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[triton/codegen] added shared memory padding for HMMA arguments and vectorized loads

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Philippe Tillet
2019-06-11 13:27:54 -07:00
parent cbd916994d
commit 1b5a742a88
9 changed files with 61 additions and 39 deletions
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8
examples/python/tensorflow/run.py
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@@ -6,7 +6,7 @@ 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 = 256,256,256
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])
@@ -30,9 +30,9 @@ result = sess.run([c], feed_dict = {locks: np.zeros(4096),
# 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))
#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")