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Fix error message of multinomial op (#27946)
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* fix multinomial doc

* fix multinomial error message

* little doc change

* fix Categorical class doc

* optimize format of error message

* fix CPU Kernel error message format

* fix isinf and isnan error in WindowsOPENBLAS CI

* delete inf and nan

* add manual_seed in sample code

* little error message change

* change error message to InvalidArgument

* add full point for error message and add manual_seed in CPU environment
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@pangyoki
pangyoki authored Oct 19, 2020
1 parent b6eff44 commit 975bd88
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Showing 6 changed files with 213 additions and 138 deletions.
15 changes: 15 additions & 0 deletions paddle/fluid/operators/multinomial_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -53,12 +53,27 @@ class MultinomialOp : public framework::OperatorWithKernel {

auto x_dim = ctx->GetInputDim("X");
int64_t x_rank = x_dim.size();
PADDLE_ENFORCE_GT(x_rank, 0,
platform::errors::InvalidArgument(
"The number of dimensions of the input probability "
"distribution should be > 0, but got %d.",
x_rank));
PADDLE_ENFORCE_LE(x_rank, 2,
platform::errors::InvalidArgument(
"The number of dimensions of the input probability "
"distribution should be <= 2, but got %d.",
x_rank));

std::vector<int64_t> out_dims(x_rank);
for (int64_t i = 0; i < x_rank - 1; i++) {
out_dims[i] = x_dim[i];
}

int64_t num_samples = ctx->Attrs().Get<int>("num_samples");
PADDLE_ENFORCE_GT(
num_samples, 0,
platform::errors::InvalidArgument(
"The number of samples should be > 0, but got %d.", num_samples));
out_dims[x_rank - 1] = num_samples;

ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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9 changes: 9 additions & 0 deletions paddle/fluid/operators/multinomial_op.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/multinomial_op.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/transform.h"

namespace paddle {
Expand All @@ -31,6 +32,14 @@ __global__ void NormalizeProbability(T* norm_probs, const T* in_data,
T* sum_rows) {
int id = threadIdx.x + blockIdx.x * blockDim.x +
blockIdx.y * gridDim.x * blockDim.x;
PADDLE_ENFORCE(
in_data[id] >= 0.0,
"The input of multinomial distribution should be >= 0, but got %f.",
in_data[id]);
PADDLE_ENFORCE(sum_rows[blockIdx.y] > 0.0,
"The sum of one multinomial distribution probability should "
"be > 0, but got %f.",
sum_rows[blockIdx.y]);
norm_probs[id] = in_data[id] / sum_rows[blockIdx.y];
}

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29 changes: 15 additions & 14 deletions paddle/fluid/operators/multinomial_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -44,28 +44,29 @@ void MultinomialFunctor(int64_t* out_data, const T* in_data,
int64_t num_zeros = 0;
for (int64_t j = 0; j < num_categories; j++) {
prob_value = in_data[i * num_categories + j];
PADDLE_ENFORCE_GE(
prob_value, 0.0,
platform::errors::OutOfRange(
"The input of multinomial distribution should be >= 0"));
PADDLE_ENFORCE_EQ((std::isinf(static_cast<double>(prob_value)) ||
std::isnan(static_cast<double>(prob_value))),
false, platform::errors::OutOfRange(
"The input of multinomial distribution "
"shoud not be infinity or NaN"));
PADDLE_ENFORCE_GE(prob_value, 0.0,
platform::errors::InvalidArgument(
"The input of multinomial distribution "
"should be >= 0, but got %f.",
prob_value));

probs_sum += prob_value;
if (prob_value == 0) {
num_zeros += 1;
}
cumulative_probs[j] = probs_sum;
}
PADDLE_ENFORCE_GT(probs_sum, 0.0, platform::errors::OutOfRange(
"The sum of input should not be 0"));
PADDLE_ENFORCE_GT(probs_sum, 0.0,
platform::errors::InvalidArgument(
"The sum of one multinomial distribution "
"probability should be > 0, but got %f.",
probs_sum));
PADDLE_ENFORCE_EQ(
(replacement || (num_categories - num_zeros >= num_samples)), true,
platform::errors::OutOfRange("When replacement is False, number of "
"samples should be less than non-zero "
"categories"));
platform::errors::InvalidArgument(
"When replacement is False, number of "
"samples should be less than non-zero "
"categories."));

for (int64_t j = 0; j < num_categories; j++) {
cumulative_probs[j] /= probs_sum;
Expand Down
197 changes: 105 additions & 92 deletions python/paddle/distribution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -662,48 +662,54 @@ class Categorical(Distribution):
Args:
logits(list|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64.
name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import Categorical
import paddle
from paddle.distribution import Categorical
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
y = paddle.rand([6])
print(y.numpy())
# [0.6365463 , 0.7278677 , 0.90260243,
# 0.5226815 , 0.35837543, 0.13981032]
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
cat2 = Categorical(y)
paddle.manual_seed(200) # on CPU device
y = paddle.rand([6])
print(y.numpy())
# [0.77663314 0.90824795 0.15685187
# 0.04279523 0.34468332 0.7955718 ]
cat.sample([2,3])
# [[5, 1, 1],
# [0, 1, 2]]
cat = Categorical(x)
cat2 = Categorical(y)
cat.entropy()
# [1.71887]
paddle.manual_seed(1000) # on CPU device
cat.sample([2,3])
# [[0, 0, 5],
# [3, 4, 5]]
cat.kl_divergence(cat2)
# [0.0278455]
cat.entropy()
# [1.77528]
value = paddle.to_tensor([2,1,3])
cat.probs(value)
# [0.341613 0.342648 0.03123]
cat.kl_divergence(cat2)
# [0.071952]
cat.log_prob(value)
# [-1.07408 -1.07105 -3.46638]
value = paddle.to_tensor([2,1,3])
cat.probs(value)
# [0.00608027 0.108298 0.269656]
cat.log_prob(value)
# [-5.10271 -2.22287 -1.31061]
"""

def __init__(self, logits, name=None):
"""
Args:
logits(list|numpy.ndarray|Variable): The logits input of categorical distribution. The data type is float32 or float64.
logits(list|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64.
name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
"""
if not in_dygraph_mode():
check_type(logits, 'logits', (np.ndarray, tensor.Variable, list),
Expand All @@ -727,27 +733,29 @@ def sample(self, shape):
"""Generate samples of the specified shape.
Args:
shape (list): Shape of the generated samples.
shape (list): Shape of the generated samples.
Returns:
Tensor: A tensor with prepended dimensions shape.
Tensor: A tensor with prepended dimensions shape.
Examples:
.. code-block:: python
.. code-block:: python
import paddle
from paddle.distribution import Categorical
import paddle
from paddle.distribution import Categorical
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
cat = Categorical(x)
cat.sample([2,3])
# [[5, 1, 1],
# [0, 1, 2]]
paddle.manual_seed(1000) # on CPU device
cat.sample([2,3])
# [[0, 0, 5],
# [3, 4, 5]]
"""
name = self.name + '_sample'
Expand Down Expand Up @@ -775,28 +783,31 @@ def kl_divergence(self, other):
other (Categorical): instance of Categorical. The data type is float32.
Returns:
Variable: kl-divergence between two Categorical distributions.
Tensor: kl-divergence between two Categorical distributions.
Examples:
.. code-block:: python
.. code-block:: python
import paddle
from paddle.distribution import Categorical
import paddle
from paddle.distribution import Categorical
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
y = paddle.rand([6])
print(y.numpy())
# [0.6365463 , 0.7278677 , 0.90260243,
# 0.5226815 , 0.35837543, 0.13981032]
paddle.manual_seed(200) # on CPU device
y = paddle.rand([6])
print(y.numpy())
# [0.77663314 0.90824795 0.15685187
# 0.04279523 0.34468332 0.7955718 ]
cat = Categorical(x)
cat2 = Categorical(y)
cat = Categorical(x)
cat2 = Categorical(y)
cat.kl_divergence(cat2)
# [0.0278455]
cat.kl_divergence(cat2)
# [0.071952]
"""
name = self.name + '_kl_divergence'
Expand All @@ -823,23 +834,24 @@ def entropy(self):
"""Shannon entropy in nats.
Returns:
Variable: Shannon entropy of Categorical distribution. The data type is float32.
Tensor: Shannon entropy of Categorical distribution. The data type is float32.
Examples:
.. code-block:: python
.. code-block:: python
import paddle
from paddle.distribution import Categorical
import paddle
from paddle.distribution import Categorical
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
cat = Categorical(x)
cat.entropy()
# [1.71887]
cat.entropy()
# [1.77528]
"""
name = self.name + '_entropy'
Expand All @@ -864,27 +876,28 @@ def probs(self, value):
with ``logits. That is, ``value[:-1] = logits[:-1]``.
Args:
value (Tensor): The input tensor represents the selected category index.
value (Tensor): The input tensor represents the selected category index.
Returns:
Tensor: probability according to the category index.
Tensor: probability according to the category index.
Examples:
.. code-block:: python
.. code-block:: python
import paddle
from paddle.distribution import Categorical
import paddle
from paddle.distribution import Categorical
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
cat = Categorical(x)
value = paddle.to_tensor([2,1,3])
cat.probs(value)
# [0.341613 0.342648 0.03123]
value = paddle.to_tensor([2,1,3])
cat.probs(value)
# [0.00608027 0.108298 0.269656]
"""
name = self.name + '_probs'
Expand Down Expand Up @@ -929,28 +942,28 @@ def log_prob(self, value):
"""Log probabilities of the given category. Refer to ``probs`` method.
Args:
value (Tensor): The input tensor represents the selected category index.
value (Tensor): The input tensor represents the selected category index.
Returns:
Tensor: Log probability.
Tensor: Log probability.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import Categorical
.. code-block:: python
x = paddle.rand([6])
print(x.numpy())
# [0.32564053, 0.99334985, 0.99034804,
# 0.09053693, 0.30820143, 0.19095989]
import paddle
from paddle.distribution import Categorical
cat = Categorical(x)
paddle.manual_seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
value = paddle.to_tensor([2,1,3])
cat = Categorical(x)
cat.log_prob(value)
# [-1.07408 -1.07105 -3.46638]
value = paddle.to_tensor([2,1,3])
cat.log_prob(value)
# [-5.10271 -2.22287 -1.31061]
"""
name = self.name + '_log_prob'
Expand Down
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