static struct ggml_tensor * ggml_dup_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ struct ggml_tensor * a,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_DUP;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_DUP;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_dup(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
+ struct ggml_tensor * a) {
return ggml_dup_impl(ctx, a, false);
}
struct ggml_tensor * ggml_dup_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
+ struct ggml_tensor * a) {
return ggml_dup_impl(ctx, a, true);
}
static struct ggml_tensor * ggml_add_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ bool inplace) {
GGML_ASSERT(ggml_can_repeat(b, a));
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_ADD;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ADD;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_add(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_add_impl(ctx, a, b, false);
}
struct ggml_tensor * ggml_add_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_add_impl(ctx, a, b, true);
}
static struct ggml_tensor * ggml_add_cast_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- enum ggml_type type) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ enum ggml_type type) {
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
a->type == GGML_TYPE_F16 ||
a->type == GGML_TYPE_BF16);
- bool is_node = false;
-
- if (a->grad || b->grad) {
- // TODO: support backward pass for broadcasting
- GGML_ASSERT(ggml_are_same_shape(a, b));
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne);
- result->op = GGML_OP_ADD;
- result->grad = is_node ? ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne) : NULL;
+ result->op = GGML_OP_ADD;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_add_cast(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- enum ggml_type type) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ enum ggml_type type) {
return ggml_add_cast_impl(ctx, a, b, type);
}
static struct ggml_tensor * ggml_add1_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ bool inplace) {
GGML_ASSERT(ggml_is_scalar(b));
GGML_ASSERT(ggml_is_padded_1d(a));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_ADD1;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ADD1;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_add1(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_add1_impl(ctx, a, b, false);
}
struct ggml_tensor * ggml_add1_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_add1_impl(ctx, a, b, true);
}
static struct ggml_tensor * ggml_acc_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- size_t nb1,
- size_t nb2,
- size_t nb3,
- size_t offset,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ size_t nb1,
+ size_t nb2,
+ size_t nb3,
+ size_t offset,
+ bool inplace) {
GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a));
GGML_ASSERT(ggml_is_contiguous(a));
GGML_ASSERT(a->type == GGML_TYPE_F32);
GGML_ASSERT(b->type == GGML_TYPE_F32);
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_ACC;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ACC;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_acc(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- size_t nb1,
- size_t nb2,
- size_t nb3,
- size_t offset) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ size_t nb1,
+ size_t nb2,
+ size_t nb3,
+ size_t offset) {
return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
}
struct ggml_tensor * ggml_acc_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- size_t nb1,
- size_t nb2,
- size_t nb3,
- size_t offset) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ size_t nb1,
+ size_t nb2,
+ size_t nb3,
+ size_t offset) {
return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
}
static struct ggml_tensor * ggml_sub_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ bool inplace) {
GGML_ASSERT(ggml_can_repeat(b, a));
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- // TODO: support backward pass for broadcasting
- GGML_ASSERT(ggml_are_same_shape(a, b));
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SUB;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SUB;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_sub(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_sub_impl(ctx, a, b, false);
}
struct ggml_tensor * ggml_sub_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
return ggml_sub_impl(ctx, a, b, true);
}
static struct ggml_tensor * ggml_mul_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ bool inplace) {
GGML_ASSERT(ggml_can_repeat(b, a));
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- // TODO: support backward pass for broadcasting
- GGML_ASSERT(ggml_are_same_shape(a, b));
- is_node = true;
- }
-
- if (inplace) {
- GGML_ASSERT(!is_node);
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_MUL;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MUL;
result->src[0] = a;
result->src[1] = b;
static struct ggml_tensor * ggml_div_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- bool inplace) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ bool inplace) {
GGML_ASSERT(ggml_can_repeat(b, a));
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
- if (inplace) {
- GGML_ASSERT(!is_node);
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_DIV;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_DIV;
result->src[0] = a;
result->src[1] = b;
static struct ggml_tensor * ggml_sqr_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ struct ggml_tensor * a,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SQR;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SQR;
result->src[0] = a;
return result;
static struct ggml_tensor * ggml_sqrt_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ struct ggml_tensor * a,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SQRT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SQRT;
result->src[0] = a;
return result;
static struct ggml_tensor * ggml_log_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_LOG;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_LOG;
result->src[0] = a;
return result;
static struct ggml_tensor * ggml_sin_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SIN;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SIN;
result->src[0] = a;
return result;
static struct ggml_tensor * ggml_cos_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_COS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_COS;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_sum(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
+ struct ggml_tensor * a) {
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
- result->op = GGML_OP_SUM;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SUM;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_sum_rows(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
+ struct ggml_tensor * a) {
int64_t ne[GGML_MAX_DIMS] = { 1 };
for (int i = 1; i < GGML_MAX_DIMS; ++i) {
ne[i] = a->ne[i];
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne);
- result->op = GGML_OP_SUM_ROWS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SUM_ROWS;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_mean(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement
- is_node = true;
- }
-
+ struct ggml_tensor * a) {
int64_t ne[4] = { 1, a->ne[1], a->ne[2], a->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
- result->op = GGML_OP_MEAN;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MEAN;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
- struct ggml_tensor * a) {
+ struct ggml_tensor * a) {
GGML_ASSERT(ggml_is_matrix(a));
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error");
- is_node = true;
- }
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]);
- result->op = GGML_OP_ARGMAX;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ARGMAX;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_repeat(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
GGML_ASSERT(ggml_can_repeat(a, b));
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne);
- result->op = GGML_OP_REPEAT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_REPEAT;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_repeat_back(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
GGML_ASSERT(ggml_can_repeat(b, a));
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
- if (ggml_are_same_shape(a, b) && !is_node) {
- return a;
- }
-
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne);
- result->op = GGML_OP_REPEAT_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_REPEAT_BACK;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_concat(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int dim) {
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int dim) {
GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
int64_t ne[GGML_MAX_DIMS];
ne[d] = a->ne[d];
}
- bool is_node = false;
-
- if (a->grad || b->grad) {
- GGML_ABORT("fatal error"); // TODO: implement
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne);
ggml_set_op_params_i32(result, 0, dim);
- result->op = GGML_OP_CONCAT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CONCAT;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_leaky_relu(
struct ggml_context * ctx,
- struct ggml_tensor * a, float negative_slope, bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- GGML_ABORT("fatal error"); // TODO: not implemented
- is_node = true;
- }
-
+ struct ggml_tensor * a,
+ float negative_slope,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &negative_slope, sizeof(negative_slope));
- result->op = GGML_OP_LEAKY_RELU;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_LEAKY_RELU;
result->src[0] = a;
return result;
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
- bool is_node = false;
-
- if (a->grad || b->grad) {
- // TODO: implement backward
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SILU_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SILU_BACK;
result->src[0] = a;
result->src[1] = b;
}
// ggml hardswish
+
struct ggml_tensor * ggml_hardswish(
struct ggml_context * ctx,
struct ggml_tensor * a) {
}
// ggml hardsigmoid
+
struct ggml_tensor * ggml_hardsigmoid(
struct ggml_context * ctx,
struct ggml_tensor * a) {
}
// ggml exp
+
struct ggml_tensor * ggml_exp(
struct ggml_context * ctx,
struct ggml_tensor * a) {
static struct ggml_tensor * ggml_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ float eps,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &eps, sizeof(eps));
- result->op = GGML_OP_NORM;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_NORM;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps) {
+ float eps) {
return ggml_norm_impl(ctx, a, eps, false);
}
struct ggml_tensor * ggml_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps) {
+ float eps) {
return ggml_norm_impl(ctx, a, eps, true);
}
static struct ggml_tensor * ggml_rms_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
+ float eps,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &eps, sizeof(eps));
- result->op = GGML_OP_RMS_NORM;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RMS_NORM;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps) {
+ float eps) {
return ggml_rms_norm_impl(ctx, a, eps, false);
}
struct ggml_tensor * ggml_rms_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
- float eps) {
+ float eps) {
return ggml_rms_norm_impl(ctx, a, eps, true);
}
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
- float eps) {
- bool is_node = false;
-
- if (a->grad) {
- // TODO: implement backward
- is_node = true;
- }
-
+ float eps) {
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &eps, sizeof(eps));
- result->op = GGML_OP_RMS_NORM_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RMS_NORM_BACK;
result->src[0] = a;
result->src[1] = b;
// ggml_group_norm
static struct ggml_tensor * ggml_group_norm_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int n_groups,
- float eps,
- bool inplace) {
-
- bool is_node = false;
- if (!inplace && (a->grad)) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int n_groups,
+ float eps,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, n_groups);
ggml_set_op_params_f32(result, 1, eps);
- result->op = GGML_OP_GROUP_NORM;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_GROUP_NORM;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_group_norm(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int n_groups,
- float eps) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int n_groups,
+ float eps) {
return ggml_group_norm_impl(ctx, a, n_groups, eps, false);
}
struct ggml_tensor * ggml_group_norm_inplace(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int n_groups,
- float eps) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int n_groups,
+ float eps) {
return ggml_group_norm_impl(ctx, a, n_groups, eps, true);
}
GGML_ASSERT(ggml_can_mul_mat(a, b));
GGML_ASSERT(!ggml_is_transposed(a));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
- result->op = GGML_OP_MUL_MAT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MUL_MAT;
result->src[0] = a;
result->src[1] = b;
GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat
GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast
- bool is_node = false;
-
- if (as->grad || b->grad) {
- is_node = true;
- }
-
const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
- result->op = GGML_OP_MUL_MAT_ID;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MUL_MAT_ID;
result->src[0] = as;
result->src[1] = b;
result->src[2] = ids;
GGML_ASSERT(ggml_can_out_prod(a, b));
GGML_ASSERT(!ggml_is_transposed(a));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
// a is broadcastable to b for ne[2] and ne[3] -> use b->ne[2] and b->ne[3]
const int64_t ne[4] = { a->ne[0], b->ne[0], b->ne[2], b->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
- result->op = GGML_OP_OUT_PROD;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_OUT_PROD;
result->src[0] = a;
result->src[1] = b;
struct ggml_context * ctx,
struct ggml_tensor * a,
float s,
- bool inplace) {
+ bool inplace) {
GGML_ASSERT(ggml_is_padded_1d(a));
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &s, sizeof(s));
- result->op = GGML_OP_SCALE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SCALE;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_scale(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- float s) {
+ struct ggml_tensor * a,
+ float s) {
return ggml_scale_impl(ctx, a, s, false);
}
struct ggml_tensor * ggml_scale_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- float s) {
+ struct ggml_tensor * a,
+ float s) {
return ggml_scale_impl(ctx, a, s, true);
}
size_t nb2,
size_t nb3,
size_t offset,
- bool inplace) {
+ bool inplace) {
GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
// make a view of the destination
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_SET;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SET;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * ggml_set(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
struct ggml_tensor * ggml_set_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
struct ggml_tensor * ggml_set_1d(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t offset) {
return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false);
}
struct ggml_tensor * ggml_set_1d_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t offset) {
return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true);
}
struct ggml_tensor * ggml_set_2d(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t nb1,
size_t offset) {
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
struct ggml_tensor * ggml_set_2d_inplace(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
size_t nb1,
size_t offset) {
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true);
struct ggml_tensor * b) {
GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- // inplace is false and either one have a grad
- is_node = true;
- }
-
// make a view of the destination
struct ggml_tensor * result = ggml_view_tensor(ctx, b);
if (strlen(b->name) > 0) {
ggml_format_name(result, "%s (copy)", a->name);
}
- result->op = GGML_OP_CPY;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CPY;
result->src[0] = a;
result->src[1] = b;
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_type type) {
- bool is_node = false;
-
struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne);
ggml_format_name(result, "%s (copy)", a->name);
- result->op = GGML_OP_CPY;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CPY;
result->src[0] = a;
- result->src[1] = result;
return result;
}
static struct ggml_tensor * ggml_cont_impl(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
ggml_format_name(result, "%s (cont)", a->name);
- result->op = GGML_OP_CONT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CONT;
result->src[0] = a;
return result;
int64_t ne3) {
GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3));
- bool is_node = false;
-
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
ggml_format_name(result, "%s (cont)", a->name);
- result->op = GGML_OP_CONT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CONT;
result->src[0] = a;
return result;
// as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous.
GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
- if (b->grad) {
- // gradient propagation is not supported
- //GGML_ABORT("fatal error");
- }
-
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
- result->op = GGML_OP_RESHAPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RESHAPE;
result->src[0] = a;
return result;
GGML_ASSERT(ggml_is_contiguous(a));
GGML_ASSERT(ggml_nelements(a) == ne0);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
const int64_t ne[1] = { ne0 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
- result->op = GGML_OP_RESHAPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RESHAPE;
result->src[0] = a;
return result;
GGML_ASSERT(ggml_is_contiguous(a));
GGML_ASSERT(ggml_nelements(a) == ne0*ne1);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
const int64_t ne[2] = { ne0, ne1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
- result->op = GGML_OP_RESHAPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RESHAPE;
result->src[0] = a;
return result;
GGML_ASSERT(ggml_is_contiguous(a));
GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
const int64_t ne[3] = { ne0, ne1, ne2 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
- result->op = GGML_OP_RESHAPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RESHAPE;
result->src[0] = a;
return result;
GGML_ASSERT(ggml_is_contiguous(a));
GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
- result->op = GGML_OP_RESHAPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RESHAPE;
result->src[0] = a;
return result;
int n_dims,
const int64_t * ne,
size_t offset) {
-
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
- result->op = GGML_OP_VIEW;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_VIEW;
result->src[0] = a;
return result;
struct ggml_tensor * a,
int64_t ne0,
size_t offset) {
-
struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset);
return result;
int64_t ne1,
size_t nb1,
size_t offset) {
-
const int64_t ne[2] = { ne0, ne1 };
struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset);
size_t nb1,
size_t nb2,
size_t offset) {
-
const int64_t ne[3] = { ne0, ne1, ne2 };
struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset);
size_t nb2,
size_t nb3,
size_t offset) {
-
const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset);
GGML_ASSERT(axis1 != axis3);
GGML_ASSERT(axis2 != axis3);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_format_name(result, "%s (permuted)", a->name);
result->nb[2] = nb[2];
result->nb[3] = nb[3];
- result->op = GGML_OP_PERMUTE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_PERMUTE;
result->src[0] = a;
int32_t params[] = { axis0, axis1, axis2, axis3 };
struct ggml_tensor * ggml_transpose(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_format_name(result, "%s (transposed)", a->name);
result->nb[0] = a->nb[1];
result->nb[1] = a->nb[0];
- result->op = GGML_OP_TRANSPOSE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_TRANSPOSE;
result->src[0] = a;
return result;
GGML_ASSERT(b->ne[3] == 1);
GGML_ASSERT(b->type == GGML_TYPE_I32);
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
// TODO: implement non F32 return
enum ggml_type type = GGML_TYPE_F32;
if (a->type == GGML_TYPE_I32) {
}
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
- result->op = GGML_OP_GET_ROWS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_GET_ROWS;
result->src[0] = a;
result->src[1] = b;
GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0]));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
-
// TODO: implement non F32 return
//struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]);
- result->op = GGML_OP_GET_ROWS_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_GET_ROWS_BACK;
result->src[0] = a;
result->src[1] = b;
struct ggml_context * ctx,
struct ggml_tensor * a) {
GGML_ASSERT(a->ne[1] == 1);
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, 4, ne);
- result->op = GGML_OP_DIAG;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_DIAG;
result->src[0] = a;
return result;
struct ggml_tensor * a,
int n_past,
bool inplace) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { n_past };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_DIAG_MASK_INF;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_DIAG_MASK_INF;
result->src[0] = a;
return result;
struct ggml_tensor * a,
int n_past,
bool inplace) {
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { n_past };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_DIAG_MASK_ZERO;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_DIAG_MASK_ZERO;
result->src[0] = a;
return result;
GGML_ASSERT(mask);
}
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
float params[] = { scale, max_bias };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_SOFT_MAX;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SOFT_MAX;
result->src[0] = a;
result->src[1] = mask;
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true; // TODO : implement backward pass
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_SOFT_MAX_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SOFT_MAX_BACK;
result->src[0] = a;
result->src[1] = b;
GGML_ASSERT(c->ne[0] >= n_dims / 2);
}
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
memcpy(params + 10, &beta_slow, sizeof(float));
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_ROPE;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ROPE;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
GGML_ASSERT(b->type == GGML_TYPE_I32);
GGML_ASSERT(a->ne[2] == b->ne[0]);
- bool is_node = false;
-
- if (a->grad) {
- GGML_ASSERT(false && "backwards pass not implemented");
- is_node = false;
- }
-
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
memcpy(params + 10, &beta_slow, sizeof(float));
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_ROPE_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ROPE_BACK;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
struct ggml_tensor * a,
float min,
float max) {
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
// TODO: when implement backward, fix this:
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
float params[] = { min, max };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_CLAMP;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CLAMP;
result->src[0] = a;
return result;
GGML_ASSERT(p0 == 0);
GGML_ASSERT(d0 == 1);
- bool is_node = false;
-
- if (a->grad || b->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
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,
int32_t params[] = { s0, p0, d0 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_CONV_TRANSPOSE_1D;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CONV_TRANSPOSE_1D;
result->src[0] = a;
result->src[1] = b;
}
// 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 * 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]),
// b: [N, IC, IH, IW]
// result: [N, OH, OW, IC*KH*KW]
struct ggml_tensor * ggml_im2col(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int s0,
- int s1,
- int p0,
- int p1,
- int d0,
- int d1,
- bool is_2D,
- enum ggml_type dst_type) {
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int s1,
+ int p0,
+ int p1,
+ int d0,
+ int d1,
+ bool is_2D,
+ enum ggml_type dst_type) {
if(is_2D) {
GGML_ASSERT(a->ne[2] == b->ne[2]);
} else {
GGML_ASSERT(a->ne[1] == b->ne[1]);
GGML_ASSERT(b->ne[3] == 1);
}
- bool is_node = false;
-
- if (/*a->grad ||*/ b->grad) { // a is only used for its shape, not its data
- is_node = true;
- }
const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0;
const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_IM2COL;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_IM2COL;
result->src[0] = a;
result->src[1] = b;
}
struct ggml_tensor * ggml_im2col_back(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int64_t * ne,
- int s0,
- int s1,
- int p0,
- int p1,
- int d0,
- int d1,
- bool is_2D) {
-
- bool is_node = false;
-
- if (/*a->grad ||*/ b->grad) { // a is only used for its shape, not its data
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int64_t * ne,
+ int s0,
+ int s1,
+ int p0,
+ int p1,
+ int d0,
+ int d1,
+ bool is_2D) {
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_IM2COL_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_IM2COL_BACK;
result->src[0] = a;
result->src[1] = b;
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
- int s0,
- int s1,
- int p0,
- int p1,
- int d0,
- int d1) {
+ int s0,
+ int s1,
+ int p0,
+ int p1,
+ int d0,
+ int d1) {
struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, a->type); // [N, OH, OW, IC * KH * KW]
struct ggml_tensor * result =
}
// ggml_conv_2d_sk_p0
+
struct ggml_tensor * ggml_conv_2d_sk_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,
int stride) {
GGML_ASSERT(a->ne[3] == b->ne[2]);
- bool is_node = false;
-
- if (a->grad || b->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
const int64_t ne[4] = {
ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/),
ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/),
ggml_set_op_params_i32(result, 0, stride);
- result->op = GGML_OP_CONV_TRANSPOSE_2D;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CONV_TRANSPOSE_2D;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * a,
enum ggml_op_pool op,
int k0,
- int s0,
- int p0) {
-
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ int s0,
+ int p0) {
const int64_t ne[4] = {
ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
a->ne[1],
int32_t params[] = { op, k0, s0, p0 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_POOL_1D;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_POOL_1D;
result->src[0] = a;
return result;
int s1,
float p0,
float p1) {
-
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result;
const int64_t ne[4] = {
ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_POOL_2D;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_POOL_2D;
result->src[0] = a;
+
return result;
}
int s1,
float p0,
float p1) {
-
- bool is_node = false;
-
- if (a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result;
result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, af->ne);
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_POOL_2D_BACK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_POOL_2D_BACK;
result->src[0] = a;
result->src[1] = af;
+
return result;
}
// ggml_upscale
static struct ggml_tensor * ggml_upscale_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int ne0,
- int ne1,
- int ne2,
- int ne3) {
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int ne0,
+ int ne1,
+ int ne2,
+ int ne3) {
GGML_ASSERT(a->ne[0] <= ne0);
GGML_ASSERT(a->ne[1] <= ne1);
GGML_ASSERT(a->ne[2] <= ne2);
GGML_ASSERT(a->ne[3] <= ne3);
- struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
- ne0,
- ne1,
- ne2,
- ne3
- );
-
- result->op = GGML_OP_UPSCALE;
+ struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_UPSCALE;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_upscale(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int scale_factor) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int scale_factor) {
return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]);
}
struct ggml_tensor * ggml_upscale_ext(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int ne0,
- int ne1,
- int ne2,
- int ne3) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int ne0,
+ int ne1,
+ int ne2,
+ int ne3) {
return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3);
}
// ggml_pad
struct ggml_tensor * ggml_pad(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- int p0, int p1, int p2, int p3) {
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int p0,
+ int p1,
+ int p2,
+ int p3) {
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
a->ne[0] + p0,
a->ne[1] + p1,
a->ne[2] + p2,
a->ne[3] + p3);
- result->op = GGML_OP_PAD;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_PAD;
result->src[0] = a;
return result;
// ggml_arange
struct ggml_tensor * ggml_arange(
- struct ggml_context * ctx,
- float start,
- float stop,
- float step) {
-
+ struct ggml_context * ctx,
+ float start,
+ float stop,
+ float step) {
GGML_ASSERT(stop > start);
const int64_t steps = (int64_t) ceilf((stop - start) / step);
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps);
- result->op = GGML_OP_ARANGE;
ggml_set_op_params_f32(result, 0, start);
ggml_set_op_params_f32(result, 1, stop);
ggml_set_op_params_f32(result, 2, step);
+ result->op = GGML_OP_ARANGE;
+
return result;
}
// ggml_timestep_embedding
struct ggml_tensor * ggml_timestep_embedding(
- struct ggml_context * ctx,
- struct ggml_tensor * timesteps,
- int dim,
- int max_period) {
- bool is_node = false;
-
- if (timesteps->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * timesteps,
+ int dim,
+ int max_period) {
int actual_dim = dim;
if (dim % 2 != 0) {
actual_dim = dim + 1;
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
- result->op = GGML_OP_TIMESTEP_EMBEDDING;
ggml_set_op_params_i32(result, 0, dim);
ggml_set_op_params_i32(result, 1, max_period);
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_TIMESTEP_EMBEDDING;
result->src[0] = timesteps;
return result;
// ggml_argsort
struct ggml_tensor * ggml_argsort(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- enum ggml_sort_order order) {
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: not implemented
- is_node = true;
- }
-
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ enum ggml_sort_order order) {
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne);
ggml_set_op_params_i32(result, 0, (int32_t) order);
- result->op = GGML_OP_ARGSORT;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ARGSORT;
result->src[0] = a;
return result;
bool is_node = false;
- if (q->grad || k->grad || v->grad) {
- is_node = true;
- }
-
// permute(0, 2, 1, 3)
int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
GGML_ASSERT(sx->ne[1] == d_inner);
GGML_ASSERT(n_t >= 0);
- bool is_node = false;
-
- if (sx->grad || c->grad) {
- GGML_ABORT("fatal error"); // TODO: implement
- is_node = true;
- }
-
struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_t, n_s);
- result->op = GGML_OP_SSM_CONV;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_SSM_CONV;
result->src[0] = sx;
result->src[1] = c;
GGML_ASSERT(B->ne[2] == n_seqs);
}
- bool is_node = false;
-
- if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad) {
- GGML_ABORT("fatal error"); // TODO: implement
- is_node = true;
- }
-
// concatenated y + ssm_states
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s));
result->op = GGML_OP_SSM_SCAN;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = s;
result->src[1] = x;
result->src[2] = dt;
GGML_ASSERT(a->ne[3] == 1);
GGML_ASSERT(a->type == GGML_TYPE_F32);
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
// padding
const int px = (w - a->ne[1]%w)%w;
const int py = (w - a->ne[2]%w)%w;
int32_t params[] = { npx, npy, w };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_WIN_PART;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_WIN_PART;
result->src[0] = a;
return result;
int w) {
GGML_ASSERT(a->type == GGML_TYPE_F32);
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
const int64_t ne[4] = { a->ne[0], w0, h0, 1, };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
int32_t params[] = { w };
ggml_set_op_params(result, params, sizeof(params));
- result->op = GGML_OP_WIN_UNPART;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_WIN_UNPART;
result->src[0] = a;
return result;
GGML_ASSERT(qh == kh);
GGML_ASSERT(2*MAX(qh, kh) - 1 == a->ne[1]);
- bool is_node = false;
-
- if (a->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
const int64_t ne[4] = { a->ne[0], kh, qh, 1, };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne);
- result->op = GGML_OP_GET_REL_POS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_GET_REL_POS;
result->src[0] = a;
return result;
GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]);
GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]);
- bool is_node = false;
-
- if (!inplace && (a->grad || pw->grad || ph->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, inplace ? 1 : 0);
- result->op = GGML_OP_ADD_REL_POS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_ADD_REL_POS;
result->src[0] = a;
result->src[1] = pw;
result->src[2] = ph;
struct ggml_tensor * ggml_rwkv_wkv(
struct ggml_context * ctx,
- struct ggml_tensor * k,
- struct ggml_tensor * v,
- struct ggml_tensor * r,
- struct ggml_tensor * tf,
- struct ggml_tensor * td,
- struct ggml_tensor * state) {
+ struct ggml_tensor * k,
+ struct ggml_tensor * v,
+ struct ggml_tensor * r,
+ struct ggml_tensor * tf,
+ struct ggml_tensor * td,
+ struct ggml_tensor * state) {
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(r));
GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs);
}
- bool is_node = false;
-
- if (k->grad || v->grad || r->grad || tf->grad || td->grad || state->grad) {
- GGML_ABORT("fatal error"); // TODO: implement backward
- is_node = true;
- }
-
// concat output and new_state
const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
- result->op = GGML_OP_RWKV_WKV;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_RWKV_WKV;
result->src[0] = k;
result->src[1] = v;
result->src[2] = r;
static struct ggml_tensor * ggml_unary_impl(
struct ggml_context * ctx,
- struct ggml_tensor * a,
- enum ggml_unary_op op,
- bool inplace) {
+ struct ggml_tensor * a,
+ enum ggml_unary_op op,
+ bool inplace) {
GGML_ASSERT(ggml_is_contiguous_1(a));
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, (int32_t) op);
- result->op = GGML_OP_UNARY;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_UNARY;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_unary(
struct ggml_context * ctx,
struct ggml_tensor * a,
- enum ggml_unary_op op) {
+ enum ggml_unary_op op) {
return ggml_unary_impl(ctx, a, op, false);
}
struct ggml_tensor * ggml_unary_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
- enum ggml_unary_op op) {
+ enum ggml_unary_op op) {
return ggml_unary_impl(ctx, a, op, true);
}
static struct ggml_tensor * ggml_map_unary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
- const ggml_unary_op_f32_t fun,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && a->grad) {
- is_node = true;
- }
-
+ const ggml_unary_op_f32_t fun,
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
- result->op = GGML_OP_MAP_UNARY;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_UNARY;
result->src[0] = a;
return result;
struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
- const ggml_unary_op_f32_t fun) {
+ const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, false);
}
struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
- const ggml_unary_op_f32_t fun) {
+ const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, true);
}
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
- const ggml_binary_op_f32_t fun,
- bool inplace) {
+ const ggml_binary_op_f32_t fun,
+ bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
- result->op = GGML_OP_MAP_BINARY;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_BINARY;
result->src[0] = a;
result->src[1] = b;
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
- const ggml_binary_op_f32_t fun) {
+ const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
}
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
- const ggml_binary_op_f32_t fun) {
+ const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && a->grad) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
- result->op = GGML_OP_MAP_CUSTOM1_F32;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM1_F32;
result->src[0] = a;
return result;
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
- result->op = GGML_OP_MAP_CUSTOM2_F32;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM2_F32;
result->src[0] = a;
result->src[1] = b;
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad || c->grad)) {
- is_node = true;
- }
-
+ bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
- result->op = GGML_OP_MAP_CUSTOM3_F32;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM3_F32;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
// ggml_map_custom1
struct ggml_map_custom1_op_params {
- ggml_custom1_op_t fun;
- int n_tasks;
- void * userdata;
+ ggml_custom1_op_t fun;
+ int n_tasks;
+ void * userdata;
};
static struct ggml_tensor * ggml_map_custom1_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- const ggml_custom1_op_t fun,
- int n_tasks,
- void * userdata,
- bool inplace) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ const ggml_custom1_op_t fun,
+ int n_tasks,
+ void * userdata,
+ bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
- bool is_node = false;
-
- if (!inplace && a->grad) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom1_op_params params = {
};
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
- result->op = GGML_OP_MAP_CUSTOM1;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM1;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_map_custom1(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- const ggml_custom1_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ const ggml_custom1_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom1_inplace(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- const ggml_custom1_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ const ggml_custom1_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
}
// ggml_map_custom2
struct ggml_map_custom2_op_params {
- ggml_custom2_op_t fun;
- int n_tasks;
- void * userdata;
+ ggml_custom2_op_t fun;
+ int n_tasks;
+ void * userdata;
};
static struct ggml_tensor * ggml_map_custom2_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- const ggml_custom2_op_t fun,
- int n_tasks,
- void * userdata,
- bool inplace) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ const ggml_custom2_op_t fun,
+ int n_tasks,
+ void * userdata,
+ bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom2_op_params params = {
};
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
- result->op = GGML_OP_MAP_CUSTOM2;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM2;
result->src[0] = a;
result->src[1] = b;
}
struct ggml_tensor * ggml_map_custom2(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- const ggml_custom2_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ const ggml_custom2_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom2_inplace(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- const ggml_custom2_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ const ggml_custom2_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
}
};
static struct ggml_tensor * ggml_map_custom3_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- struct ggml_tensor * c,
- const ggml_custom3_op_t fun,
- int n_tasks,
- void * userdata,
- bool inplace) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ const ggml_custom3_op_t fun,
+ int n_tasks,
+ void * userdata,
+ bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
- bool is_node = false;
-
- if (!inplace && (a->grad || b->grad || c->grad)) {
- is_node = true;
- }
-
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom3_op_params params = {
};
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
- result->op = GGML_OP_MAP_CUSTOM3;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_MAP_CUSTOM3;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
}
struct ggml_tensor * ggml_map_custom3(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- struct ggml_tensor * c,
- const ggml_custom3_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ const ggml_custom3_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom3_inplace(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- struct ggml_tensor * c,
- const ggml_custom3_op_t fun,
- int n_tasks,
- void * userdata) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ const ggml_custom3_op_t fun,
+ int n_tasks,
+ void * userdata) {
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
}
// ggml_cross_entropy_loss
struct ggml_tensor * ggml_cross_entropy_loss(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b) {
GGML_ASSERT(ggml_are_same_shape(a, b));
- bool is_node = false;
-
- if (a->grad || b->grad) {
- is_node = true;
- }
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
- result->op = GGML_OP_CROSS_ENTROPY_LOSS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->op = GGML_OP_CROSS_ENTROPY_LOSS;
result->src[0] = a;
result->src[1] = b;
// ggml_cross_entropy_loss_back
struct ggml_tensor * ggml_cross_entropy_loss_back(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- struct ggml_tensor * c) {
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c) {
GGML_ASSERT(ggml_are_same_shape(a, b));
GGML_ASSERT(ggml_is_scalar(c));
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
- result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
- result->grad = NULL;
+ result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
float beta2,
float eps,
float wd) {
- GGML_ASSERT(a->grad);
+ GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM);
GGML_ASSERT(alpha > 0.0f);
GGML_ASSERT(beta1 >= 0.0f && beta1 <= 1.0f);
GGML_ASSERT(beta2 >= 0.0f && beta2 <= 1.0f);
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
- result->op = GGML_OP_OPT_STEP_ADAMW;
- result->grad = NULL;
- result->src[0] = a;
- result->src[1] = a->grad;
- result->src[2] = ggml_dup_tensor(ctx, a->grad);
- result->src[3] = ggml_dup_tensor(ctx, a->grad);
-
const int64_t iter = 1;
memcpy(&result->op_params[0], &iter, sizeof(int64_t));
ggml_set_op_params_f32(result, 2, alpha);
ggml_set_op_params_f32(result, 5, eps);
ggml_set_op_params_f32(result, 6, wd);
+ result->op = GGML_OP_OPT_STEP_ADAMW;
+ result->src[0] = a;
+ result->src[1] = a->grad;
+ result->src[2] = ggml_dup_tensor(ctx, a);
+ result->src[3] = ggml_dup_tensor(ctx, a);
+
return result;
}
////////////////////////////////////////////////////////////////////////////////
-void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
- tensor->flags |= GGML_TENSOR_FLAG_PARAM;
-
- GGML_ASSERT(tensor->grad == NULL);
- tensor->grad = ggml_dup_tensor(ctx, tensor);
- ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
-}
-
-void ggml_set_loss(struct ggml_tensor * tensor) {
- GGML_ASSERT(ggml_is_scalar(tensor));
- GGML_ASSERT(tensor->type == GGML_TYPE_F32);
- GGML_ASSERT(tensor->grad);
- tensor->flags |= GGML_TENSOR_FLAG_LOSS;
-}
-
// ggml_compute_forward_dup
static void ggml_compute_forward_dup_same_cont(
struct ggml_tensor * * checkpoints,
int n_checkpoints) {
ggml_graph_cpy(gf, gb_tmp);
- ggml_build_backward_expand(ctx, gf, gb_tmp, false, true);
+ ggml_build_backward_expand(ctx, gf, gb_tmp, false);
if (n_checkpoints <= 0) {
ggml_graph_cpy(gb_tmp, gb);
ggml_soft_max_back(ctx, tensor->grad, tensor),
zero_table, acc_table);
}
-
+ GGML_ASSERT((!src1 || !src1->grad) && "backward pass for softmax mask not implemented");
} break;
case GGML_OP_SOFT_MAX_BACK:
{
beta_slow),
zero_table, acc_table);
}
+ GGML_ASSERT((!src2 || !src2->grad) && "gradients for freq factors not implemented");
} break;
case GGML_OP_ROPE_BACK:
{
}
case GGML_OP_FLASH_ATTN_EXT:
{
+ GGML_ABORT("FA backward pass not adapted after rework");
struct ggml_tensor * flash_grad = NULL;
if (src0->grad || src1->grad || tensor->src[2]->grad) {
int32_t t = ggml_get_op_params_i32(tensor, 0);
tensor->grad),
zero_table, acc_table);
}
+ GGML_ASSERT(!src1->grad && "backward pass for labels not implemented");
} break;
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
{
}
}
- if (node->op == GGML_OP_NONE && node->grad == NULL) {
+ if (node->op == GGML_OP_NONE && !(node->flags & GGML_TENSOR_FLAG_PARAM)) {
// reached a leaf node, not part of the gradient graph (e.g. a constant)
GGML_ASSERT(cgraph->n_leafs < cgraph->size);
}
cgraph->nodes[cgraph->n_nodes] = node;
- if (cgraph->grads) {
- cgraph->grads[cgraph->n_nodes] = node->grad;
- }
cgraph->n_nodes++;
}
}
ggml_build_forward_impl(cgraph, tensor, true);
}
-void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep) {
+void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate) {
GGML_ASSERT(gf->n_nodes > 0);
GGML_ASSERT(gf->grads);
- // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph
- if (keep) {
- for (int i = 0; i < gf->n_nodes; i++) {
- struct ggml_tensor * node = gf->nodes[i];
+ for (int i = 0; i < gf->n_nodes; ++i) {
+ struct ggml_tensor * node = gf->nodes[i];
+
+ bool needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM;
+ bool ignore_src[GGML_MAX_SRC] = {false};
+ switch (node->op) {
+ // gradients in node->src[0] for one reason or another have no effect on output gradients
+ case GGML_OP_IM2COL: // only used for its shape
+ case GGML_OP_IM2COL_BACK: // same as IM2COL
+ ignore_src[0] = true;
+ break;
+ case GGML_OP_UNARY: {
+ const enum ggml_unary_op uop = ggml_get_unary_op(node);
+ // SGN and STEP unary ops are piecewise constant
+ if (uop == GGML_UNARY_OP_SGN || uop == GGML_UNARY_OP_STEP) {
+ ignore_src[0] = true;
+ }
+ } break;
+
+ // gradients in node->src[1] for one reason or another have no effect on output gradients
+ case GGML_OP_CPY: // gradients in CPY target are irrelevant
+ case GGML_OP_GET_ROWS: // row indices not differentiable
+ case GGML_OP_GET_ROWS_BACK: // same as for GET_ROWS
+ case GGML_OP_ROPE: // positions not differentiable
+ ignore_src[1] = true;
+ break;
- if (node->grad) {
- node->grad = ggml_dup_tensor(ctx, node);
- gf->grads[i] = node->grad;
+ default:
+ break;
+ }
+ for (int j = 0; j < GGML_MAX_SRC; ++j) {
+ if (!node->src[j] || !node->src[j]->grad || ignore_src[j]) {
+ continue;
}
+ GGML_ASSERT(node->src[j]->type == GGML_TYPE_F32 || node->src[j]->type == GGML_TYPE_F16);
+ needs_grad = true;
+ break;
+ }
+ if (!needs_grad) {
+ continue;
}
+
+ // inplace operations are currently not supported
+ GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW ||
+ node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE);
+
+ // create a new tensor with the same type and shape as the node and set it as grad
+ node->grad = ggml_dup_tensor(ctx, node);
}
// keep tables of original gradients for replacement/accumulation logic
struct ggml_context * ctx,
struct ggml_opt_params params,
struct ggml_tensor * f) {
- GGML_ASSERT(f->grad && "ggml_set_param called for at least one parent tensor.");
-
bool free_ctx = false;
if (ctx == NULL) {
struct ggml_init_params params_ctx = {
ggml_build_forward_expand(gf, f);
struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf);
- ggml_build_backward_expand(ctx, gf, gb, false, true);
+ ggml_build_backward_expand(ctx, gf, gb, false);
return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL);
}
tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
}
+void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+ GGML_UNUSED(ctx); // TODO: remove this parameter
+ tensor->flags |= GGML_TENSOR_FLAG_PARAM;
+}
+
+void ggml_set_loss(struct ggml_tensor * tensor) {
+ GGML_ASSERT(ggml_is_scalar(tensor));
+ GGML_ASSERT(tensor->type == GGML_TYPE_F32);
+ tensor->flags |= GGML_TENSOR_FLAG_LOSS;
+}
+
////////////////////////////////////////////////////////////////////////////////
void ggml_quantize_init(enum ggml_type type) {
overview / pkg / ggml / sources / llama.cpp / commitdiff