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#include <torch/csrc/jit/codegen/cuda/arith.h>
#include <torch/csrc/jit/codegen/cuda/ir_builder.h>
#include <torch/csrc/jit/codegen/cuda/ops/composite.h>
#include <torch/csrc/jit/codegen/cuda/transform_view.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
ForwardDropoutResult dropout(TensorView* x, Val* prob) {
auto p1m = sub(IrBuilder::create<Double>(x->container(), 1.), prob);
auto zero_check =
add(eq(p1m, IrBuilder::create<Double>(x->container(), 0.)), p1m);
auto scale = div(IrBuilder::create<Double>(x->container(), 1.), zero_check);
return dropout(x, p1m, scale);
}
ForwardDropoutResult dropout(TensorView* x, Val* prob, Val* scale) {
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid.");
TORCH_INTERNAL_ASSERT(
prob != nullptr && prob->getDataType().has_value() &&
prob->getDataType().value() == DataType::Double,
"Probability is not a valid Double.");
TORCH_INTERNAL_ASSERT(
scale != nullptr && scale->getDataType().has_value() &&
scale->getDataType().value() == DataType::Double,
"Scale is not a valid Double.");
auto rand_vals = randlike(x);
auto mask = lt(rand_vals, prob);
auto apply_mask = mul(x, mask);
auto y = mul(apply_mask, scale);
return {y, mask};
}
TensorView* dropout_backward(TensorView* dy, TensorView* mask, Val* scale) {
TORCH_INTERNAL_ASSERT(dy != nullptr, "Grad Output is invalid.");
TORCH_INTERNAL_ASSERT(mask != nullptr, "Mask is invalid");
TORCH_INTERNAL_ASSERT(
scale != nullptr && scale->getDataType().has_value() &&
scale->getDataType().value() == DataType::Double,
"Scale is not a valid Double.");
auto grad_mask = mul(dy, mask);
auto dx = mul(grad_mask, scale);
return dx;
}
LstmResult lstm(
TensorView* prev_cell,
TensorView* in_x,
TensorView* forget_x,
TensorView* cell_x,
TensorView* out_x) {
TORCH_INTERNAL_ASSERT(
prev_cell != nullptr, "Previous cell state is invalid.");
TORCH_INTERNAL_ASSERT(in_x != nullptr, "In-gate input is invalid");
TORCH_INTERNAL_ASSERT(forget_x != nullptr, "Forget-gate input is invalid");
TORCH_INTERNAL_ASSERT(cell_x != nullptr, "Cell-gate input is invalid");
TORCH_INTERNAL_ASSERT(out_x != nullptr, "Out-gate input is invalid");
const auto in_gate = sigmoid(in_x);
const auto forget_gate = sigmoid(forget_x);
const auto cell_gate = tanh(cell_x);
const auto out_gate = sigmoid(out_x);
const auto cell = add(mul(forget_gate, prev_cell), mul(in_gate, cell_gate));
const auto hidden = mul(out_gate, tanh(cell));
return {cell, hidden};
}
namespace {
template <typename T>
TORCH_CUDA_CU_API T* sign(T* x) {
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid.");
auto zero = IrBuilder::create<Double>(x->container(), 0.);
auto one = IrBuilder::create<Double>(x->container(), 1.);
auto minus_one = IrBuilder::create<Double>(x->container(), -1.);
auto sign = where(gt(x, zero), one, where(lt(x, zero), minus_one, zero));
return castOp(x->getDataType().value(), sign);
}
} // namespace
TORCH_CUDA_CU_API TensorView* sign(TensorView* x) {
return sign<TensorView>(x);
}
TORCH_CUDA_CU_API Val* sign(Val* x) {
return sign<Val>(x);
}
TensorView* softplus(TensorView* x, Val* beta, Val* threshold) {
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid.");
TORCH_INTERNAL_ASSERT(beta != nullptr, "Beta is invalid.");
TORCH_INTERNAL_ASSERT(
threshold != nullptr, "Threshold is not a valid Double.");
auto op_beta = mul(x, beta);
auto maybe_result = div(log1p(exp(op_beta)), beta);
auto y = where(gt(op_beta, threshold), x, maybe_result);
return y;
}
TensorView* gelu(TensorView* x) {
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid");
auto kappa = IrBuilder::create<Double>(x->container(), M_SQRT1_2);
auto half = IrBuilder::create<Double>(x->container(), 0.5);
auto one = IrBuilder::create<Double>(x->container(), 1.);
auto cdf = mul(half, add(one, erf(mul(x, kappa))));
auto y = mul(x, cdf);
return y;
}
TensorView* gelu_backward(TensorView* dy, TensorView* x) {
TORCH_INTERNAL_ASSERT(dy != nullptr, "Grad Output is invalid.");
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid");
constexpr double kAlpha = M_2_SQRTPI * M_SQRT1_2 * 0.5;
const double kHalf = 0.5;
auto cdf_1 = mul(x, IrBuilder::create<Double>(x->container(), M_SQRT1_2));
auto cdf_2 = erf(cdf_1);
auto cdf_3 = add(cdf_2, IrBuilder::create<Double>(x->container(), 1.));
auto cdf_4 = mul(cdf_3, IrBuilder::create<Double>(x->container(), kHalf));
auto pdf_1 = mul(x, x);
auto pdf_2 = mul(pdf_1, IrBuilder::create<Double>(x->container(), -kHalf));
auto pdf_3 = exp(pdf_2);
auto out = addcmul(
cdf_4, x, pdf_3, IrBuilder::create<Double>(x->container(), kAlpha));
auto dx = mul(out, dy);
return dx;
}
TensorView* tanh_gelu(TensorView* x) {
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid");
constexpr double kBeta = M_SQRT2 * M_2_SQRTPI * 0.5;
constexpr double kKappa = 0.044715;
auto x_cube = mul(x, mul(x, x));
auto inner_1 = mul(IrBuilder::create<Double>(x->container(), kKappa), x_cube);
auto inner_2 = add(x, inner_1);
auto inner_3 = mul(IrBuilder::create<Double>(x->container(), kBeta), inner_2);
auto tanh_inner = tanh(inner_3);
auto out =
mul(x, add(IrBuilder::create<Double>(x->container(), 1.), tanh_inner));
auto y = mul(IrBuilder::create<Double>(x->container(), 0.5), out);
return y;
}
TensorView* tanh_gelu_backward(TensorView* dy, TensorView* x) {
TORCH_INTERNAL_ASSERT(dy != nullptr, "Grad Output is invalid.");
TORCH_INTERNAL_ASSERT(x != nullptr, "Input is invalid");
constexpr double kBeta = M_SQRT2 * M_2_SQRTPI * 0.5;
constexpr double kKappa = 0.044715;
auto x_sq = mul(x, x);
auto x_cube = mul(x, x_sq);
auto inner_1 = mul(IrBuilder::create<Double>(x->container(), kKappa), x_cube);
auto inner_2 = add(x, inner_1);
auto inner_3 = mul(IrBuilder::create<Double>(x->container(), kBeta), inner_2);
auto tanh_inner = tanh(inner_3);
auto left = mul(IrBuilder::create<Double>(x->container(), 0.5), x);
auto right = add(IrBuilder::create<Double>(x->container(), 1.), tanh_inner);
auto left_derivative =
mul(IrBuilder::create<Double>(x->container(), 0.5), right);
auto tanh_inner_sq = mul(tanh_inner, tanh_inner);
auto tanh_derivative =
sub(IrBuilder::create<Double>(x->container(), 1), tanh_inner_sq);
auto constant_mul_x_sq =
mul(IrBuilder::create<Double>(x->container(), kBeta * 3 * kKappa), x_sq);
auto inner_derivative =
add(IrBuilder::create<Double>(x->container(), kBeta), constant_mul_x_sq);
auto right_derivative = mul(left, mul(tanh_derivative, inner_derivative));
auto dx = mul(dy, add(left_derivative, right_derivative));
return dx;
}
TensorView* tanh_backward(TensorView* dy, TensorView* tanh_x) {
TORCH_INTERNAL_ASSERT(dy != nullptr, "Grad Output is invalid.");
TORCH_INTERNAL_ASSERT(tanh_x != nullptr, "Input is invalid");
auto one = IrBuilder::create<Double>(tanh_x->container(), 1.);
auto tanh_sq = mul(tanh_x, tanh_x);
auto sub_tanh_sq = sub(one, tanh_sq);
auto dx = mul(dy, sub_tanh_sq);
return dx;
}
TensorView* leaky_relu(TensorView* x, Val* negative_slope) {
TORCH_INTERNAL_ASSERT(x != nullptr, "input is invalid.");
TORCH_INTERNAL_ASSERT(negative_slope != nullptr, "negative_slope is invalid");
auto zero = IrBuilder::create<Double>(x->container(), 0.);
return where(ge(x, zero), x, mul(negative_slope, x));
}
TensorView* view_as_real(TensorView* x) {
auto input_type = x->getDataType().value();
TORCH_CHECK(
isComplexType(input_type),
"Operand of view_as_real must have complex type");
auto vec_type = getVectorType(getTypeFromComplexType(input_type), 2);
auto tv_vector = bitCastOp(vec_type, x);
return viewAsScalar(tv_vector);
}
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
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