# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import torch
import triton
import triton.language as tl
from deepspeed.accelerator import get_accelerator


@triton.jit
def gelu_functor(x):
    # Using approximation introduces greater parity errors.
    # return tl.sigmoid(1.702 * x) * x
    return x * 0.5 * (1.0 + tl.math.erf(x / 1.41421356237))


@triton.jit
def gelu_kernel(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(axis=0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    x = tl.load(x_ptr + offsets, mask=mask)
    output = gelu_functor(x)
    tl.store(output_ptr + offsets, output, mask=mask)


def gelu(activations: torch.Tensor) -> torch.Tensor:
    assert activations.is_contiguous()
    assert get_accelerator().on_accelerator(activations)

    output = torch.empty_like(activations)
    n_elements = output.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
    gelu_kernel[grid](activations, output, n_elements, BLOCK_SIZE=1024)
    return output