DarthAffe/stable-diffusion.cpp
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fix: chunk MiniMax-H3 MLP to respect backend buffer limits (#2257)

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leejet authored and GitHub committed 2026-11-01 22:08:29 +08:00
1 parent 45a63ac460
commit 5356982584
1 file changed
+36 -6
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src/model/diffusion/minimax_h3.hpp
+36 -6
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@@ -140,8 +140,11 @@ namespace MiniMaxH3 {
};
struct MLP : public UnaryBlock {
int64_t ffn_hidden_size;
MLP(int64_t hidden_size,
int64_t ffn_hidden_size) {
int64_t ffn_hidden_size)
: ffn_hidden_size(ffn_hidden_size) {
blocks["fc1"] = std::make_shared<Linear>(hidden_size, ffn_hidden_size * 2, false, false, true, 1.f / 128.f);
blocks["fc2"] = std::make_shared<Linear>(ffn_hidden_size, hidden_size, false, false, true, 1.f / 128.f);
}
@@ -159,11 +162,38 @@ namespace MiniMaxH3 {
}
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
auto projected = fc1->forward(ctx, x);
auto activated = ggml_swiglu(ctx->ggml_ctx, projected);
return fc2->forward(ctx, activated);
auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
auto forward_chunk = [&](ggml_tensor* input) {
auto projected = fc1->forward(ctx, input);
auto activated = ggml_swiglu(ctx->ggml_ctx, projected);
return fc2->forward(ctx, activated);
};
int64_t chunk_size = x->ne[1];
if (ctx->backend != nullptr) {
auto buft = ggml_backend_get_default_buffer_type(ctx->backend);
size_t alignment = ggml_backend_buft_get_alignment(buft);
size_t max_size = ggml_backend_buft_get_max_size(buft);
max_size -= max_size % alignment;
// Matmul outputs are F32 even for quantized weights. Split before fc1
// so its expanded activation never needs an oversized backend buffer.
size_t token_bytes = ggml_row_size(GGML_TYPE_F32, std::max(x->ne[0], 2 * ffn_hidden_size)) * x->ne[2] * x->ne[3];
chunk_size = static_cast<int64_t>(std::min<size_t>(chunk_size, std::max<size_t>(1, max_size / token_bytes)));
}
if (chunk_size == x->ne[1]) {
return forward_chunk(x);
}
std::vector<ggml_tensor*> chunks;
for (int64_t start = 0; start < x->ne[1]; start += chunk_size) {
auto input = ggml_ext_slice(ctx->ggml_ctx, x, 1, start, std::min(start + chunk_size, x->ne[1]), false);
if (!ggml_is_contiguous(input)) {
input = ggml_cont(ctx->ggml_ctx, input);
}
chunks.push_back(forward_chunk(input));
}
return ggml_ext_vec_concat(ctx->ggml_ctx, chunks, 1);
}
};