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"""PyTorch OPT model.""" |
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|
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from typing import List, Optional, Tuple, Union |
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from functools import partial |
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import torch |
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|
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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|
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from transformers.activations import ACT2FN |
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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CausalLMOutputWithPast, |
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QuestionAnsweringModelOutput, |
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SequenceClassifierOutputWithPast, |
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) |
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from enum import Flag, auto |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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add_code_sample_docstrings, |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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|
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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replace_return_docstrings, |
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|
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) |
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from .configuration_opt import OPTConfig |
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class BaseEnumOptions(Flag): |
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def __str__(self): |
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return self.name |
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|
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@classmethod |
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def list_names(cls): |
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return [m.name for m in cls] |
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|
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class AttentionGateType(BaseEnumOptions): |
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none = 0 |
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unconditional_per_head = 1 |
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conditional_per_head = 2 |
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conditional_per_token = 3 |
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|
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if is_flash_attn_2_available(): |
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from flash_attn import flash_attn_func, flash_attn_varlen_func |
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input |
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|
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logger = logging.get_logger(__name__) |
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|
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_CHECKPOINT_FOR_DOC = "facebook/opt-350m" |
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_CONFIG_FOR_DOC = "OPTConfig" |
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_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] |
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_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc" |
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_SEQ_CLASS_EXPECTED_LOSS = 1.71 |
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_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" |
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def _get_unpad_data(attention_mask): |
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) |
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() |
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max_seqlen_in_batch = seqlens_in_batch.max().item() |
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cu_seqlens = F.pad(torch.cumsum( |
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seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) |
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return ( |
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indices, |
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cu_seqlens, |
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max_seqlen_in_batch, |
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) |
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|
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class OPTLearnedPositionalEmbedding(nn.Embedding): |
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""" |
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This module learns positional embeddings up to a fixed maximum size. |
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""" |
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def __init__(self, num_embeddings: int, embedding_dim: int): |
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self.offset = 2 |
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super().__init__(num_embeddings + self.offset, embedding_dim) |
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def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): |
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"""`input_ids_shape` is expected to be [bsz x seqlen].""" |
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attention_mask = attention_mask.long() |
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positions = (torch.cumsum(attention_mask, dim=1).type_as( |
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attention_mask) * attention_mask).long() - 1 |
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positions = positions[:, past_key_values_length:] |
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return super().forward(positions + self.offset) |
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|
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def softmax_n_shifted_zeros(input: torch.Tensor, n: int, dim=-1) -> torch.Tensor: |
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""" |
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$\text(softmax)_n(x_i) = exp(x_i) / (n + \sum_j exp(x_j))$ |
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Note: softmax_n, with fixed input, is _not_ shift-symmetric when n != 0 |
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""" |
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input_maxes = input.max(dim=dim, keepdim=True).values |
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shifted_inputs = torch.subtract(input, input_maxes) |
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numerator = torch.exp(shifted_inputs) |
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original_denominator = numerator.sum(dim=dim, keepdim=True) |
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shifted_zeros = torch.multiply(input_maxes, -1) |
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denominator = torch.add(original_denominator, |
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torch.multiply(torch.exp(shifted_zeros), n)) |
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return torch.divide(numerator, denominator) |
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def softmax_1(input: torch.Tensor, dim=-1, dtype=torch.float32) -> torch.Tensor: |
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""" |
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$\text(softmax)_n(x_i) = exp(x_i) / (1 + \sum_j exp(x_j))$ |
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""" |
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output = softmax_n_shifted_zeros(input, 1, dim=dim) |
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return output if dtype is None else output.type(dtype=dtype) |
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def clipped_softmax(data, dim=1, eta=1.1, gamma=-0.1, **kw): |
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sm_out = torch.nn.functional.softmax(data, dim=dim, **kw) |
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stretched_out = sm_out * (eta - gamma) + gamma |
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return torch.clip(stretched_out, 0, 1) |
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def clipped_softmax1(data, dim=1, eta=1.1, gamma=-0.1, **kw): |
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sm_out = softmax_1(data, dim=dim, **kw) |
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stretched_out = sm_out * (eta - gamma) + gamma |
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return torch.clip(stretched_out, 0, 1) |
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|
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class OPTAttention(nn.Module): |
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"""Multi-headed attention from 'Attention Is All You Need' paper""" |
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def __init__( |
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self, |
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embed_dim: int, |
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num_heads: int, |
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dropout: float = 0.0, |
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is_decoder: bool = False, |
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bias: bool = True, |
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|
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softmax_fn=torch.nn.functional.softmax, |
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alpha=12, |
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max_seq_length=512, |
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ssm_eps=None, |
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tau=None, |
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skip_attn=False, |
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attn_gate_type=AttentionGateType.none, |
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attn_gate_init=None, |
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attn_gate_mlp=False, |
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attn_gate_mlp2=False, |
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attn_gate_linear_all_features=False, |
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fine_tuning=False, |
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attn_softmax=None, |
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): |
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super().__init__() |
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self.embed_dim = embed_dim |
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self.num_heads = num_heads |
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self.dropout = dropout |
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self.head_dim = embed_dim // num_heads |
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|
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if (self.head_dim * num_heads) != self.embed_dim: |
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raise ValueError( |
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f"embed_dim must be divisible by num_heads (got `embed_dim`: { |
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self.embed_dim}" |
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f" and `num_heads`: {num_heads})." |
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) |
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self.scaling = self.head_dim**-0.5 |
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self.is_decoder = is_decoder |
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|
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) |
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) |
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) |
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) |
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self.attn_scores = nn.Identity() |
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self.attn_probs_before_dropout = nn.Identity() |
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self.attn_probs_after_dropout = nn.Identity() |
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|
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self.alpha = alpha |
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self.max_seq_length = max_seq_length |
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self.ssm_eps = ssm_eps |
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self.tau = tau |
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self.attn_softmax = attn_softmax |
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|
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if self.alpha is not None: |
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assert self.max_seq_length is not None |
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gamma = -self.alpha / self.max_seq_length |
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if self.attn_softmax is "softmax1": |
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print("Using clipped Softmax_1!") |
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self.softmax_fn = partial( |
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clipped_softmax1, gamma=gamma, eta=1.0) |
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else: |
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self.softmax_fn = partial( |
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clipped_softmax, gamma=gamma, eta=1.0) |
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else: |
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self.softmax_fn = softmax_fn |
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|
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self.skip_attn = skip_attn |
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self.last_gate_avg_prob = None |
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self.last_gate_all_probs = None |
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|
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self.attn_gate_type = attn_gate_type |
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self.attn_gate_init = attn_gate_init |
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self.attn_gate_mlp = attn_gate_mlp |
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self.attn_gate_mlp2 = attn_gate_mlp2 |
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self.attn_gate_linear_all_features = attn_gate_linear_all_features |
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|
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self.alpha = None |
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self.ssm_eps = ssm_eps |
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self.gate_fn = torch.sigmoid |
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self.pooling_fn = partial(torch.mean, dim=1, keepdims=True) |
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|
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self.fine_tuning = fine_tuning |
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self.gate_scaling_factor = 1.0 |
|
if self.fine_tuning and self.attn_gate_init is not None: |
|
self.gate_scaling_factor = 1.0 / self.attn_gate_init |
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|
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if self.attn_gate_type == AttentionGateType.unconditional_per_head: |
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init_alpha = torch.zeros(size=(self.num_heads,)) |
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self.alpha = nn.Parameter(init_alpha, requires_grad=True) |
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|
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elif self.attn_gate_type in ( |
|
AttentionGateType.conditional_per_head, |
|
AttentionGateType.conditional_per_token, |
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): |
|
if self.attn_gate_linear_all_features: |
|
self.alpha = nn.Linear( |
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self.embed_dim, self.num_heads, bias=True) |
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|
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else: |
|
module_list = [] |
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for _ in range(self.num_heads): |
|
if self.attn_gate_mlp: |
|
fc = nn.Sequential( |
|
nn.Linear(self.head_dim, |
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self.head_dim // 4, bias=True), |
|
nn.ReLU(), |
|
nn.Linear(self.head_dim // 4, 1, bias=True), |
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) |
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elif self.attn_gate_mlp2: |
|
fc = nn.Sequential( |
|
nn.Linear(self.head_dim, self.head_dim, bias=True), |
|
nn.ReLU(), |
|
nn.Linear(self.head_dim, 1, bias=True), |
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) |
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else: |
|
fc = nn.Linear(self.head_dim, 1, bias=True) |
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|
|
if self.attn_gate_init is not None: |
|
init_bias = logit(self.attn_gate_init) |
|
torch.nn.init.constant_(fc.bias, init_bias) |
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|
|
if self.fine_tuning: |
|
|
|
torch.nn.init.normal_( |
|
fc.weight, mean=0.0, std=0.001) |
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|
|
module_list.append(fc) |
|
self.alpha = nn.ModuleList(module_list) |
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|
|
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
|
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
key_value_states: Optional[torch.Tensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
layer_head_mask: Optional[torch.Tensor] = None, |
|
output_attentions: bool = False, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
"""Input shape: Batch x Time x Channel""" |
|
|
|
|
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|
|
is_cross_attention = key_value_states is not None |
|
|
|
bsz, tgt_len, _ = hidden_states.size() |
|
|
|
|
|
query_states = self.q_proj(hidden_states) * self.scaling |
|
|
|
if is_cross_attention and past_key_value is not None: |
|
|
|
key_states = past_key_value[0] |
|
value_states = past_key_value[1] |
|
elif is_cross_attention: |
|
|
|
key_states = self._shape(self.k_proj(key_value_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(key_value_states), -1, bsz) |
|
elif past_key_value is not None: |
|
|
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) |
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
else: |
|
|
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) |
|
|
|
if self.is_decoder: |
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
past_key_value = (key_states, value_states) |
|
|
|
proj_shape = (bsz * self.num_heads, -1, self.head_dim) |
|
query_states = self._shape( |
|
query_states, tgt_len, bsz).view(*proj_shape) |
|
key_states = key_states.view(*proj_shape) |
|
value_states = value_states.view(*proj_shape) |
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|
|
src_len = key_states.size(1) |
|
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) |
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|
|
|
|
attn_weights = self.attn_scores(attn_weights) |
|
|
|
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): |
|
raise ValueError( |
|
f"Attention weights should be of size { |
|
(bsz * self.num_heads, tgt_len, src_len)}, but is" |
|
f" {attn_weights.size()}" |
|
) |
|
|
|
if attention_mask is not None: |
|
if attention_mask.size() != (bsz, 1, tgt_len, src_len): |
|
raise ValueError( |
|
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is { |
|
attention_mask.size()}" |
|
) |
|
attn_weights = attn_weights.view( |
|
bsz, self.num_heads, tgt_len, src_len) + attention_mask |
|
attn_weights = torch.max( |
|
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min) |
|
) |
|
attn_weights = attn_weights.view( |
|
bsz * self.num_heads, tgt_len, src_len) |
|
|
|
|
|
if attn_weights.dtype == torch.float16: |
|
attn_weights = self.softmax_fn(attn_weights, dim=-1, dtype=torch.float32).to( |
|
torch.float16 |
|
) |
|
else: |
|
attn_weights = self.softmax_fn(attn_weights, dim=-1) |
|
|
|
if layer_head_mask is not None: |
|
if layer_head_mask.size() != (self.num_heads,): |
|
raise ValueError( |
|
f"Head mask for a single layer should be of size { |
|
(self.num_heads,)}, but is" |
|
f" {layer_head_mask.size()}" |
|
) |
|
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view( |
|
bsz, self.num_heads, tgt_len, src_len |
|
) |
|
attn_weights = attn_weights.view( |
|
bsz * self.num_heads, tgt_len, src_len) |
|
|
|
if output_attentions: |
|
|
|
|
|
|
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|
|
attn_weights_reshaped = attn_weights.view( |
|
bsz, self.num_heads, tgt_len, src_len) |
|
attn_weights = attn_weights_reshaped.view( |
|
bsz * self.num_heads, tgt_len, src_len) |
|
else: |
|
attn_weights_reshaped = None |
|
|
|
|
|
attn_weights = self.attn_probs_before_dropout(attn_weights) |
|
|
|
attn_probs = nn.functional.dropout( |
|
attn_weights, p=self.dropout, training=self.training) |
|
|
|
|
|
attn_probs = self.attn_probs_after_dropout(attn_probs) |
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|
|
attn_output = torch.bmm(attn_probs, value_states) |
|
|
|
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): |
|
raise ValueError( |
|
f"`attn_output` should be of size { |
|
(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" |
|
f" {attn_output.size()}" |
|
) |
|
|
|
attn_output = attn_output.view( |
|
bsz, self.num_heads, tgt_len, self.head_dim) |
|
|
|
|
|
|
|
|
|
if self.attn_gate_type == AttentionGateType.unconditional_per_head: |
|
gate = self.gate_fn(self.alpha) |
|
attn_output *= gate.view(-1, 1, 1) |
|
|
|
self.last_gate_avg_prob = gate.view(-1) |
|
|
|
elif self.attn_gate_type in ( |
|
AttentionGateType.conditional_per_head, |
|
AttentionGateType.conditional_per_token, |
|
): |
|
x = hidden_states |
|
|
|
if self.attn_gate_linear_all_features: |
|
alpha = self.alpha(x) |
|
gate = self.gate_fn(alpha) |
|
gate = gate.permute(0, 2, 1).contiguous() |
|
gate = gate.unsqueeze(3) |
|
|
|
else: |
|
|
|
x = self._shape(x, -1, bsz) |
|
|
|
alpha = [] |
|
for head_idx in range(self.num_heads): |
|
x_head = x[:, head_idx, ...] |
|
fc_head = self.alpha[head_idx] |
|
alpha_head = fc_head(x_head) |
|
if self.attn_gate_type == AttentionGateType.conditional_per_head: |
|
alpha_head = self.pooling_fn(alpha_head) |
|
alpha.append(alpha_head) |
|
alpha = torch.stack(alpha, dim=1) |
|
gate = self.gate_fn(alpha) |
|
|
|
attn_output *= gate * self.gate_scaling_factor |
|
|
|
self.last_gate_all_probs = gate |
|
avg_gate = gate.mean(dim=0) |
|
self.last_gate_avg_prob = avg_gate.view( |
|
self.num_heads, -1).mean(dim=1) |
|
|
|
|
|
|
|
|
|
attn_output = attn_output.transpose(1, 2) |
|
|
|
|
|
|
|
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) |
|
|
|
attn_output = self.out_proj(attn_output) |
|
|
|
return attn_output, attn_weights_reshaped, past_key_value |
|
|
|
|
|
class OptFlashAttention2(OPTAttention): |
|
""" |
|
OPT flash attention module. This module inherits from `OPTAttention` 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. |
|
""" |
|
|
|
|
|
def __init__(self, *args, **kwargs): |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
|
|
|
|
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
key_value_states: Optional[torch.Tensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
layer_head_mask: Optional[torch.Tensor] = None, |
|
output_attentions: bool = False, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
"""Input shape: Batch x Time x Channel""" |
|
|
|
|
|
|
|
is_cross_attention = key_value_states is not None |
|
|
|
bsz, _, _ = hidden_states.size() |
|
|
|
|
|
query_states = self.q_proj(hidden_states) |
|
|
|
if is_cross_attention and past_key_value is not None: |
|
|
|
key_states = past_key_value[0] |
|
value_states = past_key_value[1] |
|
elif is_cross_attention: |
|
|
|
key_states = self._shape(self.k_proj(key_value_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(key_value_states), -1, bsz) |
|
elif past_key_value is not None: |
|
|
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) |
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
else: |
|
|
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) |
|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) |
|
|
|
if self.is_decoder: |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
past_key_value = (key_states, value_states) |
|
|
|
query_length = query_states.shape[1] |
|
tgt_len = key_states.shape[-2] |
|
|
|
|
|
|
|
query_states = query_states.view( |
|
bsz, query_length, self.num_heads, self.head_dim) |
|
key_states = key_states.transpose(1, 2).view( |
|
bsz, tgt_len, self.num_heads, self.head_dim) |
|
value_states = value_states.transpose(1, 2).view( |
|
bsz, tgt_len, self.num_heads, self.head_dim) |
|
|
|
attn_dropout = self.dropout if self.training else 0.0 |
|
|
|
|
|
|
|
|
|
input_dtype = query_states.dtype |
|
if input_dtype == torch.float32: |
|
if torch.is_autocast_enabled(): |
|
target_dtype = torch.get_autocast_gpu_dtype() |
|
|
|
elif hasattr(self.config, "_pre_quantization_dtype"): |
|
target_dtype = self.config._pre_quantization_dtype |
|
else: |
|
target_dtype = self.q_proj.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_states = query_states.to(target_dtype) |
|
key_states = key_states.to(target_dtype) |
|
value_states = value_states.to(target_dtype) |
|
|
|
attn_output = self._flash_attention_forward( |
|
query_states, key_states, value_states, attention_mask, query_length, dropout=attn_dropout |
|
) |
|
|
|
attn_weights_reshaped = attn_output.reshape( |
|
bsz, query_length, self.num_heads * self.head_dim) |
|
attn_output = self.out_proj(attn_weights_reshaped) |
|
|
|
if not output_attentions: |
|
attn_weights_reshaped = None |
|
|
|
return attn_output, attn_weights_reshaped, past_key_value |
|
|
|
|
|
def _flash_attention_forward( |
|
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None |
|
): |
|
""" |
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
|
first unpad the input, then computes the attention scores and pad the final attention scores. |
|
|
|
Args: |
|
query_states (`torch.Tensor`): |
|
Input query states to be passed to Flash Attention API |
|
key_states (`torch.Tensor`): |
|
Input key states to be passed to Flash Attention API |
|
value_states (`torch.Tensor`): |
|
Input value states to be passed to Flash Attention API |
|
attention_mask (`torch.Tensor`): |
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
|
position of padding tokens and 1 for the position of non-padding tokens. |
|
dropout (`float`): |
|
Attention dropout |
|
softmax_scale (`float`, *optional*): |
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
|
""" |
|
if not self._flash_attn_uses_top_left_mask: |
|
causal = self.is_causal |
|
else: |
|
|
|
causal = self.is_causal and query_length != 1 |
|
|
|
|
|
if attention_mask is not None: |
|
batch_size = query_states.shape[0] |
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( |
|
query_states, key_states, value_states, attention_mask, query_length |
|
) |
|
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens |
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens |
|
|
|
attn_output_unpad = flash_attn_varlen_func( |
|
query_states, |
|
key_states, |
|
value_states, |
|
cu_seqlens_q=cu_seqlens_q, |
|
cu_seqlens_k=cu_seqlens_k, |
|
max_seqlen_q=max_seqlen_in_batch_q, |
|
max_seqlen_k=max_seqlen_in_batch_k, |
|
dropout_p=dropout, |
|
softmax_scale=softmax_scale, |
|
causal=causal, |
|
) |
|
|
|
attn_output = pad_input( |
|
attn_output_unpad, indices_q, batch_size, query_length) |
|
else: |
|
attn_output = flash_attn_func( |
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal |
|
) |
|
|
|
return attn_output |
|
|
|
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): |
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data( |
|
attention_mask) |
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape |
|
|
|
key_layer = index_first_axis( |
|
key_layer.reshape(batch_size * kv_seq_len, |
|
num_key_value_heads, head_dim), indices_k |
|
) |
|
value_layer = index_first_axis( |
|
value_layer.reshape(batch_size * kv_seq_len, |
|
num_key_value_heads, head_dim), indices_k |
|
) |
|
if query_length == kv_seq_len: |
|
query_layer = index_first_axis( |
|
query_layer.reshape(batch_size * kv_seq_len, |
|
self.num_heads, head_dim), indices_k |
|
) |
|
cu_seqlens_q = cu_seqlens_k |
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k |
|
indices_q = indices_k |
|
elif query_length == 1: |
|
max_seqlen_in_batch_q = 1 |
|
cu_seqlens_q = torch.arange( |
|
batch_size + 1, dtype=torch.int32, device=query_layer.device |
|
) |
|
indices_q = cu_seqlens_q[:-1] |
|
query_layer = query_layer.squeeze(1) |
|
else: |
|
|
|
attention_mask = attention_mask[:, -query_length:] |
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( |
|
query_layer, attention_mask) |
|
|
|
return ( |
|
query_layer, |
|
key_layer, |
|
value_layer, |
|
indices_q, |
|
(cu_seqlens_q, cu_seqlens_k), |
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k), |
|
) |
|
|
|
|
|
OPT_ATTENTION_CLASSES = { |
|
"eager": OPTAttention, |
|
"flash_attention_2": OptFlashAttention2, |
|
} |
|
|
|
|
|
class OPTDecoderLayer(nn.Module): |
|
def __init__(self, config: OPTConfig): |
|
super().__init__() |
|
self.embed_dim = config.hidden_size |
|
|
|
self.self_attn = OPTAttention( |
|
config=config, is_decoder=True) |
|
|
|
self.do_layer_norm_before = config.do_layer_norm_before |
|
self.dropout = config.dropout |
|
self.activation_fn = ACT2FN[config.activation_function] |
|
|
|
self.self_attn_layer_norm = nn.LayerNorm( |
|
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine |
|
) |
|
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, |
|
bias=config.enable_bias) |
|
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, |
|
bias=config.enable_bias) |
|
self.final_layer_norm = nn.LayerNorm( |
|
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
layer_head_mask: Optional[torch.Tensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: Optional[bool] = False, |
|
use_cache: Optional[bool] = False, |
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size |
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. |
|
layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size |
|
`(encoder_attention_heads,)`. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
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`). |
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
|
""" |
|
|
|
residual = hidden_states |
|
|
|
|
|
if self.do_layer_norm_before: |
|
hidden_states = self.self_attn_layer_norm(hidden_states) |
|
|
|
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn( |
|
hidden_states=hidden_states, |
|
past_key_value=past_key_value, |
|
attention_mask=attention_mask, |
|
layer_head_mask=layer_head_mask, |
|
output_attentions=output_attentions, |
|
) |
|
hidden_states = nn.functional.dropout( |
|
hidden_states, p=self.dropout, training=self.training) |
|
hidden_states = residual + hidden_states |
|
|
|
|
|
if not self.do_layer_norm_before: |
|
hidden_states = self.self_attn_layer_norm(hidden_states) |
|
|
|
|
|
hidden_states_shape = hidden_states.shape |
|
hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) |
|
residual = hidden_states |
|
|
|
|
|
if self.do_layer_norm_before: |
|
hidden_states = self.final_layer_norm(hidden_states) |
|
|
|
hidden_states = self.fc1(hidden_states) |
|
hidden_states = self.activation_fn(hidden_states) |
|
|
|
hidden_states = self.fc2(hidden_states) |
|
hidden_states = nn.functional.dropout( |
|
hidden_states, p=self.dropout, training=self.training) |
|
|
|
hidden_states = (residual + hidden_states).view(hidden_states_shape) |
|
|
|
|
|
if not self.do_layer_norm_before: |
|
hidden_states = self.final_layer_norm(hidden_states) |
|
|
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights,) |
|
|
|
if use_cache: |
|
outputs += (present_key_value,) |
|
|
|
return outputs |
|
|
|
|
|
OPT_START_DOCSTRING = r""" |
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
|
etc.) |
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
|
and behavior. |
|
|
|
Parameters: |
|
config ([`OPTConfig`]): |
|
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. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare OPT Model outputting raw hidden-states without any specific head on top.", |
|
OPT_START_DOCSTRING, |
|
) |
|
class OPTPreTrainedModel(PreTrainedModel): |
|
config_class = OPTConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ["OPTDecoderLayer"] |
|
_supports_flash_attn_2 = True |
|
|
|
def _init_weights(self, module): |
|
std = self.config.init_std |
|
if isinstance(module, nn.Linear): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
|
|
|
|
OPT_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see |
|
`past_key_values`). |
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
|
information on the default strategy. |
|
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): |
|
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): |
|
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)`) and 2 additional tensors of shape |
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. |
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that |
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all |
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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. |
|
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 (`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 [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
class OPTDecoder(OPTPreTrainedModel): |
|
""" |
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`] |
|
|
|
Args: |
|
config: OPTConfig |
|
""" |
|
|
|
def __init__(self, config: OPTConfig): |
|
super().__init__(config) |
|
self.dropout = config.dropout |
|
self.layerdrop = config.layerdrop |
|
self.padding_idx = config.pad_token_id |
|
self.max_target_positions = config.max_position_embeddings |
|
self.vocab_size = config.vocab_size |
|
|
|
self.embed_tokens = nn.Embedding( |
|
config.vocab_size, config.word_embed_proj_dim, self.padding_idx) |
|
self.embed_positions = OPTLearnedPositionalEmbedding( |
|
config.max_position_embeddings, config.hidden_size) |
|
|
|
if config.word_embed_proj_dim != config.hidden_size: |
|
self.project_out = nn.Linear( |
|
config.hidden_size, config.word_embed_proj_dim, bias=False) |
|
else: |
|
self.project_out = None |
|
|
|
if config.word_embed_proj_dim != config.hidden_size: |
|
self.project_in = nn.Linear( |
|
config.word_embed_proj_dim, config.hidden_size, bias=False) |
|
else: |
|
self.project_in = None |
|
|
|
|
|
|
|
|
|
if config.do_layer_norm_before and not config._remove_final_layer_norm: |
|
self.final_layer_norm = nn.LayerNorm( |
|
config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine |
|
) |
|
else: |
|
self.final_layer_norm = None |
|
|
|
self.layers = nn.ModuleList( |
|
[OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]) |
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" |
|
|
|
self.gradient_checkpointing = False |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.embed_tokens = value |
|
|
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
head_mask: Optional[torch.Tensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPast]: |
|
r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you |
|
provide it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) |
|
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): |
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): |
|
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)`) and 2 additional tensors of |
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the |
|
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those |
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of |
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. |
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. |
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors |
|
than the model's internal embedding lookup matrix. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors |
|
for more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
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 decoder_input_ids and decoder_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]) |
|
elif inputs_embeds is not None: |
|
input_shape = inputs_embeds.size()[:-1] |
|
else: |
|
raise ValueError( |
|
"You have to specify either decoder_input_ids or decoder_inputs_embeds") |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embed_tokens(input_ids) |
|
|
|
batch_size, seq_length = input_shape |
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 |
|
|
|
mask_seq_length = past_key_values_length + seq_length |
|
|
|
|
|
if self._use_flash_attention_2: |
|
|
|
causal_attention_mask = attention_mask if ( |
|
attention_mask is not None and 0 in attention_mask) else None |
|
attention_mask = ( |
|
torch.ones(batch_size, mask_seq_length, |
|
device=inputs_embeds.device) |
|
if attention_mask is None |
|
else attention_mask |
|
) |
|
else: |
|
|
|
if attention_mask is None: |
|
attention_mask = torch.ones( |
|
batch_size, mask_seq_length, device=inputs_embeds.device) |
|
elif attention_mask.shape[1] != mask_seq_length: |
|
raise ValueError( |
|
f"The provided attention mask has length { |
|
attention_mask.shape[1]}, but its length should be " |
|
f"{mask_seq_length} (sum of the lengths of current and past inputs)" |
|
) |
|
causal_attention_mask = _prepare_4d_causal_attention_mask( |
|
attention_mask, input_shape, inputs_embeds, past_key_values_length |
|
) |
|
|
|
pos_embeds = self.embed_positions( |
|
attention_mask, past_key_values_length) |
|
|
|
if self.project_in is not None: |
|
inputs_embeds = self.project_in(inputs_embeds) |
|
|
|
hidden_states = inputs_embeds + pos_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 |
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attns = () if output_attentions else None |
|
next_decoder_cache = () if use_cache else None |
|
|
|
|
|
for attn_mask, mask_name in zip([head_mask], ["head_mask"]): |
|
if attn_mask is not None: |
|
if attn_mask.size()[0] != (len(self.layers)): |
|
raise ValueError( |
|
f"The `{mask_name}` should be specified for { |
|
len(self.layers)} layers, but it is for" |
|
f" {head_mask.size()[0]}." |
|
) |
|
|
|
for idx, decoder_layer in enumerate(self.layers): |
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
if self.training: |
|
dropout_probability = torch.rand([]) |
|
if dropout_probability < self.layerdrop: |
|
continue |
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None |
|
|
|
if self.gradient_checkpointing and self.training: |
|
layer_outputs = self._gradient_checkpointing_func( |
|
decoder_layer.__call__, |
|
hidden_states, |
|
causal_attention_mask, |
|
head_mask[idx] if head_mask is not None else None, |
|
None, |
|
output_attentions, |
|
use_cache, |
|
) |
|
else: |
|
layer_outputs = decoder_layer( |
|
hidden_states, |
|
attention_mask=causal_attention_mask, |
|
layer_head_mask=( |
|
head_mask[idx] if head_mask is not None else None), |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if use_cache: |
|
next_decoder_cache += ( |
|
layer_outputs[2 if output_attentions else 1],) |
|
|
|
if output_attentions: |
|
all_self_attns += (layer_outputs[1],) |
|
|
|
if self.final_layer_norm is not None: |
|
hidden_states = self.final_layer_norm(hidden_states) |
|
|
|
if self.project_out is not None: |
|
hidden_states = self.project_out(hidden_states) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
next_cache = next_decoder_cache if use_cache else None |
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attns, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare OPT Model outputting raw hidden-states without any specific head on top.", |
|
OPT_START_DOCSTRING, |
|
) |
|
class OPTModel(OPTPreTrainedModel): |
|
def __init__(self, config: OPTConfig): |
|
super().__init__(config) |
|
self.decoder = OPTDecoder(config) |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.decoder.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.decoder.embed_tokens = value |
|
|
|
def get_decoder(self): |
|
return self.decoder |
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
output_type=BaseModelOutputWithPast, |
|
config_class=_CONFIG_FOR_DOC, |
|
expected_output=_EXPECTED_OUTPUT_SHAPE, |
|
) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
head_mask: Optional[torch.Tensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPast]: |
|
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 |
|
|
|
|
|
decoder_outputs = self.decoder( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
if not return_dict: |
|
return decoder_outputs |
|
|
|
return BaseModelOutputWithPast( |
|
last_hidden_state=decoder_outputs.last_hidden_state, |
|
past_key_values=decoder_outputs.past_key_values, |
|
hidden_states=decoder_outputs.hidden_states, |
|
attentions=decoder_outputs.attentions, |
|
) |
|
|
|
|
|
class OPTForCausalLM(OPTPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.model = OPTModel(config) |
|
|
|
|
|
self.lm_head = nn.Linear( |
|
config.word_embed_proj_dim, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.model.decoder.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.decoder.embed_tokens = value |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def set_decoder(self, decoder): |
|
self.model.decoder = decoder |
|
|
|
def get_decoder(self): |
|
return self.model.decoder |
|
|
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
head_mask: Optional[torch.Tensor] = None, |
|
past_key_values: Optional[List[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, CausalLMOutputWithPast]: |
|
r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you |
|
provide it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) |
|
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): |
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): |
|
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)`) and 2 additional tensors of |
|
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional |
|
tensors are only required when the model is used as a decoder in a Sequence to Sequence model. |
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the |
|
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those |
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of |
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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. |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
|
config.vocab_size]` or -100 (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]`. |
|
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 (`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 [`~utils.ModelOutput`] instead of a plain tuple. |
|
|
|
Returns: |
|
|
|
Example: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, OPTForCausalLM |
|
|
|
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") |
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") |
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?" |
|
>>> inputs = tokenizer(prompt, return_tensors="pt") |
|
|
|
>>> # Generate |
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo." |
|
```""" |
|
|
|
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 |
|
|
|
|
|
outputs = self.model.decoder( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
logits = self.lm_head(outputs[0]).contiguous() |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
labels = labels.to(logits.device) |
|
|
|
shift_logits = logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct( |
|
shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) |
|
|
|
if not return_dict: |
|
output = (logits,) + outputs[1:] |
|
return (loss,) + output if loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=loss, |
|
logits=logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs |
|
): |
|
if past_key_values is not None: |
|
past_length = past_key_values[0][0].shape[2] |
|
|
|
|
|
if input_ids.shape[1] > past_length: |
|
remove_prefix_length = past_length |
|
else: |
|
|
|
remove_prefix_length = input_ids.shape[1] - 1 |
|
|
|
input_ids = input_ids[:, remove_prefix_length:] |
|
|
|
|
|
if inputs_embeds is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
model_inputs = {"input_ids": input_ids} |
|
|
|
model_inputs.update( |
|
{ |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"attention_mask": attention_mask, |
|
} |
|
) |
|
return model_inputs |
|
|
|
@staticmethod |
|
def _reorder_cache(past_key_values, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) |
|
for past_state in layer_past), |
|
) |
|
return reordered_past |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The OPT Model transformer with a sequence classification head on top (linear layer). |
|
|
|
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
|
(e.g. GPT-2) 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). |
|
""", |
|
OPT_START_DOCSTRING, |
|
) |
|
class OPTForSequenceClassification(OPTPreTrainedModel): |
|
def __init__(self, config: OPTConfig): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.model = OPTModel(config) |
|
self.score = nn.Linear(config.word_embed_proj_dim, |
|
self.num_labels, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, |
|
output_type=SequenceClassifierOutputWithPast, |
|
config_class=_CONFIG_FOR_DOC, |
|
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT, |
|
expected_loss=_SEQ_CLASS_EXPECTED_LOSS, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, 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 |
|
|
|
transformer_outputs = self.model( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
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 = transformer_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: |
|
sequence_lengths = -1 |
|
else: |
|
if input_ids is not None: |
|
|
|
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( |
|
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: |
|
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,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=pooled_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|
|
def get_input_embeddings(self): |
|
return self.model.decoder.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.decoder.embed_tokens = value |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD |
|
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). |
|
""", |
|
OPT_START_DOCSTRING, |
|
) |
|
class OPTForQuestionAnswering(OPTPreTrainedModel): |
|
def __init__(self, config: OPTConfig): |
|
super().__init__(config) |
|
self.model = OPTModel(config) |
|
self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
start_positions: Optional[torch.LongTensor] = None, |
|
end_positions: 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, 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. |
|
|
|
Returns: |
|
|
|
Example: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, OPTForQuestionAnswering |
|
>>> import torch |
|
|
|
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT |
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") |
|
|
|
>>> # note: we are loading a OPTForQuestionAnswering from the hub here, |
|
>>> # so the head will be randomly initialized, hence the predictions will be random |
|
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") |
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" |
|
|
|
>>> inputs = tokenizer(question, text, return_tensors="pt") |
|
>>> with torch.no_grad(): |
|
... outputs = model(**inputs) |
|
|
|
>>> answer_start_index = outputs.start_logits.argmax() |
|
>>> answer_end_index = outputs.end_logits.argmax() |
|
|
|
>>> answer_offset = len(tokenizer(question)[0]) |
|
|
|
>>> predict_answer_tokens = inputs.input_ids[ |
|
... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1 |
|
... ] |
|
>>> predicted = tokenizer.decode(predict_answer_tokens) |
|
>>> predicted |
|
' a nice puppet' |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
transformer_outputs = self.model( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
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 = transformer_outputs[0] |
|
|
|
logits = self.qa_outputs(hidden_states) |
|
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 len(start_positions.size()) > 1: |
|
start_positions = start_positions.squeeze(-1) |
|
if len(end_positions.size()) > 1: |
|
end_positions = end_positions.squeeze(-1) |
|
|
|
ignored_index = start_logits.size(1) |
|
start_positions = start_positions.clamp( |
|
0, ignored_index).to(logits.device) |
|
end_positions = end_positions.clamp( |
|
0, ignored_index).to(logits.device) |
|
|
|
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) + transformer_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=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|
|
def get_input_embeddings(self): |
|
return self.model.decoder.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.decoder.embed_tokens = value |
|
|