46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import os
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import tensorflow as tf
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import numpy as np
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from time import time
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data_files_path = tf.resource_loader.get_data_files_path()
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library_dir = os.path.dirname(os.path.realpath(__file__))
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module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
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def run_dot():
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M, N, K = 128,128,128
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a = tf.placeholder(tf.float16, shape=[M, K])
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b = tf.placeholder(tf.float16, shape=[N, K])
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locks = tf.placeholder(tf.int32, shape=[4096])
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# c = tf.matmul(a, b, transpose_a=True)
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c = module.dot(a, b, locks)
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# Reference
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ha = np.random.rand(M, K).astype(np.float16)
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hb = np.random.rand(N, K).astype(np.float16)
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# Run
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {locks: np.zeros(4096),
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a: ha,
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b: hb})[0]
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# Test
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hresult = np.dot(ha.T, hb).T
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dif = np.abs(result - hresult)
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print("dif: %f" % np.max(dif))
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def run_conv():
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BS, C, H, W = 16, 32, 32, 32
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R, S, NF = 3, 3, 32
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a = tf.placeholder(tf.float32, shape=[BS, C, H, W])
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b = tf.placeholder(tf.float32, shape=[C, R, S, NF])
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c = module.conv2d(a, b)
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# Reference
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ha = np.random.rand(BS, C, H, W)
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hb = np.random.rand(C, R, S, NF)
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# Run
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {a: ha,
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b: hb})[0]
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run_conv()
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