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from transformers import (
AutoConfig,
BlenderbotSmallForConditionalGeneration,
logging
)
from transformers.modeling_outputs import (
Seq2SeqLMOutput,
BaseModelOutput,
)
from huggingface_hub import hf_hub_url, cached_download
from onnxruntime import (GraphOptimizationLevel,
InferenceSession,
SessionOptions)
from torch import from_numpy
from torch.nn import Module
from functools import reduce
from operator import iconcat
#supress huggingface warnings
logging.set_verbosity_error()
model_vocab_size=30000
model_card="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
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, onnx_model_sessions):
config = AutoConfig.from_pretrained("facebook/blenderbot_small-90M")
config.vocab_size=model_vocab_size
super().__init__(config)
assert len(onnx_model_sessions) == 3, "all three models should be given"
encoder_sess, decoder_sess, decoder_sess_init = onnx_model_sessions
self.encoder = BlenderEncoder(encoder_sess)
self.decoder = BlenderDecoder(decoder_sess)
self.decoder_init = BlenderDecoderInit(decoder_sess_init)
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 ModelLoad:
def __init__(self, model_card,file_names):
self.model_card=model_card
self.file_names=file_names
def model_file_downloader(self,model_card,filename):
config_file_url = hf_hub_url(model_card, filename)
model_file = cached_download(config_file_url)
return model_file
def inference_session(self,file_name):
model_file=self.model_file_downloader(self.model_card,file_name)
options = SessionOptions()
options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
return InferenceSession(model_file,options=options)
def __call__(self,model_config):
model=model_config([*map(self.inference_session,
self.file_names)])
return model
model_loader=ModelLoad(model_card,model_file_names)
blender_onnx_model=model_loader(OnnxBlender) |