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提交 c66eec75 编写于 作者: P pangyoki
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support num_distribution different multinomial distributions

上级 8e14302f
隐藏空白更改
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Showing with 150 addition and 55 deletion
paddle/fluid/operators/multinomial_op.cc
浏览文件 @ c66eec75
......@@ -30,6 +30,7 @@ class MultinomialOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "A tensor contains probabilities of categories");
AddOutput("Out", "The output tensor of multinomial op");
// AddOutput("yokiOut", "yoki");
AddAttr<int>("num_samples", "number of the generated samples")
.SetDefault(1);
AddAttr<bool>("replacement", "can a category be sampled more than once")
......@@ -49,7 +50,7 @@ class MultinomialOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Multinomial");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Multinomial");
// OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Multinomial");
auto x_dim = ctx->GetInputDim("X");
int64_t x_rank = x_dim.size();
......@@ -62,6 +63,7 @@ class MultinomialOp : public framework::OperatorWithKernel {
out_dims[x_rank - 1] = num_samples;
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
// ctx->SetOutputDim("yokiOut", x_dim);
}
};
......@@ -72,11 +74,16 @@ class MultinomialOpKernel<platform::CPUDeviceContext, T>
void Compute(const framework::ExecutionContext &ctx) const override {
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());
for (int i = 0; i < x->numel(); i++) {
yokiout_data[i] = in_data[i];
}*/
auto in_dims = x->dims();
int64_t in_rank = in_dims.size();
......
paddle/fluid/operators/multinomial_op.cu
浏览文件 @ c66eec75
......@@ -70,8 +70,31 @@ 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;
norm_probs[id] = in_data[id] / sum_rows[0];
// 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,
norm_probs_data + (id + 1) * num_categories,
cumulative_probs + id * num_categories);
}
}
template <typename T>
......@@ -141,21 +164,29 @@ __global__ void sampleMultinomialWithReplacement(
// global index formula for 2D grid of 1D blocks
// int idx = blockIdx.y * gridDim.x * blockDim.x + blockIdx.x * blockDim.x +
// threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
for (int sample = blockIdx.x * blockDim.x + threadIdx.x;
sample < totalSamples; 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
// int idx = blockIdx.x * blockDim.x + threadIdx.x;
// T uniform_random = dist(rng);
T uniform_random = rng[sample];
int idx = threadIdx.x + blockIdx.x * blockDim.x +
blockIdx.y * gridDim.x * blockDim.x;
// Find the bucket that a uniform sample lies in
int choice = binarySearchForMultinomial<T>(normDistPrefixSum, normDist,
categories, uniform_random);
for (int curDist = blockIdx.y; curDist < distributions;
curDist += gridDim.y) {
for (int sample = blockIdx.x * blockDim.x + threadIdx.x;
sample < totalSamples; 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
dest[sample] = choice;
// T uniform_random = dist(rng);
T uniform_random = rng[sample + curDist * totalSamples];
// Find the bucket that a uniform sample lies in
int choice = binarySearchForMultinomial<T>(
normDistPrefixSum + curDist * categories,
normDist + curDist * categories, categories, uniform_random);
dest[sample + curDist * totalSamples] = choice;
}
}
}
......@@ -167,17 +198,48 @@ 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();
const int64_t num_categories = in_dims[in_rank - 1];
const int64_t num_distributions = in_rank > 1 ? in_dims[in_rank - 2] : 1;
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];
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),
cudaMemcpyHostToDevice);
delete[] cpu_in_data;
delete[] cpu_out_data;
return;
}
// std::vector<T> sum_rows(num_distributions);
// SumArrayCUDAKernel<T>(in_data, sum_rows,)
......@@ -188,30 +250,44 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
VLOG(3) << "Print in_rank " << in_rank << "\n";
framework::Tensor sum_rows_t;
auto* sum_rows_data = sum_rows_t.mutable_data<T>({1}, ctx.GetPlace());
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({1}), ctx.GetPlace());
// sum_rows_t->mutable_data<T>(framework::make_ddim({num_distributions}),
// ctx.GetPlace());
auto& place = *ctx.template device_context<platform::CUDADeviceContext>()
.eigen_device();
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>(0))
.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));
dim3 grid(num_distributions);
dim3 block(num_categories);
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_categories}, ctx.GetPlace());
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);
......@@ -219,43 +295,46 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
// 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_categories}, ctx.GetPlace());
auto* cumulative_probs = cumulative_probs_t.mutable_data<T>(
{num_distributions, num_categories}, ctx.GetPlace());
// T cumulative_probs[num_categories];
int64_t size = num_categories;
thrust::inclusive_scan(thrust::device, norm_probs_data,
norm_probs_data + num_categories, cumulative_probs);
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) {
dim3 block(128);
// int grid_y = 1;
dim3 grid((num_samples - 1) / block.x + 1);
/*
// std::vector<T> rng(num_samples);
T rng[num_samples];
std::uniform_real_distribution<T> dist(0, 1);
auto gen_ptr = framework::DefaultCPUGenerator();
auto engine = gen_ptr->GetCPUEngine();
for (int s = 0; s < num_samples; s++) {
rng[s] = dist(*engine);
}
*/
dim3 grid((num_samples - 1) / block.x + 1, num_distributions);
std::random_device rd;
auto seed = rd();
framework::Tensor rng_data_t;
auto* rng_data =
rng_data_t.mutable_data<T>({num_samples}, ctx.GetPlace());
auto* rng_data = rng_data_t.mutable_data<T>(
{num_distributions, num_samples}, ctx.GetPlace());
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
platform::Transform<platform::CUDADeviceContext> trans;
auto* context = static_cast<const platform::CUDADeviceContext*>(
&ctx.device_context());
trans(*context, index_sequence_begin, index_sequence_begin + num_samples,
rng_data, RandomGeneratorCudaFunctor<T>(seed));
trans(*context, index_sequence_begin,
index_sequence_begin + num_distributions * num_samples, rng_data,
RandomGeneratorCudaFunctor<T>(seed));
VLOG(3) << "Print enter\n";
// VLOG(3) << "Print size in_data " <<
......@@ -267,8 +346,12 @@ class MultinomialOpKernel<platform::CUDADeviceContext, T>
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 @ c66eec75
......@@ -38,6 +38,7 @@ class TestMultinomialOp(OpTest):
# input probability is a vector, and replacement is True
self.input_np = np.random.rand(4)
self.outputs = {"Out": np.zeros(100000).astype("int64")}
# self.outputs = {"yokiOut": np.zeros(4).astype("int64")}
self.attrs = {"num_samples": 100000, "replacement": True}
def test_check_output(self):
......@@ -53,19 +54,21 @@ class TestMultinomialOp(OpTest):
# normalize the input to get the probability
prob = self.input_np / self.input_np.sum(axis=-1, keepdims=True)
sample_prob = self.sample_output(np.array(outs[0]))
print("sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
# sample_prob = np.array(outs[0])
# print("input", self.input_np)
# print("sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
self.assertTrue(
np.allclose(
sample_prob, prob, rtol=0, atol=0.01),
"sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
"""
class TestMultinomialOp2(TestMultinomialOp):
def init_data(self):
# input probability is a matrix
self.input_np = np.random.rand(3, 4)
self.outputs = {"Out": np.zeros((3, 100000)).astype("int64")}
# self.outputs = {"yokiOut": np.zeros((3, 4)).astype("int64")}
self.attrs = {"num_samples": 100000, "replacement": True}
def sample_output(self, out):
......@@ -88,11 +91,13 @@ class TestMultinomialOp3(TestMultinomialOp):
def verify_output(self, outs):
out = np.array(outs[0])
# print("op3out", out)
unique_out = np.unique(out)
self.assertEqual(
len(unique_out), 100,
"replacement is False. categories can't be sampled repeatedly")
"""
"""
class TestReplacementError(unittest.TestCase):
def init_data(self):
......
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