def testMultipleReduces(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float16, [3])
pb = array_ops.placeholder(np.float16, [3])
a = math_ops.cast(pa, np.float32)
a = math_ops.reduce_sum(a)
a = math_ops.cast(a, np.float16)
b = math_ops.cast(pb, np.float32)
b = math_ops.reduce_sum(b)
b = math_ops.cast(b, np.float16)
c = a + b
report = ReportJSON(self, sess)
report.reset()
fd = {pa: [2.0, 0.5, 1.0], pb: [1.0, 1.0, 2.0]}
result = sess.run(c, fd)
self.assertAllClose(result, 7.5)
report.parse_log()
ok = [
'__seed*', 'host-exchange-local-copy-', 'Sum/reduce*/Reduce',
'Sum_1/reduce*/Reduce', 'add/add*/Add'
]
report.assert_all_compute_sets_and_list(ok)
def testNoCastsF16ReduceWithReshape(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float16, [3, 4])
a = gen_array_ops.reshape(pa, [4, 3])
a = math_ops.reduce_sum(a, axis=(1))
report = ReportJSON(self, sess)
report.reset()
fd = {pa: np.ones([3, 4])}
result = sess.run(a, fd)
self.assertAllClose(result, [3.0, 3.0, 3.0, 3.0])
report.parse_log()
ok = [
'__seed*',
'Sum/reduce*/Reduce',
]
report.assert_all_compute_sets_and_list(ok)
def testReduceMean(self):
with self.session() as sess:
shape = [2, 10000]
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float16, shape)
output = math_ops.reduce_mean(pa, axis=[1])
report = ReportJSON(self, sess)
report.reset()
val = np.finfo(np.float16).max / 2
result = sess.run(output, {pa: np.full(shape, val)})
self.assertAllClose(result, [val, val])
report.parse_log(assert_len=4)
ok = [
'__seed*', 'host-exchange-local-copy-', 'Mean/fusion/Reduce',
'Mean/fusion*/Op/Multiply', 'Mean/convert*/Cast'
]
report.assert_all_compute_sets_and_list(ok)
def testReductionSumVectorF16NoConverts(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float16, [4096], name="a")
output = math_ops.reduce_sum(pa, axis=[0])
report = ReportJSON(self, sess)
report.reset()
fd = {pa: np.ones([4096])}
result = sess.run(output, fd)
self.assertAllClose(result, 4096)
report.parse_log()
# Check that there are no casts to float at the beginning.
ok = [
'__seed*', 'host-exchange-local-copy-',
'Sum/reduce*/ReduceOnTile/InToIntermediateNoExchange/Reduce',
'Sum/reduce*/ReduceFinalStage/IntermediateToOutput/Reduce'
]
report.assert_all_compute_sets_and_list(ok)
def testDontRemoveCastsIfUsed(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float16, [3])
b = math_ops.cast(pa, np.float32)
const = array_ops.constant(1.0, np.float32)
b = b + const
c = math_ops.cast(b, np.float16)
report = ReportJSON(self, sess)
report.reset()
fd = {pa: [2.0, 0.5, 1.0]}
result = sess.run(c, fd)
self.assertAllClose(result, [3.0, 1.5, 2.0])
report.parse_log(assert_len=4)
ok = [
'__seed*', 'host-exchange-local-copy-', 'Cast/convert.*/Cast',
'add/fusion*/Add', 'Cast_1/convert.*/Cast'
]
report.assert_all_compute_sets_and_list(ok)
def testConvolutionsDontMatchDifferentDevices(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
x = array_ops.placeholder(np.float32, shape=[1, 4, 4, 2])
with variable_scope.variable_scope("vs", use_resource=True):
with ipu.scopes.ipu_shard(0):
y = layers.Conv2D(
2,
1,
use_bias=False,
kernel_initializer=init_ops.ones_initializer())(x)
with ipu.scopes.ipu_shard(1):
y = layers.Conv2D(
2,
1,
use_bias=False,
kernel_initializer=init_ops.ones_initializer())(y)
report = ReportJSON(self, sess, sharded=True)
sess.run(variables.global_variables_initializer())
report.reset()
sess.run(y, {x: np.zeros([1, 4, 4, 2])})
report.parse_log()
# Note how there are two convolutions
ok = [
'__seed*', '*OnTileCopy*', 'vs/conv2d/Conv2D/convolution.*',
'Copy_vs/conv2d/Conv2D/convolution.*',
'vs/conv2d_1/Conv2D/convolution.*'
]
report.assert_all_compute_sets_and_list(ok)