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https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 15:29:43 +00:00
ggml : add unary and binary map operations (#874)
* GGML map ops proof of concept. * Various cleanups. Add handling for task setting. Add handling for ggml_compute_backward. Rename functions to ggml_map_unary_f32 and ggml_map_binary_f32 Fix compiler warnings related to casting function pointers and `void *` Reorder functions and definitions based on the GGML op number. Use typedefs for map op function pointer types. * Fix position of map ops cases in ggml_compute_forward
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2 changed files with 237 additions and 2 deletions
221
ggml.c
221
ggml.c
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@ -2712,9 +2712,12 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
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"FLASH_ATTN",
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"FLASH_FF",
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"MAP_UNARY",
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"MAP_BINARY",
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};
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static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
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static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -2757,9 +2760,12 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"flash_attn(x)",
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"flash_ff(x)",
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"f(x)",
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"f(x,y)",
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};
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static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
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static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
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static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
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static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
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@ -4907,6 +4913,90 @@ struct ggml_tensor * ggml_flash_ff(
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return result;
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}
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// ggml_map_unary
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struct ggml_tensor * ggml_map_unary_impl_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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const ggml_unary_op_f32_t fun,
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bool inplace) {
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bool is_node = false;
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if (!inplace && a->grad) {
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is_node = true;
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}
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struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
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*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
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struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op = GGML_OP_MAP_UNARY;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src0 = a;
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result->opt[0] = addr_tensor;
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return result;
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}
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struct ggml_tensor * ggml_map_unary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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const ggml_unary_op_f32_t fun) {
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return ggml_map_unary_impl_f32(ctx, a, fun, false);
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}
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struct ggml_tensor * ggml_map_unary_inplace_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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const ggml_unary_op_f32_t fun) {
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return ggml_map_unary_impl_f32(ctx, a, fun, true);
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}
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// ggml_map_binary
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struct ggml_tensor * ggml_map_binary_impl_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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const ggml_binary_op_f32_t fun,
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bool inplace) {
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GGML_ASSERT(ggml_are_same_shape(a, b));
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bool is_node = false;
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if (!inplace && (a->grad || b->grad)) {
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is_node = true;
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}
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struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
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*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
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struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op = GGML_OP_MAP_BINARY;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src0 = a;
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result->src1 = b;
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result->opt[0] = addr_tensor;
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return result;
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}
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struct ggml_tensor * ggml_map_binary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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const ggml_binary_op_f32_t fun) {
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return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
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}
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struct ggml_tensor * ggml_map_binary_inplace_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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const ggml_binary_op_f32_t fun) {
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return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
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}
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////////////////////////////////////////////////////////////////////////////////
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void ggml_set_param(
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@ -8875,6 +8965,111 @@ static void ggml_compute_forward_flash_ff(
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}
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}
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// ggml_compute_forward_map_unary
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static void ggml_compute_forward_map_unary_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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struct ggml_tensor * dst,
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const ggml_unary_op_f32_t fun) {
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GGML_ASSERT(ggml_are_same_shape(src0, dst));
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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assert( dst->nb[0] == sizeof(float));
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assert(src0->nb[0] == sizeof(float));
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for (int i = 0; i < n; i++) {
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fun(nc,
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(float *) ((char *) dst->data + i*( dst->nb[1])),
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(float *) ((char *) src0->data + i*(src0->nb[1])));
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}
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}
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static void ggml_compute_forward_map_unary(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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struct ggml_tensor * dst,
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const ggml_unary_op_f32_t fun) {
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
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} break;
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_I8:
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_F16:
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case GGML_TYPE_COUNT:
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{
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GGML_ASSERT(false);
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} break;
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}
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}
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// ggml_compute_forward_map_binary
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static void ggml_compute_forward_map_binary_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst,
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const ggml_binary_op_f32_t fun) {
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assert(params->ith == 0);
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assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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assert( dst->nb[0] == sizeof(float));
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assert(src0->nb[0] == sizeof(float));
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assert(src1->nb[0] == sizeof(float));
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for (int i = 0; i < n; i++) {
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fun(nc,
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(float *) ((char *) dst->data + i*( dst->nb[1])),
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(float *) ((char *) src0->data + i*(src0->nb[1])),
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(float *) ((char *) src1->data + i*(src1->nb[1])));
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}
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}
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static void ggml_compute_forward_map_binary(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst,
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const ggml_binary_op_f32_t fun) {
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
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} break;
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_I8:
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_F16:
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case GGML_TYPE_COUNT:
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{
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GGML_ASSERT(false);
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} break;
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}
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}
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/////////////////////////////////
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static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
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@ -9024,6 +9219,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor);
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} break;
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case GGML_OP_MAP_UNARY:
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{
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const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data);
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ggml_compute_forward_map_unary(params, tensor->src0, tensor, fun);
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}
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break;
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case GGML_OP_MAP_BINARY:
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{
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const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->opt[0]->data);
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ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
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}
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break;
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case GGML_OP_NONE:
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{
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// nop
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@ -9283,6 +9490,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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{
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GGML_ASSERT(false); // not supported
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} break;
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case GGML_OP_MAP_UNARY:
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case GGML_OP_MAP_BINARY:
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{
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GGML_ASSERT(false); // not supported
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} break;
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case GGML_OP_NONE:
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{
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// nop
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@ -9775,6 +9987,11 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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work_size = MAX(work_size, cur);
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} break;
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case GGML_OP_MAP_UNARY:
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case GGML_OP_MAP_BINARY:
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{
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node->n_tasks = 1;
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} break;
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case GGML_OP_NONE:
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{
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node->n_tasks = 1;
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18
ggml.h
18
ggml.h
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@ -253,6 +253,9 @@ enum ggml_op {
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GGML_OP_FLASH_ATTN,
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GGML_OP_FLASH_FF,
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GGML_OP_MAP_UNARY,
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GGML_OP_MAP_BINARY,
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GGML_OP_COUNT,
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};
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@ -652,6 +655,21 @@ struct ggml_tensor * ggml_flash_ff(
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struct ggml_tensor * c0,
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struct ggml_tensor * c1);
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// Mapping operations
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typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
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typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
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struct ggml_tensor * ggml_map_unary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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const ggml_unary_op_f32_t fun);
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struct ggml_tensor * ggml_map_binary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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const ggml_binary_op_f32_t fun);
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//
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// automatic differentiation
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//
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