leo-hessianai-13b / modeling_flash_llama.py

Create modeling_flash_llama.py

1ef978a 9 months ago

No virus

45.4 kB

	# coding=utf-8
	# From https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py
	# With fix from Alex Birch: https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/17
	# 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 LLaMA 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 BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
	from transformers.models.llama.configuration_llama import LlamaConfig


	try:
	from flash_attn.flash_attn_interface import (
	flash_attn_kvpacked_func,
	flash_attn_varlen_kvpacked_func,
	)
	from flash_attn.bert_padding import unpad_input, pad_input
	flash_attn_v2_installed = True
	print('>>>> Flash Attention installed')
	except ImportError:
	flash_attn_v2_installed = False
	raise ImportError('Please install Flash Attention: `pip install flash-attn --no-build-isolation`')

	try:
	from flash_attn.layers.rotary import apply_rotary_emb_func
	flash_rope_installed = True
	print('>>>> Flash RoPE installed')
	except ImportError:
	flash_rope_installed = False
	raise ImportError('Please install RoPE kernels: `pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary`')


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "LlamaConfig"


	# @torch.jit.script
	def rmsnorm_func(hidden_states, weight, variance_epsilon):
	input_dtype = hidden_states.dtype
	hidden_states = hidden_states.to(torch.float32)
	variance = hidden_states.pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
	return (weight * hidden_states).to(input_dtype)


	class LlamaRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	LlamaRMSNorm is equivalent to T5LayerNorm
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.register_buffer(
	"variance_epsilon",
	torch.tensor(eps),
	persistent=False,
	)

	def forward(self, hidden_states):
	return rmsnorm_func(hidden_states, self.weight, self.variance_epsilon)


	class FlashRotaryEmbedding(torch.nn.Module):
	"""
	The rotary position embeddings from RoFormer_ (Su et. al).
	A crucial insight from the method is that the query and keys are
	transformed by rotation matrices which depend on the relative positions.

	Other implementations are available in the Rotary Transformer repo_ and in
	GPT-NeoX_, GPT-NeoX was an inspiration

	.. _RoFormer: https://arxiv.org/abs/2104.09864
	.. _repo: https://github.com/ZhuiyiTechnology/roformer
	.. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox

	If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
	A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
	Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
	"""

	def __init__(self, dim: int, base=10000.0, interleaved=False, scale_base=None,
	scaling_factor=1.0, pos_idx_in_fp32=True, device=None):
	"""
	interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
	of 1st half and 2nd half (GPT-NeoX style).
	pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
	otherwise they might be in lower precision.
	This option was added because previously (before 2023-07-02), when we construct
	the position indices, we use the dtype of self.inv_freq. In most cases this would
	be fp32, but if the model is trained in pure bf16 (not mixed precision), then
	self.inv_freq would be bf16, and the position indices are also in bf16.
	Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
	embeddings for some positions will coincide.
	To maintain compatibility with models previously trained in pure bf16,
	we add this option.
	scaling_factor: RotaryEmbedding extended with linear scaling.
	"""
	super().__init__()
	self.dim = dim
	self.base = float(base)
	self.pos_idx_in_fp32 = pos_idx_in_fp32
	# Generate and save the inverse frequency buffer (non trainable)
	inv_freq = self._compute_inv_freq(device)
	self.register_buffer("inv_freq", inv_freq, persistent=False)
	self.interleaved = interleaved
	self.scale_base = scale_base
	self.scaling_factor = scaling_factor
	scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
	/ (1.4 * dim) if scale_base is not None else None)
	self.register_buffer("scale", scale)

	self._seq_len_cached = 0
	self._cos_cached = None
	self._sin_cached = None
	self._cos_k_cached = None
	self._sin_k_cached = None

	def _compute_inv_freq(self, device=None):
	return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device,
	dtype=torch.float32) / self.dim))


	def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
	# Reset the tables if the sequence length has changed,
	# if we're on a new device (possibly due to tracing for instance),
	# or if we're switching from inference mode to training
	if (seqlen > self._seq_len_cached or self._cos_cached.device != device
	or self._cos_cached.dtype != dtype
	or (self.training and self._cos_cached.is_inference())):
	self._seq_len_cached = seqlen
	# We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
	# And the output of arange can be quite large, so bf16 would lose a lot of precision.
	# However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
	if self.pos_idx_in_fp32:
	t = torch.arange(seqlen, device=device, dtype=torch.float32)
	t /= self.scaling_factor
	# We want fp32 here as well since inv_freq will be multiplied with t, and the output
	# will be large. Having it in bf16 will lose a lot of precision and cause the
	# cos & sin output to change significantly.
	# We want to recompute self.inv_freq if it was not loaded in fp32
	if self.inv_freq.dtype != torch.float32:
	inv_freq = self.inv_freq.to(torch.float32)
	else:
	inv_freq = self.inv_freq
	else:
	t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
	t /= self.scaling_factor
	inv_freq = self.inv_freq
	# Don't do einsum, it converts fp32 to fp16 under AMP
	# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	freqs = torch.outer(t, inv_freq)
	if self.scale is None:
	self._cos_cached = torch.cos(freqs).to(dtype)
	self._sin_cached = torch.sin(freqs).to(dtype)
	else:
	power = ((torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
	- seqlen // 2) / self.scale_base)
	scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
	# We want the multiplication by scale to happen in fp32
	self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
	self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
	self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
	self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)

	def forward(self, q: torch.Tensor, k: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
	"""
	q: (batch, seqlen, nheads, headdim)
	k: (batch, seqlen, nheads, headdim)
	seqlen_offset: can be used in generation where the qkv being passed in is only the last
	token in the batch.
	"""
	self._update_cos_sin_cache(q.shape[1] + seqlen_offset, device=q.device, dtype=q.dtype)
	if self.scale is None:
	return apply_rotary_emb_func(
	q, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
	self.interleaved, True # inplace=True
	), apply_rotary_emb_func(
	k, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
	self.interleaved, True # inplace=True
	)
	else:
	assert False

	class LlamaMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
	self.act_fn = ACT2FN[config.hidden_act]

	def forward(self, x):
	if self.config.pretraining_tp > 1:
	slice = self.intermediate_size // self.config.pretraining_tp
	gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
	up_proj_slices = self.up_proj.weight.split(slice, dim=0)
	down_proj_slices = self.down_proj.weight.split(slice, dim=1)

	gate_proj = torch.cat(
	[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
	)
	up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)

	intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
	down_proj = [
	F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
	]
	down_proj = sum(down_proj)
	else:
	down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

	return down_proj

	@torch.jit.script
	def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
	"""
	This is the 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, slen, _, num_key_value_heads, head_dim = hidden_states.shape
	if n_rep == 1:
	return hidden_states
	hidden_states = hidden_states[:, :, :, :, None, :].expand(batch, slen, 2, num_key_value_heads, n_rep, head_dim)
	return hidden_states.reshape(batch, slen, 2, num_key_value_heads * n_rep, head_dim)


	class LlamaAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config: LlamaConfig):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.num_key_value_heads = config.num_key_value_heads
	self.num_key_value_groups = self.num_heads // self.num_key_value_heads
	self.max_position_embeddings = config.max_position_embeddings

	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.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
	self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

	self.register_buffer(
	"norm_factor",
	torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()),
	persistent=False,
	)

	if self.config.rope_scaling is None:
	scaling_factor = 1
	else:
	scaling_type = self.config.rope_scaling["type"]
	scaling_factor = self.config.rope_scaling["factor"]
	assert scaling_type == 'linear'

	self.rotary_emb = FlashRotaryEmbedding(
	self.head_dim, base=10000, interleaved=False, scaling_factor=scaling_factor,
	)

	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,
	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,
	is_padded_inputs: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, h_size = hidden_states.size()

	has_layer_past = past_key_value is not None

	if has_layer_past:
	past_kv = past_key_value[0]
	past_len = past_key_value[1]
	else:
	past_len = 0

	if self.config.pretraining_tp > 1:
	key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
	query_slices = self.q_proj.weight.split(
	(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
	)
	key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
	value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

	q = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
	q = torch.cat(q, dim=-1)

	k = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
	k = torch.cat(k, dim=-1)

	v = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
	v = torch.cat(v, dim=-1)

	else:
	q = self.q_proj(hidden_states)
	k = self.k_proj(hidden_states)
	v = self.v_proj(hidden_states)

	q = q.view(bsz, q_len, self.num_heads, self.head_dim)
	k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
	v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim)

	q, k = self.rotary_emb(q, k, past_len)

	kv = torch.stack([k, v], 2)
	kv = repeat_kv(kv, self.num_key_value_groups)

	# Cache QKV values
	if has_layer_past:
	new_len = past_len+q.size(1)
	if new_len > past_kv.size(1):
	past_kv = torch.cat([past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1)
	past_kv[:, past_len:new_len] = kv
	kv = past_kv[:, :new_len]
	else:
	past_kv = kv

	past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None

	if is_padded_inputs:

	# varlen, ignore padding tokens, efficient for large batch with many paddings

	assert attention_mask is not None

	unpadded_kv, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(kv, attention_mask)
	unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask[:, -q.size(1):])
	attn_outputs = flash_attn_varlen_kvpacked_func(
	unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
	max_seqlen_q, max_seqlen_k,
	dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
	causal=(not has_layer_past), return_attn_probs=output_attentions
	)

	attn_output = attn_outputs[0] if output_attentions else attn_outputs
	attn_output = pad_input(
	attn_output, indices_q, bsz, q_len
	).reshape(bsz, q_len, h_size)
	attn_weights = attn_outputs[2] if output_attentions else None

	else:

	# no padding tokens, more efficient

	attn_outputs = flash_attn_kvpacked_func(
	q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)

	attn_output = attn_outputs[0] if output_attentions else attn_outputs
	attn_output = attn_output.reshape(bsz, q_len, h_size)
	attn_weights = attn_outputs[2] if output_attentions else None

	if self.config.pretraining_tp > 1:
	attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
	o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
	attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
	else:
	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class LlamaDecoderLayer(nn.Module):
	def __init__(self, config: LlamaConfig):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = LlamaAttention(config=config)
	self.mlp = LlamaMLP(config)
	self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	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,
	is_padded_inputs: Optional[bool] = False,
	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,
	is_padded_inputs=is_padded_inputs,
	)
	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 = residual + 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 ([`LlamaConfig`]):
	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 LlamaPreTrainedModel(PreTrainedModel):
	config_class = LlamaConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["LlamaDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

	def _init_weights(self, 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_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, LlamaModel):
	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 LlamaModel(LlamaPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`LlamaDecoderLayer`]

	Args:
	config: LlamaConfig
	"""

	def __init__(self, config: LlamaConfig):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	@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,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	is_padded_inputs: Optional[bool] = False,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	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

	position_ids = None

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)

	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

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, output_attentions, None)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	None,
	is_padded_inputs
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	is_padded_inputs=is_padded_inputs,
	)

	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 BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	)


	class LlamaForCausalLM(LlamaPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = LlamaModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.model.embed_tokens

	def set_input_embeddings(self, value):
	self.model.embed_tokens = value

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def set_decoder(self, decoder):
	self.model = decoder

	def get_decoder(self):
	return self.model

	@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	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,
	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,
	is_padded_inputs: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	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 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
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	is_padded_inputs = ((attention_mask is not None) and (not attention_mask.all().item()))

	# 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,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	is_padded_inputs=is_padded_inputs,
	)

	hidden_states = outputs[0]
	if self.config.pretraining_tp > 1:
	lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
	logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
	logits = torch.cat(logits, dim=-1)
	else:
	logits = self.lm_head(hidden_states)
	logits = logits.float()

	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 = 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)

	if not return_dict:
	output = (logits,) + outputs[1:]
	return (loss,) + output if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):
	if past_key_values:
	input_ids = input_ids[:, -1:]

	position_ids = kwargs.get("position_ids", 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:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"position_ids": position_ids,
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"attention_mask": attention_mask,
	"is_padded_inputs": ((attention_mask is not None) and (not attention_mask.all().item()))
	}
	)
	return model_inputs

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	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


	@add_start_docstrings(
	"""
	The LLaMa Model transformer with a sequence classification head on top (linear layer).

	[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
	no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
	padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
	each row of the batch).
	""",
	LLAMA_START_DOCSTRING,
	)
	class LlamaForSequenceClassification(LlamaPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = LlamaModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.model.embed_tokens

	def set_input_embeddings(self, value):
	self.model.embed_tokens = value

	@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,
	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, SequenceClassifierOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.model(
	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,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size = input_ids.shape[0]
	else:
	batch_size = inputs_embeds.shape[0]

	if self.config.pad_token_id is None and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
	else:
	sequence_lengths = -1

	pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

	loss = None
	if labels is not None:
	labels = labels.to(logits.device)
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = "regression"
	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
	self.config.problem_type = "single_label_classification"
	else:
	self.config.problem_type = "multi_label_classification"

	if self.config.problem_type == "regression":
	loss_fct = MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(pooled_logits, labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(pooled_logits, labels)
	if not return_dict:
	output = (pooled_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutputWithPast(
	loss=loss,
	logits=pooled_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)