# coding=utf-8 # Copyright 2022 EleutherAI The HuggingFace Inc. 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 GPTNeoX model.""" from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from packaging import version from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, StaticCache from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS from ...modeling_utils import PreTrainedModel from ...utils import ( get_torch_version, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, ) from .configuration_gpt_neox import GPTNeoXConfig if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM" _REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b" _CONFIG_FOR_DOC = "GPTNeoXConfig" class GPTNeoXPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTNeoXConfig base_model_prefix = "gpt_neox" supports_gradient_checkpointing = True _no_split_modules = ["GPTNeoXLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_sdpa = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): 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 GPTNeoXAttention(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() self.config = config self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size if self.hidden_size % self.num_attention_heads != 0: raise ValueError( "The hidden size is not divisble by the number of attention heads! Make sure to update them" ) self.head_size = self.hidden_size // self.num_attention_heads self.rotary_ndims = int(self.head_size * config.rotary_pct) self.rope_theta = config.rotary_emb_base self._init_bias(config.max_position_embeddings) self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) self.rotary_emb = GPTNeoXRotaryEmbedding(config=self.config) if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.norm_factor = self.head_size**-0.5 self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias) self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias) self.attention_dropout = nn.Dropout(config.attention_dropout) self.is_causal = True self.layer_idx = layer_idx def _init_bias(self, max_positions, device=None): self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( 1, 1, max_positions, max_positions ), persistent=False, ) if device is not None: self.bias = self.bias.to(device) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, padding_mask: Optional[torch.Tensor] = None, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache, position_embeddings=position_embeddings, ) # Compute attention attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) # Reshape outputs attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) 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, attn_head_size): """ Splits hidden dim into attn_head_size and num_attention_heads """ # tensor: [bs, seq_len, hidden_size] new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) # -> [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, num_attention_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ # 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.size(0), tensor.size(1), num_attention_heads * attn_head_size) # -> [bs, seq_len, hidden_size] return tensor def _attn_projections_and_rope( self, hidden_states: torch.FloatTensor, position_ids: torch.LongTensor, layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] qkv = self.query_key_value(hidden_states) # [batch, seq_len, (num_heads * 3 * head_size)] # --> [batch, seq_len, num_heads, 3 * head_size] new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size) qkv = qkv.view(*new_qkv_shape) # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size] query = qkv[..., : self.head_size].permute(0, 2, 1, 3) key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3) value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3) # Compute rotary embeddings on rotary_ndims query_rot = query[..., : self.rotary_ndims] query_pass = query[..., self.rotary_ndims :] key_rot = key[..., : self.rotary_ndims] key_pass = key[..., self.rotary_ndims :] if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory." ) cos, sin = self.rotary_emb(value, position_ids) else: cos, sin = position_embeddings query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) query = torch.cat((query, query_pass), dim=-1) key = torch.cat((key, key_pass), dim=-1) # Cache QKV values if layer_past is not None: cache_kwargs = { "sin": sin, "cos": cos, "partial_rotation_size": self.rotary_ndims, "cache_position": cache_position, } key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) return query, key, value, layer_past 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.size() key_length = key.size(-2) # dynamically increase the causal mask with the key length, if needed. if key_length > self.bias.shape[-1]: self._init_bias(key_length, device=key.device) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] query = query.view(batch_size * num_attention_heads, query_length, attn_head_size) key = key.view(batch_size * num_attention_heads, key_length, attn_head_size) attn_scores = torch.zeros( batch_size * num_attention_heads, query_length, key_length, dtype=query.dtype, device=key.device, ) attn_scores = torch.baddbmm( attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=self.norm_factor, ) attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) mask_value = torch.finfo(attn_scores.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.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) attn_scores = torch.where(causal_mask, attn_scores, mask_value) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key.shape[-2]] attn_scores = attn_scores + causal_mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) attn_weights = attn_weights.to(value.dtype) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_weights = self.attention_dropout(attn_weights) attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights class GPTNeoXFlashAttention2(GPTNeoXAttention): """ GPTNeoX flash attention module. This module inherits from `GPTNeoXAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) query_length = query.shape[-2] # GPT-neo-X casts query and key in fp32 to apply rotary embedding in full precision target_dtype = value.dtype if query.dtype != target_dtype: query = query.to(target_dtype) if key.dtype != target_dtype: key = key.to(target_dtype) # Permute to get the expected shape for Flash Attention query = query.permute(0, 2, 1, 3) key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 / bfloat16 just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms input_dtype = query.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.query_key_value.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query = query.to(target_dtype) key = key.to(target_dtype) value = value.to(target_dtype) attention_dropout = self.config.attention_dropout if self.training else 0.0 # Compute attention attn_weights = _flash_attention_forward( query, key, value, attention_mask, query_length, dropout=attention_dropout, softmax_scale=self.norm_factor, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) # Reshape outputs attn_output = attn_weights.reshape( attn_weights.shape[0], attn_weights.shape[1], self.num_attention_heads * self.head_size ) attn_output = self.dense(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) return outputs class GPTNeoXSdpaAttention(GPTNeoXAttention): """ GPTNeoX attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `GPTNeoXAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to the SDPA API. """ def __init__(self, config, layer_idx=None): super().__init__(config, layer_idx=layer_idx) # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom # attn_mask, so we need to call `.contiguous()`. This was fixed in torch==2.2.0. # Reference: https://github.com/pytorch/pytorch/issues/112577 self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0") 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, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): if output_attentions or head_mask is not None: logger.warning_once( "`GPTNeoXSdpaAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not support " "`output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, but " "specifying the manual implementation will be required from Transformers version v5.0.0 onwards. " 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) bsz, q_len, _ = hidden_states.size() # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) causal_mask = attention_mask if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key.shape[-2]] # GPT-neo-X casts query and key in fp32 to apply rotary embedding in full precision target_dtype = value.dtype if query.dtype != target_dtype: query = query.to(target_dtype) if key.dtype != target_dtype: key = key.to(target_dtype) # Avoid torch==2.1.2 specific bug for the memory-efficient backend in SDPA if self.require_contiguous_qkv and query.device.type == "cuda" and attention_mask is not None: query = query.contiguous() key = key.contiguous() value = value.contiguous() # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. is_causal = True if causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query=query, key=key, value=value, attn_mask=causal_mask, dropout_p=self.attention_dropout.p if self.training else 0.0, is_causal=is_causal, ) # Reshape outputs attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, self.hidden_size) attn_output = self.dense(attn_output) return attn_output, present, None def attention_mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min) return attention_scores # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->GPTNeoX class GPTNeoXRotaryEmbedding(nn.Module): def __init__( self, dim=None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, rope_type="default", config: Optional[GPTNeoXConfig] = None, ): super().__init__() # TODO (joao): remove the `if` below, only used for BC self.rope_kwargs = {} if config is None: logger.warning_once( "`GPTNeoXRotaryEmbedding` can now be fully parameterized by passing the model config through the " "`config` argument. All other arguments will be removed in v4.46" ) self.rope_kwargs = { "rope_type": rope_type, "factor": scaling_factor, "dim": dim, "base": base, "max_position_embeddings": max_position_embeddings, } self.rope_type = rope_type self.max_seq_len_cached = max_position_embeddings self.original_max_seq_len = max_position_embeddings else: # BC: "rope_type" was originally "type" if config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq def _dynamic_frequency_update(self, position_ids, device): """ dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) """ seq_len = torch.max(position_ids) + 1 if seq_len > self.max_seq_len_cached: # growth inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device, seq_len=seq_len, **self.rope_kwargs ) self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation self.max_seq_len_cached = seq_len if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.original_max_seq_len @torch.no_grad() def forward(self, x, position_ids): if "dynamic" in self.rope_type: self._dynamic_frequency_update(position_ids, device=x.device) # Core RoPE block inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() # Force float32 (see https://github.com/huggingface/transformers/pull/29285) device_type = x.device.type device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention cos = cos * self.attention_scaling sin = sin * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->GPTNeoX class GPTNeoXLinearScalingRotaryEmbedding(GPTNeoXRotaryEmbedding): """GPTNeoXRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, *args, **kwargs): logger.warning_once( "`GPTNeoXLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " "`GPTNeoXRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)." ) kwargs["rope_type"] = "linear" super().__init__(*args, **kwargs) # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->GPTNeoX class GPTNeoXDynamicNTKScalingRotaryEmbedding(GPTNeoXRotaryEmbedding): """GPTNeoXRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, *args, **kwargs): logger.warning_once( "`GPTNeoXDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " "`GPTNeoXRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to " "__init__)." ) kwargs["rope_type"] = "dynamic" super().__init__(*args, **kwargs) 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) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class GPTNeoXMLP(nn.Module): def __init__(self, config): super().__init__() self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size) self.act = ACT2FN[config.hidden_act] def forward(self, hidden_states): hidden_states = self.dense_h_to_4h(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dense_4h_to_h(hidden_states) return hidden_states GPT_NEOX_ATTENTION_CLASSES = { "eager": GPTNeoXAttention, "flash_attention_2": GPTNeoXFlashAttention2, "sdpa": GPTNeoXSdpaAttention, } class GPTNeoXLayer(nn.Module): def __init__(self, config, layer_idx): super().__init__() self.use_parallel_residual = config.use_parallel_residual 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.post_attention_dropout = nn.Dropout(config.hidden_dropout) self.post_mlp_dropout = nn.Dropout(config.hidden_dropout) self.attention = GPT_NEOX_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = GPTNeoXMLP(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[Cache] = None, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): 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, cache_position=cache_position, position_embeddings=position_embeddings, ) attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights) attn_output = self.post_attention_dropout(attn_output) outputs = attention_layer_outputs[1:] if self.use_parallel_residual: # pseudocode: # x = x + attn(ln1(x)) + mlp(ln2(x)) mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) mlp_output = self.post_mlp_dropout(mlp_output) hidden_states = mlp_output + attn_output + hidden_states else: # pseudocode: # x = x + attn(ln1(x)) # x = x + mlp(ln2(x)) attn_output = attn_output + hidden_states mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) mlp_output = self.post_mlp_dropout(mlp_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 GPT_NEOX_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 ([`~GPTNeoXConfig`]): 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. """ GPT_NEOX_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): 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 `({0})`, *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**. inputs_embeds (`torch.FloatTensor` of shape `({0}, 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. past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. 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. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( "The bare GPTNeoX Model transformer outputting raw hidden-states without any specific head on top.", GPT_NEOX_START_DOCSTRING, ) class GPTNeoXModel(GPTNeoXPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) self.emb_dropout = nn.Dropout(config.hidden_dropout) self.layers = nn.ModuleList([GPTNeoXLayer(config, i) for i in range(config.num_hidden_layers)]) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.rotary_emb = GPTNeoXRotaryEmbedding(config=config) self._attn_implementation = config._attn_implementation 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(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @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[Union[Cache, 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, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: r""" use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = 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 None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) seq_length = inputs_embeds.shape[1] if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) # 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) hidden_states = self.emb_dropout(inputs_embeds) # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) next_decoder_cache = None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, layer in enumerate( self.layers, ): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( layer.__call__, hidden_states, causal_mask, position_ids, head_mask[i], use_cache, None, output_attentions, cache_position, position_embeddings, ) else: outputs = layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], layer_past=past_key_values, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = outputs[0] if use_cache is True: next_decoder_cache = outputs[1] if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.final_layer_norm(hidden_states) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_attentions, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda" and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @add_start_docstrings( """GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING ) class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin): _tied_weights_keys = ["embed_out.weight"] def __init__(self, config): super().__init__(config) self.gpt_neox = GPTNeoXModel(config) self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # 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(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @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[Union[Cache, 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, cache_position: Optional[torch.LongTensor] = 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]`. 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`). Returns: Example: ```python >>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") >>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b") >>> config.is_decoder = True >>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config) >>> 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.gpt_neox( 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, cache_position=cache_position, ) hidden_states = outputs[0] lm_logits = self.embed_out(hidden_states) lm_loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # 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 _reorder_cache(self, past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past @add_start_docstrings( """ The GPTNeoX Model transformer with a sequence classification head on top (linear layer). [`GPTNeoXForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, GPT_NEOX_START_DOCSTRING, ) class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.gpt_neox = GPTNeoXModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, 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[Union[Cache, 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[torch.Tensor], SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.gpt_neox( 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] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 logger.warning_once( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.gpt_neox = GPTNeoXModel(config) self.dropout = nn.Dropout(config.classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint="LarsJonasson/pythia-410m-deduped-sft-swedish", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, 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, 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, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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.gpt_neox( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = 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, ) @add_start_docstrings( """ The GPT-NeoX Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPT_NEOX_START_DOCSTRING, ) class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.gpt_neox = GPTNeoXModel(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = 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, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, 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.gpt_neox( input_ids, attention_mask=attention_mask, 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, ) 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).to(start_logits.device) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device) # 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 = 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, )