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ggml : Depthwise 2D convolution (ggml/1152)

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* ggml-cpu : kernels for faster depthwise 2D convolution

* fix compile: remove static after moving to ops.cpp

* add dilation for depthwise_conv_2d

* review: rename to ggml_conv_2d_dw_direct, remove redundant struct keywords, pass by ref, whitespace

* review: rename depthwise_conv_2d -> conv_2d_dw everywhere
This commit is contained in:
Acly
2025-04-17 14:16:45 +02:00
committed by Georgi Gerganov
parent 915c14ef10
commit d87dfcf7c0
5 changed files with 250 additions and 3 deletions
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53
ggml/src/ggml.c
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@ -956,6 +956,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CONV_TRANSPOSE_1D",
"IM2COL",
"IM2COL_BACK",
"CONV_2D_DW",
"CONV_TRANSPOSE_2D",
"POOL_1D",
"POOL_2D",
@ -993,7 +994,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1050,6 +1051,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"conv_transpose_1d(x)",
"im2col(x)",
"im2col_back(x)",
"conv_2d_dw(x)",
"conv_transpose_2d(x)",
"pool_1d(x)",
"pool_2d(x)",
@ -1087,7 +1089,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -1344,6 +1346,13 @@ bool ggml_is_permuted(const struct ggml_tensor * tensor) {
return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
}
bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor) {
return
tensor->nb[0] > tensor->nb[2] &&
tensor->nb[1] > tensor->nb[0] &&
tensor->nb[2] == ggml_type_size(tensor->type);
}
static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
@ -4050,6 +4059,46 @@ struct ggml_tensor * ggml_conv_2d_dw(
return result;
}
// ggml_conv_2d_dw_direct
struct ggml_tensor * ggml_conv_2d_dw_direct(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int stride0,
int stride1,
int pad0,
int pad1,
int dilation0,
int dilation1) {
GGML_ASSERT(a->ne[2] == 1);
GGML_ASSERT(a->ne[3] == b->ne[2]);
int64_t ne[4];
ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], stride0, pad0, dilation0);
ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], stride1, pad1, dilation1);
ne[2] = b->ne[2];
ne[3] = b->ne[3];
struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne);
if (ggml_is_contiguous_channels(b)) {
// Result will be permuted the same way as input (CWHN order)
const int64_t type_size = ggml_type_size(result->type);
GGML_ASSERT(ggml_blck_size(result->type) == 1);
result->nb[0] = result->ne[2] * type_size;
result->nb[1] = result->ne[0] * result->nb[0];
result->nb[2] = type_size;
}
int32_t params[] = { stride0, stride1, pad0, pad1, dilation0, dilation1 };
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_2D_DW;
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) {