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from transformers import AutoConfig, AutoModelForCausalLM |
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from abc import ABC, abstractmethod |
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from typing import Optional, Tuple, Union, Dict |
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from dataclasses import dataclass |
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from functools import partial, reduce |
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from PIL import Image |
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import torch.utils.checkpoint |
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from torch import nn |
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from transformers.image_processing_utils import BatchFeature, get_size_dict |
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from transformers.image_transforms import (convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format, ) |
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from transformers.image_utils import (ChannelDimension, PILImageResampling, to_numpy_array, ) |
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from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ModelOutput |
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class SigLipImageProcessor: |
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def __init__(self, |
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image_mean=(0.5, 0.5, 0.5), |
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image_std=(0.5, 0.5, 0.5), |
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size=(384, 384), |
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crop_size: Dict[str, int] = None, |
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resample=PILImageResampling.BICUBIC, |
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rescale_factor=1 / 255, |
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data_format=ChannelDimension.FIRST): |
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crop_size = crop_size if crop_size is not None else {"height": 384, "width": 384} |
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crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size") |
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self.image_mean = image_mean |
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self.image_std = image_std |
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self.size = size |
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self.resample = resample |
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self.rescale_factor = rescale_factor |
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self.data_format = data_format |
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self.crop_size = crop_size |
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def preprocess(self, images, return_tensors): |
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if isinstance(images, Image.Image): |
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images = [images] |
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else: |
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assert isinstance(images, list) |
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transforms = [ |
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convert_to_rgb, |
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to_numpy_array, |
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partial(resize, size=self.size, resample=self.resample, data_format=self.data_format), |
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partial(rescale, scale=self.rescale_factor, data_format=self.data_format), |
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partial(normalize, mean=self.image_mean, std=self.image_std, data_format=self.data_format), |
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partial(to_channel_dimension_format, channel_dim=self.data_format, input_channel_dim=self.data_format), |
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] |
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images = reduce(lambda x, f: [*map(f, x)], transforms, images) |
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data = {"pixel_values": images} |
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return BatchFeature(data=data, tensor_type=return_tensors) |
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from .configuration_gemma import SigLipVisionConfig |
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@dataclass |
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class SigLipVisionModelOutput(ModelOutput): |
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""" |
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. |
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Args: |
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): |
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The image embeddings obtained by applying the projection layer to the pooler_output. |
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
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Sequence of hidden-states at the output of the last layer of the model. |
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): |
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, |
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sequence_length)`. |
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention |
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heads. |
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""" |
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image_embeds: Optional[torch.FloatTensor] = None |
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last_hidden_state: torch.FloatTensor = None |
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
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attentions: Optional[Tuple[torch.FloatTensor]] = None |
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class SigLipVisionEmbeddings(nn.Module): |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__() |
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self.config = config |
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self.embed_dim = config.hidden_size |
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self.image_size = config.image_size |
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self.patch_size = config.patch_size |
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self.patch_embedding = nn.Conv2d( |
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in_channels=config.num_channels, |
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out_channels=self.embed_dim, |
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kernel_size=self.patch_size, |
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stride=self.patch_size, |
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padding="valid", |
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) |
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self.num_patches = (self.image_size // self.patch_size) ** 2 |
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self.num_positions = self.num_patches |
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) |
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self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) |
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: |
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patch_embeds = self.patch_embedding(pixel_values) |
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embeddings = patch_embeds.flatten(2).transpose(1, 2) |
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embeddings = embeddings + self.position_embedding(self.position_ids) |
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return embeddings |
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class SigLipAttention(nn.Module): |
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"""Multi-headed attention from 'Attention Is All You Need' paper""" |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.embed_dim = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.embed_dim // self.num_heads |
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if self.head_dim * self.num_heads != self.embed_dim: |
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raise ValueError( |
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" |
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f" {self.num_heads})." |
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) |
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self.scale = self.head_dim ** -0.5 |
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self.dropout = config.attention_dropout |
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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attention_mask: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = False, |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
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"""Input shape: Batch x Time x Channel""" |
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batch_size, q_len, _ = hidden_states.size() |
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query_states = self.q_proj(hidden_states) |
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key_states = self.k_proj(hidden_states) |
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value_states = self.v_proj(hidden_states) |
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query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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k_v_seq_len = key_states.shape[-2] |
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale |
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if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): |
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raise ValueError( |
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f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" |
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f" {attn_weights.size()}" |
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) |
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if attention_mask is not None: |
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if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): |
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raise ValueError( |
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f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" |
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) |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
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attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) |
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attn_output = torch.matmul(attn_weights, value_states) |
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if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim): |
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raise ValueError( |
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f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is" |
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f" {attn_output.size()}" |
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) |
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attn_output = attn_output.transpose(1, 2).contiguous() |
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attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) |
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attn_output = self.out_proj(attn_output) |
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return attn_output, attn_weights |
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class SigLipMLP(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.activation_fn = ACT2FN[config.hidden_act] |
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) |
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) |
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: |
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hidden_states = self.fc1(hidden_states) |
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hidden_states = self.activation_fn(hidden_states) |
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hidden_states = self.fc2(hidden_states) |
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return hidden_states |
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class SigLipEncoderLayer(nn.Module): |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__() |
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self.embed_dim = config.hidden_size |
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self.self_attn = SigLipAttention(config) |
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self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) |
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self.mlp = SigLipMLP(config) |
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self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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attention_mask: torch.Tensor, |
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output_attentions: Optional[bool] = False, |
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) -> Tuple[torch.FloatTensor]: |
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""" |
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Args: |
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hidden_states (`torch.FloatTensor`): |
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Input to the layer of shape `(batch, seq_len, embed_dim)`. |
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attention_mask (`torch.FloatTensor`): |
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Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. |
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output_attentions (`bool`, *optional*, defaults to `False`): |
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
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returned tensors for more detail. |
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""" |
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residual = hidden_states |
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hidden_states = self.layer_norm1(hidden_states) |
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hidden_states, attn_weights = self.self_attn( |
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hidden_states=hidden_states, |
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attention_mask=attention_mask, |
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output_attentions=output_attentions, |
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) |
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hidden_states = residual + hidden_states |
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residual = hidden_states |
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hidden_states = self.layer_norm2(hidden_states) |
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hidden_states = self.mlp(hidden_states) |
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hidden_states = residual + hidden_states |
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outputs = (hidden_states,) |
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if output_attentions: |
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outputs += (attn_weights,) |
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return outputs |
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class SigLipPreTrainedModel(PreTrainedModel): |
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""" |
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
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models. |
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""" |
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config_class = SigLipVisionConfig |
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base_model_prefix = "siglip" |
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supports_gradient_checkpointing = True |
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def _init_weights(self, module): |
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"""Initialize the weights""" |
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pass |
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class SigLipEncoder(nn.Module): |
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""" |
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a |
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[`SigLipEncoderLayer`]. |
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Args: |
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config: SigLipVisionConfig |
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""" |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__() |
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self.config = config |
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self.layers = nn.ModuleList([SigLipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) |
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self.gradient_checkpointing = False |
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def forward( |
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self, |
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inputs_embeds, |
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attention_mask: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, BaseModelOutput]: |
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r""" |
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Args: |
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. |
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors |
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than the model's internal embedding lookup matrix. |
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
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- 1 for tokens that are **not masked**, |
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- 0 for tokens that are **masked**. |
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[What are attention masks?](../glossary#attention-mask) |
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output_attentions (`bool`, *optional*): |
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
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returned tensors for more detail. |
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output_hidden_states (`bool`, *optional*): |
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors |
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for more detail. |
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return_dict (`bool`, *optional*): |
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
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""" |
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
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output_hidden_states = ( |
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
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) |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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encoder_states = () if output_hidden_states else None |
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all_attentions = () if output_attentions else None |
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hidden_states = inputs_embeds |
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for encoder_layer in self.layers: |
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if output_hidden_states: |
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encoder_states = encoder_states + (hidden_states,) |
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if self.gradient_checkpointing and self.training: |
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layer_outputs = self._gradient_checkpointing_func( |
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encoder_layer.__call__, |
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hidden_states, |
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attention_mask, |
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output_attentions, |
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) |
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else: |
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layer_outputs = encoder_layer( |
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hidden_states, |
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attention_mask, |
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output_attentions=output_attentions, |
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) |
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hidden_states = layer_outputs[0] |
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if output_attentions: |
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all_attentions = all_attentions + (layer_outputs[1],) |
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if output_hidden_states: |
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encoder_states = encoder_states + (hidden_states,) |
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if not return_dict: |
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return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) |
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return BaseModelOutput( |
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last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions |
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) |
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class SigLipVisionTransformer(nn.Module): |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__() |
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self.config = config |
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embed_dim = config.hidden_size |
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self.embeddings = SigLipVisionEmbeddings(config) |
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self.encoder = SigLipEncoder(config) |
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self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) |
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self.head = SigLipMultiheadAttentionPoolingHead(config) |
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def forward( |
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self, |
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pixel_values, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, BaseModelOutputWithPooling]: |
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r""" |
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Returns: |
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""" |
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
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output_hidden_states = ( |
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
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) |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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hidden_states = self.embeddings(pixel_values) |
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encoder_outputs = self.encoder( |
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inputs_embeds=hidden_states, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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last_hidden_state = encoder_outputs[0] |
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last_hidden_state = self.post_layernorm(last_hidden_state) |
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pooled_output = self.head(last_hidden_state) |
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if not return_dict: |
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return (last_hidden_state, pooled_output) + encoder_outputs[1:] |
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return BaseModelOutputWithPooling( |
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last_hidden_state=last_hidden_state, |
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pooler_output=pooled_output, |
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hidden_states=encoder_outputs.hidden_states, |
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attentions=encoder_outputs.attentions, |
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) |
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class SigLipMultiheadAttentionPoolingHead(nn.Module): |
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"""Multihead Attention Pooling.""" |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__() |
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self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) |
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self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) |
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self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
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self.mlp = SigLipMLP(config) |
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def forward(self, hidden_state): |
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batch_size = hidden_state.shape[0] |
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probe = self.probe.repeat(batch_size, 1, 1) |
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hidden_state = self.attention(probe, hidden_state, hidden_state)[0] |
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residual = hidden_state |
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hidden_state = self.layernorm(hidden_state) |
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hidden_state = residual + self.mlp(hidden_state) |
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return hidden_state[:, 0] |
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class SigLipVisionModel(SigLipPreTrainedModel): |
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config_class = SigLipVisionConfig |
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main_input_name = "pixel_values" |
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_no_split_modules = ["SigLipEncoderLayer"] |
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def __init__(self, config: SigLipVisionConfig): |
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super().__init__(config) |
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self.vision_model = SigLipVisionTransformer(config) |
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self.post_init() |
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def get_input_embeddings(self) -> nn.Module: |
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return self.vision_model.embeddings.patch_embedding |
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|
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def forward( |
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self, |
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pixel_values, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, BaseModelOutputWithPooling]: |
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r""" |
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Returns: |
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|
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Examples: |
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|
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```python |
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>>> from PIL import Image |
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>>> import requests |
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>>> from transformers import AutoProcessor, SigLipVisionModel |
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>>> model = SigLipVisionModel.from_pretrained("google/siglip-base-patch16-224") |
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>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") |
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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>>> image = Image.open(requests.get(url, stream=True).raw) |
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>>> inputs = processor(images=image, return_tensors="pt") |
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|
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>>> outputs = model(**inputs) |
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>>> last_hidden_state = outputs.last_hidden_state |
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>>> pooled_output = outputs.pooler_output # pooled features |
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```""" |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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|
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return self.vision_model( |
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pixel_values=pixel_values, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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|
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class SigLipVisionTower(nn.Module): |
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def __init__(self, vision_tower, vision_tower_cfg, delay_load=False): |
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super().__init__() |
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|
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self.is_loaded = False |
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|
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self.config = SigLipVisionConfig() |
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|
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self.vision_tower_name = vision_tower |
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|
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self.image_processor = SigLipImageProcessor() |
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|
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if not delay_load: |
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self.load_model() |
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else: |
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self.cfg_only = self.config |
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|
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def load_model(self): |
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if self.is_loaded: |
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return |
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|
|
self.vision_tower = SigLipVisionModel.from_pretrained(self.vision_tower_name) |
|
|
|
del self.vision_tower.vision_model.encoder.layers[-1:] |
|
self.vision_tower.vision_model.head = nn.Identity() |
|
self.vision_tower.requires_grad_(False) |
|
self.vision_tower.eval() |
|
|
|
self.is_loaded = True |
|
|
|
@torch.no_grad() |
|
def forward(self, images): |
|
if type(images) is list: |
|
image_features = [] |
|
for image in images: |
|
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), |
|
output_hidden_states=True) |
|
image_feature = image_forward_out.hidden_states[-1].to(image.dtype) |
|
assert image_features.shape[-2] == 729 |
|
image_features.append(image_feature) |
|
else: |
|
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), |
|
output_hidden_states=True) |
|
image_features = image_forward_outs.hidden_states[-1].to(images.dtype) |
|
assert image_features.shape[-2] == 729 |
|
|
|
return image_features |
|
|
|
@property |
|
def dummy_feature(self): |
|
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) |
|
|
|
@property |
|
def dtype(self): |
|
for p in self.vision_tower.parameters(): |
|
return p.dtype |
|
|
|
@property |
|
def device(self): |
|
for p in self.vision_tower.parameters(): |
|
return p.device |
|
|
|
@property |
|
def hidden_size(self): |
|
return self.config.hidden_size |
|
|
|
@property |
|
def num_patches(self): |
|
return (self.config.image_size // self.config.patch_size) ** 2 |
|
|
|
|
|
def build_vision_tower(vision_tower_cfg, **kwargs): |
|
vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None)) |
|
|
|
return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs) |
|
|
|
|
|
import re |
|
|
|
|
|
def build_vision_projector(config, delay_load=False, **kwargs): |
|
projector_type = getattr(config, 'mm_projector_type', 'mlp2x_gelu') |
|
|
|
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) |
|
if mlp_gelu_match: |
|
mlp_depth = int(mlp_gelu_match.group(1)) |
|
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] |
|
for _ in range(1, mlp_depth): |
|
modules.append(nn.GELU()) |
|
modules.append(nn.Linear(config.hidden_size, config.hidden_size)) |
|
return nn.Sequential(*modules) |
|
|
|
|
|
|
|
IGNORE_INDEX = -100 |
|
IMAGE_TOKEN_INDEX = -200 |
|
|
|
|
|
class CeruleMetaModel: |
|
|
|
def __init__(self, config): |
|
super(CeruleMetaModel, self).__init__(config) |
|
|
|
if hasattr(config, "mm_vision_tower"): |
|
self.vision_tower = build_vision_tower(config, delay_load=True) |
|
self.mm_projector = build_vision_projector(config) |
|
|
|
def get_vision_tower(self): |
|
vision_tower = getattr(self, 'vision_tower', None) |
|
if type(vision_tower) is list: |
|
vision_tower = vision_tower[0] |
|
return vision_tower |
|
|
|
def initialize_vision_modules(self, model_args): |
|
vision_tower = model_args.vision_tower |
|
|
|
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter |
|
|
|
self.config.mm_vision_tower = vision_tower |
|
|
|
if self.get_vision_tower() is None: |
|
vision_tower = build_vision_tower(model_args) |
|
self.vision_tower = vision_tower |
|
else: |
|
vision_tower = self.vision_tower |
|
vision_tower.load_model() |
|
|
|
self.config.use_mm_proj = True |
|
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type') |
|
self.config.mm_hidden_size = vision_tower.hidden_size |
|
|
|
if getattr(self, 'mm_projector', None) is None: |
|
self.mm_projector = build_vision_projector(self.config) |
|
else: |
|
|
|
for p in self.mm_projector.parameters(): |
|
p.requires_grad = True |
|
|
|
if pretrain_mm_mlp_adapter is not None: |
|
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') |
|
|
|
def get_w(weights, keyword): |
|
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} |
|
|
|
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) |
|
|
|
|
|
class CeruleMetaForCausalLM(ABC): |
|
|
|
@abstractmethod |
|
def get_model(self): |
|
pass |
|
|
|
def get_vision_tower(self): |
|
return self.get_model().get_vision_tower() |
|
|
|
def encode_images(self, images): |
|
image_features = self.get_model().get_vision_tower()(images) |
|
image_features = self.get_model().mm_projector(image_features) |
|
return image_features |
|
|
|
def prepare_inputs_labels_for_multimodal( |
|
self, input_ids, position_ids, attention_mask, past_key_values, labels, images |
|
): |
|
vision_tower = self.get_vision_tower() |
|
if vision_tower is None or images is None or input_ids.shape[1] == 1: |
|
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[ |
|
1] == 1: |
|
target_shape = past_key_values[-1][-1].shape[-2] + 1 |
|
attention_mask = torch.cat((attention_mask, torch.ones( |
|
(attention_mask.shape[0], target_shape - attention_mask.shape[1]), |
|
dtype=attention_mask.dtype, |
|
device=attention_mask.device |
|
)), dim=1) |
|
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 |
|
return input_ids, position_ids, attention_mask, past_key_values, None, labels |
|
|
|
if type(images) is list or images.ndim == 5: |
|
concat_images = torch.cat([image for image in images], dim=0) |
|
image_features = self.encode_images(concat_images) |
|
split_sizes = [image.shape[0] for image in images] |
|
image_features = torch.split(image_features, split_sizes, dim=0) |
|
image_features = [x.flatten(0, 1).to(self.device) for x in image_features] |
|
else: |
|
image_features = self.encode_images(images).to(self.device) |
|
|
|
|
|
|
|
|
|
|
|
_labels = labels |
|
_position_ids = position_ids |
|
_attention_mask = attention_mask |
|
if attention_mask is None: |
|
attention_mask = torch.ones_like(input_ids, dtype=torch.bool) |
|
else: |
|
attention_mask = attention_mask.bool() |
|
if position_ids is None: |
|
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device) |
|
if labels is None: |
|
labels = torch.full_like(input_ids, IGNORE_INDEX) |
|
|
|
|
|
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in |
|
zip(input_ids, attention_mask)] |
|
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)] |
|
|
|
new_input_embeds = [] |
|
new_labels = [] |
|
cur_image_idx = 0 |
|
for batch_idx, cur_input_ids in enumerate(input_ids): |
|
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() |
|
if num_images == 0: |
|
cur_image_features = image_features[cur_image_idx] |
|
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids) |
|
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0) |
|
new_input_embeds.append(cur_input_embeds) |
|
new_labels.append(labels[batch_idx]) |
|
cur_image_idx += 1 |
|
continue |
|
|
|
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [ |
|
cur_input_ids.shape[0]] |
|
cur_input_ids_noim = [] |
|
cur_labels = labels[batch_idx] |
|
cur_labels_noim = [] |
|
for i in range(len(image_token_indices) - 1): |
|
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1:image_token_indices[i + 1]]) |
|
cur_labels_noim.append(cur_labels[image_token_indices[i] + 1:image_token_indices[i + 1]]) |
|
split_sizes = [x.shape[0] for x in cur_labels_noim] |
|
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim)) |
|
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) |
|
cur_new_input_embeds = [] |
|
cur_new_labels = [] |
|
|
|
for i in range(num_images + 1): |
|
cur_new_input_embeds.append(cur_input_embeds_no_im[i]) |
|
cur_new_labels.append(cur_labels_noim[i]) |
|
if i < num_images: |
|
cur_image_features = image_features[cur_image_idx] |
|
cur_image_idx += 1 |
|
cur_new_input_embeds.append(cur_image_features) |
|
cur_new_labels.append( |
|
torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, |
|
dtype=cur_labels.dtype)) |
|
|
|
cur_new_input_embeds = torch.cat(cur_new_input_embeds) |
|
cur_new_labels = torch.cat(cur_new_labels) |
|
|
|
new_input_embeds.append(cur_new_input_embeds) |
|
new_labels.append(cur_new_labels) |
|
|
|
|
|
tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None) |
|
if tokenizer_model_max_length is not None: |
|
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds] |
|
new_labels = [x[:tokenizer_model_max_length] for x in new_labels] |
|
|
|
|
|
max_len = max(x.shape[0] for x in new_input_embeds) |
|
batch_size = len(new_input_embeds) |
|
|
|
new_input_embeds_padded = [] |
|
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, |
|
device=new_labels[0].device) |
|
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device) |
|
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device) |
|
|
|
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)): |
|
cur_len = cur_new_embed.shape[0] |
|
if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": |
|
new_input_embeds_padded.append(torch.cat(( |
|
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, |
|
device=cur_new_embed.device), |
|
cur_new_embed |
|
), dim=0)) |
|
if cur_len > 0: |
|
new_labels_padded[i, -cur_len:] = cur_new_labels |
|
attention_mask[i, -cur_len:] = True |
|
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, |
|
device=position_ids.device) |
|
else: |
|
new_input_embeds_padded.append(torch.cat(( |
|
cur_new_embed, |
|
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, |
|
device=cur_new_embed.device) |
|
), dim=0)) |
|
if cur_len > 0: |
|
new_labels_padded[i, :cur_len] = cur_new_labels |
|
attention_mask[i, :cur_len] = True |
|
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, |
|
device=position_ids.device) |
|
|
|
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) |
|
|
|
if _labels is None: |
|
new_labels = None |
|
else: |
|
new_labels = new_labels_padded |
|
|
|
if _attention_mask is None: |
|
attention_mask = None |
|
else: |
|
attention_mask = attention_mask.to(dtype=_attention_mask.dtype) |
|
|
|
if _position_ids is None: |
|
position_ids = None |
|
|
|
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" PyTorch Gemma model.""" |
|
|
|
import math |
|
import warnings |
|
from typing import List, Optional, Tuple, Union |
|
|
|
import torch |
|
import torch.nn.functional as F |
|
import torch.utils.checkpoint |
|
from torch import nn |
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
|
|
|
from transformers.activations import ACT2FN |
|
from transformers.cache_utils import Cache, DynamicCache, StaticCache |
|
from transformers.modeling_attn_mask_utils import ( |
|
AttentionMaskConverter, |
|
_prepare_4d_causal_attention_mask, |
|
) |
|
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast |
|
from transformers.modeling_utils import PreTrainedModel |
|
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13 |
|
from transformers.utils import ( |
|
add_start_docstrings, |
|
add_start_docstrings_to_model_forward, |
|
is_flash_attn_2_available, |
|
is_flash_attn_greater_or_equal_2_10, |
|
logging, |
|
replace_return_docstrings, |
|
) |
|
from transformers.utils.import_utils import is_torch_fx_available |
|
from .configuration_gemma import CeruleGemmaConfig |
|
|
|
|
|
if is_flash_attn_2_available(): |
|
from flash_attn import flash_attn_func, flash_attn_varlen_func |
|
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input |
|
|
|
|
|
|
|
|
|
if is_torch_fx_available(): |
|
if not is_torch_greater_or_equal_than_1_13: |
|
import torch.fx |
|
|
|
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) |
|
|
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
_CONFIG_FOR_DOC = "CeruleGemmaConfig" |
|
|
|
|
|
def _get_unpad_data(attention_mask): |
|
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) |
|
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() |
|
max_seqlen_in_batch = seqlens_in_batch.max().item() |
|
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) |
|
return ( |
|
indices, |
|
cu_seqlens, |
|
max_seqlen_in_batch, |
|
) |
|
|
|
|
|
class GemmaRMSNorm(nn.Module): |
|
def __init__(self, dim: int, eps: float = 1e-6): |
|
super().__init__() |
|
self.eps = eps |
|
self.weight = nn.Parameter(torch.zeros(dim)) |
|
|
|
def _norm(self, x): |
|
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) |
|
|
|
def forward(self, x): |
|
output = self._norm(x.float()).type_as(x) |
|
return output * (1 + self.weight) |
|
|
|
|
|
ALL_LAYERNORM_LAYERS.append(GemmaRMSNorm) |
|
|
|
|
|
class GemmaRotaryEmbedding(nn.Module): |
|
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): |
|
super().__init__() |
|
|
|
self.dim = dim |
|
self.max_position_embeddings = max_position_embeddings |
|
self.base = base |
|
self.register_buffer("inv_freq", None, persistent=False) |
|
|
|
@torch.no_grad() |
|
def forward(self, x, position_ids, seq_len=None): |
|
|
|
if self.inv_freq is None: |
|
self.inv_freq = 1.0 / ( |
|
self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim) |
|
) |
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) |
|
position_ids_expanded = position_ids[:, None, :].float() |
|
|
|
|
|
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) |
|
|
|
|
|
|
|
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) |
|
q_embed = (q * cos) + (rotate_half(q) * sin) |
|
k_embed = (k * cos) + (rotate_half(k) * sin) |
|
return q_embed, k_embed |
|
|
|
|
|
|
|
class GemmaMLP(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.config = config |
|
self.hidden_size = config.hidden_size |
|
self.intermediate_size = config.intermediate_size |
|
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
|
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
|
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) |
|
self.act_fn = ACT2FN[config.hidden_act] |
|
|
|
def forward(self, x): |
|
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) |
|
|
|
|
|
|
|
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 GemmaAttention(nn.Module): |
|
"""Multi-headed attention from 'Attention Is All You Need' paper""" |
|
|
|
|
|
def __init__(self, config: CeruleGemmaConfig, 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 = config.head_dim |
|
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.rope_theta = config.rope_theta |
|
self.is_causal = True |
|
|
|
if self.hidden_size % self.num_heads != 0: |
|
raise ValueError( |
|
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" |
|
f" and `num_heads`: {self.num_heads})." |
|
) |
|
|
|
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) |
|
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) |
|
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) |
|
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias) |
|
self.rotary_emb = GemmaRotaryEmbedding( |
|
self.head_dim, |
|
max_position_embeddings=self.max_position_embeddings, |
|
base=self.rope_theta, |
|
) |
|
|
|
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 = True, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
**kwargs, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
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) |
|
|
|
past_key_value = getattr(self, "past_key_value", past_key_value) |
|
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None) |
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None) |
|
|
|
if past_key_value is not None: |
|
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} |
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
|
|
|
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: |
|
if cache_position is not None: |
|
causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]] |
|
else: |
|
causal_mask = attention_mask |
|
attn_weights = attn_weights + causal_mask |
|
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_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.view(bsz, q_len, -1) |
|
attn_output = self.o_proj(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
|
|
class GemmaFlashAttention2(GemmaAttention): |
|
""" |
|
Gemma flash attention module. This module inherits from `GemmaAttention` 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. |
|
""" |
|
|
|
def __init__(self, *args, **kwargs): |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
|
|
|
|
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 = True, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
**kwargs, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
output_attentions = False |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
|
|
|
|
|
|
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) |
|
|
|
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None) |
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None) |
|
|
|
past_key_value = getattr(self, "past_key_value", past_key_value) |
|
|
|
if past_key_value is not None: |
|
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} |
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
|
|
|
|
|
|
|
query_states = query_states.transpose(1, 2) |
|
key_states = key_states.transpose(1, 2) |
|
value_states = value_states.transpose(1, 2) |
|
|
|
dropout_rate = self.attention_dropout if self.training else 0.0 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
input_dtype = query_states.dtype |
|
if input_dtype == torch.float32: |
|
if torch.is_autocast_enabled(): |
|
target_dtype = torch.get_autocast_gpu_dtype() |
|
|
|
elif hasattr(self.config, "_pre_quantization_dtype"): |
|
target_dtype = self.config._pre_quantization_dtype |
|
else: |
|
target_dtype = self.q_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) |
|
|
|
attn_output = self._flash_attention_forward( |
|
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate |
|
) |
|
|
|
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() |
|
attn_output = self.o_proj(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
def _flash_attention_forward( |
|
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None |
|
): |
|
""" |
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
|
first unpad the input, then computes the attention scores and pad the final attention scores. |
|
|
|
Args: |
|
query_states (`torch.Tensor`): |
|
Input query states to be passed to Flash Attention API |
|
key_states (`torch.Tensor`): |
|
Input key states to be passed to Flash Attention API |
|
value_states (`torch.Tensor`): |
|
Input value states to be passed to Flash Attention API |
|
attention_mask (`torch.Tensor`): |
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
|
position of padding tokens and 1 for the position of non-padding tokens. |
|
dropout (`float`): |
|
Attention dropout |
|
softmax_scale (`float`, *optional*): |
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
|
""" |
|
if not self._flash_attn_uses_top_left_mask: |
|
causal = self.is_causal |
|
else: |
|
|
|
causal = self.is_causal and query_length != 1 |
|
|
|
|
|
if attention_mask is not None: |
|
batch_size = query_states.shape[0] |
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( |
|
query_states, key_states, value_states, attention_mask, query_length |
|
) |
|
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens |
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens |
|
|
|
attn_output_unpad = flash_attn_varlen_func( |
|
query_states, |
|
key_states, |
|
value_states, |
|
cu_seqlens_q=cu_seqlens_q, |
|
cu_seqlens_k=cu_seqlens_k, |
|
max_seqlen_q=max_seqlen_in_batch_q, |
|
max_seqlen_k=max_seqlen_in_batch_k, |
|
dropout_p=dropout, |
|
softmax_scale=softmax_scale, |
|
causal=causal, |
|
) |
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) |
|
else: |
|
attn_output = flash_attn_func( |
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal |
|
) |
|
|
|
return attn_output |
|
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): |
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) |
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape |
|
|
|
key_layer = index_first_axis( |
|
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k |
|
) |
|
value_layer = index_first_axis( |
|
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k |
|
) |
|
if query_length == kv_seq_len: |
|
query_layer = index_first_axis( |
|
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k |
|
) |
|
cu_seqlens_q = cu_seqlens_k |
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k |
|
indices_q = indices_k |
|
elif query_length == 1: |
|
max_seqlen_in_batch_q = 1 |
|
cu_seqlens_q = torch.arange( |
|
batch_size + 1, dtype=torch.int32, device=query_layer.device |
|
) |
|
indices_q = cu_seqlens_q[:-1] |
|
query_layer = query_layer.squeeze(1) |
|
else: |
|
|
|
attention_mask = attention_mask[:, -query_length:] |
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) |
|
|
|
return ( |
|
query_layer, |
|
key_layer, |
|
value_layer, |
|
indices_q, |
|
(cu_seqlens_q, cu_seqlens_k), |
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k), |
|
) |
|
|
|
|
|
|
|
class GemmaSdpaAttention(GemmaAttention): |
|
""" |
|
Gemma attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from |
|
`GemmaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to |
|
SDPA API. |
|
""" |
|
|
|
|
|
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 = True, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
if output_attentions: |
|
|
|
logger.warning_once( |
|
"GemmaModel is using GemmaSdpaAttention, 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=False, |
|
cache_position=cache_position, |
|
) |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
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) |
|
|
|
|
|
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None) |
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None) |
|
|
|
past_key_value = getattr(self, "past_key_value", past_key_value) |
|
|
|
if past_key_value is not None: |
|
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} |
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
|
|
|
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 and cache_position is not None: |
|
causal_mask = causal_mask[:, :, cache_position, : key_states.shape[-2]] |
|
|
|
|
|
|
|
if query_states.device.type == "cuda" and causal_mask is not None: |
|
query_states = query_states.contiguous() |
|
key_states = key_states.contiguous() |
|
value_states = value_states.contiguous() |
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention( |
|
query_states, |
|
key_states, |
|
value_states, |
|
attn_mask=causal_mask, |
|
dropout_p=self.attention_dropout if self.training else 0.0, |
|
) |
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.view(bsz, q_len, -1) |
|
|
|
attn_output = self.o_proj(attn_output) |
|
|
|
return attn_output, None, past_key_value |
|
|
|
|
|
GEMMA_ATTENTION_CLASSES = { |
|
"eager": GemmaAttention, |
|
"flash_attention_2": GemmaFlashAttention2, |
|
"sdpa": GemmaSdpaAttention, |
|
} |
|
|
|
|
|
|
|
class GemmaDecoderLayer(nn.Module): |
|
def __init__(self, config: CeruleGemmaConfig, layer_idx: int): |
|
super().__init__() |
|
self.hidden_size = config.hidden_size |
|
|
|
self.self_attn = GEMMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) |
|
|
|
self.mlp = GemmaMLP(config) |
|
self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
self.post_attention_layernorm = GemmaRMSNorm(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_size, sequence_length)` if flash attention is used or `(batch_size, 1, |
|
query_sequence_length, key_sequence_length)` if default attention is used. |
|
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 |
|
""" |
|
if "padding_mask" in kwargs: |
|
warnings.warn( |
|
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
|
) |
|
|
|
residual = hidden_states |
|
|
|
hidden_states = self.input_layernorm(hidden_states) |
|
|
|
|
|
hidden_states, 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=False, |
|
cache_position=cache_position, |
|
**kwargs, |
|
) |
|
hidden_states = residual + hidden_states |
|
|
|
|
|
residual = hidden_states |
|
hidden_states = self.post_attention_layernorm(hidden_states) |
|
hidden_states = self.mlp(hidden_states) |
|
hidden_states = residual + hidden_states |
|
|
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights,) |
|
|
|
if use_cache: |
|
outputs += (present_key_value,) |
|
|
|
return outputs |
|
|
|
|
|
GEMMA_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 ([`CeruleGemmaConfig`]): |
|
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 Gemma Model outputting raw hidden-states without any specific head on top.", |
|
GEMMA_START_DOCSTRING, |
|
) |
|
class GemmaPreTrainedModel(PreTrainedModel): |
|
config_class = CeruleGemmaConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = True |
|
_keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"] |
|
_no_split_modules = ["GemmaDecoderLayer"] |
|
_skip_keys_device_placement = ["past_key_values", "causal_mask"] |
|
_supports_flash_attn_2 = True |
|
_supports_sdpa = True |
|
_supports_cache_class = True |
|
|
|
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_() |
|
|
|
def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None): |
|
if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache: |
|
raise ValueError( |
|
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " |
|
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" |
|
) |
|
|
|
if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device: |
|
causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device) |
|
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False) |
|
|
|
for layer in self.model.layers: |
|
weights = layer.self_attn.o_proj.weight |
|
layer.self_attn.past_key_value = cache_cls( |
|
self.config, max_batch_size, max_cache_len, device=weights.device, dtype=weights.dtype |
|
) |
|
|
|
def _reset_cache(self): |
|
for layer in self.model.layers: |
|
layer.self_attn.past_key_value = None |
|
|
|
|
|
GEMMA_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see |
|
`past_key_values`). |
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
|
information on the default strategy. |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.n_positions - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): |
|
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` |
|
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. |
|
|
|
Two formats are allowed: |
|
- a [`~cache_utils.Cache`] instance; |
|
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of |
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy |
|
cache format. |
|
|
|
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the |
|
legacy cache format will be returned. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
|
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
|
of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
|
model's internal embedding lookup matrix. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare Gemma Model outputting raw hidden-states without any specific head on top.", |
|
GEMMA_START_DOCSTRING, |
|
) |
|
|
|
class GemmaModel(GemmaPreTrainedModel): |
|
""" |
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`] |
|
|
|
Args: |
|
config: GemmaConfig |
|
""" |
|
|
|
def __init__(self, config: CeruleGemmaConfig): |
|
super().__init__(config) |
|
self.padding_idx = config.pad_token_id |
|
self.vocab_size = config.vocab_size |
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) |
|
self.layers = nn.ModuleList( |
|
[GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] |
|
) |
|
self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
self.gradient_checkpointing = False |
|
|
|
|
|
|
|
causal_mask = torch.full( |
|
(config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool |
|
) |
|
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False) |
|
|
|
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(GEMMA_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, |
|
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, |
|
) -> 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 cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" |
|
) |
|
|
|
if self.gradient_checkpointing and self.training and use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." |
|
) |
|
use_cache = False |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embed_tokens(input_ids) |
|
|
|
past_seen_tokens = 0 |
|
if use_cache: |
|
if not isinstance(past_key_values, StaticCache): |
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
|
past_seen_tokens = past_key_values.get_seq_length() |
|
|
|
if cache_position is None: |
|
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) |
|
|
|
|
|
hidden_states = inputs_embeds |
|
|
|
|
|
hidden_states = hidden_states * (self.config.hidden_size**0.5) |
|
|
|
|
|
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=False, |
|
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) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
next_cache = None |
|
if use_cache: |
|
next_cache = ( |
|
next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache |
|
) |
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attns, |
|
) |
|
|
|
|
|
|
|
|
|
|
|
def _update_causal_mask(self, attention_mask, input_tensor): |
|
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 |
|
|
|
batch_size, seq_length = input_tensor.shape[:2] |
|
dtype = input_tensor.dtype |
|
device = input_tensor.device |
|
|
|
|
|
if seq_length > self.causal_mask.shape[-1]: |
|
causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1) |
|
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False) |
|
|
|
|
|
min_dtype = torch.finfo(dtype).min |
|
|
|
causal_mask = self.causal_mask[None, None, :, :].to(dtype=dtype, device=device) * min_dtype |
|
causal_mask = causal_mask.expand(batch_size, 1, -1, -1) |
|
if attention_mask is not None: |
|
causal_mask = causal_mask.clone() |
|
if attention_mask.dim() == 2: |
|
mask_length = attention_mask.shape[-1] |
|
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0) |
|
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype) |
|
elif attention_mask.dim() == 4: |
|
mask_shape = attention_mask.shape |
|
mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype |
|
causal_mask[: mask_shape[0], : mask_shape[1], : mask_shape[2], : mask_shape[3]] = mask_slice |
|
|
|
if ( |
|
self.config._attn_implementation == "sdpa" |
|
and attention_mask is not None |
|
and attention_mask.device.type == "cuda" |
|
): |
|
|
|
is_tracing = ( |
|
torch.jit.is_tracing() |
|
or isinstance(input_tensor, torch.fx.Proxy) |
|
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling()) |
|
) |
|
if not is_tracing and torch.any(attention_mask != 1): |
|
|
|
|
|
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) |
|
|
|
return causal_mask |
|
|
|
|
|
|
|
class GemmaForCausalLM(GemmaPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.model = GemmaModel(config) |
|
self.vocab_size = config.vocab_size |
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.model.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.embed_tokens = value |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def set_decoder(self, decoder): |
|
self.model = decoder |
|
|
|
def get_decoder(self): |
|
return self.model |
|
|
|
|
|
@add_start_docstrings_to_model_forward(GEMMA_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, |
|
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, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
r""" |
|
Args: |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. |
|
|
|
Returns: |
|
|
|
Example: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, GemmaForCausalLM |
|
|
|
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b") |
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") |
|
|
|
>>> prompt = "What is your favorite condiment?" |
|
>>> 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] |
|
"What is your favorite condiment?" |
|
```""" |
|
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 |
|
|
|
|
|
outputs = self.model( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=False, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
cache_position=cache_position, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
logits = self.lm_head(hidden_states) |
|
logits = logits.float() |
|
loss = None |
|
if labels is not None: |
|
|
|
shift_logits = logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
|
shift_labels = shift_labels.view(-1) |
|
|
|
shift_labels = shift_labels.to(shift_logits.device) |
|
loss = loss_fct(shift_logits, shift_labels) |
|
|
|
if not return_dict: |
|
output = (logits,) + outputs[1:] |
|
return (loss,) + output if loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=loss, |
|
logits=logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs |
|
): |
|
past_length = 0 |
|
if past_key_values is not None: |
|
if isinstance(past_key_values, Cache): |
|
cache_length = past_key_values.get_seq_length() |
|
past_length = past_key_values.seen_tokens |
|
max_cache_length = past_key_values.get_max_length() |
|
else: |
|
cache_length = past_length = past_key_values[0][0].shape[2] |
|
max_cache_length = None |
|
|
|
|
|
|
|
|
|
|
|
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: |
|
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] |
|
|
|
|
|
elif past_length < input_ids.shape[1]: |
|
input_ids = input_ids[:, past_length:] |
|
|
|
|
|
|
|
if ( |
|
max_cache_length is not None |
|
and attention_mask is not None |
|
and cache_length + input_ids.shape[1] > max_cache_length |
|
): |
|
attention_mask = attention_mask[:, -max_cache_length:] |
|
|
|
position_ids = kwargs.get("position_ids", None) |
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -input_ids.shape[1] :] |
|
|
|
if self.generation_config.cache_implementation == "static": |
|
|
|
cache_position = kwargs.get("cache_position", None) |
|
if cache_position is None: |
|
past_length = 0 |
|
else: |
|
past_length = cache_position[-1] + 1 |
|
input_ids = input_ids[:, past_length:] |
|
position_ids = position_ids[:, past_length:] |
|
|
|
|
|
|
|
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] |
|
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) |
|
position_ids = position_ids.contiguous() if position_ids is not None else None |
|
|
|
|
|
if inputs_embeds is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
|
|
|
|
|
|
model_inputs = {"input_ids": input_ids.contiguous()} |
|
|
|
model_inputs.update( |
|
{ |
|
"position_ids": position_ids, |
|
"cache_position": cache_position, |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"attention_mask": attention_mask, |
|
} |
|
) |
|
return model_inputs |
|
|
|
@staticmethod |
|
def _reorder_cache(past_key_values, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), |
|
) |
|
return reordered_past |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The Gemma Model transformer with a sequence classification head on top (linear layer). |
|
|
|
[`GemmaForSequenceClassification`] 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). |
|
""", |
|
GEMMA_START_DOCSTRING, |
|
) |
|
|
|
class GemmaForSequenceClassification(GemmaPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.model = GemmaModel(config) |
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
|
|
|
|
|
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(GEMMA_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 |
|
|
|
transformer_outputs = self.model( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=False, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
hidden_states = transformer_outputs[0] |
|
logits = self.score(hidden_states) |
|
|
|
if input_ids is not None: |
|
batch_size = input_ids.shape[0] |
|
else: |
|
batch_size = inputs_embeds.shape[0] |
|
|
|
if self.config.pad_token_id is None and batch_size != 1: |
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") |
|
if self.config.pad_token_id is None: |
|
sequence_lengths = -1 |
|
else: |
|
if input_ids is not None: |
|
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 |
|
sequence_lengths = sequence_lengths % input_ids.shape[-1] |
|
sequence_lengths = sequence_lengths.to(logits.device) |
|
else: |
|
sequence_lengths = -1 |
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
|
|
|
loss = None |
|
if labels is not None: |
|
labels = labels.to(logits.device) |
|
if self.config.problem_type is None: |
|
if self.num_labels == 1: |
|
self.config.problem_type = "regression" |
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
|
self.config.problem_type = "single_label_classification" |
|
else: |
|
self.config.problem_type = "multi_label_classification" |
|
|
|
if self.config.problem_type == "regression": |
|
loss_fct = MSELoss() |
|
if self.num_labels == 1: |
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(pooled_logits, labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(pooled_logits, labels) |
|
if not return_dict: |
|
output = (pooled_logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=pooled_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|
|
|
|
from .configuration_gemma import CeruleGemmaConfig |
|
|
|
|
|
class CeruleGemmaModel(CeruleMetaModel, GemmaModel): |
|
config_class = CeruleGemmaConfig |
|
|
|
def __init__(self, config: CeruleGemmaConfig): |
|
super(CeruleGemmaModel, self).__init__(config) |
|
|
|
|
|
class CeruleGemmaForCausalLM(GemmaForCausalLM, CeruleMetaForCausalLM): |
|
config_class = CeruleGemmaConfig |
|
|
|
def __init__(self, config): |
|
super(GemmaForCausalLM, self).__init__(config) |
|
self.model = CeruleGemmaModel(config) |
|
self.vocab_size = config.vocab_size |
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_model(self): |
|
return self.model |
|
|
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
images: Optional[torch.FloatTensor] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
|
|
if inputs_embeds is None: |
|
( |
|
input_ids, |
|
position_ids, |
|
attention_mask, |
|
past_key_values, |
|
inputs_embeds, |
|
labels |
|
) = self.prepare_inputs_labels_for_multimodal( |
|
input_ids, |
|
position_ids, |
|
attention_mask, |
|
past_key_values, |
|
labels, |
|
images |
|
) |
|
|
|
return super().forward( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
labels=labels, |
|
use_cache=False, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict |
|
) |
|
|
|
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None, |
|
**kwargs): |
|
images = kwargs.pop("images", None) |
|
|
|
_inputs = super().prepare_inputs_for_generation( |
|
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, |
|
**kwargs |
|
) |
|
|
|
if images is not None: |
|
_inputs['images'] = images |
|
return _inputs |
|
|
|
def expand2square(self, pil_img, background_color): |
|
width, height = pil_img.size |
|
if width == height: |
|
return pil_img |
|
elif width > height: |
|
result = Image.new(pil_img.mode, (width, width), background_color) |
|
result.paste(pil_img, (0, (width - height) // 2)) |
|
return result |
|
else: |
|
result = Image.new(pil_img.mode, (height, height), background_color) |
|
result.paste(pil_img, ((height - width) // 2, 0)) |
|
return result |
|
|
|
def process_images(self, images, model_cfg): |
|
vision_tower = self.get_vision_tower() |
|
if not vision_tower.is_loaded: |
|
vision_tower.load_model() |
|
image_processor = vision_tower.image_processor |
|
image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None) |
|
new_images = [] |
|
if image_aspect_ratio == 'pad': |
|
for image in images: |
|
image = self.expand2square(image, tuple(int(x * 255) for x in image_processor.image_mean)) |
|
image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] |
|
new_images.append(image) |
|
else: |
|
return image_processor(images, return_tensors='pt')['pixel_values'] |
|
if all(x.shape == new_images[0].shape for x in new_images): |
|
new_images = torch.stack(new_images, dim=0) |
|
return new_images |
|
|
|
|
|
AutoConfig.register("phi-msft", CeruleGemmaConfig) |
|
AutoModelForCausalLM.register(CeruleGemmaConfig, CeruleGemmaForCausalLM) |
