phixtral-2x2_8 / modeling_phi.py

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	# Copyright (c) Microsoft Corporation.
	# Licensed under the MIT license.
	#
	# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
	# Licensed under the BSD 3-Clause License.

	from __future__ import annotations

	import math
	from dataclasses import dataclass, field
	from typing import Any, Dict, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	from einops import rearrange, repeat
	from transformers import PretrainedConfig, PreTrainedModel
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import CausalLMOutputWithPast

	from .configuration_phi import PhiConfig

	try:
	from flash_attn.bert_padding import pad_input, unpad_input
	from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
	from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
	from flash_attn.ops.fused_dense import FusedDense
	except:
	pad_input, unpad_input = None, None
	FlashRotaryEmbedding = None
	FlashSelfAttention, FlashCrossAttention = None, None
	FusedDense = None


	@dataclass
	class InferenceParams:
	"""Inference parameters passed to model to efficiently calculate
	and store context during inference.

	Reference:
	https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.

	Args:
	max_seqlen: Maximum sequence length.
	max_batch_size: Maximum batch size.
	seqlen_offset: Sequence length offset.
	batch_size_offset: Batch size offset.
	key_value_memory_dict: Key value memory dictionary.
	lengths_per_sample: Lengths per sample.

	"""

	max_seqlen: int = field(metadata={"help": "Maximum sequence length."})

	max_batch_size: int = field(metadata={"help": "Maximum batch size."})

	seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})

	batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})

	key_value_memory_dict: Dict[str, Any] = field(
	default_factory=dict, metadata={"help": "Key value memory dictionary."}
	)

	lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})


	class Embedding(nn.Module):
	"""Token embedding with dropout."""

	def __init__(self, config: PretrainedConfig) -> None:
	super().__init__()

	self.wte = nn.Embedding(config.vocab_size, config.n_embd)
	self.drop = nn.Dropout(config.embd_pdrop)

	def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])

	hidden_states = self.wte(input_ids)
	hidden_states = self.drop(hidden_states)

	return hidden_states


	def _apply_rotary_emb(
	x: torch.FloatTensor,
	cos: torch.FloatTensor,
	sin: torch.FloatTensor,
	) -> torch.FloatTensor:
	_, seqlen, _, _ = x.shape
	_, rotary_dim = cos.shape
	rotary_dim *= 2

	x_rot = x[:, :, :, :rotary_dim]
	x_pass = x[:, :, :, rotary_dim:]

	x1, x2 = x_rot.chunk(2, dim=-1)
	c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
	x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]]

	x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)

	return torch.cat([x_rot, x_pass], axis=-1)


	def _apply_rotary_emb_kv(
	kv: torch.FloatTensor,
	cos: torch.FloatTensor,
	sin: torch.FloatTensor,
	cos_k: Optional[torch.FloatTensor] = None,
	sin_k: Optional[torch.FloatTensor] = None,
	) -> torch.FloatTensor:
	_, seqlen, _, _, _ = kv.shape
	_, rotary_dim = cos.shape
	rotary_dim *= 2

	k_rot = kv[:, :, 0, :, :rotary_dim]
	k_pass = kv[:, :, 0, :, rotary_dim:]

	k1, k2 = k_rot.chunk(2, dim=-1)
	c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
	k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]

	k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype)

	return torch.cat(
	[
	torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
	kv[:, :, 1:2, :, :],
	],
	axis=2,
	)


	def _apply_rotary_emb_qkv(
	qkv: torch.FloatTensor,
	cos: torch.FloatTensor,
	sin: torch.FloatTensor,
	cos_k: Optional[torch.FloatTensor] = None,
	sin_k: Optional[torch.FloatTensor] = None,
	) -> torch.FloatTensor:
	_, seqlen, _, _, _ = qkv.shape
	_, rotary_dim = cos.shape
	rotary_dim *= 2

	q_rot = qkv[:, :, 0, :, :rotary_dim]
	q_pass = qkv[:, :, 0, :, rotary_dim:]

	k_rot = qkv[:, :, 1, :, :rotary_dim]
	k_pass = qkv[:, :, 1, :, rotary_dim:]

	q1, q2 = q_rot.chunk(2, dim=-1)
	k1, k2 = k_rot.chunk(2, dim=-1)
	c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
	q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]

	q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
	k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)

	return torch.cat(
	[
	torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
	torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
	qkv[:, :, 2:3, :, :],
	],
	axis=2,
	)


	class RotaryEmbedding(nn.Module):
	"""Rotary positional embedding (RoPE).

	Reference:
	RoFormer: Enhanced Transformer with Rotary Position Embedding.
	https://arxiv.org/pdf/2104.09864.pdf.

	"""

	def __init__(
	self,
	dim: int,
	base: int = 10000,
	scale_base: Optional[float] = None,
	pos_idx_in_fp32: bool = True,
	max_position_embeddings: int = 2048,
	device: Optional[str] = None,
	**kwargs,
	) -> None:
	super().__init__()

	if scale_base is not None:
	raise NotImplementedError

	self.dim = dim
	self.base = float(base)
	self.scale_base = scale_base
	self.pos_idx_in_fp32 = pos_idx_in_fp32
	self.max_position_embeddings = max_position_embeddings
	self.device = device

	# 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)

	# Generate and save the scale buffer (non-trainable)
	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, persistent=False)

	# Initialize cached attributes since ONNX can't rely on dynamic initialization
	self._update_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.float32)

	def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
	return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))

	def _update_cos_sin_cache(
	self,
	seqlen: int,
	device: Optional[str] = None,
	dtype: Optional[torch.dtype] = None,
	) -> None:
	self._seq_len_cached = seqlen

	# fp32 is preferred since the output of `torch.arange` can be quite large
	# and bf16 would lose a lot of precision
	if self.pos_idx_in_fp32:
	t = torch.arange(seqlen, device=device, dtype=torch.float32)
	if self.inv_freq.dtype != torch.float32:
	inv_freq = self._compute_inv_freq(device=device)
	else:
	inv_freq = self.inv_freq
	else:
	t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
	inv_freq = self.inv_freq

	# `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
	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) ** rearrange(power, "s -> s 1")

	# Force the scale multiplication 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,
	qkv: torch.Tensor,
	kv: Optional[torch.Tensor] = None,
	seqlen_offset: int = 0,
	**kwargs,
	) -> Tuple[torch.Tensor, torch.Tensor]:
	if (
	self._seq_len_cached < qkv.shape[1] + seqlen_offset
	or self._cos_cached.device != qkv.device
	or self._cos_cached.dtype != qkv.dtype
	or (self.training and self._cos_cached.is_inference())
	):
	self._update_cos_sin_cache(qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype)

	if kv is None:
	return _apply_rotary_emb_qkv(
	qkv,
	self._cos_cached[seqlen_offset:],
	self._sin_cached[seqlen_offset:],
	)
	else:
	q = _apply_rotary_emb(
	qkv,
	self._cos_cached[seqlen_offset:],
	self._sin_cached[seqlen_offset:],
	)
	kv = _apply_rotary_emb_kv(
	kv,
	self._cos_cached[seqlen_offset:],
	self._sin_cached[seqlen_offset:],
	)

	return q, kv


	class MoE(nn.Module):
	def __init__(
	self,
	config: PretrainedConfig,
	):
	super().__init__()
	self.mlp = nn.ModuleList([MLP(config) for i in range(config.num_local_experts)])
	self.gate = nn.Linear(config.n_embd, config.num_local_experts, bias=False)
	self.num_experts_per_tok = config.num_experts_per_tok

	def forward(self, x):
	orig_shape = x.shape
	x = x.view(-1, x.shape[-1])

	scores = self.gate(x)
	expert_weights, expert_indices = torch.topk(scores, self.num_experts_per_tok, dim=-1)
	expert_weights = expert_weights.softmax(dim=-1)
	flat_expert_indices = expert_indices.view(-1)

	x = x.repeat_interleave(self.num_experts_per_tok, dim=0)
	y = torch.empty_like(x)
	for i, expert in enumerate(self.mlp):
	y[flat_expert_indices == i] = expert(x[flat_expert_indices == i])
	y = (y.view(expert_weights.shape, -1) expert_weights.unsqueeze(-1)).sum(dim=1)
	return y.view(*orig_shape)


	class MLP(nn.Module):
	"""Multi-Layer Perceptron.

	Reference:
	Attention Is All You Need.
	https://arxiv.org/pdf/1706.03762.pdf.

	"""

	def __init__(
	self,
	config: PretrainedConfig,
	n_inner: Optional[int] = None,
	act_fn: Optional[str] = None,
	) -> None:
	super().__init__()

	act_fn = config.activation_function if act_fn is None else act_fn

	n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
	n_inner = n_inner if n_inner is not None else 4 * config.n_embd

	self.fc1 = nn.Linear(config.n_embd, n_inner)
	self.fc2 = nn.Linear(n_inner, config.n_embd)
	self.act = ACT2FN[act_fn]

	def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.fc2(hidden_states)

	return hidden_states


	class SelfAttention(nn.Module):
	"""Self-attention layer (compatible with PyTorch).

	Reference:
	https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.

	"""

	def __init__(
	self,
	causal: bool = True,
	softmax_scale: Optional[float] = None,
	attention_dropout: float = 0.0,
	) -> None:
	super().__init__()

	self.causal = causal
	self.softmax_scale = softmax_scale
	self.drop = nn.Dropout(attention_dropout)

	@torch.autocast("cpu", enabled=False)
	@torch.autocast("cuda", enabled=False)
	def forward(
	self,
	qkv: torch.FloatTensor,
	causal: bool = None,
	key_padding_mask: Optional[torch.BoolTensor] = None,
	**kwargs,
	) -> torch.FloatTensor:
	batch_size, seqlen = qkv.shape[0], qkv.shape[1]
	q, k, v = qkv.unbind(dim=2)

	q = q.to(torch.float32)
	k = k.to(torch.float32)

	causal = self.causal if causal is None else causal
	softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])

	# Autocast is manually disabled to avoid `torch.einsum` performing the operation
	# using float16, which might lead to overflow
	scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)

	if key_padding_mask is not None:
	padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device)
	padding_mask.masked_fill_(key_padding_mask, 0.0)

	scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")

	if causal:
	causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
	scores = scores + causal_mask.to(dtype=scores.dtype)

	attention = torch.softmax(scores, dim=-1).to(v.dtype)
	attention = self.drop(attention)

	output = torch.einsum("bhts,bshd->bthd", attention, v)

	return output


	class CrossAttention(nn.Module):
	"""Cross-attention layer (compatible with PyTorch).

	Reference:
	https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.

	"""

	def __init__(
	self,
	causal: bool = True,
	softmax_scale: Optional[float] = None,
	attention_dropout: float = 0.0,
	) -> None:
	super().__init__()

	self.causal = causal
	self.softmax_scale = softmax_scale
	self.drop = nn.Dropout(attention_dropout)

	@torch.autocast("cpu", enabled=False)
	@torch.autocast("cuda", enabled=False)
	def forward(
	self,
	q: torch.FloatTensor,
	kv: torch.FloatTensor,
	causal: bool = None,
	key_padding_mask: Optional[torch.BoolTensor] = None,
	**kwargs,
	) -> torch.FloatTensor:
	batch_size, seqlen_q = q.shape[0], q.shape[1]
	seqlen_k = kv.shape[1]

	if kv.shape[3] != q.shape[2]:
	kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
	k, v = kv.unbind(dim=2)

	q = q.to(torch.float32)
	k = k.to(torch.float32)

	causal = self.causal if causal is None else causal
	softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])

	# Autocast is manually disabled to avoid `torch.einsum` performing the operation
	# using float16, which might lead to overflow
	scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)

	if key_padding_mask is not None:
	padding_mask = torch.full(
	(batch_size, seqlen_k),
	-10000.0,
	dtype=scores.dtype,
	device=scores.device,
	)
	padding_mask.masked_fill_(key_padding_mask, 0.0)

	scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")

	if causal:
	rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1")
	cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
	causal_mask = cols > rows + seqlen_k - seqlen_q

	scores = scores.masked_fill(causal_mask, -10000.0)

	attention = torch.softmax(scores, dim=-1).to(v.dtype)
	attention = self.drop(attention)

	output = torch.einsum("bhts,bshd->bthd", attention, v)

	return output


	def _find_mha_dims(
	config: PretrainedConfig,
	n_head: Optional[int] = None,
	n_head_kv: Optional[int] = None,
	head_dim: Optional[int] = None,
	) -> Tuple[int, int]:
	if n_head is None and head_dim is None:
	head_dim = config.n_embd // config.n_head
	n_head = config.n_head
	elif n_head is None or head_dim is None:
	raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")

	if n_head_kv is None:
	n_head_kv = getattr(config, "n_head_kv", None) or n_head

	return n_head, n_head_kv, head_dim


	def _update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
	num_heads, head_dim = kv.shape[-2:]

	if layer_idx not in inference_params.key_value_memory_dict:
	inference_params.key_value_memory_dict[layer_idx] = torch.empty(
	inference_params.max_batch_size,
	inference_params.max_seqlen,
	2,
	num_heads,
	head_dim,
	dtype=kv.dtype,
	device=kv.device,
	)

	batch_start = inference_params.batch_size_offset
	batch_end = batch_start + kv.shape[0]

	sequence_start = inference_params.seqlen_offset
	sequence_end = sequence_start + kv.shape[1]

	# When the current sequence length is equal to or larger than the maximum sequence length,
	# we need to concatenate the current `kv` with the cached `kv` to expand its length
	if sequence_end >= inference_params.max_seqlen:
	inference_params.key_value_memory_dict[layer_idx] = torch.concatenate((inference_params.key_value_memory_dict[layer_idx], kv), dim=1)

	inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, sequence_start:sequence_end, ...] = kv
	kv = inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, :sequence_end, ...]

	return kv


	class MHA(nn.Module):
	"""Multi-head attention layer."""

	def __init__(
	self,
	config: PretrainedConfig,
	dtype: Optional[torch.dtype] = None,
	device: Optional[str] = None,
	rotary_dim: Optional[int] = None,
	rotary_base: float = 10000.0,
	rotary_scale_base: Optional[float] = None,
	n_head: Optional[int] = None,
	n_head_kv: Optional[int] = None,
	head_dim: Optional[int] = None,
	bias: bool = True,
	causal: bool = True,
	softmax_scale: Optional[float] = None,
	layer_idx: Optional[int] = None,
	return_residual: bool = False,
	checkpointing: bool = False,
	) -> None:
	super().__init__()

	# Rotary embedding
	self.rotary_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
	if self.rotary_dim > 0:
	rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
	if rotary_cls is None:
	rotary_cls = RotaryEmbedding

	rotary_kwargs = {}
	if rotary_cls is RotaryEmbedding:
	rotary_kwargs["max_position_embeddings"] = config.n_positions

	self.rotary_emb = rotary_cls(
	self.rotary_dim,
	base=rotary_base,
	scale_base=rotary_scale_base,
	device=device,
	**rotary_kwargs,
	)

	# MLP
	self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
	config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim
	)
	op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
	hidden_size = config.n_embd

	linear_cls = FusedDense if config.fused_dense else nn.Linear
	if linear_cls is None:
	linear_cls = nn.Linear

	self.Wqkv = linear_cls(hidden_size, op_size, bias=bias, device=device, dtype=dtype)
	self.out_proj = linear_cls(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype)

	# Attention
	attn_cls = FlashSelfAttention if config.flash_attn else SelfAttention
	if attn_cls is None:
	attn_cls = SelfAttention

	cross_attn_cls = FlashCrossAttention if config.flash_attn else CrossAttention
	if cross_attn_cls is None:
	cross_attn_cls = CrossAttention

	self.inner_attn = attn_cls(
	causal=causal,
	softmax_scale=softmax_scale,
	attention_dropout=config.attn_pdrop,
	)
	self.inner_cross_attn = cross_attn_cls(
	causal=causal,
	softmax_scale=softmax_scale,
	attention_dropout=config.attn_pdrop,
	)

	self.flash_attn = config.flash_attn and attn_cls is FlashSelfAttention
	self.layer_idx = layer_idx
	self.return_residual = return_residual
	self.checkpointing = checkpointing

	def _forward_self_attn(
	self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
	) -> torch.FloatTensor:
	qkv = self.Wqkv(x)
	qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)

	if self.rotary_dim > 0:
	qkv = self.rotary_emb(qkv)

	if self.flash_attn:
	batch_size, seqlen = qkv.shape[0], qkv.shape[1]

	cu_seqlens, max_seqlen = None, None
	if key_padding_mask is not None:
	# If `key_padding_mask` is supplied, we need to unpad the input and retrieve
	# the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
	qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask)

	if self.checkpointing:
	attn_output = torch.utils.checkpoint.checkpoint(
	self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
	)
	else:
	attn_output = self.inner_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen).to(qkv.device)

	# If `key_padding_mask` is supplied, we need to pad the output back to the original shape
	return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output

	if self.checkpointing:
	return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, key_padding_mask=key_padding_mask)

	return self.inner_attn(qkv, key_padding_mask=key_padding_mask)

	def _forward_cross_attn(
	self,
	x: torch.FloatTensor,
	past_key_values: Optional[InferenceParams],
	key_padding_mask: Optional[torch.BoolTensor],
	) -> torch.FloatTensor:
	batch_size = x.shape[0]

	qkv = self.Wqkv(x)

	q = qkv[..., : self.n_head * self.head_dim]
	q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)

	kv = qkv[..., self.n_head * self.head_dim :]
	kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)

	seqlen_offset = past_key_values.seqlen_offset if past_key_values is not None else 0
	causal = None if seqlen_offset == 0 else False
	if self.rotary_dim > 0:
	q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)

	if past_key_values is not None:
	kv = _update_kv_cache(kv, past_key_values, self.layer_idx)

	if self.flash_attn:
	batch_size, seqlen_q = q.shape[0], q.shape[1]
	seqlen_k = kv.shape[1]

	cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = (
	None,
	None,
	None,
	None,
	)
	if key_padding_mask is not None:
	kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask)

	if seqlen_q == 1:
	key_padding_mask = torch.ones(batch_size, 1, device=q.device)
	elif seqlen_q != seqlen_k:
	key_padding_mask = key_padding_mask[:, -seqlen_q:]

	q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, key_padding_mask)

	if self.checkpointing:
	attn_output = torch.utils.checkpoint.checkpoint(
	self.inner_cross_attn,
	q,
	kv,
	causal=causal,
	cu_seqlens=cu_seqlens_q,
	max_seqlen=max_seqlen_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_k=max_seqlen_k,
	)
	else:
	attn_output = self.inner_cross_attn(
	q,
	kv,
	causal=causal,
	cu_seqlens=cu_seqlens_q,
	max_seqlen=max_seqlen_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_k=max_seqlen_k,
	)

	return (
	pad_input(attn_output, indices_q, batch_size, max_seqlen_q)
	if key_padding_mask is not None
	else attn_output
	)

	if self.checkpointing:
	return torch.utils.checkpoint.checkpoint(
	self.inner_cross_attn,
	q,
	kv,
	key_padding_mask=key_padding_mask,
	causal=causal,
	)

	return self.inner_cross_attn(q, kv, key_padding_mask=key_padding_mask, causal=causal)

	def forward(
	self,
	x: torch.FloatTensor,
	past_key_values: Optional[InferenceParams] = None,
	attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
	**kwargs,
	) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
	if attention_mask is not None:
	attention_mask = attention_mask.bool()
	else:
	attention_mask = None

	# MHA
	if self.n_head == self.n_head_kv:
	if past_key_values is None:
	# If `past_key_values` are not supplied, we run self-attention
	attn_output = self._forward_self_attn(x, attention_mask)
	else:
	# If `past_key_values` are supplied, it means that we might have cached values and
	# could take advantage of cross-attention
	attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
	# MQA / GQA
	else:
	# Regardless of `past_key_values` being supplied or not, it always use cross-attention
	# because `q` and `kv` lengths might be different
	attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)

	output = rearrange(attn_output, "... h d -> ... (h d)")
	output = self.out_proj(output)

	return output if not self.return_residual else (output, x)


	class ParallelBlock(nn.Module):
	"""Parallel block.

	This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).

	"""

	def __init__(
	self,
	config: PretrainedConfig,
	block_idx: Optional[int] = None,
	) -> None:
	super().__init__()

	self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)
	self.block_idx = block_idx

	self.mixer = MHA(config, layer_idx=block_idx)
	self.moe = MoE(config)

	def forward(
	self,
	hidden_states: torch.FloatTensor,
	past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
	attention_mask: Optional[torch.BoolTensor] = None,
	**kwargs,
	) -> torch.FloatTensor:
	residual = hidden_states
	hidden_states = self.ln(hidden_states)

	attn_outputs = self.mixer(
	hidden_states,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	)
	if isinstance(attn_outputs, tuple):
	attn_outputs = attn_outputs[0]

	attn_outputs = self.resid_dropout(attn_outputs)
	feed_forward_hidden_states = self.resid_dropout(self.moe(hidden_states))

	hidden_states = attn_outputs + feed_forward_hidden_states + residual

	return hidden_states


	class CausalLMHead(nn.Module):
	"""Causal Language Modeling head.

	Reference:
	Improving Language Understanding by Generative Pre-Training.
	https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.

	"""

	def __init__(self, config: PretrainedConfig) -> None:
	super().__init__()

	self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
	self.linear = nn.Linear(config.n_embd, config.vocab_size)

	def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
	hidden_states = self.ln(hidden_states)
	logits = self.linear(hidden_states).to(torch.float32)

	return logits


	class CausalLMLoss(nn.Module):
	"""Causal Language Modeling loss.

	Reference:
	Improving Language Understanding by Generative Pre-Training.
	https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.

	"""

	def __init__(self, shift_labels: bool = True) -> None:
	super().__init__()

	self.shift_labels = shift_labels
	self.loss_fct = nn.CrossEntropyLoss()

	def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
	if self.shift_labels:
	logits = logits[..., :-1, :].contiguous()
	labels = labels[..., 1:].contiguous()

	loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))

	return loss


	class PhiPreTrainedModel(PreTrainedModel):
	"""Phi pre-trained model."""

	config_class = PhiConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = False
	_no_split_modules = ["ParallelBlock"]

	def __init__(self, inputs, *kwargs) -> None:
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module: nn.Module) -> None:
	if isinstance(module, (nn.Linear,)):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, nn.LayerNorm):
	if module.bias is not None:
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	def prepare_inputs_for_generation(
	self,
	input_ids: torch.LongTensor,
	past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
	attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
	**kwargs,
	) -> Dict[str, Any]:
	if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
	past_key_values = InferenceParams(
	max_seqlen=self.config.n_positions,
	max_batch_size=input_ids.shape[0],
	seqlen_offset=0,
	batch_size_offset=0,
	key_value_memory_dict={},
	lengths_per_sample=None,
	)
	else:
	# Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
	past_key_values.seqlen_offset = input_ids.shape[1] - 1
	input_ids = input_ids[:, -1].unsqueeze(-1)

	return {
	"input_ids": input_ids,
	"past_key_values": past_key_values,
	"attention_mask": attention_mask,
	}


	class PhiModel(PhiPreTrainedModel):
	"""Phi model."""

	_keys_to_ignore_on_load_missing = [""]
	_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in\|fc_out)\.(weight\|bias)"]

	def __init__(self, config: PhiConfig) -> None:
	super().__init__(config)

	self.embd = Embedding(config)
	self.h = nn.ModuleList([ParallelBlock(config, block_idx=i) for i in range(config.n_layer)])
	self.gradient_checkpointing = False
	self.post_init()

	def get_input_embeddings(self) -> nn.Embedding:
	return self.embd.wte

	def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
	self.embd.wte = new_embeddings

	def forward(
	self,
	input_ids: torch.LongTensor,
	past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
	attention_mask: Optional[torch.BoolTensor] = None,
	) -> torch.FloatTensor:
	hidden_states = self.embd(input_ids)

	for layer in self.h:
	hidden_states = layer(
	hidden_states,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	)

	return hidden_states


	class PhiForCausalLM(PhiPreTrainedModel):
	"""Phi for Causal Language Modeling."""

	_keys_to_ignore_on_load_missing = [""]
	_keys_to_ignore_on_load_unexpected = [r"transformer\.h\.\d+\.mlp.(fc_in\|fc_out)\.(weight\|bias)"]

	def __init__(self, config: PhiConfig) -> None:
	super().__init__(config)

	self.transformer = PhiModel(config)
	self.lm_head = CausalLMHead(config)
	self.loss = CausalLMLoss()

	self.post_init()

	def get_output_embeddings(self) -> nn.Linear:
	return self.lm_head.linear

	def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
	self.lm_head.linear = new_embeddings

	def forward(
	self,
	input_ids: torch.LongTensor,
	past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
	attention_mask: Optional[torch.BoolTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	**kwargs,
	) -> CausalLMOutputWithPast:
	hidden_states = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask)
	lm_logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	loss = self.loss(lm_logits, labels)

	return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)