File size: 5,093 Bytes
d90b3a8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
# Copyright (c) 2024, EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# 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.
import torch
import torch.nn as nn
import torch.nn.functional as F
from megatron.model.fused_softmax import FusedScaleMaskSoftmax
from megatron.model.activations import get_activation
from megatron.model.norms import get_norm
from megatron.model.utils import get_fusion_type
from megatron import mpu
class TinyAttention(nn.Module):
def __init__(self, neox_args, d_attn, d_ff, mask_fn):
super().__init__()
self.proj_qkv = nn.Linear(d_ff * 2, 3 * d_attn)
self.scale = d_attn**-0.5
self.proj_ffn = nn.Linear(d_attn, d_ff)
self.softmax = FusedScaleMaskSoftmax(
input_in_fp16=neox_args.precision == "fp16",
input_in_bf16=neox_args.precision == "bfloat16",
fusion_type=get_fusion_type(neox_args),
mask_func=mask_fn,
softmax_in_fp32=neox_args.attention_softmax_in_fp32,
scale=None,
)
def forward(self, x, attention_mask):
q, k, v = torch.chunk(self.proj_qkv(x), 3, dim=-1)
w = torch.einsum("bnd,bmd->bnm", q, k).unsqueeze(1) * self.scale
a = self.softmax(
w, mask=attention_mask[..., : w.size(-2), : w.size(-1)]
).squeeze(1)
x = torch.einsum("bnm,bmd->bnd", a, v)
return self.proj_ffn(x)
class SpatialGatingUnit(nn.Module):
def __init__(self, neox_args, d_ff, d_attn=None, causal=True, mask_fn=None):
super().__init__()
self.causal = causal
self.use_attn = d_attn is not None
norm, eps = get_norm(neox_args)
self.norm = norm(d_ff, eps=eps)
self.proj = nn.Linear(neox_args.seq_length, neox_args.seq_length)
if self.use_attn:
assert mask_fn is not None
self.attn = TinyAttention(
neox_args=neox_args, d_attn=d_attn, d_ff=d_ff, mask_fn=mask_fn
)
nn.init.zeros_(self.proj.weight)
nn.init.constant_(self.proj.bias, 1.0)
def forward(self, x, attention_mask):
device, n = x.device, x.shape[1]
x = x.transpose(0, 1) # [s, b, d] -> [b, s, d]
res, gate = x.chunk(2, dim=-1) # split along dim
gate = self.norm(gate)
weight, bias = self.proj.weight, self.proj.bias
if self.causal:
weight, bias = weight[:n, :n], bias[:n]
mask = torch.ones(weight.shape[:2], device=device).triu_(1).bool()
weight = weight.masked_fill(mask, 0.0)
gate = F.linear(gate.transpose(2, 1), weight, self.proj.bias).transpose(2, 1)
if self.use_attn:
gate = gate + self.attn(x, attention_mask)
return (gate * res).transpose(0, 1) # [b, s, d] -> [s, b, d]
class GMLPBlock(nn.Module):
def __init__(
self,
neox_args,
init_method,
output_layer_init_method,
layer_number,
ff_mult=4,
mask_fn=None,
):
super().__init__()
self.layer_number = layer_number
ff_dim = neox_args.hidden_size * ff_mult
norm, eps = get_norm(neox_args)
self.norm = norm(neox_args.hidden_size, eps=eps)
self.input_linear = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
output_size=ff_dim * 2,
gather_output=False,
init_method=init_method,
skip_bias_add=True,
)
self.activation_func, _ = get_activation(neox_args)
ff_dim_parallel = mpu.divide(ff_dim, mpu.get_model_parallel_world_size())
if neox_args.attention_config[layer_number] == "amlp":
d_attn = neox_args.gmlp_attn_dim
else:
d_attn = None
self.sgu = SpatialGatingUnit(
neox_args, ff_dim_parallel, d_attn, causal=True, mask_fn=mask_fn
)
self.output_linear = 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,
)
def forward(self, args):
assert len(args) == 2, "GMLPBlock expects 2 arguments"
x, attention_mask = args
x = self.norm(x)
x, _ = self.input_linear(x)
x = self.activation_func(x)
x = self.sgu(x, attention_mask)
x, _ = self.output_linear(x)
return x, attention_mask
|