def convert_fairseq_s2t_checkpoint_to_tfms(checkpoint_path,
pytorch_dump_folder_path):
m2m_100 = torch.load(checkpoint_path, map_location="cpu")
args = m2m_100["args"]
state_dict = m2m_100["model"]
lm_head_weights = state_dict["decoder.output_projection.weight"]
remove_ignore_keys_(state_dict)
rename_keys(state_dict)
vocab_size = state_dict["decoder.embed_tokens.weight"].shape[0]
tie_embeds = args.share_decoder_input_output_embed
conv_kernel_sizes = [int(i) for i in args.conv_kernel_sizes.split(",")]
config = Speech2TextConfig(
vocab_size=vocab_size,
max_source_positions=args.max_source_positions,
max_target_positions=args.max_target_positions,
encoder_layers=args.encoder_layers,
decoder_layers=args.decoder_layers,
encoder_attention_heads=args.encoder_attention_heads,
decoder_attention_heads=args.decoder_attention_heads,
encoder_ffn_dim=args.encoder_ffn_embed_dim,
decoder_ffn_dim=args.decoder_ffn_embed_dim,
d_model=args.encoder_embed_dim,
dropout=args.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.activation_dropout,
activation_function="relu",
num_conv_layers=len(conv_kernel_sizes),
conv_channels=args.conv_channels,
conv_kernel_sizes=conv_kernel_sizes,
input_feat_per_channel=args.input_feat_per_channel,
input_channels=args.input_channels,
tie_word_embeddings=tie_embeds,
num_beams=5,
max_length=200,
use_cache=True,
decoder_start_token_id=2,
early_stopping=True,
)
model = Speech2TextForConditionalGeneration(config)
missing, unexpected = model.model.load_state_dict(state_dict, strict=False)
if len(missing) > 0 and not set(missing) <= set([
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
]):
raise ValueError(
"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"
f" but all the following weights are missing {missing}")
if tie_embeds:
model.lm_head = make_linear_from_emb(model.model.decoder.embed_tokens)
else:
model.lm_head.weight.data = lm_head_weights
model.save_pretrained(pytorch_dump_folder_path)
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
attention_mask = input_dict["attention_mask"]
model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
input_features = input_features.half()
model.half()
model.generate(input_features, attention_mask=attention_mask)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
