diff options
| -rw-r--r-- | ggml.c | 379 | ||||
| -rw-r--r-- | ggml.h | 145 |
2 files changed, 413 insertions, 111 deletions
@@ -195,8 +195,8 @@ typedef void * thread_ret_t; #define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) #define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) #else -inline static void* ggml_aligned_malloc(size_t size) { - void* aligned_memory = NULL; +inline static void * ggml_aligned_malloc(size_t size) { + void * aligned_memory = NULL; #ifdef GGML_USE_METAL int result = posix_memalign(&aligned_memory, getpagesize(), size); #else @@ -3811,7 +3811,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59"); +static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3883,7 +3883,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59"); +static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -4253,7 +4253,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { +bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return @@ -6890,7 +6890,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d( ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), a->ne[2], 1, 1, }; - struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); int32_t params[] = { s0, p0, d0 }; ggml_set_op_params(result, ¶ms, sizeof(params)); @@ -6905,10 +6905,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d( // ggml_conv_2d -struct ggml_tensor* ggml_conv_2d( - struct ggml_context* ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, +struct ggml_tensor * ggml_conv_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, int s0, int s1, int p0, @@ -6929,7 +6929,7 @@ struct ggml_tensor* ggml_conv_2d( ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1), a->ne[3], b->ne[3], }; - struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1 }; ggml_set_op_params(result, ¶ms, sizeof(params)); @@ -6945,7 +6945,7 @@ struct ggml_tensor* ggml_conv_2d( // ggml_conv_1d_ph -struct ggml_tensor* ggml_conv_1d_ph( +struct ggml_tensor * ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -6963,7 +6963,7 @@ static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { // ggml_pool_1d -struct ggml_tensor* ggml_pool_1d( +struct ggml_tensor * ggml_pool_1d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, @@ -6982,7 +6982,7 @@ struct ggml_tensor* ggml_pool_1d( ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], }; - struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, ¶ms, sizeof(params)); @@ -6996,7 +6996,7 @@ struct ggml_tensor* ggml_pool_1d( // ggml_pool_2d -struct ggml_tensor* ggml_pool_2d( +struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, @@ -7019,7 +7019,7 @@ struct ggml_tensor* ggml_pool_2d( ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; - struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, ¶ms, sizeof(params)); @@ -7349,7 +7349,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32( return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } -// ggml_map_custom1 +// ggml_map_custom1_f32 static struct ggml_tensor * ggml_map_custom1_impl_f32( struct ggml_context * ctx, @@ -7366,7 +7366,7 @@ static struct ggml_tensor * ggml_map_custom1_impl_f32( ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM1; + result->op = GGML_OP_MAP_CUSTOM1_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; @@ -7387,7 +7387,7 @@ struct ggml_tensor * ggml_map_custom1_inplace_f32( return ggml_map_custom1_impl_f32(ctx, a, fun, true); } -// ggml_map_custom2 +// ggml_map_custom2_f32 static struct ggml_tensor * ggml_map_custom2_impl_f32( struct ggml_context * ctx, @@ -7405,7 +7405,7 @@ static struct ggml_tensor * ggml_map_custom2_impl_f32( ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM2; + result->op = GGML_OP_MAP_CUSTOM2_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; @@ -7429,7 +7429,7 @@ struct ggml_tensor * ggml_map_custom2_inplace_f32( return ggml_map_custom2_impl_f32(ctx, a, b, fun, true); } -// ggml_map_custom3 +// ggml_map_custom3_f32 static struct ggml_tensor * ggml_map_custom3_impl_f32( struct ggml_context * ctx, @@ -7448,7 +7448,7 @@ static struct ggml_tensor * ggml_map_custom3_impl_f32( ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM3; + result->op = GGML_OP_MAP_CUSTOM3_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; @@ -7475,6 +7475,190 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32( return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true); } +// ggml_map_custom1 +struct ggml_map_custom1_op_params { + 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) { + 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 = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + 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->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) { + 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) { + 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; +}; + +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) { + 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 = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + 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->src[0] = a; + result->src[1] = b; + + return result; +} + +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) { + 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) { + return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true); +} + +// ggml_map_custom3 + +struct ggml_map_custom3_op_params { + ggml_custom3_op_t fun; + int n_tasks; + void * userdata; +}; + +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) { + 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 = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + 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->src[0] = a; + result->src[1] = b; + result->src[2] = c; + + return result; +} + +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) { + 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) { + 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( @@ -9283,8 +9467,8 @@ static void ggml_compute_forward_sum_rows_f32( for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { - float* src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); - float* dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); + float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); + float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); float row_sum = 0; ggml_vec_sum_f32(ne00, &row_sum, src_row); dst_row[0] = row_sum; @@ -12894,7 +13078,7 @@ static void ggml_compute_forward_pool_1d( const struct ggml_tensor * src0, struct ggml_tensor * dst) { - const int32_t* opts = (const int32_t*)dst->op_params; + const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int s0 = opts[2]; @@ -14227,24 +14411,6 @@ static void ggml_compute_forward_map_custom1_f32( 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( @@ -14263,24 +14429,6 @@ static void ggml_compute_forward_map_custom2_f32( } -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( @@ -14299,24 +14447,52 @@ static void ggml_compute_forward_map_custom3_f32( fun(dst, a, b, c); } +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params; + + p->fun(dst, a, params->ith, params->nth, p->userdata); +} + +// ggml_compute_forward_map_custom2 + +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) { + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params; + + p->fun(dst, a, b, params->ith, params->nth, p->userdata); +} + +// ggml_compute_forward_map_custom3 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; + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; } + + struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params; + + p->fun(dst, a, b, c, params->ith, params->nth, p->userdata); } // ggml_compute_forward_cross_entropy_loss @@ -14838,25 +15014,40 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun); } break; - case GGML_OP_MAP_CUSTOM1: + case GGML_OP_MAP_CUSTOM1_F32: { ggml_custom1_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom1(params, tensor->src[0], tensor, fun); + ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun); } break; - case GGML_OP_MAP_CUSTOM2: + case GGML_OP_MAP_CUSTOM2_F32: { ggml_custom2_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor, fun); + ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun); } break; - case GGML_OP_MAP_CUSTOM3: + case GGML_OP_MAP_CUSTOM3_F32: { ggml_custom3_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun); + ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM1: + { + ggml_compute_forward_map_custom1(params, tensor->src[0], tensor); + } + break; + case GGML_OP_MAP_CUSTOM2: + { + ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor); + } + break; + case GGML_OP_MAP_CUSTOM3: + { + ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS: @@ -15664,6 +15855,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor } break; case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: case GGML_OP_MAP_CUSTOM1: case GGML_OP_MAP_CUSTOM2: case GGML_OP_MAP_CUSTOM3: @@ -16449,11 +16643,38 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: + { + n_tasks = 1; + } break; case GGML_OP_MAP_CUSTOM1: + { + struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; case GGML_OP_MAP_CUSTOM2: + { + struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; case GGML_OP_MAP_CUSTOM3: { - n_tasks = 1; + struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } } break; case GGML_OP_CROSS_ENTROPY_LOSS: { @@ -183,6 +183,15 @@ # define GGML_API #endif +// TODO: support for clang +#ifdef __GNUC__ +# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint))) +#elif defined(_MSC_VER) +# define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func +#else +# define GGML_DEPRECATED(func, hint) func +#endif + #include <stdint.h> #include <stddef.h> #include <stdbool.h> @@ -374,6 +383,10 @@ extern "C" { GGML_OP_MAP_UNARY, GGML_OP_MAP_BINARY, + GGML_OP_MAP_CUSTOM1_F32, + GGML_OP_MAP_CUSTOM2_F32, + GGML_OP_MAP_CUSTOM3_F32, + GGML_OP_MAP_CUSTOM1, GGML_OP_MAP_CUSTOM2, GGML_OP_MAP_CUSTOM3, @@ -570,6 +583,8 @@ extern "C" { GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor); GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor); + GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1); + // use this to compute the memory overhead of a tensor GGML_API size_t ggml_tensor_overhead(void); @@ -1240,7 +1255,7 @@ extern "C" { // conv_1d with padding = half // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d) - GGML_API struct ggml_tensor* ggml_conv_1d_ph( + GGML_API struct ggml_tensor * ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -1253,7 +1268,7 @@ extern "C" { GGML_OP_POOL_COUNT, }; - GGML_API struct ggml_tensor* ggml_pool_1d( + GGML_API struct ggml_tensor * ggml_pool_1d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, @@ -1261,7 +1276,7 @@ extern "C" { int s0, // stride int p0); // padding - GGML_API struct ggml_tensor* ggml_pool_2d( + GGML_API struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, @@ -1315,15 +1330,6 @@ extern "C" { int h0, int w); - // 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_unary( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1334,63 +1340,138 @@ extern "C" { struct ggml_tensor * a, enum ggml_unary_op op); - GGML_API struct ggml_tensor * ggml_map_unary_f32( + // 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_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, - ggml_unary_op_f32_t fun); + ggml_unary_op_f32_t fun), + "use ggml_map_custom1 instead"); - GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32( + GGML_DEPRECATED(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_unary_op_f32_t fun), + "use ggml_map_custom1_inplace instead"); - GGML_API struct ggml_tensor * ggml_map_binary_f32( + GGML_DEPRECATED(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_binary_op_f32_t fun), + "use ggml_map_custom2 instead"); - GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32( + GGML_DEPRECATED(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_binary_op_f32_t fun), + "use ggml_map_custom2_inplace instead"); - GGML_API struct ggml_tensor * ggml_map_custom1_f32( + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32( struct ggml_context * ctx, struct ggml_tensor * a, - ggml_custom1_op_f32_t fun); + ggml_custom1_op_f32_t fun), + "use ggml_map_custom1 instead"); - GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32( + GGML_DEPRECATED(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_custom1_op_f32_t fun), + "use ggml_map_custom1_inplace instead"); - GGML_API struct ggml_tensor * ggml_map_custom2_f32( + GGML_DEPRECATED(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_custom2_op_f32_t fun), + "use ggml_map_custom2 instead"); - GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32( + GGML_DEPRECATED(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_custom2_op_f32_t fun), + "use ggml_map_custom2_inplace instead"); - GGML_API struct ggml_tensor * ggml_map_custom3_f32( + GGML_DEPRECATED(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_custom3_op_f32_t fun), + "use ggml_map_custom3 instead"); - GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32( + GGML_DEPRECATED(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); + ggml_custom3_op_f32_t fun), + "use ggml_map_custom3_inplace instead"); + + // custom operators v2 + + typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); + typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata); + typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata); + + #define GGML_N_TASKS_MAX -1 + + GGML_API struct ggml_tensor * ggml_map_custom1( + struct ggml_context * ctx, + struct ggml_tensor * a, + ggml_custom1_op_t fun, + int n_tasks, + void * userdata); + + GGML_API struct ggml_tensor * ggml_map_custom1_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + ggml_custom1_op_t fun, + int n_tasks, + void * userdata); + + GGML_API struct ggml_tensor * ggml_map_custom2( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + ggml_custom2_op_t fun, + int n_tasks, + void * userdata); + + GGML_API struct ggml_tensor * ggml_map_custom2_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + ggml_custom2_op_t fun, + int n_tasks, + void * userdata); + + GGML_API struct ggml_tensor * ggml_map_custom3( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + ggml_custom3_op_t fun, + int n_tasks, + void * userdata); + + GGML_API struct ggml_tensor * ggml_map_custom3_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + ggml_custom3_op_t fun, + int n_tasks, + void * userdata); // loss function |