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# Copyright 2024 Databricks
# SPDX-License-Identifier: Apache-2.0
import torch
import torch.nn.functional as F
from megablocks.layers.arguments import Arguments
class FFN(torch.nn.Module):
def __init__(self, args: Arguments):
super().__init__()
self.w1 = torch.nn.Parameter(
torch.empty(
args.hidden_size,
args.ffn_hidden_size,
device=args.device,
dtype=torch.float16 if args.fp16 else torch.float32,
),
)
self.w2 = torch.nn.Parameter(
torch.empty(
args.ffn_hidden_size,
args.hidden_size,
device=args.device,
dtype=torch.float16 if args.fp16 else torch.float32,
),
)
def forward(self, x):
return torch.matmul(
F.gelu(torch.matmul(x, self.w1), approximate='tanh'),
self.w2,
)
class GLU(FFN):
def __init__(self, args: Arguments):
super().__init__(args)
self.v1 = torch.nn.Parameter(
torch.empty(
args.hidden_size,
args.ffn_hidden_size,
device=args.device,
dtype=torch.float16 if args.fp16 else torch.float32,
),
)
def forward(self, x):
x1 = F.gelu(torch.matmul(x, self.w1), approximate='tanh') * torch.matmul(x, self.v1)
return torch.matmul(x1, self.w2)
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