Delete friday

friday/__init__.py

@@ -1,8 +0,0 @@
-# friday/__init__.py
-from .model.friday_arch import FridayConfig, FridayForCausalLM
-
-from transformers import AutoConfig, AutoModelForCausalLM
-AutoConfig.register(FridayConfig.model_type, FridayConfig)
-AutoModelForCausalLM.register(FridayConfig, FridayForCausalLM)
-
-__all__ = ["FridayConfig", "FridayForCausalLM"]

friday/constants.py

@@ -1,11 +0,0 @@
-# Model Constants
-IMAGE_TOKEN = "<image>"
-IMG_START_TOKEN   = "<img_start>"
-IMG_END_TOKEN     = "<img_end>"
-IGNORE_INDEX      = -100
-IMAGE_TOKEN_INDEX = -200
-PAD_FOR_EOS       = -300
-
-CONTROLLER_HEART_BEAT_EXPIRATION = 30
-LOGDIR = "gradio-logs"
-WORKER_HEART_BEAT_INTERVAL = 15

friday/model/__init__.py

@@ -1,15 +0,0 @@
-from .friday_arch import (
-    build_tokenizer, 
-    FridayConfig, 
-    FridayModel, 
-    FridayForCausalLM,
-    DEFAULT_CFG_SPECIAL_TOKENS as SPECIAL_TOKENS
-)
-
-__all__ = [
-    "build_tokenizer",
-    "FridayConfig",
-    "FridayModel",
-    "FridayForCausalLM",
-    "SPECIAL_TOKENS",
-]

friday/model/friday_arch.py

@@ -1,585 +0,0 @@
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import transforms
-
-from typing import List, Tuple, Optional, Union
-
-import PIL
-
-from transformers import AutoTokenizer, AutoConfig
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-
-from friday.model.vision_adapter import MLPAdapter
-from friday.model.vision_tower import (
-    SiglipVisionTower, 
-    SiglipVisionTowerS2,
-    FastVitVisionTower,
-    FastVitVisionTowerS2
-)
-from friday.model.language_model.phi4 import (
-    Phi3Config, 
-    Phi3Model, 
-    Phi3ForCausalLM
-)
-from friday.constants import (
-    IMAGE_TOKEN,
-    IMG_START_TOKEN,
-    IMG_END_TOKEN,
-    IGNORE_INDEX
-)
-
-DEFAULT_CFG_SPECIAL_TOKENS = {
-    "image_token_id": 200029,
-    "image_start_token_id": 200030,
-    "image_end_token_id": 200031,
-}
-DEFAULT_CFG_VISION_TOWER = {
-    "pretrained_model_name_or_path": "kevin510/fast-vit-hd",
-    "type": "fastvit",
-    "s2_scales": "512,1024",
-    "use_s2": True,
-    "pad_to_square": True,
-    "freeze": False,
-    "model_params": { "trust_remote_code": True }
-}
-DEFAULT_CFG_VISION_ADAPTER = {
-    "input_dim": 6144,
-    "hidden_dim": 3072,
-    "output_dim": 3072,
-    "layers": 2,
-    "activation": "gelu",
-    "freeze": False,
-}
-
-
-class FridayConfig(Phi3Config):
-    model_type = "friday"
-
-    def __init__(self, 
-            base_model_name_or_path: str | None = "microsoft/Phi-4-mini-reasoning",
-            delay_load=False, 
-            tokenizer_model_max_length=None,
-            **kwargs
-        ):
-        base_kwargs = {}
-        if base_model_name_or_path is not None:
-            base_cfg = AutoConfig.from_pretrained(
-                base_model_name_or_path,
-                trust_remote_code=True,   # Phi‑4 uses custom code in the repo
-            )
-            base_kwargs = base_cfg.to_dict()
-
-        merged = {**base_kwargs, **kwargs}
-        self.delay_load = delay_load
-        self.tokenizer_model_max_length = tokenizer_model_max_length
-
-        self._cfg_vision_tower = DEFAULT_CFG_VISION_TOWER.copy()
-        if "cfg_vision_tower" in kwargs:
-            self._cfg_vision_tower.update(kwargs["cfg_vision_tower"])
-
-        self._cfg_vision_adapter = DEFAULT_CFG_VISION_ADAPTER.copy()
-        if "cfg_vision_adapter" in kwargs:
-            self._cfg_vision_adapter.update(kwargs["cfg_vision_adapter"])
-
-        self._cfg_special_tokens = DEFAULT_CFG_SPECIAL_TOKENS.copy()
-        if "cfg_special_tokens" in kwargs:
-            self._cfg_special_tokens.update(kwargs["cfg_special_tokens"])
-
-        super().__init__(**merged)
-        
-    
-    @property
-    def cfg_vision_tower(self):
-        return self._cfg_vision_tower
-
-    @cfg_vision_tower.setter
-    def cfg_vision_tower(self, value):
-        if not value:
-            raise ValueError("Name cannot be empty")
-        self._cfg_vision_tower.update(value)
-    
-
-    @property
-    def cfg_vision_adapter(self):
-        return self._cfg_vision_adapter
-    
-    @cfg_vision_adapter.setter
-    def cfg_vision_adapter(self, value):
-        if not value:
-            raise ValueError("Name cannot be empty")
-        self._cfg_vision_adapter.update(value)
-    
-    @property
-    def cfg_special_tokens(self):
-        return self._cfg_special_tokens
-    
-    @cfg_special_tokens.setter
-    def cfg_special_tokens(self, value):
-        if not value:
-            raise ValueError("Name cannot be empty")
-        self._cfg_special_tokens.update(value)
-
-
-class FridayModel(Phi3Model):
-    config_class = FridayConfig
-    
-    def __init__(self, config: FridayConfig):
-        super().__init__(config)
-
-        self.cfg_vision_adapter = config.cfg_vision_adapter
-        self.cfg_vision_tower = config.cfg_vision_tower
-
-        self.vision_tower = None
-        self.mm_projector    = None
-        if not config.delay_load:
-            self.initialize_vision_modules()
-    
-    def get_vision_tower(self):
-        return self.vision_tower
-    
-    def initialize_vision_modules(self):
-        if self.vision_tower is not None:
-            return
-
-        if self.cfg_vision_tower.get("type", "siglip").lower() == "siglip":
-            if self.cfg_vision_tower.get("use_s2", True):
-                self.vision_tower = SiglipVisionTowerS2(**self.cfg_vision_tower)
-            else:
-                self.vision_tower = SiglipVisionTower(**self.cfg_vision_tower)
-        elif self.cfg_vision_tower.get("type", "siglip").lower() == "fastvit":
-            if self.cfg_vision_tower.get("use_s2", True):
-                self.vision_tower = FastVitVisionTowerS2(**self.cfg_vision_tower)
-            else:
-                self.vision_tower = FastVitVisionTower(**self.cfg_vision_tower)
-        else:
-            raise ValueError(f"Unsupported vision tower type: {self.cfg_vision_tower.get('type', 'siglip')}. Supported types are 'siglip' and 'fastvit'.")
-        
-        self.vision_tower.load_model()
-        self.mm_projector = MLPAdapter(**self.cfg_vision_adapter)
-
-        if self.cfg_vision_tower.get("freeze", False):
-            self.set_vision_tower_requires_grad(False)
-        
-        if self.cfg_vision_adapter.get("freeze", False):
-            self.set_vision_adapter_requires_grad(False)
-    
-    def compute_image_features(self, imgs: torch.Tensor) -> torch.Tensor:
-        features = self.vision_tower(imgs)
-        if isinstance(features, list):
-            features = torch.stack(features, dim=1)
-        return self.mm_projector(features)
-    
-    def set_vision_tower_requires_grad(self, requires_grad: bool):
-        if self.vision_tower is not None:
-            for param in self.vision_tower.parameters():
-                param.requires_grad = requires_grad
-        else:
-            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
-    
-    def set_vision_adapter_requires_grad(self, requires_grad: bool):
-        if self.mm_projector is not None:
-            for param in self.mm_projector.parameters():
-                param.requires_grad = requires_grad
-        else:
-            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")
-    
-    def set_vision_tower_dtype(self, dtype: torch.dtype):
-        if self.vision_tower is not None:
-            for p in self.vision_tower.parameters():
-                p.data = p.data.to(dtype)
-        else:
-            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
-    
-    def set_vision_adapter_dtype(self, dtype: torch.dtype):
-        if self.mm_projector is not None:
-            for p in self.mm_projector.parameters():
-                p.data = p.data.to(dtype)
-        else:
-            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")
-    
-    def is_vision_tower_frozen(self):
-        if self.vision_tower is not None:
-            return all(not p.requires_grad for p in self.vision_tower.parameters())
-        else:
-            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
-    
-    def is_vision_adapter_frozen(self):
-        if self.mm_projector is not None:
-            return all(not p.requires_grad for p in self.mm_projector.parameters())
-        else:
-            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")
-
-
-class FridayForCausalLM(Phi3ForCausalLM):
-    config_class = FridayConfig
-
-    def __init__(self, config: FridayConfig):
-        super().__init__(config)
-
-        self.config = config
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.image_token_id = config.cfg_special_tokens["image_token_id"]
-        self.image_start_id       = config.cfg_special_tokens["image_start_token_id"]
-        self.image_end_id         = config.cfg_special_tokens["image_end_token_id"]
-
-        self.model = FridayModel(config)
-        self.post_init()
-    
-    def get_model(self) -> FridayModel:
-        return self.model
-    
-    def get_vision_tower(self) -> SiglipVisionTower:
-        return self.model.get_vision_tower()
-    
-    def get_vision_adapter(self) -> MLPAdapter:
-        return self.model.mm_projector
-
-    def get_llm_parameters(self, exclude_lora: bool = False):
-        return [
-            p for n, p in self.named_parameters() 
-            if "vision_tower" not in n and "mm_projector" not in n and (not exclude_lora or ("lora_" not in n))
-        ]
-
-    def get_llm_named_modules(self):
-        return {n: m for n, m in self.named_modules() if "vision_tower" not in n and "mm_projector" not in n}
-    
-    def set_llm_requires_grad(self, requires_grad: bool, exclude_lora: bool = True):
-        for n, p in self.named_parameters():
-            if exclude_lora and ("lora_A" in n or "lora_B" in n):
-                continue
-            if "vision_tower" in n or "mm_projector" in n:
-                continue
-            p.requires_grad = requires_grad
-    
-    def set_vision_tower_requires_grad(self, requires_grad: bool):
-        self.model.set_vision_tower_requires_grad(requires_grad)
-
-    def set_vision_adapter_requires_grad(self, requires_grad: bool):
-        self.model.set_vision_adapter_requires_grad(requires_grad)
-    
-    def set_llm_dtype(self, dtype: torch.dtype):
-        for p in self.get_llm_parameters():
-            p.data = p.data.to(dtype)
-
-    def set_vision_tower_dtype(self, dtype: torch.dtype):
-        self.model.set_vision_tower_dtype(dtype)
-    
-    def set_vision_adapter_dtype(self, dtype: torch.dtype):
-        self.model.set_vision_adapter_dtype(dtype)
-    
-    def is_llm_frozen(self):
-        return all(not p.requires_grad for p in self.get_llm_parameters())
-    
-    def is_vision_tower_frozen(self):
-        return self.model.is_vision_tower_frozen()
-    
-    def is_vision_adapter_frozen(self):
-        return self.model.is_vision_adapter_frozen()
-    
-    
-    
-    def initialize_vision_modules(self):
-        self.model.initialize_vision_modules()
-    
-    def get_multimodal_input_embeddings(self, input_ids, image_features, return_labels=True) -> torch.Tensor:
-        emb_start_image_id = self.model.embed_tokens(torch.tensor([self.image_start_id], device=self.device))
-        emb_end_image_id   = self.model.embed_tokens(torch.tensor([self.image_end_id], device=self.device))
-        id_ignore = torch.tensor([IGNORE_INDEX], device=self.device)
-
-        # repetition‑penalty safety ????
-        # input_ids[input_ids == self.image_token_id] = 0
-
-        
-        # Iterate over each batch item
-        embeds_list, labels_list = [], []
-        for batch_id, item_ids in enumerate(input_ids):
-            
-            image_token_positions = (item_ids == self.image_token_id).nonzero(as_tuple=True)[0]
-            if len(image_token_positions) != image_features[batch_id].shape[0]:
-                raise ValueError(
-                    f"Mismatch between number of image tokens ({len(image_token_positions)}) and number of image features ({image_features[batch_id].shape[0]})"
-                )
-
-
-            cursor = 0
-            emb_parts, lbl_parts = [], []
-            for indx_image, image_token_pos in enumerate(image_token_positions):
-                if image_token_pos > cursor:
-                    span = item_ids[cursor:image_token_pos]
-                    emb_parts.append(self.model.embed_tokens(span))
-                    lbl_parts.append(span)
-
-                # <image_start>
-                emb_parts.append(emb_start_image_id)
-                lbl_parts.append(id_ignore)
-
-                # vision embeddings
-                image_tokens = image_features[batch_id][indx_image]
-                if image_tokens.shape[0] == 1 and image_tokens.ndim == 3:
-                    image_tokens = image_tokens.squeeze(0)
-                emb_parts.append(image_tokens)
-                lbl_parts.append(id_ignore.repeat(image_tokens.shape[0]))
-
-                # <image_end>
-                emb_parts.append(emb_end_image_id)
-                lbl_parts.append(id_ignore)
-
-                cursor = image_token_pos + 1
-            
-            # tail text
-            if cursor < item_ids.shape[0]:
-                tail = item_ids[cursor:]
-                emb_parts.append(self.model.embed_tokens(tail))
-                lbl_parts.append(tail)
-            
-            embeds_list.append(torch.cat(emb_parts, dim=0))
-            labels_list.append(torch.cat(lbl_parts, dim=0))
-    
-        return (embeds_list, labels_list) if return_labels else embeds_list
-
-    def prepare_inputs_for_multimodal(
-        self,
-        input_ids: torch.LongTensor,
-        images: List[List[PIL.Image.Image]], # B x N
-        position_ids: Optional[torch.LongTensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_values: Optional[List[torch.FloatTensor]],
-        labels: Optional[torch.LongTensor],
-    ) -> Tuple[Optional[torch.Tensor], Optional[torch.LongTensor], Optional[torch.Tensor], Optional[List[torch.FloatTensor]], torch.Tensor, Optional[torch.Tensor]]:
-        
-        # ─────────────────── early return (no image / streaming step) ───────────────────
-        # if we have already processed images and are in a streaming step we can skip the multimodal processing
-        # but we need to ensure the attention mask and position ids are correct
-
-        if past_key_values is not None and attention_mask is not None and input_ids.shape[1] == 1:
-            tgt = past_key_values[-1][-1].shape[-2] + 1
-            attention_mask = torch.cat(
-                [attention_mask,
-                torch.ones((attention_mask.size(0),
-                            tgt - attention_mask.size(1)),
-                            dtype=attention_mask.dtype,
-                            device=attention_mask.device)],
-                dim=1,
-            )
-            position_ids = (attention_mask.sum(dim=1, keepdim=True) - 1).long()
-
-            return input_ids, position_ids, attention_mask, past_key_values, None, labels
-
-        # ─────────────────────────── images: (B, N) ───────────────────────────
-        if isinstance(images, list) and isinstance(images[0], list):
-            # images is a list of lists, each containing multiple images, B x N
-            # e.g. [[img1, img2], [img3, img4]]
-            assert len(images) == input_ids.shape[0], f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
-            image_features = []
-            for sublst_images in images:
-                if len(sublst_images) == 0:
-                    image_features.append(torch.zeros((0, self.get_model().mm_projector.output_dim), device=self.device))
-                else:
-                    if isinstance(sublst_images[0], PIL.Image.Image):
-                        image_features.append(
-                            self.model.compute_image_features(
-                                self.model.vision_tower.preprocess_images(sublst_images, pad_and_stack_tensors=True)
-                            )
-                        )
-                    elif isinstance(sublst_images[0], torch.Tensor):
-                        # This should be a list of tensors of pre-processed images, [(N X 3 X W x H), ...]
-                        image_features.append(
-                            self.model.compute_image_features(sublst_images)
-                        )
-        elif isinstance(images, list) and isinstance(images[0], PIL.Image.Image):
-            # images is a list of images for a single batch item, 1 x N
-            # e.g. [img1, img2, img3]
-            assert input_ids.shape[0] == 1, f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
-            image_features = [
-                self.model.compute_image_features(
-                    self.model.vision_tower.preprocess_images(images, pad_and_stack_tensors=True)
-                )
-            ]
-        elif isinstance(images, list) and isinstance(images[0], torch.Tensor):
-            # This should be a list of tensors of pre-processed images, [(N X 3 X W x H), ...]
-            # The list length should match the batch size
-            assert input_ids.shape[0] == len(images), f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
-            image_features = [
-                self.model.compute_image_features(imgs) for imgs in images
-            ]
-        elif isinstance(images, PIL.Image.Image):
-            # images is a single image, 1 x 1
-            # e.g. img1
-            assert input_ids.shape[0] == 1, f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
-            image_features = [
-                self.model.compute_image_features(
-                    self.model.vision_tower.preprocess_images([images])
-                )
-            ]
-        else:
-            raise ValueError(f"Unsupported images format: {type(images)}. Expected list of PIL images, a single PIL image or a Tensor of pre-processed images")
-        
-        # ─────────────────────────── image_features: (B x N x D) ───────────────────────────
-        if isinstance(image_features, list):
-            assert input_ids.shape[0] == len(image_features), f"Incorrectly formatted image_features: list length should match batch size"
-            assert isinstance(image_features[0], torch.Tensor), f"Incorrectly formatted image_features: list items should be tensors"
-        elif isinstance(image_features, torch.Tensor):
-            assert input_ids.shape[0] == image_features.shape[0], f"Incorrectly formatted image_features: tensor should match batch size"
-        
-
-        # ───────────────────────────── pad handling prelims ──────────────────────────────
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
-        else:
-            attention_mask = attention_mask.bool()
-        if position_ids is None:
-            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
-
-        input_ids_nopad = [ids[mask] for ids, mask in zip(input_ids, attention_mask)]
-        embeds_list, labels_list = self.get_multimodal_input_embeddings(
-            input_ids_nopad,
-            image_features,
-            return_labels=True
-        )
-
-        # ───────────────────── truncate then pad back to rectangle ──────────────────────
-        new_input_embeds = torch.nn.utils.rnn.pad_sequence(
-            embeds_list,
-            batch_first=True,
-            padding_value=0.0
-        ).to(dtype=self.dtype)
-
-        new_labels = torch.nn.utils.rnn.pad_sequence(
-            labels_list,
-            batch_first=True,
-            padding_value=IGNORE_INDEX
-        ).long()
-
-        if self.config.tokenizer_model_max_length is not None:
-            new_input_embeds = new_input_embeds[:, :self.config.tokenizer_model_max_length]
-            new_labels       = new_labels[:, :self.config.tokenizer_model_max_length]
-
-        
-        
-
-        # ────────────────────────────── attention mask and position ids ────────────────
-        
-        attention_mask = (
-            torch.arange(new_input_embeds.size(1), device=input_ids.device)
-                  .unsqueeze(0)
-            < torch.tensor([e.size(0) for e in embeds_list],
-                           device=input_ids.device).unsqueeze(1)
-        )
-
-        raw_pos = attention_mask.cumsum(dim=1) - 1
-        position_ids = raw_pos.masked_fill(~attention_mask, 0).long()
-
-        if not self.training:
-            new_labels = None
-
-        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
-
-    
-    
-    # ------------------------------------------------------------------
-    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,
-            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,
-            cache_position: Optional[torch.LongTensor] = None,
-            logits_to_keep: Union[int, torch.Tensor] = 0,
-            images: Optional[PIL.Image.Image] = None,
-            **kwargs: Unpack[KwargsForCausalLM],
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        is_multi_modal = images is not None and not (
-            (
-                isinstance(images, list) and (len(images) == 0 or all(i == [] for i in images))
-            )
-        )
-
-
-        if inputs_embeds is None and is_multi_modal:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_for_multimodal(
-                input_ids=input_ids,
-                images=images,
-                position_ids=position_ids,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-                labels=labels,
-            )
-
-            if cache_position is not None and inputs_embeds is not None and cache_position.shape[0] != inputs_embeds.shape[1]:
-                cache_position = torch.arange(inputs_embeds.shape[1], device=self.device)
-        
-        
-        return Phi3ForCausalLM.forward(
-            self,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-            logits_to_keep=logits_to_keep,
-            **kwargs
-        )
-    
-    def print_device_configuration(self):
-        print("*************Device Configuration*********")
-        if len(self.get_llm_parameters()) > 0:
-            llm_device = set({str(p.device) for p in self.get_llm_parameters()})
-            llm_dtype = set({p.dtype for p in self.get_llm_parameters()})
-            print(f"LLM Parameters:\t\t\tdevice: {llm_device}\tdtype: {llm_dtype}\tfrozen: {self.is_llm_frozen()}")
-        else:
-            print("LLM parameters have not been initialized")
-        
-        if self.get_model().vision_tower is not None:
-            vt_device = set({str(p.device) for p in self.get_model().vision_tower.parameters()})
-            vt_dtype = set({p.dtype for p in self.get_model().vision_tower.parameters()})
-            print(f"Vision Tower Parameters:\tdevice: {vt_device}\tdtype: {vt_dtype}\tfrozen: {self.is_vision_tower_frozen()}")
-        else:
-            print("Vision tower parameters have not been initialized")
-
-        if self.get_model().mm_projector is not None:
-            mm_device = set({str(p.device) for p in self.get_model().mm_projector.parameters()})
-            mm_dtype = set({p.dtype for p in self.get_model().mm_projector.parameters()})
-            print(f"MM Projector Parameters:\tdevice: {mm_device}\tdtype: {mm_dtype}\tfrozen: {self.is_vision_adapter_frozen()}")
-        else:
-            print("MM Projector parameters have not been initialized")
-        print("******************************************")
-
-
-
-def build_tokenizer(base_model_id: str) -> Tuple[AutoTokenizer, dict]:
-    tok = AutoTokenizer.from_pretrained(base_model_id, padding_side="right")
-    specials = {t: tok.convert_tokens_to_ids(t) for t in [IMAGE_TOKEN, IMG_START_TOKEN, IMG_END_TOKEN] if t in tok.vocab}
-    if len(specials) < 3:
-        n = tok.add_tokens([IMAGE_TOKEN, IMG_START_TOKEN, IMG_END_TOKEN], special_tokens=True)
-        tok.pad_token = tok.eos_token
-        specials = {
-            "image": tok.convert_tokens_to_ids(IMAGE_TOKEN),
-            "start": tok.convert_tokens_to_ids(IMG_START_TOKEN),
-            "end": tok.convert_tokens_to_ids(IMG_END_TOKEN),
-        }
-    return tok, specials
-

friday/model/language_model/phi4/__init__.py

@@ -1,11 +0,0 @@
-from .configuration_phi3 import Phi3Config
-from .modeling_phi3 import (
-    Phi3Model,
-    Phi3ForCausalLM,
-)
-
-__all__ = [
-    "Phi3Config",
-    "Phi3Model",
-    "Phi3ForCausalLM",
-]

friday/model/language_model/phi4/configuration_phi3.py

@@ -1,226 +0,0 @@
-# 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.
-
-"""Phi-3 model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class Phi3Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
-    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
-    defaults will yield a similar configuration to that of the
-    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32064):
-            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Phi3Model`].
-        hidden_size (`int`, *optional*, defaults to 3072):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 8192):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
-            `num_attention_heads`.
-        resid_pdrop (`float`, *optional*, defaults to 0.0):
-            Dropout probability for mlp outputs.
-        embd_pdrop (`int`, *optional*, defaults to 0.0):
-            The dropout ratio for the embeddings.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio after computing the attention scores.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 4096):
-            The maximum sequence length that this model might ever be used with.
-        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
-            The maximum sequence length that this model was trained with. This is used to determine the size of the
-            original RoPE embeddings when using long scaling.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon value used for the RMSNorm.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`dict`, *optional*):
-            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
-            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
-            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
-            divided by the number of attention heads divided by 2.
-        partial_rotary_factor (`float`, *optional*, defaults to 1.0):
-            Percentage of the query and keys which will have rotary embedding. Must be between 0.0 and 1.0.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 32000):
-            The id of the "end-of-sequence" token.
-        pad_token_id (`int`, *optional*, defaults to 32000):
-            The id of the padding token.
-        sliding_window (`int`, *optional*):
-            Sliding window attention window size. If `None`, no sliding window is applied.
-
-    Example:
-
-    ```python
-    >>> from transformers import Phi3Model, Phi3Config
-
-    >>> # Initializing a Phi-3 style configuration
-    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
-
-    >>> # Initializing a model from the configuration
-    >>> model = Phi3Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "phi3"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=32064,
-        hidden_size=3072,
-        intermediate_size=8192,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        resid_pdrop=0.0,
-        embd_pdrop=0.0,
-        attention_dropout=0.0,
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        original_max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-5,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        partial_rotary_factor=1.0,
-        bos_token_id=1,
-        eos_token_id=32000,
-        pad_token_id=32000,
-        sliding_window=None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.resid_pdrop = resid_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.attention_dropout = attention_dropout
-        self.hidden_act = hidden_act
-        self.max_position_embeddings = max_position_embeddings
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.partial_rotary_factor = partial_rotary_factor
-        self._rope_scaling_adjustment()
-        self._rope_scaling_validation()
-        self.sliding_window = sliding_window
-
-        super().__init__(
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            pad_token_id=pad_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-    def _rope_scaling_adjustment(self):
-        """
-        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
-        """
-        if self.rope_scaling is None:
-            return
-
-        rope_scaling_type = self.rope_scaling.get("type", None)
-
-        # For backward compatibility if previous version used "su" or "yarn"
-        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
-            self.rope_scaling["type"] = "longrope"
-
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
-        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
-            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
-        if not (
-            isinstance(rope_scaling_short_factor, list)
-            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
-        ):
-            raise ValueError(
-                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
-            )
-        rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor)
-        if not len(rope_scaling_short_factor) == rotary_ndims // 2:
-            raise ValueError(
-                f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_short_factor)}"
-            )
-        if not (
-            isinstance(rope_scaling_long_factor, list)
-            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
-        ):
-            raise ValueError(
-                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
-            )
-        if not len(rope_scaling_long_factor) == rotary_ndims // 2:
-            raise ValueError(
-                f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}"
-            )

friday/model/language_model/phi4/modeling_phi3.py

@@ -1,1181 +0,0 @@
-# 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 model."""
-
-from typing import Callable, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
-from transformers.generation import GenerationMixin
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    LossKwargs,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-from .configuration_phi3 import Phi3Config
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
-_CONFIG_FOR_DOC = "Phi3Config"
-
-
-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)
-
-
-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)
-
-
-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)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs,
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        attn_weights = attn_weights + causal_mask
-
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-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)
-
-    rotary_dim = cos.shape[-1]
-    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
-    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
-
-    q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1)
-    k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1)
-    return q_embed, k_embed
-
-
-class Phi3Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.is_causal = True
-
-        op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim)
-        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
-        self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        qkv = self.qkv_proj(hidden_states)
-        query_pos = self.config.num_attention_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(hidden_shape).transpose(1, 2)
-        key_states = key_states.view(hidden_shape).transpose(1, 2)
-        value_states = value_states.view(hidden_shape).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
-                logger.warning_once(
-                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
-                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-                )
-            else:
-                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scaling,
-            sliding_window=getattr(self.config, "sliding_window", None),
-            **kwargs,
-        )
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-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)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class Phi3DecoderLayer(nn.Module):
-    def __init__(self, config: Phi3Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx)
-        self.mlp = Phi3MLP(config)
-        self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.config = config
-        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
-        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
-
-    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: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`):
-                input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            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)
-            past_key_value (`Cache`, *optional*): cached past key and value projection states
-            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`).
-            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-                Indices depicting the position of the input sequence tokens in the sequence
-            kwargs (`dict`, *optional*):
-                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
-                into the model
-        """
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights = 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,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        hidden_states = residual + self.resid_attn_dropout(hidden_states)  # main diff with Llama
-
-        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)  # main diff with Llama
-
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        return outputs
-
-
-class Phi3RotaryEmbedding(nn.Module):
-    def __init__(self, config: Phi3Config, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    def _dynamic_frequency_update(self, position_ids, device):
-        """
-        dynamic RoPE layers should recompute `inv_freq` in the following situations:
-        1 - growing beyond the cached sequence length (allow scaling)
-        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
-        """
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_seq_len_cached:  # growth
-            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
-            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
-            self.max_seq_len_cached = seq_len
-
-        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
-            # This .to() is needed if the model has been moved to a device after being initialized (because
-            # the buffer is automatically moved, but not the original copy)
-            self.original_inv_freq = self.original_inv_freq.to(device)
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-            self.max_seq_len_cached = self.original_max_seq_len
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        if "dynamic" in self.rope_type:
-            self._dynamic_frequency_update(position_ids, device=x.device)
-        elif self.rope_type == "longrope":
-            self._longrope_frequency_update(position_ids, device=x.device)
-
-        # Core RoPE block
-        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 (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()
-
-        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-    def _longrope_frequency_update(self, position_ids, device):
-        """Longrope uses long factor if sequence is larger than original pretraining length, short otherwise."""
-        seq_len = torch.max(position_ids) + 1
-        if hasattr(self.config, "original_max_position_embeddings"):
-            original_max_position_embeddings = self.config.original_max_position_embeddings
-        else:
-            original_max_position_embeddings = self.config.max_position_embeddings
-        if seq_len > original_max_position_embeddings:
-            if not hasattr(self, "long_inv_freq"):
-                self.long_inv_freq, _ = self.rope_init_fn(
-                    self.config, device, seq_len=original_max_position_embeddings + 1
-                )
-            self.register_buffer("inv_freq", self.long_inv_freq, persistent=False)
-        else:
-            # This .to() is needed if the model has been moved to a device after being initialized (because
-            # the buffer is automatically moved, but not the original copy)
-            self.original_inv_freq = self.original_inv_freq.to(device)
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-
-
-PHI3_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 ([`Phi3Config`]):
-            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 Phi3 Model outputting raw hidden-states without any specific head on top.",
-    PHI3_START_DOCSTRING,
-)
-class Phi3PreTrainedModel(PreTrainedModel):
-    config_class = Phi3Config
-    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 = True
-    _supports_flex_attn = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-    _supports_attention_backend = 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_()
-
-
-PHI3_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, see our
-            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
-            - 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.
-        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.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-@add_start_docstrings(
-    "The bare Phi3 Model outputting raw hidden-states without any specific head on top.",
-    PHI3_START_DOCSTRING,
-)
-class Phi3Model(Phi3PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
-
-    Args:
-        config: Phi3Config
-    """
-
-    def __init__(self, config: Phi3Config):
-        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.layers = nn.ModuleList(
-            [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Phi3RotaryEmbedding(config=config)
-        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(PHI3_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[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> 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
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        for decoder_layer in self.layers[: self.config.num_hidden_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,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                    position_embeddings,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                    **flash_attn_kwargs,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            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,)
-
-        output = BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-        return output if return_dict else output.to_tuple()
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and past_key_values is not None:
-                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
-                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 attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if (
-            self.config._attn_implementation == "sdpa"
-            and not (using_static_cache or using_sliding_window_cache)
-            and not output_attentions
-        ):
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                sliding_window=self.config.sliding_window,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        # SlidingWindowCache or StaticCache
-        if using_sliding_window_cache or using_static_cache:
-            target_length = past_key_values.get_max_cache_shape()
-        # DynamicCache or no cache
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            device=device,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-            config=self.config,
-            past_key_values=past_key_values,
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type in ["cuda", "xpu"]
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        config: Phi3Config,
-        past_key_values: Cache,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            device (`torch.device`):
-                The device to plcae the 4D attention mask on.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-            config (`Phi3Config`):
-                The model's configuration class
-            past_key_values (`Cache`):
-                The cache class that is being used currently to generate
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
-            )
-            diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            if config.sliding_window is not None:
-                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
-                # the check is needed to verify is current checkpoint was trained with sliding window or not
-                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
-                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
-                        cache_position.reshape(-1, 1) - config.sliding_window
-                    )
-                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
-            
-            causal_mask *= diagonal_attend_mask
-            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                if attention_mask.shape[-1] > target_length:
-                    attention_mask = attention_mask[:, :target_length]
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
-                    causal_mask.device
-                )
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-        return causal_mask
-
-
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
-
-
-class Phi3ForCausalLM(Phi3PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-    _tp_plan = {"lm_head": "colwise_rep"}
-    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
-
-    def __init__(self, config):
-        super().__init__(config)
-        # self.model = Phi3Model(config)
-        # self.vocab_size = config.vocab_size
-        # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # # Initialize weights and apply final processing
-        # self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-    @add_start_docstrings_to_model_forward(PHI3_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[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
-    ) -> 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]`.
-
-            logits_to_keep (`int` or `torch.Tensor`, *optional*):
-                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-                This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, Phi3ForCausalLM
-
-        >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf")
-        >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-hf")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = outputs[0]
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        cache_position=None,
-        position_ids=None,
-        use_cache=True,
-        logits_to_keep=None,
-        **kwargs,
-    ):
-        # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
-        # process
-
-        # When the first time input length reached long and short factor switching point, enforce re-compute cache
-        # It will cause downside of slower at this single token position, however, better than current failure.
-        if (
-            past_key_values
-            and self.config.rope_scaling
-            and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
-        ):
-            past_length = cache_position[0]
-            if past_length <= self.config.original_max_position_embeddings:
-                past_key_values = None
-
-        model_inputs = super().prepare_inputs_for_generation(
-            input_ids=input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            cache_position=cache_position,
-            position_ids=position_ids,
-            use_cache=use_cache,
-            logits_to_keep=logits_to_keep,
-            **kwargs,
-        )
-        return model_inputs
-
-
-@add_start_docstrings(
-    """
-    The Phi3 Model transformer with a sequence classification head on top (linear layer).
-
-    [`Phi3ForSequenceClassification`] 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).
-    """,
-    PHI3_START_DOCSTRING,
-)
-class Phi3ForSequenceClassification(Phi3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Phi3Model(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(PHI3_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[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,
-        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
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_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:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
-            token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The Phi3 Model transformer 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.
-    """,
-    PHI3_START_DOCSTRING,
-)
-class Phi3ForTokenClassification(Phi3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Phi3Model(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # 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(PHI3_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,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, 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
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

friday/model/language_model/phi4/sample_finetune.py

@@ -1,214 +0,0 @@
-import sys
-import logging
-
-import datasets
-from datasets import load_dataset
-from peft import LoraConfig
-import torch
-import transformers
-from trl import SFTTrainer
-from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
-
-"""
-A simple example on using SFTTrainer and Accelerate to finetune Phi-4-Mini-Instruct model. For
-a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
-This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
-script can be run on V100 or later generation GPUs. Here are some suggestions on 
-futher reducing memory consumption:
-    - reduce batch size
-    - decrease lora dimension
-    - restrict lora target modules
-Please follow these steps to run the script:
-1. Install dependencies: 
-    conda install -c conda-forge accelerate=1.3.0
-    pip3 install -i https://pypi.org/simple/ bitsandbytes
-    pip3 install peft==0.14.0
-    pip3 install transformers==4.48.1
-    pip3 install trl datasets
-    pip3 install deepspeed
-2. Setup accelerate and deepspeed config based on the machine used:
-    accelerate config
-Here is a sample config for deepspeed zero3:
-    compute_environment: LOCAL_MACHINE
-    debug: false
-    deepspeed_config:
-      gradient_accumulation_steps: 1
-      offload_optimizer_device: none
-      offload_param_device: none
-      zero3_init_flag: true
-      zero3_save_16bit_model: true
-      zero_stage: 3
-    distributed_type: DEEPSPEED
-    downcast_bf16: 'no'
-    enable_cpu_affinity: false
-    machine_rank: 0
-    main_training_function: main
-    mixed_precision: bf16
-    num_machines: 1
-    num_processes: 4
-    rdzv_backend: static
-    same_network: true
-    tpu_env: []
-    tpu_use_cluster: false
-    tpu_use_sudo: false
-    use_cpu: false
-3. check accelerate config:
-    accelerate env
-4. Run the code:
-    accelerate launch sample_finetune.py
-"""
-
-logger = logging.getLogger(__name__)
-
-
-###################
-# Hyper-parameters
-###################
-training_config = {
-    "bf16": True,
-    "do_eval": False,
-    "learning_rate": 5.0e-06,
-    "log_level": "info",
-    "logging_steps": 20,
-    "logging_strategy": "steps",
-    "lr_scheduler_type": "cosine",
-    "num_train_epochs": 1,
-    "max_steps": -1,
-    "output_dir": "./checkpoint_dir",
-    "overwrite_output_dir": True,
-    "per_device_eval_batch_size": 4,
-    "per_device_train_batch_size": 4,
-    "remove_unused_columns": True,
-    "save_steps": 100,
-    "save_total_limit": 1,
-    "seed": 0,
-    "gradient_checkpointing": True,
-    "gradient_checkpointing_kwargs":{"use_reentrant": False},
-    "gradient_accumulation_steps": 1,
-    "warmup_ratio": 0.2,
-    }
-
-peft_config = {
-    "r": 16,
-    "lora_alpha": 32,
-    "lora_dropout": 0.05,
-    "bias": "none",
-    "task_type": "CAUSAL_LM",
-    "target_modules": "all-linear",
-    "modules_to_save": None,
-}
-train_conf = TrainingArguments(**training_config)
-peft_conf = LoraConfig(**peft_config)
-
-
-###############
-# Setup logging
-###############
-logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    handlers=[logging.StreamHandler(sys.stdout)],
-)
-log_level = train_conf.get_process_log_level()
-logger.setLevel(log_level)
-datasets.utils.logging.set_verbosity(log_level)
-transformers.utils.logging.set_verbosity(log_level)
-transformers.utils.logging.enable_default_handler()
-transformers.utils.logging.enable_explicit_format()
-
-# Log on each process a small summary
-logger.warning(
-    f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
-    + f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
-)
-logger.info(f"Training/evaluation parameters {train_conf}")
-logger.info(f"PEFT parameters {peft_conf}")
-
-
-################
-# Model Loading
-################
-checkpoint_path = "microsoft/Phi-4-mini-instruct"
-model_kwargs = dict(
-    use_cache=False,
-    trust_remote_code=True,
-    attn_implementation="flash_attention_2",  # loading the model with flash-attention support
-    torch_dtype=torch.bfloat16,
-    device_map=None
-)
-model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
-tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
-tokenizer.model_max_length = 2048
-tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation
-tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
-tokenizer.padding_side = 'right'
-
-
-##################
-# Data Processing
-##################
-def apply_chat_template(
-    example,
-    tokenizer,
-):
-    messages = example["messages"]
-    example["text"] = tokenizer.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=False)
-    return example
-
-
-train_dataset, test_dataset = load_dataset("HuggingFaceH4/ultrachat_200k", split=["train_sft", "test_sft"])
-column_names = list(train_dataset.features)
-
-processed_train_dataset = train_dataset.map(
-    apply_chat_template,
-    fn_kwargs={"tokenizer": tokenizer},
-    num_proc=10,
-    remove_columns=column_names,
-    desc="Applying chat template to train_sft",
-)
-
-processed_test_dataset = test_dataset.map(
-    apply_chat_template,
-    fn_kwargs={"tokenizer": tokenizer},
-    num_proc=10,
-    remove_columns=column_names,
-    desc="Applying chat template to test_sft",
-)
-
-
-###########
-# Training
-###########
-trainer = SFTTrainer(
-    model=model,
-    args=train_conf,
-    peft_config=peft_conf,
-    train_dataset=processed_train_dataset,
-    eval_dataset=processed_test_dataset,
-    max_seq_length=2048,
-    dataset_text_field="text",
-    tokenizer=tokenizer,
-    packing=True
-)
-train_result = trainer.train()
-metrics = train_result.metrics
-trainer.log_metrics("train", metrics)
-trainer.save_metrics("train", metrics)
-trainer.save_state()
-
-
-#############
-# Evaluation
-#############
-tokenizer.padding_side = 'left'
-metrics = trainer.evaluate()
-metrics["eval_samples"] = len(processed_test_dataset)
-trainer.log_metrics("eval", metrics)
-trainer.save_metrics("eval", metrics)
-
-
-# ############
-# # Save model
-# ############
-trainer.save_model(train_conf.output_dir)

friday/model/vision_adapter/__init__.py

@@ -1,3 +0,0 @@
-from .mlp_adapter import MLPAdapter
-
-__all__ = ['MLPAdapter']

friday/model/vision_adapter/mlp_adapter.py

@@ -1,61 +0,0 @@
-import torch
-import torch.nn as nn
-
-class MLPAdapter(nn.Module):
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers=2, activation='gelu', checkpoint_path=None, device=None, **kwargs):
-        """
-        Initialize the MLPAdapter with the given dimensions and activation function.
-
-        Args:
-            input_dim (int): Input dimension.
-            hidden_dim (int): Hidden dimension.
-            output_dim (int): Output dimension.
-            layers (int): Number of layers in the MLP.
-            activation (str): Activation function to use ('gelu' or 'relu').
-        """
-        super().__init__()
-        self.num_layers = num_layers
-        self.activation = activation
-        self.output_dim = output_dim
-
-        # Define the first layer
-        layers_list = [nn.Linear(input_dim, hidden_dim, device=device)]
-        if activation == 'gelu':
-            layers_list.append(nn.GELU())
-        elif activation == 'relu':
-            layers_list.append(nn.ReLU())
-        else:
-            raise ValueError("Unsupported activation function. Use 'gelu' or 'relu'.")
-        
-        # Define the subsequent layers
-        for _ in range(1, num_layers):
-            layers_list.append(nn.Linear(hidden_dim, hidden_dim, device=device))
-            if activation == 'gelu':
-                layers_list.append(nn.GELU())
-            elif activation == 'relu':
-                layers_list.append(nn.ReLU())
-        
-        # Define the final output layer
-        layers_list.append(nn.Linear(hidden_dim, output_dim, device=device))
-        self.mlp = nn.Sequential(*layers_list)
-
-        # Load checkpoint if provided
-        if checkpoint_path:
-            self.load_state_dict(torch.load(checkpoint_path, map_location=device), strict=False)
-            print(f"Loaded MLPAdapter from {checkpoint_path}")
-        
-        if device:
-            self.to(device)
-
-    def forward(self, x):
-        """
-        Forward pass through the MLPAdapter.
-
-        Args:
-            x (torch.Tensor): Input tensor.
-
-        Returns:
-            torch.Tensor: Output tensor after passing through the MLP.
-        """
-        return self.mlp(x)

friday/model/vision_tower/__init__.py

@@ -1,9 +0,0 @@
-from .siglip_encoder import SiglipVisionTower, SiglipVisionTowerS2
-from .fast_vit_encoder import FastVitVisionTower, FastVitVisionTowerS2
-
-__all__ = [
-    "SiglipVisionTower", 
-    "SiglipVisionTowerS2",
-    "FastVitVisionTower",
-    "FastVitVisionTowerS2"
-]

friday/model/vision_tower/fast_vit_encoder.py

@@ -1,160 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import PIL.Image
-from typing import List
-from friday.util import expand2square, pad_and_stack
-
-from transformers import AutoModel, AutoImageProcessor
-from friday.util.s2wrapper import forward as multiscale_forward
-
-
-class FastVitVisionTower(nn.Module):
-    def __init__(self, pretrained_model_name_or_path, model_params={}, pad_to_square=True, **kwargs):
-        super().__init__()
-
-        self.is_loaded = False
-        self.pretrained_model_name_or_path = pretrained_model_name_or_path
-        self.model_params = model_params
-        self.pad_to_square = pad_to_square
-        self.load_model()
-
-    @property
-    def output_dim(self):
-        return self.vision_tower.config.embed_dim if self.vision_tower else None
-    
-    def load_model(self):
-        if self.is_loaded:
-            return
-        self.image_processor = AutoImageProcessor.from_pretrained(self.pretrained_model_name_or_path)
-        self.image_processor.crop_size = self.image_processor.size
-        self.vision_tower = AutoModel.from_pretrained(
-            self.pretrained_model_name_or_path,
-            **self.model_params,
-        )
-        self.vision_tower.requires_grad_(False)
-
-        self.is_loaded = True
-    
-    def preprocess_images(self, imgs: List[PIL.Image.Image], pad_and_stack_tensors=True) -> torch.Tensor:
-        img_mean = tuple(int(x * 255) for x in self.image_processor.image_mean)
-        if self.pad_to_square:
-            imgs = [expand2square(img, img_mean) for img in imgs]
-        
-        imgs = [self.image_processor(img, do_resize=True, do_center_crop=False, return_tensors="pt")['pixel_values'][0] for img in imgs]
-        
-
-        if pad_and_stack_tensors:
-            imgs = pad_and_stack(imgs, pad_value=0.0)
-            imgs = imgs.to(dtype=torch.float32, device=self.device)
-        
-        return imgs
-
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_feature = self.vision_tower(
-                    image.to(device=self.device, dtype=self.dtype).unsqueeze(0)
-                )
-                image_features.append(image_feature)
-        else:
-            image_features = self.vision_tower(
-                images.to(device=self.device, dtype=self.dtype),
-            )
-
-        return image_features
-
-    @property
-    def dummy_feature(self):
-        return torch.zeros(1, self.embed_dim, device=self.device, dtype=self.dtype)
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def config(self):
-        if self.is_loaded:
-            return self.vision_tower.config
-        else:
-            return self.cfg_only
-
-    @property
-    def hidden_size(self):
-        return self.config.embed_dim
-
-    @property
-    def num_patches(self):
-        return (self.config.image_size // self.config.patch_size) ** 2
-
-
-class FastVitVisionTowerS2(FastVitVisionTower):
-    def __init__(self, pretrained_model_name_or_path, s2_scales, model_params={}, **kwargs):
-        self.s2_scales = list(map(int, s2_scales.split(',')))
-        self.s2_scales.sort()
-        self.s2_split_size = self.s2_scales[0]
-        self.s2_image_size = self.s2_scales[-1]
-
-        super().__init__(pretrained_model_name_or_path, model_params)
-
-        self.multiscale_forward = multiscale_forward
-    
-    @property
-    def output_dim(self):
-        return (2*self.vision_tower.config.embed_dim) if self.vision_tower else None
-
-    def load_model(self):
-        if self.is_loaded:
-            return
-        
-        super().load_model()
-        self.image_processor.size = self.image_processor.crop_size = {
-            "height": self.s2_image_size,
-            "width":  self.s2_image_size
-        }
-
-    def forward_feature(self, images):
-        image_size = self.vision_tower.config.image_size
-        if images.shape[2] != image_size or images.shape[3] != image_size:
-            images = F.interpolate(
-                images,
-                size=(image_size, image_size),
-                mode="bilinear",
-                align_corners=False,
-                antialias=True
-            )    
-
-        return self.vision_tower(
-            images.to(device=self.device, dtype=self.dtype),
-        )
-
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_feature = self.multiscale_forward(
-                    self.forward_feature, 
-                    image.unsqueeze(0),
-                    img_sizes=self.s2_scales, 
-                    max_split_size=self.s2_split_size
-                )
-                image_features.append(image_feature)
-        else:
-            image_features = self.multiscale_forward(
-                self.forward_feature, 
-                images, 
-                img_sizes=self.s2_scales,
-                max_split_size=self.s2_split_size
-            )
-
-        return image_features
-
-    @property
-    def hidden_size(self):
-        return self.config.embed_dim * len(self.s2_scales)

friday/model/vision_tower/siglip_encoder.py

@@ -1,155 +0,0 @@
-import torch
-import torch.nn as nn
-
-import PIL.Image
-from typing import List
-from friday.util import expand2square, pad_and_stack
-
-from transformers import SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig
-from friday.util.s2wrapper import forward as multiscale_forward
-
-
-class SiglipVisionTower(nn.Module):
-    def __init__(self, pretrained_model_name_or_path, model_params={}, pad_to_square=True, **kwargs):
-        super().__init__()
-
-        self.is_loaded = False
-        self.pretrained_model_name_or_path = pretrained_model_name_or_path
-        self.model_params = model_params
-        self.pad_to_square = pad_to_square
-        self.select_layer = -2
-        self.load_model()
-
-    @property
-    def output_dim(self):
-        return self.vision_tower.config.hidden_size if self.vision_tower else None
-    
-    def load_model(self):
-        if self.is_loaded:
-            return
-        self.image_processor = SiglipImageProcessor.from_pretrained(self.pretrained_model_name_or_path)
-        self.image_processor.crop_size = self.image_processor.size
-        self.vision_tower = SiglipVisionModel.from_pretrained(
-            self.pretrained_model_name_or_path,
-            **self.model_params,
-        )
-        self.vision_tower.requires_grad_(False)
-
-        self.is_loaded = True
-    
-    def preprocess_images(self, imgs: List[PIL.Image.Image], pad_and_stack_tensors=True) -> torch.Tensor:
-        img_mean = tuple(int(x * 255) for x in self.image_processor.image_mean)
-        if self.pad_to_square:
-            imgs = [expand2square(img, img_mean) for img in imgs]
-        imgs = [self.image_processor(img, return_tensors="pt")['pixel_values'][0] for img in imgs]
-
-        if pad_and_stack_tensors:
-            imgs = pad_and_stack(imgs, pad_value=0.0)
-            imgs = imgs.to(dtype=torch.float32, device=self.device)
-        
-        return imgs
-
-    def feature_select(self, image_forward_outs):
-        image_features = image_forward_outs.hidden_states[self.select_layer]
-
-        return image_features
-
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
-                                                      output_hidden_states=True)
-                image_feature = self.feature_select(image_forward_out).to(image.dtype)
-                image_features.append(image_feature)
-        else:
-            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
-                                                   output_hidden_states=True)
-            image_features = self.feature_select(image_forward_outs).to(images.dtype)
-
-        return image_features
-
-    @property
-    def dummy_feature(self):
-        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def config(self):
-        if self.is_loaded:
-            return self.vision_tower.config
-        else:
-            return self.cfg_only
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size
-
-    @property
-    def num_patches(self):
-        return (self.config.image_size // self.config.patch_size) ** 2
-
-
-class SiglipVisionTowerS2(SiglipVisionTower):
-    def __init__(self, pretrained_model_name_or_path, s2_scales, model_params={}, **kwargs):
-        self.s2_scales = list(map(int, s2_scales.split(',')))
-        self.s2_scales.sort()
-        self.s2_split_size = self.s2_scales[0]
-        self.s2_image_size = self.s2_scales[-1]
-
-        super().__init__(pretrained_model_name_or_path, model_params)
-
-        self.multiscale_forward = multiscale_forward
-
-        self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
-        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
-    
-    @property
-    def output_dim(self):
-        return (2*self.vision_tower.config.hidden_size) if self.vision_tower else None
-
-    def load_model(self):
-        if self.is_loaded:
-            return
-        
-        super().load_model()
-        self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
-        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
-
-    def forward_feature(self, images):
-        image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
-                                               output_hidden_states=True)
-        image_features = self.feature_select(image_forward_outs).to(images.dtype)
-        return image_features
-
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_feature = self.multiscale_forward(
-                    self.forward_feature, 
-                    image.unsqueeze(0),
-                    img_sizes=self.s2_scales, 
-                    max_split_size=self.s2_split_size
-                )
-                image_features.append(image_feature)
-        else:
-            image_features = self.multiscale_forward(
-                self.forward_feature, 
-                images, 
-                img_sizes=self.s2_scales,
-                max_split_size=self.s2_split_size
-            )
-
-        return image_features
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size * len(self.s2_scales)

friday/util/__init__.py

@@ -1,13 +0,0 @@
-from .util import *
-from .s2wrapper.core import forward as multi_scale_forward
-
-__all__ = [
-    "pad_and_stack",
-    "mask_token_segment",
-    "expand2square",
-    "maybe_zero_3",
-    "get_peft_state_maybe_zero_3",
-    "get_peft_state_non_lora_maybe_zero_3",
-    "find_all_linear_names",
-    "multi_scale_forward"
-]

friday/util/s2wrapper/__init__.py

@@ -1,2 +0,0 @@
-from .core import *
-from .utils import *

friday/util/s2wrapper/core.py

@@ -1,64 +0,0 @@
-#  ------------------------------------------------------------------------------------------
-#  Copyright (c) 2024 Baifeng Shi.
-#  All rights reserved.
-#
-#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
-#  ------------------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from .utils import split_chessboard, merge_chessboard, batched_forward
-
-def forward(model, input, scales=None, img_sizes=None, max_split_size=None, resize_output_to_idx=0, num_prefix_token=0,
-            output_shape='bnc', split_forward=False):
-
-    # print(f"Input shape: {input.shape}")
-
-    assert input.dim() == 4, "Input image must be in the shape of BxCxHxW."
-    assert input.shape[2] == input.shape[3], "Currently only square images are supported."
-    assert output_shape in ['bnc', 'bchw'], "Output shape should be either BxNxC (e.g., ViT) or BxCxHxW (e.g., ConvNet)."
-    assert output_shape == 'bnc' or num_prefix_token == 0, "For ConvNet there shouldn't be any prefix token."
-
-    b, c, input_size, _ = input.shape
-
-    # image size for each scale
-    assert scales is not None or img_sizes is not None, "Please assign either scales or img_sizes."
-    img_sizes = img_sizes or [int(input_size * scale) for scale in scales]
-
-    # prepare multiscale inputs
-    max_split_size = max_split_size or input_size   # The maximum size of each split of image. Set as the input size by default
-    num_splits = [math.ceil(size / max_split_size) for size in img_sizes]   # number of splits each scale
-    input_multiscale = []
-    for size, num_split in zip(img_sizes, num_splits):
-        x = F.interpolate(input.to(torch.float32), size=size, mode='bicubic').to(input.dtype)
-        x = split_chessboard(x, num_split=num_split)
-        input_multiscale.append(x)
-
-    # run feedforward on each scale
-    outs_multiscale = [batched_forward(model, x, b) if split_forward else model(x) for x in input_multiscale]
-    if num_prefix_token > 0:
-        outs_prefix_multiscale = [out[:, :num_prefix_token] for out in outs_multiscale]
-        outs_multiscale = [out[:, num_prefix_token:] for out in outs_multiscale]
-    if output_shape == 'bnc':
-        outs_multiscale = [rearrange(out, 'b (h w) c -> b c h w', h=int(out.shape[1] ** 0.5), w=int(out.shape[1] ** 0.5))
-                           for out in outs_multiscale]
-
-    # merge outputs of different splits for each scale separately
-    outs_multiscale = [merge_chessboard(out, num_split=num_split) for num_split, out in zip(num_splits, outs_multiscale)]
-
-    # interpolate outputs from different scales and concat together
-    output_size = outs_multiscale[resize_output_to_idx].shape[-2]
-    out = torch.cat([F.interpolate(outs_multiscale[i].to(torch.float32), size=output_size,
-                                   mode='area').to(outs_multiscale[i].dtype)
-                     for i in range(len(outs_multiscale))], dim=1)
-    if output_shape == 'bnc':
-        out = rearrange(out, 'b c h w -> b (h w) c')
-    if num_prefix_token > 0:
-        # take the mean of prefix tokens from different splits for each scale
-        outs_prefix_multiscale = [torch.stack(out.split(b, dim=0), dim=0).mean(dim=0) for out in outs_prefix_multiscale]
-        out_prefix_multiscale = torch.cat(outs_prefix_multiscale, dim=-1)
-        out = torch.cat([out_prefix_multiscale, out], dim=1)
-
-    return out

friday/util/s2wrapper/utils.py

@@ -1,41 +0,0 @@
-#  ------------------------------------------------------------------------------------------
-#  Copyright (c) 2024 Baifeng Shi.
-#  All rights reserved.
-#
-#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
-#  ------------------------------------------------------------------------------------------
-
-import torch
-
-def split_chessboard(x, num_split):
-    """
-        x: b * c * h * w
-        Deividing x into num_split**2 sub-squares, and concatenate all the sub-squares on the batch dimension
-    """
-    B, C, H, W = x.shape
-    assert H % num_split == 0 and W % num_split == 0
-    h, w = H // num_split, W // num_split
-    x_split = torch.cat([x[:, :, i*h:(i+1)*h, j*w:(j+1)*w] for i in range(num_split) for j in range(num_split)], dim=0)
-    return x_split
-
-def merge_chessboard(x, num_split):
-    """
-        x: b * c * h * w
-        Assuming x contains num_split**2 sub-squares concatenated along batch dimension, merge the sub-squares back to the original whole square.
-        (inverse of split_chessboard)
-    """
-    B, C, H, W = x.shape
-    assert B % (num_split**2) == 0
-    b = B // (num_split**2)
-    x_merge = torch.cat([torch.cat([x[(i*num_split + j)*b:(i*num_split + j + 1)*b] for j in range(num_split)], dim=-1)
-                         for i in range(num_split)], dim=-2)
-    return x_merge
-
-def batched_forward(model, x, batch_size=-1):
-    if batch_size == -1:
-        return model(x)
-    else:
-        x_batched = x.split(batch_size)
-        outs = [model(x) for x in x_batched]
-        return torch.cat(outs, dim=0)
-

friday/util/util.py

@@ -1,160 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from PIL import Image
-
-
-import torch
-
-def mask_token_segment(
-               start_id: int,
-               end_id: int,
-               input_ids: torch.Tensor,
-               fill_value: int = -100):
-    """
-    Replace *every* token from each `start_id` **through** its matching `end_id`
-    (boundaries included) with `fill_value`.  Any spans that start with some
-    other token are left untouched.
-
-    Works on CUDA, TorchScript, batched via vmap, etc.—no Python loops.
-    """
-    if input_ids.dim() != 1:
-        raise ValueError("`input_ids` must be 1-D")
-
-    device = input_ids.device
-    n       = input_ids.size(0)
-
-    # where the *target* start-tokens and end-tokens sit
-    start_pos = (input_ids == start_id).nonzero(as_tuple=True)[0]      # ascending
-    end_pos   = (input_ids == end_id).nonzero(as_tuple=True)[0]        # ascending
-
-    if start_pos.numel() == 0:
-        return input_ids.clone()
-
-    # ── pair every start with the first end that comes *after* it ────────────────
-    # searchsorted gives the insertion index into the (sorted) end positions
-    idx_in_end = torch.searchsorted(end_pos, start_pos, right=False)
-
-    have_match = idx_in_end < end_pos.size(0)                # safety: drop unmatched
-    start_pos  = start_pos[have_match]
-    end_pos    = end_pos[idx_in_end[have_match]]
-
-    # (rare) guard against pathological orderings
-    keep = end_pos > start_pos
-    start_pos, end_pos = start_pos[keep], end_pos[keep]
-
-    if start_pos.numel() == 0:
-        return input_ids
-
-    # ── differential “scan-line” trick to build the span mask in O(N) ───────────
-    # +1 at each start index, -1 at the element *after* each end
-    delta = torch.zeros(n + 1, dtype=torch.int8, device=device)
-    delta[start_pos]        += 1
-    delta[end_pos + 1]      -= 1          # +1 is safe because delta is length n+1
-
-    inside = torch.cumsum(delta[:-1], dim=0) > 0   # boolean mask, incl. boundaries
-
-    # ── apply ────────────────────────────────────────────────────────────────────
-    out = input_ids.clone()
-    out[inside] = fill_value
-    return out
-
-
-
-def maybe_zero_3(param, ignore_status=False, name=None):
-    from deepspeed import zero
-    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
-    if hasattr(param, "ds_id"):
-        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
-            if not ignore_status:
-                print(name, 'no ignore status')
-        with zero.GatheredParameters([param]):
-            param = param.data.detach().cpu().clone()
-    else:
-        param = param.detach().cpu().clone()
-    return param
-
-
-# Borrowed from peft.util.get_peft_model_state_dict
-def get_peft_state_maybe_zero_3(named_params, bias):
-    if bias == "none":
-        to_return = {k: t for k, t in named_params if "lora_" in k}
-    elif bias == "all":
-        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
-    elif bias == "lora_only":
-        to_return = {}
-        maybe_lora_bias = {}
-        lora_bias_names = set()
-        for k, t in named_params:
-            if "lora_" in k:
-                to_return[k] = t
-                bias_name = k.split("lora_")[0] + "bias"
-                lora_bias_names.add(bias_name)
-            elif "bias" in k:
-                maybe_lora_bias[k] = t
-        for k, t in maybe_lora_bias:
-            if bias_name in lora_bias_names:
-                to_return[bias_name] = t
-    else:
-        raise NotImplementedError
-    to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
-    return to_return
-
-
-def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
-    to_return = {k: t for k, t in named_params if "lora_" not in k}
-    if require_grad_only:
-        to_return = {k: t for k, t in to_return.items() if t.requires_grad}
-    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
-    return to_return
-
-
-def find_all_linear_names(modules):
-    lora_module_names = set()
-    for name, module in modules():
-        if isinstance(module, torch.nn.Linear):
-            names = name.split('.')
-            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
-
-    if 'lm_head' in lora_module_names:  # needed for 16-bit
-        lora_module_names.remove('lm_head')
-    return list(lora_module_names)
-
-
-def expand2square(pil_img, background_color):
-    width, height = pil_img.size
-    if width == height:
-        return pil_img
-    elif width > height:
-        result = Image.new(pil_img.mode, (width, width), background_color)
-        result.paste(pil_img, (0, (width - height) // 2))
-        return result
-    else:
-        result = Image.new(pil_img.mode, (height, height), background_color)
-        result.paste(pil_img, ((height - width) // 2, 0))
-        return result
-
-def pad_and_stack(img_list, pad_value=0.0):
-    """
-    img_list : list[Tensor]  each (C, H, W) already *normalised*
-    pad_value: float or tuple/list of 3 floats (one per channel)
-               Use 0.0 if your processor has already centred to mean 0.
-    Returns
-    -------
-    batch : Tensor  (B, C, H_max, W_max)
-    """
-
-    # 1. target square size ---------------------------------------------------
-    h_max = max(t.shape[1] for t in img_list)
-    w_max = max(t.shape[2] for t in img_list)
-    H, W  = max(h_max, w_max), max(h_max, w_max)
-
-    # 2. create padded copies -------------------------------------------------
-    padded = []
-    for img in img_list:
-        c, h, w = img.shape
-        canvas   = img.new_full((c, H, W), pad_value)     # filled with mean/zeros
-        canvas[:, :h, :w] = img                    # top-left corner
-        padded.append(canvas)
-
-    return torch.stack(padded, 0)                  # (B,C,H,W)