b2e89e6e80
[FT] feat: Add FasterTransformer v3.0 1. Add supporting of INT8 quantization of cpp and TensorFlow op. 2. Provide the tools to quantize the model. 3. Fix the bugs that cmake 3.15 and 3.16 cannot build this project. 4. Deprecate the FasterTransformer v1
146 lines
6.1 KiB
Python
146 lines
6.1 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
|
|
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
import argparse
|
|
import timeit
|
|
import torch
|
|
# import torch.cuda.nvtx as nvtx
|
|
|
|
from onmt.utils.misc import sequence_mask
|
|
from utils.decoder import DecoderWeights, CustomDecoder, ONMTDecoder, init_op_cache, init_onmt_cache
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('batch_size', type=int,
|
|
help='batch size')
|
|
parser.add_argument('layer_num', type=int,
|
|
help='number of layers')
|
|
parser.add_argument('seq_len', type=int,
|
|
help='sequence length')
|
|
parser.add_argument('head_num', type=int,
|
|
help='head number')
|
|
parser.add_argument('head_size', type=int,
|
|
help='size per head')
|
|
parser.add_argument('--step', type=int, default=0,
|
|
help='decoding step number')
|
|
parser.add_argument('--fp16', action='store_true',
|
|
help='is fp16')
|
|
parser.add_argument('--time', action='store_true',
|
|
help='test the time or not.')
|
|
parser.add_argument('--module_path', type=str, default='./',
|
|
help='directory containing the th_fastertransformer dynamic lib')
|
|
parser.add_argument('--ths', action='store_true',
|
|
help='use TorchScript mode')
|
|
parser.add_argument('--ths_path', type=str, default='./lib/libths_fastertransformer.so',
|
|
help='path of the ths_fastertransformer dynamic lib file')
|
|
|
|
args = parser.parse_args()
|
|
|
|
hidden_dim = args.head_num * args.head_size
|
|
|
|
if args.step <= 0:
|
|
step = args.seq_len
|
|
else:
|
|
step = args.step
|
|
|
|
print("\n=============== Argument ===============")
|
|
print('batch_size: ' + str(args.batch_size))
|
|
print('layer_num: ' + str(args.layer_num))
|
|
print('seq_len: ' + str(args.seq_len))
|
|
print('head_num: ' + str(args.head_num))
|
|
print('head_size: ' + str(args.head_size))
|
|
print('hidden_dim: ' + str(hidden_dim))
|
|
print('step: ' + str(step))
|
|
print('use_fp16: ' + str(args.fp16))
|
|
print('TorchScript mode: ' + str(args.ths))
|
|
print('test_time: ' + str(args.time))
|
|
print("========================================\n")
|
|
|
|
inp = torch.empty(args.batch_size, 1, hidden_dim).cuda()
|
|
mem = torch.empty(args.batch_size, args.seq_len, hidden_dim).cuda()
|
|
torch.nn.init.uniform_(inp, -1, 1)
|
|
torch.nn.init.uniform_(mem, -1, 1)
|
|
if args.fp16:
|
|
inp = inp.half()
|
|
mem = mem.half()
|
|
mem_seq_lens = torch.randint(1, args.seq_len+1, (args.batch_size,), dtype=torch.int32).cuda()
|
|
src_pad_mask = ~sequence_mask(mem_seq_lens, args.seq_len).unsqueeze(1)
|
|
|
|
weights = DecoderWeights(args.layer_num, hidden_dim)
|
|
onmt_decoder = ONMTDecoder(args.layer_num, args.head_num, args.head_size, weights)
|
|
onmt_decoder.cuda()
|
|
if args.fp16:
|
|
onmt_decoder.half()
|
|
onmt_decoder.eval()
|
|
|
|
weights.to_cuda()
|
|
if args.fp16:
|
|
weights.to_half()
|
|
if args.ths:
|
|
custom_decoder = CustomDecoder(args.layer_num, args.head_num, args.head_size, weights, args.fp16, os.path.abspath(args.ths_path), args.ths)
|
|
else:
|
|
custom_decoder = CustomDecoder(args.layer_num, args.head_num, args.head_size, weights, args.fp16, os.path.abspath(args.module_path))
|
|
|
|
with torch.no_grad():
|
|
self_cache, mem_cache = init_op_cache(args.layer_num, args.batch_size, 1, args.seq_len, hidden_dim, args.fp16)
|
|
cache = init_onmt_cache(args.layer_num, mem)
|
|
output1 = inp
|
|
output2 = inp
|
|
|
|
for i in range(step):
|
|
output1 = onmt_decoder(output1, mem, src_pad_mask, cache, 0)
|
|
output2, self_cache, mem_cache = custom_decoder(output2, mem, mem_seq_lens, self_cache, mem_cache)
|
|
diff = torch.abs((output1 - output2) / output1)
|
|
print('step: {} Mean relative diff: {} Max relative diff: {} Min relative diff: {}'.format(
|
|
i, torch.mean(diff), torch.max(diff), torch.min(diff)))
|
|
|
|
if args.time:
|
|
iterations = 10
|
|
|
|
for i in range(iterations):
|
|
cache = init_onmt_cache(args.layer_num, mem)
|
|
output1 = inp
|
|
for i in range(step):
|
|
output1 = onmt_decoder(output1, mem, src_pad_mask, cache, 0)
|
|
t10 = timeit.default_timer()
|
|
for i in range(iterations):
|
|
cache = init_onmt_cache(args.layer_num, mem)
|
|
output1 = inp
|
|
for i in range(step):
|
|
output1 = onmt_decoder(output1, mem, src_pad_mask, cache, 0)
|
|
t1 = timeit.default_timer() - t10
|
|
|
|
for i in range(iterations):
|
|
self_cache, mem_cache = init_op_cache(args.layer_num, args.batch_size, 1, args.seq_len, hidden_dim, args.fp16)
|
|
output2 = inp
|
|
for i in range(step):
|
|
output2, self_cache, mem_cache = custom_decoder(output2, mem, mem_seq_lens, self_cache, mem_cache)
|
|
t20 = timeit.default_timer()
|
|
for i in range(iterations):
|
|
self_cache, mem_cache = init_op_cache(args.layer_num, args.batch_size, 1, args.seq_len, hidden_dim, args.fp16)
|
|
output2 = inp
|
|
for i in range(step):
|
|
output2, self_cache, mem_cache = custom_decoder(output2, mem, mem_seq_lens, self_cache, mem_cache)
|
|
t2 = timeit.default_timer() - t20
|
|
print("[INFO] ONMTDecoder time costs: {:.2f} ms".format(t1*1000/iterations))
|
|
print("[INFO] FTDecoder time costs: {:.2f} ms".format(t2*1000/iterations))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|