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hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu
|
#include "q4_matmul.cuh"
#include "column_remap.cuh"
#include "../util.cuh"
#include "../matrix.cuh"
#include "../cuda_compat.cuh"
#include "../cuda_buffers.cuh"
const int THREADS_X = 32; // Block size and thread count along columns in w and out
const int THREADS_Y = 1; // Block size and thread count along rows in x and out
typedef void (*fp_q4_matmul_kernel)
(
const half*,
const uint32_t*,
half*,
const half*,
const uint32_t*,
const int,
const int,
const int,
const int,
const int,
const uint32_t*,
bool
);
template<bool use_half2, bool use_groupsize, bool use_x_map>
__global__ void q4_matmul_kernel
(
const half* __restrict__ x,
const uint32_t* __restrict__ w,
half* __restrict__ out,
const half* __restrict__ w_scales,
const uint32_t* __restrict__ w_zeros,
const int height,
const int dim,
const int width,
const int groupsize,
const int block_size_z,
const uint32_t* __restrict__ x_map,
bool no_zero
)
{
// Start of block
int x_column = block_size_z * blockIdx.z;
int x_column_end = min(dim, block_size_z * (blockIdx.z + 1));
int w_column = THREADS_X * blockIdx.x + threadIdx.x;
int x_row = THREADS_Y * blockIdx.y + threadIdx.y;
int iterations = (x_column_end - x_column) / 8;
// Views
MatrixView_half x_(x, height, dim);
MatrixView_half w_scales_(w_scales, dim / groupsize, width);
MatrixView_q4_row w_zeros_(w_zeros, dim / groupsize, width);
MatrixView_q4_column w_(w, dim, width);
MatrixView_half_rw out_(out, height, width);
// Zero output
if (!no_zero && blockIdx.z == 0 && (threadIdx.x & 1) == 0)
{
*((uint32_t*) out_.item_ptr(x_row, w_column)) = 0;
__syncthreads();
}
// Loop over part of x row (and w column)
half2 acc = {};
half acc_h = {};
if constexpr (use_groupsize)
{
// For quant matrices where groupsize divides BLOCK_SIZE_Z we always start on a group boundary, so this
// could be slightly faster
for (int k = x_column, group = x_column / groupsize; k < x_column + iterations * 8; group++, k += groupsize)
{
if constexpr (use_half2)
{
half2 w_scale = w_scales_.item_half2half2(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc = dot_product_8_x_map(acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8, x_map);
else acc = dot_product_8 (acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8);
}
else
{
half w_scale = w_scales_.item(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc_h = dot_product_8_x_map_h(acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8, x_map);
else acc_h = dot_product_8_h (acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8);
}
}
}
else
{
// Otherwise assume groupsize is a multiple of 8, do 8 columns per iteration and trust the cache
for (int k = x_column; k < x_column + iterations * 8; k += 8)
{
if constexpr (use_half2)
{
int group = k / groupsize;
half2 w_scale = w_scales_.item_half2half2(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc = dot_product_8_x_map(acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1, x_map);
else acc = dot_product_8 (acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1);
}
else
{
int group = k / groupsize;
half w_scale = w_scales_.item(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc_h = dot_product_8_x_map_h(acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1, x_map);
else acc_h = dot_product_8_h (acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1);
}
}
}
// Add to block result
if constexpr (use_half2)
{
half result = __hadd(acc.x, acc.y);
atomicAdd(out_.item_ptr(x_row, w_column), result);
}
else
{
atomicAdd(out_.item_ptr(x_row, w_column), acc_h);
}
}
fp_q4_matmul_kernel q4_matmul_kernel_pick(ExLlamaTuning* tuningParams, int block_size_z, int groupsize, uint32_t* x_map)
{
// <bool use_half2, bool use_groupsize, bool use_x_map>
if (tuningParams->matmul_no_half2) {
if (block_size_z % groupsize == 0) {
if (x_map) return q4_matmul_kernel<false, true, true >;
else return q4_matmul_kernel<false, true, false>;
} else {
if (x_map) return q4_matmul_kernel<false, false, true >;
else return q4_matmul_kernel<false, false, false>;
}
} else {
if (block_size_z % groupsize == 0)
{
if (x_map) return q4_matmul_kernel<true, true, true >;
else return q4_matmul_kernel<true, true, false>;
} else {
if (x_map) return q4_matmul_kernel<true, false, true >;
else return q4_matmul_kernel<true, false, false>;
}
}
};
// Compute y = x @ w
void q4_matmul_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
const Q4Matrix* w,
half* out,
bool no_zero,
cudaStream_t alt_stream
)
{
int height = x_height;
int dim = w->height;
int width = w->width;
cudaSetDevice(w->device);
uint32_t* x_map = w->cuda_x_map;
const half* x_mapped = x;
if (x_map && !tuningParams->matmul_fused_remap && !alt_stream)
{
CudaBuffers* buffers = get_buffers(w->device);
column_remap_cuda(x, buffers->temp_state, x_height, dim, w->cuda_x_map);
x_mapped = buffers->temp_state;
x_map = NULL;
}
int block_size_z;
if (w->width == 4096) block_size_z = 384; // 7B
else if (w->width == 11008) block_size_z = 256;
else if (w->width == 5120) block_size_z = 384; // 13B
else if (w->width == 13824) block_size_z = 256;
else if (w->width == 6656) block_size_z = 256; // 33B
else if (w->width == 17920) block_size_z = 128;
else block_size_z = 256;
//if (!no_zero) cudaMemsetAsync(out, 0, x_height * w->width * sizeof(half));
dim3 threads(THREADS_X, THREADS_Y, 1);
dim3 blocks
(
(width + threads.x - 1) / threads.x,
(height + threads.y - 1) / threads.y,
(dim + block_size_z - 1) / block_size_z
);
fp_q4_matmul_kernel kernel = q4_matmul_kernel_pick(tuningParams, block_size_z, w->groupsize, x_map);
kernel<<<blocks, threads, 0, alt_stream>>> (x_mapped, w->cuda_qweight, out, w->cuda_scales, w->cuda_qzeros, height, dim, width, w->groupsize, block_size_z, x_map, no_zero);
}
void q4_matmul_recons_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
Q4Matrix* w,
half* out,
const cublasHandle_t handle,
bool no_zero
)
{
int height = x_height;
int dim = w->height;
int width = w->width;
cudaSetDevice(w->device);
CudaBuffers* buffers = get_buffers(w->device);
const half* x_mapped = x;
if (w->cuda_x_map)
{
column_remap_cuda(x, buffers->temp_state, x_height, dim, w->cuda_x_map);
x_mapped = buffers->temp_state;
}
w->reconstruct(buffers->temp_dq);
const half alpha = __float2half(1.0f);
const half beta = no_zero ? __float2half(1.0f) : __float2half(0.0f);
cublasHgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, width, height, dim, &alpha, buffers->temp_dq, width, x_mapped, dim, &beta, out, width);
// const float alpha = 1.0f;
// const float beta = no_zero ? 1.0f : 0.0f;
// cublasSgemmEx(handle, CUBLAS_OP_N, CUBLAS_OP_N, width, height, dim, &alpha, buffers->temp_dq, CUDA_R_16F, width,
// x_mapped, CUDA_R_16F, dim, &beta, out, CUDA_R_16F, width);
}
| 0
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh
|
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matrix_cuh
#define _q4_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
class Q4Matrix
{
public:
int device;
int height;
int width;
int groups;
int groupsize;
uint32_t* cuda_qweight = NULL;
uint32_t* cuda_qzeros = NULL;
half* cuda_scales = NULL;
uint32_t* cuda_x_map = NULL;
Q4Matrix
(
const int _height,
const int _width,
const int _groups,
uint32_t* _qweight,
uint32_t* _qzeros,
half* _scales,
uint32_t* _g_idx,
const int _device
);
~Q4Matrix();
void reconstruct(half* out);
private:
void make_sequential(const uint32_t* cpu_g_idx);
};
void g_q4_keep_matrix(Q4Matrix* m);
void g_q4_free_matrices();
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu
|
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
const int x_height,
const uint32_t* x_map
)
{
int x_column = SHUF_BLOCKSIZE_X * blockIdx.x + threadIdx.x;
int x_row = SHUF_BLOCKSIZE_Y * blockIdx.y;
int x_stride = x_width;
int x_idx = x_row * x_stride + x_column;
int x_row_end = min(x_row + SHUF_BLOCKSIZE_Y, x_height);
int x_idx_end = x_row_end * x_stride + x_column;
int s_column = x_map[x_column];
int s_idx = x_row * x_stride + s_column;
while (x_idx < x_idx_end)
{
x_new[x_idx] = x[s_idx];
x_idx += x_stride;
s_idx += x_stride;
}
}
// Remap columns in x to correspond to sequential group index before matmul
//
// perform x -> seq_x such that seq_x @ seq_w == x @ w
void column_remap_cuda
(
const half* x,
half* x_new,
const int x_height,
const int x_width,
const uint32_t* x_map
)
{
dim3 threads(SHUF_BLOCKSIZE_X, 1, 1);
dim3 blocks
(
(x_width + SHUF_BLOCKSIZE_X - 1) / SHUF_BLOCKSIZE_X,
(x_height + SHUF_BLOCKSIZE_Y - 1) / SHUF_BLOCKSIZE_Y,
1
);
column_remap_kernel<<<blocks, threads>>>(x, x_new, x_width, x_height, x_map);
}
| 0
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels
|
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh
|
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matmul_cuh
#define _q4_matmul_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include "../tuning.h"
void q4_matmul_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
const Q4Matrix* w,
half* out,
bool no_zero = false,
cudaStream_t alt_stream = NULL
);
void q4_matmul_recons_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
Q4Matrix* w,
half* out,
const cublasHandle_t handle,
bool no_zero = false
);
#endif
| 0
|
hf_public_repos/text-generation-inference/server
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/setup.py
|
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="exllamav2_kernels",
ext_modules=[
CUDAExtension(
name="exllamav2_kernels",
sources=[
"exllamav2_kernels/ext.cpp",
"exllamav2_kernels/cuda/q_matrix.cu",
"exllamav2_kernels/cuda/q_gemm.cu",
],
)
],
cmdclass={"build_ext": BuildExtension},
)
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/ext.cpp
|
#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "config.h"
#include "cuda/q_matrix.cuh"
#include "cuda/q_gemm.cuh"
#include "cpp/util.h"
// Some decluttering macros
#define TORCH_CHECK_DTYPE(__x, __dtype) TORCH_CHECK((__x).dtype() == torch::__dtype, #__x " is incorrect datatype, must be " #__dtype)
#define TORCH_CHECK_DTYPE_OPT(__x, __dtype) TORCH_CHECK((__x).device().is_meta() || (__x).dtype() == torch::__dtype, #__x " is incorrect datatype, must be " #__dtype)
#define TORCH_CHECK_SHAPES(__x, __dim_x, __y, __dim_y, __scale_y) TORCH_CHECK((__x).size(__dim_x) == (__y).size(__dim_y) * __scale_y, #__x " and " #__y " have incompatible shapes")
#define TORCH_CHECK_SHAPES_OPT(__x, __dim_x, __y, __dim_y, __scale_y) TORCH_CHECK((__x).device().is_meta() || (__x).size(__dim_x) == (__y).size(__dim_y) * __scale_y, #__x " and " #__y " have incompatible shapes")
// Quant matrix
uintptr_t make_q_matrix
(
torch::Tensor q_weight,
torch::Tensor q_perm,
torch::Tensor q_invperm,
torch::Tensor q_scale,
torch::Tensor q_scale_max,
torch::Tensor q_groups,
torch::Tensor gptq_qzeros,
torch::Tensor gptq_scales,
torch::Tensor gptq_g_idx,
torch::Tensor temp_dq
)
{
TORCH_CHECK_DTYPE(q_weight, kInt);
TORCH_CHECK_DTYPE_OPT(q_perm, kShort);
TORCH_CHECK_DTYPE_OPT(q_invperm, kShort);
TORCH_CHECK_DTYPE_OPT(q_scale, kInt);
TORCH_CHECK_DTYPE_OPT(q_scale_max, kHalf);
TORCH_CHECK_DTYPE_OPT(q_groups, kShort);
TORCH_CHECK_DTYPE_OPT(gptq_qzeros, kInt);
TORCH_CHECK_DTYPE_OPT(gptq_scales, kHalf);
TORCH_CHECK_DTYPE_OPT(gptq_g_idx, kInt);
TORCH_CHECK_SHAPES(q_perm, 0, q_invperm, 0, 1);
int device = q_weight.device().index();
int width = q_weight.size(1);
int groups;
int height;
if (!q_scale.device().is_meta())
{
TORCH_CHECK_SHAPES(q_weight, 1, q_scale, 1, 8);
TORCH_CHECK_SHAPES(q_scale_max, 0, q_scale, 0, 1);
groups = q_scale.size(0);
height = q_invperm.size(0);
}
else
{
TORCH_CHECK_SHAPES(q_weight, 1, gptq_qzeros, 1, 8);
TORCH_CHECK_SHAPES(q_weight, 1, gptq_scales, 1, 1);
groups = gptq_qzeros.size(0);
height = q_weight.size(0) * 8;
}
TORCH_CHECK(temp_dq.size(0) >= width * height, "Insufficient size of temp_dq buffer")
QMatrix* m = new QMatrix
(
device,
height,
width,
groups,
(uint32_t*) q_weight.data_ptr(),
q_perm.device().is_meta() ? NULL : (uint16_t*) q_perm.data_ptr(),
q_invperm.device().is_meta() ? NULL : (uint16_t*) q_invperm.data_ptr(),
q_scale.device().is_meta() ? NULL : (uint32_t*) q_scale.data_ptr(),
q_scale_max.device().is_meta() ? NULL : (half*) q_scale_max.data_ptr(),
q_groups.device().is_meta() ? NULL : (uint16_t*) q_groups.data_ptr(),
gptq_qzeros.device().is_meta() ? NULL : (uint32_t*) gptq_qzeros.data_ptr(),
gptq_scales.device().is_meta() ? NULL : (half*) gptq_scales.data_ptr(),
gptq_g_idx.device().is_meta() ? NULL : (uint32_t*) gptq_g_idx.data_ptr(),
(half*) temp_dq.data_ptr()
);
return reinterpret_cast<uintptr_t> (m);
}
void gemm_half_q_half
(
torch::Tensor a,
uintptr_t b,
torch::Tensor c,
bool force_cuda
)
{
QMatrix* qm = reinterpret_cast<QMatrix*> (b);
TORCH_CHECK_DTYPE(a, kHalf);
TORCH_CHECK_DTYPE(c, kHalf);
TORCH_CHECK_SHAPES(a, 0, c, 0, 1);
TORCH_CHECK(qm->height == a.size(1), "a and b have incompatible shapes")
TORCH_CHECK(qm->width == c.size(1), "b and c have incompatible shapes")
const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
gemm_half_q_half_cuda
(
at::cuda::getCurrentCUDABlasHandle(),
(const half*) a.data_ptr(),
qm,
(half*) c.data_ptr(),
c.size(0), // m
c.size(1), // n
a.size(1), // k
true,
NULL,
force_cuda
);
}
// Bindings
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("make_q_matrix", &make_q_matrix, "make_q_matrix");
m.def("gemm_half_q_half", &gemm_half_q_half, "gemm_half_q_half");
}
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h
|
#ifndef _config_h
#define _config_h
#define MAX_Q_GEMM_ROWS 50
#define QMODE_2BIT 1
#define QMODE_3BIT 1
#define QMODE_4BIT 1
#define QMODE_5BIT 1
#define QMODE_6BIT 0
#define QMODE_8BIT 0
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h
|
#ifndef _util_h
#define _util_h
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, __y) printf("%s, %s: %i, %i\n", #__x, #__y, __x, __y)
#define DBGI3(__x, __y, __z) printf("%s, %s, %s: %i, %i, %i\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGF(__x) printf("%s: %f\n", #__x, __x)
#define DBGF2(__x, __y) printf("%s, %s: %f, %f\n", #__x, #__y, __x, __y)
#define DBGF3(__x, __y, __z) printf("%s, %s, %s: %f, %f, %f\n", #__x, #__y, #__z, __x, __y, __z)
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh
|
#ifndef _q_matrix_cuh
#define _q_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#define MAX_SUPERGROUPS 16
class QMatrix
{
public:
int device;
bool is_gptq;
int height;
int width;
int groups;
int groupsize;
int rows_8;
int rows_6;
int rows_5;
int rows_4;
int rows_3;
int rows_2;
uint32_t* cuda_q_weight = NULL;
uint16_t* cuda_q_perm = NULL;
uint16_t* cuda_q_invperm = NULL;
uint32_t* cuda_q_scale = NULL;
half* cuda_q_scale_max = NULL;
uint16_t* cuda_q_groups = NULL;
uint32_t* cuda_gptq_qzeros = NULL;
half* cuda_gptq_scales = NULL;
half* temp_dq;
bool failed;
QMatrix
(
const int _device,
const int _height,
const int _width,
const int _groups,
uint32_t* _q_weight,
uint16_t* _q_perm,
uint16_t* _q_invperm,
uint32_t* _q_scale,
half* _q_scale_max,
uint16_t* _q_groups,
uint32_t* _gptq_qzeros,
half* _gptq_scales,
uint32_t* _gptq_g_idx,
half* _temp_dq
);
~QMatrix();
void reconstruct(half* out);
bool make_sequential(const uint32_t* cpu_g_idx);
private:
};
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/compat_gemm.cuh
|
#ifndef _compat_gemm_cuh
#define _compat_gemm_cuh
#if defined(USE_ROCM)
// For some reason this include is not present anywhere in exllama_v2 codebase, but it is required
// for symbols as hipblasHalf.
#include <hipblas/hipblas.h>
__host__ __forceinline__ hipblasStatus_t __compat_hipblasHgemm(hipblasHandle_t handle,
hipblasOperation_t transA,
hipblasOperation_t transB,
int m,
int n,
int k,
const half* alpha,
const half* AP,
int lda,
const half* BP,
int ldb,
const half* beta,
half* CP,
int ldc) {
return hipblasHgemm(handle, transA, transB, m, n, k,
reinterpret_cast<const hipblasHalf *>(alpha),
reinterpret_cast<const hipblasHalf *>(AP), lda,
reinterpret_cast<const hipblasHalf *>(BP), ldb,
reinterpret_cast<const hipblasHalf *>(beta),
reinterpret_cast<hipblasHalf *>(CP), ldc);
}
#define hipblasHgemm __compat_hipblasHgemm
// Previous version of PyTorch were converting to rocBLAS instead of hipBLAS.
#define rocblas_operation_none HIPBLAS_OP_N
#define rocblas_hgemm __compat_hipblasHgemm
#endif
#endif
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh
|
#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, __y) printf("%s, %s: %i, %i\n", #__x, #__y, __x, __y)
#define DBGI3(__x, __y, __z) printf("%s, %s, %s: %i, %i, %i\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGX(__x) printf("%s: %x\n", #__x, __x)
#define DBGX2(__x, __y) printf("%s, %s: %x, %x\n", #__x, #__y, __x, __y)
#define DBGX3(__x, __y, __z) printf("%s, %s, %s: %x, %x, %x\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGF(__x) printf("%s: %f\n", #__x, __x)
#define DBGF2(__x, __y) printf("%s, %s: %f, %f\n", #__x, #__y, __x, __y)
#define DBGF3(__x, __y, __z) printf("%s, %s, %s: %f, %f, %f\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGH(__x) printf("%s: %f\n", #__x, __half2float(__x))
#define DBGH2(__x, __y) printf("%s, %s: %f, %f\n", #__x, #__y, __half2float(__x), __half2float(__y))
#define DBGH3(__x, __y, __z) printf("%s, %s, %s: %f, %f, %f\n", #__x, #__y, #__z, __half2float(__x), __half2float(__y), __half2float(__z))
#define DBGIH(__x, __y) printf("%s, %s: %i, %f\n", #__x, #__y, __x, __half2float(__y))
#define DBGIH2(__x, __y, __z) printf("%s, %s, %s: %i, %f, %f\n", #__x, #__y, #__z, __x, __half2float(__y), __half2float(__z))
__forceinline__ __device__ half dq_scale_(const int qs, const half max_scale)
{
half qs_h = __hmul(__int2half_rn(qs + 1), __float2half_rn(1.0f / 16.0f));
qs_h = __hmul(qs_h, qs_h);
qs_h = __hmul(qs_h, max_scale);
return qs_h;
}
__forceinline__ __device__ float clamp(float x, float a, float b)
{
return fmaxf(a, fminf(b, x));
}
#define cuda_check(ans) { gpu_assert((ans), __FILE__, __LINE__); }
inline void gpu_assert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"CUDA error: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cuh
|
#ifndef _q_gemm_cuh
#define _q_gemm_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q_matrix.cuh"
void gemm_half_q_half_cuda
(
cublasHandle_t cublas_handle,
const half* a,
QMatrix* b,
half* c,
int size_m,
int size_n,
int size_k,
bool clear = false,
half* reconstruct = NULL,
bool force_cuda = false
);
void clear_tensor_cuda
(
half* c,
int size_m,
int size_n
);
#endif
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/compat.cuh
|
#ifndef _compat_cuh
#define _compat_cuh
// atomicAdd for half types, to support CC < 7.x
__device__ __forceinline__ void atomicAdd_half(half* address, half val)
{
unsigned int * address_as_ui = (unsigned int *) ((char *)address - ((size_t)address & 2));
unsigned int old = *address_as_ui;
unsigned int assumed;
do
{
assumed = old;
__half_raw hsum;
hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
half tmpres = __hadd(hsum, val);
hsum = __half_raw(tmpres);
old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x;
old = atomicCAS(address_as_ui, assumed, old);
}
while (assumed != old);
}
// atomicAdd for half2 types
__device__ __forceinline__ void atomicAdd_half2(half2* address, half2 val)
{
unsigned int* address_as_ui = (unsigned int*)address;
unsigned int old = *address_as_ui;
unsigned int assumed;
do
{
assumed = old;
half2 old_val = *((half2*)&old);
half2 new_val = __hadd2(old_val, val);
old = atomicCAS(address_as_ui, assumed, *((unsigned int*)&new_val));
}
while (assumed != old);
}
//
#if defined(__CUDA_ARCH__) || defined(USE_ROCM)
#if __CUDA_ARCH__ < 700 || defined(USE_ROCM)
__device__ __forceinline__ void atomicAdd(half* address, half val) { atomicAdd_half(address, val); }
#if __CUDA_ARCH__ < 600 || defined(USE_ROCM)
__device__ __forceinline__ void atomicAdd(half2* address, half2 val) { atomicAdd_half2(address, val); }
#endif
#endif
#endif
#endif
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh
|
#include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hadd2(result, g_result);
}
__forceinline__ __device__ float dot22_8_f(half2(&dq)[4], const half* a_ptr)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __half2float(__low2half(result)) + __half2float(__high2half(result));
}
typedef void (*fp_gemm_half_q_half_gptq_kernel)
(
const half*,
const uint32_t*,
const uint32_t*,
const half*,
half*,
const int,
const int,
const int,
const int,
const int,
const uint16_t*,
const int,
const bool
);
template <bool first_block, int m_count>
__global__ void gemm_half_q_half_gptq_kernel
(
const half* __restrict__ a,
const uint32_t* __restrict__ b_q_weight,
const uint32_t* __restrict__ b_gptq_qzeros,
const half* __restrict__ b_gptq_scales,
half* __restrict__ c,
const int size_m,
const int size_n,
const int size_k,
const int groups,
const int groupsize,
const uint16_t* __restrict__ b_q_perm,
const int rows_4,
const bool clear
)
{
MatrixView_half a_(a, size_m, size_k);
MatrixView_half_rw c_(c, size_m, size_n);
MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
int t = threadIdx.x;
// Block
int offset_n = blockIdx.x * BLOCK_KN_SIZE * 4;
int offset_m = blockIdx.y * m_count;
int offset_k = blockIdx.z * BLOCK_KN_SIZE;
int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
int end_m = min(offset_m + m_count, size_m);
int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
int n = offset_n + t * 4;
// Preload block_a
__shared__ half block_a[m_count][BLOCK_KN_SIZE];
if (offset_k + t < end_k)
{
for (int m = 0; m < m_count; ++m)
{
const half* a_ptr = a_.item_ptr(offset_m + m, 0);
half* block_a_ptr = block_a[m];
half a0;
if (b_q_perm) a0 = a_ptr[b_q_perm[offset_k + t]];
else a0 = a_ptr[offset_k + t];
block_a_ptr[t] = a0;
}
}
// Zero output
if (n >= size_n) return;
if (clear && blockIdx.z == 0) // && (threadIdx.x & 1) == 0)
{
for (int m = 0; m < m_count; m++)
*((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
}
__syncthreads();
// Find initial group
int group = offset_k / groupsize;
int nextgroup = offset_k + groupsize;
// a, b offset
int qk = offset_k / (32 / 4);
const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
const half* a_ptr = &block_a[0][0];
int a_stride = BLOCK_KN_SIZE;
// Initial group
int zeros[4];
float scales[4];
half2 z1z16[4][2];
half2 y1y16[4][2];
b_gptq_qzeros_.item4(zeros, group, n);
b_gptq_scales_.item4_f(scales, group, n);
dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
// __syncthreads();
// Column result
float block_c[m_count][4] = {};
// Dequantize and multiply
int k = offset_k;
while (k < end_k)
{
if (k == nextgroup)
{
group++;
nextgroup += groupsize;
b_gptq_qzeros_.item4(zeros, group, n);
b_gptq_scales_.item4_f(scales, group, n);
dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
}
#pragma unroll
for (int j = 0; j < 4; j++)
{
const int4* b_ptr4 = (int4*) b_ptr;
int4 load_int4 = *b_ptr4;
half2 dq[4][4];
dequant_4bit_8_gptq(load_int4.x, dq[0], z1z16[0], y1y16[0], size_n, false);
dequant_4bit_8_gptq(load_int4.y, dq[1], z1z16[1], y1y16[1], size_n, false);
dequant_4bit_8_gptq(load_int4.z, dq[2], z1z16[2], y1y16[2], size_n, false);
dequant_4bit_8_gptq(load_int4.w, dq[3], z1z16[3], y1y16[3], size_n, false);
#pragma unroll
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = fma(dot22_8_f(dq[0], a_ptr + m * a_stride), scales[0], block_c[m][0]);
block_c[m][1] = fma(dot22_8_f(dq[1], a_ptr + m * a_stride), scales[1], block_c[m][1]);
block_c[m][2] = fma(dot22_8_f(dq[2], a_ptr + m * a_stride), scales[2], block_c[m][2]);
block_c[m][3] = fma(dot22_8_f(dq[3], a_ptr + m * a_stride), scales[3], block_c[m][3]);
}
b_ptr += size_n;
a_ptr += 8;
}
k += 32;
}
for (int m = 0; m < m_count; m++)
{
half2 *out = (half2*) c_.item_ptr(offset_m + m, n);
half2 result01 = __halves2half2(__float2half_rn(block_c[m][0]), __float2half_rn(block_c[m][1]));
half2 result23 = __halves2half2(__float2half_rn(block_c[m][2]), __float2half_rn(block_c[m][3]));
atomicAdd(out , result01);
atomicAdd(out + 1, result23);
}
}
fp_gemm_half_q_half_gptq_kernel pick_gemm_half_q_half_gptq_kernel(bool first_block, const int m_count)
{
#if BLOCK_M_SIZE_MAX >= 1
if (m_count == 1) return gemm_half_q_half_gptq_kernel<true, 1>;
#endif
#if BLOCK_M_SIZE_MAX >= 2
if (m_count == 2) return gemm_half_q_half_gptq_kernel<true, 2>;
#endif
#if BLOCK_M_SIZE_MAX >= 3
if (m_count == 3) return gemm_half_q_half_gptq_kernel<true, 3>;
#endif
#if BLOCK_M_SIZE_MAX >= 4
if (m_count == 4) return gemm_half_q_half_gptq_kernel<true, 4>;
#endif
#if BLOCK_M_SIZE_MAX >= 5
if (m_count == 5) return gemm_half_q_half_gptq_kernel<true, 5>;
#endif
#if BLOCK_M_SIZE_MAX >= 6
if (m_count == 6) return gemm_half_q_half_gptq_kernel<true, 6>;
#endif
#if BLOCK_M_SIZE_MAX >= 7
if (m_count == 7) return gemm_half_q_half_gptq_kernel<true, 7>;
#endif
#if BLOCK_M_SIZE_MAX >= 8
if (m_count == 8) return gemm_half_q_half_gptq_kernel<true, 8>;
#endif
return NULL;
}
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu
|
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define BLOCK_KN_SIZE 128
#define BLOCK_M_SIZE_MAX 8
#define MAX_GROUPS_IN_BLOCK (BLOCK_KN_SIZE / 32)
#define CLEAR_N_SIZE 256
#include "q_gemm_kernel.cuh"
#include "q_gemm_kernel_gptq.cuh"
#include "compat_gemm.cuh"
void gemm_half_q_half_cuda_part
(
const half* a,
QMatrix* b,
half* c,
int size_m,
int size_n,
int size_k,
int m_count,
bool clear
)
{
if (!b->is_gptq)
{
dim3 blockDim, gridDim;
blockDim.x = BLOCK_KN_SIZE;
blockDim.y = 1;
blockDim.z = 1;
gridDim.x = DIVIDE(size_n, BLOCK_KN_SIZE * 4);
gridDim.y = DIVIDE(size_m, m_count);
gridDim.z = DIVIDE(size_k, BLOCK_KN_SIZE);
fp_gemm_half_q_half_kernel kernel = pick_gemm_half_q_half_kernel(true, m_count);
kernel<<<gridDim, blockDim>>>
(
a,
b->cuda_q_weight,
b->cuda_q_scale,
b->cuda_q_scale_max,
c,
size_m,
size_n,
size_k,
b->groups,
b->groupsize,
b->cuda_q_perm,
b->rows_8,
b->rows_6,
b->rows_5,
b->rows_4,
b->rows_3,
b->rows_2,
clear
);
}
else
{
dim3 blockDim, gridDim;
blockDim.x = BLOCK_KN_SIZE;
blockDim.y = 1;
blockDim.z = 1;
gridDim.x = DIVIDE(size_n, BLOCK_KN_SIZE * 4);
gridDim.y = DIVIDE(size_m, m_count);
gridDim.z = DIVIDE(size_k, BLOCK_KN_SIZE);
fp_gemm_half_q_half_gptq_kernel kernel = pick_gemm_half_q_half_gptq_kernel(true, m_count);
// DBGX((uint64_t) b->cuda_q_perm);
// DBGI(b->rows_4);
// DBGI(b->height);
kernel<<<gridDim, blockDim>>>
(
a,
b->cuda_q_weight,
b->cuda_gptq_qzeros,
b->cuda_gptq_scales,
c,
size_m,
size_n,
size_k,
b->groups,
b->groupsize,
b->cuda_q_perm,
b->rows_4,
clear
);
}
}
void gemm_half_q_half_cuda
(
cublasHandle_t cublas_handle,
const half* a,
QMatrix* b,
half* c,
int size_m,
int size_n,
int size_k,
bool clear,
half* temp_dq,
bool force_cuda
)
{
if (size_m > MAX_Q_GEMM_ROWS && !force_cuda)
{
//printf("cublas\n");
// Reconstruct FP16 matrix, then cuBLAS
if (!temp_dq) temp_dq = b->temp_dq;
b->reconstruct(temp_dq);
//cublasSetMathMode(cublas_handle, CUBLAS_TENSOR_OP_MATH);
const half alpha = __float2half(1.0f);
const half beta = clear ? __float2half(0.0f) : __float2half(1.0f);
cublasHgemm(cublas_handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
size_n, size_m, size_k,
&alpha, temp_dq, size_n,
a, size_k,
&beta, c, size_n);
//const float alpha = 1.0f;
//const float beta = clear ? 0.0f : 1.0f;
//cublasSgemmEx(cublas_handle,
// CUBLAS_OP_N,
// CUBLAS_OP_N,
// size_n, size_m, size_k,
// &alpha, temp_dq, CUDA_R_16F, size_n,
// a, CUDA_R_16F, size_k,
// &beta, c, CUDA_R_16F, size_n);
//const float alpha = 1.0f;
//const float beta = clear ? 0.0f : 1.0f;
//cublasGemmEx(cublas_handle,
// CUBLAS_OP_N, CUBLAS_OP_N,
// size_n, size_m, size_k,
// &alpha, temp_dq, CUDA_R_16F, size_n,
// a, CUDA_R_16F, size_k,
// &beta, c, CUDA_R_16F, size_n,
// CUDA_R_16F, CUBLAS_GEMM_DFALT_TENSOR_OP);
}
else
{
//printf("cuda\n");
// Quantized matmul
//if (clear) clear_tensor_cuda(c, size_m, size_n);
int max_chunks = size_m / BLOCK_M_SIZE_MAX;
int last_chunk = max_chunks * BLOCK_M_SIZE_MAX;
int last_chunk_size = size_m - last_chunk;
if (max_chunks)
{
gemm_half_q_half_cuda_part(a, b, c, last_chunk, size_n, size_k, BLOCK_M_SIZE_MAX, clear);
}
if (last_chunk_size)
{
gemm_half_q_half_cuda_part(a + last_chunk * size_k, b, c + last_chunk * size_n, last_chunk_size, size_n, size_k, last_chunk_size, clear);
}
}
}
__global__ void clear_kernel
(
half* __restrict__ c,
const int size_m,
const int size_n
)
{
int m = blockIdx.y;
int n = (blockIdx.x * CLEAR_N_SIZE + threadIdx.x) * 8;
if (n >= size_n) return;
int4* c_ptr = (int4*)(c + m * size_n + n);
*c_ptr = {};
}
void clear_tensor_cuda
(
half* c,
int size_m,
int size_n
)
{
return;
dim3 blockDim, gridDim;
blockDim.x = CLEAR_N_SIZE;
blockDim.y = 1;
gridDim.x = DIVIDE(size_n / 8, CLEAR_N_SIZE);
gridDim.y = size_m;
clear_kernel<<<gridDim, blockDim>>>(c, size_m, size_n);
}
| 0
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hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/matrix_view.cuh
|
#ifndef _matrix_view_cuh
#define _matrix_view_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include "quant/qdq_util.cuh"
class MatrixView_half
{
public:
const half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half(const half* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
__device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
__device__ __forceinline__ half2 item_half2half2(int row, int column) const { return __half2half2(data[row * width + column]); }
__device__ __forceinline__ const half* item_ptr(int row, int column) const { return &data[row * width + column]; }
__device__ __forceinline__ void item4(half (&items)[4], int row, int column) const
{
half2* ptr = (half2*) item_ptr(row, column);
half2 i01 = ptr[0];
half2 i23 = ptr[1];
items[0] = __low2half(i01);
items[1] = __high2half(i01);
items[2] = __low2half(i23);
items[3] = __high2half(i23);
}
__device__ __forceinline__ void item4_f(float (&items)[4], int row, int column) const
{
half2* ptr = (half2*)item_ptr(row, column);
half2 i01 = ptr[0];
half2 i23 = ptr[1];
items[0] = __half2float(__low2half(i01));
items[1] = __half2float(__high2half(i01));
items[2] = __half2float(__low2half(i23));
items[3] = __half2float(__high2half(i23));
}
__device__ __forceinline__ void item4_h2(half2 (&items)[4], int row, int column) const
{
half2* ptr = (half2*)item_ptr(row, column);
half2 i01 = ptr[0];
half2 i23 = ptr[1];
items[0] = __half2half2(__low2half(i01));
items[1] = __half2half2(__high2half(i01));
items[2] = __half2half2(__low2half(i23));
items[3] = __half2half2(__high2half(i23));
}
};
class MatrixView_half_rw
{
public:
half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half_rw(half* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
__device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
__device__ __forceinline__ half* item_ptr(int row, int column) { return &data[row * width + column]; }
__device__ __forceinline__ void set(int row, int column, half value) { data[row * width + column] = value; }
__device__ __forceinline__ void set_half2(int row, int column, half2 value) { ((half2*)data)[(row * width + column) / 2] = value; }
__device__ __forceinline__ void set4(int row, int column, half v0, half v1, half v2, half v3)
{
half2 v01 = __halves2half2(v0, v1);
half2 v23 = __halves2half2(v2, v3);
half2* ptr = (half2*) item_ptr(row, column);
ptr[0] = v01;
ptr[1] = v23;
}
};
class MatrixView_q4_row
{
public:
const uint32_t* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_q4_row(const uint32_t* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ int item(int row, int column) const
{
int shift = (column & 0x07) * 4;
return (data[row * width / 8 + column / 8] >> shift) & 0x0f;
}
__device__ __forceinline__ void item2(int (&items)[2], int row, int column) const
{
int shift = (column & 0x07) * 4;
uint32_t d = data[row * width / 8 + column / 8] >> shift;
items[0] = d & 0x0f;
items[1] = (d >> 4) & 0x0f;
}
__device__ __forceinline__ void item4(int (&items)[4], int row, int column) const
{
int shift = (column & 0x07) * 4;
uint32_t d = data[row * width / 8 + column / 8] >> shift;
items[0] = d & 0x0f;
items[1] = (d >> 4) & 0x0f;
items[2] = (d >> 8) & 0x0f;
items[3] = (d >> 12) & 0x0f;
}
};
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu
|
#include "q_matrix.cuh"
#include "matrix_view.cuh"
#include "util.cuh"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define BLOCK_KN_SIZE 128
#define THREADS_X 32
#define THREADS_Y 32
// Shuffle quantized data on load
__global__ void shuffle_kernel
(
uint32_t* __restrict__ b_q_weight,
const int size_k,
const int size_n,
const int rows_8,
const int rows_6,
const int rows_5,
const int rows_4,
const int rows_3,
const int rows_2
)
{
int n = blockIdx.x * THREADS_X + threadIdx.x;
if (n >= size_n) return;
int k = 0;
uint32_t* b_ptr = b_q_weight + n;
while (k < rows_8) { shuffle_8bit_4 (b_ptr, size_n); b_ptr += 1 * size_n; k += 4; }
while (k < rows_6) { shuffle_6bit_16(b_ptr, size_n); b_ptr += 3 * size_n; k += 16; }
while (k < rows_5) { shuffle_5bit_32(b_ptr, size_n); b_ptr += 5 * size_n; k += 32; }
while (k < rows_4) { shuffle_4bit_8 (b_ptr, size_n); b_ptr += 1 * size_n; k += 8; }
while (k < rows_3) { shuffle_3bit_32(b_ptr, size_n); b_ptr += 3 * size_n; k += 32; }
while (k < rows_2) { shuffle_2bit_16(b_ptr, size_n); b_ptr += 1 * size_n; k += 16; }
}
// QMatrix constructor
QMatrix::QMatrix
(
const int _device,
const int _height,
const int _width,
const int _groups,
uint32_t* _q_weight,
uint16_t* _q_perm,
uint16_t* _q_invperm,
uint32_t* _q_scale,
half* _q_scale_max,
uint16_t* _q_groups,
uint32_t* _gptq_qzeros,
half* _gptq_scales,
uint32_t* _gptq_g_idx,
half* _temp_dq
) :
device(_device),
height(_height),
width(_width),
groups(_groups),
temp_dq(_temp_dq)
{
cudaSetDevice(device);
failed = false;
cuda_q_weight = _q_weight;
cuda_q_perm = _q_perm;
cuda_q_invperm = _q_invperm;
cuda_q_scale = _q_scale;
cuda_q_scale_max = _q_scale_max;
cuda_q_groups = _q_groups;
cuda_gptq_qzeros = _gptq_qzeros;
cuda_gptq_scales = _gptq_scales;
is_gptq = (_gptq_qzeros != NULL);
groupsize = 1;
while (groupsize * groups < height) groupsize *= 2;
// Create group map
rows_8 = 0;
rows_6 = 0;
rows_5 = 0;
rows_4 = 0;
rows_3 = 0;
rows_2 = 0;
if (!is_gptq)
{
uint16_t* cpu_q_groups = (uint16_t*)calloc(groups * 2, sizeof(uint16_t));
cudaMemcpy(cpu_q_groups, cuda_q_groups, groups * 2 * sizeof(uint16_t), cudaMemcpyDeviceToHost);
for (int i = 0; i < groups; i++)
{
int bits = cpu_q_groups[i * 2];
if (bits == 8) rows_8 += groupsize;
if (bits == 6) rows_6 += groupsize;
if (bits == 5) rows_5 += groupsize;
if (bits == 4) rows_4 += groupsize;
if (bits == 3) rows_3 += groupsize;
if (bits == 2) rows_2 += groupsize;
}
free(cpu_q_groups);
rows_6 += rows_8;
rows_5 += rows_6;
rows_4 += rows_5;
rows_3 += rows_4;
rows_2 += rows_3;
}
else
{
rows_4 = height;
rows_3 = height;
rows_2 = height;
if (_gptq_g_idx)
{
if (!make_sequential(_gptq_g_idx))
{
failed = true;
//printf("FAIL\n");
return;
}
}
}
// Shuffle quantized data
dim3 blockDim, gridDim;
blockDim.x = THREADS_X;
blockDim.y = 1;
gridDim.x = DIVIDE(width, THREADS_X);
gridDim.y = 1;
shuffle_kernel<<<gridDim, blockDim>>>(cuda_q_weight, height, width, rows_8, rows_6, rows_5, rows_4, rows_3, rows_2);
}
QMatrix::~QMatrix()
{
}
// Reconstruct b[k,n] (GPTQ)
__global__ void reconstruct_gptq_kernel
(
const uint32_t* __restrict__ b_q_weight,
const uint16_t* __restrict__ b_q_perm,
const uint32_t* __restrict__ b_gptq_qzeros,
const half* __restrict__ b_gptq_scales,
//const uint16_t* __restrict__ b_q_groups,
const int size_k,
const int size_n,
const int groupsize,
const int groups,
half* __restrict__ b,
const int rows_4
)
{
MatrixView_half_rw b_(b, size_k, size_n);
MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
int offset_k = BLOCK_KN_SIZE * blockIdx.y;
int offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
// Preload remapping table
__shared__ uint16_t perm[BLOCK_KN_SIZE];
int t = threadIdx.x;
if (b_q_perm)
{
if (offset_k + t < size_k)
perm[t] = b_q_perm[offset_k + t];
}
// Column
int n = offset_n + t * 4;
if (n >= size_n) return;
// Find initial group
int group = offset_k / groupsize;
int nextgroup = offset_k + groupsize;
// b offset
int qk = offset_k / (32 / 4);
const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
// Initial zeros/scale
int zeros[4];
half2 scales[4];
half2 z1z16[4][2];
half2 y1y16[4][2];
b_gptq_qzeros_.item4(zeros, group, n);
b_gptq_scales_.item4_h2(scales, group, n);
dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
__syncthreads();
int k = offset_k;
int lk = 0;
while (k < end_k)
{
if (k == nextgroup)
{
group++;
nextgroup += groupsize;
b_gptq_qzeros_.item4(zeros, group, n);
b_gptq_scales_.item4_h2(scales, group, n);
dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
}
for (int p = 0; p < 4; p++)
{
half2 dq[4][4];
const int4* b_ptr4 = (int4*) b_ptr;
int4 load_int4 = *b_ptr4;
dequant_4bit_8_gptq(load_int4.x, dq[0], z1z16[0], y1y16[0], size_n, false);
dequant_4bit_8_gptq(load_int4.y, dq[1], z1z16[1], y1y16[1], size_n, false);
dequant_4bit_8_gptq(load_int4.z, dq[2], z1z16[2], y1y16[2], size_n, false);
dequant_4bit_8_gptq(load_int4.w, dq[3], z1z16[3], y1y16[3], size_n, false);
b_ptr += size_n;
//half* dqh = (half*)dq;
if (b_q_perm)
{
for (int j = 0; j < 4; j++)
{
for (int v = 0; v < 4; v++) dq[v][j] = __hmul2(scales[v], dq[v][j]);
b_.set4(perm[lk++], n, __low2half(dq[0][j]), __low2half(dq[1][j]), __low2half(dq[2][j]), __low2half(dq[3][j]));
b_.set4(perm[lk++], n, __high2half(dq[0][j]), __high2half(dq[1][j]), __high2half(dq[2][j]), __high2half(dq[3][j]));
}
}
else
{
for (int j = 0; j < 4; j++)
{
for (int v = 0; v < 4; v++) dq[v][j] = __hmul2(scales[v], dq[v][j]);
b_.set4(offset_k + lk++, n, __low2half(dq[0][j]), __low2half(dq[1][j]), __low2half(dq[2][j]), __low2half(dq[3][j]));
b_.set4(offset_k + lk++, n, __high2half(dq[0][j]), __high2half(dq[1][j]), __high2half(dq[2][j]), __high2half(dq[3][j]));
}
}
}
k += 32;
}
}
// Reconstruct b[k,n]
__global__ void reconstruct_kernel
(
const uint32_t* __restrict__ b_q_weight,
const uint16_t* __restrict__ b_q_perm,
const uint32_t* __restrict__ b_q_scale,
const half* __restrict__ b_q_scale_max,
//const uint16_t* __restrict__ b_q_groups,
const int size_k,
const int size_n,
const int groupsize,
const int groups,
half* __restrict__ b,
const int rows_8,
const int rows_6,
const int rows_5,
const int rows_4,
const int rows_3,
const int rows_2
)
{
MatrixView_half_rw b_(b, size_k, size_n);
MatrixView_q4_row b_q_scale_(b_q_scale, groups, size_n);
int offset_k = BLOCK_KN_SIZE * blockIdx.y;
int offset_n = BLOCK_KN_SIZE * blockIdx.x;
// Preload remapping table
int t = threadIdx.x;
__shared__ uint16_t perm[BLOCK_KN_SIZE];
if (offset_k + t < size_k)
perm[t] = b_q_perm[offset_k + t];
// Column
int n = offset_n + t;
if (n >= size_n) return;
// Find initial group
int group = offset_k / groupsize;
int pre_rows_8 = min(rows_8, offset_k);
int pre_rows_6 = offset_k > rows_8 ? min(rows_6, offset_k) - rows_8 : 0;
int pre_rows_5 = offset_k > rows_6 ? min(rows_5, offset_k) - rows_6 : 0;
int pre_rows_4 = offset_k > rows_5 ? min(rows_4, offset_k) - rows_5 : 0;
int pre_rows_3 = offset_k > rows_4 ? min(rows_3, offset_k) - rows_4 : 0;
int pre_rows_2 = offset_k > rows_3 ? min(rows_2, offset_k) - rows_3 : 0;
int qk = 0;
qk += pre_rows_8 / 32 * 8;
qk += pre_rows_6 / 32 * 6;
qk += pre_rows_5 / 32 * 5;
qk += pre_rows_4 / 32 * 4;
qk += pre_rows_3 / 32 * 3;
qk += pre_rows_2 / 32 * 2;
const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
half qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]);
half2 qs_h2 = __halves2half2(qs_h, qs_h);
int nextgroup = offset_k + groupsize;
int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
int k = offset_k;
int lk = 0;
__syncthreads();
while (k < rows_8 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 4; p++)
{
half2 dq[4];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
uint32_t q_1 = *b_ptr; b_ptr += size_n;
dequant_8bit_8(q_0, q_1, dq, size_n);
for (int j = 0; j < 4; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 8; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
while (k < rows_6 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 2; p++)
{
half2 dq[8];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
uint32_t q_1 = *b_ptr; b_ptr += size_n;
uint32_t q_2 = *b_ptr; b_ptr += size_n;
dequant_6bit_16(q_0, q_1, q_2, dq, size_n);
for (int j = 0; j < 8; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 16; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
while (k < rows_5 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 1; p++)
{
half2 dq[16];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
uint32_t q_1 = *b_ptr; b_ptr += size_n;
uint32_t q_2 = *b_ptr; b_ptr += size_n;
uint32_t q_3 = *b_ptr; b_ptr += size_n;
uint32_t q_4 = *b_ptr; b_ptr += size_n;
dequant_5bit_32(q_0, q_1, q_2, q_3, q_4, dq, size_n);
for (int j = 0; j < 16; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 32; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
while (k < rows_4 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 4; p++)
{
half2 dq[4];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
dequant_4bit_8(q_0, dq, size_n);
for (int j = 0; j < 4; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 8; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
while (k < rows_3 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 1; p++)
{
half2 dq[16];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
uint32_t q_1 = *b_ptr; b_ptr += size_n;
uint32_t q_2 = *b_ptr; b_ptr += size_n;
dequant_3bit_32(q_0, q_1, q_2, dq, size_n);
for (int j = 0; j < 16; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 32; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
while (k < rows_2 && k < end_k)
{
if (k == nextgroup) { group++; qs_h = dq_scale(b_q_scale_.item(group, n), b_q_scale_max[group]); nextgroup += groupsize; qs_h2 = __halves2half2(qs_h, qs_h); }
for (int p = 0; p < 2; p++)
{
half2 dq[8];
uint32_t q_0 = *b_ptr; b_ptr += size_n;
dequant_2bit_16(q_0, dq, size_n);
for (int j = 0; j < 8; j++) dq[j] = __hmul2(dq[j], qs_h2);
half* dqh = (half*) dq;
for (int j = 0; j < 16; j++) b_.set(perm[lk++], n, dqh[j]);
}
k += 32;
}
}
void QMatrix::reconstruct(half* out)
{
dim3 blockDim, gridDim;
blockDim.x = BLOCK_KN_SIZE;
blockDim.y = 1;
gridDim.y = DIVIDE(height, BLOCK_KN_SIZE);
if (!is_gptq)
{
gridDim.x = DIVIDE(width, BLOCK_KN_SIZE);
reconstruct_kernel<<<gridDim, blockDim>>>
(
cuda_q_weight,
cuda_q_perm,
cuda_q_scale,
cuda_q_scale_max,
//cuda_q_groups,
height,
width,
groupsize,
groups,
out,
rows_8,
rows_6,
rows_5,
rows_4,
rows_3,
rows_2
);
}
else
{
gridDim.x = DIVIDE(width, BLOCK_KN_SIZE * 4);
reconstruct_gptq_kernel<<<gridDim, blockDim>>>
(
cuda_q_weight,
cuda_q_perm,
cuda_gptq_qzeros,
cuda_gptq_scales,
//const uint16_t* __restrict__ b_q_groups,
height,
width,
groupsize,
groups,
out,
rows_4
);
}
}
__global__ void make_sequential_kernel
(
const uint32_t* __restrict__ w,
uint32_t* __restrict__ w_new,
const uint16_t* __restrict__ q_perm,
const int w_height,
const int w_width
)
{
const uint64_t* w2 = (uint64_t*) w;
uint64_t* w_new2 = (uint64_t*) w_new;
int w2_stride = w_width >> 1;
int w2_column = THREADS_X * blockIdx.x + threadIdx.x;
if (w2_column >= w2_stride) return;
int w_new2_row = blockIdx.y;
int q_perm_idx = w_new2_row << 3;
uint64_t dst = 0;
#pragma unroll
for (int i = 0; i < 8; i++)
{
int source_row = q_perm[q_perm_idx++];
int w2_row = source_row >> 3;
int w2_subrow = source_row & 0x07;
int w2_row_shift = w2_subrow << 2;
int wnew2_row_shift = i << 2;
uint64_t src = w2[w2_row * w2_stride + w2_column];
src >>= w2_row_shift;
src &= 0x0000000f0000000f;
src <<= wnew2_row_shift;
dst |= src;
}
w_new2[w_new2_row * w2_stride + w2_column] = dst;
}
bool QMatrix::make_sequential(const uint32_t* cpu_g_idx)
{
uint32_t* cuda_new_qweight = NULL;
cudaError_t err = cudaMalloc(&cuda_new_qweight, height / 8 * width * sizeof(uint32_t));
if (err != cudaSuccess) {
cudaError_t cuda_status = cudaGetLastError(); // Clear error
return false;
}
uint32_t* cpu_g_idx_map = (uint32_t*) calloc(groups, sizeof(uint32_t));
uint32_t* cpu_x_map = (uint32_t*) malloc(height * sizeof(uint32_t));
uint32_t* cpu_x_map_inv = (uint32_t*) malloc(height * sizeof(uint32_t));
// Group histogram
for (int i = 0; i < height; i++) cpu_g_idx_map[cpu_g_idx[i]]++;
// Group map
for (int i = 0, acc = 0; i < groups; i++)
{
short tmp = cpu_g_idx_map[i];
cpu_g_idx_map[i] = acc;
acc += tmp;
}
// X map (inverse)
for (int row = 0; row < height; row++)
{
uint32_t target_group = cpu_g_idx[row];
uint32_t target_row = cpu_g_idx_map[target_group];
cpu_g_idx_map[target_group]++;
cpu_x_map_inv[row] = target_row;
}
// X map
for (int row = 0; row < height; row++) cpu_x_map[cpu_x_map_inv[row]] = row;
// Reduce to uint16_t
uint16_t* cpu_x_map16 = (uint16_t*)cpu_x_map;
uint16_t* cpu_x_map_inv16 = (uint16_t*)cpu_x_map_inv;
for (int row = 0; row < height; row++) cpu_x_map16[row] = (uint16_t) cpu_x_map[row];
for (int row = 0; row < height; row++) cpu_x_map_inv16[row] = (uint16_t) cpu_x_map_inv[row];
// Move to CUDA
cudaMemcpyAsync(cuda_q_perm, cpu_x_map16, height * sizeof(uint16_t), cudaMemcpyHostToDevice);
cudaMemcpyAsync(cuda_q_invperm, cpu_x_map_inv16, height * sizeof(uint16_t), cudaMemcpyHostToDevice);
// Rearrange rows in w
dim3 blockDim, gridDim;
blockDim.x = THREADS_X;
blockDim.y = 1;
gridDim.x = DIVIDE(width, THREADS_X);
gridDim.y = height / 8;
make_sequential_kernel<<<gridDim, blockDim>>>
(
cuda_q_weight,
cuda_new_qweight,
cuda_q_perm,
height / 8,
width
);
// Replace qweights
cudaMemcpyAsync(cuda_q_weight, cuda_new_qweight, height / 8 * width * sizeof(uint32_t), cudaMemcpyDeviceToDevice);
// Cleanup
cudaDeviceSynchronize();
cudaFree(cuda_new_qweight);
free(cpu_g_idx_map);
free(cpu_x_map);
free(cpu_x_map_inv);
return true;
}
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh
|
#include "compat.cuh"
#include <cuda_runtime.h>
#include <cuda_fp16.h>
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfma2(result, __halves2half2(qs_h, qs_h), g_result);
}
__forceinline__ __device__ half2 dot22_16(half2(&dq)[8], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 8; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfma2(result, __halves2half2(qs_h, qs_h), g_result);
}
__forceinline__ __device__ half2 dot22_32(half2(&dq)[16], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 16; i += 1) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfma2(result, __halves2half2(qs_h, qs_h), g_result);
}
__forceinline__ __device__ float dot22_8_f(half2(&dq)[4], const half* a_ptr, const float g_result, const float qs_f)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
float result_f = __half2float(__low2half(result)) + __half2float(__high2half(result));
return fma(result_f, qs_f, g_result);
}
__forceinline__ __device__ float dot22_16_f(half2(&dq)[8], const half* a_ptr, const float g_result, const float qs_f)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 8; i++) result = __hfma2(dq[i], *a2_ptr++, result);
float result_f = __half2float(__low2half(result)) + __half2float(__high2half(result));
return fma(result_f, qs_f, g_result);
}
__forceinline__ __device__ float dot22_32_f(half2(&dq)[16], const half* a_ptr, const float g_result, const float qs_f)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 16; i += 1) result = __hfma2(dq[i], *a2_ptr++, result);
float result_f = __half2float(__low2half(result)) + __half2float(__high2half(result));
return fma(result_f, qs_f, g_result);
}
typedef void (*fp_gemm_half_q_half_kernel)
(
const half*,
const uint32_t*,
const uint32_t*,
const half*,
half*,
const int,
const int,
const int,
const int,
const int,
const uint16_t*,
const int,
const int,
const int,
const int,
const int,
const int,
const bool
);
template <bool first_block, int m_count>
__global__ void gemm_half_q_half_kernel
(
const half* __restrict__ a,
const uint32_t* __restrict__ b_q_weight,
const uint32_t* __restrict__ b_q_scale,
const half* __restrict__ b_q_scale_max,
half* __restrict__ c,
const int size_m,
const int size_n,
const int size_k,
const int groups,
const int groupsize,
const uint16_t* __restrict__ b_q_perm,
const int rows_8,
const int rows_6,
const int rows_5,
const int rows_4,
const int rows_3,
const int rows_2,
const bool clear
)
{
MatrixView_half a_(a, size_m, size_k);
MatrixView_half_rw c_(c, size_m, size_n);
MatrixView_q4_row b_q_scale_(b_q_scale, groups, size_n);
int t = threadIdx.x;
// Block
int offset_n = blockIdx.x * BLOCK_KN_SIZE * 4;
int offset_m = blockIdx.y * m_count;
int offset_k = blockIdx.z * BLOCK_KN_SIZE;
int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
int end_m = min(offset_m + m_count, size_m);
int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
int n = offset_n + t * 4;
// Preload block_a
__shared__ half block_a[m_count][BLOCK_KN_SIZE];
if (offset_k + t < end_k)
{
for (int m = 0; m < m_count; ++m)
{
const half* a_ptr = a_.item_ptr(offset_m + m, 0);
half* block_a_ptr = block_a[m];
half a0 = a_ptr[b_q_perm[offset_k + t]];
block_a_ptr[t] = a0;
}
}
// Clear
if (n >= size_n) return;
if (clear && blockIdx.z == 0) // && (threadIdx.x & 1) == 0)
{
for (int m = 0; m < m_count; m++)
*((uint64_t*) c_.item_ptr(offset_m + m, n)) = 0;
}
__syncthreads();
// Find initial group
int group = offset_k / groupsize;
// Preload scales
float scales[MAX_GROUPS_IN_BLOCK][4];
int groups_in_block = DIVIDE((end_k - offset_k), groupsize);
for (int g = 0; g < groups_in_block; g++)
{
int qscales[4];
b_q_scale_.item4(qscales, group + g, n);
qscales[0]++;
qscales[1]++;
qscales[2]++;
qscales[3]++;
float maxscale = __half2float(b_q_scale_max[group + g]);
scales[g][0] = __int2float_rn(qscales[0] * qscales[0]) * maxscale;
scales[g][1] = __int2float_rn(qscales[1] * qscales[1]) * maxscale;
scales[g][2] = __int2float_rn(qscales[2] * qscales[2]) * maxscale;
scales[g][3] = __int2float_rn(qscales[3] * qscales[3]) * maxscale;
}
// a, b offset
int pre_rows_8 = min(rows_8, offset_k);
int pre_rows_6 = offset_k > rows_8 ? min(rows_6, offset_k) - rows_8 : 0;
int pre_rows_5 = offset_k > rows_6 ? min(rows_5, offset_k) - rows_6 : 0;
int pre_rows_4 = offset_k > rows_5 ? min(rows_4, offset_k) - rows_5 : 0;
int pre_rows_3 = offset_k > rows_4 ? min(rows_3, offset_k) - rows_4 : 0;
int pre_rows_2 = offset_k > rows_3 ? min(rows_2, offset_k) - rows_3 : 0;
int qk = 0;
qk += pre_rows_8 / 32 * 8;
qk += pre_rows_6 / 32 * 6;
qk += pre_rows_5 / 32 * 5;
qk += pre_rows_4 / 32 * 4;
qk += pre_rows_3 / 32 * 3;
qk += pre_rows_2 / 32 * 2;
const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
const half* a_ptr = &block_a[0][0];
int a_stride = BLOCK_KN_SIZE;
// Initial group
int scales_idx = 0;
float qs_f0 = scales[scales_idx][0];
float qs_f1 = scales[scales_idx][1];
float qs_f2 = scales[scales_idx][2];
float qs_f3 = scales[scales_idx][3];
int nextgroup = offset_k + groupsize;
// Column result
float block_c[m_count][4] = {};
// Dequantize groups
int k = offset_k;
while (k < rows_8 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 4; j++)
{
int4 load_int4[2];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[1] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][4];
dequant_8bit_8(load_int4[0].x, load_int4[1].x, dq[0], size_n);
dequant_8bit_8(load_int4[0].y, load_int4[1].y, dq[1], size_n);
dequant_8bit_8(load_int4[0].z, load_int4[1].z, dq[2], size_n);
dequant_8bit_8(load_int4[0].w, load_int4[1].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_8_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_8_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_8_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_8_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 8;
}
k += 32;
}
while (k < rows_6 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 2; j++)
{
int4 load_int4[3];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[1] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[2] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][8];
dequant_6bit_16(load_int4[0].x, load_int4[1].x, load_int4[2].x, dq[0], size_n);
dequant_6bit_16(load_int4[0].y, load_int4[1].y, load_int4[2].y, dq[1], size_n);
dequant_6bit_16(load_int4[0].z, load_int4[1].z, load_int4[2].z, dq[2], size_n);
dequant_6bit_16(load_int4[0].w, load_int4[1].w, load_int4[2].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_16_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_16_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_16_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_16_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 16;
}
k += 32;
}
while (k < rows_5 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 1; j++)
{
int4 load_int4[5];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[1] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[2] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[3] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[4] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][16];
dequant_5bit_32(load_int4[0].x, load_int4[1].x, load_int4[2].x, load_int4[3].x, load_int4[4].x, dq[0], size_n);
dequant_5bit_32(load_int4[0].y, load_int4[1].y, load_int4[2].y, load_int4[3].y, load_int4[4].y, dq[1], size_n);
dequant_5bit_32(load_int4[0].z, load_int4[1].z, load_int4[2].z, load_int4[3].z, load_int4[4].z, dq[2], size_n);
dequant_5bit_32(load_int4[0].w, load_int4[1].w, load_int4[2].w, load_int4[3].w, load_int4[4].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_32_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_32_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_32_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_32_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 32;
}
k += 32;
}
while (k < rows_4 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 4; j++)
{
int4 load_int4[1];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][4];
dequant_4bit_8(load_int4[0].x, dq[0], size_n);
dequant_4bit_8(load_int4[0].y, dq[1], size_n);
dequant_4bit_8(load_int4[0].z, dq[2], size_n);
dequant_4bit_8(load_int4[0].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_8_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_8_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_8_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_8_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 8;
}
k += 32;
}
while (k < rows_3 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 1; j++)
{
int4 load_int4[3];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[1] = *((int4*) b_ptr); b_ptr += size_n;
load_int4[2] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][16];
dequant_3bit_32(load_int4[0].x, load_int4[1].x, load_int4[2].x, dq[0], size_n);
dequant_3bit_32(load_int4[0].y, load_int4[1].y, load_int4[2].y, dq[1], size_n);
dequant_3bit_32(load_int4[0].z, load_int4[1].z, load_int4[2].z, dq[2], size_n);
dequant_3bit_32(load_int4[0].w, load_int4[1].w, load_int4[2].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_32_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_32_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_32_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_32_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 32;
}
k += 32;
}
while (k < rows_2 && k < end_k)
{
if (k == nextgroup)
{
group++;
scales_idx++;
qs_f0 = scales[scales_idx][0];
qs_f1 = scales[scales_idx][1];
qs_f2 = scales[scales_idx][2];
qs_f3 = scales[scales_idx][3];
nextgroup += groupsize;
}
#pragma unroll
for (int j = 0; j < 2; j++)
{
int4 load_int4[1];
load_int4[0] = *((int4*) b_ptr); b_ptr += size_n;
half2 dq[4][8];
dequant_2bit_16(load_int4[0].x, dq[0], size_n);
dequant_2bit_16(load_int4[0].y, dq[1], size_n);
dequant_2bit_16(load_int4[0].z, dq[2], size_n);
dequant_2bit_16(load_int4[0].w, dq[3], size_n);
for (int m = 0; m < m_count; m++)
{
block_c[m][0] = dot22_16_f(dq[0], a_ptr + m * a_stride, block_c[m][0], qs_f0);
block_c[m][1] = dot22_16_f(dq[1], a_ptr + m * a_stride, block_c[m][1], qs_f1);
block_c[m][2] = dot22_16_f(dq[2], a_ptr + m * a_stride, block_c[m][2], qs_f2);
block_c[m][3] = dot22_16_f(dq[3], a_ptr + m * a_stride, block_c[m][3], qs_f3);
}
a_ptr += 16;
}
k += 32;
}
// Accumulate column sums in c
for (int m = 0; m < m_count; m++)
{
half2* out = (half2*)c_.item_ptr(offset_m + m, n);
half2 result01 = __halves2half2(__float2half_rn(block_c[m][0]), __float2half_rn(block_c[m][1]));
half2 result23 = __halves2half2(__float2half_rn(block_c[m][2]), __float2half_rn(block_c[m][3]));
atomicAdd(out , result01);
atomicAdd(out + 1, result23);
}
}
fp_gemm_half_q_half_kernel pick_gemm_half_q_half_kernel(bool first_block, const int m_count)
{
#if BLOCK_M_SIZE_MAX >= 1
if (m_count == 1) return gemm_half_q_half_kernel<true, 1>;
#endif
#if BLOCK_M_SIZE_MAX >= 2
if (m_count == 2) return gemm_half_q_half_kernel<true, 2>;
#endif
#if BLOCK_M_SIZE_MAX >= 3
if (m_count == 3) return gemm_half_q_half_kernel<true, 3>;
#endif
#if BLOCK_M_SIZE_MAX >= 4
if (m_count == 4) return gemm_half_q_half_kernel<true, 4>;
#endif
#if BLOCK_M_SIZE_MAX >= 5
if (m_count == 5) return gemm_half_q_half_kernel<true, 5>;
#endif
#if BLOCK_M_SIZE_MAX >= 6
if (m_count == 6) return gemm_half_q_half_kernel<true, 6>;
#endif
#if BLOCK_M_SIZE_MAX >= 7
if (m_count == 7) return gemm_half_q_half_kernel<true, 7>;
#endif
#if BLOCK_M_SIZE_MAX >= 8
if (m_count == 8) return gemm_half_q_half_kernel<true, 8>;
#endif
return NULL;
}
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_5.cuh
|
#ifndef _qdq_5_cuh
#define _qdq_5_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_5BIT == 1
// Permutation:
//
// v5555533 33311111 u4444422 22200000 (u, v lsb)
// vbbbbb99 99977777 uaaaaa88 88866666
// vhhhhhff fffddddd ugggggee eeeccccc
// vnnnnnll llljjjjj ummmmmkk kkkiiiii
// vtttttrr rrrppppp usssssqq qqqooooo
__forceinline__ __device__ void shuffle_5bit_32
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0 * stride];
uint32_t qb = q[1 * stride];
uint32_t qc = q[2 * stride];
uint32_t qd = q[3 * stride];
uint32_t qe = q[4 * stride];
// qa: 66555554 44443333 32222211 11100000
// qb: ccccbbbb baaaaa99 99988888 77777666
// qc: jiiiiihh hhhggggg fffffeee eedddddc
// qd: pppooooo nnnnnmmm mmlllllk kkkkjjjj
// qe: vvvvvuuu uuttttts ssssrrrr rqqqqqpp
uint32_t qf = qe >> 22;
qe <<= 8;
qe |= qd >> 24;
qd <<= 6;
qd |= qc >> 26;
qc <<= 4;
qc |= qb >> 28;
qb <<= 2;
qb |= qa >> 30;
// qa: 555554 44443333 32222211 11100000
// qb: bbbbba aaaa9999 98888877 77766666
// qc: hhhhhg ggggffff feeeeedd dddccccc
// qd: nnnnnm mmmmllll lkkkkkjj jjjiiiii
// qe: ttttts ssssrrrr rqqqqqpp pppooooo
// qf: vv vvvuuuuu
uint32_t za = 0;
uint32_t zb = 0;
uint32_t zc = 0;
uint32_t zd = 0;
uint32_t ze = 0;
for (int i = 0; i < 3; i++) { uint32_t t0 = qa & 0x1f; uint32_t t1 = (qa & 0x3e0) >> 5; qa >>= 10; za |= (t0 << (i * 5)); za |= (t1 << (i * 5 + 16)); }
for (int i = 0; i < 3; i++) { uint32_t t0 = qb & 0x1f; uint32_t t1 = (qb & 0x3e0) >> 5; qb >>= 10; zb |= (t0 << (i * 5)); zb |= (t1 << (i * 5 + 16)); }
for (int i = 0; i < 3; i++) { uint32_t t0 = qc & 0x1f; uint32_t t1 = (qc & 0x3e0) >> 5; qc >>= 10; zc |= (t0 << (i * 5)); zc |= (t1 << (i * 5 + 16)); }
for (int i = 0; i < 3; i++) { uint32_t t0 = qd & 0x1f; uint32_t t1 = (qd & 0x3e0) >> 5; qd >>= 10; zd |= (t0 << (i * 5)); zd |= (t1 << (i * 5 + 16)); }
for (int i = 0; i < 3; i++) { uint32_t t0 = qe & 0x1f; uint32_t t1 = (qe & 0x3e0) >> 5; qe >>= 10; ze |= (t0 << (i * 5)); ze |= (t1 << (i * 5 + 16)); }
// za: 5555533 33311111 4444422 22200000
// zb: bbbbb99 99977777 aaaaa88 88866666
// zc: hhhhhff fffddddd gggggee eeeccccc
// zd: nnnnnll llljjjjj mmmmmkk kkkiiiii
// ze: tttttrr rrrppppp sssssqq qqqooooo
// qf: vv vvvuuuuu
za |= ((qf & 0x001) >> 0) << 15;
zb |= ((qf & 0x002) >> 1) << 15;
zc |= ((qf & 0x004) >> 2) << 15;
zd |= ((qf & 0x008) >> 3) << 15;
ze |= ((qf & 0x010) >> 4) << 15;
za |= ((qf & 0x020) >> 5) << 31;
zb |= ((qf & 0x040) >> 6) << 31;
zc |= ((qf & 0x080) >> 7) << 31;
zd |= ((qf & 0x100) >> 8) << 31;
ze |= ((qf & 0x200) >> 9) << 31;
// za: v5555533 33311111 u4444422 22200000 (u, v lsb)
// zb: vbbbbb99 99977777 uaaaaa88 88866666
// zc: vhhhhhff fffddddd ugggggee eeeccccc
// zd: vnnnnnll llljjjjj ummmmmkk kkkiiiii
// ze: vtttttrr rrrppppp usssssqq qqqooooo
q[0 * stride] = za;
q[1 * stride] = zb;
q[2 * stride] = zc;
q[3 * stride] = zd;
q[4 * stride] = ze;
}
__forceinline__ __device__ void dequant_5bit_32
(
const uint32_t q_0,
const uint32_t q_1,
const uint32_t q_2,
const uint32_t q_3,
const uint32_t q_4,
half2 (&dq)[16],
int stride
)
{
const uint32_t c0 = 0x64006400;
const half y32_ = __float2half_rn(1.0f / 32.0f);
const half2 y32 = __halves2half2(y32_, y32_);
const half z1_ = __float2half_rn(-1024.0f - 16.0f);
const half z32_ = __float2half_rn(-1024.0f / 32.0f - 16.0f);
const half2 z1 = __halves2half2(z1_, z1_);
const half2 z32 = __halves2half2(z32_, z32_);
uint32_t qa = q_0;
uint32_t qb = q_1;
uint32_t qc = q_2;
uint32_t qd = q_3;
uint32_t qe = q_4;
half2_uint32 q0 ((qa & 0x001f001f) | c0); // half2(q[ 0], q[ 1]) + 1024
half2_uint32 q1 ((qa & 0x03e003e0) | c0); // half2(q[ 2], q[ 3]) * 32 + 1024
qa >>= 10;
half2_uint32 q2 ((qa & 0x001f001f) | c0); // half2(q[ 4], q[ 5]) + 1024
qa >>= 5;
qa &= 0x00010001;
half2_uint32 q3 ((qb & 0x001f001f) | c0); // half2(q[ 6], q[ 7]) + 1024
half2_uint32 q4 ((qb & 0x03e003e0) | c0); // half2(q[ 8], q[ 9]) * 32 + 1024
qb >>= 10;
half2_uint32 q5 ((qb & 0x001f001f) | c0); // half2(q[10], q[11]) + 1024
qb >>= 4;
qb &= 0x00020002;
half2_uint32 q6 ((qc & 0x001f001f) | c0); // half2(q[12], q[13]) + 1024
half2_uint32 q7 ((qc & 0x03e003e0) | c0); // half2(q[14], q[15]) * 32 + 1024
qc >>= 10;
half2_uint32 q8 ((qc & 0x001f001f) | c0); // half2(q[16], q[17]) + 1024
qc >>= 3;
qc &= 0x00040004;
half2_uint32 q9 ((qd & 0x001f001f) | c0); // half2(q[18], q[19]) + 1024
half2_uint32 q10((qd & 0x03e003e0) | c0); // half2(q[20], q[21]) * 32 + 1024
qd >>= 10;
half2_uint32 q11((qd & 0x001f001f) | c0); // half2(q[22], q[23]) + 1024
qd >>= 2;
qd &= 0x00080008;
half2_uint32 q12((qe & 0x001f001f) | c0); // half2(q[24], q[25]) + 1024
half2_uint32 q13((qe & 0x03e003e0) | c0); // half2(q[26], q[27]) * 32 + 1024
qe >>= 10;
half2_uint32 q14((qe & 0x001f001f) | c0); // half2(q[28], q[29]) + 1024
qe >>= 1;
qe &= 0x00100010;
half2_uint32 q15((qa | qb | qc | qd | qe) | c0);
dq[ 0] = __hadd2( q0.as_half2, z1);
dq[ 1] = __hfma2( q1.as_half2, y32, z32);
dq[ 2] = __hadd2( q2.as_half2, z1);
dq[ 3] = __hadd2( q3.as_half2, z1);
dq[ 4] = __hfma2( q4.as_half2, y32, z32);
dq[ 5] = __hadd2( q5.as_half2, z1);
dq[ 6] = __hadd2( q6.as_half2, z1);
dq[ 7] = __hfma2( q7.as_half2, y32, z32);
dq[ 8] = __hadd2( q8.as_half2, z1);
dq[ 9] = __hadd2( q9.as_half2, z1);
dq[10] = __hfma2(q10.as_half2, y32, z32);
dq[11] = __hadd2(q11.as_half2, z1);
dq[12] = __hadd2(q12.as_half2, z1);
dq[13] = __hfma2(q13.as_half2, y32, z32);
dq[14] = __hadd2(q14.as_half2, z1);
dq[15] = __hadd2(q15.as_half2, z1);
}
#else
__forceinline__ __device__ void shuffle_5bit_32
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_5bit_32
(
const uint32_t q_0,
const uint32_t q_1,
const uint32_t q_2,
const uint32_t q_3,
const uint32_t q_4,
half2 (&dq)[16],
int stride
)
{
half dqh[32];
for (int i = 0; i < 6; i++) dqh[ i] = dq_ns(exb( q_0, i * 5 , 0x1f), 16);
dqh[ 6 ] = dq_ns(exb(q_1, q_0, 30, 0x1f), 16);
for (int i = 0; i < 5; i++) dqh[ 7 + i] = dq_ns(exb( q_1, i * 5 + 3, 0x1f), 16);
dqh[12 ] = dq_ns(exb(q_2, q_1, 28, 0x1f), 16);
for (int i = 0; i < 6; i++) dqh[13 + i] = dq_ns(exb( q_2, i * 5 + 1, 0x1f), 16);
dqh[19 ] = dq_ns(exb(q_3, q_2, 31, 0x1f), 16);
for (int i = 0; i < 5; i++) dqh[20 + i] = dq_ns(exb( q_3, i * 5 + 4, 0x1f), 16);
dqh[25 ] = dq_ns(exb(q_4, q_3, 29, 0x1f), 16);
for (int i = 0; i < 6; i++) dqh[26 + i] = dq_ns(exb( q_4, i * 5 + 2, 0x1f), 16);
for (int i = 0; i < 16; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh
|
#ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0 * stride];
uint32_t qb = q[1 * stride];
uint32_t qc = q[2 * stride];
// qa: aa999888 77766655 54443332 22111000
// qb: lkkkjjji iihhhggg fffeeedd dcccbbba
// qc: vvvuuutt tsssrrrq qqpppooo nnnmmmll
uint32_t qd = qc >> 26;
qc <<= 4;
qc |= qb >> 28;
qb <<= 2;
qb |= qa >> 30;
// qa: ..999888 77766655 54443332 22111000
// qb: ..jjjiii hhhgggff feeedddc ccbbbaaa
// qc: ..tttsss rrrqqqpp pooonnnm mmlllkkk
// qd: vvvuuu
uint32_t za = 0;
uint32_t zb = 0;
uint32_t zc = 0;
for (int i = 0; i < 5; i++) { uint32_t t0 = qa & 0x07; uint32_t t1 = (qa & 0x38) >> 3; qa >>= 6; za |= (t0 << (i * 3)); za |= (t1 << (i * 3 + 16)); }
for (int i = 0; i < 5; i++) { uint32_t t0 = qb & 0x07; uint32_t t1 = (qb & 0x38) >> 3; qb >>= 6; zb |= (t0 << (i * 3)); zb |= (t1 << (i * 3 + 16)); }
for (int i = 0; i < 5; i++) { uint32_t t0 = qc & 0x07; uint32_t t1 = (qc & 0x38) >> 3; qc >>= 6; zc |= (t0 << (i * 3)); zc |= (t1 << (i * 3 + 16)); }
// za: 9997775 55333111 8886664 44222000
// zb: jjjhhhf ffdddbbb iiiggge eecccaaa
// zc: tttrrrp ppnnnlll sssqqqo oommmkkk
// qd: vvvuuu
za |= ((qd & 0x01) >> 0) << 15;
zb |= ((qd & 0x02) >> 1) << 15;
zc |= ((qd & 0x04) >> 2) << 15;
za |= ((qd & 0x08) >> 3) << 31;
zb |= ((qd & 0x10) >> 4) << 31;
zc |= ((qd & 0x20) >> 5) << 31;
// za: v9997775 55333111 u8886664 44222000 (u, v lsb)
// zb: vjjjhhhf ffdddbbb uiiiggge eecccaaa
// zc: vtttrrrp ppnnnlll usssqqqo oommmkkk
q[0 * stride] = za;
q[1 * stride] = zb;
q[2 * stride] = zc;
}
__forceinline__ __device__ void dequant_3bit_32
(
const uint32_t q_0,
const uint32_t q_1,
const uint32_t q_2,
half2 (&dq)[16],
int stride
)
{
const uint32_t c0 = 0x64006400;
const half y8_ = __float2half_rn(1.0f / 8.0f);
const half y64_ = __float2half_rn(1.0f / 64.0f);
const half2 y8 = __halves2half2(y8_, y8_);
const half2 y64 = __halves2half2(y64_, y64_);
const half z1_ = __float2half_rn(-1024.0f - 4.0f);
const half z8_ = __float2half_rn(-1024.0f / 8.0f - 4.0f);
const half z64_ = __float2half_rn(-1024.0f / 64.0f - 4.0f);
const half2 z1 = __halves2half2(z1_, z1_);
const half2 z8 = __halves2half2(z8_, z8_);
const half2 z64 = __halves2half2(z64_, z64_);
uint32_t qa = q_0;
uint32_t qb = q_1;
uint32_t qc = q_2;
half2_uint32 q0((qa & 0x00070007) | c0); // half2(q[ 0], q[ 1]) + 1024
half2_uint32 q1((qa & 0x00380038) | c0); // half2(q[ 2], q[ 3]) * 8 + 1024
qa >>= 6;
half2_uint32 q2((qa & 0x00070007) | c0); // half2(q[ 4], q[ 5]) + 1024
half2_uint32 q3((qa & 0x00380038) | c0); // half2(q[ 6], q[ 7]) * 8 + 1024
half2_uint32 q4((qa & 0x01c001c0) | c0); // half2(q[ 8], q[ 9]) * 64 + 1024
qa >>= 9;
qa &= 0x00010001;
half2_uint32 q5((qb & 0x00070007) | c0); // half2(q[10], q[11]) + 1024
half2_uint32 q6((qb & 0x00380038) | c0); // half2(q[12], q[13]) * 8 + 1024
qb >>= 6;
half2_uint32 q7((qb & 0x00070007) | c0); // half2(q[14], q[15]) + 1024
half2_uint32 q8((qb & 0x00380038) | c0); // half2(q[16], q[17]) * 8 + 1024
half2_uint32 q9((qb & 0x01c001c0) | c0); // half2(q[18], q[19]) * 64 + 1024
qb >>= 8;
qb &= 0x00020002;
half2_uint32 q10((qc & 0x00070007) | c0); // half2(q[20], q[21]) + 1024
half2_uint32 q11((qc & 0x00380038) | c0); // half2(q[22], q[23]) * 8 + 1024
qc >>= 6;
half2_uint32 q12((qc & 0x00070007) | c0); // half2(q[24], q[25]) + 1024
half2_uint32 q13((qc & 0x00380038) | c0); // half2(q[26], q[27]) * 8 + 1024
half2_uint32 q14((qc & 0x01c001c0) | c0); // half2(q[28], q[29]) * 64 + 1024
qc >>= 7;
qc &= 0x00040004;
half2_uint32 q15((qa | qb | qc) | c0);
dq[ 0] = __hadd2( q0.as_half2, z1);
dq[ 1] = __hfma2( q1.as_half2, y8, z8);
dq[ 2] = __hadd2( q2.as_half2, z1);
dq[ 3] = __hfma2( q3.as_half2, y8, z8);
dq[ 4] = __hfma2( q4.as_half2, y64, z64);
dq[ 5] = __hadd2( q5.as_half2, z1);
dq[ 6] = __hfma2( q6.as_half2, y8, z8);
dq[ 7] = __hadd2( q7.as_half2, z1);
dq[ 8] = __hfma2( q8.as_half2, y8, z8);
dq[ 9] = __hfma2( q9.as_half2, y64, z64);
dq[10] = __hadd2(q10.as_half2, z1);
dq[11] = __hfma2(q11.as_half2, y8, z8);
dq[12] = __hadd2(q12.as_half2, z1);
dq[13] = __hfma2(q13.as_half2, y8, z8);
dq[14] = __hfma2(q14.as_half2, y64, z64);
dq[15] = __hadd2(q15.as_half2, z1);
}
#else
__forceinline__ __device__ void shuffle_3bit_32
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_3bit_32
(
const uint32_t q_0,
const uint32_t q_1,
const uint32_t q_2,
half2 (&dq)[16],
int stride
)
{
half dqh[32];
for (int i = 0; i < 10; i++) dqh[ i] = dq_ns(exb( q_0, i * 3 , 0x07), 4);
dqh[10 ] = dq_ns(exb(q_1, q_0, 30, 0x07), 4);
for (int i = 0; i < 10; i++) dqh[11 + i] = dq_ns(exb( q_1, i * 3 + 1, 0x07), 4);
dqh[21 ] = dq_ns(exb(q_2, q_1, 31, 0x07), 4);
for (int i = 0; i < 10; i++) dqh[22 + i] = dq_ns(exb( q_2, i * 3 + 2, 0x07), 4);
for (int i = 0; i < 16; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh
|
#ifndef _qdq_util_cuh
#define _qdq_util_cuh
union half2_uint32
{
uint32_t as_uint32;
half2 as_half2;
__device__ half2_uint32(uint32_t val) : as_uint32(val) {}
__device__ half2_uint32(half2 val) : as_half2(val) {}
};
union half_uint16
{
uint16_t as_uint16;
half as_half;
__device__ half_uint16(uint16_t val) : as_uint16(val) {}
__device__ half_uint16(half val) : as_half(val) {}
};
// Max_scale premultiplied by 1/256
__forceinline__ __device__ half dq_scale(const int qs, const half max_scale)
{
int qs_i = qs + 1;
half qs_h = __int2half_rn(qs_i * qs_i);
qs_h = __hmul(qs_h, max_scale);
return qs_h;
}
__forceinline__ __device__ half dq(const int q, const int qzero, const half scale)
{
return __hmul(__int2half_rn(q - qzero), scale);
}
__forceinline__ __device__ half dq_ns(const int q, const int qzero)
{
//return __hsub(__int2half_rn(q), __int2half_rn(qzero));
return __int2half_rn(q - qzero);
}
__forceinline__ __device__ int exb(const uint32_t q, const int shift, const int mask)
{
return (int)((q >> shift) & mask);
}
__forceinline__ __device__ int exb(const uint32_t q1, const uint32_t q0, const int shift, const int mask)
{
return (int)(__funnelshift_rc(q0, q1, shift) & mask);
}
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_6.cuh
|
#ifndef _qdq_6_cuh
#define _qdq_6_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_6BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_6bit_16
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_6bit_16
(
const uint32_t q_0,
const uint32_t q_1,
const uint32_t q_2,
half2 (&dq)[8],
int stride
)
{
half dqh[16];
for (int i = 0; i < 5; i++) dqh[ i] = dq_ns(exb( q_0, i * 6 , 0x3f), 32);
dqh[ 5 ] = dq_ns(exb(q_1, q_0, 30, 0x3f), 32);
for (int i = 0; i < 4; i++) dqh[ 6 + i] = dq_ns(exb( q_1, i * 6 + 4, 0x3f), 32);
dqh[10 ] = dq_ns(exb(q_2, q_1, 28, 0x3f), 32);
for (int i = 0; i < 5; i++) dqh[11 + i] = dq_ns(exb( q_2, i * 6 + 2, 0x3f), 32);
for (int i = 0; i < 8; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh
|
#ifndef _qdq_4_cuh
#define _qdq_4_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_4BIT == 1
// Permutation:
//
// 77775555 33331111 66664444 22220000
__forceinline__ __device__ void shuffle_4bit_8
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unroll
for (int i = 0; i < 4; i++)
{
uint32_t qa0 = qa & 0x0f;
uint32_t qa1 = (qa & 0xf0) >> 4;
qa >>= 8;
qb |= (qa1 << (i * 4 + 16));
qb |= (qa0 << (i * 4));
}
q[0] = qb;
}
__forceinline__ __device__ void dequant_4bit_8
(
const uint32_t q_0,
half2 (&dq)[4],
int stride
)
{
const uint32_t c0 = 0x64006400;
const half y16_ = __float2half_rn(1.0f / 16.0f);
const half2 y16 = __halves2half2(y16_, y16_);
const half z1_ = __float2half_rn(-1024.0f - 8.0f);
const half z16_ = __float2half_rn(-1024.0f / 16.0f - 8.0f);
const half2 z1 = __halves2half2(z1_, z1_);
const half2 z16 = __halves2half2(z16_, z16_);
uint32_t qa = q_0;
half2_uint32 q0((qa & 0x000f000f) | c0); // half2(q[ 0], q[ 1]) + 1024
half2_uint32 q1((qa & 0x00f000f0) | c0); // half2(q[ 2], q[ 3]) * 16 + 1024
qa >>= 8;
half2_uint32 q2((qa & 0x000f000f) | c0); // half2(q[ 4], q[ 5]) + 1024
half2_uint32 q3((qa & 0x00f000f0) | c0); // half2(q[ 6], q[ 7]) * 16 + 1024
dq[0] = __hadd2(q0.as_half2, z1);
dq[1] = __hfma2(q1.as_half2, y16, z16);
dq[2] = __hadd2(q2.as_half2, z1);
dq[3] = __hfma2(q3.as_half2, y16, z16);
}
__forceinline__ __device__ void dequant_4bit_8_prep_zero_scale
(
const uint32_t zero,
const half scale,
half2 (&z1z16)[2],
half2 (&y1y16)[2]
)
{
half_uint16 z1(0xe400 | zero); // half(-1024.0f - zero);
half z16 = __hsub(__int2half_rn(-64), __int2half_rn(zero));
half2 scale2 = __half2half2(scale);
z1z16[0] = __hmul2(scale2, __half2half2(z1.as_half));
z1z16[1] = __hmul2(scale2, __half2half2(z16));
const half y1 = __float2half_rn(1.0f);
const half y16 = __float2half_rn(1.0f / 16.0f);
y1y16[0] = __hmul2(scale2, __half2half2(y1));
y1y16[1] = __hmul2(scale2, __half2half2(y16));
}
__forceinline__ __device__ void dequant_4bit_8_prep_zero
(
const uint32_t zero,
half2(&z1z16)[2],
half2(&y1y16)[2]
)
{
half_uint16 z1(0xe400 | zero); // half(-1024.0f - zero);
half z16 = __hsub(__int2half_rn(-64), __int2half_rn(zero));
z1z16[0] = __half2half2(z1.as_half);
z1z16[1] = __half2half2(z16);
const half y1 = __float2half_rn(1.0f);
const half y16 = __float2half_rn(1.0f / 16.0f);
y1y16[0] = __half2half2(y1);
y1y16[1] = __half2half2(y16);
}
__forceinline__ __device__ void dequant_4bit_8_gptq
(
const uint32_t q_0,
half2 (&dq)[4],
half2 (&z1z16)[2],
half2 (&y1y16)[2],
int stride,
bool scaled
)
{
const uint32_t c0 = 0x64006400;
uint32_t qa = q_0;
half2_uint32 q0((qa & 0x000f000f) | c0); // half2( q[0] + 1024, q[1] + 1024 )
half2_uint32 q1((qa & 0x00f000f0) | c0); // half2( q[2] * 16 + 1024, q[3] * 16 + 1024 )
qa >>= 8;
half2_uint32 q2((qa & 0x000f000f) | c0); // half2( q[4] + 1024, q[5] + 1024 )
half2_uint32 q3((qa & 0x00f000f0) | c0); // half2( q[6] * 16 + 1024, q[7] * 16 + 1024 )
if (scaled)
{
dq[0] = __hfma2(q0.as_half2, y1y16[0], z1z16[0]); // half2( q[0] * s - z * s, q[1] * s - z * s)
dq[1] = __hfma2(q1.as_half2, y1y16[1], z1z16[1]); // half2( q[2] * s - z * s, q[3] * s - z * s)
dq[2] = __hfma2(q2.as_half2, y1y16[0], z1z16[0]);
dq[3] = __hfma2(q3.as_half2, y1y16[1], z1z16[1]);
}
else
{
dq[0] = __hadd2(q0.as_half2, z1z16[0]); // half2( q[0] - z, q[1] - z )
dq[1] = __hfma2(q1.as_half2, y1y16[1], z1z16[1]); // half2( q[2] - z, q[3] - z )
dq[2] = __hadd2(q2.as_half2, z1z16[0]); // half2( q[4] - z, q[5] - z )
dq[3] = __hfma2(q3.as_half2, y1y16[1], z1z16[1]); // half2( q[6] - z, q[7] - z )
}
}
#else
__forceinline__ __device__ void shuffle_4bit_8
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_4bit_8
(
const uint32_t q_0,
half2 (&dq)[4],
int stride
)
{
half dqh[8];
for (int i = 0; i < 8; i++) dqh[i] = dq_ns(exb(q_0, i * 4, 0x0f), 8);
for (int i = 0; i < 4; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
__forceinline__ __device__ void dequant_4bit_8_prep_zero_scale
(
const uint32_t zero,
const half scale,
half2 (&z1)[2],
half2 (&y1)[2]
)
{
half z = __int2half_rn(-((int)zero));
z = __hmul(z, scale);
z1[0] = __half2half2(z);
y1[0] = __half2half2(scale);
}
__forceinline__ __device__ void dequant_4bit_8_prep_zero
(
const uint32_t zero,
half2(&z1)[2],
half2(&y1)[2]
)
{
half z = __int2half_rn(-((int)zero));
z1[0] = __half2half2(z);
}
__forceinline__ __device__ void dequant_4bit_8_gptq
(
const uint32_t q_0,
half2 (&dq)[4],
half2 (&z1)[2],
half2 (&y1)[2],
int stride,
bool scaled
)
{
half2 dqh2[8];
uint32_t qa = q_0;
for (int i = 0; i < 4; i++)
{
half d0 = __int2half_rn(qa & 0x0f); qa >>= 4;
half d1 = __int2half_rn(qa & 0x0f); qa >>= 4;
dqh2[i] = __halves2half2(d0, d1);
}
if (scaled)
{
dq[0] = __hfma2(dqh2[0], y1[0], z1[0]);
dq[1] = __hfma2(dqh2[1], y1[0], z1[0]);
dq[2] = __hfma2(dqh2[2], y1[0], z1[0]);
dq[3] = __hfma2(dqh2[3], y1[0], z1[0]);
}
else
{
dq[0] = __hadd2(dqh2[0], z1[0]);
dq[1] = __hadd2(dqh2[1], z1[0]);
dq[2] = __hadd2(dqh2[2], z1[0]);
dq[3] = __hadd2(dqh2[3], z1[0]);
}
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh
|
#ifndef _qdq_2_cuh
#define _qdq_2_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_2BIT == 1
// Permutation:
//
// ffddbb99 77553311 eeccaa88 66442200
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unroll
for (int i = 0; i < 8; i++)
{
uint32_t qa0 = qa & 0x03;
uint32_t qa1 = (qa & 0x0c) >> 2;
qa >>= 4;
qb |= (qa1 << (i * 2 + 16));
qb |= (qa0 << (i * 2));
}
q[0] = qb;
}
__forceinline__ __device__ void dequant_2bit_16
(
const uint32_t q_0,
half2 (&dq)[8],
int stride
)
{
const uint32_t c0 = 0x64006400;
const half y4_ = __float2half_rn(1.0f / 4.0f);
const half y16_ = __float2half_rn(1.0f / 16.0f);
const half y64_ = __float2half_rn(1.0f / 64.0f);
const half2 y4 = __halves2half2(y4_, y4_);
const half2 y16 = __halves2half2(y16_, y16_);
const half2 y64 = __halves2half2(y64_, y64_);
const half z1_ = __float2half_rn(-1024.0f - 2.0f);
const half z4_ = __float2half_rn(-1024.0f / 4.0f - 2.0f);
const half z16_ = __float2half_rn(-1024.0f / 16.0f - 2.0f);
const half z64_ = __float2half_rn(-1024.0f / 64.0f - 2.0f);
const half2 z1 = __halves2half2(z1_, z1_);
const half2 z4 = __halves2half2(z4_, z4_);
const half2 z16 = __halves2half2(z16_, z16_);
const half2 z64 = __halves2half2(z64_, z64_);
uint32_t qa = q_0;
half2_uint32 q0((qa & 0x00030003) | c0); // half2(q[ 0], q[ 1]) + 1024
half2_uint32 q1((qa & 0x000c000c) | c0); // half2(q[ 2], q[ 3]) * 4 + 1024
half2_uint32 q2((qa & 0x00300030) | c0); // half2(q[ 4], q[ 5]) * 16 + 1024
half2_uint32 q3((qa & 0x00c000c0) | c0); // half2(q[ 6], q[ 7]) * 64 + 1024
qa >>= 8;
half2_uint32 q4((qa & 0x00030003) | c0); // half2(q[ 8], q[ 8]) + 1024
half2_uint32 q5((qa & 0x000c000c) | c0); // half2(q[10], q[11]) * 4 + 1024
half2_uint32 q6((qa & 0x00300030) | c0); // half2(q[12], q[13]) * 16 + 1024
half2_uint32 q7((qa & 0x00c000c0) | c0); // half2(q[14], q[15]) * 64 + 1024
dq[0] = __hadd2(q0.as_half2, z1);
dq[1] = __hfma2(q1.as_half2, y4, z4);
dq[2] = __hfma2(q2.as_half2, y16, z16);
dq[3] = __hfma2(q3.as_half2, y64, z64);
dq[4] = __hadd2(q4.as_half2, z1);
dq[5] = __hfma2(q5.as_half2, y4, z4);
dq[6] = __hfma2(q6.as_half2, y16, z16);
dq[7] = __hfma2(q7.as_half2, y64, z64);
}
#else
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_2bit_16
(
const uint32_t q_0,
half2 (&dq)[8],
int stride
)
{
half dqh[16];
for (int i = 0; i < 16; i++) dqh[i] = dq_ns(exb(q_0, i * 2, 0x03), 2);
for (int i = 0; i < 8; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda
|
hf_public_repos/text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_8.cuh
|
#ifndef _qdq_8_cuh
#define _qdq_8_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_8BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_8bit_4
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_8bit_8
(
const uint32_t q_0,
const uint32_t q_1,
half2 (&dq)[4],
int stride
)
{
half dqh[8];
for (int i = 0; i < 4; i++) dqh[i ] = dq_ns(exb(q_0, i * 8, 0xff), 128);
for (int i = 0; i < 4; i++) dqh[i + 4] = dq_ns(exb(q_1, i * 8, 0xff), 128);
for (int i = 0; i < 4; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
}
#endif
#endif
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/launcher/build.rs
|
use std::error::Error;
use vergen::EmitBuilder;
fn main() -> Result<(), Box<dyn Error>> {
// Emit cargo and rustc compile time values
EmitBuilder::builder().all_cargo().all_rustc().emit()?;
// Try to get the git sha from the local git repository
if EmitBuilder::builder()
.fail_on_error()
.git_sha(false)
.emit()
.is_err()
{
// Unable to get the git sha
if let Ok(sha) = std::env::var("GIT_SHA") {
// Set it from an env var
println!("cargo:rustc-env=VERGEN_GIT_SHA={sha}");
}
}
// Set docker label if present
if let Ok(label) = std::env::var("DOCKER_LABEL") {
// Set it from an env var
println!("cargo:rustc-env=DOCKER_LABEL={label}");
}
Ok(())
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/launcher/Cargo.toml
|
[package]
name = "text-generation-launcher"
description = "Text Generation Launcher"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
clap = { version = "4.4.5", features = ["derive", "env"] }
ctrlc = { version = "3.4.1", features = ["termination"] }
nix = "0.27.1"
serde = { version = "1.0.188", features = ["derive"] }
serde_json = "1.0.107"
tracing = "0.1.37"
tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
[dev-dependencies]
float_eq = "1.0.1"
reqwest = { version = "0.11.20", features = ["blocking", "json"] }
[build-dependencies]
vergen = { version = "8.2.5", features = ["build", "cargo", "git", "gitcl", "rustc", "si"] }
| 0
|
hf_public_repos/text-generation-inference/launcher
|
hf_public_repos/text-generation-inference/launcher/src/env_runtime.rs
|
use std::fmt;
use std::process::Command;
pub(crate) struct Env {
cargo_target: &'static str,
cargo_version: &'static str,
git_sha: &'static str,
docker_label: &'static str,
nvidia_env: String,
}
impl Env {
pub fn new() -> Self {
let nvidia_env = nvidia_smi();
Self {
nvidia_env: nvidia_env.unwrap_or("N/A".to_string()),
cargo_target: env!("VERGEN_CARGO_TARGET_TRIPLE"),
cargo_version: env!("VERGEN_RUSTC_SEMVER"),
git_sha: option_env!("VERGEN_GIT_SHA").unwrap_or("N/A"),
docker_label: option_env!("DOCKER_LABEL").unwrap_or("N/A"),
}
}
}
impl fmt::Display for Env {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
writeln!(f, "Runtime environment:")?;
writeln!(f, "Target: {}", self.cargo_target)?;
writeln!(f, "Cargo version: {}", self.cargo_version)?;
writeln!(f, "Commit sha: {}", self.git_sha)?;
writeln!(f, "Docker label: {}", self.docker_label)?;
write!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
Ok(())
}
}
fn nvidia_smi() -> Option<String> {
let output = Command::new("nvidia-smi").output().ok()?;
let nvidia_smi = String::from_utf8(output.stdout).ok()?;
let output = nvidia_smi.replace('\n', "\n ");
Some(output.trim().to_string())
}
| 0
|
hf_public_repos/text-generation-inference/launcher
|
hf_public_repos/text-generation-inference/launcher/src/main.rs
|
use clap::{Parser, ValueEnum};
use nix::sys::signal::{self, Signal};
use nix::unistd::Pid;
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
use std::io::{BufRead, BufReader, Lines, Read};
use std::os::unix::process::{CommandExt, ExitStatusExt};
use std::path::Path;
use std::process::{Child, Command, ExitStatus, Stdio};
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::mpsc::TryRecvError;
use std::sync::{mpsc, Arc};
use std::thread;
use std::thread::sleep;
use std::time::{Duration, Instant};
use std::{fs, io};
use tracing_subscriber::EnvFilter;
mod env_runtime;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Quantization {
/// 4 bit quantization. Requires a specific GTPQ quantized model:
/// https://hf.co/models?search=awq.
/// Should replace GPTQ models whereever possible because of the better latency
Awq,
/// 8 bit quantization, doesn't require specific model.
/// Should be a drop-in replacement to bitsandbytes with much better performance.
/// Kernels are from https://github.com/NetEase-FuXi/EETQ.git
Eetq,
/// 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq.
/// text-generation-inference will use exllama (faster) kernels whereever possible, and use
/// triton kernel (wider support) when it's not.
/// AWQ has faster kernels.
Gptq,
/// Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half,
/// but it is known that the model will be much slower to run than the native f16.
#[deprecated(
since = "1.1.0",
note = "Use `eetq` instead, which provides better latencies overall and is drop-in in most cases"
)]
Bitsandbytes,
/// Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x,
/// but it is known that the model will be much slower to run than the native f16.
BitsandbytesNF4,
/// Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better
/// perplexity performance for you model
BitsandbytesFP4,
}
impl std::fmt::Display for Quantization {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
// To keep in track with `server`.
match self {
Quantization::Bitsandbytes => {
write!(f, "bitsandbytes")
}
Quantization::BitsandbytesNF4 => {
write!(f, "bitsandbytes-nf4")
}
Quantization::BitsandbytesFP4 => {
write!(f, "bitsandbytes-fp4")
}
Quantization::Gptq => {
write!(f, "gptq")
}
Quantization::Awq => {
write!(f, "awq")
}
Quantization::Eetq => {
write!(f, "eetq")
}
}
}
}
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Dtype {
Float16,
#[clap(name = "bfloat16")]
BFloat16,
}
impl std::fmt::Display for Dtype {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
// To keep in track with `server`.
match self {
Dtype::Float16 => {
write!(f, "float16")
}
Dtype::BFloat16 => {
write!(f, "bfloat16")
}
}
}
}
#[derive(Clone, Copy, Debug, ValueEnum)]
enum RopeScaling {
Linear,
Dynamic,
}
impl std::fmt::Display for RopeScaling {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
// To keep in track with `server`.
match self {
RopeScaling::Linear => {
write!(f, "linear")
}
RopeScaling::Dynamic => {
write!(f, "dynamic")
}
}
}
}
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
/// The name of the model to load.
/// Can be a MODEL_ID as listed on <https://hf.co/models> like
/// `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`.
/// Or it can be a local directory containing the necessary files
/// as saved by `save_pretrained(...)` methods of transformers
#[clap(default_value = "bigscience/bloom-560m", long, env)]
model_id: String,
/// The actual revision of the model if you're referring to a model
/// on the hub. You can use a specific commit id or a branch like `refs/pr/2`.
#[clap(long, env)]
revision: Option<String>,
/// The number of tokenizer workers used for payload validation and truncation inside the
/// router.
#[clap(default_value = "2", long, env)]
validation_workers: usize,
/// Whether to shard the model across multiple GPUs
/// By default text-generation-inference will use all available GPUs to run
/// the model. Setting it to `false` deactivates `num_shard`.
#[clap(long, env)]
sharded: Option<bool>,
/// The number of shards to use if you don't want to use all GPUs on a given machine.
/// You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2`
/// and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to
/// launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance.
#[clap(long, env)]
num_shard: Option<usize>,
/// Whether you want the model to be quantized.
#[clap(long, env, value_enum)]
quantize: Option<Quantization>,
/// The dtype to be forced upon the model. This option cannot be used with `--quantize`.
#[clap(long, env, value_enum)]
dtype: Option<Dtype>,
/// Whether you want to execute hub modelling code. Explicitly passing a `revision` is
/// encouraged when loading a model with custom code to ensure no malicious code has been
/// contributed in a newer revision.
#[clap(long, env, value_enum)]
trust_remote_code: bool,
/// The maximum amount of concurrent requests for this particular deployment.
/// Having a low limit will refuse clients requests instead of having them
/// wait for too long and is usually good to handle backpressure correctly.
#[clap(default_value = "128", long, env)]
max_concurrent_requests: usize,
/// This is the maximum allowed value for clients to set `best_of`.
/// Best of makes `n` generations at the same time, and return the best
/// in terms of overall log probability over the entire generated sequence
#[clap(default_value = "2", long, env)]
max_best_of: usize,
/// This is the maximum allowed value for clients to set `stop_sequences`.
/// Stop sequences are used to allow the model to stop on more than just
/// the EOS token, and enable more complex "prompting" where users can preprompt
/// the model in a specific way and define their "own" stop token aligned with
/// their prompt.
#[clap(default_value = "4", long, env)]
max_stop_sequences: usize,
/// This is the maximum allowed value for clients to set `top_n_tokens`.
/// `top_n_tokens is used to return information about the the `n` most likely
/// tokens at each generation step, instead of just the sampled token. This
/// information can be used for downstream tasks like for classification or
/// ranking.
#[clap(default_value = "5", long, env)]
max_top_n_tokens: u32,
/// This is the maximum allowed input length (expressed in number of tokens)
/// for users. The larger this value, the longer prompt users can send which
/// can impact the overall memory required to handle the load.
/// Please note that some models have a finite range of sequence they can handle.
#[clap(default_value = "1024", long, env)]
max_input_length: usize,
/// This is the most important value to set as it defines the "memory budget"
/// of running clients requests.
/// Clients will send input sequences and ask to generate `max_new_tokens`
/// on top. with a value of `1512` users can send either a prompt of
/// `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for
/// `1511` max_new_tokens.
/// The larger this value, the larger amount each request will be in your RAM
/// and the less effective batching can be.
#[clap(default_value = "2048", long, env)]
max_total_tokens: usize,
/// This represents the ratio of waiting queries vs running queries where
/// you want to start considering pausing the running queries to include the waiting
/// ones into the same batch.
/// `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's
/// only 10 queries left in the current batch we check if we can fit those 12
/// waiting queries into the batching strategy, and if yes, then batching happens
/// delaying the 10 running queries by a `prefill` run.
///
/// This setting is only applied if there is room in the batch
/// as defined by `max_batch_total_tokens`.
#[clap(default_value = "1.2", long, env)]
waiting_served_ratio: f32,
/// Limits the number of tokens for the prefill operation.
/// Since this operation take the most memory and is compute bound, it is interesting
/// to limit the number of requests that can be sent.
#[clap(default_value = "4096", long, env)]
max_batch_prefill_tokens: u32,
/// **IMPORTANT** This is one critical control to allow maximum usage
/// of the available hardware.
///
/// This represents the total amount of potential tokens within a batch.
/// When using padding (not recommended) this would be equivalent of
/// `batch_size` * `max_total_tokens`.
///
/// However in the non-padded (flash attention) version this can be much finer.
///
/// For `max_batch_total_tokens=1000`, you could fit `10` queries of `total_tokens=100`
/// or a single query of `1000` tokens.
///
/// Overall this number should be the largest possible amount that fits the
/// remaining memory (after the model is loaded). Since the actual memory overhead
/// depends on other parameters like if you're using quantization, flash attention
/// or the model implementation, text-generation-inference cannot infer this number
/// automatically.
#[clap(long, env)]
max_batch_total_tokens: Option<u32>,
/// This setting defines how many tokens can be passed before forcing the waiting
/// queries to be put on the batch (if the size of the batch allows for it).
/// New queries require 1 `prefill` forward, which is different from `decode`
/// and therefore you need to pause the running batch in order to run `prefill`
/// to create the correct values for the waiting queries to be able to join the batch.
///
/// With a value too small, queries will always "steal" the compute to run `prefill`
/// and running queries will be delayed by a lot.
///
/// With a value too big, waiting queries could wait for a very long time
/// before being allowed a slot in the running batch. If your server is busy
/// that means that requests that could run in ~2s on an empty server could
/// end up running in ~20s because the query had to wait for 18s.
///
/// This number is expressed in number of tokens to make it a bit more
/// "model" agnostic, but what should really matter is the overall latency
/// for end users.
#[clap(default_value = "20", long, env)]
max_waiting_tokens: usize,
/// The IP address to listen on
#[clap(default_value = "0.0.0.0", long, env)]
hostname: String,
/// The port to listen on.
#[clap(default_value = "3000", long, short, env)]
port: u16,
/// The name of the socket for gRPC communication between the webserver
/// and the shards.
#[clap(default_value = "/tmp/text-generation-server", long, env)]
shard_uds_path: String,
/// The address the master shard will listen on. (setting used by torch distributed)
#[clap(default_value = "localhost", long, env)]
master_addr: String,
/// The address the master port will listen on. (setting used by torch distributed)
#[clap(default_value = "29500", long, env)]
master_port: usize,
/// The location of the huggingface hub cache.
/// Used to override the location if you want to provide a mounted disk for instance
#[clap(long, env)]
huggingface_hub_cache: Option<String>,
/// The location of the huggingface hub cache.
/// Used to override the location if you want to provide a mounted disk for instance
#[clap(long, env)]
weights_cache_override: Option<String>,
/// For some models (like bloom), text-generation-inference implemented custom
/// cuda kernels to speed up inference. Those kernels were only tested on A100.
/// Use this flag to disable them if you're running on different hardware and
/// encounter issues.
#[clap(long, env)]
disable_custom_kernels: bool,
/// Limit the CUDA available memory.
/// The allowed value equals the total visible memory multiplied by cuda-memory-fraction.
#[clap(default_value = "1.0", long, env)]
cuda_memory_fraction: f32,
/// Rope scaling will only be used for RoPE models
/// and allow rescaling the position rotary to accomodate for
/// larger prompts.
///
/// Goes together with `rope_factor`.
///
/// `--rope-factor 2.0` gives linear scaling with a factor of 2.0
/// `--rope-scaling dynamic` gives dynamic scaling with a factor of 1.0
/// `--rope-scaling linear` gives linear scaling with a factor of 1.0 (Nothing will be changed
/// basically)
///
/// `--rope-scaling linear --rope-factor` fully describes the scaling you want
#[clap(long, env)]
rope_scaling: Option<RopeScaling>,
/// Rope scaling will only be used for RoPE models
/// See `rope_scaling`
#[clap(long, env)]
rope_factor: Option<f32>,
/// Outputs the logs in JSON format (useful for telemetry)
#[clap(long, env)]
json_output: bool,
#[clap(long, env)]
otlp_endpoint: Option<String>,
#[clap(long, env)]
cors_allow_origin: Vec<String>,
#[clap(long, env)]
watermark_gamma: Option<f32>,
#[clap(long, env)]
watermark_delta: Option<f32>,
/// Enable ngrok tunneling
#[clap(long, env)]
ngrok: bool,
/// ngrok authentication token
#[clap(long, env)]
ngrok_authtoken: Option<String>,
/// ngrok edge
#[clap(long, env)]
ngrok_edge: Option<String>,
/// Display a lot of information about your runtime environment
#[clap(long, short, action)]
env: bool,
}
#[derive(Debug)]
enum ShardStatus {
Ready,
Failed(usize),
}
#[allow(clippy::too_many_arguments)]
fn shard_manager(
model_id: String,
revision: Option<String>,
quantize: Option<Quantization>,
dtype: Option<Dtype>,
trust_remote_code: bool,
uds_path: String,
rank: usize,
world_size: usize,
master_addr: String,
master_port: usize,
huggingface_hub_cache: Option<String>,
weights_cache_override: Option<String>,
disable_custom_kernels: bool,
watermark_gamma: Option<f32>,
watermark_delta: Option<f32>,
cuda_memory_fraction: f32,
rope_scaling: Option<RopeScaling>,
rope_factor: Option<f32>,
otlp_endpoint: Option<String>,
status_sender: mpsc::Sender<ShardStatus>,
shutdown: Arc<AtomicBool>,
_shutdown_sender: mpsc::Sender<()>,
) {
// Enter shard-manager tracing span
let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
// Get UDS path
let uds_string = format!("{uds_path}-{rank}");
let uds = Path::new(&uds_string);
// Clean previous runs
if uds.exists() {
fs::remove_file(uds).unwrap();
}
// Process args
let mut shard_args = vec![
"serve".to_string(),
model_id,
"--uds-path".to_string(),
uds_path,
"--logger-level".to_string(),
"INFO".to_string(),
"--json-output".to_string(),
];
// Activate trust remote code
if trust_remote_code {
shard_args.push("--trust-remote-code".to_string());
}
// Activate tensor parallelism
if world_size > 1 {
shard_args.push("--sharded".to_string());
}
if let Some(quantize) = quantize {
shard_args.push("--quantize".to_string());
shard_args.push(quantize.to_string())
}
if let Some(dtype) = dtype {
shard_args.push("--dtype".to_string());
shard_args.push(dtype.to_string())
}
// Model optional revision
if let Some(revision) = revision {
shard_args.push("--revision".to_string());
shard_args.push(revision)
}
let rope = match (rope_scaling, rope_factor) {
(None, None) => None,
(Some(scaling), None) => Some((scaling, 1.0)),
(Some(scaling), Some(factor)) => Some((scaling, factor)),
(None, Some(factor)) => Some((RopeScaling::Linear, factor)),
};
// OpenTelemetry
if let Some(otlp_endpoint) = otlp_endpoint {
shard_args.push("--otlp-endpoint".to_string());
shard_args.push(otlp_endpoint);
}
// Copy current process env
let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
// Torch Distributed Env vars
envs.push(("RANK".into(), rank.to_string().into()));
envs.push(("WORLD_SIZE".into(), world_size.to_string().into()));
envs.push(("MASTER_ADDR".into(), master_addr.into()));
envs.push(("MASTER_PORT".into(), master_port.to_string().into()));
envs.push(("NCCL_ASYNC_ERROR_HANDLING".into(), "1".into()));
// CUDA memory fraction
envs.push((
"CUDA_MEMORY_FRACTION".into(),
cuda_memory_fraction.to_string().into(),
));
// Safetensors load fast
envs.push(("SAFETENSORS_FAST_GPU".into(), "1".into()));
// Enable hf transfer for insane download speeds
let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
envs.push((
"HF_HUB_ENABLE_HF_TRANSFER".into(),
enable_hf_transfer.into(),
));
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
// Detect rope scaling
// Sending as env instead of CLI args to not bloat everything
// those only can be used by RoPE models, so passing information around
// for all models will complexify code unnecessarily
if let Some((scaling, factor)) = rope {
envs.push(("ROPE_SCALING".into(), scaling.to_string().into()));
envs.push(("ROPE_FACTOR".into(), factor.to_string().into()));
}
// If huggingface_hub_cache is some, pass it to the shard
// Useful when running inside a docker container
if let Some(huggingface_hub_cache) = huggingface_hub_cache {
envs.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
};
// If weights_cache_override is some, pass it to the shard
// Useful when running inside a HuggingFace Inference Endpoint
if let Some(weights_cache_override) = weights_cache_override {
envs.push((
"WEIGHTS_CACHE_OVERRIDE".into(),
weights_cache_override.into(),
));
};
// If disable_custom_kernels is true, pass it to the shard as an env var
if disable_custom_kernels {
envs.push(("DISABLE_CUSTOM_KERNELS".into(), "True".into()))
}
// Watermark Gamma
if let Some(watermark_gamma) = watermark_gamma {
envs.push(("WATERMARK_GAMMA".into(), watermark_gamma.to_string().into()))
}
// Watermark Delta
if let Some(watermark_delta) = watermark_delta {
envs.push(("WATERMARK_DELTA".into(), watermark_delta.to_string().into()))
}
// Start process
tracing::info!("Starting shard");
let mut p = match Command::new("text-generation-server")
.args(shard_args)
.envs(envs)
.stdout(Stdio::piped())
.stderr(Stdio::piped())
.process_group(0)
.spawn()
{
Ok(p) => p,
Err(err) => {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-server not found in PATH");
tracing::error!("Please install it with `make install-server`")
}
{
tracing::error!("{}", err);
}
status_sender.send(ShardStatus::Failed(rank)).unwrap();
return;
}
};
// Redirect STDOUT to the console
let shard_stdout_reader = BufReader::new(p.stdout.take().unwrap());
let shard_stderr_reader = BufReader::new(p.stderr.take().unwrap());
//stdout tracing thread
thread::spawn(move || {
log_lines(shard_stdout_reader.lines());
});
let mut ready = false;
let start_time = Instant::now();
let mut wait_time = Instant::now();
loop {
// Process exited
if let Some(exit_status) = p.try_wait().unwrap() {
// We read stderr in another thread as it seems that lines() can block in some cases
let (err_sender, err_receiver) = mpsc::channel();
thread::spawn(move || {
for line in shard_stderr_reader.lines().flatten() {
err_sender.send(line).unwrap_or(());
}
});
let mut err = String::new();
while let Ok(line) = err_receiver.recv_timeout(Duration::from_millis(10)) {
err = err + "\n" + &line;
}
tracing::error!("Shard complete standard error output:\n{err}");
if let Some(signal) = exit_status.signal() {
tracing::error!("Shard process was signaled to shutdown with signal {signal}");
}
status_sender.send(ShardStatus::Failed(rank)).unwrap();
return;
}
// We received a shutdown signal
if shutdown.load(Ordering::SeqCst) {
p.kill().unwrap();
let _ = p.wait();
tracing::info!("Shard terminated");
return;
}
// Shard is ready
if uds.exists() && !ready {
tracing::info!("Shard ready in {:?}", start_time.elapsed());
status_sender.send(ShardStatus::Ready).unwrap();
ready = true;
} else if !ready && wait_time.elapsed() > Duration::from_secs(10) {
tracing::info!("Waiting for shard to be ready...");
wait_time = Instant::now();
}
sleep(Duration::from_millis(100));
}
}
fn shutdown_shards(shutdown: Arc<AtomicBool>, shutdown_receiver: &mpsc::Receiver<()>) {
tracing::info!("Shutting down shards");
// Update shutdown value to true
// This will be picked up by the shard manager
shutdown.store(true, Ordering::SeqCst);
// Wait for shards to shutdown
// This will block till all shutdown_sender are dropped
let _ = shutdown_receiver.recv();
}
fn num_cuda_devices() -> Option<usize> {
let devices = match env::var("CUDA_VISIBLE_DEVICES") {
Ok(devices) => devices,
Err(_) => env::var("NVIDIA_VISIBLE_DEVICES").ok()?,
};
let n_devices = devices.split(',').count();
Some(n_devices)
}
#[derive(Deserialize)]
#[serde(rename_all = "UPPERCASE")]
enum PythonLogLevelEnum {
Trace,
Debug,
Info,
Success,
Warning,
Error,
Critical,
}
#[derive(Deserialize)]
struct PythonLogLevel {
name: PythonLogLevelEnum,
}
#[derive(Deserialize)]
struct PythonLogRecord {
level: PythonLogLevel,
}
#[derive(Deserialize)]
struct PythonLogMessage {
text: String,
record: PythonLogRecord,
}
impl PythonLogMessage {
fn trace(&self) {
match self.record.level.name {
PythonLogLevelEnum::Trace => tracing::trace!("{}", self.text),
PythonLogLevelEnum::Debug => tracing::debug!("{}", self.text),
PythonLogLevelEnum::Info => tracing::info!("{}", self.text),
PythonLogLevelEnum::Success => tracing::info!("{}", self.text),
PythonLogLevelEnum::Warning => tracing::warn!("{}", self.text),
PythonLogLevelEnum::Error => tracing::error!("{}", self.text),
PythonLogLevelEnum::Critical => tracing::error!("{}", self.text),
}
}
}
impl TryFrom<&String> for PythonLogMessage {
type Error = serde_json::Error;
fn try_from(value: &String) -> Result<Self, Self::Error> {
serde_json::from_str::<Self>(value)
}
}
fn log_lines<S: Sized + BufRead>(lines: Lines<S>) {
for line in lines.flatten() {
match PythonLogMessage::try_from(&line) {
Ok(log) => log.trace(),
Err(_) => tracing::debug!("{line}"),
}
}
}
fn find_num_shards(
sharded: Option<bool>,
num_shard: Option<usize>,
) -> Result<usize, LauncherError> {
// get the number of shards given `sharded` and `num_shard`
let num_shard = match (sharded, num_shard) {
(Some(true), None) => {
// try to default to the number of available GPUs
tracing::info!("Parsing num_shard from CUDA_VISIBLE_DEVICES/NVIDIA_VISIBLE_DEVICES");
let n_devices = num_cuda_devices()
.expect("--num-shard and CUDA_VISIBLE_DEVICES/NVIDIA_VISIBLE_DEVICES are not set");
if n_devices <= 1 {
return Err(LauncherError::NotEnoughCUDADevices(format!(
"`sharded` is true but only found {n_devices} CUDA devices"
)));
}
n_devices
}
(Some(true), Some(num_shard)) => {
// we can't have only one shard while sharded
if num_shard <= 1 {
return Err(LauncherError::ArgumentValidation(
"`sharded` is true but `num_shard` <= 1".to_string(),
));
}
num_shard
}
(Some(false), Some(num_shard)) => num_shard,
(Some(false), None) => 1,
(None, None) => num_cuda_devices().unwrap_or(1),
(None, Some(num_shard)) => num_shard,
};
if num_shard < 1 {
return Err(LauncherError::ArgumentValidation(
"`num_shard` cannot be < 1".to_string(),
));
}
Ok(num_shard)
}
#[derive(Debug)]
enum LauncherError {
ArgumentValidation(String),
NotEnoughCUDADevices(String),
DownloadError,
ShardCannotStart,
ShardDisconnected,
ShardFailed,
WebserverFailed,
WebserverCannotStart,
}
fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), LauncherError> {
// Enter download tracing span
let _span = tracing::span!(tracing::Level::INFO, "download").entered();
let mut download_args = vec![
"download-weights".to_string(),
args.model_id.to_string(),
"--extension".to_string(),
".safetensors".to_string(),
"--logger-level".to_string(),
"INFO".to_string(),
"--json-output".to_string(),
];
// Model optional revision
if let Some(revision) = &args.revision {
download_args.push("--revision".to_string());
download_args.push(revision.to_string())
}
// Trust remote code for automatic peft fusion
if args.trust_remote_code {
download_args.push("--trust-remote-code".to_string());
}
// Copy current process env
let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
// If huggingface_hub_cache is set, pass it to the download process
// Useful when running inside a docker container
if let Some(ref huggingface_hub_cache) = args.huggingface_hub_cache {
envs.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
};
// Enable hf transfer for insane download speeds
let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
envs.push((
"HF_HUB_ENABLE_HF_TRANSFER".into(),
enable_hf_transfer.into(),
));
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
// If args.weights_cache_override is some, pass it to the download process
// Useful when running inside a HuggingFace Inference Endpoint
if let Some(weights_cache_override) = &args.weights_cache_override {
envs.push((
"WEIGHTS_CACHE_OVERRIDE".into(),
weights_cache_override.into(),
));
};
// Start process
tracing::info!("Starting download process.");
let mut download_process = match Command::new("text-generation-server")
.args(download_args)
.envs(envs)
.stdout(Stdio::piped())
.stderr(Stdio::piped())
.process_group(0)
.spawn()
{
Ok(p) => p,
Err(err) => {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-server not found in PATH");
tracing::error!("Please install it with `make install-server`")
} else {
tracing::error!("{}", err);
}
return Err(LauncherError::DownloadError);
}
};
// Redirect STDOUT to the console
let download_stdout = download_process.stdout.take().unwrap();
let stdout = BufReader::new(download_stdout);
thread::spawn(move || {
log_lines(stdout.lines());
});
loop {
if let Some(status) = download_process.try_wait().unwrap() {
if status.success() {
tracing::info!("Successfully downloaded weights.");
break;
}
let mut err = String::new();
download_process
.stderr
.take()
.unwrap()
.read_to_string(&mut err)
.unwrap();
if let Some(signal) = status.signal() {
tracing::error!(
"Download process was signaled to shutdown with signal {signal}: {err}"
);
} else {
tracing::error!("Download encountered an error: {err}");
}
return Err(LauncherError::DownloadError);
}
if !running.load(Ordering::SeqCst) {
terminate("download", download_process, Duration::from_secs(10)).unwrap();
return Ok(());
}
sleep(Duration::from_millis(100));
}
Ok(())
}
#[allow(clippy::too_many_arguments)]
fn spawn_shards(
num_shard: usize,
args: &Args,
shutdown: Arc<AtomicBool>,
shutdown_receiver: &mpsc::Receiver<()>,
shutdown_sender: mpsc::Sender<()>,
status_receiver: &mpsc::Receiver<ShardStatus>,
status_sender: mpsc::Sender<ShardStatus>,
running: Arc<AtomicBool>,
) -> Result<(), LauncherError> {
// Start shard processes
for rank in 0..num_shard {
let model_id = args.model_id.clone();
let revision = args.revision.clone();
let uds_path = args.shard_uds_path.clone();
let master_addr = args.master_addr.clone();
let huggingface_hub_cache = args.huggingface_hub_cache.clone();
let weights_cache_override = args.weights_cache_override.clone();
let status_sender = status_sender.clone();
let shutdown = shutdown.clone();
let shutdown_sender = shutdown_sender.clone();
let otlp_endpoint = args.otlp_endpoint.clone();
let quantize = args.quantize;
let dtype = args.dtype;
let trust_remote_code = args.trust_remote_code;
let master_port = args.master_port;
let disable_custom_kernels = args.disable_custom_kernels;
let watermark_gamma = args.watermark_gamma;
let watermark_delta = args.watermark_delta;
let cuda_memory_fraction = args.cuda_memory_fraction;
let rope_scaling = args.rope_scaling;
let rope_factor = args.rope_factor;
thread::spawn(move || {
shard_manager(
model_id,
revision,
quantize,
dtype,
trust_remote_code,
uds_path,
rank,
num_shard,
master_addr,
master_port,
huggingface_hub_cache,
weights_cache_override,
disable_custom_kernels,
watermark_gamma,
watermark_delta,
cuda_memory_fraction,
rope_scaling,
rope_factor,
otlp_endpoint,
status_sender,
shutdown,
shutdown_sender,
)
});
}
drop(shutdown_sender);
// Wait for shard to start
let mut shard_ready = 0;
while running.load(Ordering::SeqCst) {
match status_receiver.try_recv() {
Ok(ShardStatus::Ready) => {
shard_ready += 1;
if shard_ready == num_shard {
break;
}
}
Err(TryRecvError::Empty) => {
sleep(Duration::from_millis(100));
}
Ok(ShardStatus::Failed(rank)) => {
tracing::error!("Shard {rank} failed to start");
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::ShardCannotStart);
}
Err(TryRecvError::Disconnected) => {
tracing::error!("Shard status channel disconnected");
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::ShardDisconnected);
}
}
}
Ok(())
}
fn spawn_webserver(
args: Args,
shutdown: Arc<AtomicBool>,
shutdown_receiver: &mpsc::Receiver<()>,
) -> Result<Child, LauncherError> {
// All shard started
// Start webserver
tracing::info!("Starting Webserver");
let mut router_args = vec![
"--max-concurrent-requests".to_string(),
args.max_concurrent_requests.to_string(),
"--max-best-of".to_string(),
args.max_best_of.to_string(),
"--max-stop-sequences".to_string(),
args.max_stop_sequences.to_string(),
"--max-top-n-tokens".to_string(),
args.max_top_n_tokens.to_string(),
"--max-input-length".to_string(),
args.max_input_length.to_string(),
"--max-total-tokens".to_string(),
args.max_total_tokens.to_string(),
"--max-batch-prefill-tokens".to_string(),
args.max_batch_prefill_tokens.to_string(),
"--waiting-served-ratio".to_string(),
args.waiting_served_ratio.to_string(),
"--max-waiting-tokens".to_string(),
args.max_waiting_tokens.to_string(),
"--validation-workers".to_string(),
args.validation_workers.to_string(),
"--hostname".to_string(),
args.hostname.to_string(),
"--port".to_string(),
args.port.to_string(),
"--master-shard-uds-path".to_string(),
format!("{}-0", args.shard_uds_path),
"--tokenizer-name".to_string(),
args.model_id,
];
// Model optional max batch total tokens
if let Some(max_batch_total_tokens) = args.max_batch_total_tokens {
router_args.push("--max-batch-total-tokens".to_string());
router_args.push(max_batch_total_tokens.to_string());
}
// Model optional revision
if let Some(ref revision) = args.revision {
router_args.push("--revision".to_string());
router_args.push(revision.to_string())
}
if args.json_output {
router_args.push("--json-output".to_string());
}
// OpenTelemetry
if let Some(otlp_endpoint) = args.otlp_endpoint {
router_args.push("--otlp-endpoint".to_string());
router_args.push(otlp_endpoint);
}
// CORS origins
for origin in args.cors_allow_origin.into_iter() {
router_args.push("--cors-allow-origin".to_string());
router_args.push(origin);
}
// Ngrok
if args.ngrok {
router_args.push("--ngrok".to_string());
router_args.push("--ngrok-authtoken".to_string());
router_args.push(args.ngrok_authtoken.unwrap());
router_args.push("--ngrok-edge".to_string());
router_args.push(args.ngrok_edge.unwrap());
}
// Copy current process env
let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
let mut webserver = match Command::new("text-generation-router")
.args(router_args)
.envs(envs)
.stdout(Stdio::piped())
.stderr(Stdio::piped())
.process_group(0)
.spawn()
{
Ok(p) => p,
Err(err) => {
tracing::error!("Failed to start webserver: {}", err);
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-router not found in PATH");
tracing::error!("Please install it with `make install-router`")
} else {
tracing::error!("{}", err);
}
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::WebserverCannotStart);
}
};
// Redirect STDOUT and STDERR to the console
let webserver_stdout = webserver.stdout.take().unwrap();
let webserver_stderr = webserver.stderr.take().unwrap();
thread::spawn(move || {
let stdout = BufReader::new(webserver_stdout);
let stderr = BufReader::new(webserver_stderr);
for line in stdout.lines() {
println!("{}", line.unwrap());
}
for line in stderr.lines() {
println!("{}", line.unwrap());
}
});
Ok(webserver)
}
fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::Result<ExitStatus> {
tracing::info!("Terminating {process_name}");
let terminate_time = Instant::now();
signal::kill(Pid::from_raw(process.id() as i32), Signal::SIGTERM).unwrap();
tracing::info!("Waiting for {process_name} to gracefully shutdown");
while terminate_time.elapsed() < timeout {
if let Some(status) = process.try_wait()? {
tracing::info!("{process_name} terminated");
return Ok(status);
}
sleep(Duration::from_millis(100));
}
tracing::info!("Killing {process_name}");
process.kill()?;
let exit_status = process.wait()?;
tracing::info!("{process_name} killed");
Ok(exit_status)
}
fn main() -> Result<(), LauncherError> {
// Pattern match configuration
let args: Args = Args::parse();
// Filter events with LOG_LEVEL
let env_filter =
EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
if args.json_output {
tracing_subscriber::fmt()
.with_env_filter(env_filter)
.json()
.init();
} else {
tracing_subscriber::fmt()
.with_env_filter(env_filter)
.compact()
.init();
}
if args.env {
let env_runtime = env_runtime::Env::new();
tracing::info!("{}", env_runtime);
}
tracing::info!("{:?}", args);
// Validate args
if args.max_input_length >= args.max_total_tokens {
return Err(LauncherError::ArgumentValidation(
"`max_input_length` must be < `max_total_tokens`".to_string(),
));
}
if args.max_input_length as u32 > args.max_batch_prefill_tokens {
return Err(LauncherError::ArgumentValidation(format!(
"`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {} and {}",
args.max_batch_prefill_tokens, args.max_input_length
)));
}
if args.validation_workers == 0 {
return Err(LauncherError::ArgumentValidation(
"`validation_workers` must be > 0".to_string(),
));
}
if args.trust_remote_code {
tracing::warn!(
"`trust_remote_code` is set. Trusting that model `{}` do not contain malicious code.",
args.model_id
);
}
let num_shard = find_num_shards(args.sharded, args.num_shard)?;
if num_shard > 1 {
tracing::info!("Sharding model on {num_shard} processes");
}
if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens {
if args.max_batch_prefill_tokens > *max_batch_total_tokens {
return Err(LauncherError::ArgumentValidation(format!(
"`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
args.max_batch_prefill_tokens, max_batch_total_tokens
)));
}
if args.max_total_tokens as u32 > *max_batch_total_tokens {
return Err(LauncherError::ArgumentValidation(format!(
"`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
args.max_total_tokens, max_batch_total_tokens
)));
}
}
if args.ngrok {
if args.ngrok_authtoken.is_none() {
return Err(LauncherError::ArgumentValidation(
"`ngrok-authtoken` must be set when using ngrok tunneling".to_string(),
));
}
if args.ngrok_edge.is_none() {
return Err(LauncherError::ArgumentValidation(
"`ngrok-edge` must be set when using ngrok tunneling".to_string(),
));
}
}
// Signal handler
let running = Arc::new(AtomicBool::new(true));
let r = running.clone();
ctrlc::set_handler(move || {
r.store(false, Ordering::SeqCst);
})
.expect("Error setting Ctrl-C handler");
// Download and convert model weights
download_convert_model(&args, running.clone())?;
if !running.load(Ordering::SeqCst) {
// Launcher was asked to stop
return Ok(());
}
// Shared shutdown bool
let shutdown = Arc::new(AtomicBool::new(false));
// Shared shutdown channel
// When shutting down, the main thread will wait for all senders to be dropped
let (shutdown_sender, shutdown_receiver) = mpsc::channel();
// Shared channel to track shard status
let (status_sender, status_receiver) = mpsc::channel();
spawn_shards(
num_shard,
&args,
shutdown.clone(),
&shutdown_receiver,
shutdown_sender,
&status_receiver,
status_sender,
running.clone(),
)?;
// We might have received a termination signal
if !running.load(Ordering::SeqCst) {
shutdown_shards(shutdown, &shutdown_receiver);
return Ok(());
}
let mut webserver =
spawn_webserver(args, shutdown.clone(), &shutdown_receiver).map_err(|err| {
shutdown_shards(shutdown.clone(), &shutdown_receiver);
err
})?;
// Default exit code
let mut exit_code = Ok(());
while running.load(Ordering::SeqCst) {
if let Ok(ShardStatus::Failed(rank)) = status_receiver.try_recv() {
tracing::error!("Shard {rank} crashed");
exit_code = Err(LauncherError::ShardFailed);
break;
};
match webserver.try_wait().unwrap() {
Some(_) => {
tracing::error!("Webserver Crashed");
shutdown_shards(shutdown, &shutdown_receiver);
return Err(LauncherError::WebserverFailed);
}
None => {
sleep(Duration::from_millis(100));
}
};
}
// Graceful termination
terminate("webserver", webserver, Duration::from_secs(90)).unwrap();
shutdown_shards(shutdown, &shutdown_receiver);
exit_code
}
| 0
|
hf_public_repos/text-generation-inference/clients
|
hf_public_repos/text-generation-inference/clients/python/Makefile
|
unit-tests:
python -m pytest --cov=text_generation tests
install:
pip install pip --upgrade
pip install -e .
| 0
|
hf_public_repos/text-generation-inference/clients
|
hf_public_repos/text-generation-inference/clients/python/README.md
|
# Text Generation
The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
`text-generation-inference` instance running on
[Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub.
## Get Started
### Install
```shell
pip install text-generation
```
### Inference API Usage
```python
from text_generation import InferenceAPIClient
client = InferenceAPIClient("bigscience/bloomz")
text = client.generate("Why is the sky blue?").generated_text
print(text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
or with the asynchronous client:
```python
from text_generation import InferenceAPIAsyncClient
client = InferenceAPIAsyncClient("bigscience/bloomz")
response = await client.generate("Why is the sky blue?")
print(response.generated_text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
async for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
Check all currently deployed models on the Huggingface Inference API with `Text Generation` support:
```python
from text_generation.inference_api import deployed_models
print(deployed_models())
```
### Hugging Face Inference Endpoint usage
```python
from text_generation import Client
endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
client = Client(endpoint_url)
text = client.generate("Why is the sky blue?").generated_text
print(text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
or with the asynchronous client:
```python
from text_generation import AsyncClient
endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
client = AsyncClient(endpoint_url)
response = await client.generate("Why is the sky blue?")
print(response.generated_text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
async for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
### Types
```python
# Request Parameters
class Parameters:
# Activate logits sampling
do_sample: bool
# Maximum number of generated tokens
max_new_tokens: int
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float]
# Whether to prepend the prompt to the generated text
return_full_text: bool
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str]
# Random sampling seed
seed: Optional[int]
# The value used to module the logits distribution.
temperature: Optional[float]
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int]
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float]
# truncate inputs tokens to the given size
truncate: Optional[int]
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float]
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int]
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool
# Get decoder input token logprobs and ids
decoder_input_details: bool
# Return the N most likely tokens at each step
top_n_tokens: Optional[int]
# Decoder input tokens
class InputToken:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float]
# Generated tokens
class Token:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
class BestOfSequence:
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Most likely tokens
top_tokens: Optional[List[List[Token]]]
# `generate` details
class Details:
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Most likely tokens
top_tokens: Optional[List[List[Token]]]
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]]
# `generate` return value
class Response:
# Generated text
generated_text: str
# Generation details
details: Details
# `generate_stream` details
class StreamDetails:
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# `generate_stream` return value
class StreamResponse:
# Generated token
token: Token
# Most likely tokens
top_tokens: Optional[List[Token]]
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str]
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails]
# Inference API currently deployed model
class DeployedModel:
model_id: str
sha: str
```
| 0
|
hf_public_repos/text-generation-inference/clients
|
hf_public_repos/text-generation-inference/clients/python/poetry.lock
|
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{file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955"},
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{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6"},
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf"},
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{file = "yarl-1.9.2-cp39-cp39-win32.whl", hash = "sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623"},
{file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
{file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
[[package]]
name = "zipp"
version = "3.15.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.7"
files = [
{file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
{file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.7"
content-hash = "b7fab8703967f2616ea59a98a437cd30f97f0c8d2a06e399d688814a2a2c64f8"
| 0
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hf_public_repos/text-generation-inference/clients
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hf_public_repos/text-generation-inference/clients/python/pyproject.toml
|
[tool.poetry]
name = "text-generation"
version = "0.6.1"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/text-generation-inference"
repository = "https://github.com/huggingface/text-generation-inference"
[tool.poetry.dependencies]
python = "^3.7"
pydantic = "> 1.10, < 3"
aiohttp = "^3.8"
huggingface-hub = ">= 0.12, < 1.0"
[tool.poetry.dev-dependencies]
pytest = "^6.2.5"
pytest-asyncio = "^0.17.2"
pytest-cov = "^3.0.0"
[tool.pytest.ini_options]
asyncio_mode = "auto"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
| 0
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hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/tests/test_client.py
|
import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate("test", max_new_tokens=1, decoder_input_details=True)
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
assert len(response.details.prefill) == 1
assert response.details.prefill[0] == InputToken(id=0, text="<pad>", logprob=None)
assert len(response.details.tokens) == 1
assert response.details.tokens[0].id == 3
assert response.details.tokens[0].text == " "
assert not response.details.tokens[0].special
def test_generate_best_of(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate(
"test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True
)
assert response.details.seed is not None
assert response.details.best_of_sequences is not None
assert len(response.details.best_of_sequences) == 1
assert response.details.best_of_sequences[0].seed is not None
def test_generate_not_found(fake_url, hf_headers):
client = Client(fake_url, hf_headers)
with pytest.raises(NotFoundError):
client.generate("test")
def test_generate_validation_error(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
client.generate("test", max_new_tokens=10_000)
def test_generate_stream(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
responses = [
response for response in client.generate_stream("test", max_new_tokens=1)
]
assert len(responses) == 1
response = responses[0]
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
def test_generate_stream_not_found(fake_url, hf_headers):
client = Client(fake_url, hf_headers)
with pytest.raises(NotFoundError):
list(client.generate_stream("test"))
def test_generate_stream_validation_error(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
list(client.generate_stream("test", max_new_tokens=10_000))
@pytest.mark.asyncio
async def test_generate_async(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
response = await client.generate(
"test", max_new_tokens=1, decoder_input_details=True
)
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
assert len(response.details.prefill) == 1
assert response.details.prefill[0] == InputToken(id=0, text="<pad>", logprob=None)
assert len(response.details.tokens) == 1
assert response.details.tokens[0].id == 3
assert response.details.tokens[0].text == " "
assert not response.details.tokens[0].special
@pytest.mark.asyncio
async def test_generate_async_best_of(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
response = await client.generate(
"test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True
)
assert response.details.seed is not None
assert response.details.best_of_sequences is not None
assert len(response.details.best_of_sequences) == 1
assert response.details.best_of_sequences[0].seed is not None
@pytest.mark.asyncio
async def test_generate_async_not_found(fake_url, hf_headers):
client = AsyncClient(fake_url, hf_headers)
with pytest.raises(NotFoundError):
await client.generate("test")
@pytest.mark.asyncio
async def test_generate_async_validation_error(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
await client.generate("test", max_new_tokens=10_000)
@pytest.mark.asyncio
async def test_generate_stream_async(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
responses = [
response async for response in client.generate_stream("test", max_new_tokens=1)
]
assert len(responses) == 1
response = responses[0]
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
@pytest.mark.asyncio
async def test_generate_stream_async_not_found(fake_url, hf_headers):
client = AsyncClient(fake_url, hf_headers)
with pytest.raises(NotFoundError):
async for _ in client.generate_stream("test"):
pass
@pytest.mark.asyncio
async def test_generate_stream_async_validation_error(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
async for _ in client.generate_stream("test", max_new_tokens=10_000):
pass
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/tests/test_inference_api.py
|
import pytest
from text_generation import (
InferenceAPIClient,
InferenceAPIAsyncClient,
Client,
AsyncClient,
)
from text_generation.errors import NotSupportedError, NotFoundError
from text_generation.inference_api import check_model_support, deployed_models
def test_check_model_support(flan_t5_xxl, unsupported_model, fake_model):
assert check_model_support(flan_t5_xxl)
assert not check_model_support(unsupported_model)
with pytest.raises(NotFoundError):
check_model_support(fake_model)
def test_deployed_models():
deployed_models()
def test_client(flan_t5_xxl):
client = InferenceAPIClient(flan_t5_xxl)
assert isinstance(client, Client)
def test_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIClient(unsupported_model)
def test_async_client(flan_t5_xxl):
client = InferenceAPIAsyncClient(flan_t5_xxl)
assert isinstance(client, AsyncClient)
def test_async_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIAsyncClient(unsupported_model)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/tests/test_types.py
|
import pytest
from text_generation.types import Parameters, Request
from text_generation.errors import ValidationError
def test_parameters_validation():
# Test best_of
Parameters(best_of=1)
with pytest.raises(ValidationError):
Parameters(best_of=0)
with pytest.raises(ValidationError):
Parameters(best_of=-1)
Parameters(best_of=2, do_sample=True)
with pytest.raises(ValidationError):
Parameters(best_of=2)
with pytest.raises(ValidationError):
Parameters(best_of=2, seed=1)
# Test repetition_penalty
Parameters(repetition_penalty=1)
with pytest.raises(ValidationError):
Parameters(repetition_penalty=0)
with pytest.raises(ValidationError):
Parameters(repetition_penalty=-1)
# Test seed
Parameters(seed=1)
with pytest.raises(ValidationError):
Parameters(seed=-1)
# Test temperature
Parameters(temperature=1)
with pytest.raises(ValidationError):
Parameters(temperature=0)
with pytest.raises(ValidationError):
Parameters(temperature=-1)
# Test top_k
Parameters(top_k=1)
with pytest.raises(ValidationError):
Parameters(top_k=0)
with pytest.raises(ValidationError):
Parameters(top_k=-1)
# Test top_p
Parameters(top_p=0.5)
with pytest.raises(ValidationError):
Parameters(top_p=0)
with pytest.raises(ValidationError):
Parameters(top_p=-1)
with pytest.raises(ValidationError):
Parameters(top_p=1)
# Test truncate
Parameters(truncate=1)
with pytest.raises(ValidationError):
Parameters(truncate=0)
with pytest.raises(ValidationError):
Parameters(truncate=-1)
# Test typical_p
Parameters(typical_p=0.5)
with pytest.raises(ValidationError):
Parameters(typical_p=0)
with pytest.raises(ValidationError):
Parameters(typical_p=-1)
with pytest.raises(ValidationError):
Parameters(typical_p=1)
def test_request_validation():
Request(inputs="test")
with pytest.raises(ValidationError):
Request(inputs="")
Request(inputs="test", stream=True)
Request(inputs="test", parameters=Parameters(best_of=2, do_sample=True))
with pytest.raises(ValidationError):
Request(
inputs="test", parameters=Parameters(best_of=2, do_sample=True), stream=True
)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/tests/conftest.py
|
import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def fake_model():
return "fake/model"
@pytest.fixture
def unsupported_model():
return "gpt2"
@pytest.fixture
def base_url():
return "https://api-inference.huggingface.co/models"
@pytest.fixture
def bloom_url(base_url, bloom_model):
return f"{base_url}/{bloom_model}"
@pytest.fixture
def flan_t5_xxl_url(base_url, flan_t5_xxl):
return f"{base_url}/{flan_t5_xxl}"
@pytest.fixture
def fake_url(base_url, fake_model):
return f"{base_url}/{fake_model}"
@pytest.fixture
def unsupported_url(base_url, unsupported_model):
return f"{base_url}/{unsupported_model}"
@pytest.fixture(scope="session")
def hf_headers():
return build_hf_headers(
library_name="text-generation-tests", library_version=__version__
)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/tests/test_errors.py
|
from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
payload = {"error_type": "generation", "error": "test"}
assert isinstance(parse_error(400, payload), GenerationError)
def test_incomplete_generation_error():
payload = {"error_type": "incomplete_generation", "error": "test"}
assert isinstance(parse_error(400, payload), IncompleteGenerationError)
def test_overloaded_error():
payload = {"error_type": "overloaded", "error": "test"}
assert isinstance(parse_error(400, payload), OverloadedError)
def test_validation_error():
payload = {"error_type": "validation", "error": "test"}
assert isinstance(parse_error(400, payload), ValidationError)
def test_bad_request_error():
payload = {"error": "test"}
assert isinstance(parse_error(400, payload), BadRequestError)
def test_shard_not_ready_error():
payload = {"error": "test"}
assert isinstance(parse_error(403, payload), ShardNotReadyError)
assert isinstance(parse_error(424, payload), ShardNotReadyError)
def test_shard_timeout_error():
payload = {"error": "test"}
assert isinstance(parse_error(504, payload), ShardTimeoutError)
def test_not_found_error():
payload = {"error": "test"}
assert isinstance(parse_error(404, payload), NotFoundError)
def test_rate_limit_exceeded_error():
payload = {"error": "test"}
assert isinstance(parse_error(429, payload), RateLimitExceededError)
def test_unknown_error():
payload = {"error": "test"}
assert isinstance(parse_error(500, payload), UnknownError)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/text_generation/errors.py
|
from typing import Dict
# Text Generation Inference Errors
class ValidationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class GenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class OverloadedError(Exception):
def __init__(self, message: str):
super().__init__(message)
class IncompleteGenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
# API Inference Errors
class BadRequestError(Exception):
def __init__(self, message: str):
super().__init__(message)
class ShardNotReadyError(Exception):
def __init__(self, message: str):
super().__init__(message)
class ShardTimeoutError(Exception):
def __init__(self, message: str):
super().__init__(message)
class NotFoundError(Exception):
def __init__(self, message: str):
super().__init__(message)
class RateLimitExceededError(Exception):
def __init__(self, message: str):
super().__init__(message)
class NotSupportedError(Exception):
def __init__(self, model_id: str):
message = (
f"Model `{model_id}` is not available for inference with this client. \n"
"Use `huggingface_hub.inference_api.InferenceApi` instead."
)
super(NotSupportedError, self).__init__(message)
# Unknown error
class UnknownError(Exception):
def __init__(self, message: str):
super().__init__(message)
def parse_error(status_code: int, payload: Dict[str, str]) -> Exception:
"""
Parse error given an HTTP status code and a json payload
Args:
status_code (`int`):
HTTP status code
payload (`Dict[str, str]`):
Json payload
Returns:
Exception: parsed exception
"""
# Try to parse a Text Generation Inference error
message = payload["error"]
if "error_type" in payload:
error_type = payload["error_type"]
if error_type == "generation":
return GenerationError(message)
if error_type == "incomplete_generation":
return IncompleteGenerationError(message)
if error_type == "overloaded":
return OverloadedError(message)
if error_type == "validation":
return ValidationError(message)
# Try to parse a APIInference error
if status_code == 400:
return BadRequestError(message)
if status_code == 403 or status_code == 424:
return ShardNotReadyError(message)
if status_code == 504:
return ShardTimeoutError(message)
if status_code == 404:
return NotFoundError(message)
if status_code == 429:
return RateLimitExceededError(message)
# Fallback to an unknown error
return UnknownError(message)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/text_generation/inference_api.py
|
import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.environ.get(
"HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co"
)
def deployed_models(headers: Optional[Dict] = None) -> List[DeployedModel]:
"""
Get all currently deployed models with text-generation-inference-support
Returns:
List[DeployedModel]: list of all currently deployed models
"""
resp = requests.get(
f"https://api-inference.huggingface.co/framework/text-generation-inference",
headers=headers,
timeout=5,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
models = [DeployedModel(**raw_deployed_model) for raw_deployed_model in payload]
return models
def check_model_support(repo_id: str, headers: Optional[Dict] = None) -> bool:
"""
Check if a given model is supported by text-generation-inference
Returns:
bool: whether the model is supported by this client
"""
resp = requests.get(
f"https://api-inference.huggingface.co/status/{repo_id}",
headers=headers,
timeout=5,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
framework = payload["framework"]
supported = framework == "text-generation-inference"
return supported
class InferenceAPIClient(Client):
"""Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIClient
>>> client = InferenceAPIClient("bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
class InferenceAPIAsyncClient(AsyncClient):
"""Aynschronous Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIAsyncClient
>>> client = InferenceAPIAsyncClient("bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIAsyncClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/text_generation/types.py
|
from enum import Enum
from pydantic import BaseModel, validator
from typing import Optional, List
from text_generation.errors import ValidationError
class Parameters(BaseModel):
# Activate logits sampling
do_sample: bool = False
# Maximum number of generated tokens
max_new_tokens: int = 20
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float] = None
# Whether to prepend the prompt to the generated text
return_full_text: bool = False
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str] = []
# Random sampling seed
seed: Optional[int] = None
# The value used to module the logits distribution.
temperature: Optional[float] = None
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int] = None
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float] = None
# truncate inputs tokens to the given size
truncate: Optional[int] = None
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float] = None
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int] = None
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool = False
# Get generation details
details: bool = False
# Get decoder input token logprobs and ids
decoder_input_details: bool = False
# Return the N most likely tokens at each step
top_n_tokens: Optional[int] = None
@validator("best_of")
def valid_best_of(cls, field_value, values):
if field_value is not None:
if field_value <= 0:
raise ValidationError("`best_of` must be strictly positive")
if field_value > 1 and values["seed"] is not None:
raise ValidationError("`seed` must not be set when `best_of` is > 1")
sampling = (
values["do_sample"]
| (values["temperature"] is not None)
| (values["top_k"] is not None)
| (values["top_p"] is not None)
| (values["typical_p"] is not None)
)
if field_value > 1 and not sampling:
raise ValidationError("you must use sampling when `best_of` is > 1")
return field_value
@validator("repetition_penalty")
def valid_repetition_penalty(cls, v):
if v is not None and v <= 0:
raise ValidationError("`repetition_penalty` must be strictly positive")
return v
@validator("seed")
def valid_seed(cls, v):
if v is not None and v < 0:
raise ValidationError("`seed` must be positive")
return v
@validator("temperature")
def valid_temp(cls, v):
if v is not None and v <= 0:
raise ValidationError("`temperature` must be strictly positive")
return v
@validator("top_k")
def valid_top_k(cls, v):
if v is not None and v <= 0:
raise ValidationError("`top_k` must be strictly positive")
return v
@validator("top_p")
def valid_top_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValidationError("`top_p` must be > 0.0 and < 1.0")
return v
@validator("truncate")
def valid_truncate(cls, v):
if v is not None and v <= 0:
raise ValidationError("`truncate` must be strictly positive")
return v
@validator("typical_p")
def valid_typical_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValidationError("`typical_p` must be > 0.0 and < 1.0")
return v
@validator("top_n_tokens")
def valid_top_n_tokens(cls, v):
if v is not None and v <= 0:
raise ValidationError("`top_n_tokens` must be strictly positive")
return v
class Request(BaseModel):
# Prompt
inputs: str
# Generation parameters
parameters: Optional[Parameters] = None
# Whether to stream output tokens
stream: bool = False
@validator("inputs")
def valid_input(cls, v):
if not v:
raise ValidationError("`inputs` cannot be empty")
return v
@validator("stream")
def valid_best_of_stream(cls, field_value, values):
parameters = values["parameters"]
if (
parameters is not None
and parameters.best_of is not None
and parameters.best_of > 1
and field_value
):
raise ValidationError(
"`best_of` != 1 is not supported when `stream` == True"
)
return field_value
# Decoder input tokens
class InputToken(BaseModel):
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float] = None
# Generated tokens
class Token(BaseModel):
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(str, Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
class BestOfSequence(BaseModel):
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Most likely tokens
top_tokens: Optional[List[List[Token]]] = None
# `generate` details
class Details(BaseModel):
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Most likely tokens
top_tokens: Optional[List[List[Token]]] = None
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]] = None
# `generate` return value
class Response(BaseModel):
# Generated text
generated_text: str
# Generation details
details: Details
# `generate_stream` details
class StreamDetails(BaseModel):
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int] = None
# `generate_stream` return value
class StreamResponse(BaseModel):
# Generated token
token: Token
# Most likely tokens
top_tokens: Optional[List[Token]] = None
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str] = None
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails] = None
# Inference API currently deployed model
class DeployedModel(BaseModel):
model_id: str
sha: str
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/text_generation/__init__.py
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__version__ = "0.6.0"
from text_generation.client import Client, AsyncClient
from text_generation.inference_api import InferenceAPIClient, InferenceAPIAsyncClient
| 0
|
hf_public_repos/text-generation-inference/clients/python
|
hf_public_repos/text-generation-inference/clients/python/text_generation/client.py
|
import json
import requests
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator
from text_generation.types import (
StreamResponse,
Response,
Request,
Parameters,
)
from text_generation.errors import parse_error
class Client:
"""Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import Client
>>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = timeout
def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
top_n_tokens: Optional[int] = None,
) -> Response:
"""
Given a prompt, generate the following text
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
Returns:
Response: generated response
"""
# Validate parameters
parameters = Parameters(
best_of=best_of,
details=True,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
decoder_input_details=decoder_input_details,
top_n_tokens=top_n_tokens,
)
request = Request(inputs=prompt, stream=False, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
return Response(**payload[0])
def generate_stream(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
top_n_tokens: Optional[int] = None,
) -> Iterator[StreamResponse]:
"""
Given a prompt, generate the following stream of tokens
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
Returns:
Iterator[StreamResponse]: stream of generated tokens
"""
# Validate parameters
parameters = Parameters(
best_of=None,
details=True,
decoder_input_details=False,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
top_n_tokens=top_n_tokens,
)
request = Request(inputs=prompt, stream=True, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
stream=True,
)
if resp.status_code != 200:
raise parse_error(resp.status_code, resp.json())
# Parse ServerSentEvents
for byte_payload in resp.iter_lines():
# Skip line
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
# Event data
if payload.startswith("data:"):
# Decode payload
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
# Parse payload
try:
response = StreamResponse(**json_payload)
except ValidationError:
# If we failed to parse the payload, then it is an error payload
raise parse_error(resp.status_code, json_payload)
yield response
class AsyncClient:
"""Asynchronous Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import AsyncClient
>>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout * 60)
async def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
top_n_tokens: Optional[int] = None,
) -> Response:
"""
Given a prompt, generate the following text asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
Returns:
Response: generated response
"""
# Validate parameters
parameters = Parameters(
best_of=best_of,
details=True,
decoder_input_details=decoder_input_details,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
top_n_tokens=top_n_tokens,
)
request = Request(inputs=prompt, stream=False, parameters=parameters)
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(self.base_url, json=request.dict()) as resp:
payload = await resp.json()
if resp.status != 200:
raise parse_error(resp.status, payload)
return Response(**payload[0])
async def generate_stream(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
top_n_tokens: Optional[int] = None,
) -> AsyncIterator[StreamResponse]:
"""
Given a prompt, generate the following stream of tokens asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
Returns:
AsyncIterator[StreamResponse]: stream of generated tokens
"""
# Validate parameters
parameters = Parameters(
best_of=None,
details=True,
decoder_input_details=False,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
top_n_tokens=top_n_tokens,
)
request = Request(inputs=prompt, stream=True, parameters=parameters)
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(self.base_url, json=request.dict()) as resp:
if resp.status != 200:
raise parse_error(resp.status, await resp.json())
# Parse ServerSentEvents
async for byte_payload in resp.content:
# Skip line
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
# Event data
if payload.startswith("data:"):
# Decode payload
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
# Parse payload
try:
response = StreamResponse(**json_payload)
except ValidationError:
# If we failed to parse the payload, then it is an error payload
raise parse_error(resp.status, json_payload)
yield response
| 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/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/huggingface/candle/pull/1009).
- Add the gelu-erf op and activation function
[969](https://github.com/huggingface/candle/pull/969).
- Add the mixformer/phi-v1.5 model
[930](https://github.com/huggingface/candle/pull/930).
- Add the sclice-scatter op
[927](https://github.com/huggingface/candle/pull/927).
- Add the Wuerstchen diffusion model
[911](https://github.com/huggingface/candle/pull/911).
### Modified
- Support for simd128 intrinsics in some quantized vecdots
[982](https://github.com/huggingface/candle/pull/982).
- Optimize the index-select cuda kernel
[976](https://github.com/huggingface/candle/pull/976).
- Self-contained safetensor wrappers
[946](https://github.com/huggingface/candle/pull/946).
## v0.2.2 - 2023-09-18
### Added
- Support for `top_p` sampling
[819](https://github.com/huggingface/candle/pull/819).
- T5 model including decoding
[864](https://github.com/huggingface/candle/pull/864).
- 1-d upsampling
[839](https://github.com/huggingface/candle/pull/839).
### Modified
- Bugfix for conv2d
[820](https://github.com/huggingface/candle/pull/820).
- Support tensor based indexing using `.i`
[842](https://github.com/huggingface/candle/pull/842).
## v0.2.1 - 2023-09-11
### Added
- Add some RNNs (GRU and LSTM) in `candle-nn`
[674](https://github.com/huggingface/candle/pull/674),
[688](https://github.com/huggingface/candle/pull/688).
- gguf v2 support
[725](https://github.com/huggingface/candle/pull/725).
- Quantized llama example in Python using the pyo3 api
[716](https://github.com/huggingface/candle/pull/716).
- `candle-nn` layer for conv2d-transposed
[760](https://github.com/huggingface/candle/pull/760).
- Add the Segment-Anything Model (SAM) as an example
[773](https://github.com/huggingface/candle/pull/773).
- TinyViT backbone for the segemnt anything example
[787](https://github.com/huggingface/candle/pull/787).
- Shape with holes support
[770](https://github.com/huggingface/candle/pull/770).
### Modified
- Dilations are now supported in conv-transpose2d.
[671](https://github.com/huggingface/candle/pull/671).
- Interactive mode for the quantized model
[690](https://github.com/huggingface/candle/pull/690).
- Faster softmax operation
[747](https://github.com/huggingface/candle/pull/747).
- Faster convolution operations on CPU and CUDA via im2col
[802](https://github.com/huggingface/candle/pull/802).
- Moving some models to a more central location
[796](https://github.com/huggingface/candle/pull/796).
## v0.2.0 - 2023-08-30
### Added
- Add the powf op
[664](https://github.com/huggingface/candle/pull/664).
- Stable Diffusion XL support
[647](https://github.com/huggingface/candle/pull/647).
- Add the conv-transpose2d op
[635](https://github.com/huggingface/candle/pull/635).
- Refactor the VarBuilder api
[627](https://github.com/huggingface/candle/pull/627).
- Add some quantization command
[625](https://github.com/huggingface/candle/pull/625).
- Support more quantized types, e.g. Q2K, Q4K, Q5K...
[586](https://github.com/huggingface/candle/pull/586).
- Add pose estimation to the yolo example
[589](https://github.com/huggingface/candle/pull/589).
- Api to write GGUF files
[585](https://github.com/huggingface/candle/pull/585).
- Support more quantization types
[580](https://github.com/huggingface/candle/pull/580).
- Add EfficientNet as an example Computer Vision model
[572](https://github.com/huggingface/candle/pull/572).
- Add a group parameter to convolutions
[566](https://github.com/huggingface/candle/pull/566).
- New dtype: int64
[563](https://github.com/huggingface/candle/pull/563).
- Handling of the GGUF file format.
[559](https://github.com/huggingface/candle/pull/559).
## v0.1.2 - 2023-08-21
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# candle
[](https://discord.gg/hugging-face-879548962464493619)
[](https://crates.io/crates/candle-core)
[](https://docs.rs/candle-core)
Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support)
and ease of use. Try our online demos:
[whisper](https://huggingface.co/spaces/lmz/candle-whisper),
[LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2),
[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
[yolo](https://huggingface.co/spaces/lmz/candle-yolo),
[Segment
Anything](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
## Get started
Make sure that you have [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) correctly installed as described in [**Installation**](https://huggingface.github.io/candle/guide/installation.html).
Let's see how to run a simple matrix multiplication.
Write the following to your `myapp/src/main.rs` file:
```rust
use candle_core::{Device, Tensor};
fn main() -> Result<(), Box<dyn std::error::Error>> {
let device = Device::Cpu;
let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
let b = Tensor::randn(0f32, 1., (3, 4), &device)?;
let c = a.matmul(&b)?;
println!("{c}");
Ok(())
}
```
`cargo run` should display a tensor of shape `Tensor[[2, 4], f32]`.
Having installed `candle` with Cuda support, simply define the `device` to be on GPU:
```diff
- let device = Device::Cpu;
+ let device = Device::new_cuda(0)?;
```
For more advanced examples, please have a look at the following section.
## Check out our examples
These online demos run entirely in your browser:
- [yolo](https://huggingface.co/spaces/lmz/candle-yolo): pose estimation and
object recognition.
- [whisper](https://huggingface.co/spaces/lmz/candle-whisper): speech recognition.
- [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2): text generation.
- [T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm): text generation.
- [Phi-v1.5](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm): text generation.
- [Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm): Image segmentation.
- [BLIP](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning): image captioning.
We also provide a some command line based examples using state of the art models:
- [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM.
- [Falcon](./candle-examples/examples/falcon/): general LLM.
- [Phi-v1 and Phi-v1.5](./candle-examples/examples/phi/): a 1.3b general LLM with performance on par with LLaMA-v2 7b.
- [StableLM-3B-4E1T](./candle-examples/examples/stable-lm/): a 3b general LLM
pre-trained on 1T tokens of English and code datasets.
- [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
performance larger than all publicly available 13b models as of 2023-09-28.
- [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
- [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
(English/Chinese) general LLMs with 6b and 34b parameters.
- [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
the LLaMA model using the same quantization techniques as
[llama.cpp](https://github.com/ggerganov/llama.cpp).
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/quantized/assets/aoc.gif" width="600">
- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
- [Wuerstchen](./candle-examples/examples/wuerstchen/): another text to
image generative model.
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/wuerstchen/assets/cat.jpg" width="200">
- [yolo-v3](./candle-examples/examples/yolo-v3/) and
[yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
estimation models.
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.od.jpg" width="200"><img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.pose.jpg" width="200">
- [segment-anything](./candle-examples/examples/segment-anything/): image
segmentation model with prompt.
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
- [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
[JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
using self-supervision (can be used for imagenet classification, depth
evaluation, segmentation).
- [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
generate captions for an image.
- [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
model, generates the translated text from the input text.
Run them using commands like:
```
cargo run --example quantized --release
```
In order to use **CUDA** add `--features cuda` to the example command line. If
you have cuDNN installed, use `--features cudnn` for even more speedups.
There are also some wasm examples for whisper and
[llama2.c](https://github.com/karpathy/llama2.c). You can either build them with
`trunk` or try them online:
[whisper](https://huggingface.co/spaces/lmz/candle-whisper),
[llama2](https://huggingface.co/spaces/lmz/candle-llama2),
[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
[Phi-v1.5](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm),
[Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
For LLaMA2, run the following command to retrieve the weight files and start a
test server:
```bash
cd candle-wasm-examples/llama2-c
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
trunk serve --release --port 8081
```
And then head over to
[http://localhost:8081/](http://localhost:8081/).
<!--- ANCHOR: useful_libraries --->
## Useful External Resources
- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): A
very detailed tutorial showing how to convert a PyTorch model to Candle.
- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): Efficient and ergonomic LoRA implemenation for Candle. `candle-lora` has
out-of-the-box LoRA support for many models from Candle, which can be found [here](https://github.com/EricLBuehler/candle-lora/tree/master/candle-lora-transformers/examples).
- [`optimisers`](https://github.com/KGrewal1/optimisers): A collection of optimisers
including SGD with momentum, AdaGrad, AdaDelta, AdaMax, NAdam, RAdam, and RMSprop.
- [`candle-vllm`](https://github.com/EricLBuehler/candle-vllm): Efficient platform for inference and
serving local LLMs including an OpenAI compatible API server.
- [`candle-ext`](https://github.com/mokeyish/candle-ext): An extension library to Candle that provides PyTorch functions not currently available in Candle.
- [`kalosm`](https://github.com/floneum/floneum/tree/master/interfaces/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
- [`candle-sampling`](https://github.com/EricLBuehler/candle-sampling): Sampling techniques for Candle.
If you have an addition to this list, please submit a pull request.
<!--- ANCHOR_END: useful_libraries --->
<!--- ANCHOR: features --->
## Features
- Simple syntax, looks and feels like PyTorch.
- Model training.
- Embed user-defined ops/kernels, such as [flash-attention v2](https://github.com/huggingface/candle/blob/89ba005962495f2bfbda286e185e9c3c7f5300a3/candle-flash-attn/src/lib.rs#L152).
- Backends.
- Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
- CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
- WASM support, run your models in a browser.
- Included models.
- Language Models.
- LLaMA v1 and v2.
- Falcon.
- StarCoder.
- Phi v1.5.
- Mistral 7b v0.1.
- StableLM-3B-4E1T.
- Replit-code-v1.5-3B.
- Bert.
- Yi-6B and Yi-34B.
- Quantized LLMs.
- Llama 7b, 13b, 70b, as well as the chat and code variants.
- Mistral 7b, and 7b instruct.
- Zephyr 7b a and b (Mistral based).
- OpenChat 3.5 (Mistral based).
- Text to text.
- T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
- Marian MT (Machine Translation).
- Whisper (multi-lingual support).
- Text to image.
- Stable Diffusion v1.5, v2.1, XL v1.0.
- Wurstchen v2.
- Image to text.
- BLIP.
- Computer Vision Models.
- DINOv2, ConvMixer, EfficientNet, ResNet, ViT.
- yolo-v3, yolo-v8.
- Segment-Anything Model (SAM).
- File formats: load models from safetensors, npz, ggml, or PyTorch files.
- Serverless (on CPU), small and fast deployments.
- Quantization support using the llama.cpp quantized types.
<!--- ANCHOR_END: features --->
## How to use
<!--- ANCHOR: cheatsheet --->
Cheatsheet:
| | Using PyTorch | Using Candle |
|------------|------------------------------------------|------------------------------------------------------------------|
| Creation | `torch.Tensor([[1, 2], [3, 4]])` | `Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?` |
| Creation | `torch.zeros((2, 2))` | `Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?` |
| Indexing | `tensor[:, :4]` | `tensor.i((.., ..4))?` |
| Operations | `tensor.view((2, 2))` | `tensor.reshape((2, 2))?` |
| Operations | `a.matmul(b)` | `a.matmul(&b)?` |
| Arithmetic | `a + b` | `&a + &b` |
| Device | `tensor.to(device="cuda")` | `tensor.to_device(&Device::new_cuda(0)?)?` |
| Dtype | `tensor.to(dtype=torch.float16)` | `tensor.to_dtype(&DType::F16)?` |
| Saving | `torch.save({"A": A}, "model.bin")` | `candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?` |
| Loading | `weights = torch.load("model.bin")` | `candle::safetensors::load("model.safetensors", &device)` |
<!--- ANCHOR_END: cheatsheet --->
## Structure
- [candle-core](./candle-core): Core ops, devices, and `Tensor` struct definition
- [candle-nn](./candle-nn/): Tools to build real models
- [candle-examples](./candle-examples/): Examples of using the library in realistic settings
- [candle-kernels](./candle-kernels/): CUDA custom kernels
- [candle-datasets](./candle-datasets/): Datasets and data loaders.
- [candle-transformers](./candle-transformers): transformers-related utilities.
- [candle-flash-attn](./candle-flash-attn): Flash attention v2 layer.
- [candle-onnx](./candle-onnx/): ONNX model evaluation.
## FAQ
### Why should I use Candle?
Candle's core goal is to *make serverless inference possible*. Full machine learning frameworks like PyTorch
are very large, which makes creating instances on a cluster slow. Candle allows deployment of lightweight
binaries.
Secondly, Candle lets you *remove Python* from production workloads. Python overhead can seriously hurt performance,
and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
Finally, Rust is cool! A lot of the HF ecosystem already has Rust crates, like [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers).
### Other ML frameworks
- [dfdx](https://github.com/coreylowman/dfdx) is a formidable crate, with shapes being included
in types. This prevents a lot of headaches by getting the compiler to complain about shape mismatches right off the bat.
However, we found that some features still require nightly, and writing code can be a bit daunting for non rust experts.
We're leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each
other.
- [burn](https://github.com/burn-rs/burn) is a general crate that can leverage multiple backends so you can choose the best
engine for your workload.
- [tch-rs](https://github.com/LaurentMazare/tch-rs.git) Bindings to the torch library in Rust. Extremely versatile, but they
bring in the entire torch library into the runtime. The main contributor of `tch-rs` is also involved in the development
of `candle`.
### Common Errors
#### Missing symbols when compiling with the mkl feature.
If you get some missing symbols when compiling binaries/tests using the mkl
or accelerate features, e.g. for mkl you get:
```
= note: /usr/bin/ld: (....o): in function `blas::sgemm':
.../blas-0.22.0/src/lib.rs:1944: undefined reference to `sgemm_' collect2: error: ld returned 1 exit status
= note: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
= note: use the `-l` flag to specify native libraries to link
= note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo
```
or for accelerate:
```
Undefined symbols for architecture arm64:
"_dgemm_", referenced from:
candle_core::accelerate::dgemm::h1b71a038552bcabe in libcandle_core...
"_sgemm_", referenced from:
candle_core::accelerate::sgemm::h2cf21c592cba3c47 in libcandle_core...
ld: symbol(s) not found for architecture arm64
```
This is likely due to a missing linker flag that was needed to enable the mkl library. You
can try adding the following for mkl at the top of your binary:
```rust
extern crate intel_mkl_src;
```
or for accelerate:
```rust
extern crate accelerate_src;
```
#### Cannot run the LLaMA examples: access to source requires login credentials
```
Error: request error: https://huggingface.co/meta-llama/Llama-2-7b-hf/resolve/main/tokenizer.json: status code 401
```
This is likely because you're not permissioned for the LLaMA-v2 model. To fix
this, you have to register on the huggingface-hub, accept the [LLaMA-v2 model
conditions](https://huggingface.co/meta-llama/Llama-2-7b-hf), and set up your
authentication token. See issue
[#350](https://github.com/huggingface/candle/issues/350) for more details.
#### Missing cute/cutlass headers when compiling flash-attn
```
In file included from kernels/flash_fwd_launch_template.h:11:0,
from kernels/flash_fwd_hdim224_fp16_sm80.cu:5:
kernels/flash_fwd_kernel.h:8:10: fatal error: cute/algorithm/copy.hpp: No such file or directory
#include <cute/algorithm/copy.hpp>
^~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
Error: nvcc error while compiling:
```
[cutlass](https://github.com/NVIDIA/cutlass) is provided as a git submodule so you may want to run the following command to check it in properly.
```bash
git submodule update --init
```
#### Compiling with flash-attention fails
```
/usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:
```
This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the CANDLE_NVCC_CCBIN environment variable.
```
env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
```
#### Linking error on windows when running rustdoc or mdbook tests
```
Couldn't compile the test.
---- .\candle-book\src\inference\hub.md - Using_the_hub::Using_in_a_real_model_ (line 50) stdout ----
error: linking with `link.exe` failed: exit code: 1181
//very long chain of linking
= note: LINK : fatal error LNK1181: cannot open input file 'windows.0.48.5.lib'
```
Make sure you link all native libraries that might be located outside a project target, e.g., to run mdbook tests, you should run:
```
mdbook test candle-book -L .\target\debug\deps\ `
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.42.2\lib `
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.48.5\lib
```
#### Extremely slow model load time with WSL
This may be caused by the models being loaded from `/mnt/c`, more details on
[stackoverflow](https://stackoverflow.com/questions/68972448/why-is-wsl-extremely-slow-when-compared-with-native-windows-npm-yarn-processing).
#### Tracking down errors
You can set `RUST_BACKTRACE=1` to be provided with backtraces when a candle
error is generated.
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backend-test:J
xytest"Relu
SingleReluZ
x
b
y
B
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hf_public_repos/candle/LICENSE-MIT
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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 Software, and to permit persons to whom the Software
is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice
shall be included in all copies or substantial portions
of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
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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",
"--",
"-Dwarnings",
]
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|
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,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
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"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
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"Contributor" shall mean Licensor and any individual or Legal Entity
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2. Grant of Copyright License. Subject to the terms and conditions of
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worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
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with the Work to which such Contribution(s) was submitted. If You
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or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
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(d) If the Work includes a "NOTICE" text file as part of its
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of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
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that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
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7. Disclaimer of Warranty. Unless required by applicable law or
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
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incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
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Licensed under the Apache License, Version 2.0 (the "License");
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| 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",
"candle-onnx",
]
resolver = "2"
[workspace.package]
version = "0.3.1"
edition = "2021"
description = "Minimalist ML framework."
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[workspace.dependencies]
accelerate-src = { version = "0.3.2" }
anyhow = { version = "1", features = ["backtrace"] }
byteorder = "1.4.3"
clap = { version = "4.2.4", features = ["derive"] }
cudarc = { version = "0.9.14", features = ["f16"] }
gemm = { version = "0.16.6", features = ["wasm-simd128-enable"] }
hf-hub = "0.3.0"
half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }
imageproc = { version = "0.23.0", default-features = false }
intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"] }
libc = { version = "0.2.147" }
log = "0.4"
memmap2 = { version = "0.7.1", features = ["stable_deref_trait"] }
num_cpus = "1.15.0"
num-traits = "0.2.15"
parquet = { version = "45.0.0" }
rand = "0.8.5"
rand_distr = "0.4.3"
rayon = "1.7.0"
rusttype = { version = "0.9", default-features = false }
safetensors = "0.3.1"
serde = { version = "1.0.171", features = ["derive"] }
serde_plain = "1.0.2"
serde_json = "1.0.99"
thiserror = "1"
tokenizers = { version = "0.13.4", default-features = false }
tracing = "0.1.37"
tracing-chrome = "0.7.1"
tracing-subscriber = "0.3.7"
wav = "1.0.0"
yoke = { version = "0.7.2", features = ["derive"] }
zip = { version = "0.6.6", default-features = false }
metal = { version = "0.27.1", features = ["mps"], package="candle-metal" }
[profile.release-with-debug]
inherits = "release"
debug = true
| 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,
and distribution as defined by Sections 1 through 9 of this document.
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"You" (or "Your") shall mean an individual or Legal Entity
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"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
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"Contributor" shall mean Licensor and any individual or Legal Entity
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worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
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granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
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attribution notices from the Source form of the Work,
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(d) If the Work includes a "NOTICE" text file as part of its
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within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| 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 = { workspace = true }
candle-kernels = { path = "../candle-kernels", version = "0.3.1", optional = true }
candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.1", optional = true }
metal = { workspace = true, optional = true}
cudarc = { workspace = true, optional = true }
gemm = { workspace = true }
half = { workspace = true }
intel-mkl-src = { workspace = true, optional = true }
libc = { workspace = true, optional = true }
memmap2 = { workspace = true }
num-traits = { workspace = true }
num_cpus = { workspace = true }
rand = { workspace = true }
rand_distr = { workspace = true }
rayon = { workspace = true }
safetensors = { workspace = true }
thiserror = { workspace = true }
yoke = { workspace = true }
zip = { workspace = true }
[dev-dependencies]
anyhow = { workspace = true }
clap = { workspace = true }
[features]
default = []
cuda = ["cudarc", "dep:candle-kernels"]
cudnn = ["cuda", "cudarc/cudnn"]
mkl = ["dep:libc", "dep:intel-mkl-src"]
accelerate = ["dep:libc", "dep:accelerate-src"]
metal = ["dep:metal", "dep:candle-metal-kernels"]
| 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 core::mem;
use half::{bf16, f16};
use metal;
use metal::{Buffer, CommandQueue, MTLResourceOptions, NSUInteger};
use std::sync::Arc;
/// Metal related errors
#[derive(thiserror::Error, Debug)]
pub enum MetalError {
#[error("{0}")]
Message(String),
#[error(transparent)]
KernelError(#[from] candle_metal_kernels::MetalKernelError),
#[error("matmul is only supported for contiguous tensors lstride: {lhs_stride:?} rstride: {rhs_stride:?} mnk: {mnk:?}")]
MatMulNonContiguous {
lhs_stride: Vec<usize>,
rhs_stride: Vec<usize>,
mnk: (usize, usize, usize),
},
}
impl From<String> for MetalError {
fn from(e: String) -> Self {
MetalError::Message(e)
}
}
#[derive(Clone)]
pub struct MetalDevice {
device: metal::Device,
command_queue: metal::CommandQueue,
kernels: Arc<candle_metal_kernels::Kernels>,
}
impl std::fmt::Debug for MetalDevice {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "MetalDevice({:?})", self.device.registry_id())
}
}
impl std::ops::Deref for MetalDevice {
type Target = metal::DeviceRef;
fn deref(&self) -> &Self::Target {
&self.device
}
}
impl MetalDevice {
pub fn id(&self) -> NSUInteger {
self.registry_id()
}
pub fn command_queue(&self) -> &CommandQueue {
&self.command_queue
}
pub fn kernels(&self) -> &Kernels {
&self.kernels
}
pub fn device(&self) -> &metal::Device {
&self.device
}
pub fn new_buffer(&self, element_count: usize, dtype: DType) -> Buffer {
let size = (element_count * dtype.size_in_bytes()) as NSUInteger;
self.device
.new_buffer(size, MTLResourceOptions::StorageModeManaged)
}
}
#[derive(Debug, Clone)]
pub struct MetalStorage {
buffer: metal::Buffer,
device: MetalDevice,
dtype: DType,
}
impl BackendStorage for MetalStorage {
type Device = MetalDevice;
fn try_clone(&self, _: &Layout) -> Result<Self> {
Ok(self.clone())
}
fn dtype(&self) -> DType {
self.dtype
}
fn device(&self) -> &Self::Device {
&self.device
}
fn to_cpu_storage(&self) -> Result<CpuStorage> {
let length = self.buffer.length() as usize;
let size = self.dtype.size_in_bytes();
if length % size != 0 {
crate::bail!(
"The Metal buffer length is not aligned with dtype {:?}",
self.dtype
);
}
match self.dtype {
DType::U8 => Ok(CpuStorage::U8(self.buffer.read_to_vec(length / size))),
DType::U32 => Ok(CpuStorage::U32(self.buffer.read_to_vec(length / size))),
DType::I64 => Ok(CpuStorage::I64(self.buffer.read_to_vec(length / size))),
DType::F16 => Ok(CpuStorage::F16(self.buffer.read_to_vec(length / size))),
DType::BF16 => Ok(CpuStorage::BF16(self.buffer.read_to_vec(length / size))),
DType::F32 => Ok(CpuStorage::F32(self.buffer.read_to_vec(length / size))),
DType::F64 => Ok(CpuStorage::F64(self.buffer.read_to_vec(length / size))),
}
}
fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
let device = self.device().clone();
let shape = layout.shape();
let el = shape.elem_count();
let dtype = self.dtype;
if layout.is_contiguous() || layout.start_offset() != 0 || dtype != DType::F32 {
crate::bail!("Not contiguous, non-f32 affine is not implemented yet.");
}
let mut buffer = device.new_buffer(el, self.dtype);
let command_buffer = self.device.command_queue.new_command_buffer();
candle_metal_kernels::call_affine(
&device.device,
&command_buffer,
&device.kernels,
el,
&self.buffer,
&mut buffer,
mul as f32,
add as f32,
)
.map_err(MetalError::from)?;
command_buffer.commit();
command_buffer.wait_until_completed();
return Ok(Self {
buffer,
device: device.clone(),
dtype,
});
}
fn powf(&self, _: &Layout, _: f64) -> Result<Self> {
crate::bail!("powf metal")
}
fn elu(&self, _: &Layout, _: f64) -> Result<Self> {
crate::bail!("elu metal")
}
fn reduce_op(&self, op: ReduceOp, layout: &Layout, sum_dims: &[usize]) -> Result<Self> {
if !(sum_dims.len() == 1
&& sum_dims[0] == layout.shape().rank() - 1
&& layout.is_contiguous()
&& layout.start_offset() == 0)
{
crate::bail!("Non contiguous reduce op not supported yet");
}
let device = self.device.clone();
let src_stride = layout.stride();
let src_dims = layout.shape().dims();
let src_el: usize = src_dims.iter().product();
// Source dims and strides with the sum dims at the end.
let mut dims = vec![];
let mut stride = vec![];
let mut dst_el: usize = 1;
for (dim_idx, &d) in src_dims.iter().enumerate() {
if !sum_dims.contains(&dim_idx) {
dst_el *= d;
dims.push(d);
stride.push(src_stride[dim_idx]);
}
}
for &dim_idx in sum_dims.iter() {
dims.push(src_dims[dim_idx]);
stride.push(src_stride[dim_idx]);
}
// The reduction loop requires the shared array to be properly initialized and for
// this we want the number of threads to be a power of two.
let (name, check_empty, return_index) = match (op, self.dtype) {
(ReduceOp::Sum, DType::F32) => ("fast_sum_float", false, false),
(ReduceOp::Min, DType::F32) => ("fast_min_float", true, false),
(ReduceOp::Max, DType::F32) => ("fast_max_float", true, false),
(ReduceOp::ArgMin, DType::F32) => ("fast_argmin_float", true, true),
(ReduceOp::ArgMax, DType::F32) => ("fast_argmax_float", true, true),
_ => crate::bail!("Reduce op for non float"),
};
if check_empty && layout.shape().elem_count() == 0 {
Err(crate::Error::EmptyTensor { op: "reduce" }.bt())?
}
let dtype = if return_index { DType::U32 } else { self.dtype };
let mut buffer = device.new_buffer(dst_el, dtype);
let command_buffer = self.device.command_queue.new_command_buffer();
candle_metal_kernels::call_reduce_contiguous(
&device.device,
&command_buffer,
&device.kernels,
name,
src_el,
dst_el,
&self.buffer,
&mut buffer,
)
.map_err(MetalError::from)?;
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device,
dtype,
})
}
fn cmp(&self, _: CmpOp, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
crate::bail!("cmp metal")
}
fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
let device = self.device();
let shape = layout.shape();
let el_count = shape.elem_count();
let mut buffer = device.new_buffer(el_count, dtype);
let command_buffer = device.command_queue.new_command_buffer();
if layout.is_contiguous() {
let kernel_name = match (self.dtype, dtype) {
(DType::U32, DType::F32) => "cast_u32_f32",
(left, right) => crate::bail!("to dtype {left:?} - {right:?}"),
};
candle_metal_kernels::call_cast_contiguous(
&device.device,
&command_buffer,
&device.kernels,
kernel_name,
el_count,
&self.buffer,
&mut buffer,
)
.map_err(MetalError::from)?;
} else {
crate::bail!(
"TODO Implement the kernel calling cast {:?}-{:?}",
self.dtype,
dtype
);
}
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device: device.clone(),
dtype,
})
}
fn unary_impl<B: UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
let device = self.device();
let dtype = self.dtype;
let shape = layout.shape();
let el_count = shape.elem_count();
let mut buffer = device.new_buffer(el_count, dtype);
let command_buffer = device.command_queue.new_command_buffer();
if layout.is_contiguous() && layout.start_offset() == 0 {
use candle_metal_kernels::unary::contiguous;
let kernel_name = match (B::KERNEL, dtype) {
("ucos", DType::F32) => contiguous::cos::FLOAT,
("usin", DType::F32) => contiguous::sin::FLOAT,
("usqr", DType::F32) => contiguous::sqr::FLOAT,
("usqrt", DType::F32) => contiguous::sqrt::FLOAT,
("uneg", DType::F32) => contiguous::neg::FLOAT,
("uexp", DType::F32) => contiguous::exp::FLOAT,
("ulog", DType::F32) => contiguous::log::FLOAT,
(name, dtype) => crate::bail!("Match {name} - {dtype:?}"),
};
candle_metal_kernels::call_unary_contiguous(
&device.device,
&command_buffer,
&device.kernels,
kernel_name,
el_count,
&self.buffer,
&mut buffer,
)
.map_err(MetalError::from)?;
} else {
crate::bail!("TODO Implement the kernel calling {}", B::KERNEL);
}
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device: device.clone(),
dtype,
})
}
fn binary_impl<B: BinaryOpT>(
&self,
rhs: &Self,
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
let device = self.device();
let dtype = self.dtype;
let shape = lhs_l.shape();
let el_count = shape.elem_count();
let mut buffer = device.new_buffer(el_count, dtype);
let command_buffer = device.command_queue.new_command_buffer();
if (lhs_l.is_contiguous() && lhs_l.start_offset() == 0)
&& (rhs_l.is_contiguous() && rhs_l.start_offset() == 0)
{
use candle_metal_kernels::binary::contiguous;
let kernel_name = match (B::KERNEL, dtype) {
("add", DType::F32) => contiguous::add::FLOAT,
("badd", DType::F32) => contiguous::add::FLOAT,
("sub", DType::F32) => contiguous::sub::FLOAT,
("bsub", DType::F32) => contiguous::sub::FLOAT,
("mul", DType::F32) => contiguous::mul::FLOAT,
("bmul", DType::F32) => contiguous::mul::FLOAT,
("div", DType::F32) => contiguous::div::FLOAT,
("bdiv", DType::F32) => contiguous::div::FLOAT,
(name, dtype) => crate::bail!("Match {name} - {dtype:?}"),
};
candle_metal_kernels::call_binary_contiguous(
&device.device,
&command_buffer,
&device.kernels,
kernel_name,
el_count,
&self.buffer,
&rhs.buffer,
&mut buffer,
)
.map_err(MetalError::from)?;
} else {
use candle_metal_kernels::binary::strided;
let kernel_name = match (B::KERNEL, dtype) {
("badd", DType::F32) => strided::add::FLOAT,
("bsub", DType::F32) => strided::sub::FLOAT,
("bmul", DType::F32) => strided::mul::FLOAT,
("bdiv", DType::F32) => strided::div::FLOAT,
(name, dtype) => crate::bail!("Match {name} - {dtype:?}"),
};
candle_metal_kernels::call_binary_strided(
&device.device,
&command_buffer,
&device.kernels,
kernel_name,
lhs_l.dims(),
&self.buffer,
&lhs_l.stride(),
lhs_l.start_offset() * self.dtype.size_in_bytes(),
&rhs.buffer,
&rhs_l.stride(),
rhs_l.start_offset() * rhs.dtype.size_in_bytes(),
&mut buffer,
)
.map_err(MetalError::from)?;
}
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device: device.clone(),
dtype,
})
}
fn where_cond(
&self,
layout: &Layout,
t: &Self,
t_l: &Layout,
f: &Self,
f_l: &Layout,
) -> Result<Self> {
let device = self.device.clone();
let shape = t_l.shape();
let dims = shape.dims();
let el = shape.elem_count();
let dtype = t.dtype;
let mut buffer = self.device.new_buffer(el, dtype);
let command_buffer = self.device.command_queue.new_command_buffer();
candle_metal_kernels::call_where_cond_strided(
&device.device,
&command_buffer,
&device.kernels,
"where_u8_f32",
&dims,
&self.buffer,
(
layout.stride(),
layout.start_offset() * self.dtype.size_in_bytes(),
),
&t.buffer,
(&t_l.stride(), t_l.start_offset() * t.dtype.size_in_bytes()),
&f.buffer,
(&f_l.stride(), f_l.start_offset() * f.dtype.size_in_bytes()),
&mut buffer,
)
.map_err(MetalError::from)?;
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device,
dtype,
})
}
fn conv1d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &ParamsConv1D,
) -> Result<Self> {
crate::bail!("conv1d metal")
}
fn conv_transpose1d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &ParamsConvTranspose1D,
) -> Result<Self> {
crate::bail!("conv_transpose1d metal")
}
fn conv2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &ParamsConv2D,
) -> Result<Self> {
crate::bail!("conv2d metal")
}
fn conv_transpose2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &ParamsConvTranspose2D,
) -> Result<Self> {
crate::bail!("conv_tranpose2d metal")
}
fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
crate::bail!("avg_pool2d metal")
}
fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
crate::bail!("max_pool2d metal")
}
fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
crate::bail!("upsample_nearest1d metal")
}
fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
crate::bail!("upsample_nearest2d metal")
}
fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self> {
crate::bail!("gather metal")
}
fn scatter_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
crate::bail!("scatter_add metal")
}
fn index_select(&self, ids: &Self, src_l: &Layout, ids_l: &Layout, dim: usize) -> Result<Self> {
if !(src_l.is_contiguous()
&& src_l.start_offset() == 0
&& ids_l.is_contiguous()
&& ids_l.start_offset() == 0)
{
crate::bail!("Non contiguous index select not implemented");
}
let left_size: usize = src_l.dims()[..dim].iter().product();
let right_size: usize = src_l.dims()[dim + 1..].iter().product();
let ids_el = ids_l.shape().elem_count();
let dst_el = ids_el * left_size * right_size;
let dtype = self.dtype;
let device = self.device();
let mut buffer = device.new_buffer(dst_el, dtype);
let name = match (ids.dtype, self.dtype) {
(DType::U32, DType::F32) => "is_u32_f32",
(left, right) => crate::bail!("index select metal {left:?} {right:?}"),
};
let command_buffer = self.device.command_queue.new_command_buffer();
candle_metal_kernels::call_index_select(
&device.device,
&command_buffer,
&self.device.kernels,
name,
src_l.dims(),
ids_el,
dim,
&self.buffer,
&ids.buffer,
&mut buffer,
)
.map_err(MetalError::from)?;
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer,
device: device.clone(),
dtype,
})
}
fn index_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
crate::bail!("index_add metal")
}
fn matmul(
&self,
rhs: &Self,
(b, m, n, k): (usize, usize, usize, usize),
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
// Create descriptors
use metal::mps::matrix::*;
let type_id = metal::mps::MPS_FLOATBIT_ENCODING | 32;
let size = core::mem::size_of::<f32>() as NSUInteger;
let elem_count = b * m * n;
let lhs_stride = lhs_l.stride();
let rhs_stride = rhs_l.stride();
let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
let rhs_m2 = rhs_stride[rhs_stride.len() - 2];
let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
// The a tensor has dims batching, k, n (rhs)
let transpose_left = if lhs_m1 == 1 && lhs_m2 == k {
false
} else if lhs_m1 == m && lhs_m2 == 1 {
true
} else {
Err(MetalError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?
};
let transpose_right = if rhs_m1 == 1 && rhs_m2 == n {
false
} else if rhs_m1 == k && rhs_m2 == 1 {
true
} else {
Err(MetalError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?
};
let b = b as NSUInteger;
let m = m as NSUInteger;
let n = n as NSUInteger;
let k = k as NSUInteger;
let left_descriptor = if transpose_left {
MatrixDescriptor::init_single(k, m, m * size, type_id)
} else {
MatrixDescriptor::init_single(m, k, k * size, type_id)
};
let right_descriptor = if transpose_right {
MatrixDescriptor::init_single(n, k, k * size, type_id)
} else {
MatrixDescriptor::init_single(k, n, n * size, type_id)
};
let result_descriptor = MatrixDescriptor::init_single(m, n, n * size, type_id);
// Create matrix objects
let left_matrix = Matrix::init_with_buffer_descriptor(&self.buffer, 0, &left_descriptor)
.ok_or_else(|| {
MetalError::from("Failed to create matrix multiplication kernel".to_string())
})?;
let right_matrix = Matrix::init_with_buffer_descriptor(&rhs.buffer, 0, &right_descriptor)
.ok_or_else(|| {
MetalError::from("Failed to create matrix multiplication kernel".to_string())
})?;
let out_buffer = self.device.new_buffer(elem_count, self.dtype);
let result_matrix = Matrix::init_with_buffer_descriptor(&out_buffer, 0, &result_descriptor)
.ok_or_else(|| {
MetalError::from("Failed to create matrix multiplication kernel".to_string())
})?;
let alpha = 1.0f64;
let beta = 0.0f64;
// Create kernel
let matrix_multiplication = MatrixMultiplication::init(
&self.device,
transpose_left,
transpose_right,
m,
n,
k,
alpha,
beta,
)
.ok_or_else(|| {
MetalError::from("Failed to create matrix multiplication kernel".to_string())
})?;
matrix_multiplication.set_batch_size(b);
// Encode kernel to command buffer
let command_buffer = self.device.command_queue.new_command_buffer();
matrix_multiplication.encode_to_command_buffer(
command_buffer,
&left_matrix,
&right_matrix,
&result_matrix,
);
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(Self {
buffer: out_buffer,
device: self.device.clone(),
dtype: self.dtype(),
})
}
fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
let src_shape = src_l.shape();
let el_count = src_shape.elem_count();
if el_count == 0 {
return Ok(());
}
let command_buffer = self.device.command_queue.new_command_buffer();
let kernel_name = match self.dtype {
DType::F32 => candle_metal_kernels::unary::strided::copy::FLOAT,
DType::F16 => candle_metal_kernels::unary::strided::copy::HALF,
DType::BF16 => candle_metal_kernels::unary::strided::copy::BFLOAT,
dtype => crate::bail!("copy_strided not implemented for {dtype:?}"),
};
candle_metal_kernels::call_unary_strided(
&self.device.device,
&command_buffer,
&self.device.kernels,
kernel_name,
src_l.dims(),
&self.buffer,
&src_l.stride(),
src_l.start_offset() * self.dtype.size_in_bytes(),
&mut dst.buffer,
dst_offset,
)
.map_err(MetalError::from)?;
command_buffer.commit();
command_buffer.wait_until_completed();
Ok(())
}
}
impl MetalStorage {
pub fn new(buffer: Buffer, device: MetalDevice, dtype: DType) -> Self {
Self {
buffer,
device,
dtype,
}
}
pub fn buffer(&self) -> &Buffer {
&self.buffer
}
}
impl BackendDevice for MetalDevice {
type Storage = MetalStorage;
fn new(ordinal: usize) -> Result<Self> {
let device = metal::Device::all().swap_remove(ordinal);
let command_queue = device.new_command_queue();
let kernels = Arc::new(Kernels::new());
Ok(Self {
device,
command_queue,
kernels,
})
}
fn set_seed(&self, _seed: u64) -> Result<()> {
crate::bail!("set_seed")
}
fn location(&self) -> crate::DeviceLocation {
crate::DeviceLocation::Metal {
gpu_id: self.registry_id() as usize,
}
}
fn same_device(&self, rhs: &Self) -> bool {
self.device.registry_id() == rhs.device.registry_id()
}
fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<MetalStorage> {
// TODO Is there a faster way ?
let cpu_storage = crate::cpu_backend::CpuDevice.zeros_impl(shape, dtype)?;
self.storage_from_cpu_storage(&cpu_storage)
}
fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<Self::Storage> {
// TODO Is there a faster way ?
let cpu_storage = crate::cpu_backend::CpuDevice.ones_impl(shape, dtype)?;
self.storage_from_cpu_storage(&cpu_storage)
}
fn storage_from_cpu_storage(&self, storage: &CpuStorage) -> Result<Self::Storage> {
let option = metal::MTLResourceOptions::StorageModeManaged;
let buffer = match storage {
CpuStorage::U8(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<u8>()) as NSUInteger,
option,
),
CpuStorage::U32(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<u32>()) as NSUInteger,
option,
),
CpuStorage::I64(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<i64>()) as NSUInteger,
option,
),
CpuStorage::BF16(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<bf16>()) as NSUInteger,
option,
),
CpuStorage::F16(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<f16>()) as NSUInteger,
option,
),
CpuStorage::F32(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<f32>()) as NSUInteger,
option,
),
CpuStorage::F64(storage) => self.device.new_buffer_with_data(
storage.as_ptr() as *const core::ffi::c_void,
(storage.len() * mem::size_of::<f64>()) as NSUInteger,
option,
),
};
Ok(Self::Storage {
buffer,
device: self.clone(),
dtype: storage.dtype(),
})
}
fn rand_uniform(
&self,
shape: &Shape,
dtype: DType,
mean: f64,
stddev: f64,
) -> Result<Self::Storage> {
// TODO is there a better way ?
let cpu_storage = crate::cpu_backend::CpuDevice.rand_uniform(shape, dtype, mean, stddev)?;
self.storage_from_cpu_storage(&cpu_storage)
}
fn rand_normal(
&self,
shape: &Shape,
dtype: DType,
mean: f64,
stddev: f64,
) -> Result<Self::Storage> {
// TODO is there a better way ?
let cpu_storage = crate::cpu_backend::CpuDevice.rand_normal(shape, dtype, mean, stddev)?;
self.storage_from_cpu_storage(&cpu_storage)
}
}
| 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 Error {
// === DType Errors ===
#[error("{msg}, expected: {expected:?}, got: {got:?}")]
UnexpectedDType {
msg: &'static str,
expected: DType,
got: DType,
},
#[error("dtype mismatch in {op}, lhs: {lhs:?}, rhs: {rhs:?}")]
DTypeMismatchBinaryOp {
lhs: DType,
rhs: DType,
op: &'static str,
},
#[error("unsupported dtype {0:?} for op {1}")]
UnsupportedDTypeForOp(DType, &'static str),
// === Dimension Index Errors ===
#[error("{op}: dimension index {dim} out of range for shape {shape:?}")]
DimOutOfRange {
shape: Shape,
dim: i32,
op: &'static str,
},
#[error("{op}: duplicate dim index {dims:?} for shape {shape:?}")]
DuplicateDimIndex {
shape: Shape,
dims: Vec<usize>,
op: &'static str,
},
// === Shape Errors ===
#[error("unexpected rank, expected: {expected}, got: {got} ({shape:?})")]
UnexpectedNumberOfDims {
expected: usize,
got: usize,
shape: Shape,
},
#[error("{msg}, expected: {expected:?}, got: {got:?}")]
UnexpectedShape {
msg: String,
expected: Shape,
got: Shape,
},
#[error(
"Shape mismatch, got buffer of size {buffer_size} which is compatible with shape {shape:?}"
)]
ShapeMismatch { buffer_size: usize, shape: Shape },
#[error("shape mismatch in {op}, lhs: {lhs:?}, rhs: {rhs:?}")]
ShapeMismatchBinaryOp {
lhs: Shape,
rhs: Shape,
op: &'static str,
},
#[error("shape mismatch in cat for dim {dim}, shape for arg 1: {first_shape:?} shape for arg {n}: {nth_shape:?}")]
ShapeMismatchCat {
dim: usize,
first_shape: Shape,
n: usize,
nth_shape: Shape,
},
#[error("Cannot divide tensor of shape {shape:?} equally along dim {dim} into {n_parts}")]
ShapeMismatchSplit {
shape: Shape,
dim: usize,
n_parts: usize,
},
#[error("{op} can only be performed on a single dimension")]
OnlySingleDimension { op: &'static str, dims: Vec<usize> },
#[error("empty tensor for {op}")]
EmptyTensor { op: &'static str },
// === Device Errors ===
#[error("device mismatch in {op}, lhs: {lhs:?}, rhs: {rhs:?}")]
DeviceMismatchBinaryOp {
lhs: DeviceLocation,
rhs: DeviceLocation,
op: &'static str,
},
// === Op Specific Errors ===
#[error("narrow invalid args {msg}: {shape:?}, dim: {dim}, start: {start}, len:{len}")]
NarrowInvalidArgs {
shape: Shape,
dim: usize,
start: usize,
len: usize,
msg: &'static str,
},
#[error("conv1d invalid args {msg}: inp: {inp_shape:?}, k: {k_shape:?}, pad: {padding}, stride: {stride}")]
Conv1dInvalidArgs {
inp_shape: Shape,
k_shape: Shape,
padding: usize,
stride: usize,
msg: &'static str,
},
#[error("{op} invalid index {index} with dim size {size}")]
InvalidIndex {
op: &'static str,
index: usize,
size: usize,
},
#[error("cannot broadcast {src_shape:?} to {dst_shape:?}")]
BroadcastIncompatibleShapes { src_shape: Shape, dst_shape: Shape },
#[error("cannot set variable {msg}")]
CannotSetVar { msg: &'static str },
// Box indirection to avoid large variant.
#[error("{0:?}")]
MatMulUnexpectedStriding(Box<MatMulUnexpectedStriding>),
#[error("{op} only supports contiguous tensors")]
RequiresContiguous { op: &'static str },
#[error("{op} expects at least one tensor")]
OpRequiresAtLeastOneTensor { op: &'static str },
#[error("{op} expects at least two tensors")]
OpRequiresAtLeastTwoTensors { op: &'static str },
#[error("backward is not supported for {op}")]
BackwardNotSupported { op: &'static str },
// === Other Errors ===
#[error("the candle crate has not been built with cuda support")]
NotCompiledWithCudaSupport,
#[error("the candle crate has not been built with metal support")]
NotCompiledWithMetalSupport,
#[error("cannot find tensor {path}")]
CannotFindTensor { path: String },
// === Wrapped Errors ===
#[error(transparent)]
Cuda(Box<dyn std::error::Error + Send + Sync>),
#[error("Metal error {0}")]
Metal(#[from] MetalError),
#[error(transparent)]
TryFromIntError(#[from] core::num::TryFromIntError),
#[error("npy/npz error {0}")]
Npy(String),
/// Zip file format error.
#[error(transparent)]
Zip(#[from] zip::result::ZipError),
/// Integer parse error.
#[error(transparent)]
ParseInt(#[from] std::num::ParseIntError),
/// I/O error.
#[error(transparent)]
Io(#[from] std::io::Error),
/// SafeTensor error.
#[error(transparent)]
SafeTensor(#[from] safetensors::SafeTensorError),
#[error("unsupported safetensor dtype {0:?}")]
UnsupportedSafeTensorDtype(safetensors::Dtype),
/// Arbitrary errors wrapping.
#[error(transparent)]
Wrapped(Box<dyn std::error::Error + Send + Sync>),
/// Adding path information to an error.
#[error("path: {path:?} {inner}")]
WithPath {
inner: Box<Self>,
path: std::path::PathBuf,
},
#[error("{inner}\n{backtrace}")]
WithBacktrace {
inner: Box<Self>,
backtrace: Box<std::backtrace::Backtrace>,
},
/// User generated error message, typically created via `bail!`.
#[error("{0}")]
Msg(String),
}
pub type Result<T> = std::result::Result<T, Error>;
impl Error {
pub fn wrap(err: impl std::error::Error + Send + Sync + 'static) -> Self {
Self::Wrapped(Box::new(err)).bt()
}
pub fn msg(err: impl std::error::Error + Send + Sync + 'static) -> Self {
Self::Msg(err.to_string()).bt()
}
pub fn bt(self) -> Self {
let backtrace = std::backtrace::Backtrace::capture();
match backtrace.status() {
std::backtrace::BacktraceStatus::Disabled
| std::backtrace::BacktraceStatus::Unsupported => self,
_ => Self::WithBacktrace {
inner: Box::new(self),
backtrace: Box::new(backtrace),
},
}
}
pub fn with_path<P: AsRef<std::path::Path>>(self, p: P) -> Self {
Self::WithPath {
inner: Box::new(self),
path: p.as_ref().to_path_buf(),
}
}
}
#[macro_export]
macro_rules! bail {
($msg:literal $(,)?) => {
return Err($crate::Error::Msg(format!($msg).into()).bt())
};
($err:expr $(,)?) => {
return Err($crate::Error::Msg(format!($err).into()).bt())
};
($fmt:expr, $($arg:tt)*) => {
return Err($crate::Error::Msg(format!($fmt, $($arg)*).into()).bt())
};
}
pub fn zip<T, U>(r1: Result<T>, r2: Result<U>) -> Result<(T, U)> {
match (r1, r2) {
(Ok(r1), Ok(r2)) => Ok((r1, r2)),
(Err(e), _) => Err(e),
(_, Err(e)) => Err(e),
}
}
| 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 + std::fmt::Display>(
&self,
f: &mut std::fmt::Formatter,
) -> std::fmt::Result {
let device_str = match self.device().location() {
crate::DeviceLocation::Cpu => "".to_owned(),
crate::DeviceLocation::Cuda { gpu_id } => {
format!(", cuda:{}", gpu_id)
}
crate::DeviceLocation::Metal { gpu_id } => {
format!(", metal:{}", gpu_id)
}
};
write!(f, "Tensor[")?;
match self.dims() {
[] => {
if let Ok(v) = self.to_scalar::<T>() {
write!(f, "{v}")?
}
}
[s] if *s < 10 => {
if let Ok(vs) = self.to_vec1::<T>() {
for (i, v) in vs.iter().enumerate() {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{v}")?;
}
}
}
dims => {
write!(f, "dims ")?;
for (i, d) in dims.iter().enumerate() {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{d}")?;
}
}
}
write!(f, "; {}{}]", self.dtype().as_str(), device_str)
}
}
impl std::fmt::Debug for Tensor {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
match self.dtype() {
DType::U8 => self.fmt_dt::<u8>(f),
DType::U32 => self.fmt_dt::<u32>(f),
DType::I64 => self.fmt_dt::<i64>(f),
DType::BF16 => self.fmt_dt::<bf16>(f),
DType::F16 => self.fmt_dt::<f16>(f),
DType::F32 => self.fmt_dt::<f32>(f),
DType::F64 => self.fmt_dt::<f64>(f),
}
}
}
/// Options for Tensor pretty printing
pub struct PrinterOptions {
precision: usize,
threshold: usize,
edge_items: usize,
line_width: usize,
sci_mode: Option<bool>,
}
static PRINT_OPTS: std::sync::Mutex<PrinterOptions> =
std::sync::Mutex::new(PrinterOptions::const_default());
impl PrinterOptions {
// We cannot use the default trait as it's not const.
const fn const_default() -> Self {
Self {
precision: 4,
threshold: 1000,
edge_items: 3,
line_width: 80,
sci_mode: None,
}
}
}
pub fn set_print_options(options: PrinterOptions) {
*PRINT_OPTS.lock().unwrap() = options
}
pub fn set_print_options_default() {
*PRINT_OPTS.lock().unwrap() = PrinterOptions::const_default()
}
pub fn set_print_options_short() {
*PRINT_OPTS.lock().unwrap() = PrinterOptions {
precision: 2,
threshold: 1000,
edge_items: 2,
line_width: 80,
sci_mode: None,
}
}
pub fn set_print_options_full() {
*PRINT_OPTS.lock().unwrap() = PrinterOptions {
precision: 4,
threshold: usize::MAX,
edge_items: 3,
line_width: 80,
sci_mode: None,
}
}
struct FmtSize {
current_size: usize,
}
impl FmtSize {
fn new() -> Self {
Self { current_size: 0 }
}
fn final_size(self) -> usize {
self.current_size
}
}
impl std::fmt::Write for FmtSize {
fn write_str(&mut self, s: &str) -> std::fmt::Result {
self.current_size += s.len();
Ok(())
}
}
trait TensorFormatter {
type Elem: WithDType;
fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result;
fn max_width(&self, to_display: &Tensor) -> usize {
let mut max_width = 1;
if let Ok(vs) = to_display.flatten_all().and_then(|t| t.to_vec1()) {
for &v in vs.iter() {
let mut fmt_size = FmtSize::new();
let _res = self.fmt(v, 1, &mut fmt_size);
max_width = usize::max(max_width, fmt_size.final_size())
}
}
max_width
}
fn write_newline_indent(i: usize, f: &mut std::fmt::Formatter) -> std::fmt::Result {
writeln!(f)?;
for _ in 0..i {
write!(f, " ")?
}
Ok(())
}
fn fmt_tensor(
&self,
t: &Tensor,
indent: usize,
max_w: usize,
summarize: bool,
po: &PrinterOptions,
f: &mut std::fmt::Formatter,
) -> std::fmt::Result {
let dims = t.dims();
let edge_items = po.edge_items;
write!(f, "[")?;
match dims {
[] => {
if let Ok(v) = t.to_scalar::<Self::Elem>() {
self.fmt(v, max_w, f)?
}
}
[v] if summarize && *v > 2 * edge_items => {
if let Ok(vs) = t
.narrow(0, 0, edge_items)
.and_then(|t| t.to_vec1::<Self::Elem>())
{
for v in vs.into_iter() {
self.fmt(v, max_w, f)?;
write!(f, ", ")?;
}
}
write!(f, "...")?;
if let Ok(vs) = t
.narrow(0, v - edge_items, edge_items)
.and_then(|t| t.to_vec1::<Self::Elem>())
{
for v in vs.into_iter() {
write!(f, ", ")?;
self.fmt(v, max_w, f)?;
}
}
}
[_] => {
let elements_per_line = usize::max(1, po.line_width / (max_w + 2));
if let Ok(vs) = t.to_vec1::<Self::Elem>() {
for (i, v) in vs.into_iter().enumerate() {
if i > 0 {
if i % elements_per_line == 0 {
write!(f, ",")?;
Self::write_newline_indent(indent, f)?
} else {
write!(f, ", ")?;
}
}
self.fmt(v, max_w, f)?
}
}
}
_ => {
if summarize && dims[0] > 2 * edge_items {
for i in 0..edge_items {
match t.get(i) {
Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
Err(e) => write!(f, "{e:?}")?,
}
write!(f, ",")?;
Self::write_newline_indent(indent, f)?
}
write!(f, "...")?;
Self::write_newline_indent(indent, f)?;
for i in dims[0] - edge_items..dims[0] {
match t.get(i) {
Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
Err(e) => write!(f, "{e:?}")?,
}
if i + 1 != dims[0] {
write!(f, ",")?;
Self::write_newline_indent(indent, f)?
}
}
} else {
for i in 0..dims[0] {
match t.get(i) {
Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
Err(e) => write!(f, "{e:?}")?,
}
if i + 1 != dims[0] {
write!(f, ",")?;
Self::write_newline_indent(indent, f)?
}
}
}
}
}
write!(f, "]")?;
Ok(())
}
}
struct FloatFormatter<S: WithDType> {
int_mode: bool,
sci_mode: bool,
precision: usize,
_phantom: std::marker::PhantomData<S>,
}
impl<S> FloatFormatter<S>
where
S: WithDType + num_traits::Float + std::fmt::Display,
{
fn new(t: &Tensor, po: &PrinterOptions) -> Result<Self> {
let mut int_mode = true;
let mut sci_mode = false;
// Rather than containing all values, this should only include
// values that end up being displayed according to [threshold].
let values = t
.flatten_all()?
.to_vec1()?
.into_iter()
.filter(|v: &S| v.is_finite() && !v.is_zero())
.collect::<Vec<_>>();
if !values.is_empty() {
let mut nonzero_finite_min = S::max_value();
let mut nonzero_finite_max = S::min_value();
for &v in values.iter() {
let v = v.abs();
if v < nonzero_finite_min {
nonzero_finite_min = v
}
if v > nonzero_finite_max {
nonzero_finite_max = v
}
}
for &value in values.iter() {
if value.ceil() != value {
int_mode = false;
break;
}
}
if let Some(v1) = S::from(1000.) {
if let Some(v2) = S::from(1e8) {
if let Some(v3) = S::from(1e-4) {
sci_mode = nonzero_finite_max / nonzero_finite_min > v1
|| nonzero_finite_max > v2
|| nonzero_finite_min < v3
}
}
}
}
match po.sci_mode {
None => {}
Some(v) => sci_mode = v,
}
Ok(Self {
int_mode,
sci_mode,
precision: po.precision,
_phantom: std::marker::PhantomData,
})
}
}
impl<S> TensorFormatter for FloatFormatter<S>
where
S: WithDType + num_traits::Float + std::fmt::Display + std::fmt::LowerExp,
{
type Elem = S;
fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
if self.sci_mode {
write!(
f,
"{v:width$.prec$e}",
v = v,
width = max_w,
prec = self.precision
)
} else if self.int_mode {
if v.is_finite() {
write!(f, "{v:width$.0}.", v = v, width = max_w - 1)
} else {
write!(f, "{v:max_w$.0}")
}
} else {
write!(
f,
"{v:width$.prec$}",
v = v,
width = max_w,
prec = self.precision
)
}
}
}
struct IntFormatter<S: WithDType> {
_phantom: std::marker::PhantomData<S>,
}
impl<S: WithDType> IntFormatter<S> {
fn new() -> Self {
Self {
_phantom: std::marker::PhantomData,
}
}
}
impl<S> TensorFormatter for IntFormatter<S>
where
S: WithDType + std::fmt::Display,
{
type Elem = S;
fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
write!(f, "{v:max_w$}")
}
}
fn get_summarized_data(t: &Tensor, edge_items: usize) -> Result<Tensor> {
let dims = t.dims();
if dims.is_empty() {
Ok(t.clone())
} else if dims.len() == 1 {
if dims[0] > 2 * edge_items {
Tensor::cat(
&[
t.narrow(0, 0, edge_items)?,
t.narrow(0, dims[0] - edge_items, edge_items)?,
],
0,
)
} else {
Ok(t.clone())
}
} else if dims[0] > 2 * edge_items {
let mut vs: Vec<_> = (0..edge_items)
.map(|i| get_summarized_data(&t.get(i)?, edge_items))
.collect::<Result<Vec<_>>>()?;
for i in (dims[0] - edge_items)..dims[0] {
vs.push(get_summarized_data(&t.get(i)?, edge_items)?)
}
Tensor::cat(&vs, 0)
} else {
let vs: Vec<_> = (0..dims[0])
.map(|i| get_summarized_data(&t.get(i)?, edge_items))
.collect::<Result<Vec<_>>>()?;
Tensor::cat(&vs, 0)
}
}
impl std::fmt::Display for Tensor {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
let po = PRINT_OPTS.lock().unwrap();
let summarize = self.elem_count() > po.threshold;
let to_display = if summarize {
match get_summarized_data(self, po.edge_items) {
Ok(v) => v,
Err(err) => return write!(f, "{err:?}"),
}
} else {
self.clone()
};
match self.dtype() {
DType::U8 => {
let tf: IntFormatter<u8> = IntFormatter::new();
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
DType::U32 => {
let tf: IntFormatter<u32> = IntFormatter::new();
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
DType::I64 => {
let tf: IntFormatter<i64> = IntFormatter::new();
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
DType::BF16 => {
if let Ok(tf) = FloatFormatter::<bf16>::new(&to_display, &po) {
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
}
DType::F16 => {
if let Ok(tf) = FloatFormatter::<f16>::new(&to_display, &po) {
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
}
DType::F64 => {
if let Ok(tf) = FloatFormatter::<f64>::new(&to_display, &po) {
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
}
DType::F32 => {
if let Ok(tf) = FloatFormatter::<f32>::new(&to_display, &po) {
let max_w = tf.max_width(&to_display);
tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
writeln!(f)?;
}
}
};
let device_str = match self.device().location() {
crate::DeviceLocation::Cpu => "".to_owned(),
crate::DeviceLocation::Cuda { gpu_id } => {
format!(", cuda:{}", gpu_id)
}
crate::DeviceLocation::Metal { gpu_id } => {
format!(", metal:{}", gpu_id)
}
};
write!(
f,
"Tensor[{:?}, {}{}]",
self.dims(),
self.dtype().as_str(),
device_str
)
}
}
| 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, 3), DType::F32, &Device::Cpu)?;
///
/// let c = a.i(0..1)?;
/// assert_eq!(c.shape().dims(), &[1, 3]);
///
/// let c = a.i(0)?;
/// assert_eq!(c.shape().dims(), &[3]);
///
/// let c = a.i((.., ..2) )?;
/// assert_eq!(c.shape().dims(), &[2, 2]);
///
/// let c = a.i((.., ..=2))?;
/// assert_eq!(c.shape().dims(), &[2, 3]);
///
/// # Ok::<(), candle_core::Error>(())
/// ```
fn index(&self, indexers: &[TensorIndexer]) -> Result<Self, Error> {
let mut x = self.clone();
let dims = self.shape().dims();
let mut current_dim = 0;
for (i, indexer) in indexers.iter().enumerate() {
x = match indexer {
TensorIndexer::Select(n) => x.narrow(current_dim, *n, 1)?.squeeze(current_dim)?,
TensorIndexer::Narrow(left_bound, right_bound) => {
let start = match left_bound {
Bound::Included(n) => *n,
Bound::Excluded(n) => *n + 1,
Bound::Unbounded => 0,
};
let stop = match right_bound {
Bound::Included(n) => *n + 1,
Bound::Excluded(n) => *n,
Bound::Unbounded => dims[i],
};
let out = x.narrow(current_dim, start, stop.saturating_sub(start))?;
current_dim += 1;
out
}
TensorIndexer::IndexSelect(indexes) => {
if indexes.rank() != 1 {
crate::bail!("multi-dimensional tensor indexing is not supported")
}
let out = x.index_select(&indexes.to_device(x.device())?, current_dim)?;
current_dim += 1;
out
}
TensorIndexer::Err(e) => crate::bail!("indexing error {e:?}"),
};
}
Ok(x)
}
}
#[derive(Debug)]
/// Generic structure used to index a slice of the tensor
pub enum TensorIndexer {
/// This selects the elemnts for which an index has some specific value.
Select(usize),
/// This is a regular slice, purely indexing a chunk of the tensor
Narrow(Bound<usize>, Bound<usize>),
/// Indexing via a 1d tensor
IndexSelect(Tensor),
Err(Error),
}
impl From<usize> for TensorIndexer {
fn from(index: usize) -> Self {
TensorIndexer::Select(index)
}
}
impl From<&[u32]> for TensorIndexer {
fn from(index: &[u32]) -> Self {
match Tensor::new(index, &crate::Device::Cpu) {
Ok(tensor) => TensorIndexer::IndexSelect(tensor),
Err(e) => TensorIndexer::Err(e),
}
}
}
impl From<Vec<u32>> for TensorIndexer {
fn from(index: Vec<u32>) -> Self {
let len = index.len();
match Tensor::from_vec(index, len, &crate::Device::Cpu) {
Ok(tensor) => TensorIndexer::IndexSelect(tensor),
Err(e) => TensorIndexer::Err(e),
}
}
}
impl From<&Tensor> for TensorIndexer {
fn from(tensor: &Tensor) -> Self {
TensorIndexer::IndexSelect(tensor.clone())
}
}
trait RB: RangeBounds<usize> {}
impl RB for Range<usize> {}
impl RB for RangeFrom<usize> {}
impl RB for RangeFull {}
impl RB for RangeInclusive<usize> {}
impl RB for RangeTo<usize> {}
impl RB for RangeToInclusive<usize> {}
impl<T: RB> From<T> for TensorIndexer {
fn from(range: T) -> Self {
use std::ops::Bound::*;
let start = match range.start_bound() {
Included(idx) => Included(*idx),
Excluded(idx) => Excluded(*idx),
Unbounded => Unbounded,
};
let end = match range.end_bound() {
Included(idx) => Included(*idx),
Excluded(idx) => Excluded(*idx),
Unbounded => Unbounded,
};
TensorIndexer::Narrow(start, end)
}
}
/// Trait used to implement multiple signatures for ease of use of the slicing
/// of a tensor
pub trait IndexOp<T> {
/// Returns a slicing iterator which are the chunks of data necessary to
/// reconstruct the desired tensor.
fn i(&self, index: T) -> Result<Tensor, Error>;
}
impl<T> IndexOp<T> for Tensor
where
T: Into<TensorIndexer>,
{
fn i(&self, index: T) -> Result<Tensor, Error> {
self.index(&[index.into()])
}
}
macro_rules! index_op_tuple {
($($t:ident),+) => {
#[allow(non_snake_case)]
impl<$($t),*> IndexOp<($($t,)*)> for Tensor
where
$($t: Into<TensorIndexer>,)*
{
fn i(&self, ($($t,)*): ($($t,)*)) -> Result<Tensor, Error> {
self.index(&[$($t.into(),)*])
}
}
};
}
index_op_tuple!(A);
index_op_tuple!(A, B);
index_op_tuple!(A, B, C);
index_op_tuple!(A, B, C, D);
index_op_tuple!(A, B, C, D, E);
index_op_tuple!(A, B, C, D, E, F);
index_op_tuple!(A, B, C, D, E, F, G);
| 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],
}
impl<'a> StridedIndex<'a> {
pub(crate) fn new(dims: &'a [usize], stride: &'a [usize], start_offset: usize) -> Self {
let elem_count: usize = dims.iter().product();
let next_storage_index = if elem_count == 0 {
None
} else {
// This applies to the scalar case.
Some(start_offset)
};
StridedIndex {
next_storage_index,
multi_index: vec![0; dims.len()],
dims,
stride,
}
}
pub(crate) fn from_layout(l: &'a Layout) -> Self {
Self::new(l.dims(), l.stride(), l.start_offset())
}
}
impl<'a> Iterator for StridedIndex<'a> {
type Item = usize;
fn next(&mut self) -> Option<Self::Item> {
let storage_index = match self.next_storage_index {
None => return None,
Some(storage_index) => storage_index,
};
let mut updated = false;
let mut next_storage_index = storage_index;
for ((multi_i, max_i), stride_i) in self
.multi_index
.iter_mut()
.zip(self.dims.iter())
.zip(self.stride.iter())
.rev()
{
let next_i = *multi_i + 1;
if next_i < *max_i {
*multi_i = next_i;
updated = true;
next_storage_index += stride_i;
break;
} else {
next_storage_index -= *multi_i * stride_i;
*multi_i = 0
}
}
self.next_storage_index = if updated {
Some(next_storage_index)
} else {
None
};
Some(storage_index)
}
}
#[derive(Debug)]
pub enum StridedBlocks<'a> {
SingleBlock {
start_offset: usize,
len: usize,
},
MultipleBlocks {
block_start_index: StridedIndex<'a>,
block_len: usize,
},
}
| 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) => {
#[test]
fn $test_cpu() -> Result<()> {
$fn_name(&Device::Cpu)
}
#[cfg(feature = "cuda")]
#[test]
fn $test_cuda() -> Result<()> {
$fn_name(&Device::new_cuda(0)?)
}
#[cfg(feature = "metal")]
#[test]
fn $test_metal() -> Result<()> {
$fn_name(&Device::new_metal(0)?)
}
};
}
pub fn to_vec0_round(t: &Tensor, digits: i32) -> Result<f32> {
let b = 10f32.powi(digits);
let t = t.to_vec0::<f32>()?;
Ok(f32::round(t * b) / b)
}
pub fn to_vec1_round(t: &Tensor, digits: i32) -> Result<Vec<f32>> {
let b = 10f32.powi(digits);
let t = t.to_vec1::<f32>()?;
let t = t.iter().map(|t| f32::round(t * b) / b).collect();
Ok(t)
}
pub fn to_vec2_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
let b = 10f32.powi(digits);
let t = t.to_vec2::<f32>()?;
let t = t
.iter()
.map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
.collect();
Ok(t)
}
pub fn to_vec3_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
let b = 10f32.powi(digits);
let t = t.to_vec3::<f32>()?;
let t = t
.iter()
.map(|t| {
t.iter()
.map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
.collect()
})
.collect();
Ok(t)
}
| 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 neither
// send nor sync.
thread_local! {
static CUDNN: RefCell<HashMap<crate::cuda_backend::DeviceId, Arc<Cudnn>>> = HashMap::new().into();
}
impl From<cudarc::cudnn::CudnnError> for crate::Error {
fn from(err: cudarc::cudnn::CudnnError) -> Self {
crate::Error::wrap(err)
}
}
impl From<cudarc::driver::DriverError> for crate::Error {
fn from(err: cudarc::driver::DriverError) -> Self {
crate::Error::wrap(err)
}
}
pub(crate) fn launch_conv2d<
T: DeviceRepr + WithDType + ValidAsZeroBits + cudarc::cudnn::CudnnDataType,
>(
src: &CudaView<T>,
src_l: &crate::Layout,
filter: &CudaView<T>,
dst: &mut CudaSlice<T>,
params: &crate::conv::ParamsConv2D,
dev: &crate::cuda_backend::CudaDevice,
) -> crate::Result<()> {
use crate::conv::CudnnFwdAlgo as CandleAlgo;
use cudarc::cudnn::sys::cudnnConvolutionFwdAlgo_t as A;
let device_id = dev.id();
let cudnn = CUDNN.with(|cudnn| {
if let Some(cudnn) = cudnn.borrow().get(&device_id) {
return Ok(cudnn.clone());
}
let c = Cudnn::new(dev.cuda_device());
if let Ok(c) = &c {
cudnn.borrow_mut().insert(device_id, c.clone());
}
c
})?;
let conv = cudnn.create_conv2d::<T>(
/* pad */ [params.padding as i32, params.padding as i32],
/* stride */ [params.stride as i32, params.stride as i32],
/* dilation */ [params.dilation as i32, params.dilation as i32],
cudarc::cudnn::sys::cudnnConvolutionMode_t::CUDNN_CROSS_CORRELATION,
)?;
let x_shape = [
params.b_size as i32,
params.c_in as i32,
params.i_h as i32,
params.i_w as i32,
];
// Note that `src` already starts at the proper offset.
let x = if src_l.is_contiguous() {
cudnn.create_4d_tensor(
cudarc::cudnn::sys::cudnnTensorFormat_t::CUDNN_TENSOR_NCHW,
x_shape,
)?
} else {
let s = src_l.stride();
cudnn.create_4d_tensor_ex(
x_shape,
[s[0] as i32, s[1] as i32, s[2] as i32, s[3] as i32],
)?
};
let w = cudnn.create_4d_filter(
cudarc::cudnn::sys::cudnnTensorFormat_t::CUDNN_TENSOR_NCHW,
[
params.c_out as i32,
params.c_in as i32,
params.k_h as i32,
params.k_w as i32,
],
)?;
let (w_out, h_out) = (params.out_w() as i32, params.out_h() as i32);
let y = cudnn.create_4d_tensor(
cudarc::cudnn::sys::cudnnTensorFormat_t::CUDNN_TENSOR_NCHW,
[params.b_size as i32, params.c_out as i32, h_out, w_out],
)?;
let conv2d = Conv2dForward {
conv: &conv,
x: &x,
w: &w,
y: &y,
};
let alg = match params.cudnn_fwd_algo {
None => conv2d.pick_algorithm()?,
Some(CandleAlgo::ImplicitGemm) => A::CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM,
Some(CandleAlgo::ImplicitPrecompGemm) => {
A::CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
}
Some(CandleAlgo::Gemm) => A::CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
Some(CandleAlgo::Direct) => A::CUDNN_CONVOLUTION_FWD_ALGO_DIRECT,
Some(CandleAlgo::Fft) => A::CUDNN_CONVOLUTION_FWD_ALGO_FFT,
Some(CandleAlgo::FftTiling) => A::CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING,
Some(CandleAlgo::Winograd) => A::CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD,
Some(CandleAlgo::WinogradNonFused) => A::CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED,
Some(CandleAlgo::Count) => A::CUDNN_CONVOLUTION_FWD_ALGO_COUNT,
};
let workspace_size = conv2d.get_workspace_size(alg)?;
let mut workspace = dev.cuda_device().alloc_zeros::<u8>(workspace_size)?;
unsafe {
conv2d.launch::<CudaSlice<u8>, _, _, _>(
alg,
Some(&mut workspace),
(T::one(), T::zero()),
src,
filter,
dst,
)?;
}
Ok(())
}
| 0
|
hf_public_repos/candle/candle-core
|
hf_public_repos/candle/candle-core/src/tensor.rs
|
//! Tensors are N-dimenional matrixes of elements using a single data type.
#![allow(clippy::redundant_closure_call)]
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{
BackpropOp, BinaryOp, CmpOp, CustomOp1, CustomOp2, CustomOp3, Op, ReduceOp, UnaryOp,
};
use crate::scalar::TensorOrScalar;
use crate::shape::{Dim, Dims};
use crate::{bail, storage::Storage, DType, Device, Error, Layout, Result, Shape};
use std::sync::{Arc, RwLock};
/// Unique identifier for tensors.
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub struct TensorId(usize);
impl TensorId {
fn new() -> Self {
// https://users.rust-lang.org/t/idiomatic-rust-way-to-generate-unique-id/33805
use std::sync::atomic;
static COUNTER: atomic::AtomicUsize = atomic::AtomicUsize::new(1);
Self(COUNTER.fetch_add(1, atomic::Ordering::Relaxed))
}
}
pub struct Tensor_ {
id: TensorId,
// As we provide inner mutability on the tensor content, the alternatives are:
// - Using a mutex, this would have the highest cost when retrieving the storage but would
// prevent errors when concurrent access takes place. Mutex would also be subject to
// deadlocks for example using the current code if the same tensor is used twice by a single
// binary op.
// - Using a refcell unsafe cell would have some intermediary cost, borrow checking would be
// verified dynamically, but the resulting tensors would not be send or sync.
// - Using an unsafe cell would have the lowest cost but undefined behavior on concurrent
// accesses.
// Ideally, we would use Arc<Storage> for tensors on which we don't plan on modifying the data
// and Arc<Mutex<Storage>> for tensors where the data could be modified, e.g. variables but
// that's tricky to encode in the current setup.
storage: Arc<RwLock<Storage>>,
layout: Layout,
op: BackpropOp,
is_variable: bool,
dtype: DType,
device: Device,
}
impl AsRef<Tensor> for Tensor {
fn as_ref(&self) -> &Tensor {
self
}
}
// Tensors are refcounted so that cloning is cheap when building the op graph.
// Storages are also refcounted independently so that its possible to avoid
// copying the storage for operations that only modify the shape or stride.
#[derive(Clone)]
/// The core struct for manipulating tensors.
///
/// ```rust
/// use candle_core::{Tensor, DType, Device};
///
/// let a = Tensor::arange(0f32, 6f32, &Device::Cpu)?.reshape((2, 3))?;
/// let b = Tensor::arange(0f32, 12f32, &Device::Cpu)?.reshape((3, 4))?;
///
/// let c = a.matmul(&b)?;
/// # Ok::<(), candle_core::Error>(())
/// ```
///
/// Tensors are reference counted with [`Arc`] so cloning them is cheap.
pub struct Tensor(Arc<Tensor_>);
impl std::ops::Deref for Tensor {
type Target = Tensor_;
fn deref(&self) -> &Self::Target {
self.0.as_ref()
}
}
macro_rules! unary_op {
($fn_name:ident, $op_name:ident) => {
pub fn $fn_name(&self) -> Result<Self> {
let shape = self.shape();
let storage = self
.storage()
.unary_impl::<crate::op::$op_name>(self.layout())?;
let op = BackpropOp::new1(self, |s| Op::Unary(s, UnaryOp::$op_name));
Ok(from_storage(storage, shape.clone(), op, false))
}
};
}
macro_rules! binary_op {
($fn_name:ident, $op_name:ident) => {
pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
let shape = self.same_shape_binary_op(rhs, stringify!($fn_name))?;
let storage = self.storage().binary_impl::<crate::op::$op_name>(
&*rhs.storage(),
self.layout(),
rhs.layout(),
)?;
let op = BackpropOp::new2(self, rhs, |t1, t2| Op::Binary(t1, t2, BinaryOp::$op_name));
Ok(from_storage(storage, shape.clone(), op, false))
}
};
}
macro_rules! binary_op_scalar {
($fn_name:ident, $op_name:ident) => {
pub fn $fn_name<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
let rhs = match rhs.to_tensor_scalar()? {
crate::scalar::TensorScalar::Tensor(rhs) => rhs,
crate::scalar::TensorScalar::Scalar(rhs) => rhs
.to_dtype(self.dtype())?
.to_device(self.device())?
.broadcast_as(self.shape())?,
};
let shape = self.same_shape_binary_op(&rhs, stringify!($fn_name))?;
let storage = self.storage().binary_impl::<crate::op::$op_name>(
&*rhs.storage(),
self.layout(),
rhs.layout(),
)?;
let op = BackpropOp::new2(self, &rhs, |t1, t2| Op::Binary(t1, t2, BinaryOp::$op_name));
Ok(from_storage(storage, shape.clone(), op, false))
}
};
}
macro_rules! broadcast_binary_op {
($fn_name:ident, $inner_fn_name:ident) => {
pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
let lhs = self;
let shape = lhs
.shape()
.broadcast_shape_binary_op(rhs.shape(), stringify!($fn_name))?;
let l_broadcast = shape != *lhs.shape();
let r_broadcast = shape != *rhs.shape();
match (l_broadcast, r_broadcast) {
(true, true) => lhs
.broadcast_as(&shape)?
.$inner_fn_name(&rhs.broadcast_as(&shape)?),
(false, true) => lhs.$inner_fn_name(&rhs.broadcast_as(&shape)?),
(true, false) => lhs.broadcast_as(&shape)?.$inner_fn_name(rhs),
(false, false) => lhs.$inner_fn_name(rhs),
}
}
};
}
/// Creates a fresh tensor structure based on a storage and a shape, this uses contiguous strides.
pub(crate) fn from_storage<S: Into<Shape>>(
storage: Storage,
shape: S,
op: BackpropOp,
is_variable: bool,
) -> Tensor {
let dtype = storage.dtype();
let device = storage.device();
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: Arc::new(RwLock::new(storage)),
layout: Layout::contiguous(shape),
op,
is_variable,
dtype,
device,
};
Tensor(Arc::new(tensor_))
}
impl Tensor {
pub(crate) fn ones_impl<S: Into<Shape>>(
shape: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let none = BackpropOp::none();
let shape = shape.into();
let storage = device.ones(&shape, dtype)?;
Ok(from_storage(storage, shape, none, is_variable))
}
/// Creates a new tensor filled with ones.
///
/// ```rust
/// use candle_core::{Tensor, DType, Device};
/// let a = Tensor::ones((2, 3), DType::F32, &Device::Cpu)?;
/// let b = Tensor::from_slice(&[1.0f32, 1.0, 1.0, 1.0, 1.0, 1.0], (2, 3), &Device::Cpu)?;
/// // a == b
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn ones<S: Into<Shape>>(shape: S, dtype: DType, device: &Device) -> Result<Self> {
Self::ones_impl(shape, dtype, device, false)
}
/// Creates a new tensor filled with ones with same shape, dtype, and device as the other tensor.
///
/// ```rust
/// use candle_core::{Tensor, DType, Device};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
/// let b = a.ones_like()?;
/// // b == a + 1
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn ones_like(&self) -> Result<Self> {
Tensor::ones(self.shape(), self.dtype(), self.device())
}
// Do not expose outside of the crate, the `is_variable=true` case should only be accessed from
// the variable module.
pub(crate) fn zeros_impl<S: Into<Shape>>(
shape: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let none = BackpropOp::none();
let shape = shape.into();
let storage = device.zeros(&shape, dtype)?;
Ok(from_storage(storage, shape, none, is_variable))
}
/// Creates a new tensor filled with zeros.
///
/// ```rust
/// use candle_core::{Tensor, DType, Device};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
/// let b = Tensor::from_slice(&[0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0], (2, 3), &Device::Cpu)?;
/// // a == b
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn zeros<S: Into<Shape>>(shape: S, dtype: DType, device: &Device) -> Result<Self> {
Self::zeros_impl(shape, dtype, device, false)
}
/// Creates a new tensor filled with ones with same shape, dtype, and device as the other
/// tensor.
///
/// ```rust
/// use candle_core::{Tensor, DType, Device};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
/// let b = a.zeros_like()?;
/// // b is on CPU f32.
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn zeros_like(&self) -> Result<Self> {
Tensor::zeros(self.shape(), self.dtype(), self.device())
}
pub(crate) fn rand_impl<S: Into<Shape>, T: crate::FloatDType>(
lo: T,
up: T,
s: S,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_uniform(lo, up, &s)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
pub(crate) fn rand_f64_impl<S: Into<Shape>>(
lo: f64,
up: f64,
s: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_uniform_f64(lo, up, &s, dtype)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
/// Creates a new tensor initialized with values sampled uniformly between `lo` and `up`.
pub fn rand<S: Into<Shape>, T: crate::FloatDType>(
lo: T,
up: T,
s: S,
device: &Device,
) -> Result<Self> {
Self::rand_impl(lo, up, s, device, false)
}
pub fn rand_like(&self, lo: f64, up: f64) -> Result<Self> {
Tensor::rand_f64_impl(lo, up, self.shape(), self.dtype(), self.device(), false)
}
pub(crate) fn randn_impl<S: Into<Shape>, T: crate::FloatDType>(
mean: T,
std: T,
s: S,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_normal(mean, std, &s)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
pub(crate) fn randn_f64_impl<S: Into<Shape>>(
mean: f64,
std: f64,
s: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_normal_f64(mean, std, &s, dtype)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
pub fn randn_like(&self, mean: f64, stdev: f64) -> Result<Self> {
Tensor::randn_f64_impl(
mean,
stdev,
self.shape(),
self.dtype(),
self.device(),
false,
)
}
/// Creates a new tensor initialized with values sampled from a normal distribution with the
/// specified `mean` and standard deviation `std`.
pub fn randn<S: Into<Shape>, T: crate::FloatDType>(
mean: T,
std: T,
s: S,
device: &Device,
) -> Result<Self> {
Self::randn_impl(mean, std, s, device, false)
}
pub(crate) fn new_impl<A: crate::device::NdArray>(
array: A,
shape: Shape,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let n: usize = shape.elem_count();
let buffer_size: usize = array.shape()?.elem_count();
if buffer_size != n {
return Err(Error::ShapeMismatch { buffer_size, shape }.bt());
}
let storage = device.storage(array)?;
let none = BackpropOp::none();
Ok(from_storage(storage, shape, none, is_variable))
}
/// Creates a new tensor on the specified device using the content and shape of the input.
pub fn new<A: crate::device::NdArray>(array: A, device: &Device) -> Result<Self> {
let shape = array.shape()?;
Self::new_impl(array, shape, device, false)
}
/// Creates a new 1D tensor from an iterator.
pub fn from_iter<D: crate::WithDType>(
iter: impl IntoIterator<Item = D>,
device: &Device,
) -> Result<Self> {
let data = iter.into_iter().collect::<Vec<_>>();
let len = data.len();
Self::from_vec_impl(data, len, device, false)
}
/// Creates a new 1D tensor with values from the interval `[start, end)` taken with a common
/// difference `1` from `start`.
pub fn arange<D: crate::WithDType>(start: D, end: D, device: &Device) -> Result<Self> {
Self::arange_step(start, end, D::one(), device)
}
/// Creates a new 1D tensor with values from the interval `[start, end)` taken with a common
/// difference `step` from `start`.
pub fn arange_step<D: crate::WithDType>(
start: D,
end: D,
step: D,
device: &Device,
) -> Result<Self> {
if D::is_zero(&step) {
crate::bail!("step cannot be zero")
}
let mut data = vec![];
let mut current = start;
if step >= D::zero() {
while current < end {
data.push(current);
current += step;
}
} else {
while current > end {
data.push(current);
current += step;
}
}
let len = data.len();
Self::from_vec_impl(data, len, device, false)
}
pub(crate) fn from_vec_impl<S: Into<Shape>, D: crate::WithDType>(
data: Vec<D>,
shape: S,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let shape = shape.into();
let buffer_size = data.len();
if buffer_size != shape.elem_count() {
return Err(Error::ShapeMismatch { buffer_size, shape }.bt());
}
let storage = device.storage_owned(data)?;
let none = BackpropOp::none();
Ok(from_storage(storage, shape, none, is_variable))
}
/// Creates a new tensor initialized with values from the input vector. The number of elements
/// in this vector must be the same as the number of elements defined by the shape.
/// If the device is cpu, no data copy is made.
pub fn from_vec<S: Into<Shape>, D: crate::WithDType>(
data: Vec<D>,
shape: S,
device: &Device,
) -> Result<Self> {
Self::from_vec_impl(data, shape, device, false)
}
/// Creates a new tensor initialized with values from the input slice. The number of elements
/// in this vector must be the same as the number of elements defined by the shape.
pub fn from_slice<S: Into<Shape>, D: crate::WithDType>(
array: &[D],
shape: S,
device: &Device,
) -> Result<Self> {
Self::new_impl(array, shape.into(), device, false)
}
pub(crate) fn same_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<&Shape> {
let lhs = self.shape();
let rhs = rhs.shape();
if lhs != rhs {
Err(Error::ShapeMismatchBinaryOp {
lhs: lhs.clone(),
rhs: rhs.clone(),
op,
}
.bt())
} else {
Ok(lhs)
}
}
/// Returns true if the computation graph should track this op, that is if it is
/// a variable or if it has some variable as dependencies.
pub fn track_op(&self) -> bool {
self.is_variable || self.op.is_some()
}
// TODO: Also make an inplace version or a pre-allocated? This could be tricky
// if this can create cycles in the compute graph.
binary_op!(add, Add);
binary_op!(mul, Mul);
binary_op!(sub, Sub);
binary_op!(div, Div);
binary_op_scalar!(maximum, Maximum);
binary_op_scalar!(minimum, Minimum);
broadcast_binary_op!(broadcast_add, add);
broadcast_binary_op!(broadcast_mul, mul);
broadcast_binary_op!(broadcast_sub, sub);
broadcast_binary_op!(broadcast_div, div);
broadcast_binary_op!(broadcast_maximum, maximum);
broadcast_binary_op!(broadcast_minimum, minimum);
broadcast_binary_op!(broadcast_eq, eq);
broadcast_binary_op!(broadcast_ne, ne);
broadcast_binary_op!(broadcast_lt, lt);
broadcast_binary_op!(broadcast_le, le);
broadcast_binary_op!(broadcast_gt, gt);
broadcast_binary_op!(broadcast_ge, ge);
unary_op!(recip, Recip);
unary_op!(neg, Neg);
unary_op!(exp, Exp);
unary_op!(log, Log);
unary_op!(sin, Sin);
unary_op!(cos, Cos);
unary_op!(tanh, Tanh);
unary_op!(abs, Abs);
unary_op!(sqr, Sqr);
unary_op!(sqrt, Sqrt);
unary_op!(gelu, Gelu);
unary_op!(gelu_erf, GeluErf);
unary_op!(erf, Erf);
unary_op!(relu, Relu);
unary_op!(ceil, Ceil);
unary_op!(floor, Floor);
unary_op!(round, Round);
/// Round element of the input tensor to the nearest integer.
///
/// If the number of decimals is negative, it specifies the number of positions to the left of
/// the decimal point.
pub fn round_to(&self, decimals: i32) -> Result<Self> {
let mult = 10f64.powi(decimals);
(self * mult)?.round()? * (1f64 / mult)
}
/// Retrieves the single scalar value hold in the tensor. If the tensor contains multiple
/// dimensions, an error is returned instead.
pub fn to_scalar<S: crate::WithDType>(&self) -> Result<S> {
if self.rank() != 0 {
Err(Error::UnexpectedNumberOfDims {
expected: 0,
got: self.rank(),
shape: self.shape().clone(),
}
.bt())?
}
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
Ok::<_, Error>(data[self.layout().start_offset()])
};
match &*self.storage() {
Storage::Cpu(cpu_storage) => from_cpu_storage(cpu_storage),
Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
}
}
/// An alias for `to_scalar`.
pub fn to_vec0<S: crate::WithDType>(&self) -> Result<S> {
self.to_scalar::<S>()
}
/// Repeat this tensor along the specified dimensions.
pub fn repeat<S: Into<Shape>>(&self, shape: S) -> Result<Tensor> {
// Similar to PyTorch, we extend the number of dimensions of self if needed.
let repeats = shape.into();
let repeats = repeats.dims();
let mut inp = if self.rank() < repeats.len() {
let shape = [vec![1; repeats.len() - self.rank()], self.dims().to_vec()].concat();
self.reshape(shape)?
} else {
self.clone()
};
for (idx, &repeat) in repeats.iter().enumerate() {
if repeat > 1 {
inp = Tensor::cat(&vec![&inp; repeat], idx)?
}
}
Ok(inp)
}
/// Creates grids of coordinates specified by the 1D inputs.
///
/// # Arguments
///
/// * `args` - A slice of 1D tensors.
/// * `xy_indexing` - Whether to use xy indexing or ij indexing. If xy is selected, the
/// first dimension corresponds to the cardinality of the second input and the second
/// dimension corresponds to the cardinality of the first input. If ij is selected, the
/// dimensions are in the same order as the cardinality of the inputs.
///
/// # Examples
///
/// ```rust
/// use candle_core::{Tensor, Device, Shape};
/// let x = Tensor::new(&[1f32, 2., 3.], &Device::Cpu)?;
/// let y = Tensor::new(&[4f32, 5., 6.], &Device::Cpu)?;
///
/// let grids_xy = Tensor::meshgrid(&[&x, &y], true)?;
///
/// assert_eq!(grids_xy.len(), 2);
/// assert_eq!(grids_xy[0].dims(), &[3, 3]);
///
/// assert_eq!(grids_xy[0].to_vec2::<f32>()?, &[[1., 2., 3.], [1., 2., 3.], [1., 2., 3.]]);
/// assert_eq!(grids_xy[1].to_vec2::<f32>()?, &[[4., 4., 4.], [5., 5., 5.], [6., 6., 6.]]);
///
/// let grids_ij = Tensor::meshgrid(&[&x, &y], false)?;
///
/// assert_eq!(grids_ij[0].to_vec2::<f32>()?, &[[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]]);
/// assert_eq!(grids_ij[1].to_vec2::<f32>()?, &[[4., 5., 6.], [4., 5., 6.], [4., 5., 6.]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
///
/// # Errors
///
/// * Will return `Err` if `args` contains less than 2 tensors.
///
pub fn meshgrid<A: AsRef<Tensor>>(args: &[A], xy_indexing: bool) -> Result<Vec<Self>> {
if args.len() <= 1 {
Err(Error::OpRequiresAtLeastTwoTensors { op: "meshgrid" }.bt())?
}
let args: Vec<_> = if xy_indexing {
args.iter().rev().collect()
} else {
args.iter().collect()
};
let mut shape = Vec::with_capacity(args.len());
for arg in args.iter() {
shape.push(arg.as_ref().dims1()?)
}
let mut grids = Vec::with_capacity(args.len());
for idx in 0..args.len() {
let mut ones = vec![1usize; args.len()];
ones[idx] = shape[idx];
let arg = args[idx].as_ref().reshape(ones)?;
let mut repeats = shape.clone();
repeats[idx] = 1;
let repeated_tensor = arg.repeat(repeats)?;
grids.push(repeated_tensor);
}
if xy_indexing {
grids.reverse();
}
Ok(grids)
}
/// This operation multiplies the input tensor by `mul` then adds `add` and return the result.
/// The input values `mul` and `add` are casted to the appropriate type so some rounding might
/// be performed.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let a = Tensor::new(&[[0f32, 1.], [2., 3.]], &Device::Cpu)?;
/// let a = a.affine(4., -2.)?;
/// assert_eq!(a.to_vec2::<f32>()?, &[[-2.0, 2.0], [6.0, 10.0]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn affine(&self, mul: f64, add: f64) -> Result<Self> {
let storage = self.storage().affine(self.layout(), mul, add)?;
let op = BackpropOp::new1(self, |arg| Op::Affine { arg, mul, add });
Ok(from_storage(storage, self.shape(), op, false))
}
/// Applies the Exponential Linear Unit (ELU) function on each element of the input tensor.
pub fn elu(&self, alpha: f64) -> Result<Self> {
let storage = self.storage().elu(self.layout(), alpha)?;
let op = BackpropOp::new1(self, |t| Op::Elu(t, alpha));
Ok(from_storage(storage, self.shape(), op, false))
}
/// Raise the tensor to some float exponent `e`.
pub fn powf(&self, e: f64) -> Result<Self> {
let storage = self.storage().powf(self.layout(), e)?;
let op = BackpropOp::new1(self, |t| Op::Powf(t, e));
Ok(from_storage(storage, self.shape(), op, false))
}
fn check_dim(&self, dim: usize, op: &'static str) -> Result<()> {
if dim >= self.dims().len() {
Err(Error::DimOutOfRange {
shape: self.shape().clone(),
dim: dim as i32,
op,
}
.bt())?
} else {
Ok(())
}
}
/// Split a tensor into the specified number of chunks, this may return less chunks than
/// specificed.
pub fn chunk<D: Dim>(&self, chunks: usize, dim: D) -> Result<Vec<Self>> {
let dim = dim.to_index(self.shape(), "chunk")?;
let size = self.dim(dim)?;
if size < chunks {
(0..size).map(|i| self.narrow(dim, i, 1)).collect()
} else {
let chunk_size = size / chunks;
let cnt_additional = size % chunks;
let mut tensors = vec![];
let mut sum_chunk_size = 0;
for i in 0..chunks {
let chunk_size = if i < cnt_additional {
chunk_size + 1
} else {
chunk_size
};
let tensor = self.narrow(dim, sum_chunk_size, chunk_size)?;
tensors.push(tensor);
sum_chunk_size += chunk_size
}
Ok(tensors)
}
}
/// Returns a new tensor that is a narrowed version of the input, the dimension `dim`
/// ranges from `start` to `start + len`.
pub fn narrow<D: Dim>(&self, dim: D, start: usize, len: usize) -> Result<Self> {
let dims = self.dims();
let dim = dim.to_index(self.shape(), "narrow")?;
let err = |msg| {
Err::<(), _>(
Error::NarrowInvalidArgs {
shape: self.shape().clone(),
dim,
start,
len,
msg,
}
.bt(),
)
};
if start > dims[dim] {
err("start > dim_len")?
}
if start.saturating_add(len) > dims[dim] {
err("start + len > dim_len")?
}
if start == 0 && dims[dim] == len {
Ok(self.clone())
} else {
let op = BackpropOp::new1(self, |t| Op::Narrow(t, dim, start, len));
let layout = self.layout().narrow(dim, start, len)?;
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout,
op,
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
}
fn squeeze_dims(self, dims: &[usize]) -> Result<Self> {
match dims {
[] => Ok(self),
[i] => self.squeeze(*i),
dims => {
let dims = self
.dims()
.iter()
.enumerate()
.filter_map(|(dim_idx, &v)| {
if dims.contains(&dim_idx) {
None
} else {
Some(v)
}
})
.collect::<Vec<_>>();
self.reshape(dims)
}
}
}
fn reduce_impl<D: Dim>(&self, dim: D, keepdim: bool, op: ReduceOp) -> Result<Self> {
let dim = dim.to_index(self.shape(), op.name())?;
let storage = self.storage().reduce_op(op, self.layout(), &[dim])?;
let mut dims = self.dims().to_vec();
dims[dim] = 1;
let op = match op {
ReduceOp::Sum | ReduceOp::Min | ReduceOp::Max => {
BackpropOp::new1(self, |arg| Op::Reduce(arg, op, dims.to_vec()))
}
ReduceOp::ArgMin | ReduceOp::ArgMax => BackpropOp::none(),
};
let res = from_storage(storage, dims, op, false);
if keepdim {
Ok(res)
} else {
res.squeeze_dims(&[dim])
}
}
fn sum_impl<D: Dims>(&self, sum_dims: D, keepdim: bool) -> Result<Self> {
let sum_dims = sum_dims.to_indexes(self.shape(), "sum")?;
let storage = self
.storage()
.reduce_op(ReduceOp::Sum, self.layout(), &sum_dims)?;
let mut dims = self.dims().to_vec();
for &sum_dim in sum_dims.iter() {
dims[sum_dim] = 1
}
let op = BackpropOp::new1(self, |a| Op::Reduce(a, ReduceOp::Sum, dims.to_vec()));
let sum = from_storage(storage, dims, op, false);
if keepdim {
Ok(sum)
} else {
sum.squeeze_dims(&sum_dims)
}
}
/// Returns the sum of all elements in the input tensor. The sum is performed over all the
/// input dimensions.
///
/// The resulting tensor has a shape that is similar to the shape of the input tensor, except
/// that the number of elements for each dimension index in `sum_dims` is 1.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let a = Tensor::new(&[[0f32, 1.], [2., 3.]], &Device::Cpu)?;
/// let s = a.sum_keepdim(0)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[2., 4.]]);
/// let s = a.sum_keepdim(1)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[1.], [5.]]);
/// let s = a.sum_keepdim((0, 1))?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[6.]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn sum_keepdim<D: Dims>(&self, sum_dims: D) -> Result<Self> {
self.sum_impl(sum_dims, true)
}
/// Returns the sum of all elements in the input tensor. The sum is performed over all the
/// input dimensions and compared to `sum_keepdim` these dimensions are squeezed rather than
/// kept.
pub fn sum<D: Dims>(&self, sum_dims: D) -> Result<Self> {
self.sum_impl(sum_dims, false)
}
/// Returns the mean of all elements in the input tensor. The mean is performed over all the
/// input dimensions.
///
/// The resulting tensor has a shape that is similar to the shape of the input tensor, except
/// that the number of elements for each dimension index in `mean_dims` is 1.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let a = Tensor::new(&[[0f32, 1.], [2., 3.]], &Device::Cpu)?;
/// let s = a.mean_keepdim(0)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[1., 2.]]);
/// let s = a.mean_keepdim(1)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[0.5], [2.5]]);
/// let s = a.mean_keepdim((0, 1))?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[1.5]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn mean_keepdim<D: Dims>(&self, mean_dims: D) -> Result<Self> {
let mean_dims = mean_dims.to_indexes(self.shape(), "mean-keepdim")?;
let reduced_dim: usize = mean_dims.iter().map(|i| self.dims()[*i]).product();
let scale = 1f64 / (reduced_dim as f64);
self.sum_impl(mean_dims, true)? * scale
}
/// Returns the mean of all elements in the input tensor. The mean is performed over all the
/// input dimensions and compared to `mean_keepdim` these dimensions are squeezed rather than
/// kept.
pub fn mean<D: Dims>(&self, mean_dims: D) -> Result<Self> {
let mean_dims = mean_dims.to_indexes(self.shape(), "mean")?;
let reduced_dim: usize = mean_dims.iter().map(|i| self.dims()[*i]).product();
let scale = 1f64 / (reduced_dim as f64);
self.sum_impl(mean_dims, false)? * scale
}
/// Returns the unbiased variance over the selected dimension.
pub fn var_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "var")?;
let mean = self.mean_keepdim(dim)?;
let squares = self.broadcast_sub(&mean)?.sqr()?;
squares.sum_impl(dim, true)? / (self.dim(dim)? - 1) as f64
}
/// Returns the unbiased variance over the selected dimension.
pub fn var<D: Dim>(&self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "var")?;
self.var_keepdim(dim)?.squeeze(dim)
}
/// Gathers the maximum value across the selected dimension. The resulting shape has the same
/// number of dimensions as the original tensor and the select dimension has a single element.
pub fn max_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, true, ReduceOp::Max)
}
/// Similar to `max_keepdim` but the target dimension is squeezed.
pub fn max<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, false, ReduceOp::Max)
}
/// Gathers the minimum value across the selected dimension. The resulting shape has the same
/// number of dimensions as the original tensor and the select dimension has a single element.
pub fn min_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, true, ReduceOp::Min)
}
/// Similar to `min_keepdim` but the target dimension is squeezed.
pub fn min<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, false, ReduceOp::Min)
}
pub fn argmax_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, true, ReduceOp::ArgMax)
}
/// Similar to `argmax_keepdim` but the target dimension is squeezed.
pub fn argmax<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, false, ReduceOp::ArgMax)
}
pub fn argmin_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, true, ReduceOp::ArgMin)
}
/// Similar to `argmin_keepdim` but the target dimension is squeezed.
pub fn argmin<D: Dim>(&self, dim: D) -> Result<Self> {
self.reduce_impl(dim, false, ReduceOp::ArgMin)
}
/// Element-wise comparison between two tensors, e.g. equality, greater than, ... The actual
/// comparison operation is specified by the `op` argument.
///
/// The returned tensor has the same shape as the original tensors and uses `u8` elements.
pub fn cmp<T: TensorOrScalar>(&self, rhs: T, op: CmpOp) -> Result<Self> {
let rhs = match rhs.to_tensor_scalar()? {
crate::scalar::TensorScalar::Tensor(rhs) => rhs,
crate::scalar::TensorScalar::Scalar(rhs) => rhs
.to_dtype(self.dtype())?
.to_device(self.device())?
.broadcast_as(self.shape())?,
};
let shape = self.same_shape_binary_op(&rhs, "cmp")?;
let storage = self
.storage()
.cmp(op, &rhs.storage(), self.layout(), rhs.layout())?;
let op = BackpropOp::new1(self, |a| Op::Cmp(a, op));
Ok(from_storage(storage, shape.dims(), op, false))
}
/// Element-wise equality.
pub fn eq<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Eq)
}
/// Element-wise non-equality.
pub fn ne<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Ne)
}
/// Element-wise comparison with lower-than, the returned tensor uses value 1 where `self <
/// rhs` and 0 otherwise.
pub fn lt<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Lt)
}
/// Element-wise comparison with greater-than, the returned tensor uses value 1 where `self >
/// rhs` and 0 otherwise.
pub fn gt<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Gt)
}
/// Element-wise comparison with greater-equal, the returned tensor uses value 1 where `self >=
/// rhs` and 0 otherwise.
pub fn ge<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Ge)
}
/// Element-wise comparison with lower-equal, the returned tensor uses value 1 where `self <=
/// rhs` and 0 otherwise.
pub fn le<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
self.cmp(rhs, CmpOp::Le)
}
/// Clamp the tensor values to be between `min` and `max`.
pub fn clamp<T1: TensorOrScalar, T2: TensorOrScalar>(&self, min: T1, max: T2) -> Result<Self> {
self.maximum(min)?.minimum(max)
}
/// Interpolate the input tensor to the `target_size` size, taking the value of the nearest element.
///
/// The input tensor should have three dimensions, `(batch, channels, l)`, the returned
/// tensor also has three dimensions, `(batch, channels, target_size)`.
pub fn interpolate1d(&self, target_size: usize) -> Result<Self> {
let (n, c, _l) = self.dims3()?;
let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
let storage = self
.storage()
.upsample_nearest1d(self.layout(), target_size)?;
Ok(from_storage(storage, (n, c, target_size), op, false))
}
/// Alias for `interpolate1d`.
pub fn upsample_nearest1d(&self, target_size: usize) -> Result<Self> {
self.interpolate1d(target_size)
}
/// Interpolate the input tensor to the `(target_h, target_w)` size, taking the value of the
/// nearest element.
///
/// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
/// tensor also has four dimensions, `(batch, channels, target_h, target_w)`.
pub fn interpolate2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
let (n, c, _h, _w) = self.dims4()?;
let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
let storage = self
.storage()
.upsample_nearest2d(self.layout(), target_h, target_w)?;
Ok(from_storage(storage, (n, c, target_h, target_w), op, false))
}
/// Alias for `interpolate2d`.
pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
self.interpolate2d(target_h, target_w)
}
/// 2D average pooling over an input tensor with multiple channels.
///
/// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
/// tensor also has four dimensions, `(batch, channels, h', w')`. The pooling is performed on
/// the two last dimensions using a kernel of size `sz`. The returned element is the average
/// value over the kernel window.
pub fn avg_pool2d<T: crate::ToUsize2>(&self, sz: T) -> Result<Self> {
let sz = sz.to_usize2();
self.avg_pool2d_with_stride(sz, sz)
}
/// Same as `avg_pool2d` but with a `stride` that can be set to a value different from the
/// kernel size.
pub fn avg_pool2d_with_stride<T: crate::ToUsize2>(
&self,
kernel_size: T,
stride: T,
) -> Result<Self> {
let kernel_size = kernel_size.to_usize2();
let stride = stride.to_usize2();
let (n, c, h, w) = self.dims4()?;
// https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d
let h_out = (h - kernel_size.0) / stride.0 + 1;
let w_out = (w - kernel_size.1) / stride.1 + 1;
let op = BackpropOp::new1(self, |arg| Op::AvgPool2D {
arg,
kernel_size,
stride,
});
let storage = self
.storage()
.avg_pool2d(self.layout(), kernel_size, stride)?;
Ok(from_storage(storage, (n, c, h_out, w_out), op, false))
}
/// 2D max pooling over an input tensor with multiple channels.
///
/// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
/// tensor also has four dimensions, `(batch, channels, h', w')`. The pooling is performed on
/// the two last dimensions using a kernel of size `sz`, the returned element is the maximum
/// value over the kernel window.
pub fn max_pool2d<T: crate::ToUsize2>(&self, sz: T) -> Result<Self> {
let sz = sz.to_usize2();
self.max_pool2d_with_stride(sz, sz)
}
/// Same as `max_pool2d` but with a `stride` that can be set to a value different from the
/// kernel size.
pub fn max_pool2d_with_stride<T: crate::ToUsize2>(
&self,
kernel_size: T,
stride: T,
) -> Result<Self> {
let kernel_size = kernel_size.to_usize2();
let stride = stride.to_usize2();
let (n, c, h, w) = self.dims4()?;
// https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d
let h_out = (h - kernel_size.0) / stride.0 + 1;
let w_out = (w - kernel_size.1) / stride.1 + 1;
let op = BackpropOp::new1(self, |arg| Op::MaxPool2D {
arg,
kernel_size,
stride,
});
let storage = self
.storage()
.max_pool2d(self.layout(), kernel_size, stride)?;
Ok(from_storage(storage, (n, c, h_out, w_out), op, false))
}
/// Returns the matrix-multiplication of the input tensor with the other provided tensor.
///
/// # Arguments
///
/// * `self` - A tensor with dimensions `b1, b2, ..., bi, m, k`.
/// * `rhs` - A tensor with dimensions `b1, b2, ..., bi, k, n`.
///
/// The resulting tensor has dimensions `b1, b2, ..., bi, m, n`.
pub fn matmul(&self, rhs: &Self) -> Result<Self> {
let a_dims = self.shape().dims();
let b_dims = rhs.shape().dims();
let dim = a_dims.len();
if dim < 2 || b_dims.len() != dim {
Err(Error::ShapeMismatchBinaryOp {
lhs: self.shape().clone(),
rhs: rhs.shape().clone(),
op: "matmul",
}
.bt())?
}
let m = a_dims[dim - 2];
let k = a_dims[dim - 1];
let k2 = b_dims[dim - 2];
let n = b_dims[dim - 1];
let c_shape = Shape::from(&a_dims[..dim - 2]).extend(&[m, n]);
let batching: usize = a_dims[..dim - 2].iter().product();
let batching_b: usize = b_dims[..dim - 2].iter().product();
if k != k2 || batching != batching_b {
Err(Error::ShapeMismatchBinaryOp {
lhs: self.shape().clone(),
rhs: rhs.shape().clone(),
op: "matmul",
}
.bt())?
}
let storage = self.storage().matmul(
&rhs.storage(),
(batching, m, n, k),
self.layout(),
rhs.layout(),
)?;
let op = BackpropOp::new2(self, rhs, Op::Matmul);
Ok(from_storage(storage, c_shape, op, false))
}
/// Matrix-multiplication with broadcasting support.
///
/// Compared to `matmul` the two matrixes are allowed to have different dimensions as long as
/// they are compatible for broadcast. E.g. if `self` has shape `(j, 1, n, k)` and `rhs` has
/// shape `(l, k, m)`, the output will have shape `(j, l, n, m)`.
pub fn broadcast_matmul(&self, rhs: &Self) -> Result<Self> {
let lhs = self;
let (l_shape, r_shape) = lhs.shape().broadcast_shape_matmul(rhs.shape())?;
let l_broadcast = l_shape != *lhs.shape();
let r_broadcast = r_shape != *rhs.shape();
// TODO: Avoid concretising the broadcasted matrixes via contiguous.
match (l_broadcast, r_broadcast) {
(true, true) => lhs
.broadcast_as(&l_shape)?
.contiguous()?
.matmul(&rhs.broadcast_as(&r_shape)?.contiguous()?),
(false, true) => lhs.matmul(&rhs.broadcast_as(&r_shape)?.contiguous()?),
(true, false) => lhs.broadcast_as(&l_shape)?.contiguous()?.matmul(rhs),
(false, false) => lhs.matmul(rhs),
}
}
/// Returns a tensor with the same shape as the input tensor, the values are taken from
/// `on_true` if the input tensor value is not zero, and `on_false` at the positions where the
/// input tensor is equal to zero.
pub fn where_cond(&self, on_true: &Self, on_false: &Self) -> Result<Self> {
let _shap = self.same_shape_binary_op(on_true, "where_cond")?;
let shape = self.same_shape_binary_op(on_false, "where_cond")?;
let storage = self.storage().where_cond(
self.layout(),
&on_true.storage(),
on_true.layout(),
&on_false.storage(),
on_false.layout(),
)?;
let op = BackpropOp::new3(self, on_true, on_false, Op::WhereCond);
Ok(from_storage(storage, shape, op, false))
}
/// Returns a tensor with the values from the `self` tensor at the index corresponding to the
/// values hold in the `ids` tensor.
///
/// # Arguments
///
/// * `self` - A tensor with dimensions `v, h`.
/// * `ids` - A tensor with dimensions `s` and with integer values between 0 and v (exclusive).
///
/// The resulting tensor has dimensions `s, h`. `s` is called the sequence length, `v` the
/// vocabulary size, and `h` the hidden size.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let values = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let ids = Tensor::new(&[2u32, 1u32, 2u32], &Device::Cpu)?;
/// let emb = values.embedding(&ids)?;
/// assert_eq!(emb.to_vec2::<f32>()?, &[[4., 5.], [2., 3.], [4., 5.]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn embedding(&self, ids: &Self) -> Result<Self> {
if self.rank() != 2 || ids.rank() != 1 {
Err(Error::ShapeMismatchBinaryOp {
lhs: self.shape().clone(),
rhs: ids.shape().clone(),
op: "embedding",
}
.bt())?
}
self.index_select(ids, 0)
}
pub fn scatter_add<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "scatter-add")?;
let source_dims = source.dims();
let self_dims = self.dims();
let mismatch = if source_dims.len() != self_dims.len() {
true
} else {
let mut mismatch = false;
for (i, (&d1, &d2)) in self_dims.iter().zip(source_dims.iter()).enumerate() {
if i != dim && d1 != d2 {
mismatch = true;
break;
}
}
mismatch
};
if mismatch {
Err(Error::ShapeMismatchBinaryOp {
op: "scatter-add (self, src)",
lhs: self.shape().clone(),
rhs: source.shape().clone(),
}
.bt())?
}
if indexes.dims() != source.dims() {
Err(Error::ShapeMismatchBinaryOp {
op: "scatter-add (indexes, src)",
lhs: indexes.shape().clone(),
rhs: source.shape().clone(),
}
.bt())?
}
let storage = self.storage().scatter_add(
self.layout(),
&indexes.storage(),
indexes.layout(),
&source.storage(),
source.layout(),
dim,
)?;
let op = BackpropOp::new3(self, indexes, source, |t1, t2, t3| {
Op::ScatterAdd(t1, t2, t3, dim)
});
Ok(from_storage(storage, self.shape(), op, false))
}
/// Embeds the values of the `src` tensor into the `self` tensor on the specified dimension.
pub fn slice_scatter<D: Dim>(&self, src: &Self, dim: D, start: usize) -> Result<Self> {
let dim = dim.to_index(self.shape(), "slice-scatter")?;
if dim == 0 {
self.slice_scatter0(src, start)
} else {
// TODO: Maybe we want to add a more efficient implementation at some point.
self.transpose(0, dim)?
.slice_scatter0(&src.transpose(0, dim)?, start)?
.transpose(0, dim)
}
}
/// Embeds the values of the `src` tensor into the `self` tensor on the first dimension.
pub fn slice_scatter0(&self, src: &Self, start: usize) -> Result<Self> {
if self.dtype() != src.dtype() {
Err(Error::DTypeMismatchBinaryOp {
lhs: self.dtype(),
rhs: src.dtype(),
op: "slice-scatter",
}
.bt())?
}
if self.device().location() != src.device.location() {
Err(Error::DeviceMismatchBinaryOp {
lhs: self.device().location(),
rhs: src.device().location(),
op: "slice-scatter",
}
.bt())?
}
if self.rank() != src.rank() {
Err(Error::UnexpectedNumberOfDims {
expected: self.rank(),
got: src.rank(),
shape: src.shape().clone(),
}
.bt())?
}
let shape_ok =
self.dims()
.iter()
.zip(src.dims().iter())
.enumerate()
.all(|(dim_idx, (&d1, &d2))| {
if 0 == dim_idx {
d2 + start <= d1
} else {
d1 == d2
}
});
if !shape_ok {
Err(Error::ShapeMismatchBinaryOp {
op: "slice-scatter (self, src)",
lhs: self.shape().clone(),
rhs: src.shape().clone(),
}
.bt())?
}
let mut storage = self.device().zeros(self.shape(), self.dtype())?;
self.storage()
.copy_strided_src(&mut storage, 0, self.layout())?;
let offset = start * src.dims()[1..].iter().product::<usize>();
src.storage()
.copy_strided_src(&mut storage, offset, src.layout())?;
let op = BackpropOp::new2(self, src, |t1, t2| Op::SliceScatter0(t1, t2, start));
Ok(from_storage(storage, self.shape(), op, false))
}
/// Accumulate element from `source` at indexes `indexes` and add them to `self`.
pub fn index_add<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "index-add")?;
let source_dims = source.dims();
let self_dims = self.dims();
let mismatch = if source_dims.len() != self_dims.len() {
true
} else {
let mut mismatch = false;
for (i, (&d1, &d2)) in self_dims.iter().zip(source_dims.iter()).enumerate() {
if i != dim && d1 != d2 {
mismatch = true;
break;
}
}
mismatch
};
if mismatch {
Err(Error::ShapeMismatchBinaryOp {
op: "index-add (self, source)",
lhs: self.shape().clone(),
rhs: source.shape().clone(),
}
.bt())?
}
// The number of element in indexes must match the dimension on which the add is
// performed on the source tensor (and the index values from `indexes` are taken from
// the target tensor self)
let indexes_len = indexes.dims1()?;
if source_dims[dim] != indexes_len {
Err(Error::ShapeMismatchBinaryOp {
op: "index-add (ids, source))",
lhs: indexes.shape().clone(),
rhs: source.shape().clone(),
}
.bt())?
}
let storage = self.storage().index_add(
self.layout(),
&indexes.storage(),
indexes.layout(),
&source.storage(),
source.layout(),
dim,
)?;
let op = BackpropOp::new3(self, indexes, source, |t1, t2, t3| {
Op::IndexAdd(t1, t2, t3, dim)
});
Ok(from_storage(storage, self.shape(), op, false))
}
/// Gather values across the target dimension.
///
/// # Arguments
///
/// * `self` - The input tensor.
/// * `indexes` - The indices of elements to gather, this should have the same shape as `self`
/// but can have a different number of elements on the target dimension.
/// * `dim` - the target dimension.
///
/// The resulting tensor has the same shape as `indexes` and use values from `self` indexed on
/// dimension `dim` by the values in `indexes`.
pub fn gather<D: Dim>(&self, indexes: &Self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "gather")?;
let self_dims = self.dims();
let indexes_dims = indexes.dims();
let mismatch = if indexes_dims.len() != self_dims.len() {
true
} else {
let mut mismatch = false;
for (i, (&d1, &d2)) in self_dims.iter().zip(indexes_dims.iter()).enumerate() {
if i != dim && d1 != d2 {
mismatch = true;
break;
}
}
mismatch
};
if mismatch {
Err(Error::ShapeMismatchBinaryOp {
op: "gather",
lhs: self.shape().clone(),
rhs: indexes.shape().clone(),
}
.bt())?
}
let storage =
self.storage()
.gather(self.layout(), &indexes.storage(), indexes.layout(), dim)?;
let op = BackpropOp::new2(self, indexes, |t1, t2| Op::Gather(t1, t2, dim));
Ok(from_storage(storage, indexes.shape(), op, false))
}
/// Select values for the input tensor at the target indexes across the specified dimension.
///
/// The `indexes` is argument is an int tensor with a single dimension.
/// The output has the same number of dimension as the `self` input. The target dimension of
/// the output has length the length of `indexes` and the values are taken from `self` using
/// the index from `indexes`. Other dimensions have the same number of elements as the input
/// tensor.
pub fn index_select<D: Dim>(&self, indexes: &Self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "index-select")?;
let indexes_len = match indexes.dims() {
[l] => *l,
_ => Err(Error::ShapeMismatchBinaryOp {
lhs: self.shape().clone(),
rhs: indexes.shape().clone(),
op: "index-select",
}
.bt())?,
};
let storage = self.storage().index_select(
&indexes.storage(),
self.layout(),
indexes.layout(),
dim,
)?;
let mut dims = self.dims().to_vec();
dims[dim] = indexes_len;
let op = BackpropOp::new2(self, indexes, |t1, t2| Op::IndexSelect(t1, t2, dim));
Ok(from_storage(storage, dims, op, false))
}
/// Returns an iterator over position of the elements in the storage when ranging over the
/// index tuples in lexicographic order.
pub fn strided_index(&self) -> crate::StridedIndex {
self.layout.strided_index()
}
/// Similar to `strided_index` but returns the position of the start of each contiguous block
/// as well as the length of the contiguous blocks. For a contiguous tensor, the index iterator
/// will only return the start offset and the size would be the number of elements in the
/// tensor.
pub fn strided_blocks(&self) -> crate::StridedBlocks {
self.layout.strided_blocks()
}
/// Returns the data contained in a 1D tensor as a vector of scalar values.
pub fn to_vec1<S: crate::WithDType>(&self) -> Result<Vec<S>> {
if self.rank() != 1 {
Err(Error::UnexpectedNumberOfDims {
expected: 1,
got: self.rank(),
shape: self.shape().clone(),
}
.bt())?
}
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let data = match self.layout.contiguous_offsets() {
Some((o1, o2)) => data[o1..o2].to_vec(),
None => self.strided_index().map(|i| data[i]).collect(),
};
Ok::<Vec<_>, Error>(data)
};
match &*self.storage() {
Storage::Cpu(storage) => from_cpu_storage(storage),
Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
}
}
/// Returns the data contained in a 2D tensor as a vector of vector of scalar values.
pub fn to_vec2<S: crate::WithDType>(&self) -> Result<Vec<Vec<S>>> {
let (dim1, dim2) = self.dims2()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut rows = vec![];
match self.layout.contiguous_offsets() {
Some((o1, o2)) => {
let data = &data[o1..o2];
for idx_row in 0..dim1 {
rows.push(data[idx_row * dim2..(idx_row + 1) * dim2].to_vec())
}
}
None => {
let mut src_index = self.strided_index();
for _idx_row in 0..dim1 {
let row = (0..dim2).map(|_| data[src_index.next().unwrap()]).collect();
rows.push(row)
}
assert!(src_index.next().is_none());
}
}
Ok(rows)
};
match &*self.storage() {
Storage::Cpu(storage) => from_cpu_storage(storage),
Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
}
}
/// Returns the data contained in a 3D tensor.
pub fn to_vec3<S: crate::WithDType>(&self) -> Result<Vec<Vec<Vec<S>>>> {
let (dim1, dim2, dim3) = self.dims3()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut top_rows = vec![];
match self.layout.contiguous_offsets() {
Some((o1, o2)) => {
let data = &data[o1..o2];
let dim23 = dim2 * dim3;
for idx1 in 0..dim1 {
let data = &data[idx1 * dim23..(idx1 + 1) * dim23];
let mut rows = vec![];
for idx2 in 0..dim2 {
rows.push(data[idx2 * dim3..(idx2 + 1) * dim3].to_vec())
}
top_rows.push(rows);
}
}
None => {
let mut src_index = self.strided_index();
for _idx in 0..dim1 {
let mut rows = vec![];
for _jdx in 0..dim2 {
let row = (0..dim3).map(|_| data[src_index.next().unwrap()]).collect();
rows.push(row)
}
top_rows.push(rows);
}
assert!(src_index.next().is_none());
}
}
Ok(top_rows)
};
match &*self.storage() {
Storage::Cpu(storage) => from_cpu_storage(storage),
Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
}
}
/// The dtype for the elements stored in the input tensor.
pub fn dtype(&self) -> DType {
self.dtype
}
/// The device on which the input tensor is located.
pub fn device(&self) -> &Device {
&self.device
}
/// The tensor shape, i.e. dimension sizes on each axis.
pub fn shape(&self) -> &Shape {
self.layout().shape()
}
/// The dimension size for this tensor on each axis.
pub fn dims(&self) -> &[usize] {
self.shape().dims()
}
/// The dimension size for a specified dimension index.
pub fn dim<D: Dim>(&self, dim: D) -> Result<usize> {
let dim = dim.to_index(self.shape(), "dim")?;
Ok(self.dims()[dim])
}
/// The layout of the input tensor, this stores both the shape of the tensor as well as the
/// strides and the start offset to apply to the underlying storage.
pub fn layout(&self) -> &Layout {
&self.layout
}
pub fn stride(&self) -> &[usize] {
self.layout.stride()
}
/// The number of dimensions for this tensor, 0 for a scalar tensor, 1 for a 1D tensor, etc.
pub fn rank(&self) -> usize {
self.shape().rank()
}
/// The number of elements stored in this tensor.
pub fn elem_count(&self) -> usize {
self.shape().elem_count()
}
/// The unique identifier for this tensor.
pub fn id(&self) -> TensorId {
self.id
}
/// Whether this tensor is a variable or not. A variable is a tensor for which gradient is
/// tracked and on which backpropagation can be performed.
pub fn is_variable(&self) -> bool {
self.is_variable
}
pub(crate) fn op(&self) -> &Option<Op> {
&self.op
}
/// Computes the sum of all the elements in this tensor and returns a tensor holding this
/// scalar with zero dimensions.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let tensor = tensor.sum_all()?;
/// assert_eq!(tensor.to_scalar::<f32>()?, 15.);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn sum_all(&self) -> Result<Tensor> {
let dims: Vec<_> = (0..self.rank()).collect();
self.sum(dims)
}
pub fn mean_all(&self) -> Result<Tensor> {
self.sum_all()? / self.elem_count() as f64
}
fn flatten_<D1: Dim, D2: Dim>(
&self,
start_dim: Option<D1>,
end_dim: Option<D2>,
) -> Result<Tensor> {
if self.rank() == 0 {
self.reshape(1)
} else {
let start_dim = match start_dim {
None => 0,
Some(dim) => dim.to_index(self.shape(), "flatten")?,
};
let end_dim = match end_dim {
None => self.rank() - 1,
Some(dim) => dim.to_index(self.shape(), "flatten")?,
};
if start_dim < end_dim {
let dims = self.dims();
let mut dst_dims = dims[..start_dim].to_vec();
dst_dims.push(dims[start_dim..end_dim + 1].iter().product::<usize>());
if end_dim + 1 < dims.len() {
dst_dims.extend(&dims[end_dim + 1..]);
}
self.reshape(dst_dims)
} else {
Ok(self.clone())
}
}
}
/// Flattens the input tensor on the dimension indexes from `start_dim` to `end_dim` (both
/// inclusive).
pub fn flatten<D1: Dim, D2: Dim>(&self, start_dim: D1, end_dim: D2) -> Result<Tensor> {
self.flatten_(Some(start_dim), Some(end_dim))
}
/// Flattens the input tensor on the dimension indexes from `0` to `end_dim` (inclusive).
pub fn flatten_to<D: Dim>(&self, end_dim: D) -> Result<Tensor> {
self.flatten_(None::<usize>, Some(end_dim))
}
/// Flattens the input tensor on the dimension indexes from `start_dim` (inclusive) to the last
/// dimension.
pub fn flatten_from<D: Dim>(&self, start_dim: D) -> Result<Tensor> {
self.flatten_(Some(start_dim), None::<usize>)
}
/// Flattens the input tensor by reshaping it into a one dimension tensor.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let tensor = tensor.flatten_all()?;
/// assert_eq!(tensor.to_vec1::<f32>()?, &[0., 1., 2., 3., 4., 5.]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn flatten_all(&self) -> Result<Tensor> {
self.flatten_(None::<usize>, None::<usize>)
}
/// Returns the sub-tensor fixing the index at `i` on the first dimension.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let t = tensor.get(0)?;
/// assert_eq!(t.to_vec1::<f32>()?, &[0., 1.]);
/// let t = tensor.get(1)?;
/// assert_eq!(t.to_vec1::<f32>()?, &[2., 3.]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn get(&self, i: usize) -> Result<Tensor> {
let dims = self.dims();
if dims.is_empty() {
Ok(self.clone())
} else {
self.narrow(0, i, 1)?.reshape(&dims[1..])
}
}
/// Returns the sub-tensor fixing the index at `index` on the dimension `dim`.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let t = tensor.get_on_dim(1, 0)?;
/// assert_eq!(t.to_vec1::<f32>()?, &[0., 2., 4.]);
/// let t = tensor.get_on_dim(1, 1)?;
/// assert_eq!(t.to_vec1::<f32>()?, &[1., 3., 5.]);
/// let t = tensor.get_on_dim(0, 1)?;
/// assert_eq!(t.to_vec1::<f32>()?, &[2., 3.]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn get_on_dim<D: Dim>(&self, dim: D, index: usize) -> Result<Tensor> {
let dim = dim.to_index(self.shape(), "get_on_dim")?;
self.narrow(dim, index, 1)?.squeeze(dim)
}
/// Returns a tensor that is a transposed version of the input, the two last dimensions of the
/// input are swapped.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
/// let tensor = tensor.t()?;
/// assert_eq!(tensor.to_vec2::<f32>()?, &[[0.0, 2.0, 4.0], [1.0, 3.0, 5.0]]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn t(&self) -> Result<Tensor> {
let rank = self.rank();
if rank < 2 {
Err(Error::UnexpectedNumberOfDims {
expected: 2,
got: rank,
shape: self.shape().clone(),
}
.bt())?
}
self.transpose(rank - 2, rank - 1)
}
/// Returns a tensor that is a transposed version of the input, the given dimensions are
/// swapped.
pub fn transpose<D1: Dim, D2: Dim>(&self, dim1: D1, dim2: D2) -> Result<Tensor> {
let dim1 = dim1.to_index(self.shape(), "transpose")?;
let dim2 = dim2.to_index(self.shape(), "transpose")?;
if dim1 == dim2 {
return Ok(self.clone());
}
let op = BackpropOp::new1(self, |t| Op::Transpose(t, dim1, dim2));
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout: self.layout.transpose(dim1, dim2)?,
op,
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
/// Returns a tensor with the same data as the input where the dimensions have been permuted.
/// dims must be a permutation, i.e. include each dimension index exactly once.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::arange(0u32, 120u32, &Device::Cpu)?.reshape((2, 3, 4, 5))?;
/// assert_eq!(tensor.dims(), &[2, 3, 4, 5]);
/// let tensor = tensor.permute((2, 3, 1, 0))?;
/// assert_eq!(tensor.dims(), &[4, 5, 3, 2]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn permute<D: Dims>(&self, dims: D) -> Result<Tensor> {
let dims = dims.to_indexes(self.shape(), "permute")?;
// O(n^2) permutation check but these arrays are small.
let is_permutation =
dims.len() == self.rank() && (0..dims.len()).all(|i| dims.contains(&i));
if !is_permutation {
crate::bail!(
"dimension mismatch in permute, tensor {:?}, dims: {:?}",
self.dims(),
dims
)
}
let op = BackpropOp::new1(self, |t| Op::Permute(t, dims.clone()));
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout: self.layout.permute(&dims)?,
op,
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
/// Returns true if the data is stored in a C contiguous (aka row major) way.
pub fn is_contiguous(&self) -> bool {
self.layout.is_contiguous()
}
/// Returns true if the data is stored in a Fortran contiguous (aka column major) way.
pub fn is_fortran_contiguous(&self) -> bool {
self.layout.is_fortran_contiguous()
}
/// Compared to clone, this copies the actual storage but may fail because of running out of
/// memory.
pub fn copy(&self) -> Result<Tensor> {
let op = BackpropOp::new1(self, Op::Copy);
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: Arc::new(RwLock::new(self.storage().try_clone(self.layout())?)),
layout: self.layout.clone(),
op,
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
/// Returns a new tensor detached from the current graph, gradient are not propagated through
/// this new node. The storage of this tensor is shared with the initial tensor.
///
/// If the tensor is already detached from the computation graph, the same tensor is returned.
pub fn detach(&self) -> Result<Tensor> {
if self.op.is_none() && !self.is_variable {
Ok(self.clone())
} else {
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout: self.layout.clone(),
op: BackpropOp::none(),
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
}
/// If the target device is the same as the tensor device, only a shallow copy is performed.
pub fn to_device(&self, device: &Device) -> Result<Tensor> {
if self.device().same_device(device) {
Ok(self.clone())
} else {
let storage = match (&*self.storage(), device) {
(Storage::Cpu(storage), Device::Cuda(cuda)) => {
Storage::Cuda(cuda.storage_from_cpu_storage(storage)?)
}
(Storage::Cpu(storage), Device::Metal(metal)) => {
Storage::Metal(metal.storage_from_cpu_storage(storage)?)
}
(Storage::Cuda(storage), Device::Cpu) => Storage::Cpu(storage.to_cpu_storage()?),
(Storage::Metal(storage), Device::Cpu) => {
println!("{storage:?} - {:?}", storage.to_cpu_storage()?);
Storage::Cpu(storage.to_cpu_storage()?)
}
(Storage::Cuda(storage), Device::Cuda(cuda)) => {
// TODO: Avoid passing through the cpu storage here, especially if the gpu ids
// are the same.
let cpu_storage = storage.to_cpu_storage()?;
Storage::Cuda(cuda.storage_from_cpu_storage(&cpu_storage)?)
}
(Storage::Cpu(storage), Device::Cpu) => Storage::Cpu(storage.clone()),
_ => {
bail!("not implemented yet")
}
};
let op = BackpropOp::new1(self, Op::ToDevice);
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: Arc::new(RwLock::new(storage)),
layout: self.layout.clone(),
op,
is_variable: false,
dtype: self.dtype,
device: device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
}
/// Returns a new tensor duplicating data from the original tensor. New dimensions are inserted
/// on the left.
pub fn broadcast_left<S: Into<Shape>>(&self, left_shape: S) -> Result<Self> {
let left_shape = left_shape.into();
let mut dims = left_shape.into_dims();
dims.extend(self.dims());
self.broadcast_as(dims)
}
/// Broadcast the input tensor to the target shape. This returns an error if the input shape is
/// not compatible with the target shape.
///
/// If the input shape is `i_1, i_2, ... i_k`, the target shape has to have `k` dimensions or
/// more and shape `j_1, ..., j_l, t_1, t_2, ..., t_k`. The dimensions `j_1` to `j_l` can have
/// any value, the dimension `t_a` must be equal to `i_a` if `i_a` is different from 1. If
/// `i_a` is equal to 1, any value can be used.
pub fn broadcast_as<S: Into<Shape>>(&self, shape: S) -> Result<Self> {
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout: self.layout.broadcast_as(shape)?,
op: BackpropOp::new1(self, Op::Broadcast),
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
}
/// An alias for broadcast_as.
pub fn expand<S: Into<Shape>>(&self, shape: S) -> Result<Self> {
self.broadcast_as(shape)
}
/// Casts the input tensor to the target `dtype`.
///
/// ```rust
/// use candle_core::{Tensor, Device};
/// let tensor = Tensor::new(3.14159265358979f64, &Device::Cpu)?;
/// assert_eq!(tensor.to_scalar::<f64>()?, 3.14159265358979);
/// let tensor = tensor.to_dtype(candle_core::DType::F32)?;
/// assert_eq!(tensor.to_scalar::<f32>()?, 3.1415927);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn to_dtype(&self, dtype: DType) -> Result<Self> {
if self.dtype() == dtype {
Ok(self.clone())
} else {
let shape = self.shape();
let storage = self.storage().to_dtype(self.layout(), dtype)?;
let op = BackpropOp::new1(self, Op::ToDType);
Ok(from_storage(storage, shape.clone(), op, false))
}
}
/// Returns a tensor that is in row major order. This is the same as the original tensor if it
/// was already contiguous, otherwise a copy is triggered.
pub fn contiguous(&self) -> Result<Tensor> {
if self.is_contiguous() {
Ok(self.clone())
} else {
let shape = self.shape();
let mut storage = self.device().zeros(shape, self.dtype())?;
self.storage()
.copy_strided_src(&mut storage, 0, self.layout())?;
let op = BackpropOp::new1(self, Op::Copy);
Ok(from_storage(storage, shape.clone(), op, false))
}
}
/// Create a variable based on the values currently stored in a tensor. The storage is always
/// copied.
pub(crate) fn make_var(&self) -> Result<Tensor> {
let shape = self.shape().clone();
let mut storage = self.device().zeros(&shape, self.dtype())?;
self.storage()
.copy_strided_src(&mut storage, 0, self.layout())?;
Ok(from_storage(storage, shape, BackpropOp::none(), true))
}
/// Reshape returns a tensor with the target shape provided that the number of elements of the
/// original tensor is the same.
/// If the input tensor is contiguous, this is a view on the original data. Otherwise this uses
/// a new storage and copies the data over, the returned tensor is always contiguous.
///
/// The shape can be specified using a tuple of `usize` and at most one `()` in which case
/// the behavior is the same as when using `-1` in PyTorch: this dimension size is adjusted so
/// as to match the number of elements in the tensor.
///
/// ```rust
/// # use candle_core::{Tensor, DType, Device, D};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
///
/// let c = a.reshape((1, 6))?;
/// assert_eq!(c.shape().dims(), &[1, 6]);
///
/// let c = a.reshape((3, 2))?;
/// assert_eq!(c.shape().dims(), &[3, 2]);
///
/// let c = a.reshape((2, (), 1))?;
/// assert_eq!(c.shape().dims(), &[2, 3, 1]);
///
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn reshape<S: crate::shape::ShapeWithOneHole>(&self, s: S) -> Result<Tensor> {
let shape = s.into_shape(self.elem_count())?;
if shape.elem_count() != self.elem_count() {
return Err(Error::ShapeMismatchBinaryOp {
lhs: self.shape().clone(),
rhs: shape,
op: "reshape",
}
.bt());
}
let op = BackpropOp::new1(self, Op::Reshape);
if self.is_contiguous() {
let tensor_ = Tensor_ {
id: TensorId::new(),
storage: self.storage.clone(),
layout: Layout::contiguous_with_offset(shape, self.layout.start_offset()),
op,
is_variable: false,
dtype: self.dtype,
device: self.device.clone(),
};
Ok(Tensor(Arc::new(tensor_)))
} else {
let mut storage = self.device().zeros(&shape, self.dtype())?;
self.storage()
.copy_strided_src(&mut storage, 0, self.layout())?;
Ok(from_storage(storage, shape, op, false))
}
}
/// Creates a new tensor with the specified dimension removed if its size was one.
///
/// ```rust
/// # use candle_core::{Tensor, DType, Device, D};
/// let a = Tensor::zeros((2, 3, 1), DType::F32, &Device::Cpu)?;
///
/// let c = a.squeeze(2)?;
/// assert_eq!(c.shape().dims(), &[2, 3]);
///
/// let c = a.squeeze(D::Minus1)?;
/// assert_eq!(c.shape().dims(), &[2, 3]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn squeeze<D: Dim>(&self, dim: D) -> Result<Self> {
// The PyTorch semantics are to return the same tensor if the target dimension
// does not have a size of 1.
let dims = self.dims();
let dim = dim.to_index(self.shape(), "squeeze")?;
if dims[dim] == 1 {
let mut dims = dims.to_vec();
dims.remove(dim);
self.reshape(dims)
} else {
Ok(self.clone())
}
}
/// Creates a new tensor with a dimension of size one inserted at the specified position.
///
/// ```rust
/// # use candle_core::{Tensor, DType, Device, D};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
///
/// let c = a.unsqueeze(0)?;
/// assert_eq!(c.shape().dims(), &[1, 2, 3]);
///
/// let c = a.unsqueeze(D::Minus1)?;
/// assert_eq!(c.shape().dims(), &[2, 3, 1]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn unsqueeze<D: Dim>(&self, dim: D) -> Result<Self> {
let mut dims = self.dims().to_vec();
let dim = dim.to_index_plus_one(self.shape(), "unsqueeze")?;
// Cannot panic because to_index_plus_one already checks dimensions
dims.insert(dim, 1);
self.reshape(dims)
}
/// Stacks two or more tensors along a particular dimension.
///
/// All tensors must have the same rank, and the output has one additional rank
///
/// ```rust
/// # use candle_core::{Tensor, DType, Device};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
/// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
///
/// let c = Tensor::stack(&[&a, &b], 0)?;
/// assert_eq!(c.shape().dims(), &[2, 2, 3]);
///
/// let c = Tensor::stack(&[&a, &b], 2)?;
/// assert_eq!(c.shape().dims(), &[2, 3, 2]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn stack<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
if args.is_empty() {
Err(Error::OpRequiresAtLeastOneTensor { op: "stack" }.bt())?
}
let dim = dim.to_index_plus_one(args[0].as_ref().shape(), "stack")?;
let args = args
.iter()
.map(|t| t.as_ref().unsqueeze(dim))
.collect::<Result<Vec<_>>>()?;
Self::cat(&args, dim)
}
/// Concatenates two or more tensors along a particular dimension.
///
/// All tensors must of the same rank, and the output will have
/// the same rank
///
/// ```rust
/// # use candle_core::{Tensor, DType, Device};
/// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
/// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
///
/// let c = Tensor::cat(&[&a, &b], 0)?;
/// assert_eq!(c.shape().dims(), &[4, 3]);
///
/// let c = Tensor::cat(&[&a, &b], 1)?;
/// assert_eq!(c.shape().dims(), &[2, 6]);
/// # Ok::<(), candle_core::Error>(())
/// ```
pub fn cat<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
if args.is_empty() {
Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
}
let arg0 = args[0].as_ref();
if args.len() == 1 {
return Ok(arg0.clone());
}
let dim = dim.to_index(arg0.shape(), "cat")?;
for arg in args {
arg.as_ref().check_dim(dim, "cat")?;
}
for (arg_idx, arg) in args.iter().enumerate() {
let arg = arg.as_ref();
if arg0.rank() != arg.rank() {
Err(Error::UnexpectedNumberOfDims {
expected: arg0.rank(),
got: arg.rank(),
shape: arg.shape().clone(),
}
.bt())?
}
for (dim_idx, (v1, v2)) in arg0
.shape()
.dims()
.iter()
.zip(arg.shape().dims().iter())
.enumerate()
{
if dim_idx != dim && v1 != v2 {
Err(Error::ShapeMismatchCat {
dim: dim_idx,
first_shape: arg0.shape().clone(),
n: arg_idx + 1,
nth_shape: arg.shape().clone(),
}
.bt())?
}
}
}
if dim == 0 {
Self::cat0(args)
} else {
// TODO: Avoid these transpositions and have an implementation that works
// for dim != 0...
let args: Vec<Tensor> = args
.iter()
.map(|a| a.as_ref().transpose(0, dim))
.collect::<Result<Vec<_>>>()?;
let cat = Self::cat0(&args)?;
cat.transpose(0, dim)
}
}
fn cat0<A: AsRef<Tensor>>(args: &[A]) -> Result<Self> {
if args.is_empty() {
Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
}
let arg0 = args[0].as_ref();
if args.len() == 1 {
return Ok(arg0.clone());
}
let rank = arg0.rank();
let device = arg0.device();
let dtype = arg0.dtype();
let first_dims = arg0.shape().dims();
let mut cat_dims = first_dims.to_vec();
cat_dims[0] = 0;
let mut offsets = vec![0usize];
for (arg_idx, arg) in args.iter().enumerate() {
let arg = arg.as_ref();
if arg.dtype() != dtype {
Err(Error::DTypeMismatchBinaryOp {
lhs: dtype,
rhs: arg.dtype(),
op: "cat",
}
.bt())?
}
if arg.device().location() != device.location() {
Err(Error::DeviceMismatchBinaryOp {
lhs: device.location(),
rhs: arg.device().location(),
op: "cat",
}
.bt())?
}
if rank != arg.rank() {
Err(Error::UnexpectedNumberOfDims {
expected: rank,
got: arg.rank(),
shape: arg.shape().clone(),
}
.bt())?
}
for (dim_idx, (v1, v2)) in arg0
.shape()
.dims()
.iter()
.zip(arg.shape().dims().iter())
.enumerate()
{
if dim_idx == 0 {
cat_dims[0] += v2;
}
if dim_idx != 0 && v1 != v2 {
Err(Error::ShapeMismatchCat {
dim: dim_idx,
first_shape: arg0.shape().clone(),
n: arg_idx + 1,
nth_shape: arg.shape().clone(),
}
.bt())?
}
}
let next_offset = offsets.last().unwrap() + arg.elem_count();
offsets.push(next_offset);
}
let shape = Shape::from(cat_dims);
let op = BackpropOp::new(args, |args| Op::Cat(args, 0));
let mut storage = device.zeros(&shape, dtype)?;
for (arg, &offset) in args.iter().zip(offsets.iter()) {
let arg = arg.as_ref();
arg.storage()
.copy_strided_src(&mut storage, offset, arg.layout())?;
}
Ok(from_storage(storage, shape, op, false))
}
/// Pad the input tensor using 0s along dimension `dim`. This adds `left` elements before the
/// input tensor values and `right` elements after.
pub fn pad_with_zeros<D: Dim>(&self, dim: D, left: usize, right: usize) -> Result<Self> {
if left == 0 && right == 0 {
Ok(self.clone())
} else if left == 0 {
let dim = dim.to_index(self.shape(), "pad_with_zeros")?;
let mut dims = self.dims().to_vec();
dims[dim] = right;
let right = Tensor::zeros(dims.as_slice(), self.dtype, self.device())?;
Tensor::cat(&[self, &right], dim)
} else if right == 0 {
let dim = dim.to_index(self.shape(), "pad_with_zeros")?;
let mut dims = self.dims().to_vec();
dims[dim] = left;
let left = Tensor::zeros(dims.as_slice(), self.dtype, self.device())?;
Tensor::cat(&[&left, self], dim)
} else {
let dim = dim.to_index(self.shape(), "pad_with_zeros")?;
let mut dims = self.dims().to_vec();
dims[dim] = left;
let left = Tensor::zeros(dims.as_slice(), self.dtype, self.device())?;
dims[dim] = right;
let right = Tensor::zeros(dims.as_slice(), self.dtype, self.device())?;
Tensor::cat(&[&left, self, &right], dim)
}
}
/// Pad the input tensor using same values along dimension `dim`. This adds `left` elements before the
/// input tensor values and `right` elements after.
pub fn pad_with_same<D: Dim>(&self, dim: D, left: usize, right: usize) -> Result<Self> {
if left == 0 && right == 0 {
Ok(self.clone())
} else if self.elem_count() == 0 {
crate::bail!("cannot use pad_with_same on an empty tensor")
} else if left == 0 {
let dim = dim.to_index(self.shape(), "pad_with_same")?;
let r = self.narrow(dim, self.dim(dim)? - 1, 1)?;
let mut v = vec![self];
for _ in 0..right {
v.push(&r)
}
Tensor::cat(&v, dim)
} else if right == 0 {
let dim = dim.to_index(self.shape(), "pad_with_same")?;
let l = self.narrow(dim, 0, 1)?;
let mut v = vec![];
for _ in 0..left {
v.push(&l)
}
v.push(self);
Tensor::cat(&v, dim)
} else {
let dim = dim.to_index(self.shape(), "pad_with_same")?;
let l = self.narrow(dim, 0, 1)?;
let r = self.narrow(dim, self.dim(dim)? - 1, 1)?;
let mut v = vec![];
for _ in 0..left {
v.push(&l)
}
v.push(self);
for _ in 0..right {
v.push(&r)
}
Tensor::cat(&v, dim)
}
}
/// Run the `forward` method of `m` on `self`.
pub fn apply<M: crate::Module>(&self, m: &M) -> Result<Self> {
m.forward(self)
}
/// Run the `forward` method of `m` on `self`.
pub fn apply_t<M: crate::ModuleT>(&self, m: &M, train: bool) -> Result<Self> {
m.forward_t(self, train)
}
pub(crate) fn storage(&self) -> std::sync::RwLockReadGuard<'_, Storage> {
self.storage.read().unwrap()
}
// If we extend the visibility of this function to be usable outside of this crate, we should
// make it unsafe.
pub(crate) fn storage_mut_and_layout(
&self,
) -> (std::sync::RwLockWriteGuard<'_, Storage>, &Layout) {
let storage = self.storage.write().unwrap();
(storage, &self.layout)
}
/// The storage used by this tensor, together with the layout to use to access it safely.
pub fn storage_and_layout(&self) -> (std::sync::RwLockReadGuard<'_, Storage>, &Layout) {
let storage = self.storage.read().unwrap();
(storage, &self.layout)
}
pub(crate) fn same_storage(&self, rhs: &Self) -> bool {
let lhs: &RwLock<Storage> = self.storage.as_ref();
let rhs: &RwLock<Storage> = rhs.storage.as_ref();
std::ptr::eq(lhs, rhs)
}
/// Applies a unary custom op without backward support
pub fn apply_op1_no_bwd<C: CustomOp1>(&self, c: &C) -> Result<Self> {
let (storage, shape) = self.storage().apply_op1(self.layout(), c)?;
Ok(from_storage(storage, shape, BackpropOp::none(), false))
}
/// Applies a binary custom op without backward support
pub fn apply_op2_no_bwd<C: CustomOp2>(&self, rhs: &Self, c: &C) -> Result<Self> {
let (storage, shape) =
self.storage()
.apply_op2(self.layout(), &rhs.storage(), rhs.layout(), c)?;
Ok(from_storage(storage, shape, BackpropOp::none(), false))
}
/// Applies a ternary custom op without backward support
pub fn apply_op3_no_bwd<C: CustomOp3>(&self, t2: &Self, t3: &Self, c: &C) -> Result<Self> {
let (storage, shape) = self.storage().apply_op3(
self.layout(),
&t2.storage(),
t2.layout(),
&t3.storage(),
t3.layout(),
c,
)?;
Ok(from_storage(storage, shape, BackpropOp::none(), false))
}
/// Applies a unary custom op.
pub fn apply_op1_arc(&self, c: Arc<Box<dyn CustomOp1 + Send + Sync>>) -> Result<Self> {
let (storage, shape) = self
.storage()
.apply_op1(self.layout(), c.as_ref().as_ref())?;
let op = BackpropOp::new1(self, |s| Op::CustomOp1(s, c.clone()));
Ok(from_storage(storage, shape, op, false))
}
pub fn apply_op1<C: 'static + CustomOp1 + Send + Sync>(&self, c: C) -> Result<Self> {
self.apply_op1_arc(Arc::new(Box::new(c)))
}
/// Applies a binary custom op.
pub fn apply_op2_arc(
&self,
rhs: &Self,
c: Arc<Box<dyn CustomOp2 + Send + Sync>>,
) -> Result<Self> {
let (storage, shape) = self.storage().apply_op2(
self.layout(),
&rhs.storage(),
rhs.layout(),
c.as_ref().as_ref(),
)?;
let op = BackpropOp::new2(self, rhs, |t1, t2| Op::CustomOp2(t1, t2, c.clone()));
Ok(from_storage(storage, shape, op, false))
}
pub fn apply_op2<C: 'static + CustomOp2 + Send + Sync>(&self, r: &Self, c: C) -> Result<Self> {
self.apply_op2_arc(r, Arc::new(Box::new(c)))
}
/// Applies a ternary custom op.
pub fn apply_op3_arc(
&self,
t2: &Self,
t3: &Self,
c: Arc<Box<dyn CustomOp3 + Send + Sync>>,
) -> Result<Self> {
let (storage, shape) = self.storage().apply_op3(
self.layout(),
&t2.storage(),
t2.layout(),
&t3.storage(),
t3.layout(),
c.as_ref().as_ref(),
)?;
let op = BackpropOp::new3(self, t2, t3, |t1, t2, t3| {
Op::CustomOp3(t1, t2, t3, c.clone())
});
Ok(from_storage(storage, shape, op, false))
}
pub fn apply_op3<C: 'static + CustomOp3 + Send + Sync>(
&self,
t2: &Self,
t3: &Self,
c: C,
) -> Result<Self> {
self.apply_op3_arc(t2, t3, Arc::new(Box::new(c)))
}
/// Normalize a 'relative' axis value: positive values are kept, negative
/// values means counting the dimensions from the back.
pub fn normalize_axis(&self, axis: i64) -> Result<usize> {
let rank = self.rank() as i64;
if rank <= axis {
crate::bail!("axis {axis} is too large, tensor rank {rank}")
} else if 0 <= axis {
Ok(axis as usize)
} else {
let naxis = rank + axis;
if naxis < 0 {
crate::bail!("axis {axis} is too small, tensor rank {rank}")
}
Ok(naxis as usize)
}
}
/// Returns a lower triangular matrix of ones of size n by n.
pub fn tril2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
let t = Tensor::arange(0u32, n as u32, device)?;
let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
t1.le(&t2)?.to_dtype(dtype)
}
/// Returns an upper triangular matrix of ones of size n by n.
pub fn triu2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
let t = Tensor::arange(0u32, n as u32, device)?;
let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
t1.ge(&t2)?.to_dtype(dtype)
}
/// Returns a matrix with a diagonal of ones of size n by n.
pub fn eye(n: usize, dtype: DType, device: &Device) -> Result<Self> {
let t = Tensor::arange(0u32, n as u32, device)?;
let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
t1.eq(&t2)?.to_dtype(dtype)
}
/// Returns the cumulative sum of elements of the input tensor summed over the specified
/// dimension.
///
/// This operation is most efficient when dim is the last dimension of the tensor.
pub fn cumsum<D: Dim>(&self, dim: D) -> Result<Self> {
let dim = dim.to_index(self.shape(), "cumsum")?;
let rank = self.rank();
if rank == 0 {
return Ok(self.clone());
}
let n_axis = self.dim(dim)?;
let triu = Tensor::triu2(n_axis, self.dtype(), self.device())?;
if rank == 1 {
self.unsqueeze(0)?.matmul(&triu)?.squeeze(0)
} else {
let last = rank - 1;
let t = self.transpose(dim, last)?;
let t = t.broadcast_matmul(&triu)?;
t.transpose(dim, last)
}
}
/// Returns a copy of `self` where the values within `ranges` have been replaced with the
/// content of `src`.
pub fn slice_assign<D: std::ops::RangeBounds<usize>>(
&self,
ranges: &[D],
src: &Tensor,
) -> Result<Self> {
let src_dims = src.dims();
let self_dims = self.dims();
if self_dims.len() != src_dims.len() {
crate::bail!(
"slice-assign requires input with the same rank {} <> {}",
self_dims.len(),
src_dims.len()
)
}
if self_dims.len() != ranges.len() {
crate::bail!(
"slice-assign requires input with the same rank as there are ranges {} <> {}",
self_dims.len(),
ranges.len()
)
}
let mut src = src.clone();
let mut mask = Self::ones(src.shape(), DType::U8, src.device())?;
for (i, range) in ranges.iter().enumerate() {
let start_included = match range.start_bound() {
std::ops::Bound::Unbounded => 0,
std::ops::Bound::Included(v) => *v,
std::ops::Bound::Excluded(v) => *v + 1,
};
let end_excluded = match range.end_bound() {
std::ops::Bound::Unbounded => self_dims[i],
std::ops::Bound::Included(v) => *v + 1,
std::ops::Bound::Excluded(v) => *v,
};
if end_excluded <= start_included {
crate::bail!(
"slice-assign: empty range for dim {i}, {start_included} {end_excluded}"
)
}
if self_dims[i] < end_excluded {
crate::bail!(
"slice-assign: upper bound is out of range for dim {i}, {end_excluded} {}",
self_dims[i]
)
}
if end_excluded - start_included != src_dims[i] {
crate::bail!(
"slice-assign: the range for dim {i} ({start_included}..{end_excluded}) does not match the size of src {}", src_dims[i]
)
}
src = src.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?;
mask = mask.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?
}
mask.where_cond(/* on_true= */ &src, /* on_false= */ self)
}
}
macro_rules! bin_trait {
($trait:ident, $fn1:ident, $mul:expr, $add:expr) => {
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<B> for Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: B) -> Self::Output {
Tensor::$fn1(&self, rhs.borrow())
}
}
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<B> for &Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: B) -> Self::Output {
Tensor::$fn1(&self, rhs.borrow())
}
}
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<Tensor> for Result<B> {
type Output = Result<Tensor>;
fn $fn1(self, rhs: Tensor) -> Self::Output {
Tensor::$fn1(self?.borrow(), &rhs)
}
}
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<&Tensor> for Result<B> {
type Output = Result<Tensor>;
fn $fn1(self, rhs: &Tensor) -> Self::Output {
Tensor::$fn1(self?.borrow(), rhs)
}
}
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<Result<B>> for Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: Result<B>) -> Self::Output {
Tensor::$fn1(&self, rhs?.borrow())
}
}
impl<B: std::borrow::Borrow<Tensor>> std::ops::$trait<Result<B>> for &Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: Result<B>) -> Self::Output {
Tensor::$fn1(&self, rhs?.borrow())
}
}
impl std::ops::$trait<f64> for Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: f64) -> Self::Output {
self.affine($mul(rhs), $add(rhs))
}
}
impl std::ops::$trait<f64> for &Tensor {
type Output = Result<Tensor>;
fn $fn1(self, rhs: f64) -> Self::Output {
self.affine($mul(rhs), $add(rhs))
}
}
};
}
bin_trait!(Add, add, |_| 1., |v| v);
bin_trait!(Sub, sub, |_| 1., |v: f64| -v);
bin_trait!(Mul, mul, |v| v, |_| 0.);
bin_trait!(Div, div, |v| 1. / v, |_| 0.);
impl std::ops::Add<Tensor> for f64 {
type Output = Result<Tensor>;
fn add(self, rhs: Tensor) -> Self::Output {
rhs + self
}
}
impl std::ops::Add<&Tensor> for f64 {
type Output = Result<Tensor>;
fn add(self, rhs: &Tensor) -> Self::Output {
rhs + self
}
}
impl std::ops::Mul<Tensor> for f64 {
type Output = Result<Tensor>;
fn mul(self, rhs: Tensor) -> Self::Output {
rhs * self
}
}
impl std::ops::Mul<&Tensor> for f64 {
type Output = Result<Tensor>;
fn mul(self, rhs: &Tensor) -> Self::Output {
rhs * self
}
}
impl std::ops::Sub<Tensor> for f64 {
type Output = Result<Tensor>;
fn sub(self, rhs: Tensor) -> Self::Output {
rhs.affine(-1., self)
}
}
impl std::ops::Sub<&Tensor> for f64 {
type Output = Result<Tensor>;
fn sub(self, rhs: &Tensor) -> Self::Output {
rhs.affine(-1., self)
}
}
impl std::ops::Div<Tensor> for f64 {
type Output = Result<Tensor>;
#[allow(clippy::suspicious_arithmetic_impl)]
fn div(self, rhs: Tensor) -> Self::Output {
rhs.recip()? * self
}
}
impl std::ops::Div<&Tensor> for f64 {
type Output = Result<Tensor>;
#[allow(clippy::suspicious_arithmetic_impl)]
fn div(self, rhs: &Tensor) -> Self::Output {
rhs.recip()? * self
}
}
| 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> {
tensor.to_vec1::<T>()
}
}
impl<T: WithDType> TryFrom<&Tensor> for Vec<Vec<T>> {
type Error = Error;
fn try_from(tensor: &Tensor) -> Result<Self, Self::Error> {
tensor.to_vec2::<T>()
}
}
impl<T: WithDType> TryFrom<&Tensor> for Vec<Vec<Vec<T>>> {
type Error = Error;
fn try_from(tensor: &Tensor) -> Result<Self, Self::Error> {
tensor.to_vec3::<T>()
}
}
impl<T: WithDType> TryFrom<Tensor> for Vec<T> {
type Error = Error;
fn try_from(tensor: Tensor) -> Result<Self, Self::Error> {
Vec::<T>::try_from(&tensor)
}
}
impl<T: WithDType> TryFrom<Tensor> for Vec<Vec<T>> {
type Error = Error;
fn try_from(tensor: Tensor) -> Result<Self, Self::Error> {
Vec::<Vec<T>>::try_from(&tensor)
}
}
impl<T: WithDType> TryFrom<Tensor> for Vec<Vec<Vec<T>>> {
type Error = Error;
fn try_from(tensor: Tensor) -> Result<Self, Self::Error> {
Vec::<Vec<Vec<T>>>::try_from(&tensor)
}
}
impl<T: WithDType> TryFrom<&[T]> for Tensor {
type Error = Error;
fn try_from(v: &[T]) -> Result<Self, Self::Error> {
Tensor::from_slice(v, v.len(), &Device::Cpu)
}
}
impl<T: WithDType> TryFrom<Vec<T>> for Tensor {
type Error = Error;
fn try_from(v: Vec<T>) -> Result<Self, Self::Error> {
let len = v.len();
Tensor::from_vec(v, len, &Device::Cpu)
}
}
macro_rules! from_tensor {
($typ:ident) => {
impl TryFrom<&Tensor> for $typ {
type Error = Error;
fn try_from(tensor: &Tensor) -> Result<Self, Self::Error> {
tensor.to_scalar::<$typ>()
}
}
impl TryFrom<Tensor> for $typ {
type Error = Error;
fn try_from(tensor: Tensor) -> Result<Self, Self::Error> {
$typ::try_from(&tensor)
}
}
impl TryFrom<$typ> for Tensor {
type Error = Error;
fn try_from(v: $typ) -> Result<Self, Self::Error> {
Tensor::new(v, &Device::Cpu)
}
}
};
}
from_tensor!(f64);
from_tensor!(f32);
from_tensor!(f16);
from_tensor!(bf16);
from_tensor!(i64);
from_tensor!(u32);
from_tensor!(u8);
impl Tensor {
pub fn write_bytes<W: std::io::Write>(&self, f: &mut W) -> crate::Result<()> {
use byteorder::{LittleEndian, WriteBytesExt};
let vs = self.flatten_all()?;
match self.dtype() {
DType::BF16 => {
let vs = vs.to_vec1::<bf16>()?;
for &v in vs.reinterpret_cast() {
f.write_u16::<LittleEndian>(v)?
}
}
DType::F16 => {
let vs = vs.to_vec1::<f16>()?;
for &v in vs.reinterpret_cast() {
f.write_u16::<LittleEndian>(v)?
}
}
DType::F32 => {
// TODO: Avoid using a buffer when data is already on the CPU.
for v in vs.to_vec1::<f32>()? {
f.write_f32::<LittleEndian>(v)?
}
}
DType::F64 => {
for v in vs.to_vec1::<f64>()? {
f.write_f64::<LittleEndian>(v)?
}
}
DType::U32 => {
for v in vs.to_vec1::<u32>()? {
f.write_u32::<LittleEndian>(v)?
}
}
DType::I64 => {
for v in vs.to_vec1::<i64>()? {
f.write_i64::<LittleEndian>(v)?
}
}
DType::U8 => {
let vs = vs.to_vec1::<u8>()?;
f.write_all(&vs)?;
}
}
Ok(())
}
}
| 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> {
if arg.rank() == node.rank() {
// keepdim = true
node.broadcast_as(arg.shape())
} else {
// keepdim = false
// first expand the reduced dims.
node.reshape(reduced_dims)?.broadcast_as(arg.shape())
}
}
thread_local! {
static CANDLE_GRAD_DO_NOT_DETACH: bool = {
match std::env::var("CANDLE_GRAD_DO_NOT_DETACH") {
Ok(s) => {
!s.is_empty() && s != "0"
},
Err(_) => false,
}
}
}
impl Tensor {
/// Return all the nodes that lead to this value in a topologically sorted vec, the first
/// elements having dependencies on the latter ones, e.g. the first element if any is the
/// argument.
/// This assumes that the op graph is a DAG.
fn sorted_nodes(&self) -> Vec<&Tensor> {
// The vec of sorted nodes is passed as an owned value rather than a mutable reference
// to get around some lifetime limitations.
fn walk<'a>(
node: &'a Tensor,
nodes: Vec<&'a Tensor>,
already_seen: &mut HashMap<TensorId, bool>,
) -> (bool, Vec<&'a Tensor>) {
if let Some(&tg) = already_seen.get(&node.id()) {
return (tg, nodes);
}
let mut track_grad = false;
let mut nodes = if node.is_variable() {
// Do not call recursively on the "leaf" nodes.
track_grad = true;
nodes
} else if node.dtype().is_int() {
nodes
} else if let Some(op) = node.op() {
match op {
Op::IndexAdd(t1, t2, t3, _)
| Op::ScatterAdd(t1, t2, t3, _)
| Op::CustomOp3(t1, t2, t3, _)
| Op::WhereCond(t1, t2, t3) => {
let (tg, nodes) = walk(t1, nodes, already_seen);
track_grad |= tg;
let (tg, nodes) = walk(t2, nodes, already_seen);
track_grad |= tg;
let (tg, nodes) = walk(t3, nodes, already_seen);
track_grad |= tg;
nodes
}
Op::Conv1D {
arg: lhs,
kernel: rhs,
..
}
| Op::ConvTranspose1D {
arg: lhs,
kernel: rhs,
..
}
| Op::Conv2D {
arg: lhs,
kernel: rhs,
..
}
| Op::ConvTranspose2D {
arg: lhs,
kernel: rhs,
..
}
| Op::CustomOp2(lhs, rhs, _)
| Op::Binary(lhs, rhs, _)
| Op::Gather(lhs, rhs, _)
| Op::IndexSelect(lhs, rhs, _)
| Op::Matmul(lhs, rhs)
| Op::SliceScatter0(lhs, rhs, _) => {
let (tg, nodes) = walk(lhs, nodes, already_seen);
track_grad |= tg;
let (tg, nodes) = walk(rhs, nodes, already_seen);
track_grad |= tg;
nodes
}
Op::Cat(args, _) => args.iter().fold(nodes, |nodes, arg| {
let (tg, nodes) = walk(arg, nodes, already_seen);
track_grad |= tg;
nodes
}),
Op::Affine { arg, mul, .. } => {
if *mul == 0. {
nodes
} else {
let (tg, nodes) = walk(arg, nodes, already_seen);
track_grad |= tg;
nodes
}
}
Op::Unary(_node, UnaryOp::Ceil)
| Op::Unary(_node, UnaryOp::Floor)
| Op::Unary(_node, UnaryOp::Round) => nodes,
Op::Reshape(node)
| Op::UpsampleNearest1D(node)
| Op::UpsampleNearest2D(node)
| Op::AvgPool2D { arg: node, .. }
| Op::MaxPool2D { arg: node, .. }
| Op::Copy(node)
| Op::Broadcast(node)
| Op::Cmp(node, _)
| Op::Reduce(node, ReduceOp::Min | ReduceOp::Sum | ReduceOp::Max, _)
| Op::ToDevice(node)
| Op::Transpose(node, _, _)
| Op::Permute(node, _)
| Op::Narrow(node, _, _, _)
| Op::Unary(node, _)
| Op::Elu(node, _)
| Op::Powf(node, _)
| Op::CustomOp1(node, _) => {
let (tg, nodes) = walk(node, nodes, already_seen);
track_grad |= tg;
nodes
}
Op::ToDType(node) => {
if node.dtype().is_float() {
let (tg, nodes) = walk(node, nodes, already_seen);
track_grad |= tg;
nodes
} else {
nodes
}
}
Op::Reduce(_, ReduceOp::ArgMin | ReduceOp::ArgMax, _) => nodes,
}
} else {
nodes
};
already_seen.insert(node.id(), track_grad);
if track_grad {
nodes.push(node);
}
(track_grad, nodes)
}
let (_tg, mut nodes) = walk(self, vec![], &mut HashMap::new());
nodes.reverse();
nodes
}
pub fn backward(&self) -> Result<GradStore> {
let sorted_nodes = self.sorted_nodes();
let mut grads = GradStore::new();
grads.insert(self, self.ones_like()?.contiguous()?);
for node in sorted_nodes.iter() {
if node.is_variable() {
continue;
}
let grad = grads
.remove(node)
.expect("candle internal error - grad not populated");
// https://github.com/huggingface/candle/issues/1241
// Ideally, we would make these operations in place where possible to ensure that we
// do not have to allocate too often. Here we just call `.detach` to avoid computing
// the backprop graph of the backprop itself. This would be an issue for second order
// derivatives but these are out of scope at the moment.
let do_not_detach = CANDLE_GRAD_DO_NOT_DETACH.with(|b| *b);
let grad = if do_not_detach { grad } else { grad.detach()? };
if let Some(op) = node.op() {
match op {
Op::Binary(lhs, rhs, BinaryOp::Add) => {
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.add(&grad)?;
}
Op::Binary(lhs, rhs, BinaryOp::Sub) => {
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&grad)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.sub(&grad)?;
}
Op::Binary(lhs, rhs, BinaryOp::Mul) => {
let lhs_grad = grad.mul(rhs)?;
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
let rhs_grad = grad.mul(lhs)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
}
Op::Binary(lhs, rhs, BinaryOp::Div) => {
let lhs_grad = grad.div(rhs)?;
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
let rhs_grad = grad.mul(lhs)?.div(&rhs.sqr()?)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.sub(&rhs_grad)?;
}
Op::Binary(lhs, rhs, BinaryOp::Minimum)
| Op::Binary(lhs, rhs, BinaryOp::Maximum) => {
let mask_lhs = node.eq(lhs)?.to_dtype(grad.dtype())?;
let mask_rhs = node.eq(rhs)?.to_dtype(grad.dtype())?;
// If both masks are 1 one the same point, we want to scale the
// gradient by 0.5 rather than 1.
let lhs_grad = mask_lhs.mul(&grad)?.div(&(&mask_rhs + 1.)?)?;
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
let rhs_grad = mask_rhs.mul(&grad)?.div(&(&mask_lhs + 1.)?)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
}
Op::WhereCond(pred, t, f) => {
let zeros = grad.zeros_like()?;
let t_sum_grad = grads.or_insert(t)?;
let t_grad = pred.where_cond(&grad, &zeros)?;
*t_sum_grad = t_sum_grad.add(&t_grad)?;
let f_sum_grad = grads.or_insert(f)?;
let f_grad = pred.where_cond(&zeros, &grad)?;
*f_sum_grad = f_sum_grad.add(&f_grad)?;
}
Op::Conv1D {
arg,
kernel,
padding,
stride,
dilation,
} => {
// The output height for conv_transpose1d is:
// (l_in - 1) * stride - 2 * padding + dilation * (k_size - 1) + out_padding + 1
let grad_l_in = grad.dim(2)?;
let k_size = kernel.dim(2)?;
let out_size =
(grad_l_in - 1) * stride + dilation * (k_size - 1) + 1 - 2 * padding;
let out_padding = arg.dim(2)? - out_size;
let grad_arg = grad.conv_transpose1d(
kernel,
*padding,
out_padding,
*stride,
*dilation,
)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
let grad_kernel = arg
.transpose(0, 1)?
.conv1d(&grad.transpose(0, 1)?, *padding, *dilation, *stride, 1)?
.transpose(0, 1)?;
let sum_grad = grads.or_insert(kernel)?;
let (_, _, k0) = kernel.dims3()?;
let (_, _, g_k0) = grad_kernel.dims3()?;
let grad_kernel = if g_k0 != k0 {
grad_kernel.narrow(2, 0, k0)?
} else {
grad_kernel
};
*sum_grad = sum_grad.add(&grad_kernel)?;
}
Op::Conv2D {
arg,
kernel,
padding,
stride,
dilation,
} => {
// The output height for conv_transpose2d is:
// (i_h - 1) * stride - 2 * padding + dilation * (k_h - 1) + out_padding + 1
let grad_h = grad.dim(2)?;
let k_h = kernel.dim(2)?;
let out_size =
(grad_h - 1) * stride + dilation * (k_h - 1) + 1 - 2 * padding;
let out_padding = arg.dim(2)? - out_size;
let grad_arg = grad.conv_transpose2d(
kernel,
*padding,
out_padding,
*stride,
*dilation,
)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
let grad_kernel = arg
.transpose(0, 1)?
.conv2d(&grad.transpose(0, 1)?, *padding, *dilation, *stride, 1)?
.transpose(0, 1)?;
let sum_grad = grads.or_insert(kernel)?;
let (_, _, k0, k1) = kernel.dims4()?;
let (_, _, g_k0, g_k1) = grad_kernel.dims4()?;
let grad_kernel = if g_k0 != k0 || g_k1 != k1 {
grad_kernel.narrow(2, 0, k0)?.narrow(3, 0, k1)?
} else {
grad_kernel
};
*sum_grad = sum_grad.add(&grad_kernel)?;
}
Op::ConvTranspose1D { .. } => Err(Error::BackwardNotSupported {
op: "conv-transpose1d",
})?,
Op::ConvTranspose2D { .. } => Err(Error::BackwardNotSupported {
op: "conv-transpose2d",
})?,
Op::AvgPool2D {
arg,
kernel_size,
stride,
} => {
if kernel_size != stride {
crate::bail!("backward not supported for avgpool2d if ksize {kernel_size:?} != stride {stride:?}")
}
let (_n, _c, h, w) = arg.dims4()?;
let grad_arg = grad.upsample_nearest2d(h, w)?;
let grad_arg =
(grad_arg * (1f64 / (kernel_size.0 * kernel_size.1) as f64))?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
}
Op::MaxPool2D {
arg,
kernel_size,
stride,
} => {
if kernel_size != stride {
crate::bail!("backward not supported for maxpool2d if ksize {kernel_size:?} != stride {stride:?}")
}
let (_n, _c, h, w) = arg.dims4()?;
// For computing the max-pool gradient, we compute a mask where a 1 means
// that the element is the maximum, then we apply this mask to the
// upsampled gradient (taking into account that multiple max may exist so
// we scale the gradient for this case).
let node_upsampled = node.upsample_nearest2d(h, w)?;
let mask = arg.eq(&node_upsampled)?.to_dtype(arg.dtype())?;
let avg = mask.avg_pool2d_with_stride(*kernel_size, *stride)?;
let grad_arg = ((grad * avg)?.upsample_nearest2d(h, w)? * mask)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
}
Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
op: "upsample-nearest1d",
})?,
Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
op: "upsample-nearest2d",
})?,
Op::SliceScatter0(lhs, rhs, start_rhs) => {
let rhs_sum_grad = grads.or_insert(rhs)?;
let rhs_grad = grad.narrow(0, *start_rhs, rhs.dim(0)?)?;
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
let lhs_sum_grad = grads.or_insert(lhs)?;
let lhs_grad = grad.slice_scatter0(&rhs.zeros_like()?, *start_rhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?
}
Op::Gather(arg, indexes, dim) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.scatter_add(indexes, &grad, *dim)?;
}
Op::ScatterAdd(init, indexes, src, dim) => {
let init_sum_grad = grads.or_insert(init)?;
*init_sum_grad = init_sum_grad.add(&grad)?;
let src_grad = grad.gather(indexes, *dim)?;
let src_sum_grad = grads.or_insert(src)?;
*src_sum_grad = src_sum_grad.add(&src_grad)?;
}
Op::IndexAdd(init, indexes, src, dim) => {
let init_sum_grad = grads.or_insert(init)?;
*init_sum_grad = init_sum_grad.add(&grad)?;
let src_grad = grad.index_select(indexes, *dim)?;
let src_sum_grad = grads.or_insert(src)?;
*src_sum_grad = src_sum_grad.add(&src_grad)?;
}
Op::IndexSelect(arg, indexes, dim) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.index_add(indexes, &grad, *dim)?;
}
Op::Matmul(lhs, rhs) => {
// Skipping checks, the op went ok, we can skip
// the matmul size checks for now.
let lhs_grad = grad.matmul(&rhs.t()?)?;
let lhs_sum_grad = grads.or_insert(lhs)?;
*lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?;
let rhs_grad = lhs.t()?.matmul(&grad)?;
let rhs_sum_grad = grads.or_insert(rhs)?;
*rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
}
Op::Cat(args, dim) => {
let mut start_idx = 0;
for arg in args {
let len = arg.dims()[*dim];
let arg_grad = grad.narrow(*dim, start_idx, len)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?;
start_idx += len;
}
}
Op::Broadcast(arg) => {
let arg_dims = arg.dims();
let node_dims = node.dims();
// The number of dims that have been inserted on the left.
let left_dims = node_dims.len() - arg_dims.len();
let mut sum_dims: Vec<usize> = (0..left_dims).collect();
for (dim, (node_dim, arg_dim)) in node_dims[left_dims..]
.iter()
.zip(arg_dims.iter())
.enumerate()
{
if node_dim != arg_dim {
sum_dims.push(dim + left_dims)
}
}
let mut arg_grad = grad.sum_keepdim(sum_dims.as_slice())?;
for _i in 0..left_dims {
arg_grad = arg_grad.squeeze(0)?
}
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad.broadcast_as(sum_grad.dims())?)?;
}
Op::Reduce(arg, ReduceOp::Sum, reduced_dims) => {
let grad = broadcast_back(arg, &grad, reduced_dims)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad)?;
}
Op::Cmp(_args, _) => {}
Op::Reduce(arg, ReduceOp::Max, reduced_dims) => {
let node = broadcast_back(arg, node, reduced_dims)?;
let grad = broadcast_back(arg, &grad, reduced_dims)?;
let grad = node.eq(arg)?.to_dtype(grad.dtype())?.mul(&grad)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad.broadcast_as(sum_grad.dims())?)?;
}
Op::Reduce(arg, ReduceOp::Min, reduced_dims) => {
let node = broadcast_back(arg, node, reduced_dims)?;
let grad = broadcast_back(arg, &grad, reduced_dims)?;
let grad = node.eq(arg)?.to_dtype(grad.dtype())?.mul(&grad)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad.broadcast_as(sum_grad.dims())?)?;
}
Op::ToDType(arg) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad.to_dtype(arg.dtype())?)?
}
Op::Copy(arg) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad)?
}
Op::Affine { arg, mul, .. } => {
let arg_grad = grad.affine(*mul, 0.)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Unary(arg, UnaryOp::Log) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&(grad / arg)?)?
}
Op::Unary(arg, UnaryOp::Sin) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&(&grad * arg.cos())?)?
}
Op::Unary(arg, UnaryOp::Cos) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.sub(&(&grad * arg.sin())?)?
}
Op::Unary(arg, UnaryOp::Tanh) => {
let sum_grad = grads.or_insert(arg)?;
let minus_dtanh = (node.sqr()? - 1.)?;
*sum_grad = sum_grad.sub(&(&grad * &minus_dtanh)?)?
}
Op::Unary(arg, UnaryOp::Abs) => {
let sum_grad = grads.or_insert(arg)?;
let ones = arg.ones_like()?;
let abs_grad = arg.ge(&arg.zeros_like()?)?.where_cond(&ones, &ones.neg()?);
*sum_grad = sum_grad.add(&(&grad * abs_grad)?)?
}
Op::Unary(arg, UnaryOp::Exp) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&(&grad * *node)?)?
}
Op::Unary(arg, UnaryOp::Neg) => {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.sub(&grad)?
}
Op::Unary(arg, UnaryOp::Recip) => {
let sum_grad = grads.or_insert(arg)?;
let grad = (grad / arg.sqr()?)?;
*sum_grad = sum_grad.sub(&grad)?
}
&Op::Narrow(ref arg, dim, start_idx, len) => {
let arg_dims = arg.dims();
let left_pad = if start_idx == 0 {
None
} else {
let mut dims = arg_dims.to_vec();
dims[dim] = start_idx;
Some(Tensor::zeros(dims, grad.dtype(), grad.device())?)
};
let right_pad = arg_dims[dim] - start_idx - len;
let right_pad = if right_pad == 0 {
None
} else {
let mut dims = arg_dims.to_vec();
dims[dim] = right_pad;
Some(Tensor::zeros(dims, grad.dtype(), grad.device())?)
};
let arg_grad = match (left_pad, right_pad) {
(None, None) => grad,
(Some(l), None) => Tensor::cat(&[&l, &grad], dim)?,
(None, Some(r)) => Tensor::cat(&[&grad, &r], dim)?,
(Some(l), Some(r)) => Tensor::cat(&[&l, &grad, &r], dim)?,
};
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Reduce(_, ReduceOp::ArgMin, _) => {}
Op::Reduce(_, ReduceOp::ArgMax, _) => {}
Op::Reshape(arg) => {
let arg_grad = grad.reshape(arg.dims())?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Unary(_, UnaryOp::Ceil) => Err(Error::BackwardNotSupported { op: "ceil" })?,
Op::Unary(_, UnaryOp::Floor) => {
Err(Error::BackwardNotSupported { op: "floor" })?
}
Op::Unary(_, UnaryOp::Round) => {
Err(Error::BackwardNotSupported { op: "round" })?
}
Op::Unary(arg, UnaryOp::Gelu) => {
let sum_grad = grads.or_insert(arg)?;
let cube = arg.powf(3.)?;
let tanh = (0.0356774 * &cube + (0.797885 * arg)?)?.tanh()?;
let gelu_grad = (((0.5 * &tanh)?
+ (0.0535161 * cube + (0.398942 * arg)?)? * (1. - tanh.powf(2.)?))?
+ 0.5)?;
*sum_grad = sum_grad.add(&(&grad * gelu_grad)?)?
}
Op::Unary(arg, UnaryOp::Erf) => {
let sum_grad = grads.or_insert(arg)?;
// d/dx erf(x) = 2/sqrt(pi) * e^(-x^2)
let erf_grad =
(2. / std::f64::consts::PI.sqrt()) * (arg.sqr()?.neg()?).exp()?;
*sum_grad = sum_grad.add(&(&grad * erf_grad)?)?
}
Op::Unary(arg, UnaryOp::GeluErf) => {
let sum_grad = grads.or_insert(arg)?;
// d/dx gelu_erf(x) = 0.5 + 0.398942 e^(-x^2/2) x + 0.5 erf(x/sqrt(2))
let neg_half_square = (arg.sqr()?.neg()? / 2.)?;
let scaled_exp_arg = (0.398942 * neg_half_square.exp()? * arg)?;
let arg_scaled_sqrt = (arg / 2f64.sqrt())?;
let erf_scaled_sqrt = (0.5 * arg_scaled_sqrt.erf()?)?;
let gelu_erf_grad = (0.5 + scaled_exp_arg + erf_scaled_sqrt)?;
*sum_grad = sum_grad.add(&(&grad * gelu_erf_grad)?)?;
}
Op::Unary(arg, UnaryOp::Relu) => {
let sum_grad = grads.or_insert(arg)?;
let relu_grad = arg.ge(&arg.zeros_like()?)?.to_dtype(arg.dtype())?;
*sum_grad = sum_grad.add(&(&grad * relu_grad)?)?
}
Op::Elu(arg, alpha) => {
// d/dx elu(x) = 1 for x > 0, alpha * e^x for x <= 0
let sum_grad = grads.or_insert(arg)?;
let zeros = arg.zeros_like()?;
let positive_mask = arg.gt(&zeros)?.to_dtype(arg.dtype())?;
let negative_mask = arg.le(&zeros)?.to_dtype(arg.dtype())?;
let negative_exp_mask = ((negative_mask * arg.exp())? * *alpha)?;
let combined_mask = (positive_mask + negative_exp_mask)?;
*sum_grad = sum_grad.add(&(grad * combined_mask)?)?
}
Op::Powf(arg, e) => {
let arg_grad = (&(grad * arg.powf(e - 1.)?)? * *e)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::CustomOp1(arg, c) => {
if let Some(arg_grad) = c.bwd(arg, node, &grad)? {
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
}
Op::CustomOp2(arg1, arg2, c) => {
let (arg_grad1, arg_grad2) = c.bwd(arg1, arg2, node, &grad)?;
if let Some(arg_grad1) = arg_grad1 {
let sum_grad = grads.or_insert(arg1)?;
*sum_grad = sum_grad.add(&arg_grad1)?
}
if let Some(arg_grad2) = arg_grad2 {
let sum_grad = grads.or_insert(arg2)?;
*sum_grad = sum_grad.add(&arg_grad2)?
}
}
Op::CustomOp3(arg1, arg2, arg3, c) => {
let (arg_grad1, arg_grad2, arg_grad3) =
c.bwd(arg1, arg2, arg3, node, &grad)?;
if let Some(arg_grad1) = arg_grad1 {
let sum_grad = grads.or_insert(arg1)?;
*sum_grad = sum_grad.add(&arg_grad1)?
}
if let Some(arg_grad2) = arg_grad2 {
let sum_grad = grads.or_insert(arg2)?;
*sum_grad = sum_grad.add(&arg_grad2)?
}
if let Some(arg_grad3) = arg_grad3 {
let sum_grad = grads.or_insert(arg3)?;
*sum_grad = sum_grad.add(&arg_grad3)?
}
}
Op::Unary(arg, UnaryOp::Sqr) => {
let arg_grad = arg.mul(&grad)?.affine(2., 0.)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Unary(arg, UnaryOp::Sqrt) => {
let arg_grad = grad.div(node)?.affine(0.5, 0.)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::ToDevice(arg) => {
let sum_grad = grads.or_insert(arg)?;
let arg_grad = grad.to_device(sum_grad.device())?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Transpose(arg, dim1, dim2) => {
let arg_grad = grad.transpose(*dim1, *dim2)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
Op::Permute(arg, dims) => {
let mut inv_dims = vec![0; dims.len()];
for (i, &dim_idx) in dims.iter().enumerate() {
inv_dims[dim_idx] = i
}
let arg_grad = grad.permute(inv_dims)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&arg_grad)?
}
};
}
}
Ok(grads)
}
}
#[derive(Debug)]
pub struct GradStore(HashMap<TensorId, Tensor>);
impl GradStore {
fn new() -> Self {
GradStore(HashMap::new())
}
pub fn get_id(&self, id: TensorId) -> Option<&Tensor> {
self.0.get(&id)
}
pub fn get(&self, tensor: &Tensor) -> Option<&Tensor> {
self.0.get(&tensor.id())
}
pub fn remove(&mut self, tensor: &Tensor) -> Option<Tensor> {
self.0.remove(&tensor.id())
}
pub fn insert(&mut self, tensor: &Tensor, grad: Tensor) -> Option<Tensor> {
self.0.insert(tensor.id(), grad)
}
fn or_insert(&mut self, tensor: &Tensor) -> Result<&mut Tensor> {
use std::collections::hash_map::Entry;
let grad = match self.0.entry(tensor.id()) {
Entry::Occupied(entry) => entry.into_mut(),
Entry::Vacant(entry) => {
let grad = tensor.zeros_like()?;
entry.insert(grad)
}
};
Ok(grad)
}
}
| 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.clone()))
}
}
impl<T: WithDType> TensorOrScalar for T {
fn to_tensor_scalar(self) -> Result<TensorScalar> {
let scalar = Tensor::new(self, &crate::Device::Cpu)?;
Ok(TensorScalar::Scalar(scalar))
}
}
| 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`.
use crate::{DType, Device, Error, Result, Shape, Tensor};
/// A variable is a wrapper around a tensor, however variables can have their content modified
/// whereas tensors are immutable.
#[derive(Clone, Debug)]
pub struct Var(Tensor);
impl std::fmt::Display for Var {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
std::fmt::Display::fmt(&self.0, f)
}
}
impl std::ops::Deref for Var {
type Target = Tensor;
fn deref(&self) -> &Self::Target {
self.0.as_ref()
}
}
impl Var {
pub fn zeros<S: Into<Shape>>(shape: S, dtype: DType, device: &Device) -> Result<Self> {
let inner = Tensor::zeros_impl(shape, dtype, device, true)?;
Ok(Self(inner))
}
pub fn ones<S: Into<Shape>>(shape: S, dtype: DType, device: &Device) -> Result<Self> {
let inner = Tensor::ones_impl(shape, dtype, device, true)?;
Ok(Self(inner))
}
pub fn from_tensor(t: &Tensor) -> Result<Self> {
let inner = t.make_var()?;
Ok(Self(inner))
}
pub fn rand_f64<S: Into<Shape>>(
lo: f64,
up: f64,
s: S,
dtype: DType,
device: &Device,
) -> Result<Self> {
let inner = Tensor::rand_f64_impl(lo, up, s, dtype, device, true)?;
Ok(Self(inner))
}
pub fn randn_f64<S: Into<Shape>>(
mean: f64,
std: f64,
s: S,
dtype: DType,
device: &Device,
) -> Result<Self> {
let inner = Tensor::randn_f64_impl(mean, std, s, dtype, device, true)?;
Ok(Self(inner))
}
pub fn rand<S: Into<Shape>, T: crate::FloatDType>(
lo: T,
up: T,
s: S,
device: &Device,
) -> Result<Self> {
let inner = Tensor::rand_impl(lo, up, s, device, true)?;
Ok(Self(inner))
}
pub fn randn<S: Into<Shape>, T: crate::FloatDType>(
mean: T,
std: T,
s: S,
device: &Device,
) -> Result<Self> {
let inner = Tensor::randn_impl(mean, std, s, device, true)?;
Ok(Self(inner))
}
/// Creates a new tensor on the specified device using the content and shape of the input.
/// This is similar to `new` but the resulting tensor is a variable.
pub fn new<A: crate::device::NdArray>(array: A, device: &Device) -> Result<Self> {
let shape = array.shape()?;
let inner = Tensor::new_impl(array, shape, device, true)?;
Ok(Self(inner))
}
pub fn from_vec<S: Into<Shape>, D: crate::WithDType>(
data: Vec<D>,
shape: S,
device: &Device,
) -> Result<Self> {
let inner = Tensor::from_vec_impl(data, shape, device, true)?;
Ok(Self(inner))
}
pub fn from_slice<S: Into<Shape>, D: crate::WithDType>(
array: &[D],
shape: S,
device: &Device,
) -> Result<Self> {
let inner = Tensor::new_impl(array, shape.into(), device, true)?;
Ok(Self(inner))
}
pub fn as_tensor(&self) -> &Tensor {
&self.0
}
/// Consumes this `Var` and return the underlying tensor.
pub fn into_inner(self) -> Tensor {
self.0
}
/// Sets the content of the inner tensor, this does not require a mutable reference as inner
/// mutability is used.
pub fn set(&self, src: &Tensor) -> Result<()> {
if self.same_storage(src) {
let msg = "cannot set a variable to a tensor that is derived from its value";
Err(Error::CannotSetVar { msg }.bt())?
}
let (mut dst, layout) = self.storage_mut_and_layout();
if !layout.is_contiguous() {
let msg = "cannot set a non-contiguous variable";
Err(Error::CannotSetVar { msg }.bt())?
}
let (src, src_l) = src.storage_and_layout();
if layout.shape() != src_l.shape() {
Err(Error::ShapeMismatchBinaryOp {
lhs: layout.shape().clone(),
rhs: src_l.shape().clone(),
op: "set",
}
.bt())?
}
src.copy_strided_src(&mut dst, layout.start_offset(), src_l)?;
Ok(())
}
}
| 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 (unnamed)
//! whereas a npz file can contain multiple named tensors. npz files are also compressed.
//!
//! These two formats are easy to use in Python using the numpy library.
//!
//! ```python
//! import numpy as np
//! x = np.arange(10)
//!
//! # Write a npy file.
//! np.save("test.npy", x)
//!
//! # Read a value from the npy file.
//! x = np.load("test.npy")
//!
//! # Write multiple values to a npz file.
//! values = { "x": x, "x_plus_one": x + 1 }
//! np.savez("test.npz", **values)
//!
//! # Load multiple values from a npz file.
//! values = np.loadz("test.npz")
//! ```
use crate::{DType, Device, Error, Result, Shape, Tensor};
use byteorder::{LittleEndian, ReadBytesExt};
use half::{bf16, f16, slice::HalfFloatSliceExt};
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufReader, Read, Write};
use std::path::Path;
const NPY_MAGIC_STRING: &[u8] = b"\x93NUMPY";
const NPY_SUFFIX: &str = ".npy";
fn read_header<R: Read>(reader: &mut R) -> Result<String> {
let mut magic_string = vec![0u8; NPY_MAGIC_STRING.len()];
reader.read_exact(&mut magic_string)?;
if magic_string != NPY_MAGIC_STRING {
return Err(Error::Npy("magic string mismatch".to_string()));
}
let mut version = [0u8; 2];
reader.read_exact(&mut version)?;
let header_len_len = match version[0] {
1 => 2,
2 => 4,
otherwise => return Err(Error::Npy(format!("unsupported version {otherwise}"))),
};
let mut header_len = vec![0u8; header_len_len];
reader.read_exact(&mut header_len)?;
let header_len = header_len
.iter()
.rev()
.fold(0_usize, |acc, &v| 256 * acc + v as usize);
let mut header = vec![0u8; header_len];
reader.read_exact(&mut header)?;
Ok(String::from_utf8_lossy(&header).to_string())
}
#[derive(Debug, PartialEq)]
struct Header {
descr: DType,
fortran_order: bool,
shape: Vec<usize>,
}
impl Header {
fn shape(&self) -> Shape {
Shape::from(self.shape.as_slice())
}
fn to_string(&self) -> Result<String> {
let fortran_order = if self.fortran_order { "True" } else { "False" };
let mut shape = self
.shape
.iter()
.map(|x| x.to_string())
.collect::<Vec<_>>()
.join(",");
let descr = match self.descr {
DType::BF16 => Err(Error::Npy("bf16 is not supported".into()))?,
DType::F16 => "f2",
DType::F32 => "f4",
DType::F64 => "f8",
DType::I64 => "i8",
DType::U32 => "u4",
DType::U8 => "u1",
};
if !shape.is_empty() {
shape.push(',')
}
Ok(format!(
"{{'descr': '<{descr}', 'fortran_order': {fortran_order}, 'shape': ({shape}), }}"
))
}
// Hacky parser for the npy header, a typical example would be:
// {'descr': '<f8', 'fortran_order': False, 'shape': (128,), }
fn parse(header: &str) -> Result<Header> {
let header =
header.trim_matches(|c: char| c == '{' || c == '}' || c == ',' || c.is_whitespace());
let mut parts: Vec<String> = vec![];
let mut start_index = 0usize;
let mut cnt_parenthesis = 0i64;
for (index, c) in header.chars().enumerate() {
match c {
'(' => cnt_parenthesis += 1,
')' => cnt_parenthesis -= 1,
',' => {
if cnt_parenthesis == 0 {
parts.push(header[start_index..index].to_owned());
start_index = index + 1;
}
}
_ => {}
}
}
parts.push(header[start_index..].to_owned());
let mut part_map: HashMap<String, String> = HashMap::new();
for part in parts.iter() {
let part = part.trim();
if !part.is_empty() {
match part.split(':').collect::<Vec<_>>().as_slice() {
[key, value] => {
let key = key.trim_matches(|c: char| c == '\'' || c.is_whitespace());
let value = value.trim_matches(|c: char| c == '\'' || c.is_whitespace());
let _ = part_map.insert(key.to_owned(), value.to_owned());
}
_ => return Err(Error::Npy(format!("unable to parse header {header}"))),
}
}
}
let fortran_order = match part_map.get("fortran_order") {
None => false,
Some(fortran_order) => match fortran_order.as_ref() {
"False" => false,
"True" => true,
_ => return Err(Error::Npy(format!("unknown fortran_order {fortran_order}"))),
},
};
let descr = match part_map.get("descr") {
None => return Err(Error::Npy("no descr in header".to_string())),
Some(descr) => {
if descr.is_empty() {
return Err(Error::Npy("empty descr".to_string()));
}
if descr.starts_with('>') {
return Err(Error::Npy(format!("little-endian descr {descr}")));
}
// the only supported types in tensor are:
// float64, float32, float16,
// complex64, complex128,
// int64, int32, int16, int8,
// uint8, and bool.
match descr.trim_matches(|c: char| c == '=' || c == '<' || c == '|') {
"e" | "f2" => DType::F16,
"f" | "f4" => DType::F32,
"d" | "f8" => DType::F64,
// "i" | "i4" => DType::S32,
"q" | "i8" => DType::I64,
// "h" | "i2" => DType::S16,
// "b" | "i1" => DType::S8,
"B" | "u1" => DType::U8,
"I" | "u4" => DType::U32,
"?" | "b1" => DType::U8,
// "F" | "F4" => DType::C64,
// "D" | "F8" => DType::C128,
descr => return Err(Error::Npy(format!("unrecognized descr {descr}"))),
}
}
};
let shape = match part_map.get("shape") {
None => return Err(Error::Npy("no shape in header".to_string())),
Some(shape) => {
let shape = shape.trim_matches(|c: char| c == '(' || c == ')' || c == ',');
if shape.is_empty() {
vec![]
} else {
shape
.split(',')
.map(|v| v.trim().parse::<usize>())
.collect::<std::result::Result<Vec<_>, _>>()?
}
}
};
Ok(Header {
descr,
fortran_order,
shape,
})
}
}
impl Tensor {
// TODO: Add the possibility to read directly to a device?
pub(crate) fn from_reader<R: std::io::Read>(
shape: Shape,
dtype: DType,
reader: &mut R,
) -> Result<Self> {
let elem_count = shape.elem_count();
match dtype {
DType::BF16 => {
let mut data_t = vec![bf16::ZERO; elem_count];
reader.read_u16_into::<LittleEndian>(data_t.reinterpret_cast_mut())?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::F16 => {
let mut data_t = vec![f16::ZERO; elem_count];
reader.read_u16_into::<LittleEndian>(data_t.reinterpret_cast_mut())?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::F32 => {
let mut data_t = vec![0f32; elem_count];
reader.read_f32_into::<LittleEndian>(&mut data_t)?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::F64 => {
let mut data_t = vec![0f64; elem_count];
reader.read_f64_into::<LittleEndian>(&mut data_t)?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::U8 => {
let mut data_t = vec![0u8; elem_count];
reader.read_exact(&mut data_t)?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::U32 => {
let mut data_t = vec![0u32; elem_count];
reader.read_u32_into::<LittleEndian>(&mut data_t)?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
DType::I64 => {
let mut data_t = vec![0i64; elem_count];
reader.read_i64_into::<LittleEndian>(&mut data_t)?;
Tensor::from_vec(data_t, shape, &Device::Cpu)
}
}
}
/// Reads a npy file and return the stored multi-dimensional array as a tensor.
pub fn read_npy<T: AsRef<Path>>(path: T) -> Result<Self> {
let mut reader = File::open(path.as_ref())?;
let header = read_header(&mut reader)?;
let header = Header::parse(&header)?;
if header.fortran_order {
return Err(Error::Npy("fortran order not supported".to_string()));
}
Self::from_reader(header.shape(), header.descr, &mut reader)
}
/// Reads a npz file and returns the stored multi-dimensional arrays together with their names.
pub fn read_npz<T: AsRef<Path>>(path: T) -> Result<Vec<(String, Self)>> {
let zip_reader = BufReader::new(File::open(path.as_ref())?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut result = vec![];
for i in 0..zip.len() {
let mut reader = zip.by_index(i)?;
let name = {
let name = reader.name();
name.strip_suffix(NPY_SUFFIX).unwrap_or(name).to_owned()
};
let header = read_header(&mut reader)?;
let header = Header::parse(&header)?;
if header.fortran_order {
return Err(Error::Npy("fortran order not supported".to_string()));
}
let s = Self::from_reader(header.shape(), header.descr, &mut reader)?;
result.push((name, s))
}
Ok(result)
}
/// Reads a npz file and returns the stored multi-dimensional arrays for some specified names.
pub fn read_npz_by_name<T: AsRef<Path>>(path: T, names: &[&str]) -> Result<Vec<Self>> {
let zip_reader = BufReader::new(File::open(path.as_ref())?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut result = vec![];
for name in names.iter() {
let mut reader = match zip.by_name(&format!("{name}{NPY_SUFFIX}")) {
Ok(reader) => reader,
Err(_) => Err(Error::Npy(format!(
"no array for {name} in {:?}",
path.as_ref()
)))?,
};
let header = read_header(&mut reader)?;
let header = Header::parse(&header)?;
if header.fortran_order {
return Err(Error::Npy("fortran order not supported".to_string()));
}
let s = Self::from_reader(header.shape(), header.descr, &mut reader)?;
result.push(s)
}
Ok(result)
}
fn write<T: Write>(&self, f: &mut T) -> Result<()> {
f.write_all(NPY_MAGIC_STRING)?;
f.write_all(&[1u8, 0u8])?;
let header = Header {
descr: self.dtype(),
fortran_order: false,
shape: self.dims().to_vec(),
};
let mut header = header.to_string()?;
let pad = 16 - (NPY_MAGIC_STRING.len() + 5 + header.len()) % 16;
for _ in 0..pad % 16 {
header.push(' ')
}
header.push('\n');
f.write_all(&[(header.len() % 256) as u8, (header.len() / 256) as u8])?;
f.write_all(header.as_bytes())?;
self.write_bytes(f)
}
/// Writes a multi-dimensional array in the npy format.
pub fn write_npy<T: AsRef<Path>>(&self, path: T) -> Result<()> {
let mut f = File::create(path.as_ref())?;
self.write(&mut f)
}
/// Writes multiple multi-dimensional arrays using the npz format.
pub fn write_npz<S: AsRef<str>, T: AsRef<Tensor>, P: AsRef<Path>>(
ts: &[(S, T)],
path: P,
) -> Result<()> {
let mut zip = zip::ZipWriter::new(File::create(path.as_ref())?);
let options =
zip::write::FileOptions::default().compression_method(zip::CompressionMethod::Stored);
for (name, tensor) in ts.iter() {
zip.start_file(format!("{}.npy", name.as_ref()), options)?;
tensor.as_ref().write(&mut zip)?
}
Ok(())
}
}
/// Lazy tensor loader.
pub struct NpzTensors {
index_per_name: HashMap<String, usize>,
path: std::path::PathBuf,
// We do not store a zip reader as it needs mutable access to extract data. Instead we
// re-create a zip reader for each tensor.
}
impl NpzTensors {
pub fn new<T: AsRef<Path>>(path: T) -> Result<Self> {
let path = path.as_ref().to_owned();
let zip_reader = BufReader::new(File::open(&path)?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut index_per_name = HashMap::new();
for i in 0..zip.len() {
let file = zip.by_index(i)?;
let name = {
let name = file.name();
name.strip_suffix(NPY_SUFFIX).unwrap_or(name).to_owned()
};
index_per_name.insert(name, i);
}
Ok(Self {
index_per_name,
path,
})
}
pub fn names(&self) -> Vec<&String> {
self.index_per_name.keys().collect()
}
/// This only returns the shape and dtype for a named tensor. Compared to `get`, this avoids
/// reading the whole tensor data.
pub fn get_shape_and_dtype(&self, name: &str) -> Result<(Shape, DType)> {
let index = match self.index_per_name.get(name) {
None => crate::bail!("cannot find tensor {name}"),
Some(index) => *index,
};
let zip_reader = BufReader::new(File::open(&self.path)?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut reader = zip.by_index(index)?;
let header = read_header(&mut reader)?;
let header = Header::parse(&header)?;
Ok((header.shape(), header.descr))
}
pub fn get(&self, name: &str) -> Result<Option<Tensor>> {
let index = match self.index_per_name.get(name) {
None => return Ok(None),
Some(index) => *index,
};
// We hope that the file has not changed since first reading it.
let zip_reader = BufReader::new(File::open(&self.path)?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut reader = zip.by_index(index)?;
let header = read_header(&mut reader)?;
let header = Header::parse(&header)?;
if header.fortran_order {
return Err(Error::Npy("fortran order not supported".to_string()));
}
let tensor = Tensor::from_reader(header.shape(), header.descr, &mut reader)?;
Ok(Some(tensor))
}
}
#[cfg(test)]
mod tests {
use super::Header;
#[test]
fn parse() {
let h = "{'descr': '<f8', 'fortran_order': False, 'shape': (128,), }";
assert_eq!(
Header::parse(h).unwrap(),
Header {
descr: crate::DType::F64,
fortran_order: false,
shape: vec![128]
}
);
let h = "{'descr': '<f4', 'fortran_order': True, 'shape': (256,1,128), }";
let h = Header::parse(h).unwrap();
assert_eq!(
h,
Header {
descr: crate::DType::F32,
fortran_order: true,
shape: vec![256, 1, 128]
}
);
assert_eq!(
h.to_string().unwrap(),
"{'descr': '<f4', 'fortran_order': True, 'shape': (256,1,128,), }"
);
let h = Header {
descr: crate::DType::U32,
fortran_order: false,
shape: vec![],
};
assert_eq!(
h.to_string().unwrap(),
"{'descr': '<u4', 'fortran_order': False, 'shape': (), }"
);
}
}
| 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 {
// Unsigned 8 bits integer.
U8,
// Unsigned 32 bits integer.
U32,
// Signed 64 bits integer.
I64,
// Brain floating-point using half precision (16 bits).
BF16,
// Floating-point using half precision (16 bits).
F16,
// Floating-point using single precision (32 bits).
F32,
// Floating-point using double precision (64 bits).
F64,
}
#[derive(Debug, PartialEq, Eq)]
pub struct DTypeParseError;
impl std::str::FromStr for DType {
type Err = DTypeParseError;
fn from_str(s: &str) -> std::result::Result<Self, Self::Err> {
match s {
"u8" => Ok(Self::U8),
"u32" => Ok(Self::U32),
"i64" => Ok(Self::I64),
"bf16" => Ok(Self::BF16),
"f16" => Ok(Self::F16),
"f32" => Ok(Self::F32),
"f64" => Ok(Self::F64),
_ => Err(DTypeParseError),
}
}
}
impl DType {
/// String representation for dtypes.
pub fn as_str(&self) -> &'static str {
match self {
Self::U8 => "u8",
Self::U32 => "u32",
Self::I64 => "i64",
Self::BF16 => "bf16",
Self::F16 => "f16",
Self::F32 => "f32",
Self::F64 => "f64",
}
}
/// The size used by each element in bytes, i.e. 1 for `U8`, 4 for `F32`.
pub fn size_in_bytes(&self) -> usize {
match self {
Self::U8 => 1,
Self::U32 => 4,
Self::I64 => 8,
Self::BF16 => 2,
Self::F16 => 2,
Self::F32 => 4,
Self::F64 => 8,
}
}
pub fn is_int(&self) -> bool {
match self {
Self::U8 | Self::U32 | Self::I64 => true,
Self::BF16 | Self::F16 | Self::F32 | Self::F64 => false,
}
}
pub fn is_float(&self) -> bool {
match self {
Self::U8 | Self::U32 | Self::I64 => false,
Self::BF16 | Self::F16 | Self::F32 | Self::F64 => true,
}
}
}
pub trait WithDType:
Sized
+ Copy
+ num_traits::NumAssign
+ std::cmp::PartialOrd
+ std::fmt::Display
+ 'static
+ Send
+ Sync
+ crate::cpu::kernels::VecOps
{
const DTYPE: DType;
fn from_f64(v: f64) -> Self;
fn to_f64(self) -> f64;
fn to_cpu_storage_owned(data: Vec<Self>) -> CpuStorage;
fn to_cpu_storage(data: &[Self]) -> CpuStorage {
Self::to_cpu_storage_owned(data.to_vec())
}
fn cpu_storage_as_slice(s: &CpuStorage) -> Result<&[Self]>;
fn cpu_storage_data(s: CpuStorage) -> Result<Vec<Self>>;
}
macro_rules! with_dtype {
($ty:ty, $dtype:ident, $from_f64:expr, $to_f64:expr) => {
impl WithDType for $ty {
const DTYPE: DType = DType::$dtype;
fn from_f64(v: f64) -> Self {
$from_f64(v)
}
fn to_f64(self) -> f64 {
$to_f64(self)
}
fn to_cpu_storage_owned(data: Vec<Self>) -> CpuStorage {
CpuStorage::$dtype(data)
}
fn cpu_storage_data(s: CpuStorage) -> Result<Vec<Self>> {
match s {
CpuStorage::$dtype(data) => Ok(data),
_ => Err(Error::UnexpectedDType {
expected: DType::$dtype,
got: s.dtype(),
msg: "unexpected dtype",
}
.bt()),
}
}
fn cpu_storage_as_slice(s: &CpuStorage) -> Result<&[Self]> {
match s {
CpuStorage::$dtype(data) => Ok(data),
_ => Err(Error::UnexpectedDType {
expected: DType::$dtype,
got: s.dtype(),
msg: "unexpected dtype",
}
.bt()),
}
}
}
};
}
use half::{bf16, f16};
with_dtype!(u8, U8, |v: f64| v as u8, |v: u8| v as f64);
with_dtype!(u32, U32, |v: f64| v as u32, |v: u32| v as f64);
with_dtype!(i64, I64, |v: f64| v as i64, |v: i64| v as f64);
with_dtype!(f16, F16, f16::from_f64, f16::to_f64);
with_dtype!(bf16, BF16, bf16::from_f64, bf16::to_f64);
with_dtype!(f32, F32, |v: f64| v as f32, |v: f32| v as f64);
with_dtype!(f64, F64, |v: f64| v, |v: f64| v);
pub trait IntDType: WithDType {
fn is_true(&self) -> bool;
fn as_usize(&self) -> usize;
}
impl IntDType for i64 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
impl IntDType for u32 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
impl IntDType for u8 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
pub trait FloatDType: WithDType {}
impl FloatDType for f16 {}
impl FloatDType for bf16 {}
impl FloatDType for f32 {}
impl FloatDType for f64 {}
| 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:
// /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate.tbd
#[link_name = "sgemm_"]
pub fn sgemm_ffi(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_float,
a: *const c_float,
lda: *const c_int,
b: *const c_float,
ldb: *const c_int,
beta: *const c_float,
c: *mut c_float,
ldc: *const c_int,
);
#[link_name = "dgemm_"]
pub fn dgemm_ffi(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_double,
a: *const c_double,
lda: *const c_int,
b: *const c_double,
ldb: *const c_int,
beta: *const c_double,
c: *mut c_double,
ldc: *const c_int,
);
pub fn vvexpf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvexp(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvsqrtf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvsqrt(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvsinf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvsin(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvcosf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvcos(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvlogf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvlog(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvtanhf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvtanh(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vDSP_vaddD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vadd(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vsubD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vsub(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmulD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmul(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vdivD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vdiv(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vminD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmin(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmaxD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmax(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
}
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn sgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f32,
a: &[f32],
lda: i32,
b: &[f32],
ldb: i32,
beta: f32,
c: &mut [f32],
ldc: i32,
) {
ffi::sgemm_ffi(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn dgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f64,
a: &[f64],
lda: i32,
b: &[f64],
ldb: i32,
beta: f64,
c: &mut [f64],
ldc: i32,
) {
ffi::dgemm_ffi(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[inline]
pub fn vs_exp(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvexpf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_exp(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvexp(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sqrt(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsqrtf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_sqrt(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsqrt(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sin(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsinf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_sin(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsin(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_cos(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvcosf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_cos(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvcos(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_tanh(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanhf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_tanh(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanh(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_ln(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvlogf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_ln(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvlog(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sqr(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
}
#[inline]
pub fn vd_sqr(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
}
#[inline]
pub fn vs_tanh_inplace(y: &mut [f32]) {
unsafe { ffi::vvtanhf(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vd_tanh_inplace(y: &mut [f64]) {
unsafe { ffi::vvtanh(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vs_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
#[inline]
pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vd_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
macro_rules! binary_op {
($fn_name:ident, $ty:ty, $accelerate_name:ident) => {
#[inline]
pub fn $fn_name(a: &[$ty], b: &[$ty], y: &mut [$ty]) {
let a_len = a.len();
let b_len = b.len();
let y_len = y.len();
if a_len != y_len || b_len != y_len {
panic!(
"{} a,b,y len mismatch {a_len} {b_len} {y_len}",
stringify!($fn_name)
);
}
unsafe {
// Weird quirk of accelerate, the rhs comes before the lhs.
ffi::$accelerate_name(
b.as_ptr(),
1,
a.as_ptr(),
1,
y.as_mut_ptr(),
1,
a_len as u64,
)
}
}
};
}
binary_op!(vs_add, f32, vDSP_vadd);
binary_op!(vd_add, f64, vDSP_vaddD);
binary_op!(vs_sub, f32, vDSP_vsub);
binary_op!(vd_sub, f64, vDSP_vsubD);
binary_op!(vs_mul, f32, vDSP_vmul);
binary_op!(vd_mul, f64, vDSP_vmulD);
binary_op!(vs_div, f32, vDSP_vdiv);
binary_op!(vd_div, f64, vDSP_vdivD);
binary_op!(vs_max, f32, vDSP_vmax);
binary_op!(vd_max, f64, vDSP_vmaxD);
binary_op!(vs_min, f32, vDSP_vmin);
binary_op!(vd_min, f64, vDSP_vminD);
| 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, Eq, Hash)]
pub enum DeviceLocation {
Cpu,
Cuda { gpu_id: usize },
Metal { gpu_id: usize },
}
#[derive(Debug, Clone)]
pub enum Device {
Cpu,
Cuda(crate::CudaDevice),
Metal(crate::MetalDevice),
}
pub trait NdArray {
fn shape(&self) -> Result<Shape>;
fn to_cpu_storage(&self) -> CpuStorage;
}
impl<S: WithDType> NdArray for S {
fn shape(&self) -> Result<Shape> {
Ok(Shape::from(()))
}
fn to_cpu_storage(&self) -> CpuStorage {
S::to_cpu_storage(&[*self])
}
}
impl<S: WithDType, const N: usize> NdArray for &[S; N] {
fn shape(&self) -> Result<Shape> {
Ok(Shape::from(self.len()))
}
fn to_cpu_storage(&self) -> CpuStorage {
S::to_cpu_storage(self.as_slice())
}
}
impl<S: WithDType> NdArray for &[S] {
fn shape(&self) -> Result<Shape> {
Ok(Shape::from(self.len()))
}
fn to_cpu_storage(&self) -> CpuStorage {
S::to_cpu_storage(self)
}
}
impl<S: WithDType, const N: usize, const M: usize> NdArray for &[[S; N]; M] {
fn shape(&self) -> Result<Shape> {
Ok(Shape::from((M, N)))
}
fn to_cpu_storage(&self) -> CpuStorage {
S::to_cpu_storage_owned(self.concat())
}
}
impl<S: WithDType, const N1: usize, const N2: usize, const N3: usize> NdArray
for &[[[S; N3]; N2]; N1]
{
fn shape(&self) -> Result<Shape> {
Ok(Shape::from((N1, N2, N3)))
}
fn to_cpu_storage(&self) -> CpuStorage {
let mut vec = Vec::with_capacity(N1 * N2 * N3);
for i1 in 0..N1 {
for i2 in 0..N2 {
vec.extend(self[i1][i2])
}
}
S::to_cpu_storage_owned(vec)
}
}
impl<S: WithDType, const N1: usize, const N2: usize, const N3: usize, const N4: usize> NdArray
for &[[[[S; N4]; N3]; N2]; N1]
{
fn shape(&self) -> Result<Shape> {
Ok(Shape::from((N1, N2, N3, N4)))
}
fn to_cpu_storage(&self) -> CpuStorage {
let mut vec = Vec::with_capacity(N1 * N2 * N3 * N4);
for i1 in 0..N1 {
for i2 in 0..N2 {
for i3 in 0..N3 {
vec.extend(self[i1][i2][i3])
}
}
}
S::to_cpu_storage_owned(vec)
}
}
impl<S: NdArray> NdArray for Vec<S> {
fn shape(&self) -> Result<Shape> {
if self.is_empty() {
crate::bail!("empty array")
}
let shape0 = self[0].shape()?;
let n = self.len();
for v in self.iter() {
let shape = v.shape()?;
if shape != shape0 {
crate::bail!("two elements have different shapes {shape:?} {shape0:?}")
}
}
Ok(Shape::from([[n].as_slice(), shape0.dims()].concat()))
}
fn to_cpu_storage(&self) -> CpuStorage {
// This allocates intermediary memory and shouldn't be necessary.
let storages = self.iter().map(|v| v.to_cpu_storage()).collect::<Vec<_>>();
CpuStorage::concat(storages.as_slice()).unwrap()
}
}
impl Device {
pub fn new_cuda(ordinal: usize) -> Result<Self> {
Ok(Self::Cuda(crate::CudaDevice::new(ordinal)?))
}
pub fn new_metal(ordinal: usize) -> Result<Self> {
Ok(Self::Metal(crate::MetalDevice::new(ordinal)?))
}
pub fn set_seed(&self, seed: u64) -> Result<()> {
match self {
Self::Cpu => CpuDevice.set_seed(seed),
Self::Cuda(c) => c.set_seed(seed),
Self::Metal(m) => m.set_seed(seed),
}
}
pub fn same_device(&self, rhs: &Self) -> bool {
match (self, rhs) {
(Self::Cpu, Self::Cpu) => true,
(Self::Cuda(lhs), Self::Cuda(rhs)) => lhs.same_device(rhs),
(Self::Metal(lhs), Self::Metal(rhs)) => lhs.same_device(rhs),
_ => false,
}
}
pub fn location(&self) -> DeviceLocation {
match self {
Self::Cpu => DeviceLocation::Cpu,
Self::Cuda(device) => device.location(),
Device::Metal(device) => device.location(),
}
}
pub fn is_cpu(&self) -> bool {
matches!(self, Self::Cpu)
}
pub fn is_cuda(&self) -> bool {
matches!(self, Self::Cuda(_))
}
pub fn is_metal(&self) -> bool {
matches!(self, Self::Metal(_))
}
pub fn cuda_if_available(ordinal: usize) -> Result<Self> {
if crate::utils::cuda_is_available() {
Self::new_cuda(ordinal)
} else {
Ok(Self::Cpu)
}
}
pub(crate) fn rand_uniform_f64(
&self,
lo: f64,
up: f64,
shape: &Shape,
dtype: DType,
) -> Result<Storage> {
match self {
Device::Cpu => {
let storage = CpuDevice.rand_uniform(shape, dtype, lo, up)?;
Ok(Storage::Cpu(storage))
}
Device::Cuda(device) => {
// TODO: Remove the special case if we start supporting generating f16/bf16 directly.
if dtype == DType::F16 || dtype == DType::BF16 {
let storage = device.rand_uniform(shape, DType::F32, lo, up)?;
Storage::Cuda(storage).to_dtype(&crate::Layout::contiguous(shape), dtype)
} else {
let storage = device.rand_uniform(shape, dtype, lo, up)?;
Ok(Storage::Cuda(storage))
}
}
Device::Metal(_device) => {
// let storage = device.rand_uniform(shape, dtype, lo, up)?;
// Ok(Storage::Metal(storage))
crate::bail!("Metal rand_uniform not implemented")
}
}
}
pub(crate) fn rand_uniform<T: crate::FloatDType>(
&self,
lo: T,
up: T,
shape: &Shape,
) -> Result<Storage> {
self.rand_uniform_f64(lo.to_f64(), up.to_f64(), shape, T::DTYPE)
}
pub(crate) fn rand_normal_f64(
&self,
mean: f64,
std: f64,
shape: &Shape,
dtype: DType,
) -> Result<Storage> {
match self {
Device::Cpu => {
let storage = CpuDevice.rand_normal(shape, dtype, mean, std)?;
Ok(Storage::Cpu(storage))
}
Device::Cuda(device) => {
// TODO: Remove the special case if we start supporting generating f16/bf16 directly.
if dtype == DType::F16 || dtype == DType::BF16 {
let storage = device.rand_normal(shape, DType::F32, mean, std)?;
Storage::Cuda(storage).to_dtype(&crate::Layout::contiguous(shape), dtype)
} else {
let storage = device.rand_normal(shape, dtype, mean, std)?;
Ok(Storage::Cuda(storage))
}
}
Device::Metal(device) => {
let storage = device.rand_normal(shape, dtype, mean, std)?;
Ok(Storage::Metal(storage))
}
}
}
pub(crate) fn rand_normal<T: crate::FloatDType>(
&self,
mean: T,
std: T,
shape: &Shape,
) -> Result<Storage> {
self.rand_normal_f64(mean.to_f64(), std.to_f64(), shape, T::DTYPE)
}
pub(crate) fn ones(&self, shape: &Shape, dtype: DType) -> Result<Storage> {
match self {
Device::Cpu => {
let storage = CpuDevice.ones_impl(shape, dtype)?;
Ok(Storage::Cpu(storage))
}
Device::Cuda(device) => {
let storage = device.ones_impl(shape, dtype)?;
Ok(Storage::Cuda(storage))
}
Device::Metal(device) => {
let storage = device.ones_impl(shape, dtype)?;
Ok(Storage::Metal(storage))
}
}
}
pub(crate) fn zeros(&self, shape: &Shape, dtype: DType) -> Result<Storage> {
match self {
Device::Cpu => {
let storage = CpuDevice.zeros_impl(shape, dtype)?;
Ok(Storage::Cpu(storage))
}
Device::Cuda(device) => {
let storage = device.zeros_impl(shape, dtype)?;
Ok(Storage::Cuda(storage))
}
Device::Metal(device) => {
let storage = device.zeros_impl(shape, dtype)?;
Ok(Storage::Metal(storage))
}
}
}
pub(crate) fn storage<A: NdArray>(&self, array: A) -> Result<Storage> {
match self {
Device::Cpu => Ok(Storage::Cpu(array.to_cpu_storage())),
Device::Cuda(device) => {
let storage = array.to_cpu_storage();
let storage = device.storage_from_cpu_storage(&storage)?;
Ok(Storage::Cuda(storage))
}
Device::Metal(device) => {
let storage = array.to_cpu_storage();
let storage = device.storage_from_cpu_storage(&storage)?;
Ok(Storage::Metal(storage))
}
}
}
pub(crate) fn storage_owned<S: WithDType>(&self, data: Vec<S>) -> Result<Storage> {
match self {
Device::Cpu => Ok(Storage::Cpu(S::to_cpu_storage_owned(data))),
Device::Cuda(device) => {
let storage = S::to_cpu_storage_owned(data);
let storage = device.storage_from_cpu_storage(&storage)?;
Ok(Storage::Cuda(storage))
}
Device::Metal(device) => {
let storage = S::to_cpu_storage_owned(data);
let storage = device.storage_from_cpu_storage(&storage)?;
Ok(Storage::Metal(storage))
}
}
}
}
| 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::{
CudaFunction, CudaSlice, DevicePtr, DeviceRepr, DeviceSlice, LaunchAsync, LaunchConfig,
ValidAsZeroBits,
};
use half::{bf16, f16};
use std::sync::{Arc, Mutex};
/// cudarc related errors
#[derive(thiserror::Error, Debug)]
pub enum CudaError {
#[error(transparent)]
Cuda(#[from] cudarc::driver::DriverError),
#[error(transparent)]
Compiler(#[from] cudarc::nvrtc::CompileError),
#[error(transparent)]
Cublas(#[from] cudarc::cublas::result::CublasError),
#[error(transparent)]
Curand(#[from] cudarc::curand::result::CurandError),
#[error("missing kernel '{module_name}'")]
MissingKernel { module_name: String },
#[error("unsupported dtype {dtype:?} for {op}")]
UnsupportedDtype { dtype: DType, op: &'static str },
#[error("internal error '{0}'")]
InternalError(&'static str),
#[error("matmul is only supported for contiguous tensors lstride: {lhs_stride:?} rstride: {rhs_stride:?} mnk: {mnk:?}")]
MatMulNonContiguous {
lhs_stride: Vec<usize>,
rhs_stride: Vec<usize>,
mnk: (usize, usize, usize),
},
#[error("{msg}, expected: {expected:?}, got: {got:?}")]
UnexpectedDType {
msg: &'static str,
expected: DType,
got: DType,
},
#[error("{cuda} when loading {module_name}")]
Load {
cuda: cudarc::driver::DriverError,
module_name: String,
},
}
impl From<CudaError> for crate::Error {
fn from(val: CudaError) -> Self {
crate::Error::Cuda(Box::new(val)).bt()
}
}
/// Unique identifier for cuda devices.
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub struct DeviceId(usize);
impl DeviceId {
fn new() -> Self {
// https://users.rust-lang.org/t/idiomatic-rust-way-to-generate-unique-id/33805
use std::sync::atomic;
static COUNTER: atomic::AtomicUsize = atomic::AtomicUsize::new(1);
Self(COUNTER.fetch_add(1, atomic::Ordering::Relaxed))
}
}
struct CudaRng(cudarc::curand::CudaRng);
unsafe impl Send for CudaRng {}
#[derive(Clone)]
pub struct CudaDevice {
id: DeviceId,
device: Arc<cudarc::driver::CudaDevice>,
blas: Arc<cudarc::cublas::CudaBlas>,
curand: Arc<Mutex<CudaRng>>,
}
impl std::fmt::Debug for CudaDevice {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "CudaDevice({:?})", self.id)
}
}
impl std::ops::Deref for CudaDevice {
type Target = Arc<cudarc::driver::CudaDevice>;
fn deref(&self) -> &Self::Target {
&self.device
}
}
pub trait WrapErr<O> {
fn w(self) -> std::result::Result<O, crate::Error>;
}
impl<O, E: Into<CudaError>> WrapErr<O> for std::result::Result<O, E> {
fn w(self) -> std::result::Result<O, crate::Error> {
self.map_err(|e| crate::Error::Cuda(Box::new(e.into())))
}
}
impl CudaDevice {
pub fn cuda_device(&self) -> Arc<cudarc::driver::CudaDevice> {
self.device.clone()
}
pub fn id(&self) -> DeviceId {
self.id
}
fn const_impl(&self, v: f64, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
let elem_count = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(elem_count as u32);
let slice = match dtype {
DType::U8 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<u8>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_u8", kernels::FILL)?;
let params = (&data, v as u8, elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::U8(data)
}
DType::U32 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<u32>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_u32", kernels::FILL)?;
let params = (&data, v as u32, elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::U32(data)
}
DType::I64 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<i64>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_i64", kernels::FILL)?;
let params = (&data, v as i64, elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::I64(data)
}
DType::BF16 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<bf16>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_bf16", kernels::FILL)?;
let params = (&data, bf16::from_f64(v), elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::BF16(data)
}
DType::F16 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<f16>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_f16", kernels::FILL)?;
let params = (&data, f16::from_f64(v), elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F16(data)
}
DType::F32 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_f32", kernels::FILL)?;
let params = (&data, v as f32, elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F32(data)
}
DType::F64 => {
// SAFETY: Set later by running the fill kernel.
let data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
let func = self.get_or_load_func("fill_f64", kernels::FILL)?;
let params = (&data, v, elem_count);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F64(data)
}
};
Ok(CudaStorage {
slice,
device: self.clone(),
})
}
pub fn get_or_load_func(&self, module_name: &str, ptx: &'static str) -> Result<CudaFunction> {
if !self.has_func(module_name, module_name) {
// Leaking the string here is a bit sad but we need a &'static str and this is only
// done once per kernel name.
let static_module_name = Box::leak(module_name.to_string().into_boxed_str());
self.load_ptx(ptx.into(), module_name, &[static_module_name])
.map_err(|cuda| CudaError::Load {
cuda,
module_name: module_name.to_string(),
})
.w()?;
}
self.get_func(module_name, module_name)
// Clippy recommends this `ok_or` rather than `ok_or_else` so hopefully the compiler is
// able to only build the error value if needed.
.ok_or(CudaError::MissingKernel {
module_name: module_name.to_string(),
})
.w()
}
}
impl BackendDevice for CudaDevice {
type Storage = CudaStorage;
fn new(ordinal: usize) -> Result<Self> {
let device = cudarc::driver::CudaDevice::new(ordinal).w()?;
let blas = cudarc::cublas::CudaBlas::new(device.clone()).w()?;
let curand = cudarc::curand::CudaRng::new(299792458, device.clone()).w()?;
Ok(Self {
id: DeviceId::new(),
device,
blas: Arc::new(blas),
curand: Arc::new(Mutex::new(CudaRng(curand))),
})
}
fn set_seed(&self, seed: u64) -> Result<()> {
// We do not call set_seed but instead create a new curand object. This ensures that the
// state will be identical and the same random numbers will be generated.
let mut curand = self.curand.lock().unwrap();
curand.0 = cudarc::curand::CudaRng::new(seed, self.device.clone()).w()?;
Ok(())
}
fn location(&self) -> crate::DeviceLocation {
crate::DeviceLocation::Cuda {
gpu_id: self.device.ordinal(),
}
}
fn same_device(&self, rhs: &Self) -> bool {
self.id == rhs.id
}
fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
let elem_count = shape.elem_count();
let slice = match dtype {
DType::U8 => {
let data = self.alloc_zeros::<u8>(elem_count).w()?;
CudaStorageSlice::U8(data)
}
DType::U32 => {
let data = self.alloc_zeros::<u32>(elem_count).w()?;
CudaStorageSlice::U32(data)
}
DType::I64 => {
let data = self.alloc_zeros::<i64>(elem_count).w()?;
CudaStorageSlice::I64(data)
}
DType::BF16 => {
let data = self.alloc_zeros::<bf16>(elem_count).w()?;
CudaStorageSlice::BF16(data)
}
DType::F16 => {
let data = self.alloc_zeros::<f16>(elem_count).w()?;
CudaStorageSlice::F16(data)
}
DType::F32 => {
let data = self.alloc_zeros::<f32>(elem_count).w()?;
CudaStorageSlice::F32(data)
}
DType::F64 => {
let data = self.alloc_zeros::<f64>(elem_count).w()?;
CudaStorageSlice::F64(data)
}
};
Ok(CudaStorage {
slice,
device: self.clone(),
})
}
fn rand_uniform(&self, shape: &Shape, dtype: DType, lo: f64, up: f64) -> Result<CudaStorage> {
let elem_count = shape.elem_count();
let curand = self.curand.lock().unwrap();
let slice = match dtype {
// TODO: Add support for F16 and BF16 though this is likely to require some upstream
// cudarc changes.
DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
Err(CudaError::UnsupportedDtype {
dtype,
op: "rand_uniform",
})
.w()?
}
DType::F32 => {
let mut data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
curand.0.fill_with_uniform(&mut data).w()?;
CudaStorageSlice::F32(data)
}
DType::F64 => {
let mut data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
curand.0.fill_with_uniform(&mut data).w()?;
CudaStorageSlice::F64(data)
}
};
let slice = if lo == 0. && up == 1.0 {
slice
} else {
let layout = Layout::contiguous(shape);
Affine(up - lo, lo).map(&slice, self, &layout)?
};
Ok(CudaStorage {
slice,
device: self.clone(),
})
}
fn rand_normal(&self, shape: &Shape, dtype: DType, mean: f64, std: f64) -> Result<CudaStorage> {
// TODO: Add support for F16 and BF16 though this is likely to require some upstream
// cudarc changes.
let elem_count = shape.elem_count();
let curand = self.curand.lock().unwrap();
// curand can only generate an odd number of values.
// https://github.com/huggingface/candle/issues/734
let elem_count_round = if elem_count % 2 == 1 {
elem_count + 1
} else {
elem_count
};
let slice = match dtype {
DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
Err(CudaError::UnsupportedDtype {
dtype,
op: "rand_normal",
})
.w()?
}
DType::F32 => {
let mut data = unsafe { self.alloc::<f32>(elem_count_round) }.w()?;
curand
.0
.fill_with_normal(&mut data, mean as f32, std as f32)
.w()?;
CudaStorageSlice::F32(data)
}
DType::F64 => {
let mut data = unsafe { self.alloc::<f64>(elem_count_round) }.w()?;
curand.0.fill_with_normal(&mut data, mean, std).w()?;
CudaStorageSlice::F64(data)
}
};
Ok(CudaStorage {
slice,
device: self.clone(),
})
}
fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
self.const_impl(1., shape, dtype)
}
fn storage_from_cpu_storage(&self, storage: &CpuStorage) -> Result<CudaStorage> {
let slice = match storage {
CpuStorage::U8(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::U8(data)
}
CpuStorage::U32(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::U32(data)
}
CpuStorage::I64(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::I64(data)
}
CpuStorage::BF16(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::BF16(data)
}
CpuStorage::F16(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::F16(data)
}
CpuStorage::F32(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::F32(data)
}
CpuStorage::F64(storage) => {
let data = self.htod_sync_copy(storage).w()?;
CudaStorageSlice::F64(data)
}
};
Ok(CudaStorage {
slice,
device: self.clone(),
})
}
}
#[derive(Debug)]
pub enum CudaStorageSlice {
U8(CudaSlice<u8>),
U32(CudaSlice<u32>),
I64(CudaSlice<i64>),
BF16(CudaSlice<bf16>),
F16(CudaSlice<f16>),
F32(CudaSlice<f32>),
F64(CudaSlice<f64>),
}
type S = CudaStorageSlice;
pub trait Map1 {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>>;
fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
let out = match s {
S::U8(s) => S::U8(self.f(s, d, l)?),
S::U32(s) => S::U32(self.f(s, d, l)?),
S::I64(s) => S::I64(self.f(s, d, l)?),
S::BF16(s) => S::BF16(self.f(s, d, l)?),
S::F16(s) => S::F16(self.f(s, d, l)?),
S::F32(s) => S::F32(self.f(s, d, l)?),
S::F64(s) => S::F64(self.f(s, d, l)?),
};
Ok(out)
}
}
pub trait Map2 {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src1: &CudaSlice<T>,
layout1: &Layout,
src2: &CudaSlice<T>,
layout2: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>>;
fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
let out = match (s1, s2) {
(S::U8(s1), S::U8(s2)) => S::U8(self.f(s1, l1, s2, l2, d)?),
(S::U32(s1), S::U32(s2)) => S::U32(self.f(s1, l1, s2, l2, d)?),
(S::I64(s1), S::I64(s2)) => S::I64(self.f(s1, l1, s2, l2, d)?),
(S::BF16(s1), S::BF16(s2)) => S::BF16(self.f(s1, l1, s2, l2, d)?),
(S::F16(s1), S::F16(s2)) => S::F16(self.f(s1, l1, s2, l2, d)?),
(S::F32(s1), S::F32(s2)) => S::F32(self.f(s1, l1, s2, l2, d)?),
(S::F64(s1), S::F64(s2)) => S::F64(self.f(s1, l1, s2, l2, d)?),
_ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
};
Ok(out)
}
}
pub trait Map2InPlace {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
dst: &mut CudaSlice<T>,
dst_shape: &Shape,
src: &CudaSlice<T>,
src_l: &Layout,
dev: &CudaDevice,
) -> Result<()>;
fn map(
&self,
dst: &mut S,
dst_s: &Shape,
src: &S,
src_l: &Layout,
d: &CudaDevice,
) -> Result<()> {
match (dst, src) {
(S::U8(dst), S::U8(src)) => self.f(dst, dst_s, src, src_l, d),
(S::U32(dst), S::U32(src)) => self.f(dst, dst_s, src, src_l, d),
(S::I64(dst), S::I64(src)) => self.f(dst, dst_s, src, src_l, d),
(S::BF16(dst), S::BF16(src)) => self.f(dst, dst_s, src, src_l, d),
(S::F16(dst), S::F16(src)) => self.f(dst, dst_s, src, src_l, d),
(S::F32(dst), S::F32(src)) => self.f(dst, dst_s, src, src_l, d),
(S::F64(dst), S::F64(src)) => self.f(dst, dst_s, src, src_l, d),
_ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
}
}
}
pub trait Map1Any {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits, W: Fn(CudaSlice<T>) -> S>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
wrap: W,
) -> Result<S>;
fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
let out = match s {
S::U8(s) => self.f(s, d, l, S::U8)?,
S::U32(s) => self.f(s, d, l, S::U32)?,
S::I64(s) => self.f(s, d, l, S::I64)?,
S::BF16(s) => self.f(s, d, l, S::BF16)?,
S::F16(s) => self.f(s, d, l, S::F16)?,
S::F32(s) => self.f(s, d, l, S::F32)?,
S::F64(s) => self.f(s, d, l, S::F64)?,
};
Ok(out)
}
}
pub trait Map2Any {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src1: &CudaSlice<T>,
layout1: &Layout,
src2: &CudaSlice<T>,
layout2: &Layout,
dev: &CudaDevice,
) -> Result<S>;
fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
let out = match (s1, s2) {
(S::U8(s1), S::U8(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::U32(s1), S::U32(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::I64(s1), S::I64(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::BF16(s1), S::BF16(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::F16(s1), S::F16(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::F32(s1), S::F32(s2)) => self.f(s1, l1, s2, l2, d)?,
(S::F64(s1), S::F64(s2)) => self.f(s1, l1, s2, l2, d)?,
_ => Err(CudaError::InternalError("dtype mismatch in binary op")).w()?,
};
Ok(out)
}
}
struct Clone;
impl Map1 for Clone {
fn f<T: DeviceRepr>(
&self,
s: &CudaSlice<T>,
_: &CudaDevice,
_: &Layout,
) -> Result<CudaSlice<T>> {
s.try_clone().w()
}
}
pub fn kernel_name<T: WithDType>(root: &str) -> String {
let dtype = T::DTYPE.as_str();
format!("{root}_{dtype}")
}
struct Affine(f64, f64);
impl Map1 for Affine {
fn f<T: DeviceRepr + WithDType>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let el = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("affine"), kernels::AFFINE)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el) }.w()?;
let params = (
el,
dims.len(),
&ds,
src,
&out,
T::from_f64(self.0),
T::from_f64(self.1),
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Elu(f64);
impl Map1 for Elu {
fn f<T: DeviceRepr + WithDType>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let el = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("uelu"), kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el) }.w()?;
let params = (el, dims.len(), &ds, T::from_f64(self.0), src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Im2Col1D {
l_k: usize,
stride: usize,
dilation: usize,
padding: usize,
}
impl Im2Col1D {
fn l_out(&self, l: usize) -> usize {
(l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
}
}
impl Map1 for Im2Col1D {
fn f<T: DeviceRepr + WithDType>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let l_out = self.l_out(dims[2]);
let dst_el = dims[0] * l_out * dims[1] * self.l_k;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("im2col1d"), kernels::CONV)?;
// SAFETY: Set later by running the kernel.
let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let params = (
dst_el,
l_out,
self.l_k,
self.stride,
self.padding,
self.dilation,
&ds,
src,
&dst,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(dst)
}
}
struct Im2Col {
h_k: usize,
w_k: usize,
stride: usize,
dilation: usize,
padding: usize,
}
impl Im2Col {
fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
(h_out, w_out)
}
}
impl Map1 for Im2Col {
fn f<T: DeviceRepr + WithDType>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let (h_out, w_out) = self.hw_out(dims[2], dims[3]);
let dst_el = dims[0] * h_out * w_out * dims[1] * self.h_k * self.w_k;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("im2col"), kernels::CONV)?;
// SAFETY: Set later by running the kernel.
let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let params = (
dst_el,
h_out,
w_out,
self.h_k,
self.w_k,
self.stride,
self.padding,
self.dilation,
&ds,
src,
&dst,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(dst)
}
}
struct Powf(f64);
impl Map1 for Powf {
fn f<T: DeviceRepr + WithDType>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let el = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("upowf"), kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el) }.w()?;
let params = (el, dims.len(), &ds, T::from_f64(self.0), src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Sum<'a>(&'a [usize]);
impl<'a> Map1 for Sum<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let src_dims = shape.dims();
let el = shape.elem_count();
let mut dst_el = el;
for &sum_dim in self.0.iter() {
dst_el /= src_dims[sum_dim];
}
let mut sum_dims = self.0.to_vec();
// Sort the sum_dims as they have to be processed from left to right when converting the
// indexes.
sum_dims.sort();
let sum_dims_l: Vec<usize> = sum_dims.iter().map(|&d| src_dims[d]).collect();
let sum_dims_s: Vec<usize> = sum_dims
.iter()
.map(|&d| src_dims[d + 1..].iter().product::<usize>())
.collect();
let cfg = LaunchConfig::for_num_elems(el as u32);
let ds = dev
.htod_copy([src_dims, layout.stride(), &sum_dims_l, &sum_dims_s].concat())
.w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>("sum"), kernels::REDUCE)?;
let out = dev.alloc_zeros::<T>(dst_el).w()?;
let params = (el, src_dims.len(), sum_dims.len(), &ds, src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct FastReduce<'a>(&'a [usize], ReduceOp);
impl<'a> Map1Any for FastReduce<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits, W: Fn(CudaSlice<T>) -> S>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
wrap: W,
) -> Result<S> {
let src_stride = layout.stride();
let src_dims = layout.shape().dims();
let src_el: usize = src_dims.iter().product();
// Source dims and strides with the sum dims at the end.
let mut dims = vec![];
let mut stride = vec![];
let mut dst_el: usize = 1;
for (dim_idx, &d) in src_dims.iter().enumerate() {
if !self.0.contains(&dim_idx) {
dst_el *= d;
dims.push(d);
stride.push(src_stride[dim_idx]);
}
}
for &dim_idx in self.0.iter() {
dims.push(src_dims[dim_idx]);
stride.push(src_stride[dim_idx]);
}
let el_to_sum_per_block = src_el / dst_el;
// The reduction loop requires the shared array to be properly initialized and for
// this we want the number of threads to be a power of two.
let block_dim = usize::min(1024, el_to_sum_per_block).next_power_of_two();
let cfg = LaunchConfig {
// TODO: Maybe use grid_y if the output is too large?
// TODO: Specialized implementation when reducing on no or all dimensions or when
// reducing only aggregate a small number of elements together.
grid_dim: (dst_el as u32, 1, 1),
block_dim: (block_dim as u32, 1, 1),
shared_mem_bytes: 0,
};
let ds = dev
.htod_copy([dims.as_slice(), stride.as_slice()].concat())
.w()?;
let src = &src.slice(layout.start_offset()..);
let (name, check_empty, return_index) = match self.1 {
ReduceOp::Sum => ("fast_sum", false, false),
ReduceOp::Min => ("fast_min", true, false),
ReduceOp::Max => ("fast_max", true, false),
ReduceOp::ArgMin => ("fast_argmin", true, true),
ReduceOp::ArgMax => ("fast_argmax", true, true),
};
if check_empty && layout.shape().elem_count() == 0 {
Err(crate::Error::EmptyTensor { op: "reduce" }.bt())?
}
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::REDUCE)?;
if return_index {
// SAFETY: filled in by the follow up kernel.
let out = unsafe { dev.alloc::<u32>(dst_el) }.w()?;
let params = (src_el, el_to_sum_per_block, src_dims.len(), &ds, src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(S::U32(out))
} else {
// SAFETY: filled in by the follow up kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let params = (src_el, el_to_sum_per_block, src_dims.len(), &ds, src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(wrap(out))
}
}
}
impl<U: UnaryOpT> Map1 for U {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
layout: &Layout,
) -> Result<CudaSlice<T>> {
let shape = layout.shape();
let dims = shape.dims();
let el_count = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el_count as u32);
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let src = &src.slice(layout.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>(U::KERNEL), kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el_count) }.w()?;
let params = (el_count, dims.len(), &ds, src, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct IndexSelect<'a>(&'a CudaStorage, &'a Layout, usize);
impl<'a> Map1 for IndexSelect<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
src_l: &Layout,
) -> Result<CudaSlice<T>> {
let ids_l = &self.1;
let (name, ids) = match &self.0.slice {
CudaStorageSlice::U32(slice) => {
("is_u32", *slice.slice(ids_l.start_offset()..).device_ptr())
}
CudaStorageSlice::U8(slice) => {
("is_u8", *slice.slice(ids_l.start_offset()..).device_ptr())
}
CudaStorageSlice::I64(slice) => {
("is_i64", *slice.slice(ids_l.start_offset()..).device_ptr())
}
_ => Err(CudaError::UnexpectedDType {
msg: "index_select ids should be u8 or u32",
expected: DType::U32,
got: self.0.dtype(),
})
.w()?,
};
let ids_shape = ids_l.shape();
let ids_dims = ids_shape.dims();
let ds = dev.htod_copy([ids_dims, ids_l.stride()].concat()).w()?;
let src = match src_l.contiguous_offsets() {
Some((o1, o2)) => src.slice(o1..o2),
None => Err(crate::Error::RequiresContiguous { op: "index-select" }.bt())?,
};
let left_size: usize = src_l.dims()[..self.2].iter().product();
let right_size: usize = src_l.dims()[self.2 + 1..].iter().product();
let src_dim_size = src_l.dims()[self.2];
let ids_dim_size = ids_shape.elem_count();
let dst_el = ids_shape.elem_count() * left_size * right_size;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::INDEXING)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let params = (
dst_el,
ids_dims.len(),
&ds,
ids,
&src,
&out,
left_size,
src_dim_size,
ids_dim_size,
right_size,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Gather<'a>(&'a CudaStorage, &'a Layout, usize);
impl<'a> Map1 for Gather<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
src: &CudaSlice<T>,
dev: &CudaDevice,
src_l: &Layout,
) -> Result<CudaSlice<T>> {
let ids = &self.0;
let ids_l = &self.1;
let dim = self.2;
let (ids_o1, ids_o2) = match ids_l.contiguous_offsets() {
Some(o12) => o12,
None => Err(crate::Error::RequiresContiguous { op: "gather" }.bt())?,
};
let (name, ids) = match &ids.slice {
CudaStorageSlice::U32(slice) => {
("gather_u32", *slice.slice(ids_o1..ids_o2).device_ptr())
}
CudaStorageSlice::U8(slice) => ("gather_u8", *slice.slice(ids_o1..ids_o2).device_ptr()),
CudaStorageSlice::I64(slice) => {
("gather_i64", *slice.slice(ids_o1..ids_o2).device_ptr())
}
_ => Err(CudaError::UnexpectedDType {
msg: "gather ids should be u8/u32/i64",
expected: DType::U32,
got: ids.dtype(),
})?,
};
let el = ids_l.shape().elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let src = match src_l.contiguous_offsets() {
Some((o1, o2)) => src.slice(o1..o2),
None => Err(crate::Error::RequiresContiguous { op: "gather" }.bt())?,
};
let left_sz: usize = src_l.dims()[..dim].iter().product();
let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
let src_dim_sz = src_l.dims()[dim];
let ids_dim_sz = ids_l.dims()[dim];
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::INDEXING)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el) }.w()?;
let params = (
el, ids, &src, &out, left_sz, src_dim_sz, ids_dim_sz, right_sz,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct IndexAdd<'a>(&'a CudaStorage, &'a Layout, usize);
impl<'a> Map2InPlace for IndexAdd<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
dst: &mut CudaSlice<T>,
dst_shape: &Shape,
src: &CudaSlice<T>,
src_l: &Layout,
dev: &CudaDevice,
) -> Result<()> {
let ids = &self.0;
let ids_l = &self.1;
let dim = self.2;
let (ids_o1, ids_o2) = match ids_l.contiguous_offsets() {
Some(o12) => o12,
None => Err(crate::Error::RequiresContiguous { op: "index-add" }.bt())?,
};
let (name, ids) = match &ids.slice {
CudaStorageSlice::U32(slice) => ("ia_u32", *slice.slice(ids_o1..ids_o2).device_ptr()),
CudaStorageSlice::I64(slice) => ("ia_i64", *slice.slice(ids_o1..ids_o2).device_ptr()),
CudaStorageSlice::U8(slice) => ("ia_u8", *slice.slice(ids_o1..ids_o2).device_ptr()),
_ => Err(CudaError::UnexpectedDType {
msg: "index-add ids should be u8/u32/i64",
expected: DType::U32,
got: ids.dtype(),
})?,
};
let src = match src_l.contiguous_offsets() {
Some((o1, o2)) => src.slice(o1..o2),
None => Err(crate::Error::RequiresContiguous { op: "index-add" }.bt())?,
};
let left_sz: usize = src_l.dims()[..dim].iter().product();
let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
let src_dim_sz = src_l.dims()[dim];
let dst_dim_sz = dst_shape.dims()[dim];
let ids_dim_sz = ids_l.dims()[0];
let cfg = LaunchConfig::for_num_elems((left_sz * right_sz) as u32);
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::INDEXING)?;
// SAFETY: Set later by running the kernel.
let params = (
ids, ids_dim_sz, &src, dst, left_sz, src_dim_sz, dst_dim_sz, right_sz,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(())
}
}
struct ScatterAdd<'a>(&'a CudaStorage, &'a Layout, usize);
impl<'a> Map2InPlace for ScatterAdd<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
dst: &mut CudaSlice<T>,
dst_shape: &Shape,
src: &CudaSlice<T>,
src_l: &Layout,
dev: &CudaDevice,
) -> Result<()> {
let ids = &self.0;
let ids_l = &self.1;
let dim = self.2;
let (ids_o1, ids_o2) = match ids_l.contiguous_offsets() {
Some(o12) => o12,
None => Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt())?,
};
let (name, ids) = match &ids.slice {
CudaStorageSlice::U32(slice) => ("sa_u32", *slice.slice(ids_o1..ids_o2).device_ptr()),
CudaStorageSlice::I64(slice) => ("sa_i64", *slice.slice(ids_o1..ids_o2).device_ptr()),
CudaStorageSlice::U8(slice) => ("sa_u8", *slice.slice(ids_o1..ids_o2).device_ptr()),
_ => Err(CudaError::UnexpectedDType {
msg: "scatter-add ids should be u8/u32/i64",
expected: DType::U32,
got: ids.dtype(),
})?,
};
let src = match src_l.contiguous_offsets() {
Some((o1, o2)) => src.slice(o1..o2),
None => Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt())?,
};
let left_sz: usize = src_l.dims()[..dim].iter().product();
let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
let src_dim_sz = src_l.dims()[dim];
let dst_dim_sz = dst_shape.dims()[dim];
let cfg = LaunchConfig::for_num_elems((left_sz * right_sz) as u32);
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::INDEXING)?;
// SAFETY: Set later by running the kernel.
let params = (ids, &src, dst, left_sz, src_dim_sz, dst_dim_sz, right_sz);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(())
}
}
struct Conv1D<'a>(&'a crate::conv::ParamsConv1D);
impl<'a> Map2 for Conv1D<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
inp: &CudaSlice<T>,
inp_l: &Layout,
k: &CudaSlice<T>,
k_l: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>> {
// Kernel shape: (c_out, c_in_k, k_size)
// Input shape: (b_size, c_in, l_in) or (c_in, l_in)
let p = &self.0;
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(k_l.start_offset()..);
let shape = inp_l.shape();
let dims = shape.dims();
let el = shape.elem_count();
let l_out = p.l_out();
let dst_el = p.c_out * l_out * p.b_size;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let func = dev.get_or_load_func(&kernel_name::<T>("conv1d"), kernels::CONV)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let ds = if dims.len() == 3 {
[dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
} else if dims.len() == 2 {
[&[1], dims, &[1], inp_l.stride(), k_l.dims(), k_l.stride()].concat()
} else {
crate::bail!("unexpected input shape for conv1d {dims:?}")
};
let ds = dev.htod_copy(ds).w()?;
let params = (
el, l_out, p.stride, p.padding, p.dilation, &ds, inp, k, &out,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Conv2D<'a>(&'a crate::conv::ParamsConv2D);
impl<'a> Map2 for Conv2D<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
inp: &CudaSlice<T>,
inp_l: &Layout,
k: &CudaSlice<T>,
k_l: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>> {
// Kernel shape: (c_out, c_in_k, h_k, w_k)
// Input shape: (b_size, c_in, h_in, w_in)
let p = &self.0;
let (out_w, out_h) = (p.out_w(), p.out_h());
let dst_el = p.c_out * out_w * out_h * p.b_size;
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(k_l.start_offset()..);
let shape = inp_l.shape();
let dims = shape.dims();
let el = shape.elem_count();
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let func = dev.get_or_load_func(&kernel_name::<T>("conv2d"), kernels::CONV)?;
let ds = if dims.len() == 4 {
[dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
} else {
crate::bail!("unexpected input shape for conv2d {dims:?}")
};
let ds = dev.htod_copy(ds).w()?;
let params = (
el, out_w, out_h, p.stride, p.padding, p.dilation, &ds, inp, k, &out,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct ConvTranspose2D<'a>(&'a crate::conv::ParamsConvTranspose2D);
impl<'a> Map2 for ConvTranspose2D<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
inp: &CudaSlice<T>,
inp_l: &Layout,
k: &CudaSlice<T>,
k_l: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>> {
// Kernel shape: (c_in_k, c_out, h_k, w_k)
// Input shape: (b_size, c_in, h_in, w_in)
let p = &self.0;
let (out_w, out_h) = (p.out_w(), p.out_h());
let dst_el = p.c_out * out_w * out_h * p.b_size;
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(k_l.start_offset()..);
let shape = inp_l.shape();
let dims = shape.dims();
let el = shape.elem_count();
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let func = dev.get_or_load_func(&kernel_name::<T>("conv_transpose2d"), kernels::CONV)?;
let ds = if dims.len() == 4 {
[dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
} else {
crate::bail!("unexpected input shape for conv_transpose2d {dims:?}")
};
let ds = dev.htod_copy(ds).w()?;
let params = (
el,
out_w,
out_h,
p.stride,
p.padding,
p.output_padding,
p.dilation,
&ds,
inp,
k,
&out,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
enum PoolOp {
Max,
Avg,
}
struct Pool2D {
w_k: usize,
h_k: usize,
w_stride: usize,
h_stride: usize,
op: PoolOp,
}
impl Map1 for Pool2D {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
inp: &CudaSlice<T>,
dev: &CudaDevice,
inp_l: &Layout,
) -> Result<CudaSlice<T>> {
// Input shape: (b_size, c, h, w)
let inp = &inp.slice(inp_l.start_offset()..);
let shape = inp_l.shape();
let dims = shape.dims();
let ds = if dims.len() == 4 {
[dims, inp_l.stride()].concat()
} else {
crate::bail!("unexpected input shape for pool {dims:?}")
};
let el = shape.elem_count();
let out_w = (dims[2] - self.w_k) / self.w_stride + 1;
let out_h = (dims[3] - self.h_k) / self.h_stride + 1;
let dst_el = out_w * out_h * dims[0] * dims[1];
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let kname = match self.op {
PoolOp::Max => "max_pool2d",
PoolOp::Avg => "avg_pool2d",
};
let func = dev.get_or_load_func(&kernel_name::<T>(kname), kernels::CONV)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let ds = dev.htod_copy(ds).w()?;
let params = (
el,
self.w_k,
self.h_k,
self.w_stride,
self.h_stride,
&ds,
inp,
&out,
);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct UpsampleNearest2D(usize, usize);
impl Map1 for UpsampleNearest2D {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
inp: &CudaSlice<T>,
dev: &CudaDevice,
inp_l: &Layout,
) -> Result<CudaSlice<T>> {
// Input shape: (b_size, c, h, w)
let inp = &inp.slice(inp_l.start_offset()..);
let shape = inp_l.shape();
let dims = shape.dims();
let ds = if dims.len() == 4 {
[dims, inp_l.stride()].concat()
} else {
crate::bail!("unexpected input shape for upsample {dims:?}")
};
let (out_w, out_h) = (self.0, self.1);
let dst_el = out_w * out_h * dims[0] * dims[1];
let cfg = LaunchConfig::for_num_elems(dst_el as u32);
let func = dev.get_or_load_func(&kernel_name::<T>("upsample_nearest2d"), kernels::CONV)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
let ds = dev.htod_copy(ds).w()?;
let scale_w = dims[2] as f64 / out_w as f64;
let scale_h = dims[3] as f64 / out_h as f64;
let params = (out_w, out_h, scale_w, scale_h, &ds, inp, &out);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct WhereCond<'a>(&'a CudaStorage, &'a Layout);
impl<'a> Map2 for WhereCond<'a> {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
t: &CudaSlice<T>,
layout_t: &Layout,
f: &CudaSlice<T>,
layout_f: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>> {
let ids_l = &self.1;
let (ids, name) = match &self.0.slice {
CudaStorageSlice::U8(slice) => {
let ptr = *slice.slice(ids_l.start_offset()..).device_ptr();
(ptr, "where_u8")
}
CudaStorageSlice::U32(slice) => {
let ptr = *slice.slice(ids_l.start_offset()..).device_ptr();
(ptr, "where_u32")
}
CudaStorageSlice::I64(slice) => {
let ptr = *slice.slice(ids_l.start_offset()..).device_ptr();
(ptr, "where_i64")
}
_ => Err(CudaError::UnexpectedDType {
msg: "where conditions should be u8/u32/i64",
expected: DType::U32,
got: self.0.dtype(),
})
.w()?,
};
let shape = ids_l.shape();
let dims = shape.dims();
let el = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let ds = dev
.htod_copy([dims, ids_l.stride(), layout_t.stride(), layout_f.stride()].concat())
.w()?;
let t = &t.slice(layout_t.start_offset()..);
let f = &f.slice(layout_f.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::TERNARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(el) }.w()?;
let params = (el, dims.len(), &ds, ids, t, f, &out);
// SAFETY: ffi
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
impl<U: crate::op::BinaryOpT> Map2 for U {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
lhs: &CudaSlice<T>,
lhs_l: &Layout,
rhs: &CudaSlice<T>,
rhs_l: &Layout,
dev: &CudaDevice,
) -> Result<CudaSlice<T>> {
let shape = lhs_l.shape();
let dims = shape.dims();
let elem_count = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(elem_count as u32);
let dims_and_strides = dev
.htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
.w()?;
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let func = dev.get_or_load_func(&kernel_name::<T>(U::KERNEL), kernels::BINARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<T>(elem_count) }.w()?;
let params = (elem_count, dims.len(), &dims_and_strides, lhs, rhs, &out);
// SAFETY: ffi
unsafe { func.launch(cfg, params) }.w()?;
Ok(out)
}
}
struct Cmp(CmpOp);
impl Map2Any for Cmp {
fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
&self,
lhs: &CudaSlice<T>,
lhs_l: &Layout,
rhs: &CudaSlice<T>,
rhs_l: &Layout,
dev: &CudaDevice,
) -> Result<S> {
let shape = lhs_l.shape();
let dims = shape.dims();
let elem_count = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(elem_count as u32);
let dims_and_strides = dev
.htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
.w()?;
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let name = match self.0 {
CmpOp::Eq => "eq",
CmpOp::Ne => "ne",
CmpOp::Lt => "lt",
CmpOp::Le => "le",
CmpOp::Gt => "gt",
CmpOp::Ge => "ge",
};
let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::BINARY)?;
// SAFETY: Set later by running the kernel.
let out = unsafe { dev.alloc::<u8>(elem_count) }.w()?;
let params = (elem_count, dims.len(), &dims_and_strides, lhs, rhs, &out);
// SAFETY: ffi
unsafe { func.launch(cfg, params) }.w()?;
Ok(S::U8(out))
}
}
fn slice_src_and_dst<'a, T>(
src: &'a CudaSlice<T>,
src_l: &Layout,
dst: &'a mut CudaSlice<T>,
dst_offset: usize,
) -> (
cudarc::driver::CudaView<'a, T>,
cudarc::driver::CudaViewMut<'a, T>,
) {
let src_offset = src_l.start_offset();
let to_copy = dst
.len()
.saturating_sub(dst_offset)
.min(src.len().saturating_sub(src_offset));
let src = src.slice(src_offset..src_offset + to_copy);
let dst = dst.slice_mut(dst_offset..dst_offset + to_copy);
(src, dst)
}
#[derive(Debug)]
pub struct CudaStorage {
pub slice: CudaStorageSlice,
pub device: CudaDevice,
}
pub trait CudaDType: Sized {
fn as_cuda_slice(s: &CudaStorage) -> Result<&CudaSlice<Self>>;
fn wrap_cuda_slice(s: CudaSlice<Self>, dev: CudaDevice) -> CudaStorage;
}
macro_rules! cuda_dtype {
($ty:ty, $dtype:ident) => {
impl CudaDType for $ty {
fn as_cuda_slice(s: &CudaStorage) -> Result<&CudaSlice<Self>> {
match &s.slice {
CudaStorageSlice::$dtype(data) => Ok(&data),
_ => Err(crate::Error::UnexpectedDType {
expected: DType::$dtype,
got: s.dtype(),
msg: "unexpected dtype",
}
.bt()),
}
}
fn wrap_cuda_slice(slice: CudaSlice<Self>, device: CudaDevice) -> CudaStorage {
let slice = CudaStorageSlice::$dtype(slice);
CudaStorage { slice, device }
}
}
};
}
cuda_dtype!(u8, U8);
cuda_dtype!(u32, U32);
cuda_dtype!(i64, I64);
cuda_dtype!(f16, F16);
cuda_dtype!(bf16, BF16);
cuda_dtype!(f32, F32);
cuda_dtype!(f64, F64);
impl CudaStorage {
pub fn wrap_cuda_slice<T: CudaDType>(slice: CudaSlice<T>, device: CudaDevice) -> CudaStorage {
T::wrap_cuda_slice(slice, device)
}
pub fn as_cuda_slice<T: CudaDType>(&self) -> Result<&CudaSlice<T>> {
T::as_cuda_slice(self)
}
}
fn gemm_config<T>(
alpha: T,
beta: T,
(b, m, n, k): (usize, usize, usize, usize),
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<StridedBatchedConfig<T>> {
// https://docs.nvidia.com/cuda/cublas/index.html#cublas-t-gemm
use cudarc::cublas::sys::cublasOperation_t;
let lhs_stride = lhs_l.stride();
let rhs_stride = rhs_l.stride();
let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
let rhs_m2 = rhs_stride[rhs_stride.len() - 2];
let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
// The a tensor has dims batching, k, n (rhs)
let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
(n as i32, cublasOperation_t::CUBLAS_OP_N)
} else if rhs_m1 == k && rhs_m2 == 1 {
(k as i32, cublasOperation_t::CUBLAS_OP_T)
} else {
Err(CudaError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?
};
// The b tensor has dims batching, m, k (lhs)
let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
(k as i32, cublasOperation_t::CUBLAS_OP_N)
} else if lhs_m1 == m && lhs_m2 == 1 {
(m as i32, cublasOperation_t::CUBLAS_OP_T)
} else {
Err(CudaError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?
};
// The setup below was copied from:
// https://github.com/lebedov/scikit-cuda/blob/7e7300474286019c917a6c8a4bca59405c64fbce/tests/test_cublas.py#L531
let gemm = GemmConfig {
alpha,
beta,
m: n as i32,
n: m as i32,
k: k as i32,
lda,
ldb,
ldc: n as i32,
transa,
transb,
};
let stride_b: usize = match lhs_stride[..lhs_stride.len() - 2] {
[s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
[stride] => stride,
[] => m * k,
_ => Err(CudaError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?,
};
let stride_a: usize = match rhs_stride[..rhs_stride.len() - 2] {
[s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
[stride] => stride,
[] => n * k,
_ => Err(CudaError::MatMulNonContiguous {
lhs_stride: lhs_stride.to_vec(),
rhs_stride: rhs_stride.to_vec(),
mnk: (m, n, k),
})?,
};
Ok(StridedBatchedConfig {
batch_size: b as i32,
gemm,
stride_a: stride_a as i64,
stride_b: stride_b as i64,
stride_c: (m * n) as i64,
})
}
impl BackendStorage for CudaStorage {
type Device = CudaDevice;
fn try_clone(&self, layout: &Layout) -> Result<Self> {
let slice = Clone.map(&self.slice, self.device(), layout)?;
let device = self.device.clone();
Ok(Self { slice, device })
}
fn dtype(&self) -> DType {
match self.slice {
CudaStorageSlice::U8(_) => DType::U8,
CudaStorageSlice::U32(_) => DType::U32,
CudaStorageSlice::I64(_) => DType::I64,
CudaStorageSlice::BF16(_) => DType::BF16,
CudaStorageSlice::F16(_) => DType::F16,
CudaStorageSlice::F32(_) => DType::F32,
CudaStorageSlice::F64(_) => DType::F64,
}
}
fn device(&self) -> &CudaDevice {
&self.device
}
fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
let shape = layout.shape();
let dims = shape.dims();
let el = shape.elem_count();
let cfg = LaunchConfig::for_num_elems(el as u32);
let dev = self.device();
let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
let start_o = layout.start_offset();
// This returns an i64 rather than a &i64, this is useful to get around some temporary
// lifetime issue and is safe as long as self.slice does not go out of scope before inp
// is used.
let inp = match &self.slice {
CudaStorageSlice::U8(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::U32(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::I64(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::BF16(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::F16(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::F32(inp) => *inp.slice(start_o..).device_ptr(),
CudaStorageSlice::F64(inp) => *inp.slice(start_o..).device_ptr(),
};
let inp = &inp;
let kernel_name = format!("cast_{}_{}", self.dtype().as_str(), dtype.as_str());
let func = dev.get_or_load_func(&kernel_name, kernels::CAST)?;
let slice = match dtype {
DType::U8 => {
let out = unsafe { dev.alloc::<u8>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::U8(out)
}
DType::U32 => {
let out = unsafe { dev.alloc::<u32>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::U32(out)
}
DType::I64 => {
let out = unsafe { dev.alloc::<i64>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::I64(out)
}
DType::BF16 => {
let out = unsafe { dev.alloc::<bf16>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::BF16(out)
}
DType::F16 => {
let out = unsafe { dev.alloc::<f16>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F16(out)
}
DType::F32 => {
let out = unsafe { dev.alloc::<f32>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F32(out)
}
DType::F64 => {
let out = unsafe { dev.alloc::<f64>(el) }.w()?;
let params = (el, dims.len(), &ds, *inp, &out);
unsafe { func.launch(cfg, params) }.w()?;
CudaStorageSlice::F64(out)
}
};
Ok(Self {
slice,
device: dev.clone(),
})
}
fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
let device = self.device().clone();
let slice = Affine(mul, add).map(&self.slice, &device, layout)?;
Ok(Self { slice, device })
}
fn powf(&self, layout: &Layout, e: f64) -> Result<Self> {
let device = self.device().clone();
let slice = Powf(e).map(&self.slice, &device, layout)?;
Ok(Self { slice, device })
}
fn elu(&self, layout: &Layout, alpha: f64) -> Result<Self> {
let device = self.device().clone();
let slice = Elu(alpha).map(&self.slice, &device, layout)?;
Ok(Self { slice, device })
}
fn reduce_op(&self, op: ReduceOp, layout: &Layout, sum_dims: &[usize]) -> Result<Self> {
let device = self.device().clone();
let slice = FastReduce(sum_dims, op).map(&self.slice, &device, layout)?;
Ok(Self { slice, device })
}
fn cmp(&self, op: CmpOp, rhs: &Self, lhs_l: &Layout, rhs_l: &Layout) -> Result<Self> {
let device = self.device().clone();
let slice = Cmp(op).map(&self.slice, lhs_l, &rhs.slice, rhs_l, &device)?;
Ok(Self { slice, device })
}
fn unary_impl<U: UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
let device = self.device().clone();
let slice = U::V.map(&self.slice, &device, layout)?;
Ok(Self { slice, device })
}
fn binary_impl<B: BinaryOpT>(
&self,
rhs: &Self,
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
let device = self.device().clone();
let slice = B::V.map(&self.slice, lhs_l, &rhs.slice, rhs_l, &device)?;
Ok(Self { slice, device })
}
fn to_cpu_storage(&self) -> Result<CpuStorage> {
match &self.slice {
CudaStorageSlice::U8(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::U8(cpu_storage))
}
CudaStorageSlice::U32(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::U32(cpu_storage))
}
CudaStorageSlice::I64(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::I64(cpu_storage))
}
CudaStorageSlice::BF16(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::BF16(cpu_storage))
}
CudaStorageSlice::F16(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::F16(cpu_storage))
}
CudaStorageSlice::F32(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::F32(cpu_storage))
}
CudaStorageSlice::F64(slice) => {
let dev = slice.device();
let cpu_storage = dev.dtoh_sync_copy(slice).w()?;
Ok(CpuStorage::F64(cpu_storage))
}
}
}
fn where_cond(
&self,
layout: &Layout,
t: &Self,
t_l: &Layout,
f: &Self,
f_l: &Layout,
) -> Result<Self> {
let device = self.device().clone();
let slice = WhereCond(self, layout).map(&t.slice, t_l, &f.slice, f_l, &device)?;
Ok(Self { slice, device })
}
fn conv1d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv1D,
) -> Result<Self> {
const USE_IM2COL_CONV1D: bool = true;
let device = self.device().clone();
if !USE_IM2COL_CONV1D {
let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
return Ok(Self { slice, device });
}
let col = Im2Col1D {
l_k: params.k_size,
stride: params.stride,
dilation: params.dilation,
padding: params.padding,
}
.map(&self.slice, &device, l)?;
let col = Self { slice: col, device };
let l_out = params.l_out();
let b = params.b_size;
let n = params.c_out;
let k = params.k_size * params.c_in;
let m = l_out;
let col_l = Layout::contiguous((b, m, k));
let res = if kernel_l.is_contiguous() {
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
} else {
// Make the kernel contiguous if not already the case.
let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
};
let res_l = Layout::contiguous((b, l_out, n)).transpose(1, 2)?;
let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
res.copy_strided_src(&mut res_t, 0, &res_l)?;
Ok(res_t)
}
fn conv_transpose1d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self> {
todo!()
}
#[cfg(not(feature = "cudnn"))]
fn conv2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv2D,
) -> Result<Self> {
const USE_IM2COL_CONV2D: bool = true;
let device = self.device().clone();
if !USE_IM2COL_CONV2D {
let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
return Ok(Self { slice, device });
}
let col = Im2Col {
h_k: params.k_h,
w_k: params.k_w,
stride: params.stride,
dilation: params.dilation,
padding: params.padding,
}
.map(&self.slice, &device, l)?;
let col = Self { slice: col, device };
let h_out = params.out_h();
let w_out = params.out_w();
let b = params.b_size;
let n = params.c_out;
let k = params.k_h * params.k_w * params.c_in;
let m = h_out * w_out;
let col_l = Layout::contiguous((b, m, k));
let res = if kernel_l.is_contiguous() {
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
} else {
// Make the kernel contiguous if not already the case.
let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
};
let res_l = Layout::contiguous((b, h_out, w_out, n))
.transpose(1, 2)?
.transpose(1, 3)?;
let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
res.copy_strided_src(&mut res_t, 0, &res_l)?;
Ok(res_t)
}
#[cfg(feature = "cudnn")]
fn conv2d(
&self,
inp_l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv2D,
) -> Result<Self> {
let device = self.device().clone();
if !kernel_l.is_contiguous() {
let slice = Conv2D(params).map(&self.slice, inp_l, &kernel.slice, kernel_l, &device)?;
return Ok(Self { slice, device });
}
let (out_w, out_h) = (params.out_w(), params.out_h());
let dst_el = params.c_out * out_w * out_h * params.b_size;
let slice = match (&self.slice, &kernel.slice) {
(S::U8(inp), S::U8(k)) => {
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(kernel_l.start_offset()..);
let mut out = unsafe { device.alloc::<u8>(dst_el) }.w()?;
crate::cudnn::launch_conv2d::<u8>(inp, inp_l, k, &mut out, params, &device)
.map_err(crate::Error::wrap)?;
S::U8(out)
}
(S::BF16(inp), S::BF16(k)) => {
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(kernel_l.start_offset()..);
let mut out = unsafe { device.alloc::<bf16>(dst_el) }.w()?;
crate::cudnn::launch_conv2d::<bf16>(inp, inp_l, k, &mut out, params, &device)
.map_err(crate::Error::wrap)?;
S::BF16(out)
}
(S::F16(inp), S::F16(k)) => {
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(kernel_l.start_offset()..);
let mut out = unsafe { device.alloc::<f16>(dst_el) }.w()?;
crate::cudnn::launch_conv2d::<f16>(inp, inp_l, k, &mut out, params, &device)
.map_err(crate::Error::wrap)?;
S::F16(out)
}
(S::F32(inp), S::F32(k)) => {
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(kernel_l.start_offset()..);
let mut out = unsafe { device.alloc::<f32>(dst_el) }.w()?;
crate::cudnn::launch_conv2d::<f32>(inp, inp_l, k, &mut out, params, &device)
.map_err(crate::Error::wrap)?;
S::F32(out)
}
(S::F64(inp), S::F64(k)) => {
let inp = &inp.slice(inp_l.start_offset()..);
let k = &k.slice(kernel_l.start_offset()..);
let mut out = unsafe { device.alloc::<f64>(dst_el) }.w()?;
crate::cudnn::launch_conv2d::<f64>(inp, inp_l, k, &mut out, params, &device)
.map_err(crate::Error::wrap)?;
S::F64(out)
}
(S::U32(_), S::U32(_)) => Err(CudaError::InternalError("conv2d does not support u32"))?,
(S::I64(_), S::I64(_)) => Err(CudaError::InternalError("conv2d does not support i64"))?,
_ => Err(CudaError::InternalError("dtype mismatch in conv2d"))?,
};
Ok(Self { slice, device })
}
fn conv_transpose2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self> {
let device = self.device().clone();
let slice =
ConvTranspose2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
Ok(Self { slice, device })
}
fn avg_pool2d(&self, l: &Layout, k: (usize, usize), stride: (usize, usize)) -> Result<Self> {
let device = self.device().clone();
let slice = Pool2D {
w_k: k.0,
h_k: k.1,
w_stride: stride.0,
h_stride: stride.1,
op: PoolOp::Avg,
}
.map(&self.slice, &device, l)?;
Ok(Self { slice, device })
}
fn max_pool2d(&self, l: &Layout, k: (usize, usize), stride: (usize, usize)) -> Result<Self> {
let device = self.device().clone();
let slice = Pool2D {
w_k: k.0,
h_k: k.1,
w_stride: stride.0,
h_stride: stride.1,
op: PoolOp::Max,
}
.map(&self.slice, &device, l)?;
Ok(Self { slice, device })
}
fn upsample_nearest1d(&self, _: &Layout, _out_sz: usize) -> Result<Self> {
crate::bail!("upsample-nearest1d is not supported on cuda")
}
fn upsample_nearest2d(&self, l: &Layout, out_w: usize, out_h: usize) -> Result<Self> {
let device = self.device().clone();
let slice = UpsampleNearest2D(out_w, out_h).map(&self.slice, &device, l)?;
Ok(Self { slice, device })
}
fn index_select(&self, ids: &Self, l: &Layout, ids_l: &Layout, dim: usize) -> Result<Self> {
let device = self.device().clone();
let slice = IndexSelect(ids, ids_l, dim).map(&self.slice, &device, l)?;
Ok(Self { slice, device })
}
fn gather(&self, l: &Layout, ids: &Self, ids_l: &Layout, dim: usize) -> Result<Self> {
let device = self.device().clone();
let slice = Gather(ids, ids_l, dim).map(&self.slice, &device, l)?;
Ok(Self { slice, device })
}
fn scatter_add(
&self,
l: &Layout,
ids: &Self,
ids_l: &Layout,
src: &Self,
src_l: &Layout,
dim: usize,
) -> Result<Self> {
let device = self.device().clone();
let mut acc = device.zeros_impl(l.shape(), self.dtype())?;
self.copy_strided_src(&mut acc, 0, l)?;
ScatterAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
Ok(acc)
}
fn index_add(
&self,
l: &Layout,
ids: &Self,
ids_l: &Layout,
src: &Self,
src_l: &Layout,
dim: usize,
) -> Result<Self> {
let device = self.device().clone();
let mut acc = device.zeros_impl(l.shape(), self.dtype())?;
self.copy_strided_src(&mut acc, 0, l)?;
IndexAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
Ok(acc)
}
fn matmul(
&self,
rhs: &Self,
(b, m, n, k): (usize, usize, usize, usize),
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
let elem_count = b * m * n;
let dev = &self.device;
let slice = match (&self.slice, &rhs.slice) {
(CudaStorageSlice::BF16(lhs), CudaStorageSlice::BF16(rhs)) => {
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let cfg = gemm_config(bf16::ONE, bf16::ZERO, (b, m, n, k), lhs_l, rhs_l)?;
let mut out = unsafe { dev.alloc::<bf16>(elem_count) }.w()?;
unsafe {
self.device
.blas
.gemm_strided_batched(cfg, rhs, lhs, &mut out)
}
.w()?;
CudaStorageSlice::BF16(out)
}
(CudaStorageSlice::F16(lhs), CudaStorageSlice::F16(rhs)) => {
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let cfg = gemm_config(f16::ONE, f16::ZERO, (b, m, n, k), lhs_l, rhs_l)?;
let mut out = unsafe { dev.alloc::<f16>(elem_count) }.w()?;
unsafe {
self.device
.blas
.gemm_strided_batched(cfg, rhs, lhs, &mut out)
}
.w()?;
CudaStorageSlice::F16(out)
}
(CudaStorageSlice::F32(lhs), CudaStorageSlice::F32(rhs)) => {
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let cfg = gemm_config(1., 0., (b, m, n, k), lhs_l, rhs_l)?;
let mut out = unsafe { dev.alloc::<f32>(elem_count) }.w()?;
unsafe {
self.device
.blas
.gemm_strided_batched(cfg, rhs, lhs, &mut out)
}
.w()?;
CudaStorageSlice::F32(out)
}
(CudaStorageSlice::F64(lhs), CudaStorageSlice::F64(rhs)) => {
let lhs = &lhs.slice(lhs_l.start_offset()..);
let rhs = &rhs.slice(rhs_l.start_offset()..);
let cfg = gemm_config(1., 0., (b, m, n, k), lhs_l, rhs_l)?;
let mut out = unsafe { dev.alloc::<f64>(elem_count) }.w()?;
unsafe {
self.device
.blas
.gemm_strided_batched(cfg, rhs, lhs, &mut out)
}
.w()?;
CudaStorageSlice::F64(out)
}
_ => Err(CudaError::InternalError("dtype mismatch in matmul op"))?,
};
let device = dev.clone();
Ok(Self { slice, device })
}
fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
let src_shape = src_l.shape();
let dims = src_shape.dims();
let el_count = src_shape.elem_count();
if el_count == 0 {
return Ok(());
}
let cfg = LaunchConfig::for_num_elems(el_count as u32);
let dev = &self.device;
let ds = dev.htod_copy([dims, src_l.stride()].concat()).w()?;
match (&self.slice, &mut dst.slice) {
(CudaStorageSlice::BF16(src), CudaStorageSlice::BF16(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_bf16", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::F16(src), CudaStorageSlice::F16(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_f16", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::F32(src), CudaStorageSlice::F32(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_f32", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::U8(src), CudaStorageSlice::U8(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_u8", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::U32(src), CudaStorageSlice::U32(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_u32", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::I64(src), CudaStorageSlice::I64(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_i64", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?
}
}
(CudaStorageSlice::F64(src), CudaStorageSlice::F64(dst)) => {
let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);
if src_l.is_contiguous() {
dev.dtod_copy(&src, &mut dst).w()?
} else {
let func = dev.get_or_load_func("ucopy_f64", kernels::UNARY)?;
// SAFETY: Set later by running the kernel.
let params = (el_count, dims.len(), &ds, &src, &mut dst);
// SAFETY: ffi.
unsafe { func.launch(cfg, params) }.w()?;
}
}
_ => Err(CudaError::InternalError(
"dtype mismatch in copy_strided op",
))?,
}
Ok(())
}
}
| 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) -> Self {
Self {
shape,
stride,
start_offset,
}
}
pub fn contiguous_with_offset<S: Into<Shape>>(shape: S, start_offset: usize) -> Self {
let shape = shape.into();
let stride = shape.stride_contiguous();
Self {
shape,
stride,
start_offset,
}
}
pub fn contiguous<S: Into<Shape>>(shape: S) -> Self {
Self::contiguous_with_offset(shape, 0)
}
pub fn dims(&self) -> &[usize] {
self.shape.dims()
}
pub fn shape(&self) -> &Shape {
&self.shape
}
pub fn stride(&self) -> &[usize] {
&self.stride
}
pub fn start_offset(&self) -> usize {
self.start_offset
}
/// Returns the appropriate start and stop offset if the data is stored in a C
/// contiguous (aka row major) way.
pub fn contiguous_offsets(&self) -> Option<(usize, usize)> {
if self.is_contiguous() {
let start_o = self.start_offset;
Some((start_o, start_o + self.shape.elem_count()))
} else {
None
}
}
/// Returns true if the data is stored in a C contiguous (aka row major) way.
/// Note that this does not implies that the start offset is 0 or that there are no extra
/// elements at the end of the storage.
pub fn is_contiguous(&self) -> bool {
self.shape.is_contiguous(&self.stride)
}
/// Returns true if the data is stored in a Fortran contiguous (aka column major) way.
pub fn is_fortran_contiguous(&self) -> bool {
self.shape.is_fortran_contiguous(&self.stride)
}
pub(crate) fn narrow(&self, dim: usize, start: usize, len: usize) -> Result<Self> {
let dims = self.shape().dims();
if dim >= dims.len() {
Err(Error::DimOutOfRange {
shape: self.shape().clone(),
dim: dim as i32,
op: "narrow",
}
.bt())?
}
if start + len > dims[dim] {
Err(Error::NarrowInvalidArgs {
shape: self.shape.clone(),
dim,
start,
len,
msg: "start + len > dim_len",
}
.bt())?
}
let mut dims = dims.to_vec();
dims[dim] = len;
Ok(Self {
shape: Shape::from(dims),
stride: self.stride.clone(),
start_offset: self.start_offset + self.stride[dim] * start,
})
}
pub(crate) fn transpose(&self, dim1: usize, dim2: usize) -> Result<Self> {
let rank = self.shape.rank();
if rank <= dim1 || rank <= dim2 {
Err(Error::UnexpectedNumberOfDims {
expected: usize::max(dim1, dim2),
got: rank,
shape: self.shape().clone(),
}
.bt())?
}
let mut stride = self.stride().to_vec();
let mut dims = self.shape().dims().to_vec();
dims.swap(dim1, dim2);
stride.swap(dim1, dim2);
Ok(Self {
shape: Shape::from(dims),
stride,
start_offset: self.start_offset,
})
}
pub(crate) fn permute(&self, idxs: &[usize]) -> Result<Self> {
let is_permutation =
idxs.len() == self.shape.rank() && (0..idxs.len()).all(|i| idxs.contains(&i));
if !is_permutation {
crate::bail!(
"dimension mismatch in permute, tensor {:?}, dims: {:?}",
self.dims(),
idxs
)
}
let stride = self.stride();
let dims = self.shape().dims();
let mut perm_stride = stride.to_vec();
let mut perm_dims = dims.to_vec();
for (i, &idx) in idxs.iter().enumerate() {
perm_stride[i] = stride[idx];
perm_dims[i] = dims[idx];
}
Ok(Self {
shape: Shape::from(perm_dims),
stride: perm_stride,
start_offset: self.start_offset,
})
}
pub fn broadcast_as<S: Into<Shape>>(&self, shape: S) -> Result<Self> {
let shape = shape.into();
if shape.rank() < self.shape().rank() {
return Err(Error::BroadcastIncompatibleShapes {
src_shape: self.shape().clone(),
dst_shape: shape,
}
.bt());
}
let added_dims = shape.rank() - self.shape().rank();
let mut stride = vec![0; added_dims];
for (&dst_dim, (&src_dim, &src_stride)) in shape.dims()[added_dims..]
.iter()
.zip(self.dims().iter().zip(self.stride()))
{
let s = if dst_dim == src_dim {
src_stride
} else if src_dim != 1 {
return Err(Error::BroadcastIncompatibleShapes {
src_shape: self.shape().clone(),
dst_shape: shape,
}
.bt());
} else {
0
};
stride.push(s)
}
Ok(Self {
shape,
stride,
start_offset: self.start_offset,
})
}
pub(crate) fn strided_index(&self) -> crate::StridedIndex {
crate::StridedIndex::from_layout(self)
}
pub(crate) fn strided_blocks(&self) -> crate::StridedBlocks {
let mut block_len = 1;
let mut contiguous_dims = 0; // These are counted from the right.
for (&stride, &dim) in self.stride().iter().zip(self.dims().iter()).rev() {
if stride != block_len {
break;
}
block_len *= dim;
contiguous_dims += 1;
}
let index_dims = self.dims().len() - contiguous_dims;
if index_dims == 0 {
crate::StridedBlocks::SingleBlock {
start_offset: self.start_offset,
len: block_len,
}
} else {
let block_start_index = crate::StridedIndex::new(
&self.dims()[..index_dims],
&self.stride[..index_dims],
self.start_offset,
);
crate::StridedBlocks::MultipleBlocks {
block_start_index,
block_len,
}
}
}
// Returns the contiguous offsets with broadcast if applicable.
pub(crate) fn offsets_b(&self) -> Option<ContiguousOffsetsWithBroadcast> {
let mut left_broadcast = 1;
let mut right_broadcast = 1;
let strides = self.stride();
let dims = self.dims();
let mut start_cont = 0;
let mut end_cont = dims.len();
for (&s, &d) in strides.iter().zip(dims.iter()) {
if s != 0 {
break;
}
start_cont += 1;
left_broadcast *= d;
}
if start_cont == dims.len() {
return Some(ContiguousOffsetsWithBroadcast {
start: self.start_offset,
len: 1,
left_broadcast,
right_broadcast: 1,
});
}
for (&s, &d) in strides.iter().zip(dims.iter()).rev() {
if s != 0 {
break;
}
end_cont -= 1;
right_broadcast *= d;
}
// Check that the inner dims are contiguous
let strides = &strides[start_cont..end_cont];
let dims = &dims[start_cont..end_cont];
let mut len = 1;
for (&stride, &dim) in strides.iter().zip(dims.iter()).rev() {
if stride != len {
return None;
}
len *= dim;
}
Some(ContiguousOffsetsWithBroadcast {
start: self.start_offset,
len,
left_broadcast,
right_broadcast,
})
}
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ContiguousOffsetsWithBroadcast {
pub start: usize,
pub len: usize,
pub left_broadcast: usize,
pub right_broadcast: usize,
}
| 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 = a.matmul(&b)?;
//! # Ok(())}
//! ```
//!
//! ## Features
//!
//! - Simple syntax (looks and like PyTorch)
//! - CPU and Cuda backends (and M1 support)
//! - Enable serverless (CPU) small and fast deployments
//! - Model training
//! - Distributed computing (NCCL).
//! - Models out of the box (Llama, Whisper, Falcon, ...)
//!
//! ## FAQ
//!
//! - Why Candle?
//!
//! Candle stems from the need to reduce binary size in order to *enable serverless*
//! possible by making the whole engine smaller than PyTorch very large library volume
//!
//! And simply *removing Python* from production workloads.
//! Python can really add overhead in more complex workflows and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
//!
//! Rust is cool, and a lot of the HF ecosystem already has Rust crates [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers)
#[cfg(feature = "accelerate")]
mod accelerate;
pub mod backend;
pub mod backprop;
mod conv;
mod convert;
pub mod cpu;
pub mod cpu_backend;
#[cfg(feature = "cuda")]
pub mod cuda_backend;
#[cfg(feature = "cudnn")]
pub mod cudnn;
mod device;
pub mod display;
mod dtype;
mod dummy_cuda_backend;
mod dummy_metal_backend;
pub mod error;
mod indexer;
pub mod layout;
#[cfg(feature = "metal")]
pub mod metal_backend;
#[cfg(feature = "mkl")]
mod mkl;
pub mod npy;
mod op;
pub mod pickle;
pub mod quantized;
pub mod safetensors;
pub mod scalar;
pub mod shape;
mod storage;
mod strided_index;
mod tensor;
pub mod test_utils;
pub mod utils;
mod variable;
pub use cpu_backend::CpuStorage;
pub use device::{Device, DeviceLocation};
pub use dtype::{DType, FloatDType, IntDType, WithDType};
pub use error::{Error, Result};
pub use indexer::IndexOp;
pub use layout::Layout;
pub use op::{CustomOp1, CustomOp2, CustomOp3};
pub use shape::{Shape, D};
pub use storage::Storage;
pub use strided_index::{StridedBlocks, StridedIndex};
pub use tensor::{Tensor, TensorId};
pub use variable::Var;
#[cfg(feature = "cuda")]
pub use cuda_backend::{CudaDevice, CudaStorage};
#[cfg(not(feature = "cuda"))]
pub use dummy_cuda_backend::{CudaDevice, CudaStorage};
#[cfg(feature = "metal")]
pub use metal_backend::{MetalDevice, MetalError, MetalStorage};
#[cfg(not(feature = "metal"))]
pub use dummy_metal_backend::{MetalDevice, MetalError, MetalStorage};
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
pub trait ToUsize2 {
fn to_usize2(self) -> (usize, usize);
}
impl ToUsize2 for usize {
fn to_usize2(self) -> (usize, usize) {
(self, self)
}
}
impl ToUsize2 for (usize, usize) {
fn to_usize2(self) -> (usize, usize) {
self
}
}
// A simple trait defining a module with forward method using a single argument.
pub trait Module {
fn forward(&self, xs: &Tensor) -> Result<Tensor>;
}
impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
self(xs)
}
}
// A trait defining a module with forward method using a single tensor argument and a flag to
// separate the training and evaluation behaviors.
pub trait ModuleT {
fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor>;
}
impl<M: Module> ModuleT for M {
fn forward_t(&self, xs: &Tensor, _train: bool) -> Result<Tensor> {
self.forward(xs)
}
}
| 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_out: usize,
pub(crate) c_in: usize,
pub(crate) k_size: usize,
pub(crate) padding: usize,
pub(crate) stride: usize,
pub(crate) dilation: usize,
}
impl ParamsConv1D {
pub(crate) fn l_out(&self) -> usize {
(self.l_in + 2 * self.padding - self.dilation * (self.k_size - 1) - 1) / self.stride + 1
}
pub(crate) fn out_dims(&self) -> Vec<usize> {
let l_out = self.l_out();
vec![self.b_size, self.c_out, l_out]
}
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConvTranspose1D {
pub(crate) b_size: usize,
pub(crate) l_in: usize,
pub(crate) c_out: usize,
pub(crate) c_in: usize,
pub(crate) k_size: usize,
pub(crate) padding: usize,
pub(crate) output_padding: usize,
pub(crate) stride: usize,
pub(crate) dilation: usize,
}
impl ParamsConvTranspose1D {
pub(crate) fn l_out(&self) -> usize {
(self.l_in - 1) * self.stride - 2 * self.padding
+ self.dilation * (self.k_size - 1)
+ self.output_padding
+ 1
}
pub(crate) fn out_dims(&self) -> Vec<usize> {
let l_out = self.l_out();
vec![self.b_size, self.c_out, l_out]
}
}
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum CudnnFwdAlgo {
ImplicitGemm,
ImplicitPrecompGemm,
Gemm,
Direct,
Fft,
FftTiling,
Winograd,
WinogradNonFused,
Count,
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConv2D {
pub(crate) b_size: usize,
pub(crate) i_h: usize,
pub(crate) i_w: usize,
pub(crate) k_h: usize,
pub(crate) k_w: usize,
pub(crate) c_out: usize,
pub(crate) c_in: usize,
pub(crate) padding: usize,
pub(crate) stride: usize,
pub(crate) dilation: usize,
pub cudnn_fwd_algo: Option<CudnnFwdAlgo>,
}
impl ParamsConv2D {
pub(crate) fn out_h(&self) -> usize {
(self.i_h + 2 * self.padding - self.dilation * (self.k_h - 1) - 1) / self.stride + 1
}
pub(crate) fn out_w(&self) -> usize {
(self.i_w + 2 * self.padding - self.dilation * (self.k_w - 1) - 1) / self.stride + 1
}
pub(crate) fn out_dims(&self) -> Vec<usize> {
vec![self.b_size, self.c_out, self.out_h(), self.out_w()]
}
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConvTranspose2D {
pub(crate) b_size: usize,
pub(crate) i_h: usize,
pub(crate) i_w: usize,
pub(crate) k_h: usize,
pub(crate) k_w: usize,
pub(crate) c_out: usize,
pub(crate) c_in: usize,
pub(crate) padding: usize,
pub(crate) output_padding: usize,
pub(crate) stride: usize,
pub(crate) dilation: usize,
}
impl ParamsConvTranspose2D {
pub(crate) fn out_h(&self) -> usize {
(self.i_h - 1) * self.stride + self.dilation * (self.k_h - 1) + self.output_padding + 1
- 2 * self.padding
}
pub(crate) fn out_w(&self) -> usize {
(self.i_w - 1) * self.stride + self.dilation * (self.k_w - 1) + self.output_padding + 1
- 2 * self.padding
}
pub(crate) fn out_dims(&self) -> Vec<usize> {
vec![self.b_size, self.c_out, self.out_h(), self.out_w()]
}
}
impl Tensor {
fn conv1d_single_group(&self, kernel: &Self, params: &ParamsConv1D) -> Result<Self> {
let storage =
self.storage()
.conv1d(self.layout(), &kernel.storage(), kernel.layout(), params)?;
let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv1D {
arg,
kernel,
padding: params.padding,
stride: params.stride,
dilation: params.dilation,
});
let out_dims = params.out_dims();
Ok(crate::tensor::from_storage(storage, out_dims, op, false))
}
/// Applies a 1D convolution over the input tensor.
pub fn conv1d(
&self,
kernel: &Self,
padding: usize,
stride: usize,
dilation: usize,
groups: usize,
) -> Result<Self> {
let (c_out, c_in_k, k_size) = kernel.dims3()?;
let (b_size, c_in, l_in) = self.dims3()?;
if c_in != c_in_k * groups {
Err(Error::Conv1dInvalidArgs {
inp_shape: self.shape().clone(),
k_shape: kernel.shape().clone(),
padding,
stride,
msg: "the number of in-channels on the input doesn't match the kernel size",
}
.bt())?
}
let params = ParamsConv1D {
b_size,
l_in,
c_out: c_out / groups,
c_in: c_in / groups,
k_size,
padding,
stride,
dilation,
};
if groups == 1 {
self.conv1d_single_group(kernel, ¶ms)
} else {
let blocks = self.chunk(groups, 1)?;
let kernel = kernel.chunk(groups, 0)?;
let blocks = blocks
.iter()
.zip(&kernel)
.map(|(block, kernel)| block.conv1d_single_group(kernel, ¶ms))
.collect::<Result<Vec<_>>>()?;
Tensor::cat(&blocks, 1)
}
}
/// Applies a 1D transposed convolution over the input tensor.
pub fn conv_transpose1d(
&self,
kernel: &Self,
padding: usize,
output_padding: usize,
stride: usize,
dilation: usize,
) -> Result<Self> {
let (b_size, c_in, l_in) = self.dims3()?;
let (c_in_k, c_out, k_size) = kernel.dims3()?;
if c_in != c_in_k {
crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
}
let params = ParamsConvTranspose1D {
b_size,
l_in,
k_size,
c_out,
c_in,
padding,
output_padding,
stride,
dilation,
};
let storage = self.storage().conv_transpose1d(
self.layout(),
&kernel.storage(),
kernel.layout(),
¶ms,
)?;
let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::ConvTranspose1D {
arg,
kernel,
padding: params.padding,
output_padding: params.output_padding,
stride: params.stride,
dilation: params.dilation,
});
let out_dims = params.out_dims();
Ok(crate::tensor::from_storage(storage, out_dims, op, false))
}
fn conv2d_single_group(&self, kernel: &Self, params: &ParamsConv2D) -> Result<Self> {
let storage =
self.storage()
.conv2d(self.layout(), &kernel.storage(), kernel.layout(), params)?;
let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv2D {
arg,
kernel,
padding: params.padding,
stride: params.stride,
dilation: params.dilation,
});
let out_dims = params.out_dims();
Ok(crate::tensor::from_storage(storage, out_dims, op, false))
}
/// Applies a 2D convolution over the input tensor.
pub fn conv2d(
&self,
kernel: &Self,
padding: usize,
stride: usize,
dilation: usize,
groups: usize,
) -> Result<Self> {
let (b_size, c_in, i_h, i_w) = self.dims4()?;
let (c_out, c_in_k, k_h, k_w) = kernel.dims4()?;
if c_in != c_in_k * groups {
crate::bail!(
"in_channel mismatch between input ({c_in}, groups {groups}) and kernel ({c_in_k})"
)
}
let params = ParamsConv2D {
b_size,
i_h,
i_w,
k_h,
k_w,
c_out: c_out / groups,
c_in: c_in / groups,
padding,
stride,
dilation,
cudnn_fwd_algo: None,
};
if groups == 1 {
self.conv2d_single_group(kernel, ¶ms)
} else {
let blocks = self.chunk(groups, 1)?;
let kernel = kernel.chunk(groups, 0)?;
let blocks = blocks
.iter()
.zip(&kernel)
.map(|(block, kernel)| block.conv2d_single_group(kernel, ¶ms))
.collect::<Result<Vec<_>>>()?;
Tensor::cat(&blocks, 1)
}
}
/// Applies a 2D transposed convolution over the input tensor.
pub fn conv_transpose2d(
&self,
kernel: &Self,
padding: usize,
output_padding: usize,
stride: usize,
dilation: usize,
) -> Result<Self> {
let (b_size, c_in, i_h, i_w) = self.dims4()?;
let (c_in_k, c_out, k_h, k_w) = kernel.dims4()?;
if c_in != c_in_k {
crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
}
let params = ParamsConvTranspose2D {
b_size,
i_h,
i_w,
k_h,
k_w,
c_out,
c_in,
padding,
output_padding,
stride,
dilation,
};
let storage = self.storage().conv_transpose2d(
self.layout(),
&kernel.storage(),
kernel.layout(),
¶ms,
)?;
let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::ConvTranspose2D {
arg,
kernel,
padding: params.padding,
output_padding: params.output_padding,
stride: params.stride,
dilation: params.dilation,
});
let out_dims = params.out_dims();
Ok(crate::tensor::from_storage(storage, out_dims, op, false))
}
}
| 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::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "{:?}", &self.dims())
}
}
impl<const C: usize> From<&[usize; C]> for Shape {
fn from(dims: &[usize; C]) -> Self {
Self(dims.to_vec())
}
}
impl From<&[usize]> for Shape {
fn from(dims: &[usize]) -> Self {
Self(dims.to_vec())
}
}
impl From<&Shape> for Shape {
fn from(shape: &Shape) -> Self {
Self(shape.0.to_vec())
}
}
impl From<()> for Shape {
fn from(_: ()) -> Self {
Self(vec![])
}
}
impl From<usize> for Shape {
fn from(d1: usize) -> Self {
Self(vec![d1])
}
}
impl From<(usize,)> for Shape {
fn from(d1: (usize,)) -> Self {
Self(vec![d1.0])
}
}
impl From<(usize, usize)> for Shape {
fn from(d12: (usize, usize)) -> Self {
Self(vec![d12.0, d12.1])
}
}
impl From<(usize, usize, usize)> for Shape {
fn from(d123: (usize, usize, usize)) -> Self {
Self(vec![d123.0, d123.1, d123.2])
}
}
impl From<(usize, usize, usize, usize)> for Shape {
fn from(d1234: (usize, usize, usize, usize)) -> Self {
Self(vec![d1234.0, d1234.1, d1234.2, d1234.3])
}
}
impl From<(usize, usize, usize, usize, usize)> for Shape {
fn from(d12345: (usize, usize, usize, usize, usize)) -> Self {
Self(vec![d12345.0, d12345.1, d12345.2, d12345.3, d12345.4])
}
}
impl From<(usize, usize, usize, usize, usize, usize)> for Shape {
fn from(d123456: (usize, usize, usize, usize, usize, usize)) -> Self {
Self(vec![
d123456.0, d123456.1, d123456.2, d123456.3, d123456.4, d123456.5,
])
}
}
impl From<Vec<usize>> for Shape {
fn from(dims: Vec<usize>) -> Self {
Self(dims)
}
}
macro_rules! extract_dims {
($fn_name:ident, $cnt:tt, $dims:expr, $out_type:ty) => {
pub fn $fn_name(dims: &[usize]) -> Result<$out_type> {
if dims.len() != $cnt {
Err(Error::UnexpectedNumberOfDims {
expected: $cnt,
got: dims.len(),
shape: Shape::from(dims),
}
.bt())
} else {
Ok($dims(dims))
}
}
impl Shape {
pub fn $fn_name(&self) -> Result<$out_type> {
$fn_name(self.0.as_slice())
}
}
impl crate::Tensor {
pub fn $fn_name(&self) -> Result<$out_type> {
self.shape().$fn_name()
}
}
impl std::convert::TryInto<$out_type> for Shape {
type Error = crate::Error;
fn try_into(self) -> std::result::Result<$out_type, Self::Error> {
self.$fn_name()
}
}
};
}
impl Shape {
pub fn from_dims(dims: &[usize]) -> Self {
Self(dims.to_vec())
}
/// The rank is the number of dimensions, 0 for a scalar value, 1 for a vector, etc.
pub fn rank(&self) -> usize {
self.0.len()
}
pub fn into_dims(self) -> Vec<usize> {
self.0
}
/// The dimensions as a slice of `usize`.
pub fn dims(&self) -> &[usize] {
&self.0
}
/// The total number of elements, this is the product of all dimension sizes.
pub fn elem_count(&self) -> usize {
self.0.iter().product()
}
/// The strides given in number of elements for a contiguous n-dimensional
/// arrays using this shape.
pub(crate) fn stride_contiguous(&self) -> Vec<usize> {
let mut stride: Vec<_> = self
.0
.iter()
.rev()
.scan(1, |prod, u| {
let prod_pre_mult = *prod;
*prod *= u;
Some(prod_pre_mult)
})
.collect();
stride.reverse();
stride
}
/// Returns true if the strides are C contiguous (aka row major).
pub fn is_contiguous(&self, stride: &[usize]) -> bool {
if self.0.len() != stride.len() {
return false;
}
let mut acc = 1;
for (&stride, &dim) in stride.iter().zip(self.0.iter()).rev() {
if stride != acc {
return false;
}
acc *= dim;
}
true
}
/// Returns true if the strides are Fortran contiguous (aka column major).
pub fn is_fortran_contiguous(&self, stride: &[usize]) -> bool {
if self.0.len() != stride.len() {
return false;
}
let mut acc = 1;
for (&stride, &dim) in stride.iter().zip(self.0.iter()) {
if stride != acc {
return false;
}
acc *= dim;
}
true
}
/// Modifies the shape by adding a list of additional dimensions at the end of the existing
/// dimensions.
pub fn extend(mut self, additional_dims: &[usize]) -> Self {
self.0.extend(additional_dims);
self
}
/// Check whether the two shapes are compatible for broadcast, and if it is the case return the
/// broadcasted shape. This is to be used for binary pointwise ops.
pub fn broadcast_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<Shape> {
let lhs = self;
let lhs_dims = lhs.dims();
let rhs_dims = rhs.dims();
let lhs_ndims = lhs_dims.len();
let rhs_ndims = rhs_dims.len();
let bcast_ndims = usize::max(lhs_ndims, rhs_ndims);
let mut bcast_dims = vec![0; bcast_ndims];
for (idx, bcast_value) in bcast_dims.iter_mut().enumerate() {
let rev_idx = bcast_ndims - idx;
let l_value = if lhs_ndims < rev_idx {
1
} else {
lhs_dims[lhs_ndims - rev_idx]
};
let r_value = if rhs_ndims < rev_idx {
1
} else {
rhs_dims[rhs_ndims - rev_idx]
};
*bcast_value = if l_value == r_value {
l_value
} else if l_value == 1 {
r_value
} else if r_value == 1 {
l_value
} else {
Err(Error::ShapeMismatchBinaryOp {
lhs: lhs.clone(),
rhs: rhs.clone(),
op,
}
.bt())?
}
}
Ok(Shape::from(bcast_dims))
}
pub(crate) fn broadcast_shape_matmul(&self, rhs: &Self) -> Result<(Shape, Shape)> {
let lhs = self;
let lhs_dims = lhs.dims();
let rhs_dims = rhs.dims();
if lhs_dims.len() < 2 || rhs_dims.len() < 2 {
crate::bail!("only 2d matrixes are supported {lhs:?} {rhs:?}")
}
let (m, lhs_k) = (lhs_dims[lhs_dims.len() - 2], lhs_dims[lhs_dims.len() - 1]);
let (rhs_k, n) = (rhs_dims[rhs_dims.len() - 2], rhs_dims[rhs_dims.len() - 1]);
if lhs_k != rhs_k {
crate::bail!("different inner dimensions in broadcast matmul {lhs:?} {rhs:?}")
}
let lhs_b = Self::from(&lhs_dims[..lhs_dims.len() - 2]);
let rhs_b = Self::from(&rhs_dims[..rhs_dims.len() - 2]);
let bcast = lhs_b.broadcast_shape_binary_op(&rhs_b, "broadcast_matmul")?;
let bcast_dims = bcast.dims();
let bcast_lhs = [bcast_dims, &[m, lhs_k]].concat();
let bcast_rhs = [bcast_dims, &[rhs_k, n]].concat();
Ok((Shape::from(bcast_lhs), Shape::from(bcast_rhs)))
}
}
pub trait Dim {
fn to_index(&self, shape: &Shape, op: &'static str) -> Result<usize>;
fn to_index_plus_one(&self, shape: &Shape, op: &'static str) -> Result<usize>;
}
impl Dim for usize {
fn to_index(&self, shape: &Shape, op: &'static str) -> Result<usize> {
let dim = *self;
if dim >= shape.dims().len() {
Err(Error::DimOutOfRange {
shape: shape.clone(),
dim: dim as i32,
op,
}
.bt())?
} else {
Ok(dim)
}
}
fn to_index_plus_one(&self, shape: &Shape, op: &'static str) -> Result<usize> {
let dim = *self;
if dim > shape.dims().len() {
Err(Error::DimOutOfRange {
shape: shape.clone(),
dim: dim as i32,
op,
}
.bt())?
} else {
Ok(dim)
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
pub enum D {
Minus1,
Minus2,
}
impl D {
fn out_of_range(&self, shape: &Shape, op: &'static str) -> Error {
let dim = match self {
Self::Minus1 => -1,
Self::Minus2 => -2,
};
Error::DimOutOfRange {
shape: shape.clone(),
dim,
op,
}
.bt()
}
}
impl Dim for D {
fn to_index(&self, shape: &Shape, op: &'static str) -> Result<usize> {
let rank = shape.rank();
match self {
Self::Minus1 if rank >= 1 => Ok(rank - 1),
Self::Minus2 if rank >= 2 => Ok(rank - 2),
_ => Err(self.out_of_range(shape, op)),
}
}
fn to_index_plus_one(&self, shape: &Shape, op: &'static str) -> Result<usize> {
let rank = shape.rank();
match self {
Self::Minus1 => Ok(rank),
Self::Minus2 if rank >= 1 => Ok(rank - 1),
_ => Err(self.out_of_range(shape, op)),
}
}
}
pub trait Dims: Sized {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>>;
fn to_indexes(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let dims = self.to_indexes_internal(shape, op)?;
for (i, &dim) in dims.iter().enumerate() {
if dims[..i].contains(&dim) {
Err(Error::DuplicateDimIndex {
shape: shape.clone(),
dims: dims.clone(),
op,
}
.bt())?
}
if dim >= shape.rank() {
Err(Error::DimOutOfRange {
shape: shape.clone(),
dim: dim as i32,
op,
}
.bt())?
}
}
Ok(dims)
}
}
impl Dims for Vec<usize> {
fn to_indexes_internal(self, _: &Shape, _: &'static str) -> Result<Vec<usize>> {
Ok(self)
}
}
impl<const N: usize> Dims for [usize; N] {
fn to_indexes_internal(self, _: &Shape, _: &'static str) -> Result<Vec<usize>> {
Ok(self.to_vec())
}
}
impl Dims for &[usize] {
fn to_indexes_internal(self, _: &Shape, _: &'static str) -> Result<Vec<usize>> {
Ok(self.to_vec())
}
}
impl Dims for () {
fn to_indexes_internal(self, _: &Shape, _: &'static str) -> Result<Vec<usize>> {
Ok(vec![])
}
}
impl<D: Dim + Sized> Dims for D {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let dim = self.to_index(shape, op)?;
Ok(vec![dim])
}
}
impl<D: Dim> Dims for (D,) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let dim = self.0.to_index(shape, op)?;
Ok(vec![dim])
}
}
impl<D1: Dim, D2: Dim> Dims for (D1, D2) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let d0 = self.0.to_index(shape, op)?;
let d1 = self.1.to_index(shape, op)?;
Ok(vec![d0, d1])
}
}
impl<D1: Dim, D2: Dim, D3: Dim> Dims for (D1, D2, D3) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let d0 = self.0.to_index(shape, op)?;
let d1 = self.1.to_index(shape, op)?;
let d2 = self.2.to_index(shape, op)?;
Ok(vec![d0, d1, d2])
}
}
impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim> Dims for (D1, D2, D3, D4) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let d0 = self.0.to_index(shape, op)?;
let d1 = self.1.to_index(shape, op)?;
let d2 = self.2.to_index(shape, op)?;
let d3 = self.3.to_index(shape, op)?;
Ok(vec![d0, d1, d2, d3])
}
}
impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim> Dims for (D1, D2, D3, D4, D5) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let d0 = self.0.to_index(shape, op)?;
let d1 = self.1.to_index(shape, op)?;
let d2 = self.2.to_index(shape, op)?;
let d3 = self.3.to_index(shape, op)?;
let d4 = self.4.to_index(shape, op)?;
Ok(vec![d0, d1, d2, d3, d4])
}
}
impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim, D6: Dim> Dims for (D1, D2, D3, D4, D5, D6) {
fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
let d0 = self.0.to_index(shape, op)?;
let d1 = self.1.to_index(shape, op)?;
let d2 = self.2.to_index(shape, op)?;
let d3 = self.3.to_index(shape, op)?;
let d4 = self.4.to_index(shape, op)?;
let d5 = self.5.to_index(shape, op)?;
Ok(vec![d0, d1, d2, d3, d4, d5])
}
}
extract_dims!(dims0, 0, |_: &[usize]| (), ());
extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
extract_dims!(
dims3,
3,
|d: &[usize]| (d[0], d[1], d[2]),
(usize, usize, usize)
);
extract_dims!(
dims4,
4,
|d: &[usize]| (d[0], d[1], d[2], d[3]),
(usize, usize, usize, usize)
);
extract_dims!(
dims5,
5,
|d: &[usize]| (d[0], d[1], d[2], d[3], d[4]),
(usize, usize, usize, usize, usize)
);
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn stride() {
let shape = Shape::from(());
assert_eq!(shape.stride_contiguous(), Vec::<usize>::new());
let shape = Shape::from(42);
assert_eq!(shape.stride_contiguous(), [1]);
let shape = Shape::from((42, 1337));
assert_eq!(shape.stride_contiguous(), [1337, 1]);
let shape = Shape::from((299, 792, 458));
assert_eq!(shape.stride_contiguous(), [458 * 792, 458, 1]);
}
}
pub trait ShapeWithOneHole {
fn into_shape(self, el_count: usize) -> Result<Shape>;
}
impl<S: Into<Shape>> ShapeWithOneHole for S {
fn into_shape(self, _el_count: usize) -> Result<Shape> {
Ok(self.into())
}
}
impl ShapeWithOneHole for ((),) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
Ok(el_count.into())
}
}
fn hole_size(el_count: usize, prod_d: usize, s: &dyn std::fmt::Debug) -> Result<usize> {
if prod_d == 0 {
crate::bail!("cannot reshape tensor of {el_count} elements to {s:?}")
}
if el_count % prod_d != 0 {
crate::bail!("cannot reshape tensor with {el_count} elements to {s:?}")
}
Ok(el_count / prod_d)
}
impl ShapeWithOneHole for ((), usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let ((), d1) = self;
Ok((hole_size(el_count, d1, &self)?, d1).into())
}
}
impl ShapeWithOneHole for (usize, ()) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, ()) = self;
Ok((d1, hole_size(el_count, d1, &self)?).into())
}
}
impl ShapeWithOneHole for ((), usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let ((), d1, d2) = self;
Ok((hole_size(el_count, d1 * d2, &self)?, d1, d2).into())
}
}
impl ShapeWithOneHole for (usize, (), usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, (), d2) = self;
Ok((d1, hole_size(el_count, d1 * d2, &self)?, d2).into())
}
}
impl ShapeWithOneHole for (usize, usize, ()) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, ()) = self;
Ok((d1, d2, hole_size(el_count, d1 * d2, &self)?).into())
}
}
impl ShapeWithOneHole for ((), usize, usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let ((), d1, d2, d3) = self;
let d = hole_size(el_count, d1 * d2 * d3, &self)?;
Ok((d, d1, d2, d3).into())
}
}
impl ShapeWithOneHole for (usize, (), usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, (), d2, d3) = self;
let d = hole_size(el_count, d1 * d2 * d3, &self)?;
Ok((d1, d, d2, d3).into())
}
}
impl ShapeWithOneHole for (usize, usize, (), usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, (), d3) = self;
let d = hole_size(el_count, d1 * d2 * d3, &self)?;
Ok((d1, d2, d, d3).into())
}
}
impl ShapeWithOneHole for (usize, usize, usize, ()) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, d3, ()) = self;
let d = hole_size(el_count, d1 * d2 * d3, &self)?;
Ok((d1, d2, d3, d).into())
}
}
impl ShapeWithOneHole for ((), usize, usize, usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let ((), d1, d2, d3, d4) = self;
let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
Ok((d, d1, d2, d3, d4).into())
}
}
impl ShapeWithOneHole for (usize, (), usize, usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, (), d2, d3, d4) = self;
let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
Ok((d1, d, d2, d3, d4).into())
}
}
impl ShapeWithOneHole for (usize, usize, (), usize, usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, (), d3, d4) = self;
let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
Ok((d1, d2, d, d3, d4).into())
}
}
impl ShapeWithOneHole for (usize, usize, usize, (), usize) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, d3, (), d4) = self;
let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
Ok((d1, d2, d3, d, d4).into())
}
}
impl ShapeWithOneHole for (usize, usize, usize, usize, ()) {
fn into_shape(self, el_count: usize) -> Result<Shape> {
let (d1, d2, d3, d4, ()) = self;
let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
Ok((d1, d2, d3, d4, d).into())
}
}
| 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 should return a Cow instead so that no copy is done on the cpu case.
fn to_cpu_storage(&self) -> Result<CpuStorage>;
fn affine(&self, _: &Layout, _: f64, _: f64) -> Result<Self>;
fn powf(&self, _: &Layout, _: f64) -> Result<Self>;
fn elu(&self, _: &Layout, _: f64) -> Result<Self>;
fn reduce_op(&self, _: ReduceOp, _: &Layout, _: &[usize]) -> Result<Self>;
fn cmp(&self, _: CmpOp, _: &Self, _: &Layout, _: &Layout) -> Result<Self>;
fn to_dtype(&self, _: &Layout, _: DType) -> Result<Self>;
fn unary_impl<B: UnaryOpT>(&self, _: &Layout) -> Result<Self>;
fn binary_impl<B: BinaryOpT>(&self, _: &Self, _: &Layout, _: &Layout) -> Result<Self>;
fn where_cond(&self, _: &Layout, _: &Self, _: &Layout, _: &Self, _: &Layout) -> Result<Self>;
fn conv1d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConv1D,
) -> Result<Self>;
fn conv_transpose1d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self>;
fn conv2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConv2D,
) -> Result<Self>;
fn conv_transpose2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self>;
fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self>;
fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self>;
fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self>;
fn scatter_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self>;
fn index_select(&self, _: &Self, _: &Layout, _: &Layout, _: usize) -> Result<Self>;
fn index_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self>;
fn matmul(
&self,
_: &Self,
_: (usize, usize, usize, usize),
_: &Layout,
_: &Layout,
) -> Result<Self>;
fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()>;
}
pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
type Storage: BackendStorage;
// TODO: Make the usize generic and part of a generic DeviceLocation.
fn new(_: usize) -> Result<Self>;
fn location(&self) -> crate::DeviceLocation;
fn same_device(&self, _: &Self) -> bool;
fn zeros_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage>;
fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage>;
fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage>;
fn set_seed(&self, _: u64) -> Result<()>;
}
| 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 std::collections::HashMap;
use std::io::BufRead;
const VERBOSE: bool = false;
// https://docs.juliahub.com/Pickle/LAUNc/0.1.0/opcode/
#[repr(u8)]
#[derive(Debug, Eq, PartialEq, Clone)]
pub enum OpCode {
// https://github.com/python/cpython/blob/ed25f097160b5cbb0c9a1f9a746d2f1bbc96515a/Lib/pickletools.py#L2123
Proto = 0x80,
Global = b'c',
BinPut = b'q',
LongBinPut = b'r',
EmptyTuple = b')',
Reduce = b'R',
Mark = b'(',
BinUnicode = b'X',
BinInt = b'J',
Tuple = b't',
BinPersId = b'Q',
BinInt1 = b'K',
BinInt2 = b'M',
Tuple1 = 0x85,
Tuple2 = 0x86,
Tuple3 = 0x87,
NewTrue = 0x88,
NewFalse = 0x89,
None = b'N',
BinGet = b'h',
LongBinGet = b'j',
SetItem = b's',
SetItems = b'u',
EmptyDict = b'}',
Dict = b'd',
Build = b'b',
Stop = b'.',
NewObj = 0x81,
EmptyList = b']',
BinFloat = b'g',
Append = b'a',
Appends = b'e',
}
// Avoid using FromPrimitive so as not to drag another dependency.
impl TryFrom<u8> for OpCode {
type Error = u8;
fn try_from(value: u8) -> std::result::Result<Self, Self::Error> {
match value {
0x80 => Ok(Self::Proto),
b'c' => Ok(Self::Global),
b'q' => Ok(Self::BinPut),
b'r' => Ok(Self::LongBinPut),
b')' => Ok(Self::EmptyTuple),
b'R' => Ok(Self::Reduce),
b'(' => Ok(Self::Mark),
b'X' => Ok(Self::BinUnicode),
b'J' => Ok(Self::BinInt),
b't' => Ok(Self::Tuple),
b'Q' => Ok(Self::BinPersId),
b'K' => Ok(Self::BinInt1),
b'M' => Ok(Self::BinInt2),
b'N' => Ok(Self::None),
0x85 => Ok(Self::Tuple1),
0x86 => Ok(Self::Tuple2),
0x87 => Ok(Self::Tuple3),
0x88 => Ok(Self::NewTrue),
0x89 => Ok(Self::NewFalse),
b'h' => Ok(Self::BinGet),
b'j' => Ok(Self::LongBinGet),
b's' => Ok(Self::SetItem),
b'u' => Ok(Self::SetItems),
b'}' => Ok(Self::EmptyDict),
b'd' => Ok(Self::EmptyDict),
b'b' => Ok(Self::Build),
b'.' => Ok(Self::Stop),
0x81 => Ok(Self::NewObj),
b']' => Ok(Self::EmptyList),
b'G' => Ok(Self::BinFloat),
b'a' => Ok(Self::Append),
b'e' => Ok(Self::Appends),
value => Err(value),
}
}
}
fn read_to_newline<R: BufRead>(r: &mut R) -> Result<Vec<u8>> {
let mut data: Vec<u8> = Vec::with_capacity(32);
r.read_until(b'\n', &mut data)?;
data.pop();
if data.last() == Some(&b'\r') {
data.pop();
}
Ok(data)
}
#[derive(Debug, Clone, PartialEq)]
pub enum Object {
Class {
module_name: String,
class_name: String,
},
Int(i32),
Float(f64),
Unicode(String),
Bool(bool),
None,
Tuple(Vec<Object>),
List(Vec<Object>),
Mark,
Dict(Vec<(Object, Object)>),
Reduce {
callable: Box<Object>,
args: Box<Object>,
},
Build {
callable: Box<Object>,
args: Box<Object>,
},
PersistentLoad(Box<Object>),
}
type OResult<T> = std::result::Result<T, Object>;
impl Object {
pub fn unicode(self) -> OResult<String> {
match self {
Self::Unicode(t) => Ok(t),
_ => Err(self),
}
}
pub fn reduce(self) -> OResult<(Self, Self)> {
match self {
Self::Reduce { callable, args } => Ok((*callable, *args)),
_ => Err(self),
}
}
pub fn none(self) -> OResult<()> {
match self {
Self::None => Ok(()),
_ => Err(self),
}
}
pub fn persistent_load(self) -> OResult<Self> {
match self {
Self::PersistentLoad(t) => Ok(*t),
_ => Err(self),
}
}
pub fn bool(self) -> OResult<bool> {
match self {
Self::Bool(t) => Ok(t),
_ => Err(self),
}
}
pub fn int(self) -> OResult<i32> {
match self {
Self::Int(t) => Ok(t),
_ => Err(self),
}
}
pub fn tuple(self) -> OResult<Vec<Self>> {
match self {
Self::Tuple(t) => Ok(t),
_ => Err(self),
}
}
pub fn dict(self) -> OResult<Vec<(Self, Self)>> {
match self {
Self::Dict(t) => Ok(t),
_ => Err(self),
}
}
pub fn class(self) -> OResult<(String, String)> {
match self {
Self::Class {
module_name,
class_name,
} => Ok((module_name, class_name)),
_ => Err(self),
}
}
pub fn into_tensor_info(
self,
name: Self,
dir_name: &std::path::Path,
) -> Result<Option<TensorInfo>> {
let name = match name.unicode() {
Ok(name) => name,
Err(_) => return Ok(None),
};
let (callable, args) = match self.reduce() {
Ok(callable_args) => callable_args,
_ => return Ok(None),
};
let (callable, args) = match callable {
Object::Class {
module_name,
class_name,
} if module_name == "torch._tensor" && class_name == "_rebuild_from_type_v2" => {
let mut args = args.tuple()?;
let callable = args.remove(0);
let args = args.remove(1);
(callable, args)
}
_ => (callable, args),
};
match callable {
Object::Class {
module_name,
class_name,
} if module_name == "torch._utils" && class_name == "_rebuild_tensor_v2" => {}
_ => return Ok(None),
};
let (layout, dtype, file_path, storage_size) = rebuild_args(args)?;
let mut path = dir_name.to_path_buf();
path.push(file_path);
Ok(Some(TensorInfo {
name,
dtype,
layout,
path: path.to_string_lossy().into_owned(),
storage_size,
}))
}
}
impl TryFrom<Object> for String {
type Error = Object;
fn try_from(value: Object) -> std::result::Result<Self, Self::Error> {
match value {
Object::Unicode(s) => Ok(s),
other => Err(other),
}
}
}
impl TryFrom<Object> for usize {
type Error = Object;
fn try_from(value: Object) -> std::result::Result<Self, Self::Error> {
match value {
Object::Int(s) if s >= 0 => Ok(s as usize),
other => Err(other),
}
}
}
impl<T: TryFrom<Object, Error = Object>> TryFrom<Object> for Vec<T> {
type Error = Object;
fn try_from(value: Object) -> std::result::Result<Self, Self::Error> {
match value {
Object::Tuple(values) => {
// This does not return the appropriate value in the error case but instead return
// the object related to the first error.
values
.into_iter()
.map(|v| T::try_from(v))
.collect::<std::result::Result<Vec<T>, Self::Error>>()
}
other => Err(other),
}
}
}
#[derive(Debug)]
pub struct Stack {
stack: Vec<Object>,
memo: HashMap<u32, Object>,
}
impl Stack {
pub fn empty() -> Self {
Self {
stack: Vec::with_capacity(512),
memo: HashMap::new(),
}
}
pub fn stack(&self) -> &[Object] {
self.stack.as_slice()
}
pub fn read_loop<R: BufRead>(&mut self, r: &mut R) -> Result<()> {
loop {
if self.read(r)? {
break;
}
}
Ok(())
}
pub fn finalize(mut self) -> Result<Object> {
self.pop()
}
fn push(&mut self, obj: Object) {
self.stack.push(obj)
}
fn pop(&mut self) -> Result<Object> {
match self.stack.pop() {
None => crate::bail!("unexpected empty stack"),
Some(obj) => Ok(obj),
}
}
// https://docs.juliahub.com/Pickle/LAUNc/0.1.0/opcode/#Pickle.OpCodes.BUILD
fn build(&mut self) -> Result<()> {
let args = self.pop()?;
let obj = self.pop()?;
let obj = match (obj, args) {
(Object::Dict(mut obj), Object::Dict(mut args)) => {
obj.append(&mut args);
Object::Dict(obj)
}
(obj, args) => Object::Build {
callable: Box::new(obj),
args: Box::new(args),
},
};
self.push(obj);
Ok(())
}
fn reduce(&mut self) -> Result<()> {
let args = self.pop()?;
let callable = self.pop()?;
#[allow(clippy::single_match)]
let reduced = match &callable {
Object::Class {
module_name,
class_name,
} => {
if module_name == "collections" && class_name == "OrderedDict" {
// TODO: have a separate ordered dict.
Some(Object::Dict(vec![]))
} else {
None
}
}
_ => None,
};
let reduced = reduced.unwrap_or_else(|| Object::Reduce {
callable: Box::new(callable),
args: Box::new(args),
});
self.push(reduced);
Ok(())
}
fn last(&mut self) -> Result<&mut Object> {
match self.stack.last_mut() {
None => crate::bail!("unexpected empty stack"),
Some(obj) => Ok(obj),
}
}
fn memo_get(&self, id: u32) -> Result<Object> {
match self.memo.get(&id) {
None => crate::bail!("missing object in memo {id}"),
Some(obj) => {
// Maybe we should use refcounting rather than doing potential large clones here.
Ok(obj.clone())
}
}
}
fn memo_put(&mut self, id: u32) -> Result<()> {
let obj = self.last()?.clone();
self.memo.insert(id, obj);
Ok(())
}
fn persistent_load(&self, id: Object) -> Result<Object> {
Ok(Object::PersistentLoad(Box::new(id)))
}
fn new_obj(&self, class: Object, args: Object) -> Result<Object> {
Ok(Object::Reduce {
callable: Box::new(class),
args: Box::new(args),
})
}
fn pop_to_marker(&mut self) -> Result<Vec<Object>> {
let mut mark_idx = None;
for (idx, obj) in self.stack.iter().enumerate().rev() {
if obj == &Object::Mark {
mark_idx = Some(idx);
break;
}
}
match mark_idx {
Some(mark_idx) => {
let objs = self.stack.split_off(mark_idx + 1);
self.stack.pop();
Ok(objs)
}
None => {
crate::bail!("marker object not found")
}
}
}
pub fn read<R: BufRead>(&mut self, r: &mut R) -> Result<bool> {
let op_code = match OpCode::try_from(r.read_u8()?) {
Ok(op_code) => op_code,
Err(op_code) => {
crate::bail!("unknown op-code {op_code}")
}
};
// println!("op: {op_code:?}");
// println!("{:?}", self.stack);
match op_code {
OpCode::Proto => {
let version = r.read_u8()?;
if VERBOSE {
println!("proto {version}");
}
}
OpCode::Global => {
let module_name = read_to_newline(r)?;
let class_name = read_to_newline(r)?;
let module_name = String::from_utf8_lossy(&module_name).to_string();
let class_name = String::from_utf8_lossy(&class_name).to_string();
self.push(Object::Class {
module_name,
class_name,
})
}
OpCode::BinInt1 => {
let arg = r.read_u8()?;
self.push(Object::Int(arg as i32))
}
OpCode::BinInt2 => {
let arg = r.read_u16::<LittleEndian>()?;
self.push(Object::Int(arg as i32))
}
OpCode::BinInt => {
let arg = r.read_i32::<LittleEndian>()?;
self.push(Object::Int(arg))
}
OpCode::BinFloat => {
let arg = r.read_f64::<LittleEndian>()?;
self.push(Object::Float(arg))
}
OpCode::BinUnicode => {
let len = r.read_u32::<LittleEndian>()?;
let mut data = vec![0u8; len as usize];
r.read_exact(&mut data)?;
let data = String::from_utf8(data).map_err(E::wrap)?;
self.push(Object::Unicode(data))
}
OpCode::BinPersId => {
let id = self.pop()?;
let obj = self.persistent_load(id)?;
self.push(obj)
}
OpCode::Tuple => {
let objs = self.pop_to_marker()?;
self.push(Object::Tuple(objs))
}
OpCode::Tuple1 => {
let obj = self.pop()?;
self.push(Object::Tuple(vec![obj]))
}
OpCode::Tuple2 => {
let obj2 = self.pop()?;
let obj1 = self.pop()?;
self.push(Object::Tuple(vec![obj1, obj2]))
}
OpCode::Tuple3 => {
let obj3 = self.pop()?;
let obj2 = self.pop()?;
let obj1 = self.pop()?;
self.push(Object::Tuple(vec![obj1, obj2, obj3]))
}
OpCode::NewTrue => self.push(Object::Bool(true)),
OpCode::NewFalse => self.push(Object::Bool(false)),
OpCode::Append => {
let value = self.pop()?;
let pylist = self.last()?;
if let Object::List(d) = pylist {
d.push(value)
} else {
crate::bail!("expected a list, got {pylist:?}")
}
}
OpCode::Appends => {
let objs = self.pop_to_marker()?;
let pylist = self.last()?;
if let Object::List(d) = pylist {
d.extend(objs)
} else {
crate::bail!("expected a list, got {pylist:?}")
}
}
OpCode::SetItem => {
let value = self.pop()?;
let key = self.pop()?;
let pydict = self.last()?;
if let Object::Dict(d) = pydict {
d.push((key, value))
} else {
crate::bail!("expected a dict, got {pydict:?}")
}
}
OpCode::SetItems => {
let mut objs = self.pop_to_marker()?;
let pydict = self.last()?;
if let Object::Dict(d) = pydict {
if objs.len() % 2 != 0 {
crate::bail!("setitems: not an even number of objects")
}
while let Some(value) = objs.pop() {
let key = objs.pop().unwrap();
d.push((key, value))
}
} else {
crate::bail!("expected a dict, got {pydict:?}")
}
}
OpCode::None => self.push(Object::None),
OpCode::Stop => {
return Ok(true);
}
OpCode::Build => self.build()?,
OpCode::EmptyDict => self.push(Object::Dict(vec![])),
OpCode::Dict => {
let mut objs = self.pop_to_marker()?;
let mut pydict = vec![];
if objs.len() % 2 != 0 {
crate::bail!("setitems: not an even number of objects")
}
while let Some(value) = objs.pop() {
let key = objs.pop().unwrap();
pydict.push((key, value))
}
self.push(Object::Dict(pydict))
}
OpCode::Mark => self.push(Object::Mark),
OpCode::Reduce => self.reduce()?,
OpCode::EmptyTuple => self.push(Object::Tuple(vec![])),
OpCode::EmptyList => self.push(Object::List(vec![])),
OpCode::BinGet => {
let arg = r.read_u8()?;
let obj = self.memo_get(arg as u32)?;
self.push(obj)
}
OpCode::LongBinGet => {
let arg = r.read_u32::<LittleEndian>()?;
let obj = self.memo_get(arg)?;
self.push(obj)
}
OpCode::BinPut => {
let arg = r.read_u8()?;
self.memo_put(arg as u32)?
}
OpCode::LongBinPut => {
let arg = r.read_u32::<LittleEndian>()?;
self.memo_put(arg)?
}
OpCode::NewObj => {
let args = self.pop()?;
let class = self.pop()?;
let obj = self.new_obj(class, args)?;
self.push(obj)
}
}
Ok(false)
}
}
impl From<Object> for E {
fn from(value: Object) -> Self {
E::Msg(format!("conversion error on {value:?}"))
}
}
// https://github.com/pytorch/pytorch/blob/4eac43d046ded0f0a5a5fa8db03eb40f45bf656e/torch/_utils.py#L198
// Arguments: storage, storage_offset, size, stride, requires_grad, backward_hooks
fn rebuild_args(args: Object) -> Result<(Layout, DType, String, usize)> {
let mut args = args.tuple()?;
let stride = Vec::<usize>::try_from(args.remove(3))?;
let size = Vec::<usize>::try_from(args.remove(2))?;
let offset = args.remove(1).int()? as usize;
let storage = args.remove(0).persistent_load()?;
let mut storage = storage.tuple()?;
let storage_size = storage.remove(4).int()? as usize;
let path = storage.remove(2).unicode()?;
let (_module_name, class_name) = storage.remove(1).class()?;
let dtype = match class_name.as_str() {
"FloatStorage" => DType::F32,
"DoubleStorage" => DType::F64,
"HalfStorage" => DType::F16,
"BFloat16Storage" => DType::BF16,
"ByteStorage" => DType::U8,
"LongStorage" => DType::I64,
other => {
crate::bail!("unsupported storage type {other}")
}
};
let layout = Layout::new(crate::Shape::from(size), stride, offset);
Ok((layout, dtype, path, storage_size))
}
#[derive(Debug, Clone)]
pub struct TensorInfo {
pub name: String,
pub dtype: DType,
pub layout: Layout,
pub path: String,
pub storage_size: usize,
}
pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
file: P,
verbose: bool,
) -> Result<Vec<TensorInfo>> {
let file = std::fs::File::open(file)?;
let zip_reader = std::io::BufReader::new(file);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let zip_file_names = zip
.file_names()
.map(|f| f.to_string())
.collect::<Vec<String>>();
let mut tensor_infos = vec![];
for file_name in zip_file_names.iter() {
if !file_name.ends_with("data.pkl") {
continue;
}
let dir_name = std::path::PathBuf::from(file_name.strip_suffix(".pkl").unwrap());
let reader = zip.by_name(file_name)?;
let mut reader = std::io::BufReader::new(reader);
let mut stack = Stack::empty();
stack.read_loop(&mut reader)?;
let obj = stack.finalize()?;
if VERBOSE || verbose {
println!("{obj:?}");
}
let obj = match obj {
Object::Build { callable, args } => match *callable {
Object::Reduce { callable, args: _ } => match *callable {
Object::Class {
module_name,
class_name,
} if module_name == "__torch__" && class_name == "Module" => *args,
_ => continue,
},
_ => continue,
},
obj => obj,
};
if let Object::Dict(key_values) = obj {
for (name, value) in key_values.into_iter() {
match value.into_tensor_info(name, &dir_name) {
Ok(Some(tensor_info)) => tensor_infos.push(tensor_info),
Ok(None) => {}
Err(err) => eprintln!("skipping: {err:?}"),
}
}
}
}
Ok(tensor_infos)
}
/// Lazy tensor loader.
pub struct PthTensors {
tensor_infos: HashMap<String, TensorInfo>,
path: std::path::PathBuf,
// We do not store a zip reader as it needs mutable access to extract data. Instead we
// re-create a zip reader for each tensor.
}
impl PthTensors {
pub fn new<P: AsRef<std::path::Path>>(path: P) -> Result<Self> {
let tensor_infos = read_pth_tensor_info(path.as_ref(), false)?;
let tensor_infos = tensor_infos
.into_iter()
.map(|ti| (ti.name.to_string(), ti))
.collect();
let path = path.as_ref().to_owned();
Ok(Self { tensor_infos, path })
}
pub fn tensor_infos(&self) -> &HashMap<String, TensorInfo> {
&self.tensor_infos
}
pub fn get(&self, name: &str) -> Result<Option<Tensor>> {
let tensor_info = match self.tensor_infos.get(name) {
None => return Ok(None),
Some(tensor_info) => tensor_info,
};
// We hope that the file has not changed since first reading it.
let zip_reader = std::io::BufReader::new(std::fs::File::open(&self.path)?);
let mut zip = zip::ZipArchive::new(zip_reader)?;
let mut reader = zip.by_name(&tensor_info.path)?;
// Reading the data is a bit tricky as it can be strided, use an offset, etc.
// For now only support the basic case.
if tensor_info.layout.start_offset() != 0 || !tensor_info.layout.is_contiguous() {
crate::bail!(
"cannot retrieve non-contiguous tensors {:?}",
tensor_info.layout
)
}
let tensor = Tensor::from_reader(
tensor_info.layout.shape().clone(),
tensor_info.dtype,
&mut reader,
)?;
Ok(Some(tensor))
}
}
/// Read all the tensors from a PyTorch pth file.
pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
let pth = PthTensors::new(path)?;
let tensor_names = pth.tensor_infos.keys();
let mut tensors = Vec::with_capacity(tensor_names.len());
for name in tensor_names {
if let Some(tensor) = pth.get(name)? {
tensors.push((name.to_string(), tensor))
}
}
Ok(tensors)
}
| 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_clone should be used.
#[derive(Debug)]
pub enum Storage {
Cpu(CpuStorage),
Cuda(CudaStorage),
Metal(MetalStorage),
}
impl Storage {
pub fn try_clone(&self, layout: &Layout) -> Result<Self> {
match self {
Self::Cpu(storage) => Ok(Self::Cpu(storage.clone())),
Self::Cuda(storage) => {
let storage = storage.try_clone(layout)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.try_clone(layout)?;
Ok(Self::Metal(storage))
}
}
}
pub fn device(&self) -> Device {
match self {
Self::Cpu(_) => Device::Cpu,
Self::Cuda(storage) => Device::Cuda(storage.device().clone()),
Self::Metal(storage) => Device::Metal(storage.device().clone()),
}
}
pub fn dtype(&self) -> DType {
match self {
Self::Cpu(storage) => storage.dtype(),
Self::Cuda(storage) => storage.dtype(),
Self::Metal(storage) => storage.dtype(),
}
}
pub(crate) fn same_device(&self, rhs: &Self, op: &'static str) -> Result<()> {
let lhs = self.device().location();
let rhs = rhs.device().location();
if lhs != rhs {
Err(Error::DeviceMismatchBinaryOp { lhs, rhs, op }.bt())
} else {
Ok(())
}
}
pub(crate) fn same_dtype(&self, rhs: &Self, op: &'static str) -> Result<()> {
let lhs = self.dtype();
let rhs = rhs.dtype();
if lhs != rhs {
Err(Error::DTypeMismatchBinaryOp { lhs, rhs, op }.bt())
} else {
Ok(())
}
}
pub(crate) fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.affine(layout, mul, add)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.affine(layout, mul, add)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.affine(layout, mul, add)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn powf(&self, layout: &Layout, alpha: f64) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.powf(layout, alpha)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.powf(layout, alpha)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.powf(layout, alpha)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn elu(&self, layout: &Layout, alpha: f64) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.elu(layout, alpha)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.elu(layout, alpha)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.elu(layout, alpha)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn cmp(
&self,
op: CmpOp,
rhs: &Self,
lhs_layout: &Layout,
rhs_layout: &Layout,
) -> Result<Self> {
self.same_device(rhs, "cmp")?;
self.same_dtype(rhs, "cmp")?;
match (self, rhs) {
(Storage::Cpu(lhs), Storage::Cpu(rhs)) => {
let storage = lhs.cmp(op, rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.cmp(op, rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(lhs), Self::Metal(rhs)) => {
let storage = lhs.cmp(op, rhs, lhs_layout, rhs_layout)?;
Ok(Self::Metal(storage))
}
(lhs, rhs) => {
// Should not happen because of the same device check above but we're defensive
// anyway.
Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "cmp",
}
.bt())
}
}
}
pub(crate) fn reduce_op(&self, op: ReduceOp, layout: &Layout, s: &[usize]) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.reduce_op(op, layout, s)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.reduce_op(op, layout, s)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.reduce_op(op, layout, s)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.to_dtype(layout, dtype)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.to_dtype(layout, dtype)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.to_dtype(layout, dtype)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn apply_op1(&self, l: &Layout, c: &dyn CustomOp1) -> Result<(Self, Shape)> {
match self {
Self::Cpu(storage) => {
let (storage, shape) = c.cpu_fwd(storage, l)?;
Ok((Self::Cpu(storage), shape))
}
Self::Cuda(storage) => {
let (storage, shape) = c.cuda_fwd(storage, l)?;
Ok((Self::Cuda(storage), shape))
}
Self::Metal(storage) => {
let (storage, shape) = c.metal_fwd(storage, l)?;
Ok((Self::Metal(storage), shape))
}
}
}
pub(crate) fn apply_op2(
&self,
l1: &Layout,
t2: &Self,
l2: &Layout,
c: &dyn CustomOp2,
) -> Result<(Self, Shape)> {
self.same_device(t2, c.name())?;
match (self, t2) {
(Self::Cpu(s1), Self::Cpu(s2)) => {
let (s, shape) = c.cpu_fwd(s1, l1, s2, l2)?;
Ok((Self::Cpu(s), shape))
}
(Self::Cuda(s1), Self::Cuda(s2)) => {
let (s, shape) = c.cuda_fwd(s1, l1, s2, l2)?;
Ok((Self::Cuda(s), shape))
}
(Self::Metal(s1), Self::Metal(s2)) => {
let (s, shape) = c.metal_fwd(s1, l1, s2, l2)?;
Ok((Self::Metal(s), shape))
}
_ => unreachable!(),
}
}
pub(crate) fn apply_op3(
&self,
l1: &Layout,
t2: &Self,
l2: &Layout,
t3: &Self,
l3: &Layout,
c: &dyn CustomOp3,
) -> Result<(Self, Shape)> {
self.same_device(t2, c.name())?;
self.same_device(t3, c.name())?;
match (self, t2, t3) {
(Self::Cpu(s1), Self::Cpu(s2), Self::Cpu(s3)) => {
let (s, shape) = c.cpu_fwd(s1, l1, s2, l2, s3, l3)?;
Ok((Self::Cpu(s), shape))
}
(Self::Cuda(s1), Self::Cuda(s2), Self::Cuda(s3)) => {
let (s, shape) = c.cuda_fwd(s1, l1, s2, l2, s3, l3)?;
Ok((Self::Cuda(s), shape))
}
(Self::Metal(s1), Self::Metal(s2), Self::Metal(s3)) => {
let (s, shape) = c.metal_fwd(s1, l1, s2, l2, s3, l3)?;
Ok((Self::Metal(s), shape))
}
_ => unreachable!(),
}
}
pub(crate) fn unary_impl<B: op::UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.unary_impl::<B>(layout)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.unary_impl::<B>(layout)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.unary_impl::<B>(layout)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn binary_impl<B: op::BinaryOpT>(
&self,
rhs: &Self,
lhs_layout: &Layout,
rhs_layout: &Layout,
) -> Result<Self> {
self.same_device(rhs, B::NAME)?;
self.same_dtype(rhs, B::NAME)?;
match (self, rhs) {
(Storage::Cpu(lhs), Storage::Cpu(rhs)) => {
let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(lhs), Self::Metal(rhs)) => {
let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
Ok(Self::Metal(storage))
}
(lhs, rhs) => {
// Should not happen because of the same device check above but we're defensive
// anyway.
Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: B::NAME,
}
.bt())
}
}
}
pub(crate) fn conv1d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv1D,
) -> Result<Self> {
self.same_device(kernel, "conv1d")?;
self.same_dtype(kernel, "conv1d")?;
match (self, &kernel) {
(Storage::Cpu(inp), Storage::Cpu(kernel)) => {
let s = inp.conv1d(l, kernel, kernel_l, params)?;
Ok(Self::Cpu(s))
}
(Storage::Cuda(inp), Storage::Cuda(kernel)) => {
let s = inp.conv1d(l, kernel, kernel_l, params)?;
Ok(Self::Cuda(s))
}
(Storage::Metal(inp), Storage::Metal(kernel)) => {
let s = inp.conv1d(l, kernel, kernel_l, params)?;
Ok(Self::Metal(s))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "conv1d",
}
.bt()),
}
}
pub(crate) fn conv_transpose1d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self> {
self.same_device(kernel, "conv-transpose1d")?;
self.same_dtype(kernel, "conv-transpose1d")?;
match (self, &kernel) {
(Storage::Cpu(inp), Storage::Cpu(kernel)) => {
let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
Ok(Self::Cpu(s))
}
(Storage::Cuda(inp), Storage::Cuda(kernel)) => {
let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
Ok(Self::Cuda(s))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "conv-transpose1d",
}
.bt()),
}
}
pub(crate) fn conv2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv2D,
) -> Result<Self> {
self.same_device(kernel, "conv2d")?;
self.same_dtype(kernel, "conv2d")?;
match (self, &kernel) {
(Storage::Cpu(inp), Storage::Cpu(kernel)) => {
let s = inp.conv2d(l, kernel, kernel_l, params)?;
Ok(Self::Cpu(s))
}
(Storage::Cuda(inp), Storage::Cuda(kernel)) => {
let s = inp.conv2d(l, kernel, kernel_l, params)?;
Ok(Self::Cuda(s))
}
(Storage::Metal(inp), Storage::Metal(kernel)) => {
let s = inp.conv2d(l, kernel, kernel_l, params)?;
Ok(Self::Metal(s))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "conv2d",
}
.bt()),
}
}
pub(crate) fn conv_transpose2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self> {
self.same_device(kernel, "conv_transpose2d")?;
self.same_dtype(kernel, "conv_transpose2d")?;
match (self, &kernel) {
(Storage::Cpu(inp), Storage::Cpu(kernel)) => {
let s = inp.conv_transpose2d(l, kernel, kernel_l, params)?;
Ok(Self::Cpu(s))
}
(Storage::Cuda(inp), Storage::Cuda(kernel)) => {
let s = inp.conv_transpose2d(l, kernel, kernel_l, params)?;
Ok(Self::Cuda(s))
}
(Storage::Metal(inp), Storage::Metal(kernel)) => {
let s = inp.conv_transpose2d(l, kernel, kernel_l, params)?;
Ok(Self::Metal(s))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "conv_transpose2d",
}
.bt()),
}
}
pub(crate) fn avg_pool2d(
&self,
layout: &Layout,
kernel_size: (usize, usize),
stride: (usize, usize),
) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.avg_pool2d(layout, kernel_size, stride)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.avg_pool2d(layout, kernel_size, stride)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.avg_pool2d(layout, kernel_size, stride)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn max_pool2d(
&self,
layout: &Layout,
kernel_size: (usize, usize),
stride: (usize, usize),
) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.max_pool2d(layout, kernel_size, stride)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.max_pool2d(layout, kernel_size, stride)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.max_pool2d(layout, kernel_size, stride)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.upsample_nearest1d(layout, sz)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.upsample_nearest1d(layout, sz)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.upsample_nearest1d(layout, sz)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
match self {
Storage::Cpu(storage) => {
let storage = storage.upsample_nearest2d(layout, h, w)?;
Ok(Self::Cpu(storage))
}
Self::Cuda(storage) => {
let storage = storage.upsample_nearest2d(layout, h, w)?;
Ok(Self::Cuda(storage))
}
Self::Metal(storage) => {
let storage = storage.upsample_nearest2d(layout, h, w)?;
Ok(Self::Metal(storage))
}
}
}
pub(crate) fn where_cond(
&self,
layout: &Layout,
t: &Self,
layout_t: &Layout,
f: &Self,
layout_f: &Layout,
) -> Result<Self> {
self.same_device(t, "where")?;
self.same_device(f, "where")?;
t.same_dtype(f, "where")?;
match (self, t, f) {
(Storage::Cpu(cond), Storage::Cpu(t), Storage::Cpu(f)) => {
let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(cond), Self::Cuda(t), Self::Cuda(f)) => {
let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(cond), Self::Metal(t), Self::Metal(f)) => {
let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
Ok(Self::Metal(storage))
}
(_, lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "where",
}
.bt()),
}
}
pub(crate) fn gather(
&self,
l: &Layout,
indexes: &Self,
indexes_l: &Layout,
d: usize,
) -> Result<Self> {
self.same_device(indexes, "index-add")?;
match (self, indexes) {
(Self::Cpu(s), Self::Cpu(indexes)) => {
let storage = s.gather(l, indexes, indexes_l, d)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(s), Self::Cuda(indexes)) => {
let storage = s.gather(l, indexes, indexes_l, d)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(s), Self::Metal(indexes)) => {
let storage = s.gather(l, indexes, indexes_l, d)?;
Ok(Self::Metal(storage))
}
_ => unreachable!(),
}
}
pub(crate) fn scatter_add(
&self,
l: &Layout,
indexes: &Self,
indexes_l: &Layout,
source: &Self,
source_l: &Layout,
d: usize,
) -> Result<Self> {
self.same_device(indexes, "scatter-add")?;
self.same_device(source, "scatter-add")?;
match (self, indexes, source) {
(Self::Cpu(s), Self::Cpu(indexes), Self::Cpu(source)) => {
let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(s), Self::Cuda(indexes), Self::Cuda(source)) => {
let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Metal(storage))
}
_ => unreachable!(),
}
}
pub(crate) fn index_add(
&self,
l: &Layout,
indexes: &Self,
indexes_l: &Layout,
source: &Self,
source_l: &Layout,
d: usize,
) -> Result<Self> {
self.same_device(indexes, "index-add")?;
self.same_device(source, "index-add")?;
match (self, indexes, source) {
(Self::Cpu(s), Self::Cpu(indexes), Self::Cpu(source)) => {
let storage = s.index_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(s), Self::Cuda(indexes), Self::Cuda(source)) => {
let storage = s.index_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
let storage = s.index_add(l, indexes, indexes_l, source, source_l, d)?;
Ok(Self::Metal(storage))
}
_ => unreachable!(),
}
}
pub(crate) fn index_select(
&self,
rhs: &Self,
lhs_l: &Layout,
rhs_l: &Layout,
d: usize,
) -> Result<Self> {
self.same_device(rhs, "index-select")?;
match (self, rhs) {
(Self::Cpu(lhs), Self::Cpu(rhs)) => {
let storage = lhs.index_select(rhs, lhs_l, rhs_l, d)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.index_select(rhs, lhs_l, rhs_l, d)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(lhs), Self::Metal(rhs)) => {
let storage = lhs.index_select(rhs, lhs_l, rhs_l, d)?;
Ok(Self::Metal(storage))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "index-select",
}
.bt()),
}
}
pub(crate) fn matmul(
&self,
rhs: &Self,
bmnk: (usize, usize, usize, usize),
lhs_layout: &Layout,
rhs_layout: &Layout,
) -> Result<Self> {
self.same_device(rhs, "matmul")?;
self.same_dtype(rhs, "matmul")?;
match (self, rhs) {
(Self::Cpu(lhs), Self::Cpu(rhs)) => {
let storage = lhs.matmul(rhs, bmnk, lhs_layout, rhs_layout)?;
Ok(Self::Cpu(storage))
}
(Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.matmul(rhs, bmnk, lhs_layout, rhs_layout)?;
Ok(Self::Cuda(storage))
}
(Self::Metal(lhs), Self::Metal(rhs)) => {
let storage = lhs.matmul(rhs, bmnk, lhs_layout, rhs_layout)?;
Ok(Self::Metal(storage))
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "matmul",
}
.bt()),
}
}
// self, the source can be strided whereas dst is contiguous.
pub(crate) fn copy_strided_src(
&self,
dst: &mut Self,
dst_offset: usize,
src_l: &Layout,
) -> Result<()> {
match (self, dst) {
(Self::Cpu(src), Self::Cpu(dst)) => src.copy_strided_src(dst, dst_offset, src_l),
(Self::Cuda(src), Self::Cuda(dst)) => Ok(src.copy_strided_src(dst, dst_offset, src_l)?),
(Self::Metal(src), Self::Metal(dst)) => {
Ok(src.copy_strided_src(dst, dst_offset, src_l)?)
}
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
lhs: lhs.device().location(),
rhs: rhs.device().location(),
op: "copy",
}
.bt()),
}
}
}
| 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(String),
}
impl From<String> for MetalError {
fn from(e: String) -> Self {
MetalError::Message(e)
}
}
macro_rules! fail {
() => {
unimplemented!("metal support has not been enabled, add `metal` feature to enable.")
};
}
impl crate::backend::BackendStorage for MetalStorage {
type Device = MetalDevice;
fn try_clone(&self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn dtype(&self) -> DType {
fail!()
}
fn device(&self) -> &Self::Device {
fail!()
}
fn to_cpu_storage(&self) -> Result<CpuStorage> {
Err(Error::NotCompiledWithMetalSupport)
}
fn affine(&self, _: &Layout, _: f64, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn powf(&self, _: &Layout, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn elu(&self, _: &Layout, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn reduce_op(&self, _: ReduceOp, _: &Layout, _: &[usize]) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn cmp(&self, _: CmpOp, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn to_dtype(&self, _: &Layout, _: DType) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn unary_impl<B: UnaryOpT>(&self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn binary_impl<B: BinaryOpT>(&self, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn where_cond(&self, _: &Layout, _: &Self, _: &Layout, _: &Self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn conv1d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConv1D,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn conv_transpose1d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn conv2d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConv2D,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn conv_transpose2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn index_select(&self, _: &Self, _: &Layout, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn scatter_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn index_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn matmul(
&self,
_: &Self,
_: (usize, usize, usize, usize),
_: &Layout,
_: &Layout,
) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()> {
Err(Error::NotCompiledWithMetalSupport)
}
fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
}
impl crate::backend::BackendDevice for MetalDevice {
type Storage = MetalStorage;
fn new(_: usize) -> Result<Self> {
Err(Error::NotCompiledWithMetalSupport)
}
fn set_seed(&self, _: u64) -> Result<()> {
Err(Error::NotCompiledWithMetalSupport)
}
fn location(&self) -> crate::DeviceLocation {
fail!()
}
fn same_device(&self, _: &Self) -> bool {
fail!()
}
fn zeros_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
Err(Error::NotCompiledWithMetalSupport)
}
fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
Err(Error::NotCompiledWithMetalSupport)
}
fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage> {
Err(Error::NotCompiledWithMetalSupport)
}
fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
Err(Error::NotCompiledWithMetalSupport)
}
fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
Err(Error::NotCompiledWithMetalSupport)
}
}
| 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 => st::Dtype::U8,
DType::U32 => st::Dtype::U32,
DType::I64 => st::Dtype::I64,
DType::BF16 => st::Dtype::BF16,
DType::F16 => st::Dtype::F16,
DType::F32 => st::Dtype::F32,
DType::F64 => st::Dtype::F64,
}
}
}
impl TryFrom<st::Dtype> for DType {
type Error = Error;
fn try_from(value: st::Dtype) -> Result<Self> {
match value {
st::Dtype::U8 => Ok(DType::U8),
st::Dtype::U32 => Ok(DType::U32),
st::Dtype::I64 => Ok(DType::I64),
st::Dtype::BF16 => Ok(DType::BF16),
st::Dtype::F16 => Ok(DType::F16),
st::Dtype::F32 => Ok(DType::F32),
st::Dtype::F64 => Ok(DType::F64),
dtype => Err(Error::UnsupportedSafeTensorDtype(dtype)),
}
}
}
impl st::View for Tensor {
fn dtype(&self) -> st::Dtype {
self.dtype().into()
}
fn shape(&self) -> &[usize] {
self.shape().dims()
}
fn data(&self) -> Cow<[u8]> {
// This copies data from GPU to CPU.
// TODO: Avoid the unwrap here.
Cow::Owned(convert_back(self).unwrap())
}
fn data_len(&self) -> usize {
let n: usize = self.shape().elem_count();
let bytes_per_element = self.dtype().size_in_bytes();
n * bytes_per_element
}
}
impl st::View for &Tensor {
fn dtype(&self) -> st::Dtype {
(*self).dtype().into()
}
fn shape(&self) -> &[usize] {
self.dims()
}
fn data(&self) -> Cow<[u8]> {
// This copies data from GPU to CPU.
// TODO: Avoid the unwrap here.
Cow::Owned(convert_back(self).unwrap())
}
fn data_len(&self) -> usize {
let n: usize = self.dims().iter().product();
let bytes_per_element = (*self).dtype().size_in_bytes();
n * bytes_per_element
}
}
impl Tensor {
pub fn save_safetensors<P: AsRef<Path>>(&self, name: &str, filename: P) -> Result<()> {
let data = [(name, self.clone())];
Ok(st::serialize_to_file(data, &None, filename.as_ref())?)
}
}
fn convert_slice<T: WithDType>(data: &[u8], shape: &[usize], device: &Device) -> Result<Tensor> {
let size_in_bytes = T::DTYPE.size_in_bytes();
let elem_count = data.len() / size_in_bytes;
if (data.as_ptr() as usize) % size_in_bytes == 0 {
// SAFETY This is safe because we just checked that this
// was correctly aligned.
let data: &[T] =
unsafe { std::slice::from_raw_parts(data.as_ptr() as *const T, elem_count) };
Tensor::from_slice(data, shape, device)
} else {
// XXX: We need to specify `T` here, otherwise the compiler will infer u8 because of the following cast
// Making this vector too small to fit a full f16/f32/f64 weights, resulting in out-of-bounds access
let mut c: Vec<T> = Vec::with_capacity(elem_count);
// SAFETY: We just created c, so the allocated memory is necessarily
// contiguous and non overlapping with the view's data.
// We're downgrading the `c` pointer from T to u8, which removes alignment
// constraints.
unsafe {
std::ptr::copy_nonoverlapping(data.as_ptr(), c.as_mut_ptr() as *mut u8, data.len());
c.set_len(elem_count)
}
Tensor::from_slice(&c, shape, device)
}
}
fn convert_slice_with_cast<T: Sized + Copy, U: WithDType, F: Fn(T) -> Result<U>>(
data: &[u8],
shape: &[usize],
device: &Device,
conv: F,
) -> Result<Tensor> {
let size_in_bytes = std::mem::size_of::<T>();
let elem_count = data.len() / size_in_bytes;
if (data.as_ptr() as usize) % size_in_bytes == 0 {
// SAFETY This is safe because we just checked that this
// was correctly aligned.
let data: &[T] =
unsafe { std::slice::from_raw_parts(data.as_ptr() as *const T, elem_count) };
let data = data.iter().map(|t| conv(*t)).collect::<Result<Vec<_>>>()?;
Tensor::from_vec(data, shape, device)
} else {
// XXX: We need to specify `T` here, otherwise the compiler will infer u8 because of the following cast
// Making this vector too small to fit a full f16/f32/f64 weights, resulting in out-of-bounds access
let mut c: Vec<T> = Vec::with_capacity(elem_count);
// SAFETY: We just created c, so the allocated memory is necessarily
// contiguous and non overlapping with the view's data.
// We're downgrading the `c` pointer from T to u8, which removes alignment
// constraints.
unsafe {
std::ptr::copy_nonoverlapping(data.as_ptr(), c.as_mut_ptr() as *mut u8, data.len());
c.set_len(elem_count)
}
let c = c.into_iter().map(conv).collect::<Result<Vec<_>>>()?;
Tensor::from_vec(c, shape, device)
}
}
fn convert_with_cast_<T: Sized + Copy, U: WithDType, F: Fn(T) -> Result<U>>(
view: &st::TensorView<'_>,
device: &Device,
conv: F,
) -> Result<Tensor> {
convert_slice_with_cast::<T, U, F>(view.data(), view.shape(), device, conv)
}
fn convert_<T: WithDType>(view: &st::TensorView<'_>, device: &Device) -> Result<Tensor> {
convert_slice::<T>(view.data(), view.shape(), device)
}
fn convert_back_<T: WithDType>(mut vs: Vec<T>) -> Vec<u8> {
let size_in_bytes = T::DTYPE.size_in_bytes();
let length = vs.len() * size_in_bytes;
let capacity = vs.capacity() * size_in_bytes;
let ptr = vs.as_mut_ptr() as *mut u8;
// Don't run the destructor for Vec<T>
std::mem::forget(vs);
// SAFETY:
//
// Every T is larger than u8, so there is no issue regarding alignment.
// This re-interpret the Vec<T> as a Vec<u8>.
unsafe { Vec::from_raw_parts(ptr, length, capacity) }
}
pub trait Load {
fn load(&self, device: &Device) -> Result<Tensor>;
}
impl<'a> Load for st::TensorView<'a> {
fn load(&self, device: &Device) -> Result<Tensor> {
convert(self, device)
}
}
impl Tensor {
pub fn from_raw_buffer(
data: &[u8],
dtype: DType,
shape: &[usize],
device: &Device,
) -> Result<Self> {
match dtype {
DType::U8 => convert_slice::<u8>(data, shape, device),
DType::U32 => convert_slice::<u32>(data, shape, device),
DType::I64 => convert_slice::<i64>(data, shape, device),
DType::BF16 => convert_slice::<half::bf16>(data, shape, device),
DType::F16 => convert_slice::<half::f16>(data, shape, device),
DType::F32 => convert_slice::<f32>(data, shape, device),
DType::F64 => convert_slice::<f64>(data, shape, device),
}
}
}
fn convert(view: &st::TensorView<'_>, device: &Device) -> Result<Tensor> {
match view.dtype() {
st::Dtype::U8 => convert_::<u8>(view, device),
st::Dtype::U16 => {
let conv = |x| Ok(u32::from(x));
convert_with_cast_::<u16, u32, _>(view, device, conv)
}
st::Dtype::U32 => convert_::<u32>(view, device),
st::Dtype::I32 => {
let conv = |x| Ok(i64::from(x));
convert_with_cast_::<i32, i64, _>(view, device, conv)
}
st::Dtype::I64 => convert_::<i64>(view, device),
st::Dtype::BF16 => convert_::<half::bf16>(view, device),
st::Dtype::F16 => convert_::<half::f16>(view, device),
st::Dtype::F32 => convert_::<f32>(view, device),
st::Dtype::F64 => convert_::<f64>(view, device),
dtype => Err(Error::UnsupportedSafeTensorDtype(dtype)),
}
}
fn convert_back(tensor: &Tensor) -> Result<Vec<u8>> {
// TODO: This makes an unnecessary copy when the tensor is on the cpu.
let tensor = tensor.flatten_all()?;
match tensor.dtype() {
DType::U8 => Ok(convert_back_::<u8>(tensor.to_vec1()?)),
DType::U32 => Ok(convert_back_::<u32>(tensor.to_vec1()?)),
DType::I64 => Ok(convert_back_::<i64>(tensor.to_vec1()?)),
DType::F16 => Ok(convert_back_::<half::f16>(tensor.to_vec1()?)),
DType::BF16 => Ok(convert_back_::<half::bf16>(tensor.to_vec1()?)),
DType::F32 => Ok(convert_back_::<f32>(tensor.to_vec1()?)),
DType::F64 => Ok(convert_back_::<f64>(tensor.to_vec1()?)),
}
}
pub fn load<P: AsRef<Path>>(filename: P, device: &Device) -> Result<HashMap<String, Tensor>> {
let data = std::fs::read(filename.as_ref())?;
load_buffer(&data[..], device)
}
pub fn load_buffer(data: &[u8], device: &Device) -> Result<HashMap<String, Tensor>> {
let st = safetensors::SafeTensors::deserialize(data)?;
st.tensors()
.into_iter()
.map(|(name, view)| Ok((name, view.load(device)?)))
.collect()
}
pub fn save<K: AsRef<str> + Ord + std::fmt::Display, P: AsRef<Path>>(
tensors: &HashMap<K, Tensor>,
filename: P,
) -> Result<()> {
Ok(st::serialize_to_file(tensors, &None, filename.as_ref())?)
}
#[derive(yoke::Yokeable)]
struct SafeTensors_<'a>(SafeTensors<'a>);
pub struct MmapedSafetensors {
safetensors: Vec<yoke::Yoke<SafeTensors_<'static>, memmap2::Mmap>>,
routing: Option<HashMap<String, usize>>,
}
impl MmapedSafetensors {
/// Creates a wrapper around a memory mapped file and deserialize the safetensors header.
///
/// # Safety
///
/// The unsafe is inherited from [`memmap2::MmapOptions`].
pub unsafe fn new<P: AsRef<Path>>(p: P) -> Result<Self> {
let p = p.as_ref();
let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
let file = memmap2::MmapOptions::new()
.map(&file)
.map_err(|e| Error::from(e).with_path(p))?;
let safetensors = yoke::Yoke::<SafeTensors_<'static>, memmap2::Mmap>::try_attach_to_cart(
file,
|data: &[u8]| {
let st = safetensors::SafeTensors::deserialize(data)
.map_err(|e| Error::from(e).with_path(p))?;
Ok::<_, Error>(SafeTensors_(st))
},
)?;
Ok(Self {
safetensors: vec![safetensors],
routing: None,
})
}
/// Creates a wrapper around multiple memory mapped file and deserialize the safetensors headers.
///
/// If a tensor name appears in multiple files, the last entry is returned.
///
/// # Safety
///
/// The unsafe is inherited from [`memmap2::MmapOptions`].
pub unsafe fn multi<P: AsRef<Path>>(paths: &[P]) -> Result<Self> {
let mut routing = HashMap::new();
let mut safetensors = vec![];
for (index, p) in paths.iter().enumerate() {
let p = p.as_ref();
let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
let file = memmap2::MmapOptions::new()
.map(&file)
.map_err(|e| Error::from(e).with_path(p))?;
let data = yoke::Yoke::<SafeTensors_<'static>, memmap2::Mmap>::try_attach_to_cart(
file,
|data: &[u8]| {
let st = safetensors::SafeTensors::deserialize(data)
.map_err(|e| Error::from(e).with_path(p))?;
Ok::<_, Error>(SafeTensors_(st))
},
)?;
for k in data.get().0.names() {
routing.insert(k.to_string(), index);
}
safetensors.push(data)
}
Ok(Self {
safetensors,
routing: Some(routing),
})
}
pub fn load(&self, name: &str, dev: &Device) -> Result<Tensor> {
self.get(name)?.load(dev)
}
pub fn tensors(&self) -> Vec<(String, st::TensorView<'_>)> {
let mut tensors = vec![];
for safetensors in self.safetensors.iter() {
tensors.push(safetensors.get().0.tensors())
}
tensors.into_iter().flatten().collect()
}
pub fn get(&self, name: &str) -> Result<st::TensorView<'_>> {
let index = match &self.routing {
None => 0,
Some(routing) => {
let index = routing.get(name).ok_or_else(|| {
Error::CannotFindTensor {
path: name.to_string(),
}
.bt()
})?;
*index
}
};
Ok(self.safetensors[index].get().0.tensor(name)?)
}
}
pub struct BufferedSafetensors {
safetensors: yoke::Yoke<SafeTensors_<'static>, Vec<u8>>,
}
impl BufferedSafetensors {
/// Creates a wrapper around a binary buffer and deserialize the safetensors header.
pub fn new(buffer: Vec<u8>) -> Result<Self> {
let safetensors = yoke::Yoke::<SafeTensors_<'static>, Vec<u8>>::try_attach_to_cart(
buffer,
|data: &[u8]| {
let st = safetensors::SafeTensors::deserialize(data)?;
Ok::<_, Error>(SafeTensors_(st))
},
)?;
Ok(Self { safetensors })
}
pub fn load(&self, name: &str, dev: &Device) -> Result<Tensor> {
self.get(name)?.load(dev)
}
pub fn tensors(&self) -> Vec<(String, st::TensorView<'_>)> {
self.safetensors.get().0.tensors()
}
pub fn get(&self, name: &str) -> Result<st::TensorView<'_>> {
Ok(self.safetensors.get().0.tensor(name)?)
}
}
pub struct MmapedFile {
path: std::path::PathBuf,
inner: memmap2::Mmap,
}
impl MmapedFile {
/// Creates a wrapper around a memory mapped file from which you can retrieve
/// tensors using [`MmapedFile::deserialize`]
///
/// # Safety
///
/// The unsafe is inherited from [`memmap2::MmapOptions`].
pub unsafe fn new<P: AsRef<Path>>(p: P) -> Result<Self> {
let p = p.as_ref();
let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
let inner = memmap2::MmapOptions::new()
.map(&file)
.map_err(|e| Error::from(e).with_path(p))?;
Ok(Self {
inner,
path: p.to_path_buf(),
})
}
pub fn deserialize(&self) -> Result<SafeTensors<'_>> {
let st = safetensors::SafeTensors::deserialize(&self.inner)
.map_err(|e| Error::from(e).with_path(&self.path))?;
Ok(st)
}
}
#[cfg(test)]
mod tests {
use super::*;
use std::collections::HashMap;
#[test]
fn save_single_tensor() {
let t = Tensor::zeros((2, 2), DType::F32, &Device::Cpu).unwrap();
t.save_safetensors("t", "t.safetensors").unwrap();
let bytes = std::fs::read("t.safetensors").unwrap();
assert_eq!(bytes, b"@\0\0\0\0\0\0\0{\"t\":{\"dtype\":\"F32\",\"shape\":[2,2],\"data_offsets\":[0,16]}} \0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0");
std::fs::remove_file("t.safetensors").unwrap();
}
#[test]
fn save_load_multiple_tensors() {
let t = Tensor::zeros((2, 2), DType::F32, &Device::Cpu).unwrap();
let u = Tensor::zeros((1, 2), DType::F32, &Device::Cpu).unwrap();
let map: HashMap<_, _> = [("t", t), ("u", u)].into_iter().collect();
save(&map, "multi.safetensors").unwrap();
let weights = load("multi.safetensors", &Device::Cpu).unwrap();
assert_eq!(weights.get("t").unwrap().dims(), &[2, 2]);
assert_eq!(weights.get("u").unwrap().dims(), &[1, 2]);
let bytes = std::fs::read("multi.safetensors").unwrap();
assert_eq!(bytes, b"x\0\0\0\0\0\0\0{\"t\":{\"dtype\":\"F32\",\"shape\":[2,2],\"data_offsets\":[0,16]},\"u\":{\"dtype\":\"F32\",\"shape\":[1,2],\"data_offsets\":[16,24]}} \0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0");
std::fs::remove_file("multi.safetensors").unwrap();
}
}
| 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, PartialEq, Eq)]
pub enum ReduceOp {
Sum,
Min,
Max,
ArgMin,
ArgMax,
}
impl ReduceOp {
pub(crate) fn name(&self) -> &'static str {
match self {
Self::ArgMax => "argmax",
Self::ArgMin => "argmin",
Self::Min => "min",
Self::Max => "max",
Self::Sum => "sum",
}
}
}
// These ops return the same type as their input type.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum BinaryOp {
Add,
Mul,
Sub,
Div,
Maximum,
Minimum,
}
// Unary ops with no argument
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum UnaryOp {
Exp,
Log,
Sin,
Cos,
Abs,
Neg,
Recip,
Sqr,
Sqrt,
Gelu,
GeluErf,
Erf,
Relu,
Tanh,
Floor,
Ceil,
Round,
}
#[derive(Clone)]
pub enum Op {
Binary(Tensor, Tensor, BinaryOp),
Unary(Tensor, UnaryOp),
Cmp(Tensor, CmpOp),
// The third argument is the reduced shape with `keepdim=true`.
Reduce(Tensor, ReduceOp, Vec<usize>),
Matmul(Tensor, Tensor),
Gather(Tensor, Tensor, usize),
ScatterAdd(Tensor, Tensor, Tensor, usize),
IndexSelect(Tensor, Tensor, usize),
IndexAdd(Tensor, Tensor, Tensor, usize),
WhereCond(Tensor, Tensor, Tensor),
#[allow(dead_code)]
Conv1D {
arg: Tensor,
kernel: Tensor,
padding: usize,
stride: usize,
dilation: usize,
},
#[allow(dead_code)]
ConvTranspose1D {
arg: Tensor,
kernel: Tensor,
padding: usize,
output_padding: usize,
stride: usize,
dilation: usize,
},
#[allow(dead_code)]
Conv2D {
arg: Tensor,
kernel: Tensor,
padding: usize,
stride: usize,
dilation: usize,
},
#[allow(dead_code)]
ConvTranspose2D {
arg: Tensor,
kernel: Tensor,
padding: usize,
output_padding: usize,
stride: usize,
dilation: usize,
},
AvgPool2D {
arg: Tensor,
kernel_size: (usize, usize),
stride: (usize, usize),
},
MaxPool2D {
arg: Tensor,
kernel_size: (usize, usize),
stride: (usize, usize),
},
UpsampleNearest1D(Tensor),
UpsampleNearest2D(Tensor),
Cat(Vec<Tensor>, usize),
#[allow(dead_code)] // add is currently unused.
Affine {
arg: Tensor,
mul: f64,
add: f64,
},
ToDType(Tensor),
Copy(Tensor),
Broadcast(Tensor),
Narrow(Tensor, usize, usize, usize),
SliceScatter0(Tensor, Tensor, usize),
Reshape(Tensor),
ToDevice(Tensor),
Transpose(Tensor, usize, usize),
Permute(Tensor, Vec<usize>),
Elu(Tensor, f64),
Powf(Tensor, f64),
CustomOp1(Tensor, std::sync::Arc<Box<dyn CustomOp1 + Send + Sync>>),
CustomOp2(
Tensor,
Tensor,
std::sync::Arc<Box<dyn CustomOp2 + Send + Sync>>,
),
CustomOp3(
Tensor,
Tensor,
Tensor,
std::sync::Arc<Box<dyn CustomOp3 + Send + Sync>>,
),
}
/// Unary ops that can be defined in user-land.
pub trait CustomOp1 {
// Box<dyn> does not support const yet, so use a function to get the name.
fn name(&self) -> &'static str;
/// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)>;
/// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cuda_fwd(&self, _storage: &CudaStorage, _layout: &Layout) -> Result<(CudaStorage, Shape)> {
Err(crate::Error::Cuda(
format!("no cuda implementation for {}", self.name()).into(),
))
}
/// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn metal_fwd(
&self,
_storage: &MetalStorage,
_layout: &Layout,
) -> Result<(MetalStorage, Shape)> {
Err(crate::Error::Metal(
format!("no metal implementation for {}", self.name()).into(),
))
}
/// This function takes as argument the argument `arg` used in the forward pass, the result
/// produced by the forward operation `res` and the gradient of the result `grad_res`.
/// The function should return the gradient of the argument.
fn bwd(&self, _arg: &Tensor, _res: &Tensor, _grad_res: &Tensor) -> Result<Option<Tensor>> {
Err(crate::Error::BackwardNotSupported { op: self.name() })
}
}
pub trait CustomOp2 {
fn name(&self) -> &'static str;
/// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cpu_fwd(
&self,
s1: &CpuStorage,
l1: &Layout,
s2: &CpuStorage,
l2: &Layout,
) -> Result<(CpuStorage, Shape)>;
/// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cuda_fwd(
&self,
_: &CudaStorage,
_: &Layout,
_: &CudaStorage,
_: &Layout,
) -> Result<(CudaStorage, Shape)> {
Err(crate::Error::Cuda(
format!("no cuda implementation for {}", self.name()).into(),
))
}
/// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn metal_fwd(
&self,
_: &MetalStorage,
_: &Layout,
_: &MetalStorage,
_: &Layout,
) -> Result<(MetalStorage, Shape)> {
Err(crate::Error::Metal(
format!("no metal implementation for {}", self.name()).into(),
))
}
fn bwd(
&self,
_arg1: &Tensor,
_arg2: &Tensor,
_res: &Tensor,
_grad_res: &Tensor,
) -> Result<(Option<Tensor>, Option<Tensor>)> {
Err(crate::Error::BackwardNotSupported { op: self.name() })
}
}
pub trait CustomOp3 {
fn name(&self) -> &'static str;
/// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cpu_fwd(
&self,
s1: &CpuStorage,
l1: &Layout,
s2: &CpuStorage,
l2: &Layout,
s3: &CpuStorage,
l3: &Layout,
) -> Result<(CpuStorage, Shape)>;
/// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn cuda_fwd(
&self,
_: &CudaStorage,
_: &Layout,
_: &CudaStorage,
_: &Layout,
_: &CudaStorage,
_: &Layout,
) -> Result<(CudaStorage, Shape)> {
Err(crate::Error::Cuda(
format!("no cuda implementation for {}", self.name()).into(),
))
}
/// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it.
fn metal_fwd(
&self,
_: &MetalStorage,
_: &Layout,
_: &MetalStorage,
_: &Layout,
_: &MetalStorage,
_: &Layout,
) -> Result<(MetalStorage, Shape)> {
Err(crate::Error::Metal(
format!("no metal implementation for {}", self.name()).into(),
))
}
fn bwd(
&self,
_arg1: &Tensor,
_arg2: &Tensor,
_arg3: &Tensor,
_res: &Tensor,
_grad_res: &Tensor,
) -> Result<(Option<Tensor>, Option<Tensor>, Option<Tensor>)> {
Err(crate::Error::BackwardNotSupported { op: self.name() })
}
}
pub trait UnaryOpT {
const NAME: &'static str;
const KERNEL: &'static str;
const V: Self;
fn bf16(v1: bf16) -> bf16;
fn f16(v1: f16) -> f16;
fn f32(v1: f32) -> f32;
fn f64(v1: f64) -> f64;
fn u8(v1: u8) -> u8;
fn u32(v1: u32) -> u32;
fn i64(v1: i64) -> i64;
// There is no very good way to represent optional function in traits so we go for an explicit
// boolean flag to mark the function as existing.
const BF16_VEC: bool = false;
fn bf16_vec(_xs: &[bf16], _ys: &mut [bf16]) {}
const F16_VEC: bool = false;
fn f16_vec(_xs: &[f16], _ys: &mut [f16]) {}
const F32_VEC: bool = false;
fn f32_vec(_xs: &[f32], _ys: &mut [f32]) {}
const F64_VEC: bool = false;
fn f64_vec(_xs: &[f64], _ys: &mut [f64]) {}
}
pub trait BinaryOpT {
const NAME: &'static str;
const KERNEL: &'static str;
const V: Self;
fn bf16(v1: bf16, v2: bf16) -> bf16;
fn f16(v1: f16, v2: f16) -> f16;
fn f32(v1: f32, v2: f32) -> f32;
fn f64(v1: f64, v2: f64) -> f64;
fn u8(v1: u8, v2: u8) -> u8;
fn u32(v1: u32, v2: u32) -> u32;
fn i64(v1: i64, v2: i64) -> i64;
const BF16_VEC: bool = false;
fn bf16_vec(_xs1: &[bf16], _xs2: &[bf16], _ys: &mut [bf16]) {}
const F16_VEC: bool = false;
fn f16_vec(_xs1: &[f16], _xs2: &[f16], _ys: &mut [f16]) {}
const F32_VEC: bool = false;
fn f32_vec(_xs1: &[f32], _xs2: &[f32], _ys: &mut [f32]) {}
const F64_VEC: bool = false;
fn f64_vec(_xs1: &[f64], _xs2: &[f64], _ys: &mut [f64]) {}
const U8_VEC: bool = false;
fn u8_vec(_xs1: &[u8], _xs2: &[u8], _ys: &mut [u8]) {}
const U32_VEC: bool = false;
fn u32_vec(_xs1: &[u32], _xs2: &[u32], _ys: &mut [u32]) {}
const I64_VEC: bool = false;
fn i64_vec(_xs1: &[i64], _xs2: &[i64], _ys: &mut [i64]) {}
}
pub(crate) struct Add;
pub(crate) struct Div;
pub(crate) struct Mul;
pub(crate) struct Sub;
pub(crate) struct Maximum;
pub(crate) struct Minimum;
pub(crate) struct Exp;
pub(crate) struct Log;
pub(crate) struct Sin;
pub(crate) struct Cos;
pub(crate) struct Abs;
pub(crate) struct Neg;
pub(crate) struct Recip;
pub(crate) struct Sqr;
pub(crate) struct Sqrt;
pub(crate) struct Gelu;
pub(crate) struct GeluErf;
pub(crate) struct Erf;
pub(crate) struct Relu;
pub(crate) struct Tanh;
pub(crate) struct Floor;
pub(crate) struct Ceil;
pub(crate) struct Round;
macro_rules! bin_op {
($op:ident, $name: literal, $e: expr, $f32_vec: ident, $f64_vec: ident) => {
impl BinaryOpT for $op {
const NAME: &'static str = $name;
const KERNEL: &'static str = concat!("b", $name);
const V: Self = $op;
#[inline(always)]
fn bf16(v1: bf16, v2: bf16) -> bf16 {
$e(v1, v2)
}
#[inline(always)]
fn f16(v1: f16, v2: f16) -> f16 {
$e(v1, v2)
}
#[inline(always)]
fn f32(v1: f32, v2: f32) -> f32 {
$e(v1, v2)
}
#[inline(always)]
fn f64(v1: f64, v2: f64) -> f64 {
$e(v1, v2)
}
#[inline(always)]
fn u8(v1: u8, v2: u8) -> u8 {
$e(v1, v2)
}
#[inline(always)]
fn u32(v1: u32, v2: u32) -> u32 {
$e(v1, v2)
}
#[inline(always)]
fn i64(v1: i64, v2: i64) -> i64 {
$e(v1, v2)
}
#[cfg(feature = "mkl")]
const F32_VEC: bool = true;
#[cfg(feature = "mkl")]
const F64_VEC: bool = true;
#[cfg(feature = "mkl")]
#[inline(always)]
fn f32_vec(xs1: &[f32], xs2: &[f32], ys: &mut [f32]) {
crate::mkl::$f32_vec(xs1, xs2, ys)
}
#[cfg(feature = "mkl")]
#[inline(always)]
fn f64_vec(xs1: &[f64], xs2: &[f64], ys: &mut [f64]) {
crate::mkl::$f64_vec(xs1, xs2, ys)
}
#[cfg(feature = "accelerate")]
const F32_VEC: bool = true;
#[cfg(feature = "accelerate")]
const F64_VEC: bool = true;
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f32_vec(xs1: &[f32], xs2: &[f32], ys: &mut [f32]) {
crate::accelerate::$f32_vec(xs1, xs2, ys)
}
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f64_vec(xs1: &[f64], xs2: &[f64], ys: &mut [f64]) {
crate::accelerate::$f64_vec(xs1, xs2, ys)
}
}
};
}
bin_op!(Add, "add", |v1, v2| v1 + v2, vs_add, vd_add);
bin_op!(Sub, "sub", |v1, v2| v1 - v2, vs_sub, vd_sub);
bin_op!(Mul, "mul", |v1, v2| v1 * v2, vs_mul, vd_mul);
bin_op!(Div, "div", |v1, v2| v1 / v2, vs_div, vd_div);
bin_op!(
Minimum,
"minimum",
|v1, v2| if v1 > v2 { v2 } else { v1 },
vs_min,
vd_min
);
bin_op!(
Maximum,
"maximum",
|v1, v2| if v1 < v2 { v2 } else { v1 },
vs_max,
vd_max
);
#[allow(clippy::redundant_closure_call)]
macro_rules! unary_op {
($op: ident, $name: literal, $a: ident, $e: expr) => {
impl UnaryOpT for $op {
const NAME: &'static str = $name;
const KERNEL: &'static str = concat!("u", $name);
const V: Self = $op;
#[inline(always)]
fn bf16($a: bf16) -> bf16 {
$e
}
#[inline(always)]
fn f16($a: f16) -> f16 {
$e
}
#[inline(always)]
fn f32($a: f32) -> f32 {
$e
}
#[inline(always)]
fn f64($a: f64) -> f64 {
$e
}
#[inline(always)]
fn u8(_: u8) -> u8 {
todo!("no unary function for u8")
}
#[inline(always)]
fn u32(_: u32) -> u32 {
todo!("no unary function for u32")
}
#[inline(always)]
fn i64(_: i64) -> i64 {
todo!("no unary function for i64")
}
}
};
($op: ident, $name: literal, $a: ident, $e: expr, $f32_vec:ident, $f64_vec:ident) => {
impl UnaryOpT for $op {
const NAME: &'static str = $name;
const KERNEL: &'static str = concat!("u", $name);
const V: Self = $op;
#[inline(always)]
fn bf16($a: bf16) -> bf16 {
$e
}
#[inline(always)]
fn f16($a: f16) -> f16 {
$e
}
#[inline(always)]
fn f32($a: f32) -> f32 {
$e
}
#[inline(always)]
fn f64($a: f64) -> f64 {
$e
}
#[inline(always)]
fn u8(_: u8) -> u8 {
todo!("no unary function for u8")
}
#[inline(always)]
fn u32(_: u32) -> u32 {
todo!("no unary function for u32")
}
#[inline(always)]
fn i64(_: i64) -> i64 {
todo!("no unary function for i64")
}
#[cfg(feature = "mkl")]
const F32_VEC: bool = true;
#[cfg(feature = "mkl")]
const F64_VEC: bool = true;
#[cfg(feature = "mkl")]
#[inline(always)]
fn f32_vec(xs: &[f32], ys: &mut [f32]) {
crate::mkl::$f32_vec(xs, ys)
}
#[cfg(feature = "mkl")]
#[inline(always)]
fn f64_vec(xs: &[f64], ys: &mut [f64]) {
crate::mkl::$f64_vec(xs, ys)
}
#[cfg(feature = "accelerate")]
const F32_VEC: bool = true;
#[cfg(feature = "accelerate")]
const F64_VEC: bool = true;
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f32_vec(xs: &[f32], ys: &mut [f32]) {
crate::accelerate::$f32_vec(xs, ys)
}
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f64_vec(xs: &[f64], ys: &mut [f64]) {
crate::accelerate::$f64_vec(xs, ys)
}
}
};
}
unary_op!(Exp, "exp", v, v.exp(), vs_exp, vd_exp);
unary_op!(Log, "log", v, v.ln(), vs_ln, vd_ln);
unary_op!(Sin, "sin", v, v.sin(), vs_sin, vd_sin);
unary_op!(Cos, "cos", v, v.cos(), vs_cos, vd_cos);
unary_op!(Tanh, "tanh", v, v.tanh(), vs_tanh, vd_tanh);
unary_op!(Neg, "neg", v, -v);
unary_op!(Recip, "recip", v, v.recip());
unary_op!(Sqr, "sqr", v, v * v, vs_sqr, vd_sqr);
unary_op!(Sqrt, "sqrt", v, v.sqrt(), vs_sqrt, vd_sqrt);
/// Tanh based approximation of the `gelu` operation
/// GeluErf is the more precise one.
/// <https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions>
impl UnaryOpT for Gelu {
const NAME: &'static str = "gelu";
const V: Self = Gelu;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
bf16::from_f32_const(0.5)
* v
* (bf16::ONE
+ bf16::tanh(
(bf16::from_f32_const(2.0) / bf16::PI).sqrt()
* v
* (bf16::ONE + bf16::from_f32_const(0.044715) * v * v),
))
}
#[inline(always)]
fn f16(v: f16) -> f16 {
f16::from_f32_const(0.5)
* v
* (f16::ONE
+ f16::tanh(
(f16::from_f32_const(2.0) / f16::PI).sqrt()
* v
* (f16::ONE + f16::from_f32_const(0.044715) * v * v),
))
}
#[inline(always)]
fn f32(v: f32) -> f32 {
0.5 * v
* (1.0
+ f32::tanh((2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)))
}
#[inline(always)]
fn f64(v: f64) -> f64 {
0.5 * v
* (1.0
+ f64::tanh((2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)))
}
#[inline(always)]
fn u8(_: u8) -> u8 {
0
}
#[inline(always)]
fn u32(_: u32) -> u32 {
0
}
#[inline(always)]
fn i64(_: i64) -> i64 {
0
}
const KERNEL: &'static str = "ugelu";
#[cfg(feature = "mkl")]
const F32_VEC: bool = true;
#[cfg(feature = "mkl")]
#[inline(always)]
fn f32_vec(xs: &[f32], ys: &mut [f32]) {
crate::mkl::vs_gelu(xs, ys)
}
#[cfg(feature = "mkl")]
const F64_VEC: bool = true;
#[cfg(feature = "mkl")]
#[inline(always)]
fn f64_vec(xs: &[f64], ys: &mut [f64]) {
crate::mkl::vd_gelu(xs, ys)
}
#[cfg(feature = "accelerate")]
const F32_VEC: bool = true;
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f32_vec(xs: &[f32], ys: &mut [f32]) {
crate::accelerate::vs_gelu(xs, ys)
}
#[cfg(feature = "accelerate")]
const F64_VEC: bool = true;
#[cfg(feature = "accelerate")]
#[inline(always)]
fn f64_vec(xs: &[f64], ys: &mut [f64]) {
crate::accelerate::vd_gelu(xs, ys)
}
}
/// `erf` operation
/// <https://en.wikipedia.org/wiki/Error_function>
impl UnaryOpT for Erf {
const NAME: &'static str = "erf";
const KERNEL: &'static str = "uerf";
const V: Self = Erf;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
bf16::from_f64(Self::f64(v.to_f64()))
}
#[inline(always)]
fn f16(v: f16) -> f16 {
f16::from_f64(Self::f64(v.to_f64()))
}
#[inline(always)]
fn f32(v: f32) -> f32 {
Self::f64(v as f64) as f32
}
#[inline(always)]
fn f64(v: f64) -> f64 {
crate::cpu::erf::erf(v)
}
#[inline(always)]
fn u8(_: u8) -> u8 {
0
}
#[inline(always)]
fn u32(_: u32) -> u32 {
0
}
#[inline(always)]
fn i64(_: i64) -> i64 {
0
}
}
impl UnaryOpT for Abs {
const NAME: &'static str = "abs";
const KERNEL: &'static str = "uabs";
const V: Self = Abs;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
v.abs()
}
#[inline(always)]
fn f16(v: f16) -> f16 {
v.abs()
}
#[inline(always)]
fn f32(v: f32) -> f32 {
v.abs()
}
#[inline(always)]
fn f64(v: f64) -> f64 {
v.abs()
}
#[inline(always)]
fn u8(v: u8) -> u8 {
v
}
#[inline(always)]
fn u32(v: u32) -> u32 {
v
}
#[inline(always)]
fn i64(v: i64) -> i64 {
v.abs()
}
}
impl UnaryOpT for Ceil {
const NAME: &'static str = "ceil";
const KERNEL: &'static str = "uceil";
const V: Self = Ceil;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
v.ceil()
}
#[inline(always)]
fn f16(v: f16) -> f16 {
v.ceil()
}
#[inline(always)]
fn f32(v: f32) -> f32 {
v.ceil()
}
#[inline(always)]
fn f64(v: f64) -> f64 {
v.ceil()
}
#[inline(always)]
fn u8(v: u8) -> u8 {
v
}
#[inline(always)]
fn u32(v: u32) -> u32 {
v
}
#[inline(always)]
fn i64(v: i64) -> i64 {
v
}
}
impl UnaryOpT for Floor {
const NAME: &'static str = "floor";
const KERNEL: &'static str = "ufloor";
const V: Self = Floor;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
v.floor()
}
#[inline(always)]
fn f16(v: f16) -> f16 {
v.floor()
}
#[inline(always)]
fn f32(v: f32) -> f32 {
v.floor()
}
#[inline(always)]
fn f64(v: f64) -> f64 {
v.floor()
}
#[inline(always)]
fn u8(v: u8) -> u8 {
v
}
#[inline(always)]
fn u32(v: u32) -> u32 {
v
}
#[inline(always)]
fn i64(v: i64) -> i64 {
v
}
}
impl UnaryOpT for Round {
const NAME: &'static str = "round";
const KERNEL: &'static str = "uround";
const V: Self = Round;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
v.round()
}
#[inline(always)]
fn f16(v: f16) -> f16 {
v.round()
}
#[inline(always)]
fn f32(v: f32) -> f32 {
v.round()
}
#[inline(always)]
fn f64(v: f64) -> f64 {
v.round()
}
#[inline(always)]
fn u8(v: u8) -> u8 {
v
}
#[inline(always)]
fn u32(v: u32) -> u32 {
v
}
#[inline(always)]
fn i64(v: i64) -> i64 {
v
}
}
impl UnaryOpT for GeluErf {
const NAME: &'static str = "gelu_erf";
const KERNEL: &'static str = "ugelu_erf";
const V: Self = GeluErf;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
bf16::from_f64(Self::f64(v.to_f64()))
}
#[inline(always)]
fn f16(v: f16) -> f16 {
f16::from_f64(Self::f64(v.to_f64()))
}
#[inline(always)]
fn f32(v: f32) -> f32 {
Self::f64(v as f64) as f32
}
#[inline(always)]
fn f64(v: f64) -> f64 {
(crate::cpu::erf::erf(v / 2f64.sqrt()) + 1.) * 0.5 * v
}
#[inline(always)]
fn u8(_: u8) -> u8 {
0
}
#[inline(always)]
fn u32(_: u32) -> u32 {
0
}
#[inline(always)]
fn i64(_: i64) -> i64 {
0
}
}
impl UnaryOpT for Relu {
const NAME: &'static str = "relu";
const KERNEL: &'static str = "urelu";
const V: Self = Relu;
#[inline(always)]
fn bf16(v: bf16) -> bf16 {
v.max(bf16::ZERO)
}
#[inline(always)]
fn f16(v: f16) -> f16 {
v.max(f16::ZERO)
}
#[inline(always)]
fn f32(v: f32) -> f32 {
v.max(0f32)
}
#[inline(always)]
fn f64(v: f64) -> f64 {
v.max(0f64)
}
#[inline(always)]
fn u8(v: u8) -> u8 {
v
}
#[inline(always)]
fn u32(v: u32) -> u32 {
v
}
#[inline(always)]
fn i64(v: i64) -> i64 {
v
}
}
/// `BackpropOp` is a wrapper around `Option<Op>`. The main goal is to ensure that dependencies are
/// properly checked when creating a new value
#[derive(Clone)]
pub struct BackpropOp(Option<Op>);
impl BackpropOp {
pub(crate) fn none() -> Self {
BackpropOp(None)
}
pub(crate) fn new1(arg: &Tensor, f: impl Fn(Tensor) -> Op) -> Self {
let op = if arg.track_op() {
Some(f(arg.clone()))
} else {
None
};
Self(op)
}
pub(crate) fn new2(arg1: &Tensor, arg2: &Tensor, f: impl Fn(Tensor, Tensor) -> Op) -> Self {
let op = if arg1.track_op() || arg2.track_op() {
Some(f(arg1.clone(), arg2.clone()))
} else {
None
};
Self(op)
}
pub(crate) fn new3(
arg1: &Tensor,
arg2: &Tensor,
arg3: &Tensor,
f: impl Fn(Tensor, Tensor, Tensor) -> Op,
) -> Self {
let op = if arg1.track_op() || arg2.track_op() || arg3.track_op() {
Some(f(arg1.clone(), arg2.clone(), arg3.clone()))
} else {
None
};
Self(op)
}
pub(crate) fn new<A: AsRef<Tensor>>(args: &[A], f: impl Fn(Vec<Tensor>) -> Op) -> Self {
let op = if args.iter().any(|arg| arg.as_ref().track_op()) {
let args: Vec<Tensor> = args.iter().map(|arg| arg.as_ref().clone()).collect();
Some(f(args))
} else {
None
};
Self(op)
}
pub(crate) fn is_none(&self) -> bool {
self.0.is_none()
}
}
impl std::ops::Deref for BackpropOp {
type Target = Option<Op>;
fn deref(&self) -> &Self::Target {
&self.0
}
}
| 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);
pub fn vdExp(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsLn(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdLn(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsSin(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdSin(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsCos(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdCos(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsSqrt(n: c_int, a: *const c_float, y: *mut c_float);
pub fn vdSqrt(n: c_int, a: *const c_double, y: *mut c_double);
pub fn vsAdd(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdAdd(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn vsSub(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdSub(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn vsMul(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdMul(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn vsDiv(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdDiv(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn vsFmax(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdFmax(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn vsFmin(n: c_int, a: *const c_float, b: *const c_float, y: *mut c_float);
pub fn vdFmin(n: c_int, a: *const c_double, b: *const c_double, y: *mut c_double);
pub fn sgemm_(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_float,
a: *const c_float,
lda: *const c_int,
b: *const c_float,
ldb: *const c_int,
beta: *const c_float,
c: *mut c_float,
ldc: *const c_int,
);
pub fn dgemm_(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_double,
a: *const c_double,
lda: *const c_int,
b: *const c_double,
ldb: *const c_int,
beta: *const c_double,
c: *mut c_double,
ldc: *const c_int,
);
pub fn hgemm_(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const half::f16,
a: *const half::f16,
lda: *const c_int,
b: *const half::f16,
ldb: *const c_int,
beta: *const half::f16,
c: *mut half::f16,
ldc: *const c_int,
);
}
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn sgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f32,
a: &[f32],
lda: i32,
b: &[f32],
ldb: i32,
beta: f32,
c: &mut [f32],
ldc: i32,
) {
ffi::sgemm_(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn dgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f64,
a: &[f64],
lda: i32,
b: &[f64],
ldb: i32,
beta: f64,
c: &mut [f64],
ldc: i32,
) {
ffi::dgemm_(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn hgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: half::f16,
a: &[half::f16],
lda: i32,
b: &[half::f16],
ldb: i32,
beta: half::f16,
c: &mut [half::f16],
ldc: i32,
) {
ffi::hgemm_(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[inline]
pub fn vs_exp(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsExp(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_exp(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdExp(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_ln(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsLn(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_ln(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdLn(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_sin(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsSin(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_sin(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdSin(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_cos(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsCos(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_cos(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdCos(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_sqrt(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsSqrt(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_sqrt(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdSqrt(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_sqr(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsMul(a_len as i32, a.as_ptr(), a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_sqr(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdMul(a_len as i32, a.as_ptr(), a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_tanh(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vsTanh(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_tanh(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vdTanh(a_len as i32, a.as_ptr(), y.as_mut_ptr()) }
}
// The vector functions from mkl can be performed in place by using the same array for input and
// output.
// https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-2/vector-mathematical-functions.html
#[inline]
pub fn vs_tanh_inplace(y: &mut [f32]) {
unsafe { ffi::vsTanh(y.len() as i32, y.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vd_tanh_inplace(y: &mut [f64]) {
unsafe { ffi::vdTanh(y.len() as i32, y.as_ptr(), y.as_mut_ptr()) }
}
#[inline]
pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vs_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
#[inline]
pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vd_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
macro_rules! binary_op {
($fn_name:ident, $ty:ty, $mkl_name:ident) => {
#[inline]
pub fn $fn_name(a: &[$ty], b: &[$ty], y: &mut [$ty]) {
let a_len = a.len();
let b_len = b.len();
let y_len = y.len();
if a_len != y_len || b_len != y_len {
panic!(
"{} a,b,y len mismatch {a_len} {b_len} {y_len}",
stringify!($fn_name)
);
}
unsafe { ffi::$mkl_name(a_len as i32, a.as_ptr(), b.as_ptr(), y.as_mut_ptr()) }
}
};
}
binary_op!(vs_add, f32, vsAdd);
binary_op!(vd_add, f64, vdAdd);
binary_op!(vs_sub, f32, vsSub);
binary_op!(vd_sub, f64, vdSub);
binary_op!(vs_mul, f32, vsMul);
binary_op!(vd_mul, f64, vdMul);
binary_op!(vs_div, f32, vsDiv);
binary_op!(vd_div, f64, vdDiv);
binary_op!(vs_max, f32, vsFmax);
binary_op!(vd_max, f64, vdFmax);
binary_op!(vs_min, f32, vsFmin);
binary_op!(vd_min, f64, vdFmin);
| 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, add `cuda` feature to enable.")
};
}
impl crate::backend::BackendStorage for CudaStorage {
type Device = CudaDevice;
fn try_clone(&self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn dtype(&self) -> DType {
fail!()
}
fn device(&self) -> &Self::Device {
fail!()
}
fn to_cpu_storage(&self) -> Result<CpuStorage> {
Err(Error::NotCompiledWithCudaSupport)
}
fn affine(&self, _: &Layout, _: f64, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn powf(&self, _: &Layout, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn elu(&self, _: &Layout, _: f64) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn reduce_op(&self, _: ReduceOp, _: &Layout, _: &[usize]) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn cmp(&self, _: CmpOp, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn to_dtype(&self, _: &Layout, _: DType) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn unary_impl<B: UnaryOpT>(&self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn binary_impl<B: BinaryOpT>(&self, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn where_cond(&self, _: &Layout, _: &Self, _: &Layout, _: &Self, _: &Layout) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn conv1d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConv1D,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn conv_transpose1d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn conv2d(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &crate::conv::ParamsConv2D,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn conv_transpose2d(
&self,
_l: &Layout,
_kernel: &Self,
_kernel_l: &Layout,
_params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn index_select(&self, _: &Self, _: &Layout, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn scatter_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn index_add(
&self,
_: &Layout,
_: &Self,
_: &Layout,
_: &Self,
_: &Layout,
_: usize,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn matmul(
&self,
_: &Self,
_: (usize, usize, usize, usize),
_: &Layout,
_: &Layout,
) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()> {
Err(Error::NotCompiledWithCudaSupport)
}
fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
}
impl crate::backend::BackendDevice for CudaDevice {
type Storage = CudaStorage;
fn new(_: usize) -> Result<Self> {
Err(Error::NotCompiledWithCudaSupport)
}
fn set_seed(&self, _: u64) -> Result<()> {
Err(Error::NotCompiledWithCudaSupport)
}
fn location(&self) -> crate::DeviceLocation {
fail!()
}
fn same_device(&self, _: &Self) -> bool {
fail!()
}
fn zeros_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
Err(Error::NotCompiledWithCudaSupport)
}
fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
Err(Error::NotCompiledWithCudaSupport)
}
fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage> {
Err(Error::NotCompiledWithCudaSupport)
}
fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
Err(Error::NotCompiledWithCudaSupport)
}
fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
Err(Error::NotCompiledWithCudaSupport)
}
}
| 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 has_accelerate() -> bool {
cfg!(feature = "accelerate")
}
pub fn has_mkl() -> bool {
cfg!(feature = "mkl")
}
pub fn cuda_is_available() -> bool {
cfg!(feature = "cuda")
}
pub fn metal_is_available() -> bool {
cfg!(feature = "metal")
}
pub fn with_avx() -> bool {
cfg!(target_feature = "avx")
}
pub fn with_neon() -> bool {
cfg!(target_feature = "neon")
}
pub fn with_simd128() -> bool {
cfg!(target_feature = "simd128")
}
pub fn with_f16c() -> bool {
cfg!(target_feature = "f16c")
}
| 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 should not implement [Clone] here and instead have an explicit allocator +
// intercept the oom errors to avoid panicking and provide a proper error.
#[derive(Debug, Clone)]
pub enum CpuStorage {
U8(Vec<u8>),
U32(Vec<u32>),
I64(Vec<i64>),
BF16(Vec<bf16>),
F16(Vec<f16>),
F32(Vec<f32>),
F64(Vec<f64>),
}
#[derive(Debug, Clone)]
pub struct CpuDevice;
pub trait Map1 {
fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>>;
fn map(&self, vs: &CpuStorage, layout: &Layout) -> Result<CpuStorage> {
match vs {
CpuStorage::U8(vs) => Ok(CpuStorage::U8(self.f(vs, layout)?)),
CpuStorage::U32(vs) => Ok(CpuStorage::U32(self.f(vs, layout)?)),
CpuStorage::I64(vs) => Ok(CpuStorage::I64(self.f(vs, layout)?)),
CpuStorage::BF16(vs) => Ok(CpuStorage::BF16(self.f(vs, layout)?)),
CpuStorage::F16(vs) => Ok(CpuStorage::F16(self.f(vs, layout)?)),
CpuStorage::F32(vs) => Ok(CpuStorage::F32(self.f(vs, layout)?)),
CpuStorage::F64(vs) => Ok(CpuStorage::F64(self.f(vs, layout)?)),
}
}
}
pub trait Map1Any {
fn f<T: WithDType, W: Fn(Vec<T>) -> CpuStorage>(
&self,
vs: &[T],
layout: &Layout,
wrap: W,
) -> Result<CpuStorage>;
fn map(&self, vs: &CpuStorage, layout: &Layout) -> Result<CpuStorage> {
match vs {
CpuStorage::U8(vs) => Ok(self.f(vs, layout, CpuStorage::U8)?),
CpuStorage::U32(vs) => Ok(self.f(vs, layout, CpuStorage::U32)?),
CpuStorage::I64(vs) => Ok(self.f(vs, layout, CpuStorage::I64)?),
CpuStorage::BF16(vs) => Ok(self.f(vs, layout, CpuStorage::BF16)?),
CpuStorage::F16(vs) => Ok(self.f(vs, layout, CpuStorage::F16)?),
CpuStorage::F32(vs) => Ok(self.f(vs, layout, CpuStorage::F32)?),
CpuStorage::F64(vs) => Ok(self.f(vs, layout, CpuStorage::F64)?),
}
}
}
type C = CpuStorage;
pub trait Map2 {
const OP: &'static str;
fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<T>>;
fn map(
&self,
v1: &CpuStorage,
l1: &Layout,
v2: &CpuStorage,
l2: &Layout,
) -> Result<CpuStorage> {
match (v1, v2) {
(C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::U32(v1), C::U32(v2)) => Ok(C::U32(self.f(v1, l1, v2, l2)?)),
(C::I64(v1), C::I64(v2)) => Ok(C::I64(self.f(v1, l1, v2, l2)?)),
(C::BF16(v1), C::BF16(v2)) => Ok(C::BF16(self.f(v1, l1, v2, l2)?)),
(C::F16(v1), C::F16(v2)) => Ok(C::F16(self.f(v1, l1, v2, l2)?)),
(C::F32(v1), C::F32(v2)) => Ok(C::F32(self.f(v1, l1, v2, l2)?)),
(C::F64(v1), C::F64(v2)) => Ok(C::F64(self.f(v1, l1, v2, l2)?)),
_ => Err(Error::DTypeMismatchBinaryOp {
lhs: v1.dtype(),
rhs: v2.dtype(),
op: Self::OP,
}
.bt()),
}
}
}
pub trait Map2U8 {
const OP: &'static str;
fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<u8>>;
fn map(
&self,
v1: &CpuStorage,
l1: &Layout,
v2: &CpuStorage,
l2: &Layout,
) -> Result<CpuStorage> {
match (v1, v2) {
(C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::U32(v1), C::U32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::I64(v1), C::I64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::BF16(v1), C::BF16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::F16(v1), C::F16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::F32(v1), C::F32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
(C::F64(v1), C::F64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
_ => Err(Error::DTypeMismatchBinaryOp {
lhs: v1.dtype(),
rhs: v2.dtype(),
op: Self::OP,
}
.bt()),
}
}
}
struct Cmp(CmpOp);
impl Map2U8 for Cmp {
const OP: &'static str = "cmp";
#[inline(always)]
fn f<T: WithDType>(
&self,
lhs: &[T],
lhs_l: &Layout,
rhs: &[T],
rhs_l: &Layout,
) -> Result<Vec<u8>> {
let dst = match self.0 {
CmpOp::Eq => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x == y)),
CmpOp::Ne => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x != y)),
CmpOp::Lt => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x < y)),
CmpOp::Le => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x <= y)),
CmpOp::Gt => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x > y)),
CmpOp::Ge => binary_map(lhs_l, rhs_l, lhs, rhs, |x, y| u8::from(x >= y)),
};
Ok(dst)
}
}
struct WCond<'a, T: IntDType>(&'a [T], &'a Layout);
impl<'a, I: IntDType> Map2 for WCond<'a, I> {
const OP: &'static str = "where";
#[inline(always)]
fn f<T: WithDType>(&self, t: &[T], t_l: &Layout, f: &[T], f_l: &Layout) -> Result<Vec<T>> {
let vs = match (
self.1.contiguous_offsets(),
t_l.contiguous_offsets(),
f_l.contiguous_offsets(),
) {
(Some((o1, o2)), Some((o_t1, o_t2)), Some((o_f1, o_f2))) => {
let pred = &self.0[o1..o2];
let t = &t[o_t1..o_t2];
let f = &f[o_f1..o_f2];
pred.iter()
.zip(t.iter().zip(f.iter()))
.map(|(p, (&t, &f))| if p.is_true() { t } else { f })
.collect::<Vec<_>>()
}
_ => self
.1
.strided_index()
.zip(t_l.strided_index().zip(f_l.strided_index()))
.map(|(i_p, (i_t, i_f))| {
if self.0[i_p].is_true() {
t[i_t]
} else {
f[i_f]
}
})
.collect::<Vec<_>>(),
};
Ok(vs)
}
}
struct ReduceIndex {
reduce_dim_index: usize,
use_min: bool,
return_index: bool,
}
impl ReduceIndex {
// The value gets replaced if f(s[current_acc], s[i]) returns true.
#[inline(always)]
fn fold_impl<T, U, F, G>(&self, src: &[T], src_l: &Layout, f: F, g: G) -> Result<Vec<U>>
where
T: Clone + Copy,
U: Clone + Copy,
F: Fn(T, T) -> bool,
G: Fn(T, usize) -> U,
{
let reduce_dim_size = src_l.dims()[self.reduce_dim_index];
let reduce_dim_stride = src_l.stride()[self.reduce_dim_index];
let dst_len = src_l.shape().elem_count() / reduce_dim_size;
let mut dst: Vec<U> = Vec::with_capacity(dst_len);
let dst_to_set = dst.spare_capacity_mut();
let dst_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(dst_to_set) };
match src_l.contiguous_offsets() {
Some((o1, o2)) => {
let src = &src[o1..o2];
if reduce_dim_stride == 1 {
for (start_src_i, dst_v) in dst_to_set.iter_mut().enumerate() {
let start_src_i = start_src_i * reduce_dim_size;
let src = &src[start_src_i..start_src_i + reduce_dim_size];
let mut acc = 0;
let mut val = src[0];
for (src_i, &s) in src.iter().enumerate() {
if f(val, s) {
acc = src_i;
val = s
}
}
*dst_v = g(val, acc)
}
} else {
for (start_src_i, dst_v) in dst_to_set.iter_mut().enumerate() {
let (p, q) = (
start_src_i / reduce_dim_stride,
start_src_i % reduce_dim_stride,
);
// start_src_i = p * reduce_dim_stride + q
let start_src_i = p * reduce_dim_stride * reduce_dim_size + q;
let src = &src[start_src_i..];
let mut acc = 0;
let mut val = src[0];
for src_i in 0..reduce_dim_size {
let s = src[src_i * reduce_dim_stride];
if f(val, s) {
acc = src_i;
val = s
}
}
*dst_v = g(val, acc)
}
}
}
None => {
let l = src_l.narrow(self.reduce_dim_index, 0, 1)?;
for (unstr_index, src_index) in l.strided_index().enumerate() {
let src = &src[src_index..];
let mut acc = 0;
let mut val = src[0];
for src_i in 0..reduce_dim_size {
let s = src[src_i * reduce_dim_stride];
if f(val, s) {
acc = src_i;
val = s
}
}
dst_to_set[unstr_index] = g(val, acc)
}
}
}
unsafe { dst.set_len(dst_len) };
Ok(dst)
}
}
impl Map1Any for ReduceIndex {
#[inline(always)]
fn f<T: WithDType, W: Fn(Vec<T>) -> CpuStorage>(
&self,
src: &[T],
src_l: &Layout,
wrap: W,
) -> Result<CpuStorage> {
if src_l.shape().elem_count() == 0 {
Err(Error::EmptyTensor { op: "reduce" }.bt())?
}
let dst = match (self.return_index, self.use_min) {
(false, true) => wrap(self.fold_impl(src, src_l, |x, y| x > y, |v, _i| v)?),
(false, false) => wrap(self.fold_impl(src, src_l, |x, y| x < y, |v, _i| v)?),
(true, true) => {
CpuStorage::U32(self.fold_impl(src, src_l, |x, y| x > y, |_v, i| i as u32)?)
}
(true, false) => {
CpuStorage::U32(self.fold_impl(src, src_l, |x, y| x < y, |_v, i| i as u32)?)
}
};
Ok(dst)
}
}
struct ReduceSum<'a> {
dst_shape: &'a Shape,
reduce_dims: &'a [usize],
reduce_dims_and_stride: Vec<(usize, usize)>,
}
impl<'a> ReduceSum<'a> {
#[inline(always)]
fn fold_impl<T>(&self, src: &[T], src_l: &Layout, start_elt: T) -> Result<Vec<T>>
where
T: WithDType,
{
let mut dst = vec![start_elt; self.dst_shape.elem_count()];
match src_l.contiguous_offsets() {
Some((o1, o2)) => {
let src = &src[o1..o2];
// Handle the case where we reduce over the last dimensions separately as it is
// fairly common and easy to optimize. This rely on the layout being contiguous!
// reduce_dims is sorted, check if it is ranging from a to n-1.
let reduce_over_last_dims = self
.reduce_dims
.iter()
.rev()
.enumerate()
.all(|(i, &v)| v == src_l.shape().rank() - 1 - i);
if reduce_over_last_dims {
let reduce_sz = self
.reduce_dims_and_stride
.iter()
.map(|(u, _)| u)
.product::<usize>();
for (dst_i, dst_v) in dst.iter_mut().enumerate() {
let src_i = dst_i * reduce_sz;
unsafe {
T::vec_reduce_sum(
src[src_i..src_i + reduce_sz].as_ptr(),
dst_v,
reduce_sz,
)
};
}
return Ok(dst);
};
for (unstr_index, &src) in src.iter().enumerate() {
let mut dst_index = unstr_index;
// Set the reduce_dims indexes to 0.
for &(dim, stride) in self.reduce_dims_and_stride.iter() {
// The compiler is able to optimize the following in a single divmod op.
let (pre, post) = (dst_index / stride, dst_index % stride);
dst_index = (pre / dim) * stride + post;
}
dst[dst_index] += src;
}
}
None => {
for (unstr_index, src_index) in src_l.strided_index().enumerate() {
let mut dst_index = unstr_index;
// Set the reduce_dims indexes to 0.
for &(dim, stride) in self.reduce_dims_and_stride.iter() {
// The compiler is able to optimize the following in a single divmod op.
let (pre, post) = (dst_index / stride, dst_index % stride);
dst_index = (pre / dim) * stride + post;
}
dst[dst_index] += src[src_index];
}
}
}
Ok(dst)
}
}
impl<'a> Map1 for ReduceSum<'a> {
#[inline(always)]
fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
self.fold_impl(src, src_l, T::zero())
}
}
pub fn unary_map<T: Copy, U: Copy, F: FnMut(T) -> U>(
vs: &[T],
layout: &Layout,
mut f: F,
) -> Vec<U> {
match layout.strided_blocks() {
crate::StridedBlocks::SingleBlock { start_offset, len } => vs
[start_offset..start_offset + len]
.iter()
.map(|&v| f(v))
.collect(),
crate::StridedBlocks::MultipleBlocks {
block_start_index,
block_len,
} => {
let mut result = Vec::with_capacity(layout.shape().elem_count());
// Specialize the case where block_len is one to avoid the second loop.
if block_len == 1 {
for index in block_start_index {
let v = unsafe { vs.get_unchecked(index) };
result.push(f(*v))
}
} else {
for index in block_start_index {
for offset in 0..block_len {
let v = unsafe { vs.get_unchecked(index + offset) };
result.push(f(*v))
}
}
}
result
}
}
}
pub fn unary_map_vec<T: Copy, U: Copy, F: FnMut(T) -> U, FV: FnMut(&[T], &mut [U])>(
vs: &[T],
layout: &Layout,
mut f: F,
mut f_vec: FV,
) -> Vec<U> {
match layout.strided_blocks() {
crate::StridedBlocks::SingleBlock { start_offset, len } => {
let mut ys: Vec<U> = Vec::with_capacity(len);
let ys_to_set = ys.spare_capacity_mut();
let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
f_vec(&vs[start_offset..start_offset + len], ys_to_set);
// SAFETY: values are all set by f_vec.
unsafe { ys.set_len(len) };
ys
}
crate::StridedBlocks::MultipleBlocks {
block_start_index,
block_len,
} => {
let el_count = layout.shape().elem_count();
// Specialize the case where block_len is one to avoid the second loop.
if block_len == 1 {
let mut result = Vec::with_capacity(el_count);
for index in block_start_index {
let v = unsafe { vs.get_unchecked(index) };
result.push(f(*v))
}
result
} else {
let mut ys: Vec<U> = Vec::with_capacity(el_count);
let ys_to_set = ys.spare_capacity_mut();
let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
let mut dst_index = 0;
for src_index in block_start_index {
let vs = &vs[src_index..src_index + block_len];
let ys = &mut ys_to_set[dst_index..dst_index + block_len];
f_vec(vs, ys);
dst_index += block_len;
}
// SAFETY: values are all set by f_vec.
unsafe { ys.set_len(el_count) };
ys
}
}
}
}
// This function maps over two strided index sequences.
pub fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
lhs_l: &Layout,
rhs_l: &Layout,
lhs: &[T],
rhs: &[T],
mut f: F,
) -> Vec<U> {
match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
(Some((o_l1, o_l2)), Some((o_r1, o_r2))) => lhs[o_l1..o_l2]
.iter()
.zip(rhs[o_r1..o_r2].iter())
.map(|(&l, &r)| f(l, r))
.collect(),
(Some((o_l1, o_l2)), None) => {
// TODO: Maybe we want to avoid going through the layout twice.
match rhs_l.offsets_b() {
Some(ob) => {
let mut i_in_block = 0;
let mut i_right_broadcast = 0;
lhs[o_l1..o_l2]
.iter()
.map(|&l| {
let r = unsafe { rhs.get_unchecked(i_in_block + ob.start) };
i_right_broadcast += 1;
if i_right_broadcast >= ob.right_broadcast {
i_in_block += 1;
i_right_broadcast = 0;
}
if i_in_block >= ob.len {
i_in_block = 0
}
f(l, *r)
})
.collect()
}
None => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
}
}
(None, Some((o_r1, o_r2))) => {
// TODO: Maybe we want to avoid going through the layout twice.
match lhs_l.offsets_b() {
Some(ob) => {
let mut i_in_block = 0;
let mut i_right_broadcast = 0;
rhs[o_r1..o_r2]
.iter()
.map(|&r| {
let l = unsafe { lhs.get_unchecked(i_in_block + ob.start) };
i_right_broadcast += 1;
if i_right_broadcast >= ob.right_broadcast {
i_in_block += 1;
i_right_broadcast = 0;
}
if i_in_block >= ob.len {
i_in_block = 0
}
f(*l, r)
})
.collect()
}
None => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
}
}
_ => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
}
}
// Similar to binary_map but with vectorized variants.
pub fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>(
lhs_l: &Layout,
rhs_l: &Layout,
lhs: &[T],
rhs: &[T],
mut f: F,
mut f_vec: FV,
) -> Vec<T> {
let el_count = lhs_l.shape().elem_count();
match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
(Some((o_l1, o_l2)), Some((o_r1, o_r2))) => {
let mut ys: Vec<T> = Vec::with_capacity(el_count);
let ys_to_set = ys.spare_capacity_mut();
let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
f_vec(&lhs[o_l1..o_l2], &rhs[o_r1..o_r2], ys_to_set);
// SAFETY: values are all set by f_vec.
unsafe { ys.set_len(el_count) };
ys
}
(Some((o_l1, o_l2)), None) => match rhs_l.offsets_b() {
Some(ob) if ob.right_broadcast == 1 => {
let rhs = &rhs[ob.start..ob.start + ob.len];
let mut ys: Vec<T> = Vec::with_capacity(el_count);
let ys_to_set = ys.spare_capacity_mut();
let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
let mut dst_i = 0;
for src_i in (o_l1..o_l2).step_by(ob.len) {
f_vec(
&lhs[src_i..src_i + ob.len],
rhs,
&mut ys_to_set[dst_i..dst_i + ob.len],
);
dst_i += ob.len;
}
// SAFETY: values are all set by f_vec.
unsafe { ys.set_len(el_count) };
ys
}
Some(ob) => {
let rhs = &rhs[ob.start..ob.start + ob.len];
let mut ys = lhs[o_l1..o_l2].to_vec();
for idx_l in 0..ob.left_broadcast {
let start = idx_l * ob.len * ob.right_broadcast;
for (i, &r) in rhs.iter().enumerate() {
let start = start + i * ob.right_broadcast;
for v in ys[start..start + ob.right_broadcast].iter_mut() {
*v = f(*v, r)
}
}
}
ys
}
None => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
},
(None, Some((o_r1, o_r2))) => match lhs_l.offsets_b() {
Some(ob) if ob.right_broadcast == 1 => {
let lhs = &lhs[ob.start..ob.start + ob.len];
let mut ys: Vec<T> = Vec::with_capacity(el_count);
let ys_to_set = ys.spare_capacity_mut();
let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
let mut dst_i = 0;
for src_i in (o_r1..o_r2).step_by(ob.len) {
f_vec(
lhs,
&rhs[src_i..src_i + ob.len],
&mut ys_to_set[dst_i..dst_i + ob.len],
);
dst_i += ob.len;
}
// SAFETY: values are all set by f_vec.
unsafe { ys.set_len(el_count) };
ys
}
Some(ob) => {
let lhs = &lhs[ob.start..ob.start + ob.len];
let mut ys = rhs[o_r1..o_r2].to_vec();
for idx_l in 0..ob.left_broadcast {
let start = idx_l * ob.len * ob.right_broadcast;
for (i, &l) in lhs.iter().enumerate() {
let start = start + i * ob.right_broadcast;
for v in ys[start..start + ob.right_broadcast].iter_mut() {
*v = f(l, *v)
}
}
}
ys
}
None => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
},
_ => lhs_l
.strided_index()
.zip(rhs_l.strided_index())
.map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
.collect(),
}
}
struct Affine(f64, f64);
impl Map1 for Affine {
fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
let mul = T::from_f64(self.0);
let add = T::from_f64(self.1);
Ok(unary_map(vs, layout, |v| v * mul + add))
}
}
struct AvgPool2D((usize, usize), (usize, usize));
impl Map1 for AvgPool2D {
fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
// https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html
let (k_h, k_w) = self.0;
let (s_h, s_w) = self.1;
let (b_sz, c, h, w) = layout.shape().dims4()?;
let stride = layout.stride();
let (stride_h, stride_w) = (stride[2], stride[3]);
let h_out = (h - k_h) / s_h + 1;
let w_out = (w - k_w) / s_w + 1;
let src_index = layout.start_offset();
let mut dst = vec![T::zero(); b_sz * c * h_out * w_out];
let scale = 1f64 / (k_h * k_w) as f64;
let scale = T::from_f64(scale);
for b_idx in 0..b_sz {
let dst = &mut dst[b_idx * c * h_out * w_out..];
let src_index = src_index + b_idx * stride[0];
for c_idx in 0..c {
let dst = &mut dst[c_idx * h_out * w_out..];
let src_index = src_index + c_idx * stride[1];
for h_idx in 0..h_out {
for w_idx in 0..w_out {
let mut sum = T::zero();
for m in 0..k_h {
for n in 0..k_w {
let m = s_h * h_idx + m;
let n = s_w * w_idx + n;
sum += src[src_index + m * stride_h + n * stride_w]
}
}
dst[h_idx * w_out + w_idx] = sum * scale;
}
}
}
}
Ok(dst)
}
}
struct MaxPool2D((usize, usize), (usize, usize));
impl Map1 for MaxPool2D {
fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
// https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html
let (k_h, k_w) = self.0;
let (s_h, s_w) = self.1;
let (b_sz, c, h, w) = layout.shape().dims4()?;
let stride = layout.stride();
let (stride_h, stride_w) = (stride[2], stride[3]);
let h_out = (h - k_h) / s_h + 1;
let w_out = (w - k_w) / s_w + 1;
let src_index = layout.start_offset();
let mut dst = vec![T::zero(); b_sz * c * h_out * w_out];
for b_idx in 0..b_sz {
let dst = &mut dst[b_idx * c * h_out * w_out..];
let src_index = src_index + b_idx * stride[0];
for c_idx in 0..c {
let dst = &mut dst[c_idx * h_out * w_out..];
let src_index = src_index + c_idx * stride[1];
for h_idx in 0..h_out {
for w_idx in 0..w_out {
let mut largest =
src[src_index + s_h * h_idx * stride_h + s_w * w_idx * stride_w];
for m in 0..k_h {
for n in 0..k_w {
let m = s_h * h_idx + m;
let n = s_w * w_idx + n;
if largest < src[src_index + m * stride_h + n * stride_w] {
largest = src[src_index + m * stride_h + n * stride_w]
}
}
}
dst[h_idx * w_out + w_idx] = largest;
}
}
}
}
Ok(dst)
}
}
struct UpsampleNearest1D(usize);
impl Map1 for UpsampleNearest1D {
fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
// TODO: Specialized implementation for the case 2*sz?
let dst_sz = self.0;
let (b_sz, c, src_sz) = layout.shape().dims3()?;
let stride = layout.stride();
let stride_sz = stride[2];
let src_index = layout.start_offset();
let scale_sz = src_sz as f64 / dst_sz as f64;
let mut dst = vec![T::zero(); b_sz * c * dst_sz];
let src_idxs = (0..dst_sz)
.map(|idx| usize::min(src_sz - 1, (idx as f64 * scale_sz) as usize))
.collect::<Vec<_>>();
for b_idx in 0..b_sz {
let dst = &mut dst[b_idx * c * dst_sz..];
let src_index = src_index + b_idx * stride[0];
for c_idx in 0..c {
let dst = &mut dst[c_idx * dst_sz..];
let src_index = src_index + c_idx * stride[1];
for (idx, src_idx) in src_idxs.iter().enumerate() {
dst[idx] = src[src_index + src_idx * stride_sz]
}
}
}
Ok(dst)
}
}
struct UpsampleNearest2D(usize, usize);
impl Map1 for UpsampleNearest2D {
fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
// TODO: Specialized implementation for the case 2*h, 2*w?
let (dst_h, dst_w) = (self.0, self.1);
let (b_sz, c, src_h, src_w) = layout.shape().dims4()?;
let stride = layout.stride();
let (stride_h, stride_w) = (stride[2], stride[3]);
let src_index = layout.start_offset();
let scale_h = src_h as f64 / dst_h as f64;
let scale_w = src_w as f64 / dst_w as f64;
let mut dst = vec![T::zero(); b_sz * c * dst_h * dst_w];
let src_h_idxs = (0..dst_h)
.map(|h_idx| usize::min(src_h - 1, (h_idx as f64 * scale_h) as usize))
.collect::<Vec<_>>();
let src_w_idxs = (0..dst_w)
.map(|w_idx| usize::min(src_w - 1, (w_idx as f64 * scale_w) as usize))
.collect::<Vec<_>>();
for b_idx in 0..b_sz {
let dst = &mut dst[b_idx * c * dst_h * dst_w..];
let src_index = src_index + b_idx * stride[0];
for c_idx in 0..c {
let dst = &mut dst[c_idx * dst_h * dst_w..];
let src_index = src_index + c_idx * stride[1];
for (h_idx, src_h_idx) in src_h_idxs.iter().enumerate() {
for (w_idx, src_w_idx) in src_w_idxs.iter().enumerate() {
let src_index = src_index + src_h_idx * stride_h + src_w_idx * stride_w;
dst[h_idx * dst_w + w_idx] = src[src_index]
}
}
}
}
Ok(dst)
}
}
struct Gather<'a, I: IntDType> {
ids: &'a [I],
ids_l: &'a Layout,
dim: usize,
}
impl<'a, I: IntDType> Map1 for Gather<'a, I> {
fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
let ids = match self.ids_l.contiguous_offsets() {
Some((a, b)) => &self.ids[a..b],
None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
};
let src = match src_l.contiguous_offsets() {
Some((a, b)) => &src[a..b],
None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
};
let dim = self.dim;
let ids_dims = self.ids_l.dims();
let src_dims = src_l.dims();
let dst_len: usize = ids_dims.iter().product();
let dst_left_len: usize = ids_dims[..dim].iter().product();
let dst_dim_len = ids_dims[dim];
let dst_right_len: usize = ids_dims[dim + 1..].iter().product();
let src_dim_len = src_dims[dim];
let src_right_len: usize = src_dims[dim + 1..].iter().product();
let mut dst = vec![T::zero(); dst_len];
for left_i in 0..dst_left_len {
let start_src_idx = left_i * src_right_len * src_dim_len;
let start_dst_idx = left_i * dst_right_len * dst_dim_len;
for i in 0..dst_dim_len {
let start_dst_idx = start_dst_idx + i * dst_right_len;
for right_i in 0..dst_right_len {
let dst_idx = start_dst_idx + right_i;
let index = ids[dst_idx].as_usize();
if index >= src_dim_len {
Err(Error::InvalidIndex {
index,
size: src_dim_len,
op: "gather",
}
.bt())?
}
let src_idx = start_src_idx + index * src_right_len + right_i;
dst[dst_idx] = src[src_idx]
}
}
}
Ok(dst)
}
}
struct IndexSelect<'a, T: IntDType> {
ids: &'a [T],
ids_l: &'a Layout,
dim: usize,
}
impl<'a, I: IntDType> Map1 for IndexSelect<'a, I> {
fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
let src = match layout.contiguous_offsets() {
Some((a, b)) => &src[a..b],
None => Err(Error::RequiresContiguous { op: "index-select" }.bt())?,
};
let dim = self.dim;
let n_ids = match self.ids_l.dims() {
[n_ids] => *n_ids,
d => Err(Error::UnexpectedNumberOfDims {
expected: 1,
got: d.len(),
shape: self.ids_l.shape().clone(),
}
.bt())?,
};
let stride_ids = self.ids_l.stride()[0];
let mut dst_dims = layout.dims().to_vec();
let src_dim = dst_dims[dim];
dst_dims[dim] = n_ids;
let dst_len: usize = dst_dims.iter().product();
let left_len: usize = dst_dims[..dim].iter().product();
let right_len: usize = dst_dims[dim + 1..].iter().product();
let mut dst = vec![T::zero(); dst_len];
for left_i in 0..left_len {
let start_src_idx = left_i * right_len * src_dim;
let start_dst_idx = left_i * right_len * n_ids;
for i in 0..n_ids {
let index = self.ids[self.ids_l.start_offset() + stride_ids * i].as_usize();
if index >= src_dim {
Err(Error::InvalidIndex {
index,
size: src_dim,
op: "index-select",
}
.bt())?
}
let start_src_idx = start_src_idx + index * right_len;
let start_dst_idx = start_dst_idx + i * right_len;
dst[start_dst_idx..start_dst_idx + right_len]
.copy_from_slice(&src[start_src_idx..start_src_idx + right_len])
}
}
Ok(dst)
}
}
struct ScatterAdd<'a, I: IntDType> {
ids: &'a [I],
ids_l: &'a Layout,
dim: usize,
}
impl<'a, I: IntDType> Map2 for ScatterAdd<'a, I> {
const OP: &'static str = "scatter-add";
fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
let dst_len = l1.shape().elem_count();
let mut dst = vec![T::zero(); dst_len];
copy_strided_src_(v1, &mut dst, 0, l1);
let src = match src_l.contiguous_offsets() {
None => Err(Error::RequiresContiguous { op: "scatter-add" }.bt())?,
Some((o1, o2)) => &src[o1..o2],
};
let dim = self.dim;
let ids_dims = self.ids_l.dims();
let dst_dims = l1.dims();
let dst_dim_len = dst_dims[dim];
let dst_right_len: usize = dst_dims[dim + 1..].iter().product();
let ids_left_len: usize = ids_dims[..dim].iter().product();
let ids_dim_len = ids_dims[dim];
let ids_right_len: usize = ids_dims[dim + 1..].iter().product();
let ids = match self.ids_l.contiguous_offsets() {
Some((a, b)) => &self.ids[a..b],
None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
};
for left_i in 0..ids_left_len {
let start_ids_idx = left_i * ids_right_len * ids_dim_len;
let start_dst_idx = left_i * dst_right_len * dst_dim_len;
for i in 0..ids_dim_len {
let start_ids_idx = start_ids_idx + i * ids_right_len;
for right_i in 0..dst_right_len {
let ids_idx = start_ids_idx + right_i;
let index = ids[ids_idx].as_usize();
if index >= dst_dim_len {
Err(Error::InvalidIndex {
index,
size: dst_dim_len,
op: "gather",
}
.bt())?
}
let dst_idx = start_dst_idx + index * dst_right_len + right_i;
dst[dst_idx] += src[ids_idx]
}
}
}
Ok(dst)
}
}
struct IndexAdd<'a, I: IntDType> {
ids: &'a [I],
dim: usize,
}
impl<'a, I: IntDType> Map2 for IndexAdd<'a, I> {
const OP: &'static str = "index-add";
// https://pytorch.org/docs/stable/generated/torch.Tensor.index_add_.html#torch.Tensor.index_add_
// v1, l1 -> self
fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
let dst_len = l1.shape().elem_count();
let mut dst = vec![T::zero(); dst_len];
copy_strided_src_(v1, &mut dst, 0, l1);
let src = match src_l.contiguous_offsets() {
None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
Some((o1, o2)) => &src[o1..o2],
};
let dim = self.dim;
let max_idx = l1.dims()[dim];
let pre_dim = src_l.dims()[..dim].iter().product::<usize>();
let src_dim_sz = src_l.dims()[dim];
let post_dim = src_l.dims()[dim + 1..].iter().product::<usize>();
if dim == 0 {
for (src_idx, dst_idx) in self.ids.iter().enumerate() {
let dst_idx = dst_idx.as_usize();
if dst_idx >= max_idx {
Err(Error::InvalidIndex {
index: dst_idx,
op: "index-add",
size: max_idx,
})?
}
let src_idx = src_idx * post_dim;
let dst_idx = dst_idx * post_dim;
let src = &src[src_idx..src_idx + post_dim];
let dst = &mut dst[dst_idx..dst_idx + post_dim];
for (d, &s) in dst.iter_mut().zip(src.iter()) {
*d += s
}
}
} else {
for (src_idx, dst_idx) in self.ids.iter().enumerate() {
let dst_idx = dst_idx.as_usize();
if dst_idx >= max_idx {
Err(Error::InvalidIndex {
index: dst_idx,
op: "index-add",
size: max_idx,
})?
}
for pre_i in 0..pre_dim {
let pre_src_i = (pre_i * src_dim_sz + src_idx) * post_dim;
let pre_dst_i = (pre_i * max_idx + dst_idx) * post_dim;
let src = &src[pre_src_i..pre_src_i + post_dim];
let dst = &mut dst[pre_dst_i..pre_dst_i + post_dim];
for (d, &s) in dst.iter_mut().zip(src.iter()) {
*d += s
}
}
}
}
Ok(dst)
}
}
fn copy_strided_src_<T: Copy>(src: &[T], dst: &mut [T], dst_offset: usize, src_l: &Layout) {
match src_l.strided_blocks() {
crate::StridedBlocks::SingleBlock { start_offset, len } => {
let to_copy = (dst.len() - dst_offset).min(len);
dst[dst_offset..dst_offset + to_copy]
.copy_from_slice(&src[start_offset..start_offset + to_copy])
}
crate::StridedBlocks::MultipleBlocks {
block_start_index,
block_len: 1,
} => {
for (dst_index, src_index) in block_start_index.enumerate() {
let dst_index = dst_index + dst_offset;
if dst_index >= dst.len() {
break;
}
dst[dst_index] = src[src_index]
}
}
crate::StridedBlocks::MultipleBlocks {
block_start_index,
block_len,
} => {
let mut dst_index = dst_offset;
for src_index in block_start_index {
let next_dst_index = dst_index + block_len;
if dst_index >= dst.len() {
break;
}
let to_copy = usize::min(block_len, dst.len() - dst_index);
dst[dst_index..dst_index + to_copy]
.copy_from_slice(&src[src_index..src_index + to_copy]);
dst_index = next_dst_index
}
}
}
}
struct Conv1D<'a>(&'a crate::conv::ParamsConv1D);
impl<'a> Map2 for Conv1D<'a> {
const OP: &'static str = "conv1d";
fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
let p = self.0;
let inp = &inp[inp_l.start_offset()..];
let k = &k[k_l.start_offset()..];
let (inp_s0, inp_s1, inp_s2) = crate::shape::dims3(inp_l.stride())?;
let (k_s0, k_s1, k_s2) = crate::shape::dims3(k_l.stride())?;
let l_out = p.l_out();
let dst_elems = p.c_out * l_out * p.b_size;
// The output shape is [b_size, c_out, l_out]
let dst = vec![T::zero(); dst_elems];
// TODO: Avoid making this copy if `inp` already has the appropriate layout.
let mut inp_cont = vec![T::zero(); p.b_size * p.c_in * p.l_in];
for b_idx in 0..p.b_size {
for src_l in 0..p.l_in {
for src_c_idx in 0..p.c_in {
let inp_idx = b_idx * inp_s0 + src_c_idx * inp_s1 + src_l * inp_s2;
inp_cont[b_idx * p.l_in * p.c_in + src_l * p.c_in + src_c_idx] = inp[inp_idx]
}
}
}
for offset in 0..p.k_size {
(0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
let dst_idx = dst_c_idx * l_out;
let k_cont = (0..p.c_in)
.map(|c_in_idx| k[dst_c_idx * k_s0 + c_in_idx * k_s1 + offset * k_s2])
.collect::<Vec<_>>();
for b_idx in 0..p.b_size {
let dst_idx = dst_idx + b_idx * p.c_out * l_out;
for dst_l in 0..l_out {
let dst_idx = dst_idx + dst_l;
let src_l = p.stride * dst_l + offset * p.dilation;
if src_l < p.padding || src_l >= p.padding + p.l_in {
continue;
}
let src_l = src_l - p.padding;
let inp_cont = &inp_cont[b_idx * p.l_in * p.c_in + src_l * p.c_in..];
assert!(inp_cont.len() >= p.c_in);
assert!(k_cont.len() >= p.c_in);
let mut d = T::zero();
unsafe { T::vec_dot(inp_cont.as_ptr(), k_cont.as_ptr(), &mut d, p.c_in) }
let dst_p = dst.as_ptr();
// Safety: dst_idx are uniques per dst_c_idx which is used to parallelise
// the different tasks so no two threads can try to write at the same
// location.
unsafe {
let ptr = dst_p.add(dst_idx) as *mut T;
*ptr += d
}
}
}
})
}
Ok(dst)
}
}
struct Im2Col1D {
l_k: usize,
stride: usize,
dilation: usize,
padding: usize,
}
impl Im2Col1D {
fn l_out(&self, l: usize) -> usize {
(l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
}
}
impl Map1 for Im2Col1D {
fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
let &Self {
l_k,
stride,
dilation,
padding,
} = self;
let (b, c, l) = layout.shape().dims3()?;
let l_out = self.l_out(l);
let src = &vs[layout.start_offset()..];
let mut dst = vec![T::zero(); b * l_out * c * l_k];
let (src_s0, src_s1, src_s2) = {
let s = layout.stride();
(s[0], s[1], s[2])
};
// TODO: provide specialized kernels for the common use cases.
// - l_k = 1
// - padding = 0
// - stride = 1
// - dilation = 1
for b_idx in 0..b {
let src_idx = b_idx * src_s0;
let dst_idx = b_idx * l_out * c * l_k;
for l_idx in 0..l_out {
let dst_idx = dst_idx + l_idx * c * l_k;
for c_idx in 0..c {
let dst_idx = dst_idx + c_idx * l_k;
let src_idx = c_idx * src_s1 + src_idx;
for l_k_idx in 0..l_k {
let src_l = l_idx * stride + l_k_idx * dilation;
if padding != 0 && (src_l < padding || src_l >= l + padding) {
continue;
}
let src_l = src_l - padding;
let src_idx = src_idx + src_l * src_s2;
let dst_idx = dst_idx + l_k_idx;
dst[dst_idx] = src[src_idx]
}
}
}
}
Ok(dst)
}
}
struct Im2Col {
h_k: usize,
w_k: usize,
stride: usize,
dilation: usize,
padding: usize,
}
impl Im2Col {
fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
(h_out, w_out)
}
}
impl Map1 for Im2Col {
fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
let &Self {
h_k,
w_k,
stride,
dilation,
padding,
} = self;
let (b, c, h, w) = layout.shape().dims4()?;
let (h_out, w_out) = self.hw_out(h, w);
let src = &vs[layout.start_offset()..];
let mut dst = vec![T::zero(); b * h_out * w_out * c * h_k * w_k];
let (src_s0, src_s1, src_s2, src_s3) = {
let s = layout.stride();
(s[0], s[1], s[2], s[3])
};
// TODO: provide specialized kernels for the common use cases.
// - h_k = w_k = 1
// - padding = 0
// - stride = 1
// - dilation = 1
for b_idx in 0..b {
let src_idx = b_idx * src_s0;
let dst_idx = b_idx * h_out * w_out * c * h_k * w_k;
for h_idx in 0..h_out {
let dst_idx = dst_idx + h_idx * w_out * c * h_k * w_k;
for w_idx in 0..w_out {
let dst_idx = dst_idx + w_idx * c * h_k * w_k;
for c_idx in 0..c {
let dst_idx = dst_idx + c_idx * h_k * w_k;
let src_idx = c_idx * src_s1 + src_idx;
for h_k_idx in 0..h_k {
let src_h = h_idx * stride + h_k_idx * dilation;
if padding != 0 && (src_h < padding || src_h >= h + padding) {
continue;
}
let src_h = src_h - padding;
let src_idx = src_idx + src_h * src_s2;
let dst_idx = dst_idx + h_k_idx * w_k;
for w_k_idx in 0..w_k {
let src_w = w_idx * stride + w_k_idx * dilation;
if padding != 0 && (src_w < padding || src_w >= w + padding) {
continue;
}
let src_w = src_w - padding;
let src_idx = src_idx + src_w * src_s3;
let dst_idx = dst_idx + w_k_idx;
dst[dst_idx] = src[src_idx]
}
}
}
}
}
}
Ok(dst)
}
}
struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
impl<'a> Map2 for ConvTranspose1D<'a> {
const OP: &'static str = "conv_transpose1d";
fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
let p = self.0;
let inp = &inp[inp_l.start_offset()..];
let (inp_s0, inp_s1, inp_s2) = crate::shape::dims3(inp_l.stride())?;
let (k_s0, k_s1, k_s2) = crate::shape::dims3(k_l.stride())?;
let l_out = p.l_out();
// Output shape: [b_size, c_out, l_out].
let dst_elems = p.c_out * l_out * p.b_size;
let dst = vec![T::zero(); dst_elems];
let dst_s0 = p.c_out * l_out;
let dst_s1 = l_out;
let dst_s2 = 1;
// TODO: Avoid making this copy if `inp` already has the appropriate layout.
let mut inp_cont = vec![T::zero(); p.b_size * p.c_in * p.l_in];
let cont_s0 = p.l_in * p.c_in;
let cont_s1 = p.c_in;
for b_idx in 0..p.b_size {
for l_idx in 0..p.l_in {
for c_idx in 0..p.c_in {
let src_idx = b_idx * inp_s0 + c_idx * inp_s1 + l_idx * inp_s2;
let dst_idx = b_idx * cont_s0 + l_idx * cont_s1 + c_idx;
inp_cont[dst_idx] = inp[src_idx]
}
}
}
for k_idx in 0..p.k_size {
(0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
let k_cont = (0..p.c_in)
.map(|c_in_idx| k[c_in_idx * k_s0 + dst_c_idx * k_s1 + k_idx * k_s2])
.collect::<Vec<_>>();
for b_idx in 0..p.b_size {
for l_idx in 0..p.l_in {
let out_idx = l_idx * p.stride + k_idx * p.dilation;
if out_idx < p.padding {
continue;
}
let out_idx = out_idx - p.padding;
if out_idx < l_out {
let inp_cont = &inp_cont[b_idx * cont_s0 + l_idx * cont_s1..];
let dst_idx = b_idx * dst_s0 + out_idx * dst_s2 + dst_c_idx * dst_s1;
let mut d = T::zero();
unsafe {
T::vec_dot(inp_cont.as_ptr(), k_cont.as_ptr(), &mut d, p.c_in)
}
let dst_p = dst.as_ptr();
// Safety: dst_idx are uniques per dst_c_idx which is used to
// parallelise the different tasks so no two threads can try to
// write at the same location.
unsafe {
let ptr = dst_p.add(dst_idx) as *mut T;
*ptr += d
}
}
}
}
})
}
Ok(dst)
}
}
struct Conv2D<'a>(&'a crate::conv::ParamsConv2D);
impl<'a> Map2 for Conv2D<'a> {
const OP: &'static str = "conv2d";
fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
let p = self.0;
let inp = &inp[inp_l.start_offset()..];
let (inp_s0, inp_s1, inp_s2, inp_s3) = crate::shape::dims4(inp_l.stride())?;
let k = &k[k_l.start_offset()..];
let (k_s0, k_s1, k_s2, k_s3) = crate::shape::dims4(k_l.stride())?;
let (out_h, out_w) = (p.out_h(), p.out_w());
// Output shape: [b_size, c_out, out_h, out_w].
let dst = vec![T::zero(); p.b_size * p.c_out * out_h * out_w];
// TODO: Avoid making this copy if `inp` already has the appropriate layout.
let mut inp_cont = vec![T::zero(); p.b_size * p.c_in * p.i_h * p.i_w];
let cont_s0 = p.i_h * p.i_w * p.c_in;
let cont_s1 = p.i_w * p.c_in;
let cont_s2 = p.c_in;
for b_idx in 0..p.b_size {
for h_idx in 0..p.i_h {
for w_idx in 0..p.i_w {
for c_idx in 0..p.c_in {
let src_idx =
b_idx * inp_s0 + c_idx * inp_s1 + h_idx * inp_s2 + w_idx * inp_s3;
let dst_idx = b_idx * cont_s0 + h_idx * cont_s1 + w_idx * cont_s2 + c_idx;
inp_cont[dst_idx] = inp[src_idx]
}
}
}
}
for offset_h in 0..p.k_h {
for offset_w in 0..p.k_w {
(0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
let dst_idx = dst_c_idx * out_w * out_h;
let k_cont = (0..p.c_in)
.map(|c_in_idx| {
k[dst_c_idx * k_s0
+ c_in_idx * k_s1
+ offset_h * k_s2
+ offset_w * k_s3]
})
.collect::<Vec<_>>();
for b_idx in 0..p.b_size {
let dst_idx = dst_idx + b_idx * p.c_out * out_h * out_w;
for dst_h in 0..out_h {
let dst_idx = dst_idx + dst_h * out_w;
let src_h = p.stride * dst_h + offset_h * p.dilation;
if src_h < p.padding || src_h >= p.i_h + p.padding {
continue;
}
let src_h = src_h - p.padding;
for dst_w in 0..out_w {
let dst_idx = dst_idx + dst_w;
let src_w = p.stride * dst_w + offset_w * p.dilation;
if src_w < p.padding || src_w >= p.i_w + p.padding {
continue;
}
let src_w = src_w - p.padding;
let inp_cont = &inp_cont
[b_idx * cont_s0 + src_h * cont_s1 + src_w * cont_s2..];
assert!(inp_cont.len() >= p.c_in);
assert!(k_cont.len() >= p.c_in);
let mut d = T::zero();
unsafe {
T::vec_dot(inp_cont.as_ptr(), k_cont.as_ptr(), &mut d, p.c_in)
}
let dst_p = dst.as_ptr();
// Safety: dst_idx are uniques per dst_c_idx which is used to parallelise
// the different tasks so no two threads can try to write at the same
// location.
unsafe {
let ptr = dst_p.add(dst_idx) as *mut T;
*ptr += d
}
}
}
}
});
}
}
Ok(dst)
}
}
struct ConvTranspose2D<'a>(&'a crate::conv::ParamsConvTranspose2D);
impl<'a> Map2 for ConvTranspose2D<'a> {
const OP: &'static str = "conv_transpose2d";
fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
let p = self.0;
let inp = &inp[inp_l.start_offset()..];
let (inp_s0, inp_s1, inp_s2, inp_s3) = crate::shape::dims4(inp_l.stride())?;
let k = &k[k_l.start_offset()..];
let (k_s0, k_s1, k_s2, k_s3) = crate::shape::dims4(k_l.stride())?;
let (out_h, out_w) = (p.out_h(), p.out_w());
// Output shape: [b_size, c_out, out_h, out_w].
let dst = vec![T::zero(); p.b_size * p.c_out * out_h * out_w];
let dst_s0 = p.c_out * out_h * out_w;
let dst_s1 = out_h * out_w;
let dst_s2 = out_w;
let dst_s3 = 1;
// TODO: Avoid making this copy if `inp` already has the appropriate layout.
let mut inp_cont = vec![T::zero(); p.b_size * p.c_in * p.i_h * p.i_w];
let cont_s0 = p.i_h * p.i_w * p.c_in;
let cont_s1 = p.i_w * p.c_in;
let cont_s2 = p.c_in;
for b_idx in 0..p.b_size {
for h_idx in 0..p.i_h {
for w_idx in 0..p.i_w {
for c_idx in 0..p.c_in {
let src_idx =
b_idx * inp_s0 + c_idx * inp_s1 + h_idx * inp_s2 + w_idx * inp_s3;
let dst_idx = b_idx * cont_s0 + h_idx * cont_s1 + w_idx * cont_s2 + c_idx;
inp_cont[dst_idx] = inp[src_idx]
}
}
}
}
for k_y in 0..p.k_h {
for k_x in 0..p.k_w {
(0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
let k_cont = (0..p.c_in)
.map(|c_in_idx| {
k[c_in_idx * k_s0 + dst_c_idx * k_s1 + k_y * k_s2 + k_x * k_s3]
})
.collect::<Vec<_>>();
for b_idx in 0..p.b_size {
for inp_y in 0..p.i_h {
for inp_x in 0..p.i_w {
let out_x = inp_x * p.stride + k_x * p.dilation;
let out_y = inp_y * p.stride + k_y * p.dilation;
if out_x < p.padding || out_y < p.padding {
continue;
}
let out_x = out_x - p.padding;
let out_y = out_y - p.padding;
if out_x < out_w && out_y < out_h {
let inp_cont = &inp_cont
[b_idx * cont_s0 + inp_y * cont_s1 + inp_x * cont_s2..];
let dst_idx = b_idx * dst_s0
+ out_y * dst_s2
+ out_x * dst_s3
+ dst_c_idx * dst_s1;
let mut d = T::zero();
unsafe {
T::vec_dot(
inp_cont.as_ptr(),
k_cont.as_ptr(),
&mut d,
p.c_in,
)
}
let dst_p = dst.as_ptr();
// Safety: dst_idx are uniques per dst_c_idx which is used to
// parallelise the different tasks so no two threads can try to
// write at the same location.
unsafe {
let ptr = dst_p.add(dst_idx) as *mut T;
*ptr += d
}
}
}
}
}
})
}
}
Ok(dst)
}
}
struct MatMul((usize, usize, usize, usize));
impl MatMul {
fn striding_error(&self, lhs_l: &Layout, rhs_l: &Layout, msg: &'static str) -> Error {
Error::MatMulUnexpectedStriding(Box::new(crate::error::MatMulUnexpectedStriding {
lhs_l: lhs_l.clone(),
rhs_l: rhs_l.clone(),
bmnk: self.0,
msg,
}))
.bt()
}
}
impl Map2 for MatMul {
const OP: &'static str = "mat_mul";
#[cfg(all(not(feature = "mkl"), not(feature = "accelerate")))]
fn f<T: 'static + WithDType + num_traits::Num + Copy>(
&self,
lhs: &[T],
lhs_l: &Layout,
rhs: &[T],
rhs_l: &Layout,
) -> Result<Vec<T>> {
use gemm::{gemm, Parallelism};
match T::DTYPE {
DType::F16 | DType::F32 | DType::F64 => {}
_ => Err(Error::UnsupportedDTypeForOp(T::DTYPE, "matmul").bt())?,
}
let (b, m, n, k) = self.0;
let lhs = &lhs[lhs_l.start_offset()..];
let rhs = &rhs[rhs_l.start_offset()..];
let lhs_stride = lhs_l.stride();
let rhs_stride = rhs_l.stride();
let rank = lhs_stride.len();
let lhs_cs = lhs_stride[rank - 1];
let lhs_rs = lhs_stride[rank - 2];
let rhs_cs = rhs_stride[rank - 1];
let rhs_rs = rhs_stride[rank - 2];
let a_skip: usize = match lhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
[stride] => stride,
[] => m * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
};
let b_skip: usize = match rhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
[stride] => stride,
[] => n * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
};
let c_skip: usize = m * n;
let dst_shape: Shape = (m, n).into();
let dst_strides = dst_shape.stride_contiguous();
let dst_rs = dst_strides[0];
let dst_cs = dst_strides[1];
let mut dst = vec![T::zero(); b * m * n];
let num_threads = crate::utils::get_num_threads();
let parallelism = if num_threads > 1 {
Parallelism::Rayon(num_threads)
} else {
Parallelism::None
};
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
gemm(
/* m: usize = */ m,
/* n: usize = */ n,
/* k: usize = */ k,
/* dst: *mut T = */ dst_p.as_mut_ptr(),
/* dst_cs: isize = */ dst_cs as isize,
/* dst_rs: isize = */ dst_rs as isize,
/* read_dst: bool = */ false,
/* lhs: *const T = */ lhs_p.as_ptr(),
/* lhs_cs: isize = */ lhs_cs as isize,
/* lhs_rs: isize = */ lhs_rs as isize,
/* rhs: *const T = */ rhs_p.as_ptr(),
/* rhs_cs: isize = */ rhs_cs as isize,
/* rhs_rs: isize = */ rhs_rs as isize,
/* alpha: T = */ T::zero(),
/* beta: T = */ T::one(),
/* conj_dst: bool = */ false,
/* conj_lhs: bool = */ false,
/* conj_rhs: bool = */ false,
parallelism,
)
}
}
Ok(dst)
}
#[cfg(feature = "accelerate")]
fn f<T: 'static + WithDType + num_traits::Num + Copy>(
&self,
lhs: &[T],
lhs_l: &Layout,
rhs: &[T],
rhs_l: &Layout,
) -> Result<Vec<T>> {
let (b, m, n, k) = self.0;
let lhs = &lhs[lhs_l.start_offset()..];
let rhs = &rhs[rhs_l.start_offset()..];
let lhs_stride = lhs_l.stride();
let rhs_stride = rhs_l.stride();
let rank = lhs_stride.len();
let a_skip: usize = match lhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
[stride] => stride,
[] => m * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
};
let b_skip: usize = match rhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
[stride] => stride,
[] => n * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
};
let c_skip: usize = m * n;
let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
let rhs_m2 = rhs_stride[rhs_stride.len() - 2];
let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
(n as i32, b'N')
} else if rhs_m1 == k && rhs_m2 == 1 {
(k as i32, b'T')
} else {
Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?
};
// The b tensor has dims batching, m, k (lhs)
let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
(k as i32, b'N')
} else if lhs_m1 == m && lhs_m2 == 1 {
(m as i32, b'T')
} else {
Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?
};
let mut dst = vec![T::zero(); b * m * n];
match T::DTYPE {
DType::F16 => {
crate::bail!("the accelerate backend does not support f16 matmul")
}
DType::F32 => {
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
let a = rhs_p.as_ptr() as *const f32;
let b = lhs_p.as_ptr() as *const f32;
let c = dst_p.as_mut_ptr() as *mut f32;
let a = std::slice::from_raw_parts(a, a_skip);
let b = std::slice::from_raw_parts(b, b_skip);
let c = std::slice::from_raw_parts_mut(c, c_skip);
crate::accelerate::sgemm(
transa, transb, /* m= */ n as i32, /* n= */ m as i32,
/* k= */ k as i32, /* alpha= */ 1., /* a= */ a,
/* lda= */ lda, /* b= */ b, /* ldb= */ ldb,
/* beta= */ 0., /* c= */ c, /* ldc= */ n as i32,
)
}
}
}
DType::F64 => {
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
let a = rhs_p.as_ptr() as *const f64;
let b = lhs_p.as_ptr() as *const f64;
let c = dst_p.as_mut_ptr() as *mut f64;
let a = std::slice::from_raw_parts(a, a_skip);
let b = std::slice::from_raw_parts(b, b_skip);
let c = std::slice::from_raw_parts_mut(c, c_skip);
crate::accelerate::dgemm(
transa, transb, /* m= */ n as i32, /* n= */ m as i32,
/* k= */ k as i32, /* alpha= */ 1., /* a= */ a,
/* lda= */ lda, /* b= */ b, /* ldb= */ ldb,
/* beta= */ 0., /* c= */ c, /* ldc= */ n as i32,
)
}
}
}
dtype => Err(Error::UnsupportedDTypeForOp(dtype, "matmul").bt())?,
}
Ok(dst)
}
#[cfg(feature = "mkl")]
fn f<T: 'static + WithDType + num_traits::Num + Copy>(
&self,
lhs: &[T],
lhs_l: &Layout,
rhs: &[T],
rhs_l: &Layout,
) -> Result<Vec<T>> {
let (b, m, n, k) = self.0;
let lhs = &lhs[lhs_l.start_offset()..];
let rhs = &rhs[rhs_l.start_offset()..];
let lhs_stride = lhs_l.stride();
let rhs_stride = rhs_l.stride();
let rank = lhs_stride.len();
let a_skip: usize = match lhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
[stride] => stride,
[] => m * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
};
let b_skip: usize = match rhs_stride[..rank - 2] {
[s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
[stride] => stride,
[] => n * k,
_ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
};
let c_skip: usize = m * n;
let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
let rhs_m2 = rhs_stride[rhs_stride.len() - 2];
let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
(n as i32, b'N')
} else if rhs_m1 == k && rhs_m2 == 1 {
(k as i32, b'T')
} else {
Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?
};
// The b tensor has dims batching, m, k (lhs)
let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
(k as i32, b'N')
} else if lhs_m1 == m && lhs_m2 == 1 {
(m as i32, b'T')
} else {
Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?
};
let mut dst = vec![T::zero(); b * m * n];
match T::DTYPE {
DType::F16 => {
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
let a = rhs_p.as_ptr() as *const f16;
let b = lhs_p.as_ptr() as *const f16;
let c = dst_p.as_mut_ptr() as *mut f16;
let a = std::slice::from_raw_parts(a, a_skip);
let b = std::slice::from_raw_parts(b, b_skip);
let c = std::slice::from_raw_parts_mut(c, c_skip);
crate::mkl::hgemm(
transa,
transb,
/* m= */ n as i32,
/* n= */ m as i32,
/* k= */ k as i32,
/* alpha= */ f16::ONE,
/* a= */ a,
/* lda= */ lda,
/* b= */ b,
/* ldb= */ ldb,
/* beta= */ f16::ZERO,
/* c= */ c,
/* ldc= */ n as i32,
)
}
}
}
DType::F32 => {
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
let a = rhs_p.as_ptr() as *const f32;
let b = lhs_p.as_ptr() as *const f32;
let c = dst_p.as_mut_ptr() as *mut f32;
let a = std::slice::from_raw_parts(a, a_skip);
let b = std::slice::from_raw_parts(b, b_skip);
let c = std::slice::from_raw_parts_mut(c, c_skip);
crate::mkl::sgemm(
transa, transb, /* m= */ n as i32, /* n= */ m as i32,
/* k= */ k as i32, /* alpha= */ 1., /* a= */ a,
/* lda= */ lda, /* b= */ b, /* ldb= */ ldb,
/* beta= */ 0., /* c= */ c, /* ldc= */ n as i32,
)
}
}
}
DType::F64 => {
for step in 0..b {
let lhs_p = &lhs[step * a_skip..];
let rhs_p = &rhs[step * b_skip..];
let dst_p = &mut dst[step * c_skip..];
unsafe {
let a = rhs_p.as_ptr() as *const f64;
let b = lhs_p.as_ptr() as *const f64;
let c = dst_p.as_mut_ptr() as *mut f64;
let a = std::slice::from_raw_parts(a, a_skip);
let b = std::slice::from_raw_parts(b, b_skip);
let c = std::slice::from_raw_parts_mut(c, c_skip);
crate::mkl::dgemm(
transa, transb, /* m= */ n as i32, /* n= */ m as i32,
/* k= */ k as i32, /* alpha= */ 1., /* a= */ a,
/* lda= */ lda, /* b= */ b, /* ldb= */ ldb,
/* beta= */ 0., /* c= */ c, /* ldc= */ n as i32,
)
}
}
}
dtype => Err(Error::UnsupportedDTypeForOp(dtype, "matmul").bt())?,
}
Ok(dst)
}
}
fn elu<T: num_traits::Float>(v: T, alpha: T) -> T {
if v.is_sign_positive() {
v
} else {
(v.exp() - T::one()) * alpha
}
}
impl CpuStorage {
pub fn as_slice<D: WithDType>(&self) -> Result<&[D]> {
D::cpu_storage_as_slice(self)
}
pub fn concat(storages: &[CpuStorage]) -> Result<CpuStorage> {
let storage0 = &storages[0];
let s = match storage0 {
Self::U8(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::U8(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::U8(storages)
}
Self::U32(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::U32(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::U32(storages)
}
Self::I64(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::I64(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::I64(storages)
}
Self::BF16(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::BF16(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::BF16(storages)
}
Self::F16(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::F16(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::F16(storages)
}
Self::F32(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::F32(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::F32(storages)
}
Self::F64(_) => {
let storages = storages
.iter()
.map(|s| match s {
Self::F64(s) => Ok(s.as_slice()),
_ => crate::bail!("dtype mismatch"),
})
.collect::<Result<Vec<_>>>()?
.concat();
Self::F64(storages)
}
};
Ok(s)
}
}
impl BackendStorage for CpuStorage {
type Device = CpuDevice;
fn dtype(&self) -> DType {
match self {
Self::U8(_) => DType::U8,
Self::U32(_) => DType::U32,
Self::I64(_) => DType::I64,
Self::BF16(_) => DType::BF16,
Self::F16(_) => DType::F16,
Self::F32(_) => DType::F32,
Self::F64(_) => DType::F64,
}
}
fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
// TODO: find a way around the quadratic number of cases below.
match (self, dtype) {
(Self::U8(storage), DType::BF16) => {
let data = unary_map(storage, layout, |v| bf16::from_f32(v as f32));
Ok(Self::BF16(data))
}
(Self::U32(storage), DType::BF16) => {
let data = unary_map(storage, layout, |v| bf16::from_f32(v as f32));
Ok(Self::BF16(data))
}
(Self::I64(storage), DType::BF16) => {
let data = unary_map(storage, layout, |v| bf16::from_f32(v as f32));
Ok(Self::BF16(data))
}
(Self::BF16(storage), DType::BF16) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::BF16(data))
}
(Self::F16(storage), DType::BF16) => {
let data = unary_map(storage, layout, |v| bf16::from_f32(v.to_f32()));
Ok(Self::BF16(data))
}
(Self::F32(storage), DType::BF16) => {
let data = unary_map(storage, layout, bf16::from_f32);
Ok(Self::BF16(data))
}
(Self::F64(storage), DType::BF16) => {
let data = unary_map(storage, layout, bf16::from_f64);
Ok(Self::BF16(data))
}
(Self::U8(storage), DType::F16) => {
let data = unary_map(storage, layout, |v| f16::from_f32(v as f32));
Ok(Self::F16(data))
}
(Self::U32(storage), DType::F16) => {
let data = unary_map(storage, layout, |v| f16::from_f32(v as f32));
Ok(Self::F16(data))
}
(Self::I64(storage), DType::F16) => {
let data = unary_map(storage, layout, |v| f16::from_f32(v as f32));
Ok(Self::F16(data))
}
(Self::BF16(storage), DType::F16) => {
let data = unary_map(storage, layout, |v| f16::from_f32(v.to_f32()));
Ok(Self::F16(data))
}
(Self::F16(storage), DType::F16) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::F16(data))
}
(Self::F32(storage), DType::F16) => {
let data = unary_map(storage, layout, f16::from_f32);
Ok(Self::F16(data))
}
(Self::F64(storage), DType::F16) => {
let data = unary_map(storage, layout, f16::from_f64);
Ok(Self::F16(data))
}
(Self::U8(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v as f32);
Ok(Self::F32(data))
}
(Self::U32(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v as f32);
Ok(Self::F32(data))
}
(Self::I64(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v as f32);
Ok(Self::F32(data))
}
(Self::BF16(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v.to_f32());
Ok(Self::F32(data))
}
(Self::F16(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v.to_f32());
Ok(Self::F32(data))
}
(Self::F32(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::F32(data))
}
(Self::F64(storage), DType::F32) => {
let data = unary_map(storage, layout, |v| v as f32);
Ok(Self::F32(data))
}
(Self::U8(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::U8(data))
}
(Self::BF16(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v.to_f32() as u8);
Ok(Self::U8(data))
}
(Self::F16(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v.to_f32() as u8);
Ok(Self::U8(data))
}
(Self::F32(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v as u8);
Ok(Self::U8(data))
}
(Self::F64(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v as u8);
Ok(Self::U8(data))
}
(Self::U32(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v as u8);
Ok(Self::U8(data))
}
(Self::I64(storage), DType::U8) => {
let data = unary_map(storage, layout, |v| v as u8);
Ok(Self::U8(data))
}
(Self::U8(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v as u32);
Ok(Self::U32(data))
}
(Self::U32(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::U32(data))
}
(Self::I64(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v as u32);
Ok(Self::U32(data))
}
(Self::BF16(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v.to_f32() as u32);
Ok(Self::U32(data))
}
(Self::F16(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v.to_f32() as u32);
Ok(Self::U32(data))
}
(Self::F32(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v as u32);
Ok(Self::U32(data))
}
(Self::F64(storage), DType::U32) => {
let data = unary_map(storage, layout, |v| v as u32);
Ok(Self::U32(data))
}
(Self::U8(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v as i64);
Ok(Self::I64(data))
}
(Self::U32(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v as i64);
Ok(Self::I64(data))
}
(Self::I64(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::I64(data))
}
(Self::BF16(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v.to_f32() as i64);
Ok(Self::I64(data))
}
(Self::F16(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v.to_f32() as i64);
Ok(Self::I64(data))
}
(Self::F32(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v as i64);
Ok(Self::I64(data))
}
(Self::F64(storage), DType::I64) => {
let data = unary_map(storage, layout, |v| v as i64);
Ok(Self::I64(data))
}
(Self::U8(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v as f64);
Ok(Self::F64(data))
}
(Self::U32(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v as f64);
Ok(Self::F64(data))
}
(Self::I64(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v as f64);
Ok(Self::F64(data))
}
(Self::BF16(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v.to_f64());
Ok(Self::F64(data))
}
(Self::F16(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v.to_f64());
Ok(Self::F64(data))
}
(Self::F32(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v as f64);
Ok(Self::F64(data))
}
(Self::F64(storage), DType::F64) => {
let data = unary_map(storage, layout, |v| v);
Ok(Self::F64(data))
}
}
}
fn reduce_op(&self, op: ReduceOp, layout: &Layout, reduce_dims: &[usize]) -> Result<Self> {
match op {
ReduceOp::Sum => {
let src_dims = layout.dims();
let mut dst_dims = src_dims.to_vec();
for &dim in reduce_dims.iter() {
dst_dims[dim] = 1;
}
let dst_shape = Shape::from(dst_dims);
let mut reduce_dims = reduce_dims.to_vec();
// Sort the reduce_dims as they have to be processed from left to right when converting the
// indexes.
reduce_dims.sort();
let reduce_dims_and_stride: Vec<_> = reduce_dims
.iter()
.map(|&d| (src_dims[d], src_dims[d + 1..].iter().product::<usize>()))
.collect();
ReduceSum {
dst_shape: &dst_shape,
reduce_dims: &reduce_dims,
reduce_dims_and_stride,
}
.map(self, layout)
}
ReduceOp::Min | ReduceOp::ArgMin | ReduceOp::Max | ReduceOp::ArgMax => {
let reduce_dim_index = match reduce_dims {
[reduce_dim_index] => *reduce_dim_index,
_ => {
let op = match op {
ReduceOp::Min => "min",
ReduceOp::ArgMin => "argmin",
ReduceOp::Max => "max",
ReduceOp::ArgMax => "argmax",
_ => unreachable!(),
};
let dims = reduce_dims.to_vec();
Err(Error::OnlySingleDimension { op, dims })?
}
};
let (use_min, return_index) = match op {
ReduceOp::Min => (true, false),
ReduceOp::ArgMin => (true, true),
ReduceOp::Max => (false, false),
ReduceOp::ArgMax => (false, true),
_ => unreachable!(),
};
ReduceIndex {
reduce_dim_index,
use_min,
return_index,
}
.map(self, layout)
}
}
}
fn cmp(&self, op: CmpOp, rhs: &Self, lhs_l: &Layout, rhs_l: &Layout) -> Result<Self> {
Cmp(op).map(self, lhs_l, rhs, rhs_l)
}
fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
Affine(mul, add).map(self, layout)
}
fn avg_pool2d(
&self,
layout: &Layout,
kernel_size: (usize, usize),
stride: (usize, usize),
) -> Result<Self> {
AvgPool2D(kernel_size, stride).map(self, layout)
}
fn max_pool2d(
&self,
layout: &Layout,
kernel_size: (usize, usize),
stride: (usize, usize),
) -> Result<Self> {
MaxPool2D(kernel_size, stride).map(self, layout)
}
fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
UpsampleNearest1D(sz).map(self, layout)
}
fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
UpsampleNearest2D(h, w).map(self, layout)
}
fn powf(&self, layout: &Layout, e: f64) -> Result<Self> {
use num_traits::Float;
// TODO: Have some generic map for functions that apply on num_traits::Float elements.
match self {
Self::BF16(storage) => {
let data = unary_map(storage, layout, |v| v.powf(bf16::from_f64(e)));
Ok(Self::BF16(data))
}
Self::F16(storage) => {
let data = unary_map(storage, layout, |v| v.powf(f16::from_f64(e)));
Ok(Self::F16(data))
}
Self::F32(storage) => {
let data = unary_map(storage, layout, |v| v.powf(e as f32));
Ok(Self::F32(data))
}
Self::F64(storage) => {
let data = unary_map(storage, layout, |v| v.powf(e));
Ok(Self::F64(data))
}
Self::U8(_) => Err(Error::UnsupportedDTypeForOp(DType::U8, "elu").bt()),
Self::U32(_) => Err(Error::UnsupportedDTypeForOp(DType::U32, "elu").bt()),
Self::I64(_) => Err(Error::UnsupportedDTypeForOp(DType::I64, "elu").bt()),
}
}
fn elu(&self, layout: &Layout, alpha: f64) -> Result<Self> {
// TODO: Have some generic map for functions that apply on num_traits::Float elements.
match self {
Self::BF16(storage) => {
let data = unary_map(storage, layout, |v| elu(v, bf16::from_f64(alpha)));
Ok(Self::BF16(data))
}
Self::F16(storage) => {
let data = unary_map(storage, layout, |v| elu(v, f16::from_f64(alpha)));
Ok(Self::F16(data))
}
Self::F32(storage) => {
let data = unary_map(storage, layout, |v| elu(v, f32::from_f64(alpha)));
Ok(Self::F32(data))
}
Self::F64(storage) => {
let data = unary_map(storage, layout, |v| elu(v, alpha));
Ok(Self::F64(data))
}
Self::U8(_) => Err(Error::UnsupportedDTypeForOp(DType::U8, "elu").bt()),
Self::U32(_) => Err(Error::UnsupportedDTypeForOp(DType::U32, "elu").bt()),
Self::I64(_) => Err(Error::UnsupportedDTypeForOp(DType::I64, "elu").bt()),
}
}
fn unary_impl<B: UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
match self {
Self::BF16(storage) => {
if B::BF16_VEC {
let data = unary_map_vec(storage, layout, B::bf16, B::bf16_vec);
Ok(Self::BF16(data))
} else {
let data = unary_map(storage, layout, B::bf16);
Ok(Self::BF16(data))
}
}
Self::F16(storage) => {
if B::F16_VEC {
let data = unary_map_vec(storage, layout, B::f16, B::f16_vec);
Ok(Self::F16(data))
} else {
let data = unary_map(storage, layout, B::f16);
Ok(Self::F16(data))
}
}
Self::F32(storage) => {
if B::F32_VEC {
let data = unary_map_vec(storage, layout, B::f32, B::f32_vec);
Ok(Self::F32(data))
} else {
let data = unary_map(storage, layout, B::f32);
Ok(Self::F32(data))
}
}
Self::F64(storage) => {
if B::F64_VEC {
let data = unary_map_vec(storage, layout, B::f64, B::f64_vec);
Ok(Self::F64(data))
} else {
let data = unary_map(storage, layout, B::f64);
Ok(Self::F64(data))
}
}
Self::U8(storage) => {
let data = unary_map(storage, layout, B::u8);
Ok(Self::U8(data))
}
Self::U32(storage) => {
let data = unary_map(storage, layout, B::u32);
Ok(Self::U32(data))
}
Self::I64(storage) => {
let data = unary_map(storage, layout, B::i64);
Ok(Self::I64(data))
}
}
}
fn binary_impl<B: BinaryOpT>(
&self,
rhs: &Self,
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
match (self, rhs) {
(Self::BF16(lhs), Self::BF16(rhs)) => {
let data = if B::BF16_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::bf16, B::bf16_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::bf16)
};
Ok(Self::BF16(data))
}
(Self::F16(lhs), Self::F16(rhs)) => {
let data = if B::F16_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::f16, B::f16_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::f16)
};
Ok(Self::F16(data))
}
(Self::F32(lhs), Self::F32(rhs)) => {
let data = if B::F32_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::f32, B::f32_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::f32)
};
Ok(Self::F32(data))
}
(Self::F64(lhs), Self::F64(rhs)) => {
let data = if B::F64_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::f64, B::f64_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::f64)
};
Ok(Self::F64(data))
}
(Self::U32(lhs), Self::U32(rhs)) => {
let data = if B::U32_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::u32, B::u32_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::u32)
};
Ok(Self::U32(data))
}
(Self::I64(lhs), Self::I64(rhs)) => {
let data = if B::I64_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::i64, B::i64_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::i64)
};
Ok(Self::I64(data))
}
(Self::U8(lhs), Self::U8(rhs)) => {
let data = if B::U8_VEC {
binary_map_vec(lhs_l, rhs_l, lhs, rhs, B::u8, B::u8_vec)
} else {
binary_map(lhs_l, rhs_l, lhs, rhs, B::u8)
};
Ok(Self::U8(data))
}
_ => {
// This should be covered by the dtype check above.
Err(Error::DTypeMismatchBinaryOp {
lhs: self.dtype(),
rhs: rhs.dtype(),
op: B::NAME,
}
.bt())
}
}
}
fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
match (self, dst) {
(Self::U8(src), Self::U8(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::U32(src), Self::U32(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::I64(src), Self::I64(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::BF16(src), Self::BF16(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::F16(src), Self::F16(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::F32(src), Self::F32(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(Self::F64(src), Self::F64(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
(_, dst) => {
// This should be covered by the dtype check above.
return Err(Error::DTypeMismatchBinaryOp {
lhs: self.dtype(),
rhs: dst.dtype(),
op: "copy_strided",
}
.bt());
}
}
Ok(())
}
fn where_cond(
&self,
layout: &Layout,
t: &Self,
t_l: &Layout,
f: &Self,
f_l: &Layout,
) -> Result<Self> {
match self {
Self::U8(pred) => WCond(pred, layout).map(t, t_l, f, f_l),
Self::U32(pred) => WCond(pred, layout).map(t, t_l, f, f_l),
Self::I64(pred) => WCond(pred, layout).map(t, t_l, f, f_l),
_ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "where-cond")),
}
}
fn conv1d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv1D,
) -> Result<Self> {
if !USE_IM2COL_CONV1D {
return Conv1D(params).map(self, l, kernel, kernel_l);
}
let op = Im2Col1D {
l_k: params.k_size,
padding: params.padding,
stride: params.stride,
dilation: params.dilation,
};
let col = op.map(self, l)?;
let b = params.b_size;
let n = params.c_out;
let l_out = params.l_out();
let k = op.l_k * params.c_in;
let m = l_out;
let col_l = Layout::contiguous((b, m, k));
let res = if kernel_l.is_contiguous() {
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
} else {
// Make the kernel contiguous if not already the case.
let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
};
let res_l = Layout::contiguous((b, l_out, params.c_out)).transpose(1, 2)?;
let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
res.copy_strided_src(&mut res_t, 0, &res_l)?;
Ok(res_t)
}
fn conv_transpose1d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConvTranspose1D,
) -> Result<Self> {
ConvTranspose1D(params).map(self, l, kernel, kernel_l)
}
fn conv2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConv2D,
) -> Result<Self> {
if !USE_IM2COL_CONV2D {
return Conv2D(params).map(self, l, kernel, kernel_l);
}
let op = Im2Col {
h_k: params.k_h,
w_k: params.k_w,
padding: params.padding,
stride: params.stride,
dilation: params.dilation,
};
let col = op.map(self, l)?;
let b = params.b_size;
let n = params.c_out;
let (h_out, w_out) = (params.out_h(), params.out_w());
let k = op.h_k * op.w_k * params.c_in;
let m = h_out * w_out;
let col_l = Layout::contiguous((b, m, k));
let res = if kernel_l.is_contiguous() {
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
} else {
// Make the kernel contiguous if not already the case.
let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
.transpose(1, 2)?
.broadcast_as((b, k, n))?;
col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
};
let res_l = Layout::contiguous((b, h_out, w_out, params.c_out))
.transpose(1, 2)?
.transpose(1, 3)?;
let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
res.copy_strided_src(&mut res_t, 0, &res_l)?;
Ok(res_t)
}
fn conv_transpose2d(
&self,
l: &Layout,
kernel: &Self,
kernel_l: &Layout,
params: &crate::conv::ParamsConvTranspose2D,
) -> Result<Self> {
ConvTranspose2D(params).map(self, l, kernel, kernel_l)
}
fn index_select(&self, ids: &Self, l: &Layout, ids_l: &Layout, dim: usize) -> Result<Self> {
match ids {
Self::U8(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
Self::U32(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
Self::I64(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
_ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-select")),
}
}
fn gather(&self, l: &Layout, ids: &Self, ids_l: &Layout, dim: usize) -> Result<Self> {
match ids {
Self::U8(ids) => Gather { ids, ids_l, dim }.map(self, l),
Self::U32(ids) => Gather { ids, ids_l, dim }.map(self, l),
Self::I64(ids) => Gather { ids, ids_l, dim }.map(self, l),
_ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "gather")),
}
}
fn scatter_add(
&self,
l: &Layout,
ids: &Self,
ids_l: &Layout,
src: &Self,
src_l: &Layout,
dim: usize,
) -> Result<Self> {
match ids {
Self::U8(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
Self::U32(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
Self::I64(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
_ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add")),
}
}
fn index_add(
&self,
l: &Layout,
ids: &Self,
ids_l: &Layout,
src: &Self,
src_l: &Layout,
dim: usize,
) -> Result<Self> {
match ids {
Self::U8(ids) => {
let ids = match ids_l.contiguous_offsets() {
Some((a, b)) => &ids[a..b],
None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
};
IndexAdd { ids, dim }.map(self, l, src, src_l)
}
Self::U32(ids) => {
let ids = match ids_l.contiguous_offsets() {
Some((a, b)) => &ids[a..b],
None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
};
IndexAdd { ids, dim }.map(self, l, src, src_l)
}
Self::I64(ids) => {
let ids = match ids_l.contiguous_offsets() {
Some((a, b)) => &ids[a..b],
None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
};
IndexAdd { ids, dim }.map(self, l, src, src_l)
}
_ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-add").bt()),
}
}
fn matmul(
&self,
rhs: &Self,
bmnk: (usize, usize, usize, usize),
lhs_l: &Layout,
rhs_l: &Layout,
) -> Result<Self> {
MatMul(bmnk).map(self, lhs_l, rhs, rhs_l)
}
fn device(&self) -> &Self::Device {
&CpuDevice
}
fn try_clone(&self, _: &Layout) -> Result<Self> {
Ok(self.clone())
}
fn to_cpu_storage(&self) -> Result<CpuStorage> {
Ok(self.clone())
}
}
impl BackendDevice for CpuDevice {
type Storage = CpuStorage;
fn location(&self) -> crate::DeviceLocation {
crate::DeviceLocation::Cpu
}
fn same_device(&self, _: &Self) -> bool {
true
}
fn storage_from_cpu_storage(&self, s: &CpuStorage) -> Result<Self::Storage> {
Ok(s.clone())
}
fn new(_: usize) -> Result<Self> {
Ok(Self)
}
fn set_seed(&self, _seed: u64) -> Result<()> {
crate::bail!("cannot seed the CPU rng with set_seed")
}
fn rand_uniform(&self, shape: &Shape, dtype: DType, min: f64, max: f64) -> Result<CpuStorage> {
use rand::prelude::*;
let elem_count = shape.elem_count();
let mut rng = rand::thread_rng();
match dtype {
DType::U8 | DType::U32 | DType::I64 => {
Err(Error::UnsupportedDTypeForOp(dtype, "rand_uniform").bt())
}
DType::BF16 => {
let mut data = Vec::with_capacity(elem_count);
let uniform =
rand::distributions::Uniform::new(bf16::from_f64(min), bf16::from_f64(max));
for _i in 0..elem_count {
data.push(rng.sample::<bf16, _>(uniform))
}
Ok(CpuStorage::BF16(data))
}
DType::F16 => {
let mut data = Vec::with_capacity(elem_count);
let uniform =
rand::distributions::Uniform::new(f16::from_f64(min), f16::from_f64(max));
for _i in 0..elem_count {
data.push(rng.sample::<f16, _>(uniform))
}
Ok(CpuStorage::F16(data))
}
DType::F32 => {
let mut data = Vec::with_capacity(elem_count);
let uniform = rand::distributions::Uniform::new(min as f32, max as f32);
for _i in 0..elem_count {
data.push(rng.sample::<f32, _>(uniform))
}
Ok(CpuStorage::F32(data))
}
DType::F64 => {
let mut data = Vec::with_capacity(elem_count);
let uniform = rand::distributions::Uniform::new(min, max);
for _i in 0..elem_count {
data.push(rng.sample::<f64, _>(uniform))
}
Ok(CpuStorage::F64(data))
}
}
}
fn rand_normal(&self, shape: &Shape, dtype: DType, mean: f64, std: f64) -> Result<CpuStorage> {
use rand::prelude::*;
let elem_count = shape.elem_count();
let mut rng = rand::thread_rng();
match dtype {
DType::U8 | DType::U32 | DType::I64 => {
Err(Error::UnsupportedDTypeForOp(dtype, "rand_normal").bt())
}
DType::BF16 => {
let mut data = Vec::with_capacity(elem_count);
let normal = rand_distr::Normal::new(bf16::from_f64(mean), bf16::from_f64(std))
.map_err(Error::wrap)?;
for _i in 0..elem_count {
data.push(normal.sample(&mut rng))
}
Ok(CpuStorage::BF16(data))
}
DType::F16 => {
let mut data = Vec::with_capacity(elem_count);
let normal = rand_distr::Normal::new(f16::from_f64(mean), f16::from_f64(std))
.map_err(Error::wrap)?;
for _i in 0..elem_count {
data.push(normal.sample(&mut rng))
}
Ok(CpuStorage::F16(data))
}
DType::F32 => {
let mut data = Vec::with_capacity(elem_count);
let normal =
rand_distr::Normal::new(mean as f32, std as f32).map_err(Error::wrap)?;
for _i in 0..elem_count {
data.push(normal.sample(&mut rng))
}
Ok(CpuStorage::F32(data))
}
DType::F64 => {
let mut data = Vec::with_capacity(elem_count);
let normal = rand_distr::Normal::new(mean, std).map_err(Error::wrap)?;
for _i in 0..elem_count {
data.push(normal.sample(&mut rng))
}
Ok(CpuStorage::F64(data))
}
}
}
fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
let elem_count = shape.elem_count();
let storage = match dtype {
DType::U8 => CpuStorage::U8(vec![1u8; elem_count]),
DType::U32 => CpuStorage::U32(vec![1u32; elem_count]),
DType::I64 => CpuStorage::I64(vec![1i64; elem_count]),
DType::BF16 => CpuStorage::BF16(vec![bf16::ONE; elem_count]),
DType::F16 => CpuStorage::F16(vec![f16::ONE; elem_count]),
DType::F32 => CpuStorage::F32(vec![1f32; elem_count]),
DType::F64 => CpuStorage::F64(vec![1f64; elem_count]),
};
Ok(storage)
}
fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
let elem_count = shape.elem_count();
let storage = match dtype {
DType::U8 => CpuStorage::U8(vec![0u8; elem_count]),
DType::U32 => CpuStorage::U32(vec![0u32; elem_count]),
DType::I64 => CpuStorage::I64(vec![0i64; elem_count]),
DType::BF16 => CpuStorage::BF16(vec![bf16::ZERO; elem_count]),
DType::F16 => CpuStorage::F16(vec![f16::ZERO; elem_count]),
DType::F32 => CpuStorage::F32(vec![0f32; elem_count]),
DType::F64 => CpuStorage::F64(vec![0f64; elem_count]),
};
Ok(storage)
}
}
#[macro_export]
macro_rules! map_dtype {
($name:expr, $storage:ident, $fn:expr, ($($dtypes:ident),+)) => {
match $storage {
$(CpuStorage::$dtypes(__e) => CpuStorage::$dtypes($fn(__e)),)*
s => Err(Error::UnsupportedDTypeForOp(s.dtype(), $name).bt())?,
}
};
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/neon.rs
|
use super::k_quants::{
BlockQ2K, BlockQ3K, BlockQ4K, BlockQ4_0, BlockQ5K, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K,
};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
#[allow(unused_imports)]
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[allow(unused_imports)]
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
#[inline(always)]
pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
let nb = n / qk;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
}
unsafe {
let mut sumv0 = vdupq_n_f32(0.0f32);
for i in 0..nb {
let x0 = &xs[i];
let y0 = &ys[i];
let m4b = vdupq_n_u8(0x0F);
let s8b = vdupq_n_s8(0x8);
let v0_0 = vld1q_u8(x0.qs.as_ptr());
// 4-bit -> 8-bit
let v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
let v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
// sub 8
let v0_0ls = vsubq_s8(v0_0l, s8b);
let v0_0hs = vsubq_s8(v0_0h, s8b);
// load y
let v1_0l = vld1q_s8(y0.qs.as_ptr());
let v1_0h = vld1q_s8(y0.qs.as_ptr().add(16));
// TODO: Support dotprod when it's available outside of nightly.
let pl0l = vmull_s8(vget_low_s8(v0_0ls), vget_low_s8(v1_0l));
let pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
let ph0l = vmull_s8(vget_low_s8(v0_0hs), vget_low_s8(v1_0h));
let ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
let pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
let ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
sumv0 = vmlaq_n_f32(
sumv0,
vcvtq_f32_s32(vaddq_s32(pl0, ph0)),
x0.d.to_f32() * y0.d.to_f32(),
);
}
Ok(vaddvq_f32(sumv0))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
}
let nb = n / QK8_0;
unsafe {
let mut sumv0 = vdupq_n_f32(0.0f32);
for i in 0..nb {
let x0 = &xs[i];
let y0 = &ys[i];
let x0_0 = vld1q_s8(x0.qs.as_ptr());
let x0_1 = vld1q_s8(x0.qs.as_ptr().add(16));
// load y
let y0_0 = vld1q_s8(y0.qs.as_ptr());
let y0_1 = vld1q_s8(y0.qs.as_ptr().add(16));
// TODO dotprod once this is the intrinsics are.
let p0_0 = vmull_s8(vget_low_s8(x0_0), vget_low_s8(y0_0));
let p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
let p0_2 = vmull_s8(vget_low_s8(x0_1), vget_low_s8(y0_1));
let p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
let p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
let p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
sumv0 = vmlaq_n_f32(
sumv0,
vcvtq_f32_s32(vaddq_s32(p0, p1)),
x0.d.to_f32() * y0.d.to_f32(),
);
}
Ok(vaddvq_f32(sumv0))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
let qk = QK_K;
if n % QK_K != 0 {
crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
}
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
unsafe {
let mut sum_i = vdupq_n_s32(0);
let scale = xs.d * ys.d;
let xs = xs.qs.as_ptr();
let ys = ys.qs.as_ptr();
for i in (0..QK_K).step_by(16) {
let xs = vld1q_s8(xs.add(i));
let ys = vld1q_s8(ys.add(i));
let xy_lo = vmull_s8(vget_low_s8(xs), vget_low_s8(ys));
let xy_up = vmull_s8(vget_high_s8(xs), vget_high_s8(ys));
let xy = vaddq_s32(vpaddlq_s16(xy_lo), vpaddlq_s16(xy_up));
sum_i = vaddq_s32(sum_i, xy)
}
sumf += vaddvq_s32(sum_i) as f32 * scale
}
}
Ok(sumf)
}
#[inline(always)]
pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q6k_q8k: {n} is not divisible by {QK_K}")
}
let mut sum = 0f32;
unsafe {
let m4b = vdupq_n_u8(0xF);
let mone = vdupq_n_u8(3);
for (x, y) in xs.iter().zip(ys.iter()) {
let d_all = x.d.to_f32();
let mut q6 = x.ql.as_ptr();
let mut qh = x.qh.as_ptr();
let mut q8 = y.qs.as_ptr();
let mut scale = x.scales.as_ptr();
let q8sums = vld1q_s16_x2(y.bsums.as_ptr());
let scales = vld1q_s8(scale);
let q6scales = int16x8x2_t(
vmovl_s8(vget_low_s8(scales)),
vmovl_s8(vget_high_s8(scales)),
);
let prod = vaddq_s32(
vaddq_s32(
vmull_s16(vget_low_s16(q8sums.0), vget_low_s16(q6scales.0)),
vmull_s16(vget_high_s16(q8sums.0), vget_high_s16(q6scales.0)),
),
vaddq_s32(
vmull_s16(vget_low_s16(q8sums.1), vget_low_s16(q6scales.1)),
vmull_s16(vget_high_s16(q8sums.1), vget_high_s16(q6scales.1)),
),
);
let isum_mins = vaddvq_s32(prod);
let mut isum = 0i32;
for _j in 0..QK_K / 128 {
let qhbits = vld1q_u8_x2(qh);
qh = qh.add(32);
let q6bits = vld1q_u8_x4(q6);
q6 = q6.add(64);
let q8bytes = vld1q_s8_x4(q8);
q8 = q8.add(64);
let q6h_0 = vshlq_n_u8(vandq_u8(mone, qhbits.0), 4);
let q6h_1 = vshlq_n_u8(vandq_u8(mone, qhbits.1), 4);
let shifted = vshrq_n_u8(qhbits.0, 2);
let q6h_2 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let shifted = vshrq_n_u8(qhbits.1, 2);
let q6h_3 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let q6bytes_0 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.0, m4b), q6h_0));
let q6bytes_1 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.1, m4b), q6h_1));
let q6bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.2, m4b), q6h_2));
let q6bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.3, m4b), q6h_3));
// TODO: dotprod
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q6bytes_0), vget_high_s8(q8bytes.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q6bytes_1), vget_high_s8(q8bytes.1)),
);
let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
isum += vaddvq_s16(p0) as i32 * scale0 + vaddvq_s16(p1) as i32 * scale1;
scale = scale.add(2);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_2), vget_low_s8(q8bytes.2)),
vmull_s8(vget_high_s8(q6bytes_2), vget_high_s8(q8bytes.2)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_3), vget_low_s8(q8bytes.3)),
vmull_s8(vget_high_s8(q6bytes_3), vget_high_s8(q8bytes.3)),
);
let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
isum += vaddvq_s16(p2) as i32 * scale0 + vaddvq_s16(p3) as i32 * scale1;
scale = scale.add(2);
let q8bytes = vld1q_s8_x4(q8);
q8 = q8.add(64);
let shifted = vshrq_n_u8(qhbits.0, 4);
let q6h_0 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let shifted = vshrq_n_u8(qhbits.1, 4);
let q6h_1 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let shifted = vshrq_n_u8(qhbits.0, 6);
let q6h_2 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let shifted = vshrq_n_u8(qhbits.1, 6);
let q6h_3 = vshlq_n_u8(vandq_u8(mone, shifted), 4);
let q6bytes_0 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.0, 4), q6h_0));
let q6bytes_1 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.1, 4), q6h_1));
let q6bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.2, 4), q6h_2));
let q6bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.3, 4), q6h_3));
// TODO: dotprod case.
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q6bytes_0), vget_high_s8(q8bytes.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q6bytes_1), vget_high_s8(q8bytes.1)),
);
let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
isum += vaddvq_s16(p0) as i32 * scale0 + vaddvq_s16(p1) as i32 * scale1;
scale = scale.add(2);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_2), vget_low_s8(q8bytes.2)),
vmull_s8(vget_high_s8(q6bytes_2), vget_high_s8(q8bytes.2)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q6bytes_3), vget_low_s8(q8bytes.3)),
vmull_s8(vget_high_s8(q6bytes_3), vget_high_s8(q8bytes.3)),
);
let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
isum += vaddvq_s16(p2) as i32 * scale0 + vaddvq_s16(p3) as i32 * scale1;
scale = scale.add(2);
}
sum += d_all * y.d * ((isum - 32 * isum_mins) as f32);
}
}
Ok(sum)
}
#[inline(always)]
pub(crate) fn vec_dot_q5k_q8k(n: usize, xs: &[BlockQ5K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q5k_q8k: {n} is not divisible by {QK_K}")
}
let mut sumf = 0f32;
let mut utmp = [0u32; 4];
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
unsafe {
let m4b = vdupq_n_u8(0xF);
let mone = vdupq_n_u8(1);
let mtwo = vdupq_n_u8(2);
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = y.d * x.dmin.to_f32();
let q8sums = vpaddq_s16(
vld1q_s16(y.bsums.as_ptr()),
vld1q_s16(y.bsums.as_ptr().add(8)),
);
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
let mins8 = vld1_u8((utmp.as_ptr() as *const u8).add(8));
let mins = vreinterpretq_s16_u16(vmovl_u8(mins8));
let prod = vaddq_s32(
vmull_s16(vget_low_s16(q8sums), vget_low_s16(mins)),
vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins)),
);
let sumi_mins = vaddvq_s32(prod);
let mut scales = utmp.as_ptr() as *const u8;
let mut q5 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let mut qhbits = vld1q_u8_x2(x.qh.as_ptr());
let mut sumi = 0i32;
for _j in 0..QK_K / 64 {
let q5bits = vld1q_u8_x2(q5);
q5 = q5.add(32);
let q8bytes = vld1q_s8_x4(q8);
q8 = q8.add(64);
let q5h_0 = vshlq_n_u8(vandq_u8(mone, qhbits.0), 4);
let q5h_1 = vshlq_n_u8(vandq_u8(mone, qhbits.1), 4);
let q5h_2 = vshlq_n_u8(vandq_u8(mtwo, qhbits.0), 3);
let q5h_3 = vshlq_n_u8(vandq_u8(mtwo, qhbits.1), 3);
qhbits.0 = vshrq_n_u8(qhbits.0, 2);
qhbits.1 = vshrq_n_u8(qhbits.1, 2);
let q5bytes_0 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.0, m4b), q5h_0));
let q5bytes_1 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.1, m4b), q5h_1));
let q5bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.0, 4), q5h_2));
let q5bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.1, 4), q5h_3));
// TODO: dotprod
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q5bytes_0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q5bytes_0), vget_high_s8(q8bytes.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q5bytes_1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q5bytes_1), vget_high_s8(q8bytes.1)),
);
sumi += vaddvq_s16(vaddq_s16(p0, p1)) as i32 * *scales as i32;
scales = scales.add(1);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q5bytes_2), vget_low_s8(q8bytes.2)),
vmull_s8(vget_high_s8(q5bytes_2), vget_high_s8(q8bytes.2)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q5bytes_3), vget_low_s8(q8bytes.3)),
vmull_s8(vget_high_s8(q5bytes_3), vget_high_s8(q8bytes.3)),
);
sumi += vaddvq_s16(vaddq_s16(p2, p3)) as i32 * *scales as i32;
scales = scales.add(1);
}
sumf += d * sumi as f32 - dmin * sumi_mins as f32;
}
}
Ok(sumf)
}
#[inline(always)]
pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
}
let mut sumf = 0f32;
let mut utmp = [0u32; 4];
let mut scales = [0u8; 16];
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
unsafe {
let m4b = vdupq_n_u8(0xF);
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = y.d * x.dmin.to_f32();
let q8sums = vpaddq_s16(
vld1q_s16(y.bsums.as_ptr()),
vld1q_s16(y.bsums.as_ptr().add(8)),
);
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
let mins8 = vld1_u32(
[
utmp[1] & KMASK1,
((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4),
]
.as_ptr(),
);
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[0] &= KMASK1;
let mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8)));
let prod = vaddq_s32(
vmull_s16(vget_low_s16(q8sums), vget_low_s16(mins)),
vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins)),
);
sumf -= dmin * vaddvq_s32(prod) as f32;
LittleEndian::write_u32_into(&utmp, &mut scales);
let mut q4 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let mut sumi1 = 0i32;
let mut sumi2 = 0i32;
for j in 0..QK_K / 64 {
let q4bits = vld1q_u8_x2(q4);
q4 = q4.add(32);
// TODO: dotprod
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
let q4bytes = int8x16x2_t(
vreinterpretq_s8_u8(vandq_u8(q4bits.0, m4b)),
vreinterpretq_s8_u8(vandq_u8(q4bits.1, m4b)),
);
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q4bytes.0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q4bytes.0), vget_high_s8(q8bytes.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q4bytes.1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q4bytes.1), vget_high_s8(q8bytes.1)),
);
sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) as i32 * scales[2 * j] as i32;
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
let q4bytes = int8x16x2_t(
vreinterpretq_s8_u8(vshrq_n_u8(q4bits.0, 4)),
vreinterpretq_s8_u8(vshrq_n_u8(q4bits.1, 4)),
);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q4bytes.0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q4bytes.0), vget_high_s8(q8bytes.0)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q4bytes.1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q4bytes.1), vget_high_s8(q8bytes.1)),
);
sumi2 += vaddvq_s16(vaddq_s16(p2, p3)) as i32 * scales[2 * j + 1] as i32;
}
sumf += d * (sumi1 + sumi2) as f32;
}
}
Ok(sumf)
}
#[inline(always)]
pub(crate) fn vec_dot_q3k_q8k(n: usize, xs: &[BlockQ3K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q3k_q8k: {n} is not divisible by {QK_K}")
}
let mut sumf = 0f32;
let mut utmp = [0u32; 4];
let mut aux = [0u32; 3];
const KMASK1: u32 = 0x03030303;
const KMASK2: u32 = 0x0f0f0f0f;
unsafe {
let m3b = vdupq_n_u8(0x3);
let m0 = vdupq_n_u8(1);
let m1 = vshlq_n_u8(m0, 1);
let m2 = vshlq_n_u8(m0, 2);
let m3 = vshlq_n_u8(m0, 3);
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let mut q3 = x.qs.as_ptr();
let qh = x.hmask.as_ptr();
let mut q8 = y.qs.as_ptr();
let mut qhbits = vld1q_u8_x2(qh);
let mut isum = 0i32;
// Set up scales
LittleEndian::read_u32_into(&x.scales, &mut aux);
utmp[3] = ((aux[1] >> 4) & KMASK2) | (((aux[2] >> 6) & KMASK1) << 4);
utmp[2] = ((aux[0] >> 4) & KMASK2) | (((aux[2] >> 4) & KMASK1) << 4);
utmp[1] = (aux[1] & KMASK2) | (((aux[2] >> 2) & KMASK1) << 4);
utmp[0] = (aux[0] & KMASK2) | ((aux[2] & KMASK1) << 4);
let mut scale = utmp.as_mut_ptr() as *mut i8;
for j in 0..16 {
*scale.add(j) -= 32i8
}
for j in 0..QK_K / 128 {
let q3bits = vld1q_u8_x2(q3);
q3 = q3.add(32);
let q8bytes_1 = vld1q_s8_x4(q8);
q8 = q8.add(64);
let q8bytes_2 = vld1q_s8_x4(q8);
q8 = q8.add(64);
let q3h_0 = vshlq_n_u8(vbicq_u8(m0, qhbits.0), 2);
let q3h_1 = vshlq_n_u8(vbicq_u8(m0, qhbits.1), 2);
let q3h_2 = vshlq_n_u8(vbicq_u8(m1, qhbits.0), 1);
let q3h_3 = vshlq_n_u8(vbicq_u8(m1, qhbits.1), 1);
let q3bytes_0 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(q3bits.0, m3b)),
vreinterpretq_s8_u8(q3h_0),
);
let q3bytes_1 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(q3bits.1, m3b)),
vreinterpretq_s8_u8(q3h_1),
);
let q3bytes_2 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.0, 2), m3b)),
vreinterpretq_s8_u8(q3h_2),
);
let q3bytes_3 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.1, 2), m3b)),
vreinterpretq_s8_u8(q3h_3),
);
// TODO: dotprod
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_0), vget_low_s8(q8bytes_1.0)),
vmull_s8(vget_high_s8(q3bytes_0), vget_high_s8(q8bytes_1.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_1), vget_low_s8(q8bytes_1.1)),
vmull_s8(vget_high_s8(q3bytes_1), vget_high_s8(q8bytes_1.1)),
);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_2), vget_low_s8(q8bytes_1.2)),
vmull_s8(vget_high_s8(q3bytes_2), vget_high_s8(q8bytes_1.2)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_3), vget_low_s8(q8bytes_1.3)),
vmull_s8(vget_high_s8(q3bytes_3), vget_high_s8(q8bytes_1.3)),
);
isum += vaddvq_s16(p0) as i32 * *scale as i32
+ vaddvq_s16(p1) as i32 * *scale.add(1) as i32
+ vaddvq_s16(p2) as i32 * *scale.add(2) as i32
+ vaddvq_s16(p3) as i32 * *scale.add(3) as i32;
scale = scale.add(4);
let q3h_0 = vbicq_u8(m2, qhbits.0);
let q3h_1 = vbicq_u8(m2, qhbits.1);
let q3h_2 = vshrq_n_u8(vbicq_u8(m3, qhbits.0), 1);
let q3h_3 = vshrq_n_u8(vbicq_u8(m3, qhbits.1), 1);
let q3bytes_0 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.0, 4), m3b)),
vreinterpretq_s8_u8(q3h_0),
);
let q3bytes_1 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.1, 4), m3b)),
vreinterpretq_s8_u8(q3h_1),
);
let q3bytes_2 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.0, 6), m3b)),
vreinterpretq_s8_u8(q3h_2),
);
let q3bytes_3 = vsubq_s8(
vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.1, 6), m3b)),
vreinterpretq_s8_u8(q3h_3),
);
// TODO: dotprod
let p0 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_0), vget_low_s8(q8bytes_2.0)),
vmull_s8(vget_high_s8(q3bytes_0), vget_high_s8(q8bytes_2.0)),
);
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_1), vget_low_s8(q8bytes_2.1)),
vmull_s8(vget_high_s8(q3bytes_1), vget_high_s8(q8bytes_2.1)),
);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_2), vget_low_s8(q8bytes_2.2)),
vmull_s8(vget_high_s8(q3bytes_2), vget_high_s8(q8bytes_2.2)),
);
let p3 = vaddq_s16(
vmull_s8(vget_low_s8(q3bytes_3), vget_low_s8(q8bytes_2.3)),
vmull_s8(vget_high_s8(q3bytes_3), vget_high_s8(q8bytes_2.3)),
);
isum += vaddvq_s16(p0) as i32 * *scale as i32
+ vaddvq_s16(p1) as i32 * *scale.add(1) as i32
+ vaddvq_s16(p2) as i32 * *scale.add(2) as i32
+ vaddvq_s16(p3) as i32 * *scale.add(3) as i32;
scale = scale.add(4);
if j == 0 {
qhbits.0 = vshrq_n_u8(qhbits.0, 4);
qhbits.1 = vshrq_n_u8(qhbits.1, 4);
}
}
sumf += d * isum as f32;
}
}
Ok(sumf)
}
#[inline(always)]
pub(crate) fn vec_dot_q2k_q8k(n: usize, xs: &[BlockQ2K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
}
let mut sumf = 0f32;
let mut aux = [0u8; 16];
unsafe {
let m3 = vdupq_n_u8(0x3);
let m4 = vdupq_n_u8(0xF);
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = -y.d * x.dmin.to_f32();
let mut q2 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let sc = x.scales.as_ptr();
let mins_and_scales = vld1q_u8(sc);
let scales = vandq_u8(mins_and_scales, m4);
vst1q_u8(aux.as_mut_ptr(), scales);
let mins = vshrq_n_u8(mins_and_scales, 4);
let q8sums = vld1q_s16_x2(y.bsums.as_ptr());
let mins16 = int16x8x2_t(
vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))),
vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins))),
);
let s0 = vaddq_s32(
vmull_s16(vget_low_s16(mins16.0), vget_low_s16(q8sums.0)),
vmull_s16(vget_high_s16(mins16.0), vget_high_s16(q8sums.0)),
);
let s1 = vaddq_s32(
vmull_s16(vget_low_s16(mins16.1), vget_low_s16(q8sums.1)),
vmull_s16(vget_high_s16(mins16.1), vget_high_s16(q8sums.1)),
);
sumf += dmin * vaddvq_s32(vaddq_s32(s0, s1)) as f32;
let mut isum = 0i32;
let mut is = 0usize;
// TODO: dotprod
for _j in 0..QK_K / 128 {
let q2bits = vld1q_u8_x2(q2);
q2 = q2.add(32);
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
let mut q2bytes = int8x16x2_t(
vreinterpretq_s8_u8(vandq_u8(q2bits.0, m3)),
vreinterpretq_s8_u8(vandq_u8(q2bits.1, m3)),
);
isum += multiply_accum_with_scale(&aux, is, 0, q2bytes, q8bytes);
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
q2bytes.0 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.0, 2), m3));
q2bytes.1 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.1, 2), m3));
isum += multiply_accum_with_scale(&aux, is, 2, q2bytes, q8bytes);
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
q2bytes.0 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.0, 4), m3));
q2bytes.1 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.1, 4), m3));
isum += multiply_accum_with_scale(&aux, is, 4, q2bytes, q8bytes);
let q8bytes = vld1q_s8_x2(q8);
q8 = q8.add(32);
q2bytes.0 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.0, 6), m3));
q2bytes.1 = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.1, 6), m3));
isum += multiply_accum_with_scale(&aux, is, 6, q2bytes, q8bytes);
is += 8;
}
sumf += d * isum as f32;
}
}
Ok(sumf)
}
#[inline(always)]
unsafe fn multiply_accum_with_scale(
aux: &[u8; 16],
is: usize,
index: usize,
q2bytes: int8x16x2_t,
q8bytes: int8x16x2_t,
) -> i32 {
let p1 = vaddq_s16(
vmull_s8(vget_low_s8(q2bytes.0), vget_low_s8(q8bytes.0)),
vmull_s8(vget_high_s8(q2bytes.0), vget_high_s8(q8bytes.0)),
);
let p2 = vaddq_s16(
vmull_s8(vget_low_s8(q2bytes.1), vget_low_s8(q8bytes.1)),
vmull_s8(vget_high_s8(q2bytes.1), vget_high_s8(q8bytes.1)),
);
vaddvq_s16(p1) as i32 * aux[is + index] as i32
+ vaddvq_s16(p2) as i32 * aux[is + 1 + index] as i32
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/avx.rs
|
use super::k_quants::{
BlockQ2K, BlockQ3K, BlockQ4K, BlockQ4_0, BlockQ5K, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K,
};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
#[inline(always)]
pub(crate) unsafe fn sum_i16_pairs_float(x: __m256i) -> __m256 {
let ones = _mm256_set1_epi16(1);
let summed_pairs = _mm256_madd_epi16(ones, x);
_mm256_cvtepi32_ps(summed_pairs)
}
#[inline(always)]
pub(crate) unsafe fn mul_sum_us8_pairs_float(ax: __m256i, sy: __m256i) -> __m256 {
let dot = _mm256_maddubs_epi16(ax, sy);
sum_i16_pairs_float(dot)
}
#[inline(always)]
pub(crate) unsafe fn hsum_float_8(x: __m256) -> f32 {
let res = _mm256_extractf128_ps(x, 1);
let res = _mm_add_ps(res, _mm256_castps256_ps128(x));
let res = _mm_add_ps(res, _mm_movehl_ps(res, res));
let res = _mm_add_ss(res, _mm_movehdup_ps(res));
_mm_cvtss_f32(res)
}
#[inline(always)]
pub(crate) unsafe fn bytes_from_nibbles_32(rsi: *const u8) -> __m256i {
let tmp = _mm_loadu_si128(rsi as *const __m128i);
let bytes = _mm256_insertf128_si256::<1>(_mm256_castsi128_si256(tmp), _mm_srli_epi16(tmp, 4));
let low_mask = _mm256_set1_epi8(0xF);
_mm256_and_si256(low_mask, bytes)
}
#[inline(always)]
pub(crate) unsafe fn mul_sum_i8_pairs_float(x: __m256i, y: __m256i) -> __m256 {
let ax = _mm256_sign_epi8(x, x);
let sy = _mm256_sign_epi8(y, x);
mul_sum_us8_pairs_float(ax, sy)
}
#[inline(always)]
pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = _mm256_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = _mm256_set1_ps(f16::to_f32(x.d) * f16::to_f32(y.d));
let bx = bytes_from_nibbles_32(x.qs.as_ptr());
let off = _mm256_set1_epi8(8);
let bx = _mm256_sub_epi8(bx, off);
let by = _mm256_loadu_si256(y.qs.as_ptr() as *const __m256i);
let q = mul_sum_i8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(d, q, acc);
}
Ok(hsum_float_8(acc))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = _mm256_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = _mm256_set1_ps(f16::to_f32(x.d) * f16::to_f32(y.d));
let bx = _mm256_loadu_si256(x.qs.as_ptr() as *const __m256i);
let by = _mm256_loadu_si256(y.qs.as_ptr() as *const __m256i);
let q = mul_sum_i8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(d, q, acc);
}
Ok(hsum_float_8(acc))
}
}
#[inline(always)]
unsafe fn get_scale_shuffle(i: usize) -> __m128i {
const K_SHUFFLE: [u8; 128] = [
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7,
7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10,
11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13,
13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15,
];
_mm_loadu_si128((K_SHUFFLE.as_ptr() as *const __m128i).add(i))
}
#[inline(always)]
unsafe fn get_scale_shuffle_k4(i: usize) -> __m256i {
const K_SHUFFLE: [u8; 256] = [
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
2, 3, 2, 3, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,
4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
6, 7, 6, 7, 6, 7, 6, 7, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,
8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10,
11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 10, 11, 12, 13, 12, 13, 12, 13,
12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12,
13, 12, 13, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15,
14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15,
];
_mm256_loadu_si256((K_SHUFFLE.as_ptr() as *const __m256i).add(i))
}
#[inline(always)]
unsafe fn get_scale_shuffle_q3k(i: usize) -> __m256i {
const K_SHUFFLE: [u8; 128] = [
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
2, 3, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
6, 7, 6, 7, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10, 11, 10, 11, 10, 11, 10, 11,
10, 11, 10, 11, 10, 11, 10, 11, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12,
13, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15, 14, 15,
];
_mm256_loadu_si256((K_SHUFFLE.as_ptr() as *const __m256i).add(i))
}
#[inline(always)]
pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Result<f32> {
let qk = QK_K;
if n % qk != 0 {
crate::bail!("vec_dot_q6k_8k: {n} is not divisible by {qk}")
}
unsafe {
let m4 = _mm256_set1_epi8(0xF);
let m2 = _mm256_set1_epi8(3);
let m32s = _mm256_set1_epi8(32);
let mut acc = _mm256_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let mut q4 = x.ql.as_ptr();
let mut qh = x.qh.as_ptr();
let mut q8 = y.qs.as_ptr();
let scales = _mm_loadu_si128(x.scales.as_ptr() as *const __m128i);
let mut sumi = _mm256_setzero_si256();
for j in 0..QK_K / 128 {
let is = j * 4;
let scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is));
let scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1));
let scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2));
let scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3));
let q4bits1 = _mm256_loadu_si256(q4 as *const __m256i);
q4 = q4.add(32);
let q4bits2 = _mm256_loadu_si256(q4 as *const __m256i);
q4 = q4.add(32);
let q4bits_h = _mm256_loadu_si256(qh as *const __m256i);
qh = qh.add(32);
let q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bits_h, m2), 4);
let q4h_1 =
_mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bits_h, 2), m2), 4);
let q4h_2 =
_mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bits_h, 4), m2), 4);
let q4h_3 =
_mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bits_h, 6), m2), 4);
let q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
let q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1);
let q4_2 =
_mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2);
let q4_3 =
_mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3);
let q8_0 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_1 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_2 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_3 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8s_0 = _mm256_maddubs_epi16(m32s, q8_0);
let q8s_1 = _mm256_maddubs_epi16(m32s, q8_1);
let q8s_2 = _mm256_maddubs_epi16(m32s, q8_2);
let q8s_3 = _mm256_maddubs_epi16(m32s, q8_3);
let p16_0 = _mm256_maddubs_epi16(q4_0, q8_0);
let p16_1 = _mm256_maddubs_epi16(q4_1, q8_1);
let p16_2 = _mm256_maddubs_epi16(q4_2, q8_2);
let p16_3 = _mm256_maddubs_epi16(q4_3, q8_3);
let p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
let p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
let p16_2 = _mm256_sub_epi16(p16_2, q8s_2);
let p16_3 = _mm256_sub_epi16(p16_3, q8s_3);
let p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0);
let p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1);
let p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2);
let p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3);
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3));
}
acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
}
Ok(hsum_float_8(acc))
}
}
#[inline(always)]
unsafe fn mm256_set_m128i(a: __m128i, b: __m128i) -> __m256i {
_mm256_insertf128_si256(_mm256_castsi128_si256(b), a, 1)
}
#[inline(always)]
pub(crate) fn vec_dot_q2k_q8k(n: usize, xs: &[BlockQ2K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
}
unsafe {
let m3 = _mm256_set1_epi8(3);
let m4 = _mm_set1_epi8(0xF);
let mut acc = _mm256_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = -y.d * x.dmin.to_f32();
let mut q2 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let mins_and_scales = _mm_loadu_si128(x.scales.as_ptr() as *const __m128i);
let scales8 = _mm_and_si128(mins_and_scales, m4);
let mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4);
let mins = _mm256_cvtepi8_epi16(mins8);
let prod =
_mm256_madd_epi16(mins, _mm256_loadu_si256(y.bsums.as_ptr() as *const __m256i));
acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc);
let all_scales = _mm256_cvtepi8_epi16(scales8);
let l_scales = _mm256_extracti128_si256(all_scales, 0);
let h_scales = _mm256_extracti128_si256(all_scales, 1);
let scales = [
mm256_set_m128i(l_scales, l_scales),
mm256_set_m128i(h_scales, h_scales),
];
let mut sumi = _mm256_setzero_si256();
for scale in scales {
let q2bits = _mm256_loadu_si256(q2 as *const __m256i);
q2 = q2.add(32);
let q8_0 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_1 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_2 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_3 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q2_0 = _mm256_and_si256(q2bits, m3);
let q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3);
let q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3);
let q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3);
let p0 = _mm256_maddubs_epi16(q2_0, q8_0);
let p1 = _mm256_maddubs_epi16(q2_1, q8_1);
let p2 = _mm256_maddubs_epi16(q2_2, q8_2);
let p3 = _mm256_maddubs_epi16(q2_3, q8_3);
let p0 =
_mm256_madd_epi16(_mm256_shuffle_epi8(scale, get_scale_shuffle_q3k(0)), p0);
let p1 =
_mm256_madd_epi16(_mm256_shuffle_epi8(scale, get_scale_shuffle_q3k(1)), p1);
let p2 =
_mm256_madd_epi16(_mm256_shuffle_epi8(scale, get_scale_shuffle_q3k(2)), p2);
let p3 =
_mm256_madd_epi16(_mm256_shuffle_epi8(scale, get_scale_shuffle_q3k(3)), p3);
let p0 = _mm256_add_epi32(p0, p1);
let p2 = _mm256_add_epi32(p2, p3);
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2));
}
acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
}
Ok(hsum_float_8(acc))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q3k_q8k(n: usize, xs: &[BlockQ3K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q3k_q8k: {n} is not divisible by {QK_K}")
}
const KMASK1: u32 = 0x03030303;
const KMASK2: u32 = 0x0f0f0f0f;
let mut aux = [0u32; 3];
unsafe {
let m3 = _mm256_set1_epi8(3);
let mone = _mm256_set1_epi8(1);
let m32 = _mm_set1_epi8(32);
let mut acc = _mm256_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let mut q3 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
LittleEndian::read_u32_into(&x.scales, &mut aux);
let scales128 = _mm_set_epi32(
(((aux[1] >> 4) & KMASK2) | (((aux[2] >> 6) & KMASK1) << 4)) as i32,
(((aux[0] >> 4) & KMASK2) | (((aux[2] >> 4) & KMASK1) << 4)) as i32,
((aux[1] & KMASK2) | (((aux[2] >> 2) & KMASK1) << 4)) as i32,
((aux[0] & KMASK2) | (((aux[2]) & KMASK1) << 4)) as i32,
);
let scales128 = _mm_sub_epi8(scales128, m32);
let all_scales = _mm256_cvtepi8_epi16(scales128);
let l_scales = _mm256_extracti128_si256(all_scales, 0);
let h_scales = _mm256_extracti128_si256(all_scales, 1);
let scales = [
mm256_set_m128i(l_scales, l_scales),
mm256_set_m128i(h_scales, h_scales),
];
// high bit
let hbits = _mm256_loadu_si256(x.hmask.as_ptr() as *const __m256i);
let mut sumi = _mm256_setzero_si256();
for (j, scale) in scales.iter().enumerate() {
// load low 2 bits
let q3bits = _mm256_loadu_si256(q3 as *const __m256i);
q3 = q3.add(32);
// Prepare low and high bits
// We hardcode the shifts here to avoid loading them into a seperate register
let q3l_0 = _mm256_and_si256(q3bits, m3);
let q3h_0 = if j == 0 {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 0)), 0)
} else {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 4)), 4)
};
let q3h_0 = _mm256_slli_epi16(q3h_0, 2);
let q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3);
let q3h_1 = if j == 0 {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 1)), 1)
} else {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 5)), 5)
};
let q3h_1 = _mm256_slli_epi16(q3h_1, 2);
let q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3);
let q3h_2 = if j == 0 {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 2)), 2)
} else {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 6)), 6)
};
let q3h_2 = _mm256_slli_epi16(q3h_2, 2);
let q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3);
let q3h_3 = if j == 0 {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 3)), 3)
} else {
_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 7)), 7)
};
let q3h_3 = _mm256_slli_epi16(q3h_3, 2);
// load Q8 quants
let q8_0 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_1 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_2 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_3 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
// Dot product: we multiply the 2 low bits and 1 high bit part separately, so we
// can use _mm256_maddubs_epi16, and then subtract. The high bit part has the 2
// already subtracted (and so, it is zero if the high bit was not set, and 2 if the
// high bit was set)
let q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0);
let q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1);
let q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2);
let q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3);
let p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0);
let p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1);
let p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2);
let p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3);
let p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
let p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
let p16_2 = _mm256_sub_epi16(p16_2, q8s_2);
let p16_3 = _mm256_sub_epi16(p16_3, q8s_3);
// multiply with scales
let p16_0 =
_mm256_madd_epi16(_mm256_shuffle_epi8(*scale, get_scale_shuffle_q3k(0)), p16_0);
let p16_1 =
_mm256_madd_epi16(_mm256_shuffle_epi8(*scale, get_scale_shuffle_q3k(1)), p16_1);
let p16_2 =
_mm256_madd_epi16(_mm256_shuffle_epi8(*scale, get_scale_shuffle_q3k(2)), p16_2);
let p16_3 =
_mm256_madd_epi16(_mm256_shuffle_epi8(*scale, get_scale_shuffle_q3k(3)), p16_3);
// accumulate
let p16_0 = _mm256_add_epi32(p16_0, p16_1);
let p16_2 = _mm256_add_epi32(p16_2, p16_3);
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2));
}
// multiply with block scale and accumulate
acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
}
Ok(hsum_float_8(acc))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
}
let mut utmp = [0u32; 4];
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
unsafe {
let m4 = _mm256_set1_epi8(0xF);
let mut acc = _mm256_setzero_ps();
let mut acc_m = _mm_setzero_ps();
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = -y.d * x.dmin.to_f32();
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
let mut q4 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(
utmp[3] as i32,
utmp[2] as i32,
utmp[1] as i32,
utmp[0] as i32,
));
let q8sums = _mm256_loadu_si256(y.bsums.as_ptr() as *const __m256i);
let q8s = _mm_hadd_epi16(
_mm256_extracti128_si256(q8sums, 0),
_mm256_extracti128_si256(q8sums, 1),
);
let prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s);
acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
let sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
let scales = mm256_set_m128i(sc128, sc128);
let mut sumi = _mm256_setzero_si256();
for j in 0..QK_K / 64 {
let scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2 * j));
let scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2 * j + 1));
let q4bits = _mm256_loadu_si256(q4 as *const __m256i);
q4 = q4.add(32);
let q4l = _mm256_and_si256(q4bits, m4);
let q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4);
let q8l = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let p16l = _mm256_maddubs_epi16(q4l, q8l);
let p16l = _mm256_madd_epi16(scale_l, p16l);
sumi = _mm256_add_epi32(sumi, p16l);
let q8h = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let p16h = _mm256_maddubs_epi16(q4h, q8h);
let p16h = _mm256_madd_epi16(scale_h, p16h);
sumi = _mm256_add_epi32(sumi, p16h);
}
let vd = _mm256_set1_ps(d);
acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc);
}
let acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m));
let acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m));
Ok(hsum_float_8(acc) + _mm_cvtss_f32(acc_m))
}
}
#[inline(always)]
pub(crate) fn vec_dot_q5k_q8k(n: usize, xs: &[BlockQ5K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q5k_q8k: {n} is not divisible by {QK_K}")
}
let mut utmp = [0u32; 4];
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
unsafe {
let m4 = _mm256_set1_epi8(0xF);
let mzero = _mm_setzero_si128();
let mone = _mm256_set1_epi8(1);
let mut acc = _mm256_setzero_ps();
let mut summs = 0.0;
for (x, y) in xs.iter().zip(ys.iter()) {
let d = y.d * x.d.to_f32();
let dmin = -y.d * x.dmin.to_f32();
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
let mut q5 = x.qs.as_ptr();
let mut q8 = y.qs.as_ptr();
let mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(
utmp[3] as i32,
utmp[2] as i32,
utmp[1] as i32,
utmp[0] as i32,
));
let q8sums = _mm256_loadu_si256(y.bsums.as_ptr() as *const __m256i);
let q8s = _mm_hadd_epi16(
_mm256_extracti128_si256(q8sums, 0),
_mm256_extracti128_si256(q8sums, 1),
);
let prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s);
let hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero);
summs += dmin * _mm_extract_epi32(hsum, 0) as f32;
let sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
let scales = mm256_set_m128i(sc128, sc128);
let hbits = _mm256_loadu_si256(x.qh.as_ptr() as *const __m256i);
let mut hmask = mone;
let mut sumi = _mm256_setzero_si256();
for j in 0..QK_K / 64 {
let scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2 * j));
let scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2 * j + 1));
let q5bits = _mm256_loadu_si256(q5 as *const __m256i);
q5 = q5.add(32);
//Similar to q3k we hardcode the shifts here to avoid loading them into a seperate register
let q5l_0 = _mm256_and_si256(q5bits, m4);
let q5l_0_shift_input = _mm256_and_si256(hbits, hmask);
let q5l_0_right_shift = match j {
0 => _mm256_srli_epi16(q5l_0_shift_input, 0),
1 => _mm256_srli_epi16(q5l_0_shift_input, 2),
2 => _mm256_srli_epi16(q5l_0_shift_input, 4),
3 => _mm256_srli_epi16(q5l_0_shift_input, 6),
_ => unreachable!(),
};
let q5h_0 = _mm256_slli_epi16(q5l_0_right_shift, 4);
let q5_0 = _mm256_add_epi8(q5l_0, q5h_0);
hmask = _mm256_slli_epi16(hmask, 1);
let q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4);
let q5l_1_shift_input = _mm256_and_si256(hbits, hmask);
let q5l_1_right_shift = match j {
0 => _mm256_srli_epi16(q5l_1_shift_input, 1),
1 => _mm256_srli_epi16(q5l_1_shift_input, 3),
2 => _mm256_srli_epi16(q5l_1_shift_input, 5),
3 => _mm256_srli_epi16(q5l_1_shift_input, 7),
_ => unreachable!(),
};
let q5h_1 = _mm256_slli_epi16(q5l_1_right_shift, 4);
let q5_1 = _mm256_add_epi8(q5l_1, q5h_1);
hmask = _mm256_slli_epi16(hmask, 1);
let q8_0 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let q8_1 = _mm256_loadu_si256(q8 as *const __m256i);
q8 = q8.add(32);
let p16_0 = _mm256_maddubs_epi16(q5_0, q8_0);
let p16_1 = _mm256_maddubs_epi16(q5_1, q8_1);
let p16_0 = _mm256_madd_epi16(scale_0, p16_0);
let p16_1 = _mm256_madd_epi16(scale_1, p16_1);
sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
}
let vd = _mm256_set1_ps(d);
acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc);
}
Ok(hsum_float_8(acc) + summs)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
let qk = QK_K;
if n % qk != 0 {
crate::bail!("vec_dot_q8k_8k: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = _mm256_setzero_ps();
for (xs, ys) in xs.iter().zip(ys.iter()) {
let mut sumi = _mm256_setzero_si256();
let x_qs = xs.qs.as_ptr();
let y_qs = ys.qs.as_ptr();
for j in (0..QK_K).step_by(32) {
let xs = _mm256_loadu_si256(x_qs.add(j) as *const __m256i);
let ys = _mm256_loadu_si256(y_qs.add(j) as *const __m256i);
let xs0 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(xs, 0));
let ys0 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(ys, 0));
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(xs0, ys0));
let xs1 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(xs, 1));
let ys1 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(ys, 1));
sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(xs1, ys1));
}
let d = _mm256_set1_ps(xs.d * ys.d);
acc = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi), acc);
}
Ok(hsum_float_8(acc))
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/k_quants.rs
|
use super::utils::{
get_scale_min_k4, group_for_dequantization, group_for_quantization, make_q3_quants,
make_qkx1_quants, make_qx_quants, nearest_int,
};
use super::GgmlDType;
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
use rayon::prelude::*;
// Default to QK_K 256 rather than 64.
pub const QK_K: usize = 256;
pub const K_SCALE_SIZE: usize = 12;
pub const QK4_0: usize = 32;
pub const QK4_1: usize = 32;
pub const QK5_0: usize = 32;
pub const QK5_1: usize = 32;
pub const QK8_0: usize = 32;
pub const QK8_1: usize = 32;
pub trait GgmlType: Sized + Clone + Send + Sync {
const DTYPE: GgmlDType;
const BLCK_SIZE: usize;
type VecDotType: GgmlType;
// This is only safe for types that include immediate values such as float/int/...
fn zeros() -> Self {
unsafe { std::mem::MaybeUninit::zeroed().assume_init() }
}
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()>;
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()>;
/// Dot product used as a building block for quantized mat-mul.
/// n is the number of elements to be considered.
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32>;
/// Generic implementation of the dot product without simd optimizations.
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32>;
}
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ4_0 {
pub(crate) d: f16,
pub(crate) qs: [u8; QK4_0 / 2],
}
const _: () = assert!(std::mem::size_of::<BlockQ4_0>() == 18);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ4_1 {
pub(crate) d: f16,
pub(crate) m: f16,
pub(crate) qs: [u8; QK4_1 / 2],
}
const _: () = assert!(std::mem::size_of::<BlockQ4_1>() == 20);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ5_0 {
pub(crate) d: f16,
pub(crate) qh: [u8; 4],
pub(crate) qs: [u8; QK5_0 / 2],
}
const _: () = assert!(std::mem::size_of::<BlockQ5_0>() == 22);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ5_1 {
pub(crate) d: f16,
pub(crate) m: f16,
pub(crate) qh: [u8; 4],
pub(crate) qs: [u8; QK5_1 / 2],
}
const _: () = assert!(std::mem::size_of::<BlockQ5_1>() == 24);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ8_0 {
pub(crate) d: f16,
pub(crate) qs: [i8; QK8_0],
}
const _: () = assert!(std::mem::size_of::<BlockQ8_0>() == 34);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ8_1 {
pub(crate) d: f16,
pub(crate) s: f16,
pub(crate) qs: [i8; QK8_1],
}
const _: () = assert!(std::mem::size_of::<BlockQ8_1>() == 36);
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ2K {
pub(crate) scales: [u8; QK_K / 16],
pub(crate) qs: [u8; QK_K / 4],
pub(crate) d: f16,
pub(crate) dmin: f16,
}
const _: () = assert!(QK_K / 16 + QK_K / 4 + 2 * 2 == std::mem::size_of::<BlockQ2K>());
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ3K {
pub(crate) hmask: [u8; QK_K / 8],
pub(crate) qs: [u8; QK_K / 4],
pub(crate) scales: [u8; 12],
pub(crate) d: f16,
}
const _: () = assert!(QK_K / 8 + QK_K / 4 + 12 + 2 == std::mem::size_of::<BlockQ3K>());
#[derive(Debug, Clone, PartialEq)]
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/k_quants.h#L82
#[repr(C)]
pub struct BlockQ4K {
pub(crate) d: f16,
pub(crate) dmin: f16,
pub(crate) scales: [u8; K_SCALE_SIZE],
pub(crate) qs: [u8; QK_K / 2],
}
const _: () = assert!(QK_K / 2 + K_SCALE_SIZE + 2 * 2 == std::mem::size_of::<BlockQ4K>());
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ5K {
pub(crate) d: f16,
pub(crate) dmin: f16,
pub(crate) scales: [u8; K_SCALE_SIZE],
pub(crate) qh: [u8; QK_K / 8],
pub(crate) qs: [u8; QK_K / 2],
}
const _: () =
assert!(QK_K / 8 + QK_K / 2 + 2 * 2 + K_SCALE_SIZE == std::mem::size_of::<BlockQ5K>());
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ6K {
pub(crate) ql: [u8; QK_K / 2],
pub(crate) qh: [u8; QK_K / 4],
pub(crate) scales: [i8; QK_K / 16],
pub(crate) d: f16,
}
const _: () = assert!(3 * QK_K / 4 + QK_K / 16 + 2 == std::mem::size_of::<BlockQ6K>());
#[derive(Debug, Clone, PartialEq)]
#[repr(C)]
pub struct BlockQ8K {
pub(crate) d: f32,
pub(crate) qs: [i8; QK_K],
pub(crate) bsums: [i16; QK_K / 16],
}
const _: () = assert!(4 + QK_K + QK_K / 16 * 2 == std::mem::size_of::<BlockQ8K>());
impl GgmlType for BlockQ4_0 {
const DTYPE: GgmlDType = GgmlDType::Q4_0;
const BLCK_SIZE: usize = QK4_0;
type VecDotType = BlockQ8_0;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L1525
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
let qk = Self::BLCK_SIZE;
if k % qk != 0 {
crate::bail!("dequantize_row_q4_0: {k} is not divisible by {qk}")
}
let nb = k / qk;
for i in 0..nb {
let d = xs[i].d.to_f32();
for j in 0..(qk / 2) {
let x0 = (xs[i].qs[j] & 0x0F) as i16 - 8;
let x1 = (xs[i].qs[j] >> 4) as i16 - 8;
ys[i * qk + j] = (x0 as f32) * d;
ys[i * qk + j + qk / 2] = (x1 as f32) * d;
}
}
Ok(())
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q4_0
let qk = Self::BLCK_SIZE;
let k = xs.len();
if k % qk != 0 {
crate::bail!("{k} is not divisible by {}", qk);
};
let nb = k / qk;
if ys.len() != nb {
crate::bail!("size mismatch {} {} {}", xs.len(), ys.len(), qk,)
}
for (i, ys) in ys.iter_mut().enumerate() {
let mut amax = 0f32;
let mut max = 0f32;
let xs = &xs[i * qk..(i + 1) * qk];
for &x in xs.iter() {
if amax < x.abs() {
amax = x.abs();
max = x;
}
}
let d = max / -8.0;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
for (j, q) in ys.qs.iter_mut().enumerate() {
let x0 = xs[j] * id;
let x1 = xs[qk / 2 + j] * id;
let xi0 = u8::min(15, (x0 + 8.5) as u8);
let xi1 = u8::min(15, (x1 + 8.5) as u8);
*q = xi0 | (xi1 << 4)
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/ggml.c#L2361C10-L2361C122
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q4_0_q8_0(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q4_0_q8_0(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q4_0_q8_0(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let mut sum_i = 0;
for j in 0..qk / 2 {
let v0 = (xs.qs[j] & 0x0F) as i32 - 8;
let v1 = (xs.qs[j] >> 4) as i32 - 8;
sum_i += v0 * ys.qs[j] as i32 + v1 * ys.qs[j + qk / 2] as i32
}
sumf += sum_i as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
}
Ok(sumf)
}
}
impl GgmlType for BlockQ4_1 {
const DTYPE: GgmlDType = GgmlDType::Q4_1;
const BLCK_SIZE: usize = QK4_1;
type VecDotType = BlockQ8_1;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
// ggml_vec_dot_q4_1_q8_1
let qk = QK8_1;
if n % qk != 0 {
crate::bail!("vec_dot_q4_1_q8_1: {n} is not divisible by {qk}")
}
let nb = n / qk;
if nb % 2 != 0 {
crate::bail!("vec_dot_q4_1_q8_1: {n}, nb is not divisible by 2")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let mut sumi = 0i32;
for j in 0..qk / 2 {
let v0 = xs.qs[j] as i32 & 0x0F;
let v1 = xs.qs[j] as i32 >> 4;
sumi += (v0 * ys.qs[j] as i32) + (v1 * ys.qs[j + qk / 2] as i32);
}
sumf += sumi as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
+ f16::to_f32(xs.m) * f16::to_f32(ys.s)
}
Ok(sumf)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q4_1
let qk = Self::BLCK_SIZE;
if ys.len() * qk != xs.len() {
crate::bail!("size mismatch {} {} {}", xs.len(), ys.len(), qk,)
}
for (i, ys) in ys.iter_mut().enumerate() {
let xs = &xs[i * qk..(i + 1) * qk];
let mut min = f32::INFINITY;
let mut max = f32::NEG_INFINITY;
for &x in xs.iter() {
min = f32::min(x, min);
max = f32::max(x, max);
}
let d = (max - min) / ((1 << 4) - 1) as f32;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
ys.m = f16::from_f32(min);
for (j, q) in ys.qs.iter_mut().take(qk / 2).enumerate() {
let x0 = (xs[j] - min) * id;
let x1 = (xs[qk / 2 + j] - min) * id;
let xi0 = u8::min(15, (x0 + 0.5) as u8);
let xi1 = u8::min(15, (x1 + 0.5) as u8);
*q = xi0 | (xi1 << 4);
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L1545
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK4_1 != 0 {
crate::bail!("dequantize_row_q4_1: {k} is not divisible by {QK4_1}");
}
let nb = k / QK4_1;
for i in 0..nb {
let d = xs[i].d.to_f32();
let m = xs[i].m.to_f32();
for j in 0..(QK4_1 / 2) {
let x0 = xs[i].qs[j] & 0x0F;
let x1 = xs[i].qs[j] >> 4;
ys[i * QK4_1 + j] = (x0 as f32) * d + m;
ys[i * QK4_1 + j + QK4_1 / 2] = (x1 as f32) * d + m;
}
}
Ok(())
}
}
impl GgmlType for BlockQ5_0 {
const DTYPE: GgmlDType = GgmlDType::Q5_0;
const BLCK_SIZE: usize = QK5_0;
type VecDotType = BlockQ8_0;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = Self::BLCK_SIZE;
if n % Self::BLCK_SIZE != 0 {
crate::bail!("vec_dot_q5_0_q8_0: {n} is not divisible by {qk}")
}
let nb = n / qk;
if nb % 2 != 0 {
crate::bail!("vec_dot_q5_0_q8_0: {n}, nb is not divisible by 2")
}
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(_n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let qh = LittleEndian::read_u32(&xs.qh);
let mut sumi = 0i32;
for j in 0..Self::BLCK_SIZE / 2 {
let xh_0 = (((qh & (1u32 << j)) >> j) << 4) as u8;
let xh_1 = ((qh & (1u32 << (j + 16))) >> (j + 12)) as u8;
let x0 = ((xs.qs[j] & 0x0F) as i32 | xh_0 as i32) - 16;
let x1 = ((xs.qs[j] >> 4) as i32 | xh_1 as i32) - 16;
sumi += (x0 * ys.qs[j] as i32) + (x1 * ys.qs[j + Self::BLCK_SIZE / 2] as i32);
}
sumf += sumi as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
}
Ok(sumf)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q5_0
let k = xs.len();
if ys.len() * Self::BLCK_SIZE != k {
crate::bail!("size mismatch {k} {} {}", ys.len(), Self::BLCK_SIZE)
}
for (i, ys) in ys.iter_mut().enumerate() {
let xs = &xs[i * Self::BLCK_SIZE..(i + 1) * Self::BLCK_SIZE];
let mut amax = 0f32;
let mut max = 0f32;
for &x in xs.iter() {
if amax < x.abs() {
amax = x.abs();
max = x;
}
}
let d = max / -16.;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
let mut qh = 0u32;
for j in 0..Self::BLCK_SIZE / 2 {
let x0 = xs[j] * id;
let x1 = xs[j + Self::BLCK_SIZE / 2] * id;
let xi0 = ((x0 + 16.5) as i8).min(31) as u8;
let xi1 = ((x1 + 16.5) as i8).min(31) as u8;
ys.qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
qh |= ((xi0 as u32 & 0x10) >> 4) << j;
qh |= ((xi1 as u32 & 0x10) >> 4) << (j + Self::BLCK_SIZE / 2);
}
LittleEndian::write_u32(&mut ys.qh, qh)
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L1566
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK5_0 != 0 {
crate::bail!("dequantize_row_q5_0: {k} is not divisible by {QK5_0}");
}
let nb = k / QK5_0;
for i in 0..nb {
let d = xs[i].d.to_f32();
let qh: u32 = LittleEndian::read_u32(&xs[i].qh);
for j in 0..(QK5_0 / 2) {
let xh_0 = (((qh >> j) << 4) & 0x10) as u8;
let xh_1 = ((qh >> (j + 12)) & 0x10) as u8;
let x0 = ((xs[i].qs[j] & 0x0F) | xh_0) as i32 - 16;
let x1 = ((xs[i].qs[j] >> 4) | xh_1) as i32 - 16;
ys[i * QK5_0 + j] = (x0 as f32) * d;
ys[i * QK5_0 + j + QK5_0 / 2] = (x1 as f32) * d;
}
}
Ok(())
}
}
impl GgmlType for BlockQ5_1 {
const DTYPE: GgmlDType = GgmlDType::Q5_1;
const BLCK_SIZE: usize = QK5_1;
type VecDotType = BlockQ8_1;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = Self::BLCK_SIZE;
if n % Self::BLCK_SIZE != 0 {
crate::bail!("vec_dot_q5_1_q8_1: {n} is not divisible by {qk}")
}
let nb = n / qk;
if nb % 2 != 0 {
crate::bail!("vec_dot_q5_1_q8_1: {n}, nb is not divisible by 2")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let qh = LittleEndian::read_u32(&xs.qh);
let mut sumi = 0i32;
for j in 0..Self::BLCK_SIZE / 2 {
let xh_0 = ((qh >> j) << 4) & 0x10;
let xh_1 = (qh >> (j + 12)) & 0x10;
let x0 = (xs.qs[j] as i32 & 0xF) | xh_0 as i32;
let x1 = (xs.qs[j] as i32 >> 4) | xh_1 as i32;
sumi += (x0 * ys.qs[j] as i32) + (x1 * ys.qs[j + Self::BLCK_SIZE / 2] as i32);
}
sumf += sumi as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
+ f16::to_f32(xs.m) * f16::to_f32(ys.s)
}
Ok(sumf)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q5_1
let qk = Self::BLCK_SIZE;
if ys.len() * qk != xs.len() {
crate::bail!("size mismatch {} {} {}", xs.len(), ys.len(), qk,)
}
for (i, ys) in ys.iter_mut().enumerate() {
let xs = &xs[i * qk..(i + 1) * qk];
let mut min = f32::INFINITY;
let mut max = f32::NEG_INFINITY;
for &x in xs.iter() {
min = f32::min(x, min);
max = f32::max(x, max);
}
let d = (max - min) / ((1 << 5) - 1) as f32;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
ys.m = f16::from_f32(min);
let mut qh = 0u32;
for (j, q) in ys.qs.iter_mut().take(qk / 2).enumerate() {
let x0 = (xs[j] - min) * id;
let x1 = (xs[qk / 2 + j] - min) * id;
let xi0 = (x0 + 0.5) as u8;
let xi1 = (x1 + 0.5) as u8;
*q = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
// get the 5-th bit and store it in qh at the right position
qh |= ((xi0 as u32 & 0x10) >> 4) << j;
qh |= ((xi1 as u32 & 0x10) >> 4) << (j + qk / 2);
}
LittleEndian::write_u32(&mut ys.qh, qh);
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L1592
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK5_1 != 0 {
crate::bail!("dequantize_row_q5_1: {k} is not divisible by {QK5_1}");
}
let nb = k / QK5_1;
for i in 0..nb {
let d = xs[i].d.to_f32();
let m = xs[i].m.to_f32();
let qh: u32 = LittleEndian::read_u32(&xs[i].qh);
for j in 0..(QK5_1 / 2) {
let xh_0 = (((qh >> j) << 4) & 0x10) as u8;
let xh_1 = ((qh >> (j + 12)) & 0x10) as u8;
let x0 = (xs[i].qs[j] & 0x0F) | xh_0;
let x1 = (xs[i].qs[j] >> 4) | xh_1;
ys[i * QK5_1 + j] = (x0 as f32) * d + m;
ys[i * QK5_1 + j + QK5_1 / 2] = (x1 as f32) * d + m;
}
}
Ok(())
}
}
impl GgmlType for BlockQ8_0 {
const DTYPE: GgmlDType = GgmlDType::Q8_0;
const BLCK_SIZE: usize = QK8_0;
type VecDotType = BlockQ8_0;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L1619
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK8_0 != 0 {
crate::bail!("dequantize_row_q8_0: {k} is not divisible by {QK8_0}");
}
let nb = k / QK8_0;
for i in 0..nb {
let d = xs[i].d.to_f32();
for j in 0..QK8_0 {
ys[i * QK8_0 + j] = xs[i].qs[j] as f32 * d;
}
}
Ok(())
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q8_0
let k = xs.len();
if k % Self::BLCK_SIZE != 0 {
crate::bail!("{k} is not divisible by {}", Self::BLCK_SIZE);
};
let nb = k / Self::BLCK_SIZE;
if ys.len() != nb {
crate::bail!(
"size mismatch {} {} {}",
xs.len(),
ys.len(),
Self::BLCK_SIZE
)
}
for (i, ys) in ys.iter_mut().enumerate() {
let mut amax = 0f32;
let xs = &xs[i * Self::BLCK_SIZE..(i + 1) * Self::BLCK_SIZE];
for &x in xs.iter() {
amax = amax.max(x.abs())
}
let d = amax / ((1 << 7) - 1) as f32;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
for (y, &x) in ys.qs.iter_mut().zip(xs.iter()) {
*y = f32::round(x * id) as i8
}
}
Ok(())
}
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q8_0_q8_0(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q8_0_q8_0(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q8_0_q8_0(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let sum_i = xs
.qs
.iter()
.zip(ys.qs.iter())
.map(|(&x, &y)| x as i32 * y as i32)
.sum::<i32>();
sumf += sum_i as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
}
Ok(sumf)
}
}
impl GgmlType for BlockQ8_1 {
const DTYPE: GgmlDType = GgmlDType::Q8_1;
const BLCK_SIZE: usize = QK8_1;
type VecDotType = BlockQ8_1;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(_n: usize, _xs: &[Self], _ys: &[Self::VecDotType]) -> Result<f32> {
unimplemented!("no support for vec-dot on Q8_1")
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
// quantize_row_q8_1
let k = xs.len();
if ys.len() * Self::BLCK_SIZE != k {
crate::bail!("size mismatch {k} {} {}", ys.len(), Self::BLCK_SIZE)
}
for (i, ys) in ys.iter_mut().enumerate() {
let mut amax = 0f32;
let xs = &xs[i * Self::BLCK_SIZE..(i + 1) * Self::BLCK_SIZE];
for &x in xs.iter() {
amax = amax.max(x.abs())
}
let d = amax / ((1 << 7) - 1) as f32;
let id = if d != 0f32 { 1. / d } else { 0. };
ys.d = f16::from_f32(d);
let mut sum = 0i32;
for j in 0..Self::BLCK_SIZE / 2 {
let v0 = xs[j] * id;
let v1 = xs[j + Self::BLCK_SIZE / 2] * id;
ys.qs[j] = f32::round(v0) as i8;
ys.qs[j + Self::BLCK_SIZE / 2] = f32::round(v1) as i8;
sum += ys.qs[j] as i32 + ys.qs[j + Self::BLCK_SIZE / 2] as i32;
}
ys.s = f16::from_f32(sum as f32) * ys.d;
}
Ok(())
}
fn to_float(_xs: &[Self], _ys: &mut [f32]) -> Result<()> {
unimplemented!("no support for vec-dot on Q8_1")
}
}
impl GgmlType for BlockQ2K {
const DTYPE: GgmlDType = GgmlDType::Q2K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q2k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q2k_q8k(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q2k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
}
let mut sumf = 0.0;
for (x, y) in xs.iter().zip(ys.iter()) {
let mut q2: &[_] = &x.qs;
let mut q8: &[_] = &y.qs;
let sc = &x.scales;
let mut summs = 0;
for (bsum, scale) in y.bsums.iter().zip(sc) {
summs += *bsum as i32 * ((scale >> 4) as i32);
}
let dall = y.d * x.d.to_f32();
let dmin = y.d * x.dmin.to_f32();
let mut isum = 0;
let mut is = 0;
for _ in 0..(QK_K / 128) {
let mut shift = 0;
for _ in 0..4 {
let d = (sc[is] & 0xF) as i32;
is += 1;
let mut isuml = 0;
for l in 0..16 {
isuml += q8[l] as i32 * (((q2[l] >> shift) & 3) as i32);
}
isum += d * isuml;
let d = (sc[is] & 0xF) as i32;
is += 1;
isuml = 0;
for l in 16..32 {
isuml += q8[l] as i32 * (((q2[l] >> shift) & 3) as i32);
}
isum += d * isuml;
shift += 2;
// adjust the indexing
q8 = &q8[32..];
}
// adjust the indexing
q2 = &q2[32..];
}
sumf += dall * isum as f32 - dmin * summs as f32;
}
Ok(sumf)
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L279
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
const Q4SCALE: f32 = 15.0;
for (block, x) in group_for_quantization(xs, ys)? {
//calculate scales and mins
let mut mins: [f32; QK_K / 16] = [0.0; QK_K / 16];
let mut scales: [f32; QK_K / 16] = [0.0; QK_K / 16];
for (j, x_scale_slice) in x.chunks(16).enumerate() {
(scales[j], mins[j]) = make_qkx1_quants(3, 5, x_scale_slice);
}
// get max scale and max min and ensure they are >= 0.0
let max_scale = scales.iter().fold(0.0, |max, &val| val.max(max));
let max_min = mins.iter().fold(0.0, |max, &val| val.max(max));
if max_scale > 0.0 {
let iscale = Q4SCALE / max_scale;
for (j, scale) in scales.iter().enumerate().take(QK_K / 16) {
block.scales[j] = nearest_int(iscale * scale) as u8;
}
block.d = f16::from_f32(max_scale / Q4SCALE);
} else {
for j in 0..QK_K / 16 {
block.scales[j] = 0;
}
block.d = f16::from_f32(0.0);
}
if max_min > 0.0 {
let iscale = Q4SCALE / max_min;
for (j, scale) in block.scales.iter_mut().enumerate() {
let l = nearest_int(iscale * mins[j]) as u8;
*scale |= l << 4;
}
block.dmin = f16::from_f32(max_min / Q4SCALE);
} else {
block.dmin = f16::from_f32(0.0);
}
let mut big_l: [u8; QK_K] = [0; QK_K];
for j in 0..QK_K / 16 {
let d = block.d.to_f32() * (block.scales[j] & 0xF) as f32;
if d == 0.0 {
continue;
}
let dm = block.dmin.to_f32() * (block.scales[j] >> 4) as f32;
for ii in 0..16 {
let ll = nearest_int((x[16 * j + ii] + dm) / d).clamp(0, 3);
big_l[16 * j + ii] = ll as u8;
}
}
for j in (0..QK_K).step_by(128) {
for ll in 0..32 {
block.qs[j / 4 + ll] = big_l[j + ll]
| (big_l[j + ll + 32] << 2)
| (big_l[j + ll + 64] << 4)
| (big_l[j + ll + 96] << 6);
}
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L354
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
for (block, y) in group_for_dequantization(xs, ys)? {
let d = block.d.to_f32();
let min = block.dmin.to_f32();
let mut is = 0;
for (y_block, qs) in y.chunks_exact_mut(128).zip(block.qs.chunks_exact(32)) {
// Step by 32 over q.
let mut shift = 0;
let mut y_block_index = 0;
for _j in 0..4 {
let sc = block.scales[is];
is += 1;
let dl = d * (sc & 0xF) as f32;
let ml = min * (sc >> 4) as f32;
for q in &qs[..16] {
let y = dl * ((q >> shift) & 3) as f32 - ml;
y_block[y_block_index] = y;
y_block_index += 1;
}
let sc = block.scales[is];
is += 1;
let dl = d * (sc & 0xF) as f32;
let ml = min * (sc >> 4) as f32;
for q in &qs[16..] {
let y = dl * ((q >> shift) & 3) as f32 - ml;
y_block[y_block_index] = y;
y_block_index += 1;
}
shift += 2;
}
}
}
Ok(())
}
}
impl GgmlType for BlockQ3K {
const DTYPE: GgmlDType = GgmlDType::Q3K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q3k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q3k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q3k_q8k: {n} is not divisible by {QK_K}")
}
const KMASK1: u32 = 0x03030303;
const KMASK2: u32 = 0x0f0f0f0f;
let mut aux8: [i8; QK_K] = [0; QK_K];
let mut aux16: [i16; 8] = [0; 8];
let mut sums: [f32; 8] = [0.0; 8];
let mut aux32: [i32; 8] = [0; 8];
let mut auxs: [u32; 4] = [0; 4];
for (x, y) in xs.iter().zip(ys.iter()) {
let mut q3: &[u8] = &x.qs;
let hmask: &[u8] = &x.hmask;
let mut q8: &[i8] = &y.qs;
aux32.fill(0);
let mut a = &mut aux8[..];
let mut m = 1;
//Like the GGML original this is written this way to enable the compiler to vectorize it.
for _ in 0..QK_K / 128 {
a.iter_mut()
.take(32)
.zip(q3)
.for_each(|(a_val, q3_val)| *a_val = (q3_val & 3) as i8);
a.iter_mut()
.take(32)
.zip(hmask)
.for_each(|(a_val, hmask_val)| {
*a_val -= if hmask_val & m != 0 { 0 } else { 4 }
});
a = &mut a[32..];
m <<= 1;
a.iter_mut()
.take(32)
.zip(q3)
.for_each(|(a_val, q3_val)| *a_val = ((q3_val >> 2) & 3) as i8);
a.iter_mut()
.take(32)
.zip(hmask)
.for_each(|(a_val, hmask_val)| {
*a_val -= if hmask_val & m != 0 { 0 } else { 4 }
});
a = &mut a[32..];
m <<= 1;
a.iter_mut()
.take(32)
.zip(q3)
.for_each(|(a_val, q3_val)| *a_val = ((q3_val >> 4) & 3) as i8);
a.iter_mut()
.take(32)
.zip(hmask)
.for_each(|(a_val, hmask_val)| {
*a_val -= if hmask_val & m != 0 { 0 } else { 4 }
});
a = &mut a[32..];
m <<= 1;
a.iter_mut()
.take(32)
.zip(q3)
.for_each(|(a_val, q3_val)| *a_val = ((q3_val >> 6) & 3) as i8);
a.iter_mut()
.take(32)
.zip(hmask)
.for_each(|(a_val, hmask_val)| {
*a_val -= if hmask_val & m != 0 { 0 } else { 4 }
});
a = &mut a[32..];
m <<= 1;
q3 = &q3[32..];
}
a = &mut aux8[..];
LittleEndian::read_u32_into(&x.scales, &mut auxs[0..3]);
let tmp = auxs[2];
auxs[2] = ((auxs[0] >> 4) & KMASK2) | (((tmp >> 4) & KMASK1) << 4);
auxs[3] = ((auxs[1] >> 4) & KMASK2) | (((tmp >> 6) & KMASK1) << 4);
auxs[0] = (auxs[0] & KMASK2) | (((tmp) & KMASK1) << 4);
auxs[1] = (auxs[1] & KMASK2) | (((tmp >> 2) & KMASK1) << 4);
for aux in auxs {
for scale in aux.to_le_bytes() {
let scale = i8::from_be_bytes([scale]);
for l in 0..8 {
aux16[l] = q8[l] as i16 * a[l] as i16;
}
for l in 0..8 {
aux32[l] += (scale as i32 - 32) * aux16[l] as i32;
}
q8 = &q8[8..];
a = &mut a[8..];
for l in 0..8 {
aux16[l] = q8[l] as i16 * a[l] as i16;
}
for l in 0..8 {
aux32[l] += (scale as i32 - 32) * aux16[l] as i32;
}
q8 = &q8[8..];
a = &mut a[8..];
}
}
let d = x.d.to_f32() * y.d;
for l in 0..8 {
sums[l] += d * aux32[l] as f32;
}
}
Ok(sums.iter().sum())
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
for (block, x) in group_for_quantization(xs, ys)? {
let mut scales: [f32; QK_K / 16] = [0.0; QK_K / 16];
for (j, x_scale_slice) in x.chunks_exact(16).enumerate() {
scales[j] = make_q3_quants(x_scale_slice, 4, true);
}
// Get max scale by absolute value.
let mut max_scale: f32 = 0.0;
for &scale in scales.iter() {
if scale.abs() > max_scale.abs() {
max_scale = scale;
}
}
block.scales.fill(0);
if max_scale != 0.0 {
let iscale = -32.0 / max_scale;
for (j, scale) in scales.iter().enumerate() {
let l_val = nearest_int(iscale * scale);
let l_val = l_val.clamp(-32, 31) + 32;
if j < 8 {
block.scales[j] = (l_val & 0xF) as u8;
} else {
block.scales[j - 8] |= ((l_val & 0xF) << 4) as u8;
}
let l_val = l_val >> 4;
block.scales[j % 4 + 8] |= (l_val << (2 * (j / 4))) as u8;
}
block.d = f16::from_f32(1.0 / iscale);
} else {
block.d = f16::from_f32(0.0);
}
let mut l: [i8; QK_K] = [0; QK_K];
for j in 0..QK_K / 16 {
let sc = if j < 8 {
block.scales[j] & 0xF
} else {
block.scales[j - 8] >> 4
};
let sc = (sc | (((block.scales[8 + j % 4] >> (2 * (j / 4))) & 3) << 4)) as i8 - 32;
let d = block.d.to_f32() * sc as f32;
if d != 0.0 {
for ii in 0..16 {
let l_val = nearest_int(x[16 * j + ii] / d);
l[16 * j + ii] = (l_val.clamp(-4, 3) + 4) as i8;
}
}
}
block.hmask.fill(0);
let mut m = 0;
let mut hm = 1;
for ll in l.iter_mut() {
if *ll > 3 {
block.hmask[m] |= hm;
*ll -= 4;
}
m += 1;
if m == QK_K / 8 {
m = 0;
hm <<= 1;
}
}
for j in (0..QK_K).step_by(128) {
for l_val in 0..32 {
block.qs[j / 4 + l_val] = (l[j + l_val]
| (l[j + l_val + 32] << 2)
| (l[j + l_val + 64] << 4)
| (l[j + l_val + 96] << 6))
as u8;
}
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L533
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
const KMASK1: u32 = 0x03030303;
const KMASK2: u32 = 0x0f0f0f0f;
for (block, y) in group_for_dequantization(xs, ys)? {
//Reconstruct the scales
let mut aux = [0; 4];
LittleEndian::read_u32_into(&block.scales, &mut aux[0..3]);
let tmp = aux[2];
aux[2] = ((aux[0] >> 4) & KMASK2) | (((tmp >> 4) & KMASK1) << 4);
aux[3] = ((aux[1] >> 4) & KMASK2) | (((tmp >> 6) & KMASK1) << 4);
aux[0] = (aux[0] & KMASK2) | (((tmp) & KMASK1) << 4);
aux[1] = (aux[1] & KMASK2) | (((tmp >> 2) & KMASK1) << 4);
//Transfer the scales into an i8 array
let scales: &mut [i8] =
unsafe { std::slice::from_raw_parts_mut(aux.as_mut_ptr() as *mut i8, 16) };
let d_all = block.d.to_f32();
let mut m = 1;
let mut is = 0;
// Dequantize both 128 long blocks
// 32 qs values per 128 long block
// Each 16 elements get a scale
for (y, qs) in y.chunks_exact_mut(128).zip(block.qs.chunks_exact(32)) {
let mut shift = 0;
for shift_scoped_y in y.chunks_exact_mut(32) {
for (scale_index, scale_scoped_y) in
shift_scoped_y.chunks_exact_mut(16).enumerate()
{
let dl = d_all * (scales[is] as f32 - 32.0);
for (i, inner_y) in scale_scoped_y.iter_mut().enumerate() {
let new_y = dl
* (((qs[i + 16 * scale_index] >> shift) & 3) as i8
- if (block.hmask[i + 16 * scale_index] & m) == 0 {
4
} else {
0
}) as f32;
*inner_y = new_y;
}
// 16 block finished => advance scale index
is += 1;
}
// 32 block finished => increase shift and m
shift += 2;
m <<= 1;
}
}
}
Ok(())
}
}
impl GgmlType for BlockQ4K {
const DTYPE: GgmlDType = GgmlDType::Q4K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q4k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q4k_q8k(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q4k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
}
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
let mut utmp: [u32; 4] = [0; 4];
let mut scales: [u8; 8] = [0; 8];
let mut mins: [u8; 8] = [0; 8];
let mut aux8: [i8; QK_K] = [0; QK_K];
let mut aux16: [i16; 8] = [0; 8];
let mut sums: [f32; 8] = [0.0; 8];
let mut aux32: [i32; 8] = [0; 8];
let mut sumf = 0.0;
for (y, x) in ys.iter().zip(xs.iter()) {
let q4 = &x.qs;
let q8 = &y.qs;
aux32.fill(0);
let mut a = &mut aux8[..];
let mut q4 = &q4[..];
for _ in 0..QK_K / 64 {
for l in 0..32 {
a[l] = (q4[l] & 0xF) as i8;
}
a = &mut a[32..];
for l in 0..32 {
a[l] = (q4[l] >> 4) as i8;
}
a = &mut a[32..];
q4 = &q4[32..];
}
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
//extract scales and mins
LittleEndian::write_u32_into(&utmp[0..2], &mut scales);
LittleEndian::write_u32_into(&utmp[2..4], &mut mins);
let mut sumi = 0;
for j in 0..QK_K / 16 {
sumi += y.bsums[j] as i32 * mins[j / 2] as i32;
}
let mut a = &mut aux8[..];
let mut q8 = &q8[..];
for scale in scales {
let scale = scale as i32;
for _ in 0..4 {
for l in 0..8 {
aux16[l] = q8[l] as i16 * a[l] as i16;
}
for l in 0..8 {
aux32[l] += scale * aux16[l] as i32;
}
q8 = &q8[8..];
a = &mut a[8..];
}
}
let d = x.d.to_f32() * y.d;
for l in 0..8 {
sums[l] += d * aux32[l] as f32;
}
let dmin = x.dmin.to_f32() * y.d;
sumf -= dmin * sumi as f32;
}
Ok(sumf + sums.iter().sum::<f32>())
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
for (block, x) in group_for_quantization(xs, ys)? {
let mut mins: [f32; QK_K / 32] = [0.0; QK_K / 32];
let mut scales: [f32; QK_K / 32] = [0.0; QK_K / 32];
for (j, x_scale_slice) in x.chunks_exact(32).enumerate() {
(scales[j], mins[j]) = make_qkx1_quants(15, 5, x_scale_slice);
}
// get max scale and max min and ensure they are >= 0.0
let max_scale = scales.iter().fold(0.0, |max, &val| val.max(max));
let max_min = mins.iter().fold(0.0, |max, &val| val.max(max));
let inv_scale = if max_scale > 0.0 {
63.0 / max_scale
} else {
0.0
};
let inv_min = if max_min > 0.0 { 63.0 / max_min } else { 0.0 };
for j in 0..QK_K / 32 {
let ls = nearest_int(inv_scale * scales[j]).min(63) as u8;
let lm = nearest_int(inv_min * mins[j]).min(63) as u8;
if j < 4 {
block.scales[j] = ls;
block.scales[j + 4] = lm;
} else {
block.scales[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
block.scales[j - 4] |= (ls >> 4) << 6;
block.scales[j] |= (lm >> 4) << 6;
}
}
block.d = f16::from_f32(max_scale / 63.0);
block.dmin = f16::from_f32(max_min / 63.0);
let mut l: [u8; QK_K] = [0; QK_K];
for j in 0..QK_K / 32 {
let (sc, m) = get_scale_min_k4(j, &block.scales);
let d = block.d.to_f32() * sc as f32;
if d != 0.0 {
let dm = block.dmin.to_f32() * m as f32;
for ii in 0..32 {
let l_val = nearest_int((x[32 * j + ii] + dm) / d);
l[32 * j + ii] = l_val.clamp(0, 15) as u8;
}
}
}
let q = &mut block.qs;
for j in (0..QK_K).step_by(64) {
for l_val in 0..32 {
let offset_index = (j / 64) * 32 + l_val;
q[offset_index] = l[j + l_val] | (l[j + l_val + 32] << 4);
}
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L735
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
for (block, y) in group_for_dequantization(xs, ys)? {
let d = block.d.to_f32();
let min = block.dmin.to_f32();
let q = &block.qs;
let mut is = 0;
let mut ys_index = 0;
for j in (0..QK_K).step_by(64) {
let q = &q[j / 2..j / 2 + 32];
let (sc, m) = get_scale_min_k4(is, &block.scales);
let d1 = d * sc as f32;
let m1 = min * m as f32;
let (sc, m) = get_scale_min_k4(is + 1, &block.scales);
let d2 = d * sc as f32;
let m2 = min * m as f32;
for q in q {
y[ys_index] = d1 * (q & 0xF) as f32 - m1;
ys_index += 1;
}
for q in q {
y[ys_index] = d2 * (q >> 4) as f32 - m2;
ys_index += 1;
}
is += 2;
}
}
Ok(())
}
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L928
impl GgmlType for BlockQ5K {
const DTYPE: GgmlDType = GgmlDType::Q5K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q5k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q5k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q5k_q8k: {n} is not divisible by {QK_K}")
}
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
let mut utmp: [u32; 4] = [0; 4];
let mut scales: [u8; 8] = [0; 8];
let mut mins: [u8; 8] = [0; 8];
let mut aux8: [i8; QK_K] = [0; QK_K];
let mut aux16: [i16; 8] = [0; 8];
let mut sums: [f32; 8] = [0.0; 8];
let mut aux32: [i32; 8] = [0; 8];
let mut sumf = 0.0;
for (y, x) in ys.iter().zip(xs.iter()) {
let q5 = &x.qs;
let hm = &x.qh;
let q8 = &y.qs;
aux32.fill(0);
let mut a = &mut aux8[..];
let mut q5 = &q5[..];
let mut m = 1u8;
for _ in 0..QK_K / 64 {
for l in 0..32 {
a[l] = (q5[l] & 0xF) as i8;
a[l] += if hm[l] & m != 0 { 16 } else { 0 };
}
a = &mut a[32..];
m <<= 1;
for l in 0..32 {
a[l] = (q5[l] >> 4) as i8;
a[l] += if hm[l] & m != 0 { 16 } else { 0 };
}
a = &mut a[32..];
m <<= 1;
q5 = &q5[32..];
}
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
//extract scales and mins
LittleEndian::write_u32_into(&utmp[0..2], &mut scales);
LittleEndian::write_u32_into(&utmp[2..4], &mut mins);
let mut sumi = 0;
for j in 0..QK_K / 16 {
sumi += y.bsums[j] as i32 * mins[j / 2] as i32;
}
let mut a = &mut aux8[..];
let mut q8 = &q8[..];
for scale in scales {
let scale = scale as i32;
for _ in 0..4 {
for l in 0..8 {
aux16[l] = q8[l] as i16 * a[l] as i16;
}
for l in 0..8 {
aux32[l] += scale * aux16[l] as i32;
}
q8 = &q8[8..];
a = &mut a[8..];
}
}
let d = x.d.to_f32() * y.d;
for l in 0..8 {
sums[l] += d * aux32[l] as f32;
}
let dmin = x.dmin.to_f32() * y.d;
sumf -= dmin * sumi as f32;
}
Ok(sumf + sums.iter().sum::<f32>())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L793
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
for (block, x) in group_for_quantization(xs, ys)? {
let mut mins: [f32; QK_K / 32] = [0.0; QK_K / 32];
let mut scales: [f32; QK_K / 32] = [0.0; QK_K / 32];
for (j, x_scale_slice) in x.chunks_exact(32).enumerate() {
(scales[j], mins[j]) = make_qkx1_quants(31, 5, x_scale_slice);
}
// get max scale and max min and ensure they are >= 0.0
let max_scale = scales.iter().fold(0.0, |max, &val| val.max(max));
let max_min = mins.iter().fold(0.0, |max, &val| val.max(max));
let inv_scale = if max_scale > 0.0 {
63.0 / max_scale
} else {
0.0
};
let inv_min = if max_min > 0.0 { 63.0 / max_min } else { 0.0 };
for j in 0..QK_K / 32 {
let ls = nearest_int(inv_scale * scales[j]).min(63) as u8;
let lm = nearest_int(inv_min * mins[j]).min(63) as u8;
if j < 4 {
block.scales[j] = ls;
block.scales[j + 4] = lm;
} else {
block.scales[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
block.scales[j - 4] |= (ls >> 4) << 6;
block.scales[j] |= (lm >> 4) << 6;
}
}
block.d = f16::from_f32(max_scale / 63.0);
block.dmin = f16::from_f32(max_min / 63.0);
let mut l: [u8; QK_K] = [0; QK_K];
for j in 0..QK_K / 32 {
let (sc, m) = get_scale_min_k4(j, &block.scales);
let d = block.d.to_f32() * sc as f32;
if d == 0.0 {
continue;
}
let dm = block.dmin.to_f32() * m as f32;
for ii in 0..32 {
let ll = nearest_int((x[32 * j + ii] + dm) / d);
l[32 * j + ii] = ll.clamp(0, 31) as u8;
}
}
let qh = &mut block.qh;
let ql = &mut block.qs;
qh.fill(0);
let mut m1 = 1;
let mut m2 = 2;
for n in (0..QK_K).step_by(64) {
let offset = (n / 64) * 32;
for j in 0..32 {
let mut l1 = l[n + j];
if l1 > 15 {
l1 -= 16;
qh[j] |= m1;
}
let mut l2 = l[n + j + 32];
if l2 > 15 {
l2 -= 16;
qh[j] |= m2;
}
ql[offset + j] = l1 | (l2 << 4);
}
m1 <<= 2;
m2 <<= 2;
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L928
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
for (block, y) in group_for_dequantization(xs, ys)? {
let d = block.d.to_f32();
let min = block.dmin.to_f32();
let ql = &block.qs;
let qh = &block.qh;
let mut is = 0;
let mut u1 = 1;
let mut u2 = 2;
let mut ys_index = 0;
for j in (0..QK_K).step_by(64) {
let ql = &ql[j / 2..j / 2 + 32];
let (sc, m) = get_scale_min_k4(is, &block.scales);
let d1 = d * sc as f32;
let m1 = min * m as f32;
let (sc, m) = get_scale_min_k4(is + 1, &block.scales);
let d2 = d * sc as f32;
let m2 = min * m as f32;
for (ql, qh) in ql.iter().zip(qh) {
let to_add = if qh & u1 != 0 { 16 } else { 1 };
y[ys_index] = d1 * ((ql & 0xF) + to_add) as f32 - m1;
ys_index += 1;
}
for (ql, qh) in ql.iter().zip(qh) {
let to_add = if qh & u2 != 0 { 16 } else { 1 };
y[ys_index] = d2 * ((ql >> 4) + to_add) as f32 - m2;
ys_index += 1;
}
is += 2;
u1 <<= 2;
u2 <<= 2;
}
}
Ok(())
}
}
impl GgmlType for BlockQ6K {
const DTYPE: GgmlDType = GgmlDType::Q6K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q6k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q6k_q8k(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q6k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q6k_q8k: {n} is not divisible by {QK_K}")
}
let mut aux8 = [0i8; QK_K];
let mut aux16 = [0i16; 8];
let mut sums = [0f32; 8];
let mut aux32 = [0f32; 8];
for (x, y) in xs.iter().zip(ys.iter()) {
let q4 = &x.ql;
let qh = &x.qh;
let q8 = &y.qs;
aux32.fill(0f32);
for j in (0..QK_K).step_by(128) {
let aux8 = &mut aux8[j..];
let q4 = &q4[j / 2..];
let qh = &qh[j / 4..];
for l in 0..32 {
aux8[l] = (((q4[l] & 0xF) | ((qh[l] & 3) << 4)) as i32 - 32) as i8;
aux8[l + 32] =
(((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) as i32 - 32) as i8;
aux8[l + 64] = (((q4[l] >> 4) | (((qh[l] >> 4) & 3) << 4)) as i32 - 32) as i8;
aux8[l + 96] =
(((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) as i32 - 32) as i8;
}
}
for (j, &scale) in x.scales.iter().enumerate() {
let scale = scale as f32;
let q8 = &q8[16 * j..];
let aux8 = &aux8[16 * j..];
for l in 0..8 {
aux16[l] = q8[l] as i16 * aux8[l] as i16;
}
for l in 0..8 {
aux32[l] += scale * aux16[l] as f32
}
let q8 = &q8[8..];
let aux8 = &aux8[8..];
for l in 0..8 {
aux16[l] = q8[l] as i16 * aux8[l] as i16;
}
for l in 0..8 {
aux32[l] += scale * aux16[l] as f32
}
}
let d = x.d.to_f32() * y.d;
for (sum, &a) in sums.iter_mut().zip(aux32.iter()) {
*sum += a * d;
}
}
Ok(sums.iter().sum())
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
if xs.len() != ys.len() * Self::BLCK_SIZE {
crate::bail!(
"quantize_row_q6k: size mismatch {} {} {}",
xs.len(),
ys.len(),
Self::BLCK_SIZE
)
}
let mut l = [0i8; QK_K];
let mut scales = [0f32; QK_K / 16];
let mut x = xs.as_ptr();
let l = l.as_mut_ptr();
unsafe {
for y in ys.iter_mut() {
let mut max_scale = 0f32;
let mut max_abs_scale = 0f32;
for (ib, scale_) in scales.iter_mut().enumerate() {
let scale = make_qx_quants(16, 32, x.add(16 * ib), l.add(16 * ib), 1);
*scale_ = scale;
let abs_scale = scale.abs();
if abs_scale > max_abs_scale {
max_abs_scale = abs_scale;
max_scale = scale
}
}
let iscale = -128f32 / max_scale;
y.d = f16::from_f32(1.0 / iscale);
for (y_scale, scale) in y.scales.iter_mut().zip(scales.iter()) {
*y_scale = nearest_int(iscale * scale).min(127) as i8
}
for (j, &y_scale) in y.scales.iter().enumerate() {
let d = y.d.to_f32() * y_scale as f32;
if d == 0. {
continue;
}
for ii in 0..16 {
let ll = nearest_int(*x.add(16 * j + ii) / d).clamp(-32, 31);
*l.add(16 * j + ii) = (ll + 32) as i8
}
}
let mut ql = y.ql.as_mut_ptr();
let mut qh = y.qh.as_mut_ptr();
for j in (0..QK_K).step_by(128) {
for l_idx in 0..32 {
let q1 = *l.add(j + l_idx) & 0xF;
let q2 = *l.add(j + l_idx + 32) & 0xF;
let q3 = *l.add(j + l_idx + 64) & 0xF;
let q4 = *l.add(j + l_idx + 96) & 0xF;
*ql.add(l_idx) = (q1 | (q3 << 4)) as u8;
*ql.add(l_idx + 32) = (q2 | (q4 << 4)) as u8;
*qh.add(l_idx) = ((*l.add(j + l_idx) >> 4)
| ((*l.add(j + l_idx + 32) >> 4) << 2)
| ((*l.add(j + l_idx + 64) >> 4) << 4)
| ((*l.add(j + l_idx + 96) >> 4) << 6))
as u8;
}
ql = ql.add(64);
qh = qh.add(32);
}
x = x.add(QK_K)
}
}
Ok(())
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L1067
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK_K != 0 {
crate::bail!("dequantize_row_q6k: {k} is not divisible by {QK_K}")
}
for (idx_x, x) in xs.iter().enumerate() {
let d = x.d.to_f32();
let ql = &x.ql;
let qh = &x.qh;
let sc = &x.scales;
for n in (0..QK_K).step_by(128) {
let idx = n / 128;
let ys = &mut ys[idx_x * QK_K + n..];
let sc = &sc[8 * idx..];
let ql = &ql[64 * idx..];
let qh = &qh[32 * idx..];
for l in 0..32 {
let is = l / 16;
let q1 = ((ql[l] & 0xF) | ((qh[l] & 3) << 4)) as i8 - 32;
let q2 = ((ql[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) as i8 - 32;
let q3 = ((ql[l] >> 4) | (((qh[l] >> 4) & 3) << 4)) as i8 - 32;
let q4 = ((ql[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) as i8 - 32;
ys[l] = d * sc[is] as f32 * q1 as f32;
ys[l + 32] = d * sc[is + 2] as f32 * q2 as f32;
ys[l + 64] = d * sc[is + 4] as f32 * q3 as f32;
ys[l + 96] = d * sc[is + 6] as f32 * q4 as f32;
}
}
}
Ok(())
}
}
impl GgmlType for BlockQ8K {
const DTYPE: GgmlDType = GgmlDType::Q8K;
const BLCK_SIZE: usize = QK_K;
type VecDotType = BlockQ8K;
#[allow(unreachable_code)]
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
#[cfg(target_feature = "avx")]
return super::avx::vec_dot_q8k_q8k(n, xs, ys);
#[cfg(target_feature = "neon")]
return super::neon::vec_dot_q8k_q8k(n, xs, ys);
#[cfg(target_feature = "simd128")]
return super::simd128::vec_dot_q8k_q8k(n, xs, ys);
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = QK_K;
if n % QK_K != 0 {
crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let sum_i = xs
.qs
.iter()
.zip(ys.qs.iter())
.map(|(&x, &y)| x as i32 * y as i32)
.sum::<i32>();
sumf += sum_i as f32 * xs.d * ys.d
}
Ok(sumf)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
let k = xs.len();
if k % QK_K != 0 {
crate::bail!("quantize_row_q8k: {k} is not divisible by {QK_K}")
}
for (i, y) in ys.iter_mut().enumerate() {
let mut max = 0f32;
let mut amax = 0f32;
let xs = &xs[i * QK_K..(i + 1) * QK_K];
for &x in xs.iter() {
if amax < x.abs() {
amax = x.abs();
max = x;
}
}
if amax == 0f32 {
y.d = 0f32;
y.qs.fill(0)
} else {
let iscale = -128f32 / max;
for (j, q) in y.qs.iter_mut().enumerate() {
// ggml uses nearest_int with bit magic here, maybe we want the same
// but we would have to test and benchmark it.
let v = (iscale * xs[j]).round();
*q = v.min(127.) as i8
}
for j in 0..QK_K / 16 {
let mut sum = 0i32;
for ii in 0..16 {
sum += y.qs[j * 16 + ii] as i32
}
y.bsums[j] = sum as i16
}
y.d = 1.0 / iscale
}
}
Ok(())
}
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
let k = ys.len();
if k % QK_K != 0 {
crate::bail!("dequantize_row_q8k: {k} is not divisible by {QK_K}")
}
for (i, x) in xs.iter().enumerate() {
for (j, &q) in x.qs.iter().enumerate() {
ys[i * QK_K + j] = x.d * q as f32
}
}
Ok(())
}
}
// https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/ggml.c#L10605
pub fn matmul<T: GgmlType>(
mkn: (usize, usize, usize),
lhs: &[f32],
rhs_t: &[T],
dst: &mut [f32],
) -> Result<()> {
let (m, k, n) = mkn;
if m * k != lhs.len() {
crate::bail!("unexpected lhs length {} {mkn:?}", lhs.len());
}
let k_in_lhs_blocks = (k + T::BLCK_SIZE - 1) / T::BLCK_SIZE;
let k_in_rhs_blocks = (k + T::VecDotType::BLCK_SIZE - 1) / T::VecDotType::BLCK_SIZE;
// TODO: Do not make this copy if the DotType is f32.
// TODO: Pre-allocate this.
let mut lhs_b = vec![T::VecDotType::zeros(); m * k_in_lhs_blocks];
for row_idx in 0..m {
let lhs_b = &mut lhs_b[row_idx * k_in_lhs_blocks..(row_idx + 1) * k_in_lhs_blocks];
let lhs = &lhs[row_idx * k..(row_idx + 1) * k];
T::VecDotType::from_float(lhs, lhs_b)?
}
let lhs_b = lhs_b.as_slice();
for row_idx in 0..m {
let lhs_row = &lhs_b[row_idx * k_in_lhs_blocks..(row_idx + 1) * k_in_lhs_blocks];
let dst_row = &mut dst[row_idx * n..(row_idx + 1) * n];
let result: Result<Vec<_>> = dst_row
.into_par_iter()
.enumerate()
.with_min_len(128)
.with_max_len(512)
.map(|(col_idx, dst)| {
let rhs_col = &rhs_t[col_idx * k_in_rhs_blocks..(col_idx + 1) * k_in_rhs_blocks];
T::vec_dot(k, rhs_col, lhs_row).map(|value| *dst = value)
})
.collect();
result?;
}
Ok(())
}
impl GgmlType for f32 {
const DTYPE: GgmlDType = GgmlDType::F32;
const BLCK_SIZE: usize = 1;
type VecDotType = f32;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if xs.len() < n {
crate::bail!("size mismatch {} < {n}", xs.len())
}
if ys.len() < n {
crate::bail!("size mismatch {} < {n}", ys.len())
}
let mut res = 0f32;
unsafe { crate::cpu::vec_dot_f32(xs.as_ptr(), ys.as_ptr(), &mut res, n) };
Ok(res)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
if xs.len() != ys.len() {
crate::bail!("size mismatch {} {}", xs.len(), ys.len());
}
ys.copy_from_slice(xs);
Ok(())
}
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
if xs.len() != ys.len() {
crate::bail!("size mismatch {} {}", xs.len(), ys.len());
}
ys.copy_from_slice(xs);
Ok(())
}
}
impl GgmlType for f16 {
const DTYPE: GgmlDType = GgmlDType::F16;
const BLCK_SIZE: usize = 1;
type VecDotType = f16;
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
Self::vec_dot_unopt(n, xs, ys)
}
fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
if xs.len() < n {
crate::bail!("size mismatch {} < {n}", xs.len())
}
if ys.len() < n {
crate::bail!("size mismatch {} < {n}", ys.len())
}
let mut res = 0f32;
unsafe { crate::cpu::vec_dot_f16(xs.as_ptr(), ys.as_ptr(), &mut res, n) };
Ok(res)
}
fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
if xs.len() != ys.len() {
crate::bail!("size mismatch {} {}", xs.len(), ys.len());
}
// TODO: vectorize
for (x, y) in xs.iter().zip(ys.iter_mut()) {
*y = f16::from_f32(*x)
}
Ok(())
}
fn to_float(xs: &[Self], ys: &mut [f32]) -> Result<()> {
if xs.len() != ys.len() {
crate::bail!("size mismatch {} {}", xs.len(), ys.len());
}
// TODO: vectorize
for (x, y) in xs.iter().zip(ys.iter_mut()) {
*y = x.to_f32()
}
Ok(())
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/mod.rs
|
use crate::{Device, Result, Shape, Tensor};
#[cfg(target_feature = "avx")]
pub mod avx;
pub mod ggml_file;
pub mod gguf_file;
pub mod k_quants;
#[cfg(target_feature = "neon")]
pub mod neon;
#[cfg(target_feature = "simd128")]
pub mod simd128;
pub mod utils;
pub use k_quants::GgmlType;
pub struct QTensor {
data: Box<dyn QuantizedType>,
shape: Shape,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum GgmlDType {
F32,
F16,
Q4_0,
Q4_1,
Q5_0,
Q5_1,
Q8_0,
Q8_1,
Q2K,
Q3K,
Q4K,
Q5K,
Q6K,
Q8K,
}
impl GgmlDType {
pub(crate) fn from_u32(u: u32) -> Result<Self> {
let dtype = match u {
0 => Self::F32,
1 => Self::F16,
2 => Self::Q4_0,
3 => Self::Q4_1,
6 => Self::Q5_0,
7 => Self::Q5_1,
8 => Self::Q8_0,
9 => Self::Q8_1,
10 => Self::Q2K,
11 => Self::Q3K,
12 => Self::Q4K,
13 => Self::Q5K,
14 => Self::Q6K,
15 => Self::Q8K,
_ => crate::bail!("unknown dtype for tensor {u}"),
};
Ok(dtype)
}
pub(crate) fn to_u32(self) -> u32 {
match self {
Self::F32 => 0,
Self::F16 => 1,
Self::Q4_0 => 2,
Self::Q4_1 => 3,
Self::Q5_0 => 6,
Self::Q5_1 => 7,
Self::Q8_0 => 8,
Self::Q8_1 => 9,
Self::Q2K => 10,
Self::Q3K => 11,
Self::Q4K => 12,
Self::Q5K => 13,
Self::Q6K => 14,
Self::Q8K => 15,
}
}
/// The type size for blocks in bytes.
pub fn type_size(&self) -> usize {
use k_quants::*;
match self {
Self::F32 => 4,
Self::F16 => 2,
Self::Q4_0 => std::mem::size_of::<BlockQ4_0>(),
Self::Q4_1 => std::mem::size_of::<BlockQ4_1>(),
Self::Q5_0 => std::mem::size_of::<BlockQ5_0>(),
Self::Q5_1 => std::mem::size_of::<BlockQ5_1>(),
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/ggml.c#L932
Self::Q8_0 => std::mem::size_of::<BlockQ8_0>(),
Self::Q8_1 => std::mem::size_of::<BlockQ8_1>(),
Self::Q2K => std::mem::size_of::<BlockQ2K>(),
Self::Q3K => std::mem::size_of::<BlockQ3K>(),
Self::Q4K => std::mem::size_of::<BlockQ4K>(),
Self::Q5K => std::mem::size_of::<BlockQ5K>(),
Self::Q6K => std::mem::size_of::<BlockQ6K>(),
Self::Q8K => std::mem::size_of::<BlockQ8K>(),
}
}
/// The block size, i.e. the number of elements stored in each block.
pub fn blck_size(&self) -> usize {
match self {
Self::F32 => 1,
Self::F16 => 1,
Self::Q4_0 => k_quants::QK4_0,
Self::Q4_1 => k_quants::QK4_1,
Self::Q5_0 => k_quants::QK5_0,
Self::Q5_1 => k_quants::QK5_1,
Self::Q8_0 => k_quants::QK8_0,
Self::Q8_1 => k_quants::QK8_1,
Self::Q2K | Self::Q3K | Self::Q4K | Self::Q5K | Self::Q6K | Self::Q8K => k_quants::QK_K,
}
}
}
// A version of GgmlType without `vec_dot` so that it can be dyn boxed.
pub trait QuantizedType: Send + Sync {
fn dtype(&self) -> GgmlDType;
fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()>;
fn to_float(&self, ys: &mut [f32]) -> Result<()>;
fn storage_size_in_bytes(&self) -> usize;
fn as_ptr(&self) -> *const u8;
}
impl<T: k_quants::GgmlType + Send + Sync> QuantizedType for Vec<T> {
fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()> {
k_quants::matmul(mkn, lhs, self.as_slice(), dst)
}
fn dtype(&self) -> GgmlDType {
T::DTYPE
}
fn to_float(&self, ys: &mut [f32]) -> Result<()> {
T::to_float(self.as_slice(), ys)
}
fn storage_size_in_bytes(&self) -> usize {
self.len() * std::mem::size_of::<T>()
}
fn as_ptr(&self) -> *const u8 {
self.as_ptr() as *const u8
}
}
impl std::fmt::Debug for QTensor {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
write!(f, "QTensor[{:?}; {:?}]", self.shape, self.dtype())
}
}
fn check_shape<T: k_quants::GgmlType>(shape: &Shape) -> Result<()> {
let dims = shape.dims();
if dims.is_empty() {
crate::bail!("scalar tensor cannot be quantized {shape:?}")
}
if dims[dims.len() - 1] % T::BLCK_SIZE != 0 {
crate::bail!(
"quantized tensor must have their last dim divisible by block size {shape:?} {}",
T::BLCK_SIZE
)
}
Ok(())
}
impl QTensor {
pub fn new<S: Into<Shape>, T: k_quants::GgmlType + Send + Sync + 'static>(
data: Vec<T>,
shape: S,
) -> Result<Self> {
let shape = shape.into();
check_shape::<T>(&shape)?;
Ok(Self {
data: Box::new(data),
shape,
})
}
pub fn quantize<T: k_quants::GgmlType + Send + Sync + 'static>(src: &Tensor) -> Result<Self> {
let shape = src.shape();
check_shape::<T>(shape)?;
let src = src
.to_dtype(crate::DType::F32)?
.flatten_all()?
.to_vec1::<f32>()?;
if src.len() % T::BLCK_SIZE != 0 {
crate::bail!(
"tensor size ({shape:?}) is not divisible by block size {}",
T::BLCK_SIZE
)
}
let mut data = vec![T::zeros(); src.len() / T::BLCK_SIZE];
T::from_float(&src, &mut data)?;
Ok(Self {
data: Box::new(data),
shape: shape.clone(),
})
}
pub fn dtype(&self) -> GgmlDType {
self.data.dtype()
}
pub fn rank(&self) -> usize {
self.shape.rank()
}
pub fn shape(&self) -> &Shape {
&self.shape
}
pub fn dequantize(&self, device: &Device) -> Result<Tensor> {
let mut f32_data = vec![0f32; self.shape.elem_count()];
self.data.to_float(&mut f32_data)?;
Tensor::from_vec(f32_data, &self.shape, device)
}
pub fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()> {
self.data.matmul_t(mkn, lhs, dst)
}
pub fn storage_size_in_bytes(&self) -> usize {
self.data.storage_size_in_bytes()
}
pub fn as_ptr(&self) -> *const u8 {
self.data.as_ptr()
}
}
#[derive(Clone, Debug)]
pub enum QMatMul {
QTensor(std::sync::Arc<QTensor>),
Tensor(Tensor),
}
thread_local! {
static DEQUANTIZE_ALL: bool = {
match std::env::var("CANDLE_DEQUANTIZE_ALL") {
Ok(s) => {
!s.is_empty() && s != "0"
},
Err(_) => false,
}
}
}
impl QMatMul {
pub fn from_arc(qtensor: std::sync::Arc<QTensor>) -> Result<Self> {
let dequantize = match qtensor.dtype() {
GgmlDType::F32 | GgmlDType::F16 => true,
_ => DEQUANTIZE_ALL.with(|b| *b),
};
let t = if dequantize {
let tensor = qtensor.dequantize(&Device::Cpu)?;
Self::Tensor(tensor)
} else {
Self::QTensor(qtensor)
};
Ok(t)
}
pub fn from_qtensor(qtensor: QTensor) -> Result<Self> {
Self::from_arc(std::sync::Arc::new(qtensor))
}
}
impl crate::CustomOp1 for QTensor {
fn name(&self) -> &'static str {
"qmatmul"
}
fn cpu_fwd(
&self,
storage: &crate::CpuStorage,
layout: &crate::Layout,
) -> Result<(crate::CpuStorage, Shape)> {
if !layout.is_contiguous() {
crate::bail!("input tensor is not contiguous {layout:?}")
}
let src_shape = layout.shape();
// self is transposed so n is first then k.
let (n, k) = self.shape.dims2()?;
if src_shape.rank() < 2 {
crate::bail!("input tensor has only one dimension {layout:?}")
}
let mut dst_shape = src_shape.dims().to_vec();
let last_k = dst_shape.pop().unwrap();
if last_k != k {
crate::bail!("input tensor {layout:?} incompatible with {:?}", self.shape)
}
dst_shape.push(n);
let dst_shape = Shape::from(dst_shape);
let storage = storage.as_slice::<f32>()?;
let storage =
&storage[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
let mut dst_storage = vec![0f32; dst_shape.elem_count()];
self.matmul_t(
(dst_shape.elem_count() / n, k, n),
storage,
&mut dst_storage,
)?;
Ok((crate::CpuStorage::F32(dst_storage), dst_shape))
}
}
impl crate::Module for QMatMul {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
match self {
Self::QTensor(t) => xs.apply_op1_no_bwd(t.as_ref()),
Self::Tensor(w) => {
let w = match *xs.dims() {
[b1, b2, _, _] => w.broadcast_left((b1, b2))?.t()?,
[bsize, _, _] => w.broadcast_left(bsize)?.t()?,
_ => w.t()?,
};
xs.matmul(&w)
}
}
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/gguf_file.rs
|
//! Support for the GGUF file format.
//!
//! Spec: https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md
use super::{GgmlDType, QTensor};
use crate::Result;
use byteorder::{LittleEndian, ReadBytesExt, WriteBytesExt};
use std::collections::HashMap;
pub const DEFAULT_ALIGNMENT: u64 = 32;
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum Magic {
Gguf,
}
impl TryFrom<u32> for Magic {
type Error = crate::Error;
fn try_from(value: u32) -> Result<Self> {
let magic = match value {
0x46554747 | 0x47475546 => Self::Gguf,
_ => crate::bail!("unknown magic 0x{value:08x}"),
};
Ok(magic)
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VersionedMagic {
GgufV1,
GgufV2,
GgufV3,
}
impl VersionedMagic {
fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
let magic = reader.read_u32::<LittleEndian>()?;
let magic = Magic::try_from(magic)?;
let version = reader.read_u32::<LittleEndian>()?;
let versioned_magic = match (magic, version) {
(Magic::Gguf, 1) => Self::GgufV1,
(Magic::Gguf, 2) => Self::GgufV2,
(Magic::Gguf, 3) => Self::GgufV3,
_ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
};
Ok(versioned_magic)
}
}
#[derive(Debug)]
pub struct TensorInfo {
pub ggml_dtype: GgmlDType,
pub shape: crate::Shape,
pub offset: u64,
}
impl TensorInfo {
pub fn read<R: std::io::Seek + std::io::Read>(
&self,
reader: &mut R,
tensor_data_offset: u64,
) -> Result<QTensor> {
let tensor_elems = self.shape.elem_count();
let blck_size = self.ggml_dtype.blck_size();
if tensor_elems % blck_size != 0 {
crate::bail!(
"the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
)
}
let size_in_bytes = tensor_elems / blck_size * self.ggml_dtype.type_size();
let mut raw_data = vec![0u8; size_in_bytes];
reader.seek(std::io::SeekFrom::Start(tensor_data_offset + self.offset))?;
reader.read_exact(&mut raw_data)?;
super::ggml_file::qtensor_from_ggml(self.ggml_dtype, &raw_data, self.shape.dims().to_vec())
}
}
#[derive(Debug)]
pub struct Content {
pub magic: VersionedMagic,
pub metadata: HashMap<String, Value>,
pub tensor_infos: HashMap<String, TensorInfo>,
pub tensor_data_offset: u64,
}
fn read_string<R: std::io::Read>(reader: &mut R, magic: &VersionedMagic) -> Result<String> {
let len = match magic {
VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
reader.read_u64::<LittleEndian>()? as usize
}
};
let mut v = vec![0u8; len];
reader.read_exact(&mut v)?;
// GGUF strings are supposed to be non-null terminated but in practice this happens.
while let Some(0) = v.last() {
v.pop();
}
// GGUF strings are utf8 encoded but there are cases that don't seem to be valid.
Ok(String::from_utf8_lossy(&v).into_owned())
}
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum ValueType {
// The value is a 8-bit unsigned integer.
U8,
// The value is a 8-bit signed integer.
I8,
// The value is a 16-bit unsigned little-endian integer.
U16,
// The value is a 16-bit signed little-endian integer.
I16,
// The value is a 32-bit unsigned little-endian integer.
U32,
// The value is a 32-bit signed little-endian integer.
I32,
// The value is a 64-bit unsigned little-endian integer.
U64,
// The value is a 64-bit signed little-endian integer.
I64,
// The value is a 32-bit IEEE754 floating point number.
F32,
// The value is a 64-bit IEEE754 floating point number.
F64,
// The value is a boolean.
// 1-byte value where 0 is false and 1 is true.
// Anything else is invalid, and should be treated as either the model being invalid or the reader being buggy.
Bool,
// The value is a UTF-8 non-null-terminated string, with length prepended.
String,
// The value is an array of other values, with the length and type prepended.
///
// Arrays can be nested, and the length of the array is the number of elements in the array, not the number of bytes.
Array,
}
#[derive(Debug, Clone)]
pub enum Value {
U8(u8),
I8(i8),
U16(u16),
I16(i16),
U32(u32),
I32(i32),
U64(u64),
I64(i64),
F32(f32),
F64(f64),
Bool(bool),
String(String),
Array(Vec<Value>),
}
impl Value {
pub fn value_type(&self) -> ValueType {
match self {
Self::U8(_) => ValueType::U8,
Self::I8(_) => ValueType::I8,
Self::U16(_) => ValueType::U16,
Self::I16(_) => ValueType::I16,
Self::U32(_) => ValueType::U32,
Self::I32(_) => ValueType::I32,
Self::U64(_) => ValueType::U64,
Self::I64(_) => ValueType::I64,
Self::F32(_) => ValueType::F32,
Self::F64(_) => ValueType::F64,
Self::Bool(_) => ValueType::Bool,
Self::String(_) => ValueType::String,
Self::Array(_) => ValueType::Array,
}
}
pub fn to_u8(&self) -> Result<u8> {
match self {
Self::U8(v) => Ok(*v),
v => crate::bail!("not a u8 {v:?}"),
}
}
pub fn to_i8(&self) -> Result<i8> {
match self {
Self::I8(v) => Ok(*v),
v => crate::bail!("not a i8 {v:?}"),
}
}
pub fn to_u16(&self) -> Result<u16> {
match self {
Self::U16(v) => Ok(*v),
v => crate::bail!("not a u16 {v:?}"),
}
}
pub fn to_i16(&self) -> Result<i16> {
match self {
Self::I16(v) => Ok(*v),
v => crate::bail!("not a i16 {v:?}"),
}
}
pub fn to_u32(&self) -> Result<u32> {
match self {
Self::U32(v) => Ok(*v),
v => crate::bail!("not a u32 {v:?}"),
}
}
pub fn to_i32(&self) -> Result<i32> {
match self {
Self::I32(v) => Ok(*v),
v => crate::bail!("not a i32 {v:?}"),
}
}
pub fn to_u64(&self) -> Result<u64> {
match self {
Self::U64(v) => Ok(*v),
v => crate::bail!("not a u64 {v:?}"),
}
}
pub fn to_i64(&self) -> Result<i64> {
match self {
Self::I64(v) => Ok(*v),
v => crate::bail!("not a i64 {v:?}"),
}
}
pub fn to_f32(&self) -> Result<f32> {
match self {
Self::F32(v) => Ok(*v),
v => crate::bail!("not a f32 {v:?}"),
}
}
pub fn to_f64(&self) -> Result<f64> {
match self {
Self::F64(v) => Ok(*v),
v => crate::bail!("not a f64 {v:?}"),
}
}
pub fn to_bool(&self) -> Result<bool> {
match self {
Self::Bool(v) => Ok(*v),
v => crate::bail!("not a bool {v:?}"),
}
}
pub fn to_vec(&self) -> Result<&Vec<Value>> {
match self {
Self::Array(v) => Ok(v),
v => crate::bail!("not a vec {v:?}"),
}
}
pub fn to_string(&self) -> Result<&String> {
match self {
Self::String(v) => Ok(v),
v => crate::bail!("not a string {v:?}"),
}
}
fn read<R: std::io::Read>(
reader: &mut R,
value_type: ValueType,
magic: &VersionedMagic,
) -> Result<Self> {
let v = match value_type {
ValueType::U8 => Self::U8(reader.read_u8()?),
ValueType::I8 => Self::I8(reader.read_i8()?),
ValueType::U16 => Self::U16(reader.read_u16::<LittleEndian>()?),
ValueType::I16 => Self::I16(reader.read_i16::<LittleEndian>()?),
ValueType::U32 => Self::U32(reader.read_u32::<LittleEndian>()?),
ValueType::I32 => Self::I32(reader.read_i32::<LittleEndian>()?),
ValueType::U64 => Self::U64(reader.read_u64::<LittleEndian>()?),
ValueType::I64 => Self::I64(reader.read_i64::<LittleEndian>()?),
ValueType::F32 => Self::F32(reader.read_f32::<LittleEndian>()?),
ValueType::F64 => Self::F64(reader.read_f64::<LittleEndian>()?),
ValueType::Bool => match reader.read_u8()? {
0 => Self::Bool(false),
1 => Self::Bool(true),
b => crate::bail!("unexpected bool value {b}"),
},
ValueType::String => Self::String(read_string(reader, magic)?),
ValueType::Array => {
let value_type = reader.read_u32::<LittleEndian>()?;
let value_type = ValueType::from_u32(value_type)?;
let len = match magic {
VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
reader.read_u64::<LittleEndian>()? as usize
}
};
let mut vs = Vec::with_capacity(len);
for _ in 0..len {
vs.push(Value::read(reader, value_type, magic)?)
}
Self::Array(vs)
}
};
Ok(v)
}
fn write<W: std::io::Write>(&self, w: &mut W) -> Result<()> {
match self {
&Self::U8(v) => w.write_u8(v)?,
&Self::I8(v) => w.write_i8(v)?,
&Self::U16(v) => w.write_u16::<LittleEndian>(v)?,
&Self::I16(v) => w.write_i16::<LittleEndian>(v)?,
&Self::U32(v) => w.write_u32::<LittleEndian>(v)?,
&Self::I32(v) => w.write_i32::<LittleEndian>(v)?,
&Self::U64(v) => w.write_u64::<LittleEndian>(v)?,
&Self::I64(v) => w.write_i64::<LittleEndian>(v)?,
&Self::F32(v) => w.write_f32::<LittleEndian>(v)?,
&Self::F64(v) => w.write_f64::<LittleEndian>(v)?,
&Self::Bool(v) => w.write_u8(u8::from(v))?,
Self::String(v) => write_string(w, v.as_str())?,
Self::Array(v) => {
// The `Value` type does not enforce that all the values in an Array have the same
// type.
let value_type = if v.is_empty() {
// Doesn't matter, the array is empty.
ValueType::U32
} else {
let value_type: std::collections::HashSet<_> =
v.iter().map(|elem| elem.value_type()).collect();
if value_type.len() != 1 {
crate::bail!("multiple value-types in the same array {value_type:?}")
}
value_type.into_iter().next().unwrap()
};
w.write_u32::<LittleEndian>(value_type.to_u32())?;
w.write_u64::<LittleEndian>(v.len() as u64)?;
for elem in v.iter() {
elem.write(w)?
}
}
}
Ok(())
}
}
impl ValueType {
fn from_u32(v: u32) -> Result<Self> {
let v = match v {
0 => Self::U8,
1 => Self::I8,
2 => Self::U16,
3 => Self::I16,
4 => Self::U32,
5 => Self::I32,
6 => Self::F32,
7 => Self::Bool,
8 => Self::String,
9 => Self::Array,
10 => Self::U64,
11 => Self::I64,
12 => Self::F64,
v => crate::bail!("unrecognized value-type {v:#08x}"),
};
Ok(v)
}
fn to_u32(self) -> u32 {
match self {
Self::U8 => 0,
Self::I8 => 1,
Self::U16 => 2,
Self::I16 => 3,
Self::U32 => 4,
Self::I32 => 5,
Self::F32 => 6,
Self::Bool => 7,
Self::String => 8,
Self::Array => 9,
Self::U64 => 10,
Self::I64 => 11,
Self::F64 => 12,
}
}
}
impl Content {
pub fn read<R: std::io::Seek + std::io::Read>(reader: &mut R) -> Result<Self> {
let magic = VersionedMagic::read(reader)?;
let tensor_count = match magic {
VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
reader.read_u64::<LittleEndian>()? as usize
}
};
let metadata_kv_count = match magic {
VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
reader.read_u64::<LittleEndian>()? as usize
}
};
let mut metadata = HashMap::new();
for _idx in 0..metadata_kv_count {
let key = read_string(reader, &magic)?;
let value_type = reader.read_u32::<LittleEndian>()?;
let value_type = ValueType::from_u32(value_type)?;
let value = Value::read(reader, value_type, &magic)?;
metadata.insert(key, value);
}
let mut tensor_infos = HashMap::new();
for _idx in 0..tensor_count {
let tensor_name = read_string(reader, &magic)?;
let n_dimensions = reader.read_u32::<LittleEndian>()?;
let mut dimensions: Vec<usize> = match magic {
VersionedMagic::GgufV1 => {
let mut dimensions = vec![0; n_dimensions as usize];
reader.read_u32_into::<LittleEndian>(&mut dimensions)?;
dimensions.into_iter().map(|c| c as usize).collect()
}
VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
let mut dimensions = vec![0; n_dimensions as usize];
reader.read_u64_into::<LittleEndian>(&mut dimensions)?;
dimensions.into_iter().map(|c| c as usize).collect()
}
};
dimensions.reverse();
let ggml_dtype = reader.read_u32::<LittleEndian>()?;
let ggml_dtype = GgmlDType::from_u32(ggml_dtype)?;
let offset = reader.read_u64::<LittleEndian>()?;
tensor_infos.insert(
tensor_name,
TensorInfo {
shape: crate::Shape::from(dimensions),
offset,
ggml_dtype,
},
);
}
let position = reader.stream_position()?;
let alignment = match metadata.get("general.alignment") {
Some(Value::U8(v)) => *v as u64,
Some(Value::U16(v)) => *v as u64,
Some(Value::U32(v)) => *v as u64,
Some(Value::I8(v)) if *v >= 0 => *v as u64,
Some(Value::I16(v)) if *v >= 0 => *v as u64,
Some(Value::I32(v)) if *v >= 0 => *v as u64,
_ => DEFAULT_ALIGNMENT,
};
let tensor_data_offset = (position + alignment - 1) / alignment * alignment;
Ok(Self {
magic,
metadata,
tensor_infos,
tensor_data_offset,
})
}
pub fn tensor<R: std::io::Seek + std::io::Read>(
&self,
reader: &mut R,
name: &str,
) -> Result<QTensor> {
let tensor_info = match self.tensor_infos.get(name) {
Some(tensor_info) => tensor_info,
None => crate::bail!("cannot find tensor-infor for {name}"),
};
tensor_info.read(reader, self.tensor_data_offset)
}
}
fn write_string<W: std::io::Write>(w: &mut W, str: &str) -> Result<()> {
let bytes = str.as_bytes();
w.write_u64::<LittleEndian>(bytes.len() as u64)?;
w.write_all(bytes)?;
Ok(())
}
pub fn write<W: std::io::Seek + std::io::Write>(
w: &mut W,
metadata: &[(&str, &Value)],
tensors: &[(&str, &QTensor)],
) -> Result<()> {
w.write_u32::<LittleEndian>(0x46554747)?;
w.write_u32::<LittleEndian>(2)?; // version 2.
w.write_u64::<LittleEndian>(tensors.len() as u64)?;
w.write_u64::<LittleEndian>(metadata.len() as u64)?;
for (name, value) in metadata.iter() {
write_string(w, name)?;
w.write_u32::<LittleEndian>(value.value_type().to_u32())?;
value.write(w)?;
}
let mut offset = 0usize;
let mut offsets = Vec::with_capacity(tensors.len());
for (name, tensor) in tensors.iter() {
write_string(w, name)?;
let dims = tensor.shape().dims();
w.write_u32::<LittleEndian>(dims.len() as u32)?;
for &dim in dims.iter().rev() {
w.write_u64::<LittleEndian>(dim as u64)?;
}
w.write_u32::<LittleEndian>(tensor.dtype().to_u32())?;
w.write_u64::<LittleEndian>(offset as u64)?;
offsets.push(offset);
let size_in_bytes = tensor.storage_size_in_bytes();
let padding = 31 - (31 + size_in_bytes) % 32;
offset += size_in_bytes + padding;
}
let pos = w.stream_position()? as usize;
let padding = 31 - (31 + pos) % 32;
w.write_all(&vec![0u8; padding])?;
let tensor_start_pos = w.stream_position()? as usize;
for (offset, (_name, tensor)) in offsets.iter().zip(tensors.iter()) {
let pos = w.stream_position()? as usize;
if tensor_start_pos + offset != pos {
crate::bail!(
"internal error, unexpected current position {tensor_start_pos} {offset} {pos}"
)
}
let data_ptr = tensor.as_ptr();
let size_in_bytes = tensor.storage_size_in_bytes();
let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
w.write_all(data)?;
let padding = 31 - (31 + size_in_bytes) % 32;
w.write_all(&vec![0u8; padding])?;
}
Ok(())
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/utils.rs
|
use crate::Result;
pub(super) fn nearest_int(v: f32) -> i32 {
v.round() as i32
}
/// Validates that the input and output are the right size and returns an iterator which maps each
/// input region `xs` to its corresponding output block in `ys`. Each output region is guaranteed
/// to be `T::BLCK_SIZE` long.
pub(super) fn group_for_quantization<'a, 'b, T: super::k_quants::GgmlType>(
xs: &'b [f32],
ys: &'a mut [T],
) -> Result<Vec<(&'a mut T, &'b [f32])>> {
let block_size = T::BLCK_SIZE;
let dtype = T::DTYPE;
let expected_blocks = xs.len() / block_size;
let actual_blocks = ys.len();
// Validate that the input is the right size
if expected_blocks != actual_blocks {
crate::bail!("quantize {dtype:?}: expected {expected_blocks} blocks but only {actual_blocks} were provided!")
}
Ok(ys.iter_mut().zip(xs.chunks_exact(block_size)).collect())
}
/// Validates that the input and output are the right size and returns an iterator which maps each
/// input block `xs` to its corresponding output region in `ys`. Each output region is guaranteed
/// to be `T::BLCK_SIZE` long.
pub(super) fn group_for_dequantization<'a, 'b, T: super::k_quants::GgmlType>(
xs: &'a [T],
ys: &'b mut [f32],
) -> Result<Vec<(&'a T, &'b mut [f32])>> {
let block_size = T::BLCK_SIZE;
let dtype = T::DTYPE;
let actual_output_len = ys.len();
let expected_output_len = xs.len() * block_size;
// Validate that the output is the right size
if expected_output_len != actual_output_len {
crate::bail!("dequantize {dtype:?}: ys (len = {actual_output_len}) does not match the expected length of {expected_output_len}!")
}
// Zip the blocks and outputs together
Ok(xs.iter().zip(ys.chunks_exact_mut(block_size)).collect())
}
pub(super) fn get_scale_min_k4(j: usize, q: &[u8]) -> (u8, u8) {
if j < 4 {
let d = q[j] & 63;
let m = q[j + 4] & 63;
(d, m)
} else {
let d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
let m = (q[j + 4] >> 4) | ((q[j] >> 6) << 4);
(d, m)
}
}
pub(super) unsafe fn make_qx_quants(
n: usize,
nmax: i32,
x: *const f32,
ls: *mut i8,
rmse_type: i32,
) -> f32 {
let mut max = 0f32;
let mut amax = 0f32;
for i in 0..n {
let x = *x.add(i);
let ax = x.abs();
if ax > amax {
amax = ax;
max = x;
}
}
if amax == 0. {
// all zero
for i in 0..n {
*ls.add(i) = 0;
}
return 0.;
}
let mut iscale = -(nmax as f32) / max;
if rmse_type == 0 {
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
*ls.add(i) = (nmax + l.clamp(-nmax, nmax - 1)) as i8;
}
return 1.0 / iscale;
}
let weight_type = rmse_type % 2;
let mut sumlx = 0f32;
let mut suml2 = 0f32;
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
let l = l.clamp(-nmax, nmax - 1);
*ls.add(i) = (l + nmax) as i8;
let w = if weight_type == 1 { x * x } else { 1.0 };
let l = l as f32;
sumlx += w * x * l;
suml2 += w * l * l;
}
let mut scale = sumlx / suml2;
let mut best = scale * sumlx;
for _itry in 0..3 {
let iscale = 1.0 / scale;
let mut slx = 0f32;
let mut sl2 = 0f32;
let mut changed = false;
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
let l = l.clamp(-nmax, nmax - 1);
if l + nmax != *ls.add(i) as i32 {
changed = true;
}
let w = if weight_type == 1 { x * x } else { 1f32 };
let l = l as f32;
slx += w * x * l;
sl2 += w * l * l;
}
if !changed || sl2 == 0.0 || slx * slx <= best * sl2 {
break;
}
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
*ls.add(i) = (nmax + l.clamp(-nmax, nmax - 1)) as i8;
}
sumlx = slx;
suml2 = sl2;
scale = sumlx / suml2;
best = scale * sumlx;
}
for _itry in 0..5 {
let mut n_changed = 0;
for i in 0..n {
let x = *x.add(i);
let w = if weight_type == 1 { x * x } else { 1. };
let l = *ls.add(i) as i32 - nmax;
let mut slx = sumlx - w * x * l as f32;
if slx > 0. {
let mut sl2 = suml2 - w * l as f32 * l as f32;
let new_l = nearest_int(x * sl2 / slx);
let new_l = new_l.clamp(-nmax, nmax - 1);
if new_l != l {
slx += w * x * new_l as f32;
sl2 += w * new_l as f32 * new_l as f32;
if sl2 > 0. && slx * slx * suml2 > sumlx * sumlx * sl2 {
*ls.add(i) = (nmax + new_l) as i8;
sumlx = slx;
suml2 = sl2;
scale = sumlx / suml2;
best = scale * sumlx;
n_changed += 1;
}
}
}
}
if n_changed == 0 {
break;
}
}
if rmse_type < 3 {
return scale;
}
for is in -4..4 {
if is == 0 {
continue;
}
iscale = -(nmax as f32 + 0.1f32 * is as f32) / max;
let mut sumlx = 0.;
let mut suml2 = 0.;
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
let l = l.clamp(-nmax, nmax - 1);
let w = if weight_type == 1 { x * x } else { 1. };
let l = l as f32;
sumlx += w * x * l;
suml2 += w * l * l;
}
if suml2 > 0. && sumlx * sumlx > best * suml2 {
for i in 0..n {
let x = *x.add(i);
let l = nearest_int(iscale * x);
*ls.add(i) = (nmax + l.clamp(-nmax, nmax - 1)) as i8;
}
scale = sumlx / suml2;
best = scale * sumlx;
}
}
scale
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L224
pub(super) fn make_qkx1_quants(nmax: i32, ntry: usize, x: &[f32]) -> (f32, f32) {
let n = x.len();
let mut l = vec![0; n];
// Get min/max
let min = *x
.iter()
.take(n)
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(&x[0]);
let max = *x.iter().max_by(|a, b| a.total_cmp(b)).unwrap_or(&x[0]);
// If min == max, all values are the same => nothing to do here
if max == min {
return (0.0, 0.0);
}
// Ensure min <= 0.0
let mut min = min.min(0.);
// Compute scale and inverse scale
let mut iscale = nmax as f32 / (max - min);
let mut scale = 1.0 / iscale;
for _ in 0..ntry {
let mut sumlx = 0.0;
let mut suml2 = 0;
let mut did_change = false;
for (i, value) in x.iter().enumerate().take(n) {
let li = nearest_int(iscale * (value - min)).clamp(0, nmax);
let clamped_li = li as u8;
if clamped_li != l[i] {
l[i] = clamped_li;
did_change = true;
}
sumlx += (value - min) * li as f32;
suml2 += li * li;
}
scale = sumlx / suml2 as f32;
let sum: f32 = x
.iter()
.take(n)
.zip(l.iter().take(n))
.map(|(xi, &li)| xi - scale * li as f32)
.sum();
min = sum / n as f32;
if min > 0.0 {
min = 0.0;
}
iscale = 1.0 / scale;
if !did_change {
break;
}
}
(scale, -min)
}
// https://github.com/ggerganov/llama.cpp/blob/8183159cf3def112f6d1fe94815fce70e1bffa12/k_quants.c#L165
pub(super) fn make_q3_quants(x: &[f32], nmax: i32, do_rmse: bool) -> f32 {
let n = x.len();
let mut l = vec![0i8; n];
let mut max = 0.0;
let mut amax = 0.0;
for &xi in x.iter().take(n) {
let ax = xi.abs();
if ax > amax {
amax = ax;
max = xi;
}
}
if amax == 0.0 {
return 0.0;
}
let iscale = -(nmax as f32) / max;
if do_rmse {
let mut sumlx = 0.0;
let mut suml2 = 0.0;
for i in 0..n {
let li = (iscale * x[i]).round() as i32;
let li = li.clamp(-nmax, nmax - 1);
l[i] = li as i8;
let w = x[i] * x[i];
sumlx += w * x[i] * li as f32;
suml2 += w * (li * li) as f32;
}
for _ in 0..5 {
let mut n_changed = 0;
for i in 0..n {
let w = x[i] * x[i];
let mut slx = sumlx - w * x[i] * l[i] as f32;
if slx > 0.0 {
let mut sl2 = suml2 - w * (l[i] as i32 * l[i] as i32) as f32;
let mut new_l = (x[i] * sl2 / slx).round() as i32;
new_l = new_l.clamp(-nmax, nmax - 1);
if new_l != l[i] as i32 {
slx += w * x[i] * new_l as f32;
sl2 += w * (new_l * new_l) as f32;
if sl2 > 0.0 && slx * slx * suml2 > sumlx * sumlx * sl2 {
l[i] = new_l as i8;
sumlx = slx;
suml2 = sl2;
n_changed += 1;
}
}
}
}
if n_changed == 0 {
break;
}
}
for li in l.iter_mut() {
*li += nmax as i8;
}
return sumlx / suml2;
}
for i in 0..n {
let li = (iscale * x[i]).round() as i32;
l[i] = (li.clamp(-nmax, nmax - 1) + nmax) as i8;
}
1.0 / iscale
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/ggml_file.rs
|
//! Support for the GGML file format.
use super::{k_quants, GgmlDType};
use crate::Result;
use byteorder::{LittleEndian, ReadBytesExt};
use std::collections::HashMap;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum Magic {
Ggjt,
Ggla,
Ggmf,
Ggml,
Ggsn,
}
impl TryFrom<u32> for Magic {
type Error = crate::Error;
fn try_from(value: u32) -> Result<Self> {
let magic = match value {
0x67676a74 => Self::Ggjt,
0x67676c61 => Self::Ggla,
0x67676d66 => Self::Ggmf,
0x67676d6c => Self::Ggml,
0x6767736e => Self::Ggsn,
_ => crate::bail!("unknown magic {value:08x}"),
};
Ok(magic)
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VersionedMagic {
GgmlUnversioned,
GgmfV1,
GgjtV1,
GgjtV2,
GgjtV3,
}
impl VersionedMagic {
fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
let magic = reader.read_u32::<LittleEndian>()?;
let magic = Magic::try_from(magic)?;
if magic == Magic::Ggml {
return Ok(Self::GgmlUnversioned);
}
let version = reader.read_u32::<LittleEndian>()?;
let versioned_magic = match (magic, version) {
(Magic::Ggmf, 1) => Self::GgmfV1,
(Magic::Ggjt, 1) => Self::GgjtV1,
(Magic::Ggjt, 2) => Self::GgjtV2,
(Magic::Ggjt, 3) => Self::GgjtV3,
_ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
};
Ok(versioned_magic)
}
fn align32(&self) -> bool {
match self {
Self::GgmlUnversioned | Self::GgmfV1 => false,
Self::GgjtV1 | Self::GgjtV2 | Self::GgjtV3 => true,
}
}
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct HParams {
pub n_vocab: u32,
pub n_embd: u32,
pub n_mult: u32,
pub n_head: u32,
pub n_layer: u32,
pub n_rot: u32,
pub ftype: u32,
}
impl HParams {
fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
let n_vocab = reader.read_u32::<LittleEndian>()?;
let n_embd = reader.read_u32::<LittleEndian>()?;
let n_mult = reader.read_u32::<LittleEndian>()?;
let n_head = reader.read_u32::<LittleEndian>()?;
let n_layer = reader.read_u32::<LittleEndian>()?;
let n_rot = reader.read_u32::<LittleEndian>()?;
let ftype = reader.read_u32::<LittleEndian>()?;
Ok(Self {
n_vocab,
n_embd,
n_mult,
n_head,
n_layer,
n_rot,
ftype,
})
}
}
#[derive(Debug, Clone, PartialEq)]
pub struct Vocab {
pub token_score_pairs: Vec<(Vec<u8>, f32)>,
}
impl Vocab {
fn read<R: std::io::Read>(reader: &mut R, n_vocab: usize) -> Result<Self> {
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L556
let mut token_score_pairs = Vec::with_capacity(n_vocab);
for _index in 0..n_vocab {
let len = reader.read_u32::<LittleEndian>()? as usize;
let mut word = vec![0u8; len];
reader.read_exact(&mut word)?;
let score = reader.read_f32::<LittleEndian>()?;
token_score_pairs.push((word, score))
}
Ok(Self { token_score_pairs })
}
}
fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
raw_data: &[u8],
size_in_bytes: usize,
dims: Vec<usize>,
) -> Result<super::QTensor> {
let raw_data_ptr = raw_data.as_ptr();
let n_blocks = size_in_bytes / std::mem::size_of::<T>();
let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
super::QTensor::new(data.to_vec(), dims)
}
/// Creates a [Tensor] from a raw GGML tensor.
pub fn qtensor_from_ggml(
ggml_dtype: GgmlDType,
raw_data: &[u8],
dims: Vec<usize>,
) -> Result<super::QTensor> {
let tensor_elems = dims.iter().product::<usize>();
let blck_size = ggml_dtype.blck_size();
if tensor_elems % blck_size != 0 {
crate::bail!(
"the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
)
}
let size_in_bytes = tensor_elems / blck_size * ggml_dtype.type_size();
match ggml_dtype {
GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims),
GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims),
GgmlDType::Q4_0 => from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims),
GgmlDType::Q4_1 => from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims),
GgmlDType::Q5_0 => from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims),
GgmlDType::Q5_1 => from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims),
GgmlDType::Q8_0 => from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims),
GgmlDType::Q2K => from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims),
GgmlDType::Q3K => from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims),
GgmlDType::Q4K => from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims),
GgmlDType::Q5K => from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims),
GgmlDType::Q6K => from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims),
_ => crate::bail!("quantized type {ggml_dtype:?} is not supported yet"),
}
}
fn read_one_tensor<R: std::io::Seek + std::io::Read>(
reader: &mut R,
magic: VersionedMagic,
) -> Result<(String, super::QTensor)> {
let n_dims = reader.read_u32::<LittleEndian>()?;
let name_len = reader.read_u32::<LittleEndian>()?;
let ggml_dtype = reader.read_u32::<LittleEndian>()?;
let ggml_dtype = GgmlDType::from_u32(ggml_dtype)?;
let mut dims = vec![0u32; n_dims as usize];
reader.read_u32_into::<LittleEndian>(&mut dims)?;
// The dimensions are stored in reverse order, see for example:
// https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/convert.py#L969
dims.reverse();
let mut name = vec![0u8; name_len as usize];
reader.read_exact(&mut name)?;
let name = String::from_utf8_lossy(&name).into_owned();
if magic.align32() {
let pos = reader.stream_position()?;
reader.seek(std::io::SeekFrom::Current(((32 - pos % 32) % 32) as i64))?;
}
let dims = dims.iter().map(|&u| u as usize).collect::<Vec<_>>();
let tensor_elems = dims.iter().product::<usize>();
let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
// TODO: Mmap version to avoid copying the data around?
let mut raw_data = vec![0u8; size_in_bytes];
reader.read_exact(&mut raw_data)?;
match qtensor_from_ggml(ggml_dtype, &raw_data, dims) {
Ok(tensor) => Ok((name, tensor)),
Err(e) => crate::bail!("Error creating tensor {name}: {e}"),
}
}
pub struct Content {
pub magic: VersionedMagic,
pub hparams: HParams,
pub vocab: Vocab,
pub tensors: HashMap<String, super::QTensor>,
}
impl Content {
pub fn read<R: std::io::Seek + std::io::Read>(reader: &mut R) -> Result<Content> {
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L505
let last_position = reader.seek(std::io::SeekFrom::End(0))?;
reader.seek(std::io::SeekFrom::Start(0))?;
let magic = VersionedMagic::read(reader)?;
let hparams = HParams::read(reader)?;
let vocab = Vocab::read(reader, hparams.n_vocab as usize)?;
let mut tensors = HashMap::new();
while reader.stream_position()? != last_position {
let (name, tensor) = read_one_tensor(reader, magic)?;
tensors.insert(name, tensor);
}
Ok(Self {
magic,
hparams,
vocab,
tensors,
})
}
pub fn remove(&mut self, name: &str) -> Result<super::QTensor> {
match self.tensors.remove(name) {
None => crate::bail!("cannot find tensor with name '{name}'"),
Some(tensor) => Ok(tensor),
}
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/quantized/simd128.rs
|
use super::k_quants::{BlockQ2K, BlockQ4K, BlockQ4_0, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
use core::arch::wasm32::*;
#[inline(always)]
pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = f32x4_splat(0.0f32);
for (x, y) in xs.iter().zip(ys.iter()) {
let x1234 = v128_load(x.qs.as_ptr() as *const v128);
let x12 = v128_and(x1234, u8x16_splat(0x0F));
let x12 = i8x16_sub(x12, i8x16_splat(8));
let x34 = u8x16_shr(x1234, 4);
let x34 = i8x16_sub(x34, i8x16_splat(8));
let x1 = i16x8_extend_low_i8x16(x12);
let y1 = i16x8_load_extend_i8x8(y.qs.as_ptr());
let sum_xy = i32x4_dot_i16x8(x1, y1);
let x2 = i16x8_extend_high_i8x16(x12);
let y2 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(8));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x2, y2));
let x3 = i16x8_extend_low_i8x16(x34);
let y3 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(16));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x3, y3));
let x4 = i16x8_extend_high_i8x16(x34);
let y4 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(24));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x4, y4));
let sum_xy = f32x4_convert_i32x4(sum_xy);
// f32x4_relaxed_madd is nightly only.
let d = f32x4_splat(f16::to_f32(x.d) * f16::to_f32(y.d));
let scaled = f32x4_mul(sum_xy, d);
acc = f32x4_add(acc, scaled)
}
let res = f32x4_extract_lane::<0>(acc)
+ f32x4_extract_lane::<1>(acc)
+ f32x4_extract_lane::<2>(acc)
+ f32x4_extract_lane::<3>(acc);
Ok(res)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = f32x4_splat(0.0f32);
for (x, y) in xs.iter().zip(ys.iter()) {
let x1 = i16x8_load_extend_i8x8(x.qs.as_ptr());
let y1 = i16x8_load_extend_i8x8(y.qs.as_ptr());
let sum_xy = i32x4_dot_i16x8(x1, y1);
let x2 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(8));
let y2 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(8));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x2, y2));
let x3 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(16));
let y3 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(16));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x3, y3));
let x4 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(24));
let y4 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(24));
let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x4, y4));
let sum_xy = f32x4_convert_i32x4(sum_xy);
// f32x4_relaxed_madd is nightly only.
let d = f32x4_splat(f16::to_f32(x.d) * f16::to_f32(y.d));
let scaled = f32x4_mul(sum_xy, d);
acc = f32x4_add(acc, scaled)
}
let res = f32x4_extract_lane::<0>(acc)
+ f32x4_extract_lane::<1>(acc)
+ f32x4_extract_lane::<2>(acc)
+ f32x4_extract_lane::<3>(acc);
Ok(res)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q2k_q8k(n: usize, xs: &[BlockQ2K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
}
unsafe {
let mut sumf = f32x4_splat(0f32);
for (x, y) in xs.iter().zip(ys.iter()) {
let mut q2: &[_] = &x.qs;
let mut q8: &[_] = &y.qs;
let sc = &x.scales;
let mut summs = i32x4_splat(0);
for i in (0..(QK_K / 16)).step_by(4) {
let bsums = i32x4_load_extend_i16x4(y.bsums.as_ptr().add(i));
let scales = i32x4_shr(
i32x4(
sc[i] as i32,
sc[i + 1] as i32,
sc[i + 2] as i32,
sc[i + 3] as i32,
),
4,
);
summs = i32x4_add(summs, i32x4_mul(bsums, scales))
}
let summs = f32x4_convert_i32x4(summs);
let dall = y.d * x.d.to_f32();
let dmin = y.d * x.dmin.to_f32();
let mut isum = i32x4_splat(0);
let mut is = 0;
for _ in 0..(QK_K / 128) {
let mut shift = 0;
for _ in 0..4 {
let d = (sc[is] & 0xF) as i32;
is += 1;
let mut isuml = i16x8_splat(0);
for l in (0..16).step_by(8) {
let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(l));
let q2 = i16x8_load_extend_u8x8(q2.as_ptr().add(l));
let q2 = v128_and(i16x8_shr(q2, shift), i16x8_splat(3));
isuml = i16x8_add(isuml, i16x8_mul(q2, q8))
}
let dd = i32x4_splat(d);
isum = i32x4_add(isum, i32x4_mul(i32x4_extend_low_i16x8(isuml), dd));
isum = i32x4_add(isum, i32x4_mul(i32x4_extend_high_i16x8(isuml), dd));
let d = (sc[is] & 0xF) as i32;
is += 1;
let mut isuml = i16x8_splat(0);
for l in (16..32).step_by(8) {
let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(l));
let q2 = i16x8_load_extend_u8x8(q2.as_ptr().add(l));
let q2 = v128_and(i16x8_shr(q2, shift), i16x8_splat(3));
isuml = i16x8_add(isuml, i16x8_mul(q2, q8))
}
let dd = i32x4_splat(d);
isum = i32x4_add(isum, i32x4_mul(i32x4_extend_low_i16x8(isuml), dd));
isum = i32x4_add(isum, i32x4_mul(i32x4_extend_high_i16x8(isuml), dd));
shift += 2;
// adjust the indexing
q8 = &q8[32..];
}
// adjust the indexing
q2 = &q2[32..];
}
let isum = f32x4_convert_i32x4(isum);
sumf = f32x4_add(
sumf,
f32x4_sub(
f32x4_mul(isum, f32x4_splat(dall)),
f32x4_mul(summs, f32x4_splat(dmin)),
),
);
}
let sumf = f32x4_extract_lane::<0>(sumf)
+ f32x4_extract_lane::<1>(sumf)
+ f32x4_extract_lane::<2>(sumf)
+ f32x4_extract_lane::<3>(sumf);
Ok(sumf)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
}
const KMASK1: u32 = 0x3f3f3f3f;
const KMASK2: u32 = 0x0f0f0f0f;
const KMASK3: u32 = 0x03030303;
let mut utmp: [u32; 4] = [0; 4];
let mut scales: [u8; 8] = [0; 8];
let mut mins: [u8; 8] = [0; 8];
let mut aux8: [u8; QK_K] = [0; QK_K];
let mut sums = f32x4_splat(0f32);
unsafe {
for (y, x) in ys.iter().zip(xs.iter()) {
let q4 = &x.qs;
let q8 = &y.qs;
for j in 0..QK_K / 64 {
let q4_1 = v128_load(q4.as_ptr().add(32 * j) as *const v128);
let q4_2 = v128_load(q4.as_ptr().add(32 * j + 16) as *const v128);
v128_store(
aux8.as_mut_ptr().add(64 * j) as *mut v128,
v128_and(q4_1, u8x16_splat(0x0F)),
);
v128_store(
aux8.as_mut_ptr().add(64 * j + 16) as *mut v128,
v128_and(q4_2, u8x16_splat(0x0F)),
);
v128_store(
aux8.as_mut_ptr().add(64 * j + 32) as *mut v128,
u8x16_shr(q4_1, 4),
);
v128_store(
aux8.as_mut_ptr().add(64 * j + 48) as *mut v128,
u8x16_shr(q4_2, 4),
);
}
LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
let uaux = utmp[1] & KMASK1;
utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
utmp[2] = uaux;
utmp[0] &= KMASK1;
//extract scales and mins
LittleEndian::write_u32_into(&utmp[0..2], &mut scales);
LittleEndian::write_u32_into(&utmp[2..4], &mut mins);
let mut sumi = i32x4_splat(0);
for j in (0..QK_K / 16).step_by(4) {
let bsums = i32x4_load_extend_i16x4(y.bsums.as_ptr().add(j));
let (m1, m2) = (mins[j / 2] as i32, mins[j / 2 + 1] as i32);
let mins = i32x4(m1, m1, m2, m2);
sumi = i32x4_add(sumi, i32x4_mul(bsums, mins));
}
let mut aux32 = i32x4_splat(0i32);
for (scale_i, scale) in scales.iter().enumerate() {
let scale = i32x4_splat(*scale as i32);
for j in 0..4 {
let i = 32 * scale_i + 8 * j;
let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(i));
let aux8 = i16x8_load_extend_u8x8(aux8.as_ptr().add(i));
let aux16 = i16x8_mul(q8, aux8);
aux32 = i32x4_add(aux32, i32x4_mul(scale, i32x4_extend_low_i16x8(aux16)));
aux32 = i32x4_add(aux32, i32x4_mul(scale, i32x4_extend_high_i16x8(aux16)));
}
}
let aux32 = f32x4_convert_i32x4(aux32);
let d = f32x4_splat(x.d.to_f32() * y.d);
sums = f32x4_add(sums, f32x4_mul(aux32, d));
let dmin = x.dmin.to_f32() * y.d;
let dmin = f32x4_splat(dmin);
let sumi = f32x4_convert_i32x4(sumi);
sums = f32x4_sub(sums, f32x4_mul(sumi, dmin));
}
let sums = f32x4_extract_lane::<0>(sums)
+ f32x4_extract_lane::<1>(sums)
+ f32x4_extract_lane::<2>(sums)
+ f32x4_extract_lane::<3>(sums);
Ok(sums)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Result<f32> {
if n % QK_K != 0 {
crate::bail!("vec_dot_q6k_q8k: {n} is not divisible by {QK_K}")
}
let mut aux8 = [0i8; QK_K];
unsafe {
let mut sums = f32x4_splat(0f32);
for (x, y) in xs.iter().zip(ys.iter()) {
let q4 = &x.ql;
let qh = &x.qh;
let q8 = &y.qs;
let mut aux32 = f32x4_splat(0f32);
for j in (0..QK_K).step_by(128) {
let aux8 = aux8.as_mut_ptr().add(j);
let q4 = &q4.as_ptr().add(j / 2);
let qh = &qh.as_ptr().add(j / 4);
for l in (0..32).step_by(16) {
// aux8[l] = (((q4[l] & 0xF) | ((qh[l] & 3) << 4)) as i32 - 32) as i8;
let a8 = v128_or(
v128_and(v128_load(q4.add(l) as *const v128), u8x16_splat(0xF)),
u8x16_shl(
v128_and(v128_load(qh.add(l) as *const v128), u8x16_splat(3)),
4,
),
);
let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
v128_store(
aux8.add(l) as *mut v128,
i8x16_narrow_i16x8(a8_low, a8_high),
);
// aux8[l + 32] =
// (((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) as i32 - 32) as i8;
let a8 = v128_or(
v128_and(v128_load(q4.add(l + 32) as *const v128), u8x16_splat(0xF)),
u8x16_shl(
v128_and(
u8x16_shr(v128_load(qh.add(l) as *const v128), 2),
u8x16_splat(3),
),
4,
),
);
let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
v128_store(
aux8.add(l + 32) as *mut v128,
i8x16_narrow_i16x8(a8_low, a8_high),
);
// aux8[l + 64] = (((q4[l] >> 4) | (((qh[l] >> 4) & 3) << 4)) as i32 - 32) as i8;
let a8 = v128_or(
u8x16_shr(v128_load(q4.add(l) as *const v128), 4),
u8x16_shl(
v128_and(
u8x16_shr(v128_load(qh.add(l) as *const v128), 4),
u8x16_splat(3),
),
4,
),
);
let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
v128_store(
aux8.add(l + 64) as *mut v128,
i8x16_narrow_i16x8(a8_low, a8_high),
);
// aux8[l + 96] =
// (((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) as i32 - 32) as i8;
let a8 = v128_or(
u8x16_shr(v128_load(q4.add(l + 32) as *const v128), 4),
u8x16_shl(
v128_and(
u8x16_shr(v128_load(qh.add(l) as *const v128), 6),
u8x16_splat(3),
),
4,
),
);
let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
v128_store(
aux8.add(l + 96) as *mut v128,
i8x16_narrow_i16x8(a8_low, a8_high),
);
}
}
for (j, &scale) in x.scales.iter().enumerate() {
let scale = f32x4_splat(scale as f32);
for offset in [0, 8] {
let aux16 = i16x8_mul(
i16x8_load_extend_i8x8(q8.as_ptr().add(16 * j + offset)),
i16x8_load_extend_i8x8(aux8.as_ptr().add(16 * j + offset)),
);
aux32 = f32x4_add(
aux32,
f32x4_mul(f32x4_convert_i32x4(i32x4_extend_low_i16x8(aux16)), scale),
);
aux32 = f32x4_add(
aux32,
f32x4_mul(f32x4_convert_i32x4(i32x4_extend_high_i16x8(aux16)), scale),
);
}
}
let d = f32x4_splat(x.d.to_f32() * y.d);
sums = f32x4_add(sums, f32x4_mul(aux32, d));
}
let sums = f32x4_extract_lane::<0>(sums)
+ f32x4_extract_lane::<1>(sums)
+ f32x4_extract_lane::<2>(sums)
+ f32x4_extract_lane::<3>(sums);
Ok(sums)
}
}
#[inline(always)]
pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
let qk = QK_K;
if n % QK_K != 0 {
crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
}
unsafe {
let mut acc = f32x4_splat(0.0f32);
for (xs, ys) in xs.iter().zip(ys.iter()) {
let x_qs = xs.qs.as_ptr();
let y_qs = ys.qs.as_ptr();
let mut sumi = i32x4_splat(0);
for j in (0..QK_K).step_by(8) {
let xs = i16x8_load_extend_i8x8(x_qs.add(j));
let ys = i16x8_load_extend_i8x8(y_qs.add(j));
let sum_xy = i32x4_dot_i16x8(xs, ys);
sumi = i32x4_add(sumi, sum_xy)
}
let d = f32x4_splat(xs.d * ys.d);
acc = f32x4_add(acc, f32x4_mul(f32x4_convert_i32x4(sumi), d))
}
let res = f32x4_extract_lane::<0>(acc)
+ f32x4_extract_lane::<1>(acc)
+ f32x4_extract_lane::<2>(acc)
+ f32x4_extract_lane::<3>(acc);
Ok(res)
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/neon.rs
|
use super::Cpu;
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl CurrentCpu {
#[cfg(target_arch = "aarch64")]
unsafe fn reduce_one(x: float32x4_t) -> f32 {
vaddvq_f32(x)
}
#[cfg(target_arch = "arm")]
unsafe fn reduce_one(x: float32x4_t) -> f32 {
vgetq_lane_f32(x, 0) + vgetq_lane_f32(x, 1) + vgetq_lane_f32(x, 2) + vgetq_lane_f32(x, 3)
}
}
impl Cpu<ARR> for CurrentCpu {
type Unit = float32x4_t;
type Array = [float32x4_t; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
ARR
}
unsafe fn zero() -> Self::Unit {
vdupq_n_f32(0.0)
}
unsafe fn from_f32(x: f32) -> Self::Unit {
vdupq_n_f32(x)
}
unsafe fn zero_array() -> Self::Array {
[Self::zero(); ARR]
}
unsafe fn load(mem_addr: *const f32) -> Self::Unit {
vld1q_f32(mem_addr)
}
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit {
vaddq_f32(a, b)
}
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit {
vfmaq_f32(a, b, c)
}
unsafe fn vec_store(mem_addr: *mut f32, a: Self::Unit) {
vst1q_f32(mem_addr, a);
}
unsafe fn vec_reduce(mut x: Self::Array, y: *mut f32) {
for i in 0..ARR / 2 {
x[2 * i] = vaddq_f32(x[2 * i], x[2 * i + 1]);
}
for i in 0..ARR / 4 {
x[4 * i] = vaddq_f32(x[4 * i], x[4 * i + 2]);
}
*y = Self::reduce_one(x[0]);
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/avx.rs
|
use super::{Cpu, CpuF16};
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
use half::f16;
pub struct CurrentCpu {}
const STEP: usize = 32;
const EPR: usize = 8;
const ARR: usize = STEP / EPR;
impl Cpu<ARR> for CurrentCpu {
type Unit = __m256;
type Array = [__m256; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
ARR
}
unsafe fn zero() -> Self::Unit {
_mm256_setzero_ps()
}
unsafe fn zero_array() -> Self::Array {
[Self::zero(); ARR]
}
unsafe fn from_f32(v: f32) -> Self::Unit {
_mm256_set1_ps(v)
}
unsafe fn load(mem_addr: *const f32) -> Self::Unit {
_mm256_loadu_ps(mem_addr)
}
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit {
_mm256_add_ps(a, b)
}
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit {
_mm256_add_ps(_mm256_mul_ps(b, c), a)
}
unsafe fn vec_store(mem_addr: *mut f32, a: Self::Unit) {
_mm256_storeu_ps(mem_addr, a);
}
unsafe fn vec_reduce(mut x: Self::Array, y: *mut f32) {
for i in 0..ARR / 2 {
x[2 * i] = _mm256_add_ps(x[2 * i], x[2 * i + 1]);
}
for i in 0..ARR / 4 {
x[4 * i] = _mm256_add_ps(x[4 * i], x[4 * i + 2]);
}
#[allow(clippy::reversed_empty_ranges)]
for i in 0..ARR / 8 {
x[8 * i] = _mm256_add_ps(x[8 * i], x[8 * i + 4]);
}
let t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), _mm256_extractf128_ps(x[0], 1));
let t1 = _mm_hadd_ps(t0, t0);
*y = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));
}
}
pub struct CurrentCpuF16 {}
impl CpuF16<ARR> for CurrentCpuF16 {
type Unit = __m256;
type Array = [__m256; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
ARR
}
unsafe fn zero() -> Self::Unit {
_mm256_setzero_ps()
}
unsafe fn zero_array() -> Self::Array {
[Self::zero(); ARR]
}
unsafe fn from_f32(v: f32) -> Self::Unit {
_mm256_set1_ps(v)
}
#[cfg(target_feature = "f16c")]
unsafe fn load(mem_addr: *const f16) -> Self::Unit {
_mm256_cvtph_ps(_mm_loadu_si128(mem_addr as *const __m128i))
}
#[cfg(not(target_feature = "f16c"))]
unsafe fn load(mem_addr: *const f16) -> Self::Unit {
let mut tmp = [0.0f32; 8];
for i in 0..8 {
tmp[i] = (*mem_addr.add(i)).to_f32();
}
_mm256_loadu_ps(tmp.as_ptr())
}
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit {
_mm256_add_ps(a, b)
}
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit {
_mm256_add_ps(_mm256_mul_ps(b, c), a)
}
#[cfg(target_feature = "f16c")]
unsafe fn vec_store(mem_addr: *mut f16, a: Self::Unit) {
_mm_storeu_si128(mem_addr as *mut __m128i, _mm256_cvtps_ph(a, 0))
}
#[cfg(not(target_feature = "f16c"))]
unsafe fn vec_store(mem_addr: *mut f16, a: Self::Unit) {
let mut tmp = [0.0f32; 8];
_mm256_storeu_ps(tmp.as_mut_ptr(), a);
for i in 0..8 {
*mem_addr.add(i) = f16::from_f32(tmp[i]);
}
}
unsafe fn vec_reduce(mut x: Self::Array, y: *mut f32) {
let mut offset = ARR >> 1;
for i in 0..offset {
x[i] = _mm256_add_ps(x[i], x[offset + i]);
}
offset >>= 1;
for i in 0..offset {
x[i] = _mm256_add_ps(x[i], x[offset + i]);
}
offset >>= 1;
for i in 0..offset {
x[i] = _mm256_add_ps(x[i], x[offset + i]);
}
let t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), _mm256_extractf128_ps(x[0], 1));
let t1 = _mm_hadd_ps(t0, t0);
*y = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/erf.rs
|
#![allow(clippy::excessive_precision)]
// Code taken from https://github.com/statrs-dev/statrs
//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
//! related functions
mod evaluate {
//! Provides functions that don't have a numerical solution and must
//! be solved computationally (e.g. evaluation of a polynomial)
/// evaluates a polynomial at `z` where `coeff` are the coeffecients
/// to a polynomial of order `k` where `k` is the length of `coeff` and the
/// coeffecient
/// to the `k`th power is the `k`th element in coeff. E.g. [3,-1,2] equates to
/// `2z^2 - z + 3`
///
/// # Remarks
///
/// Returns 0 for a 0 length coefficient slice
pub fn polynomial(z: f64, coeff: &[f64]) -> f64 {
let n = coeff.len();
if n == 0 {
return 0.0;
}
let mut sum = *coeff.last().unwrap();
for c in coeff[0..n - 1].iter().rev() {
sum = *c + z * sum;
}
sum
}
}
use std::f64;
/// `erf` calculates the error function at `x`.
pub fn erf(x: f64) -> f64 {
if x.is_nan() {
f64::NAN
} else if x >= 0.0 && x.is_infinite() {
1.0
} else if x <= 0.0 && x.is_infinite() {
-1.0
} else if x == 0. {
0.0
} else {
erf_impl(x, false)
}
}
/// `erf_inv` calculates the inverse error function
/// at `x`.
pub fn erf_inv(x: f64) -> f64 {
if x == 0.0 {
0.0
} else if x >= 1.0 {
f64::INFINITY
} else if x <= -1.0 {
f64::NEG_INFINITY
} else if x < 0.0 {
erf_inv_impl(-x, 1.0 + x, -1.0)
} else {
erf_inv_impl(x, 1.0 - x, 1.0)
}
}
/// `erfc` calculates the complementary error function
/// at `x`.
pub fn erfc(x: f64) -> f64 {
if x.is_nan() {
f64::NAN
} else if x == f64::INFINITY {
0.0
} else if x == f64::NEG_INFINITY {
2.0
} else {
erf_impl(x, true)
}
}
/// `erfc_inv` calculates the complementary inverse
/// error function at `x`.
pub fn erfc_inv(x: f64) -> f64 {
if x <= 0.0 {
f64::INFINITY
} else if x >= 2.0 {
f64::NEG_INFINITY
} else if x > 1.0 {
erf_inv_impl(-1.0 + x, 2.0 - x, -1.0)
} else {
erf_inv_impl(1.0 - x, x, 1.0)
}
}
// **********************************************************
// ********** Coefficients for erf_impl polynomial **********
// **********************************************************
/// Polynomial coefficients for a numerator of `erf_impl`
/// in the interval [1e-10, 0.5].
const ERF_IMPL_AN: &[f64] = &[
0.00337916709551257388990745,
-0.00073695653048167948530905,
-0.374732337392919607868241,
0.0817442448733587196071743,
-0.0421089319936548595203468,
0.0070165709512095756344528,
-0.00495091255982435110337458,
0.000871646599037922480317225,
];
/// Polynomial coefficients for a denominator of `erf_impl`
/// in the interval [1e-10, 0.5]
const ERF_IMPL_AD: &[f64] = &[
1.0,
-0.218088218087924645390535,
0.412542972725442099083918,
-0.0841891147873106755410271,
0.0655338856400241519690695,
-0.0120019604454941768171266,
0.00408165558926174048329689,
-0.000615900721557769691924509,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [0.5, 0.75].
const ERF_IMPL_BN: &[f64] = &[
-0.0361790390718262471360258,
0.292251883444882683221149,
0.281447041797604512774415,
0.125610208862766947294894,
0.0274135028268930549240776,
0.00250839672168065762786937,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [0.5, 0.75].
const ERF_IMPL_BD: &[f64] = &[
1.0,
1.8545005897903486499845,
1.43575803037831418074962,
0.582827658753036572454135,
0.124810476932949746447682,
0.0113724176546353285778481,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [0.75, 1.25].
const ERF_IMPL_CN: &[f64] = &[
-0.0397876892611136856954425,
0.153165212467878293257683,
0.191260295600936245503129,
0.10276327061989304213645,
0.029637090615738836726027,
0.0046093486780275489468812,
0.000307607820348680180548455,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [0.75, 1.25].
const ERF_IMPL_CD: &[f64] = &[
1.0,
1.95520072987627704987886,
1.64762317199384860109595,
0.768238607022126250082483,
0.209793185936509782784315,
0.0319569316899913392596356,
0.00213363160895785378615014,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [1.25, 2.25].
const ERF_IMPL_DN: &[f64] = &[
-0.0300838560557949717328341,
0.0538578829844454508530552,
0.0726211541651914182692959,
0.0367628469888049348429018,
0.00964629015572527529605267,
0.00133453480075291076745275,
0.778087599782504251917881e-4,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [1.25, 2.25].
const ERF_IMPL_DD: &[f64] = &[
1.0,
1.75967098147167528287343,
1.32883571437961120556307,
0.552528596508757581287907,
0.133793056941332861912279,
0.0179509645176280768640766,
0.00104712440019937356634038,
-0.106640381820357337177643e-7,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [2.25, 3.5].
const ERF_IMPL_EN: &[f64] = &[
-0.0117907570137227847827732,
0.014262132090538809896674,
0.0202234435902960820020765,
0.00930668299990432009042239,
0.00213357802422065994322516,
0.00025022987386460102395382,
0.120534912219588189822126e-4,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [2.25, 3.5].
const ERF_IMPL_ED: &[f64] = &[
1.0,
1.50376225203620482047419,
0.965397786204462896346934,
0.339265230476796681555511,
0.0689740649541569716897427,
0.00771060262491768307365526,
0.000371421101531069302990367,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [3.5, 5.25].
const ERF_IMPL_FN: &[f64] = &[
-0.00546954795538729307482955,
0.00404190278731707110245394,
0.0054963369553161170521356,
0.00212616472603945399437862,
0.000394984014495083900689956,
0.365565477064442377259271e-4,
0.135485897109932323253786e-5,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [3.5, 5.25].
const ERF_IMPL_FD: &[f64] = &[
1.0,
1.21019697773630784832251,
0.620914668221143886601045,
0.173038430661142762569515,
0.0276550813773432047594539,
0.00240625974424309709745382,
0.891811817251336577241006e-4,
-0.465528836283382684461025e-11,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [5.25, 8].
const ERF_IMPL_GN: &[f64] = &[
-0.00270722535905778347999196,
0.0013187563425029400461378,
0.00119925933261002333923989,
0.00027849619811344664248235,
0.267822988218331849989363e-4,
0.923043672315028197865066e-6,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [5.25, 8].
const ERF_IMPL_GD: &[f64] = &[
1.0,
0.814632808543141591118279,
0.268901665856299542168425,
0.0449877216103041118694989,
0.00381759663320248459168994,
0.000131571897888596914350697,
0.404815359675764138445257e-11,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [8, 11.5].
const ERF_IMPL_HN: &[f64] = &[
-0.00109946720691742196814323,
0.000406425442750422675169153,
0.000274499489416900707787024,
0.465293770646659383436343e-4,
0.320955425395767463401993e-5,
0.778286018145020892261936e-7,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [8, 11.5].
const ERF_IMPL_HD: &[f64] = &[
1.0,
0.588173710611846046373373,
0.139363331289409746077541,
0.0166329340417083678763028,
0.00100023921310234908642639,
0.24254837521587225125068e-4,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [11.5, 17].
const ERF_IMPL_IN: &[f64] = &[
-0.00056907993601094962855594,
0.000169498540373762264416984,
0.518472354581100890120501e-4,
0.382819312231928859704678e-5,
0.824989931281894431781794e-7,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [11.5, 17].
const ERF_IMPL_ID: &[f64] = &[
1.0,
0.339637250051139347430323,
0.043472647870310663055044,
0.00248549335224637114641629,
0.535633305337152900549536e-4,
-0.117490944405459578783846e-12,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [17, 24].
const ERF_IMPL_JN: &[f64] = &[
-0.000241313599483991337479091,
0.574224975202501512365975e-4,
0.115998962927383778460557e-4,
0.581762134402593739370875e-6,
0.853971555085673614607418e-8,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [17, 24].
const ERF_IMPL_JD: &[f64] = &[
1.0,
0.233044138299687841018015,
0.0204186940546440312625597,
0.000797185647564398289151125,
0.117019281670172327758019e-4,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [24, 38].
const ERF_IMPL_KN: &[f64] = &[
-0.000146674699277760365803642,
0.162666552112280519955647e-4,
0.269116248509165239294897e-5,
0.979584479468091935086972e-7,
0.101994647625723465722285e-8,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [24, 38].
const ERF_IMPL_KD: &[f64] = &[
1.0,
0.165907812944847226546036,
0.0103361716191505884359634,
0.000286593026373868366935721,
0.298401570840900340874568e-5,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [38, 60].
const ERF_IMPL_LN: &[f64] = &[
-0.583905797629771786720406e-4,
0.412510325105496173512992e-5,
0.431790922420250949096906e-6,
0.993365155590013193345569e-8,
0.653480510020104699270084e-10,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [38, 60].
const ERF_IMPL_LD: &[f64] = &[
1.0,
0.105077086072039915406159,
0.00414278428675475620830226,
0.726338754644523769144108e-4,
0.477818471047398785369849e-6,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [60, 85].
const ERF_IMPL_MN: &[f64] = &[
-0.196457797609229579459841e-4,
0.157243887666800692441195e-5,
0.543902511192700878690335e-7,
0.317472492369117710852685e-9,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [60, 85].
const ERF_IMPL_MD: &[f64] = &[
1.0,
0.052803989240957632204885,
0.000926876069151753290378112,
0.541011723226630257077328e-5,
0.535093845803642394908747e-15,
];
/// Polynomial coefficients for a numerator in `erf_impl`
/// in the interval [85, 110].
const ERF_IMPL_NN: &[f64] = &[
-0.789224703978722689089794e-5,
0.622088451660986955124162e-6,
0.145728445676882396797184e-7,
0.603715505542715364529243e-10,
];
/// Polynomial coefficients for a denominator in `erf_impl`
/// in the interval [85, 110].
const ERF_IMPL_ND: &[f64] = &[
1.0,
0.0375328846356293715248719,
0.000467919535974625308126054,
0.193847039275845656900547e-5,
];
// **********************************************************
// ********** Coefficients for erf_inv_impl polynomial ******
// **********************************************************
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0, 0.5].
const ERF_INV_IMPL_AN: &[f64] = &[
-0.000508781949658280665617,
-0.00836874819741736770379,
0.0334806625409744615033,
-0.0126926147662974029034,
-0.0365637971411762664006,
0.0219878681111168899165,
0.00822687874676915743155,
-0.00538772965071242932965,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0, 0.5].
const ERF_INV_IMPL_AD: &[f64] = &[
1.0,
-0.970005043303290640362,
-1.56574558234175846809,
1.56221558398423026363,
0.662328840472002992063,
-0.71228902341542847553,
-0.0527396382340099713954,
0.0795283687341571680018,
-0.00233393759374190016776,
0.000886216390456424707504,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.5, 0.75].
const ERF_INV_IMPL_BN: &[f64] = &[
-0.202433508355938759655,
0.105264680699391713268,
8.37050328343119927838,
17.6447298408374015486,
-18.8510648058714251895,
-44.6382324441786960818,
17.445385985570866523,
21.1294655448340526258,
-3.67192254707729348546,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.5, 0.75].
const ERF_INV_IMPL_BD: &[f64] = &[
1.0,
6.24264124854247537712,
3.9713437953343869095,
-28.6608180499800029974,
-20.1432634680485188801,
48.5609213108739935468,
10.8268667355460159008,
-22.6436933413139721736,
1.72114765761200282724,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.75, 1] with x less than 3.
const ERF_INV_IMPL_CN: &[f64] = &[
-0.131102781679951906451,
-0.163794047193317060787,
0.117030156341995252019,
0.387079738972604337464,
0.337785538912035898924,
0.142869534408157156766,
0.0290157910005329060432,
0.00214558995388805277169,
-0.679465575181126350155e-6,
0.285225331782217055858e-7,
-0.681149956853776992068e-9,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.75, 1] with x less than 3.
const ERF_INV_IMPL_CD: &[f64] = &[
1.0,
3.46625407242567245975,
5.38168345707006855425,
4.77846592945843778382,
2.59301921623620271374,
0.848854343457902036425,
0.152264338295331783612,
0.01105924229346489121,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 3 and 6.
const ERF_INV_IMPL_DN: &[f64] = &[
-0.0350353787183177984712,
-0.00222426529213447927281,
0.0185573306514231072324,
0.00950804701325919603619,
0.00187123492819559223345,
0.000157544617424960554631,
0.460469890584317994083e-5,
-0.230404776911882601748e-9,
0.266339227425782031962e-11,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 3 and 6.
const ERF_INV_IMPL_DD: &[f64] = &[
1.0,
1.3653349817554063097,
0.762059164553623404043,
0.220091105764131249824,
0.0341589143670947727934,
0.00263861676657015992959,
0.764675292302794483503e-4,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 6 and 18.
const ERF_INV_IMPL_EN: &[f64] = &[
-0.0167431005076633737133,
-0.00112951438745580278863,
0.00105628862152492910091,
0.000209386317487588078668,
0.149624783758342370182e-4,
0.449696789927706453732e-6,
0.462596163522878599135e-8,
-0.281128735628831791805e-13,
0.99055709973310326855e-16,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 6 and 18.
const ERF_INV_IMPL_ED: &[f64] = &[
1.0,
0.591429344886417493481,
0.138151865749083321638,
0.0160746087093676504695,
0.000964011807005165528527,
0.275335474764726041141e-4,
0.282243172016108031869e-6,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 18 and 44.
const ERF_INV_IMPL_FN: &[f64] = &[
-0.0024978212791898131227,
-0.779190719229053954292e-5,
0.254723037413027451751e-4,
0.162397777342510920873e-5,
0.396341011304801168516e-7,
0.411632831190944208473e-9,
0.145596286718675035587e-11,
-0.116765012397184275695e-17,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.75, 1] with x between 18 and 44.
const ERF_INV_IMPL_FD: &[f64] = &[
1.0,
0.207123112214422517181,
0.0169410838120975906478,
0.000690538265622684595676,
0.145007359818232637924e-4,
0.144437756628144157666e-6,
0.509761276599778486139e-9,
];
/// Polynomial coefficients for a numerator of `erf_inv_impl`
/// in the interval [0.75, 1] with x greater than 44.
const ERF_INV_IMPL_GN: &[f64] = &[
-0.000539042911019078575891,
-0.28398759004727721098e-6,
0.899465114892291446442e-6,
0.229345859265920864296e-7,
0.225561444863500149219e-9,
0.947846627503022684216e-12,
0.135880130108924861008e-14,
-0.348890393399948882918e-21,
];
/// Polynomial coefficients for a denominator of `erf_inv_impl`
/// in the interval [0.75, 1] with x greater than 44.
const ERF_INV_IMPL_GD: &[f64] = &[
1.0,
0.0845746234001899436914,
0.00282092984726264681981,
0.468292921940894236786e-4,
0.399968812193862100054e-6,
0.161809290887904476097e-8,
0.231558608310259605225e-11,
];
/// `erf_impl` computes the error function at `z`.
/// If `inv` is true, `1 - erf` is calculated as opposed to `erf`
fn erf_impl(z: f64, inv: bool) -> f64 {
if z < 0.0 {
if !inv {
return -erf_impl(-z, false);
}
if z < -0.5 {
return 2.0 - erf_impl(-z, true);
}
return 1.0 + erf_impl(-z, false);
}
let result = if z < 0.5 {
if z < 1e-10 {
z * 1.125 + z * 0.003379167095512573896158903121545171688
} else {
z * 1.125
+ z * evaluate::polynomial(z, ERF_IMPL_AN) / evaluate::polynomial(z, ERF_IMPL_AD)
}
} else if z < 110.0 {
let (r, b) = if z < 0.75 {
(
evaluate::polynomial(z - 0.5, ERF_IMPL_BN)
/ evaluate::polynomial(z - 0.5, ERF_IMPL_BD),
0.3440242112,
)
} else if z < 1.25 {
(
evaluate::polynomial(z - 0.75, ERF_IMPL_CN)
/ evaluate::polynomial(z - 0.75, ERF_IMPL_CD),
0.419990927,
)
} else if z < 2.25 {
(
evaluate::polynomial(z - 1.25, ERF_IMPL_DN)
/ evaluate::polynomial(z - 1.25, ERF_IMPL_DD),
0.4898625016,
)
} else if z < 3.5 {
(
evaluate::polynomial(z - 2.25, ERF_IMPL_EN)
/ evaluate::polynomial(z - 2.25, ERF_IMPL_ED),
0.5317370892,
)
} else if z < 5.25 {
(
evaluate::polynomial(z - 3.5, ERF_IMPL_FN)
/ evaluate::polynomial(z - 3.5, ERF_IMPL_FD),
0.5489973426,
)
} else if z < 8.0 {
(
evaluate::polynomial(z - 5.25, ERF_IMPL_GN)
/ evaluate::polynomial(z - 5.25, ERF_IMPL_GD),
0.5571740866,
)
} else if z < 11.5 {
(
evaluate::polynomial(z - 8.0, ERF_IMPL_HN)
/ evaluate::polynomial(z - 8.0, ERF_IMPL_HD),
0.5609807968,
)
} else if z < 17.0 {
(
evaluate::polynomial(z - 11.5, ERF_IMPL_IN)
/ evaluate::polynomial(z - 11.5, ERF_IMPL_ID),
0.5626493692,
)
} else if z < 24.0 {
(
evaluate::polynomial(z - 17.0, ERF_IMPL_JN)
/ evaluate::polynomial(z - 17.0, ERF_IMPL_JD),
0.5634598136,
)
} else if z < 38.0 {
(
evaluate::polynomial(z - 24.0, ERF_IMPL_KN)
/ evaluate::polynomial(z - 24.0, ERF_IMPL_KD),
0.5638477802,
)
} else if z < 60.0 {
(
evaluate::polynomial(z - 38.0, ERF_IMPL_LN)
/ evaluate::polynomial(z - 38.0, ERF_IMPL_LD),
0.5640528202,
)
} else if z < 85.0 {
(
evaluate::polynomial(z - 60.0, ERF_IMPL_MN)
/ evaluate::polynomial(z - 60.0, ERF_IMPL_MD),
0.5641309023,
)
} else {
(
evaluate::polynomial(z - 85.0, ERF_IMPL_NN)
/ evaluate::polynomial(z - 85.0, ERF_IMPL_ND),
0.5641584396,
)
};
let g = (-z * z).exp() / z;
g * b + g * r
} else {
0.0
};
if inv && z >= 0.5 {
result
} else if z >= 0.5 || inv {
1.0 - result
} else {
result
}
}
// `erf_inv_impl` computes the inverse error function where
// `p`,`q`, and `s` are the first, second, and third intermediate
// parameters respectively
fn erf_inv_impl(p: f64, q: f64, s: f64) -> f64 {
let result = if p <= 0.5 {
let y = 0.0891314744949340820313;
let g = p * (p + 10.0);
let r = evaluate::polynomial(p, ERF_INV_IMPL_AN) / evaluate::polynomial(p, ERF_INV_IMPL_AD);
g * y + g * r
} else if q >= 0.25 {
let y = 2.249481201171875;
let g = (-2.0 * q.ln()).sqrt();
let xs = q - 0.25;
let r =
evaluate::polynomial(xs, ERF_INV_IMPL_BN) / evaluate::polynomial(xs, ERF_INV_IMPL_BD);
g / (y + r)
} else {
let x = (-q.ln()).sqrt();
if x < 3.0 {
let y = 0.807220458984375;
let xs = x - 1.125;
let r = evaluate::polynomial(xs, ERF_INV_IMPL_CN)
/ evaluate::polynomial(xs, ERF_INV_IMPL_CD);
y * x + r * x
} else if x < 6.0 {
let y = 0.93995571136474609375;
let xs = x - 3.0;
let r = evaluate::polynomial(xs, ERF_INV_IMPL_DN)
/ evaluate::polynomial(xs, ERF_INV_IMPL_DD);
y * x + r * x
} else if x < 18.0 {
let y = 0.98362827301025390625;
let xs = x - 6.0;
let r = evaluate::polynomial(xs, ERF_INV_IMPL_EN)
/ evaluate::polynomial(xs, ERF_INV_IMPL_ED);
y * x + r * x
} else if x < 44.0 {
let y = 0.99714565277099609375;
let xs = x - 18.0;
let r = evaluate::polynomial(xs, ERF_INV_IMPL_FN)
/ evaluate::polynomial(xs, ERF_INV_IMPL_FD);
y * x + r * x
} else {
let y = 0.99941349029541015625;
let xs = x - 44.0;
let r = evaluate::polynomial(xs, ERF_INV_IMPL_GN)
/ evaluate::polynomial(xs, ERF_INV_IMPL_GD);
y * x + r * x
}
};
s * result
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/kernels.rs
|
pub trait VecOps: num_traits::NumAssign + Copy {
fn min(self, rhs: Self) -> Self;
fn max(self, rhs: Self) -> Self;
/// Dot-product of two vectors.
///
/// # Safety
///
/// The length of `lhs` and `rhs` have to be at least `len`. `res` has to point to a valid
/// element.
#[inline(always)]
unsafe fn vec_dot(lhs: *const Self, rhs: *const Self, res: *mut Self, len: usize) {
*res = Self::zero();
for i in 0..len {
*res += *lhs.add(i) * *rhs.add(i)
}
}
/// Sum of all elements in a vector.
///
/// # Safety
///
/// The length of `xs` must be at least `len`. `res` has to point to a valid
/// element.
#[inline(always)]
unsafe fn vec_reduce_sum(xs: *const Self, res: *mut Self, len: usize) {
*res = Self::zero();
for i in 0..len {
*res += *xs.add(i)
}
}
/// Maximum element in a non-empty vector.
///
/// # Safety
///
/// The length of `xs` must be at least `len` and positive. `res` has to point to a valid
/// element.
#[inline(always)]
unsafe fn vec_reduce_max(xs: *const Self, res: *mut Self, len: usize) {
*res = *xs;
for i in 1..len {
*res = (*res).max(*xs.add(i))
}
}
/// Minimum element in a non-empty vector.
///
/// # Safety
///
/// The length of `xs` must be at least `len` and positive. `res` has to point to a valid
/// element.
#[inline(always)]
unsafe fn vec_reduce_min(xs: *const Self, res: *mut Self, len: usize) {
*res = *xs;
for i in 1..len {
*res = (*res).min(*xs.add(i))
}
}
}
impl VecOps for f32 {
#[inline(always)]
fn min(self, other: Self) -> Self {
Self::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
Self::max(self, other)
}
#[inline(always)]
unsafe fn vec_dot(lhs: *const Self, rhs: *const Self, res: *mut Self, len: usize) {
super::vec_dot_f32(lhs, rhs, res, len)
}
#[inline(always)]
unsafe fn vec_reduce_sum(xs: *const Self, res: *mut Self, len: usize) {
super::vec_sum(xs, res, len)
}
}
impl VecOps for half::f16 {
#[inline(always)]
fn min(self, other: Self) -> Self {
Self::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
Self::max(self, other)
}
#[inline(always)]
unsafe fn vec_dot(lhs: *const Self, rhs: *const Self, res: *mut Self, len: usize) {
let mut res_f32 = 0f32;
super::vec_dot_f16(lhs, rhs, &mut res_f32, len);
*res = half::f16::from_f32(res_f32);
}
}
impl VecOps for f64 {
#[inline(always)]
fn min(self, other: Self) -> Self {
Self::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
Self::max(self, other)
}
}
impl VecOps for half::bf16 {
#[inline(always)]
fn min(self, other: Self) -> Self {
Self::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
Self::max(self, other)
}
}
impl VecOps for u8 {
#[inline(always)]
fn min(self, other: Self) -> Self {
<Self as Ord>::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
<Self as Ord>::max(self, other)
}
}
impl VecOps for u32 {
#[inline(always)]
fn min(self, other: Self) -> Self {
<Self as Ord>::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
<Self as Ord>::max(self, other)
}
}
impl VecOps for i64 {
#[inline(always)]
fn min(self, other: Self) -> Self {
<Self as Ord>::min(self, other)
}
#[inline(always)]
fn max(self, other: Self) -> Self {
<Self as Ord>::max(self, other)
}
}
#[inline(always)]
pub fn par_for_each(n_threads: usize, func: impl Fn(usize) + Send + Sync) {
if n_threads == 1 {
func(0)
} else {
rayon::scope(|s| {
for thread_idx in 0..n_threads {
let func = &func;
s.spawn(move |_| func(thread_idx));
}
})
}
}
#[inline(always)]
pub fn par_range(lo: usize, up: usize, n_threads: usize, func: impl Fn(usize) + Send + Sync) {
if n_threads == 1 {
for i in lo..up {
func(i)
}
} else {
rayon::scope(|s| {
for thread_idx in 0..n_threads {
let func = &func;
s.spawn(move |_| {
for i in (thread_idx..up).step_by(n_threads) {
func(i)
}
});
}
})
}
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/mod.rs
|
pub mod erf;
pub mod kernels;
trait Cpu<const ARR: usize> {
type Unit;
type Array;
const STEP: usize;
const EPR: usize;
fn n() -> usize;
unsafe fn zero() -> Self::Unit;
unsafe fn zero_array() -> Self::Array;
unsafe fn load(mem_addr: *const f32) -> Self::Unit;
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit;
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit;
unsafe fn vec_reduce(x: Self::Array, y: *mut f32);
unsafe fn from_f32(v: f32) -> Self::Unit;
unsafe fn vec_store(mem_addr: *mut f32, a: Self::Unit);
}
trait CpuF16<const ARR: usize> {
type Unit;
type Array;
const STEP: usize;
const EPR: usize;
fn n() -> usize;
unsafe fn zero() -> Self::Unit;
unsafe fn zero_array() -> Self::Array;
unsafe fn load(mem_addr: *const f16) -> Self::Unit;
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit;
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit;
unsafe fn vec_reduce(x: Self::Array, y: *mut f32);
unsafe fn from_f32(v: f32) -> Self::Unit;
unsafe fn vec_store(mem_addr: *mut f16, a: Self::Unit);
}
use half::f16;
#[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
#[cfg(target_feature = "avx")]
pub mod avx;
#[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
#[cfg(target_feature = "avx")]
pub use avx::{CurrentCpu, CurrentCpuF16};
#[cfg(target_arch = "wasm32")]
#[cfg(target_feature = "simd128")]
pub mod simd128;
#[cfg(target_arch = "wasm32")]
#[cfg(target_feature = "simd128")]
pub use simd128::CurrentCpu;
#[cfg(any(target_arch = "arm", target_arch = "aarch64"))]
#[cfg(target_feature = "neon")]
pub mod neon;
#[cfg(any(target_arch = "arm", target_arch = "aarch64"))]
#[cfg(target_feature = "neon")]
pub use neon::CurrentCpu;
#[cfg(any(
target_feature = "neon",
target_feature = "avx",
target_feature = "simd128"
))]
#[inline(always)]
pub(crate) unsafe fn vec_dot_f32(a_row: *const f32, b_row: *const f32, c: *mut f32, k: usize) {
let np = k & !(CurrentCpu::STEP - 1);
let mut sum = CurrentCpu::zero_array();
let mut ax = CurrentCpu::zero_array();
let mut ay = CurrentCpu::zero_array();
for i in (0..np).step_by(CurrentCpu::STEP) {
for j in 0..CurrentCpu::n() {
ax[j] = CurrentCpu::load(a_row.add(i + j * CurrentCpu::EPR));
ay[j] = CurrentCpu::load(b_row.add(i + j * CurrentCpu::EPR));
sum[j] = CurrentCpu::vec_fma(sum[j], ax[j], ay[j]);
}
}
CurrentCpu::vec_reduce(sum, c);
// leftovers
for i in np..k {
*c += *a_row.add(i) * (*b_row.add(i));
}
}
#[cfg(not(any(
target_feature = "neon",
target_feature = "avx",
target_feature = "simd128"
)))]
#[inline(always)]
pub(crate) unsafe fn vec_dot_f32(a_row: *const f32, b_row: *const f32, c: *mut f32, k: usize) {
// leftovers
for i in 0..k {
*c += *a_row.add(i) * (*b_row.add(i));
}
}
#[cfg(any(
target_feature = "neon",
target_feature = "avx",
target_feature = "simd128"
))]
#[inline(always)]
pub(crate) unsafe fn vec_sum(row: *const f32, b: *mut f32, k: usize) {
let np = k & !(CurrentCpu::STEP - 1);
let mut sum = CurrentCpu::zero_array();
let mut x = CurrentCpu::zero_array();
for i in (0..np).step_by(CurrentCpu::STEP) {
for j in 0..CurrentCpu::n() {
x[j] = CurrentCpu::load(row.add(i + j * CurrentCpu::EPR));
sum[j] = CurrentCpu::vec_add(sum[j], x[j]);
}
}
CurrentCpu::vec_reduce(sum, b);
// leftovers
for i in np..k {
*b += *row.add(i)
}
}
#[cfg(not(any(
target_feature = "neon",
target_feature = "avx",
target_feature = "simd128"
)))]
#[inline(always)]
pub(crate) unsafe fn vec_sum(row: *const f32, b: *mut f32, k: usize) {
*b = 0f32;
for i in 0..k {
*b += *row.add(i)
}
}
#[cfg(target_feature = "avx")]
#[inline(always)]
pub(crate) unsafe fn vec_dot_f16(a_row: *const f16, b_row: *const f16, c: *mut f32, k: usize) {
let mut sumf = 0.0f32;
let np = k & !(CurrentCpuF16::STEP - 1);
let mut sum = CurrentCpuF16::zero_array();
let mut ax = CurrentCpuF16::zero_array();
let mut ay = CurrentCpuF16::zero_array();
for i in (0..np).step_by(CurrentCpuF16::STEP) {
for j in 0..CurrentCpuF16::n() {
ax[j] = CurrentCpuF16::load(a_row.add(i + j * CurrentCpuF16::EPR));
ay[j] = CurrentCpuF16::load(b_row.add(i + j * CurrentCpuF16::EPR));
sum[j] = CurrentCpuF16::vec_fma(sum[j], ax[j], ay[j]);
}
}
CurrentCpuF16::vec_reduce(sum, &mut sumf);
// leftovers
for i in np..k {
sumf += (*a_row.add(i)).to_f32() * (*b_row.add(i)).to_f32();
}
*c = sumf;
}
#[cfg(not(target_feature = "avx"))]
#[inline(always)]
pub(crate) unsafe fn vec_dot_f16(a_row: *const f16, b_row: *const f16, c: *mut f32, k: usize) {
// leftovers
let mut sum = 0.0;
for i in 0..k {
sum += (*a_row.add(i)).to_f32() * (*b_row.add(i)).to_f32();
}
*c = sum;
}
| 0
|
hf_public_repos/candle/candle-core/src
|
hf_public_repos/candle/candle-core/src/cpu/simd128.rs
|
use super::Cpu;
use core::arch::wasm32::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl Cpu<ARR> for CurrentCpu {
type Unit = v128;
type Array = [v128; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
ARR
}
unsafe fn zero() -> Self::Unit {
f32x4_splat(0.0)
}
unsafe fn zero_array() -> Self::Array {
[Self::zero(); ARR]
}
unsafe fn from_f32(v: f32) -> Self::Unit {
f32x4_splat(v)
}
unsafe fn load(mem_addr: *const f32) -> Self::Unit {
v128_load(mem_addr as *mut v128)
}
unsafe fn vec_add(a: Self::Unit, b: Self::Unit) -> Self::Unit {
f32x4_add(a, b)
}
unsafe fn vec_fma(a: Self::Unit, b: Self::Unit, c: Self::Unit) -> Self::Unit {
f32x4_add(f32x4_mul(b, c), a)
}
unsafe fn vec_store(mem_addr: *mut f32, a: Self::Unit) {
v128_store(mem_addr as *mut v128, a);
}
unsafe fn vec_reduce(mut x: Self::Array, y: *mut f32) {
for i in 0..ARR / 2 {
x[2 * i] = f32x4_add(x[2 * i], x[2 * i + 1]);
}
for i in 0..ARR / 4 {
x[4 * i] = f32x4_add(x[4 * i], x[4 * i + 2]);
}
for i in 0..ARR / 8 {
x[8 * i] = f32x4_add(x[8 * i], x[8 * i + 4]);
}
*y = f32x4_extract_lane::<0>(x[0])
+ f32x4_extract_lane::<1>(x[0])
+ f32x4_extract_lane::<2>(x[0])
+ f32x4_extract_lane::<3>(x[0]);
}
}
| 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 = grads.get(x).context("no grad for x")?;
assert_eq!(x.to_vec1::<f32>()?, [3., 1., 4.]);
// y = x^2 + 5.x + 4
assert_eq!(y.to_vec1::<f32>()?, [28., 10., 40.]);
// dy/dx = 2.x + 5
assert_eq!(grad_x.to_vec1::<f32>()?, [11., 7., 13.]);
Ok(())
}
fn sum_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4.], device)?;
let x = x.as_tensor();
let y = (x.sqr()?.sum_keepdim(0)? * 2.)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [52.]);
// y = 2.x^2 so dy/dx = 4.x
assert_eq!(grad_x.to_vec1::<f32>()?, &[12., 4., 16.]);
// Same test as before but squeezing on the last dimension.
let y = (x.sqr()?.sum_keepdim(0)? * 2.)?.squeeze(0)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_scalar::<f32>()?, 52.);
// y = 2.x^2 so dy/dx = 4.x
assert_eq!(grad_x.to_vec1::<f32>()?, &[12., 4., 16.]);
Ok(())
}
fn matmul_grad(device: &Device) -> Result<()> {
let data: Vec<_> = (0..12).map(|i| i as f32).collect();
let x = Var::from_slice(&data, (2, 2, 3), device)?;
let data: Vec<_> = (0..12).map(|i| i as f32).collect();
let y = Var::from_slice(&data, (2, 3, 2), device)?;
let c = x.matmul(&y)?;
let grads = c.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
let grad_y = grads.get(&y).context("no grad for y")?;
assert_eq!(grad_x.shape(), &Shape::from((2, 2, 3)));
assert_eq!(grad_y.shape(), &Shape::from((2, 3, 2)));
assert_eq!(
&*grad_x.to_vec3::<f32>()?,
&[
[[1., 5., 9.], [1., 5., 9.]],
[[13., 17., 21.], [13., 17., 21.]]
]
);
assert_eq!(
&*grad_y.to_vec3::<f32>()?,
&[
[[3., 3.], [5., 5.], [7., 7.]],
[[15., 15.], [17., 17.], [19., 19.]]
]
);
Ok(())
}
// The simplest gradient descent, using scalar variable.
fn grad_descent(device: &Device) -> Result<()> {
let x = Var::new(0f32, device)?;
let learning_rate = 0.1;
for _step in 0..100 {
let xt = x.as_tensor();
let c = ((xt - 4.2)? * (xt - 4.2)?)?;
let grads = c.backward()?;
let x_grad = grads.get(&x).context("no grad for x")?;
x.set(&(xt - x_grad * learning_rate)?)?
}
assert_eq!(x.to_scalar::<f32>()?, 4.199999);
Ok(())
}
fn unary_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4., 0.15], device)?;
let x = x.as_tensor();
let y = (x.log()? + 1.)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[2.0986, 1.0, 2.3863, -0.8971]
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[0.3333, 1.0, 0.25, 6.6667]
);
let y = x.exp()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
y.to_vec1::<f32>()?,
[20.085537, 2.7182817, 54.59815, 1.1618342]
);
assert_eq!(
grad_x.to_vec1::<f32>()?,
[20.085537, 2.7182817, 54.59815, 1.1618342]
);
let y = x.exp()?.sqr()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
y.to_vec1::<f32>()?,
[403.4288, 7.3890557, 2980.9578, 1.3498588]
);
// exp(x)^2 = exp(2*x)
assert_eq!(
grad_x.to_vec1::<f32>()?,
[806.8576, 14.778111, 5961.9155, 2.6997175]
);
let y = x.sin()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[0.1411, 0.8415, -0.7568, 0.1494],
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[-0.99, 0.5403, -0.6536, 0.9888],
);
let y = x.cos()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[-0.99, 0.5403, -0.6536, 0.9888],
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[-0.1411, -0.8415, 0.7568, -0.1494],
);
let y = x.sqr()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [9.0, 1.0, 16.0, 0.0225]);
assert_eq!(grad_x.to_vec1::<f32>()?, [6.0, 2.0, 8.0, 0.3]);
let y = x.sqr()?.sqrt()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [3.0, 1.0, 4.0, 0.15]);
assert_eq!(test_utils::to_vec1_round(grad_x, 4)?, [1.0, 1.0, 1.0, 1.0]);
let y = x.neg()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [-3.0, -1.0, -4.0, -0.15]);
assert_eq!(grad_x.to_vec1::<f32>()?, [-1.0, -1.0, -1.0, -1.0]);
let y = x.affine(0.2, 1.)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [1.6, 1.2, 1.8, 1.03]);
assert_eq!(grad_x.to_vec1::<f32>()?, [0.2, 0.2, 0.2, 0.2]);
let y = Tensor::new(1f32, device)?.broadcast_div(x)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[0.3333, 1.0, 0.25, 6.6667]
);
assert_eq!(
grad_x.to_vec1::<f32>()?,
[-0.11111111, -1.0, -0.0625, -44.444443],
);
let y = x.broadcast_div(&Tensor::new(0.5f32, device)?)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [6., 2., 8., 0.3]);
assert_eq!(grad_x.to_vec1::<f32>()?, [2., 2., 2., 2.]);
let x = Var::new(&[3f32, 1., 4., 0.15], device)?;
let y = x.powf(2.5)?;
let grads = y.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
assert_eq!(test_utils::to_vec1_round(&y, 2)?, [15.59, 1.0, 32.0, 0.01]);
assert_eq!(
test_utils::to_vec1_round(grad_x, 2)?,
[12.99, 2.5, 20.0, 0.15]
);
let y = x.tanh()?;
let grads = y.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
assert_eq!(test_utils::to_vec1_round(&y, 2)?, [1.0, 0.76, 1.0, 0.15]);
assert_eq!(
test_utils::to_vec1_round(grad_x, 2)?,
[0.01, 0.42, 0.0, 0.98],
);
// testing compared to pytorch nn.GELU(approximate = 'tanh')
let y = x.gelu()?;
let grads = y.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[2.9964, 0.8412, 3.9999, 0.0839]
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[1.0116, 1.0830, 1.0003, 0.6188],
);
// Testing compared to pytorch torch.erf
//
// import torch
// x = torch.tensor([3.0, 1.0, 4.0, 0.15], requires_grad=True)
// y = x.erf()
// print(y)
// loss = y.sum()
// loss.backward()
// print(x.grad)
let y = x.erf()?;
let grads = y.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
assert_eq!(test_utils::to_vec1_round(&y, 4)?, [1.0, 0.8427, 1.0, 0.168]);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[0.0001, 0.4151, 0.0, 1.1033],
);
// Testing compared to pytorch nn.GELU(approximate = 'none')
//
// import torch
// import torch.nn.functional as F
// x = torch.tensor([3.0, 1.0, 4.0, 0.15], requires_grad=True)
// y = F.gelu(x, approximate='none')
// print(y)
// loss = y.sum()
// loss.backward()
// print(x.grad)
let y = x.gelu_erf()?;
let grads = y.backward()?;
let grad_x = grads.get(&x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[2.9960, 0.8413, 3.9999, 0.0839]
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[1.0119, 1.0833, 1.0005, 0.6188],
);
// Testing compared to pytorch elu
//
// import torch
// import torch.nn.functional as F
// x = torch.tensor([-1.0, 0.0, -2.0, 3.0], requires_grad=True)
// y = F.elu(x, alpha=2.0)
// print(y)
// loss = y.min
// loss = y.sum()
// loss.backward()
// print(x.grad)
let elu_x = Var::new(&[-1.0f32, 0., -2., 3.], device)?;
let y = elu_x.elu(2.)?;
let grads = y.backward()?;
let grad_x = grads.get(&elu_x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[-1.2642, 0.0000, -1.7293, 3.0000]
);
assert_eq!(
test_utils::to_vec1_round(grad_x, 4)?,
[0.7358, 2.0000, 0.2707, 1.0000]
);
Ok(())
}
fn binary_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., -4., -1.], device)?;
let x = x.as_tensor();
// leaky relu
let y = x.maximum(&(x * 0.1)?)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(x.to_vec1::<f32>()?, [3., 1., -4., -1.]);
assert_eq!(y.to_vec1::<f32>()?, [3., 1., -0.4, -0.1]);
assert_eq!(grad_x.to_vec1::<f32>()?, [1., 1., 0.1, 0.1]);
let y = x.minimum(&(x * 0.1)?)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [0.3, 0.1, -4., -1.]);
assert_eq!(grad_x.to_vec1::<f32>()?, [0.1, 0.1, 1., 1.]);
// This one is easy to mess up, we want the gradient to be one as it is the identity function.
let y = x.minimum(x)?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(y.to_vec1::<f32>()?, [3., 1., -4., -1.]);
assert_eq!(grad_x.to_vec1::<f32>()?, [1., 1., 1., 1.]);
let x_var = Var::new(&[3f32, 1., -4., -1., 5., 9.], device)?;
let x = x_var.as_tensor();
let y_var = Var::new(&[2f32, 7., 1.], device)?;
let y = y_var.as_tensor();
let ss = x
.reshape((2, 3))?
.slice_scatter0(&y.reshape((1, 3))?, 1)?
.sqr()?;
let grads = ss.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
let grad_y = grads.get(y).context("no grad for y")?;
assert_eq!(ss.to_vec2::<f32>()?, [[9., 1., 16.], [4., 49., 1.]]);
assert_eq!(grad_x.to_vec1::<f32>()?, [6.0, 2.0, -8.0, 0.0, 0.0, 0.0]);
assert_eq!(grad_y.to_vec1::<f32>()?, [4.0, 14.0, 2.0]);
Ok(())
}
test_device!(
simple_grad,
simple_grad_cpu,
simple_grad_gpu,
simple_grad_metal
);
test_device!(sum_grad, sum_grad_cpu, sum_grad_gpu, sum_grad_metal);
test_device!(
matmul_grad,
matmul_grad_cpu,
matmul_grad_gpu,
matmul_grad_metal
);
test_device!(
grad_descent,
grad_descent_cpu,
grad_descent_gpu,
grad_descent_metal
);
test_device!(unary_grad, unary_grad_cpu, unary_grad_gpu, unary_grad_metal);
test_device!(
binary_grad,
binary_grad_cpu,
binary_grad_gpu,
binary_grad_metal
);
| 0
|
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