# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # This code is based on OpenAI's GPT-2 library. It has been modified from its # original forms to accommodate architectural differences compared to GPT-2. # # 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 FLM model.""" from typing import Optional, Tuple, Union import math import torch from einops import rearrange from torch import einsum, nn from torch.cuda.amp import autocast from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_conv1d_layer from transformers.utils import logging from transformers.utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_flm import FLMConfig class Conv1D(nn.Module): def __init__(self, nf, nx): super().__init__() self.nf = nf self.weight = nn.Parameter(torch.empty(nx, nf)) self.bias = nn.Parameter(torch.zeros(nf)) nn.init.normal_(self.weight, std=0.02) def forward(self, x): x = torch.matmul(x, self.weight) + self.bias return x logger = logging.get_logger(__name__) def exists(v): return v is not None class RotaryEmbedding(nn.Module): def __init__(self, dim, use_xpos=False, xpos_scale_base=512, theta=10000): super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) self.cache = dict() self.cache_scale = dict() self.use_xpos = use_xpos if not use_xpos: self.register_buffer('scale', None) return scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) self.register_buffer('scale', scale) self.scale_base = xpos_scale_base def forward(self, seq, cache_key=None): if cache_key is not None and cache_key in self.cache: return self.cache[cache_key] inv_freq = self.inv_freq.to(device=seq.device) freqs = einsum('i , j -> i j', seq, inv_freq) # first part even vector components, second part odd vector components, # 2 * dim in dimension size scale = torch.cat((freqs, freqs), dim=-1) if exists(cache_key): self.cache[cache_key] = scale return scale def rotate_queries_and_keys(self, q, k, seq_dim=-2): """ use this only when xpos is activated. """ assert self.use_xpos and q.device == k.device device, seq_len_k, seq_len_q = k.device, k.shape[seq_dim], q.shape[seq_dim] pos_seq_k = torch.arange(seq_len_k, device=device, dtype=torch.float32) pos_seq_q = torch.arange(seq_len_k - seq_len_q, seq_len_k, device=device, dtype=torch.float32) freqs_k = self.forward(pos_seq_k, cache_key=f"{0}:{seq_len_k}") freqs_q = self.forward(pos_seq_q, cache_key=f"{seq_len_k - seq_len_q}:{seq_len_k}") scale_k = self.get_scale(pos_seq_k) scale_q = self.get_scale(pos_seq_q, offset=seq_len_k - seq_len_q) # 这里的offset是Q相对于K的offset rotated_q = apply_rotary_emb(freqs_q, q, scale=scale_q) rotated_k = apply_rotary_emb(freqs_k, k, scale=scale_k ** -1) return rotated_q, rotated_k def rotate_queries_or_keys(self, t, seq_dim=-2, offset=0): """ use this only when xpos is NOT activated. """ # t's shape e.g. -> (batchsize, headnum, seqlen, dimofhead) assert not self.use_xpos, 'you must use `.rotate_queries_and_keys` method instead and pass in both queries and keys, for length extrapolatable rotary embeddings' device, seq_len = t.device, t.shape[seq_dim] pos_seq_t = torch.arange(offset, offset + seq_len, device=device, dtype=torch.float32) freqs = self.forward(pos_seq_t, cache_key=f"{offset}:{offset+seq_len}") # freqs seqlen x dim return apply_rotary_emb(freqs, t) def get_scale(self, t, cache_key=None, offset=0, ): assert self.use_xpos, 'This function is only useful for xpos.' if exists(cache_key) and cache_key in self.cache_scale: return self.cache_scale[cache_key] if callable(t): t = t() length = len(t) min_pos = -(length + offset) // 2 max_pos = length + offset + min_pos power = torch.arange(min_pos, max_pos, 1).to(device=self.scale.device) / self.scale_base scale = self.scale ** rearrange(power, 'n -> n 1') scale = scale[-length:, :] scale = torch.cat((scale, scale), dim=-1) if exists(cache_key): self.cache_scale[cache_key] = scale return scale def rotate_half(x): """ change sign so the last dimension becomes [-odd, +even] """ x1, x2 = torch.chunk(x, 2, dim=-1) return torch.cat((-x2, x1), dim=-1) def apply_rotary_emb(freqs, t, start_index=0, scale=1.): """ freq: seqlen x dim t: [batchsize * headnum , seqlen , dim (dim_of_head actually)] """ dtype_t = t.dtype freqs = freqs.to(device=t.device) if isinstance(scale, torch.Tensor): scale = scale.to(device=t.device) rot_dim = freqs.shape[-1] end_index = start_index + rot_dim t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] t = (t * freqs.cos() + rotate_half(t) * freqs.sin()) * scale rotated = torch.cat((t_left, t, t_right), dim=-1) rotated = rotated.to(dtype=dtype_t) return rotated class FLMAttention(nn.Module): def __init__(self, config, is_cross_attention=False, layer_idx=None): super().__init__() max_positions = config.max_position_embeddings self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( 1, 1, max_positions, max_positions ), ) self.register_buffer("masked_bias", torch.tensor(-1e4)) self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads self.split_size = self.embed_dim if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale_attn_weights = config.scale_attn_weights self.is_cross_attention = is_cross_attention # Layer-wise attention scaling, reordering, and upcasting self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx # for alignment with megatron-lm in softmax scale self.layer_idx = max(1, layer_idx) self.reorder_and_upcast_attn = config.reorder_and_upcast_attn self.relative_encoding = config.relative_encoding self.rotary_use_xpos = config.rotary_use_xpos self.use_mup = config.use_mup if self.is_cross_attention: self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) self.q_attn = Conv1D(self.embed_dim, self.embed_dim) else: self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) self.c_proj = Conv1D(self.embed_dim, self.embed_dim) self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.pruned_heads = set() def set_max_positions(self, max_positions, device='cuda'): self.max_positions = max_positions self.register_buffer( "bias", torch.tril(torch.ones((self.max_positions, self.max_positions), dtype=torch.bool)).view( 1, 1, self.max_positions, self.max_positions ).to(device=device) ) def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) # Prune conv1d layers self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) # Update hyper params self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) self.num_heads = self.num_heads - len(heads) self.pruned_heads = self.pruned_heads.union(heads) def _attn(self, query, key, value, attention_mask=None, head_mask=None): # (batch, head, seq_length, head_features) # batch_size, head_num, k_seq_len(q_seq_len), head_features batch_size, head_num, k_seq_len, head_features = key.shape _, _, q_seq_len, _ = query.shape attn_weights = torch.matmul(query, key.transpose(-1, -2)) if self.scale_attn_weights: if self.use_mup: attn_weights = attn_weights / torch.full( [], value.size(-1) / (value.size(-1) ** 0.5), dtype=attn_weights.dtype, device=attn_weights.device ) else: attn_weights = attn_weights / torch.full( [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device ) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length: key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # 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 def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM) bsz, num_heads, q_seq_len, dk = query.size() _, _, k_seq_len, _ = key.size() # Preallocate attn_weights for `baddbmm` attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=query.dtype, device=query.device) # Compute Scale Factor scale_factor = 1.0 if self.scale_attn_weights: scale_factor /= float(value.size(-1)) ** 0.5 if self.scale_attn_by_inverse_layer_idx: scale_factor /= float(self.layer_idx) # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk)) with autocast(enabled=False): q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) attn_weights = torch.baddbmm(attn_weights, q, k, beta=0, alpha=scale_factor) attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) if not self.is_cross_attention: attn_weights = attn_weights.float() if self.scale_attn_by_inverse_layer_idx: attn_weights *= self.layer_idx # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length: key_length, :key_length] mask_value = -10000.0 # align with megatron-lm # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise if attn_weights.dtype != torch.float32: raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # 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 def _split_heads(self, tensor, num_heads, attn_head_size): """ Splits hidden_size dim into attn_head_size and num_heads """ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) tensor = tensor.view(new_shape) return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def _merge_heads(self, tensor, num_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden_size """ tensor = tensor.permute(0, 2, 1, 3).contiguous() new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) return tensor.view(new_shape) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, rotary_embedding: Optional[RotaryEmbedding] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(hidden_states) key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) attention_mask = encoder_attention_mask else: query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key, past_value = layer_past key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) if use_cache is True: present = (key, value) else: present = None batch_size, head_num, k_seq_len, head_features = key.shape _, _, q_seq_len, _ = query.shape query_offset = k_seq_len - q_seq_len if rotary_embedding is not None: query = query.contiguous().view(batch_size * head_num, q_seq_len, head_features) key = key.contiguous().view(batch_size * head_num, k_seq_len, head_features) # batch_size * head_num, k_seq_len(q_seq_len), head_features if self.rotary_use_xpos: # query: [batch_size * head_num, seqlen, hn] query, key = rotary_embedding.rotate_queries_and_keys(query, key) else: query = rotary_embedding.rotate_queries_or_keys(query, offset=query_offset) key = rotary_embedding.rotate_queries_or_keys(key) # batch_size * head_num, k_seq_len(q_seq_len), head_features query = query.view(batch_size, head_num, q_seq_len, head_features) key = key.view(batch_size, head_num, k_seq_len, head_features) if self.reorder_and_upcast_attn: attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) else: attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) attn_output = self.c_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs class FLMMLP(nn.Module): def __init__(self, intermediate_size, config): super().__init__() embed_dim = config.hidden_size self.c_fc = Conv1D(intermediate_size, embed_dim) self.c_proj = Conv1D(embed_dim, intermediate_size) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(config.resid_pdrop) def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FLMBlock(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() hidden_size = config.hidden_size inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size self.layer_idx = layer_idx self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.attn = FLMAttention(config, layer_idx=layer_idx) self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) if config.add_cross_attention: self.crossattention = FLMAttention(config, is_cross_attention=True, layer_idx=layer_idx) self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = FLMMLP(inner_dim, config) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, rotary_embedding: Optional[RotaryEmbedding] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, rotary_embedding=rotary_embedding, use_cache=use_cache, output_attentions=output_attentions ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] # residual connection hidden_states = attn_output + residual residual = hidden_states hidden_states = self.ln_2(hidden_states) feed_forward_hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + feed_forward_hidden_states if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs class FLMPretrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FLMConfig load_tf_weights = None base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["FLMBlock"] def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear, Conv1D)): # 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) # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name == "c_proj.weight": # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, FLMTransformer): module.gradient_checkpointing = value class FLMTransformer(FLMPretrainedModel): _keys_to_ignore_on_load_missing = ["attn.masked_bias"] def __init__(self, config): super().__init__(config) self.embed_dim = config.hidden_size self.relative_encoding = config.relative_encoding self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.use_mup = config.use_mup if self.use_mup: self.input_mult = config.input_mult if self.relative_encoding is None: self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) elif self.relative_encoding == 'rotary': pe_dim = config.n_embd // config.n_head self.wpe = RotaryEmbedding(pe_dim, use_xpos=config.rotary_use_xpos, xpos_scale_base=config.rotary_xpos_scale_base, theta=config.rotary_theta ) else: raise RuntimeError( f'Unknown relative positional encoding type: `relative_encoding`={self.relative_encoding}') self.drop = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList([FLMBlock(config, layer_idx=i + 1) for i in range(config.num_hidden_layers)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() # @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): # Check validity of device_map self.device_map = ( get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.h)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) self.wte = self.wte.to(self.first_device) self.wpe = self.wpe.to(self.first_device) # Load onto devices for k, v in self.device_map.items(): for block in v: cuda_device = "cuda:" + str(k) self.h[block] = self.h[block].to(cuda_device) # ln_f to last self.ln_f = self.ln_f.to(self.last_device) def deparallelize(self): self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" self.wte = self.wte.to("cpu") self.wpe = self.wpe.to("cpu") for index in range(len(self.h)): self.h[index] = self.h[index].to("cpu") self.ln_f = self.ln_f.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ for layer, heads in heads_to_prune.items(): self.h[layer].attn.prune_heads(heads) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: 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, BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is 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() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) if position_ids is not None: position_ids = position_ids.view(-1, input_shape[-1]) if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) # GPT2Attention mask. if attention_mask is not None: if batch_size <= 0: raise ValueError("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 # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.add_cross_attention and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_attention_mask = None # 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 # head_mask has shape n_layer x batch x n_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) if inputs_embeds is None: inputs_embeds = self.wte(input_ids) # Mup if self.use_mup: inputs_embeds = inputs_embeds * self.input_mult if self.relative_encoding is None: position_embeds = self.wpe(position_ids) hidden_states = inputs_embeds + position_embeds elif self.relative_encoding == 'rotary': hidden_states = inputs_embeds if token_type_ids is not None: token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = input_shape + (hidden_states.size(-1),) presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure layer_past is on same device as hidden_states (might not be correct) if layer_past is not None: layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) 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 def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, use_cache, output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, None, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, rotary_embedding=self.wpe if self.relative_encoding == 'rotary' else None, 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_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # 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_self_attentions, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class FLM(FLMPretrainedModel): _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = FLMTransformer(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.use_mup = config.use_mup if self.use_mup: self.mup_scale_factor = config.mup_scale_factor self.output_mult = config.output_mult / self.mup_scale_factor # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() def set_max_positions(self, max_positions): for layer in self.transformer.h: device = layer.ln_1.weight.device layer.attn.set_max_positions(max_positions, device=device) def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.model_parallel = True def deparallelize(self): self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past: input_ids = input_ids[:, -1].unsqueeze(-1) if token_type_ids is not None: token_type_ids = token_type_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) 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: position_ids = position_ids[:, -1].unsqueeze(-1) else: position_ids = None return { "input_ids": input_ids, "past_key_values": past, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) # Mup if self.use_mup: lm_logits = lm_logits * self.output_mult loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, )