# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Idefics model."""
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
dataclass,
)
from transformers.modeling_utils import PretrainedConfig
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_vision import (
IdeficsVisionTransformer,
)
from text_generation_server.models.custom_modeling.idefics_perceiver import (
IdeficsPerceiverResampler,
)
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
PositionRotaryEmbedding,
FastLinear,
)
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
if IS_CUDA_SYSTEM:
import dropout_layer_norm
elif IS_ROCM_SYSTEM:
from vllm import layernorm_ops
@dataclass
class BaseModelOutputWithPastImage(BaseModelOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
class CausalLMOutputWithPastImage(CausalLMOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
# logger = logging.get_logger(__name__)
# _CONFIG_FOR_DOC = "IdeficsConfig"
# IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = [
# "HuggingFaceM4/idefics-9b",
# "HuggingFaceM4/idefics-80b",
# # See all Idefics models at https://huggingface.co/models?filter=idefics
# ]
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = (
torch.arange(input_ids.shape[0])
.view(-1, 1)
.repeat(1, expand_size)
.view(-1)
.to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(
0, expanded_return_idx
)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(
0, expanded_return_idx
)
model_kwargs["image_attention_mask"] = model_kwargs[
"image_attention_mask"
].index_select(0, expanded_return_idx)
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(
0, expanded_return_idx
)
if is_encoder_decoder:
if encoder_outputs is None:
raise ValueError(
"If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined."
)
encoder_outputs[
"last_hidden_state"
] = encoder_outputs.last_hidden_state.index_select(
0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
# must have this key set to at least None
model_kwargs["past_key_values"] = model_kwargs.get("past_key_values", None)
# update past
if "past_key_values" in outputs:
model_kwargs["past"] = outputs.past_key_values
elif "mems" in outputs:
model_kwargs["past"] = outputs.mems
elif "past_buckets_states" in outputs:
model_kwargs["past"] = outputs.past_buckets_states
else:
model_kwargs["past"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat(
[token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1
)
# update attention masks
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))],
dim=-1,
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
model_kwargs["image_attention_mask"] = last_mask
return model_kwargs
def prepare_inputs_for_generation(input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
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:
position_ids = position_ids[:, -1].unsqueeze(-1)
pixel_values = kwargs.get("pixel_values", None)
image_attention_mask = kwargs.get("image_attention_mask", None)
# if pixel_values is None or image_attention_mask is None:
# raise ValueError("pixel values and image attention mask cannot be None")
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"pixel_values": pixel_values,
"image_attention_mask": image_attention_mask,
}
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": nn.LayerNorm,
"Linear": nn.Linear,
"Embedding": nn.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
for module in model.modules():
if module_exceptions and any(
[isinstance(module, t) for t in module_exceptions_mapped]
):
module.requires_grad_(
True
) # Explicitely setting it to true to avoid any mistakes
else:
module.requires_grad_(False)
return model
class IdeficsDecoupledPartialTPEmbedding(nn.Module):
def __init__(
self,
config,
weights,
):
super().__init__()
self.num_embeddings = config.vocab_size
self.weight = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.additional_weight = nn.Parameter(
weights.get_tensor(f"model.embed_tokens.additional_embedding.weight")
)
def forward(self, input_ids):
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = torch.nn.functional.embedding(
input_ids_additional_vocab - self.num_embeddings, self.additional_weight
)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = self.weight(input_ids)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
class IdeficsDecoupledTensorParallelLinear(nn.Module):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
config,
weights,
) -> None:
super().__init__()
self.fc = TensorParallelHead.load(
config=config, prefix="lm_head", weights=weights
)
self.additional_fc = FastLinear.load(
config=config,
prefix="lm_head.additional_fc",
weights=weights,
bias=False,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = self.fc(input)
additional_features = self.additional_fc(input)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[
torch.zeros(
tgt_len, past_key_values_length, dtype=dtype, device=device
),
mask,
],
dim=-1,
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
class IdeficsRMSNorm(nn.Module):
def __init__(self, prefix, weights, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
weight = weights.get_tensor(f"{prefix}.weight")
self.weight = nn.Parameter(weight)
self.variance_epsilon = eps
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(
variance + self.variance_epsilon
)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
elif IS_CUDA_SYSTEM:
# faster post attention rms norm
unwrap = False
if len(hidden_states.shape) > 2:
unwrap = True
shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, shape[-1])
normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
None,
None,
None,
None,
None,
0.0,
self.variance_epsilon,
1.0,
0,
None,
False,
True, # Activate RMSNorm
)
if res is None:
res = hidden_states
if unwrap:
normed_hidden_states = normed_hidden_states.view(*shape)
return normed_hidden_states
elif IS_ROCM_SYSTEM:
# We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not.
if residual is not None:
hidden_states += residual
residual = hidden_states
unwrap = False
if len(hidden_states.shape) > 2:
unwrap = True
shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, shape[-1])
out = torch.empty_like(hidden_states)
layernorm_ops.rms_norm(
out,
hidden_states,
self.weight.data,
self.variance_epsilon,
)
if unwrap:
out = out.view(*shape)
return out
else:
raise ValueError(
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
)
# this was adapted from LlamaMLP
class IdeficsMLP(nn.Module):
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
shape = gate_up_states.shape
gate_up_states = gate_up_states.view(*shape[:-1], 2, shape[-1] // 2)
return self.down_proj(
self.act_fn(gate_up_states[:, :, 0]) * gate_up_states[:, :, 1]
)
# this was adapted from LlamaAttention
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config,
prefix,
weights,
qk_layer_norms: bool = False,
is_cross_attention: bool = False,
):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.dropout = config.dropout
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.is_cross_attention = is_cross_attention
# if not hasattr(nn.functional, "scaled_dot_product_attention"):
# raise ValueError("this model requires pytorch 2.0 or higher")
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads //= weights.process_group.size()
if self.is_cross_attention:
# kv_input_dim = (
# self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
# )
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
else:
self.qkv = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
self.o_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.o_proj", weights=weights, bias=False
)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_dim, base=10000.0, device=weights.device
)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm",
weights=weights,
eps=config.rms_norm_eps,
)
self.k_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm",
weights=weights,
eps=config.rms_norm_eps,
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
if is_cross_attention:
query_states = self.q_proj(hidden_states).view(
bsz, q_len, self.num_heads, self.head_dim
) # .transpose(1, 2)
query_states = query_states.transpose(1, 2)
(
_,
kv_len,
_,
) = (
key_value_states.size()
) # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = (
self.k_proj(key_value_states)
.view(bsz, kv_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(key_value_states)
.view(bsz, kv_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
else:
qkv = self.qkv(hidden_states)
query_states, key_states, value_states = qkv.split(
self.num_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_heads, self.head_dim
) # . transpose(1, 2)
value_states = value_states.view(
bsz, q_len, self.num_heads, self.head_dim
) # .transpose(1, 2)
kv_seq_len = q_len
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
max_s = max(kv_seq_len, q_len)
cos, sin = self.rotary_emb.get_cos_sin(
position_ids.view(-1), max_s, hidden_states.dtype
)
query_shape = query_states.shape
key_shape = key_states.shape
self.rotary_emb(
query_states.view(-1, *query_shape[2:]),
key_states.reshape(-1, *key_shape[2:]),
cos,
sin,
)
query_states = query_states.view(query_shape)
key_states = key_states.view(key_shape)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_output = nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout,
)
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)
attn_output = attn_output.reshape(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
# this was adapted from LlamaDecoderLayer
class IdeficsDecoderLayer(nn.Module):
def __init__(self, layer_id: int, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.layers.{layer_id}"
self.self_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.self_attn",
weights=weights,
qk_layer_norms=False,
is_cross_attention=False,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
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`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.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,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
# hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, layer_id, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.gated_cross_attn_layers.{layer_id}"
self.cross_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.cross_attn",
weights=weights,
qk_layer_norms=True,
is_cross_attention=True,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
self.alpha_cross_attn = nn.Parameter(
weights.get_tensor(f"{prefix}.alpha_cross_attn")
)
self.alpha_dense = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_dense"))
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
no_images: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
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`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if past_key_value is not None:
raise NotImplementedError(
"Past key value states are not implemented for Idefics cross attention module."
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
# when there are no images the model is used in pure language mode
gate = 0 if no_images else 1
hidden_states = (
residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states
)
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
# base_model_prefix = "model"
# supports_gradient_checkpointing = True
# _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
# def _init_weights(self, module):
# # important: this ported version of Idefics isn't meant for training from scratch - only
# # inference and fine-tuning - so the proper init weights code has been removed - the m4 code
# # base should be used for training from scratch and it contains the correct code.
# 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_()
# def _set_gradient_checkpointing(self, module, value=False):
# if isinstance(module, IdeficsModel):
# module.gradient_checkpointing = value
# LLAMA_INPUTS_DOCSTRING = r"""
# Args:
# input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
# Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
# it.
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# [What are input IDs?](../glossary#input-ids)
# attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
# Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
# - 1 for tokens that are **not masked**,
# - 0 for tokens that are **masked**.
# [What are attention masks?](../glossary#attention-mask)
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
# `past_key_values`).
# If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
# and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
# information on the default strategy.
# - 1 indicates the head is **not masked**,
# - 0 indicates the head is **masked**.
# position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
# Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
# config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
# past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
# Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
# `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
# `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
# Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
# blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
# If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
# don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
# `decoder_input_ids` of shape `(batch_size, sequence_length)`.
# inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
# Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
# is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
# model's internal embedding lookup matrix.
# use_cache (`bool`, *optional*):
# If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
# `past_key_values`).
# output_attentions (`bool`, *optional*):
# Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
# tensors for more detail.
# output_hidden_states (`bool`, *optional*):
# Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
# more detail.
# return_dict (`bool`, *optional*):
# Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
# """
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsModel(IdeficsPreTrainedModel):
# """
# Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
# Args:
# config: IdeficsConfig
# """
def __init__(self, config: IdeficsConfig, weights):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = IdeficsDecoupledPartialTPEmbedding(
config=config,
weights=weights,
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = IdeficsVisionTransformer(
prefix="model.vision_model",
config=config.vision_config,
weights=weights,
)
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = IdeficsPerceiverResampler(
prefix=f"model.perceiver_resampler",
config=config,
embed_dim=config.vision_config.embed_dim,
depth=perceiver_config.resampler_depth,
n_heads=perceiver_config.resampler_n_heads,
head_dim=perceiver_config.resampler_head_dim,
n_latents=perceiver_config.resampler_n_latents,
weights=weights,
)
self.layers = nn.ModuleList(
[
IdeficsDecoderLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[
IdeficsGatedCrossAttentionLayer(layer_id, config, weights)
for layer_id in range(num_cross_layers)
]
)
# self.gradient_checkpointing = False
self.norm = IdeficsRMSNorm(
prefix=f"model.norm", weights=weights, eps=config.rms_norm_eps
)
# self.gradient_checkpointing = False
# Initialize weights and apply final processing
# self.post_init()
# self.freeze_relevant_params(config)
# def freeze_relevant_params(self, config=None):
# if config is None:
# config = self.config
# if config.freeze_text_layers:
# self.freeze_text_layers(config.freeze_text_module_exceptions)
# if config.freeze_vision_layers:
# freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
# def freeze_text_layers(self, module_exceptions=[]):
# for module in [self.layers, self.norm]:
# freeze_model(module, module_exceptions=module_exceptions)
# def freeze_vision_layers(self, module_exceptions=[]):
# freeze_model(self.vision_model, module_exceptions=module_exceptions)
# def get_input_embeddings(self):
# return self.embed_tokens
# def set_input_embeddings(self, value):
# self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
# @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: 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, BaseModelOutputWithPastImage]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
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
)
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
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)
elif position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
no_images = False
if image_hidden_states is None:
if pixel_values is None and image_embeddings is None:
raise ValueError(
"Either pixel_values and image_embeddings have to be not-None."
)
elif pixel_values is not None and image_embeddings is not None:
raise ValueError(
"You cannot specify both pixel_values and image_embeddings at the same time"
)
elif pixel_values is not None:
no_images = len(torch.nonzero(pixel_values)) == 0
pixel_values = pixel_values.to(
dtype=self.dtype, device=device
) # fp16 compatibility
batch_size, num_images = pixel_values.shape[:2]
pixel_values = pixel_values.contiguous().view(
batch_size * num_images, *pixel_values.shape[2:]
)
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values
).last_hidden_state
elif image_embeddings is not None:
(
batch_size,
num_images,
image_seq_len,
image_hidden_size,
) = image_embeddings.size()
image_hidden_states = image_embeddings.to(
dtype=self.dtype, device=input_ids.device
)
image_hidden_states = image_hidden_states.view(
batch_size * num_images, image_seq_len, image_hidden_size
)
if self.config.use_resampler:
image_hidden_states = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = image_hidden_states.size(
1
), image_hidden_states.size(2)
image_hidden_states = image_hidden_states.view(
batch_size, num_images * image_seq_len, image_hidden_size
)
else:
no_images = False
num_images = pixel_values.shape[1]
image_seq_len = image_hidden_states.shape[1] // num_images
# # Hack to use the model in full language modeling mode
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(
batch_size, text_seq_len, num_images * image_seq_len
)
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
# if list(image_attention_mask.shape) != [4, 1, 1024, 64]:
# raise ValueError(f"Image hidden_states {image_hidden_states.shape} - mask {image_attention_mask.shape} {num_images} {image_seq_len} {text_seq_len}")
# if image_hidden_states is not None:
# else:
# image_attention_mask = None
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
hidden_states = 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
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
no_images,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
no_images=no_images,
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
# if self.gradient_checkpointing and self.training:
# past_key_value = None
# if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
# use_cache = False
# layer_outputs = torch.utils.checkpoint.checkpoint(
# vblock,
# decoder_layer,
# hidden_states,
# attention_mask,
# position_ids,
# past_key_value,
# image_hidden_states,
# image_attention_mask,
# output_attentions,
# use_cache,
# no_images,
# idx,
# self.cross_layer_interval,
# self.gated_cross_attn_layers,
# )
# else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
no_images=no_images,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPastImage(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
def __init__(
self,
config,
weights,
):
super().__init__(config)
self.model = IdeficsModel(
config=config,
weights=weights,
)
self.lm_head = IdeficsDecoupledTensorParallelLinear(
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_attention_mask: 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,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPastImage]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? 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 consciours? Can you talk to me?\nI'm not consciours, 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
)
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.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_embeddings=image_embeddings,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
return CausalLMOutputWithPastImage(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=False
):
return update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder
)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (
tuple(
past_state.index_select(0, beam_idx) for past_state in layer_past
),
)
return reordered_past