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提交 80537a1d 编写于 作者: P pangyoki
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add multinomial python api unittest

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paddle/fluid/operators/multinomial_op.cu
浏览文件 @ 80537a1d
......@@ -26,69 +26,17 @@ limitations under the License. */
namespace paddle {
namespace operators {
/*
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
*/
/*
template <class T>
__global__ void SumArrayCUDAKernel(T **in, T *out, size_t in_size) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
// T total(read_dst ? out[id] : static_cast<T>(0));
T total(static_cast<T>(0))
for (int i = 0; i < in_size; ++i) {
const T *tmp = in[i];
if (tmp) {
total += tmp[id];
}
}
out[id] = total;
id += blockDim.x * gridDim.x;
}*/
/*
template <typename T>
__global__ void NormalizeProbability(T* probs, int64_t rows, int64_t cols) {
extern __shared__ std::vector<T> sum_rows(rows);
T val;
for (int64_t i = blockId.x; i < rows; i += gridDim.x) {
T sum = static_cast<T>(0);
for (int64_t j = threadIdx.x; j < cols; j += blockDim.x) {
val = probs[i * cols + j];
sum += val;
}
}
}*/
template <typename T>
__global__ void NormalizeProbability(T* norm_probs, const T* in_data,
T* sum_rows) {
// int id = blockIdx.x * blockDim.x + threadIdx.x;
// int id = threadIdx.x;
int id = threadIdx.x + blockIdx.x * blockDim.x +
blockIdx.y * gridDim.x * blockDim.x;
norm_probs[id] = in_data[id] / sum_rows[blockIdx.y];
}
template <typename T>
__global__ void yokiFunc(const T* in_data, T* out) {
// int id = blockIdx.x * blockDim.x + threadIdx.x;
// int id = threadIdx.x;
int id = threadIdx.x + blockIdx.x * blockDim.x +
blockIdx.y * gridDim.x * blockDim.x;
out[id] = in_data[id];
}
template <typename T>
__global__ void Cumsum(T* norm_probs_data, int64_t num_distributions,
int64_t num_categories, T* cumulative_probs) {
// int id = blockIdx.x;
for (int id = blockIdx.x; id < num_distributions; id += gridDim.x) {
thrust::inclusive_scan(thrust::device,
norm_probs_data + id * num_categories,
......@@ -111,52 +59,43 @@ struct RandomGeneratorCudaFunctor {
}
};
/*
template <typename T>
class MultinomialCudaFunctor(T* out_data, const T* in_data,
const int64_t num_samples, const bool replacement,
const int64_t num_categories,
const int64_t num_distributions) {
}*/
template <typename T>
__device__ int binarySearchForMultinomial(T* cumdist, T* dist, int size,
T val) {
int start = 0;
int end = size;
__device__ int binarySearchFunctor(T* cumdist, T* dist, int size, T val) {
int left = 0;
int right = size;
// cumdist[size - 1] = 0 => all zero prob dist
// CUDA_KERNEL_ASSERT(cumdist[size - 1] > static_cast<T>(0));
while (end - start > 0) {
int mid = start + (end - start) / 2;
while (right - left > 0) {
int mid = left + (right - left) / 2;
T midVal = cumdist[mid];
if (midVal < val) {
start = mid + 1;
left = mid + 1;
} else {
end = mid;
right = mid;
}
}
if (start == size) {
if (left == size) {
// No probability mass or precision problems; just return the
// first non-zero element by setting start to size-1 here,
// first non-zero element by setting left to size-1 here,
// the code below will move it to the last non-zero probability
// this actually can happen when the random number is 1
// (github pytorch issue #4858).
start = size - 1;
left = size - 1;
}
while (start >= 1 && dist[start] == 0) start--;
while (left >= 1 && dist[left] == 0) left--;
return start;
return left;
}
template <typename T>
__global__ void sampleMultinomialWithReplacement(
T* rng, const int64_t totalSamples, T* dest, const int64_t distributions,
const int64_t categories, T* normDistPrefixSum, T* normDist) {
T* rng_data, const int64_t num_samples, T* out_data,
const int64_t num_distributions, const int64_t num_categories,
T* cumulative_probs, T* norm_probs_data) {
// At the moment, each warp computes one sample value in the binary
// search due to divergence. It seems possible to compute multiple
// values and limit divergence though later on.
......@@ -170,22 +109,23 @@ __global__ void sampleMultinomialWithReplacement(
int idx = threadIdx.x + blockIdx.x * blockDim.x +
blockIdx.y * gridDim.x * blockDim.x;
for (int curDist = blockIdx.y; curDist < distributions;
for (int curDist = blockIdx.y; curDist < num_distributions;
curDist += gridDim.y) {
for (int sample = blockIdx.x * blockDim.x + threadIdx.x;
sample < totalSamples; sample += blockDim.x * gridDim.x) {
sample < num_samples; sample += blockDim.x * gridDim.x) {
// we are losing 3 out of 4 generated numbers but it's ok
// this kernel is not very efficient anyway
// T uniform_random = dist(rng);
T uniform_random = rng[sample + curDist * totalSamples];
T uniform_random = rng_data[sample + curDist * num_samples];
// Find the bucket that a uniform sample lies in
int choice = binarySearchForMultinomial<T>(
normDistPrefixSum + curDist * categories,
normDist + curDist * categories, categories, uniform_random);
int choice =
binarySearchFunctor<T>(cumulative_probs + curDist * num_categories,
norm_probs_data + curDist * num_categories,
num_categories, uniform_random);
dest[sample + curDist * totalSamples] = choice;
out_data[sample + curDist * num_samples] = choice;
}
}
}
......@@ -198,14 +138,11 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
const auto x = ctx.Input<framework::Tensor>("X");
auto out = ctx.Output<framework::Tensor>("Out");
// auto yokiout = ctx.Output<framework::Tensor>("yokiOut");
const int64_t num_samples = ctx.Attr<int>("num_samples");
const bool replacement = ctx.Attr<bool>("replacement");
auto* in_data = x->data<T>();
auto* out_data = out->mutable_data<T>(ctx.GetPlace());
// auto* yokiout_data = yokiout->mutable_data<T>(ctx.GetPlace());
auto in_dims = x->dims();
int64_t in_rank = in_dims.size();
......@@ -215,10 +152,6 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
if (!replacement) {
int in_data_numel = x->numel();
int out_data_numel = out->numel();
// std::vector<T> cpu_in_data(in_data_numel);
// std::vector<T> cpu_out_data(out_data_numel);
// T cpu_in_data[in_data_numel];
// T cpu_out_data[out_data_numel];
T* cpu_in_data = new T[in_data_numel];
T* cpu_out_data = new T[out_data_numel];
......@@ -226,10 +159,6 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
cudaMemcpy(cpu_in_data, in_data, in_data_numel * sizeof(T),
cudaMemcpyDeviceToHost);
VLOG(3) << "Print cpu_in_data " << cpu_in_data[0] << "\n";
VLOG(3) << "Print in_data_numel " << in_data_numel << "\n";
VLOG(3) << "Print out_data_numel " << out_data_numel << "\n";
MultinomialFunctor<T>(cpu_out_data, cpu_in_data, num_samples, replacement,
num_categories, num_distributions);
cudaMemcpy(out_data, cpu_out_data, out_data_numel * sizeof(T),
......@@ -240,21 +169,9 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
return;
}
// std::vector<T> sum_rows(num_distributions);
// SumArrayCUDAKernel<T>(in_data, sum_rows,)
VLOG(3) << "Print num_distributions " << num_distributions << "\n";
VLOG(3) << "Print num_categories " << num_categories << "\n";
VLOG(3) << "Print in_rank " << in_rank << "\n";
framework::Tensor sum_rows_t;
auto* sum_rows_data =
sum_rows_t.mutable_data<T>({num_distributions}, ctx.GetPlace());
// auto* sum_rows_data =
// sum_rows_t->mutable_data<T>(framework::make_ddim({num_distributions}),
// ctx.GetPlace());
auto& place = *ctx.template device_context<platform::CUDADeviceContext>()
.eigen_device();
......@@ -262,58 +179,34 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
if (num_distributions == 1) {
auto eigen_input = framework::EigenVector<T>::Flatten(*x);
auto eigen_sum_rows = framework::EigenVector<T>::From(sum_rows_t);
// auto eigen_sum_rows = framework::EigenScalar<T>::From(sum_rows_t);
eigen_sum_rows.device(place) =
eigen_input.sum(Eigen::DSizes<int, 1>(1))
.eval()
.reshape(Eigen::DSizes<int, 1>(sum_rows_t.dims()[0]));
} else {
auto eigen_input = framework::EigenMatrix<T>::From(*x);
// auto eigen_sum_rows = framework::EigenVector<T>::From(sum_rows_t);
auto eigen_sum_rows = framework::EigenVector<T>::From(sum_rows_t);
eigen_sum_rows.device(place) = eigen_input.sum(Eigen::DSizes<int, 1>(1));
// .eval()
// .reshape(Eigen::DSizes<int, 1>(sum_rows_t.dims()[0]));
// eigen_sum_rows.device(place) =
// eigen_input.sum().eval().reshape(Eigen::DSizes<int, 1>(1));
}
// std::vector<T> in_data_norm(num_categories);
framework::Tensor norm_probs_t;
auto* norm_probs_data = norm_probs_t.mutable_data<T>(
{num_distributions, num_categories}, ctx.GetPlace());
// dim3 grid(num_distributions);
// dim3 block(num_categories);
dim3 block(num_categories < 512 ? num_categories : 512);
dim3 grid((num_categories - 1) / block.x + 1, num_distributions);
NormalizeProbability<
T><<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
norm_probs_data, in_data, sum_rows_data);
// num_distributions can only be 1.
// std::vector<T> cumulative_probs(num_categories);
framework::Tensor cumulative_probs_t;
auto* cumulative_probs = cumulative_probs_t.mutable_data<T>(
{num_distributions, num_categories}, ctx.GetPlace());
// T cumulative_probs[num_categories];
dim3 block1(1);
dim3 grid1(num_distributions);
Cumsum<T><<<grid1, block1, 0, ctx.cuda_device_context().stream()>>>(
norm_probs_data, num_distributions, num_categories, cumulative_probs);
/*
dim3 block2(num_categories < 512 ? num_categories : 512);
dim3 grid2((num_categories-1)/block2.x+1, num_distributions);
yokiFunc<T><<<grid2, block2, 0, ctx.cuda_device_context().stream()>>>(
cumulative_probs, yokiout_data);*/
// int64_t size = num_categories;
// thrust::inclusive_scan(thrust::device, norm_probs_data,
// norm_probs_data + num_categories,
// cumulative_probs);
VLOG(3) << "Print cumsum " << cumulative_probs << "\n";
if (replacement) {
......@@ -336,24 +229,11 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
index_sequence_begin + num_distributions * num_samples, rng_data,
RandomGeneratorCudaFunctor<T>(seed));
VLOG(3) << "Print enter\n";
// VLOG(3) << "Print size in_data " <<
// sizeof(in_data)/sizeof(in_data[num_categories-1]) << "\n";
// VLOG(3) << "Print norm_probs_data0 " <<
// sizeof(norm_probs_data[num_categories-1]) << "\n";
sampleMultinomialWithReplacement<
T><<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
rng_data, num_samples, out_data, num_distributions, num_categories,
cumulative_probs, norm_probs_data);
VLOG(3) << "Print end\n" << out_data;
}
VLOG(3) << "Print final end\n";
// MultinomialCudaFunctor<T>(out_data, in_data, num_samples, replacement,
// num_categories, num_distributions);
}
};
......
python/paddle/fluid/tests/unittests/test_multinomial_op.py
浏览文件 @ 80537a1d
......@@ -126,6 +126,49 @@ class TestMultinomialApi(unittest.TestCase):
sample_prob, prob, rtol=0, atol=0.01),
"sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
def test_dygraph2(self):
paddle.disable_static()
x = paddle.rand([3, 4])
out = paddle.multinomial(x, num_samples=100000, replacement=True)
x_numpy = x.numpy()
out_list = np.split(out.numpy(), 3, axis=0)
count_array = [0] * 3
for i in range(3):
count_array[i] = np.unique(
out_list[i], return_counts=True)[1].astype("float32")
sample_prob = np.stack(count_array, axis=0)
sample_prob /= sample_prob.sum(axis=-1, keepdims=True)
prob = x_numpy / x_numpy.sum(axis=-1, keepdims=True)
self.assertTrue(
np.allclose(
sample_prob, prob, rtol=0, atol=0.01),
"sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
paddle.enable_static()
def test_dygraph3(self):
paddle.disable_static()
x = paddle.rand([1000])
out = paddle.multinomial(x, num_samples=100, replacement=False)
x_numpy = x.numpy()
unique_out = np.unique(out.numpy())
self.assertEqual(
len(unique_out), 100,
"replacement is False. categories can't be sampled repeatedly")
paddle.enable_static()
"""
def test_replacement_error(self):
def test_error():
paddle.disable_static()
x = paddle.rand([5])
out = paddle.multinomial(x, num_samples=10, replacement=False)
self.assertRaises(OutOfRangeError, test_error) # not OutOfRangeError
"""
if __name__ == "__main__":
unittest.main()
python/paddle/tensor/random.py
浏览文件 @ 80537a1d
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: define random functions
# TODO: define random functions
from ..fluid import core
from ..fluid.framework import in_dygraph_mode, Variable, convert_np_dtype_to_dtype_
......@@ -40,18 +40,18 @@ def bernoulli(x, name=None):
This OP returns a Tensor filled with random binary(0 or 1) number from a Bernoulli distribution.
The input ``x`` is a tensor with probabilities for generating the random binary number.
Each element in ``x`` should be in [0, 1], and the out is generated by:
.. math::
out_i ~ Bernoulli (x_i)
Args:
x(Tensor): A tensor with probabilities for generating the random binary number. The data type
x(Tensor): A tensor with probabilities for generating the random binary number. The data type
should be float32, float64.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Returns:
Tensor: A Tensor filled with random binary number with the same shape and dtype as ``x``.
Examples:
......@@ -80,7 +80,7 @@ def bernoulli(x, name=None):
helper = LayerHelper("randint", **locals())
out = helper.create_variable_for_type_inference(
dtype=x.dtype) # maybe set out to int32 ?
dtype=x.dtype) # maybe set out to int32 ?
helper.append_op(
type='bernoulli', inputs={"X": x}, outputs={'Out': out}, attrs={})
return out
......@@ -88,8 +88,23 @@ def bernoulli(x, name=None):
def multinomial(x, num_samples=1, replacement=False, name=None):
"""
This OP returns a Tensor filled with random values sampled from a Multinomical
distribution. The input ``x`` is a tensor with probabilities for generating the
random number. Each element in ``x`` should be larger or equal to 0, but not all
0. ``replacement`` indicates whether it is a replaceable sample. If ``replacement``
is True, a category can be sampled more than once.
Args:
x(Tensor): A tensor with probabilities for generating the random number. The data type
should be float32, float64.
num_samples(int, optional): Number of samples, default is 1.
replacement(bool, optional): whether it is a replaceable sample, default is False.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor filled with sampled category index after ``num_samples`` times samples.
Examples:
.. code-block:: python
......@@ -97,15 +112,24 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
paddle.disable_static()
x = paddle.rand([2, 3])
x = paddle.rand([2,4])
print(x.numpy())
# [[0.11272584 0.3890902 0.7730957 ]
# [0.10351662 0.8510418 0.63806665]]
# [[0.7713825 0.4055941 0.433339 0.70706886]
# [0.9223313 0.8519825 0.04574518 0.16560672]]
out = paddle.bernoulli(x)
print(out.numpy())
# [[0. 0. 1.]
# [0. 0. 1.]]
out1 = paddle.multinomial(x, num_samples=5, replacement=True)
print(out1.numpy())
# [[3. 3. 1. 1. 0.]
# [0. 0. 0. 0. 1.]]
out2 = paddle.multinomial(x, num_samples=5)
# OutOfRangeError: When replacement is False, number of samples
# should be less than non-zero categories
out3 = paddle.multinomial(x, num_samples=3)
print(out3.numpy())
# [[0. 2. 3.]
# [0. 1. 3.]]
"""
......@@ -152,7 +176,7 @@ def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
Returns:
Tensor: A Tensor filled with random values sampled from a Gaussian
distribution, with ``shape`` and ``dtype``.
distribution, with ``shape`` and ``dtype``.
"""
op_type_for_check = 'gaussian/standard_normal/randn/normal'
seed = 0
......@@ -393,7 +417,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
......@@ -481,7 +505,7 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Returns:
Tensor: A Tensor filled with random integers from a discrete uniform
distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
......@@ -591,7 +615,7 @@ def randperm(n, dtype="int64", name=None):
out2 = paddle.randperm(7, 'int32')
# [1, 6, 2, 0, 4, 3, 5] # random
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
......
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