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2695 lines (2355 loc) · 108 KB
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/**
* @brief Tensor implementation for NumKong Python bindings.
* @file python/tensor.c
*
* This file implements the Tensor N-dimensional array type with NumPy-like
* interface and the TensorIter iterator.
*
* Features:
* - Support for all NumKong dtypes (f32, f64, f16, bf16, i8, complex, etc.)
* - Arbitrary strides for views and slices
* - Zero-copy views with reference counting
* - Python buffer protocol for interoperability
* - Arithmetic operators (+, -, *, @)
* - Reduction operations (sum, min, max, argmin, argmax)
*/
#include "tensor.h"
#include "matrix.h"
#include <float.h>
#include <math.h>
#include <stdint.h>
int buffers_shapes_match(Py_buffer const *first, Py_buffer const *second) {
if (first->ndim != second->ndim) {
PyErr_SetString(PyExc_ValueError, "Input tensor ranks don't match");
return 0;
}
for (int dimension = 0; dimension < first->ndim; ++dimension) {
if (first->shape[dimension] != second->shape[dimension]) {
PyErr_Format(
PyExc_ValueError,
"Input tensor shapes don't match at dimension %d (%zd vs %zd). " "NumKong does not support " "implicit " "shape " "broadcasting.",
dimension, first->shape[dimension], second->shape[dimension]);
return 0;
}
}
return 1;
}
size_t shared_contiguous_tail_dimensions(Py_buffer const *buffers[], size_t num_buffers, size_t num_dims) {
size_t num_contiguous_dims = 0;
for (size_t dimension = num_dims; dimension-- > 0;) {
// Compute the expected stride for this dimension if it were packed
int all_packed = 1;
for (size_t buffer_idx = 0; buffer_idx < num_buffers; ++buffer_idx) {
Py_ssize_t expected_stride = buffers[buffer_idx]->itemsize;
for (size_t inner_dim = num_dims - 1; inner_dim > dimension; --inner_dim)
expected_stride *= buffers[buffer_idx]->shape[inner_dim];
if (buffers[buffer_idx]->strides[dimension] != expected_stride) {
all_packed = 0;
break;
}
}
if (!all_packed) break;
++num_contiguous_dims;
}
return num_contiguous_dims;
}
void each_sum_recursive( //
nk_each_sum_punned_t kernel, //
char const *a_data, char const *b_data, char *result_data, //
Py_ssize_t const *shape, Py_ssize_t const *a_strides, //
Py_ssize_t const *b_strides, Py_ssize_t const *result_strides, //
size_t remaining_dims, size_t contiguous_tail_dims) {
// Base case: all remaining dimensions are contiguous — one kernel call
if (remaining_dims <= contiguous_tail_dims) {
size_t contiguous_elements = 1;
for (size_t dimension = 0; dimension < remaining_dims; ++dimension)
contiguous_elements *= (size_t)shape[dimension];
kernel(a_data, b_data, contiguous_elements, result_data);
return;
}
// Iterate over the outermost non-contiguous dimension, then recurse
size_t const dim_extent = (size_t)shape[0];
for (size_t position = 0; position < dim_extent; ++position) {
Py_ssize_t const signed_position = (Py_ssize_t)position;
each_sum_recursive(kernel, //
a_data + signed_position * a_strides[0], //
b_data + signed_position * b_strides[0], //
result_data + signed_position * result_strides[0], //
shape + 1, a_strides + 1, //
b_strides + 1, result_strides + 1, //
remaining_dims - 1, contiguous_tail_dims);
}
}
void each_scale_recursive( //
nk_each_scale_punned_t kernel, //
char const *a_data, char *result_data, //
nk_scalar_buffer_t const *alpha, nk_scalar_buffer_t const *beta, //
Py_ssize_t const *shape, Py_ssize_t const *a_strides, //
Py_ssize_t const *result_strides, //
size_t remaining_dims, size_t contiguous_tail_dims) {
if (remaining_dims <= contiguous_tail_dims) {
size_t contiguous_elements = 1;
for (size_t dimension = 0; dimension < remaining_dims; ++dimension)
contiguous_elements *= (size_t)shape[dimension];
kernel(a_data, contiguous_elements, alpha, beta, result_data);
return;
}
size_t const dim_extent = (size_t)shape[0];
for (size_t position = 0; position < dim_extent; ++position) {
Py_ssize_t const signed_position = (Py_ssize_t)position;
each_scale_recursive(kernel, //
a_data + signed_position * a_strides[0], //
result_data + signed_position * result_strides[0], //
alpha, beta, //
shape + 1, a_strides + 1, //
result_strides + 1, //
remaining_dims - 1, contiguous_tail_dims);
}
}
void each_fma_recursive( //
nk_each_fma_punned_t kernel, //
char const *a_data, char const *b_data, char const *c_data, char *result_data, //
nk_scalar_buffer_t const *alpha, nk_scalar_buffer_t const *beta, //
Py_ssize_t const *shape, Py_ssize_t const *a_strides, //
Py_ssize_t const *b_strides, Py_ssize_t const *c_strides, //
Py_ssize_t const *result_strides, //
size_t remaining_dims, size_t contiguous_tail_dims) {
if (remaining_dims <= contiguous_tail_dims) {
size_t contiguous_elements = 1;
for (size_t dimension = 0; dimension < remaining_dims; ++dimension)
contiguous_elements *= (size_t)shape[dimension];
kernel(a_data, b_data, c_data, contiguous_elements, alpha, beta, result_data);
return;
}
size_t const dim_extent = (size_t)shape[0];
for (size_t position = 0; position < dim_extent; ++position) {
Py_ssize_t const signed_position = (Py_ssize_t)position;
each_fma_recursive(kernel, //
a_data + signed_position * a_strides[0], //
b_data + signed_position * b_strides[0], //
c_data + signed_position * c_strides[0], //
result_data + signed_position * result_strides[0], //
alpha, beta, //
shape + 1, a_strides + 1, //
b_strides + 1, c_strides + 1, //
result_strides + 1, //
remaining_dims - 1, contiguous_tail_dims);
}
}
void each_blend_recursive( //
nk_each_blend_punned_t kernel, //
char const *a_data, char const *b_data, char *result_data, //
nk_scalar_buffer_t const *alpha, nk_scalar_buffer_t const *beta, //
Py_ssize_t const *shape, Py_ssize_t const *a_strides, //
Py_ssize_t const *b_strides, Py_ssize_t const *result_strides, //
size_t remaining_dims, size_t contiguous_tail_dims) {
if (remaining_dims <= contiguous_tail_dims) {
size_t contiguous_elements = 1;
for (size_t dimension = 0; dimension < remaining_dims; ++dimension)
contiguous_elements *= (size_t)shape[dimension];
kernel(a_data, b_data, contiguous_elements, alpha, beta, result_data);
return;
}
size_t const dim_extent = (size_t)shape[0];
for (size_t position = 0; position < dim_extent; ++position) {
Py_ssize_t const signed_position = (Py_ssize_t)position;
each_blend_recursive(kernel, //
a_data + signed_position * a_strides[0], //
b_data + signed_position * b_strides[0], //
result_data + signed_position * result_strides[0], //
alpha, beta, //
shape + 1, a_strides + 1, //
b_strides + 1, result_strides + 1, //
remaining_dims - 1, contiguous_tail_dims);
}
}
static int tensor_is_c_contig(Tensor *tensor, size_t item_size);
static int tensor_is_f_contig(Tensor *tensor, size_t item_size);
/** @brief Return a Python scalar from a tensor byte offset using scalar_to_py_number. */
static PyObject *tensor_read_scalar(Tensor *tensor, size_t byte_offset) {
size_t elem_size = bytes_per_dtype(tensor->dtype);
if (!elem_size) {
PyErr_SetString(PyExc_TypeError, "unsupported dtype for indexing");
return NULL;
}
nk_scalar_buffer_t buf;
memset(&buf, 0, sizeof(buf));
memcpy(&buf, tensor->data + byte_offset, elem_size);
return scalar_to_py_number(&buf, tensor->dtype);
}
/** @brief Check if a tensor is C-contiguous (row-major). */
static int tensor_is_c_contig(Tensor *tensor, size_t item_size) {
if (tensor->rank == 0) return 1;
Py_ssize_t expected = (Py_ssize_t)item_size;
for (size_t i = tensor->rank; i > 0; i--) {
if (tensor->strides[i - 1] != expected) return 0;
expected *= tensor->shape[i - 1];
}
return 1;
}
/** @brief Check if a tensor is Fortran-contiguous (column-major). */
static int tensor_is_f_contig(Tensor *tensor, size_t item_size) {
if (tensor->rank == 0) return 1;
Py_ssize_t expected = (Py_ssize_t)item_size;
for (size_t i = 0; i < tensor->rank; i++) {
if (tensor->strides[i] != expected) return 0;
expected *= tensor->shape[i];
}
return 1;
}
static void Tensor_dealloc(PyObject *self) {
Tensor *tensor = (Tensor *)self;
Py_XDECREF(tensor->parent);
Py_TYPE(self)->tp_free(self);
}
Tensor *Tensor_new(nk_dtype_t dtype, size_t rank, Py_ssize_t const *shape) {
if (rank > NK_TENSOR_MAX_RANK) {
PyErr_Format(PyExc_ValueError, "Tensor rank %zu exceeds maximum %d", rank, NK_TENSOR_MAX_RANK);
return NULL;
}
size_t const item_size = bytes_per_dtype(dtype);
size_t total_items = 1;
for (size_t i = 0; i < rank; i++) {
if (shape[i] > 0 && total_items > SIZE_MAX / (size_t)shape[i]) {
PyErr_SetString(PyExc_OverflowError, "Tensor shape too large");
return NULL;
}
total_items *= (size_t)shape[i];
}
if (item_size > 0 && total_items > SIZE_MAX / item_size) {
PyErr_SetString(PyExc_OverflowError, "Tensor allocation too large");
return NULL;
}
size_t const total_bytes = total_items * item_size;
Tensor *tensor = PyObject_NewVar(Tensor, &TensorType, total_bytes + NK_TENSOR_PADDING_);
if (!tensor) {
PyErr_NoMemory();
return NULL;
}
tensor->dtype = dtype;
tensor->rank = rank;
for (size_t i = 0; i < NK_TENSOR_MAX_RANK; i++) {
tensor->shape[i] = (i < rank) ? shape[i] : 0;
tensor->strides[i] = 0;
}
if (rank > 0) {
tensor->strides[rank - 1] = (Py_ssize_t)item_size;
for (size_t i = rank - 1; i > 0; i--) tensor->strides[i - 1] = tensor->strides[i] * tensor->shape[i];
}
tensor->parent = NULL;
tensor->data = tensor->start;
return tensor;
}
Tensor *Tensor_view(Tensor *parent, char *data_ptr, nk_dtype_t dtype, size_t rank, Py_ssize_t const *shape,
Py_ssize_t const *strides) {
if (rank > NK_TENSOR_MAX_RANK) {
PyErr_Format(PyExc_ValueError, "View rank %zu exceeds maximum %d", rank, NK_TENSOR_MAX_RANK);
return NULL;
}
Tensor *view = PyObject_NewVar(Tensor, &TensorType, 0);
if (!view) {
PyErr_NoMemory();
return NULL;
}
view->dtype = dtype;
view->rank = rank;
for (size_t i = 0; i < NK_TENSOR_MAX_RANK; i++) {
view->shape[i] = (i < rank) ? shape[i] : 0;
view->strides[i] = (i < rank) ? strides[i] : 0;
}
view->parent = (PyObject *)parent;
Py_INCREF(parent);
view->data = data_ptr;
return view;
}
/** @brief Create a 0D scalar tensor. */
static Tensor *Tensor_scalar(nk_dtype_t dtype, void const *value) {
size_t const item_size = bytes_per_dtype(dtype);
Tensor *tensor = PyObject_NewVar(Tensor, &TensorType, item_size);
if (!tensor) {
PyErr_NoMemory();
return NULL;
}
tensor->dtype = dtype;
tensor->rank = 0;
for (size_t i = 0; i < NK_TENSOR_MAX_RANK; i++) {
tensor->shape[i] = 0;
tensor->strides[i] = 0;
}
tensor->parent = NULL;
tensor->data = tensor->start;
memcpy(tensor->data, value, item_size);
return tensor;
}
/** @brief Convert a 0D Tensor to a Python float. */
static PyObject *Tensor_float(PyObject *self) {
Tensor *tensor = (Tensor *)self;
if (tensor->rank != 0) {
PyErr_SetString(PyExc_TypeError, "only 0-dimensional tensors can be converted to float");
return NULL;
}
PyObject *scalar = tensor_read_scalar(tensor, 0);
if (!scalar) return NULL;
PyObject *result = PyNumber_Float(scalar);
Py_DECREF(scalar);
return result;
}
/** @brief Convert a 0D Tensor to a Python int. */
static PyObject *Tensor_int(PyObject *self) {
Tensor *tensor = (Tensor *)self;
if (tensor->rank != 0) {
PyErr_SetString(PyExc_TypeError, "only 0-dimensional tensors can be converted to int");
return NULL;
}
PyObject *scalar = tensor_read_scalar(tensor, 0);
if (!scalar) return NULL;
PyObject *result = PyNumber_Long(scalar);
Py_DECREF(scalar);
return result;
}
static PyObject *Tensor_positive(PyObject *self) { return Tensor_copy(self, NULL); }
/** @brief Compute C-contiguous strides for a tensor shape. */
void compute_contiguous_strides(size_t rank, Py_ssize_t const *shape, size_t item_size, Py_ssize_t *strides_out) {
if (rank == 0) return;
strides_out[rank - 1] = (Py_ssize_t)item_size;
for (size_t d = rank - 1; d > 0; --d) strides_out[d - 1] = strides_out[d] * shape[d];
}
/**
* @brief Recursive stride walker for linearize_cast_into.
*
* Walks non-contiguous outer dimensions recursively. Once only contiguous tail
* dimensions remain, processes the entire contiguous slice with memcpy or nk_cast.
*/
static void linearize_cast_recursive( //
char const *src_data, nk_dtype_t src_dtype, char *dest_data, //
nk_dtype_t dest_dtype, size_t src_element_size, size_t dest_element_size, //
Py_ssize_t const *shape, Py_ssize_t const *strides, //
size_t remaining_dims, size_t contiguous_tail_dims) {
// Base case: all remaining dimensions are contiguous — one operation
if (remaining_dims <= contiguous_tail_dims) {
size_t slice_elements = 1;
for (size_t dim = 0; dim < remaining_dims; ++dim) slice_elements *= (size_t)shape[dim];
if (src_dtype == dest_dtype) memcpy(dest_data, src_data, slice_elements * src_element_size);
else nk_cast(src_data, src_dtype, (nk_size_t)slice_elements, dest_data, dest_dtype);
return;
}
// Recursive case: iterate outermost non-contiguous dimension
size_t const dim_extent = (size_t)shape[0];
// Compute the contiguous dest stride for this level
size_t inner_elements = 1;
for (size_t dim = 1; dim < remaining_dims; ++dim) inner_elements *= (size_t)shape[dim];
size_t const dest_row_bytes = inner_elements * dest_element_size;
for (size_t position = 0; position < dim_extent; ++position) {
linearize_cast_recursive( //
src_data + (Py_ssize_t)position * strides[0], src_dtype, //
dest_data + (Py_ssize_t)position * (Py_ssize_t)dest_row_bytes, //
dest_dtype, src_element_size, dest_element_size, //
shape + 1, strides + 1, //
remaining_dims - 1, contiguous_tail_dims);
}
}
void linearize_cast_into(char const *src_data, nk_dtype_t src_dtype, char *dest_data, nk_dtype_t dest_dtype,
size_t rank, Py_ssize_t const *shape, Py_ssize_t const *strides, size_t total_elements) {
nk_unused_(total_elements);
size_t src_element_size = bytes_per_dtype(src_dtype);
size_t dest_element_size = bytes_per_dtype(dest_dtype);
// Count how many trailing dims are contiguous in src
size_t contiguous_tail_dims = 0;
Py_ssize_t expected_stride = (Py_ssize_t)src_element_size;
for (size_t dim = rank; dim-- > 0;) {
if (strides[dim] != expected_stride) break;
expected_stride *= shape[dim];
contiguous_tail_dims++;
}
linearize_cast_recursive(src_data, src_dtype, dest_data, dest_dtype, src_element_size, dest_element_size, shape,
strides, rank, contiguous_tail_dims);
}
char *ensure_contiguous_buffer(char const *src_data, nk_dtype_t src_dtype, nk_dtype_t target_dtype, size_t rank,
Py_ssize_t const *shape, Py_ssize_t const *strides, size_t total_elements,
int *needs_free) {
size_t src_element_size = bytes_per_dtype(src_dtype);
size_t dest_element_size = bytes_per_dtype(target_dtype);
// Check full contiguity
int is_contiguous = 1;
Py_ssize_t expected_stride = (Py_ssize_t)src_element_size;
for (size_t dim = rank; dim-- > 0;) {
if (strides[dim] != expected_stride) {
is_contiguous = 0;
break;
}
expected_stride *= shape[dim];
}
// Zero-copy: contiguous + same dtype
if (is_contiguous && src_dtype == target_dtype) {
*needs_free = 0;
return (char *)src_data;
}
// Single allocation, delegate
char *output = PyMem_Malloc(total_elements * dest_element_size + NK_TENSOR_PADDING_);
if (!output) {
PyErr_NoMemory();
return NULL;
}
linearize_cast_into(src_data, src_dtype, output, target_dtype, rank, shape, strides, total_elements);
*needs_free = 1;
return output;
}
/** @brief Shared helper for tensor-scalar elementwise operations via scale kernel.
* Computes: result = alpha * a + beta */
static PyObject *tensor_elementwise_scalar(Tensor *a, double alpha_value, double beta_value) {
nk_each_scale_punned_t kernel = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_each_scale_k, a->dtype, static_capabilities, (nk_kernel_punned_t *)&kernel, &cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_NotImplementedError, "scale not supported for dtype '%s'", dtype_to_python_string(a->dtype));
return NULL;
}
Tensor *r = Tensor_new(a->dtype, a->rank, a->shape);
if (!r) return NULL;
size_t item_size = bytes_per_dtype(a->dtype);
Py_ssize_t r_strides[NK_TENSOR_MAX_RANK];
compute_contiguous_strides(a->rank, a->shape, item_size, r_strides);
Py_buffer a_buf = {.ndim = (int)a->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = a->shape,
.strides = a->strides};
Py_buffer const *bufs[] = {&a_buf};
size_t contiguous_tail = shared_contiguous_tail_dimensions(bufs, 1, a->rank);
nk_scalar_buffer_t alpha_buf, beta_buf;
nk_dtype_t scalar_dtype = nk_each_scale_input_dtype(a->dtype);
nk_scalar_buffer_set_f64(&alpha_buf, alpha_value, scalar_dtype);
nk_scalar_buffer_set_f64(&beta_buf, beta_value, scalar_dtype);
PyThreadState *gil = PyEval_SaveThread();
each_scale_recursive(kernel, a->data, r->data, &alpha_buf, &beta_buf, a->shape, a->strides, r_strides, a->rank,
contiguous_tail);
PyEval_RestoreThread(gil);
return (PyObject *)r;
}
static PyObject *Tensor_negative(PyObject *self) { return tensor_elementwise_scalar((Tensor *)self, -1.0, 0.0); }
static PyObject *Tensor_add(PyObject *self, PyObject *other) {
if (!PyObject_TypeCheck(self, &TensorType)) { Py_RETURN_NOTIMPLEMENTED; }
Tensor *a = (Tensor *)self;
// Tensor([1,2,3]) + Tensor([4,5,6]) → element-wise sum via sum kernel
if (PyObject_TypeCheck(other, &TensorType)) {
Tensor *b = (Tensor *)other;
if (a->rank != b->rank || a->dtype != b->dtype) {
PyErr_SetString(PyExc_ValueError, "shape/dtype mismatch");
return NULL;
}
for (size_t i = 0; i < a->rank; i++)
if (a->shape[i] != b->shape[i]) {
PyErr_SetString(PyExc_ValueError, "shape mismatch");
return NULL;
}
nk_each_sum_punned_t kernel = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_each_sum_k, a->dtype, static_capabilities, (nk_kernel_punned_t *)&kernel, &cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_NotImplementedError, "add not supported for dtype '%s'",
dtype_to_python_string(a->dtype));
return NULL;
}
Tensor *r = Tensor_new(a->dtype, a->rank, a->shape);
if (!r) return NULL;
size_t item_size = bytes_per_dtype(a->dtype);
Py_ssize_t r_strides[NK_TENSOR_MAX_RANK];
compute_contiguous_strides(a->rank, a->shape, item_size, r_strides);
Py_buffer a_buf = {.ndim = (int)a->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = a->shape,
.strides = a->strides};
Py_buffer b_buf = {.ndim = (int)b->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = b->shape,
.strides = b->strides};
Py_buffer const *bufs[] = {&a_buf, &b_buf};
size_t contiguous_tail = shared_contiguous_tail_dimensions(bufs, 2, a->rank);
PyThreadState *gil = PyEval_SaveThread();
each_sum_recursive(kernel, a->data, b->data, r->data, a->shape, a->strides, b->strides, r_strides, a->rank,
contiguous_tail);
PyEval_RestoreThread(gil);
return (PyObject *)r;
}
// Tensor([1,2,3]) + 5.0 → broadcast scalar addition via scale kernel (α=1, β=scalar)
if (PyFloat_Check(other) || PyLong_Check(other)) {
double sc = PyFloat_Check(other) ? PyFloat_AsDouble(other) : (double)PyLong_AsLong(other);
return tensor_elementwise_scalar(a, 1.0, sc);
}
Py_RETURN_NOTIMPLEMENTED;
}
static PyObject *Tensor_subtract(PyObject *self, PyObject *other) {
if (!PyObject_TypeCheck(self, &TensorType)) { Py_RETURN_NOTIMPLEMENTED; }
Tensor *a = (Tensor *)self;
// Tensor([4,5,6]) - Tensor([1,2,3]) → element-wise difference via blend kernel (α=1, β=−1)
if (PyObject_TypeCheck(other, &TensorType)) {
Tensor *b = (Tensor *)other;
if (a->rank != b->rank || a->dtype != b->dtype) {
PyErr_SetString(PyExc_ValueError, "shape/dtype mismatch");
return NULL;
}
for (size_t i = 0; i < a->rank; i++)
if (a->shape[i] != b->shape[i]) {
PyErr_SetString(PyExc_ValueError, "shape mismatch");
return NULL;
}
// Single-pass subtract via blend: result = 1*a + (-1)*b
nk_each_blend_punned_t kernel = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_each_blend_k, a->dtype, static_capabilities, (nk_kernel_punned_t *)&kernel,
&cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_NotImplementedError, "subtract not supported for dtype '%s'",
dtype_to_python_string(a->dtype));
return NULL;
}
Tensor *r = Tensor_new(a->dtype, a->rank, a->shape);
if (!r) return NULL;
size_t item_size = bytes_per_dtype(a->dtype);
Py_ssize_t r_strides[NK_TENSOR_MAX_RANK];
compute_contiguous_strides(a->rank, a->shape, item_size, r_strides);
Py_buffer a_buf = {.ndim = (int)a->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = a->shape,
.strides = a->strides};
Py_buffer b_buf = {.ndim = (int)b->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = b->shape,
.strides = b->strides};
Py_buffer const *bufs[] = {&a_buf, &b_buf};
size_t contiguous_tail = shared_contiguous_tail_dimensions(bufs, 2, a->rank);
nk_scalar_buffer_t alpha_buf, beta_buf;
nk_dtype_t scalar_dtype = nk_each_scale_input_dtype(a->dtype);
nk_scalar_buffer_set_f64(&alpha_buf, 1.0, scalar_dtype);
nk_scalar_buffer_set_f64(&beta_buf, -1.0, scalar_dtype);
PyThreadState *gil = PyEval_SaveThread();
each_blend_recursive(kernel, a->data, b->data, r->data, &alpha_buf, &beta_buf, a->shape, a->strides, b->strides,
r_strides, a->rank, contiguous_tail);
PyEval_RestoreThread(gil);
return (PyObject *)r;
}
// Tensor([4,5,6]) - 1.0 → broadcast scalar subtraction via scale kernel (α=1, β=−scalar)
if (PyFloat_Check(other) || PyLong_Check(other)) {
double sc = PyFloat_Check(other) ? PyFloat_AsDouble(other) : (double)PyLong_AsLong(other);
return tensor_elementwise_scalar(a, 1.0, -sc);
}
Py_RETURN_NOTIMPLEMENTED;
}
static PyObject *Tensor_multiply(PyObject *self, PyObject *other) {
if (!PyObject_TypeCheck(self, &TensorType)) { Py_RETURN_NOTIMPLEMENTED; }
Tensor *a = (Tensor *)self;
// Tensor([1,2,3]) × Tensor([4,5,6]) → element-wise product via fma kernel (α=1, β=0)
if (PyObject_TypeCheck(other, &TensorType)) {
Tensor *b = (Tensor *)other;
if (a->rank != b->rank || a->dtype != b->dtype) {
PyErr_SetString(PyExc_ValueError, "shape/dtype mismatch");
return NULL;
}
for (size_t i = 0; i < a->rank; i++)
if (a->shape[i] != b->shape[i]) {
PyErr_SetString(PyExc_ValueError, "shape mismatch");
return NULL;
}
nk_each_fma_punned_t kernel = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_each_fma_k, a->dtype, static_capabilities, (nk_kernel_punned_t *)&kernel, &cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_NotImplementedError, "multiply not supported for dtype '%s'",
dtype_to_python_string(a->dtype));
return NULL;
}
Tensor *r = Tensor_new(a->dtype, a->rank, a->shape);
if (!r) return NULL;
size_t item_size = bytes_per_dtype(a->dtype);
size_t total_items = 1;
for (size_t i = 0; i < a->rank; i++) total_items *= (size_t)a->shape[i];
memset(r->data, 0, total_items * item_size); // prevent 0*NaN=NaN from uninitialized memory
Py_ssize_t r_strides[NK_TENSOR_MAX_RANK];
compute_contiguous_strides(a->rank, a->shape, item_size, r_strides);
Py_buffer a_buf = {.ndim = (int)a->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = a->shape,
.strides = a->strides};
Py_buffer b_buf = {.ndim = (int)b->rank,
.itemsize = (Py_ssize_t)item_size,
.shape = b->shape,
.strides = b->strides};
Py_buffer const *bufs[] = {&a_buf, &b_buf};
size_t contiguous_tail = shared_contiguous_tail_dimensions(bufs, 2, a->rank);
// fma(a, b, dummy, n, alpha=1, beta=0) -> 1*a*b + 0*dummy
nk_scalar_buffer_t alpha_buf, beta_buf;
nk_dtype_t scalar_dtype = nk_each_scale_input_dtype(a->dtype);
nk_scalar_buffer_set_f64(&alpha_buf, 1.0, scalar_dtype);
nk_scalar_buffer_set_f64(&beta_buf, 0.0, scalar_dtype);
PyThreadState *gil = PyEval_SaveThread();
each_fma_recursive(kernel, a->data, b->data, r->data, r->data, &alpha_buf, &beta_buf, a->shape, a->strides,
b->strides, r_strides, r_strides, a->rank, contiguous_tail);
PyEval_RestoreThread(gil);
return (PyObject *)r;
}
// Tensor([1,2,3]) × 5.0 → broadcast scalar multiply via scale kernel (α=scalar, β=0)
if (PyFloat_Check(other) || PyLong_Check(other)) {
double sc = PyFloat_Check(other) ? PyFloat_AsDouble(other) : (double)PyLong_AsLong(other);
return tensor_elementwise_scalar(a, sc, 0.0);
}
Py_RETURN_NOTIMPLEMENTED;
}
static PyNumberMethods Tensor_as_number = {
.nb_add = Tensor_add,
.nb_subtract = Tensor_subtract,
.nb_multiply = Tensor_multiply,
.nb_matrix_multiply = Tensor_matmul,
.nb_negative = Tensor_negative,
.nb_positive = Tensor_positive,
.nb_float = Tensor_float,
.nb_int = Tensor_int,
};
static PyObject *Tensor_get_shape(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
PyObject *shape_tuple = PyTuple_New(tensor->rank);
if (!shape_tuple) return NULL;
for (size_t i = 0; i < tensor->rank; i++) {
PyTuple_SET_ITEM(shape_tuple, i, PyLong_FromSsize_t(tensor->shape[i]));
}
return shape_tuple;
}
static PyObject *Tensor_get_dtype(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
return PyUnicode_FromString(dtype_to_string(tensor->dtype));
}
static PyObject *Tensor_get_ndim(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
return PyLong_FromSize_t(tensor->rank);
}
static PyObject *Tensor_get_size(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
Py_ssize_t total = 1;
for (size_t i = 0; i < tensor->rank; i++) total *= tensor->shape[i];
return PyLong_FromSsize_t(total);
}
static PyObject *Tensor_get_nbytes(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
Py_ssize_t total = 1;
for (size_t i = 0; i < tensor->rank; i++) total *= tensor->shape[i];
return PyLong_FromSsize_t(total * (Py_ssize_t)bytes_per_dtype(tensor->dtype));
}
static PyObject *Tensor_get_strides(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
PyObject *strides_tuple = PyTuple_New(tensor->rank);
if (!strides_tuple) return NULL;
for (size_t i = 0; i < tensor->rank; i++) {
PyTuple_SET_ITEM(strides_tuple, i, PyLong_FromSsize_t(tensor->strides[i]));
}
return strides_tuple;
}
static PyObject *Tensor_get_itemsize(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
return PyLong_FromSize_t(bytes_per_dtype(tensor->dtype));
}
static PyObject *Tensor_get_T(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
if (tensor->rank < 2) {
// 0D or 1D: transpose is a view of itself
Py_INCREF(self);
return self;
}
// Reverse shape and strides
Py_ssize_t new_shape[NK_TENSOR_MAX_RANK];
Py_ssize_t new_strides[NK_TENSOR_MAX_RANK];
for (size_t i = 0; i < tensor->rank; i++) {
new_shape[i] = tensor->shape[tensor->rank - 1 - i];
new_strides[i] = tensor->strides[tensor->rank - 1 - i];
}
Tensor *root_parent = tensor->parent ? (Tensor *)tensor->parent : tensor;
return (PyObject *)Tensor_view(root_parent, tensor->data, tensor->dtype, tensor->rank, new_shape, new_strides);
}
static PyObject *Tensor_get_array_interface(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
PyObject *dict = PyDict_New();
if (!dict) return NULL;
PyObject *shape = Tensor_get_shape(self, NULL);
if (!shape) {
Py_DECREF(dict);
return NULL;
}
PyDict_SetItemString(dict, "shape", shape);
Py_DECREF(shape);
char const *typestr = dtype_to_array_typestr(tensor->dtype);
PyObject *typestr_obj = PyUnicode_FromString(typestr);
if (!typestr_obj) {
Py_DECREF(dict);
return NULL;
}
PyDict_SetItemString(dict, "typestr", typestr_obj);
Py_DECREF(typestr_obj);
PyObject *data_ptr = PyLong_FromVoidPtr(tensor->data);
if (!data_ptr) {
Py_DECREF(dict);
return NULL;
}
PyObject *data_tuple = PyTuple_Pack(2, data_ptr, Py_False);
Py_DECREF(data_ptr);
if (!data_tuple) {
Py_DECREF(dict);
return NULL;
}
PyDict_SetItemString(dict, "data", data_tuple);
Py_DECREF(data_tuple);
PyObject *strides = Tensor_get_strides(self, NULL);
if (!strides) {
Py_DECREF(dict);
return NULL;
}
PyDict_SetItemString(dict, "strides", strides);
Py_DECREF(strides);
PyObject *version = PyLong_FromLong(3);
if (!version) {
Py_DECREF(dict);
return NULL;
}
PyDict_SetItemString(dict, "version", version);
Py_DECREF(version);
return dict;
}
static PyObject *Tensor_get_is_contiguous(PyObject *self, void *closure) {
nk_unused_(closure);
Tensor *tensor = (Tensor *)self;
size_t item_size = bytes_per_dtype(tensor->dtype);
return PyBool_FromLong(tensor_is_c_contig(tensor, item_size));
}
static PyGetSetDef Tensor_getset[] = {
{"shape", Tensor_get_shape, NULL, "Shape of the array", NULL},
{"dtype", Tensor_get_dtype, NULL, "Data type of the array", NULL},
{"ndim", Tensor_get_ndim, NULL, "Number of dimensions", NULL},
{"size", Tensor_get_size, NULL, "Total number of elements", NULL},
{"nbytes", Tensor_get_nbytes, NULL, "Total bytes of data", NULL},
{"strides", Tensor_get_strides, NULL, "Strides in bytes", NULL},
{"itemsize", Tensor_get_itemsize, NULL, "Size of one element in bytes", NULL},
{"T", Tensor_get_T, NULL, "Transposed view of the array", NULL},
{"__array_interface__", Tensor_get_array_interface, NULL, "NumPy array interface", NULL},
{"is_contiguous", Tensor_get_is_contiguous, NULL, "Whether the tensor is C-contiguous", NULL},
{NULL, NULL, NULL, NULL, NULL},
};
PyObject *Tensor_copy(PyObject *self, PyObject *args) {
nk_unused_(args);
Tensor *tensor = (Tensor *)self;
size_t total_elements = 1;
for (size_t i = 0; i < tensor->rank; i++) total_elements *= (size_t)tensor->shape[i];
Tensor *result = Tensor_new(tensor->dtype, tensor->rank, tensor->shape);
if (!result) return NULL;
linearize_cast_into(tensor->data, tensor->dtype, result->data, tensor->dtype, tensor->rank, tensor->shape,
tensor->strides, total_elements);
return (PyObject *)result;
}
PyObject *Tensor_reshape(PyObject *self, PyObject *const *args, Py_ssize_t nargs) {
Tensor *tensor = (Tensor *)self;
Py_ssize_t new_shape[NK_TENSOR_MAX_RANK];
size_t new_rank = 0;
if (nargs == 1 && PyTuple_Check(args[0])) {
PyObject *shape_tuple = args[0];
new_rank = PyTuple_GET_SIZE(shape_tuple);
if (new_rank > NK_TENSOR_MAX_RANK) {
PyErr_Format(PyExc_ValueError, "reshape: too many dimensions (%zu > %d)", new_rank, NK_TENSOR_MAX_RANK);
return NULL;
}
for (size_t i = 0; i < new_rank; i++) {
PyObject *item = PyTuple_GET_ITEM(shape_tuple, i);
if (!PyLong_Check(item)) {
PyErr_SetString(PyExc_TypeError, "reshape: shape dimensions must be integers");
return NULL;
}
new_shape[i] = PyLong_AsSsize_t(item);
if (new_shape[i] < 0) {
PyErr_SetString(PyExc_ValueError, "reshape: negative dimensions not supported");
return NULL;
}
}
}
else {
new_rank = (size_t)nargs;
if (new_rank > NK_TENSOR_MAX_RANK) {
PyErr_Format(PyExc_ValueError, "reshape: too many dimensions (%zu > %d)", new_rank, NK_TENSOR_MAX_RANK);
return NULL;
}
for (size_t i = 0; i < new_rank; i++) {
PyObject *item = args[i];
if (!PyLong_Check(item)) {
PyErr_SetString(PyExc_TypeError, "reshape: shape dimensions must be integers");
return NULL;
}
new_shape[i] = PyLong_AsSsize_t(item);
if (new_shape[i] < 0) {
PyErr_SetString(PyExc_ValueError, "reshape: negative dimensions not supported");
return NULL;
}
}
}
Py_ssize_t new_total = 1;
for (size_t i = 0; i < new_rank; i++) new_total *= new_shape[i];
Py_ssize_t old_total = 1;
for (size_t i = 0; i < tensor->rank; i++) old_total *= tensor->shape[i];
if (new_total != old_total) {
PyErr_Format(PyExc_ValueError, "reshape: cannot reshape tensor of size %zd into shape with size %zd", old_total,
new_total);
return NULL;
}
size_t item_size = bytes_per_dtype(tensor->dtype);
if (tensor_is_c_contig(tensor, item_size)) {
Py_ssize_t new_strides[NK_TENSOR_MAX_RANK];
Py_ssize_t stride = item_size;
for (size_t i = new_rank; i > 0; i--) {
new_strides[i - 1] = stride;
stride *= new_shape[i - 1];
}
Tensor *root_parent = tensor->parent ? (Tensor *)tensor->parent : tensor;
return (PyObject *)Tensor_view(root_parent, tensor->data, tensor->dtype, new_rank, new_shape, new_strides);
}
// Non-contiguous: must copy
Tensor *result = Tensor_new(tensor->dtype, new_rank, new_shape);
if (!result) return NULL;
linearize_cast_into(tensor->data, tensor->dtype, result->data, tensor->dtype, tensor->rank, tensor->shape,
tensor->strides, (size_t)old_total);
return (PyObject *)result;
}
PyObject *Tensor_flatten(PyObject *self, PyObject *args) {
nk_unused_(args);
Tensor *tensor = (Tensor *)self;
Py_ssize_t total_elements = 1;
for (size_t i = 0; i < tensor->rank; i++) total_elements *= tensor->shape[i];
size_t item_size = bytes_per_dtype(tensor->dtype);
if (tensor_is_c_contig(tensor, item_size)) {
Py_ssize_t flat_shape[1] = {total_elements};
Py_ssize_t flat_strides[1] = {(Py_ssize_t)item_size};
Tensor *root_parent = tensor->parent ? (Tensor *)tensor->parent : tensor;
return (PyObject *)Tensor_view(root_parent, tensor->data, tensor->dtype, 1, flat_shape, flat_strides);
}
// Non-contiguous: must copy then flatten
Py_ssize_t flat_shape[1] = {total_elements};
Tensor *result = Tensor_new(tensor->dtype, 1, flat_shape);
if (!result) return NULL;
linearize_cast_into(tensor->data, tensor->dtype, result->data, tensor->dtype, tensor->rank, tensor->shape,
tensor->strides, (size_t)total_elements);
return (PyObject *)result;
}
PyObject *Tensor_squeeze(PyObject *self, PyObject *const *args, Py_ssize_t nargs) {
Tensor *tensor = (Tensor *)self;
if (nargs > 1) {
PyErr_SetString(PyExc_TypeError, "squeeze() takes at most 1 argument");
return NULL;
}
Py_ssize_t new_shape[NK_TENSOR_MAX_RANK];
Py_ssize_t new_strides[NK_TENSOR_MAX_RANK];
size_t new_rank = 0;
if (nargs == 1) {
// Squeeze a specific axis
Py_ssize_t axis = PyLong_AsSsize_t(args[0]);
if (axis == -1 && PyErr_Occurred()) return NULL;
if (axis < 0) axis += (Py_ssize_t)tensor->rank;
if (axis < 0 || (size_t)axis >= tensor->rank) {
PyErr_Format(PyExc_ValueError, "squeeze: axis %zd out of range for tensor with %zu dimensions", axis,
tensor->rank);
return NULL;
}
if (tensor->shape[axis] != 1) {
// Axis is not size 1, return a view of the same tensor
Tensor *root_parent = tensor->parent ? (Tensor *)tensor->parent : tensor;
return (PyObject *)Tensor_view(root_parent, tensor->data, tensor->dtype, tensor->rank, tensor->shape,
tensor->strides);
}
for (size_t i = 0; i < tensor->rank; i++) {
You can’t perform that action at this time.
