diff --git "a/modeling_phi4mm.py" "b/modeling_phi4mm.py" new file mode 100644--- /dev/null +++ "b/modeling_phi4mm.py" @@ -0,0 +1,2407 @@ +# 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-4-MM model.""" +import math +import warnings +from typing import List, Optional, Tuple, Union + +import numpy as np + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +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 _flash_attention_forward +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig + +from .configuration_phi4mm import Phi4MMConfig +from .processing_phi4mm import InputMode +from .vision_siglip_navit import get_siglip_vision_model +from .speech_conformer_encoder import ConformerEncoder + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "TBA" +_CONFIG_FOR_DOC = "Phi4MMConfig" + +# Special token ids +_IMAGE_SPECIAL_TOKEN_ID = 200010 # '<|endoftext10|>', or we can better name it (in `tokenizer_config.json`) +_AUDIO_SPECIAL_TOKEN_ID = 200011 # '<|endoftext11|>' +_COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE = [-9999, -1] # For backward compatibility +_COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE = [float('-inf'), -10000] # For backward compatibility + + +class Phi4MMImageEmbedding(nn.Module): + """Image embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + + # n_embed or hidden_size + hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size + if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'): + embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop + self.drop = nn.Dropout(embd_drop) + else: + self.drop = None + + logger.info(f"create image tower {config.img_processor}") + enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False) + + # Load SigLIP model + self.img_processor = get_siglip_vision_model( + _flash_attn_2_enabled=config._attn_implementation == 'flash_attention_2' + ) + + pe_weight = self.img_processor.embeddings.position_embedding.weight + L, D = pe_weight.size() + H = int(math.sqrt(L)) + assert H**2 == L + if H % 2 != 0: #and kwargs.get('image_token_compression_cls', None) is None: + self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1)) + H += 1 + image_dim_out = D + # ((448/14)//2)**2 + self.num_img_tokens = (H//2)**2 + self.base_feat_height_target = H + + if enable_gradient_checkpointing: + self.img_processor.encoder.gradient_checkpointing = True + + self.image_dim_out = image_dim_out + self.img_sizes = None + self.image_attention_mask = None + + # global_gn and sub_gn for hd transform, serves as line separator + self.use_hd_transform = kwargs.get('use_hd_transform', False) + self.with_learnable_separator = kwargs.get('with_learnable_separator', False) + self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub') + self.freeze_img_processor = kwargs.get('freeze_img_processor', False) + self.crop_size = kwargs.get('crop_size', 336) + logger.info(f'freeze_img_processor = {self.freeze_img_processor}') + + # image token compression + self.image_token_compression_cls = kwargs.get('image_token_compression_cls', None) + if self.image_token_compression_cls == 'avg_pool_2d': + self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2) + self.base_feat_height_reduction = 1 + self.base_feat_height_target = self.base_feat_height_target // 2 + elif self.image_token_compression_cls is None: + self.image_token_compression = None + self.base_feat_height_reduction = 2 + else: + raise NotImplementedError(f'image_token_compression_cls = {self.image_token_compression_cls}, not implemented') + + # with_hd_transform and with_learnable_separator should have same value + assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value' + if self.with_learnable_separator: + assert self.use_hd_transform, 'learnable separator is only for hd transform' + # 1024 * 4, merge spatial to channel dimension + self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * self.base_feat_height_reduction**2])) + self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * self.base_feat_height_reduction**2])) + logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}') + + projection_cls = kwargs.get('projection_cls', 'linear') + if projection_cls == 'linear': + self.img_projection = nn.Linear(image_dim_out, hidden_size) + elif projection_cls == 'mlp' and self.use_hd_transform: + dim_projection = hidden_size + depth = 2 + layers = [nn.Linear(image_dim_out * self.base_feat_height_reduction**2, dim_projection)] + for _ in range(1, depth): + layers.extend([nn.GELU(), + nn.Linear(dim_projection, dim_projection)]) + self.img_projection = nn.Sequential(*layers) + elif projection_cls == 'mlp': + # follow llava-v1.5's implementation + # (do not use image_projection and image_proj_norm) + dim_projection = hidden_size + depth = 2 + layers = [nn.Linear(image_dim_out, dim_projection)] + for _ in range(1, depth): + layers.extend([nn.GELU(), + nn.Linear(dim_projection, dim_projection)]) + self.img_projection = nn.Sequential(*layers) + else: + raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented') + + self.vocab_size = config.vocab_size + self.img_features = None + + if isinstance(config.img_processor, dict): + self.layer_idx = config.img_processor.get('layer_idx', -2) + self.type_feature = config.img_processor.get('type_feature', 'patch') + else: + self.layer_idx = -2 + self.type_feature = 'patch' + + def set_img_features(self, img_features: torch.FloatTensor) -> None: + self.img_features = img_features + + def set_img_sizes(self, img_sizes: torch.LongTensor) -> None: + self.img_sizes = img_sizes + + def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None: + self.image_attention_mask = image_attention_mask + + def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor: + LAYER_IDX = self.layer_idx + TYPE_FEATURE = self.type_feature + + if self.freeze_img_processor: + with torch.no_grad(): + if attention_mask is not None: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask) + else: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True) + img_feature = img_processor_output.hidden_states[LAYER_IDX] + else: + if attention_mask is not None: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask) + else: + img_processor_output = self.img_processor(img_embeds, output_hidden_states=True) + img_feature = img_processor_output.hidden_states[LAYER_IDX] + + if TYPE_FEATURE == "patch": + patch_feature = img_feature + if self.image_token_compression is not None: + # reshape to 2D tensor + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + if getattr(self, 'img_processor_padding', None) is not None: + patch_feature = self.img_processor_padding(patch_feature) + patch_feature = self.image_token_compression(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + elif getattr(self, 'img_processor_padding', None) is not None: + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + patch_feature = self.img_processor_padding(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + return patch_feature + + if TYPE_FEATURE == "cls_patch": + if self.image_token_compression is not None: + # reshape to 2D tensor + patch_feature = img_feature[:, 1:] + cls_feature = img_feature[:, 0] + width = math.sqrt(patch_feature.size(1)) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + patch_feature = self.image_token_compression(patch_feature) + patch_feature = patch_feature.view(-1, patch_feature.size(-2) * patch_feature.size(-1)) + img_feature = torch.cat([cls_feature, patch_feature], dim=1) + return img_feature + + logger.info(f'processed img feature size = {img_feature.size()}') + raise NotImplementedError + + def spatiotemporal_pool(self, x, num_img_tokens, batch_size=1, T=1): + + if self.image_pos_embed is not None: + x = x.view(batch_size * T, -1, x.shape[-1]) + num_tokens = x.shape[-2] + h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5) + assert h * w == num_tokens, 'only support square feature maps for now' + x = x.view(batch_size * T, h, w, x.shape[-1]) + pos_embed = self.image_pos_embed(x) + x = x + pos_embed + x = x.view(batch_size, T * h * w, x.shape[-1]) + + if self.visual_temporal_embed is not None: + visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0]) + x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1]) + + new_x = [] + # [bsz, T * H' * W', C] -> [bsz, T, C] + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + new_x.append(spatial_avg_pool_x) + + # [bsz, T * H' * W', C] -> [bsz, H'*W', C] + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + new_x.append(temporal_avg_pool_x) + + x = torch.cat(new_x, dim=1).view(-1, self.image_dim_out) + num_img_tokens += T + return x, num_img_tokens + + def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, image_sizes=None, **kwargs) -> torch.FloatTensor: + + if isinstance(input_ids, tuple): + # # pipeline parallel + input_ids, input_embeds = input_ids + + img_embeds = input_embeds + if image_sizes is None and 'image_sizes' in kwargs: + image_sizes = kwargs['image_sizes'] + img_sizes = image_sizes + + if self.img_features is not None: + img_embeds = self.img_features.clone() + self.img_features = None + + if self.img_sizes is not None: + img_sizes = self.img_sizes + + dtype = self.img_processor.embeddings.patch_embedding.weight.dtype + if img_embeds is not None: + # convert to bf16 + img_embeds = img_embeds.to(dtype) + + if self.image_attention_mask is not None: + image_attention_mask = self.image_attention_mask.clone() + self.image_attention_mask = None + elif 'image_attention_mask' in kwargs: + image_attention_mask = kwargs['image_attention_mask'] + else: + image_attention_mask = None + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + with torch.no_grad(): + positions = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=False) + positions_tuple = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=True) + + # logger.info(f'position size: {positions.size()} ...') + fake_image_forward = False + select = False + hd_transform = False + + if isinstance(self.img_projection, nn.Sequential): + target_device = self.img_projection[0].bias.device + target_dtype = self.img_projection[0].bias.dtype + else: # It's a single nn.Linear layer + target_device = self.img_projection.bias.device + target_dtype = self.img_projection.bias.dtype + + num_img_tokens = self.num_img_tokens + if len(positions.tolist()) > 0: + if self.use_hd_transform and img_sizes is not None and len(img_sizes): + hd_transform = True + assert img_embeds.ndim == 5, f'(branch 1) img_embeds size: {img_embeds.size()}, expect 5D tensor for hd transform' + # img_embeds: (num_images, max_num_crops, 3, H, W) + # img_sizes: (num_images, 2).view(1, -1) + + bs = img_embeds.shape[0] + # Nx(HW)xC + if image_attention_mask is not None and len(image_attention_mask) > 0: + img_features = self.get_img_features(img_embeds.flatten(0, 1), attention_mask=image_attention_mask.type(torch.BoolTensor).flatten(0,1).to(target_device)) + else: + img_features = self.get_img_features(img_embeds.flatten(0, 1)) + + base_feat_height_target = self.base_feat_height_target + base_resolution = self.crop_size + base_feat_height_reduction = self.base_feat_height_reduction + + base_feat_height = base_feat_width = int(np.sqrt(img_features.shape[1])) + + assert base_feat_height == base_feat_height_target and base_feat_width == base_feat_height_target, f'base_feat_height: {base_feat_height}, base_feat_width: {base_feat_width}, expect {base_feat_height_target} features for hd transform' + + # bs x max_num_crops x (24x24) x C + img_features = img_features.view(bs, -1, base_feat_height * base_feat_width, self.image_dim_out) + C = self.image_dim_out + H = base_feat_height + + output_imgs = [] + output_len = [] + # training is tensor, inference is list + if isinstance(img_sizes, torch.Tensor): + img_sizes = img_sizes.view(-1, 2) + for _bs in range(bs): + h, w = img_sizes[_bs] + h = h // base_resolution + w = w // base_resolution + B_ = h * w + + # 1 x (24x24) x 1024 + global_img_feature = img_features[_bs, :1] + + # 1 x 12 x 12 x 4096 + glb_img = global_img_feature.reshape(1,H,H,C).reshape(1,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C).contiguous() + temp_glb_GN = self.sub_GN.repeat(1, H//base_feat_height_reduction, 1, 1) + + # 1 x 156 x 4096 + glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C) + + # (max_num_crops-1) x (12x12) x C + sub_img = img_features[_bs, 1:] + # 16x574x1024 + # get rid of padding sub_img + sub_img = sub_img[:B_] + + # (num_crops, 12, 2, 12, 2, 1024) -> (num_crops, 12, 12, 2, 2, 1024) -> (num_crops, 12*12, 4*1024) + sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,base_feat_height_reduction*base_feat_height_reduction*C).contiguous() + sub_img = sub_img.reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction, -1).permute(0,1,3,2,4,5).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C) + + if image_attention_mask is not None and len(image_attention_mask) > 0: + reshaped_image_attention_mask = image_attention_mask[_bs,1:B_+1,0::2,0::2].reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction).permute(0,1,3,2,4).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction) + useful_height = int(reshaped_image_attention_mask[0,:,0].sum().item()) + useful_width = int(reshaped_image_attention_mask[0,0,:].sum().item()) + sub_img = sub_img[:,:useful_height, :useful_width] + temp_sub_GN = self.sub_GN.repeat(1, useful_height, 1, 1) + temp_len = int(image_attention_mask[_bs,:B_+1,0::2,0::2].sum().item()) + (useful_height+1) + base_feat_height//base_feat_height_reduction + else: + temp_sub_GN = self.sub_GN.repeat(1, h*base_feat_height//base_feat_height_reduction, 1, 1) + temp_len = int((h*w+1)*self.num_img_tokens+ 1 + (h+1)*base_feat_height//base_feat_height_reduction) + + sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C) + # (1, num_img_tokens, 1024*4) + + # glb + sub + if self.hd_transform_order == 'glb_sub': + output_imgs.append(torch.cat([glb_img, self.glb_GN, sub_img], dim=1)) + elif self.hd_transform_order == 'sub_glb': + output_imgs.append(torch.cat([sub_img, self.glb_GN, glb_img], dim=1)) + else: + raise NotImplementedError(f'hd_transform_order = {self.hd_transform_order}, not implemented') + + #temp_len = int((h*w+1)*144 + 1 + (h+1)*12) + assert temp_len == output_imgs[-1].shape[1], f'temp_len: {temp_len}, output_imgs[-1].shape[1]: {output_imgs[-1].shape[1]}' + output_len.append(temp_len) + + num_img_tokens = output_len + img_set_tensor = [] + for _output_img in output_imgs: + img_feature_proj = self.img_projection(_output_img.to(target_device).to(target_dtype)) + img_set_tensor.append(img_feature_proj) + #logger.info(f'img_embeds size: {img_embeds.size()}, image sizes: {img_sizes} loading time {datetime.now() - start_time}') + #assert sum(num_img_tokens) == len(g_values), f'(branch 1) sum(num_img_tokens): {sum(num_img_tokens)}, g_values size: {len(g_values)}, g_values {g_values}' + + else: + raise NotImplementedError + select = True + else: + # # create a fake image tensor + # # TODO: need define image size for different vision model + if self.training: + img_embeds = torch.zeros(1, 3, self.crop_size, self.crop_size, dtype=target_dtype, device=input_ids.device) + + tt = ( + self.get_img_features(img_embeds) + .to(target_device) + .to(target_dtype) + .reshape(-1, 1024) + ) + if self.use_hd_transform: + img_set_tensor = self.img_projection(tt.reshape(-1, self.image_dim_out*self.base_feat_height_reduction**2) * self.glb_GN[0] * self.sub_GN[0, 0]) + else: + img_set_tensor = self.img_projection(tt) # adapted visual features. + fake_image_forward = True + + # we use the token embedding layer from the huggingface model, this is REQUIRED to make sure we are using the loaded weights. + hidden_states = kwargs['wte'](input_ids) + + if select: + if hd_transform: + # new implementation without in-place operation + # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233 + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_ + # img_set_tensor: a list of tensors, each tensor has shape (1, N_tokens, C) + assert all([_img_set_tensor.shape[0] == 1 for _img_set_tensor in img_set_tensor]), 'img_set_tensor should have shape (1, N_tokens, C)' + # Shape: (merged_N_tokens, C) + merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0) + merged_img_set_tensor = merged_img_set_tensor.to(hidden_states.dtype).to(hidden_states.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with torch.autocast(device_type=hidden_states.device.type, enabled=False): + new_hidden_states = hidden_states.index_put( + indices=positions_tuple, + values=merged_img_set_tensor, + accumulate=False + ) + hidden_states = new_hidden_states + else: + raise NotImplementedError + + if fake_image_forward and self.training: + hidden_states = hidden_states + (0 * img_set_tensor[0].to(hidden_states.dtype).to(hidden_states.device)).sum() + + if self.drop is not None: + hidden_states = self.drop(hidden_states) + + return hidden_states + + +class Phi4MMAudioEmbedding(nn.Module): + """Audio embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + self.config = config + # n_embed or hidden_size for text LM + hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size + + if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'): + embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop + self.drop = nn.Dropout(embd_drop) + else: + self.drop = None + + audio_dim_out = None # Set this variable according to the actual audio processor + logger.info(f"create audio processor {config.audio_processor}") + self.layer_idx = -2 + + if isinstance(config.audio_processor, dict) and config.audio_processor.get('name', None) == "cascades": + encoder_config = config.audio_processor.get("config", None) + assert encoder_config is not None + self.encoder = ConformerEncoder(**encoder_config) + + # fake initialization, create encoder_embedding layer only so that + # in decoding, all parameters can be loaded in from_pretrained_function + # in training, we do post init after from_pretrained function to make sure the correct initialization + self.encoder.post_init({}) + + audio_dim_out = encoder_config["attention_dim"] + n_mels = encoder_config["input_size"] + else: + raise NotImplementedError + + assert audio_dim_out is not None, "Remember to set values for audio_dim_out" + self.audio_dim_out = audio_dim_out + self.audio_dim_in = n_mels + + self.freeze_audio_processor = kwargs.get('freeze_audio_processor', False) + logger.info(f'freeze_audio_processor = {self.freeze_audio_processor}') + + self.downsample_rate = kwargs.get('downsample_rate', 1) + + enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False) + if enable_gradient_checkpointing: + self.encoder.gradient_checkpointing_enable() + logger.info(f'gradient checkpointing enabled for audio processor') + + projection_cls = kwargs.get('projection_cls', 'linear') + if projection_cls == 'linear': + self.audio_projection = nn.Linear(audio_dim_out, hidden_size) + elif projection_cls == 'mlp': + # follow llava-v1.5's implementation + # (do not use image_projection and image_proj_norm) + dim_projection = hidden_size + depth = 2 + self.linear_downsample_rate = self.downsample_rate + + layers_for_speech = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)] + for _ in range(1, depth): + layers_for_speech.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)]) + audio_projection_for_speech = nn.Sequential(*layers_for_speech) + + layers_for_vision = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)] + for _ in range(1, depth): + layers_for_vision.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)]) + audio_projection_for_vision = nn.Sequential(*layers_for_vision) + + self.audio_projection = nn.ModuleDict({ + 'speech': audio_projection_for_speech, + 'vision': audio_projection_for_vision + }) + else: + raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented') + + self.vocab_size = config.vocab_size + self.input_embeds = None + self.audio_embed_sizes = None + + def post_init(self, audio_config): + # execute after the from_pretrained() initialization of the phi4mm model + if audio_config.get('name', None) == "cascades": + init_model_config = audio_config.get("init_model", {}) + self.encoder.post_init(init_model_config) + # remove the init model in config so it is not saved in the config. + # This might affect the model loading in resuming training and decoding. + if "init_model" in audio_config: + audio_config.pop("init_model") + + def set_audio_embeds(self, input_embeds: torch.FloatTensor) -> None: + self.input_embeds = input_embeds + + def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None: + self.audio_embed_sizes = audio_embed_sizes + + def get_audio_features(self, input_embeds: torch.FloatTensor, audio_attention_mask: torch.Tensor, audio_projection_mode: str='speech'): + + if self.freeze_audio_processor: + with torch.no_grad(): + audio_features, masks = self.encoder(input_embeds, audio_attention_mask) + else: + audio_features, masks = self.encoder(input_embeds, audio_attention_mask) + + if isinstance(self.audio_projection, nn.Sequential): + audio_set_tensor = self.audio_projection(audio_features) + elif isinstance(self.audio_projection, nn.ModuleDict): + audio_set_tensor = self.audio_projection[audio_projection_mode](audio_features) + else: + raise NotImplementedError + + return audio_set_tensor + + def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, audio_embed_sizes=None, audio_attention_mask=None, audio_projection_mode='speech', **kwargs) -> torch.FloatTensor: + ''' + arguments: + input_ids: input text ids (B, U) + input_embeds: audio features (B, T, D) B: num audios in a sequence + ''' + if self.input_embeds is not None: + input_embeds = self.input_embeds.clone() + if self.audio_embed_sizes is not None: + audio_embed_sizes = self.audio_embed_sizes.clone() + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + MAX_INPUT_ID = int(1e9) + + with torch.no_grad(): + positions = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=False) + positions_tuple = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=True) + + if isinstance(self.audio_projection, nn.Sequential): + target_device = self.audio_projection[0].bias.device + target_dtype = self.audio_projection[0].bias.dtype + elif isinstance(self.audio_projection, nn.ModuleDict): + target_device = self.audio_projection[audio_projection_mode][0].bias.device + target_dtype = self.audio_projection[audio_projection_mode][0].bias.dtype + else: # It's a single nn.Linear layer + target_device = self.audio_projection.bias.device + target_dtype = self.audio_projection.bias.dtype + + if input_embeds is not None: + input_embeds = input_embeds.to(target_device).to(target_dtype) + + if len(positions.tolist()) > 0: + audio_set_tensor = self.get_audio_features(input_embeds, audio_attention_mask, audio_projection_mode) + else: + # # create an audio tensor + # To do: not sure if this is required for text only input + if self.training: + audio_embeds = torch.zeros(1, 500, self.audio_dim_in).to(target_device).to(target_dtype) + audio_attention_mask = audio_embeds.new_ones(audio_embeds.size()[:2]).long() + audio_set_tensor = self.get_audio_features(audio_embeds, audio_attention_mask, audio_projection_mode) + + hidden_states = kwargs['wte'](input_ids) + + if len(positions.tolist()) > 0: + + assert audio_embed_sizes.sum().item() == len(positions), \ + f"please ensure the encoder outputs have the same length as defined in input_ids! \n audio_embed_sizes.sum().item(): {audio_embed_sizes.sum().item()} \n len(positions): {len(positions)} \n audio_embed_sizes: {audio_embed_sizes} \n positions: {positions} \n input_ids.shape \n {input_ids.shape}" + + # new implementation without in-place operation + # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233 + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html + # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_ + # audio_set_tensor: shape (N_audios, N_padded_tokens, C) + # Shape: (merged_N_tokens, C) + merged_audio_set_tensor = torch.cat([ + audio_set_tensor[i, :audio_embed_sizes[i], :] + for i in range(len(audio_embed_sizes)) + ], dim=0) + merged_audio_set_tensor = merged_audio_set_tensor.to(hidden_states.dtype).to(hidden_states.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with torch.autocast(device_type=hidden_states.device.type, enabled=False): + new_hidden_states = hidden_states.index_put( + indices=positions_tuple, + values=merged_audio_set_tensor, + accumulate=False + ) + hidden_states = new_hidden_states + else: + if self.training: + hidden_states = hidden_states + (0 * audio_set_tensor[:,0].to(hidden_states.dtype).to(hidden_states.device)).sum() + + if self.drop is not None: + hidden_states = self.drop(hidden_states) + + return hidden_states + + + +class Phi4MMImageAudioEmbedding(nn.Module): + """Image-audio embedding.""" + + def __init__(self, config: PretrainedConfig, **kwargs) -> None: + super().__init__() + + self.vocab_size = config.vocab_size + + self.image_input_id = kwargs.get('image_input_id', -1) + self.audio_input_id = kwargs.get('audio_input_id', -10000) + assert self.image_input_id != self.audio_input_id, 'image_input_id and audio_input_id should be different' + + self.image_embd_layer_kwargs = kwargs['image_embd_layer'] + self.image_embed = Phi4MMImageEmbedding(config, **self.image_embd_layer_kwargs) + self.audio_embd_layer_kwargs = kwargs['audio_embd_layer'] + self.audio_embed = Phi4MMAudioEmbedding(config, **self.audio_embd_layer_kwargs) + + self.input_image_embeds = None + self.image_sizes = None + self.image_attention_mask = None + self.input_audio_embeds = None + self.audio_embed_sizes = None + + def post_init(self, audio_config): + # post init for audio embedding + # ref: model.model.embed_tokens_extend.post_init(audio_config) in phyagi/getters/model.py + self.audio_embed.post_init(audio_config) + + def set_input_image_embeds(self, input_image_embeds: torch.FloatTensor) -> None: + self.input_image_embeds = input_image_embeds + + def set_image_sizes(self, image_sizes: torch.LongTensor) -> None: + self.image_sizes = image_sizes + + def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None: + self.image_attention_mask = image_attention_mask + + def set_input_audio_embeds(self, input_audio_embeds: torch.FloatTensor) -> None: + self.input_audio_embeds = input_audio_embeds + + def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None: + self.audio_embed_sizes = audio_embed_sizes + + def forward( + self, + input_ids: torch.LongTensor, + input_embeds, + input_image_embeds: Optional[torch.FloatTensor]=None, + input_audio_embeds: Optional[torch.FloatTensor]=None, + image_sizes=None, + image_attention_mask=None, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode='speech', + wte=None, + ) -> torch.FloatTensor: + MAX_INPUT_ID = int(1e9) + assert -MAX_INPUT_ID < self.audio_input_id < self.image_input_id + + # override image and audio embeddings and sizes from object itself + # this is for inference + # ref: phyagi/eval/utils/text_generation_vision_audio_pipeline.py + if self.input_image_embeds is not None: + assert input_image_embeds is None + input_image_embeds = self.input_image_embeds.clone() + # NOTE weijian: set input_image_embeds to None after first call in for eval stage + # during evaluation, it will call model's forward() multiple times + # the first time input_ids contains the prompt (including <|image_{}|>) and input_embeds exists + # from the second time, the input_ids will only contain the generated text + # thus, the input_image_embeds is no longer needed + self.input_image_embeds = None + + if self.image_sizes is not None: + assert image_sizes is None + image_sizes = self.image_sizes + + if self.input_audio_embeds is not None: + assert input_audio_embeds is None + input_audio_embeds = self.input_audio_embeds.clone() + self.input_audio_embeds = None + + if self.audio_embed_sizes is not None: + assert audio_embed_sizes is None + audio_embed_sizes = self.audio_embed_sizes.clone() + + if self.image_attention_mask is not None: + assert image_attention_mask is None + image_attention_mask = self.image_attention_mask.clone() + self.image_attention_mask = None + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + # backward compatibility + with torch.no_grad(): + new_input_ids = input_ids.clone() + new_input_ids[(input_ids >= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[0]) & + (input_ids <= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[1])] = _IMAGE_SPECIAL_TOKEN_ID + new_input_ids[(input_ids >= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[0]) & + (input_ids <= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[1])] = _AUDIO_SPECIAL_TOKEN_ID + input_ids = new_input_ids + + with torch.no_grad(): + image_position_mask = input_ids == _IMAGE_SPECIAL_TOKEN_ID + non_image_position_mask = ~image_position_mask + + assert input_embeds is None + if self.training: + assert input_image_embeds is not None or input_audio_embeds is not None + + if input_image_embeds is not None: + image_hidden_states = self.image_embed( + input_ids=input_ids, + input_embeds=input_image_embeds, + image_sizes=image_sizes, + wte=wte, + image_attention_mask=image_attention_mask + ) + if input_audio_embeds is not None: + audio_hidden_states = self.audio_embed( + input_ids=input_ids, + input_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + wte=wte, + audio_projection_mode=audio_projection_mode, + ) + + # merge image and audio hidden states + # NOTE weijian: for non-image-audio tokens, here we use audio hidden states + # actually, in the debug code above, the non-image-audio tokens from image_hidden_states and audio_hidden_states should be the same + if input_image_embeds is not None and input_audio_embeds is not None: + dtype = image_hidden_states.dtype + hidden_states = image_hidden_states * image_position_mask.to(dtype).unsqueeze(-1) + audio_hidden_states * non_image_position_mask.to(dtype).unsqueeze(-1) + elif input_image_embeds is not None: + hidden_states = image_hidden_states + elif input_audio_embeds is not None: + hidden_states = audio_hidden_states + else: + assert wte is not None + hidden_states = wte(input_ids) + + return hidden_states + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3 +class Phi4MMRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Phi4MMRMSNorm 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}" + + +# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3 +class Phi4MMRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)) + self.register_buffer("inv_freq", tensor=inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + self.inv_freq.to(x.device) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMSuScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + warnings.warn( + "The class Phi4MMSuScaledRotaryEmbedding is deprecated and will be removed in version 5 of Transformers. Please" + " use Phi4MMLongRoPEScaledRotaryEmbedding instead.", + FutureWarning, + ) + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMYarnScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + warnings.warn( + "The class Phi4MMYarnScaledRotaryEmbedding is deprecated and will be removed in version 5 of Transformers", + FutureWarning, + ) + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = 0.1 * math.log(scale) + 1.0 + + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Phi4MMLongRoPEScaledRotaryEmbedding(Phi4MMRotaryEmbedding): + def __init__(self, dim, config, device=None): + super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) + + self.short_factor = config.rope_scaling["short_factor"] + self.long_factor = config.rope_scaling["long_factor"] + self.original_max_position_embeddings = config.original_max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + seq_len = seq_len or torch.max(position_ids) + 1 + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) + + inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim + self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) + + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + + scale = self.max_position_embeddings / self.original_max_position_embeddings + if scale <= 1.0: + scaling_factor = 1.0 + else: + scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) + + cos = emb.cos() * scaling_factor + sin = emb.sin() * scaling_factor + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +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 Phi4MMMLP(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + + self.activation_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + up_states = self.gate_up_proj(hidden_states) + + gate, up_states = up_states.chunk(2, dim=-1) + up_states = up_states * self.activation_fn(gate) + + return self.down_proj(up_states) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Phi4MMAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Phi4MMConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.original_max_position_embeddings = config.original_max_position_embeddings + self.rope_theta = config.rope_theta + self.rope_scaling = config.rope_scaling + self.rotary_ndims = int(self.head_dim * config.partial_rotary_factor) + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False) + self._init_rope() + + def _init_rope(self): + if self.rope_scaling is None: + self.rotary_emb = Phi4MMRotaryEmbedding( + self.rotary_ndims, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + if scaling_type == "longrope": + self.rotary_emb = Phi4MMLongRoPEScaledRotaryEmbedding(self.rotary_ndims, self.config) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.") + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights += causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Phi4MMFlashAttention2(Phi4MMAttention): + """ + Phi-4-MM flash attention module. This module inherits from `Phi4MMAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # Phi4MMFlashAttention2 attention does not support output_attentions + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = ( + max(kv_seq_len, position_ids[:, -1].max().item() + 1) if position_ids is not None else kv_seq_len + ) + + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len, position_ids=position_ids) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_dropout = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. + + if query_states.dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.qkv_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=attn_dropout, + sliding_window=getattr(self.config, "sliding_window", None), + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi +# TODO @Arthur no longer copied from LLama after static cache +class Phi4MMSdpaAttention(Phi4MMAttention): + """ + Phi4MM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Phi4MMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Phi4MMAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Phi4MMModel is using Phi4MMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + query_pos = self.num_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +PHI4MM_ATTENTION_CLASSES = { + "eager": Phi4MMAttention, + "flash_attention_2": Phi4MMFlashAttention2, + "sdpa": Phi4MMSdpaAttention, +} + + +class Phi4MMDecoderLayer(nn.Module): + def __init__(self, config: Phi4MMConfig, layer_idx: int): + super().__init__() + + self.config = config + self.self_attn = PHI4MM_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) + + self.mlp = Phi4MMMLP(config) + self.input_layernorm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) + self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) + self.post_attention_layernorm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> 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) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + 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 + attn_outputs, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = residual + self.resid_attn_dropout(attn_outputs) + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.resid_mlp_dropout(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +PHI4MM_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 ([`Phi4MMConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Phi-4-MM model outputting raw hidden-states without any specific head on top.", + PHI4MM_START_DOCSTRING, +) +class Phi4MMPreTrainedModel(PreTrainedModel): + config_class = Phi4MMConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi4MMDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + _version = "0.0.5" + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +PHI4MM_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 Phi-4-MM model outputting raw hidden-states without any specific head on top.", + PHI4MM_START_DOCSTRING, +) +class Phi4MMModel(Phi4MMPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi4MMDecoderLayer`] + + Args: + config: Phi4MMConfig + """ + + def __init__(self, config: Phi4MMConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.embed_dropout = nn.Dropout(config.embd_pdrop) + + self.embed_tokens_extend = None + if isinstance(config.embd_layer, dict): + embedding_config = { + 'embedding_cls': config.embd_layer['embedding_cls'], + **config.embd_layer + } + self.embed_tokens_extend = Phi4MMImageAudioEmbedding(config, **embedding_config) + + self.layers = nn.ModuleList( + [Phi4MMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Phi4MMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + input_image_embeds: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + image_attention_mask=None, + input_audio_embeds: Optional[torch.FloatTensor] = None, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode=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, + **kwargs, + ) -> 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: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens_extend( + input_ids=input_ids, + input_embeds=inputs_embeds, + input_image_embeds=input_image_embeds, + input_audio_embeds=input_audio_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + wte=self.embed_tokens, + ) + + 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 + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + ) + 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, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + 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 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 == "cuda" + 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 + # Copied from transformers.models.mistral.modeling_mistral.MistralModel._prepare_4d_causal_attention_mask_with_cache_position with Mistral->Phi3 + 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: Phi4MMConfig, + 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 (`Phi4MMConfig`): + 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, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + return causal_mask + + +class Phi4MMForCausalLM(Phi4MMPreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi + def __init__(self, config): + super().__init__(config) + self.model = Phi4MMModel(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() + + # LoRA related settings + assert getattr(config, "vision_lora", None) is not None + from peft import LoraConfig, get_peft_model + vision_lora_config = LoraConfig( + r=config.vision_lora['r'], + lora_alpha=config.vision_lora['lora_alpha'], + target_modules=config.vision_lora['layer'], + lora_dropout=config.vision_lora['dp'], + task_type="CAUSAL_LM", + ) + peft_model = get_peft_model(self.model, vision_lora_config, adapter_name="vision") + self.config.vision_lora['r'] = config.vision_lora['r'] + self.config.vision_lora['lora_alpha'] = config.vision_lora['lora_alpha'] + self.config.vision_lora['layer'] = config.vision_lora['layer'] + self.config.vision_lora['dp'] = config.vision_lora['dp'] + + assert getattr(config, "speech_lora", None) is not None + speech_lora_config = LoraConfig( + r=config.speech_lora['r'], + lora_alpha=config.speech_lora['lora_alpha'], + target_modules=config.speech_lora['layer'], + lora_dropout=config.speech_lora['dp'], + task_type="CAUSAL_LM", + ) + peft_model.base_model.active_adapter.append("speech") + peft_model.add_adapter("speech", speech_lora_config) + self.config.speech_lora['r'] = config.speech_lora['r'] + self.config.speech_lora['lora_alpha'] = config.speech_lora['lora_alpha'] + self.config.speech_lora['layer'] = config.speech_lora['layer'] + self.config.speech_lora['dp'] = config.speech_lora['dp'] + + def set_lora_adapter(self, adapter_name) -> None: + from peft.tuners.lora.layer import LoraLayer + for module in self.modules(): + if isinstance(module, LoraLayer): + if module.merged: + warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.") + module.unmerge() + module.set_adapter(adapter_name) + module._disable_adapters = False + + def unset_lora_adapter(self) -> None: + # Ref: peft/tuners/tuners_utils.py - enable_adapters() + # Ref: peft/tuners/lora/layer.py + from peft.tuners.lora.layer import LoraLayer + for module in self.modules(): + if isinstance(module, LoraLayer): + # disable grads on all adapter layers + # TODO weijian: may use enable_adapters() instead + for layer_name in module.adapter_layer_names: + layer = getattr(module, layer_name) + layer.requires_grad_(False) + module._disable_adapters = True + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings + def get_input_embeddings(self): + return self.model.embed_tokens + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings + def get_output_embeddings(self): + return self.lm_head + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder + def set_decoder(self, decoder): + self.model = decoder + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder + def get_decoder(self): + return self.model + + # Ignore copy + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + input_image_embeds: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + image_attention_mask=None, + input_audio_embeds: Optional[torch.FloatTensor] = None, + audio_embed_sizes=None, + audio_attention_mask=None, + input_mode=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, + num_logits_to_keep: int = 0, + ) -> 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]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_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. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Phi4MMForCausalLM + + >>> model = Phi4MMForCausalLM.from_pretrained("TBA") + >>> tokenizer = AutoTokenizer.from_pretrained("TBA") + + >>> prompt = "This is an example script ." + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' + ```""" + if ( + use_cache + and self.config.rope_scaling + and cache_position is not None + and cache_position[0] == self.config.original_max_position_embeddings + ): + logger.warning( + f"If you are not using the generate method, you may encounter nonsensical outputs after the {self.config.original_max_position_embeddings}th token, as the KV cache needs to be recomputed." + ) + + 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 + + if isinstance(input_mode, torch.Tensor): + assert len(input_mode) == 1 + input_mode = input_mode[0].item() + input_mode = InputMode(input_mode) + + if input_mode in [InputMode.VISION_SPEECH, InputMode.VISION]: + self.set_lora_adapter('vision') + audio_projection_mode = 'vision' + elif input_mode == InputMode.SPEECH: + self.set_lora_adapter('speech') + audio_projection_mode = 'speech' + elif input_mode == InputMode.LANGUAGE: + self.unset_lora_adapter() + audio_projection_mode = 'speech' + else: + raise ValueError(f"Invalid input_mode: {input_mode}") + + # 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, + input_image_embeds=input_image_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + input_audio_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size) + + 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, + input_image_embeds=None, + image_sizes=None, + image_attention_mask=None, + input_audio_embeds=None, + audio_embed_sizes=None, + audio_attention_mask=None, + input_mode=None, + cache_position=None, + position_ids=None, + use_cache=True, + num_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, + input_image_embeds=input_image_embeds, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + input_audio_embeds=input_audio_embeds, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + input_mode=input_mode, + cache_position=cache_position, + position_ids=position_ids, + use_cache=use_cache, + num_logits_to_keep=num_logits_to_keep, + **kwargs, + ) + return model_inputs + + +@add_start_docstrings( + """ + The [`Phi4MMModel`] with a sequence classification head on top (linear layer). + + [`Phi4MMForSequenceClassification`] 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). + """, + PHI4MM_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi, LLAMA->PHI, self.transformer->self.model, transformer_outputs->model_outputs +class Phi4MMForSequenceClassification(Phi4MMPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = Phi4MMModel(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(PHI4MM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + model_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = model_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config) + + if not return_dict: + output = (pooled_logits,) + model_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=model_outputs.past_key_values, + hidden_states=model_outputs.hidden_states, + attentions=model_outputs.attentions, + ) + + +@add_start_docstrings( + """ + [`Phi4MMModel`] 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. + """, + PHI4MM_START_DOCSTRING, +) +# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi,MPT->PHI,self.transformer->self.model,transformer_outputs->model_outputs +class Phi4MMForTokenClassification(Phi4MMPreTrainedModel): + def __init__(self, config: Phi4MMConfig): + super().__init__(config) + self.num_labels = config.num_labels + + self.model = Phi4MMModel(config) + if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: + classifier_dropout = config.classifier_dropout + elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: + classifier_dropout = config.hidden_dropout + else: + classifier_dropout = 0.1 + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(PHI4MM_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **deprecated_arguments, + ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + model_outputs = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = model_outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + batch_size, seq_length = labels.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct( + logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) + ) + + if not return_dict: + output = (logits,) + model_outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=model_outputs.hidden_states, + attentions=model_outputs.attentions, + ) + + +AutoConfig.register("phi4mm", Phi4MMConfig) +AutoModelForCausalLM.register(Phi4MMConfig, Phi4MMForCausalLM) +Phi4MMConfig.register_for_auto_class() +Phi4MMForCausalLM.register_for_auto_class("AutoModelForCausalLM")