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[general] hmma baseline setup

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Philippe Tillet
2019-06-05 14:43:38 -07:00
parent 49fcfd6fc7
commit f58c9a4d2b
14 changed files with 50 additions and 33 deletions
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19
examples/python/tensorflow/run.py
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@@ -3,18 +3,23 @@ import tensorflow as tf
import numpy as np
data_files_path = tf.resource_loader.get_data_files_path()
library_dir = '/home/philippe/Development/triton/build/examples/python/tensorflow'
library_dir = '/home/philippe/development/triton/build/examples/python/tensorflow'
module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
M, N, K = 512, 512, 512
a = tf.placeholder(tf.float32, shape=[M, K])
b = tf.placeholder(tf.float32, shape=[N, K])
a = tf.placeholder(tf.float16, shape=[M, K])
b = tf.placeholder(tf.float16, shape=[N, K])
locks = tf.placeholder(tf.int32, shape=[4096])
c = module.block_sparse_mat_mul(a, b, locks)
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)
hresult = np.dot(hb.T, ha)
# Run
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {locks: np.zeros(4096),
a: np.random.rand(M, K),
b: np.random.rand(N, K)})
print(result)
a: ha,
b: hb})
print(result - hresult)