chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,108 @@
+-- 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)