"""largely copy from llama and adapt for cogvlm""" import warnings from typing import TYPE_CHECKING, Optional, Tuple, List, Union, Literal, Dict, Any import math import torch from torch import nn from torch.nn import functional as F from torch.nn import CrossEntropyLoss from torchvision import transforms from einops import rearrange from transformers import PreTrainedModel, PreTrainedTokenizer from transformers.utils.logging import get_logger from transformers.activations import ACT2FN from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from .configuration_cogvlm import CogVLMConfig from .visual import EVA2CLIPModel if TYPE_CHECKING: from transformers.utils import ModelOutput logger = get_logger(__name__) LANGUAGE_TOKEN_TYPE = 0 VISION_TOKEN_TYPE = 1 # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return (self.weight * hidden_states).to(input_dtype) class MLP(nn.Module): def __init__(self, config): super().__init__() 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): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj def get_expert_mask(token_type_ids: "torch.LongTensor(B, L)") -> "[torch.BoolTensor(B, L), torch.BoolTensor(B, L)]": vision_token_mask = torch.zeros_like(token_type_ids, dtype=torch.bool) vision_token_mask[:, :-1] = (token_type_ids[:, :-1] == VISION_TOKEN_TYPE) & (token_type_ids[:, 1:] == VISION_TOKEN_TYPE) language_token_mask = ~vision_token_mask return vision_token_mask, language_token_mask class VisionExpertMLP(nn.Module): def __init__(self, config): super().__init__() self.language_mlp = MLP(config) self.vision_mlp = MLP(config) def forward(self, hidden_states: "torch.Tensor(B, L, D)", token_type_ids: "torch.LongTensor(B, L)"): output = torch.empty(hidden_states.shape, dtype=hidden_states.dtype, device=hidden_states.device) vision_token_mask, language_token_mask = get_expert_mask(token_type_ids) output[vision_token_mask] = self.vision_mlp(hidden_states[vision_token_mask]) output[language_token_mask] = self.language_mlp(hidden_states[language_token_mask]) return output def attention_fn( query_layer: "torch.tensor(B, H, L, HD)", key_layer: "torch.tensor(B, H, L, HD)", value_layer: "torch.tensor(B, H, L, HD)", attention_mask: "torch.tensor(B, H, L, HD)", *, scaling_attention_score: bool = True, attention_dropout: nn.Module = None ): attention_mask_bool = (attention_mask == 0) is_low_triangle = (attention_mask_bool == torch.ones_like(attention_mask_bool, dtype=torch.float).tril()).all() is_full = (attention_mask_bool > 0).all() if not (int(torch.__version__.split('.')[0]) >= 2): warnings.warn("It's recommended to use torch2.0 or higher.") if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score and (is_full or is_low_triangle): dropout_p = 0. if attention_dropout is None or not attention_dropout.training else attention_dropout.p return torch.nn.functional.scaled_dot_product_attention( query_layer, key_layer, value_layer, attn_mask=None, dropout_p=dropout_p, is_causal=not is_full ) else: if scaling_attention_score: query_layer = query_layer / math.sqrt(query_layer.shape[-1]) attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores + attention_mask attention_scores = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype) if attention_dropout is not None: attention_scores = attention_dropout(attention_scores) context_layer = torch.matmul(attention_scores, value_layer) return context_layer class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = self._compute_inv_freq(device) self.register_buffer("inv_freq", inv_freq) self.max_seq_len_cached = 0 def _compute_inv_freq(self, device=None): return 1.0 / ( self.base ** (torch.arange(0, self.dim, 2, device=device) / self.dim) ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[:, None, :].to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin()[:, None, :].to(dtype), persistent=False) def forward(self, x, seq_len): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len, ...].to(dtype=x.dtype), self.sin_cached[:seq_len, ...].to(dtype=x.dtype), ) def rotate_half(x): x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=x1.ndim - 1) def apply_rotary_pos_emb_index_bhs(q, k, cos, sin, position_id): # batch_size, num_head, seq_len, hidden_size cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(1), \ F.embedding(position_id, sin.squeeze(1)).unsqueeze(1) q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) return q, k class VisionExpertAttention(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.max_position_embeddings = config.max_position_embeddings self.rotary_emb = RotaryEmbedding(self.head_dim) self.vision_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False) self.vision_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.language_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False) self.language_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False) def _transpose_for_scores(self, tensor): """Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD].""" new_tensor_shape = tensor.size()[:-1] + (self.num_heads, self.head_dim) tensor = tensor.view(*new_tensor_shape) return tensor.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, token_type_ids: torch.LongTensor, position_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, print_values: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() vision_token_mask, language_token_mask = get_expert_mask(token_type_ids) shape = list(hidden_states.shape) shape[-1] = shape[-1] * 3 mixed_raw_layer = torch.empty(shape, dtype=hidden_states.dtype, device=hidden_states.device) mixed_raw_layer[vision_token_mask] = self.vision_expert_query_key_value(hidden_states[vision_token_mask]) mixed_raw_layer[language_token_mask] = self.language_expert_query_key_value(hidden_states[language_token_mask]) query_states, key_states, value_states = torch.split(mixed_raw_layer, self.hidden_size, dim=-1) query_states = self._transpose_for_scores(query_states) # B, H, L, HD key_states = self._transpose_for_scores(key_states) # B, H, L, HD value_states = self._transpose_for_scores(value_states) # B, H, L, HD # if print_values: # torch.save(query_states, "query_states.pt") # torch.save(key_states, "key_states.pt") # torch.save(value_states, "value_states.pt") # from huggingface_hub import HfApi # api = HfApi() # api.upload_file( # path_or_fileobj="query_states.pt", # path_in_repo="query_states.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api = HfApi() # api.upload_file( # path_or_fileobj="key_states.pt", # path_in_repo="key_states.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api = HfApi() # api.upload_file( # path_or_fileobj="value_states.pt", # path_in_repo="value_states.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=position_ids.max() + 1) query_states, key_states = apply_rotary_pos_emb_index_bhs(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None context_layer = attention_fn( query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask, scaling_attention_score=True, attention_dropout=None) if context_layer.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" {context_layer.size()}" ) context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.hidden_size) attn_output = torch.empty(context_layer.shape, dtype=hidden_states.dtype, device=hidden_states.device) attn_output[vision_token_mask] = self.vision_expert_dense(context_layer[vision_token_mask]) attn_output[language_token_mask] = self.language_expert_dense(context_layer[language_token_mask]) if output_attentions: warnings.warn("output_attentions is not implemented.") return attn_output, None, past_key_value class CogVLMDecoderLayer(nn.Module): def __init__(self, config): super().__init__() self.hidden_size = config.hidden_size self.self_attn = VisionExpertAttention(config=config) self.mlp = VisionExpertMLP(config) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, token_type_ids: torch.LongTensor, position_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, print_values = False, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # if print_values: # print("Hidden states before RMS norm:", hidden_states[0, :3, :3]) hidden_states = self.input_layernorm(hidden_states) # if print_values: # print("Hidden states after RMS norm, before self attention:", hidden_states[0,:3,:3]) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, print_values=print_values, ) # if print_values: # print("Hidden states after self attention:", hidden_states[0,:3,:3]) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states, token_type_ids=token_type_ids) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs # type: ignore class CogVLMPreTrainedModel(PreTrainedModel): config_class = CogVLMConfig base_model_prefix = "model" supports_gradient_checkpointing = False _no_split_modules = ["CogVLMDecoderLayer", "TransformerLayer"] _skip_keys_device_placement = "past_key_values" 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 is_empty(images_list: Optional[List[List[torch.Tensor]]]): if images_list is None or len(images_list) == 0: return True for image_list in images_list: if len(image_list): return False return True def build_position_ids(x: "torch.BoolTensor(B, L)", attention_mask: Optional["torch.BoolTensor(B, L)"] = None) -> "torch.LongTensor(B, L)": if attention_mask is not None: tmp = x.clone() tmp[~(attention_mask.bool())] = -1 else: tmp = x.clone() # image boi eoi token as LANGUAGE_TOKEN_TYPE is_boi_eoi = torch.zeros_like(x, dtype=torch.bool) is_boi_eoi[:, 1:] |= (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE) is_boi_eoi[:, 0] |= (tmp[:, 0] == VISION_TOKEN_TYPE) is_boi_eoi[:, :-1] |= (tmp[:, :-1] == VISION_TOKEN_TYPE) & (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) is_boi_eoi[:, -1] |= (tmp[:, -1] == VISION_TOKEN_TYPE) tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE # final position ids y = torch.zeros_like(x, dtype=torch.long) y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) | ((tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE)) y = y.cumsum(dim=-1) return y class CogVLMModel(CogVLMPreTrainedModel): def __init__(self, config): 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([CogVLMDecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.vision = EVA2CLIPModel(config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def encode_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor: images_list, images = images, [] images = [] for image_list in images_list: for image in image_list: images.append(image) images = torch.stack(images) images_features = self.vision(images) return images_features def forward( self, input_ids: torch.LongTensor = None, images: List[List[torch.Tensor]] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: """take care of image_encode, token_type_ids, position_ids and (attention_mask = None is fine)""" if past_key_values is not None: pass # generate mode with past_key_values. the image features are already mapped else: # not allow for inputs_embeds, because we want to process image feature assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}" if not is_empty(images): # multi-modality assert token_type_ids is not None, f"multi-modality requires `token_type_ids`!" assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}" inputs_embeds = self.embed_tokens(input_ids) images_features = self.encode_images(images) images_features = rearrange(images_features, 'b n d -> (b n) d') images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device) # from huggingface_hub import HfApi # torch.save(images_features, "images_features.pt") # torch.save(inputs_embeds, "inputs_embeds.pt") # torch.save(token_type_ids, "token_type_ids.pt") # api = HfApi() # api.upload_file( # path_or_fileobj="images_features.pt", # path_in_repo="images_features.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api.upload_file( # path_or_fileobj="inputs_embeds.pt", # path_in_repo="inputs_embeds.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api.upload_file( # path_or_fileobj="token_type_ids.pt", # path_in_repo="token_type_ids.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # print("First values of text embeddings:", inputs_embeds[0, :3, :3]) # print("First values of images_features:", images_features[0, :3]) inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features) # print("First values of inputs_embeds after index_put:", inputs_embeds[0, :3, :3]) else: # single-modality if token_type_ids is None: token_type_ids = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device) * LANGUAGE_TOKEN_TYPE assert not (token_type_ids == VISION_TOKEN_TYPE).any(), f"{(token_type_ids == VISION_TOKEN_TYPE).sum()}" inputs_embeds = self.embed_tokens(input_ids) if position_ids is None: position_ids = build_position_ids(token_type_ids, attention_mask) input_ids = None return self.llm_forward( input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) def llm_forward( self, input_ids: torch.LongTensor = None, token_type_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, ) -> Union[Tuple, BaseModelOutputWithPast]: """largely copy from llama forward and adapt for cogvlm with `token_type_ids`""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] seq_length_with_past = seq_length_with_past + past_key_values_length if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # embed positions if attention_mask is None: attention_mask = torch.ones( (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device ) attention_mask = self._prepare_decoder_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds # torch.save(hidden_states, "initial_hidden_states.pt") # torch.save(attention_mask, "initial_attention_mask.pt") # torch.save(token_type_ids, "initial_token_type_ids.pt") # torch.save(position_ids, "initial_position_ids.pt") # from huggingface_hub import HfApi # api = HfApi() # api.upload_file( # path_or_fileobj="initial_hidden_states.pt", # path_in_repo="initial_hidden_states.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api = HfApi() # api.upload_file( # path_or_fileobj="initial_attention_mask.pt", # path_in_repo="initial_attention_mask.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api = HfApi() # api.upload_file( # path_or_fileobj="initial_token_type_ids.pt", # path_in_repo="initial_token_type_ids.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # api = HfApi() # api.upload_file( # path_or_fileobj="initial_position_ids.pt", # path_in_repo="initial_position_ids.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = () if use_cache else None for idx, decoder_layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) past_key_value = past_key_values[idx] if past_key_values is not None else None layer_outputs = decoder_layer( hidden_states, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, print_values=idx==0, ) hidden_states = layer_outputs[0] # if idx == 0: # torch.save(hidden_states, "hidden_states_after_layer_0.pt") # api = HfApi() # api.upload_file( # path_or_fileobj="hidden_states_after_layer_0.pt", # path_in_repo="hidden_states_after_layer_0.pt", # repo_id="nielsr/test-cogvlm", # repo_type="dataset", # ) if use_cache: next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if 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 get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value # noinspection PyMethodMayBeStatic # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def _history_to_prompt(signal_type, history, query): if signal_type == 'base': return query elif signal_type == 'vqa': answer_format = 'Short answer:' elif signal_type == 'chat': answer_format = 'Answer:' else: assert False, f"Unknown signal type {signal_type}" prompt = '' for i, (old_query, response) in enumerate(history): prompt += 'Question: ' + old_query + " {} ".format(answer_format) + response + "\n" prompt += 'Question: {} {}'.format(query, answer_format) return prompt class CogVLMForCausalLM(CogVLMPreTrainedModel): _auto_class = "AutoModelForCausalLM" def __init__(self, config): super().__init__(config) self.model = CogVLMModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def forward( self, input_ids: torch.LongTensor = None, images: List[List[torch.Tensor]] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, images=images, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) logits = logits.float() loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def _prepare_attention_mask_for_generation( self, inputs: torch.Tensor, pad_token_id: Optional[int], eos_token_id: Optional[Union[int, List[int]]], ) -> torch.LongTensor: return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device) # type: ignore def prepare_inputs_for_generation( self, input_ids, token_type_ids, images=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): # build position_ids if needed position_ids = kwargs.get("position_ids", None) if position_ids is None: position_ids = build_position_ids(token_type_ids, attention_mask) if past_key_values: input_ids = input_ids[:, -1:] token_type_ids = token_type_ids[:, -1:] position_ids = position_ids[:, -1:] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "token_type_ids": token_type_ids, "images": images, "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs def _update_model_kwargs_for_generation( self, outputs: "ModelOutput", model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, standardize_cache_format: bool = False, model_inputs: Optional[Dict[str, Any]] = None, ) -> Dict[str, Any]: # update past_key_values model_kwargs["past_key_values"] = self._extract_past_from_model_output( outputs, standardize_cache_format=standardize_cache_format ) if getattr(outputs, "state", None) is not None: model_kwargs["state"] = outputs.state # update token_type_ids with last value if "token_type_ids" in model_kwargs: token_type_ids = model_kwargs["token_type_ids"] new_token_type_ids = torch.ones(size=(token_type_ids.shape[0], 1), dtype=token_type_ids.dtype, device=token_type_ids.device) * LANGUAGE_TOKEN_TYPE model_kwargs["token_type_ids"] = torch.cat([token_type_ids, new_token_type_ids], dim=-1) if not is_encoder_decoder: # update attention mask if "attention_mask" in model_kwargs: attention_mask = model_kwargs["attention_mask"] model_kwargs["attention_mask"] = torch.cat( [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 ) else: # update decoder attention mask if "decoder_attention_mask" in model_kwargs: decoder_attention_mask = model_kwargs["decoder_attention_mask"] model_kwargs["decoder_attention_mask"] = torch.cat( [decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))], dim=-1, ) return model_kwargs def _reorder_cache(self, 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 def build_conversation_input_ids( self, tokenizer: "PreTrainedTokenizer", *, query: str, history: Optional[List[Tuple[str, str]]] = None, images: Optional[List["PIL.Image"]] = None, template_version: Optional[Literal["base", "chat", "vqa"]] = None, ): image_size: int = self.config.vision_config['image_size'] patch_size: int = self.config.vision_config['patch_size'] template_version = template_version or self.config.template_version assert images is None or len(images) <= 1, f"not support multi images by now." history = history or [] text = _history_to_prompt(template_version, history, query) input_ids = [tokenizer.bos_token_id] token_type_ids = [LANGUAGE_TOKEN_TYPE] if images is not None and len(images) == 1: # vision transform = transforms.Compose( [ transforms.Resize( (image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC ), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ] ) images = [transform(images[0])] # language vision_token_num = (image_size // patch_size) * (image_size // patch_size) + 2 input_ids += [tokenizer.pad_token_id] * vision_token_num token_type_ids += [VISION_TOKEN_TYPE] * vision_token_num text_ids = tokenizer.encode(text, add_special_tokens=False) input_ids += text_ids token_type_ids += [LANGUAGE_TOKEN_TYPE] * len(text_ids) attention_mask = [1] * len(input_ids) return { 'input_ids': torch.tensor(input_ids, dtype=torch.long), 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long), 'attention_mask': torch.tensor(attention_mask, dtype=torch.long), 'images': images, }