tqcq/llama.cpp
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Merge branch 'master' into compilade/mamba2

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This commit is contained in:
Francis Couture-Harpin
2025-06-10 19:22:15 -04:00
parent e94f3932f2 dad5c44398
commit 9864bfcd01
581 changed files with 159374 additions and 37809 deletions
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159
tests/test-backend-ops.cpp
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@ -823,7 +823,7 @@ struct test_case {
ggml_build_forward_expand(gf, out);
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx.get(), ctx.get(), gb, false);
ggml_build_backward_expand(ctx.get(), gb, nullptr);
if (expect.size() != 1 || expect[0] != 0.0f) {
GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
@ -1026,7 +1026,7 @@ struct test_example : public test_case {
// Step 3: return the output tensor.
return out;
}
// In order to also check the gradients for your op, add calls like ggml_set_param(ctx, a)
// In order to also check the gradients for your op, add calls like ggml_set_param(a)
// immediately after you create the tensors.
// This is optional and only makes sense if a backward pass has actually been implemented for the new op.
};
@ -1058,7 +1058,7 @@ struct test_unary : public test_case {
auto ne = ne_a; ne[0] *= 3;
a = ggml_new_tensor(ctx, type, 4, ne.data());
if (grad_supported) {
ggml_set_param(ctx, a);
ggml_set_param(a);
}
ggml_set_name(a, "a");
@ -1067,7 +1067,7 @@ struct test_unary : public test_case {
} else {
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
if (grad_supported) {
ggml_set_param(ctx, a);
ggml_set_param(a);
}
ggml_set_name(a, "a");
}
@ -1133,7 +1133,7 @@ struct test_get_rows : public test_case {
const bool grad_supported = ggml_is_matrix(in) && ggml_is_vector(rows);
if (grad_supported) {
ggml_set_param(ctx, in);
ggml_set_param(in);
// rows is a constant input -> no gradients
}
@ -1322,7 +1322,7 @@ struct test_repeat : public test_case {
ggml_set_name(target, "target");
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, src);
ggml_set_param(src);
ggml_set_name(src, "src");
ggml_tensor * out = ggml_repeat(ctx, src, target);
@ -1406,7 +1406,7 @@ struct test_dup : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, src);
ggml_set_param(src);
ggml_set_name(src, "src");
if (_use_permute) {
@ -1442,7 +1442,7 @@ struct test_set : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
ggml_set_param(ctx, src);
ggml_set_param(src);
ggml_set_name(src, "src");
auto ne_dst = ne;
@ -1450,7 +1450,7 @@ struct test_set : public test_case {
ne_dst[i] *= 2;
}
ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
ggml_set_param(ctx, dst);
ggml_set_param(dst);
ggml_set_name(dst, "dst");
size_t offset = 0;
@ -1498,7 +1498,7 @@ struct test_cpy : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
ggml_set_param(ctx, src);
ggml_set_param(src);
ggml_set_name(src, "src");
if (_src_use_permute) {
@ -1536,7 +1536,7 @@ struct test_cont : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, src);
ggml_set_param(src);
ggml_set_name(src, "src");
src = ggml_transpose(ctx, src);
@ -1583,8 +1583,8 @@ struct test_bin_bcast : public test_case {
// The backward pass supports broadcasting only for GGML_ADD:
const bool grad_supported = op == ggml_add || ggml_are_same_shape(a, b);
if (grad_supported) {
ggml_set_param(ctx, a);
ggml_set_param(ctx, b);
ggml_set_param(a);
ggml_set_param(b);
}
ggml_tensor * out = op(ctx, a, b);
@ -1632,11 +1632,11 @@ struct test_add1 : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * b = ggml_new_tensor_1d(ctx, type, 1);
// ggml_set_param(ctx, b); // TODO: implement
// ggml_set_param(b); // TODO: implement
ggml_set_name(b, "b");
ggml_tensor * out = ggml_add1(ctx, a, b);
@ -1667,7 +1667,7 @@ struct test_scale : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_scale(ctx, a, scale);
@ -1762,7 +1762,7 @@ struct test_rms_norm : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
if (v) {
@ -2058,9 +2058,9 @@ struct test_mul_mat : public test_case {
b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(ctx, a);
ggml_set_param(a);
}
ggml_set_param(ctx, b);
ggml_set_param(b);
}
ggml_set_name(a, "a");
ggml_set_name(b, "b");
@ -2070,22 +2070,29 @@ struct test_mul_mat : public test_case {
ggml_set_name(a, "a_permuted");
ggml_set_name(b, "b_permuted");
} else {
if (v) {
a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(a);
}
ggml_set_param(b);
}
a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0);
b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->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) {
ggml_set_param(ctx, a);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(a);
}
ggml_set_param(b);
}
ggml_set_param(ctx, b);
}
ggml_set_name(a, "a");
ggml_set_name(b, "b");
@ -2234,7 +2241,7 @@ struct test_sqr : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sqr(ctx, a);
@ -2263,7 +2270,7 @@ struct test_sqrt : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sqrt(ctx, a);
@ -2303,7 +2310,7 @@ struct test_log : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_log(ctx, a);
@ -2339,7 +2346,7 @@ struct test_sin : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sin(ctx, a);
@ -2382,7 +2389,7 @@ struct test_cos : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_cos(ctx, a);
@ -2462,7 +2469,7 @@ struct test_diag_mask_inf : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past);
@ -2501,7 +2508,7 @@ struct test_soft_max : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * mask = nullptr;
@ -2583,7 +2590,7 @@ struct test_rope : public test_case {
auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
a = ggml_new_tensor(ctx, type, 4, ne.data());
if (forward) {
ggml_set_param(ctx, a);
ggml_set_param(a);
}
ggml_set_name(a, "a");
@ -2592,7 +2599,7 @@ struct test_rope : public test_case {
} else {
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
if (forward) {
ggml_set_param(ctx, a);
ggml_set_param(a);
}
ggml_set_name(a, "a");
}
@ -2706,7 +2713,7 @@ struct test_pool2d : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
ggml_set_param(ctx, input);
ggml_set_param(input);
ggml_set_name(input, "input");
ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
@ -2729,8 +2736,8 @@ struct test_conv_transpose_1d : public test_case {
return VARS_TO_STR5(ne_input, ne_kernel, s0, p0, d0);
}
test_conv_transpose_1d(std::array<int64_t, 4> ne_input = {197, 32, 1, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {16, 32, 32, 1}, // [kernel_width, kernel_height, input_channels, 1]
test_conv_transpose_1d(std::array<int64_t, 4> ne_input = {197, 32, 1, 1}, // [input_width, input_channels, 1 /* assert in cpu kernel*/, 1 (should be batch)]
std::array<int64_t, 4> ne_kernel = {16, 32, 32, 1}, // [kernel_width, output_channels, input_channels, 1 (should be batch)]
int s0 = 1, int p0 = 0, int d0 = 1)
: ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), p0(p0), d0(d0) {}
@ -2782,7 +2789,7 @@ struct test_im2col : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
ggml_set_param(ctx, input);
ggml_set_param(input);
ggml_set_name(input, "input");
ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
@ -2795,6 +2802,48 @@ struct test_im2col : public test_case {
}
};
// GGML_OP_CONV_2D_DW
struct test_conv_2d_dw : public test_case {
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
const int stride;
const int padding;
const int dilation;
const bool cwhn;
std::string vars() override {
return VARS_TO_STR6(ne_input, ne_kernel, stride, padding, dilation, cwhn);
}
test_conv_2d_dw(std::array<int64_t, 4> ne_input = {64, 64, 16, 1},
std::array<int64_t, 4> ne_kernel = {3, 3, 1, 16},
int stride = 1, int padding = 0, int dilation = 1, bool cwhn = false)
: ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), padding(padding), dilation(dilation), cwhn(cwhn) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
ggml_set_name(input, "input");
ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data());
ggml_set_name(kernel, "kernel");
if (cwhn) {
// change memory layout to channel-most-contiguous (CWHN),
// then permute it back so NE matches the original input
input = ggml_cont(ctx, ggml_permute(ctx, input, 1, 2, 0, 3));
input = ggml_permute(ctx, input, 2, 0, 1, 3);
kernel = ggml_cont(ctx, ggml_permute(ctx, kernel, 2, 3, 1, 0));
kernel = ggml_permute(ctx, kernel, 3, 2, 0, 1);
}
ggml_tensor * out = ggml_conv_2d_dw_direct(
ctx, kernel, input,
stride, stride, padding, padding, dilation, dilation);
ggml_set_name(out, "out");
return out;
}
};
// GGML_OP_CONCAT
struct test_concat : public test_case {
const ggml_type type;
@ -2917,7 +2966,7 @@ struct test_sum : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sum(ctx, a);
@ -2946,7 +2995,7 @@ struct test_sum_rows : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sum_rows(ctx, a);
@ -2971,7 +3020,7 @@ struct test_mean : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_mean(ctx, a);
@ -3117,11 +3166,11 @@ struct test_acc : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_set_param(ctx, a);
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_set_param(ctx, b);
ggml_set_param(b);
ggml_set_name(b, "b");
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
@ -3358,7 +3407,7 @@ struct test_cross_entropy_loss : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_param(ctx, logits);
ggml_set_param(logits);
ggml_set_name(logits, "logits");
ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data());
@ -3440,7 +3489,7 @@ struct test_opt_step_adamw : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
ggml_set_param(ctx, a); // Despite tensor a having gradients the output tensor will not.
ggml_set_param(a); // Despite tensor a having gradients the output tensor will not.
ggml_set_name(a, "a");
ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
@ -4005,6 +4054,23 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
// test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true));
// test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, false));
test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, true));
test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, false));
test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, true));
for(uint32_t Cout : {1, 9}){
for(uint32_t Cin : {1, 7}){
for(uint32_t K : {1, 3, 1337}){
for(uint32_t L : {1, 2, 13}){
for(uint32_t s0: {1, 2, 3}){
test_cases.emplace_back(new test_conv_transpose_1d({L,Cin,1,1}, {K,Cout,Cin,1}, s0, 0, 1));
}
}
}
}
}
test_cases.emplace_back(new test_conv_transpose_1d());
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 2, 0, 1));
@ -4580,6 +4646,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
}
}
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false));
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true));
return test_cases;
}