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 testGRUNotCached(self):
with self.session() as sess:
# Note here the second GRU is larger.
pinputs1 = array_ops.placeholder(dataType,
[seq_len, batch_size, input_size],
name="inputs1")
pinputs2 = array_ops.placeholder(dataType,
[seq_len * 2, batch_size, input_size],
name="inputs2")
plabels = array_ops.placeholder(np.int32, [batch_size], name="labels")
with ops.device("/device:IPU:0"):
def gru_layer(inputs, name):
initial_state = _get_variable(
"initial_state",
shape=[batch_size, num_channels],
initializer=init_ops.constant_initializer(0.1, dataType))
return self._GRULayer(inputs=inputs,
weights_value=1.,
initial_state=initial_state,
training=True,
name=name)
with variable_scope.variable_scope("gru_layer1", use_resource=True):
logits1 = gru_layer(pinputs1, "layer1")
with variable_scope.variable_scope("gru_layer2", use_resource=True):
logits2 = gru_layer(pinputs2, "layer2")
logits = (math_ops.reduce_mean(logits1, axis=0) +
math_ops.reduce_mean(logits2, axis=0))
softmax = nn.sparse_softmax_cross_entropy_with_logits_v2(
logits=logits, labels=array_ops.stop_gradient(plabels))
loss = math_ops.reduce_mean(softmax)
train = gradient_descent.GradientDescentOptimizer(0.01).minimize(loss)
report = ReportJSON(self, sess)
sess.run(variables.global_variables_initializer())
report.reset()
sess.run(
[loss, train], {
pinputs1: _createGRUInput(0.5, batch_size, seq_len, input_size),
pinputs2: _createGRUInput(1.5, batch_size, seq_len * 2,
input_size),
plabels: np.ones(shape=[batch_size], dtype=np.int32),
})
report.parse_log()
report.assert_compute_sets_matches(
'*BasicGruCell/ProcessUnits/Weight/Conv*/Convolve', 4,
"There should be four fwd GRUs")
report.assert_compute_sets_matches('*/MulOGate/Op/Multiply', 2,
"There should be two bwd GRUs")
def testNoCastsF32ToF16ToF32(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float32, [3])
b = math_ops.cast(pa, np.float16)
c = math_ops.cast(b, np.float32)
report = ReportJSON(self, sess)
report.reset()
fd = {pa: [2.0, 0.5, 1.0]}
result = sess.run(c, fd)
self.assertAllClose(result, [2.0, 0.5, 1.0])
report.parse_log(assert_len=0)
report.assert_no_compute_set()
def testBatchNormalizeFused(self):
with self.session() as sess:
a = array_ops.placeholder(np.float32, [4, 64, 64, 4],
name="input_a")
def my_graph(a):
with ops.device("/device:IPU:0"):
with variable_scope.variable_scope("", use_resource=True):
beta = variable_scope.get_variable(
"x",
dtype=np.float32,
shape=[4],
initializer=init_ops.constant_initializer(0.0))
gamma = variable_scope.get_variable(
"y",
dtype=np.float32,
shape=[4],
initializer=init_ops.constant_initializer(1.0))
b_mean, b_var = nn.moments(a, [0, 1, 2],
name='moments')
normed = nn.fused_batch_norm(a,
gamma,
beta,
b_mean,
b_var,
is_training=False)
return normed
report = ReportJSON(self, sess)
out = ipu.ipu_compiler.compile(my_graph, [a])
sess.run(variables.global_variables_initializer())
report.reset()
result, _, _ = sess.run(out, {a: np.zeros([4, 64, 64, 4])})
self.assertAllClose(result, np.zeros([4, 64, 64, 4]))
report.parse_log()
bl = ['*convert*/Cast*']
report.assert_compute_sets_not_in_blacklist(bl)
report.assert_tensor_input_names("input_a", "x", "y")
def testArgMaxBasic(self, dtype):
def model(a):
return math_ops.argmax(a, output_type=dtypes.int32)
with self.session() as sess:
report = ReportJSON(self, sess)
report.reset()
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input, axis=0))
report.parse_log(assert_len=4)
def testBatchNormalizeLayerFusedFp16(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
with variable_scope.variable_scope("", use_resource=True):
a = array_ops.placeholder(np.float16, [4, 64, 64, 4],
name="input_a")
normed = layers_norm.batch_normalization(a, fused=True)
report = ReportJSON(self, sess)
sess.run(variables.global_variables_initializer())
report.reset()
result = sess.run(normed, {a: np.zeros([4, 64, 64, 4])})
self.assertAllClose(result, np.zeros([4, 64, 64, 4]))
report.parse_log()
bl = ['*convert*/Cast*']
report.assert_compute_sets_not_in_blacklist(bl)
report.assert_tensor_input_names("input_a")
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)
