1398 lines
63 KiB
Python
1398 lines
63 KiB
Python
# 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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from typing import Optional, 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 ...cache_utils import Cache, DynamicCache, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import (
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AttentionMaskConverter,
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)
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from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
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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_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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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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logging,
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)
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from .configuration_falcon import FalconConfig
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if is_flash_attn_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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logger = logging.get_logger(__name__)
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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.llama.modeling_llama.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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`, *optional*):
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Deprecated and unused.
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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.unsqueeze(unsqueeze_dim)
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sin = sin.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.LlamaRotaryEmbedding with Llama->Falcon
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class FalconRotaryEmbedding(nn.Module):
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def __init__(self, config: FalconConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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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`):
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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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class FalconAttention(nn.Module):
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def __init__(self, config: FalconConfig, layer_idx=None):
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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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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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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 = 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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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`): [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]]
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value = qkv[:, :, :, [-1]]
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key = torch.broadcast_to(key, query.shape)
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value = torch.broadcast_to(value, query.shape)
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query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
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return query, key, value
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elif not self.multi_query:
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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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return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
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else:
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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 + 2, self.head_dim)
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return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
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# Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
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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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alibi: Optional[torch.Tensor],
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attention_mask: torch.Tensor,
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position_ids: Optional[torch.LongTensor] = None,
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layer_past: Optional[Cache] = None,
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head_mask: Optional[torch.Tensor] = None,
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use_cache: bool = False,
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output_attentions: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
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):
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, query_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
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key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
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value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
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if alibi is None:
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cos, sin = position_embeddings
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query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin)
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if layer_past is not None:
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cache_kwargs = {"cache_position": cache_position}
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if alibi is None:
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cache_kwargs.update({"sin": sin, "cos": cos})
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key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
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kv_length = key_layer.shape[-2]
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if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None:
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# For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask,
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# Reference: https://github.com/pytorch/pytorch/issues/112577.
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query_layer = query_layer.contiguous()
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key_layer = key_layer.contiguous()
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value_layer = value_layer.contiguous()
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key_layer.shape[-2]]
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if alibi is None:
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if self._use_sdpa and not output_attentions:
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# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
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# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
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# The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not
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# create a causal mask in case query_length == 1.
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is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attn_mask=attention_mask,
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dropout_p=0.0,
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is_causal=is_causal,
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)
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attention_scores = None
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else:
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attention_scores = query_layer @ key_layer.transpose(-1, -2)
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attention_scores /= math.sqrt(self.head_dim)
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attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
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# It is unclear why neither dropout nor head_mask is applied here (while it is with alibi).
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attn_output = attention_scores @ value_layer
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attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
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attn_output = attn_output.permute(0, 2, 1, 3)
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attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
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attn_output = self.dense(attn_output)
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return attn_output, attention_scores
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else:
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if self._use_sdpa and not output_attentions and head_mask is None:
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# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
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# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
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is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attn_mask=attention_mask,
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dropout_p=self.attention_dropout.p if self.training else 0.0,
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is_causal=is_causal,
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)
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attention_probs = None
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
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attn_output = self.dense(attn_output)
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else:
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
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# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `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)
|
|
|
|
return attn_output, attention_probs
|
|
|
|
|
|
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.
|
|
"""
|
|
|
|
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 alignment, 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 = flash_attn_supports_top_left_mask()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
alibi: Optional[torch.Tensor],
|
|
attention_mask: torch.Tensor,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
layer_past: Optional[Cache] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
use_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
|
):
|
|
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)
|
|
|
|
if alibi is None:
|
|
cos, sin = position_embeddings
|
|
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin)
|
|
|
|
if layer_past is not None:
|
|
cache_kwargs = {"cache_position": cache_position}
|
|
if alibi is None:
|
|
cache_kwargs.update({"sin": sin, "cos": cos})
|
|
key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
|
|
|
|
# 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
|
|
device_type = query_layer.device.type if query_layer.device.type != "mps" else "cpu"
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = (
|
|
torch.get_autocast_dtype(device_type)
|
|
if hasattr(torch, "get_autocast_dtype")
|
|
else 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 = _flash_attention_forward(
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
attention_mask,
|
|
query_length,
|
|
position_ids=position_ids,
|
|
dropout=attn_dropout,
|
|
is_causal=self.is_causal,
|
|
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
|
)
|
|
|
|
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, attn_weights
|
|
|
|
|
|
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(GradientCheckpointingLayer):
|
|
def __init__(self, config: FalconConfig, layer_idx=None):
|
|
super().__init__()
|
|
hidden_size = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
|
|
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
self.mlp = FalconMLP(config)
|
|
self.hidden_dropout = config.hidden_dropout
|
|
self.config = config
|
|
|
|
if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture:
|
|
config.num_ln_in_parallel_attn = 2
|
|
|
|
if not config.parallel_attn:
|
|
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
|
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
|
else:
|
|
if config.num_ln_in_parallel_attn == 2:
|
|
# 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)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
alibi: Optional[torch.Tensor],
|
|
attention_mask: torch.Tensor,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
layer_past: Optional[Union[Cache, tuple[torch.Tensor, torch.Tensor]]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
use_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
|
**kwargs,
|
|
):
|
|
residual = hidden_states
|
|
|
|
if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2:
|
|
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.
|
|
attention_output, attn_weights = 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,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
|
|
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)
|
|
|
|
if (
|
|
self.config.new_decoder_architecture
|
|
and self.config.parallel_attn
|
|
and self.config.num_ln_in_parallel_attn == 1
|
|
):
|
|
mlp_layernorm_out = attention_layernorm_out
|
|
|
|
# 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)
|
|
|
|
return output, attn_weights
|
|
|
|
|
|
@auto_docstring
|
|
class FalconPreTrainedModel(PreTrainedModel):
|
|
config: FalconConfig
|
|
base_model_prefix = "transformer"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["FalconDecoderLayer"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
|
|
_can_compile_fullgraph = 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):
|
|
_is_bettertransformer = getattr(cls, "use_bettertransformer", False)
|
|
if _is_bettertransformer:
|
|
return config
|
|
|
|
if not hard_check_only:
|
|
config._attn_implementation = "sdpa"
|
|
return config
|
|
|
|
|
|
@auto_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, layer_idx=i) for i 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.rotary_emb = FalconRotaryEmbedding(config=config)
|
|
|
|
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
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, 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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[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:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
|
|
if not isinstance(past_key_values, (type(None), Cache)):
|
|
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
# Compute alibi tensor: check build_alibi_tensor documentation
|
|
alibi = None
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
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=inputs_embeds.dtype)
|
|
|
|
if cache_position is None:
|
|
cache_position = torch.arange(
|
|
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions, head_mask, alibi
|
|
)
|
|
|
|
# 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)
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
all_self_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
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,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask[i],
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
alibi=alibi,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: torch.Tensor,
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool,
|
|
head_mask: torch.Tensor,
|
|
alibi: torch.Tensor,
|
|
):
|
|
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
|
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
|
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
|
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
|
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_static_cache = isinstance(past_key_values, StaticCache)
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and not using_static_cache
|
|
and not output_attentions
|
|
and head_mask is None
|
|
and alibi is None
|
|
):
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype, device = input_tensor.dtype, input_tensor.device
|
|
min_dtype = torch.finfo(dtype).min
|
|
batch_size, sequence_length, _ = input_tensor.shape
|
|
if using_static_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
device=device,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
# We take care to integrate alibi bias in the causal_mask here
|
|
if head_mask is None and alibi is not None:
|
|
alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
|
|
causal_mask = torch.masked_fill(
|
|
alibi / math.sqrt(self.config.hidden_size // self.num_heads),
|
|
causal_mask < -1,
|
|
min_dtype,
|
|
)
|
|
|
|
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
|
|
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 Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
|
|
"""
|
|
)
|
|
class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin):
|
|
_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 set_output_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.lm_head = new_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, 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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs,
|
|
) -> Union[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,
|
|
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,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
lm_logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
lm_logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
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).
|
|
"""
|
|
)
|
|
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()
|
|
|
|
@auto_docstring
|
|
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"""
|
|
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,
|
|
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:
|
|
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 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()
|
|
|
|
@auto_docstring
|
|
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"""
|
|
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,
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_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()
|
|
|
|
@auto_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"""
|
|
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,
|
|
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,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"FalconForCausalLM",
|
|
"FalconModel",
|
|
"FalconPreTrainedModel",
|
|
"FalconForSequenceClassification",
|
|
"FalconForTokenClassification",
|
|
"FalconForQuestionAnswering",
|
|
]
|