40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import os
|
|
import tensorflow as tf
|
|
import numpy as np
|
|
from time import time
|
|
data_files_path = tf.resource_loader.get_data_files_path()
|
|
library_dir = os.path.dirname(os.path.realpath(__file__))
|
|
module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
|
|
|
|
M, N, K = 8192, 8192, 8192
|
|
a = tf.placeholder(tf.float16, shape=[M, K])
|
|
b = tf.placeholder(tf.float16, shape=[N, K])
|
|
locks = tf.placeholder(tf.int32, shape=[4096])
|
|
# c = tf.matmul(a, b, transpose_a=True)
|
|
c = module.dot(a, b, locks)
|
|
|
|
# Reference
|
|
ha = np.random.rand(M, K).astype(np.float16)
|
|
hb = np.random.rand(N, K).astype(np.float16)
|
|
|
|
# Run
|
|
sess = tf.InteractiveSession()
|
|
sess.run(tf.global_variables_initializer())
|
|
result = sess.run([c], feed_dict = {locks: np.zeros(4096),
|
|
a: ha,
|
|
b: hb})[0]
|
|
|
|
#bench = tf.test.Benchmark().run_op_benchmark(sess=sess,
|
|
# op_or_tensor=c,
|
|
# feed_dict={a: ha, b: hb},
|
|
# min_iters=100)
|
|
#print(end - start)
|
|
#print(2*M*N*K / (end - start) * 1e-12)
|
|
#hresult = np.dot(ha.T, hb).T
|
|
#dif = np.abs(result - hresult)
|
|
#print("dif: %f" % np.max(dif))
|
|
|
|
#np.savetxt("dif.txt", dif, fmt="%5.2f")
|
|
#np.savetxt("gpu.txt", result, fmt="%5.2f")
|
|
#np.savetxt("cpu.txt", hresult, fmt="%5.2f")
|