repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos | hf_public_repos/candle/LICENSE-MIT | Permission is hereby granted, free of charge, to any
person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the
Software without restriction, including without
limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of
the ... | 0 |
hf_public_repos | hf_public_repos/candle/.pre-commit-config.yaml | repos:
- repo: https://github.com/Narsil/pre-commit-rust
rev: 2eed6366172ef2a5186e8785ec0e67243d7d73d0
hooks:
- id: fmt
name: "Rust (fmt)"
- id: clippy
name: "Rust (clippy)"
args:
[
"--tests",
"--examples",
"--",
"-D... | 0 |
hf_public_repos | hf_public_repos/candle/test.onnx | backend-test:J
xytest"Relu
SingleReluZ
x
b
y
B | 0 |
hf_public_repos | hf_public_repos/candle/README.md | # candle
[](https://discord.gg/hugging-face-879548962464493619)
[](https://crates.io/crates/candle-core)
[](htt... | 0 |
hf_public_repos | hf_public_repos/candle/LICENSE-APACHE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
hf_public_repos | hf_public_repos/candle/CHANGELOG.md | # Changelog
This documents the main changes to the `candle` crate.
## v0.3.1 - Unreleased
### Added
### Modified
## v0.3.0 - 2023-10-01
### Added
- Added the Mistral 7b v0.1 model
[983](https://github.com/huggingface/candle/pull/983).
- Quantized version of the Mistral model
[1009](https://github.com/huggingf... | 0 |
hf_public_repos | hf_public_repos/candle/Makefile | .PHONY: clean-ptx clean test
clean-ptx:
find target -name "*.ptx" -type f -delete
echo "" > candle-kernels/src/lib.rs
touch candle-kernels/build.rs
touch candle-examples/build.rs
touch candle-flash-attn/build.rs
clean:
cargo clean
test:
cargo test
all: test
| 0 |
hf_public_repos | hf_public_repos/candle/Cargo.toml | [workspace]
members = [
"candle-core",
"candle-datasets",
"candle-examples",
"candle-book",
"candle-nn",
"candle-pyo3",
"candle-transformers",
"candle-wasm-examples/*",
"candle-wasm-tests",
]
exclude = [
"candle-flash-attn",
"candle-kernels",
"candle-metal-kernels",
"cand... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/build.rs | fn main() {
pyo3_build_config::add_extension_module_link_args();
}
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/test.py | import candle
print(f"mkl: {candle.utils.has_mkl()}")
print(f"accelerate: {candle.utils.has_accelerate()}")
print(f"num-threads: {candle.utils.get_num_threads()}")
print(f"cuda: {candle.utils.cuda_is_available()}")
t = candle.Tensor(42.0)
print(t)
print(t.shape, t.rank, t.device)
print(t + t)
t = can... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/README.md | ## Installation
From the `candle-pyo3` directory, enable a virtual env where you will want the
candle package to be installed then run.
```bash
maturin develop -r
python test.py
```
## Generating Stub Files for Type Hinting
For type hinting support, the `candle-pyo3` package requires `*.pyi` files. You can automa... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/pyproject.toml | [project]
name = 'candle-nn'
requires-python = '>=3.7'
authors = [
{name = 'The Candle Team'},
]
dynamic = [
'description',
'license',
'readme',
]
[project.urls]
Homepage = 'https://github.com/huggingface/candle'
Source = 'https://github.com/huggingface/candle'
[build-system]
requires = ["maturin>=1.... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/stub.py | # See: https://raw.githubusercontent.com/huggingface/tokenizers/main/bindings/python/stub.py
import argparse
import inspect
import os
from typing import Optional
import black
from pathlib import Path
import re
INDENT = " " * 4
GENERATED_COMMENT = "# Generated content DO NOT EDIT\n"
TYPING = """from typing import Any,... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/quant-llama.py | # This example shows how the candle Python api can be used to replicate llama.cpp.
import sys
from typing import Dict, Tuple, Any
import candle
from candle.models.llama import QuantizedLlama
from candle import utils
MAX_SEQ_LEN = 4096
def gguf_rename(tensor_name: str):
if tensor_name == "token_embd.weight":
... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/e5.py | from candle.utils import load_safetensors, save_gguf, load_gguf
from candle.models.bert import BertModel, Config
import json
from candle import Tensor
from tqdm import tqdm
from dataclasses import fields
import os
import time
from huggingface_hub import hf_hub_download
from transformers import BertTokenizer, AutoModel... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/test_pytorch.py | import candle
import torch
# convert from candle tensor to torch tensor
t = candle.randn((3, 512, 512))
torch_tensor = t.to_torch()
print(torch_tensor)
print(type(torch_tensor))
# convert from torch tensor to candle tensor
t = torch.randn((3, 512, 512))
candle_tensor = candle.Tensor(t)
print(candle_tensor)
print(type... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-pyo3/Cargo.toml | [package]
name = "candle-pyo3"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[lib]
name = "candle"
crate-type = ["cdylib"]
[dependencies]
accelerate-src = { ... | 0 |
hf_public_repos/candle/candle-pyo3/py_src | hf_public_repos/candle/candle-pyo3/py_src/candle/__init__.py | import logging
try:
from .candle import *
except ImportError as e:
# If we are in development mode, or we did not bundle the DLLs, we try to locate them here
# PyO3 wont give us any information about what DLLs are missing, so we can only try to load
# the DLLs and re-import the module
logging.warni... | 0 |
hf_public_repos/candle/candle-pyo3/py_src | hf_public_repos/candle/candle-pyo3/py_src/candle/__init__.pyi | # Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
class bf16(DType):
pass
@staticmethod
def cat(tensors: List[Tensor], dim: int) -> Tensor:
"""
Concatenat... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/container.py | # see https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/container.py
from .module import Module
from typing import (
Any,
Dict,
Iterable,
Iterator,
Mapping,
Optional,
overload,
Tuple,
TypeVar,
Union,
)
from collections import OrderedDict, abc as container_abcs
import ... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/__init__.py | from .module import Module
from .container import Sequential, ModuleList, ModuleDict
from .sparse import Embedding
from .normalization import LayerNorm
from .linear import Linear
| 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/linear.py | import math
from typing import Any
import candle
from candle import Tensor
from .module import Module
# See https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/linear.py
class Identity(Module):
r"""A placeholder identity operator that is argument-insensitive.
Args:
args: any argument (unu... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/normalization.py | import candle
from candle import Tensor
from .module import Module
from typing import Union, List, Tuple, Optional, Any
_shape_t = Union[int, List[int]]
import numbers
class LayerNorm(Module):
r"""Applies Layer Normalization over a mini-batch of inputs as described in
the paper `Layer Normalization <https://... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/module.py | from candle import Tensor, QTensor, DType
from typing import (
Dict,
Tuple,
Any,
Optional,
Union,
Iterator,
Set,
overload,
Mapping,
TypeVar,
List,
)
from collections import OrderedDict, namedtuple
TensorLike = Union[Tensor, QTensor]
T = TypeVar("T", bound="Module")
class _... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/nn/sparse.py | from .module import Module
from typing import Optional, Tuple, Any
from candle import Tensor
import candle
class Embedding(Module):
"""A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them using indices.
The input... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/onnx/__init__.py | # Generated content DO NOT EDIT
from .. import onnx
ONNXModel = onnx.ONNXModel
ONNXTensorDescription = onnx.ONNXTensorDescription
| 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/onnx/__init__.pyi | # Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
class ONNXModel:
"""
A wrapper around an ONNX model.
"""
d... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/models/llama.py | import candle
from typing import Dict, Tuple, Any
from candle import Tensor, QTensor, utils, nn
from candle.nn import Module, ModuleList
def masked_fill(on_false: Tensor, mask: Tensor, on_true: Tensor):
shape = mask.shape
on_true = candle.tensor(on_true).broadcast_as(shape)
return mask.where_cond(on_true,... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/models/bert.py | from dataclasses import dataclass
from typing import Optional
from candle.nn import Module, Embedding, LayerNorm, Linear, ModuleList
from candle import Tensor
import candle
import candle.functional as F
from typing import Tuple, Optional
@dataclass
class Config:
vocab_size: int = 30522
hidden_size: int = 768
... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/functional/__init__.py | # Generated content DO NOT EDIT
from .. import functional
avg_pool2d = functional.avg_pool2d
gelu = functional.gelu
max_pool2d = functional.max_pool2d
relu = functional.relu
silu = functional.silu
softmax = functional.softmax
tanh = functional.tanh
| 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/functional/__init__.pyi | # Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
@staticmethod
def avg_pool2d(tensor: Tensor, ksize: int, stride: int = 1) -... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/utils/__init__.py | # Generated content DO NOT EDIT
from .. import utils
cuda_is_available = utils.cuda_is_available
get_num_threads = utils.get_num_threads
has_accelerate = utils.has_accelerate
has_mkl = utils.has_mkl
load_ggml = utils.load_ggml
load_gguf = utils.load_gguf
load_safetensors = utils.load_safetensors
save_gguf = utils.save... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/utils/__init__.pyi | # Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
@staticmethod
def cuda_is_available() -> bool:
"""
Returns true if ... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/testing/__init__.py | import candle
from candle import Tensor
_UNSIGNED_DTYPES = set([str(candle.u8), str(candle.u32)])
def _assert_tensor_metadata(
actual: Tensor,
expected: Tensor,
check_device: bool = True,
check_dtype: bool = True,
check_layout: bool = True,
check_stride: bool = False,
):
if check_device:... | 0 |
hf_public_repos/candle/candle-pyo3/py_src/candle | hf_public_repos/candle/candle-pyo3/py_src/candle/typing/__init__.py | from typing import TypeVar, Union, Sequence
_T = TypeVar("_T")
_ArrayLike = Union[
_T,
Sequence[_T],
Sequence[Sequence[_T]],
Sequence[Sequence[Sequence[_T]]],
Sequence[Sequence[Sequence[Sequence[_T]]]],
]
CPU: str = "cpu"
CUDA: str = "cuda"
Device = TypeVar("Device", CPU, CUDA)
Scalar = Union[i... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/bindings/test_testing.py | import candle
from candle import Tensor
from candle.testing import assert_equal, assert_almost_equal
import pytest
@pytest.mark.parametrize("dtype", [candle.f32, candle.f64, candle.f16, candle.u32, candle.u8, candle.i64])
def test_assert_equal_asserts_correctly(dtype: candle.DType):
a = Tensor([1, 2, 3]).to(dtype... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/bindings/test_linear.py | import candle
from candle import Tensor
from candle.nn import Linear
def test_linear_layer_can_be_constructed():
linear = Linear(10, 10)
assert linear is not None
def test_linear_layer_can_forward_a_singular_input():
linear = Linear(384, 1536)
input_tensor = candle.randn((8, 384))
output = linea... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/bindings/test_module.py | import candle
from candle import Tensor, QTensor
from candle.nn import Module, Linear
from candle.utils import cuda_is_available
import pytest
def test_module_can_be_constructed():
class A(Module):
pass
a = A()
assert a is not None
assert len(list(a.buffers())) == 0
def test_module_registe... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/native/test_shape.py | from candle import Tensor
from candle import rand
import pytest
def test_absolute_shapes_are_valid():
a = rand((10, 20))
assert a.shape == (10, 20)
b = rand(10, 20)
assert b.shape == (10, 20)
pytest.raises(OverflowError, lambda: rand((10, 20, -1)))
pytest.raises(OverflowError, lambda: rand(-1... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/native/test_tensor.py | import candle
from candle import Tensor
from candle.utils import cuda_is_available
from candle.testing import assert_equal
import pytest
def test_tensor_can_be_constructed():
t = Tensor(42.0)
assert t.values() == 42.0
def test_tensor_can_be_constructed_from_list():
t = Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])... | 0 |
hf_public_repos/candle/candle-pyo3/tests | hf_public_repos/candle/candle-pyo3/tests/native/test_utils.py | import candle
from candle import Tensor, QTensor
from candle.utils import load_safetensors, save_gguf, load_gguf, save_safetensors
from pathlib import Path
TEST_DIR = Path(__file__).parent.parent / "_workdir"
TEST_DIR.mkdir(exist_ok=True)
def test_can_roundtrip_safetensors():
tensors = {
"a": candle.rand... | 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/_additional_typing/README.md | This python module contains external typehinting for certain `candle` classes. This is only necessary for `magic` methodes e.g. `__add__` as their text signature cant be set via pyo3.
The classes in this module will be parsed by the `stub.py` script and interleafed with the signatures of the actual pyo3 `candle.candle... | 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/_additional_typing/__init__.py | from typing import Union, Sequence
class Tensor:
"""
This contains the type hints for the magic methodes of the `candle.Tensor` class.
"""
def __add__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
"""
Add a scalar to a tensor or two tensors together.
"""
pass
... | 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/src/lib.rs | #![allow(clippy::redundant_closure_call)]
use pyo3::exceptions::{PyTypeError, PyValueError};
use pyo3::prelude::*;
use pyo3::pyclass::CompareOp;
use pyo3::types::{IntoPyDict, PyDict, PyTuple};
use pyo3::ToPyObject;
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
use std::os::raw::c_long;
u... | 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/src/shape.rs | use ::candle::Tensor;
use pyo3::prelude::*;
#[derive(Clone, Debug)]
/// Represents an absolute shape e.g. (1, 2, 3)
pub struct PyShape(Vec<usize>);
impl<'source> pyo3::FromPyObject<'source> for PyShape {
fn extract(ob: &'source PyAny) -> PyResult<Self> {
if ob.is_none() {
return Err(PyErr::new... | 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/src/utils.rs | use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;
pub fn wrap_err(err: ::candle::Error) -> PyErr {
PyErr::new::<PyValueError, _>(format!("{err:?}"))
}
| 0 |
hf_public_repos/candle/candle-pyo3 | hf_public_repos/candle/candle-pyo3/src/onnx.rs | use std::collections::HashMap;
use crate::utils::wrap_err;
use crate::{PyDType, PyTensor};
use candle_onnx::eval::{dtype, get_tensor, simple_eval};
use candle_onnx::onnx::tensor_proto::DataType;
use candle_onnx::onnx::tensor_shape_proto::dimension::Value;
use candle_onnx::onnx::type_proto::{Tensor as ONNXTensor, Value... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-core/README.md | # candle
Minimalist ML framework for Rust
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-core/Cargo.toml | [package]
name = "candle-core"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
accelerate-src = { workspace = true, optional = true }
byteorder =... | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-core/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/benches/bench_main.rs | mod benchmarks;
use criterion::criterion_main;
criterion_main!(
benchmarks::matmul::benches,
benchmarks::affine::benches,
benchmarks::where_cond::benches
);
| 0 |
hf_public_repos/candle/candle-core/benches | hf_public_repos/candle/candle-core/benches/benchmarks/mod.rs | pub(crate) mod affine;
pub(crate) mod matmul;
pub(crate) mod where_cond;
use candle_core::{Device, Result};
pub(crate) trait BenchDevice {
fn sync(&self) -> Result<()>;
fn bench_name<S: Into<String>>(&self, name: S) -> String;
}
impl BenchDevice for Device {
fn sync(&self) -> Result<()> {
match ... | 0 |
hf_public_repos/candle/candle-core/benches | hf_public_repos/candle/candle-core/benches/benchmarks/affine.rs | use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(a: &Tensor) {
a.affine(12.34, 56.78).unwrap();
}
fn run_affine_benchmark(c: &mut Criterion, device: &Device, dtype:... | 0 |
hf_public_repos/candle/candle-core/benches | hf_public_repos/candle/candle-core/benches/benchmarks/matmul.rs | use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(a: &Tensor, b: &Tensor) {
a.matmul(&b.t().unwrap()).unwrap();
}
fn run_bench(c: &mut Criterion, device: &Device) {
... | 0 |
hf_public_repos/candle/candle-core/benches | hf_public_repos/candle/candle-core/benches/benchmarks/where_cond.rs | use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(a: &Tensor, b: &Tensor, c: &Tensor) {
a.where_cond(b, c).unwrap();
}
const fn create_cond_arr<const N: usize>() -> ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/examples/cuda_sum_benchmark.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::str::FromStr;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn cos_sin(n: usize, device: &Device) -> Result<Tensor> {
let thetas: Vec<_> = (0..n).map(|i| (i as f32 / n as f32)).colle... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/examples/tensor-tools.rs | use candle_core::quantized::{gguf_file, GgmlDType, QTensor};
use candle_core::{Device, Result};
use clap::{Parser, Subcommand, ValueEnum};
use rayon::prelude::*;
#[derive(ValueEnum, Debug, Clone)]
enum QuantizationMode {
/// The default quantization includes all 2d tensors, except the output tensor which always
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/examples/basics.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let a = Tensor::new(&[[0.0f32, 1.0, 2.0], [3.0, 4.0, 5.0]], &Device::Cpu)?;
let b = Tensor::new(&[[88.0f32, 99.0]], ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/examples/cuda_basics.rs | #[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let device = Device::new_cuda(0)?;
let in_t = Tensor::rand(-1f32, 1f32, (1, 3, 12, 7), &device)?;
let k_t = Tens... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/display_tests.rs | use anyhow::Result;
use candle_core::{DType, Device::Cpu, Tensor};
#[test]
fn display_scalar() -> Result<()> {
let t = Tensor::new(1234u32, &Cpu)?;
let s = format!("{t}");
assert_eq!(&s, "[1234]\nTensor[[], u32]");
let t = t.to_dtype(DType::F32)?.neg()?;
let s = format!("{}", (&t / 10.0)?);
ass... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/layout_tests.rs | use candle::{test_device, Device, IndexOp, Result, Tensor};
use candle_core as candle;
fn contiguous(device: &Device) -> Result<()> {
let tensor = Tensor::arange(0u32, 24u32, device)?.reshape((2, 3, 4))?;
assert_eq!(
tensor.to_vec3::<u32>()?,
&[
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 1... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/grad_tests.rs | use anyhow::{Context, Result};
use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
fn simple_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4.], device)?;
let x = x.as_tensor();
let y = (((x * x)? + x * 5f64)? + 4f64)?;
let grads = y.backward()?;
let grad_x =... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/tensor_tests.rs | use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor, D};
fn zeros(device: &Device) -> Result<()> {
let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
let (dim1, dim2) = tensor.dims2()?;
assert_eq!(dim1, 5);
assert_eq!(dim2, 2);
Ok(())
}
fn ones(device: &Device) ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/indexing_tests.rs | use anyhow::Result;
use candle_core::{Device, IndexOp, Tensor};
#[test]
fn integer_index() -> Result<()> {
let dev = Device::Cpu;
let tensor = Tensor::arange(0u32, 2 * 3, &dev)?.reshape((2, 3))?;
let result = tensor.i(1)?;
assert_eq!(result.dims(), &[3]);
assert_eq!(result.to_vec1::<u32>()?, &[3, ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/custom_op_tests.rs | use candle_core::backend::BackendStorage;
use candle_core::cpu_backend;
use candle_core::test_utils::to_vec1_round;
use candle_core::{CpuStorage, CustomOp1, DType, Device, Error, Layout, Result, Shape, Tensor};
fn fwd<T: num_traits::Float>(v: T, alpha: f64) -> T {
if v.is_sign_positive() {
v
} else {
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/serialization_tests.rs | use candle_core::{DType, Result, Tensor};
#[test]
fn npy() -> Result<()> {
let npy = Tensor::read_npy("tests/test.npy")?;
assert_eq!(
npy.to_dtype(DType::U8)?.to_vec1::<u8>()?,
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
);
Ok(())
}
#[test]
fn npz() -> Result<()> {
let npz = Tensor::read_npz("t... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/quantized_tests.rs | use candle_core::{
bail,
quantized::{self, GgmlDType},
test_device,
test_utils::to_vec2_round,
Device, Module, Result, Tensor,
};
use quantized::{k_quants, GgmlType};
use rand::prelude::*;
const GGML_TEST_SIZE: usize = 32 * 128;
const GGML_MAX_QUANTIZATION_TOTAL_ERROR: f32 = 0.002;
const GGML_MAX_... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/pool_tests.rs | use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let t = Tensor::from_vec(data, (... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/npy.py | import numpy as np
x = np.arange(10)
# Write a npy file.
np.save("test.npy", x)
# Write multiple values to a npz file.
values = { "x": x, "x_plus_one": x + 1 }
np.savez("test.npz", **values)
| 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/tests/conv_tests.rs | use anyhow::Result;
use candle_core::{test_device, test_utils, Device, IndexOp, Tensor};
/* This test is based on the following script.
import torch
torch.manual_seed(4242)
t = torch.randn((1, 4, 5))
w = torch.randn((2, 4, 3))
print(t.flatten())
print(w.flatten())
res = torch.nn.functional.conv1d(t, w)
print(res.flat... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/layout.rs | use crate::{Error, Result, Shape};
#[derive(Debug, PartialEq, Eq, Clone)]
pub struct Layout {
shape: Shape,
// The strides are given in number of elements and not in bytes.
stride: Vec<usize>,
start_offset: usize,
}
impl Layout {
pub fn new(shape: Shape, stride: Vec<usize>, start_offset: usize) ->... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/device.rs | use crate::backend::BackendDevice;
use crate::cpu_backend::CpuDevice;
use crate::{CpuStorage, DType, Result, Shape, Storage, WithDType};
/// A `DeviceLocation` represents a physical device whereas multiple `Device`
/// can live on the same location (typically for cuda devices).
#[derive(Debug, Copy, Clone, PartialEq, ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/dtype.rs | //! Types for elements that can be stored and manipulated using tensors.
#![allow(clippy::redundant_closure_call)]
use crate::backend::BackendStorage;
use crate::{CpuStorage, Error, Result};
/// The different types of elements allowed in tensors.
#[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
pub enum DType {
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/cudnn.rs | use crate::WithDType;
use cudarc;
use cudarc::cudnn::safe::{Conv2dForward, Cudnn};
use cudarc::driver::{CudaSlice, CudaView, DeviceRepr, ValidAsZeroBits};
use std::cell::RefCell;
use std::collections::HashMap;
use std::sync::Arc;
// The cudnn handles are stored per thread here rather than on the CudaDevice as they are... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/storage.rs | use crate::backend::BackendStorage;
use crate::op::{self, CmpOp, CustomOp1, CustomOp2, CustomOp3, ReduceOp};
use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
// We do not want to implement Clone on Storage as cloning may fail because of
// out of memory. Instead try_clon... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/conv.rs | use crate::{op::BackpropOp, op::Op, Error, Result, Tensor};
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConv1D {
pub(crate) b_size: usize,
// Maybe we should have a version without l_in as this bit depends on the input and not only on
// the weights.
pub(crate) l_in: usize,
pub(crate) c... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/scalar.rs | use crate::{Result, Tensor, WithDType};
pub enum TensorScalar {
Tensor(Tensor),
Scalar(Tensor),
}
pub trait TensorOrScalar {
fn to_tensor_scalar(self) -> Result<TensorScalar>;
}
impl TensorOrScalar for &Tensor {
fn to_tensor_scalar(self) -> Result<TensorScalar> {
Ok(TensorScalar::Tensor(self.... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/lib.rs | //! ML framework for Rust
//!
//! ```rust
//! use candle_core::{Tensor, DType, Device};
//! # use candle_core::Error;
//! # fn main() -> Result<(), Error>{
//!
//! let a = Tensor::arange(0f32, 6f32, &Device::Cpu)?.reshape((2, 3))?;
//! let b = Tensor::arange(0f32, 12f32, &Device::Cpu)?.reshape((3, 4))?;
//!
//! let c =... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/backprop.rs | use crate::op::{BinaryOp, Op, ReduceOp, UnaryOp};
use crate::{Error, Result, Tensor, TensorId};
use std::collections::HashMap;
// arg has been reduced to node via reduce_dims, expand it back to arg.
// This has to handle keepdims.
fn broadcast_back(arg: &Tensor, node: &Tensor, reduced_dims: &[usize]) -> Result<Tensor>... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/strided_index.rs | use crate::Layout;
/// An iterator over offset position for items of an N-dimensional arrays stored in a
/// flat buffer using some potential strides.
#[derive(Debug)]
pub struct StridedIndex<'a> {
next_storage_index: Option<usize>,
multi_index: Vec<usize>,
dims: &'a [usize],
stride: &'a [usize],
}
im... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/dummy_cuda_backend.rs | #![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct CudaDevice;
#[derive(Debug)]
pub struct CudaStorage;
macro_rules! fail {
() => {
unimplemented!("cuda support has not been enabled, ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/dummy_metal_backend.rs | #![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct MetalDevice;
#[derive(Debug)]
pub struct MetalStorage;
#[derive(thiserror::Error, Debug)]
pub enum MetalError {
#[error("{0}")]
Message(... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/mkl.rs | #![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int};
mod ffi {
use super::*;
extern "C" {
pub fn vsTanh(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdTanh(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsExp(n: c_int, a: *const c_float, y: *mut c_float);... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/cpu_backend.rs | use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
use half::{bf16, f16};
use rayon::prelude::*;
const USE_IM2COL_CONV1D: bool = true;
const USE_IM2COL_CONV2D: bool = true;
// TODO: Maybe we... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/convert.rs | //! Implement conversion traits for tensors
use crate::{DType, Device, Error, Tensor, WithDType};
use half::{bf16, f16, slice::HalfFloatSliceExt};
use std::convert::TryFrom;
impl<T: WithDType> TryFrom<&Tensor> for Vec<T> {
type Error = Error;
fn try_from(tensor: &Tensor) -> Result<Self, Self::Error> {
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/shape.rs | //! The shape of a tensor is a tuple with the size of each of its dimensions.
#![allow(clippy::redundant_closure_call)]
use crate::{Error, Result};
#[derive(Clone, PartialEq, Eq)]
pub struct Shape(Vec<usize>);
pub const SCALAR: Shape = Shape(vec![]);
impl std::fmt::Debug for Shape {
fn fmt(&self, f: &mut std::fm... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/cuda_backend.rs | use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape, WithDType};
pub use candle_kernels as kernels;
pub use cudarc;
use cudarc::cublas::{Gemm, GemmConfig, StridedBatchedConfig};
use cudarc::driver::{
CudaFun... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/utils.rs | use std::str::FromStr;
pub fn get_num_threads() -> usize {
// Respond to the same environment variable as rayon.
match std::env::var("RAYON_NUM_THREADS")
.ok()
.and_then(|s| usize::from_str(&s).ok())
{
Some(x) if x > 0 => x,
Some(_) | None => num_cpus::get(),
}
}
pub fn... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/npy.rs | //! Numpy support for tensors.
//!
//! The spec for the npy format can be found in
//! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
//! The functions from this module can be used to read tensors from npy/npz files
//! or write tensors to these files. A npy file contains a single tensor (u... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/variable.rs | // Variables are wrappers around tensors that can be modified, they are typically used for holding
// weights and being modified by gradient descent.
// We do not expose a public way to create variables as this would break the invariant that the
// tensor within a variable is actually with `is_variable` set to `true`.
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/accelerate.rs | #![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int, c_long, c_ulong};
mod ffi {
use super::*;
extern "C" {
// It would be nice to be able to switch to the NEWLAPACK version of the function but this
// seems to trigger some link error. Available function names can be seen here:
... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/pickle.rs | // Just enough pickle support to be able to read PyTorch checkpoints.
// This hardcodes objects that are required for tensor reading, we may want to make this a bit more
// composable/tensor agnostic at some point.
use crate::{DType, Error as E, Layout, Result, Tensor};
use byteorder::{LittleEndian, ReadBytesExt};
use ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/indexer.rs | use crate::{Error, Tensor};
use std::ops::{
Bound, Range, RangeBounds, RangeFrom, RangeFull, RangeInclusive, RangeTo, RangeToInclusive,
};
impl Tensor {
/// Intended to be use by the trait `.i()`
///
/// ```
/// # use candle_core::{Tensor, DType, Device, IndexOp};
/// let a = Tensor::zeros((2, ... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/display.rs | /// Pretty printing of tensors
/// This implementation should be in line with the PyTorch version.
/// https://github.com/pytorch/pytorch/blob/7b419e8513a024e172eae767e24ec1b849976b13/torch/_tensor_str.py
use crate::{DType, Result, Tensor, WithDType};
use half::{bf16, f16};
impl Tensor {
fn fmt_dt<T: WithDType + s... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/safetensors.rs | use crate::{DType, Device, Error, Result, Tensor, WithDType};
use safetensors::tensor as st;
use safetensors::tensor::SafeTensors;
use std::borrow::Cow;
use std::collections::HashMap;
use std::path::Path;
impl From<DType> for st::Dtype {
fn from(value: DType) -> Self {
match value {
DType::U8 =... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/op.rs | #![allow(clippy::redundant_closure_call)]
use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
use half::{bf16, f16};
use num_traits::float::Float;
#[derive(Clone, Copy, PartialEq, Eq)]
pub enum CmpOp {
Eq,
Ne,
Le,
Ge,
Lt,
Gt,
}
#[derive(Debug, Clone, Copy, Partia... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/metal_backend.rs | use crate::backend::{BackendDevice, BackendStorage};
use crate::conv::{ParamsConv1D, ParamsConv2D, ParamsConvTranspose1D, ParamsConvTranspose2D};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape};
use candle_metal_kernels;
use candle_metal_kernels::Kernels;
use... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/backend.rs | use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape};
pub trait BackendStorage: Sized {
type Device: BackendDevice;
fn try_clone(&self, _: &Layout) -> Result<Self>;
fn dtype(&self) -> DType;
fn device(&self) -> &Self::Device;
// Maybe this... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/error.rs | use crate::{DType, DeviceLocation, Layout, MetalError, Shape};
#[derive(Debug, Clone)]
pub struct MatMulUnexpectedStriding {
pub lhs_l: Layout,
pub rhs_l: Layout,
pub bmnk: (usize, usize, usize, usize),
pub msg: &'static str,
}
/// Main library error type.
#[derive(thiserror::Error, Debug)]
pub enum E... | 0 |
hf_public_repos/candle/candle-core | hf_public_repos/candle/candle-core/src/test_utils.rs | use crate::{Result, Tensor};
#[macro_export]
macro_rules! test_device {
// TODO: Switch to generating the two last arguments automatically once concat_idents is
// stable. https://github.com/rust-lang/rust/issues/29599
($fn_name: ident, $test_cpu: ident, $test_cuda: ident, $test_metal: ident) => {
... | 0 |
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