def output_spec(self):
spec = super().output_spec() # has 'output_text'
spec.update({
"input_tokens":
lit_types.Tokens(parent="input_text"),
"encoder_final_embedding":
lit_types.Embeddings(),
# If target text is given, the following will also be populated.
"target_tokens":
lit_types.Tokens(parent="target_text"),
"pred_tokens":
lit_types.TokenTopKPreds(align="target_tokens"),
})
if self.config.num_to_generate > 1:
spec["output_text"] = lit_types.GeneratedTextCandidates(
parent="target_text")
if self.config.output_attention:
# Add attention for each layer.
for i in range(self.num_layers):
spec[
f"encoder_layer_{i:d}_attention"] = lit_types.AttentionHeads(
align_in="input_tokens", align_out="input_tokens")
spec[
f"decoder_layer_{i:d}_attention"] = lit_types.AttentionHeads(
align_in="target_tokens", align_out="target_tokens")
return spec
def output_spec(self):
spec = {
# the "parent" keyword tells LIT which field in the input spec we should
# compare this to when computing metrics.
"pred_tokens": lit_types.TokenTopKPreds(align="tokens"),
"tokens": lit_types.Tokens(parent="text"), # all tokens
}
# Add attention and embeddings from each layer.
for i in range(self.num_layers):
spec[f"layer_{i:d}_attention"] = lit_types.AttentionHeads(
align_in="tokens", align_out="tokens")
spec[f"layer_{i:d}_avg_embedding"] = lit_types.Embeddings()
return spec
def output_spec(self):
spec = {
"input_tokens": lit_types.Tokens(parent="input_text"),
"generation": lit_types.GeneratedText(parent="target_text"),
"encoder_final_embedding": lit_types.Embeddings(),
# If target text is given, the following will also be populated.
"target_tokens": lit_types.Tokens(parent="target_text"),
"pred_tokens": lit_types.TokenTopKPreds(align="target_tokens"),
"rougeL": lit_types.Scalar(),
}
if self.config.output_attention:
# Add attention for each layer.
for i in range(self.num_layers):
spec[f"encoder_layer_{i:d}_attention"] = lit_types.AttentionHeads(
align=("input_tokens", "input_tokens"))
spec[f"decoder_layer_{i:d}_attention"] = lit_types.AttentionHeads(
align=("target_tokens", "target_tokens"))
return spec
def output_spec(self) -> lit_types.Spec:
return {
"src_tokens":
lit_types.Tokens(parent="src_text"),
"trg_text":
lit_types.GeneratedText(parent="ref_text"),
"trg_tokens":
lit_types.Tokens(parent="trg_text"),
"attention":
lit_types.AttentionHeads(align_in="src_tokens",
align_out="trg_tokens"),
"pred_tokens":
lit_types.TokenTopKPreds(align="trg_tokens", parent="trg_text"),
"encoder_final_embedding":
lit_types.Embeddings(),
"ter":
lit_types.Scalar(),
"chrf3":
lit_types.Scalar(),
}
def output_spec(self):
return {
"tokens": lit_types.Tokens(parent="text"),
"pred_tokens": lit_types.TokenTopKPreds(align="tokens"),
"cls_emb": lit_types.Embeddings(),
}