# Copyright (c) Huawei Technologies Co., Ltd. 2025. All rights reserved.
# Copyright 2018-2020 Philippe Tillet
# Copyright 2020-2022 OpenAI
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"""
Vector Addition
===============

In this tutorial, you will write a simple vector addition using Triton.

In doing so, you will learn about:

* The basic programming model of Triton.

* The `triton.jit` decorator, which is used to define Triton kernels.

* The best practices for validating and benchmarking your custom ops against native reference implementations.

"""

# %%
# Compute Kernel
# --------------

import torch
import torch_npu

import triton
import triton.language as tl


@triton.jit
def add_kernel(x_ptr,  # *Pointer* to first input vector.
               y_ptr,  # *Pointer* to second input vector.
               output_ptr,  # *Pointer* to output vector.
               n_elements,  # Size of the vector.
               BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process.
               # NOTE: `constexpr` so it can be used as a shape value.
               ):
    # There are multiple 'programs' processing different data. We identify which program
    # we are here:
    pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
    # This program will process inputs that are offset from the initial data.
    # For instance, if you had a vector of length 256 and block_size of 64, the programs
    # would each access the elements [0:64, 64:128, 128:192, 192:256].
    # Note that offsets is a list of pointers:
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    # Create a mask to guard memory operations against out-of-bounds accesses.
    mask = offsets < n_elements
    # Load x and y from DRAM, masking out any extra elements in case the input is not a
    # multiple of the block size.
    x = tl.load(x_ptr + offsets, mask=mask)
    y = tl.load(y_ptr + offsets, mask=mask)
    output = x + y
    # Write x + y back to DRAM.
    tl.store(output_ptr + offsets, output, mask=mask)


# %%
# Let's also declare a helper function to (1) allocate the `z` tensor
# and (2) enqueue the above kernel with appropriate grid/block sizes:

def add(x: torch.Tensor, y: torch.Tensor):
    output = torch.empty_like(x)
    n_elements = output.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
    return output


# %%
# We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:
torch.manual_seed(0)
size = 98432
x = torch.rand(size, device='npu')
y = torch.rand(size, device='npu')
output_torch = x + y
output_triton = add(x, y)
print(output_torch)
print(output_triton)
print(f'The maximum difference between torch and triton is '
      f'{torch.max(torch.abs(output_torch - output_triton))}')