def test_ntg_hidden_states(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
model.to(torch_device)
encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
decoder_prev_ids = torch.tensor([[
2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913,
1032, 351
]]).to(torch_device)
output = model(input_ids=encoder_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=decoder_prev_ids)
output_predited_logis = output[0]
expected_shape = torch.Size((1, 14, 250012))
self.assertEqual(output_predited_logis.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor([[[-8.8815, -9.2996, -4.4506],
[-6.7202, -7.8944, -0.9402],
[-8.6890, -7.4528,
-1.9437]]]).to(torch_device)
self.assertTrue(
torch.allclose(output_predited_logis[:, :3, :3],
expected_slice,
atol=1e-4))
def test_xprophetnet_ntg_inference(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
model.to(torch_device)
model.config.max_length = 512
tokenizer = XLMProphetNetTokenizer.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
EN_SENTENCE = "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after January 14, 2020, according to the official portal of the organization. From that day, users of this system will not be able to receive security updates, which could make their computers vulnerable to cyber attacks."
RU_SENTENCE = "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7 после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми к кибератакам."
ZH_SENTENCE = (
"根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
)
input_ids = tokenizer([EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE],
padding=True,
max_length=255,
return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
summary_ids = model.generate(input_ids,
num_beams=10,
length_penalty=1.0,
no_repeat_ngram_size=3,
early_stopping=True)
generated_titles = [
tokenizer.decode(g, skip_special_tokens=True) for g in summary_ids
]
EXPECTED_TITLE_EN = "Microsoft to end Windows 7 free support after January 14, 2020"
EXPECTED_TITLE_RU = "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
EXPECTED_TITLE_ZH = "微软打算终止对Windows 7操作系统的免费支持"
self.assertListEqual(
[EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH],
generated_titles,
)
summary_ids_beam1 = model.generate(input_ids,
num_beams=1,
length_penalty=1.0,
no_repeat_ngram_size=3,
early_stopping=True)
generated_titles_beam1_tok = [
tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)
for g in summary_ids_beam1
]
EXPECTED_TITLE_EN_BEAM1_TOK = "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(
" ")
EXPECTED_TITLE_RU_BEAM1_TOK = "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
" ")
EXPECTED_TITLE_ZH_BEAM1_TOK = "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(
" ")
self.assertListEqual(
[
EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK,
EXPECTED_TITLE_ZH_BEAM1_TOK
],
generated_titles_beam1_tok,
)
def test_pretrained_checkpoint_hidden_states(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased")
model.to(torch_device)
# encoder-decoder outputs
encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
decoder_prev_ids = torch.tensor([[
2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913,
1032, 351
]]).to(torch_device)
output = model(input_ids=encoder_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=decoder_prev_ids)
output_predited_logis = output[0]
expected_shape = torch.Size((1, 14, 250012))
self.assertEqual(output_predited_logis.shape, expected_shape)
expected_slice = torch.tensor([[[-6.6042, -8.3838, 12.4717],
[-6.4426, -8.1994, 12.4542],
[-6.0851, -7.8209,
12.9493]]]).to(torch_device)
self.assertTrue(
torch.allclose(output_predited_logis[:, :3, :3],
expected_slice,
atol=1e-4))
# encoder outputs
encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
expected_encoder_outputs_slice = torch.tensor(
[[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807],
[-1.7678, 0.0114, 0.4646]]]).to(torch_device)
expected_shape_encoder = torch.Size((1, 4, 1024))
self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
self.assertTrue(
torch.allclose(encoder_outputs[:, :3, :3],
expected_encoder_outputs_slice,
atol=1e-4))
# decoder outputs
decoder_outputs = model.prophetnet.decoder(
decoder_prev_ids,
encoder_hidden_states=encoder_outputs,
)
predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 14,
-1)
predicting_streams_logits = model.lm_head(predicting_streams)
next_first_stream_logits = predicting_streams_logits[:, 0]
self.assertTrue(
torch.allclose(next_first_stream_logits[:, :3, :3],
expected_slice,
atol=1e-4))
def convert_prophetnet_checkpoint_to_pytorch(prophetnet_checkpoint_path: str, pytorch_dump_folder_path: str):
"""
Copy/paste/tweak prohpetnet's weights to our prophetnet structure.
"""
if "xprophetnet" in prophetnet_checkpoint_path:
prophet_old = XLMProphetNetForConditionalGenerationOld.from_pretrained(prophetnet_checkpoint_path)
prophet, loading_info = XLMProphetNetForConditionalGeneration.from_pretrained(
prophetnet_checkpoint_path, output_loading_info=True
)
else:
prophet_old = ProphetNetForConditionalGenerationOld.from_pretrained(prophetnet_checkpoint_path)
prophet, loading_info = ProphetNetForConditionalGeneration.from_pretrained(
prophetnet_checkpoint_path, output_loading_info=True
)
special_keys = ["key_proj", "value_proj", "query_proj"]
mapping = {
"self_attn": "ngram_self_attn",
"cross_attn": "encoder_attn",
"cross_attn_layer_norm": "encoder_attn_layer_norm",
"feed_forward_layer_norm": "final_layer_norm",
"feed_forward": "",
"intermediate": "fc1",
"output": "fc2",
"key_proj": "k_proj",
"query_proj": "q_proj",
"value_proj": "v_proj",
"word_embeddings": "embed_tokens",
"embeddings_layer_norm": "emb_layer_norm",
"relative_pos_embeddings": "relative_linear",
"ngram_embeddings": "ngram_input_embed",
"position_embeddings": "embed_positions",
}
for key in loading_info["missing_keys"]:
attributes = key.split(".")
if attributes[0] == "lm_head":
model = prophet
old_model = prophet_old
else:
model = prophet.prophetnet
old_model = prophet_old.model
is_key_init = False
for attribute in attributes:
if attribute in mapping:
old_attribute = mapping[attribute]
if not hasattr(old_model, old_attribute) and len(old_attribute) > 0:
old_attribute = attribute
elif hasattr(old_model, attribute):
old_attribute = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
model.weight = old_model.weight
logger.info(f"{attribute} is initialized.")
is_key_init = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
model.bias = old_model.bias
logger.info(f"{attribute} is initialized")
is_key_init = True
break
elif attribute in special_keys and hasattr(old_model, "in_proj_weight"):
embed_dim = old_model.in_proj_weight.shape[0] // 3
param = getattr(model, attribute)
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
model.query_proj.weight = torch.nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
model.query_proj.bias = torch.nn.Parameter(old_model.in_proj_bias[:embed_dim])
elif attribute == "key_proj":
model.key_proj.weight = torch.nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
model.key_proj.bias = torch.nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
elif attribute == "value_proj":
model.value_proj.weight = torch.nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
model.value_proj.bias = torch.nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
is_key_init = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
model.position_embeddings.weight = torch.nn.Parameter(old_model.embed_positions.weight[:512, :])
is_key_init = True
break
if attribute.isdigit():
model = model[int(attribute)]
old_model = old_model[int(old_attribute)]
else:
model = getattr(model, attribute)
if old_attribute == "":
old_model = old_model
else:
if not hasattr(old_model, old_attribute):
raise ValueError(f"{old_model} does not have {old_attribute}")
old_model = getattr(old_model, old_attribute)
if not is_key_init:
raise ValueError(f"{key} was not correctly initialized!")
print(f"Saving model to {pytorch_dump_folder_path}")
prophet.save_pretrained(pytorch_dump_folder_path)