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transformer.py
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transformer.py
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# Copyright (c) 2021 EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2020, 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
#
# https://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
import torch
import torch.nn.functional as F
import torch.nn as nn
from .norms import get_norm
from megatron import mpu
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_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
# 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
p: number of model parallel partitions
np: n/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
):
super().__init__()
self.activation_func = get_activation(neox_args)
self.activation_type = neox_args.activation
self.bias_gelu_fusion = neox_args.bias_gelu_fusion
# auto scale so geglu has equal parameters
ff_mult = int(4 * 2 / 3) if self.activation_type == "geglu" else 4
ff_dim = (
int(ff_mult * neox_args.hidden_size) * 2
if self.activation_type == "geglu"
else ff_mult * neox_args.hidden_size
)
self.dense_h_to_4h = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=ff_dim,
gather_output=False,
init_method=init_method,
skip_bias_add=True,
)
ff_dim_in = ff_dim // 2 if self.activation_type == "geglu" else ff_dim
# Project back to h.
self.dense_4h_to_h = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=ff_dim_in,
output_size=neox_args.hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method,
skip_bias_add=True,
parallel_output=parallel_output,
)
def forward(self, hidden_states):
# [s, b, 4hp]
intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states)
if (
self.activation_type == "gelu" and self.bias_gelu_fusion
) or self.activation_type == "geglu":
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.dense_4h_to_h(intermediate_parallel)
return output, output_bias
class LLaMAParallelMLP(nn.Module):
"""LLaMA's 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.
Note: multiple_of is used to compute the hidden dimension of the MLP
"""
def __init__(
self,
neox_args,
init_method,
output_layer_init_method,
parallel_output=False,
multiple_of=256,
):
super().__init__()
self.activation_func = get_activation(neox_args)
self.activation_type = neox_args.activation
self.multiple_of = multiple_of
ff_dim = int(2 * neox_args.hidden_size * 4 / 3)
ff_dim = self.multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
self.w1 = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=ff_dim,
gather_output=False,
init_method=init_method,
skip_bias_add=True,
bias=False,
)
self.w3 = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=ff_dim,
gather_output=False,
init_method=init_method,
skip_bias_add=True,
bias=False,
)
self.w2 = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=ff_dim,
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=False,
)
def forward(self, hidden_states):
w1_out, _ = self.w1(hidden_states)
w3_out, _ = self.w3(hidden_states)
return self.w2(self.activation_func(w1_out) * w3_out)
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__()
parallelism = neox_args.output_layer_parallelism
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
)
# else:
# 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
# )
def forward(self, hidden_states):
return self.final_linear(hidden_states)
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_tensor_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
# 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,
)
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_tensor_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,
)
else:
self.rotary_emb = None
self.attention_type = neox_args.attention_config[layer_number]
self.use_flash_attention = self.attention_type == "flash"
self.use_ds_ulysses_attention = self.attention_type == "ulysses"
self.sparse = self.attention_type not in ("global", "flash")
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:
from megatron.model.flash_attention import (
# flash_attn_unpadded_qkvpacked_func_cuda,
# flash_attn_unpadded_kvpacked_func_cuda,
# Change of function names going from flash attention 1 -> flash attention 2
flash_attn_varlen_qkvpacked_func,
flash_attn_varlen_kvpacked_func,
flash_attn_unpadded_unpacked_func_triton,
)
self.flash_triton_fn = flash_attn_unpadded_unpacked_func_triton
self.flash_qkv_fn = flash_attn_varlen_qkvpacked_func
self.flash_kv_fn = flash_attn_varlen_kvpacked_func
elif self.use_ds_ulysses_attention:
print('USING ULYSSES')
try:
from deepspeed.sequence.layer import DistributedAttention
self.ds_ulysses_attention_fn = DistributedAttention(self.attention, mpu.get_sequence_parallel_group())
except ImportError as e:
print(f'Error. You passed a gpt-neox ulysses config but DeepSpeed ulysses could not be imported with the error: {e}')
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.pos_emb != "alibi":
# [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], 1, output_size[1], -1
)
value_layer = value_layer.transpose(0, 1).reshape(
output_size[0] * output_size[3], 1, output_size[1], -1
)
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,
)
if not self.training:
# [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
)
# Combined k/v into [b * sk, 2, np, hn].
kv = torch.cat([key_layer, value_layer], dim=1)
output = self.flash_kv_fn(
query_layer,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
self.dropout_p if self.training else 0.0,
softmax_scale=None,
causal=True,
)
else:
# [sq, b, np, hn] -> [b * sq, 1, np, hn]
query_layer = query_layer.transpose(0, 1).reshape(
output_size[0] * output_size[2], 1, output_size[1], -1
)
# Combined q/k/v into [b * s, 3, np, hn].
qkv = torch.cat([query_layer, key_layer, value_layer], dim=1)
output = self.flash_qkv_fn(
qkv,
cu_seqlens_q,
max_seqlen_q,
self.dropout_p if self.training else 0.0,
softmax_scale=None,
causal=True,
)
# [b * sq, np, hn] -> [b, sq, np, hn]
matmul_result = output.view(
output_size[0], output_size[2], output.shape[1], output.shape[2]
)
# [b, sq, np, hn] -> [b, np, sq, hn]
matmul_result = matmul_result.transpose(1, 2)
else:
# [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 forward(self, hidden_states, attention_mask, layer_past=None):
# hidden_states: [sq, b, h]
# =====================
# Query, Key, and Value
# =====================
# 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
)
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
apply_rotary_fn = (
apply_rotary_pos_emb_torch if self.bf16 else apply_rotary_pos_emb
)
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)
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 self.use_ds_ulysses_attention:
context_layer = self.ds_ulysses_attention_fn(query_layer, key_layer, value_layer, attention_mask)
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
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.mlp_type = neox_args.mlp_type
if self.gpt_j_residual:
self.reduce = mpu.mappings.reduce_from_model_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
if neox_args.mlp_type == "regular":
self.mlp = ParallelMLP(
neox_args=neox_args,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
parallel_output=self.gpt_j_residual,
)
elif neox_args.mlp_type == "llama":
self.mlp = LLaMAParallelMLP(
neox_args=neox_args,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
parallel_output=self.gpt_j_residual,
)
else:
raise KeyError(neox_args.mlp_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()
# 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
with torch.enable_grad():
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)
with torch.enable_grad():
output = bias_dropout_fn(
mlp_output,
bias=mlp_bias.expand_as(mlp_output),
residual=attention_output,
prob=self.hidden_dropout,
)
# 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 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))
mlp_output, mlp_bias = self.mlp(
self.post_attention_layernorm(attention_output)
)
with torch.enable_grad():
if self.mlp_type == "llama":
# 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
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]
return super().forward(hidden_states, attention_mask), 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, bias=None):
"""LM logits using word embedding weights."""
# Parallel logits.
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)