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	# coding=utf-8
	# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
	#
	# 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 Phi model."""


	import math
	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.cache_utils import Cache, DynamicCache
	from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	SequenceClassifierOutputWithPast,
	TokenClassifierOutput,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	is_flash_attn_2_available,
	is_flash_attn_greater_or_equal_2_10,
	logging,
	replace_return_docstrings,
	)
	from .configuration_phi import PhiConfig


	try:
	from flash_attn import flash_attn_func, flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
	except:
	pass


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "microsoft/phi-2"
	_CONFIG_FOR_DOC = "PhiConfig"

	PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"microsoft/phi-2",
	# See all Phi models at https://huggingface.co/models?filter=phi
	]


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	def _get_unpad_data(attention_mask):
	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.torch.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
	class PhiRotaryEmbedding(nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()

	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).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	# Build here to make `torch.jit.trace` work.
	self._set_cos_sin_cache(
	seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
	)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

	freqs = torch.outer(t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	if seq_len > self.max_seq_len_cached:
	self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

	return (
	self.cos_cached[:seq_len].to(dtype=x.dtype),
	self.sin_cached[:seq_len].to(dtype=x.dtype),
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
	class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
	"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
	t = t / self.scaling_factor

	freqs = torch.outer(t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


	# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
	class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
	"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len

	if seq_len > self.max_position_embeddings:
	base = self.base * (
	(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
	) ** (self.dim / (self.dim - 2))
	inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

	freqs = torch.outer(t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


	# Copied from transformers.models.llama.modeling_llama.rotate_half
	def rotate_half(x):
	"""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)


	# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
	"""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.
	position_ids (`torch.Tensor`):
	The position indices of the tokens corresponding to the query and key tensors. For example, this can be
	used to pass offsetted position ids when working with a KV-cache.
	unsqueeze_dim (`int`, optional, defaults to 1):
	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
	sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
	that cos[position_ids] and sin[position_ids] 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[position_ids] and sin[position_ids] 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[position_ids].unsqueeze(unsqueeze_dim)
	sin = sin[position_ids].unsqueeze(unsqueeze_dim)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
	class PhiMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.activation_fn = ACT2FN[config.hidden_act]
	self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
	self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)
	hidden_states = self.fc2(hidden_states)
	return hidden_states


	# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
	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, 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)


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

	def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
	super().__init__()
	self.config = config
	self.layer_idx = layer_idx
	if layer_idx is None:
	logger.warning_once(
	f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
	"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
	"when creating this class."
	)

	self.attention_dropout = config.attention_dropout
	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
	self.rope_theta = config.rope_theta
	self.partial_rotary_factor = config.partial_rotary_factor
	self.is_causal = True

	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=True)
	self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
	self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
	self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)

	self.qk_layernorm = config.qk_layernorm
	if self.qk_layernorm:
	self.q_layernorm = nn.LayerNorm(
	config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
	)
	self.k_layernorm = nn.LayerNorm(
	config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
	)

	self._init_rope()

	def _init_rope(self):
	if self.config.rope_scaling is None:
	self.rotary_emb = PhiRotaryEmbedding(
	int(self.partial_rotary_factor * self.head_dim),
	max_position_embeddings=self.max_position_embeddings,
	base=self.rope_theta,
	)
	else:
	scaling_type = self.config.rope_scaling["type"]
	scaling_factor = self.config.rope_scaling["factor"]
	if scaling_type == "linear":
	self.rotary_emb = PhiLinearScalingRotaryEmbedding(
	int(self.partial_rotary_factor * self.head_dim),
	max_position_embeddings=self.max_position_embeddings,
	scaling_factor=scaling_factor,
	base=self.rope_theta,
	)
	elif scaling_type == "dynamic":
	self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
	int(self.partial_rotary_factor * self.head_dim),
	max_position_embeddings=self.max_position_embeddings,
	scaling_factor=scaling_factor,
	base=self.rope_theta,
	)
	else:
	raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

	# Phi-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
	@torch.autocast("cpu", enabled=False)
	@torch.autocast("cuda", enabled=False)
	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	if self.qk_layernorm:
	query_states = self.q_layernorm(query_states)
	key_states = self.k_layernorm(key_states)

	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)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	if self.layer_idx is None:
	raise ValueError(
	f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
	"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
	"with a layer index."
	)
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

	# Partial rotary embedding
	query_rot, query_pass = (
	query_states[..., : self.rotary_emb.dim],
	query_states[..., self.rotary_emb.dim :],
	)
	key_rot, key_pass = (
	key_states[..., : self.rotary_emb.dim],
	key_states[..., self.rotary_emb.dim :],
	)
	# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
	query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)

	# [batch_size, seq_length, num_heads, head_dim]
	query_states = torch.cat((query_rot, query_pass), dim=-1)
	key_states = torch.cat((key_rot, key_pass), dim=-1)

	if past_key_value is not None:
	cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_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)

	# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
	attn_weights = torch.matmul(
	query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
	) / math.sqrt(self.head_dim)

	if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
	f" {attn_weights.size()}"
	)

	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_weights = attn_weights + attention_mask

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
	attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, 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.dense(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class PhiFlashAttention2(PhiAttention):
	"""
	Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
	untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
	flash attention and deal with padding tokens in case the input contains any of them.
	"""

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)

	# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
	# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
	# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
	self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

	def forward(
	self,
	hidden_states: torch.Tensor,
	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,
	**kwargs,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	# PhiFlashAttention2 attention does not support output_attentions

	output_attentions = False

	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	if self.qk_layernorm:
	query_states = self.q_layernorm(query_states)
	key_states = self.k_layernorm(key_states)

	# 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)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

	# Partial rotary embedding
	query_rot, query_pass = (
	query_states[..., : self.rotary_emb.dim],
	query_states[..., self.rotary_emb.dim :],
	)
	key_rot, key_pass = (
	key_states[..., : self.rotary_emb.dim],
	key_states[..., self.rotary_emb.dim :],
	)
	# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
	query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)

	# [batch_size, seq_length, num_heads, head_dim]
	query_states = torch.cat((query_rot, query_pass), dim=-1)
	key_states = torch.cat((key_rot, key_pass), dim=-1)

	if past_key_value is not None:
	cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

	# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
	# to be able to avoid many of these transpose/reshape/view.
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	attn_dropout = self.attention_dropout if self.training else 0.0

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in 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.

	if query_states.dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	f"The input hidden states seems to be silently casted in float32, this might be related to"
	f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_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=attn_dropout, softmax_scale=None
	)

	attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
	attn_output = self.dense(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
	def _flash_attention_forward(
	self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
	):
	"""
	Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
	first unpad the input, then computes the attention scores and pad the final attention scores.

	Args:
	query_states (`torch.Tensor`):
	Input query states to be passed to Flash Attention API
	key_states (`torch.Tensor`):
	Input key states to be passed to Flash Attention API
	value_states (`torch.Tensor`):
	Input value states to be passed to Flash Attention API
	attention_mask (`torch.Tensor`):
	The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
	position of padding tokens and 1 for the position of non-padding tokens.
	dropout (`int`, optional):
	Attention dropout
	softmax_scale (`float`, optional):
	The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
	"""
	if not self._flash_attn_uses_top_left_mask:
	causal = self.is_causal
	else:
	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# Contains at least one padding token in the sequence
	if attention_mask is not None:
	batch_size = query_states.shape[0]
	query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
	query_states, key_states, value_states, attention_mask, query_length
	)

	cu_seqlens_q, cu_seqlens_k = cu_seq_lens
	max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

	attn_output_unpad = flash_attn_varlen_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens_q=cu_seqlens_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_q=max_seqlen_in_batch_q,
	max_seqlen_k=max_seqlen_in_batch_k,
	dropout_p=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)

	attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
	else:
	attn_output = flash_attn_func(
	query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
	)

	return attn_output

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
	def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
	indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
	batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

	key_layer = index_first_axis(
	key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	value_layer = index_first_axis(
	value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	if query_length == kv_seq_len:
	query_layer = index_first_axis(
	query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
	)
	cu_seqlens_q = cu_seqlens_k
	max_seqlen_in_batch_q = max_seqlen_in_batch_k
	indices_q = indices_k
	elif query_length == 1:
	max_seqlen_in_batch_q = 1
	cu_seqlens_q = torch.arange(
	batch_size + 1, dtype=torch.int32, device=query_layer.device
	) # There is a memcpy here, that is very bad.
	indices_q = cu_seqlens_q[:-1]
	query_layer = query_layer.squeeze(1)
	else:
	# The -q_len: slice assumes left padding.
	attention_mask = attention_mask[:, -query_length:]
	query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

	return (
	query_layer,
	key_layer,
	value_layer,
	indices_q,
	(cu_seqlens_q, cu_seqlens_k),
	(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
	)


	PHI_ATTENTION_CLASSES = {
	"eager": PhiAttention,
	"flash_attention_2": PhiFlashAttention2,
	}


	class PhiDecoderLayer(nn.Module):
	def __init__(self, config: PhiConfig, layer_idx: int):
	super().__init__()
	self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
	self.mlp = PhiMLP(config)
	self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	) -> 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.
	position_ids (`torch.LongTensor` of shape `({0})`, 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)
	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
	attn_outputs, 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,
	)
	attn_outputs = self.resid_dropout(attn_outputs)

	feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
	hidden_states = attn_outputs + feed_forward_hidden_states + residual
	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	PHI_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 ([`PhiConfig`]):
	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 Phi Model outputting raw hidden-states without any specific head on top.",
	PHI_START_DOCSTRING,
	)
	class PhiPreTrainedModel(PreTrainedModel):
	config_class = PhiConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["PhiDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_supports_flash_attn_2 = True
	_supports_cache_class = True

	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_()


	PHI_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, optional):
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
	returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

	Two formats are allowed:
	- a [`~cache_utils.Cache`] instance;
	- 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)`). This is also known as the legacy
	cache format.

	The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
	legacy cache format will be returned.

	If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
	have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 Phi Model outputting raw hidden-states without any specific head on top.",
	PHI_START_DOCSTRING,
	)
	class PhiModel(PhiPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`PhiDecoderLayer`]

	Args:
	config: PhiConfig
	"""

	def __init__(self, config: PhiConfig):
	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.embed_dropout = nn.Dropout(config.embd_pdrop)
	self.layers = nn.ModuleList(
	[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
	)
	self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"

	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(PHI_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,
	) -> 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 input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape[:2]
	elif inputs_embeds is not None:
	batch_size, seq_length = inputs_embeds.shape[:2]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	past_key_values_length = 0

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	if use_cache:
	use_legacy_cache = not isinstance(past_key_values, Cache)
	if use_legacy_cache:
	past_key_values = DynamicCache.from_legacy_cache(past_key_values)
	past_key_values_length = past_key_values.get_usable_length(seq_length)

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(
	past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
	)
	position_ids = position_ids.unsqueeze(0)

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

	inputs_embeds = self.embed_dropout(inputs_embeds)

	# Attention mask.
	if self._use_flash_attention_2:
	# 2d mask is passed through the layers
	attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
	else:
	# 4d mask is passed through the layers
	attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
	)

	hidden_states = inputs_embeds

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = None

	for decoder_layer in self.layers:
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	attention_mask,
	position_ids,
	past_key_values,
	output_attentions,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_values,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	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.final_layernorm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	next_cache = None
	if use_cache:
	next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_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] 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 PhiForCausalLM(PhiPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
	def __init__(self, config):
	super().__init__(config)
	self.model = PhiModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)

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

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
	def get_input_embeddings(self):
	return self.model.embed_tokens

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
	def set_input_embeddings(self, value):
	self.model.embed_tokens = value

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
	def get_output_embeddings(self):
	return self.lm_head

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
	def set_decoder(self, decoder):
	self.model = decoder

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
	def get_decoder(self):
	return self.model

	@add_start_docstrings_to_model_forward(PHI_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,
	) -> 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, PhiForCausalLM

	>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
	>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")

	>>> prompt = "This is an example script ."
	>>> 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]
	'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
	```"""

	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,
	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)
	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,
	)

	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):
	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 `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,
	}
	)
	return model_inputs

	@staticmethod
	# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
	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 PhiModel with a sequence classification head on top (linear layer).

	[`PhiForSequenceClassification`] 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).
	""",
	PHI_START_DOCSTRING,
	)
	# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs
	class PhiForSequenceClassification(PhiPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = PhiModel(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(PHI_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

	model_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 = model_outputs[0]
	logits = self.score(hidden_states)

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

	if self.config.pad_token_id is None and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
	sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
	sequence_lengths = sequence_lengths % input_ids.shape[-1]
	sequence_lengths = sequence_lengths.to(logits.device)
	else:
	sequence_lengths = -1

	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,) + model_outputs[1:]
	return ((loss,) + output) if loss is not None else output

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


	@add_start_docstrings(
	"""
	PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
	Named-Entity-Recognition (NER) tasks.
	""",
	PHI_START_DOCSTRING,
	)
	# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs
	class PhiForTokenClassification(PhiPreTrainedModel):
	def __init__(self, config: PhiConfig):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.model = PhiModel(config)
	if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
	classifier_dropout = config.classifier_dropout
	elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
	classifier_dropout = config.hidden_dropout
	else:
	classifier_dropout = 0.1
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

	@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TokenClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	**deprecated_arguments,
	) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	model_outputs = self.model(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = model_outputs[0]
	hidden_states = self.dropout(hidden_states)
	logits = self.classifier(hidden_states)

	loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	batch_size, seq_length = labels.shape
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(
	logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
	)

	if not return_dict:
	output = (logits,) + model_outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return TokenClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=model_outputs.hidden_states,
	attentions=model_outputs.attentions,
	)