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add categorical

This commit is contained in:
baihuawei 2020-08-19 11:21:15 +08:00
parent 2cbb280bea
commit 772e14d00d
6 changed files with 269 additions and 10 deletions
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10
mindspore/ccsrc/backend/kernel_compiler/gpu/math/multinomial_gpu_kernel.h
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@ -54,11 +54,15 @@ class MultinomialGpuKernel : public GpuKernel {
CHECK_CUDA_RET_WITH_EXCEPT(cudaMalloc(reinterpret_cast<void **>(&cflag), sizeof(T)), "cudaMalloc failed.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMallocHost(&flag, sizeof(T)), "cudaMallocHost failed.");
CalFloatStatus(input_size_0_ / sizeof(T), input_addr, cflag, reinterpret_cast<cudaStream_t>(stream_ptr));
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaStreamSynchronize failed.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(flag, cflag, sizeof(T), cudaMemcpyDeviceToHost), "cudaMemcpyAsync failed.");
if (*flag > 0) {
MS_LOG(EXCEPTION) << "Input is invalid (containing NaN, -inf or inf)";
}
CheckNonNeg(input_size_0_ / sizeof(T), input_addr, cflag, reinterpret_cast<cudaStream_t>(stream_ptr));
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaStreamSynchronize failed.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(flag, cflag, sizeof(T), cudaMemcpyDeviceToHost), "cudaMemcpyAsync failed.");
if (*flag > 0) {
MS_LOG(EXCEPTION) << "Input is invalid (input element < 0)";
@ -68,14 +72,16 @@ class MultinomialGpuKernel : public GpuKernel {
"cudaMalloc failed.");
CumSum(input_addr, cum_sum_input, cum_sum_input, IntToSize(distributions_), IntToSize(categories), 1,
IntToSize(categories), 1, false, false, reinterpret_cast<cudaStream_t>(stream_ptr));
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaStreamSynchronize failed.");
CheckZero(IntToSize(distributions_), IntToSize(categories), cum_sum_input, cflag,
reinterpret_cast<cudaStream_t>(stream_ptr));
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaStreamSynchronize failed.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(flag, cflag, sizeof(T), cudaMemcpyDeviceToHost), "cudaMemcpyAsync failed.");
if (*flag > 0) {
MS_LOG(EXCEPTION) << "Input is invalid (sum <= 0)";
}
CHECK_CUDA_RET_WITH_EXCEPT(cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr)),
"cudaStreamSynchronize failed.");
Multinomial(seed_, cum_sum_input, num_sample, devStates, output_addr, IntToSize(distributions_),
IntToSize(categories), reinterpret_cast<cudaStream_t>(stream_ptr));

2
mindspore/nn/probability/distribution/__init__.py
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@ -25,6 +25,7 @@ from .bernoulli import Bernoulli
from .exponential import Exponential
from .uniform import Uniform
from .geometric import Geometric
from .categorical import Categorical
__all__ = ['Distribution',
'TransformedDistribution',
@ -32,4 +33,5 @@ __all__ = ['Distribution',
'Bernoulli',
'Exponential',
'Uniform',
'Categorical',
'Geometric',]

6
mindspore/nn/probability/distribution/_utils/utils.py
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@ -256,16 +256,16 @@ def check_tensor_type(name, inputs, valid_type):
Check if inputs is proper.
Args:
inputs: Tensor to be checked.
name: inputs name
inputs: Tensor to be checked.
Raises:
ValueError: if inputs is not a proper Tensor.
"""
if not isinstance(inputs, Tensor):
raise TypeError(f"{name} should be a Tensor")
inputs = P.DType()(inputs)
if inputs not in valid_type:
input_type = P.DType()(inputs)
if input_type not in valid_type:
raise TypeError(f"{name} dtype is invalid")
def check_type(data_type, value_type, name):

217
mindspore/nn/probability/distribution/categorical.py Normal file
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@ -0,0 +1,217 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Categorical Distribution"""
import numpy as np
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import logits_to_probs, probs_to_logits, check_tensor_type, cast_to_tensor
class Categorical(Distribution):
"""
Creates a categorical distribution parameterized by either probs or logits (but not both).
Args:
probs (Tensor, list, numpy.ndarray, Parameter, float): event probabilities.
logits (Tensor, list, numpy.ndarray, Parameter, float): event log-odds.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Categorical.
Note:
probs must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1.
Examples:
>>> # To initialize a Categorical distribution of prob is [0.5, 0.5]
>>> import mindspore.nn.probability.distribution as msd
>>> b = msd.Categorical(probs = [0.5, 0.5], dtype=mstype.int32)
>>>
>>> # To use Categorical in a network
>>> class net(Cell):
>>> def __init__(self, probs):
>>> super(net, self).__init__():
>>> self.ca = msd.Categorical(probs=probs, dtype=mstype.int32)
>>> # All the following calls in construct are valid
>>> def construct(self, value):
>>>
>>> # Similar calls can be made to logits
>>> ans = self.ca.probs
>>> # value should be Tensor
>>> ans = self.ca.log_prob(value)
>>>
>>> # Usage of enumerate_support
>>> ans = self.ca.enumerate_support()
>>>
>>> # Usage of entropy
>>> ans = self.ca.entropy()
>>>
>>> # Sample
>>> ans = self.ca.sample()
>>> ans = self.ca.sample((2,3))
>>> ans = self.ca.sample((2,))
"""
def __init__(self,
probs=None,
logits=None,
seed=0,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
super(Categorical, self).__init__(seed, dtype, name, param)
if (probs is None) == (logits is None):
raise ValueError("Either 'prob' or 'logits' must be specified, but not both.")
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.log = P.Log()
self.exp = P.Exp()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.div = P.RealDiv()
self.size = P.Size()
self.mutinomial = P.Multinomial(seed=seed)
self.cast = P.Cast()
self.expandim = P.ExpandDims()
self.gather = P.GatherNd()
self.concat = P.Concat(-1)
if probs is not None:
self._probs = cast_to_tensor(probs, mstype.float32)
input_sum = self.reduce_sum(self._probs, -1)
self._probs = self.div(self._probs, input_sum)
self._logits = probs_to_logits(self._probs)
self._param = self._probs
else:
self._logits = cast_to_tensor(logits, mstype.float32)
input_sum = self.reduce_sum(self.exp(self._logits), -1)
self._logits = self._logits - self.log(input_sum)
self._probs = logits_to_probs(self._logits)
self._param = self._logits
self._num_events = self.shape(self._param)[-1]
self._param2d = self.reshape(self._param, (-1, self._num_events))
self._batch_shape = self.shape(self._param2d)[0]
@property
def logits(self):
"""
Returns the logits.
"""
return self._logits
@property
def probs(self):
"""
Returns the probability.
"""
return self._probs
def _sample(self, sample_shape=(1,)):
"""
Sampling.
Args:
sample_shape (tuple): shape of the sample. Default: (1,).
Returns:
Tensor, shape is shape(probs)[:-1] + sample_shape
"""
if not isinstance(sample_shape, tuple):
raise ValueError("sample shape must be a tuple")
num_sample = 1
for i in sample_shape:
num_sample *= i
probs_2d = self.reshape(self._probs, (-1, self._num_events))
samples = self.mutinomial(probs_2d, num_sample)
extend_shape = sample_shape
if len(self.shape(self._probs)) > 1:
extend_shape = self.shape(self._probs)[:-1] + sample_shape
return self.cast(self.reshape(samples, extend_shape), self.dtype)
def _broad_cast_shape(self, a, b):
"""
Broadcast Tensor shape.
Args:
a (Tensor): A Tensor need to Broadcast.
b (Tensor): Another Tensor need to Broadcast.
Returns:
Tuple, Broadcast shape.
"""
shape_a = self.shape(a)
shape_b = self.shape(b)
size_a = len(shape_a)
size_b = len(shape_b)
if size_a > size_b:
size = size_a
shape_out = list(shape_a)
shape_short = list(shape_b)
diff_size = size_a - size_b
else:
size = size_b
shape_out = list(shape_b)
shape_short = list(shape_a)
diff_size = size_b - size_a
for i in range(diff_size, size):
if shape_out[i] == shape_short[i - diff_size]:
continue
if shape_out[i] == 1 or shape_short[i - diff_size] == 1:
shape_out[i] = shape_out[i] * shape_short[i - diff_size]
else:
raise ValueError(f"Shape {shape_a} and {shape_b} is not broadcastable.")
return tuple(shape_out)
def _log_prob(self, value):
r"""
Evaluate log probability.
Args:
value (Tensor): value to be evaluated. The dtype could be mstype.float32, bool, mstype.int32.
"""
if value is not None:
check_tensor_type("value", value, [mstype.float32, bool, mstype.int32])
value = self.expandim(self.cast(value, mstype.float32), -1)
broad_shape = self._broad_cast_shape(value, self._logits)
broad = P.BroadcastTo(broad_shape)
value = broad(value)[..., :1]
index = cast_to_tensor(np.arange(broad_shape[-1]).astype(np.float32))
index = self.expandim(index, -1)
index = broad(index)[..., :1]
value = self.concat((index, value))
value = self.cast(value, mstype.int32)
return self.gather(self._logits, value)
return None
def _entropy(self):
r"""
Evaluate entropy.
.. math::
H(X) = -\sum(logits * probs)
"""
p_log_p = self._logits * self._probs
return self.reduce_sum(-p_log_p, -1)
def enumerate_support(self, expand=True):
r"""
Enumerate categories.
"""
num_events = self._num_events
values = cast_to_tensor(np.arange(num_events).astype(np.int32), mstype.int32)
values = self.reshape(values, (num_events, 1))
if expand:
values = P.BroadcastTo((num_events, self._batch_shape))(values)
values = self.cast(values, mstype.int32)
return values

8
mindspore/ops/composite/random_ops.py
View File

@ -202,7 +202,7 @@ def poisson(shape, mean, seed=0):
value = random_poisson(shape, mean)
return value
def multinomial(inputs, num_sample=None, replacement=True, seed=0):
def multinomial(inputs, num_sample, replacement=True, seed=0):
r"""
Returns a tensor sampled from the multinomial probability distribution located in the corresponding
row of tensor input.
@ -216,7 +216,7 @@ def multinomial(inputs, num_sample=None, replacement=True, seed=0):
Inputs:
- **input** (Tensor) - the input tensor containing probabilities, must be 1 or 2 dims.
- **num_samples** (int) - number of samples to draw, default None.
- **num_samples** (int) - number of samples to draw.
- **replacement** (bool, optional) - whether to draw with replacement or not, default True.
Outputs:
@ -239,9 +239,7 @@ def multinomial(inputs, num_sample=None, replacement=True, seed=0):
n_dist = 1
if len(shape(inputs)) > 1:
n_dist = shape(inputs)[-2]
a = Tensor(0.0, mstype.float32)
b = Tensor(1.0, mstype.float32)
random_uniform = P.UniformReal(seed=seed)((n_dist * num_sample,), a, b)
random_uniform = P.UniformReal(seed=seed)((n_dist * num_sample,))
if n_dist != 1:
random_uniform = reshape(random_uniform, (n_dist, num_sample))
vals = P.RealDiv()(P.Log()(random_uniform), inputs + 1e-6)

36
tests/st/ops/gpu/test_categorical_op.py Normal file
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@ -0,0 +1,36 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import mindspore.context as context
import mindspore.nn.probability.distribution as msd
context.set_context(device_target='GPU')
def test_categorical1():
cat1 = msd.Categorical(probs=[[0.9, 0.2], [0.9, 0.2]])
cat1out1 = cat1.sample((1,))
cat1out2 = cat1.sample((3, 2))
cat1out3 = cat1.sample((6,))
assert cat1out1.asnumpy().shape == (2, 1)
assert cat1out2.asnumpy().shape == (2, 3, 2)
assert cat1out3.asnumpy().shape == (2, 6)
cat1 = msd.Categorical(probs=[0.9, 0.2])
cat1out1 = cat1.sample((1,))
cat1out2 = cat1.sample((3, 2))
cat1out3 = cat1.sample((6,))
assert cat1out1.asnumpy().shape == (1,)
assert cat1out2.asnumpy().shape == (3, 2)
assert cat1out3.asnumpy().shape == (6,)