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773 lines
32 KiB
773 lines
32 KiB
# Copyright 2023 HuggingFace Inc. team and MosaicML NLP team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch MPT model."""
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import math
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
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from torch.nn import functional as F
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import auto_docstring, logging
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from .configuration_mpt import MptConfig
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logger = logging.get_logger(__name__)
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def build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max=8, device=None):
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r"""
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Link to paper: https://huggingface.co/papers/2108.12409 - Alibi tensor is not causal as the original paper mentions, it
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relies on a translation invariance of softmax for quick implementation. This implementation has been copied from
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the alibi implementation of MPT source code that led to slightly different results than the Bloom alibi:
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https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L292
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"""
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alibi = torch.arange(1 - sequence_length, 1, dtype=torch.int32, device=device).view(1, 1, 1, sequence_length)
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num_heads_power_of_2 = 2 ** math.ceil(math.log2(num_heads))
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base = torch.arange(1, num_heads_power_of_2 + 1, dtype=torch.int64, device=device).float()
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base = base * (alibi_bias_max / num_heads_power_of_2)
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slopes = 1.0 / torch.pow(2, base)
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slopes = slopes.view(1, num_heads_power_of_2, 1, 1)
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if num_heads_power_of_2 != num_heads:
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slopes = torch.concat([slopes[:, 1::2, ...], slopes[:, ::2, ...]], dim=1)[:, :num_heads, ...]
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alibi = alibi * slopes
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return alibi.squeeze(0)
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class MptAttention(nn.Module):
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"""Multi-head self attention.
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Using torch or triton attention implementation enables user to also use additive bias.
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"""
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def __init__(self, config: MptConfig, layer_idx: int | None = None):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.n_heads = config.n_heads
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self.max_seq_length = config.max_seq_len
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self.head_dim = self.hidden_size // self.n_heads
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self.softmax_scale = config.attn_config.softmax_scale
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if self.softmax_scale is None:
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self.softmax_scale = 1 / math.sqrt(self.hidden_size / self.n_heads)
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self.attn_dropout_p = config.attn_config.attn_pdrop
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self.clip_qkv = config.attn_config.clip_qkv
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self.Wqkv = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
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self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
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self.layer_idx = layer_idx
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_bias: torch.Tensor,
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past_key_values: Cache | None = None,
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attention_mask: torch.Tensor | None = None,
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cache_position: torch.Tensor | None = None,
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):
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batch_size, seq_length = hidden_states.shape[:2]
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mixed_qkv = self.Wqkv(hidden_states)
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if self.clip_qkv:
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mixed_qkv = mixed_qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)
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query_states, key_states, value_states = mixed_qkv.chunk(3, dim=2)
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query_states = query_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
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if past_key_values is not None:
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cache_kwargs = {"cache_position": cache_position}
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key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) * self.softmax_scale
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query_length = seq_length if past_key_values is None else seq_length + past_key_values.get_seq_length()
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if position_bias is not None:
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if len(position_bias.shape) != 3:
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raise ValueError(f"Expecting position_bias shape to be 3 dimensions, got {len(position_bias.shape)}")
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key_length = key_states.shape[-2]
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position_bias_query_index = max(0, position_bias.size(1) - query_length)
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position_bias_key_index = max(0, position_bias.size(2) - key_length)
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position_bias = position_bias[:, position_bias_query_index:, position_bias_key_index:]
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attention_scores = attention_scores + position_bias
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if attention_mask is not None:
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attention_scores = attention_scores.masked_fill(attention_mask, torch.finfo(query_states.dtype).min)
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# (batch_size, n_heads, seq_length, key_length)
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attn_weights = nn.functional.softmax(attention_scores.float(), dim=-1).to(value_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attn_dropout_p, training=self.training)
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context_states = torch.matmul(attn_weights, value_states)
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context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
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attn_output = self.out_proj(context_states)
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return attn_output, attn_weights
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class MptMLP(nn.Module):
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def __init__(self, config: MptConfig):
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super().__init__()
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hidden_size = config.hidden_size
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self.up_proj = nn.Linear(hidden_size, 4 * hidden_size, bias=False)
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self.act = nn.GELU(approximate="none")
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self.down_proj = nn.Linear(4 * hidden_size, hidden_size, bias=False)
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self.hidden_dropout = config.attn_config.attn_pdrop
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def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
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hidden_states = self.act(self.up_proj(hidden_states))
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intermediate_output = self.down_proj(hidden_states)
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output = F.dropout(intermediate_output, p=self.hidden_dropout, training=self.training)
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output = output + residual
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return output
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class MptBlock(GradientCheckpointingLayer):
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def __init__(self, config: MptConfig, layer_idx: int | None = None):
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super().__init__()
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hidden_size = config.hidden_size
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self.norm_1 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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# backward compatibility with weights on the Hub
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self.norm_1.bias = None
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self.num_heads = config.n_heads
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self.attn = MptAttention(config, layer_idx)
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self.norm_2 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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# backward compatibility with weights on the Hub
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self.norm_2.bias = None
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self.ffn = MptMLP(config)
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self.dropout_rate = config.attn_config.attn_pdrop
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self.resid_attn_dropout = nn.Dropout(self.dropout_rate)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_bias: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Cache | None = None,
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use_cache: bool = False,
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output_attentions: bool = False,
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cache_position: torch.Tensor | None = None,
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):
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# hidden_states: [batch_size, seq_length, hidden_size]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.norm_1(hidden_states)
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residual = hidden_states
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# Self attention.
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attn_outputs, attn_weights = self.attn(
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layernorm_output,
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position_bias=position_bias,
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attention_mask=attention_mask,
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past_key_values=layer_past,
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cache_position=cache_position,
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)
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hidden_states = self.resid_attn_dropout(attn_outputs) + residual
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layernorm_output = self.norm_2(hidden_states)
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# Get residual
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residual = hidden_states
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# MLP.
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output = self.ffn(layernorm_output, residual)
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return output, attn_weights
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@auto_docstring
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class MptPreTrainedModel(PreTrainedModel):
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config: MptConfig
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base_model_prefix = "transformer"
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supports_gradient_checkpointing = True
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_no_split_modules = ["MptBlock"]
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@auto_docstring
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class MptModel(MptPreTrainedModel):
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def __init__(self, config: MptConfig):
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super().__init__(config)
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self.hidden_size = config.hidden_size
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self.num_heads = config.n_heads
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# Embedding + LN Embedding
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self.wte = nn.Embedding(config.vocab_size, self.hidden_size)
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# Transformer blocks
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self.blocks = nn.ModuleList([MptBlock(config, layer_idx=i) for i in range(config.n_layers)])
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# Final Layer Norm
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self.norm_f = LayerNorm(self.hidden_size, eps=config.layer_norm_epsilon)
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# backward compatibility with weights on the Hub
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self.norm_f.bias = None
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.wte
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def build_mpt_alibi_tensor(self, num_heads, sequence_length, alibi_bias_max=8, device=None):
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return build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max, device)
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def set_input_embeddings(self, new_embeddings: torch.Tensor):
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self.wte = new_embeddings
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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attention_mask: torch.Tensor | None = None,
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inputs_embeds: torch.LongTensor | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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return_dict: bool | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs, # NOOP kwargs, for now
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) -> tuple[torch.Tensor, ...] | BaseModelOutputWithPastAndCrossAttentions:
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r"""
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input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
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`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
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(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
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If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
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`input_ids`.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.wte(input_ids)
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache(config=self.config)
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hidden_states = inputs_embeds
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all_self_attentions = () if output_attentions else None
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all_hidden_states = () if output_hidden_states else None
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# Compute alibi tensor: check build_alibi_tensor documentation
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past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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seq_length_with_past = seq_length + past_key_values_length
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
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else:
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attention_mask = attention_mask.to(hidden_states.device)
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alibi = self.build_mpt_alibi_tensor(self.num_heads, self.config.max_seq_len, device=hidden_states.device)
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causal_mask = _prepare_4d_causal_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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causal_mask = causal_mask.bool()
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for block in self.blocks:
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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outputs = block(
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hidden_states,
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layer_past=past_key_values,
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attention_mask=causal_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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position_bias=alibi,
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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (outputs[1],)
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# Add last hidden state
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hidden_states = self.norm_f(hidden_states)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(
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v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attentions] if v is not None
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)
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return BaseModelOutputWithPastAndCrossAttentions(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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@auto_docstring(
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custom_intro="""
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The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
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embeddings).
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"""
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)
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class MptForCausalLM(MptPreTrainedModel, GenerationMixin):
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_tied_weights_keys = {"lm_head.weight": "transformer.wte.weight"}
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def __init__(self, config: MptConfig):
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super().__init__(config)
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self.transformer = MptModel(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def set_output_embeddings(self, new_embeddings: torch.Tensor):
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self.lm_head = new_embeddings
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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attention_mask: torch.Tensor | None = None,
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inputs_embeds: torch.Tensor | None = None,
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labels: torch.Tensor | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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return_dict: bool | None = None,
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cache_position: torch.Tensor | None = None,
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logits_to_keep: int | torch.Tensor = 0,
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**kwargs,
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) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions:
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r"""
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input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
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`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
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(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
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If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
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`input_ids`.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
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`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
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are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.transformer(
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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hidden_states = transformer_outputs[0]
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# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
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slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
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logits = self.lm_head(hidden_states[:, slice_indices, :])
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loss = None
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if labels is not None:
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loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The MPT Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`MptForSequenceClassification`] 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).
|
|
"""
|
|
)
|
|
class MptForSequenceClassification(MptPreTrainedModel):
|
|
def __init__(self, config: MptConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = MptModel(config)
|
|
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.score = new_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor] | SequenceClassifierOutputWithPast:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
|
|
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
|
|
|
|
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
|
|
`input_ids`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
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.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_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 = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
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:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -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), last_non_pad_token]
|
|
|
|
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, labels)
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class MptForTokenClassification(MptPreTrainedModel):
|
|
def __init__(self, config: MptConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = MptModel(config)
|
|
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
|
classifier_dropout = config.classifier_dropout
|
|
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
|
classifier_dropout = config.hidden_dropout
|
|
else:
|
|
classifier_dropout = 0.1
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
**deprecated_arguments,
|
|
) -> tuple[torch.Tensor] | TokenClassifierOutput:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
|
|
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
|
|
|
|
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
|
|
`input_ids`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
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.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_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]
|
|
hidden_states = self.dropout(hidden_states)
|
|
logits = self.classifier(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device
|
|
labels = labels.to(logits.device)
|
|
batch_size, seq_length = labels.shape
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(
|
|
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class MptForQuestionAnswering(MptPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = MptModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
start_positions: torch.LongTensor | None = None,
|
|
end_positions: torch.LongTensor | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
**kwargs,
|
|
) -> tuple | QuestionAnsweringModelOutput:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
|
|
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
|
|
|
|
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
|
|
`input_ids`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_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)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = 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,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"MptForCausalLM",
|
|
"MptModel",
|
|
"MptPreTrainedModel",
|
|
"MptForSequenceClassification",
|
|
"MptForTokenClassification",
|
|
"MptForQuestionAnswering",
|
|
]
|