# coding=utf-8 # Copyright 2024 The GTE Team Authors and Alibaba Group. # Copyright (c) 2018, NVIDIA CORPORATION. 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 NEW model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging try: import xformers.ops as xops except ImportError as e: xops = None from .configuration import NewConfig logger = logging.get_logger(__name__) # Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py # Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py class IndexFirstAxis(torch.autograd.Function): @staticmethod def forward(ctx, input, indices): ctx.save_for_backward(indices) assert input.ndim >= 2 ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:] second_dim = other_shape.numel() # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing. # return input[indices] # return torch.gather( # rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim) # ).reshape(-1, *other_shape) return torch.gather( input.view(ctx.first_axis_dim, second_dim), 0, indices.unsqueeze(-1).expand(indices.size(0), second_dim) ).reshape(-1, *other_shape) @staticmethod def backward(ctx, grad_output): (indices,) = ctx.saved_tensors assert grad_output.ndim >= 2 other_shape = grad_output.shape[1:] # grad_output = rearrange(grad_output, "b ... -> b (...)") grad_output = grad_output.view(grad_output.size(0), other_shape.numel()) grad_input = torch.zeros( [ctx.first_axis_dim, grad_output.shape[1]], device=grad_output.device, dtype=grad_output.dtype, ) # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing. # grad_input[indices] = grad_output # grad_input.scatter_(0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output) grad_input.scatter_( 0, indices.unsqueeze(-1).expand(indices.size(0), grad_output.size(1)), grad_output ) return grad_input.reshape(ctx.first_axis_dim, *other_shape), None index_first_axis = IndexFirstAxis.apply def unpad_input(hidden_states, attention_mask=None, indices=None): """ Arguments: hidden_states: (batch, seqlen, ...) attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid. indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence. Return: hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask. """ if indices is None: assert attention_mask is not None indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to # index with integer indices. Moreover, torch's index is a bit slower than it needs to be, # so we write custom forward and backward to make it a bit faster. hidden_states = hidden_states.view(-1, *hidden_states.shape[2:]) return index_first_axis(hidden_states, indices) class IndexPutFirstAxis(torch.autograd.Function): @staticmethod def forward( ctx, values: torch.Tensor, indices: torch.Tensor, first_axis_dim ) -> torch.Tensor: ctx.save_for_backward(indices) assert indices.ndim == 1 assert values.ndim >= 2 output = torch.zeros( first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype ) output[indices] = values return output @staticmethod def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]: indices, = ctx.saved_tensors grad_values = grad_output[indices] return grad_values, None, None index_put_first_axis = IndexPutFirstAxis.apply def pad_input(inputs: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor: """Add padding to sequences. Arguments: inputs: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask. indices: (total_nnz), `indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()` batch: int batch_size seqlen: int max sequence length Returns: inputs: (batch, seqlen, ...) """ output = index_put_first_axis(inputs, indices, batch * seqlen) return output.view(batch, seqlen, *inputs.shape[1:]) 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): """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. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos, sin = cos.to(q.dtype), sin.to(q.dtype) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=512, base=10000.0, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) 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=torch.float32) 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().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # 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), ) class NTKScalingRotaryEmbedding(RotaryEmbedding): """RotaryEmbedding extended with fixed and mixed NTK scaling. https://kexue.fm/archives/9706 """ def __init__(self, dim, max_position_embeddings=512, base=10000, device=None, scaling_factor=1.0, mixed_b=None): self.scaling_factor = scaling_factor self.mixed_b = mixed_b super().__init__(dim, max_position_embeddings, base, device) max_position_embeddings = max_position_embeddings * self.scaling_factor self._set_cos_sin_cache(max_position_embeddings, self.inv_freq.device, torch.get_default_dtype()) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * (self.scaling_factor if self.mixed_b is None else 1) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) if self.mixed_b is None: inv_freq = inv_freq / self.scaling_factor ** (2 / self.dim) # (6) else: a = torch.tensor(self.scaling_factor).log() / (self.dim / 2) ** self.mixed_b # (13) lambda_1_m = (a * torch.arange(1, self.dim // 2 + 1).float().to(device) ** self.mixed_b).exp() # (12) inv_freq = inv_freq / lambda_1_m # (10) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32) 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().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) LAYER_NORM = { 'layer_norm': nn.LayerNorm, 'rms_norm': RMSNorm } class NewEmbeddings(nn.Module): """ Embedding and Unpadding. """ def __init__(self, config: NewConfig): super().__init__() self.padding_idx = config.pad_token_id self.word_embeddings = nn.Embedding( config.vocab_size, config.hidden_size, padding_idx=self.padding_idx ) self.position_embedding_type = config.position_embedding_type if self.position_embedding_type == 'absolute': self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) elif self.position_embedding_type == 'rope': self._init_rope(config) else: raise ValueError self.type_vocab_size = config.type_vocab_size if self.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids is contiguous in memory and excluded when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings), persistent=False ) def _init_rope(self, config): kwargs = dict( dim=int(config.hidden_size / config.num_attention_heads), max_position_embeddings=config.max_position_embeddings, base=config.rope_theta ) if config.rope_scaling is None: self.rotary_emb = RotaryEmbedding(**kwargs) else: kwargs.update(scaling_factor=config.rope_scaling["factor"]) scaling_type = config.rope_scaling["type"] if scaling_type == 'ntk': kwargs.update(mixed_b=config.rope_scaling.get('mixed_b', None)) self.rotary_emb = NTKScalingRotaryEmbedding(**kwargs) # elif scaling_type == "linear": # self.rotary_emb = LinearScalingRotaryEmbedding(**kwargs) # elif scaling_type == "dynamic": # self.rotary_emb = DynamicNTKScalingRotaryEmbedding(**kwargs) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def forward( self, unpad_inputs: bool, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, length: Optional[List[int]] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[Tuple], Optional[List[int]]]: """ """ if inputs_embeds is None: device, input_shape = input_ids.device, input_ids.shape else: device, input_shape = inputs_embeds.device, inputs_embeds.shape[:2] batch_size, seq_length = input_shape # Set attention_mask if it's None if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if length is not None: for i, l in enumerate(length): attention_mask[i, l:] = 0 # Set attention_mask_bool for unpadding if unpad_inputs: attention_mask_bool = attention_mask.bool() if length is None: length = attention_mask.sum(-1).tolist() # Get word embeddings if inputs_embeds is None: if unpad_inputs: input_ids = input_ids[attention_mask_bool].unsqueeze(0) inputs_embeds = self.word_embeddings(input_ids) else: if unpad_inputs: inputs_embeds = inputs_embeds[attention_mask_bool].unsqueeze(0) embeddings = inputs_embeds # Set and unpad position_ids if position_ids is None: if seq_length > self.position_ids.size(0): self.register_buffer( "position_ids", torch.arange(seq_length), persistent=False ) if unpad_inputs: # [1, cumsum_seq_len] position_ids = torch.cat([self.position_ids[:l] for l in length]).unsqueeze(0) else: # [bs, seq_len] position_ids = self.position_ids[:seq_length].expand(batch_size, -1) elif unpad_inputs: position_ids = position_ids[attention_mask_bool].unsqueeze(0) # [1, cumsum_seq_len] # Compute rotary embedding if self.position_embedding_type == 'rope': rope_cos, rope_sin = self.rotary_emb(inputs_embeds, seq_len=seq_length) rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] rope_sin = rope_sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] rope_embeds = rope_cos, rope_sin else: rope_embeds = None if self.type_vocab_size > 0: if token_type_ids is None: token_type_ids = position_ids.mul(0) elif unpad_inputs: token_type_ids = token_type_ids[attention_mask_bool].unsqueeze(0) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings += token_type_embeddings # BERT position if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings, attention_mask, rope_embeds, length class NewAttention(nn.Module): def __init__(self, config: NewConfig, pack_qkv=None, use_memory_efficient_attention=None): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.hidden_size = config.hidden_size self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size if pack_qkv is None: pack_qkv = config.pack_qkv self.pack_qkv = pack_qkv if self.pack_qkv: self.qkv_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=True) else: self.q_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.k_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.v_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True) if use_memory_efficient_attention is None: use_memory_efficient_attention = self.config.use_memory_efficient_attention self.use_memory_efficient_attention = use_memory_efficient_attention self.memory_efficient_attention = None if xops is None else xops.memory_efficient_attention if self.use_memory_efficient_attention: assert self.memory_efficient_attention is not None, 'please install xformers' if self.config.unpad_inputs: assert self.config.use_memory_efficient_attention, 'unpad only with xformers' def forward( self, hidden_states: torch.Tensor, attention_bias: torch.FloatTensor, rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, attention_scale: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, qkv_inputs: Optional[Tuple] = None, # For RetroMAE padding_inputs: Optional[Tuple] = None, # indices, batch, seqlen ) -> Tuple[torch.Tensor, ...]: shape_hd = (self.num_attention_heads, self.attention_head_size) # qkv if self.pack_qkv and qkv_inputs is None: qkv_pack = self.qkv_proj(hidden_states).split(self.all_head_size, dim=-1) else: if qkv_inputs is None: qkv_inputs = (hidden_states, hidden_states, hidden_states) qkv_pack = [ getattr(self, n + '_proj')(s) for s, n in zip(qkv_inputs, 'qkv') ] query_states, key_states, value_states = [t.view(t.shape[:-1] + shape_hd) for t in qkv_pack] if self.config.position_embedding_type == 'rope': query_states, key_states = apply_rotary_pos_emb(query_states, key_states, *rope_embeds) dtype = query_states.dtype if self.config.logn_attention_scale and attention_scale is not None: # https://kexue.fm/archives/8823 query_states = query_states * attention_scale.to(dtype) if padding_inputs is not None: query_states = pad_input(query_states.squeeze(), *padding_inputs) key_states = pad_input(key_states.squeeze(), *padding_inputs) value_states = pad_input(value_states.squeeze(), *padding_inputs) if self.use_memory_efficient_attention: assert self.memory_efficient_attention is not None, "xformers is not loaded" assert output_attentions is False, "memory_efficient_attention do not output attentions" assert head_mask is None, "Not support yet" attention_probs = None if torch.is_tensor(attention_bias): attention_bias = attention_bias.to(dtype) context_layer = self.memory_efficient_attention( query_states, key_states, value_states, attn_bias=attention_bias, p=self.dropout.p ) else: context_layer = self._attention(query_states, key_states, value_states, attention_bias, head_mask) if padding_inputs is not None: context_layer = unpad_input(context_layer, indices=padding_inputs[0]) new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) # output proj attn_output = self.o_proj(context_layer) # add attentions if we output them outputs = (attn_output, attention_probs) if output_attentions else (attn_output,) return outputs def _attention(self, query_states, key_states, value_states, attention_bias, head_mask): """ Args: q/k/v: (B, L, n_head, head_dim), Returns: attn_output: (B L, n_head, head_dim) """ query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_bias is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_bias # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_states) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() return context_layer class NewSdpaAttention(NewAttention): """ New attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `NewAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ def __init__(self, config: NewConfig, **kwargs): super().__init__(config, **kwargs) torch.backends.cuda.enable_mem_efficient_sdp(False) logger.warning( "Disable memory efficient attention kernel for `NewSdpaAttention`, you can set " "`use_memory_efficient_attention=True` if it expected to use." ) def _attention(self, query_states, key_states, value_states, attention_bias, head_mask): attn_output = torch.nn.functional.scaled_dot_product_attention( query_states.transpose(1, 2), key_states.transpose(1, 2), value_states.transpose(1, 2), attn_mask=attention_bias, dropout_p=self.dropout.p if self.training else 0.0, ) attn_output = attn_output.permute(0, 2, 1, 3).contiguous() return attn_output NEW_ATTENTION_CLASSES = { "eager": NewAttention, # "flash_attention_2": , # TODO: xformers will dispatch to flash_attn "sdpa": NewSdpaAttention, } class NewGatedMLP(nn.Module): """ GLU Variants Improve Transformer. """ def __init__(self, config: NewConfig): super().__init__() self.intermediate_size = config.intermediate_size self.up_gate_proj = nn.Linear(config.hidden_size, self.intermediate_size * 2, bias=False) self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=True) self.act_fn = ACT2FN[config.hidden_act] if config.hidden_dropout_prob > 0: self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob) else: self.hidden_dropout = None def forward(self, hidden_states): up_gate = self.up_gate_proj(hidden_states) up_states, gate = torch.split(up_gate, self.intermediate_size, dim=-1) gate = self.act_fn(gate) gated_states = gate * up_states if self.hidden_dropout is not None: gated_states = self.hidden_dropout(gated_states) down_states = self.down_proj(gated_states) return down_states class NewLayer(nn.Module): def __init__( self, config: NewConfig, pack_qkv=None, use_memory_efficient_attention=None, attn_implementation=None ): super().__init__() if attn_implementation is None: attn_implementation = config._attn_implementation if attn_implementation != 'eager': use_memory_efficient_attention = False self.attention = NEW_ATTENTION_CLASSES[attn_implementation]( config, pack_qkv=pack_qkv, use_memory_efficient_attention=use_memory_efficient_attention ) self.mlp = NewGatedMLP(config) ln_class = LAYER_NORM[config.layer_norm_type] self.attn_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps) self.mlp_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps) if config.hidden_dropout_prob > 0: self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob) else: self.hidden_dropout = None def forward( self, hidden_states: torch.Tensor, attention_bias: torch.FloatTensor, rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, attention_scale: Optional[torch.FloatTensor] = None, subset_indices: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, qkv_inputs: Optional[Tuple] = None, # For RetroMAE padding_inputs: Optional[Tuple] = None, ) -> Tuple[torch.Tensor, ...]: # Multi head self attention residual = hidden_states if qkv_inputs is None else qkv_inputs[0] attention_outputs = self.attention( hidden_states, attention_bias, rope_embeds, attention_scale, head_mask, output_attentions=output_attentions, qkv_inputs=qkv_inputs, padding_inputs=padding_inputs, ) hidden_states = attention_outputs[0] if self.hidden_dropout is not None: hidden_states = self.hidden_dropout(hidden_states) hidden_states = residual + hidden_states # In pretraining, after the attention of last layer, we only need the masked tokens. if subset_indices is not None: hidden_states = hidden_states[subset_indices] hidden_states = self.attn_ln(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.mlp(hidden_states) if self.hidden_dropout is not None: hidden_states = self.hidden_dropout(hidden_states) hidden_states = residual + hidden_states hidden_states = self.mlp_ln(hidden_states) # add self attentions if we output attention weights outputs = (hidden_states,) + attention_outputs[1:] return outputs class NewEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([NewLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_bias: Optional[torch.FloatTensor] = None, rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, attention_scale: Optional[torch.FloatTensor] = None, subset_indices: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if i >= len(self.layer) - 1: layer_subset_indices = subset_indices else: layer_subset_indices = None layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_bias, rope_embeds, attention_scale, layer_subset_indices, layer_head_mask, ) else: layer_outputs = layer_module( hidden_states, attention_bias, rope_embeds, attention_scale, layer_subset_indices, layer_head_mask, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, all_hidden_states, all_self_attentions, ] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->New class NewPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class NewPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = NewConfig base_model_prefix = "new" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class NewModel(NewPreTrainedModel): """ The bare New Model transformer outputting raw hidden-states without any specific head on top. """ def __init__(self, config: NewConfig, add_pooling_layer=False): super().__init__(config) self.config = config self.embeddings = NewEmbeddings(config) self.encoder = NewEncoder(config) self.pooler = NewPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, length: Optional[List[int]] = None, subset_indices: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]: r""" length (`list` of length `batch_size`, *optional*): If is `None`, return padded `last_hidden_state`. subset_indices (): pass unpad_inputs (`bool`, *optional*): pass """ 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 unpad_inputs = unpad_inputs if unpad_inputs is not None else self.config.unpad_inputs output_padded = length is None 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: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) 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") # TODO: not used # # 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) # Get embeddings, may unpad them (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( unpad_inputs, input_ids=input_ids, attention_mask=attention_mask, length=length, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds ) batch_size, seq_length = input_shape if unpad_inputs: assert self.config.use_memory_efficient_attention attention_bias = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(length) else: # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. attention_bias = self.get_extended_attention_mask(attention_mask, input_shape) if self.config.use_memory_efficient_attention: # Invalid shape for attention bias: torch.Size([48, 1, 1, 512]) (expected (48, 12, 512, 512)) attention_bias = attention_bias.expand(-1, self.config.num_attention_heads, seq_length, -1) if self.config.logn_attention_scale: # attention scale log_512(input_len) attention_scale = attention_mask.sum(1).log() / torch.tensor(self.config.max_position_embeddings).log() # inference-time logn scale need clip 1 if self.config.logn_attention_clip1: attention_scale.clip_(1) attention_scale = attention_scale[:, None, None, None] else: attention_scale = None encoder_outputs = self.encoder( embedding_output, attention_bias=attention_bias, rope_embeds=rope_embeds, attention_scale=attention_scale, subset_indices=subset_indices, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if unpad_inputs and output_padded: indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() sequence_output = pad_input( sequence_output.squeeze(), indices, batch_size, seq_length ) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class NewLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.transform_act_fn = ACT2FN[config.hidden_act] self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.norm(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class NewForMaskedLM(NewPreTrainedModel): _tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"] def __init__(self, config: NewConfig): super().__init__(config) self.new = NewModel(config, add_pooling_layer=False) self.lm_head = NewLMPredictionHead(config) self.loss_fct = nn.CrossEntropyLoss() # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. 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 in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is None or not self.new.config.unpad_inputs: length = None subset_indices = None else: length = attention_mask.sum(-1).tolist() labels = labels[attention_mask.bool()].unsqueeze(0) subset_indices = labels > -100 outputs = self.new( input_ids, attention_mask=attention_mask, length=length, subset_indices=subset_indices, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, unpad_inputs=unpad_inputs, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: if subset_indices is None: mask = attention_mask.bool() prediction_scores = prediction_scores[mask] labels = labels[mask] else: labels = labels[subset_indices] masked_lm_loss = self.loss_fct(prediction_scores, labels) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class NewForSequenceClassification(NewPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.new = NewModel(config, add_pooling_layer=True) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.new( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, unpad_inputs=unpad_inputs, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: 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 = nn.MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = nn.BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class NewForMultipleChoice(NewPreTrainedModel): def __init__(self, config): super().__init__(config) self.new = NewModel(config, add_pooling_layer=True) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.new( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, unpad_inputs=unpad_inputs, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class NewForTokenClassification(NewPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.new = NewModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.new( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, unpad_inputs=unpad_inputs, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class NewForQuestionAnswering(NewPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.new = NewModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, unpad_inputs: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.new( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, unpad_inputs=unpad_inputs, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )