte the same for vision embedding
+            # self.vision_embed_tokens.wte.weight = self.embed_tokens.weight
+
+        self.layers = nn.ModuleList(
+            [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        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
+
+    @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: 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, 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
+
+        # retrieve input_ids and inputs_embeds
+        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:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_key_values_length = 0
+
+        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
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            if pixel_values is not None and image_sizes is not None:
+                assert self.vision_embed_tokens is not None, "Vision embedding layer is not defined"
+                # inputs_embeds = self.vision_embed_tokens(input_ids, pixel_values=pixel_values, image_sizes=image_sizes)
+                inputs_embeds = self.vision_embed_tokens(pixel_values=pixel_values, image_sizes=image_sizes)
+            else:
+                inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Phi3. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    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],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        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,
+        )
+
+
+class Phi3VForCausalLM(Phi3VPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Phi3VModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING)
+    @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,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.LongTensor] = 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]:
+        r"""
+        Args:
+            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]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Phi3ForCausalLM
+
+        >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+
+        >>> prompt = "This is an example script ."
+        >>> 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]
+        'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
+        ```"""
+
+        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(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        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,
+        )
+
+    # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, pixel_values=None, image_sizes=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        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(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "image_sizes": image_sizes,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The [`Phi3VModel`] with a sequence classification head on top (linear layer).
+
+    [`Phi3VForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    PHI3V_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
+class Phi3VForSequenceClassification(Phi3VPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Phi3VModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.LongTensor] = 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, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        model_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = model_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + model_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=model_outputs.past_key_values,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    [`Phi3VModel`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    PHI3V_START_DOCSTRING,
+)
+# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
+class Phi3VForTokenClassification(Phi3VPreTrainedModel):
+    def __init__(self, config: Phi3VConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = Phi3VModel(config)
+        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+            classifier_dropout = config.classifier_dropout
+        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **deprecated_arguments,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        model_outputs = self.model(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = model_outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            batch_size, seq_length = labels.shape
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
+            )
+
+        if not return_dict:
+            output = (logits,) + model_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )