1641 lines
74 KiB
Python
1641 lines
74 KiB
Python
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# coding=utf-8
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# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
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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 Falcon model."""
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import math
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import warnings
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from typing import TYPE_CHECKING, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
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from torch.nn import functional as F
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from ...activations import get_activation
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from ...modeling_attn_mask_utils import (
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AttentionMaskConverter,
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_prepare_4d_causal_attention_mask,
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_prepare_4d_causal_attention_mask_for_sdpa,
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)
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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 ...pytorch_utils import is_torch_greater_or_equal_than_2_0
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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)
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from .configuration_falcon import FalconConfig
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if TYPE_CHECKING:
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from ...configuration_utils import PretrainedConfig
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import FALCON_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
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_CONFIG_FOR_DOC = "FalconConfig"
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# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
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# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
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class FalconLinear(nn.Linear):
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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hidden_states = input @ self.weight.T
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if self.bias is None:
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return hidden_states
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return hidden_states + self.bias
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Falcon
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class FalconRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
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)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon
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# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
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class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
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"""FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
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t = t / self.scaling_factor
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon
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# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
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class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding):
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"""FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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if seq_len > self.max_position_embeddings:
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base = self.base * (
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
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) ** (self.dim / (self.dim - 2))
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inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
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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].bfloat16() * arange_tensor
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return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
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# Copied from transformers.models.bloom.modeling_bloom.dropout_add
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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`, *required*):
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input tensor
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residual (`torch.tensor`, *required*):
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residual tensor
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prob (`float`, *required*):
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dropout probability
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training (`bool`, *required*):
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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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class FalconAttention(nn.Module):
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def __init__(self, config: FalconConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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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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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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self._use_sdpa = config._attn_implementation == "sdpa"
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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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if config.rotary:
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self._init_rope()
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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 = self.inv_norm_factor
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if config.new_decoder_architecture:
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qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
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elif config.multi_query:
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qkv_out_dim = self.hidden_size + 2 * self.head_dim
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else:
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qkv_out_dim = 3 * self.hidden_size
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self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
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self.new_decoder_architecture = config.new_decoder_architecture
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self.multi_query = config.multi_query
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self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
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self.attention_dropout = nn.Dropout(config.attention_dropout)
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self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
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# Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Falcon
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def _init_rope(self):
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if self.config.rope_scaling is None:
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self.rotary_emb = FalconRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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else:
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scaling_type = self.config.rope_scaling["type"]
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_type == "linear":
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self.rotary_emb = FalconLinearScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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elif scaling_type == "dynamic":
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self.rotary_emb = FalconDynamicNTKScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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else:
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raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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def _split_heads(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), results share same memory storage as `fused_qkv`
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Args:
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fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
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Returns:
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query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
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value: [batch_size, seq_length, num_heads, head_dim]
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"""
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if self.new_decoder_architecture:
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batch, seq_len, _ = fused_qkv.shape
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qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
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query = qkv[:, :, :, :-2]
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||
|
key = qkv[:, :, :, [-2]]
|
||
|
value = qkv[:, :, :, [-1]]
|
||
|
key = torch.broadcast_to(key, query.shape)
|
||
|
value = torch.broadcast_to(value, query.shape)
|
||
|
|
||
|
query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
|
||
|
return query, key, value
|
||
|
elif not self.multi_query:
|
||
|
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
||
|
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
|
||
|
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
|
||
|
else:
|
||
|
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
||
|
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
|
||
|
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
|
||
|
|
||
|
# Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
|
||
|
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
"""
|
||
|
Merge heads together over the last dimension
|
||
|
|
||
|
Args:
|
||
|
x (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
|
||
|
|
||
|
Returns:
|
||
|
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
|
||
|
"""
|
||
|
# What we want to achieve is:
|
||
|
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
|
||
|
batch_size_and_num_heads, seq_length, _ = x.shape
|
||
|
batch_size = batch_size_and_num_heads // self.num_heads
|
||
|
|
||
|
# First view to decompose the batch size
|
||
|
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
|
||
|
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
|
||
|
|
||
|
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
|
||
|
x = x.permute(0, 2, 1, 3)
|
||
|
|
||
|
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
|
||
|
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
alibi: Optional[torch.Tensor],
|
||
|
attention_mask: torch.Tensor,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
use_cache: bool = False,
|
||
|
output_attentions: bool = False,
|
||
|
**kwargs,
|
||
|
):
|
||
|
if "padding_mask" in kwargs:
|
||
|
warnings.warn(
|
||
|
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
|
)
|
||
|
|
||
|
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
||
|
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
||
|
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
||
|
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
||
|
|
||
|
batch_size, query_length, _, _ = query_layer.shape
|
||
|
|
||
|
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
|
||
|
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
|
||
|
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
|
||
|
|
||
|
kv_seq_len = key_layer.shape[-2]
|
||
|
if layer_past is not None:
|
||
|
kv_seq_len += layer_past[0].shape[-2]
|
||
|
if alibi is None:
|
||
|
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
|
||
|
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
|
||
|
|
||
|
if layer_past is not None:
|
||
|
past_key, past_value = layer_past
|
||
|
# concatenate along seq_length dimension:
|
||
|
# - key: [batch_size, self.num_heads, kv_length, head_dim]
|
||
|
# - value: [batch_size, self.num_heads, kv_length, head_dim]
|
||
|
key_layer = torch.cat((past_key, key_layer), dim=-2)
|
||
|
value_layer = torch.cat((past_value, value_layer), dim=-2)
|
||
|
|
||
|
kv_length = key_layer.shape[-2]
|
||
|
if use_cache:
|
||
|
present = (key_layer, value_layer)
|
||
|
else:
|
||
|
present = None
|
||
|
|
||
|
if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None:
|
||
|
# For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask,
|
||
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||
|
query_layer = query_layer.contiguous()
|
||
|
key_layer = key_layer.contiguous()
|
||
|
value_layer = value_layer.contiguous()
|
||
|
|
||
|
if alibi is None:
|
||
|
if self._use_sdpa and not output_attentions:
|
||
|
attn_output = F.scaled_dot_product_attention(
|
||
|
query_layer,
|
||
|
key_layer,
|
||
|
value_layer,
|
||
|
attention_mask,
|
||
|
0.0,
|
||
|
# The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case query_length == 1.
|
||
|
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
||
|
)
|
||
|
|
||
|
attention_scores = None
|
||
|
else:
|
||
|
attention_scores = query_layer @ key_layer.transpose(-1, -2)
|
||
|
attention_scores /= math.sqrt(self.head_dim)
|
||
|
|
||
|
attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
||
|
# It is unclear why neither dropout nor head_mask is applied here (while it is with alibi).
|
||
|
attn_output = attention_scores @ value_layer
|
||
|
|
||
|
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
|
||
|
attn_output = attn_output.permute(0, 2, 1, 3)
|
||
|
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
|
||
|
|
||
|
attn_output = self.dense(attn_output)
|
||
|
|
||
|
if output_attentions:
|
||
|
return attn_output, present, attention_scores
|
||
|
else:
|
||
|
return attn_output, present
|
||
|
|
||
|
else:
|
||
|
if self._use_sdpa and not output_attentions and head_mask is None:
|
||
|
attn_output = F.scaled_dot_product_attention(
|
||
|
query_layer,
|
||
|
key_layer,
|
||
|
value_layer,
|
||
|
attn_mask=attention_mask,
|
||
|
dropout_p=self.attention_dropout.p if self.training else 0.0,
|
||
|
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
||
|
)
|
||
|
attn_output = attn_output.transpose(1, 2)
|
||
|
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
|
||
|
|
||
|
attn_output = self.dense(attn_output)
|
||
|
else:
|
||
|
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
||
|
|
||
|
# change view to [batch_size, num_heads, q_length, kv_length]
|
||
|
attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
|
||
|
|
||
|
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
|
||
|
input_dtype = attention_scores.dtype
|
||
|
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
||
|
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
||
|
attention_scores = attention_scores.to(torch.float32)
|
||
|
|
||
|
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
|
||
|
attention_logits *= self.inv_norm_factor
|
||
|
attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
||
|
# [batch_size, num_heads, q_length, kv_length]
|
||
|
attention_probs = self.attention_dropout(attention_probs)
|
||
|
|
||
|
if head_mask is not None:
|
||
|
attention_probs = attention_probs * head_mask
|
||
|
|
||
|
# change view [batch_size, num_heads, q_length, kv_length]
|
||
|
attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
|
||
|
|
||
|
# matmul: [batch_size * num_heads, q_length, head_dim]
|
||
|
attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
|
||
|
|
||
|
# change view [batch_size, q_length, num_heads * head_dim]
|
||
|
attn_output = self._merge_heads(attn_output)
|
||
|
|
||
|
attn_output = self.dense(attn_output)
|
||
|
|
||
|
if output_attentions:
|
||
|
return attn_output, present, attention_probs
|
||
|
else:
|
||
|
return attn_output, present
|
||
|
|
||
|
|
||
|
class FalconFlashAttention2(FalconAttention):
|
||
|
"""
|
||
|
Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays
|
||
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
||
|
flash attention and deal with padding tokens in case the input contains any of them.
|
||
|
"""
|
||
|
|
||
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
||
|
def __init__(self, *args, **kwargs):
|
||
|
super().__init__(*args, **kwargs)
|
||
|
|
||
|
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
||
|
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
||
|
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
||
|
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
alibi: Optional[torch.Tensor],
|
||
|
attention_mask: torch.Tensor,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
use_cache: bool = False,
|
||
|
output_attentions: bool = False,
|
||
|
**kwargs,
|
||
|
):
|
||
|
if "padding_mask" in kwargs:
|
||
|
warnings.warn(
|
||
|
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
|
)
|
||
|
|
||
|
# overwrite attention_mask with padding_mask
|
||
|
attention_mask = kwargs.pop("padding_mask")
|
||
|
|
||
|
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
||
|
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
||
|
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
||
|
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
||
|
|
||
|
batch_size, query_length, _, _ = query_layer.shape
|
||
|
|
||
|
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
|
||
|
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
|
||
|
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
|
||
|
|
||
|
kv_seq_len = key_layer.shape[-2]
|
||
|
if layer_past is not None:
|
||
|
kv_seq_len += layer_past[0].shape[-2]
|
||
|
if alibi is None:
|
||
|
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
|
||
|
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
|
||
|
|
||
|
if layer_past is not None and use_cache:
|
||
|
past_key, past_value = layer_past
|
||
|
# concatenate along seq_length dimension:
|
||
|
# - key: [batch_size, self.num_heads, kv_length, head_dim]
|
||
|
# - value: [batch_size, self.num_heads, kv_length, head_dim]
|
||
|
key_layer = torch.cat((past_key, key_layer), dim=-2)
|
||
|
value_layer = torch.cat((past_value, value_layer), dim=-2)
|
||
|
|
||
|
past_key_value = (key_layer, value_layer) if use_cache else None
|
||
|
|
||
|
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||
|
# to be able to avoid many of these transpose/reshape/view.
|
||
|
query_layer = query_layer.transpose(1, 2)
|
||
|
key_layer = key_layer.transpose(1, 2)
|
||
|
value_layer = value_layer.transpose(1, 2)
|
||
|
|
||
|
if alibi is not None:
|
||
|
raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
|
||
|
|
||
|
attn_dropout = self.config.attention_dropout if self.training else 0.0
|
||
|
|
||
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||
|
# cast them back in float16 just to be sure everything works as expected.
|
||
|
input_dtype = query_layer.dtype
|
||
|
if input_dtype == torch.float32:
|
||
|
if torch.is_autocast_enabled():
|
||
|
target_dtype = torch.get_autocast_gpu_dtype()
|
||
|
# Handle the case where the model is quantized
|
||
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
||
|
target_dtype = self.config._pre_quantization_dtype
|
||
|
else:
|
||
|
target_dtype = self.query_key_value.weight.dtype
|
||
|
|
||
|
logger.warning_once(
|
||
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
||
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||
|
f" {target_dtype}."
|
||
|
)
|
||
|
|
||
|
query_layer = query_layer.to(target_dtype)
|
||
|
key_layer = key_layer.to(target_dtype)
|
||
|
value_layer = value_layer.to(target_dtype)
|
||
|
|
||
|
attn_output = self._flash_attention_forward(
|
||
|
query_layer, key_layer, value_layer, attention_mask, query_length, dropout=attn_dropout
|
||
|
)
|
||
|
|
||
|
attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
|
||
|
attn_output = self.dense(attn_weights)
|
||
|
|
||
|
if not output_attentions:
|
||
|
attn_weights = None
|
||
|
|
||
|
return attn_output, past_key_value, attn_weights
|
||
|
|
||
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
||
|
def _flash_attention_forward(
|
||
|
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
||
|
):
|
||
|
"""
|
||
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
|
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
|
||
|
Args:
|
||
|
query_states (`torch.Tensor`):
|
||
|
Input query states to be passed to Flash Attention API
|
||
|
key_states (`torch.Tensor`):
|
||
|
Input key states to be passed to Flash Attention API
|
||
|
value_states (`torch.Tensor`):
|
||
|
Input value states to be passed to Flash Attention API
|
||
|
attention_mask (`torch.Tensor`):
|
||
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
|
position of padding tokens and 1 for the position of non-padding tokens.
|
||
|
dropout (`float`):
|
||
|
Attention dropout
|
||
|
softmax_scale (`float`, *optional*):
|
||
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
|
"""
|
||
|
if not self._flash_attn_uses_top_left_mask:
|
||
|
causal = self.is_causal
|
||
|
else:
|
||
|
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
||
|
causal = self.is_causal and query_length != 1
|
||
|
|
||
|
# Contains at least one padding token in the sequence
|
||
|
if attention_mask is not None:
|
||
|
batch_size = query_states.shape[0]
|
||
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
|
query_states, key_states, value_states, attention_mask, query_length
|
||
|
)
|
||
|
|
||
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
|
|
||
|
attn_output_unpad = flash_attn_varlen_func(
|
||
|
query_states,
|
||
|
key_states,
|
||
|
value_states,
|
||
|
cu_seqlens_q=cu_seqlens_q,
|
||
|
cu_seqlens_k=cu_seqlens_k,
|
||
|
max_seqlen_q=max_seqlen_in_batch_q,
|
||
|
max_seqlen_k=max_seqlen_in_batch_k,
|
||
|
dropout_p=dropout,
|
||
|
softmax_scale=softmax_scale,
|
||
|
causal=causal,
|
||
|
)
|
||
|
|
||
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
|
else:
|
||
|
attn_output = flash_attn_func(
|
||
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||
|
)
|
||
|
|
||
|
return attn_output
|
||
|
|
||
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
||
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||
|
|
||
|
key_layer = index_first_axis(
|
||
|
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
)
|
||
|
value_layer = index_first_axis(
|
||
|
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
)
|
||
|
if query_length == kv_seq_len:
|
||
|
query_layer = index_first_axis(
|
||
|
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||
|
)
|
||
|
cu_seqlens_q = cu_seqlens_k
|
||
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||
|
indices_q = indices_k
|
||
|
elif query_length == 1:
|
||
|
max_seqlen_in_batch_q = 1
|
||
|
cu_seqlens_q = torch.arange(
|
||
|
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||
|
) # There is a memcpy here, that is very bad.
|
||
|
indices_q = cu_seqlens_q[:-1]
|
||
|
query_layer = query_layer.squeeze(1)
|
||
|
else:
|
||
|
# The -q_len: slice assumes left padding.
|
||
|
attention_mask = attention_mask[:, -query_length:]
|
||
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||
|
|
||
|
return (
|
||
|
query_layer,
|
||
|
key_layer,
|
||
|
value_layer,
|
||
|
indices_q,
|
||
|
(cu_seqlens_q, cu_seqlens_k),
|
||
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||
|
)
|
||
|
|
||
|
|
||
|
class FalconMLP(nn.Module):
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__()
|
||
|
hidden_size = config.hidden_size
|
||
|
|
||
|
self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias)
|
||
|
self.act = get_activation(config.activation)
|
||
|
self.dense_4h_to_h = FalconLinear(config.ffn_hidden_size, hidden_size, bias=config.bias)
|
||
|
self.hidden_dropout = config.hidden_dropout
|
||
|
|
||
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
x = self.act(self.dense_h_to_4h(x))
|
||
|
x = self.dense_4h_to_h(x)
|
||
|
return x
|
||
|
|
||
|
|
||
|
FALCON_ATTENTION_CLASSES = {
|
||
|
"eager": FalconAttention,
|
||
|
"sdpa": FalconAttention, # FalconAttention originally implemented both a forward with & without SDPA
|
||
|
"flash_attention_2": FalconFlashAttention2,
|
||
|
}
|
||
|
|
||
|
|
||
|
class FalconDecoderLayer(nn.Module):
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__()
|
||
|
hidden_size = config.hidden_size
|
||
|
self.num_heads = config.num_attention_heads
|
||
|
|
||
|
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config)
|
||
|
self.mlp = FalconMLP(config)
|
||
|
self.hidden_dropout = config.hidden_dropout
|
||
|
self.config = config
|
||
|
|
||
|
if config.new_decoder_architecture:
|
||
|
# The layer norm before self-attention
|
||
|
self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
||
|
# The layer norm before the MLP
|
||
|
self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
||
|
else:
|
||
|
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
||
|
if not config.parallel_attn:
|
||
|
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
alibi: Optional[torch.Tensor],
|
||
|
attention_mask: torch.Tensor,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
use_cache: bool = False,
|
||
|
output_attentions: bool = False,
|
||
|
**kwargs,
|
||
|
):
|
||
|
if "padding_mask" in kwargs:
|
||
|
warnings.warn(
|
||
|
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
|
)
|
||
|
|
||
|
residual = hidden_states
|
||
|
|
||
|
if self.config.new_decoder_architecture:
|
||
|
attention_layernorm_out = self.ln_attn(hidden_states)
|
||
|
mlp_layernorm_out = self.ln_mlp(hidden_states)
|
||
|
else:
|
||
|
attention_layernorm_out = self.input_layernorm(hidden_states)
|
||
|
|
||
|
# Self attention.
|
||
|
attn_outputs = self.self_attention(
|
||
|
attention_layernorm_out,
|
||
|
layer_past=layer_past,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
alibi=alibi,
|
||
|
head_mask=head_mask,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
**kwargs,
|
||
|
)
|
||
|
|
||
|
attention_output = attn_outputs[0]
|
||
|
|
||
|
if not self.config.new_decoder_architecture:
|
||
|
if self.config.parallel_attn:
|
||
|
mlp_layernorm_out = attention_layernorm_out
|
||
|
else:
|
||
|
residual = dropout_add(
|
||
|
attention_output, residual, self.config.attention_dropout, training=self.training
|
||
|
)
|
||
|
mlp_layernorm_out = self.post_attention_layernorm(residual)
|
||
|
|
||
|
outputs = attn_outputs[1:]
|
||
|
|
||
|
# MLP.
|
||
|
mlp_output = self.mlp(mlp_layernorm_out)
|
||
|
|
||
|
if self.config.new_decoder_architecture or self.config.parallel_attn:
|
||
|
mlp_output += attention_output
|
||
|
|
||
|
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
|
||
|
|
||
|
if use_cache:
|
||
|
outputs = (output,) + outputs
|
||
|
else:
|
||
|
outputs = (output,) + outputs[1:]
|
||
|
|
||
|
return outputs # hidden_states, present, attentions
|
||
|
|
||
|
|
||
|
FALCON_START_DOCSTRING = r"""
|
||
|
|
||
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||
|
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
|
||
|
|
||
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||
|
and behavior.
|
||
|
|
||
|
Parameters:
|
||
|
config ([`FalconConfig`]): Model configuration class with all the parameters of the model.
|
||
|
Initializing with a config file does not load the weights associated with the model, only the
|
||
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||
|
"""
|
||
|
|
||
|
FALCON_INPUTS_DOCSTRING = r"""
|
||
|
Args:
|
||
|
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[0][0].shape[2]`
|
||
|
(`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)
|
||
|
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`):
|
||
|
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
|
||
|
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
|
||
|
their past given to this model should not be passed as `input_ids` as they have already been computed.
|
||
|
|
||
|
Each element of `past_key_values` is a tuple (past_key, past_value):
|
||
|
- past_key: [batch_size * num_heads, head_dim, kv_length]
|
||
|
- past_value: [batch_size * num_heads, kv_length, head_dim]
|
||
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||
|
|
||
|
- 1 for tokens that are **not masked**,
|
||
|
- 0 for tokens that are **masked**.
|
||
|
|
||
|
[What are attention masks?](../glossary#attention-mask)
|
||
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
||
|
config.n_positions - 1]`.
|
||
|
|
||
|
[What are position IDs?](../glossary#position-ids)
|
||
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
||
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
||
|
|
||
|
- 1 indicates the head is **not masked**,
|
||
|
- 0 indicates the head is **masked**.
|
||
|
|
||
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
||
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
||
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
||
|
model's internal embedding lookup matrix.
|
||
|
|
||
|
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
|
||
|
`past_key_values`).
|
||
|
use_cache (`bool`, *optional*):
|
||
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
||
|
`past_key_values`).
|
||
|
output_attentions (`bool`, *optional*):
|
||
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||
|
tensors for more detail.
|
||
|
output_hidden_states (`bool`, *optional*):
|
||
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||
|
more detail.
|
||
|
return_dict (`bool`, *optional*):
|
||
|
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
|
||
|
"""
|
||
|
|
||
|
|
||
|
class FalconPreTrainedModel(PreTrainedModel):
|
||
|
"""
|
||
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
|
models.
|
||
|
"""
|
||
|
|
||
|
config_class = FalconConfig
|
||
|
base_model_prefix = "transformer"
|
||
|
supports_gradient_checkpointing = True
|
||
|
_no_split_modules = ["FalconDecoderLayer"]
|
||
|
_supports_flash_attn_2 = True
|
||
|
_supports_sdpa = True
|
||
|
|
||
|
def __init__(self, *inputs, **kwargs):
|
||
|
super().__init__(*inputs, **kwargs)
|
||
|
|
||
|
def _init_weights(self, module: nn.Module):
|
||
|
"""Initialize the weights."""
|
||
|
if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
|
||
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
||
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
||
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||
|
if module.bias is not None:
|
||
|
module.bias.data.zero_()
|
||
|
elif isinstance(module, nn.Embedding):
|
||
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||
|
if module.padding_idx is not None:
|
||
|
module.weight.data[module.padding_idx].zero_()
|
||
|
elif isinstance(module, LayerNorm):
|
||
|
module.bias.data.zero_()
|
||
|
module.weight.data.fill_(1.0)
|
||
|
|
||
|
# Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
|
||
|
@classmethod
|
||
|
def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig":
|
||
|
# NOTE: Falcon supported SDPA from PyTorch 2.0. We keep it like that for backward compatibility (automatically use SDPA for torch>=2.0).
|
||
|
if hard_check_only:
|
||
|
if not is_torch_greater_or_equal_than_2_0:
|
||
|
raise ImportError("PyTorch SDPA requirements in Transformers are not met. Please install torch>=2.0.")
|
||
|
|
||
|
if not is_torch_greater_or_equal_than_2_0:
|
||
|
return config
|
||
|
|
||
|
_is_bettertransformer = getattr(cls, "use_bettertransformer", False)
|
||
|
if _is_bettertransformer:
|
||
|
return config
|
||
|
|
||
|
if not hard_check_only:
|
||
|
config._attn_implementation = "sdpa"
|
||
|
return config
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
|
||
|
FALCON_START_DOCSTRING,
|
||
|
)
|
||
|
class FalconModel(FalconPreTrainedModel):
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__(config)
|
||
|
|
||
|
self.embed_dim = config.hidden_size
|
||
|
self.num_heads = config.num_attention_heads
|
||
|
self.use_alibi = config.alibi
|
||
|
|
||
|
# Embedding + LN Embedding
|
||
|
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
||
|
|
||
|
# Transformer blocks
|
||
|
self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||
|
self._use_sdpa = config._attn_implementation == "sdpa"
|
||
|
|
||
|
# 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 get_input_embeddings(self):
|
||
|
return self.word_embeddings
|
||
|
|
||
|
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
||
|
self.word_embeddings = new_embeddings
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
|
||
|
@add_code_sample_docstrings(
|
||
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
||
|
output_type=BaseModelOutputWithPastAndCrossAttentions,
|
||
|
config_class=_CONFIG_FOR_DOC,
|
||
|
)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
head_mask: Optional[torch.LongTensor] = None,
|
||
|
inputs_embeds: Optional[torch.LongTensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
||
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
output_hidden_states = (
|
||
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
)
|
||
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
if input_ids is not None and inputs_embeds is not None:
|
||
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||
|
elif input_ids is not None:
|
||
|
batch_size, seq_length = input_ids.shape
|
||
|
elif inputs_embeds is not None:
|
||
|
batch_size, seq_length, _ = inputs_embeds.shape
|
||
|
else:
|
||
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
|
||
|
if past_key_values is None:
|
||
|
past_key_values = tuple([None] * len(self.h))
|
||
|
|
||
|
if inputs_embeds is None:
|
||
|
inputs_embeds = self.word_embeddings(input_ids)
|
||
|
|
||
|
hidden_states = inputs_embeds
|
||
|
|
||
|
if self.gradient_checkpointing and self.training:
|
||
|
if use_cache:
|
||
|
logger.warning(
|
||
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
|
)
|
||
|
use_cache = False
|
||
|
presents = () if use_cache else None
|
||
|
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
|
||
|
past_key_values_length = 0
|
||
|
if past_key_values[0] is not None:
|
||
|
past_key_values_length = past_key_values[0][0].shape[-2]
|
||
|
|
||
|
if self.use_alibi:
|
||
|
mask = (
|
||
|
torch.ones(
|
||
|
(batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
|
||
|
)
|
||
|
if attention_mask is None
|
||
|
else attention_mask
|
||
|
)
|
||
|
alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
|
||
|
else:
|
||
|
alibi = None
|
||
|
if position_ids is None:
|
||
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
|
position_ids = torch.arange(
|
||
|
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||
|
)
|
||
|
position_ids = position_ids.unsqueeze(0)
|
||
|
|
||
|
if self._use_flash_attention_2:
|
||
|
# 2d mask is passed through the layers
|
||
|
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
||
|
elif self._use_sdpa and not output_attentions:
|
||
|
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
||
|
# the manual implementation that requires a 4D causal mask in all cases.
|
||
|
if alibi is None:
|
||
|
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
||
|
attention_mask,
|
||
|
(batch_size, seq_length),
|
||
|
inputs_embeds,
|
||
|
past_key_values_length,
|
||
|
)
|
||
|
elif head_mask is None:
|
||
|
alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
|
||
|
|
||
|
# We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched.
|
||
|
attention_mask = _prepare_4d_causal_attention_mask(
|
||
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
|
)
|
||
|
|
||
|
# We take care to integrate alibi bias in the attention_mask here.
|
||
|
min_dtype = torch.finfo(alibi.dtype).min
|
||
|
attention_mask = torch.masked_fill(
|
||
|
alibi / math.sqrt(self.config.hidden_size // self.num_heads),
|
||
|
attention_mask < -1,
|
||
|
min_dtype,
|
||
|
)
|
||
|
|
||
|
# From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
|
||
|
# produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
|
||
|
if seq_length > 1 and attention_mask.device.type == "cuda":
|
||
|
attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype)
|
||
|
else:
|
||
|
# PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case.
|
||
|
attention_mask = _prepare_4d_causal_attention_mask(
|
||
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
|
)
|
||
|
else:
|
||
|
# 4d mask is passed through the layers
|
||
|
attention_mask = _prepare_4d_causal_attention_mask(
|
||
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
|
)
|
||
|
|
||
|
# Prepare head mask if needed
|
||
|
# 1.0 in head_mask indicate we keep the head
|
||
|
# attention_probs has shape batch_size x num_heads x N x N
|
||
|
# head_mask has shape n_layer x batch x num_heads x N x N
|
||
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
||
|
|
||
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
||
|
if output_hidden_states:
|
||
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
||
|
|
||
|
if self.gradient_checkpointing and self.training:
|
||
|
outputs = self._gradient_checkpointing_func(
|
||
|
block.__call__,
|
||
|
hidden_states,
|
||
|
alibi,
|
||
|
attention_mask,
|
||
|
position_ids,
|
||
|
head_mask[i],
|
||
|
layer_past,
|
||
|
use_cache,
|
||
|
output_attentions,
|
||
|
)
|
||
|
else:
|
||
|
outputs = block(
|
||
|
hidden_states,
|
||
|
layer_past=layer_past,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
head_mask=head_mask[i],
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
alibi=alibi,
|
||
|
)
|
||
|
|
||
|
hidden_states = outputs[0]
|
||
|
if use_cache is True:
|
||
|
presents = presents + (outputs[1],)
|
||
|
|
||
|
if output_attentions:
|
||
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
||
|
|
||
|
# 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, presents, all_hidden_states, all_self_attentions] if v is not None)
|
||
|
|
||
|
return BaseModelOutputWithPastAndCrossAttentions(
|
||
|
last_hidden_state=hidden_states,
|
||
|
past_key_values=presents,
|
||
|
hidden_states=all_hidden_states,
|
||
|
attentions=all_self_attentions,
|
||
|
)
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
|
||
|
FALCON_START_DOCSTRING,
|
||
|
)
|
||
|
class FalconForCausalLM(FalconPreTrainedModel):
|
||
|
_tied_weights_keys = ["lm_head.weight"]
|
||
|
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__(config)
|
||
|
self.transformer = FalconModel(config)
|
||
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
|
||
|
# Initialize weights and apply final processing
|
||
|
self.post_init()
|
||
|
|
||
|
def get_output_embeddings(self):
|
||
|
return self.lm_head
|
||
|
|
||
|
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
||
|
self.lm_head = new_embeddings
|
||
|
|
||
|
def prepare_inputs_for_generation(
|
||
|
self,
|
||
|
input_ids: torch.LongTensor,
|
||
|
past_key_values: Optional[torch.Tensor] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
position_ids: Optional[torch.Tensor] = None,
|
||
|
**kwargs,
|
||
|
) -> dict:
|
||
|
if past_key_values is not None:
|
||
|
past_length = past_key_values[0][0].shape[2]
|
||
|
|
||
|
# Some generation methods already pass only the last input ID
|
||
|
if input_ids.shape[1] > past_length:
|
||
|
remove_prefix_length = past_length
|
||
|
else:
|
||
|
# Default to old behavior: keep only final ID
|
||
|
remove_prefix_length = input_ids.shape[1] - 1
|
||
|
|
||
|
input_ids = input_ids[:, remove_prefix_length:]
|
||
|
|
||
|
# Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE.
|
||
|
if not self.transformer.use_alibi and attention_mask is not None and position_ids is None:
|
||
|
# create position_ids on the fly for batch generation
|
||
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
||
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
||
|
if past_key_values:
|
||
|
position_ids = position_ids[:, -input_ids.shape[1] :]
|
||
|
|
||
|
return {
|
||
|
"input_ids": input_ids,
|
||
|
"position_ids": position_ids,
|
||
|
"past_key_values": past_key_values,
|
||
|
"use_cache": kwargs.get("use_cache"),
|
||
|
"attention_mask": attention_mask,
|
||
|
}
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
|
||
|
@add_code_sample_docstrings(
|
||
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
||
|
output_type=CausalLMOutputWithCrossAttentions,
|
||
|
config_class=_CONFIG_FOR_DOC,
|
||
|
)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
inputs_embeds: Optional[torch.Tensor] = None,
|
||
|
labels: Optional[torch.Tensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
||
|
r"""
|
||
|
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,
|
||
|
position_ids=position_ids,
|
||
|
head_mask=head_mask,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
return_dict=return_dict,
|
||
|
)
|
||
|
hidden_states = transformer_outputs[0]
|
||
|
|
||
|
lm_logits = self.lm_head(hidden_states)
|
||
|
|
||
|
loss = None
|
||
|
if labels is not None:
|
||
|
# Shift so that tokens < n predict n
|
||
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
||
|
shift_labels = labels[..., 1:].contiguous()
|
||
|
batch_size, seq_length, vocab_size = shift_logits.shape
|
||
|
# Flatten the tokens
|
||
|
loss_fct = CrossEntropyLoss()
|
||
|
loss = loss_fct(
|
||
|
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
||
|
)
|
||
|
|
||
|
if not return_dict:
|
||
|
output = (lm_logits,) + transformer_outputs[1:]
|
||
|
return ((loss,) + output) if loss is not None else output
|
||
|
|
||
|
return CausalLMOutputWithCrossAttentions(
|
||
|
loss=loss,
|
||
|
logits=lm_logits,
|
||
|
past_key_values=transformer_outputs.past_key_values,
|
||
|
hidden_states=transformer_outputs.hidden_states,
|
||
|
attentions=transformer_outputs.attentions,
|
||
|
)
|
||
|
|
||
|
def _reorder_cache(
|
||
|
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
||
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
||
|
"""
|
||
|
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
||
|
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
||
|
beam_idx at every generation step.
|
||
|
|
||
|
Output shares the same memory storage as `past`.
|
||
|
"""
|
||
|
|
||
|
# Get a copy of `beam_idx` on all the devices where we need those indices.
|
||
|
device_to_beam_idx = {
|
||
|
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
|
||
|
}
|
||
|
reordered_past = tuple(
|
||
|
(
|
||
|
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
||
|
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
||
|
)
|
||
|
for layer_past in past
|
||
|
)
|
||
|
return reordered_past
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"""
|
||
|
The Falcon Model transformer with a sequence classification head on top (linear layer).
|
||
|
|
||
|
[`FalconForSequenceClassification`] 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).
|
||
|
""",
|
||
|
FALCON_START_DOCSTRING,
|
||
|
)
|
||
|
class FalconForSequenceClassification(FalconPreTrainedModel):
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__(config)
|
||
|
self.num_labels = config.num_labels
|
||
|
self.transformer = FalconModel(config)
|
||
|
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
||
|
|
||
|
# Initialize weights and apply final processing
|
||
|
self.post_init()
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
|
||
|
@add_code_sample_docstrings(
|
||
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
||
|
output_type=SequenceClassifierOutputWithPast,
|
||
|
config_class=_CONFIG_FOR_DOC,
|
||
|
)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
inputs_embeds: Optional[torch.Tensor] = None,
|
||
|
labels: Optional[torch.Tensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
||
|
r"""
|
||
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||
|
"""
|
||
|
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
transformer_outputs = self.transformer(
|
||
|
input_ids,
|
||
|
past_key_values=past_key_values,
|
||
|
attention_mask=attention_mask,
|
||
|
head_mask=head_mask,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
return_dict=return_dict,
|
||
|
)
|
||
|
|
||
|
hidden_states = transformer_outputs[0]
|
||
|
logits = self.score(hidden_states)
|
||
|
|
||
|
if input_ids is not None:
|
||
|
batch_size = 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:
|
||
|
sequence_lengths = -1
|
||
|
else:
|
||
|
if input_ids is not None:
|
||
|
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
||
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
||
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
||
|
sequence_lengths = sequence_lengths.to(logits.device)
|
||
|
else:
|
||
|
sequence_lengths = -1
|
||
|
logger.warning(
|
||
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
||
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
||
|
)
|
||
|
|
||
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||
|
|
||
|
loss = None
|
||
|
if labels is not None:
|
||
|
if self.config.problem_type is None:
|
||
|
if self.num_labels == 1:
|
||
|
self.config.problem_type = "regression"
|
||
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||
|
self.config.problem_type = "single_label_classification"
|
||
|
else:
|
||
|
self.config.problem_type = "multi_label_classification"
|
||
|
|
||
|
if self.config.problem_type == "regression":
|
||
|
loss_fct = MSELoss()
|
||
|
if self.num_labels == 1:
|
||
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||
|
else:
|
||
|
loss = loss_fct(pooled_logits, labels)
|
||
|
elif self.config.problem_type == "single_label_classification":
|
||
|
loss_fct = CrossEntropyLoss()
|
||
|
loss = loss_fct(pooled_logits, 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,
|
||
|
)
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"""
|
||
|
Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
||
|
Named-Entity-Recognition (NER) tasks.
|
||
|
""",
|
||
|
FALCON_START_DOCSTRING,
|
||
|
)
|
||
|
class FalconForTokenClassification(FalconPreTrainedModel):
|
||
|
def __init__(self, config: FalconConfig):
|
||
|
super().__init__(config)
|
||
|
self.num_labels = config.num_labels
|
||
|
|
||
|
self.transformer = FalconModel(config)
|
||
|
if getattr(config, "classifier_dropout", None) is not None:
|
||
|
classifier_dropout = config.classifier_dropout
|
||
|
elif getattr(config, "hidden_dropout", None) 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()
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
|
||
|
@add_code_sample_docstrings(
|
||
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
||
|
output_type=TokenClassifierOutput,
|
||
|
config_class=_CONFIG_FOR_DOC,
|
||
|
)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
head_mask: Optional[torch.Tensor] = None,
|
||
|
inputs_embeds: Optional[torch.Tensor] = None,
|
||
|
labels: Optional[torch.Tensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
||
|
r"""
|
||
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||
|
"""
|
||
|
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
transformer_outputs = self.transformer(
|
||
|
input_ids,
|
||
|
past_key_values=past_key_values,
|
||
|
attention_mask=attention_mask,
|
||
|
head_mask=head_mask,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
return_dict=return_dict,
|
||
|
)
|
||
|
|
||
|
hidden_states = transformer_outputs[0]
|
||
|
hidden_states = self.dropout(hidden_states)
|
||
|
logits = self.classifier(hidden_states)
|
||
|
|
||
|
loss = None
|
||
|
if labels is not None:
|
||
|
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,
|
||
|
)
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"""
|
||
|
The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like
|
||
|
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
||
|
""",
|
||
|
FALCON_START_DOCSTRING,
|
||
|
)
|
||
|
class FalconForQuestionAnswering(FalconPreTrainedModel):
|
||
|
def __init__(self, config):
|
||
|
super().__init__(config)
|
||
|
self.transformer = FalconModel(config)
|
||
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
||
|
|
||
|
# Initialize weights and apply final processing
|
||
|
self.post_init()
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
attention_mask: Optional[torch.FloatTensor] = None,
|
||
|
head_mask: Optional[torch.FloatTensor] = None,
|
||
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
start_positions: Optional[torch.LongTensor] = None,
|
||
|
end_positions: Optional[torch.LongTensor] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
||
|
r"""
|
||
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
||
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
||
|
are not taken into account for computing the loss.
|
||
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
||
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
||
|
are not taken into account for computing the loss.
|
||
|
"""
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
outputs = self.transformer(
|
||
|
input_ids,
|
||
|
attention_mask=attention_mask,
|
||
|
head_mask=head_mask,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
return_dict=return_dict,
|
||
|
)
|
||
|
|
||
|
sequence_output = outputs[0]
|
||
|
|
||
|
logits = self.qa_outputs(sequence_output)
|
||
|
start_logits, end_logits = logits.split(1, dim=-1)
|
||
|
start_logits = start_logits.squeeze(-1).contiguous()
|
||
|
end_logits = end_logits.squeeze(-1).contiguous()
|
||
|
|
||
|
total_loss = None
|
||
|
if start_positions is not None and end_positions is not None:
|
||
|
# If we are on multi-GPU, split add a dimension
|
||
|
if len(start_positions.size()) > 1:
|
||
|
start_positions = start_positions.squeeze(-1)
|
||
|
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,
|
||
|
)
|