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DistributionKernels.cpp
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DistributionKernels.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/CPUGeneratorImpl.h>
#include <ATen/Dispatch.h>
#include <ATen/Generator.h>
#include <ATen/core/DistributionsHelper.h>
#include <ATen/native/Distributions.h>
#include <ATen/native/cpu/DistributionTemplates.h>
#include <ATen/native/UnaryOps.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
#include <ATen/ops/empty.h>
#endif
#include <cmath>
#include <limits>
#include <type_traits>
#if AT_MKL_ENABLED()
#include <mkl.h>
#include <cpuinfo.h>
#endif
namespace at::native {
namespace {
static void cauchy_kernel(TensorIteratorBase& iter, double median, double sigma, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::cauchy_kernel(iter, median, sigma, generator);
}
void bernoulli_tensor_kernel(const TensorBase &self, const TensorBase &p_, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::bernoulli_kernel(self, p_, generator);
}
#if !AT_MKL_ENABLED()
void bernoulli_scalar_kernel_default(const TensorBase &self, double p, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::bernoulli_kernel(self, p, generator);
}
void bernoulli_scalar_kernel(const TensorBase &self, double p, std::optional<Generator> gen) {
bernoulli_scalar_kernel_default(self, p, gen);
}
#else
void bernoulli_scalar_kernel(const TensorBase &self, double p, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
int64_t seed;
{
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(generator->mutex_);
seed = generator->random();
}
int64_t n = self.numel();
bool contig = self.is_contiguous();
AT_DISPATCH_ALL_TYPES_AND3(at::ScalarType::Bool, at::ScalarType::BFloat16, at::ScalarType::Half,
self.scalar_type(), "bernoulli_scalar_cpu_", [&] {
at::Tensor tmp_int_tensor;
if (std::is_same<scalar_t, int>::value && contig) {
tmp_int_tensor = self;
} else {
tmp_int_tensor = at::empty(self.sizes(), self.options().dtype(at::kInt));
}
scalar_t *self_ptr = self.data_ptr<scalar_t>();
int *sample_int_ptr = tmp_int_tensor.data_ptr<int>();
auto sample = [&](int64_t begin, int64_t end) {
int64_t len = end - begin;
if (len > 0) {
VSLStreamStatePtr stream;
vslNewStream(&stream, VSL_BRNG_MCG31, seed);
vslSkipAheadStream(stream, begin);
viRngBernoulli(VSL_RNG_METHOD_BERNOULLI_ICDF, stream, len,
sample_int_ptr + begin, p);
vslDeleteStream(&stream);
// vectorized copy if using buffer and contiguous, i.e., being non-int
// type and contiguous
if (!std::is_same<scalar_t, int>::value && contig) {
scalar_t *self_seg = self_ptr + begin;
int* tmp_seg = sample_int_ptr + begin;
at::vec::convert<int, scalar_t>(tmp_seg, self_seg, len);
}
}
};
parallel_for(0, n, /* grain_size= */ 800, sample);
// copy_ if using buffer and non contiguous
if (!contig) {
OptionalTensorRef(self)->copy_(tmp_int_tensor);
}
});
}
#endif
static void exponential_kernel_default(TensorIteratorBase& iter, double lambda, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::exponential_kernel(iter, lambda, generator);
}
#if (!AT_MKL_ENABLED() || defined(FBCODE_CAFFE2))
void exponential_kernel(TensorIteratorBase& iter, double lambda, std::optional<Generator> gen) {
exponential_kernel_default(iter, lambda, gen);
}
#else
void exponential_kernel(TensorIteratorBase &iter, double lambda, std::optional<Generator> gen) {
TORCH_CHECK(isFloatingType(iter.dtype()), "Exponential distribution is a continuous probability distribution. dtype must be a floating point but you specified ", iter.dtype());
Tensor self = iter.tensor(0);
if (lambda > 0 && !std::isinf(lambda) && !std::isnan(lambda)) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
int64_t seed;
{
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(generator->mutex_);
if (self.scalar_type() == at::kDouble)
seed = generator->random64();
else
seed = generator->random();
}
int64_t n = self.numel();
bool contig = self.is_contiguous();
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "exponential_cpu", [&] {
at::Tensor tmp_tensor;
constexpr bool is_df = std::is_same<scalar_t, float>::value || std::is_same<scalar_t, double>::value;
if (is_df && contig) {
tmp_tensor = self;
} else if (std::is_same<scalar_t, double>::value) {
tmp_tensor = at::empty(self.sizes(), self.options().dtype(at::kDouble));
} else {
tmp_tensor = at::empty(self.sizes(), self.options().dtype(at::kFloat));
}
scalar_t *self_ptr = self.data_ptr<scalar_t>();
using tmp_scalar_t = typename std::conditional_t<std::is_same<scalar_t, double>::value, double, float>;
tmp_scalar_t *sample_ptr = tmp_tensor.data_ptr<tmp_scalar_t>();
// Intel MKL vRngExponential variate originally does not exclude 0.
// However, to align with pytorch exponential variate definition which excludes 0,
// we shift the MKL vRngExponential distribution location by adding a very small constant, eps.
// If X ~ Exp(lambda), then E(X) = 1/lambda, and V(X) = 1/lambda**2.
// If Y = X + eps, where eps ~= 0, then E(Y) = (1/lambda) + eps, and V(Y) = 1/lambda**2.
// If eps is very small, the two distributions are indistinguishable, and are almost identical.
// The detail of location-shifted MKL vRngExponential is as follows.
// PDF: f(x) = lambda * exp( -lambda * (x - eps) )
// CDF: F(x) = 1 - exp( -lambda * (x - eps) )
// Mean: E[X+eps] = (1/lambda) + eps
// Variance: V[X+eps] = 1/lambda**2
auto eps = std::numeric_limits<tmp_scalar_t>::min();
auto sample = [&](int64_t begin, int64_t end) {
int64_t len = end - begin;
if (len > 0) {
VSLStreamStatePtr stream;
if constexpr (std::is_same<scalar_t, double>::value) {
vslNewStream(&stream, VSL_BRNG_MCG31, seed);
vslSkipAheadStream(stream, begin);
vdRngExponential(VSL_RNG_METHOD_EXPONENTIAL_ICDF, stream, len,
(double *)(sample_ptr + begin), eps, 1./lambda);
vslDeleteStream(&stream);
} else {
vslNewStream(&stream, VSL_BRNG_MCG31, seed);
vslSkipAheadStream(stream, begin);
vsRngExponential(VSL_RNG_METHOD_EXPONENTIAL_ICDF, stream, len,
(float *) (sample_ptr + begin), eps, 1./lambda);
vslDeleteStream(&stream);
}
// vectorized copy if using buffer and contiguous
if (!is_df && contig) {
scalar_t *self_seg = self_ptr + begin;
tmp_scalar_t *tmp_seg = sample_ptr + begin;
at::vec::convert<tmp_scalar_t, scalar_t>(tmp_seg, self_seg, len);
}
}
};
parallel_for(0, n, /* grain_size= */ 800, sample);
// copy_ if using buffer and non contiguous
if (!contig) {
self.copy_(tmp_tensor);
}
});
} else {
// The situation of inf and nan, move to using the default version
exponential_kernel_default(iter, lambda, gen);
}
}
#endif
static void geometric_kernel(TensorIteratorBase& iter, double p, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::geometric_kernel(iter, p, generator);
}
static void log_normal_kernel(TensorIteratorBase& iter, double mean, double std, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::log_normal_kernel(iter, mean, std, generator);
}
void uniform_kernel(TensorIteratorBase& iter, double from, double to, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::uniform_kernel(iter, from, to, generator);
}
void normal_kernel(const TensorBase &self, double mean, double std, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::normal_kernel(self, mean, std, generator);
}
static void random_from_to_kernel(TensorIteratorBase& iter, uint64_t range, int64_t base, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::random_from_to_kernel(iter, range, base, generator);
}
static void random_kernel(TensorIteratorBase& iter, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::random_kernel(iter, generator);
}
// This is the special kernel to handle single specific case:
// from(inclusive) = std::numeric_limits<int64_t>::lowest()
// to(exclusive) = None (= std::numeric_limits<int64_t>::max() + 1)
static void random_full_64_bits_range_kernel(TensorIteratorBase& iter, std::optional<Generator> gen) {
CPUGeneratorImpl* generator = get_generator_or_default<CPUGeneratorImpl>(gen, detail::getDefaultCPUGenerator());
templates::cpu::random_full_64_bits_range_kernel(iter, generator);
}
} // namespace (anonymous)
REGISTER_DISPATCH(bernoulli_tensor_stub, &bernoulli_tensor_kernel);
REGISTER_DISPATCH(bernoulli_scalar_stub, &bernoulli_scalar_kernel);
REGISTER_DISPATCH(cauchy_stub, &cauchy_kernel);
REGISTER_DISPATCH(exponential_stub, &exponential_kernel);
REGISTER_DISPATCH(geometric_stub, &geometric_kernel);
REGISTER_DISPATCH(log_normal_stub, &log_normal_kernel);
REGISTER_DISPATCH(normal_stub, &normal_kernel);
REGISTER_DISPATCH(uniform_stub, &uniform_kernel);
REGISTER_DISPATCH(random_from_to_stub, &random_from_to_kernel);
REGISTER_DISPATCH(random_full_64_bits_range_stub, &random_full_64_bits_range_kernel);
REGISTER_DISPATCH(random_stub, &random_kernel);
} // namespace at::native