diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index b0c1ac9c..c714fc8c 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1564,17 +1564,6 @@ extern "C" {
int d1, // dilation dimension 1
bool is_2D);
- GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
- struct ggml_context * ctx,
- struct ggml_tensor * a, // convolution kernel
- struct ggml_tensor * b, // data
- 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_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
@@ -1592,6 +1581,23 @@ extern "C" {
int s, // stride
int d); // dilation
+ // depthwise
+ // TODO: this is very likely wrong for some cases! - needs more testing
+ GGML_API struct ggml_tensor * ggml_conv_1d_dw(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a, // convolution kernel
+ struct ggml_tensor * b, // data
+ int s0, // stride
+ int p0, // padding
+ int d0); // dilation
+
+ GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a, // convolution kernel
+ struct ggml_tensor * b, // data
+ int s0, // stride
+ int d0); // dilation
+
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
@@ -1611,7 +1617,6 @@ extern "C" {
int d0, // dilation dimension 0
int d1); // dilation dimension 1
-
// kernel size is a->ne[0] x a->ne[1]
// stride is equal to kernel size
// padding is zero
@@ -1638,6 +1643,18 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
+ // depthwise
+ GGML_API struct ggml_tensor * ggml_conv_2d_dw(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a, // convolution kernel
+ struct ggml_tensor * b, // data
+ 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_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 0efd2b2e..2bbe5f48 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3760,104 +3760,10 @@ struct ggml_tensor * ggml_clamp(
return result;
}
-// ggml_conv_1d
-
static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
}
-GGML_API struct ggml_tensor * ggml_conv_1d(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int s0,
- int p0,
- int d0) {
- struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
-
- struct ggml_tensor * result =
- ggml_mul_mat(ctx,
- ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
- ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K]
-
- result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
-
- return result;
-}
-
-// ggml_conv_1d_ph
-
-struct ggml_tensor* ggml_conv_1d_ph(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int s,
- int d) {
- return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
-}
-
-// ggml_conv_transpose_1d
-
-static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
- return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
-}
-
-GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int s0,
- int p0,
- int d0) {
- GGML_ASSERT(ggml_is_matrix(b));
- GGML_ASSERT(a->ne[2] == b->ne[1]);
- GGML_ASSERT(a->ne[3] == 1);
-
- GGML_ASSERT(p0 == 0);
- GGML_ASSERT(d0 == 1);
-
- const int64_t ne[4] = {
- ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
- a->ne[1], b->ne[2], 1,
- };
- struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
-
- int32_t params[] = { s0, p0, d0 };
- ggml_set_op_params(result, params, sizeof(params));
-
- result->op = GGML_OP_CONV_TRANSPOSE_1D;
- result->src[0] = a;
- result->src[1] = b;
-
- return result;
-}
-
-// ggml_conv_depthwise
-
-struct ggml_tensor * ggml_conv_depthwise_2d(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int s0,
- int s1,
- int p0,
- int p1,
- int d0,
- int d1) {
- struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
- struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
- ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
- s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
- struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
-
- new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
- struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
- result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
-
- return result;
-}
-// ggml_conv_2d
-
// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
// a: [OC，IC, KH, KW]
// b: [N, IC, IH, IW]
@@ -3874,10 +3780,11 @@ struct ggml_tensor * ggml_im2col(
int d1,
bool is_2D,
enum ggml_type dst_type) {
- if(is_2D) {
+ if (is_2D) {
GGML_ASSERT(a->ne[2] == b->ne[2]);
} else {
- GGML_ASSERT(a->ne[1] == b->ne[1]);
+ //GGML_ASSERT(b->ne[1] % a->ne[1] == 0);
+ GGML_ASSERT(b->ne[1] == a->ne[1]);
GGML_ASSERT(b->ne[3] == 1);
}
@@ -3928,6 +3835,108 @@ struct ggml_tensor * ggml_im2col_back(
return result;
}
+// ggml_conv_1d
+
+struct ggml_tensor * ggml_conv_1d(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int p0,
+ int d0) {
+ struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
+
+ struct ggml_tensor * result =
+ ggml_mul_mat(ctx,
+ ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
+ ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K]
+
+ result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
+
+ return result;
+}
+
+// ggml_conv_1d_ph
+
+struct ggml_tensor* ggml_conv_1d_ph(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s,
+ int d) {
+ return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
+}
+
+// ggml_conv_1d_dw
+
+struct ggml_tensor * ggml_conv_1d_dw(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int p0,
+ int d0) {
+ struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]);
+ struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]);
+
+ struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
+
+ struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a);
+
+ result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1);
+
+ return result;
+}
+
+// ggml_conv_1d_dw_ph
+
+struct ggml_tensor * ggml_conv_1d_dw_ph(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int d0) {
+ return ggml_conv_1d_dw(ctx, a, b, s0, a->ne[0] / 2, d0);
+}
+
+// ggml_conv_transpose_1d
+
+static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
+ return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
+}
+
+GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int p0,
+ int d0) {
+ GGML_ASSERT(ggml_is_matrix(b));
+ GGML_ASSERT(a->ne[2] == b->ne[1]);
+ GGML_ASSERT(a->ne[3] == 1);
+
+ GGML_ASSERT(p0 == 0);
+ GGML_ASSERT(d0 == 1);
+
+ const int64_t ne[4] = {
+ ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
+ a->ne[1], b->ne[2], 1,
+ };
+ struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+ int32_t params[] = { s0, p0, d0 };
+ ggml_set_op_params(result, params, sizeof(params));
+
+ result->op = GGML_OP_CONV_TRANSPOSE_1D;
+ result->src[0] = a;
+ result->src[1] = b;
+
+ return result;
+}
+
+// ggml_conv_2d
+
// a: [OC，IC, KH, KW]
// b: [N, IC, IH, IW]
// result: [N, OC, OH, OW]
@@ -3973,6 +3982,31 @@ struct ggml_tensor * ggml_conv_2d_s1_ph(
return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1);
}
+// ggml_conv_2d_dw
+
+struct ggml_tensor * ggml_conv_2d_dw(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int s1,
+ int p0,
+ int p1,
+ int d0,
+ int d1) {
+ struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
+ struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
+ ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
+ s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
+ struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
+
+ new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
+ struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
+ result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
+
+ 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) {