phi-1_5_Wizard_Vicuna_uncensored / modeling_mixformer_sequential.py

Jan Philipp Harries

v01

401cb9c 8 months ago

No virus

32.2 kB

	# Copyright (c) Microsoft Corporation.
	# Licensed under the MIT license.

	# BSD 3-Clause License
	#
	# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
	# All rights reserved.
	#
	# Redistribution and use in source and binary forms, with or without
	# modification, are permitted provided that the following conditions are met:
	#
	# * Redistributions of source code must retain the above copyright notice, this
	# list of conditions and the following disclaimer.
	#
	# * Redistributions in binary form must reproduce the above copyright notice,
	# this list of conditions and the following disclaimer in the documentation
	# and/or other materials provided with the distribution.
	#
	# * Neither the name of the copyright holder nor the names of its
	# contributors may be used to endorse or promote products derived from
	# this software without specific prior written permission.
	#
	# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
	# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
	# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
	# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
	# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
	# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
	# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
	# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
	# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
	# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

	from __future__ import annotations

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

	import torch
	import torch.nn as nn

	from einops import rearrange
	from transformers.activations import ACT2FN
	from transformers import PretrainedConfig, PreTrainedModel
	from transformers.modeling_outputs import CausalLMOutputWithPast

	from .configuration_mixformer_sequential import MixFormerSequentialConfig

	@dataclass
	class InferenceParams:
	"""Inference parameters that are passed to the main model in order
	to efficienly calculate and store the context during inference.
	Adapted from https://github.com/Dao-AILab/flash-attention."""
	max_sequence_len: int
	max_batch_size: int
	sequence_len_offset: int = 0
	batch_size_offset: int = 0
	key_value_memory_dict: dict = field(default_factory=dict)
	fused_ft_kernel: bool = False
	lengths_per_sample: Optional[torch.Tensor] = None


	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

	class RotaryEmbedding(nn.Module):
	"""PyTorch implementation of `flash-attn` RotaryEmbedding layer.
	Adapted from https://github.com/Dao-AILab/flash-attention."""

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

	if scale_base is not None:
	raise NotImplementedError

	# Generate and save the inverse frequency buffer (non-trainable)
	self.dim = dim
	self.base = base
	self.scale_base = scale_base
	self.device = device

	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
	self.register_buffer("inv_freq", inv_freq)

	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 _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0) -> None:
	# Reset the tables if the sequence length has changed,
	# or if we're on a new device (possibly due to tracing for instance)
	seqlen = x.shape[1] + seqlen_offset

	# Re-generate the inverse frequency buffer if it's not fp32
	# (for instance if model.half() was called)
	if self.inv_freq.dtype != "torch.float32":
	self.inv_freq = 1.0 / (
	self.base ** (torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32) / self.dim)
	)

	if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
	self._seq_len_cached = seqlen
	t = torch.arange(seqlen, device=x.device, dtype=torch.float32)

	# Don't do einsum, it converts fp32 to fp16
	# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
	if self.scale is None:
	self._cos_cached = torch.cos(freqs).to(x.dtype)
	self._sin_cached = torch.sin(freqs).to(x.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")

	# We want the multiplication by scale to happen in fp32
	self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
	self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
	self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
	self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)

	def apply_rotary_emb_qkv(
	self,
	qkv: torch.FloatTensor,
	sin: torch.FloatTensor,
	cos: torch.FloatTensor,
	sin_k: Optional[torch.FloatTensor] = None,
	cos_k: Optional[torch.FloatTensor] = None,
	) -> torch.FloatTensor:
	_, seqlen, three, _, headdim = qkv.shape
	assert three == 3

	rotary_seqlen, rotary_dim = cos.shape
	rotary_dim *= 2
	assert rotary_dim <= headdim
	assert seqlen <= rotary_seqlen

	cos_k = cos if cos_k is None else cos_k
	sin_k = sin if sin_k is None else sin_k
	assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, 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:]

	# Splits the queries and keys in half
	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")

	# Casts to fp32 are necessary to prevent fp16 overflow issues
	q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]

	# Computes the new keys and queries, recasting to original dtype
	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,
	)

	def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
	"""Perform the forward pass.

	Args:
	qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
	seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.

	Returns:
	New `qkv` and the cached sinusoids.

	"""

	self._update_cos_sin_cache(qkv, seqlen_offset)

	return self.apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])

	def _update_kv_cache(kv, inference_params, layer_idx):
	"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
	Adapted from https://github.com/Dao-AILab/flash-attention."""
	# Pre-allocate memory for key-values for inference.
	num_heads, head_dim = kv.shape[-2:]
	if layer_idx not in inference_params.key_value_memory_dict:
	kv_cache = torch.empty(
	inference_params.max_batch_size, inference_params.max_sequence_len, 2,
	num_heads, head_dim, dtype=kv.dtype, device=kv.device
	)
	inference_params.key_value_memory_dict[layer_idx] = kv_cache
	else:
	kv_cache = inference_params.key_value_memory_dict[layer_idx]

	# Adjust key and value for inference
	batch_start = inference_params.batch_size_offset
	batch_end = batch_start + kv.shape[0]
	sequence_start = inference_params.sequence_len_offset
	sequence_end = sequence_start + kv.shape[1]
	assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
	assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])

	assert kv_cache is not None
	kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
	kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
	return kv


	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
	assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."

	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 _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
	old_keys = [prefix + "fc_in.weight", prefix + "fc_out.weight", prefix + "fc_in.bias", prefix + "fc_out.bias"]
	new_keys = [prefix + "fc1.weight", prefix + "fc2.weight", prefix + "fc1.bias", prefix + "fc2.bias"]

	if all(k in state_dict for k in old_keys) and not all(k in state_dict for k in new_keys):
	# Older version of `MLP` saved with different key names.
	for old_key, new_key in zip(old_keys, new_keys):
	state_dict[new_key] = state_dict.pop(old_key)

	return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

	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 FusedMLP(nn.Module):
	"""Fused Multi-Layer Perceptron from `flash-attn`.

	Reference:
	https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.

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

	act_fn = config.activation_function if act_fn is None else act_fn
	assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."

	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

	gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]
	activation = "gelu_approx" if act_fn in gelu_activations else "relu"

	self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)

	def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
	return self.mlp(hidden_states)

	class SelfAttention(nn.Module):
	"""Implement the scaled dot product attention with softmax.
	Adapted from https://github.com/Dao-AILab/flash-attention.
	Arguments
	---------
	softmax_scale: The temperature to use for the softmax attention.
	(default: 1/sqrt(d_keys) where d_keys is computed at
	runtime)
	attention_dropout: The dropout rate to apply to the attention
	(default: 0.0)
	"""
	def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
	super().__init__()
	self.causal = causal
	self.softmax_scale = softmax_scale
	self.drop = nn.Dropout(attention_dropout)

	def forward(self, qkv, causal=None, key_padding_mask=None):
	"""Implements the multihead softmax attention.
	Arguments
	---------
	qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
	causal: if passed, will override self.causal
	key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
	False means to mask out. (B, S)
	"""
	batch_size, seqlen = qkv.shape[0], qkv.shape[1]
	causal = self.causal if causal is None else causal
	q, k, v = qkv.unbind(dim=2)
	softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
	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)
	# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
	scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
	if causal:
	# "triu_tril_cuda_template" not implemented for 'BFloat16'
	# So we have to construct the mask in float
	causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
	# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
	scores = scores + causal_mask.to(dtype=scores.dtype)
	attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
	attention_drop = self.drop(attention)
	output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
	return output


	class CrossAttention(nn.Module):
	"""Implement the scaled dot product attention with softmax.
	Adapted from https://github.com/Dao-AILab/flash-attention.
	Arguments
	---------
	softmax_scale: The temperature to use for the softmax attention.
	(default: 1/sqrt(d_keys) where d_keys is computed at
	runtime)
	attention_dropout: The dropout rate to apply to the attention
	(default: 0.0)
	"""
	def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
	super().__init__()
	self.causal = causal
	self.softmax_scale = softmax_scale
	self.drop = nn.Dropout(attention_dropout)

	def forward(self, q, kv, causal=None, key_padding_mask=None):
	"""Implements the multihead softmax attention.
	Arguments
	---------
	q: The tensor containing the query. (B, Sq, H, D)
	kv: The tensor containing the key and value. (B, Sk, 2, H, D)
	causal: if passed, will override self.causal
	key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
	False means to mask out. (B, Sk)
	"""
	batch_size, seqlen_q = q.shape[0], q.shape[1]
	causal = self.causal if causal is None else causal
	seqlen_k = kv.shape[1]
	assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
	k, v = kv.unbind(dim=2)
	softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
	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)
	# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
	scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
	if causal:
	# "triu_tril_cuda_template" not implemented for 'BFloat16'
	# So we have to construct the mask in float
	causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
	device=scores.device), 1)
	# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
	scores = scores + causal_mask.to(dtype=scores.dtype)
	attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
	attention_drop = self.drop(attention)
	output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
	return output

	def find_mha_dims(
	config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
	) -> Tuple[int, int]:
	"""Validate and return the number of heads and head dimension for multi-head attention.

	Args:
	config: Model configuration.
	n_head: Number of heads.
	head_dim: Head dimension.

	Returns:
	Number of heads and head dimension.

	"""

	assert all(
	hasattr(config, attr) for attr in ["n_embd", "n_head"]
	), "`config` must have `n_embd` and `n_head` attributes."

	if head_dim is None:
	assert (
	config.n_embd % config.n_head == 0
	), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."

	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`.")

	return n_head, head_dim


	class MHA(nn.Module):
	"""Multi-head attention layer.
	Adapted from https://github.com/Dao-AILab/flash-attention."""

	def __init__(
	self,
	config: PretrainedConfig,
	rotary_dim: Optional[int] = None,
	n_head: Optional[int] = None,
	head_dim: Optional[int] = None,
	bias: Optional[bool] = True,
	dropout: Optional[float] = 0.0,
	softmax_scale: Optional[float] = None,
	causal: Optional[bool] = True,
	layer_idx: Optional[int] = None,
	rotary_emb_scale_base: Optional[float] = None,
	return_residual: Optional[bool] = False,
	checkpointing: Optional[bool] = False,
	device: Optional[str] = None,
	dtype: Optional[torch.dtype] = None,
	fused_dense: Optional[bool] = True,
	flash_attn: Optional[bool] = True,
	cutlass_attn: Optional[bool] = False,
	flash_rotary: Optional[bool] = True,
	raise_on_missing: Optional[bool] = False
	) -> None:
	super().__init__()

	factory_kwargs = {"device": device, "dtype": dtype}
	n_head, head_dim = find_mha_dims(config, n_head, head_dim)

	self.hidden_size = config.n_embd
	self.n_head = n_head
	self.head_dim = head_dim
	self.op_size = n_head * head_dim

	self.causal = causal
	self.layer_idx = layer_idx
	self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
	self.fused_dense = fused_dense
	self.flash_attn = flash_attn
	self.cutlass_attn = cutlass_attn
	self.flash_rotary = flash_rotary
	self.return_residual = return_residual
	self.checkpointing = checkpointing

	if self.rotary_emb_dim > 0:
	rotary_kwargs = {"device": device}
	if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
	rotary_kwargs["scale_base"] = rotary_emb_scale_base

	self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
	else:
	pass

	self.Wqkv = nn.Linear(self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs)
	self.out_proj = nn.Linear(self.op_size, self.hidden_size, bias=bias, **factory_kwargs)

	self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
	self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)

	def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
	"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
	Adapted from https://github.com/Dao-AILab/flash-attention."""

	assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"

	return _update_kv_cache(kv, inference_params, self.layer_idx)

	def forward(
	self,
	x: torch.FloatTensor,
	x_kv: Optional[torch.FloatTensor] = None,
	key_padding_mask: Optional[torch.BoolTensor] = None,
	cu_seqlens: Optional[torch.LongTensor] = None,
	max_seqlen: Optional[int] = None,
	mixer_subset: Optional[torch.LongTensor] = None,
	past_cache: Optional[InferenceParams] = None,
	**kwargs
	) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
	"""Perform the forward pass.

	Args:
	x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
	cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
	is the is the sum of the sequence lengths in the batch.
	x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
	key_padding_mask: boolean mask, True means to keep, False means to mask out.
	(batch, seqlen). Only applicable when not using FlashAttention.
	cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
	of the sequences in the batch, used to index into x. Only applicable when using
	FlashAttention.
	max_seqlen: int. Maximum sequence length in the batch.
	mixer_subset: for cross-attention only. If not None, will take a subset of x
	before applying the query projection. Useful for e.g., ViT where we only care
	about the CLS token in the last layer.
	past_cache: For generation only.

	Returns:
	(batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
	else (total, hidden_dim) where total is the is the sum of the sequence lengths
	in the batch.

	"""

	if cu_seqlens is not None:
	assert max_seqlen is not None
	assert key_padding_mask is None
	assert self.flash_attn
	assert self.rotary_emb_dim == 0

	if key_padding_mask is not None:
	assert cu_seqlens is None
	assert max_seqlen is None
	assert not self.flash_attn

	if past_cache is not None:
	assert key_padding_mask is None
	assert cu_seqlens is None and max_seqlen is None

	attn_kwargs = {"key_padding_mask": key_padding_mask}

	assert x_kv is None and mixer_subset is None

	qkv = self.Wqkv(x)
	qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)

	if past_cache is None:
	if self.rotary_emb_dim > 0:
	qkv = self.rotary_emb(qkv)
	context = self.inner_attn(qkv, **attn_kwargs)

	else:
	if self.rotary_emb_dim > 0:
	qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
	q = qkv[:, :, 0]
	kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
	# If we're processing the prompt, causal=None (use self.causal).
	# If we're decoding, then causal=False.
	causal = None if past_cache.sequence_len_offset == 0 else False
	context = self.inner_cross_attn(q, kv, causal=causal)

	out = rearrange(context, "... h d -> ... (h d)")
	out = self.out_proj(out)

	return out if not self.return_residual else (out, 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,
	mixer: Optional[Dict[str, Any]] = None,
	mlp: Optional[Dict[str, Any]] = None,
	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=config, **mixer, layer_idx=block_idx)
	mlp_cls = mlp.pop('mlp_cls')
	if mlp_cls == 'fused_mlp':
	self.mlp = FusedMLP(config=config, **mlp)
	else:
	self.mlp = MLP(config=config, **mlp)

	def forward(self, hidden_states: torch.FloatTensor,
	past_cache: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
	residual = hidden_states
	hidden_states = self.ln(hidden_states)

	attn_outputs = self.mixer(hidden_states, past_cache=past_cache)
	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.mlp(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: Optional[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 MixFormerSequentialPreTrainedModel(PreTrainedModel):
	"""MixFormer (sequential for DeepSpeed) pre-trained model."""

	config_class = MixFormerSequentialConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True

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

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs) -> Dict[str, Any]:
	if "use_cache" in kwargs and not kwargs["use_cache"]:
	return {"input_ids": input_ids}

	if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
	past_key_values = InferenceParams(
	max_batch_size=input_ids.shape[0],
	max_sequence_len=self.config.n_positions,
	sequence_len_offset=0,
	batch_size_offset=0,
	fused_ft_kernel=False,
	key_value_memory_dict={},
	)
	else:
	# assume past_key_values has cached all but last token in input_ids
	past_key_values.sequence_len_offset = len(input_ids[0]) - 1
	input_ids = input_ids[:, -1].unsqueeze(-1)

	return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}


	class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
	"""MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""

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

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

	modules = [Embedding(config)]
	block_config = config.architecture

	if not isinstance(block_config, list):
	block_config = [block_config for _ in range(config.n_layer)]

	if config.n_layer != len(block_config):
	config.n_layer = len(block_config)

	for block_idx, block in enumerate(block_config):
	# `block_cls` with `legacy` value is for backward compatibility
	# `path` key is for backward compatibility
	block = copy.deepcopy(block) or {"block_cls": "parallel"}
	block_cls = block.pop("path", None) or block.pop("block_cls", None)

	block["block_idx"] = block_idx
	modules.append(ParallelBlock(config, **block))

	modules.append(CausalLMHead(config))

	self.layers = nn.Sequential(*modules)
	self.loss = CausalLMLoss()

	self.post_init()

	def get_input_embeddings(self) -> nn.Embedding:
	return self.layers[0].wte

	def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
	self.layers[0].wte = new_embeddings

	def get_output_embeddings(self) -> nn.Linear:
	return self.layers[-1].linear

	def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
	self.layers[-1].linear = new_embeddings

	def forward(
	self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None,
	past_key_values: Optional[torch.FloatTensor] = None, **kwargs
	) -> CausalLMOutputWithPast:

	if not past_key_values:
	lm_logits = self.layers(input_ids)
	else:
	hidden_layer = self.layers[0](input_ids)
	for module in self.layers[1:-1]:
	hidden_layer = module(hidden_layer, past_cache=past_key_values)
	lm_logits = self.layers[-1](hidden_layer)

	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)