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Add an example of exporting BartModel + BeamSearch to ONNX module. (#13765)

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* Add all example files.

* Reformat files by black.

* Style.

* Remove unused imports.

Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
This commit is contained in:
Jay Zhang
2021-10-07 18:07:02 +08:00
committed by GitHub
parent 0d309ce39a
commit 279ce5b705
4 changed files with 1147 additions and 0 deletions
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examples/onnx/pytorch/translation/bart_onnx/generation_onnx.py Normal file
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import copy
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation_utils import GenerationMixin
def flatten_list(past):
values = []
if past is not None:
for i, p in enumerate(past):
for j, q in enumerate(p):
values.append(q)
return values
def list_to_tuple(past):
results = ()
temp_result = ()
count_n = len(past) // 4
for idx in range(count_n):
real_idx = idx * 4
temp_result = tuple(past[real_idx : real_idx + 4])
results += ((temp_result),)
return results
class EncoderForONNX(torch.nn.Module):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
def forward(self, input_ids, attention_mask):
return self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False,
)
class DecoderForONNX(torch.nn.Module):
def __init__(self, decoder):
super().__init__()
self.decoder = decoder
def forward(self, input_ids, encoder_state, attention_mask, past=None):
all_results = None
if past is not None:
all_results = list_to_tuple(past)
input_ids = input_ids[:, -1:]
last_hidden_state, past_key_values = self.decoder(
input_ids=input_ids,
encoder_hidden_states=encoder_state,
encoder_attention_mask=attention_mask,
past_key_values=all_results,
return_dict=False,
)
past_values = []
for past in past_key_values:
past_values = past_values + list(past)
return last_hidden_state, past_values
def create_traced_encoder(encoder, input_ids, attention_mask):
encoder_c = copy.deepcopy(encoder)
encoder_for_onnx = EncoderForONNX(encoder_c)
# return torch.jit.trace(encoder, (input_ids, attention_mask))
return torch.jit.trace(encoder_for_onnx, (input_ids, attention_mask))
def create_traced_decoder(decoder, input_ids, encoder_state, attention_mask, past=None):
decoder_c = copy.deepcopy(decoder)
decoder_for_onnx = DecoderForONNX(decoder_c)
past_values = flatten_list(past)
# Do this twice so we got 2 different decoders for further work.
if past_values is None or len(past_values) == 0:
return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask))
else:
return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask, past_values))
class BartConfigTS(BartConfig, torch.nn.Module):
def init_module(self):
torch.nn.Module.__init__(self)
class MinLengthLogitsProcessorTS(torch.nn.Module):
r"""
:class:`transformers.LogitsProcessor` enforcing a min-length by setting EOS probability to 0.
Args:
min_length (:obj:`int`):
The minimum length below which the score of :obj:`eos_token_id` is set to :obj:`-float("Inf")`.
eos_token_id (:obj:`int`):
The id of the `end-of-sequence` token.
"""
def __init__(self, min_length: int, eos_token_id: int):
super().__init__()
if not isinstance(min_length, int) or min_length < 0:
raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}")
if not isinstance(eos_token_id, int) or eos_token_id < 0:
raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}")
self.min_length = min_length
self.eos_token_id = eos_token_id
def forward(self, input_ids, scores) -> torch.Tensor:
cur_len = input_ids.shape[-1]
if cur_len < self.min_length:
scores[:, self.eos_token_id] = -float("inf")
return scores
class BARTGenerator(torch.nn.Module, GenerationMixin):
def __init__(self, model):
super().__init__()
self.config = BartConfigTS(model.config)
self.config.init_module()
self.config.force_bos_token_to_be_generated = False
self._trace_modules(model)
self.logits_processor = MinLengthLogitsProcessorTS(self.config.min_length, self.config.eos_token_id)
self.final_logits_weight = model.model.shared.weight
self.final_logits_bias = model.final_logits_bias
self.decoder_layers = model.config.decoder_layers
def _trace_modules(self, model):
# Be aware of the last one 2 should be kept.
input_ids = torch.tensor(
[
[
19,
669,
18,
420,
8,
664,
57,
42,
8,
664,
21,
3028,
195,
4445,
331,
1293,
34,
21,
10,
6174,
1100,
6,
69,
104,
42,
32,
2621,
1638,
144,
4,
6174,
558,
108,
4419,
1091,
28,
4,
1668,
9,
1509,
1621,
279,
35,
867,
2734,
85,
11,
2216,
2734,
85,
203,
2244,
7,
6,
15,
8102,
7,
57,
8629,
5,
2,
]
],
device=model.device,
dtype=torch.long,
)
attention_mask = torch.tensor(
[
[
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
]
],
device=model.device,
dtype=torch.bool,
)
self.encoder = create_traced_encoder(model.get_encoder(), input_ids, attention_mask)
encoder_outputs = model.get_encoder()(input_ids, attention_mask=attention_mask, return_dict=True)
decoder = model.model.decoder
decoder_outputs = decoder(input_ids, attention_mask, encoder_outputs["last_hidden_state"], None, None, None)
self.decoder_no_past = create_traced_decoder(
model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask
)
self.decoder_with_past = create_traced_decoder(
model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask, decoder_outputs[1]
)
def _encoder_forward(self, input_ids, attention_mask):
return self.encoder(input_ids, attention_mask)[0]
@staticmethod
def _init_sequence_length_for_generation(
input_ids: torch.LongTensor, max_length: int
) -> Tuple[torch.Tensor, torch.Tensor, int]:
unfinished_sequences = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + 1
sequence_lengths = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + max_length
cur_len = input_ids.shape[-1]
return sequence_lengths, unfinished_sequences, cur_len
def _decoder_forward(self, input_ids, encoder_output, attention_mask, past: List[torch.Tensor]):
# Update here to use different decoder for different values of past.
if past is None or len(past) == 0:
decoder_output, past = self.decoder_no_past(
input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask
)
else:
decoder_output, past = self.decoder_with_past(
input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask, past=past
)
lm_logits = F.linear(decoder_output, self.final_logits_weight, bias=self.final_logits_bias)
return lm_logits, past
def greedy_search(
self, input_ids, encoder_output, attention_mask, max_length, pad_token_id: int, eos_token_id: int
):
# init sequence length tensors
sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation(
input_ids, max_length
)
past: List[torch.Tensor] = []
while cur_len < max_length:
logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past)
next_token_logits = logits[:, -1, :]
# pre-process distribution
scores = self.logits_processor(input_ids, next_token_logits)
# argmax
next_tokens = torch.argmax(scores, dim=-1)
# add code that transfomers next_tokens to tokens_to_add
if eos_token_id is not None:
assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
next_tokens = next_tokens * unfinished_sequences + (pad_token_id) * (1 - unfinished_sequences)
# add token and increase length by one
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
# update sequence length
if eos_token_id is not None:
sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation(
sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id
)
# stop when there is a </s> in each sentence, or if we exceed the maximul length
if unfinished_sequences.max() == 0:
break
# increase cur_len
cur_len = cur_len + 1
return input_ids
def _prepare_decoder_input_ids_for_generation(
self,
input_ids: torch.LongTensor,
decoder_start_token_id,
bos_token_id: Optional[int] = None,
) -> torch.LongTensor:
decoder_input_ids = (
torch.ones((input_ids.shape[0], 1), dtype=input_ids.dtype, device=input_ids.device)
* decoder_start_token_id
)
return decoder_input_ids
def forward(self, input_ids, attention_mask, max_length, decoder_start_token_id):
pad_token_id = self.config.pad_token_id
bos_token_id = self.config.bos_token_id
eos_token_id = self.config.eos_token_id
# special case if pad_token_id is not defined
if pad_token_id is None and eos_token_id is not None:
# Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.
pad_token_id = eos_token_id
encoder_output = self._encoder_forward(input_ids, attention_mask)
input_ids = self._prepare_decoder_input_ids_for_generation(
input_ids,
decoder_start_token_id=decoder_start_token_id,
bos_token_id=bos_token_id,
)
return self.greedy_search(
input_ids,
encoder_output,
attention_mask,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
)
# TorchScript compatible BeamSearchScorer
class BeamSearchScorerTS(torch.nn.Module):
def __init__(self):
super().__init__()
self.max_length: int = 200
self.num_beams: int = 3
self.batch_size: int = 1
self.length_penalty: float = 1.0
self.do_early_stopping: bool = True
self.num_beam_hyps_to_keep: int = 1
self.num_beam_groups: int = 1
self.group_size: int = self.num_beams // self.num_beam_groups
self._done = torch.zeros(self.batch_size, dtype=torch.bool)
self._beam_hyps_count = torch.zeros(self.batch_size, dtype=torch.long)
self._beam_hyps_worst_scores = torch.zeros(self.batch_size) + 1e9
self._beam_hyps_max_length: int = self.max_length - 1
self._beam_hyps: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatible
self._beam_scores: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatible
def is_done(self) -> torch.Tensor:
return self._done.all()
def init(
self,
batch_size: int,
max_length: int,
num_beams: int,
device: torch.device,
length_penalty: float = 1.0,
do_early_stopping: bool = False,
num_beam_hyps_to_keep: int = 1,
num_beam_groups: int = 1,
):
self.max_length = max_length
self.num_beams = num_beams
self.batch_size = batch_size
self.length_penalty = length_penalty
self.do_early_stopping = do_early_stopping
self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
self.num_beam_groups = num_beam_groups
self.group_size = self.num_beams // self.num_beam_groups
# NOTE: TorchScript does not support List of Modules
# Rewritten BeamHypotheses with tensors and list of tensors.
self._done = torch.zeros(batch_size, dtype=torch.bool, device=device)
self._beam_hyps_count = torch.zeros(batch_size, dtype=torch.long, device=device)
self._beam_hyps_worst_scores = torch.zeros(batch_size, device=device) + 1e9
self._beam_hyps = []
self._beam_scores = []
self._beam_hyps_max_length = max_length - 1 # ignoring bos_token
if not isinstance(num_beams, int) or num_beams <= 1:
raise ValueError(
f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1, one should make use of `greedy_search` instead."
)
if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0):
raise ValueError(
f"`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` "
f"has to be divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}."
)
def hypo_len(self, hypo_idx: int):
"""
Number of hypotheses in the list.
"""
return self._beam_hyps_count[hypo_idx]
def hypo_add(self, hyp: torch.Tensor, sum_logprobs: float, hypo_idx: int):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / (hyp.shape[-1] ** self.length_penalty)
hyps_count = self.hypo_len(hypo_idx)
if hyps_count < self.num_beams or score > self._beam_hyps_worst_scores[hypo_idx]:
# NOTE: work around difference of torch.sum(empty_tensor) = 0, while error in onnx.
beam_idx = (
torch.sum(self._beam_hyps_count[:hypo_idx]) if hypo_idx != 0 else torch.tensor(0, dtype=torch.long)
)
# beam_idx = torch.sum(_beam_hyps_count[:hypo_idx])
self._beam_scores.insert(beam_idx, torch.tensor([score]))
self._beam_hyps.insert(beam_idx, hyp)
if hyps_count + 1 > self.num_beams:
sorted_next_scores, sorted_indices = torch.topk(
torch.cat(self._beam_scores)[beam_idx : beam_idx + hyps_count + 1], hyps_count + 1, largest=False
)
del self._beam_hyps[int((sorted_indices[0] + beam_idx))]
del self._beam_scores[int((sorted_indices[0] + beam_idx))]
self._beam_hyps_worst_scores[hypo_idx] = sorted_next_scores[1]
else:
self._beam_hyps_worst_scores[hypo_idx] = min(score, self._beam_hyps_worst_scores[hypo_idx])
self._beam_hyps_count[hypo_idx] = hyps_count + 1
def hypo_is_done(self, hypo_idx: int, best_sum_logprobs: float, cur_len: int) -> bool:
"""
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
if self.hypo_len(hypo_idx) < self.num_beams:
return False
elif self.do_early_stopping:
return True
else:
cur_score = best_sum_logprobs / cur_len ** self.length_penalty
ret = self._beam_hyps_worst_scores[hypo_idx].item() >= cur_score
return ret
def process(
self,
input_ids: torch.Tensor,
next_scores: torch.Tensor,
next_tokens: torch.Tensor,
next_indices: torch.Tensor,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
cur_len = input_ids.shape[-1]
batch_size = len(self._beam_hyps_count)
assert batch_size == (input_ids.shape[0] // self.group_size)
device = input_ids.device
next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device)
next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device)
next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=device)
for batch_idx in range(batch_size):
if self._done[batch_idx]:
assert (
self.hypo_len(batch_idx) >= self.num_beams
), "Batch can only be done if at least {} beams have been generated".format(self.num_beams)
assert (
eos_token_id is not None and pad_token_id is not None
), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
# pad the batch
next_beam_scores[batch_idx, :] = 0
next_beam_tokens[batch_idx, :] = pad_token_id
next_beam_indices[batch_idx, :] = 0
continue
# next tokens for this sentence
beam_idx = 0
for beam_token_rank, (next_token, next_score, next_index) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx])
):
batch_beam_idx = batch_idx * self.group_size + next_index
# add to generated hypotheses if end of sentence
if (eos_token_id is not None) and (next_token == eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= self.group_size
if is_beam_token_worse_than_top_num_beams:
continue
self.hypo_add(
input_ids[batch_beam_idx].clone(),
next_score.item(),
batch_idx,
)
else:
# add next predicted token since it is not eos_token
next_beam_scores[batch_idx, beam_idx] = next_score
next_beam_tokens[batch_idx, beam_idx] = next_token
next_beam_indices[batch_idx, beam_idx] = batch_beam_idx
beam_idx += 1
# once the beam for next step is full, don't add more tokens to it.
if beam_idx == self.group_size:
break
if beam_idx < self.group_size:
raise ValueError(
f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id: {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected."
)
# Check if we are done so that we can save a pad step if all(done)
self._done[batch_idx] = self._done[batch_idx] or self.hypo_is_done(
batch_idx,
next_scores[batch_idx].max().item(),
cur_len,
)
return next_beam_scores.view(-1), next_beam_tokens.view(-1), next_beam_indices.view(-1)
def finalize(
self,
input_ids: torch.Tensor,
final_beam_scores: torch.Tensor,
final_beam_tokens: torch.Tensor,
final_beam_indices: torch.Tensor,
pad_token_id: int,
eos_token_id: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = len(self._beam_hyps_count)
# finalize all open beam hypotheses and add to generated hypotheses
for batch_idx in range(batch_size):
if self._done[batch_idx]:
continue
# all open beam hypotheses are added to the beam hypothesis
# beam hypothesis class automatically keeps the best beams
for beam_id in range(self.num_beams):
batch_beam_idx = batch_idx * self.num_beams + beam_id
final_score = final_beam_scores[batch_beam_idx].item()
final_tokens = input_ids[batch_beam_idx]
self.hypo_add(final_tokens, final_score, batch_idx)
# select the best hypotheses
# NOTE: new is not scriptable
sent_lengths = torch.zeros(batch_size * self.num_beam_hyps_to_keep, dtype=torch.long)
best = []
best_scores = torch.zeros(
batch_size * self.num_beam_hyps_to_keep, device=input_ids.device, dtype=torch.float32
)
# retrieve best hypotheses
for i in range(batch_size):
# NOTE: lambda is not scriptable
batch_hypo_start = torch.sum(self._beam_hyps_count[:i]) if i > 0 else torch.tensor(0, dtype=torch.long)
batch_hypo_end = torch.sum(self._beam_hyps_count[: i + 1])
beam_scores = torch.cat(self._beam_scores)[batch_hypo_start:batch_hypo_end]
sorted_next_scores, sorted_indices = torch.topk(beam_scores, len(beam_scores), largest=True)
for j in range(self.num_beam_hyps_to_keep):
best_score = beam_scores[sorted_indices[j]]
best_hyp = self._beam_hyps[batch_hypo_start + sorted_indices[j]]
sent_lengths[self.num_beam_hyps_to_keep * i + j] = len(best_hyp)
# append to lists
best.append(best_hyp)
best_scores[i * self.num_beam_hyps_to_keep + j] = best_score
# prepare for adding eos
sent_max_len = min(sent_lengths.max() + 1, self.max_length)
decoded = torch.zeros(batch_size * self.num_beam_hyps_to_keep, sent_max_len, dtype=torch.long)
# shorter batches are padded if needed
if sent_lengths.min() != sent_lengths.max():
assert pad_token_id is not None, "`pad_token_id` has to be defined"
decoded.fill_(pad_token_id)
# fill with hypotheses and eos_token_id if the latter fits in
for i, hypo in enumerate(best):
decoded[i, : sent_lengths[i]] = hypo
if sent_lengths[i] < self.max_length:
decoded[i, sent_lengths[i]] = eos_token_id
return decoded, best_scores
class BARTBeamSearchGenerator(BARTGenerator):
def __init__(self, model):
super().__init__(model)
self.beam_scorer = BeamSearchScorerTS()
self.device = model.device
@staticmethod
def _expand_inputs_for_generation(
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
last_hidden_state: torch.Tensor,
expand_size: int = 1,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
expanded_return_idx = (
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
attention_mask = attention_mask.index_select(0, expanded_return_idx)
last_hidden_state = last_hidden_state.index_select(0, expanded_return_idx.to(last_hidden_state.device))
return input_ids, attention_mask, last_hidden_state
def adjust_logits_during_generation(self, logits, cur_len: int, max_length: int):
if cur_len == 1 and self.config.force_bos_token_to_be_generated:
logits = self._force_token_id_to_be_generated(logits, self.config.bos_token_id)
elif cur_len == max_length - 1 and self.config.eos_token_id is not None:
logits = self._force_token_id_to_be_generated(logits, self.config.eos_token_id)
return logits
@staticmethod
def _force_token_id_to_be_generated(scores, token_id: int):
"""force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))"""
mask = torch.full_like(scores, 1, dtype=torch.bool)
mask[:, token_id] = False
return scores.masked_fill(mask, -float("inf"))
def _reorder_cache(self, past: List[torch.Tensor], beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
reordered_decoder_past = []
for state in past:
reordered_decoder_past.append(state.index_select(0, beam_idx))
return reordered_decoder_past
def beam_search(
self, input_ids, encoder_output, attention_mask, num_beams, max_length, pad_token_id: int, eos_token_id: int
):
batch_size = self.beam_scorer.batch_size
num_beams = self.beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
assert (
num_beams * batch_size == batch_beam_size
), "Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view((batch_size * num_beams,))
next_tokens = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device)
next_indices = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device)
past: List[torch.Tensor] = []
while cur_len < max_length:
logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past)
next_token_logits = logits[:, -1, :]
# adjust tokens for Bart, *e.g.*
next_token_logits = self.adjust_logits_during_generation(
next_token_logits, cur_len=cur_len, max_length=max_length
)
next_token_scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)
# pre-process distribution
next_token_scores = self.logits_processor(input_ids, next_token_scores)
next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores)
# reshape for beam search
vocab_size = next_token_scores.shape[-1]
next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
next_token_scores, next_tokens = torch.topk(
next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
)
next_indices = next_tokens // vocab_size
next_tokens = next_tokens % vocab_size
beam_scores, beam_next_tokens, beam_idx = self.beam_scorer.process(
input_ids,
next_token_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
)
input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
cur_len = cur_len + 1
if len(past) > 0:
past = self._reorder_cache(past, beam_idx)
if self.beam_scorer.is_done():
break
sequences, sequence_scores = self.beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
)
return sequences
def forward(self, input_ids, attention_mask, num_beams, max_length, decoder_start_token_id):
pad_token_id = self.config.pad_token_id
bos_token_id = self.config.bos_token_id
eos_token_id = self.config.eos_token_id
# special case if pad_token_id is not defined
if pad_token_id is None and eos_token_id is not None:
# logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
pad_token_id = eos_token_id
encoder_output = self._encoder_forward(input_ids, attention_mask)
input_ids = self._prepare_decoder_input_ids_for_generation(
input_ids,
decoder_start_token_id=decoder_start_token_id,
bos_token_id=bos_token_id,
)
# from generation_utils.py
batch_size = input_ids.shape[0]
length_penalty = self.config.length_penalty
num_return_sequences = self.config.num_return_sequences
early_stopping = True
self.beam_scorer.init(
batch_size=batch_size,
max_length=max_length,
num_beams=num_beams,
device=self.device,
length_penalty=length_penalty,
do_early_stopping=early_stopping,
num_beam_hyps_to_keep=num_return_sequences,
)
input_ids, attention_mask, encoder_output = self._expand_inputs_for_generation(
input_ids,
attention_mask,
encoder_output,
expand_size=num_beams,
)
return self.beam_search(
input_ids=input_ids,
encoder_output=encoder_output,
attention_mask=attention_mask,
num_beams=num_beams,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
)

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import os
import numpy
import onnx
def is_equal_tensor_proto(a, b):
name_a = a.name
name_b = b.name
a.name = ""
b.name = ""
res = a == b
a.name = name_a
b.name = name_b
return res
def node_replace_input_with(node_proto, name, new_name):
for i, input_name in enumerate(node_proto.input):
if input_name == name:
node_proto.input.insert(i, new_name)
node_proto.input.pop(i + 1)
if node_proto.op_type == "If":
graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
graph_replace_input_with(node_proto.attribute[1].g, name, new_name)
if node_proto.op_type == "Loop":
graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
def graph_replace_input_with(graph_proto, name, new_name):
for n in graph_proto.node:
node_replace_input_with(n, name, new_name)
def remove_dup_initializers_from_model(model, model_without_ext, ind_to_replace):
inits_with_data = [i for i in model.graph.initializer]
inits = [i for i in model_without_ext.graph.initializer]
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
name_i = inits[i].name
name_ref = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i])
# for n in model.graph.node:
graph_replace_input_with(model_without_ext.graph, name_i, name_ref)
def remove_dup_initializers(onnx_file_path):
model_file_folder = os.path.dirname(onnx_file_path)
model_file_name = os.path.basename(onnx_file_path)
model = onnx.load(os.path.join(model_file_folder, model_file_name))
inits = [i for i in model.graph.initializer]
dup_set = set()
dup_map = {}
ind_to_replace = []
total_reduced_size = 0
for i in range(len(inits)):
if i in dup_set:
continue
for j in range(i + 1, len(inits)):
if j in dup_set:
continue
if is_equal_tensor_proto(inits[i], inits[j]):
dup_set.add(i)
dup_set.add(j)
dtype = inits[j].data_type
mem_size = numpy.prod(inits[j].dims)
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("unexpected data type: ", dtype)
total_reduced_size += mem_size
name_i = inits[i].name
name_j = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(name_j)
else:
dup_map[name_i] = [name_j]
ind_to_replace.append((j, i))
print("total reduced size: ", total_reduced_size / 1024 / 1024 / 1024, "GB")
ind_to_replace = sorted(ind_to_replace, key=lambda x: x[0])
remove_dup_initializers_from_model(model, model, ind_to_replace)
optimized_model_file_name = "optimized_" + model_file_name
new_model = os.path.join(model_file_folder, optimized_model_file_name)
onnx.save(model, new_model)
return new_model

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examples/onnx/pytorch/translation/requirements.txt Normal file
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torch >= 1.8

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examples/onnx/pytorch/translation/run_onnx_exporter.py Normal file
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#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
"""
import argparse
import logging
import os
import sys
from datetime import datetime
import numpy as np
import torch
import onnxruntime
import transformers
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger(__name__)
model_dict = {"facebook/bart-base": BartForConditionalGeneration}
tokenizer_dict = {"facebook/bart-base": BartTokenizer}
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
)
parser.add_argument(
"--max_length",
type=int,
default=5,
help=("The maximum total input sequence length after tokenization."),
)
parser.add_argument(
"--num_beams",
type=int,
default=None,
help="Number of beams to use for evaluation. This argument will be "
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--config_name",
type=str,
default=None,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--device",
type=str,
default="cpu",
help="Device where the model will be run",
)
parser.add_argument("--output_file_path", type=str, default=None, help="Where to store the final ONNX file.")
args = parser.parse_args()
return args
def load_model_tokenizer(model_name, device="cpu"):
huggingface_model = model_dict[model_name].from_pretrained(model_name).to(device)
tokenizer = tokenizer_dict[model_name].from_pretrained(model_name)
if model_name in ["facebook/bart-base"]:
huggingface_model.config.no_repeat_ngram_size = 0
huggingface_model.config.forced_bos_token_id = None
huggingface_model.config.min_length = 0
return huggingface_model, tokenizer
def export_and_validate_model(model, tokenizer, onnx_file_path, num_beams, max_length):
model.eval()
ort_sess = None
onnx_bart = torch.jit.script(BARTBeamSearchGenerator(model))
with torch.no_grad():
ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt").to(model.device)
# Test export here.
summary_ids = model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
num_beams=num_beams,
max_length=max_length,
early_stopping=True,
decoder_start_token_id=model.config.decoder_start_token_id,
)
if not ort_sess:
torch.onnx.export(
onnx_bart,
(
inputs["input_ids"],
inputs["attention_mask"],
num_beams,
max_length,
model.config.decoder_start_token_id,
),
onnx_file_path,
opset_version=14,
input_names=["input_ids", "attention_mask", "num_beams", "max_length", "decoder_start_token_id"],
output_names=["output_ids"],
dynamic_axes={
"input_ids": {0: "batch", 1: "seq"},
"output_ids": {0: "batch", 1: "seq_out"},
},
verbose=False,
strip_doc_string=False,
example_outputs=summary_ids,
)
new_onnx_file_path = remove_dup_initializers(os.path.abspath(onnx_file_path))
ort_sess = onnxruntime.InferenceSession(new_onnx_file_path)
ort_out = ort_sess.run(
None,
{
"input_ids": inputs["input_ids"].cpu().numpy(),
"attention_mask": inputs["attention_mask"].cpu().numpy(),
"num_beams": np.array(num_beams),
"max_length": np.array(max_length),
"decoder_start_token_id": np.array(model.config.decoder_start_token_id),
},
)
np.testing.assert_allclose(summary_ids.cpu().numpy(), ort_out[0], rtol=1e-3, atol=1e-3)
print("========= Pass - Results are matched! =========")
def main():
args = parse_args()
local_device = None
local_max_length = 5
local_num_beams = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.setLevel(logging.ERROR)
transformers.utils.logging.set_verbosity_error()
if args.model_name_or_path:
model, tokenizer = load_model_tokenizer(args.model_name_or_path, local_device)
else:
raise ValueError("Make sure that model name has been passed")
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
if args.device:
if args.device == "cuda" and not torch.cuda.is_available():
raise ValueError("CUDA is not available in this server.")
local_device = torch.device(args.device)
else:
local_device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(local_device)
if args.max_length:
local_max_length = args.max_length
if args.num_beams:
local_num_beams = args.num_beams
if args.output_file_path:
output_name = args.output_file_path
else:
output_name = "onnx_model_{}.onnx".format(datetime.now().utcnow().microsecond)
export_and_validate_model(model, tokenizer, output_name, local_num_beams, local_max_length)
logger.info("***** Running export *****")
if __name__ == "__main__":
main()