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-rw-r--r--llama.cpp/tests/test-alloc.cpp608
1 files changed, 608 insertions, 0 deletions
diff --git a/llama.cpp/tests/test-alloc.cpp b/llama.cpp/tests/test-alloc.cpp
new file mode 100644
index 0000000..95e09c9
--- /dev/null
+++ b/llama.cpp/tests/test-alloc.cpp
@@ -0,0 +1,608 @@
+#include <ggml-alloc.h>
+#include <ggml-backend-impl.h>
+#include <ggml-cpp.h>
+#include <ggml-impl.h>
+#include <ggml.h>
+
+#include <algorithm>
+#include <exception>
+#include <memory>
+#include <vector>
+
+//
+// dummy backend with configurable max_buffer_size, tracks allocations
+
+uint8_t * const alloc_base = (uint8_t *) 16;
+
+struct dummy_backend_context {
+ size_t max_buffer_size = 64;
+ size_t alignment = 8;
+
+ ggml_backend_buffer_i buffer_interface;
+ std::vector<ggml_backend_buffer_t> buffers;
+
+ size_t allocated_total() const {
+ size_t n = 0;
+ for (ggml_backend_buffer_t buf : buffers) {
+ n += ggml_backend_buffer_get_size(buf);
+ }
+ return n;
+ }
+};
+
+// ggml_backend_buffer_type interface
+
+static const char * dummy_backend_buffer_type_get_name(ggml_backend_buffer_type_t) {
+ return "dummy_buffer_type";
+}
+
+static ggml_backend_buffer_t dummy_backend_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+ dummy_backend_context * ctx = (dummy_backend_context *) buft->context;
+ ggml_backend_buffer_t & buffer = ctx->buffers.emplace_back();
+ buffer = ggml_backend_buffer_init(buft, ctx->buffer_interface, ctx, size);
+ return buffer;
+}
+
+static size_t dummy_backend_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+ dummy_backend_context * ctx = (dummy_backend_context *) buft->context;
+ return ctx->alignment;
+}
+
+static size_t dummy_backend_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+ dummy_backend_context * ctx = (dummy_backend_context *) buft->context;
+ return ctx->max_buffer_size;
+}
+
+static bool dummy_backend_buffer_type_is_host(ggml_backend_buffer_type_t) {
+ return true;
+}
+
+// ggml_backend_buffer interface
+
+static void dummy_backend_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+ dummy_backend_context * ctx = (dummy_backend_context *) buffer->context;
+
+ auto i = std::find(ctx->buffers.begin(), ctx->buffers.end(), buffer);
+ GGML_ASSERT(i != ctx->buffers.end());
+ ctx->buffers.erase(i);
+}
+
+static void * dummy_backend_buffer_get_base(ggml_backend_buffer_t) {
+ return alloc_base;
+}
+
+static ggml_status dummy_backend_buffer_init_tensor(ggml_backend_buffer_t, ggml_tensor *) {
+ return GGML_STATUS_SUCCESS;
+}
+
+static void dummy_backend_buffer_memset_tensor(ggml_backend_buffer_t, ggml_tensor *, uint8_t, size_t, size_t) {}
+
+static void dummy_backend_buffer_set_tensor(ggml_backend_buffer_t, ggml_tensor *, const void *, size_t, size_t) {}
+
+static void dummy_backend_buffer_get_tensor(ggml_backend_buffer_t, const ggml_tensor *, void *, size_t, size_t) {}
+
+static void dummy_backend_buffer_clear(ggml_backend_buffer_t, uint8_t) {}
+
+// dummy_backend (not really a full backend, just provides what gallocr needs)
+
+struct dummy_backend {
+ std::unique_ptr<dummy_backend_context> context;
+ ggml_backend_buffer_type buffer_type;
+};
+
+static dummy_backend dummy_backend_init(size_t max_buffer_size, size_t alignment = 8) {
+ dummy_backend b{};
+ b.context = std::make_unique<dummy_backend_context>();
+ b.context->alignment = alignment;
+ b.context->max_buffer_size = max_buffer_size;
+
+ b.context->buffer_interface.free_buffer = dummy_backend_buffer_free_buffer;
+ b.context->buffer_interface.get_base = dummy_backend_buffer_get_base;
+ b.context->buffer_interface.init_tensor = dummy_backend_buffer_init_tensor;
+ b.context->buffer_interface.memset_tensor = dummy_backend_buffer_memset_tensor;
+ b.context->buffer_interface.set_tensor = dummy_backend_buffer_set_tensor;
+ b.context->buffer_interface.get_tensor = dummy_backend_buffer_get_tensor;
+ b.context->buffer_interface.clear = dummy_backend_buffer_clear;
+
+ b.buffer_type.context = b.context.get();
+ b.buffer_type.iface.get_name = dummy_backend_buffer_type_get_name;
+ b.buffer_type.iface.alloc_buffer = dummy_backend_buffer_type_alloc_buffer;
+ b.buffer_type.iface.get_alignment = dummy_backend_buffer_type_get_alignment;
+ b.buffer_type.iface.get_max_size = dummy_backend_buffer_type_get_max_size;
+ b.buffer_type.iface.is_host = dummy_backend_buffer_type_is_host;
+ return b;
+}
+
+//
+// test utilities
+
+struct test_context_with_graph {
+ ggml_context * ctx;
+ ggml_cgraph * graph;
+ ggml_context_ptr ctx_ptr;
+};
+
+static test_context_with_graph make_context() {
+ ggml_init_params params{};
+ params.mem_size = 48 * ggml_tensor_overhead() + ggml_graph_overhead();
+ params.no_alloc = true;
+
+ ggml_context * ctx = ggml_init(params);
+ ggml_context_ptr ctx_ptr = ggml_context_ptr(ctx);
+ ggml_cgraph * graph = ggml_new_graph(ctx);
+ return { ctx, graph, std::move(ctx_ptr) };
+}
+
+static ggml_tensor * make_input_1d(ggml_context * ctx, int64_t n_elements) {
+ ggml_tensor * t = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+ ggml_set_input(t);
+ return t;
+}
+
+static ggml_tensor * make_input_with_size(ggml_context * ctx, size_t size_bytes) {
+ GGML_ASSERT(size_bytes % 4 == 0);
+ return make_input_1d(ctx, size_bytes / 4);
+}
+
+static void assign_names(ggml_context * ctx, const char * prefix = "x") {
+ int i = 0;
+ for (ggml_tensor * t = ggml_get_first_tensor(ctx); t; t = ggml_get_next_tensor(ctx, t)) {
+ ggml_format_name(t, "%s%d", prefix, i++);
+ }
+}
+
+static int get_leaf_id(ggml_cgraph * graph, const char * tensor_name) {
+ for (int i = 0; i < graph->n_leafs; ++i) {
+ if (strncmp(graph->leafs[i]->name, tensor_name, GGML_MAX_NAME) == 0) {
+ return i;
+ }
+ }
+ fprintf(stderr, "leaf not found: %s\n", tensor_name);
+ return -1;
+}
+
+static int get_node_id(ggml_cgraph * graph, const char * tensor_name) {
+ for (int i = 0; i < graph->n_nodes; ++i) {
+ if (strncmp(graph->nodes[i]->name, tensor_name, GGML_MAX_NAME) == 0) {
+ return i;
+ }
+ }
+ fprintf(stderr, "node not found: %s", tensor_name);
+ return -1;
+}
+
+static ggml_gallocr_ptr allocate_graph(ggml_cgraph * graph, ggml_tensor * out, ggml_backend_buffer_type_t buft) {
+ ggml_set_output(out);
+ ggml_build_forward_expand(graph, out);
+
+ ggml_gallocr_ptr galloc = ggml_gallocr_ptr(ggml_gallocr_new(buft));
+ bool result = ggml_gallocr_alloc_graph(galloc.get(), graph);
+ GGML_ASSERT(result);
+ return galloc;
+}
+
+//
+// correctness checks for result allocations
+
+static void check_all_allocated(ggml_cgraph * graph) {
+ for (int i = 0; i < ggml_graph_n_nodes(graph); ++i) {
+ ggml_tensor * t = ggml_graph_node(graph, i);
+ GGML_ASSERT(t->buffer != nullptr);
+ GGML_ASSERT(t->data != nullptr);
+ }
+}
+
+static void check_max_size(ggml_context * ctx) {
+ for (ggml_tensor * t = ggml_get_first_tensor(ctx); t; t = ggml_get_next_tensor(ctx, t)) {
+ auto buft = ggml_backend_buffer_get_type(t->buffer);
+ size_t max_size = ggml_backend_buft_get_max_size(buft);
+ size_t offset = (char *) t->data - (char *) ggml_backend_buffer_get_base(t->buffer);
+ GGML_ASSERT(t->data >= ggml_backend_buffer_get_base(t->buffer));
+ GGML_ASSERT((size_t) offset + ggml_nbytes(t) <= max_size);
+ }
+}
+
+static bool can_reuse_memory(ggml_cgraph * graph, int current_i, ggml_tensor * current, ggml_tensor * other) {
+ if (other->flags & GGML_TENSOR_FLAG_OUTPUT) {
+ return false;
+ }
+ // Check if `other` is still "alive", ie. an input to any node after the `current` op
+ for (int i = current_i; i < ggml_graph_n_nodes(graph); ++i) {
+ ggml_tensor * t = ggml_graph_node(graph, i);
+ for (int s = 0; s < GGML_MAX_SRC; s++) {
+ if (t == current && ggml_op_can_inplace(t->op)) {
+ continue;
+ }
+ if (t->src[s] == other) {
+ return false;
+ }
+ if (t->src[s] && t->src[s]->view_src == other) {
+ return false;
+ }
+ }
+ }
+ return true;
+}
+
+static bool memory_overlap(ggml_tensor * a, ggml_tensor * b) {
+ if (a->buffer != b->buffer) {
+ return false;
+ }
+ int64_t a0 = (int64_t) a->data;
+ int64_t a1 = a0 + ggml_nbytes(a);
+ int64_t b0 = (int64_t) b->data;
+ int64_t b1 = b0 + ggml_nbytes(b);
+ return a1 > b0 && b1 > a0;
+}
+
+static ggml_tensor * get_view_source(ggml_tensor * t) {
+ while (t->view_src) {
+ t = t->view_src;
+ }
+ return t;
+}
+
+static void check_no_overlap(ggml_cgraph * graph) {
+ for (int i = 0; i < ggml_graph_n_nodes(graph); ++i) {
+ for (int j = 0; j < i; ++j) {
+ ggml_tensor * t = ggml_graph_node(graph, i);
+ ggml_tensor * o = ggml_graph_node(graph, j);
+ GGML_ASSERT(t != o);
+
+ if (get_view_source(t) == get_view_source(o)) {
+ continue;
+ }
+ if (memory_overlap(t, o)) {
+ GGML_ASSERT(can_reuse_memory(graph, i, t, o));
+ }
+ }
+ }
+}
+
+//
+// test cases
+
+// Scenario where the first backend buffer is completely exhausted and there are further
+// tensors which require a second buffer
+static void test_max_size_too_many_tensors() {
+ dummy_backend backend = dummy_backend_init(16);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[7];
+ x[0] = make_input_with_size(ctx, 8);
+ x[1] = make_input_with_size(ctx, 8);
+ x[2] = make_input_with_size(ctx, 8);
+ x[3] = ggml_mul(ctx, x[0], x[1]);
+ x[4] = ggml_add(ctx, x[1], x[2]);
+ x[5] = ggml_add(ctx, x[3], x[0]);
+ x[6] = ggml_add(ctx, x[4], x[5]);
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[6], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 16 + 16);
+}
+
+// Scenario where there is some space left in the first buffer, but not enough to accomodate
+// a larger tensor, so a second buffer is required
+static void test_max_size_tensor_too_large() {
+ dummy_backend backend = dummy_backend_init(32);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[3];
+ x[0] = make_input_with_size(ctx, 16); // chunk 0, [0 , 16)
+ x[1] = make_input_with_size(ctx, 8); // chunk 0, [16, 24)
+ x[2] = ggml_concat(ctx, x[0], x[1], 0); // chunk 1, [0 , 24)
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[2], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 32 + 24);
+}
+
+// Scenario where a single tensor exceeds the max buffer size - in this case the allocator
+// should try to create a bigger buffer anyway, and wait for the backend to throw an error.
+// Backends may report an artificially lower max size in some cases for compatibility reasons.
+static void test_tensor_larger_than_max_size() {
+ dummy_backend backend = dummy_backend_init(16);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[2];
+ x[0] = make_input_with_size(ctx, 24);
+ x[1] = ggml_scale(ctx, x[0], 2.0f);
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[1], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ GGML_ASSERT(backend.context->allocated_total() == 24);
+}
+
+// This test assumes a max of 16 buffer chunks, and tries to allocate tensors that would
+// require more. Expectation is that the last buffer should grow to fit everything,
+// leaving it to the backend to error out if it can't allocate that much.
+static void test_not_enough_chunks() {
+ const int max_chunks = 16;
+ const int max_size = 8;
+
+ dummy_backend backend = dummy_backend_init(max_size);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[max_chunks + 1];
+ for (int i = 0; i < max_chunks + 1; ++i) {
+ x[i] = make_input_with_size(ctx, max_size);
+ }
+ ggml_tensor * acc = x[0];
+ for (int i = 0; i < max_chunks; ++i) {
+ acc = ggml_add(ctx, acc, x[i + 1]);
+ }
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, acc, &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ GGML_ASSERT(backend.context->allocated_total() > max_chunks * max_size);
+}
+
+// Fill up leftover unallocated space of a chunk after allocating a large tensor that
+// requires a new chunk.
+static void test_fill_leftover_space() {
+ dummy_backend backend = dummy_backend_init(16);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[4];
+ x[0] = make_input_with_size(ctx, 8);
+ x[1] = ggml_pad(ctx, x[0], 2, 0, 0, 0);
+ x[3] = ggml_mean(ctx, x[1]);
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[3], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 12 + 16);
+}
+
+// Check that views don't require any extra memory
+static void test_view_inplace() {
+ dummy_backend backend = dummy_backend_init(32);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[6];
+ x[0] = make_input_1d(ctx, 4); // chunk 0, [0, 16)
+ x[1] = ggml_reshape_2d(ctx, x[0], 2, 2); // view of x0
+ x[2] = ggml_permute(ctx, x[1], 1, 0, 2, 3); // view of x0
+ x[3] = ggml_view_1d(ctx, x[2], 2, 4); // view of x0
+ x[4] = make_input_1d(ctx, 2); // chunk 0, [16, 24)
+ x[5] = ggml_add(ctx, x[3], x[4]); // reuse (inplace add)
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[5], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 24);
+}
+
+static void test_reuse_and_free() {
+ dummy_backend backend = dummy_backend_init(40);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[9];
+ x[0] = make_input_with_size(ctx, 24);
+ x[1] = make_input_with_size(ctx, 8);
+ x[2] = make_input_with_size(ctx, 8);
+ x[3] = ggml_add(ctx, x[1], x[2]); // reuse, free x2
+ x[4] = ggml_pad(ctx, x[0], 2, 0, 0, 0); // alloc new buffer, free x0
+ x[5] = ggml_scale(ctx, x[4], 2.0f); // alloc from free block
+ x[6] = ggml_add(ctx, x[4], x[5]); // reuse, free x5
+ x[7] = ggml_view_1d(ctx, x[6], 2, 8); // view
+ x[8] = ggml_add(ctx, x[3], x[7]); // reuse
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[8], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 40 + 32 + 32);
+}
+
+static void test_merge_free_block(size_t max_buffer_size) {
+ dummy_backend backend = dummy_backend_init(max_buffer_size);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[9];
+ x[0] = make_input_with_size(ctx, 16);
+ x[1] = make_input_with_size(ctx, 16);
+ x[2] = make_input_with_size(ctx, 16);
+ x[3] = ggml_mean(ctx, x[0]);
+ x[4] = ggml_mean(ctx, x[1]);
+ x[5] = ggml_pad(ctx, x[2], 2, 0, 0, 0);
+ x[6] = ggml_add(ctx, x[3], x[4]);
+ x[7] = ggml_pad(ctx, x[6], 5, 0, 0, 0);
+ x[8] = ggml_add(ctx, x[5], x[7]);
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[8], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend.context->allocated_total() <= 32 + 32 + 24);
+}
+
+// Check that previously allocated but freed memory is preferred over allocating
+// additional memory, even if the remaining space in a chunk would match tensor size better
+static void test_prefer_already_allocated_memory() {
+ dummy_backend backend = dummy_backend_init(32, /*align*/ 4);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[3];
+ x[0] = make_input_with_size(ctx, 24); // [24b][8b unused]
+ x[1] = ggml_mean(ctx, x[0]); // [24b free][4b][4b unused]
+ x[2] = ggml_mean(ctx, x[1]); // should be allocated in the 24b block
+ assign_names(ctx);
+
+ ggml_gallocr_ptr galloc = allocate_graph(graph, x[2], &backend.buffer_type);
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ GGML_ASSERT(backend.context->allocated_total() <= 28);
+}
+
+// test for allocating on multiple devices with some tensors in the graph
+// allocated externally (not by gallocr).
+static void test_multiple_buffer_types() {
+ dummy_backend backend_a = dummy_backend_init(32);
+ dummy_backend backend_b = dummy_backend_init(SIZE_MAX);
+
+ auto [ctx_a, _a, ctx_a_ptr] = make_context();
+ auto [ctx_b, _b, ctx_b_ptr] = make_context();
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * a[2];
+ a[0] = make_input_with_size(ctx_a, 16);
+ a[1] = make_input_with_size(ctx_a, 16);
+ assign_names(ctx_a, "a");
+
+ ggml_tensor * b[2];
+ b[0] = make_input_with_size(ctx_b, 24);
+ b[1] = make_input_with_size(ctx_b, 4);
+ assign_names(ctx_b, "b");
+
+ ggml_tensor * x[9];
+ x[0] = make_input_with_size(ctx, 16);
+ x[1] = ggml_mul(ctx, x[0], a[0]);
+ x[2] = ggml_pad(ctx, x[1], 2, 0, 0, 0);
+ x[3] = ggml_mul(ctx, x[2], b[0]);
+ x[4] = ggml_mean(ctx, x[3]);
+ x[5] = ggml_add(ctx, x[4], b[1]);
+ x[6] = ggml_pad(ctx, x[5], 3, 0, 0, 0);
+ x[7] = ggml_add(ctx, x[6], a[1]);
+ x[8] = ggml_scale(ctx, x[7], 2.0f);
+ assign_names(ctx, "x");
+
+ ggml_backend_buffer_ptr buf_a(ggml_backend_alloc_ctx_tensors_from_buft(ctx_a, &backend_a.buffer_type));
+ ggml_backend_buffer_ptr buf_b(ggml_backend_alloc_ctx_tensors_from_buft(ctx_b, &backend_b.buffer_type));
+ ggml_backend_buffer_type_t bufts[2] = { &backend_a.buffer_type, &backend_b.buffer_type };
+
+ // assign buffer types manually to avoid extra complexity from backend scheduler
+ ggml_set_output(x[8]);
+ ggml_build_forward_expand(graph, x[8]);
+
+ GGML_ASSERT(graph->n_leafs == 5);
+ int leaf_buffer_ids[5];
+ leaf_buffer_ids[get_leaf_id(graph, "a0")] = 0;
+ leaf_buffer_ids[get_leaf_id(graph, "a1")] = 0;
+ leaf_buffer_ids[get_leaf_id(graph, "b0")] = 1;
+ leaf_buffer_ids[get_leaf_id(graph, "b1")] = 1;
+ leaf_buffer_ids[get_leaf_id(graph, "x0")] = 0;
+
+ GGML_ASSERT(graph->n_nodes == 8);
+ int node_buffer_ids[8];
+ node_buffer_ids[get_node_id(graph, "x1")] = 0;
+ node_buffer_ids[get_node_id(graph, "x2")] = 0;
+ node_buffer_ids[get_node_id(graph, "x3")] = 1;
+ node_buffer_ids[get_node_id(graph, "x4")] = 1;
+ node_buffer_ids[get_node_id(graph, "x5")] = 1;
+ node_buffer_ids[get_node_id(graph, "x6")] = 1;
+ node_buffer_ids[get_node_id(graph, "x7")] = 0;
+ node_buffer_ids[get_node_id(graph, "x8")] = 0;
+
+ ggml_gallocr_ptr galloc(ggml_gallocr_new_n(bufts, 2));
+ ggml_gallocr_reserve_n(galloc.get(), graph, node_buffer_ids, leaf_buffer_ids);
+ ggml_gallocr_alloc_graph(galloc.get(), graph);
+
+ check_all_allocated(graph);
+ check_no_overlap(graph);
+ check_max_size(ctx);
+ GGML_ASSERT(backend_a.context->allocated_total() <= 32 + 32 + 24);
+ GGML_ASSERT(backend_b.context->allocated_total() <= 32 + 24);
+}
+
+static void test_buffer_size_zero() {
+ dummy_backend backend_a = dummy_backend_init(SIZE_MAX);
+ dummy_backend backend_b = dummy_backend_init(SIZE_MAX);
+ auto [ctx, graph, ctx_ptr] = make_context();
+
+ ggml_tensor * x[2];
+ x[0] = make_input_with_size(ctx, 16);
+ x[1] = ggml_scale(ctx, x[0], 2.0f);
+
+ ggml_set_output(x[1]);
+ ggml_build_forward_expand(graph, x[1]);
+
+ int leaf_buffer_ids[1] = { 0 };
+ int node_buffer_ids[1] = { 0 };
+
+ ggml_backend_buffer_type_t bufts[2] = { &backend_a.buffer_type, &backend_b.buffer_type };
+ ggml_gallocr_ptr galloc = ggml_gallocr_ptr(ggml_gallocr_new_n(bufts, 2));
+ bool res1 = ggml_gallocr_reserve_n(galloc.get(), graph, node_buffer_ids, leaf_buffer_ids);
+ bool res2 = ggml_gallocr_alloc_graph(galloc.get(), graph);
+ GGML_ASSERT(res1 && res2);
+
+ check_all_allocated(graph);
+ GGML_ASSERT(backend_a.context->allocated_total() == 16);
+ GGML_ASSERT(backend_b.context->allocated_total() == 0);
+}
+
+// Test re-using gallocr for a different graph. The new graph has the same
+// total size, but one of the chunks is larger, so reallocation is required.
+static void test_reallocation() {
+ dummy_backend backend = dummy_backend_init(32, /*align*/ 4);
+ ggml_gallocr_ptr galloc;
+ {
+ auto [ctx, graph, ctx_ptr] = make_context();
+ ggml_tensor * x[4];
+ x[0] = make_input_with_size(ctx, 24);
+ x[1] = make_input_with_size(ctx, 16);
+ x[2] = ggml_view_1d(ctx, x[0], 4, 0);
+ x[3] = ggml_add(ctx, x[2], x[1]);
+ assign_names(ctx);
+
+ galloc = allocate_graph(graph, x[3], &backend.buffer_type);
+ check_all_allocated(graph);
+ GGML_ASSERT(backend.context->allocated_total() == 40);
+ }
+ {
+ auto [ctx, graph, ctx_ptr] = make_context();
+ ggml_tensor * x[3];
+ x[0] = make_input_with_size(ctx, 20);
+ x[1] = make_input_with_size(ctx, 20);
+ x[2] = ggml_add(ctx, x[0], x[1]);
+ assign_names(ctx);
+ ggml_set_output(x[2]);
+ ggml_build_forward_expand(graph, x[2]);
+
+ bool result = ggml_gallocr_alloc_graph(galloc.get(), graph);
+ GGML_ASSERT(result);
+ check_all_allocated(graph);
+ GGML_ASSERT(backend.context->allocated_total() == 40);
+ }
+}
+
+static void run(const char * name, void (*f)()) {
+ printf("%s ", name);
+ fflush(stdout);
+ f();
+ printf("PASSED\n");
+}
+
+int main() {
+ run("test_max_size_too_many_tensors", test_max_size_too_many_tensors);
+ run("test_max_size_tensor_too_large", test_max_size_tensor_too_large);
+ run("test_tensor_larger_than_max_size", test_tensor_larger_than_max_size);
+ run("test_not_enough_chunks", test_not_enough_chunks);
+ run("test_fill_leftover_space", test_fill_leftover_space);
+ run("test_view_inplace", test_view_inplace);
+ run("test_reuse_and_free", test_reuse_and_free);
+ run("test_merge_free_block(32)", []() { test_merge_free_block(32); });
+ run("test_merge_free_block(SIZE_MAX)", []() { test_merge_free_block(SIZE_MAX); });
+ run("test_prefer_already_allocated_memory", test_prefer_already_allocated_memory);
+ run("test_multiple_buffer_types", test_multiple_buffer_types);
+ run("test_buffer_size_zero", test_buffer_size_zero);
+ run("test_reallocation", test_reallocation);
+ return 0;
+}
generated by cgit v1.2.3 (git 2.46.0) at 2026-04-29 14:41:46 +0000