# coding=utf-8 # Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved. # Modifications Copyright 2023 Better Planet Investments and labml.ai team. ALl rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch GeoV model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers.file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from .configuration_geov import GeoVConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b" _REAL_CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b" _CONFIG_FOR_DOC = "GeoVConfig" GEOV_PRETRAINED_MODEL_ARCHIVE_LIST = [ "GeoV/GeoV-9b", # See all GeoV models at https://huggingface.co/models?filter=geov ] class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, base=10000): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq) self.max_seq_len_cached = -1 def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.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).to(x.device) self.cos_cached = emb.cos()[None, None, :, :].to(x.dtype) self.sin_cached = emb.sin()[None, None, :, :].to(x.dtype) return self.cos_cached.to(x.device), self.sin_cached.to(x.device) 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, cos, sin, position_ids): """Apply positional embeddings""" gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) q_embed = (q * cos) + (rotate_half(q) * sin) return q_embed def apply_rotary_pos_emb_reverse(q, cos, sin, position_ids): """Apply positional embeddings in reverse""" gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) q_embed = (q * cos) - (rotate_half(q) * sin) return q_embed class GeoVAttention(nn.Module): """ Attention module """ def __init__(self, config): super().__init__() self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.num_attention_heads max_positions = config.max_position_embeddings self.register_buffer("causal_mask", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool))) self.rotary_emb = RotaryEmbedding(self.head_size, base=config.rotary_emb_base) self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size) self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ): has_layer_past = layer_past is not None # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] qkv = self.qkv(hidden_states) query, key, value = torch.tensor_split(qkv, 3, dim=-1) # 'b l (h q) -> b h l q' query = self._split_heads(query, self.num_attention_heads) key = self._split_heads(key, self.num_attention_heads) value = self._split_heads(value, self.num_attention_heads) # Compute token offset for rotary embeddings (when decoding) seq_len = key.shape[-2] offset = 0 if has_layer_past: seq_len += layer_past[0].shape[-2] cos, sin = self.rotary_emb(query, seq_len=seq_len) query = apply_rotary_pos_emb(query, cos, sin, position_ids) key = apply_rotary_pos_emb(key, cos, sin, position_ids) value = apply_rotary_pos_emb(value, cos, sin, position_ids) # Cache QKV values if has_layer_past: past_key = layer_past[0] past_value = layer_past[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) present = (key, value) if use_cache else None # Compute attention attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = apply_rotary_pos_emb_reverse(attn_output, cos, sin, position_ids) # Reshape outputs attn_output = self._merge_heads(attn_output) attn_output = self.dense(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs @classmethod def _split_heads(cls, tensor, num_attention_heads): """ Splits hidden dim into num_attention_heads """ # tensor: [bs, seq_len, hidden_size] new_shape = tensor.shape[:-1] + (num_attention_heads, tensor.shape[-1] // num_attention_heads) # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view(new_shape) # -> [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3) return tensor @classmethod def _merge_heads(cls, tensor): """ Merges heads """ # tensor [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3).contiguous() # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view(*tensor.shape[:2], tensor.shape[2] * tensor.shape[3]) # -> [bs, seq_len, hidden_size] return tensor def _attn(self, query, key, value, attention_mask=None, head_mask=None): # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size] # compute causal mask from causal mask buffer batch_size, num_attention_heads, query_length, attn_head_size = query.shape key_length = key.shape[-2] causal_mask = self.causal_mask[None, None, key_length - query_length: key_length, :key_length] attn_scores = torch.einsum("bhid,bhjd->bhij", query, key) / math.sqrt(attn_head_size) attn_scores.masked_fill_(causal_mask == 0, torch.finfo(attn_scores.dtype).min) if attention_mask is not None: # Apply the attention mask attn_scores = attn_scores + attention_mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights class GeoVMLP(nn.Module): """Position wise Feed-forward network""" def __init__(self, config: "GeoVConfig"): super().__init__() self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) self.dense_2h_to_h = nn.Linear( config.intermediate_size // 2, config.hidden_size, bias=config.use_extra_biases_ffn ) self.act = nn.GELU() def forward(self, hidden_states): hidden_states = self.dense_h_to_4h(hidden_states) # Gated GELU gate, pass_through = torch.tensor_split(hidden_states, 2, dim=-1) gate = self.act(gate) hidden_states = gate * pass_through hidden_states = self.dense_2h_to_h(hidden_states) return hidden_states class GeoVLayer(nn.Module): """GeoV transformer layer""" def __init__(self, config: "GeoVConfig"): super().__init__() self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = GeoVAttention(config) self.mlp = GeoVMLP(config) def forward( self, hidden_states: Optional[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, layer_past: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, ): attention_layer_outputs = self.attention( self.input_layernorm(hidden_states), attention_mask=attention_mask, position_ids=position_ids, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights) outputs = attention_layer_outputs[1:] attn_output = attn_output + hidden_states mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) hidden_states = mlp_output + attn_output if use_cache: outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights) else: outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights) return outputs class GeoVPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GeoVConfig base_model_prefix = "geov" supports_gradient_checkpointing = True _no_split_modules = ["GeoVLayer"] def _init_weights(self, module): pass def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, GeoVModel): module.gradient_checkpointing = value GEOV_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`~GeoVConfig`]): 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. """ GEOV_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *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) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`Tuple[Tuple[torch.FloatTensor]]` of length `n_layers`, with each tuple having 2 tensors of shape `(batch_size, n_heads, seq_len - 1, head_size)`, *optional*): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. 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`). inputs_embeds (`torch.FloatTensor` of shape `(batch_size, seq_len, 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. 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 [`~file_utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare GeoV Model transformer outputting raw hidden-states without any specific head on top.", GEOV_START_DOCSTRING, ) class GeoVModel(GeoVPreTrainedModel): def __init__(self, config: "GeoVConfig"): super().__init__(config) self.config = config self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([GeoVLayer(config) for _ in range(config.num_hidden_layers)]) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.embed_in.to(torch.bfloat16) self.layers.to(torch.bfloat16) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_in def set_input_embeddings(self, value): self.embed_in = value @add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[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]: 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 use_cache = use_cache if use_cache is not None else self.config.use_cache if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_length = 0 past_key_values = tuple([None] * self.config.num_hidden_layers) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange(past_length, seq_length + past_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() # Attention mask. if attention_mask is not None: assert batch_size > 0, "batch_size has to be defined and > 0" attention_mask = attention_mask.view(batch_size, -1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask = attention_mask[:, None, None, :] # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) hidden_states = inputs_embeds if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False presents = () if use_cache else None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for layer_past return module(*inputs, use_cache, None, output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, attention_mask, position_ids, head_mask[i], ) else: outputs = layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask[i], layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],) # Cast the hidden state to final layer norm data type (this is the modification from GPTNeoX) hidden_states = self.final_layer_norm(hidden_states.to(self.final_layer_norm.weight.dtype)) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) @add_start_docstrings( """GeoV Model with a `language modeling` head on top for CLM fine-tuning.""", GEOV_START_DOCSTRING ) class GeoVForCausalLM(GeoVPreTrainedModel): _keys_to_ignore_on_load_missing = [r"causal_mask", r"inv_freq"] def __init__(self, config: "GeoVConfig"): super().__init__(config) self.geov = GeoVModel(config) self.embed_out = nn.Linear(config.hidden_size, config.vocab_size) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.embed_out def set_output_embeddings(self, new_embeddings): self.embed_out = new_embeddings @add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[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, CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, GeoVForCausalLM, GeoVConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("GeoV/GeoV-9b") >>> model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.geov( input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] lm_logits = self.embed_out(hidden_states) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithPast( loss=lm_loss, logits=lm_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, **kwargs): input_shape = input_ids.shape # cut decoder_input_ids if past is used if past_key_values and past_key_values[0] is not None: input_ids = input_ids[:, -1:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) return { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past_key_values, } 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) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past