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-rw-r--r--ggml.c369
-rw-r--r--ggml.h60
2 files changed, 397 insertions, 32 deletions
diff --git a/ggml.c b/ggml.c
index 7104be0..e3f0c93 100644
--- a/ggml.c
+++ b/ggml.c
@@ -1,5 +1,5 @@
-// Defines CLOCK_MONOTONIC on Linux
-#define _GNU_SOURCE
+#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux
+#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#include "ggml.h"
@@ -131,6 +131,34 @@ typedef void* thread_ret_t;
#define GGML_MEM_ALIGN 16
#endif
+//
+// logging
+//
+
+#if (GGML_DEBUG >= 1)
+#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG(...)
+#endif
+
+#if (GGML_DEBUG >= 5)
+#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_5(...)
+#endif
+
+#if (GGML_DEBUG >= 10)
+#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_10(...)
+#endif
+
+#define GGML_PRINT(...) printf(__VA_ARGS__)
+
+//
+// end of logging block
+//
+
#if defined(_MSC_VER) || defined(__MINGW32__)
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
@@ -144,6 +172,17 @@ inline static void* ggml_aligned_malloc(size_t size) {
#endif
if (result != 0) {
// Handle allocation failure
+ const char *error_desc = "unknown allocation error";
+ switch (result) {
+ case EINVAL:
+ error_desc = "invalid alignment value";
+ break;
+ case ENOMEM:
+ error_desc = "insufficient memory";
+ break;
+ }
+ GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n",
+ __func__, error_desc, size/(1024.0*1024.0));
return NULL;
}
return aligned_memory;
@@ -3531,30 +3570,6 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
}
//
-// logging
-//
-
-#if (GGML_DEBUG >= 1)
-#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
-#else
-#define GGML_PRINT_DEBUG(...)
-#endif
-
-#if (GGML_DEBUG >= 5)
-#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
-#else
-#define GGML_PRINT_DEBUG_5(...)
-#endif
-
-#if (GGML_DEBUG >= 10)
-#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
-#else
-#define GGML_PRINT_DEBUG_10(...)
-#endif
-
-#define GGML_PRINT(...) printf(__VA_ARGS__)
-
-//
// data types
//
@@ -3713,11 +3728,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"MAP_UNARY",
"MAP_BINARY",
+ "MAP_CUSTOM1",
+ "MAP_CUSTOM2",
+ "MAP_CUSTOM3",
+
"CROSS_ENTROPY_LOSS",
"CROSS_ENTROPY_LOSS_BACK",
};
-static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61");
+static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -3785,11 +3804,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"f(x)",
"f(x,y)",
+ "custom(x)",
+ "custom(x,y)",
+ "custom(x,y,z)",
+
"cross_entropy_loss(x,y)",
"cross_entropy_loss_back(x,y)",
};
-static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61");
+static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -7094,9 +7117,14 @@ struct ggml_tensor * ggml_map_unary_impl_f32(
is_node = true;
}
+ struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_save(ctx);
+
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
- struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_UNARY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7136,9 +7164,14 @@ struct ggml_tensor * ggml_map_binary_impl_f32(
is_node = true;
}
+ struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_save(ctx);
+
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
- struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_BINARY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7165,6 +7198,150 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
+// ggml_map_custom1
+
+struct ggml_tensor * ggml_map_custom1_impl_f32(
+ 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;
+ }
+
+ struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_save(ctx);
+
+ struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
+ *((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
+
+ ggml_scratch_load(ctx);
+
+ result->op = GGML_OP_MAP_CUSTOM1;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src0 = a;
+ result->opt[0] = addr_tensor;
+
+ return result;
+}
+
+struct ggml_tensor * ggml_map_custom1_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ const ggml_custom1_op_f32_t fun) {
+ return ggml_map_custom1_impl_f32(ctx, a, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom1_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ const ggml_custom1_op_f32_t fun) {
+ return ggml_map_custom1_impl_f32(ctx, a, fun, true);
+}
+
+// ggml_map_custom2
+
+struct ggml_tensor * ggml_map_custom2_impl_f32(
+ struct ggml_context * ctx,
+ 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;
+ }
+
+ struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_save(ctx);
+
+ struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
+ *((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
+
+ ggml_scratch_load(ctx);
+
+ result->op = GGML_OP_MAP_CUSTOM2;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src0 = a;
+ result->src1 = b;
+ result->opt[0] = addr_tensor;
+
+ return result;
+}
+
+struct ggml_tensor * ggml_map_custom2_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ const ggml_custom2_op_f32_t fun) {
+ return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom2_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ const ggml_custom2_op_f32_t fun) {
+ return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
+}
+
+// ggml_map_custom3
+
+struct ggml_tensor * ggml_map_custom3_impl_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ 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;
+ }
+
+ struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_scratch_save(ctx);
+
+ struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
+ *((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
+
+ ggml_scratch_load(ctx);
+
+ result->op = GGML_OP_MAP_CUSTOM3;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src0 = a;
+ result->src1 = b;
+ result->opt[0] = addr_tensor;
+ result->opt[1] = c;
+
+ return result;
+}
+
+struct ggml_tensor * ggml_map_custom3_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ const ggml_custom3_op_f32_t fun) {
+ return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom3_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ const ggml_custom3_op_f32_t fun) {
+ return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
+}
+
// ggml_cross_entropy_loss
struct ggml_tensor * ggml_cross_entropy_loss(
@@ -14621,6 +14798,114 @@ static void ggml_compute_forward_map_binary(
}
}
+// ggml_compute_forward_map_custom1
+
+static void ggml_compute_forward_map_custom1_f32(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ struct ggml_tensor * dst,
+ const ggml_custom1_op_f32_t fun) {
+ assert(params->ith == 0);
+
+ if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ fun(dst, a);
+}
+
+
+static void ggml_compute_forward_map_custom1(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ struct ggml_tensor * dst,
+ const ggml_custom1_op_f32_t fun) {
+ switch (a->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_map_custom1_f32(params, a, dst, fun);
+ } break;
+ default:
+ {
+ GGML_ASSERT(false);
+ } break;
+ }
+}
+
+// ggml_compute_forward_map_custom2
+
+static void ggml_compute_forward_map_custom2_f32(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ const struct ggml_tensor * b,
+ struct ggml_tensor * dst,
+ const ggml_custom2_op_f32_t fun) {
+ assert(params->ith == 0);
+
+ if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ fun(dst, a, b);
+}
+
+
+static void ggml_compute_forward_map_custom2(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ const struct ggml_tensor * b,
+ struct ggml_tensor * dst,
+ const ggml_custom2_op_f32_t fun) {
+ switch (a->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun);
+ } break;
+ default:
+ {
+ GGML_ASSERT(false);
+ } break;
+ }
+}
+
+// ggml_compute_forward_map_custom3
+
+static void ggml_compute_forward_map_custom3_f32(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ const struct ggml_tensor * b,
+ const struct ggml_tensor * c,
+ struct ggml_tensor * dst,
+ const ggml_custom3_op_f32_t fun) {
+ assert(params->ith == 0);
+
+ if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ fun(dst, a, b, c);
+}
+
+
+static void ggml_compute_forward_map_custom3(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * a,
+ const struct ggml_tensor * b,
+ const struct ggml_tensor * c,
+ struct ggml_tensor * dst,
+ const ggml_custom3_op_f32_t fun) {
+ switch (a->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun);
+ } break;
+ default:
+ {
+ GGML_ASSERT(false);
+ } break;
+ }
+}
+
// ggml_compute_forward_cross_entropy_loss
static void ggml_compute_forward_cross_entropy_loss_f32(
@@ -15158,6 +15443,24 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
}
break;
+ case GGML_OP_MAP_CUSTOM1:
+ {
+ const ggml_custom1_op_f32_t fun = *((ggml_custom1_op_f32_t *)tensor->opt[0]->data);
+ ggml_compute_forward_map_custom1(params, tensor->src0, tensor, fun);
+ }
+ break;
+ case GGML_OP_MAP_CUSTOM2:
+ {
+ const ggml_custom2_op_f32_t fun = *((ggml_custom2_op_f32_t *)tensor->opt[0]->data);
+ ggml_compute_forward_map_custom2(params, tensor->src0, tensor->src1, tensor, fun);
+ }
+ break;
+ case GGML_OP_MAP_CUSTOM3:
+ {
+ const ggml_custom3_op_f32_t fun = *((ggml_custom3_op_f32_t *)tensor->opt[0]->data);
+ ggml_compute_forward_map_custom3(params, tensor->src0, tensor->src1, tensor->opt[1], tensor, fun);
+ }
+ break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
ggml_compute_forward_cross_entropy_loss(params, tensor->src0, tensor->src1, tensor);
@@ -15964,6 +16267,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
case GGML_OP_WIN_UNPART:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
+ case GGML_OP_MAP_CUSTOM1:
+ case GGML_OP_MAP_CUSTOM2:
+ case GGML_OP_MAP_CUSTOM3:
{
GGML_ASSERT(false); // not supported
} break;
@@ -16605,6 +16911,9 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
case GGML_OP_WIN_UNPART:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
+ case GGML_OP_MAP_CUSTOM1:
+ case GGML_OP_MAP_CUSTOM2:
+ case GGML_OP_MAP_CUSTOM3:
{
node->n_tasks = 1;
} break;
diff --git a/ggml.h b/ggml.h
index 4b6b728..5ebd9c4 100644
--- a/ggml.h
+++ b/ggml.h
@@ -345,6 +345,10 @@ extern "C" {
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
+ GGML_OP_MAP_CUSTOM1,
+ GGML_OP_MAP_CUSTOM2,
+ GGML_OP_MAP_CUSTOM3,
+
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
@@ -1167,21 +1171,73 @@ extern "C" {
int h0,
int w);
- // Mapping operations
- typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
+ // custom operators
+
+ typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+ typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
+ typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+ typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
+ GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ ggml_unary_op_f32_t fun);
+
GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
+ GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ ggml_binary_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom1_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ ggml_custom1_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ ggml_custom1_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom2_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ ggml_custom2_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ ggml_custom2_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom3_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ ggml_custom3_op_f32_t fun);
+
+ GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ struct ggml_tensor * c,
+ ggml_custom3_op_f32_t fun);
+
// loss function
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
generated by cgit v1.2.3 (git 2.25.1) at 2024-12-25 20:26:19 +0000