"""PyTorch Dbrx model."""
import math
import warnings
from copy import deepcopy
from functools import partial
from typing import Any, Callable, Dict, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import (MoeCausalLMOutputWithPast,
MoeModelOutputWithPast)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import is_flash_attn_2_available, logging
from .configuration_dbrx import DbrxAttentionConfig, DbrxConfig, DbrxFFNConfig
if is_flash_attn_2_available():
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import pad_input # noqa
from flash_attn.bert_padding import index_first_axis, unpad_input
except:
pass
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = 'DbrxConfig'
#############################################################################
# Copied from LLaMaRotaryEmbedding
#############################################################################
class DbrxRotaryEmbedding(nn.Module):
def __init__(self,
dim: int,
max_position_embeddings: int = 2048,
base: float = 10000.0,
scaling_factor: float = 1.0):
super().__init__()
self.scaling_factor = scaling_factor
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base**(
torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
self.register_buffer('inv_freq', inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
@torch.no_grad()
def forward(
self, x: torch.Tensor, position_ids: torch.LongTensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# x: [bs, num_attention_heads, seq_len, head_size]
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(
position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 since bfloat16 loses precision on long contexts
# See https://github.com/huggingface/transformers/pull/29285
device_type = x.device.type
device_type = device_type if isinstance(
device_type, str) and device_type != 'mps' else 'cpu'
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float()
@ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x: torch.Tensor) -> torch.Tensor:
"""Rotates half the hidden dims of the input."""
x1 = x[..., :x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
unsqueeze_dim: int = 1) -> Tuple[torch.Tensor, torch.Tensor]:
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos and
sin so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos and sin have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos and sin broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""Equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
(batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :,
None, :, :].expand(batch, num_key_value_heads,
n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
head_dim)
#############################################################################
#############################################################################
# Modified from modeling_mixtral
#############################################################################
def load_balancing_loss_func(
gate_logits: torch.Tensor,
num_experts: int,
top_k: int,
attention_mask: Optional[torch.Tensor],
) -> torch.Tensor:
r"""Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts (`int`):
Number of experts.
top_k (`int`):
The number of experts each token is routed to.
attention_mask (`torch.Tensor`, None):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return torch.tensor(0.0)
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat(
[layer_gate.to(compute_device) for layer_gate in gate_logits],
dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits,
dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (
batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (attention_mask[None, :, :, None, None].expand(
(num_hidden_layers, batch_size, sequence_length, top_k,
num_experts)).reshape(-1, top_k, num_experts).to(compute_device))
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(
expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None].expand(
(num_hidden_layers, batch_size, sequence_length,
num_experts)).reshape(-1, num_experts).to(compute_device))
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(
routing_weights * router_per_expert_attention_mask,
dim=0) / torch.sum(router_per_expert_attention_mask, dim=0)
overall_loss = torch.sum(tokens_per_expert *
router_prob_per_expert.unsqueeze(0))
return overall_loss * num_experts
#############################################################################
def resolve_ffn_act_fn(
ffn_act_fn: dict) -> Callable[[torch.Tensor], torch.Tensor]:
"""Resolve the activation function for the feed-forward network.
Args:
ffn_act_fn (dict): The configuration dictionary for the activation function.
The dict config must specify the 'name' of a torch.nn.functional activation
function. All of other key values pairs are bound to the function as a partial.
Returns:
Callable[[torch.Tensor], torch.Tensor]: The activation function.
"""
config = deepcopy(ffn_act_fn)
name = config.pop('name')
if not hasattr(nn.functional, name):
raise ValueError(f'Unrecognised activation function name ({name}).')
act = getattr(nn.functional, name)
return partial(act, **config)
#############################################################################
# Copied from LLaMaAttention
#############################################################################
def _get_unpad_data(attention_mask: torch.Tensor):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32),
(1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
class DbrxAttention(nn.Module):
"""Multi-head self attention."""
def __init__(self,
hidden_size: int,
num_heads: int,
max_position_embeddings: int,
attn_config: DbrxAttentionConfig,
block_idx: Optional[int] = None):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = max_position_embeddings
self.block_idx = block_idx
self.config = attn_config
if block_idx is None:
logger.warning_once(
f'Instantiating {self.__class__.__name__} without passing a `block_idx` is not recommended and will '
+
'lead to errors during the forward call if caching is used. Please make sure to provide a `block_idx` '
+ 'when creating this class.')
self.attn_pdrop = attn_config.attn_pdrop
self.clip_qkv = attn_config.clip_qkv
self.num_key_value_heads = attn_config.kv_n_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.rope_theta = attn_config.rope_theta
self.Wqkv = nn.Linear(self.hidden_size,
self.hidden_size +
2 * self.num_key_value_heads * self.head_dim,
bias=False)
self.out_proj = nn.Linear(self.hidden_size,
self.hidden_size,
bias=False)
self.rotary_emb = DbrxRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
bsz, q_len, _ = hidden_states.size()
qkv_states = self.Wqkv(hidden_states)
if self.clip_qkv is not None:
qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
query_states, key_states, value_states = qkv_states.split(
[
self.hidden_size,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=2,
)
query_states = query_states.view(bsz, q_len, self.num_heads,
self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
past_key_value = getattr(self, 'past_key_value', past_key_value)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states,
key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {
'sin': sin,
'cos': cos,
'cache_position': cache_position
}
key_states, value_states = past_key_value.update(
key_states, value_states, self.block_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(
2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, :key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights,
dim=-1,
dtype=torch.float32).to(
query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights,
p=self.attn_pdrop,
training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
+ f' {attn_output.size()}')
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DbrxFlashAttention2(DbrxAttention):
"""Dbrx flash attention module.
This module inherits from `DbrxAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it
calls the public API of flash attention.
"""
def __init__(self, *args: Any, **kwargs: Any):
if not is_flash_attn_2_available():
raise ImportError(
'Flash Attention 2 is not available. Please install it with `pip install flash-attn`.'
)
super().__init__(*args, **kwargs)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
logger.info(
'Implicitly setting `output_attentions` to False as it is not supported in Flash Attention.'
)
output_attentions = False
bsz, q_len, _ = hidden_states.size()
qkv_states = self.Wqkv(hidden_states)
if self.clip_qkv is not None:
qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
query_states, key_states, value_states = qkv_states.split(
[
self.hidden_size,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=2,
)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads,
self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states,
key_states, cos, sin)
past_key_value = getattr(self, 'past_key_value', past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {
'sin': sin,
'cos': cos,
'cache_position': cache_position
}
key_states, value_states = past_key_value.update(
key_states, value_states, self.block_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_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attn_pdrop 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 the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.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 = query_states.dtype
logger.warning_once(
f'The input hidden states seems to be silently casted in float32, this might be '
+
f'related to the fact you have upcasted embedding or layer norm layers in '
+ f'float32. We will cast back the input in {target_dtype}.')
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
)
attn_output = attn_output.reshape(bsz, q_len,
self.hidden_size).contiguous()
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value # type: ignore
def _flash_attention_forward(
self,
query_states: torch.Tensor,
key_states: torch.Tensor,
value_states: torch.Tensor,
attention_mask: Union[torch.LongTensor, None],
query_length: int,
dropout: float = 0.0,
softmax_scale: Optional[float] = None,
):
"""Use FlashAttention, stripping padding tokens if necessary.
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.LongTensor | None): 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.
query_length (int): The length of the query sequence
dropout (float): Attention dropout
softmax_scale (float, optional): The scaling of QK^T before applying softmax.
Defaults to 1 / sqrt(head_dim)
"""
causal = True
# 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
def _upad_input(self, query_layer: torch.Tensor, key_layer: torch.Tensor,
value_layer: torch.Tensor, attention_mask: torch.Tensor,
query_length: int):
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),
)
DBRX_ATTENTION_CLASSES = {
'eager': DbrxAttention,
'flash_attention_2': DbrxFlashAttention2,
}
class DbrxNormAttentionNorm(nn.Module):
def __init__(
self,
hidden_size: int,
num_heads: int,
max_position_embeddings: int,
resid_pdrop: float,
attn_implementation: str,
attn_config: DbrxAttentionConfig,
block_idx: Optional[int] = None,
):
super().__init__()
self.block_idx = block_idx
self.resid_pdrop = resid_pdrop
self.norm_1 = nn.LayerNorm(hidden_size, bias=False)
self.attn = DBRX_ATTENTION_CLASSES[attn_implementation](
hidden_size=hidden_size,
num_heads=num_heads,
max_position_embeddings=max_position_embeddings,
attn_config=attn_config,
block_idx=block_idx,
)
self.norm_2 = nn.LayerNorm(hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor],
Optional[Cache]]:
residual_states = hidden_states
hidden_states = self.norm_1(hidden_states).to(hidden_states.dtype)
hidden_states, attn_weights, past_key_value = self.attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = nn.functional.dropout(hidden_states,
p=self.resid_pdrop,
training=self.training)
hidden_states = hidden_states + residual_states
residual_states = hidden_states
hidden_states = self.norm_2(hidden_states).to(hidden_states.dtype)
return residual_states, hidden_states, attn_weights, past_key_value
class DbrxRouter(nn.Module):
def __init__(self, hidden_size: int, moe_num_experts: int, moe_top_k: int,
moe_jitter_eps: Optional[float],
moe_normalize_expert_weights: Optional[float],
uniform_expert_assignment: bool):
super().__init__()
self.hidden_size = hidden_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.moe_jitter_eps = moe_jitter_eps
self.moe_normalize_expert_weights = moe_normalize_expert_weights
self.uniform_expert_assignment = uniform_expert_assignment
self.layer = nn.Linear(self.hidden_size,
self.moe_num_experts,
bias=False)
def jitter(self, x: torch.Tensor) -> torch.Tensor:
if self.moe_jitter_eps is None:
raise RuntimeError('The router does not have moe_jitter_eps set.')
low = 1.0 - self.moe_jitter_eps
high = 1.0 + self.moe_jitter_eps
noise = torch.rand(x.size(), dtype=x.dtype, device=x.device)
return low + noise * (high - low)
def forward(
self, x: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.LongTensor]:
if self.training and self.moe_jitter_eps is not None:
x = x * self.jitter(x)
weights = self.layer(x.view(-1,
x.shape[-1])).softmax(dim=-1,
dtype=torch.float32)
top_weights, top_experts = torch.topk(weights, self.moe_top_k, dim=-1)
if self.moe_normalize_expert_weights:
top_weights = top_weights / torch.norm(
top_weights,
p=self.moe_normalize_expert_weights,
dim=-1,
keepdim=True)
if self.uniform_expert_assignment:
with torch.no_grad():
uniform_tensor = torch.arange(
0,
top_experts.numel(),
device=top_experts.device,
dtype=top_experts.dtype) % self.moe_num_experts
top_experts = uniform_tensor.reshape(top_experts.shape)
# Note, weights and top_weights are not changed
weights = weights.to(x.dtype)
top_weights = top_weights.to(x.dtype)
return weights, top_weights, top_experts # type: ignore
class DbrxExpertGLU(nn.Module):
def __init__(self, hidden_size: int, ffn_hidden_size: int,
moe_num_experts: int, ffn_act_fn: dict):
super().__init__()
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.moe_num_experts = moe_num_experts
self.w1 = nn.Parameter(
torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
self.v1 = nn.Parameter(
torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
self.w2 = nn.Parameter(
torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
self.activation_fn = resolve_ffn_act_fn(ffn_act_fn)
def forward(self, x: torch.Tensor, expert_idx: int) -> torch.Tensor:
expert_w1 = self.w1.view(self.moe_num_experts, self.ffn_hidden_size,
self.hidden_size)[expert_idx]
expert_v1 = self.v1.view(self.moe_num_experts, self.ffn_hidden_size,
self.hidden_size)[expert_idx]
expert_w2 = self.w2.view(self.moe_num_experts, self.ffn_hidden_size,
self.hidden_size)[expert_idx]
x1 = x.matmul(expert_w1.t())
x2 = x.matmul(expert_v1.t())
x1 = self.activation_fn(x1)
x1 = x1 * x2
x1 = x1.matmul(expert_w2)
return x1
class DbrxExperts(nn.Module):
def __init__(self, hidden_size: int, ffn_hidden_size: int,
moe_num_experts: int, ffn_act_fn: dict):
super().__init__()
self.moe_num_experts = moe_num_experts
self.mlp = DbrxExpertGLU(hidden_size=hidden_size,
ffn_hidden_size=ffn_hidden_size,
moe_num_experts=moe_num_experts,
ffn_act_fn=ffn_act_fn)
def forward(self, x: torch.Tensor, weights: torch.Tensor,
top_weights: torch.Tensor,
top_experts: torch.LongTensor) -> torch.Tensor:
bsz, q_len, hidden_size = x.shape
x = x.view(-1, hidden_size)
out = torch.zeros_like(x)
expert_mask = nn.functional.one_hot(
top_experts, num_classes=self.moe_num_experts).permute(2, 1, 0)
for expert_idx in range(0, self.moe_num_experts):
topk_idx, token_idx = torch.where(expert_mask[expert_idx])
if token_idx.shape[0] == 0:
continue
token_list = token_idx.tolist()
topk_list = topk_idx.tolist()
expert_tokens = x[None, token_list].reshape(-1, hidden_size)
expert_out = self.mlp(
expert_tokens, expert_idx) * top_weights[token_list, topk_list,
None]
out.index_add_(0, token_idx, expert_out)
out = out.reshape(bsz, q_len, hidden_size)
return out
class DbrxFFN(nn.Module):
def __init__(self, hidden_size: int, ffn_config: DbrxFFNConfig):
super().__init__()
self.router = DbrxRouter(
hidden_size,
moe_num_experts=ffn_config.moe_num_experts,
moe_top_k=ffn_config.moe_top_k,
moe_jitter_eps=ffn_config.moe_jitter_eps,
moe_normalize_expert_weights=ffn_config.
moe_normalize_expert_weights,
uniform_expert_assignment=ffn_config.uniform_expert_assignment,
)
self.experts = DbrxExperts(
hidden_size=hidden_size,
ffn_hidden_size=ffn_config.ffn_hidden_size,
moe_num_experts=ffn_config.moe_num_experts,
ffn_act_fn=ffn_config.ffn_act_fn,
)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
weights, top_weights, top_experts = self.router(x)
out = self.experts(x, weights, top_weights, top_experts)
return out, weights
class DbrxBlock(nn.Module):
def __init__(self, config: DbrxConfig, block_idx: int):
super().__init__()
self.hidden_size = config.d_model
self.resid_pdrop = config.resid_pdrop
self.block_idx = block_idx
self.norm_attn_norm = DbrxNormAttentionNorm(
hidden_size=config.d_model,
num_heads=config.n_heads,
max_position_embeddings=config.max_seq_len,
resid_pdrop=config.resid_pdrop,
attn_implementation=config._attn_implementation,
attn_config=config.attn_config,
block_idx=block_idx,
)
self.ffn = DbrxFFN(hidden_size=config.d_model,
ffn_config=config.ffn_config)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[Cache]], Tuple[
torch.Tensor, Optional[torch.Tensor], Optional[Cache]],
Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[torch.Tensor]], Tuple[
torch.Tensor, Optional[Cache], Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache],
Optional[torch.Tensor]],]:
"""Forward function for DbrxBlock.
Args:
hidden_states (`torch.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
position_ids (`torch.LongTensor`): position ids of shape `(batch, seq_len)`
attention_mask (`torch.Tensor`, optional): attention mask of size (batch_size, sequence_length)
if flash attention is used or (batch_size, 1, query_sequence_length, key_sequence_length)
if default attention is used.
past_key_value (`Tuple(torch.Tensor)`, optional): cached past key and value projection states
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_router_logits (`bool`, optional): Whether or not to return the router logits.
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`).
cache_position (`torch.LongTensor`, optional): position ids of the cache
"""
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.`'
)
# Norm + Attention + Norm
resid_states, hidden_states, self_attn_weights, present_key_value = self.norm_attn_norm(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
# Fully Connected
hidden_states, router_logits = self.ffn(hidden_states)
hidden_states = nn.functional.dropout(hidden_states,
p=self.resid_pdrop,
training=self.training)
hidden_states = resid_states + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
class DbrxPreTrainedModel(PreTrainedModel):
config_class = DbrxConfig
base_model_prefix = 'transformer'
supports_gradient_checkpointing = True
_no_split_modules = ['DbrxBlock']
_skip_keys_device_placement = ['past_key_values']
_supports_flash_attn_2 = True
_supports_sdpa = False
_supports_cache_class = True
def _init_weights(self, module: nn.Module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, DbrxExpertGLU):
module.w1.data.normal_(mean=0.0, std=std)
module.v1.data.normal_(mean=0.0, std=std)
module.w2.data.normal_(mean=0.0, std=std)
def _setup_cache(self, cache_cls: Any, max_batch_size: int,
max_cache_len: int): # TODO: how to set var type of class?
if self.config._attn_implementation == 'flash_attention_2' and cache_cls == StaticCache:
raise ValueError(
'`static` cache implementation is not compatible with ' +
'`attn_implementation==flash_attention_2`. Make sure to use ' +
'`spda` in the mean time and open an issue at https://github.com/huggingface/transformers.'
)
for block in self.transformer.blocks:
device = block.norm_attn_norm.norm_1.weight.device
if hasattr(self.config, '_pre_quantization_dtype'):
dtype = self.config._pre_quantization_dtype
else:
dtype = block.norm_attn_norm.attn.out_proj.weight.dtype
block.norm_attn_norm.attn.past_key_value = cache_cls(self.config,
max_batch_size,
max_cache_len,
device=device,
dtype=dtype)
def _reset_cache(self):
for block in self.transformer.blocks:
block.norm_attn_norm.attn.past_key_value = None
class DbrxModel(DbrxPreTrainedModel):
"""Transformer decoder consisting of *config.num_hidden_layers*
[`DbrxBlock`] layers.
Args:
config: DbrxConfig
"""
def __init__(self, config: DbrxConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.emb_pdrop = config.emb_pdrop
self.wte = nn.Embedding(config.vocab_size, config.d_model,
self.padding_idx)
self.blocks = nn.ModuleList([
DbrxBlock(config, block_idx) for block_idx in range(config.n_layers)
])
self.norm_f = nn.LayerNorm(config.d_model, bias=False)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.wte
def set_input_embeddings(self, value: nn.Embedding):
self.wte = value
def _autocast_input_embeddings(self,
inputs_embeds: torch.Tensor) -> torch.Tensor:
if inputs_embeds.device.type == 'cuda' and torch.is_autocast_enabled():
return inputs_embeds.to(dtype=torch.get_autocast_gpu_dtype())
elif inputs_embeds.device.type == 'cpu' and torch.is_autocast_cpu_enabled(
):
return inputs_embeds.to(dtype=torch.get_autocast_cpu_dtype())
else:
return inputs_embeds
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, MoeModelOutputWithPast]:
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)
output_router_logits = (output_router_logits
if output_router_logits is not None else
self.config.output_router_logits)
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 cannot specify both input_ids and inputs_embeds at the same time, and must specify either one'
)
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
'`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.'
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
inputs_embeds = self._autocast_input_embeddings(
inputs_embeds) # type: ignore
inputs_embeds = nn.functional.dropout(inputs_embeds,
p=self.emb_pdrop,
training=self.training)
past_seen_tokens = 0
if use_cache: # kept for BC (cache positions)
if not isinstance(past_key_values, StaticCache):
past_key_values = DynamicCache.from_legacy_cache(
past_key_values)
past_seen_tokens = past_key_values.get_seq_length( # type: ignore
)
if cache_position is None:
if isinstance(past_key_values, StaticCache):
raise ValueError(
'cache_position is a required argument when using StaticCache.'
)
cache_position = torch.arange( # type: ignore
past_seen_tokens,
past_seen_tokens + inputs_embeds.shape[1],
device=inputs_embeds.device)
if position_ids is None:
position_ids = cache_position.unsqueeze(0) # type: ignore
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds,
cache_position) # type: ignore
# embed positions
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
next_decoder_cache = None
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,) # type: ignore
if self.gradient_checkpointing and self.training:
block_outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
cache_position=cache_position,
)
else:
block_outputs = block(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = block_outputs[0]
if use_cache:
next_decoder_cache = block_outputs[
2 if output_attentions else 1]
if output_attentions:
all_self_attns += (block_outputs[1],) # type: ignore
if output_router_logits:
all_router_logits += (block_outputs[-1],) # type: ignore
hidden_states = self.norm_f(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,) # type: ignore
next_cache = None
if use_cache:
next_cache = (
next_decoder_cache.to_legacy_cache() # type: ignore
if isinstance(next_decoder_cache, Cache) else
next_decoder_cache)
if not return_dict:
return tuple(v for v in [
hidden_states, next_cache, all_hidden_states, all_self_attns,
all_router_logits
] if v is not None)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
# 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
def _update_causal_mask(
self, attention_mask: Optional[torch.Tensor],
input_tensor: torch.Tensor,
cache_position: torch.Tensor) -> Optional[torch.Tensor]:
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
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
if hasattr(self.blocks[0].norm_attn_norm.attn,
'past_key_value'): # static cache
target_length = self.config.max_position_embeddings
else: # dynamic cache
target_length = (attention_mask.shape[-1] if isinstance(
attention_mask, torch.Tensor) else cache_position[-1] + 1)
target_length = int(target_length)
causal_mask = torch.full((sequence_length, target_length),
fill_value=min_dtype,
dtype=dtype,
device=device)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(
target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None,
None, :, :].expand(input_tensor.shape[0], 1,
-1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone(
) # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(
0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[
..., :mask_length].masked_fill(padding_mask, min_dtype)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
if attention_mask.shape[
-2] < cache_position[0] + sequence_length:
offset = cache_position[0]
else:
offset = 0
mask_shape = attention_mask.shape
mask_slice = (attention_mask.eq(0.0)).to(
dtype=dtype) * min_dtype
causal_mask[:mask_shape[0], :mask_shape[1],
offset:mask_shape[2] +
offset, :mask_shape[3]] = mask_slice
if (self.config._attn_implementation == 'sdpa' and
attention_mask is not None and
attention_mask.device.type == 'cuda'):
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
is_tracing = (
torch.jit.is_tracing() or
isinstance(input_tensor, torch.fx.Proxy) or # type: ignore
(hasattr(torch, '_dynamo') and torch._dynamo.is_compiling()))
if not is_tracing and torch.any(attention_mask != 1):
# 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
class DbrxForCausalLM(DbrxPreTrainedModel):
def __init__(self, config: DbrxConfig):
super().__init__(config)
self.transformer = DbrxModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size,
config.vocab_size,
bias=False)
self.router_aux_loss_coef = config.router_aux_loss_coef
self.num_experts = config.ffn_config.moe_num_experts
self.num_experts_per_tok = config.ffn_config.moe_top_k
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.transformer.get_input_embeddings()
def set_input_embeddings(self, value: nn.Embedding):
self.transformer.set_input_embeddings(value)
def get_output_embeddings(self) -> nn.Linear:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Linear):
self.lm_head = new_embeddings
def set_decoder(self, decoder: DbrxModel):
self.transformer = decoder
def get_decoder(self) -> DbrxModel:
return self.transformer
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
r"""Forward function for causal language modeling.
Example:
```python
>>> from transformers import AutoTokenizer, DbrxForCausalLM
>>> model = DbrxForCausalLM.from_pretrained("databricks/dbrx")
>>> tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```
"""
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)
output_router_logits = (output_router_logits
if output_router_logits is not None else
self.config.output_router_logits)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None and loss is not None:
loss += self.router_aux_loss_coef * aux_loss.to(
loss.device) # make sure to reside in the same device
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.Tensor,
past_key_values: Optional[Cache] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: Any) -> Dict[str, Any]:
past_length = 0
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
max_cache_length = past_key_values.get_max_length()
else:
cache_length = past_length = past_key_values[0][0].shape[2]
max_cache_length = None
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[
1] > input_ids.shape[1]:
input_ids = input_ids[:,
-(attention_mask.shape[1] - past_length):]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
if (max_cache_length is not None and attention_mask is not None and
cache_length + input_ids.shape[1] > max_cache_length):
attention_mask = attention_mask[:, -max_cache_length:]
position_ids = kwargs.get('position_ids', None)
if 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]:]
if self.generation_config.cache_implementation == 'static':
# generation with static cache
cache_position = kwargs.get('cache_position', None)
if cache_position is None:
past_length = 0
else:
past_length = cache_position[-1] + 1
input_ids = input_ids[:, past_length:]
position_ids = position_ids[:,
past_length:] if position_ids is not None else None
# TODO @gante we should only keep a `cache_position` in generate, and do +=1.
# same goes for position ids. Could also help with continued generation.
input_length = position_ids.shape[
-1] if position_ids is not None else input_ids.shape[-1]
cache_position = torch.arange(past_length,
past_length + input_length,
device=input_ids.device)
position_ids = position_ids.contiguous(
) if position_ids is not None else None
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {'inputs_embeds': inputs_embeds}
else:
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
# TODO: use `next_tokens` directly instead.
model_inputs = {'input_ids': input_ids.contiguous()}
model_inputs.update(
{ # type: ignore
'position_ids': position_ids,
'cache_position': cache_position,
'past_key_values': past_key_values,
'use_cache': kwargs.get('use_cache'),
'attention_mask': attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values: Cache, beam_idx: torch.LongTensor):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(
past_state.index_select(0, beam_idx.to(past_state.device))
for past_state in layer_past),)
return reordered_past