import tensorflow as tf
from tensorflow.contrib import antares
if tf.version.VERSION.startswith('2.'):
tf = tf.compat.v1
tf.disable_eager_execution()
input0 = tf.get_variable('input0', [1024, 512],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=False)
input1 = tf.get_variable('input1', [512, 512],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=True)
op = antares.make_op(
ir=
'temp0[K, N] = input0[N, K] + 100; output0[N, M] +=! temp0[K, N] * input1[K, M] where K in 10',
feed_dict={
'input0': input0,
'input1': input1
}).tune(step=100, use_cache=True, timeout=600).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('The result of tensor `%s` is:\n%s' % (op, sess.run(op)))
return tf.get_variable(name, shape, tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.001), trainable=True)
input_tensor = tf.get_variable('input_tensor', [64, 3, 227, 227], tf.float32, initializer=tf.initializers.ones(tf.float32), trainable=False)
output_logits = antares.make_op(ir=f'''
conv_0[N, F, HO, WO] +=! input_tensor[N, C, HO * 4 + KH, WO * 4 + KW] * const_0_[KH, KW, C, F] where HO in 55, WO in 55;
mpool_0[N, C, HO, WO ] >=! conv_0[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 27, WO in 27, KH in 3, KW in 3;
conv_1[N, F, HO, WO] +=! mpool_0[N, C, -2 + HO + KH, -2 + WO + KW].when([-2 + HO + KH >= 0, -2 + HO + KH < 27, -2 + WO + KW >= 0, -2 + WO + KW < 27], 0.0) * const_1_[KH, KW, C, F] where HO in 27, WO in 27;
mpool_1[N, C, HO, WO ] >=! conv_1[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 13, WO in 13, KH in 3, KW in 3;
conv_2[N, F, HO, WO] +=! mpool_1[N, C, -1 + HO + KH, -1 + WO + KW].when([-1 + HO + KH >= 0, -1 + HO + KH < 13, -1 + WO + KW >= 0, -1 + WO + KW < 13], 0.0) * const_2_[KH, KW, C, F] where HO in 13, WO in 13;
conv_3[N, F, HO, WO] +=! conv_2[N, C, -1 + HO + KH, -1 + WO + KW].call(`max`, [0.0]).when([-1 + HO + KH >= 0, -1 + HO + KH < 13, -1 + WO + KW >= 0, -1 + WO + KW < 13], 0.0) * const_3_[KH, KW, C, F] where HO in 13, WO in 13;
mpool_2[N, C, HO, WO] >=! conv_3[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 6, WO in 6, KH in 3, KW in 3;
reshape_0[N0, N1] = mpool_2[N0, N1 // 36 % 256, N1 // 6 % 6, N1 % 6] where N1 in 9216;
dense_0[N, M] +=! reshape_0[N, K] * const_5_[K, M];
dense_1[N, M] +=! dense_0[N, K].call(`max`, [0.0]) * const_6_[K, M];
dense_2[N, M] +=! dense_1[N, K].call(`max`, [0.0]) * const_7_[K, M];
''', feed_dict={
'input_tensor': input_tensor,
'const_0_': create_param('const_0_', [11, 11, 3, 64]),
'const_1_': create_param('const_1_', [5, 5, 64, 192]),
'const_2_': create_param('const_2_', [3, 3, 192, 384]),
'const_3_': create_param('const_3_', [3, 3, 384, 256]),
'const_4_': create_param('const_4_', [3, 3, 256, 256]),
'const_5_': create_param('const_5_', [9216, 4096]),
'const_6_': create_param('const_6_', [4096, 4096]),
'const_7_': create_param('const_7_', [4096, 1000]),
}).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
import tensorflow as tf
from tensorflow.contrib import antares
if tf.version.VERSION.startswith('2.'):
tf = tf.compat.v1
tf.disable_eager_execution()
input0 = tf.get_variable('input0', [1024 * 512],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=False)
input1 = tf.get_variable('input1', [1024 * 512],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=False)
op = antares.make_op(
ir=
'output0[N] = input0[N] + input1[N]; output1[N] = input0[N].call(`exp`); output2[N] = input1[N] + output1[N];',
extra_outputs=['output0', 'output1', 'output2'],
feed_dict={
'input0': input0,
'input1': input1
}).tune(step=100, use_cache=True, timeout=600).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('The result of tensor `%s` is:\n%s' % (op, sess.run(op)))
tf_out = tf.nn.relu(tf_out)
tf_out = tf.add(tf.matmul(tf_out, w1), b1)
tf_out = tf.nn.relu(tf_out)
tf_out = tf.add(tf.matmul(tf_out, w2), b2)
out = x
out = antares.make_op(ir='''
data_0[N, M] +=! data[N, K] * weight_0[K, M];
data_0_bias[N, K] = data_0[N, K] + bias_0[K];
data_1[N, K] = data_0_bias[N, K].call(`max`, [0.0]);
data_2[N, M] +=! data_1[N, K] * weight_1[K, M];
data_2_bias[N, K] = data_2[N, K] + bias_1[K];
data_3[N, K] = data_2_bias[N, K].call(`max`, [0.0]);
data_4[N, M] +=! data_3[N, K] * weight_2[K, M];
data_5[N, K] = (data_4[N, K] + bias_2[K]);
''',
feed_dict={
'data': x,
'weight_0': w0,
'weight_1': w1,
'weight_2': w2,
'bias_0': b0,
'bias_1': b1,
'bias_2': b2
}).tune(step=200, use_cache=True, timeout=600).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('[Tensorflow Result]')
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
from _common import *
x = create_variable([1024, 3072], dtype=tf.float32)
compare_ops(
tf.reduce_sum(x, axis=1),
antares.make_op('output0[N] +=! input0[N, M]', [x]),
)
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
from _common import *
x = create_variable([1024], dtype=tf.int32)
compare_ops(
tf.one_hot(x, depth=128),
antares.make_op(
'output0[N, F] = const(1.0).when([input0[N] == F], 0.0) where F in 128',
[x]),
)
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
import os
x = tf.random.uniform([1024, 512])
y = tf.random.uniform([1024, 512])
op = antares.make_op('output0[N, M] = input0[N, M] * input1[N, M] + 1234', [x, y], server_addr=os.environ.get('ANTARES_ADDR', 'localhost:8880'))
with tf.Session() as sess:
print(sess.run(op))
sess.run([op] * 100)
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
if tf.version.VERSION.startswith('2.'):
tf = tf.compat.v1
tf.disable_eager_execution()
x = tf.get_variable('x', [128, 1024],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=False)
y = tf.get_variable('y', [1024, 1024],
tf.float32,
initializer=tf.initializers.ones(tf.float32),
trainable=False)
op = antares.make_op(ir='dot_0[N, M] +=! data[N, K] * weight[K, M]',
feed_dict={
'data': x,
'weight': y
}).tune(step=100, use_cache=True, timeout=600).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('The result of tensor `%s` is:\n%s' % (op, sess.run(op)))
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
from _common import *
x = create_variable([1024, 64], dtype=tf.float32)
compare_ops(
tf.broadcast_to(tf.reshape(x, [1024, 64, 1]), shape=[1024, 64, 16]),
antares.make_op('output0[N, M, K] = input0[N, M] where K in 16', [x]),
)
layer_output_norm = antares.make_op(ir=f'''
merged_layer_local[R, B, S1, N1, H1] +=! input_tensor[B, S1, N, H] * qkv_weight[R, N, H, N1, H1];
merged_layer_trans[R, B, N1, S1, H1] = merged_layer_local[R, B, S1, N1, H1] + qkv_bias[R, N1, H1];
attention_scores[B, N1, S1, S2] +=! merged_layer_trans[0, B, N1, S1, H1] * merged_layer_trans[1, B, N1, S2, H1] / const({H}).cast(`float32`);
softmax_1_temp0[B, N1] >=! attention_scores[B, N1, S1, S2];
softmax_1_temp1[B, N1] +=! (attention_scores[B, N1, S1, S2] - softmax_1_temp0[B, N1]).call(`exp`);
attention_probs[B, N1, S1, S2] = (attention_scores[B, N1, S1, S2] - softmax_1_temp0[B, N1]).call(`exp`) / softmax_1_temp1[B, N1];
context_layer_trans[B, S1, N1, H1] +=! attention_probs[B, N1, S1, S2] * merged_layer_trans[2, B, N1, S2, H1];
attention_local[B, S1, N2, H2] +=! context_layer_trans[B, S1, N1, H1] * attention_weight[N1, H1, N2, H2];
attention_output[B, S1, N2, H2] = attention_local[B, S1, N2, H2] + attention_bias[N2, H2];
layer_norm_1_src[B, S1, N2, H2] = attention_output[B, S1, N2, H2] + input_tensor[B, S1, N2, H2];
layer_norm_1_temp0[B, S1] += layer_norm_1_src[B, S1, N2, H2];
layer_norm_1_temp1[B, S1] += layer_norm_1_src[B, S1, N2, H2] * layer_norm_1_src[B, S1, N2, H2];
attention_output_norm[B, S1, N2, H2] = (layer_norm_1_src[B, S1, N2, H2] * {N * H} - layer_norm_1_temp0[B, S1]) * (layer_norm_1_temp0[B, S1] * {N * H} - layer_norm_1_temp1[B, S1] * layer_norm_1_temp1[B, S1]).call(`max`, [1e-8]).call(`rsqrt`);
intermediate_local[B, S1, I] +=! attention_output_norm[B, S1, N2, H2] * intermediate_weight[N2, H2, I];
intermediate[B, S1, I] = intermediate_local[B, S1, I] + intermediate_bias[I];
intermediate_gelu[B, S1, I] = 0.5 * (1.0 + (0.79788456 * (intermediate[B, S1, I] + 0.044715 * intermediate[B, S1, I] * intermediate[B, S1, I] * intermediate[B, S1, I])).call(`tanh`));
layer_output_local[B, S1, N2, H2] +=! intermediate_gelu[B, S1, I] * output_weight[I, N2, H2];
layer_output[B, S1, N2, H2] = layer_output_local[B, S1, N2, H2] + output_bias[N2, H2];
layer_norm_2_src[B, S1, N2, H2] = layer_output[B, S1, N2, H2] + attention_output_norm[B, S1, N2, H2];
layer_norm_2_temp0[B, S1] += layer_norm_2_src[B, S1, N2, H2];
layer_norm_2_temp1[B, S1] += layer_norm_2_src[B, S1, N2, H2] * layer_norm_2_src[B, S1, N2, H2];
layer_output_norm[B, S1, N2, H2] = (layer_norm_2_src[B, S1, N2, H2] * {N * H} - layer_norm_2_temp0[B, S1]) * (layer_norm_2_temp0[B, S1] * {N * H} - layer_norm_2_temp1[B, S1] * layer_norm_2_temp1[B, S1]).call(`max`, [1e-8]).call(`rsqrt`);
''',
feed_dict={
'input_tensor':
input_tensor,
'qkv_weight':
create_param('qkv_weight',
[3, N, H, N, H]),
'qkv_bias':
create_param('qkv_bias', [3, N, H]),
'attention_weight':
create_param('attention_weight',
[N, H, N, H]),
'attention_bias':
create_param('attention_bias', [N, H]),
'intermediate_weight':
create_param('intermediate_weight',
[N, H, I]),
'intermediate_bias':
create_param('intermediate_bias', [I]),
'output_weight':
create_param('output_weight',
[I, N, H]),
'output_bias':
create_param('output_bias', [N, H]),
}).emit()
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
from _common import *
x = create_variable([64, 224, 224, 3], dtype=tf.float32)
compare_ops(
tf.transpose(x, [0, 3, 1, 2]),
antares.make_op('output0[N, C, H, W] = input0[N, H, W, C]', [x]),
)
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
if tf.version.VERSION.startswith('2.'):
tf = tf.compat.v1
tf.disable_eager_execution()
from _common import *
x = create_variable([1024, 64])
y = create_variable([1024, 64])
compare_ops(
tf.add(x, y),
antares.make_op('output0[N, M] = input0[N, M] + input1[N, M]', [x, y]),
)
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
from _common import *
x = create_variable([1024, 64])
y = create_variable([64, 4096])
compare_ops(
tf.matmul(x, y),
antares.make_op('output0[N, M] +=! input0[N, K] * input1[K, M]', [x, y]),
)
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import tensorflow as tf
from tensorflow.contrib import antares
import os
x = tf.random.uniform([1024, 512])
op = antares.make_op('reduce_sum_0[N] +=! data[N, M]', {'data': x},
server_addr=os.environ.get('ANTARES_ADDR',
'localhost:8880'))
with tf.Session() as sess:
print('The result of tensor `%s` is:\n%s' %
(op._output_names[0], sess.run(op)))
sess.run([op] * 100)
reshape_0 = tf.reshape(mpool_2, [input_tensor.shape[0], -1])
dense_0 = tf.matmul(reshape_0, feed_dict['const_5_'])
dense_1 = tf.matmul(tf.nn.relu(dense_0), feed_dict['const_6_'])
dense_2 = tf.matmul(tf.nn.relu(dense_1), feed_dict['const_7_'])
output_logits_tf = dense_2
output_logits = antares.make_op(ir=f'''
conv_0[N, F, HO, WO] +=! input_tensor[N, C, HO * 4 + KH, WO * 4 + KW] * const_0_[KH, KW, C, F] where HO in 55, WO in 55;
mpool_0[N, C, HO, WO] >=! conv_0[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 27, WO in 27, KH in 3, KW in 3;
conv_1[N, F, HO, WO] +=! mpool_0[N, C, -2 + HO + KH, -2 + WO + KW].when([-2 + HO + KH >= 0, -2 + HO + KH < 27, -2 + WO + KW >= 0, -2 + WO + KW < 27], 0.0) * const_1_[KH, KW, C, F] where HO in 27, WO in 27;
mpool_1[N, C, HO, WO] >=! conv_1[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 13, WO in 13, KH in 3, KW in 3;
conv_2[N, F, HO, WO] +=! mpool_1[N, C, -1 + HO + KH, -1 + WO + KW].when([-1 + HO + KH >= 0, -1 + HO + KH < 13, -1 + WO + KW >= 0, -1 + WO + KW < 13], 0.0) * const_2_[KH, KW, C, F] where HO in 13, WO in 13;
conv_2_relu[N, F, HO, WO] = conv_2[N, F, HO, WO].call(`max`, [0.0]);
conv_3[N, F, HO, WO] +=! conv_2_relu[N, C, -1 + HO + KH, -1 + WO + KW].when([-1 + HO + KH >= 0, -1 + HO + KH < 13, -1 + WO + KW >= 0, -1 + WO + KW < 13], 0.0) * const_3_[KH, KW, C, F] where HO in 13, WO in 13;
conv_3_relu[N, F, HO, WO] = conv_3[N, F, HO, WO].call(`max`, [0.0]);
conv_4[N, F, HO, WO] +=! conv_3_relu[N, C, -1 + HO + KH, -1 + WO + KW].when([-1 + HO + KH >= 0, -1 + HO + KH < 13, -1 + WO + KW >= 0, -1 + WO + KW < 13], 0.0) * const_4_[KH, KW, C, F] where HO in 13, WO in 13;
mpool_2[N, C, HO, WO] >=! conv_4[N, C, HO * 2 + KH, WO * 2 + KW].call(`max`, [0.0]) where HO in 6, WO in 6, KH in 3, KW in 3;
reshape_0[N0, N1] = mpool_2[N0, N1 // 36 % 256, N1 // 6 % 6, N1 % 6] where N1 in 9216;
dense_0[N, M] +=! reshape_0[N, K] * const_5_[K, M];
dense_0_relu[N, M] = dense_0[N, M].call(`max`, [0.0]);
dense_1[N, M] +=! dense_0_relu[N, K] * const_6_[K, M];
dense_1_relu[N, M] = dense_1[N, M].call(`max`, [0.0]);
dense_2[N, M] +=! dense_1_relu[N, K] * const_7_[K, M];
''',
feed_dict=feed_dict).emit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('Result from Antares = %s' % sess.run([output_logits]))