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-- Copyright (c) Facebook, Inc. and its affiliates.
--
-- This source code is licensed under the MIT license found in the
-- LICENSE file in the root directory of this source tree.
--
-- Usage: convert_model.lua <model_epoch1.th7>
require 'torch'
local fairseq = require 'fairseq'
model = torch.load(arg[1])
function find_weight_norm(container, module)
for _, wn in ipairs(container:listModules()) do
if torch.type(wn) == 'nn.WeightNorm' and wn.modules[1] == module then
return wn
end
end
end
function push_state(dict, key, module)
if torch.type(module) == 'nn.Linear' then
local wn = find_weight_norm(model.module, module)
assert(wn)
dict[key .. '.weight_v'] = wn.v:float()
dict[key .. '.weight_g'] = wn.g:float()
elseif torch.type(module) == 'nn.TemporalConvolutionTBC' then
local wn = find_weight_norm(model.module, module)
assert(wn)
local v = wn.v:float():view(wn.viewOut):transpose(2, 3)
dict[key .. '.weight_v'] = v
dict[key .. '.weight_g'] = wn.g:float():view(module.weight:size(3), 1, 1)
else
dict[key .. '.weight'] = module.weight:float()
end
if module.bias then
dict[key .. '.bias'] = module.bias:float()
end
end
encoder_dict = {}
decoder_dict = {}
combined_dict = {}
function encoder_state(encoder)
luts = encoder:findModules('nn.LookupTable')
push_state(encoder_dict, 'embed_tokens', luts[1])
push_state(encoder_dict, 'embed_positions', luts[2])
fcs = encoder:findModules('nn.Linear')
assert(#fcs >= 2)
local nInputPlane = fcs[1].weight:size(1)
push_state(encoder_dict, 'fc1', table.remove(fcs, 1))
push_state(encoder_dict, 'fc2', table.remove(fcs, #fcs))
for i, module in ipairs(encoder:findModules('nn.TemporalConvolutionTBC')) do
push_state(encoder_dict, 'convolutions.' .. tostring(i - 1), module)
if nInputPlane ~= module.weight:size(3) / 2 then
push_state(encoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1))
end
nInputPlane = module.weight:size(3) / 2
end
assert(#fcs == 0)
end
function decoder_state(decoder)
luts = decoder:findModules('nn.LookupTable')
push_state(decoder_dict, 'embed_tokens', luts[1])
push_state(decoder_dict, 'embed_positions', luts[2])
fcs = decoder:findModules('nn.Linear')
local nInputPlane = fcs[1].weight:size(1)
push_state(decoder_dict, 'fc1', table.remove(fcs, 1))
push_state(decoder_dict, 'fc2', fcs[#fcs - 1])
push_state(decoder_dict, 'fc3', fcs[#fcs])
table.remove(fcs, #fcs)
table.remove(fcs, #fcs)
for i, module in ipairs(decoder:findModules('nn.TemporalConvolutionTBC')) do
if nInputPlane ~= module.weight:size(3) / 2 then
push_state(decoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1))
end
nInputPlane = module.weight:size(3) / 2
local prefix = 'attention.' .. tostring(i - 1)
push_state(decoder_dict, prefix .. '.in_projection', table.remove(fcs, 1))
push_state(decoder_dict, prefix .. '.out_projection', table.remove(fcs, 1))
push_state(decoder_dict, 'convolutions.' .. tostring(i - 1), module)
end
assert(#fcs == 0)
end
_encoder = model.module.modules[2]
_decoder = model.module.modules[3]
encoder_state(_encoder)
decoder_state(_decoder)
for k, v in pairs(encoder_dict) do
combined_dict['encoder.' .. k] = v
end
for k, v in pairs(decoder_dict) do
combined_dict['decoder.' .. k] = v
end
torch.save('state_dict.t7', combined_dict)
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