diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 3bfabfc..629df3f 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -14,23 +14,22 @@ on: jobs: deploy: - runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install build - - name: Build package - run: python -m build - - name: Publish package - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build + - name: Build package + run: python -m build + - name: Publish package + uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..db920cf --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,40 @@ +name: Tests + +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + workflow_dispatch: + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + - name: Set up Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + + - name: Install dependencies + run: | + uv sync --dev + + - name: Run tests with pytest + run: | + uv run pytest --cov=rotary_embedding_torch --cov-report=xml --cov-report=term-missing + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v4 + with: + file: ./coverage.xml + fail_ci_if_error: false diff --git a/.gitignore b/.gitignore index b6e4761..13ca9e4 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,6 @@ dmypy.json # Pyre type checker .pyre/ + +# IDE stuff +.idea/ \ No newline at end of file diff --git a/README.md b/README.md index 54dda94..c56f293 100644 --- a/README.md +++ b/README.md @@ -8,10 +8,29 @@ My gut also tells me there is something =0.7", + "torch>=2.0", +] + +[project.urls] +Homepage = "https://github.com/lucidrains/rotary-embedding-torch" +Repository = "https://github.com/lucidrains/rotary-embedding-torch" + +[dependency-groups] +dev = [ + "pytest>=8.4.1", + "pytest-cov>=6.2.1", + "coverage>=7.10.0", + "ruff>=0.12.5", +] + +[tool.ruff] +target-version = "py39" +line-length = 120 + +[tool.ruff.lint] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "UP", # pyupgrade + "N", # pep8-naming + "ANN", # annotations + "S", # bandit + "A", # builtins + "C4", # comprehensions + "EM", # errmsg + "ICN", # import-conventions + "PIE", # pie + "PT", # pytest-style + "RSE", # raise + "RET", # return + "TID", # tidy-imports + "ARG", # unused-arguments + "TD", # todos + "PL", # pylint + "FLY", # flynt + "FA", # flake8-future-annotations +] + +[tool.pydoclint] +style = "google" +arg-type-hints-in-docstring = false +check-return-types = false +check-yield-types = false + +[tool.coverage.run] +source = ["rotary_embedding_torch"] +omit = ["tests/*"] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "def __repr__", + "raise AssertionError", + "raise NotImplementedError" +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/rotary_embedding_torch/__init__.py b/rotary_embedding_torch/__init__.py index fa7caab..52eba75 100644 --- a/rotary_embedding_torch/__init__.py +++ b/rotary_embedding_torch/__init__.py @@ -1,6 +1,8 @@ from rotary_embedding_torch.rotary_embedding_torch import ( - apply_rotary_emb, RotaryEmbedding, apply_learned_rotations, - broadcat + apply_rotary_emb, + broadcast, ) + +__all__ = ["RotaryEmbedding", "apply_learned_rotations", "apply_rotary_emb", "broadcast"] diff --git a/rotary_embedding_torch/rotary_embedding_torch.py b/rotary_embedding_torch/rotary_embedding_torch.py index 5811879..d1fad89 100644 --- a/rotary_embedding_torch/rotary_embedding_torch.py +++ b/rotary_embedding_torch/rotary_embedding_torch.py @@ -1,52 +1,54 @@ from __future__ import annotations -from math import pi, log -import torch -from torch.amp import autocast -from torch.nn import Module, ModuleList -from torch import nn, einsum, broadcast_tensors, is_tensor, tensor, Tensor +from math import pi +from typing import Literal +import torch from einops import rearrange, repeat +from torch import Tensor, broadcast_tensors, einsum, is_tensor, nn, tensor +from torch.amp import autocast +from torch.nn import Module -from typing import Literal # helper functions - def exists(val): return val is not None + def default(val, d): return val if exists(val) else d -# broadcat, as tortoise-tts was using it -def broadcat(tensors, dim = -1): +# broadcast, as tortoise-tts was using it +def broadcast(tensors: list[Tensor], dim: int = -1) -> Tensor: broadcasted_tensors = broadcast_tensors(*tensors) - return torch.cat(broadcasted_tensors, dim = dim) + return torch.cat(broadcasted_tensors, dim=dim) + -def slice_at_dim(t, dim_slice: slice, *, dim): - dim += (t.ndim if dim < 0 else 0) +def slice_at_dim(t: Tensor, dim_slice: slice, *, dim: int) -> Tensor: + dim += t.ndim if dim < 0 else 0 colons = [slice(None)] * t.ndim colons[dim] = dim_slice return t[tuple(colons)] + # rotary embedding helper functions +def rotate_half(x: Tensor) -> Tensor: + x = rearrange(x, "... (d r) -> ... d r", r=2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return rearrange(x, "... d r -> ... (d r)") -def rotate_half(x): - x = rearrange(x, '... (d r) -> ... d r', r = 2) - x1, x2 = x.unbind(dim = -1) - x = torch.stack((-x2, x1), dim = -1) - return rearrange(x, '... d r -> ... (d r)') -@autocast('cuda', enabled = False) +@autocast("cuda", enabled=False) def apply_rotary_emb( - freqs, - t, - start_index = 0, - scale = 1., - seq_dim = -2, - freqs_seq_dim = None -): + freqs: Tensor, + t: Tensor, + start_index: int = 0, + scale: Tensor | float = 1.0, + seq_dim: int = -2, + freqs_seq_dim: int = None, +) -> Tensor: dtype = t.dtype if not exists(freqs_seq_dim): @@ -55,86 +57,89 @@ def apply_rotary_emb( if t.ndim == 3 or exists(freqs_seq_dim): seq_len = t.shape[seq_dim] - freqs = slice_at_dim(freqs, slice(-seq_len, None), dim = freqs_seq_dim) + freqs = slice_at_dim(freqs, slice(-seq_len, None), dim=freqs_seq_dim) rot_dim = freqs.shape[-1] end_index = start_index + rot_dim - assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + if rot_dim > t.shape[-1]: + msg = f"feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}" + raise ValueError(msg) # Split t into three parts: left, middle (to be transformed), and right t_left = t[..., :start_index] t_middle = t[..., start_index:end_index] t_right = t[..., end_index:] - # Apply rotary embeddings without modifying t in place + # Apply rotary embeddings without modifying t in place t_transformed = (t_middle * freqs.cos() * scale) + (rotate_half(t_middle) * freqs.sin() * scale) - + out = torch.cat((t_left, t_transformed, t_right), dim=-1) return out.type(dtype) -# learned rotation helpers -def apply_learned_rotations(rotations, t, start_index = 0, freq_ranges = None): +# learned rotation helpers +def apply_learned_rotations(rotations: Tensor, t: Tensor, start_index: int = 0, freq_ranges=None): if exists(freq_ranges): - rotations = einsum('..., f -> ... f', rotations, freq_ranges) - rotations = rearrange(rotations, '... r f -> ... (r f)') + rotations = einsum("..., f -> ... f", rotations, freq_ranges) + rotations = rearrange(rotations, "... r f -> ... (r f)") - rotations = repeat(rotations, '... n -> ... (n r)', r = 2) - return apply_rotary_emb(rotations, t, start_index = start_index) + rotations = repeat(rotations, "... n -> ... (n r)", r=2) + return apply_rotary_emb(rotations, t, start_index=start_index) -# classes +# classes class RotaryEmbedding(Module): def __init__( self, - dim, + dim: int, custom_freqs: Tensor | None = None, - freqs_for: Literal['lang', 'pixel', 'constant'] = 'lang', - theta = 10000, - max_freq = 10, - num_freqs = 1, - learned_freq = False, - use_xpos = False, - xpos_scale_base = 512, - interpolate_factor = 1., - theta_rescale_factor = 1., - seq_before_head_dim = False, - cache_if_possible = True, - cache_max_seq_len = 8192 - ): + freqs_for: Literal["lang", "pixel", "constant"] = "lang", + theta: int | float = 10000, + max_freq: int | float = 10, + num_freqs: int = 1, + learned_freq: bool = False, + use_xpos: bool = False, + xpos_scale_base: int = 512, + interpolate_factor: float = 1.0, + theta_rescale_factor: float = 1.0, + seq_before_head_dim: bool = False, + cache_if_possible: bool = True, + cache_max_seq_len: int = 8192, + ) -> None: super().__init__() # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning # has some connection to NTK literature # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ - theta *= theta_rescale_factor ** (dim / (dim - 2)) + if dim > 2: + theta *= theta_rescale_factor ** (dim / (dim - 2)) self.freqs_for = freqs_for if exists(custom_freqs): freqs = custom_freqs - elif freqs_for == 'lang': - freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) - elif freqs_for == 'pixel': - freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi - elif freqs_for == 'constant': + elif freqs_for == "lang": + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + elif freqs_for == "pixel": + freqs = torch.linspace(1.0, max_freq / 2, dim // 2) * pi + elif freqs_for == "constant": freqs = torch.ones(num_freqs).float() self.cache_if_possible = cache_if_possible self.cache_max_seq_len = cache_max_seq_len - self.register_buffer('cached_freqs', torch.zeros(cache_max_seq_len, dim), persistent = False) + self.register_buffer("cached_freqs", torch.zeros(cache_max_seq_len, dim), persistent=False) self.cached_freqs_seq_len = 0 - self.freqs = nn.Parameter(freqs, requires_grad = learned_freq) + self.freqs = nn.Parameter(freqs, requires_grad=learned_freq) self.learned_freq = learned_freq # dummy for device - self.register_buffer('dummy', torch.tensor(0), persistent = False) + self.register_buffer("dummy", torch.tensor(0), persistent=False) # default sequence dimension @@ -142,12 +147,11 @@ def __init__( self.default_seq_dim = -3 if seq_before_head_dim else -2 # interpolation factors - - assert interpolate_factor >= 1. + if interpolate_factor < 1.0: + raise ValueError("interpolation factor must be at least 1.0") self.interpolate_factor = interpolate_factor # xpos - self.use_xpos = use_xpos if not use_xpos: @@ -156,8 +160,8 @@ def __init__( scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) self.scale_base = xpos_scale_base - self.register_buffer('scale', scale, persistent = False) - self.register_buffer('cached_scales', torch.zeros(cache_max_seq_len, dim), persistent = False) + self.register_buffer("scale", scale, persistent=False) + self.register_buffer("cached_scales", torch.zeros(cache_max_seq_len, dim), persistent=False) self.cached_scales_seq_len = 0 # add apply_rotary_emb as static method @@ -168,99 +172,102 @@ def __init__( def device(self): return self.dummy.device - def get_seq_pos(self, seq_len, device = None, dtype = None, offset = 0): + def get_seq_pos( + self, + seq_len: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + offset: int = 0, + ) -> Tensor: device = default(device, self.device) - dtype = default(dtype, self.cached_freqs.device) + dtype = default(dtype, self.cached_freqs.dtype) - return (torch.arange(seq_len, device = device, dtype = dtype) + offset) / self.interpolate_factor + return (torch.arange(seq_len, device=device, dtype=dtype) + offset) / self.interpolate_factor - def rotate_queries_or_keys(self, t, seq_dim = None, offset = 0, scale = None): + def rotate_queries_or_keys( + self, t: Tensor, seq_dim: int | None = None, offset: int = 0, scale: Tensor | int | None = None + ) -> Tensor: seq_dim = default(seq_dim, self.default_seq_dim) - assert not self.use_xpos or exists(scale), 'you must use `.rotate_queries_and_keys` method instead and pass in both queries and keys, for length extrapolatable rotary embeddings' + assert not self.use_xpos or exists(scale), ( + "you must use `.rotate_queries_and_keys` method instead and pass in both queries " + "and keys, for length extrapolatable rotary embeddings" + ) device, dtype, seq_len = t.device, t.dtype, t.shape[seq_dim] - seq = self.get_seq_pos(seq_len, device = device, dtype = dtype, offset = offset) + seq = self.get_seq_pos(seq_len, device=device, dtype=dtype, offset=offset) - freqs = self.forward(seq, seq_len = seq_len, offset = offset) + freqs = self.forward(seq, seq_len=seq_len, offset=offset) if seq_dim == -3: - freqs = rearrange(freqs, 'n d -> n 1 d') + freqs = rearrange(freqs, "n d -> n 1 d") - return apply_rotary_emb(freqs, t, scale = default(scale, 1.), seq_dim = seq_dim) + return apply_rotary_emb(freqs, t, scale=default(scale, 1.0), seq_dim=seq_dim) - def rotate_queries_with_cached_keys(self, q, k, seq_dim = None, offset = 0): + def rotate_queries_with_cached_keys( + self, q: Tensor, k: Tensor, seq_dim: int | None = None, offset: int = 0 + ) -> tuple[Tensor, Tensor]: dtype, device, seq_dim = q.dtype, q.device, default(seq_dim, self.default_seq_dim) q_len, k_len = q.shape[seq_dim], k.shape[seq_dim] - assert q_len <= k_len - q_scale = k_scale = 1. + if q_len > k_len: + msg = "query length must be less than or equal to key length" + raise ValueError(msg) + + q_scale = k_scale = 1.0 if self.use_xpos: - seq = self.get_seq_pos(k_len, dtype = dtype, device = device) + seq = self.get_seq_pos(k_len, dtype=dtype, device=device) q_scale = self.get_scale(seq[-q_len:]).type(dtype) k_scale = self.get_scale(seq).type(dtype) - rotated_q = self.rotate_queries_or_keys(q, seq_dim = seq_dim, scale = q_scale, offset = k_len - q_len + offset) - rotated_k = self.rotate_queries_or_keys(k, seq_dim = seq_dim, scale = k_scale ** -1) + rotated_q = self.rotate_queries_or_keys(q, seq_dim=seq_dim, scale=q_scale, offset=k_len - q_len + offset) + rotated_k = self.rotate_queries_or_keys(k, seq_dim=seq_dim, scale=k_scale**-1) rotated_q = rotated_q.type(q.dtype) rotated_k = rotated_k.type(k.dtype) return rotated_q, rotated_k - def rotate_queries_and_keys(self, q, k, seq_dim = None): + def rotate_queries_and_keys(self, q: Tensor, k: Tensor, seq_dim: int | None = None) -> tuple[Tensor, Tensor]: seq_dim = default(seq_dim, self.default_seq_dim) assert self.use_xpos device, dtype, seq_len = q.device, q.dtype, q.shape[seq_dim] - seq = self.get_seq_pos(seq_len, dtype = dtype, device = device) + seq = self.get_seq_pos(seq_len, dtype=dtype, device=device) - freqs = self.forward(seq, seq_len = seq_len) - scale = self.get_scale(seq, seq_len = seq_len).to(dtype) + freqs = self.forward(seq, seq_len=seq_len) + scale = self.get_scale(seq, seq_len=seq_len).to(dtype) if seq_dim == -3: - freqs = rearrange(freqs, 'n d -> n 1 d') - scale = rearrange(scale, 'n d -> n 1 d') + freqs = rearrange(freqs, "n d -> n 1 d") + scale = rearrange(scale, "n d -> n 1 d") - rotated_q = apply_rotary_emb(freqs, q, scale = scale, seq_dim = seq_dim) - rotated_k = apply_rotary_emb(freqs, k, scale = scale ** -1, seq_dim = seq_dim) + rotated_q = apply_rotary_emb(freqs, q, scale=scale, seq_dim=seq_dim) + rotated_k = apply_rotary_emb(freqs, k, scale=scale**-1, seq_dim=seq_dim) rotated_q = rotated_q.type(q.dtype) rotated_k = rotated_k.type(k.dtype) return rotated_q, rotated_k - def get_scale( - self, - t: Tensor, - seq_len: int | None = None, - offset = 0 - ): + def get_scale(self, t: Tensor, seq_len: int | None = None, offset: int = 0) -> Tensor | float: assert self.use_xpos - should_cache = ( - self.cache_if_possible and - exists(seq_len) and - (offset + seq_len) <= self.cache_max_seq_len - ) + should_cache = self.cache_if_possible and exists(seq_len) and (offset + seq_len) <= self.cache_max_seq_len - if ( - should_cache and \ - exists(self.cached_scales) and \ - (seq_len + offset) <= self.cached_scales_seq_len - ): - return self.cached_scales[offset:(offset + seq_len)] + if should_cache and exists(self.cached_scales) and (seq_len + offset) <= self.cached_scales_seq_len: + return self.cached_scales[offset : (offset + seq_len)] - scale = 1. + scale = 1.0 if self.use_xpos: power = (t - len(t) // 2) / self.scale_base - scale = self.scale ** rearrange(power, 'n -> n 1') - scale = repeat(scale, 'n d -> n (d r)', r = 2) + scale = self.scale ** rearrange(power, "n -> n 1") + scale = repeat(scale, "n d -> n (d r)", r=2) if should_cache and offset == 0: self.cached_scales[:seq_len] = scale.detach() @@ -268,15 +275,7 @@ def get_scale( return scale - def get_axial_freqs( - self, - *dims, - offsets: ( - tuple[int | float, ...] | - Tensor | - None - ) = None - ): + def get_axial_freqs(self, *dims: int, offsets: tuple[int | float, ...] | Tensor | None = None) -> Tensor: Colon = slice(None) all_freqs = [] @@ -286,24 +285,24 @@ def get_axial_freqs( if not is_tensor(offsets): offsets = tensor(offsets) - assert len(offsets) == len(dims) + if len(offsets) != len(dims): + msg = "offsets must be the same length as dims" + raise ValueError(msg) # get frequencies for each axis - for ind, dim in enumerate(dims): - offset = 0 if exists(offsets): offset = offsets[ind] - if self.freqs_for == 'pixel': - pos = torch.linspace(-1, 1, steps = dim, device = self.device) + if self.freqs_for == "pixel": + pos = torch.linspace(-1, 1, steps=dim, device=self.device) else: - pos = torch.arange(dim, device = self.device) + pos = torch.arange(dim, device=self.device) pos = pos + offset - freqs = self.forward(pos, seq_len = dim) + freqs = self.forward(pos, seq_len=dim) all_axis = [None] * len(dims) all_axis[ind] = Colon @@ -314,34 +313,25 @@ def get_axial_freqs( # concat all freqs all_freqs = broadcast_tensors(*all_freqs) - return torch.cat(all_freqs, dim = -1) + return torch.cat(all_freqs, dim=-1) - @autocast('cuda', enabled = False) - def forward( - self, - t: Tensor, - seq_len: int | None = None, - offset = 0 - ): + @autocast("cuda", enabled=False) + def forward(self, t: Tensor, seq_len: int | None = None, offset: int = 0) -> Tensor: should_cache = ( - self.cache_if_possible and - not self.learned_freq and - exists(seq_len) and - self.freqs_for != 'pixel' and - (offset + seq_len) <= self.cache_max_seq_len + self.cache_if_possible + and not self.learned_freq + and exists(seq_len) + and self.freqs_for != "pixel" + and (offset + seq_len) <= self.cache_max_seq_len ) - if ( - should_cache and \ - exists(self.cached_freqs) and \ - (offset + seq_len) <= self.cached_freqs_seq_len - ): - return self.cached_freqs[offset:(offset + seq_len)].detach() + if should_cache and exists(self.cached_freqs) and (offset + seq_len) <= self.cached_freqs_seq_len: + return self.cached_freqs[offset : (offset + seq_len)].detach() freqs = self.freqs - freqs = einsum('..., f -> ... f', t.type(freqs.dtype), freqs) - freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = einsum("..., f -> ... f", t.type(freqs.dtype), freqs) + freqs = repeat(freqs, "... n -> ... (n r)", r=2) if should_cache and offset == 0: self.cached_freqs[:seq_len] = freqs.detach() diff --git a/setup.py b/setup.py deleted file mode 100644 index 07d990b..0000000 --- a/setup.py +++ /dev/null @@ -1,29 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name = 'rotary-embedding-torch', - packages = find_packages(), - version = '0.8.8', - license='MIT', - description = 'Rotary Embedding - Pytorch', - long_description_content_type = 'text/markdown', - author = 'Phil Wang', - author_email = 'lucidrains@gmail.com', - url = 'https://github.com/lucidrains/rotary-embedding-torch', - keywords = [ - 'artificial intelligence', - 'deep learning', - 'positional embedding' - ], - install_requires=[ - 'einops>=0.7', - 'torch>=2.0' - ], - classifiers=[ - 'Development Status :: 4 - Beta', - 'Intended Audience :: Developers', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python :: 3.6', - ], -) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..7c8b88d --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# Tests package for rotary-embedding-torch diff --git a/tests/test_rope.py b/tests/test_rope.py new file mode 100644 index 0000000..cd1ba8c --- /dev/null +++ b/tests/test_rope.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import pytest +import torch + +from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb + + +def manual_apply_rope(input_vec: torch.Tensor, position: int | float, dim: int, theta: float) -> torch.Tensor: + """Manually applies rotary positional embedding to the input vector. + + This function computes the rotary frequencies based on the given theta and dimension, and calculates the + cosine and sine values for the position, and applies the rotation to pairs of elements in the input vector. + + Args: + input_vec: The input tensor to apply rotations to. The last dimension should match `dim`. + position: The position index for which to compute the embedding. + dim: The embedding dimension. Must be even. + theta: The base value for computing frequencies (e.g., 10000). + + Returns: + The input vector with rotary embeddings applied. + """ + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim)) + pos_freqs = position * freqs + cos_vals = torch.cos(pos_freqs) + sin_vals = torch.sin(pos_freqs) + output = input_vec.clone() + for i in range(dim // 2): + j = 2 * i + x0 = input_vec[..., j] + x1 = input_vec[..., j + 1] + output[..., j] = x0 * cos_vals[i] - x1 * sin_vals[i] + output[..., j + 1] = x0 * sin_vals[i] + x1 * cos_vals[i] + return output + + +def compute_expected_freqs(dim: int, theta: float) -> torch.Tensor: + """Computes the expected base frequencies for RoPE. + + Args: + dim: The embedding dimension. + theta: The base value for computing frequencies. + + Returns: + The expected base frequencies. + """ + return 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim)) + + +@pytest.mark.parametrize("dim", [4, 8, 16, 32]) +@pytest.mark.parametrize("theta", [10, 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