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1130 lines
48 KiB
1130 lines
48 KiB
# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
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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 BLOOM model."""
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import math
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from typing import Union
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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, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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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 (
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auto_docstring,
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is_torch_flex_attn_available,
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logging,
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)
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from ...utils.generic import is_flash_attention_requested
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from .configuration_bloom import BloomConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from ...integrations.flex_attention import make_flex_block_causal_mask
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logger = logging.get_logger(__name__)
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def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
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"""
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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: with l being a tensor, and a fixed value
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`softmax(l+a) = softmax(l)`. Based on
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https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742
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TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly.
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Args:
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Returns tensor shaped (batch_size * num_heads, 1, max_seq_len)
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attention_mask (`torch.Tensor`):
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Token-wise attention mask, this should be of shape (batch_size, max_seq_len).
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num_heads (`int`):
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number of heads
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dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`):
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dtype of the output tensor
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"""
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batch_size, seq_length = attention_mask.shape
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closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
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base = torch.tensor(
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
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)
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powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
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)
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num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
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extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
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# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
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# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
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# => the query_length dimension will then be broadcasted correctly
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# This is more or less identical to T5's relative position bias:
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# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
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arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
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alibi = slopes[..., None] * arange_tensor
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return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
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def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
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"""
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Dropout add function
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Args:
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x (`torch.tensor`):
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input tensor
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residual (`torch.tensor`):
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residual tensor
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prob (`float`):
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dropout probability
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training (`bool`):
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training mode
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"""
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out = F.dropout(x, p=prob, training=training)
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out = residual + out
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return out
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def bloom_gelu_forward(x: torch.Tensor) -> torch.Tensor:
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"""
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Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to
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make the model jitable.
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Args:
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x (`torch.tensor`):
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input hidden states
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"""
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return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
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def bloom_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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"""
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gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) +
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0.3989423 * x * torch.exp(-0.5 * x * x)
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Args:
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g (`torch.tensor`):
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gradient output tensor
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x (`torch.tensor`):
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input tensor
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"""
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x = x[0] # x is a tuple of 1 element, needs to unpack it first
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tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
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# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
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ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
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return ff * g
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class GeLUFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input: torch.Tensor) -> torch.Tensor:
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ctx.save_for_backward(input)
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return bloom_gelu_forward(input)
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@staticmethod
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def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
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input = ctx.saved_tensors
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tmp = bloom_gelu_back(grad_output, input)
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return tmp
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class BloomGelu(nn.Module):
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"""
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Partly copied from Megatron-DeepSpeed code and adapted for our needs
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"""
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def __init__(self):
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super().__init__()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return GeLUFunction.apply(x)
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class BloomAttention(nn.Module):
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def __init__(self, config: BloomConfig, layer_idx: int | None = None):
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super().__init__()
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self.pretraining_tp = config.pretraining_tp
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self.slow_but_exact = config.slow_but_exact
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self.hidden_size = config.hidden_size
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self.num_heads = config.n_head
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self.head_dim = self.hidden_size // self.num_heads
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self.split_size = self.hidden_size
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self.hidden_dropout = config.hidden_dropout
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if self.head_dim * self.num_heads != self.hidden_size:
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raise ValueError(
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f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
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f" {self.num_heads})."
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)
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# Layer-wise attention scaling
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self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
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self.beta = 1.0
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True)
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self.dense = nn.Linear(self.hidden_size, self.hidden_size)
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self.attention_dropout = nn.Dropout(config.attention_dropout)
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def _reshape(self, fused_qkv: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Split the last dimension into (num_heads, head_dim) and reshapes to (bs, heads, len, dim) shape
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without making any copies, results share same memory storage as `fused_qkv`
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Args:
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fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim]
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Returns:
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query: [batch_size, num_heads, seq_length, head_dim]
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key: [batch_size, num_heads, seq_length, head_dim]
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value: [batch_size, num_heads, seq_length, head_dim]
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"""
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batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
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fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
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query_layer = fused_qkv[..., 0, :].transpose(1, 2)
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key_layer = fused_qkv[..., 1, :].transpose(1, 2)
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value_layer = fused_qkv[..., 2, :].transpose(1, 2)
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return query_layer, key_layer, value_layer
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def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Merge heads together over the last dimension
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Args:
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x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim]
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Returns:
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torch.tensor: [batch_size, seq_length, num_heads * head_dim]
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"""
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# What we want to achieve is:
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# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
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batch_size_and_num_heads, seq_length, _ = x.shape
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batch_size = batch_size_and_num_heads // self.num_heads
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# First view to decompose the batch size
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# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
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x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
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# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
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x = x.permute(0, 2, 1, 3)
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# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
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return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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residual: torch.Tensor,
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alibi: 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.LongTensor | None = None,
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):
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batch_size, q_length, _ = hidden_states.shape
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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# 3 x [batch_size, num_heads, seq_length, head_dim]
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query_layer, key_layer, value_layer = self._reshape(fused_qkv)
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if layer_past is not None:
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cache_kwargs = {"cache_position": cache_position}
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key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
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# reshape qkv for further computations
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query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
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key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
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value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
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# [batch_size * num_heads, q_length, kv_length]
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attention_scores = alibi.baddbmm(
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batch1=query_layer,
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batch2=key_layer,
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beta=self.beta,
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alpha=self.inv_norm_factor,
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)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
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attn_weights = attn_weights + causal_mask
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# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
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attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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# change view [batch_size x num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
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# matmul: [batch_size * num_heads, q_length, head_dim]
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context_layer = torch.bmm(attention_probs_reshaped, value_layer)
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# change view [batch_size, q_length, num_heads * head_dim]
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context_layer = self._merge_heads(context_layer)
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# aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
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if self.pretraining_tp > 1 and self.slow_but_exact:
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slices = self.hidden_size / self.pretraining_tp
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output_tensor = torch.zeros_like(context_layer)
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for i in range(self.pretraining_tp):
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output_tensor = output_tensor + F.linear(
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context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
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self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
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)
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else:
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output_tensor = self.dense(context_layer)
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output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
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return output_tensor, attention_probs
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class BloomMLP(nn.Module):
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def __init__(self, config: BloomConfig):
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super().__init__()
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hidden_size = config.hidden_size
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self.pretraining_tp = config.pretraining_tp
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self.slow_but_exact = config.slow_but_exact
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self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size)
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self.gelu_impl = BloomGelu()
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self.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size)
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self.hidden_dropout = config.hidden_dropout
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def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
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hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states))
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if self.pretraining_tp > 1 and self.slow_but_exact:
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intermediate_output = torch.zeros_like(residual)
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slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp
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for i in range(self.pretraining_tp):
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intermediate_output = intermediate_output + F.linear(
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hidden_states[:, :, int(i * slices) : int((i + 1) * slices)],
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self.dense_4h_to_h.weight[:, int(i * slices) : int((i + 1) * slices)],
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)
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else:
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intermediate_output = self.dense_4h_to_h(hidden_states)
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output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
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return output
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class BloomBlock(GradientCheckpointingLayer):
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def __init__(self, config: BloomConfig, 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.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.num_heads = config.n_head
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self.self_attention = BloomAttention(config, layer_idx)
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self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = BloomMLP(config)
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self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
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self.hidden_dropout = config.hidden_dropout
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def forward(
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self,
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hidden_states: torch.Tensor,
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alibi: 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.LongTensor | 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.input_layernorm(hidden_states)
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# Layer norm post the self attention.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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# Self attention.
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attention_output, attn_weights = self.self_attention(
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layernorm_output,
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residual,
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layer_past=layer_past,
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attention_mask=attention_mask,
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alibi=alibi,
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use_cache=use_cache,
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output_attentions=output_attentions,
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cache_position=cache_position,
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)
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layernorm_output = self.post_attention_layernorm(attention_output)
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# Get residual
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = attention_output
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# MLP.
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output = self.mlp(layernorm_output, residual)
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return output, attn_weights # hidden_states, attentions
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@auto_docstring
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class BloomPreTrainedModel(PreTrainedModel):
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config: BloomConfig
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base_model_prefix = "transformer"
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supports_gradient_checkpointing = True
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_no_split_modules = ["BloomBlock"]
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_skip_keys_device_placement = "past_key_values"
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_can_compile_fullgraph = True
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@auto_docstring
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class BloomModel(BloomPreTrainedModel):
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def __init__(self, config: BloomConfig):
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super().__init__(config)
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self.embed_dim = config.hidden_size
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self.num_heads = config.n_head
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# Embedding + LN Embedding
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self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
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|
self.word_embeddings_layernorm = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
|
|
# Transformer blocks
|
|
self.h = nn.ModuleList([BloomBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
|
|
|
|
# Final Layer Norm
|
|
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def build_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
|
|
return build_alibi_tensor(attention_mask, num_heads, dtype)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.word_embeddings
|
|
|
|
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.word_embeddings = 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.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor, ...] | BaseModelOutputWithPastAndCrossAttentions:
|
|
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)
|
|
"""
|
|
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 None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training and use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache(config=self.config)
|
|
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
seq_length_with_past = seq_length + past_length
|
|
if cache_position is None:
|
|
cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
|
|
|
|
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
|
|
|
|
all_self_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
# Compute alibi tensor: check build_alibi_tensor documentation
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
|
else:
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
|
|
alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
for i, block in enumerate(self.h):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=past_key_values,
|
|
attention_mask=causal_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
alibi=alibi,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[1],)
|
|
|
|
# Add last hidden state
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attentions] if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if is_flash_attention_requested(self.config):
|
|
if attention_mask is not None and (attention_mask == 0.0).any():
|
|
return attention_mask
|
|
return None
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_compilable_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
|
|
`(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache,
|
|
to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
"""
|
|
)
|
|
class BloomForCausalLM(BloomPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {"lm_head.weight": "transformer.word_embeddings.weight"}
|
|
|
|
def __init__(self, config: BloomConfig):
|
|
super().__init__(config)
|
|
self.transformer = BloomModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.lm_head = new_embeddings
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
use_cache=True,
|
|
is_first_iteration=False,
|
|
**kwargs,
|
|
):
|
|
# Overwritten because of the fixed-shape attention mask creation
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
cache_position=cache_position,
|
|
use_cache=use_cache,
|
|
is_first_iteration=is_first_iteration,
|
|
**kwargs,
|
|
)
|
|
|
|
# This part differs from other models because BLOOM needs a 2D mask to construct alibi tensor
|
|
# The only difference is the usage of 2D instead of 4D mask, but the shape will be static
|
|
if isinstance(past_key_values, StaticCache) and attention_mask is not None:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
batch_size, seq_length = attention_mask.shape
|
|
diff = target_length - seq_length
|
|
|
|
new_attn_mask = torch.zeros(batch_size, diff, device=attention_mask.device, dtype=attention_mask.dtype)
|
|
attention_mask = torch.cat([attention_mask, new_attn_mask], dim=-1)
|
|
model_inputs["attention_mask"] = attention_mask
|
|
|
|
return model_inputs
|
|
|
|
@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,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions:
|
|
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, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
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,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = transformer_outputs[0]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
num_items_in_batch=kwargs.get("num_items_in_batch"),
|
|
)
|
|
|
|
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 Bloom Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`BloomForSequenceClassification`] 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 BloomForSequenceClassification(BloomPreTrainedModel):
|
|
def __init__(self, config: BloomConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = BloomModel(config)
|
|
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
|
|
|
# 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,
|
|
**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 BloomForTokenClassification(BloomPreTrainedModel):
|
|
def __init__(self, config: BloomConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = BloomModel(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,
|
|
**kwargs,
|
|
) -> 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 BloomForQuestionAnswering(BloomPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = BloomModel(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__ = [
|
|
"BloomForCausalLM",
|
|
"BloomModel",
|
|
"BloomPreTrainedModel",
|
|
"BloomForSequenceClassification",
|
|
"BloomForTokenClassification",
|
|
"BloomForQuestionAnswering",
|
|
]
|