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Add Conv2d for CPU (#14388)

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* Conv2D: Add CPU version

* Half decent

* Tiled approach for F32

* remove file

* Fix tests

* Support F16 operations

* add assert about size

* Review: further formatting fixes, add assert and use CPU version of fp32->fp16
This commit is contained in:
Aman Gupta
2025-06-30 23:57:04 +08:00
committed by GitHub
parent 745f11fed0
commit 0a5a3b5cdf
5 changed files with 250 additions and 3 deletions
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12
ggml/include/ggml.h
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@@ -482,6 +482,7 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_1D,
GGML_OP_IM2COL,
GGML_OP_IM2COL_BACK,
GGML_OP_CONV_2D,
GGML_OP_CONV_2D_DW,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
@@ -1813,6 +1814,17 @@ extern "C" {
struct ggml_tensor * b,
int stride);
GGML_API struct ggml_tensor * ggml_conv_2d_direct(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel [KW, KH, IC, OC]
struct ggml_tensor * b, // input data [W, H, C, N]
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1); // dilation dimension 1
enum ggml_op_pool {
GGML_OP_POOL_MAX,
GGML_OP_POOL_AVG,

11
ggml/src/ggml-cpu/ggml-cpu.c
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@@ -1193,7 +1193,7 @@ static void ggml_compute_forward_mul_mat_one_chunk(
}
}
static void ggml_compute_forward_mul_mat(
void ggml_compute_forward_mul_mat(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
@@ -1866,6 +1866,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_im2col_back_f32(params, tensor);
} break;
case GGML_OP_CONV_2D:
{
ggml_compute_forward_conv_2d(params, tensor);
} break;
case GGML_OP_CONV_2D_DW:
{
ggml_compute_forward_conv_2d_dw(params, tensor);
@@ -2228,6 +2232,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_IM2COL:
case GGML_OP_IM2COL_BACK:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_CONV_TRANSPOSE_2D:
@@ -2746,6 +2751,10 @@ struct ggml_cplan ggml_graph_plan(
GGML_ABORT("fatal error");
}
} break;
case GGML_OP_CONV_2D:
{
cur = GGML_IM2COL_WORK_SIZE;
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W

181
ggml/src/ggml-cpu/ops.cpp
View File

@@ -3,6 +3,7 @@
#include "ggml-cpu.h"
#include "ggml-impl.h"
#include "binary-ops.h"
#include "ggml.h"
#include "unary-ops.h"
#include "vec.h"
@@ -6545,6 +6546,186 @@ void ggml_compute_forward_im2col_back_f32(
}
}
static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params, int64_t m, int64_t n, int64_t k,
void * a, void * b, float * c) {
const ggml_type_traits * traits = ggml_get_type_traits(type);
struct ggml_tensor src1 = {};
src1.type = type;
src1.ne[0] = k;
src1.ne[1] = m;
src1.ne[2] = 1;
src1.ne[3] = 1;
src1.nb[0] = traits->type_size;
src1.nb[1] = k * traits->type_size;
src1.nb[2] = src1.nb[1];
src1.nb[3] = src1.nb[2];
src1.data = a;
struct ggml_tensor src0 = {};
src0.type = type;
src0.ne[0] = k;
src0.ne[1] = n;
src0.ne[2] = 1;
src0.ne[3] = 1;
src0.nb[0] = traits->type_size;
src0.nb[1] = k * traits->type_size;
src0.nb[2] = src0.nb[1];
src0.nb[3] = src0.nb[2];
src0.data = b;
struct ggml_tensor dst = {};
dst.ne[0] = n;
dst.ne[1] = m;
dst.ne[2] = 1;
dst.ne[3] = 1;
dst.nb[0] = sizeof(float);
dst.nb[1] = n * sizeof(float);
dst.nb[2] = dst.nb[1];
dst.nb[3] = dst.nb[2];
dst.data = c;
dst.src[0] = &src0;
dst.src[1] = &src1;
ggml_compute_forward_mul_mat(params, &dst);
}
// ggml_compute_forward_conv_2d
static void ggml_compute_forward_conv_2d_impl(const ggml_compute_params * params,
const ggml_tensor * kernel, // [KW, KH, IC, OC]
const ggml_tensor * src, // [W, H, C, N]
ggml_tensor * dst, // [OW, OH, OC, N]
ggml_type kernel_type) {
GGML_ASSERT(ggml_is_contiguous(kernel));
GGML_ASSERT(kernel_type == GGML_TYPE_F16 || kernel_type == GGML_TYPE_F32);
GGML_ASSERT(kernel->type == kernel_type);
const ggml_type_traits * traits = ggml_get_type_traits(kernel_type);
const int32_t stride_x = dst->op_params[0];
const int32_t stride_y = dst->op_params[1];
const int32_t pad_x = dst->op_params[2];
const int32_t pad_y = dst->op_params[3];
const int32_t dilation_x = dst->op_params[4];
const int32_t dilation_y = dst->op_params[5];
const int64_t c_in = src->ne[2];
const int64_t c_out = kernel->ne[3];
GGML_ASSERT(c_in == kernel->ne[2]);
const int64_t src_w = src->ne[0];
const int64_t src_h = src->ne[1];
const int64_t knl_w = kernel->ne[0];
const int64_t knl_h = kernel->ne[1];
const int64_t dst_w = dst->ne[0];
const int64_t dst_h = dst->ne[1];
const float * src_data = (float *) src->data;
void * knl_data = kernel->data;
float * dst_data = (float *) dst->data;
const int64_t knl_n = knl_w * knl_h * c_in;
const int64_t patch_total = dst->ne[3] * dst_w * dst_h;
const int64_t space_per_patch = knl_n * traits->type_size + c_out * sizeof(float);
const int64_t batch_size = params->wsize / space_per_patch;
const int64_t patches_per_batch = batch_size > 8 ? (batch_size / 8) * 8 : batch_size;
const int64_t batch_n = (patch_total + patches_per_batch - 1) / patches_per_batch;
GGML_ASSERT(patches_per_batch > 0 && batch_size >= 1);
void * tmp = params->wdata;
for (int64_t batch_i = 0; batch_i < batch_n; ++batch_i) {
const int64_t patch_start_batch = batch_i * patches_per_batch;
const int64_t patch_end_batch = std::min(patch_start_batch + patches_per_batch,
patch_total);
const int64_t patch_n = patch_end_batch - patch_start_batch;
const int64_t patch_per_thread = (patch_n + params->nth - 1) / params->nth;
const int64_t patch_start = patch_start_batch + params->ith * patch_per_thread;
const int64_t patch_end = std::min(patch_start + patch_per_thread, patch_end_batch);
//im2col for a patch
for (int64_t p = patch_start; p < patch_end; ++p) {
const int64_t batch_n = p / (dst_w * dst_h);
const int64_t src_x = (p / dst_w) % dst_h;
const int64_t src_y = p % dst_w;
const float * src_base = (const float *)((const char *)src_data + batch_n * src->nb[3]);
char * dst_row = (char *) tmp + (p % patches_per_batch) * knl_n * traits->type_size;
for (int64_t ic = 0; ic < c_in; ++ic) {
for (int64_t ky = 0; ky < knl_h; ++ky) {
for (int64_t kx = 0; kx < knl_w; ++kx) {
const int64_t sy = src_x * stride_y + ky * dilation_y - pad_y;
const int64_t sx = src_y * stride_x + kx * dilation_x - pad_x;
int64_t dst_idx = ic * (knl_h * knl_w) + ky * knl_w + kx;
float src_val;
if (sy < 0 || sy >= src_h || sx < 0 || sx >= src_w) {
src_val = 0.0f;
} else {
const float * src_ptr = (const float *)((const char *)src_base + sx * src->nb[0] + sy * src->nb[1] + ic * src->nb[2]);
src_val = *src_ptr;
}
char * element_ptr = dst_row + dst_idx * traits->type_size;
if (kernel_type == GGML_TYPE_F32) {
*(float *) element_ptr = src_val;
} else if (kernel_type == GGML_TYPE_F16) {
*(ggml_fp16_t *) element_ptr = GGML_CPU_FP32_TO_FP16(src_val);
}
}
}
}
} // patches handled by this thread
ggml_barrier(params->threadpool);
float * gemm_output = (float *) ((char *) tmp + patches_per_batch * knl_n * traits->type_size);
GGML_ASSERT(gemm_output + patch_n * c_out <= (float*)tmp + params->wsize);
// GEMM: patches[patch_n, knl_n] × kernel[knl_n, c_out] = output[patch_n, c_out]
ggml_call_mul_mat(kernel_type, params, patch_n, c_out, knl_n, tmp, knl_data, gemm_output);
ggml_barrier(params->threadpool);
//permute back [OC, N, OH, OW] to [N, OC, OH, OW]
const int64_t permute_per_thread = (patch_n + params->nth - 1) / params->nth;
const int64_t permute_start = params->ith * permute_per_thread;
const int64_t permute_end = std::min(permute_start + permute_per_thread, patch_n);
for (int64_t i = permute_start; i < permute_end; ++i) {
const int64_t p = patch_start_batch + i;
const int64_t batch_n = p / (dst_w * dst_h);
const int64_t dst_y = (p / dst_w) % dst_h;
const int64_t dst_x = p % dst_w;
for (int64_t oc = 0; oc < c_out; ++oc) {
const float value = gemm_output[i * c_out + oc];
float * dst_ptr = (float *)((char *)dst_data + dst_x * dst->nb[0] + dst_y * dst->nb[1] + oc * dst->nb[2] + batch_n * dst->nb[3]);
*dst_ptr = value;
}
}
}
}
void ggml_compute_forward_conv_2d(
const ggml_compute_params * params,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
ggml_compute_forward_conv_2d_impl(params, src0, src1, dst, src0->type);
}
// ggml_compute_forward_conv_transpose_2d
void ggml_compute_forward_conv_transpose_2d(

5
ggml/src/ggml-cpu/ops.h
View File

@@ -20,6 +20,9 @@
static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
// Work buffer size for im2col operations in CONV2D
#define GGML_IM2COL_WORK_SIZE (16 * 1024 * 1024)
#ifdef __cplusplus
extern "C" {
#endif
@@ -65,6 +68,7 @@ void ggml_compute_forward_clamp(const struct ggml_compute_params * params, struc
void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_conv_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_conv_2d_dw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pool_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -107,6 +111,7 @@ void ggml_compute_forward_custom(const struct ggml_compute_params * params, stru
void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
#ifdef __cplusplus
}

44
ggml/src/ggml.c
View File

@@ -945,6 +945,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CONV_TRANSPOSE_1D",
"IM2COL",
"IM2COL_BACK",
"CONV_2D",
"CONV_2D_DW",
"CONV_TRANSPOSE_2D",
"POOL_1D",
@@ -986,7 +987,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"GLU",
};
static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -1044,6 +1045,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"conv_transpose_1d(x)",
"im2col(x)",
"im2col_back(x)",
"conv_2d(x)",
"conv_2d_dw(x)",
"conv_transpose_2d(x)",
"pool_1d(x)",
@@ -1085,7 +1087,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"glu(x)",
};
static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -4291,6 +4293,44 @@ struct ggml_tensor * ggml_conv_2d_dw_direct(
return result;
}
// ggml_conv_2d_direct
struct ggml_tensor * ggml_conv_2d_direct(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel [KW, KH, IC, OC]
struct ggml_tensor * b, // input data [W, H, C, N]
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1) {// dilation dimension 1
GGML_ASSERT(a->ne[2] == b->ne[2]);
//GGML_ASSERT(a->type == b->type);
int64_t ne[4];
ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1);
ne[2] = a->ne[3];
ne[3] = b->ne[3];
struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne);
ggml_set_op_params_i32(result, 0, s0);
ggml_set_op_params_i32(result, 1, s1);
ggml_set_op_params_i32(result, 2, p0);
ggml_set_op_params_i32(result, 3, p1);
ggml_set_op_params_i32(result, 4, d0);
ggml_set_op_params_i32(result, 5, d1);
result->op = GGML_OP_CONV_2D;
result->src[0] = a;
result->src[1] = b;
return result;
}
// ggml_conv_transpose_2d_p0
static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {