Revert "Fully quantize Fairseq transformer (#1993)" (#2032)

Summary:
This reverts commit 6379573.

It doesn't tie weights and breaks old checkpoints.
Pull Request resolved: #2032

Reviewed By: cndn, ngoyal2707

Differential Revision: D21141945

Pulled By: myleott

fbshipit-source-id: b2f2ce8092a1bf8bcd6a7e422a69306e342b8cdd

fairseq/models/transformer.py

@@ -669,6 +669,11 @@ def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
  factor=args.adaptive_softmax_factor,
  tie_proj=args.tie_adaptive_proj,
  )
+ elif not self.share_input_output_embed:
+ self.embed_out = nn.Parameter(
+ torch.Tensor(len(dictionary), self.output_embed_dim)
+ )
+ nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5)
 
  if args.decoder_normalize_before and not getattr(
  args, "no_decoder_final_norm", False
@@ -681,16 +686,6 @@ def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
  else:
  self.layernorm_embedding = None
 
- if self.share_input_output_embed:
- self.output_projection = nn.Linear(
- self.embed_tokens.weight.shape[1], self.embed_tokens.weight.shape[0], bias=False
- )
- else:
- self.output_projection = nn.Linear(
- self.output_embed_dim, len(dictionary), bias=False
- )
- nn.init.normal_(self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5)
-
  def build_decoder_layer(self, args, no_encoder_attn=False):
  return TransformerDecoderLayer(args, no_encoder_attn)
 
@@ -857,7 +852,10 @@ def output_layer(self, features):
  """Project features to the vocabulary size."""
  if self.adaptive_softmax is None:
  # project back to size of vocabulary
- return self.output_projection(features)
+ if self.share_input_output_embed:
+ return F.linear(features, self.embed_tokens.weight)
+ else:
+ return F.linear(features, self.embed_out)
  else:
  return features
 
@@ -892,18 +890,6 @@ def upgrade_state_dict_named(self, state_dict, name):
  "{}.embed_positions._float_tensor".format(name)
  ] = torch.FloatTensor(1)
 
- embed_tokens_weights_key = f"{name}.embed_tokens.weights"
- embed_out_key = f"{name}.embed_out"
- if embed_tokens_weights_key in state_dict:
- state_dict[f"{name}.output_projection.weight"] = state_dict[
- embed_tokens_weights_key
- ]
- if embed_out_key in state_dict:
- state_dict[f"{name}.output_projection.weight"] = state_dict[
- embed_out_key
- ]
- del state_dict[embed_out_key]
-
  for i in range(self.num_layers):
  # update layer norms
  layer_norm_map = {