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from functools import reduce
from operator import iconcat
from typing import List
from huggingface_hub import hf_hub_download
from onnxruntime import InferenceSession
from torch import from_numpy
from torch.nn import Module
from transformers import (AutoConfig, BlenderbotSmallForConditionalGeneration,
BlenderbotSmallTokenizer)
from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
model_vocab_size = 30000
original_repo_id = "facebook/blenderbot_small-90M"
repo_id = "remzicam/xs_blenderbot_onnx"
model_file_names = [
"blenderbot_small-90M-encoder-quantized.onnx",
"blenderbot_small-90M-decoder-quantized.onnx",
"blenderbot_small-90M-init-decoder-quantized.onnx",
]
class BlenderEncoder(Module):
def __init__(self, encoder_sess):
super().__init__()
self.encoder = encoder_sess
self.main_input_name = "input_ids"
def forward(
self,
input_ids,
attention_mask,
inputs_embeds=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
encoder_hidden_state = from_numpy(
self.encoder.run(
None,
{
"input_ids": input_ids.cpu().numpy(),
"attention_mask": attention_mask.cpu().numpy(),
},
)[0]
)
return BaseModelOutput(encoder_hidden_state)
class BlenderDecoderInit(Module):
def __init__(self, decoder_sess):
super().__init__()
self.decoder = decoder_sess
def forward(self, input_ids, encoder_attention_mask, encoder_hidden_states):
decoder_outputs = self.decoder.run(
None,
{
"input_ids": input_ids.cpu().numpy(),
"encoder_attention_mask": encoder_attention_mask.cpu().numpy(),
"encoder_hidden_states": encoder_hidden_states.cpu().numpy(),
},
)
list_pkv = tuple(from_numpy(x) for x in decoder_outputs[1:])
out_past_key_values = tuple(
list_pkv[i : i + 4] for i in range(0, len(list_pkv), 4)
)
return from_numpy(decoder_outputs[0]), out_past_key_values
class BlenderDecoder(Module):
def __init__(self, decoder_sess):
super().__init__()
self.decoder = decoder_sess
def forward(self, input_ids, attention_mask, encoder_output, past_key_values):
decoder_inputs = {
"input_ids": input_ids.cpu().numpy(),
"encoder_attention_mask": attention_mask.cpu().numpy(),
}
flat_past_key_values = reduce(iconcat, past_key_values, [])
past_key_values = {
f"pkv_{i}": pkv.cpu().numpy() for i, pkv in enumerate(flat_past_key_values)
}
decoder_outputs = self.decoder.run(None, {**decoder_inputs, **past_key_values})
# converts each value of the list to tensor from numpy
list_pkv = tuple(from_numpy(x) for x in decoder_outputs[1:])
# creates a tuple of tuples of shape 6x4 from the above tuple
out_past_key_values = tuple(
list_pkv[i : i + 4] for i in range(0, len(list_pkv), 4)
)
return from_numpy(decoder_outputs[0]), out_past_key_values
class OnnxBlender(BlenderbotSmallForConditionalGeneration):
"""creates a Blender model using onnx sessions (encode, decoder & init_decoder)"""
def __init__(self, original_repo_id, repo_id, file_names):
config = AutoConfig.from_pretrained(original_repo_id)
config.vocab_size = model_vocab_size
super().__init__(config)
self.files = self.files_downloader(repo_id, file_names)
self.onnx_model_sessions = self.onnx_sessions_starter(self.files)
assert len(self.onnx_model_sessions) == 3, "all three models should be given"
encoder_sess, decoder_sess, decoder_sess_init = self.onnx_model_sessions
self.encoder = BlenderEncoder(encoder_sess)
self.decoder = BlenderDecoder(decoder_sess)
self.decoder_init = BlenderDecoderInit(decoder_sess_init)
@staticmethod
def files_downloader(repo_id: str, file_names: List[str]) -> List[str]:
"""Downloads files from huggingface given file names
Args:
repo_id (str): repo name at huggingface.
file_names (List[str]): The names of the files in the repo.
Returns:
List[str]: Local paths of files
"""
return [hf_hub_download(repo_id, file) for file in file_names]
@staticmethod
def onnx_sessions_starter(files: List[str]) -> List[object]:
"""initiates onnx inference sessions
Args:
files (List[str]): Local paths of files
Returns:
List[object]: onnx sessions for each file
"""
return [*map(InferenceSession, files)]
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
encoder_hidden_states = encoder_outputs[0]
if past_key_values is not None:
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_inputs_embeds is not None:
decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
if past_key_values is None:
# runs only for the first time:
init_onnx_outputs = self.decoder_init(
decoder_input_ids, attention_mask, encoder_hidden_states
)
logits, past_key_values = init_onnx_outputs
else:
onnx_outputs = self.decoder(
decoder_input_ids,
attention_mask,
encoder_hidden_states,
past_key_values,
)
logits, past_key_values = onnx_outputs
return Seq2SeqLMOutput(logits=logits, past_key_values=past_key_values)
class TextGenerationPipeline:
"""Pipeline for text generation of blenderbot model.
Returns:
str: generated text
"""
# load tokenizer and the model
tokenizer = BlenderbotSmallTokenizer.from_pretrained(original_repo_id)
model = OnnxBlender(original_repo_id, repo_id, model_file_names)
def __init__(self, **kwargs):
"""Specififying text generation parameters.
For example: max_length=100 which generates text shorter than
100 tokens. Visit:
https://huggingface.co/docs/transformers/main_classes/text_generation
for more parameters
"""
self.__dict__.update(kwargs)
def preprocess(self, text) -> str:
"""Tokenizes input text.
Args:
text (str): user specified text
Returns:
torch.Tensor (obj): text representation as tensors
"""
return self.tokenizer(text, return_tensors="pt")
def postprocess(self, outputs) -> str:
"""Converts tensors into text.
Args:
outputs (torch.Tensor obj): model text generation output
Returns:
str: generated text
"""
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
def __call__(self, text: str) -> str:
"""Generates text from input text.
Args:
text (str): user specified text
Returns:
str: generated text
"""
tokenized_text = self.preprocess(text)
output = self.model.generate(**tokenized_text, **self.__dict__)
return self.postprocess(output)
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