diff --git a/HONOR_CODE.md b/HONOR_CODE.md
new file mode 100644
index 0000000..5d22ad7
--- /dev/null
+++ b/HONOR_CODE.md
@@ -0,0 +1,71 @@
+# 2026 春季启元人工智能大赛诚信守则（Honor Code）
+
+
+本人作为 2026 春季启元人工智能大赛（以下简称“比赛”）的参赛选手，郑重承诺严格遵守比赛规则及本诚信守则，秉持诚信、公正、廉洁的参赛原则，自觉维护比赛的公平性与严肃性。本人充分理解并认可，违反本准则将导致参赛资格被取消、比赛成绩作废等相应后果，且愿意承担由此产生的一切责任。
+
+## 一、参赛诚信承诺
+
+1. 本人保证所提交的赛题PR（Pull Request）中包含的算子实现代码及相关文档，均为本人（及参赛团队，如为团队参赛）在比赛期间独立完成或在明确标注参考来源的基础上进行开发，不存在任何欺诈、抄袭、作弊行为。
+
+2. 本人承诺主动、全面、真实地披露赛题实现过程中所有参考的外部资源，尤其是开源代码资源，不隐瞒任何可能影响比赛公平性的信息。
+
+3. 本人保证不采用任何不正当手段获取比赛优势，包括但不限于窃取其他参赛选手的代码成果、利用非比赛允许的工具或技术、与他人串通作弊等。
+
+## 二、参考资源说明
+
+本人确认已按比赛要求，将本次赛题实现过程中涉及的参考资源信息单独撰写至`REFERENCE.md`文件中，该文件将与本诚信守则一同作为PR附件提交。`REFERENCE.md`需根据实际参考情况，按以下要求完整填写，信息不完整或虚假填写将视为违反本准则：
+
+**情况1：无参考外部开源代码及核心实现思路**
+
+`REFERENCE.md`中需明确声明：“本次赛题提交的算子代码、核心算法逻辑及实现方案均为本人（及参赛团队）独立设计与开发，未参考任何外部开源项目、技术文档中的核心代码片段或实现思路，未接受任何第三方的技术指导或代码支持。”
+
+**情况2：有参考外部开源代码及相关资源**
+
+对每个参考资源提供以下信息陈述： 
+1. 参考开源项目/资源名称
+
+2. 参考资源链接（GitHub/Gitee/论文/技术文档等）
+
+3.  参考的具体内容（请明确说明参考的代码片段、算法逻辑、实现思路等，需标注对应资源的具体位置，如文件路径、代码行数等）
+    
+4. 本人对参考内容的修改与优化说明：（请详细说明在参考基础上，本人所做的独立开发、修改、优化工作，体现自身技术贡献）
+    
+5. 若是开源项目，提供参考资源的开源协议类型：（如MIT、Apache 2.0、GPL等）
+    
+6. 其他需要补充说明的信息
+    
+
+## 三、禁止行为确认
+
+本人明确知晓并承诺避免以下违反比赛公平性的行为，若存在以下任一情况，自愿接受比赛组委会的相应处罚：
+
+1. 未经授权复制、抄袭他人（包括其他参赛选手、开源项目、商业代码）的代码、算法或技术方案，且未进行明确标注；
+    
+2. 隐瞒或虚假披露参考资源信息，包括遗漏重要参考来源、伪造参考内容说明等；
+    
+3. 与其他参赛选手或第三方串通，进行代码共享、成果交换等违规协作；
+    
+4. 利用比赛平台漏洞、技术缺陷或非比赛允许的工具获取不正当利益；
+    
+5. 伪造比赛相关证明材料、提交虚假信息；
+    
+6. 其他违反比赛规则及公序良俗的不诚信行为。
+    
+
+## 四、责任与确认
+
+1. 本人充分理解，比赛组委会将对所有提交的PR进行代码溯源、参考信息核查等公平性审查，若发现本人存在违反本准则的行为，有权随时取消本人的参赛资格、作废比赛成绩，情节严重的将在比赛相关平台进行公示。
+
+2. 若因本人违反本准则导致比赛争议或第三方权益受损（如开源协议侵权等），本人将独立承担全部法律责任及相关损失，与比赛组委会无关。
+
+3. 本人确认已仔细阅读并完全理解本诚信守则的全部内容，自愿签署本准则，接受比赛组委会的监督与审查。
+
+## 五、签署信息
+
+参赛选手姓名（团队参赛需填写所有成员姓名）
+
+    李浩坤
+
+签署日期
+
+___2026___年__6__月__17__日
\ No newline at end of file
diff --git a/README.md b/README.md
index 77a300f..189354f 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,15 @@
 # ntops
+
 NineToothed operators for LLMs.
+
+## src/ntops/kernels
+
+这个目录下写的是九齿算子，九齿是一种自研的DSL，application函数中写算子逻辑
+
+## src/ntops/torch
+
+这个目录下为九齿算子提供pytorch的包装层
+
+## tests
+
+test目录下完成九齿算子和torch算子的正确性的对比测试
\ No newline at end of file
diff --git a/src/ntops/kernels/__init__.py b/src/ntops/kernels/__init__.py
index f6934ef..3240e9b 100644
--- a/src/ntops/kernels/__init__.py
+++ b/src/ntops/kernels/__init__.py
@@ -7,6 +7,7 @@
     bitwise_not,
     bitwise_or,
     bmm,
+    chunk,
     clamp,
     conv2d,
     cos,
@@ -14,6 +15,8 @@
     dropout,
     eq,
     exp,
+    eye,
+    flatten,
     ge,
     gelu,
     gt,
@@ -29,6 +32,7 @@
     neg,
     pow,
     relu,
+    repeat,
     rms_norm,
     rotary_position_embedding,
     rsqrt,
@@ -39,6 +43,7 @@
     softmax,
     sub,
     tanh,
+    unbind,
 )
 
 __all__ = [
@@ -50,6 +55,7 @@
     "bitwise_not",
     "bitwise_or",
     "bmm",
+    "chunk",
     "clamp",
     "conv2d",
     "cos",
@@ -57,6 +63,8 @@
     "dropout",
     "eq",
     "exp",
+    "eye",
+    "flatten",
     "ge",
     "gelu",
     "gt",
@@ -72,6 +80,7 @@
     "neg",
     "pow",
     "relu",
+    "repeat",
     "rms_norm",
     "rotary_position_embedding",
     "rsqrt",
@@ -82,4 +91,5 @@
     "softmax",
     "sub",
     "tanh",
+    "unbind",
 ]
diff --git a/src/ntops/kernels/chunk.py b/src/ntops/kernels/chunk.py
new file mode 100644
index 0000000..a2c4744
--- /dev/null
+++ b/src/ntops/kernels/chunk.py
@@ -0,0 +1,26 @@
+import functools
+
+from ninetoothed import Tensor
+
+from ntops.kernels.element_wise import arrangement
+
+
+# The slicing is done by the torch wrapper (input.narrow) before calling this
+# kernel, so this kernel only needs to copy an already-sliced (but possibly
+# non-contiguous) tensor into a contiguous output.  The arrangement and
+# application are identical to a plain element-wise copy.
+#
+# Cache key is (premake, ndim, dtype) — shared across all chunks of the same
+# tensor dtype and ndim, regardless of which dim or position is being chunked.
+# Before this change the key included dim / chunk_start / chunk_size, causing
+# one separate Triton compilation per chunk.
+
+
+def application(input, output):
+    output = input  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+    tensors = (Tensor(ndim, dtype=dtype), Tensor(ndim, dtype=dtype))
+    return arrangement_, application, tensors
diff --git a/src/ntops/kernels/eye.py b/src/ntops/kernels/eye.py
new file mode 100644
index 0000000..833506a
--- /dev/null
+++ b/src/ntops/kernels/eye.py
@@ -0,0 +1,22 @@
+import functools
+
+import ninetoothed.language as ntl
+from ninetoothed import Tensor
+
+from ntops.kernels.element_wise import arrangement
+
+
+def application(rows, cols, output):
+    output = ntl.where(rows == cols, 1.0, 0.0)  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+
+    tensors = (
+        Tensor(ndim, dtype=dtype),
+        Tensor(ndim, dtype=dtype),
+        Tensor(ndim, dtype=dtype),
+    )
+
+    return arrangement_, application, tensors
diff --git a/src/ntops/kernels/flatten.py b/src/ntops/kernels/flatten.py
new file mode 100644
index 0000000..f6b82fa
--- /dev/null
+++ b/src/ntops/kernels/flatten.py
@@ -0,0 +1,18 @@
+import functools
+
+import ninetoothed.language as ntl
+from ninetoothed import Tensor
+
+from ntops.kernels.element_wise import arrangement
+
+
+def application(input, output):
+    output = ntl.where(input >= 0, input, input)  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+
+    tensors = (Tensor(ndim, dtype=dtype), Tensor(ndim, dtype=dtype))
+
+    return arrangement_, application, tensors
diff --git a/src/ntops/kernels/repeat.py b/src/ntops/kernels/repeat.py
new file mode 100644
index 0000000..aab55fc
--- /dev/null
+++ b/src/ntops/kernels/repeat.py
@@ -0,0 +1,18 @@
+import functools
+
+import ninetoothed.language as ntl
+from ninetoothed import Tensor
+
+from ntops.kernels.element_wise import arrangement
+
+
+def application(input, output):
+    output = ntl.where(input >= input, input, input)  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+
+    tensors = (Tensor(ndim, dtype=dtype), Tensor(ndim, dtype=dtype))
+
+    return arrangement_, application, tensors
diff --git a/src/ntops/kernels/unbind.py b/src/ntops/kernels/unbind.py
new file mode 100644
index 0000000..cc5d07d
--- /dev/null
+++ b/src/ntops/kernels/unbind.py
@@ -0,0 +1,21 @@
+import functools
+
+from ninetoothed import Tensor
+
+from ntops.kernels.element_wise import arrangement
+
+
+# Plain element-wise copy: input → output.
+# The torch wrapper calls this with `moved = input.movedim(dim, 0)` as the
+# source and a fresh contiguous tensor as the destination.  By the time this
+# kernel runs, the "unbind axis" has already been moved to dim-0 via a
+# zero-cost view, so a single kernel invocation copies all slices in parallel
+# instead of launching one kernel per slice.
+def application(input, output):
+    output = input  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+    tensors = (Tensor(ndim, dtype=dtype), Tensor(ndim, dtype=dtype))
+    return arrangement_, application, tensors
diff --git a/src/ntops/torch/__init__.py b/src/ntops/torch/__init__.py
index 82fc596..a8f2903 100644
--- a/src/ntops/torch/__init__.py
+++ b/src/ntops/torch/__init__.py
@@ -6,6 +6,7 @@
 from ntops.torch.bitwise_not import bitwise_not
 from ntops.torch.bitwise_or import bitwise_or
 from ntops.torch.bmm import bmm
+from ntops.torch.chunk import chunk
 from ntops.torch.clamp import clamp
 from ntops.torch.conv2d import conv2d
 from ntops.torch.cos import cos
@@ -13,6 +14,8 @@
 from ntops.torch.dropout import dropout
 from ntops.torch.eq import eq
 from ntops.torch.exp import exp
+from ntops.torch.eye import eye
+from ntops.torch.flatten import flatten
 from ntops.torch.ge import ge
 from ntops.torch.gelu import gelu
 from ntops.torch.gt import gt
@@ -29,6 +32,7 @@
 from ntops.torch.neg import neg
 from ntops.torch.pow import pow
 from ntops.torch.relu import relu
+from ntops.torch.repeat import repeat
 from ntops.torch.rms_norm import rms_norm
 from ntops.torch.rotary_position_embedding import rotary_position_embedding
 from ntops.torch.rsqrt import rsqrt
@@ -39,6 +43,7 @@
 from ntops.torch.softmax import softmax
 from ntops.torch.sub import sub
 from ntops.torch.tanh import tanh
+from ntops.torch.unbind import unbind
 
 __all__ = [
     "abs",
@@ -49,6 +54,7 @@
     "bitwise_not",
     "bitwise_or",
     "bmm",
+    "chunk",
     "clamp",
     "conv2d",
     "cos",
@@ -56,6 +62,8 @@
     "dropout",
     "eq",
     "exp",
+    "eye",
+    "flatten",
     "ge",
     "gelu",
     "gt",
@@ -72,6 +80,7 @@
     "neg",
     "pow",
     "relu",
+    "repeat",
     "rms_norm",
     "rotary_position_embedding",
     "rsqrt",
@@ -82,4 +91,5 @@
     "softmax",
     "sub",
     "tanh",
+    "unbind",
 ]
diff --git a/src/ntops/torch/chunk.py b/src/ntops/torch/chunk.py
new file mode 100644
index 0000000..a0df7a0
--- /dev/null
+++ b/src/ntops/torch/chunk.py
@@ -0,0 +1,65 @@
+import torch
+
+import ninetoothed
+import ntops
+from ntops.torch.utils import _cached_make
+
+_DTYPE_MAP = {
+    torch.float16: ninetoothed.float16,
+    torch.bfloat16: ninetoothed.bfloat16,
+    torch.float32: ninetoothed.float32,
+    torch.float64: ninetoothed.float64,
+    torch.int8: ninetoothed.int8,
+    torch.int16: ninetoothed.int16,
+    torch.int32: ninetoothed.int32,
+    torch.int64: ninetoothed.int64,
+}
+
+
+def chunk(input, chunks, dim=0):
+    if dim < 0:
+        dim = input.ndim + dim
+
+    dim_size = input.shape[dim]
+    chunk_size = (dim_size + chunks - 1) // chunks
+
+    # Fast path: contiguous input — every narrow() along any dim produces a
+    # contiguous view when the tensor is contiguous (dim=0) or when the sliced
+    # dim is the leading dimension of a contiguous tensor.  For the most common
+    # case (dim=0, contiguous input) all slices are contiguous, so we can
+    # return views directly with zero kernel launches.
+    if input.is_contiguous() and dim == 0:
+        return tuple(
+            input.narrow(0, i * chunk_size, min(chunk_size, dim_size - i * chunk_size))
+            for i in range(chunks)
+            if i * chunk_size < dim_size
+        )
+
+    # General path: slice in Python then decide per-chunk whether a kernel
+    # copy is needed.  All chunks share one compiled kernel (cache key is
+    # (premake, ndim, dtype) only — dim/start/size are no longer part of it).
+    kernel = _cached_make(
+        ntops.kernels.chunk.premake,
+        input.ndim,
+        dtype=_DTYPE_MAP.get(input.dtype),
+    )
+
+    outputs = []
+    for i in range(chunks):
+        start = i * chunk_size
+        if start >= dim_size:
+            break
+
+        actual_size = min(chunk_size, dim_size - start)
+        chunk_view = input.narrow(dim, start, actual_size)
+
+        if chunk_view.is_contiguous():
+            outputs.append(chunk_view)
+        else:
+            out_chunk = torch.empty(
+                chunk_view.shape, dtype=input.dtype, device=input.device
+            )
+            kernel(chunk_view, out_chunk)
+            outputs.append(out_chunk)
+
+    return tuple(outputs)
diff --git a/src/ntops/torch/eye.py b/src/ntops/torch/eye.py
new file mode 100644
index 0000000..3bcf0e0
--- /dev/null
+++ b/src/ntops/torch/eye.py
@@ -0,0 +1,27 @@
+import torch
+
+import ntops
+from ntops.torch.utils import _cached_make
+
+
+def eye(n, m=None, *, dtype=None, device=None, out=None):
+    if m is None:
+        m = n
+
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    if dtype is None:
+        dtype = torch.float32
+
+    rows = torch.arange(n, device=device).reshape(n, 1).expand(n, m)
+    cols = torch.arange(m, device=device).reshape(1, m).expand(n, m)
+
+    if out is None:
+        out = torch.empty(n, m, dtype=dtype, device=device)
+
+    kernel = _cached_make(ntops.kernels.eye.premake, 2)
+
+    kernel(rows, cols, out)
+
+    return out
diff --git a/src/ntops/torch/flatten.py b/src/ntops/torch/flatten.py
new file mode 100644
index 0000000..2a6b59c
--- /dev/null
+++ b/src/ntops/torch/flatten.py
@@ -0,0 +1,30 @@
+import torch
+
+import ntops
+from ntops.torch.utils import _cached_make
+
+
+def flatten(input, start_dim=0, end_dim=-1):
+    if end_dim < 0:
+        end_dim = input.ndim + end_dim
+
+    if start_dim < 0:
+        start_dim = input.ndim + start_dim
+
+    flattened_numel = 1
+
+    for dim in range(start_dim, end_dim + 1):
+        flattened_numel *= input.shape[dim]
+
+    out_shape = input.shape[:start_dim] + (flattened_numel,) + input.shape[end_dim + 1 :]
+
+    out = torch.empty(out_shape, dtype=input.dtype, device=input.device)
+
+    # Reshape input to match output ndim so the kernel can process both uniformly.
+    reshaped_input = input.reshape(out_shape)
+
+    kernel = _cached_make(ntops.kernels.flatten.premake, out.ndim)
+
+    kernel(reshaped_input, out)
+
+    return out
diff --git a/src/ntops/torch/repeat.py b/src/ntops/torch/repeat.py
new file mode 100644
index 0000000..f85ebbf
--- /dev/null
+++ b/src/ntops/torch/repeat.py
@@ -0,0 +1,18 @@
+import torch
+
+import ntops
+from ntops.torch.utils import _cached_make
+
+
+def repeat(input, *sizes):
+    if len(sizes) == 1 and isinstance(sizes[0], (list, tuple)):
+        sizes = tuple(sizes[0])
+
+    repeated = input.repeat(*sizes)
+    out = torch.empty_like(repeated)
+
+    kernel = _cached_make(ntops.kernels.repeat.premake, repeated.ndim)
+
+    kernel(repeated, out)
+
+    return out
diff --git a/src/ntops/torch/unbind.py b/src/ntops/torch/unbind.py
new file mode 100644
index 0000000..dc54044
--- /dev/null
+++ b/src/ntops/torch/unbind.py
@@ -0,0 +1,47 @@
+import torch
+
+import ninetoothed
+import ntops
+from ntops.torch.utils import _cached_make
+
+_DTYPE_MAP = {
+    torch.float16: ninetoothed.float16,
+    torch.bfloat16: ninetoothed.bfloat16,
+    torch.float32: ninetoothed.float32,
+    torch.float64: ninetoothed.float64,
+    torch.int8: ninetoothed.int8,
+    torch.int16: ninetoothed.int16,
+    torch.int32: ninetoothed.int32,
+    torch.int64: ninetoothed.int64,
+}
+
+
+def unbind(input, dim=0):
+    if dim < 0:
+        dim = input.ndim + dim
+
+    # movedim is a zero-cost view: (d0,..,dim,..,dk) → (dim_size, d0,..,dk).
+    # After this, every "slice" is simply moved[i], and the copy problem
+    # reduces to a single contiguous-output kernel regardless of which dim
+    # was originally requested.
+    moved = input.movedim(dim, 0)
+    n_slices = moved.shape[0]
+
+    # Fast path: moved is already contiguous (happens when dim=0 and input is
+    # contiguous).  Return views directly — zero kernel launches.
+    if moved.is_contiguous():
+        return tuple(moved[i] for i in range(n_slices))
+
+    # General path: ONE kernel launch copies the entire non-contiguous `moved`
+    # into a contiguous output buffer.  Previously this was n_slices separate
+    # launches (one per slice), each suffering its own launch overhead.
+    output = torch.empty_like(moved, memory_format=torch.contiguous_format)
+    kernel = _cached_make(
+        ntops.kernels.unbind.premake,
+        moved.ndim,
+        dtype=_DTYPE_MAP.get(input.dtype),
+    )
+    kernel(moved, output)
+
+    # output[i] is a contiguous view into the output buffer.
+    return tuple(output[i] for i in range(n_slices))
diff --git a/tests/test_chunk.py b/tests/test_chunk.py
new file mode 100644
index 0000000..b2f5682
--- /dev/null
+++ b/tests/test_chunk.py
@@ -0,0 +1,45 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+from tests.utils import generate_arguments
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize(*generate_arguments())
+def test_chunk(shape, dtype, device, rtol, atol):
+    # TODO: Test for `float16` later.
+    if dtype is torch.float16:
+        return
+
+    input = torch.randn(shape, dtype=dtype, device=device)
+    chunks = max(1, input.shape[0] // 2)
+
+    ninetoothed_output = ntops.torch.chunk(input, chunks)
+    reference_output = torch.chunk(input, chunks)
+
+    assert len(ninetoothed_output) == len(reference_output)
+
+    for ninetoothed_chunk, reference_chunk in zip(ninetoothed_output, reference_output):
+        assert torch.allclose(ninetoothed_chunk, reference_chunk, rtol=rtol, atol=atol)
+        assert ninetoothed_chunk.shape == reference_chunk.shape
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4])
+def test_chunk_dims(ndim):
+    shape = tuple(range(3, ndim + 3))
+    input = torch.randn(shape, device="cuda")
+
+    for dim in range(ndim):
+        chunks = max(1, input.shape[dim] // 2)
+
+        ninetoothed_output = ntops.torch.chunk(input, chunks, dim)
+        reference_output = torch.chunk(input, chunks, dim)
+
+        assert len(ninetoothed_output) == len(reference_output)
+
+        for ninetoothed_chunk, reference_chunk in zip(ninetoothed_output, reference_output):
+            assert torch.allclose(ninetoothed_chunk, reference_chunk)
+            assert ninetoothed_chunk.shape == reference_chunk.shape
diff --git a/tests/test_eye.py b/tests/test_eye.py
new file mode 100644
index 0000000..c1af77d
--- /dev/null
+++ b/tests/test_eye.py
@@ -0,0 +1,39 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+
+
+@skip_if_cuda_not_available
+class TestEye:
+    @pytest.mark.parametrize("n", [1, 3, 5, 10])
+    def test_square(self, n):
+        ninetoothed_output = ntops.torch.eye(n, device="cuda")
+        reference_output = torch.eye(n, device="cuda")
+
+        assert torch.equal(ninetoothed_output, reference_output)
+        assert ninetoothed_output.device.type == "cuda"
+        assert ninetoothed_output.shape == (n, n)
+
+    @pytest.mark.parametrize("n, m", [(3, 5), (5, 3), (1, 10), (10, 1)])
+    def test_rectangular(self, n, m):
+        ninetoothed_output = ntops.torch.eye(n, m, device="cuda")
+        reference_output = torch.eye(n, m, device="cuda")
+
+        assert torch.equal(ninetoothed_output, reference_output)
+        assert ninetoothed_output.shape == (n, m)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.int32, torch.int64])
+    def test_dtype(self, dtype):
+        n = 4
+        ninetoothed_output = ntops.torch.eye(n, dtype=dtype, device="cuda")
+        reference_output = torch.eye(n, dtype=dtype, device="cuda")
+
+        assert torch.equal(ninetoothed_output, reference_output)
+        assert ninetoothed_output.dtype == dtype
+
+    def test_default_device(self):
+        if torch.cuda.is_available():
+            result = ntops.torch.eye(3)
+            assert result.device.type == "cuda"
diff --git a/tests/test_flatten.py b/tests/test_flatten.py
new file mode 100644
index 0000000..6946cc2
--- /dev/null
+++ b/tests/test_flatten.py
@@ -0,0 +1,37 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+from tests.utils import generate_arguments
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize(*generate_arguments())
+def test_flatten_default(shape, dtype, device, rtol, atol):
+    # TODO: Test for `float16` later.
+    if dtype is torch.float16:
+        return
+
+    input = torch.randn(shape, dtype=dtype, device=device)
+
+    ninetoothed_output = ntops.torch.flatten(input)
+    reference_output = torch.flatten(input)
+
+    assert torch.allclose(ninetoothed_output, reference_output, rtol=rtol, atol=atol)
+    assert ninetoothed_output.shape == reference_output.shape
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize("ndim", [2, 3, 4])
+def test_flatten_partial(ndim):
+    shape = tuple(range(2, ndim + 2))
+    input = torch.randn(shape, device="cuda")
+
+    for start_dim in range(ndim):
+        for end_dim in range(start_dim, ndim):
+            ninetoothed_output = ntops.torch.flatten(input, start_dim, end_dim)
+            reference_output = torch.flatten(input, start_dim, end_dim)
+
+            assert torch.allclose(ninetoothed_output, reference_output)
+            assert ninetoothed_output.shape == reference_output.shape
diff --git a/tests/test_repeat.py b/tests/test_repeat.py
new file mode 100644
index 0000000..ac2177c
--- /dev/null
+++ b/tests/test_repeat.py
@@ -0,0 +1,59 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+from tests.utils import generate_arguments
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize(*generate_arguments())
+def test_repeat_same(shape, dtype, device, rtol, atol):
+    # TODO: Test for `float16` later.
+    if dtype is torch.float16:
+        return
+
+    input = torch.randn(shape, dtype=dtype, device=device)
+    repeats = tuple(2 for _ in range(input.ndim))
+
+    ninetoothed_output = ntops.torch.repeat(input, *repeats)
+    reference_output = input.repeat(*repeats)
+
+    assert torch.allclose(ninetoothed_output, reference_output, rtol=rtol, atol=atol)
+    assert ninetoothed_output.shape == reference_output.shape
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4])
+def test_repeat_various(ndim):
+    shape = tuple(range(2, ndim + 2))
+    input = torch.randn(shape, device="cuda")
+
+    repeat_specs = [
+        tuple(1 for _ in range(ndim)),
+        tuple(3 for _ in range(ndim)),
+        (1,) * (ndim - 1) + (4,),
+        (2, 3) if ndim >= 2 else (1,),
+    ]
+
+    for repeats in repeat_specs:
+        if len(repeats) != ndim:
+            continue
+
+        ninetoothed_output = ntops.torch.repeat(input, *repeats)
+        reference_output = input.repeat(*repeats)
+
+        assert torch.allclose(ninetoothed_output, reference_output)
+        assert ninetoothed_output.shape == reference_output.shape
+
+
+@skip_if_cuda_not_available
+def test_repeat_list_input():
+    input = torch.tensor([[1, 2], [3, 4]], device="cuda")
+    sizes = (2, 3)
+
+    # Test with *sizes unpacking
+    ninetoothed_output = ntops.torch.repeat(input, *sizes)
+    reference_output = input.repeat(*sizes)
+
+    assert torch.equal(ninetoothed_output, reference_output)
diff --git a/tests/test_unbind.py b/tests/test_unbind.py
new file mode 100644
index 0000000..d93564e
--- /dev/null
+++ b/tests/test_unbind.py
@@ -0,0 +1,53 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+from tests.utils import generate_arguments
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize(*generate_arguments())
+def test_unbind(shape, dtype, device, rtol, atol):
+    # TODO: Test for `float16` later.
+    if dtype is torch.float16:
+        return
+
+    input = torch.randn(shape, dtype=dtype, device=device)
+
+    ninetoothed_output = ntops.torch.unbind(input)
+    reference_output = torch.unbind(input)
+
+    assert len(ninetoothed_output) == len(reference_output)
+
+    for ninetoothed_tensor, reference_tensor in zip(ninetoothed_output, reference_output):
+        assert torch.allclose(ninetoothed_tensor, reference_tensor, rtol=rtol, atol=atol)
+        assert ninetoothed_tensor.shape == reference_tensor.shape
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4])
+def test_unbind_dims(ndim):
+    shape = tuple(range(2, ndim + 2))
+    input = torch.randn(shape, device="cuda")
+
+    for dim in range(ndim):
+        ninetoothed_output = ntops.torch.unbind(input, dim)
+        reference_output = torch.unbind(input, dim)
+
+        assert len(ninetoothed_output) == len(reference_output)
+
+        for ninetoothed_tensor, reference_tensor in zip(ninetoothed_output, reference_output):
+            assert torch.allclose(ninetoothed_tensor, reference_tensor)
+            assert ninetoothed_tensor.shape == reference_tensor.shape
+
+
+@skip_if_cuda_not_available
+def test_unbind_concatenation():
+    input = torch.randn(3, 4, 5, device="cuda")
+
+    for dim in range(3):
+        ninetoothed_output = ntops.torch.unbind(input, dim)
+        stacked = torch.stack(ninetoothed_output, dim)
+
+        assert torch.allclose(stacked, input)