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# Copyright (c) 2024 EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2024, NVIDIA CORPORATION. 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.
"""Transformer."""
import math
from contextlib import nullcontext
import torch
import torch.nn.functional as F
import torch.nn as nn
from pkg_resources import packaging
from importlib.metadata import version
from .norms import get_norm
from megatron import mpu
from megatron.model import megablocks_utils
from megatron.model.fused_softmax import FusedScaleMaskSoftmax
from megatron.model.activations import get_activation
from megatron.model.utils import exists, get_fusion_type
from megatron.model.positional_embeddings import (
RotaryEmbedding,
apply_rotary_pos_emb_torch,
apply_rotary_pos_emb,
AliBi,
)
from megatron.model.fused_rope import (
FusedRoPEFunc,
fused_apply_rotary_pos_emb_cached,
)
from megatron.model.fused_bias_dropout import (
get_bias_dropout_add,
bias_dropout_add_fused_train,
bias_dropout_add_fused_inference,
)
from megatron.model.utils import configure_sparse_attention
from deepspeed.moe.layer import MoE
try:
from flash_attn.ops.activations import swiglu
except ImportError:
swiglu = None
# flags required to enable jit fusion kernels
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
""" We use the following notation throughout this file:
h: hidden size
n: number of attention heads
kv: number of key or value heads
p: number of model parallel partitions
np: n/p
kvp: kv/p
hp: h/p
hn: h/n
b: batch size
s: sequence length
l: number of layers
Transformer takes input of size [s, b, h] and returns a
tensor of the same size. We use the following arguments:
hyperparameters: transformer hyperparameters
attention_mask_func: a function that takes `unmasked-attention-scores`
with size [b, np, s, s] and an `attention-mask` and will apply
the masking. The function should return a masked score of the
same size [b, np, s, s].
masked-attention-scores = attention_mask_func(
unmasked-attention-scores, attention-mask)
"""
class ParallelMLP(nn.Module):
"""MLP.
MLP will take the input with h hidden state, project it to 4*h
hidden dimension, perform nonlinear transformation, and project the
state back into h hidden dimension. At the end, dropout is also
applied.
"""
def __init__(
self,
neox_args,
init_method,
output_layer_init_method,
parallel_output=False,
multiple_of=256,
MOE=False,
MoE_mp_size=1,
):
super().__init__()
assert (
neox_args.intermediate_size == None or neox_args.expansion_factor == None
), "Must pass either the absolute intermediate size or the relative expansion factor for the mamba projections"
self.activation_func, self.is_gated = get_activation(neox_args)
self.activation_type = neox_args.activation
self.bias_gelu_fusion = neox_args.bias_gelu_fusion
self.multiple_of = multiple_of
if neox_args.intermediate_size:
ffn_dim = neox_args.intermediate_size
elif neox_args.expansion_factor:
ffn_dim = int(neox_args.expansion_factor * neox_args.hidden_size)
else:
# 4h is default for ffn_dim
ffn_dim = 4 * neox_args.hidden_size
ffn_dim_in = ffn_dim
if self.is_gated:
# set activation function to be gated implementation
self.activation_func = Gated_Activation(
self.activation_func,
(swiglu is not None)
and (neox_args.activation == "swiglu")
and neox_args.use_flashattn_swiglu,
)
# auto scale so gated activations has equal parameters
ffn_dim = int(ffn_dim * 2 / 3)
ffn_dim_in = ffn_dim // 2
# set multiple
ffn_dim = int(
(2 * self.multiple_of)
* ((ffn_dim + (2 * multiple_of) - 1) // (2 * multiple_of))
)
ffn_dim_in = int(
self.multiple_of * ((ffn_dim_in + multiple_of - 1) // multiple_of)
)
self.linear1 = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=ffn_dim,
gather_output=False,
init_method=init_method,
skip_bias_add=True,
MOE=MOE,
MoE_mp_size=MoE_mp_size,
bias=neox_args.use_bias_in_mlp,
)
# Project back to h.
self.linear2 = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=ffn_dim_in,
output_size=neox_args.hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method,
parallel_output=parallel_output,
skip_bias_add=True,
MOE=MOE,
MoE_mp_size=MoE_mp_size,
bias=neox_args.use_bias_in_mlp,
)
def forward(self, hidden_states):
# [s, b, intermediate_size]
intermediate_parallel, bias_parallel = self.linear1(hidden_states)
if self.is_gated or (self.activation_type == "gelu" and self.bias_gelu_fusion):
intermediate_parallel = self.activation_func(
intermediate_parallel, bias_parallel
)
else:
intermediate_parallel = self.activation_func(
intermediate_parallel + bias_parallel
)
# [s, b, h]
output, output_bias = self.linear2(intermediate_parallel)
return output, output_bias
class Gated_Activation(torch.nn.Module):
def __init__(self, activation_func, use_swiglu=False):
super().__init__()
self.activation_func = activation_func
self.use_swiglu = use_swiglu
def forward(self, x, bias=None):
x, gate = x.chunk(2, dim=-1)
if bias is not None:
bias_1, bias_2 = bias.chunk(2, dim=-1)
x = x + bias_1
gate = gate + bias_2
if not self.use_swiglu:
intermediate_parallel = self.activation_func(gate)
return intermediate_parallel * x
else:
return swiglu(gate, x)
class ParallelLinear(nn.Module):
"""
A Parallel Linear Layer transforming the transformer outputs from hidden_size -> vocab_size
"""
def __init__(
self,
neox_args,
parallel_output=True,
init_method=nn.init.xavier_normal_,
is_last_layer=False,
):
super().__init__()
self.is_rm = neox_args.train_impl == "rm"
parallelism = neox_args.output_layer_parallelism if not self.is_rm else "row"
if parallelism == "column":
self.final_linear = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=neox_args.padded_vocab_size,
bias=False,
init_method=init_method,
gather_output=not parallel_output,
skip_bias_add=False,
mup_rescale_parameters=is_last_layer, # rescale params only called if neox_args.use_mup = True, despite it not being included here
seq_dim=1, # important: must mark that this layer receives shape [b, s, h] not [s, b, h] and so Seq. Parallel comms must gather along dim=1 rather than dim=0
)
else:
if not self.is_rm:
print(
'ERROR: Output layer parallelism over the hidden dim is currently broken (https://github.com/EleutherAI/gpt-neox/issues/905). Please run with output_layer_parallelism = "column" until this issue is fixed.'
)
exit()
# self.final_linear = mpu.RowParallelLinear(
# neox_args=neox_args,
# input_size=neox_args.hidden_size,
# output_size=neox_args.padded_vocab_size,
# bias=False,
# input_is_parallel=False,
# init_method=init_method,
# parallel_output=parallel_output,
# skip_bias_add=False,
# mup_rescale_parameters=is_last_layer, # only called if neox_args.use_mup = True, despite it not being included here
# )
else: # Not using cross entropy loss for RMs
self.rm_linear = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=1,
bias=False,
input_is_parallel=False,
init_method=init_method,
parallel_output=False,
skip_bias_add=False,
mup_rescale_parameters=is_last_layer, # only called if neox_args.use_mup = True, despite it not being included here
)
def forward(self, hidden_states):
if not self.is_rm:
return self.final_linear(hidden_states)
else:
return self.rm_linear(hidden_states)
class _MegablocksAdapter(nn.Module):
def __init__(
self, neox_args, layer_cls, init_method, output_layer_init_method, ep_group
):
super().__init__()
megablocks_utils.assert_megablocks_is_available()
args = megablocks_utils.as_megablocks_args(neox_args)
args.device = torch.cuda.current_device()
args.init_method = init_method
args.output_layer_init_method = output_layer_init_method
# NOTE: Shard the MoE layers over the data parallel group. Expert
# parallel sharding and data parallel sharding could be decoupled
# by extending the optimizer to handle data parallel reductions for
# MoE and non-MoE parameters separately.
if args.moe_expert_model_parallelism:
args.expert_parallel_group = ep_group
self.moe = layer_cls(args)
def forward(self, x):
return self.moe.forward(x)
class MbMoE(_MegablocksAdapter):
def __init__(self, neox_args, init_method, output_layer_init_method, ep_group):
super().__init__(
neox_args,
megablocks_utils.moe.MoE,
init_method,
output_layer_init_method,
ep_group,
)
class dMoE(_MegablocksAdapter):
def __init__(self, neox_args, init_method, output_layer_init_method, ep_group):
super().__init__(
neox_args,
megablocks_utils.dmoe.dMoE,
init_method,
output_layer_init_method,
ep_group,
)
class ParallelSelfAttention(nn.Module):
"""Parallel self-attention layer abstract class.
Self-attention layer takes input with size [b, s, h]
and returns output of the same size.
"""
def __init__(
self,
neox_args,
attention_mask_func,
init_method,
output_layer_init_method,
layer_number,
rpe=None,
rotary=False,
use_cache=False,
parallel_output=False,
):
super().__init__()
self.fp16 = neox_args.precision == "fp16"
self.bf16 = neox_args.precision == "bfloat16"
self.attention_mask_func = attention_mask_func
self.apply_query_key_layer_scaling = neox_args.apply_query_key_layer_scaling
self.use_cache = use_cache
self.attention_softmax_in_fp32 = neox_args.attention_softmax_in_fp32
if self.apply_query_key_layer_scaling:
self.attention_softmax_in_fp32 = True
self.layer_number = layer_number
# Per attention head and per partition values.
world_size = mpu.get_model_parallel_world_size()
self.hidden_size_per_partition = mpu.divide(neox_args.hidden_size, world_size)
self.hidden_size_per_attention_head = mpu.divide(
neox_args.hidden_size, neox_args.num_attention_heads
)
self.num_attention_heads_per_partition = mpu.divide(
neox_args.num_attention_heads, world_size
)
self.pos_emb = neox_args.pos_emb
self.use_qk_layernorm = neox_args.use_qk_layernorm
if self.use_qk_layernorm:
norm, eps = get_norm(neox_args)
self.qk_layernorm = norm(
[
self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head,
],
eps=eps,
)
self.sliding_window_width = neox_args.sliding_window_width
if (
not neox_args.num_kv_heads
or neox_args.num_kv_heads == neox_args.num_attention_heads
):
self.gqa = False
else:
self.gqa = True
if self.gqa:
self.num_kv_heads_per_partition = mpu.divide(
neox_args.num_kv_heads, world_size
) # we do not yet clone KV heads in MQA across TP ranks...
self.kv_hidden_size = (
neox_args.num_kv_heads * self.hidden_size_per_attention_head
) # how large the total hidden dim for each of K and V is
else:
self.num_kv_heads_per_partition = self.num_attention_heads_per_partition
self.kv_hidden_size = neox_args.hidden_size
if not self.gqa:
# Strided linear layer.
self.query_key_value = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=3 * neox_args.hidden_size,
gather_output=False,
init_method=init_method,
bias=neox_args.use_bias_in_attn_linear,
)
else:
# QKV proj is smaller if we are using GQA / MQA
self.query_key_value = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=neox_args.hidden_size + 2 * self.kv_hidden_size,
gather_output=False,
init_method=init_method,
bias=neox_args.use_bias_in_attn_linear,
)
coeff = None
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
if self.apply_query_key_layer_scaling:
coeff = max(1, self.layer_number)
self.norm_factor *= coeff
if neox_args.use_mup:
self.norm_factor = self.hidden_size_per_attention_head
self.rpe = rpe
if self.pos_emb == "alibi":
self.alibi_embed = AliBi(
neox_args.num_attention_heads,
neox_args.model_parallel_size,
mpu.get_model_parallel_rank(),
)
# TODO: this arg shouldn't need to be passed in - get from neox_args
if rotary:
if neox_args.rotary_pct == 1:
self.rotary_ndims = None
else:
assert neox_args.rotary_pct < 1
self.rotary_ndims = int(
self.hidden_size_per_attention_head * neox_args.rotary_pct
)
dim = (
self.rotary_ndims
if self.rotary_ndims is not None
else self.hidden_size_per_attention_head
)
self.rotary_emb = RotaryEmbedding(
dim,
base=neox_args.rotary_emb_base,
max_seq_len=neox_args.seq_length,
precision=neox_args.params_dtype,
save_inv_freqs=neox_args.rotary_save_freqs_buffer,
)
else:
self.rotary_emb = None
self.rope_fusion = neox_args.rope_fusion
self.attention_type = neox_args.attention_config[layer_number]
self.use_flash_attention = self.attention_type == "flash"
self.use_triton = (
self.use_flash_attention
and self.pos_emb == "alibi"
and (
not packaging.version.Version(version("flash-attn"))
>= packaging.version.Version("2.4.0.post1")
)
)
self.sparse = self.attention_type not in ("global", "flash")
if self.gqa:
assert not self.sparse
if self.sparse:
self.sparse_attn = configure_sparse_attention(
neox_args,
self.attention_type,
self.num_attention_heads_per_partition,
mpu=mpu,
)
else:
if self.use_flash_attention:
# we now use Flash Attention 2's provided interface.
# TODO: we no longer need to use flash_triton_fn since flash cuda supports alibi.
# consider adding OpenAI's more recent Flash-2 Triton kernel in future
# from https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py
from flash_attn.flash_attn_interface import (
flash_attn_func,
flash_attn_varlen_func,
)
from flash_attn.flash_attn_triton import (
flash_attn_func as flash_attn_unpadded_unpacked_func_triton,
)
self.flash_triton_fn = flash_attn_unpadded_unpacked_func_triton
self.flash_qkv_fn = flash_attn_func
self.flash_varlen_qkv_fn = flash_attn_varlen_func
else:
self.scale_mask_softmax = FusedScaleMaskSoftmax(
input_in_fp16=self.fp16,
input_in_bf16=self.bf16,
fusion_type=get_fusion_type(neox_args),
mask_func=self.attention_mask_func,
softmax_in_fp32=self.attention_softmax_in_fp32,
scale=coeff,
)
# Dropout. Note that for a single iteration, this layer will generate
# different outputs on different number of parallel partitions but
# on average it should not be partition dependent.
self.dropout_p = neox_args.attention_dropout
self.attention_dropout = nn.Dropout(self.dropout_p)
# Output.
self.dense = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=neox_args.hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method,
skip_bias_add=True,
parallel_output=parallel_output,
bias=neox_args.use_bias_in_attn_linear,
)
def attention(
self, query_layer, key_layer, value_layer, layer_past, attention_mask
):
# ===================================
# Raw attention scores. [b, np, s, s]
# ===================================
# [b, np, sq, sk]
output_size = (
query_layer.size(1),
query_layer.size(2),
query_layer.size(0),
key_layer.size(0),
)
# [sq, b, np, hn] -> [sq, b * np, hn]
query_layer = query_layer.view(
output_size[2], output_size[0] * output_size[1], -1
)
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
# preallocating result tensor: [b * np, sq, sk]
matmul_result = torch.empty(
output_size[0] * output_size[1],
output_size[2],
output_size[3],
dtype=query_layer.dtype,
device=torch.cuda.current_device(),
)
# Raw attention scores. [b * np, sq, sk]
matmul_result = torch.baddbmm(
matmul_result,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=(1.0 / self.norm_factor),
)
# change view to [b, np, sq, sk]
attention_scores = matmul_result.view(*output_size)
# ==================================================
# Update attention mask for inference. [b, np, sq, sk]
# ==================================================
if self.use_cache:
with torch.no_grad():
attention_mask = attention_mask[
..., : attention_scores.size(3), : attention_scores.size(3)
]
# ===========================
# Attention probs and dropout
# ===========================
if exists(self.rpe):
rpe = self.rpe(query_layer.size(0), key_layer.size(0))
attention_scores += rpe # [1, np, sq, sk]
if self.pos_emb == "alibi":
attention_scores = self.alibi_embed(attention_scores)
# attention scores and attention mask [b, np, sq, sk]
attention_probs = self.scale_mask_softmax(attention_scores, attention_mask)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
with mpu.get_cuda_rng_tracker().fork():
attention_probs = self.attention_dropout(attention_probs)
# =========================
# Context layer. [sq, b, hp]
# =========================
# value_layer -> context layer.
# [sk, b, np, hn] --> [b, np, sq, hn]
# context layer shape: [b, np, sq, hn]
output_size = (
value_layer.size(1),
value_layer.size(2),
query_layer.size(0),
value_layer.size(3),
)
# change view [sk, b * np, hn]
value_layer = value_layer.view(
value_layer.size(0), output_size[0] * output_size[1], -1
)
# change view [b * np, sq, sk]
attention_probs = attention_probs.view(
output_size[0] * output_size[1], output_size[2], -1
)
# matmul: [b * np, sq, hn]
context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
# change view [b, np, sq, hn]
context_layer = context_layer.view(*output_size)
return context_layer
def flash_attention(self, query_layer, key_layer, value_layer):
# [b, np, sq, sk]
output_size = (
query_layer.size(1),
query_layer.size(2),
query_layer.size(0),
key_layer.size(0),
)
if self.use_flash_attention and not self.use_triton:
# [sk, b, np, hn] -> [b, sk, np, hn] -> [b * sk, 1, np, hn]
key_layer = key_layer.transpose(0, 1).reshape(
output_size[0], output_size[3], self.num_kv_heads_per_partition, -1
)
value_layer = value_layer.transpose(0, 1).reshape(
output_size[0], output_size[3], self.num_kv_heads_per_partition, -1
)
# [sq, b, np, hn] -> [b, sq, np, hn]
query_layer = query_layer.transpose(0, 1).reshape(
output_size[0], output_size[2], output_size[1], -1
)
# only pass in window_size or alibi_slopes kwarg
# if we use Sliding Window Attention / AliBi.
# Flash attn defaults to (-1,-1), or
# does not have this kwarg prior to v2.3.0
extra_kwargs = (
{"window_size": (self.sliding_window_width, -1)}
if self.sliding_window_width is not None
else {}
)
if self.pos_emb == "alibi":
extra_kwargs["alibi_slopes"] = self.alibi_embed.slopes.to(
query_layer.device
).to(torch.float32)
if not self.training:
batch_size = output_size[0]
max_seqlen_q = output_size[2]
max_seqlen_k = output_size[3]
cu_seqlens_q = torch.arange(
0,
(batch_size + 1) * max_seqlen_q,
step=max_seqlen_q,
dtype=torch.int32,
device=query_layer.device,
)
cu_seqlens_k = torch.arange(
0,
(batch_size + 1) * max_seqlen_k,
step=max_seqlen_k,
dtype=torch.int32,
device=key_layer.device,
)
q_shape = query_layer.shape
k_shape = key_layer.shape
v_shape = value_layer.shape
is_causal = max_seqlen_q == max_seqlen_k
output = self.flash_varlen_qkv_fn(
query_layer.reshape(
(q_shape[0] * q_shape[1], q_shape[2], q_shape[3])
),
key_layer.reshape(
(k_shape[0] * k_shape[1], k_shape[2], k_shape[3])
),
value_layer.reshape(
(v_shape[0] * v_shape[1], v_shape[2], v_shape[3])
),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
softmax_scale=None,
causal=is_causal,
**extra_kwargs,
)
output = output.reshape(q_shape)
else:
output = self.flash_qkv_fn(
query_layer,
key_layer,
value_layer,
self.dropout_p if self.training else 0.0,
softmax_scale=None,
causal=True,
**extra_kwargs,
)
matmul_result = output
# [b, sq, np, hn] -> [b, np, sq, hn]
matmul_result = matmul_result.transpose(1, 2)
else:
# we still use Triton if using AliBi with flash-attn<2.4.0.post1.
# [sq, b, np, hn] -> [b, sq, np, hn]
sq = query_layer.size(0)
b = query_layer.size(1)
sk = key_layer.size(0)
query_layer = query_layer.transpose(0, 1)
key_layer = key_layer.transpose(0, 1)
value_layer = value_layer.transpose(0, 1)
bias = self.alibi_embed.bias(sq, sk, query_layer.device, query_layer.dtype)
bias = bias.unsqueeze(0).tile((b, 1, 1, 1))
matmul_result = self.flash_triton_fn(
query_layer, key_layer, value_layer, bias=bias, causal=True
)
matmul_result = matmul_result.transpose(1, 2)
return matmul_result
def sparse_attention(self, query_layer, key_layer, value_layer, attention_mask):
# TODO: sparse attn dropout?
# TODO: pad to block size
# shape of q/k/v is [sq, b, np, hn] and needs to be transposed to [b, np, sq, hn]
query_layer, key_layer, value_layer = map(
lambda t: t.permute(1, 2, 0, 3).contiguous(),
(query_layer, key_layer, value_layer),
)
# output shape [b, np(heads), sq, hn]
attn_mask = attention_mask.to(query_layer.dtype) * -10000
if exists(self.rpe):
rpe = self.rpe(query_layer.size(0), key_layer.size(0))
else:
rpe = None
return self.sparse_attn(
query_layer, key_layer, value_layer, attn_mask=attn_mask, rpe=rpe
)
def gqa_project(self, hidden_states, attention_mask, layer_past=None):
# QKV projection and separation into separate Q/K/V layers for GQA,
# where KV projections may be smaller than Q projection.
# the logic for this is explained in comments of this function
# detailing the intermediate sizes of tensors at each reshape.
# pass through projection: [sq, b, h] --> [sq, b, ((np + 2 * kvp) * hn)]
mixed_x_layer, _ = self.query_key_value(hidden_states)
# First: reshape so we have seqlen, batch, and num. query heads each as separate dims
# Final dim is not exactly head dim: the first (head dim) dims are query heads,
# The last (head dim * ratio of kv to q heads) each are the "k/v heads"
# (right now we treat like we have same num. heads, but smaller head dim)
# [sq, b, ((np + 2 * kvp) * hn)] --> [sq, b, np, (hn * (1 + 2 * (kvp / np)))]
new_qkv_shape = (
mixed_x_layer.shape[0],
mixed_x_layer.shape[1],
self.num_attention_heads_per_partition,
int(
self.hidden_size_per_attention_head
* (
1
+ 2
* (
self.num_kv_heads_per_partition
/ self.num_attention_heads_per_partition
)
)
),
)
mixed_x_layer = mixed_x_layer.reshape(*new_qkv_shape)
# Next: split our fake head dim. (last dim) so that the first (head dim) dimensions go to Q,
# the last smaller 2 * (head dim * kv to q head ratio) each divided between K and V separately
split_sizes = (
self.hidden_size_per_attention_head,
int(
(
self.num_kv_heads_per_partition
/ self.num_attention_heads_per_partition
)
* self.hidden_size_per_attention_head
),
int(
(
self.num_kv_heads_per_partition
/ self.num_attention_heads_per_partition
)
* self.hidden_size_per_attention_head
),
)
# [sq, b, np, (hn * (1 + 2 * (kvp / np)))] --> 1 x [sq, b, np, hn] , 2 x [sq, b, np, (hn * (kvp / np))]
(query_layer, key_layer, value_layer) = [
x.contiguous()
for x in torch.split(
mixed_x_layer,
split_sizes,
dim=mixed_x_layer.dim() - 1,
)
]
# reshape K/V to proper output shape (last dim = correct full "real" head size again)
# 2 x [sq, b, np, (hn * (kvp / np))] --> 2 x [sq, b, kvp, hn]
new_kv_shape = (
key_layer.size(0),
key_layer.size(1),
self.num_kv_heads_per_partition,
self.hidden_size_per_attention_head,
)
key_layer = key_layer.view(*new_kv_shape)
value_layer = value_layer.view(*new_kv_shape)
# if not using Flash attention, we repeat K/V heads to match Q head counts
if not self.use_flash_attention:
key_layer = torch.repeat_interleave(
key_layer,
repeats=int(
self.num_attention_heads_per_partition
// self.num_kv_heads_per_partition
),
dim=2,
)
value_layer = torch.repeat_interleave(
value_layer,
repeats=int(
self.num_attention_heads_per_partition
// self.num_kv_heads_per_partition
),
dim=2,
)
return query_layer, key_layer, value_layer
def forward(self, hidden_states, attention_mask, layer_past=None):
# hidden_states: [sq, b, h]
# =====================
# Query, Key, and Value
# =====================
if not self.gqa:
# QKV projection for MHA.
# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
mixed_x_layer, _ = self.query_key_value(hidden_states)
# [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn]
new_tensor_shape = mixed_x_layer.size()[:-1] + (
self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head,
)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
(query_layer, key_layer, value_layer) = mpu.split_tensor_along_last_dim(
mixed_x_layer, 3
)
else:
# Grouped Query Attention (GQA) - specific logic for performing QKV proj
# and separating out Q, K, and V outputs.
# output shapes: 1 x [sq, b, np, hn], 2 x [sq, b, kvp, hn] if using flash
query_layer, key_layer, value_layer = self.gqa_project(
hidden_states, attention_mask, layer_past=layer_past
)
# QK Normalization https://arxiv.org/abs/2302.05442
if self.use_qk_layernorm:
query_layer = self.qk_layernorm(query_layer)
key_layer = self.qk_layernorm(key_layer)
if exists(self.rotary_emb):
if exists(self.rotary_ndims):
# partial rotary
query_rot, query_pass = (
query_layer[..., : self.rotary_ndims],
query_layer[..., self.rotary_ndims :],
)
key_rot, key_pass = (
key_layer[..., : self.rotary_ndims],
key_layer[..., self.rotary_ndims :],
)
else:
# full rotary
query_rot, key_rot = query_layer, key_layer
seq_len = key_layer.shape[0]
offset = 0
if exists(layer_past) and layer_past.numel() > 0:
offset = layer_past[0].shape[0]
seq_len += offset
cos, sin = self.rotary_emb(value_layer, seq_len=seq_len)
if self.rope_fusion:
query_layer, key_layer = (
fused_apply_rotary_pos_emb_cached(rot, cos, sin)
for rot in [query_rot, key_rot]
)
else:
if self.bf16:
apply_rotary_fn = apply_rotary_pos_emb_torch
else:
apply_rotary_fn = apply_rotary_pos_emb
query_layer, key_layer = apply_rotary_fn(
query_rot, key_rot, cos, sin, offset=offset
)
if exists(self.rotary_ndims):
query_layer = torch.cat((query_layer, query_pass), dim=-1)
key_layer = torch.cat((key_layer, key_pass), dim=-1)
# ==================================
# Cache key and value for inference
# ==================================
if exists(layer_past) and layer_past.numel() > 0:
past_key, past_value = layer_past
key_layer = torch.cat((past_key.type_as(key_layer), key_layer), dim=0)
value_layer = torch.cat(
(past_value.type_as(value_layer), value_layer), dim=0
)
if self.use_cache:
present = torch.stack((key_layer, value_layer))
if self.use_flash_attention:
context_layer = self.flash_attention(query_layer, key_layer, value_layer)
elif not self.sparse:
context_layer = self.attention(
query_layer, key_layer, value_layer, layer_past, attention_mask
)
else:
context_layer = self.sparse_attention(
query_layer, key_layer, value_layer, attention_mask
)
# [b, np, sq, hn] --> [sq, b, np, hn]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sq, b, np, hn] --> [sq, b, hp]
new_context_layer_shape = context_layer.size()[:-2] + (
self.hidden_size_per_partition,
)
context_layer = context_layer.view(*new_context_layer_shape)
# =================
# Output. [sq, b, h]
# =================
output, bias = self.dense(context_layer)
if self.use_cache:
output = [output, present]
return output, bias
class ParallelTransformerLayer(nn.Module):
"""A single transformer layer.
Transformer layer takes input with size [b, s, h] and returns an
output of the same size.
"""
def __init__(
self,
neox_args,
attention_mask_func,
init_method,
output_layer_init_method,
layer_number,
rpe=None,
rotary=False,
use_cache=False,
):
super().__init__()
self.layer_number = layer_number
self.neox_args = neox_args
norm, eps = get_norm(neox_args)
# Layernorm on the input data.
self.input_layernorm = norm(neox_args.hidden_size, eps=eps)
self.use_cache = use_cache
self.hidden_dropout = neox_args.hidden_dropout
self.bias_dropout_fusion = neox_args.bias_dropout_fusion
self.gpt_j_residual = neox_args.gpt_j_residual
self.gpt_j_tied = neox_args.gpt_j_tied
self.moe_type = neox_args.moe_type
self.activation = neox_args.activation
if self.gpt_j_residual:
# GPT-J style layers allow us to defer the reduction of results across TP ranks until the end of the two sublayers.
# the reduction we use is a simple allreduce for pure Tensor Parallel,
# but needs to be a reduce-scatter when using Megatron-style Sequence Parallel (LN sharding.)
self.reduce = (
mpu.mappings.reduce_from_model_parallel_region
if not neox_args.sequence_parallel
else mpu.mappings.reduce_scatter_to_sequence_parallel_region
)
# Self attention.
self.attention = ParallelSelfAttention(
neox_args=neox_args,
attention_mask_func=attention_mask_func,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
layer_number=layer_number,
rpe=rpe,
use_cache=self.use_cache,
rotary=rotary,
parallel_output=self.gpt_j_residual,
)
# Layernorm on the output of the attention layer.
# If GPT-J residuals are used, this is surpurfulous but leaving it in
# leads to cleaner code
self.post_attention_layernorm = norm(neox_args.hidden_size, eps=eps)
# MLP
def get_mlp(**kw):
return ParallelMLP(
neox_args=neox_args,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
parallel_output=self.gpt_j_residual,
multiple_of=neox_args.mlp_multiple_of,
**kw,
)
self.num_experts = (
neox_args.moe_num_experts
if layer_number % neox_args.expert_interval == 0
else 1
)
args = neox_args
if self.num_experts <= 1:
self.mlp = get_mlp()
else:
from torch import distributed as dist
if self.num_experts > dist.get_world_size():
moe_mp_size = 1
else:
moe_mp_size = dist.get_world_size() // self.num_experts
if neox_args.moe_type == "deepspeed":
self.mlp = MoE(
args.hidden_size,
get_mlp(
"regular",
MOE=True,
MoE_mp_size=moe_mp_size,
),
num_experts=self.num_experts,
ep_size=args.moe_expert_parallel_size,
k=args.moe_top_k,
use_residual=args.moe_use_residual,
capacity_factor=args.moe_train_capacity_factor,
eval_capacity_factor=args.moe_eval_capacity_factor,
min_capacity=args.moe_min_capacity,
drop_tokens=args.moe_token_dropping,
use_tutel=args.use_tutel,
enable_expert_tensor_parallelism=args.enable_expert_tensor_parallelism,
)
elif neox_args.moe_type == "megablocks":
def integrate_megablocks_with_ds_expert_parallelism():
# We make megablocks work with DS parallelism.
#
# We fool DS into accepting these MoE parameters as its own DS MoE params,
# which makes things work with the underlying expert parallelism,
# including TED parallelism.
#
# Effectively, we want to:
#
# - Make DS's data parallel gradient all-reduction skip these params.
# - But make these params participate in the expert parallel all-reduction!
#
# Further background:
#
# Normally, with the original megablocks demo codebase, it
# only supports 1 copy of any expert throughout
# the network, since it uses EP group = DP group.
#
# First, we trigger DS initialization of the MoE expert parallel groups and internal state.
throwaway = MoE(
args.hidden_size,
get_mlp(
"regular",
MOE=True,
MoE_mp_size=moe_mp_size,
),
num_experts=self.num_experts,
ep_size=args.moe_expert_parallel_size,
k=args.moe_top_k,
use_residual=args.moe_use_residual,
capacity_factor=args.moe_train_capacity_factor,
eval_capacity_factor=args.moe_eval_capacity_factor,
min_capacity=args.moe_min_capacity,
drop_tokens=args.moe_token_dropping,
use_tutel=args.use_tutel,
enable_expert_tensor_parallelism=args.enable_expert_tensor_parallelism,
)
throwaway.set_deepspeed_parallelism()
ep_group = throwaway.deepspeed_moe.ep_group
if args.moe_token_dropping:
self.mlp = MbMoE(
neox_args, init_method, output_layer_init_method, ep_group
)
else:
self.mlp = dMoE(
neox_args, init_method, output_layer_init_method, ep_group
)
# Next, we trick DS into seeing these as its own MoE params.
for param in self.mlp.parameters():
if getattr(param, "expert_model_parallel", None) is not None:
# is_moe_param looks for this attr.
param.allreduce = False
param.group_name = throwaway.expert_group_name
integrate_megablocks_with_ds_expert_parallelism()
else:
raise KeyError(neox_args.moe_type)
self.layer_past = None # used to cache k/v pairs in inference
def _get_bias_dropout(self):
if self.bias_dropout_fusion:
fn = (
bias_dropout_add_fused_train
if self.training
else bias_dropout_add_fused_inference
)
else:
fn = get_bias_dropout_add(self.training)
return fn
def forward(self, x, attention_mask, layer_past=None):
layer_past = layer_past if layer_past is not None else self.layer_past
bias_dropout_fn = self._get_bias_dropout()
moe_loss = torch.tensor(0.0, device=x.device, dtype=x.dtype)
# x: [b, s, h]
if self.gpt_j_residual:
# pseudocode:
# x = x + attn(ln(x)) + mlp(ln(x))
# this means we can avoid doing the allreduce in the attn / mlp outputs
# to save communication time (we can do a single allreduce after we add mlp / attn outputs).
# due to a bug, the two layernorms are not tied in GPT-NeoX-20B. This is non-desirable, but
# we preserve the functionality for backwards compatibility
residual = x
# applies the correct normalization depending on if the norms are tied
if self.gpt_j_tied:
x = self.input_layernorm(x)
x1, x2 = x, x
else:
x1, x2 = self.input_layernorm(x), self.post_attention_layernorm(x)
# attention operator
attention_output, attention_bias = self.attention(
x1, attention_mask, layer_past=layer_past
)
if self.use_cache:
attention_output, presents = attention_output
self.layer_past = presents
if attention_bias is not None:
with torch.enable_grad() if not self.eval else nullcontext():
attention_output = bias_dropout_fn(
attention_output,
bias=attention_bias.expand_as(attention_output),
residual=None,
prob=self.hidden_dropout,
)
# mlp operator
mlp_output, mlp_bias = self.mlp(x2)
if mlp_bias is not None:
with torch.enable_grad() if not self.eval else nullcontext():
output = bias_dropout_fn(
mlp_output,
bias=mlp_bias.expand_as(mlp_output),
residual=attention_output,
prob=self.hidden_dropout,
)
else:
output = mlp_output
# output = (x + attn(ln(x)) + mlp(ln(x))
output = residual + self.reduce(output)
else:
# pseudocode:
# x = x + attn(ln1(x))
# x = x + mlp(ln2(x))
residual = x
# x = x + attn(ln1(x))
attention_output, attention_bias = self.attention(
self.input_layernorm(x), attention_mask, layer_past=layer_past
)
if self.use_cache:
attention_output, presents = attention_output
self.layer_past = presents
with torch.enable_grad() if not self.eval else nullcontext():
if attention_bias is not None:
# Use special bias_dropout_fn if we have a bias term from the above attention layer
attention_output = bias_dropout_fn(
attention_output,
bias=attention_bias.expand_as(residual),
residual=residual,
prob=self.hidden_dropout,
)
else:
# Otherwise just apply dropout + residual
attention_output = (
torch.nn.functional.dropout(
attention_output,
p=self.hidden_dropout,
training=self.training,
)
+ residual
)
# output = x + mlp(ln2(x))
layernorm_output = self.post_attention_layernorm(attention_output)
mlp_bias = torch.tensor(
0.0, device=layernorm_output.device, dtype=layernorm_output.dtype
)
if self.num_experts == 1:
mlp_output, mlp_bias = self.mlp(layernorm_output)
else:
if self.moe_type == "deepspeed":
mlp_output, moe_loss, _ = self.mlp(layernorm_output)
mlp_bias = (
None # deepspeed.moe.layer.MoE.forward ignores the bias term
)
elif self.moe_type == "megablocks":
mlp_output, mlp_bias = self.mlp(layernorm_output)
else:
raise KeyError(self.moe_type)
with torch.enable_grad() if not self.eval else nullcontext():
if (
self.activation == "swiglu"
or self.num_experts > 1
and self.moe_type == "deepspeed"
):
# No dropout either
assert mlp_bias is None
output = mlp_output + attention_output
else:
output = bias_dropout_fn(
mlp_output,
bias=mlp_bias.expand_as(attention_output),
residual=attention_output,
prob=self.hidden_dropout,
)
return output, moe_loss
class ParallelTransformerLayerPipe(ParallelTransformerLayer):
"""Extends ParallelTransformerLayer to forward attention_mask through the pipeline."""
def forward(self, args):
assert (
len(args) == 2
), "ParallelTransformerLayerPipe expects 2 arguments - hidden_states and attention_mask"
hidden_states, attention_mask = args
# we are returning just [hidden_states, mask]
output, moe_loss = super().forward(hidden_states, attention_mask)
# auxiliary output
self.last_moe_loss = moe_loss
return output, attention_mask
class ParallelLinearPipe(ParallelLinear):
"""Another helper class to pass presents through to the output when doing inference with a Pipe Parallel model"""
def forward(self, args):
assert isinstance(
args, torch.Tensor
), "ParallelLinearPipe expects a single argument - hidden_states"
hidden_state = args
logits, bias = super().forward(hidden_state)
return logits
class NormPipe(nn.Module):
"""Just a helper class to pass presents through to the output when doing inference with a Pipe Parallel model"""
def __init__(self, norm_class, hidden_size, eps):
super().__init__()
self.norm = norm_class(hidden_size, eps=eps)
def forward(self, args):
assert not isinstance(
args, tuple
), "NormPipe should only receive a single tensor as input"
return self.norm(args)
def parallel_lm_logits(
input_,
word_embeddings_weight,
parallel_output,
seq_parallel=False,
seq_dim=1,
bias=None,
):
"""LM logits using word embedding weights."""
# Parallel logits.
if seq_parallel:
# if using Sequence Parallelism, our logits are sharded along the sequence dimension.
# gather them here. (backward pass: reduce-scatter)
input_parallel = mpu.gather_from_sequence_parallel_region(
input_, seq_dim=seq_dim
)
else:
# Set up backprop all-reduce.
input_parallel = mpu.copy_to_model_parallel_region(input_)
# Matrix multiply.
if bias is None:
logits_parallel = F.linear(input_parallel, word_embeddings_weight)
else:
logits_parallel = F.linear(input_parallel, word_embeddings_weight, bias)
# Gather if needed.
if parallel_output:
return logits_parallel
return mpu.gather_from_model_parallel_region(logits_parallel)
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