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import time
import torch
import transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5ForConditionalGeneration
from transformer_deploy.utils.fastseq import code_patcher
def _generate(
model,
tokenizer,
batch_count,
slines,
max_token_length: int = 1024,
num_beams: int = 4,
min_gen_length: int = 55,
max_gen_length: int = 199,
no_repeat_ngram_size: int = 3,
early_stopping: bool = True,
use_cache: bool = True,
do_log=True,
):
"""Generate the summaries.
Args:
slines (List(str)): a list of input sentences.
max_token_length (int): max tokenized sentence length.
num_beams (int): beam number.
min_gen_length (int): min generation length.
max_gen_length (int): maxium length for the generation output.
no_repeat_ngram_size (int): size of no repeat gram.
early_stopping (bool): indicate if the beam search will be early
stopped.
use_cache (bool): If `use_cache` is True, past key values are used
to speed up decoding if applicable to model.
Returns:
List(str): a list of generated summaries.
"""
start = time.time()
with torch.no_grad():
inputs = tokenizer(slines, max_length=max_token_length, padding=True, truncation=True, return_tensors="pt")
# Generate Summary
summary_ids = model.generate(
inputs["input_ids"].cuda(),
num_beams=num_beams,
min_length=min_gen_length,
max_length=max_gen_length,
no_repeat_ngram_size=no_repeat_ngram_size,
early_stopping=early_stopping,
use_cache=use_cache,
)
outputs = [tokenizer.decode(g) for g in summary_ids]
end = time.time()
if do_log:
print(f"batch-{batch_count}: Process {len(slines)} samples in {end - start:.2f} seconds")
return outputs
def transformers_modifications_test(
modify_transformers: bool,
source_path: str,
expected_output_path: str,
nbr_examples: int = 100,
batch_size: int = 8,
max_token_length: int = 1024,
num_beams: int = 4,
min_gen_length: int = 55,
max_gen_length: int = 199,
no_repeat_ngram_size: int = 3,
early_stopping: bool = True,
use_cache: bool = True,
):
test_name = "with transformers modifications" if modify_transformers else "without transformers modifications"
print(f"Start test {test_name}")
if modify_transformers:
modification = (
"value_states = project(\n hidden_states, self.v, key_value_states, "
"past_key_value[1] if past_key_value is not None else None\n )\n"
)
forward_modifications = {
modification: modification + " if self.is_decoder and use_cache is True:\n "
"key_states = key_states.contiguous()\n "
"value_states = value_states.contiguous()\n"
}
code_patcher(
module_name="transformers.models.t5.modeling_t5",
function=transformers.models.t5.modeling_t5.T5Attention.forward,
new_function_name="updatedForward",
modifications=forward_modifications,
)
transformers.models.t5.modeling_t5.T5Attention.forward = transformers.models.t5.modeling_t5.updatedForward
modification = (
"reordered_layer_past_states = reordered_layer_past_states + (\n "
"layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),\n )\n"
)
reorder_cache_modifications = {
"for layer_past_state in layer_past_states": "for layer_past_state in layer_past_states[0:2]",
modification: modification + "\n reordered_layer_past_states ="
" (reordered_layer_past_states + layer_past_states[2:])\n",
}
code_patcher(
module_name="transformers.models.t5.modeling_t5",
function=transformers.models.t5.modeling_t5.T5ForConditionalGeneration._reorder_cache,
new_function_name="updated_reorder_cache",
modifications=reorder_cache_modifications,
)
transformers.models.t5.modeling_t5.T5ForConditionalGeneration._reorder_cache = (
transformers.models.t5.modeling_t5.updated_reorder_cache
)
expected_outputs = []
with open(expected_output_path, "rt", encoding="utf-8") as expected_output_file:
for idx, line in enumerate(expected_output_file):
expected_outputs.append(line.strip())
if idx + 1 == nbr_examples:
break
model_name = "t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=512)
model: T5ForConditionalGeneration = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.cuda()
model.eval()
processed_sample_count = 0
outputs = []
slines = []
start = time.time()
batch_count = 0
with open(source_path, "rt", encoding="utf-8") as source:
for s_idx, sline in enumerate(source):
slines.append(sline)
if len(slines) % batch_size:
if s_idx == nbr_examples:
break
continue
if batch_count == 0:
# warmup model:
_generate(
model,
tokenizer,
batch_count,
slines,
max_token_length,
num_beams,
min_gen_length,
max_gen_length,
no_repeat_ngram_size,
early_stopping,
use_cache,
do_log=False,
)
outputs.extend(
_generate(
model,
tokenizer,
batch_count,
slines,
max_token_length,
num_beams,
min_gen_length,
max_gen_length,
no_repeat_ngram_size,
early_stopping,
use_cache,
)
)
processed_sample_count += len(slines)
slines = []
batch_count += 1
if s_idx + 1 == nbr_examples:
break
if slines:
outputs.extend(
_generate(
model,
tokenizer,
batch_count,
slines,
max_token_length,
num_beams,
min_gen_length,
max_gen_length,
no_repeat_ngram_size,
early_stopping,
use_cache,
do_log=False,
)
)
processed_sample_count += len(slines)
end = time.time()
samples_per_second = processed_sample_count / (end - start)
print(
f"Finish the processing of {processed_sample_count} samples with the speed {samples_per_second:.2f} "
f"samples/second. Full time: {end - start} "
)
for i, output in enumerate(outputs):
if output != expected_outputs[i]:
pass
if __name__ == "__main__":
source_path = "./data/cnndm_128.txt"
expected_output_path = "./data/expected_t5_output.hypo"
# test without transformers modifications
modify_transformers = False
transformers_modifications_test(
modify_transformers,
source_path,
expected_output_path,
nbr_examples=40,
)
# test with transformers modifications
modify_transformers = True
transformers_modifications_test(
modify_transformers,
source_path,
expected_output_path,
nbr_examples=40,
)
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