add warmup for paraformer-torch

雾聪

2024-06-26 38c1f6393a16f4d15a8897647b8f8693a48f737d

 runtime/onnxruntime/src/paraformer-torch.cpp

@@ -55,6 +55,9 @@
        torch::jit::setGraphExecutorOptimize(false);
        torch::jit::FusionStrategy static0 = {{torch::jit::FusionBehavior::STATIC, 0}};
        torch::jit::setFusionStrategy(static0);
        #ifdef USE_GPU
        WarmUp();
        #endif
    } catch (std::exception const &e) {
        LOG(ERROR) << "Error when load am model: " << am_model << e.what();
        exit(-1);
@@ -471,6 +474,51 @@
    return results;
}

void ParaformerTorch::WarmUp()
{
    int32_t in_feat_dim = fbank_opts_.mel_opts.num_bins;
    int32_t feature_dim = lfr_m*in_feat_dim;
    int batch_in = 1;
    int max_frames = 10;
    std::vector<int32_t> paraformer_length;
    paraformer_length.push_back(max_frames);

    std::vector<float> all_feats(batch_in * max_frames * feature_dim, 0.1);
    torch::Tensor feats =
        torch::from_blob(all_feats.data(),
                {batch_in, max_frames, feature_dim}, torch::kFloat).contiguous();
    torch::Tensor feat_lens = torch::from_blob(paraformer_length.data(),
                        {batch_in}, torch::kInt32);

    // 2. forward
    feats = feats.to(at::kCUDA);
    feat_lens = feat_lens.to(at::kCUDA);
    std::vector<torch::jit::IValue> inputs = {feats, feat_lens};

    if (use_hotword) {
        std::string hotwords_wp = "";
        std::vector<std::vector<float>> hw_emb =  CompileHotwordEmbedding(hotwords_wp);
        std::vector<float> embedding;
        embedding.reserve(hw_emb.size() * hw_emb[0].size());
        for (auto item : hw_emb) {
            embedding.insert(embedding.end(), item.begin(), item.end());
        }
        torch::Tensor tensor_hw_emb =
            torch::from_blob(embedding.data(),
                    {batch_in, static_cast<int64_t>(hw_emb.size()), static_cast<int64_t>(hw_emb[0].size())}, torch::kFloat).contiguous();
        tensor_hw_emb = tensor_hw_emb.to(at::kCUDA);
        inputs.emplace_back(tensor_hw_emb);
    }

    try {
        auto outputs = model_->forward(inputs).toTuple()->elements();
    }
    catch (std::exception const &e)
    {
        LOG(ERROR)<<e.what();
    }
}

std::vector<std::vector<float>> ParaformerTorch::CompileHotwordEmbedding(std::string &hotwords) {
    int embedding_dim = encoder_size;
    std::vector<std::vector<float>> hw_emb;