mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-08 13:07:36 +00:00
Merge branch 'master' into compilade/mamba2
This commit is contained in:
Francis Couture-Harpin
@ -18,20 +18,22 @@
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#include <ggml.h>
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#include <ggml-alloc.h>
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#include <ggml-backend.h>
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#include <ggml-cpp.h>
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#include <algorithm>
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#include <array>
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#include <cfloat>
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#include <cstdint>
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#include <cstring>
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#include <cinttypes>
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#include <cstdint>
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <future>
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#include <memory>
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#include <random>
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#include <stdio.h>
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#include <stdlib.h>
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#include <regex>
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#include <string>
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#include <thread>
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#include <future>
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#include <vector>
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static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
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@ -257,6 +259,10 @@ static std::string var_to_str(ggml_type type) {
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return ggml_type_name(type);
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}
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static std::string var_to_str(ggml_prec prec) {
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return prec == GGML_PREC_F32 ? "f32" : "def";
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}
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static std::string var_to_str(ggml_op_pool pool) {
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switch (pool) {
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case GGML_OP_POOL_AVG: return "avg";
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@ -265,6 +271,14 @@ static std::string var_to_str(ggml_op_pool pool) {
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}
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}
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static std::string var_to_str(ggml_scale_mode mode) {
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switch (mode) {
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case GGML_SCALE_MODE_NEAREST: return "nearest";
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case GGML_SCALE_MODE_BILINEAR: return "bilinear";
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default: return std::to_string(mode);
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}
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}
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#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
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#define VARS_TO_STR1(a) VAR_TO_STR(a)
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@ -467,6 +481,7 @@ struct test_case {
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// allocate
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
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if (buf == NULL) {
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printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1));
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ggml_free(ctx);
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@ -588,14 +603,13 @@ struct test_case {
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/* .mem_base = */ NULL,
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/* .no_alloc = */ true,
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};
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ggml_context * ctx = ggml_init(params);
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ggml_context_ptr ctx(ggml_init(params)); // smart ptr
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GGML_ASSERT(ctx);
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ggml_tensor * out = build_graph(ctx);
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ggml_tensor * out = build_graph(ctx.get());
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if (op_name != nullptr && op_desc(out) != op_name) {
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//printf(" %s: skipping\n", op_desc(out).c_str());
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ggml_free(ctx);
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return true;
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}
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@ -605,7 +619,6 @@ struct test_case {
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// check if backends support op
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if (!ggml_backend_supports_op(backend, out)) {
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printf("not supported\n");
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ggml_free(ctx);
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return true;
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}
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@ -618,22 +631,26 @@ struct test_case {
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printf("%*s", last - len, "");
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// allocate
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
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ggml_backend_buffer_ptr buf(ggml_backend_alloc_ctx_tensors(ctx.get(), backend)); // smart ptr
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if (buf == NULL) {
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printf("failed to allocate tensors\n");
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ggml_free(ctx);
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return false;
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}
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// randomize tensors
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initialize_tensors(ctx);
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initialize_tensors(ctx.get());
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// build graph
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ggml_cgraph * gf = ggml_new_graph_custom(ctx, graph_nodes, false);
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ggml_cgraph * gf = ggml_new_graph_custom(ctx.get(), graph_nodes, false);
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ggml_build_forward_expand(gf, out);
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// warmup run
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ggml_backend_graph_compute(backend, gf);
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ggml_status status = ggml_backend_graph_compute(backend, gf);
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if (status != GGML_STATUS_SUCCESS) {
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
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}
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// determine number of runs
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int n_runs;
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@ -684,7 +701,11 @@ struct test_case {
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int total_runs = 0;
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do {
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int64_t start_time = ggml_time_us();
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ggml_backend_graph_compute(backend, gf);
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ggml_status status = ggml_backend_graph_compute(backend, gf);
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if (status != GGML_STATUS_SUCCESS) {
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
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}
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int64_t end_time = ggml_time_us();
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total_time_us += end_time - start_time;
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@ -722,10 +743,6 @@ struct test_case {
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}
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printf("\n");
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ggml_backend_buffer_free(buf);
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ggml_free(ctx);
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return true;
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}
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@ -738,17 +755,16 @@ struct test_case {
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/* .mem_base = */ NULL,
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/* .no_alloc = */ true,
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};
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ggml_context * ctx = ggml_init(params);
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ggml_context_ptr ctx(ggml_init(params)); // smart ptr
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GGML_ASSERT(ctx);
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gf = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true);
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gb = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true);
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gf = ggml_new_graph_custom(ctx.get(), GGML_DEFAULT_GRAPH_SIZE, true);
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gb = ggml_new_graph_custom(ctx.get(), GGML_DEFAULT_GRAPH_SIZE, true);
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ggml_tensor * out = build_graph(ctx);
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ggml_tensor * out = build_graph(ctx.get());
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if ((op_name != nullptr && op_desc(out) != op_name) || out->op == GGML_OP_OPT_STEP_ADAMW) {
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//printf(" %s: skipping\n", op_desc(out).c_str());
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ggml_free(ctx);
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return true;
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}
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@ -756,7 +772,6 @@ struct test_case {
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fflush(stdout);
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if (out->type != GGML_TYPE_F32) {
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ggml_free(ctx);
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printf("not supported [%s->type != FP32]\n", out->name);
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return true;
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}
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@ -764,7 +779,7 @@ struct test_case {
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// check if the backend supports the ops
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bool supported = true;
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bool any_params = false;
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for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
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if (!ggml_backend_supports_op(backend, t)) {
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printf("not supported [%s] ", ggml_backend_name(backend));
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supported = false;
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@ -785,40 +800,38 @@ struct test_case {
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}
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if (!supported) {
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printf("\n");
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ggml_free(ctx);
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return true;
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}
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int64_t ngrads = 0;
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for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
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if (t->flags & GGML_TENSOR_FLAG_PARAM) {
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ngrads += ggml_nelements(t);
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}
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}
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if (ngrads > grad_nmax()) {
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printf("skipping large tensors for speed \n");
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ggml_free(ctx);
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return true;
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}
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if (!ggml_is_scalar(out)) {
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out = ggml_sum(ctx, out);
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out = ggml_sum(ctx.get(), out);
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ggml_set_name(out, "sum_of_out");
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}
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ggml_set_loss(out);
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ggml_build_forward_expand(gf, out);
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ggml_graph_cpy(gf, gb);
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ggml_build_backward_expand(ctx, ctx, gb, false);
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ggml_build_backward_expand(ctx.get(), ctx.get(), gb, false);
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if (expect.size() != 1 || expect[0] != 0.0f) {
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GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
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for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
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GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || ggml_graph_get_grad(gb, t)->op != GGML_OP_NONE);
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}
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}
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for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
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if (!ggml_backend_supports_op(backend, t)) {
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printf("not supported [%s] ", ggml_backend_name(backend));
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supported = false;
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@ -832,27 +845,32 @@ struct test_case {
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}
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if (!supported) {
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printf("\n");
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ggml_free(ctx);
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return true;
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}
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// allocate
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
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ggml_backend_buffer_ptr buf(ggml_backend_alloc_ctx_tensors(ctx.get(), backend)); // smart ptr
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if (buf == NULL) {
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printf("failed to allocate tensors [%s] ", ggml_backend_name(backend));
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ggml_free(ctx);
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return false;
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}
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initialize_tensors(ctx); // Randomizes all tensors (including gradients).
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initialize_tensors(ctx.get()); // Randomizes all tensors (including gradients).
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ggml_graph_reset(gb); // Sets gradients to 1 if loss, 0 otherwise.
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ggml_backend_graph_compute(backend, gf);
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ggml_backend_graph_compute(backend, gb);
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ggml_status status = ggml_backend_graph_compute(backend, gf);
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if (status != GGML_STATUS_SUCCESS) {
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
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}
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status = ggml_backend_graph_compute(backend, gb);
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if (status != GGML_STATUS_SUCCESS) {
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
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}
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bool ok = true;
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for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
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for (struct ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
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if (!(t->flags & GGML_TENSOR_FLAG_PARAM)) {
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continue;
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}
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@ -897,20 +915,36 @@ struct test_case {
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float fu, fuh, fdh, fd; // output values for xiu, xiuh, xid, xidh
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ggml_backend_tensor_set(t, &xiu, i*sizeof(float), sizeof(float));
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ggml_backend_graph_compute(backend, gf);
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status = ggml_backend_graph_compute(backend, gf);
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if (status != GGML_STATUS_SUCCESS) {
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
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}
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ggml_backend_tensor_get(out, &fu, 0, ggml_nbytes(out));
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|
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ggml_backend_tensor_set(t, &xid, i*sizeof(float), sizeof(float));
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ggml_backend_graph_compute(backend, gf);
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status = ggml_backend_graph_compute(backend, gf);
|
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if (status != GGML_STATUS_SUCCESS) {
|
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
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return false;
|
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}
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ggml_backend_tensor_get(out, &fd, 0, ggml_nbytes(out));
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|
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if (grad_precise()) {
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ggml_backend_tensor_set(t, &xiuh, i*sizeof(float), sizeof(float));
|
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ggml_backend_graph_compute(backend, gf);
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status = ggml_backend_graph_compute(backend, gf);
|
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if (status != GGML_STATUS_SUCCESS) {
|
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
|
||||
return false;
|
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}
|
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ggml_backend_tensor_get(out, &fuh, 0, ggml_nbytes(out));
|
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|
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ggml_backend_tensor_set(t, &xidh, i*sizeof(float), sizeof(float));
|
||||
ggml_backend_graph_compute(backend, gf);
|
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status = ggml_backend_graph_compute(backend, gf);
|
||||
if (status != GGML_STATUS_SUCCESS) {
|
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fprintf(stderr, "%s: ggml_backend_graph_compute failed. status=%s \n", __func__, ggml_status_to_string(status));
|
||||
return false;
|
||||
}
|
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ggml_backend_tensor_get(out, &fdh, 0, ggml_nbytes(out));
|
||||
|
||||
gn[i] = (8.0*(double)fuh + (double)fd - (8.0*(double)fdh + (double)fu)) / (6.0*(double)eps);
|
||||
@ -936,10 +970,6 @@ struct test_case {
|
||||
printf("compare failed ");
|
||||
}
|
||||
|
||||
ggml_backend_buffer_free(buf);
|
||||
|
||||
ggml_free(ctx);
|
||||
|
||||
if (ok) {
|
||||
printf("\033[1;32mOK\033[0m\n");
|
||||
return true;
|
||||
@ -1441,11 +1471,13 @@ struct test_cpy : public test_case {
|
||||
const ggml_type type_src;
|
||||
const ggml_type type_dst;
|
||||
const std::array<int64_t, 4> ne;
|
||||
const std::array<int64_t, 4> permute;
|
||||
const std::array<int64_t, 4> permute_src;
|
||||
const std::array<int64_t, 4> permute_dst;
|
||||
bool _src_use_permute;
|
||||
bool _dst_use_permute;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR4(type_src, type_dst, ne, permute);
|
||||
return VARS_TO_STR5(type_src, type_dst, ne, permute_src, permute_dst);
|
||||
}
|
||||
|
||||
double max_nmse_err() override {
|
||||
@ -1458,9 +1490,11 @@ struct test_cpy : public test_case {
|
||||
|
||||
test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {10, 10, 10, 1},
|
||||
std::array<int64_t, 4> permute = {0, 0, 0, 0})
|
||||
: type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
|
||||
_src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
|
||||
std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
|
||||
std::array<int64_t, 4> permute_dst = {0, 0, 0, 0})
|
||||
: type_src(type_src), type_dst(type_dst), ne(ne), permute_src(permute_src), permute_dst(permute_dst),
|
||||
_src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
|
||||
_dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
|
||||
@ -1468,13 +1502,18 @@ struct test_cpy : public test_case {
|
||||
ggml_set_name(src, "src");
|
||||
|
||||
if (_src_use_permute) {
|
||||
src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
|
||||
src = ggml_permute(ctx, src, permute_src[0], permute_src[1], permute_src[2], permute_src[3]);
|
||||
ggml_set_name(src, "src_permuted");
|
||||
}
|
||||
|
||||
ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
|
||||
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
|
||||
ggml_set_name(dst, "dst");
|
||||
|
||||
if (_dst_use_permute) {
|
||||
dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
|
||||
ggml_set_name(dst, "dst_permuted");
|
||||
}
|
||||
|
||||
ggml_tensor * out = ggml_cpy(ctx, src, dst);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
@ -1928,6 +1967,40 @@ struct test_gla : public test_case {
|
||||
}
|
||||
};
|
||||
|
||||
// GGML_OP_RWKV_WKV7
|
||||
struct test_rwkv_wkv7 : public test_case {
|
||||
const ggml_type type;
|
||||
|
||||
const int64_t head_count;
|
||||
const int64_t head_size;
|
||||
const int64_t n_seq_tokens;
|
||||
const int64_t n_seqs;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs);
|
||||
}
|
||||
|
||||
test_rwkv_wkv7(ggml_type type = GGML_TYPE_F32,
|
||||
int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32)
|
||||
: type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
const int64_t n_tokens = n_seq_tokens * n_seqs;
|
||||
ggml_tensor * r = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
ggml_tensor * w = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
ggml_tensor * k = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
ggml_tensor * v = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
ggml_tensor * b = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
|
||||
// Outputs may become NaN with long seqlen without these normalization
|
||||
a = ggml_l2_norm(ctx, a, 1e-7F);
|
||||
b = ggml_l2_norm(ctx, b, 1e-7F);
|
||||
ggml_tensor * s = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size * head_size * head_count, n_seqs }.data());
|
||||
ggml_tensor * out = ggml_rwkv_wkv7(ctx, r, w, k, v, a, b, s);
|
||||
return out;
|
||||
}
|
||||
};
|
||||
|
||||
// GGML_OP_MUL_MAT
|
||||
struct test_mul_mat : public test_case {
|
||||
const ggml_type type_a;
|
||||
@ -1938,9 +2011,10 @@ struct test_mul_mat : public test_case {
|
||||
const std::array<int64_t, 2> bs; // dims 3 and 4
|
||||
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
|
||||
const std::array<int64_t, 4> per; // permutation of dimensions
|
||||
const bool v; // whether a is a non-contiguous view
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR8(type_a, type_b, m, n, k, bs, nr, per);
|
||||
return VARS_TO_STR9(type_a, type_b, m, n, k, bs, nr, per, v);
|
||||
}
|
||||
|
||||
double max_nmse_err() override {
|
||||
@ -1960,8 +2034,9 @@ struct test_mul_mat : public test_case {
|
||||
int64_t m = 32, int64_t n = 32, int64_t k = 32,
|
||||
std::array<int64_t, 2> bs = {10, 10},
|
||||
std::array<int64_t, 2> nr = {2, 2},
|
||||
std::array<int64_t, 4> per = {0, 1, 2, 3})
|
||||
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per) {}
|
||||
std::array<int64_t, 4> per = {0, 1, 2, 3},
|
||||
bool v = false)
|
||||
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per), v(v) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
|
||||
@ -1971,6 +2046,7 @@ struct test_mul_mat : public test_case {
|
||||
const int npermuted = (per[0] != 0) + (per[1] != 1) + (per[2] != 2) + (per[3] != 3);
|
||||
if (npermuted > 0) {
|
||||
GGML_ASSERT(npermuted == 2);
|
||||
GGML_ASSERT(!v); // not handled
|
||||
GGML_ASSERT(!ggml_is_quantized(type_a) || per[0] == 0);
|
||||
GGML_ASSERT(!ggml_is_quantized(type_b) || per[0] == 0);
|
||||
|
||||
@ -1994,7 +2070,13 @@ struct test_mul_mat : public test_case {
|
||||
ggml_set_name(a, "a_permuted");
|
||||
ggml_set_name(b, "b_permuted");
|
||||
} else {
|
||||
a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
|
||||
|
||||
if (v) {
|
||||
a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]);
|
||||
a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0);
|
||||
} else {
|
||||
a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
|
||||
}
|
||||
b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
|
||||
if (!ggml_is_quantized(type_a)) {
|
||||
if (bs[1] == 1 && nr[1] == 1) {
|
||||
@ -2019,7 +2101,7 @@ struct test_mul_mat_id : public test_case {
|
||||
const ggml_type type_b;
|
||||
const int n_mats;
|
||||
const int n_used;
|
||||
const bool b; // brodcast b matrix
|
||||
const bool b; // broadcast b matrix
|
||||
const int64_t m;
|
||||
const int64_t n;
|
||||
const int64_t k;
|
||||
@ -2554,6 +2636,8 @@ struct test_rope : public test_case {
|
||||
} else {
|
||||
out = ggml_rope_ext_back(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
|
||||
}
|
||||
|
||||
// TODO: add test with a non-contiguous view as input ; this case is needed for build_rope_2d in clip.cpp
|
||||
}
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
@ -2904,15 +2988,16 @@ struct test_upscale : public test_case {
|
||||
const std::array<int64_t, 4> ne;
|
||||
const int32_t scale_factor;
|
||||
const bool transpose;
|
||||
const ggml_scale_mode mode;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR4(type, ne, scale_factor, transpose);
|
||||
return VARS_TO_STR5(type, ne, scale_factor, mode, transpose);
|
||||
}
|
||||
|
||||
test_upscale(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {512, 512, 3, 1},
|
||||
int32_t scale_factor = 2, bool transpose = false)
|
||||
: type(type), ne(ne), scale_factor(scale_factor), transpose(transpose) {}
|
||||
int32_t scale_factor = 2, ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST, bool transpose = false)
|
||||
: type(type), ne(ne), scale_factor(scale_factor), transpose(transpose), mode(mode) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||
@ -2923,7 +3008,7 @@ struct test_upscale : public test_case {
|
||||
ggml_set_name(a, "a_transposed");
|
||||
}
|
||||
|
||||
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
|
||||
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor, mode);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
return out;
|
||||
@ -2935,21 +3020,23 @@ struct test_upscale_ext : public test_case {
|
||||
const ggml_type type;
|
||||
const std::array<int64_t, 4> ne;
|
||||
const std::array<int64_t, 4> ne_tgt;
|
||||
const ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR3(type, ne, ne_tgt);
|
||||
return VARS_TO_STR4(type, ne, ne_tgt, mode);
|
||||
}
|
||||
|
||||
test_upscale_ext(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {2, 5, 7, 11},
|
||||
std::array<int64_t, 4> ne_tgt = {5, 7, 11, 13})
|
||||
: type(type), ne(ne), ne_tgt(ne_tgt) {}
|
||||
std::array<int64_t, 4> ne_tgt = {5, 7, 11, 13},
|
||||
ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST)
|
||||
: type(type), ne(ne), ne_tgt(ne_tgt), mode(mode) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||
ggml_set_name(a, "a");
|
||||
|
||||
ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3]);
|
||||
ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3], mode);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
return out;
|
||||
@ -2984,6 +3071,32 @@ struct test_group_norm : public test_case {
|
||||
}
|
||||
};
|
||||
|
||||
// GGML_OP_L2_NORM
|
||||
struct test_l2_norm : public test_case {
|
||||
const ggml_type type;
|
||||
const std::array<int64_t, 4> ne;
|
||||
const float eps;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR2(type, ne);
|
||||
}
|
||||
|
||||
test_l2_norm(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {64, 64, 320, 1},
|
||||
float eps = 1e-12f)
|
||||
: type(type), ne(ne), eps(eps) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||
ggml_set_name(a, "a");
|
||||
|
||||
ggml_tensor * out = ggml_l2_norm(ctx, a, eps);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
return out;
|
||||
}
|
||||
};
|
||||
|
||||
// GGML_OP_ACC
|
||||
struct test_acc : public test_case {
|
||||
const ggml_type type;
|
||||
@ -3147,7 +3260,8 @@ struct test_leaky_relu : public test_case {
|
||||
|
||||
// GGML_OP_FLASH_ATTN_EXT
|
||||
struct test_flash_attn_ext : public test_case {
|
||||
const int64_t hs; // head size
|
||||
const int64_t hsk; // K head size
|
||||
const int64_t hsv; // V head size
|
||||
const int64_t nh; // num heads
|
||||
const int64_t nr; // repeat in Q, tests for grouped-query attention
|
||||
const int64_t kv; // kv size
|
||||
@ -3158,11 +3272,12 @@ struct test_flash_attn_ext : public test_case {
|
||||
const float max_bias; // ALiBi
|
||||
const float logit_softcap; // Gemma 2
|
||||
|
||||
const ggml_prec prec;
|
||||
const ggml_type type_KV;
|
||||
std::array<int32_t, 4> permute;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR10(hs, nh, nr, kv, nb, mask, max_bias, logit_softcap, type_KV, permute);
|
||||
return VARS_TO_STR12(hsk, hsv, nh, nr, kv, nb, mask, max_bias, logit_softcap, prec, type_KV, permute);
|
||||
}
|
||||
|
||||
double max_nmse_err() override {
|
||||
@ -3172,17 +3287,18 @@ struct test_flash_attn_ext : public test_case {
|
||||
uint64_t op_flops(ggml_tensor * t) override {
|
||||
GGML_UNUSED(t);
|
||||
// Just counting matmul costs:
|
||||
// Q*K^T is nb x hs x kv, P*V is nb x kv x hs, per head
|
||||
return 2 * 2 * nh*nr * nb * hs * kv;
|
||||
// Q*K^T is nb x hsk x kv, P*V is nb x kv x hsv, per head
|
||||
return 2 * nh*nr * nb * (hsk + hsv) * kv;
|
||||
}
|
||||
|
||||
test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t nr = 1, int64_t kv = 96, int64_t nb = 8,
|
||||
bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16,
|
||||
std::array<int32_t, 4> permute = {0, 1, 2, 3})
|
||||
: hs(hs), nh(nh), nr(nr), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV), permute(permute) {}
|
||||
test_flash_attn_ext(int64_t hsk = 128, int64_t hsv = 128, int64_t nh = 32, int64_t nr = 1, int64_t kv = 96, int64_t nb = 8,
|
||||
bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_prec prec = GGML_PREC_F32,
|
||||
ggml_type type_KV = GGML_TYPE_F16, std::array<int32_t, 4> permute = {0, 1, 2, 3})
|
||||
: hsk(hsk), hsv(hsv), nh(nh), nr(nr), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), prec(prec), type_KV(type_KV), permute(permute) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
|
||||
const int64_t hsk_padded = GGML_PAD(hsk, ggml_blck_size(type_KV));
|
||||
const int64_t hsv_padded = GGML_PAD(hsv, ggml_blck_size(type_KV));
|
||||
|
||||
auto const &create_permuted = [&](ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) -> ggml_tensor * {
|
||||
int64_t ne[4] = {ne0, ne1, ne2, ne3};
|
||||
@ -3197,13 +3313,13 @@ struct test_flash_attn_ext : public test_case {
|
||||
return t;
|
||||
};
|
||||
|
||||
ggml_tensor * q = create_permuted(GGML_TYPE_F32, hs_padded, nb, nh*nr, 1);
|
||||
ggml_tensor * q = create_permuted(GGML_TYPE_F32, hsk_padded, nb, nh*nr, 1);
|
||||
ggml_set_name(q, "q");
|
||||
|
||||
ggml_tensor * k = create_permuted(type_KV, hs_padded, kv, nh, 1);
|
||||
ggml_tensor * k = create_permuted(type_KV, hsk_padded, kv, nh, 1);
|
||||
ggml_set_name(k, "k");
|
||||
|
||||
ggml_tensor * v = create_permuted(type_KV, hs_padded, kv, nh, 1);
|
||||
ggml_tensor * v = create_permuted(type_KV, hsv_padded, kv, nh, 1);
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
ggml_tensor * m = nullptr;
|
||||
@ -3212,7 +3328,8 @@ struct test_flash_attn_ext : public test_case {
|
||||
ggml_set_name(m, "m");
|
||||
}
|
||||
|
||||
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias, logit_softcap);
|
||||
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hsk), max_bias, logit_softcap);
|
||||
ggml_flash_attn_ext_set_prec(out, prec);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
return out;
|
||||
@ -3783,10 +3900,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
std::default_random_engine rng(0);
|
||||
|
||||
// unary ops
|
||||
for (int v : {0, 1}) {
|
||||
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
|
||||
test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 2, 2, 2 }, v));
|
||||
test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 5, 7, 11, 13 }, v));
|
||||
for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
|
||||
for (int v : {0, 1}) {
|
||||
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
|
||||
test_cases.emplace_back(new test_unary((ggml_unary_op) op, type, { 128, 2, 2, 2 }, v));
|
||||
test_cases.emplace_back(new test_unary((ggml_unary_op) op, type, { 5, 7, 11, 13 }, v));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -3939,14 +4058,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim));
|
||||
}
|
||||
|
||||
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
|
||||
// same-type copy
|
||||
for (ggml_type type : all_types) {
|
||||
const auto nk = ggml_blck_size(type);
|
||||
|
||||
for (int k = 1; k < 4; ++k) {
|
||||
test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}));
|
||||
test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 3, 1, 2}, {0, 2, 1, 3}));
|
||||
}
|
||||
}
|
||||
|
||||
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
|
||||
for (ggml_type type_dst : all_types) {
|
||||
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
|
||||
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
|
||||
}
|
||||
}
|
||||
for (ggml_type type_dst : {GGML_TYPE_F32}) {
|
||||
for (ggml_type type_src : all_types) {
|
||||
for (ggml_type type_src : all_types) {
|
||||
for (ggml_type type_dst : {GGML_TYPE_F32}) {
|
||||
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
|
||||
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
|
||||
}
|
||||
@ -3973,37 +4103,38 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
|
||||
}
|
||||
};
|
||||
for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
|
||||
add_test_bin_bcast(type, {1, 1, 8, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 1, 1}, {32, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 320, 320}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 1, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 1, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 1});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 2});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 2});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 2, 2});
|
||||
add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 2, 2, 2});
|
||||
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 1, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {2, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 2});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 2});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 2, 2});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {2, 2, 2, 2});
|
||||
|
||||
// stable diffusion
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 16, 16, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1280, 16, 16, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 256, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {16, 16, 1280, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 2560, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
|
||||
add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
|
||||
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
|
||||
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
|
||||
// stable diffusion
|
||||
add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 16, 16, 1});
|
||||
add_test_bin_bcast(type, {1280, 16, 16, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 256, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 1280, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(type, {16, 16, 1280, 1}, {1, 1, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 1920, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 2560, 1}, {16, 16, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 1280, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 1920, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(type, {1, 1, 640, 1}, {32, 32, 1, 1});
|
||||
add_test_bin_bcast(type, {5120, 1, 1, 1}, {1, 256, 1, 1});
|
||||
add_test_bin_bcast(type, {640, 1, 1, 1}, {1, 1, 1, 1});
|
||||
//add_test_bin_bcast(type, {3, 3, 2560, 1280}, {1, 1, 1, 1});
|
||||
//add_test_bin_bcast(type, {3, 3, 2560, 1280}, {2, 1, 1, 1});
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_add1());
|
||||
test_cases.emplace_back(new test_scale());
|
||||
@ -4015,8 +4146,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 5, 4, 3}, v, eps));
|
||||
}
|
||||
test_cases.emplace_back(new test_rms_norm_back(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
|
||||
test_cases.emplace_back(new test_l2_norm (GGML_TYPE_F32, {64, 5, 4, 3}, eps));
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, {64, 5, 4, 3}, 1e-12f));
|
||||
|
||||
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 1536, 1, 1}, {4, 1536, 1, 1}));
|
||||
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {8, 1536, 1, 1}, {4, 1536, 1, 1}));
|
||||
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 1536, 4, 1}, {4, 1536, 1, 1}));
|
||||
@ -4029,6 +4163,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 4));
|
||||
test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 128, 4));
|
||||
|
||||
test_cases.emplace_back(new test_rwkv_wkv7(GGML_TYPE_F32, 32, 64, 1, 1));
|
||||
test_cases.emplace_back(new test_rwkv_wkv7(GGML_TYPE_F32, 32, 64, 32, 1));
|
||||
test_cases.emplace_back(new test_rwkv_wkv7(GGML_TYPE_F32, 32, 64, 32, 4));
|
||||
test_cases.emplace_back(new test_rwkv_wkv7(GGML_TYPE_F32, 32, 64, 128, 4));
|
||||
|
||||
test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 1, 1));
|
||||
test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 1));
|
||||
test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 4));
|
||||
@ -4076,6 +4215,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
|
||||
|
||||
// test cases with large ne00/ne10 to cover stream-k fixup
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 1024, {3, 2}, {1, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 1024, {3, 2}, {1, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 1024, {3, 2}, {1, 1}));
|
||||
}
|
||||
}
|
||||
for (ggml_type type_a : other_types) {
|
||||
@ -4111,6 +4255,19 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 64, { 8, 1}, {4, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 193, {1, 1}, {4, 1}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67, {1, 1}, {4, 1}, {0, 2, 1, 3}));
|
||||
|
||||
for (auto bs : {1,2,4,8}) {
|
||||
for (auto nr : {1,4}) {
|
||||
for (uint32_t m = 0; m < 2; ++m) {
|
||||
for (uint32_t k = 0; k < 2; ++k) {
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056 + m, 1, 128 + k, {bs, 1}, {nr, 1}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128 + m, 1, 1056 + k, {bs, 1}, {nr, 1}, {0, 1, 2, 3}, true));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// sycl backend will limit task global_range < MAX_INT
|
||||
// test case for f16-type-convert-to-fp32 kernel with large k under fp32 compute dtype (occurs in stable-diffusion)
|
||||
@ -4123,7 +4280,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
for (int n_mats : {4, 8}) {
|
||||
for (int n_used : {1, 2, 4}) {
|
||||
for (bool b : {false, true}) {
|
||||
for (int n : {1, 32}) {
|
||||
for (int n : {1, 32, 129}) {
|
||||
int m = 512;
|
||||
int k = 256;
|
||||
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k));
|
||||
@ -4168,12 +4325,14 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
}
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_sqr());
|
||||
test_cases.emplace_back(new test_sqrt());
|
||||
test_cases.emplace_back(new test_log());
|
||||
test_cases.emplace_back(new test_sin());
|
||||
test_cases.emplace_back(new test_cos());
|
||||
test_cases.emplace_back(new test_clamp());
|
||||
for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
|
||||
test_cases.emplace_back(new test_sqr(type));
|
||||
test_cases.emplace_back(new test_sqrt(type));
|
||||
test_cases.emplace_back(new test_log(type));
|
||||
test_cases.emplace_back(new test_sin(type));
|
||||
test_cases.emplace_back(new test_cos(type));
|
||||
test_cases.emplace_back(new test_clamp(type));
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
|
||||
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 3, 1}, 5));
|
||||
@ -4289,12 +4448,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
|
||||
}
|
||||
|
||||
for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR}) {
|
||||
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode));
|
||||
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode, true));
|
||||
test_cases.emplace_back(new test_upscale_ext(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode));
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_sum());
|
||||
test_cases.emplace_back(new test_sum_rows());
|
||||
test_cases.emplace_back(new test_mean());
|
||||
test_cases.emplace_back(new test_upscale());
|
||||
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true));
|
||||
test_cases.emplace_back(new test_upscale_ext());
|
||||
test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1}));
|
||||
test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
|
||||
test_cases.emplace_back(new test_acc());
|
||||
@ -4304,23 +4466,34 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_timestep_embedding());
|
||||
test_cases.emplace_back(new test_leaky_relu());
|
||||
|
||||
for (int hs : { 64, 80, 128, 256, }) {
|
||||
for (bool mask : { true, false } ) {
|
||||
for (float max_bias : { 0.0f, 8.0f }) {
|
||||
if (!mask && max_bias > 0.0f) continue;
|
||||
for (float logit_softcap : {0.0f, 10.0f}) {
|
||||
if (hs != 128 && logit_softcap != 0.0f) continue;
|
||||
for (int nh : { 4, }) {
|
||||
for (int nr : { 1, 4, 16 }) {
|
||||
if (nr == 16 && hs != 128) continue;
|
||||
for (int kv : { 512, 1024, }) {
|
||||
if (nr != 1 && kv != 512) continue;
|
||||
for (int nb : { 1, 3, 32, 35, }) {
|
||||
for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, nr, kv, nb, mask, max_bias, logit_softcap, type_KV));
|
||||
// run fewer test cases permuted
|
||||
if (mask == true && max_bias == 0.0f && logit_softcap == 0 && kv == 512) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, nr, kv, nb, mask, max_bias, logit_softcap, type_KV, {0, 2, 1, 3}));
|
||||
for (int hsk : { 64, 80, 128, 192, 256, 576 }) {
|
||||
for (int hsv : { 64, 80, 128, 192, 256, 512 }) {
|
||||
if (hsk != 192 && hsk != 576 && hsk != hsv) continue;
|
||||
if (hsk == 192 && (hsv != 128 && hsv != 192)) continue;
|
||||
if (hsk == 576 && hsv != 512) continue; // DeepSeek MLA
|
||||
|
||||
for (bool mask : { true, false } ) {
|
||||
for (float max_bias : { 0.0f, 8.0f }) {
|
||||
if (!mask && max_bias > 0.0f) continue;
|
||||
for (float logit_softcap : {0.0f, 10.0f}) {
|
||||
if (hsk != 128 && logit_softcap != 0.0f) continue;
|
||||
for (int nh : { 4, }) {
|
||||
for (int nr : { 1, 4, 16 }) {
|
||||
if (nr == 16 && hsk != 128) continue;
|
||||
for (int kv : { 512, 1024, }) {
|
||||
if (nr != 1 && kv != 512) continue;
|
||||
for (int nb : { 1, 3, 32, 35, }) {
|
||||
for (ggml_prec prec : {GGML_PREC_F32, GGML_PREC_DEFAULT}) {
|
||||
if (hsk != 128 && prec == GGML_PREC_DEFAULT) continue;
|
||||
for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(
|
||||
hsk, hsv, nh, nr, kv, nb, mask, max_bias, logit_softcap, prec, type_KV));
|
||||
// run fewer test cases permuted
|
||||
if (mask == true && max_bias == 0.0f && logit_softcap == 0 && kv == 512) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(
|
||||
hsk, hsv, nh, nr, kv, nb, mask, max_bias, logit_softcap, prec, type_KV, {0, 2, 1, 3}));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -4373,6 +4546,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
|
||||
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));
|
||||
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1}));
|
||||
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8, 1}, {4, 1}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8, 1}, {4, 1}, {0, 1, 2, 3}, true));
|
||||
|
||||
for (int bs : {1, 2, 3, 4, 5, 8, 512}) {
|
||||
for (ggml_type type_a : all_types) {
|
||||
for (ggml_type type_b : {GGML_TYPE_F32}) {
|
||||
@ -4393,12 +4569,38 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
|
||||
}
|
||||
}
|
||||
|
||||
for (int kv : { 4096, 8192, 16384, }) {
|
||||
for (int hs : { 64, 128, }) {
|
||||
for (int nr : { 1, 4, }) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(hs, hs, 8, nr, kv, 1, true, 0, 0, GGML_PREC_F32, GGML_TYPE_F16));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return test_cases;
|
||||
}
|
||||
|
||||
static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
|
||||
static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name, const char * params_filter) {
|
||||
auto filter_test_cases = [](std::vector<std::unique_ptr<test_case>> & test_cases, const char * params_filter) {
|
||||
if (params_filter == nullptr) {
|
||||
return;
|
||||
}
|
||||
|
||||
std::regex params_filter_regex(params_filter);
|
||||
|
||||
for (auto it = test_cases.begin(); it != test_cases.end();) {
|
||||
if (!std::regex_search((*it)->vars(), params_filter_regex)) {
|
||||
it = test_cases.erase(it);
|
||||
continue;
|
||||
}
|
||||
|
||||
it++;
|
||||
}
|
||||
};
|
||||
|
||||
if (mode == MODE_TEST) {
|
||||
auto test_cases = make_test_cases_eval();
|
||||
filter_test_cases(test_cases, params_filter);
|
||||
ggml_backend_t backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, NULL);
|
||||
if (backend_cpu == NULL) {
|
||||
printf(" Failed to initialize CPU backend\n");
|
||||
@ -4420,6 +4622,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
|
||||
if (mode == MODE_GRAD) {
|
||||
auto test_cases = make_test_cases_eval();
|
||||
filter_test_cases(test_cases, params_filter);
|
||||
size_t n_ok = 0;
|
||||
for (auto & test : test_cases) {
|
||||
if (test->eval_grad(backend, op_name)) {
|
||||
@ -4433,6 +4636,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
|
||||
if (mode == MODE_PERF) {
|
||||
auto test_cases = make_test_cases_perf();
|
||||
filter_test_cases(test_cases, params_filter);
|
||||
for (auto & test : test_cases) {
|
||||
test->eval_perf(backend, op_name);
|
||||
}
|
||||
@ -4443,7 +4647,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
}
|
||||
|
||||
static void usage(char ** argv) {
|
||||
printf("Usage: %s [mode] [-o op] [-b backend]\n", argv[0]);
|
||||
printf("Usage: %s [mode] [-o <op>] [-b <backend>] [-p <params regex>]\n", argv[0]);
|
||||
printf(" valid modes:\n");
|
||||
printf(" - test (default, compare with CPU backend for correctness)\n");
|
||||
printf(" - grad (compare gradients from backpropagation with method of finite differences)\n");
|
||||
@ -4453,8 +4657,9 @@ static void usage(char ** argv) {
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
test_mode mode = MODE_TEST;
|
||||
const char * op_name_filter = NULL;
|
||||
const char * backend_filter = NULL;
|
||||
const char * op_name_filter = nullptr;
|
||||
const char * backend_filter = nullptr;
|
||||
const char * params_filter = nullptr;
|
||||
|
||||
for (int i = 1; i < argc; i++) {
|
||||
if (strcmp(argv[i], "test") == 0) {
|
||||
@ -4477,6 +4682,13 @@ int main(int argc, char ** argv) {
|
||||
usage(argv);
|
||||
return 1;
|
||||
}
|
||||
} else if (strcmp(argv[i], "-p") == 0) {
|
||||
if (i + 1 < argc) {
|
||||
params_filter = argv[++i];
|
||||
} else {
|
||||
usage(argv);
|
||||
return 1;
|
||||
}
|
||||
} else {
|
||||
usage(argv);
|
||||
return 1;
|
||||
@ -4523,7 +4735,7 @@ int main(int argc, char ** argv) {
|
||||
printf(" Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024);
|
||||
printf("\n");
|
||||
|
||||
bool ok = test_backend(backend, mode, op_name_filter);
|
||||
bool ok = test_backend(backend, mode, op_name_filter, params_filter);
|
||||
|
||||
printf(" Backend %s: ", ggml_backend_name(backend));
|
||||
if (ok) {
|
||||
|
||||
Reference in New Issue
Block a user