def comparison_ops(self):
a = torch.randn(4)
b = torch.randn(4)
return (
torch.allclose(a, b),
torch.argsort(a),
torch.eq(a, b),
torch.equal(a, b),
torch.ge(a, b),
torch.greater_equal(a, b),
torch.gt(a, b),
torch.greater(a, b),
torch.isclose(a, b),
torch.isfinite(a),
torch.isin(a, b),
torch.isinf(a),
torch.isposinf(a),
torch.isneginf(a),
torch.isnan(a),
torch.isreal(a),
torch.kthvalue(a, 1),
torch.le(a, b),
torch.less_equal(a, b),
torch.lt(a, b),
torch.less(a, b),
torch.maximum(a, b),
torch.minimum(a, b),
torch.fmax(a, b),
torch.fmin(a, b),
torch.ne(a, b),
torch.not_equal(a, b),
torch.sort(a),
torch.topk(a, 1),
torch.msort(a),
)
def test_forced_bos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
bos_token_id = 0
logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id)
# check that all scores are -inf except the bos_token_id score
input_ids = ids_tensor((batch_size, 1), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertFalse(torch.isinf(scores).any())
def forward(self):
a = torch.tensor(0)
b = torch.tensor(1)
return len(
torch.allclose(a, b),
torch.argsort(a),
torch.eq(a, b),
torch.eq(a, 1),
torch.equal(a, b),
torch.ge(a, b),
torch.ge(a, 1),
torch.greater_equal(a, b),
torch.greater_equal(a, 1),
torch.gt(a, b),
torch.gt(a, 1),
torch.greater(a, b),
torch.isclose(a, b),
torch.isfinite(a),
torch.isin(a, b),
torch.isinf(a),
torch.isposinf(a),
torch.isneginf(a),
torch.isnan(a),
torch.isreal(a),
torch.kthvalue(a, 1),
torch.le(a, b),
torch.le(a, 1),
torch.less_equal(a, b),
torch.lt(a, b),
torch.lt(a, 1),
torch.less(a, b),
torch.maximum(a, b),
torch.minimum(a, b),
torch.fmax(a, b),
torch.fmin(a, b),
torch.ne(a, b),
torch.ne(a, 1),
torch.not_equal(a, b),
torch.sort(a),
torch.topk(a, 1),
torch.msort(a),
)
def test_forced_eos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
eos_token_id = 0
max_length = 5
logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id)
# check that all scores are -inf except the eos_token_id when max_length is reached
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
input_ids = ids_tensor((batch_size, 3), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertFalse(torch.isinf(scores).any())
def forward(self):
builder = flatbuffers.Builder(64)
# construct MessageBody
ppx_Run.RunStart(builder)
message_body = ppx_Run.RunEnd(builder)
# construct Message
ppx_Message.MessageStart(builder)
ppx_Message.MessageAddBodyType(builder,
ppx_MessageBody.MessageBody().Run)
ppx_Message.MessageAddBody(builder, message_body)
message = ppx_Message.MessageEnd(builder)
builder.Finish(message)
message = builder.Output()
self._requester.send_request(message)
while True:
reply = self._requester.receive_reply()
message_body = self._get_message_body(reply)
if isinstance(message_body, ppx_RunResult.RunResult):
result = self._protocol_tensor_to_variable(
message_body.Result())
return result
elif isinstance(message_body, ppx_Sample.Sample):
address = message_body.Address().decode('utf-8')
name = message_body.Name().decode('utf-8')
if name == '':
name = None
control = bool(message_body.Control())
replace = bool(message_body.Replace())
distribution_type = message_body.DistributionType()
if distribution_type == ppx_Distribution.Distribution(
).Uniform:
uniform = ppx_Uniform.Uniform()
uniform.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
low = self._protocol_tensor_to_variable(uniform.Low())
high = self._protocol_tensor_to_variable(uniform.High())
dist = Uniform(low, high)
elif distribution_type == ppx_Distribution.Distribution(
).Normal:
normal = ppx_Normal.Normal()
normal.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
mean = self._protocol_tensor_to_variable(normal.Mean())
stddev = self._protocol_tensor_to_variable(normal.Stddev())
dist = Normal(mean, stddev)
elif distribution_type == ppx_Distribution.Distribution(
).Categorical:
categorical = ppx_Categorical.Categorical()
categorical.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
probs = self._protocol_tensor_to_variable(
categorical.Probs())
dist = Categorical(probs)
elif distribution_type == ppx_Distribution.Distribution(
).Poisson:
poisson = ppx_Poisson.Poisson()
poisson.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
rate = self._protocol_tensor_to_variable(poisson.Rate())
dist = Poisson(rate)
elif distribution_type == ppx_Distribution.Distribution(
).Bernoulli:
bernoulli = ppx_Bernoulli.Bernoulli()
bernoulli.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
probs = self._protocol_tensor_to_variable(
bernoulli.Probs())
dist = Bernoulli(probs)
elif distribution_type == ppx_Distribution.Distribution().Beta:
beta = ppx_Beta.Beta()
beta.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
concentration1 = self._protocol_tensor_to_variable(
beta.Concentration1())
concentration0 = self._protocol_tensor_to_variable(
beta.Concentration0())
dist = Beta(concentration1, concentration0)
elif distribution_type == ppx_Distribution.Distribution(
).Exponential:
exponential = ppx_Exponential.Exponential()
exponential.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
rate = self._protocol_tensor_to_variable(
exponential.Rate())
dist = Exponential(rate)
elif distribution_type == ppx_Distribution.Distribution(
).Gamma:
gamma = ppx_Gamma.Gamma()
gamma.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
concentration = self._protocol_tensor_to_variable(
gamma.Concentration())
rate = self._protocol_tensor_to_variable(gamma.Rate())
dist = Gamma(concentration, rate)
elif distribution_type == ppx_Distribution.Distribution(
).LogNormal:
log_normal = ppx_LogNormal.LogNormal()
log_normal.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
loc = self._protocol_tensor_to_variable(log_normal.Loc())
scale = self._protocol_tensor_to_variable(
log_normal.Scale())
dist = LogNormal(loc, scale)
elif distribution_type == ppx_Distribution.Distribution(
).Binomial:
binomial = ppx_Binomial.Binomial()
binomial.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
total_count = self._protocol_tensor_to_variable(
binomial.TotalCount())
probs = self._protocol_tensor_to_variable(binomial.Probs())
dist = Binomial(total_count, probs)
elif distribution_type == ppx_Distribution.Distribution(
).Weibull:
weibull = ppx_Weibull.Weibull()
weibull.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
scale = self._protocol_tensor_to_variable(weibull.Scale())
concentration = self._protocol_tensor_to_variable(
weibull.Concentration())
dist = Weibull(scale, concentration)
else:
raise RuntimeError(
'ppx (Python): Sample from an unexpected distribution requested.'
)
result = state.sample(distribution=dist,
control=control,
name=name,
address=address)
builder = flatbuffers.Builder(64)
result = self._variable_to_protocol_tensor(builder, result)
ppx_SampleResult.SampleResultStart(builder)
ppx_SampleResult.SampleResultAddResult(builder, result)
message_body = ppx_SampleResult.SampleResultEnd(builder)
# construct Message
ppx_Message.MessageStart(builder)
ppx_Message.MessageAddBodyType(
builder,
ppx_MessageBody.MessageBody().SampleResult)
ppx_Message.MessageAddBody(builder, message_body)
message = ppx_Message.MessageEnd(builder)
builder.Finish(message)
message = builder.Output()
self._requester.send_request(message)
elif isinstance(message_body, ppx_Observe.Observe):
address = message_body.Address().decode('utf-8')
name = message_body.Name().decode('utf-8')
if name == '':
name = None
value = self._protocol_tensor_to_variable(message_body.Value())
distribution_type = message_body.DistributionType()
if distribution_type == ppx_Distribution.Distribution().NONE:
dist = None
elif distribution_type == ppx_Distribution.Distribution(
).Uniform:
uniform = ppx_Uniform.Uniform()
uniform.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
low = self._protocol_tensor_to_variable(uniform.Low())
high = self._protocol_tensor_to_variable(uniform.High())
dist = Uniform(low, high)
elif distribution_type == ppx_Distribution.Distribution(
).Normal:
normal = ppx_Normal.Normal()
normal.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
mean = self._protocol_tensor_to_variable(normal.Mean())
stddev = self._protocol_tensor_to_variable(normal.Stddev())
dist = Normal(mean, stddev)
elif distribution_type == ppx_Distribution.Distribution(
).Categorical:
categorical = ppx_Categorical.Categorical()
categorical.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
probs = self._protocol_tensor_to_variable(
categorical.Probs())
dist = Categorical(probs)
elif distribution_type == ppx_Distribution.Distribution(
).Poisson:
poisson = ppx_Poisson.Poisson()
poisson.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
rate = self._protocol_tensor_to_variable(poisson.Rate())
dist = Poisson(rate)
elif distribution_type == ppx_Distribution.Distribution(
).Bernoulli:
bernoulli = ppx_Bernoulli.Bernoulli()
bernoulli.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
probs = self._protocol_tensor_to_variable(
bernoulli.Probs())
dist = Bernoulli(probs)
elif distribution_type == ppx_Distribution.Distribution().Beta:
beta = ppx_Beta.Beta()
beta.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
concentration1 = self._protocol_tensor_to_variable(
beta.Concentration1())
concentration0 = self._protocol_tensor_to_variable(
beta.Concentration0())
dist = Beta(concentration1, concentration0)
elif distribution_type == ppx_Distribution.Distribution(
).Exponential:
exponential = ppx_Exponential.Exponential()
exponential.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
rate = self._protocol_tensor_to_variable(
exponential.Rate())
dist = Exponential(rate)
elif distribution_type == ppx_Distribution.Distribution(
).Gamma:
gamma = ppx_Gamma.Gamma()
gamma.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
concentration = self._protocol_tensor_to_variable(
gamma.Concentration())
rate = self._protocol_tensor_to_variable(gamma.Rate())
dist = Gamma(concentration, rate)
elif distribution_type == ppx_Distribution.Distribution(
).LogNormal:
log_normal = ppx_LogNormal.LogNormal()
log_normal.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
loc = self._protocol_tensor_to_variable(log_normal.Loc())
scale = self._protocol_tensor_to_variable(
log_normal.Scale())
dist = LogNormal(loc, scale)
elif distribution_type == ppx_Distribution.Distribution(
).Binomial:
binomial = ppx_Binomial.Binomial()
binomial.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
total_count = self._protocol_tensor_to_variable(
binomial.TotalCount())
probs = self._protocol_tensor_to_variable(binomial.Probs())
dist = Binomial(total_count, probs)
elif distribution_type == ppx_Distribution.Distribution(
).Weibull:
weibull = ppx_Weibull.Weibull()
weibull.Init(message_body.Distribution().Bytes,
message_body.Distribution().Pos)
scale = self._protocol_tensor_to_variable(weibull.Scale())
concentration = self._protocol_tensor_to_variable(
weibull.Concentration())
dist = Weibull(scale, concentration)
else:
raise RuntimeError(
'ppx (Python): Sample from an unexpected distribution requested: {}'
.format(distribution_type))
result = state.observe(distribution=dist,
value=value,
name=name,
address=address)
if self._kill_on_zero_likelihood and result is not None and torch.any(
torch.isneginf(dist.log_prob(result, sum=True))):
# result is None if the observed value is not provided (neither as an observe argument nor in the observation dictionary)
raise ZeroLikelihoodException(name)
builder = flatbuffers.Builder(64)
ppx_ObserveResult.ObserveResultStart(builder)
message_body = ppx_ObserveResult.ObserveResultEnd(builder)
# construct Message
ppx_Message.MessageStart(builder)
ppx_Message.MessageAddBodyType(
builder,
ppx_MessageBody.MessageBody().ObserveResult)
ppx_Message.MessageAddBody(builder, message_body)
message = ppx_Message.MessageEnd(builder)
builder.Finish(message)
message = builder.Output()
self._requester.send_request(message)
elif isinstance(message_body, ppx_Tag.Tag):
address = message_body.Address().decode('utf-8')
name = message_body.Name().decode('utf-8')
if name == '':
name = None
value = self._protocol_tensor_to_variable(message_body.Value())
state.tag(value=value, name=name, address=address)
builder = flatbuffers.Builder(64)
ppx_TagResult.TagResultStart(builder)
message_body = ppx_TagResult.TagResultEnd(builder)
# construct Message
ppx_Message.MessageStart(builder)
ppx_Message.MessageAddBodyType(
builder,
ppx_MessageBody.MessageBody().TagResult)
ppx_Message.MessageAddBody(builder, message_body)
message = ppx_Message.MessageEnd(builder)
builder.Finish(message)
message = builder.Output()
self._requester.send_request(message)
elif isinstance(message_body, ppx_Reset.Reset):
raise RuntimeError(
'ppx (Python): Received a reset request. Protocol out of sync.'
)
else:
raise RuntimeError(
'ppx (Python): Received unexpected message.')
# print(torch.inner(c, d))
"""flip"""
x = torch.arange(4).view(2, 2)
print(torch.flipud(x))
print(torch.fliplr(x))
# logical
print("logical function:")
print(torch.eq(c, d))
print(torch.ne(c, d))
print(torch.gt(c, d))
print(torch.logical_and(c, d))
print(torch.logical_or(c, d))
print(torch.logical_xor(c, d))
print(torch.logical_not(c))
print(torch.equal(c, d)) # if all equal
a = torch.rand(2, 2).bool()
print(a)
print(torch.all(a))
print(torch.all(a, dim=0)) # 按列
print(torch.all(a, dim=1)) # 按行
print(torch.any(a))
print(torch.any(a, dim=0)) # 按列
print(torch.any(a, dim=1)) # 按行
to_test = torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])
print(torch.isfinite(to_test))
print(torch.isinf(to_test))
print(torch.isposinf(to_test))
print(torch.isneginf(to_test))
print(torch.isnan(to_test))
