From 532200de2a451036670bbb4509727653136938b5 Mon Sep 17 00:00:00 2001 From: mrq Date: Mon, 23 Dec 2024 22:23:43 -0600 Subject: [PATCH] nvm fixed --- vall_e.cpp/README.md | 7 +- vall_e.cpp/vall_e.cpp | 237 +++++++++++++++++++----------------------- vall_e.cpp/vall_e.h | 14 ++- 3 files changed, 120 insertions(+), 138 deletions(-) diff --git a/vall_e.cpp/README.md b/vall_e.cpp/README.md index 916df18..914783a 100644 --- a/vall_e.cpp/README.md +++ b/vall_e.cpp/README.md @@ -2,7 +2,7 @@ This is an implementation that makes use of [llama.cpp](https://github.com/ggerganov/llama.cpp/) and [encodec.cpp](https://github.com/PABannier/encodec.cpp). -At the moment it's ***very*** barebones as I try and wrestle with `llama.cpp`'s API without needing to modify its code. +At the moment it's ***very*** work in progress. ## Build @@ -14,15 +14,14 @@ Run `make`. ### Required Modifications -[`encodec.cpp`](https://github.com/e-c-k-e-r/encodec.cpp) requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working. +[`encodec.cpp`](https://github.com/PABannier/encodec.cpp) requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working (per my [fork](https://github.com/e-c-k-e-r/encodec.cpp)). -[`llama.cpp`](https://github.com/e-c-k-e-r/llama.cpp) only possible modification needs to ensure that a non-causal attention mask is used; everything necessary can be hacked together with clever tricks. +[`llama.cpp`](https://github.com/ggerganov/llama.cpp) only possible modification needs to ensure that a non-causal attention mask is used; everything necessary can be hacked together with clever tricks. ## To-Do * [x] converted model to GGUF * [ ] convert it without modifying any of the existing code, as the tokenizer requires some care - * [ ] *actually* convert the model properly, as the embeddings differ from the real model * [x] basic framework * [x] load the quantized model * [x] orchestrate the required embeddings diff --git a/vall_e.cpp/vall_e.cpp b/vall_e.cpp/vall_e.cpp index 0fbdd0f..aa279cd 100644 --- a/vall_e.cpp/vall_e.cpp +++ b/vall_e.cpp/vall_e.cpp @@ -40,10 +40,9 @@ std::vector VALL_E_API read_2d_tensor( struct ggml_tensor* tensor ) { size_t size = tensor->ne[0] * tensor->ne[1]; std::vector res( size ); - auto* qtype = ggml_get_type_traits(tensor->type); - // dequantize if needed - if ( ggml_is_quantized(tensor->type) ) { - qtype->to_float(tensor->data, res.data(), res.size()); + auto* type_trait = ggml_get_type_traits(tensor->type); + if ( type_trait->to_float ) { + type_trait->to_float(tensor->data, res.data(), res.size()); } else { memcpy( res.data(), tensor->data, res.size() * sizeof(float) ); } @@ -78,27 +77,16 @@ ggml_tensor* VALL_E_API view_2d_tensor( struct ggml_context* ctx, struct ggml_te ggml_tensor* res = ggml_view_2d( ctx, tensor, tensor->ne[0], end - start, tensor->nb[1], tensor->nb[1] * start ); - /* - printf("%p: %i | %i | %i | %i || %p: %i | %i | %i | %i\n", - tensor->data, tensor->ne[0], tensor->ne[1], tensor->nb[1], tensor->nb[2], - res->data, res->ne[0], res->ne[1], res->nb[1], res->nb[2] - ); - */ - return res; } - -struct ggml_tensor * VALL_E_API vall_e_get_prom_embds( llama_vall_e_userdata& userdata, int32_t idx ) { - return userdata.prom_embds[idx]; +void VALL_E_API print_tokens( const std::vector& tokens, const std::string& prefix ) { + printf("%s[", prefix.c_str()); + for ( auto i = 0; i < tokens.size(); ++i ) { + printf("%i%s", tokens[i], i + 1 < tokens.size() ? ", " : ""); + } + printf("]\n"); } -struct ggml_tensor * VALL_E_API vall_e_get_resp_embds( llama_vall_e_userdata& userdata, int32_t idx ) { - return userdata.resp_embds[idx]; -} -struct ggml_tensor * VALL_E_API vall_e_get_aux_embds( llama_vall_e_userdata& userdata, int32_t idx ) { - return userdata.aux_embds[idx]; -} - const io_t& VALL_E_API vall_e_inputs_map_get( io_map_t& io_map, const std::string& name ) { return io_map.io[name]; @@ -140,43 +128,37 @@ void VALL_E_API vall_e_inputs_map_init( io_map_t& io_map, llama_model* model ) { io_map.io[entry.name].head = entry.head_idx < 0 ? NULL : userdata.heads[entry.head_idx]; } - io_map.io["text"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 0)); - io_map.io["rvq_l"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 1)); - io_map.io["lang"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 2)); - io_map.io["task"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 3)); - io_map.io["len"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 4)); - io_map.io["tone"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 5)); - io_map.io["sep"].embds = read_2d_tensor(vall_e_get_aux_embds(userdata, 6)); + io_map.io["text"].embds = read_2d_tensor(userdata.aux_embds[0]); + io_map.io["rvq_l"].embds = read_2d_tensor(userdata.aux_embds[1]); + io_map.io["lang"].embds = read_2d_tensor(userdata.aux_embds[2]); + io_map.io["task"].embds = read_2d_tensor(userdata.aux_embds[3]); + io_map.io["len"].embds = read_2d_tensor(userdata.aux_embds[4]); + io_map.io["tone"].embds = read_2d_tensor(userdata.aux_embds[5]); + io_map.io["sep"].embds = read_2d_tensor(userdata.aux_embds[6]); - io_map.io["prom|0"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 0)); - io_map.io["prom|1"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 1)); - io_map.io["prom|2"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 2)); - io_map.io["prom|3"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 3)); - io_map.io["prom|4"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 4)); - io_map.io["prom|5"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 5)); - io_map.io["prom|6"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 6)); - io_map.io["prom|7"].embds = read_2d_tensor(vall_e_get_prom_embds(userdata, 7)); + io_map.io["prom|0"].embds = read_2d_tensor(userdata.prom_embds[0]); + io_map.io["prom|1"].embds = read_2d_tensor(userdata.prom_embds[1]); + io_map.io["prom|2"].embds = read_2d_tensor(userdata.prom_embds[2]); + io_map.io["prom|3"].embds = read_2d_tensor(userdata.prom_embds[3]); + io_map.io["prom|4"].embds = read_2d_tensor(userdata.prom_embds[4]); + io_map.io["prom|5"].embds = read_2d_tensor(userdata.prom_embds[5]); + io_map.io["prom|6"].embds = read_2d_tensor(userdata.prom_embds[6]); + io_map.io["prom|7"].embds = read_2d_tensor(userdata.prom_embds[7]); - io_map.io["resps|AR:0:0"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 0)); - io_map.io["resps|NAR:0:1"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 1)); - io_map.io["resps|NAR:1:2"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 2)); - io_map.io["resps|NAR:2:3"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 3)); - io_map.io["resps|NAR:3:4"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 4)); - io_map.io["resps|NAR:4:5"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 5)); - io_map.io["resps|NAR:5:6"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 6)); - io_map.io["resps|NAR:6:7"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 7)); - io_map.io["resps|NAR:0:0"].embds = read_2d_tensor(vall_e_get_resp_embds(userdata, 8)); - - - for ( auto& entry : io_ranges ) { - for ( auto i = 0; i < 32; ++i ) printf("%s: %i: %f\n", entry.name.c_str(), i, io_map.io[entry.name].embds[i] ); - } + io_map.io["resps|AR:0:0"].embds = read_2d_tensor(userdata.resp_embds[0]); + io_map.io["resps|NAR:0:1"].embds = read_2d_tensor(userdata.resp_embds[1]); + io_map.io["resps|NAR:1:2"].embds = read_2d_tensor(userdata.resp_embds[2]); + io_map.io["resps|NAR:2:3"].embds = read_2d_tensor(userdata.resp_embds[3]); + io_map.io["resps|NAR:3:4"].embds = read_2d_tensor(userdata.resp_embds[4]); + io_map.io["resps|NAR:4:5"].embds = read_2d_tensor(userdata.resp_embds[5]); + io_map.io["resps|NAR:5:6"].embds = read_2d_tensor(userdata.resp_embds[6]); + io_map.io["resps|NAR:6:7"].embds = read_2d_tensor(userdata.resp_embds[7]); + io_map.io["resps|NAR:0:0"].embds = read_2d_tensor(userdata.resp_embds[8]); #else auto* embds = llama_get_embedding_weights( model ); auto* heads = llama_get_output_head_tensor( model ); // prepare slices - // std::vector raw_embeddings = read_2d_tensor( embds ); for ( auto& entry : io_ranges ) { io_map.io[entry.name] = entry; @@ -184,16 +166,6 @@ void VALL_E_API vall_e_inputs_map_init( io_map_t& io_map, llama_model* model ) { io_map.io[entry.name].n_vocab = entry.end - entry.start; io_map.io[entry.name].embds = read_2d_tensor(view_2d_tensor( io_map.ctx, embds, entry.start, entry.end )); io_map.io[entry.name].head = entry.head_idx < 0 ? NULL : view_2d_tensor( io_map.ctx, heads, entry.start, entry.end ); - - // these two differ after the first embedding and I don't know why......... - /* - auto raw_embd = std::vector( raw_embeddings.data() + entry.start * n_embd, raw_embeddings.data() + entry.end * n_embd ); - auto sliced_embd = read_2d_tensor( embd_tensor ); - - io_map.io[entry.name].embds = raw_embd; - - for ( auto i = 0; i < 32; ++i ) printf("%s: %i: %f == %f \n", entry.name.c_str(), i, raw_embd[i], sliced_embd[i] ); - */ } #endif } @@ -228,9 +200,6 @@ void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const for (size_t i = 0; i < seq_ids.size(); ++i) batch.seq_id[batch.n_tokens][i] = seq_ids[i]; batch.logits[batch.n_tokens] = output ? 1 : 0; - // printf("[%i] Adding: %i | %i | %p | %i\n", batch.n_tokens, id, batch.pos[batch.n_tokens], &batch.embd[batch.n_tokens], batch.logits[batch.n_tokens] ); - // printf("[%i] Adding: %i | %i | %p | %i\n", batch.n_tokens, id, pos, embds, output ); - batch.n_tokens++; } // reads a waveform from disk @@ -283,13 +252,13 @@ std::vector> VALL_E_API encode_audio_from_disk( struct enco std::vector wavform; if(!read_wav_from_disk(path, wavform)) { - printf("%s: error during reading wav file\n", __func__); + fprintf(stderr, "%s: error during reading wav file\n", __func__); return {}; } // compress audio if (!encodec_compress_audio(ectx, wavform.data(), wavform.size(), 1)) { - printf("%s: error during compression \n", __func__); + fprintf(stderr, "%s: error during compression \n", __func__); return {}; } @@ -325,7 +294,7 @@ std::vector VALL_E_API decode_audio( struct encodec_context* ectx, const // decompress audio if (!encodec_decompress_audio(ectx, codes.data(), codes.size(), 1)) { - printf("%s: error during decompression\n", __func__); + fprintf(stderr, "%s: error during decompression\n", __func__); return {}; } @@ -366,10 +335,27 @@ std::vector> VALL_E_API sum_embeddings( const std::vector VALL_E_API soft_max( int n_logits, const float* logits ) { std::vector res( n_logits, 0.0f ); + std::vector expd( n_logits, 0.0f ); float denom = 0.0f; for ( auto i = 0; i < n_logits; ++i ) { - denom += exp( logits[i] ); + expd[i] = exp( logits[i] ); + denom += expd[i]; + } + // to-do: assert denom != 0.0f + for ( auto i = 0; i < n_logits; ++i ) { + res[i] = expd[i] / denom; + } + + return res; +} + +std::vector VALL_E_API log_soft_max( int n_logits, const float* logits ) { + std::vector res( n_logits, 0.0f ); + float denom = 0.0f; + + for ( auto i = 0; i < n_logits; ++i ) { + denom += logits[i]; } // to-do: assert denom != 0.0f for ( auto i = 0; i < n_logits; ++i ) { @@ -503,7 +489,7 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m int32_t n_vocab = io.n_vocab; llama_token stop_token = io.end - io.start - 1; - printf("Generating in %s (%i) mode (%i:%i) (%i)\n", embd_name.c_str(), io.head_idx, io.start, io.end, stop_token); + if ( verbose ) printf("Generating in %s (%i) mode (%i:%i) (%i)\n", embd_name.c_str(), io.head_idx, io.start, io.end, stop_token); // update model's output heads / causal mode llama_set_output_head( model, io.head ); @@ -515,15 +501,12 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m // if INFERENCE_MODE_AR || INFERENCE_MODE_LEN if ( causal ) { output_tokens.reserve(max_tokens); - if ( verbose ) { - printf("["); - fflush(stdout); - } while ( output_tokens.size() < max_tokens ) { if ( llama_decode(ctx, batch) ) { fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return output_tokens; } + llama_kv_cache_clear(ctx); // necessary for many reasons // sample token auto t = llama_sampler_sample(smpl, ctx, -1); @@ -537,21 +520,15 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m output_tokens.emplace_back(t); // update batch with token batch_add( batch, t, io_map.n_embd, embds, output_tokens.size(), true ); - if ( verbose ) { - printf("%i, ", t); - fflush(stdout); - } - } - if ( verbose ) { - printf("]\n"); - fflush(stdout); + + if ( verbose ) print_tokens( output_tokens ); } } else if ( mode == INFERENCE_MODE_NAR_DEMASK ) { // to-do: assert n_outputs == input.resp[rvq_l-1].size() const llama_token MASK_TOKEN = 1024; // token value for masking const float PI = 3.141592653589793f; // to-do: derive from sampling arguments - int32_t steps = 30; // number of demasking steps + int32_t steps = 10; // number of demasking steps int32_t seq_len = n_outputs; float temperature = 1.5f; float cfg_strength = 2.5f; @@ -572,34 +549,40 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m // do one step on many tokens for ( auto step = 0; step < steps; ++step ) { - if ( verbose ) { - printf("[%i/%i] [", step, steps); - fflush(stdout); - } - - float timestep = (step+1) / steps; // to-do: align with torch.linspace + float timestep = ((float)step) / steps; // to-do: align with torch.linspace + float annealing = 1.0f - timestep; - float noise_p = cos( timestep * PI * 0.5f ); - float remask_p = 0.5f / steps; - int32_t n_masked_tokens = std::min(int(noise_p * seq_len), 1); + float sampling_temperature = temperature * annealing; float sampling_cfg_strength = timestep * cfg_strength; - std::vector is_masked(n_outputs, false); - std::vector masked_indices; - masked_indices.reserve(n_masked_tokens); - std::vector sorted = scores; - std::sort(sorted.begin(), sorted.end()); - masked_indices.insert( masked_indices.end(), sorted.begin(), sorted.begin() + n_masked_tokens ); + float noise_p = cos( timestep * PI * 0.5f ); + float remask_p = 0.0f; // 0.5f / steps; + + int32_t n_masked_tokens = (noise_p + remask_p) * seq_len; + if ( n_masked_tokens < 1 ) { + n_masked_tokens = 1; + } + if ( n_masked_tokens > n_outputs ) { + n_masked_tokens = n_outputs; + } + + // masked mask + std::vector is_masked(n_outputs, false); + // sort previous scores + std::vector sorted_scores( n_outputs ); + for ( auto i = 0; i < n_outputs; ++i ) sorted_scores[i] = { i, scores[i] }; + std::sort(sorted_scores.begin(), sorted_scores.end()); + + // and top-k pick the worst scores + for ( auto i = 0; i < n_masked_tokens; ++i ) { + auto idx = sorted_scores[i].idx; - // mask off tokens - for ( auto& idx : masked_indices ) { output_tokens[idx] = MASK_TOKEN; + is_masked[idx] = true; } - // update token mask - for ( auto i = 0; i < n_outputs; ++i ) { - is_masked[i] = output_tokens[i] == MASK_TOKEN; - } + + if ( verbose ) print_tokens( output_tokens, "Masked tokens:" ); // update batch // to-do: only update the embeddings instead @@ -611,15 +594,14 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m null_batch.n_tokens = 0; fill_batch( null_batch, input, io_map, mode ); - // to-do: update sampling temperature - // cfg decode if ( llama_decode(ctx, null_batch) ) { fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return output_tokens; } + llama_kv_cache_clear(ctx); // necessary for many reasons // copy null probabilities - std::vector null_logits(n_outputs * n_vocab, -INFINITY); + std::vector null_logits(n_outputs * n_vocab, 0.0f); // to-do: copy once for ( auto idx = 0; idx < n_outputs; ++idx ) { memcpy( &null_logits[idx * n_vocab], llama_get_logits_ith( ctx, null_batch.n_tokens - n_outputs + idx ), sizeof(float) * n_vocab ); @@ -630,6 +612,17 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return output_tokens; } + llama_kv_cache_clear(ctx); // necessary for many reasons + + auto sparams = llama_sampler_chain_default_params(); + sparams.no_perf = false; + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp (sampling_temperature)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist (1130)); + // to-do: figure out why all logits are the same for each token...... // "reverse" iterate from backwards indexing for ( auto idx = 0; idx < n_outputs; ++idx ) { @@ -652,16 +645,12 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m // store token if it was masked output_tokens[idx] = t; // update score if it was masked - scores[idx] = 1.0f - softmaxed[t]; // invert so we pick the worst tokens later - if ( verbose ) { - printf("%i, ", t); - fflush(stdout); - } - } - if ( verbose ) { - printf("\n"); - fflush(stdout); + scores[idx] = softmaxed[t]; // invert so we pick the worst tokens later } + + llama_sampler_free(smpl); + + if ( verbose ) print_tokens( output_tokens ); } } else if ( mode == INFERENCE_MODE_NAR ) { // to-do: assert n_outputs == input.resp[rvq_l-1].size() @@ -671,27 +660,17 @@ std::vector VALL_E_API generate( llama_context* ctx, llama_model* m fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return output_tokens; } + llama_kv_cache_clear(ctx); // necessary for many reasons // to-do: figure out why all logits are the same for each token...... // "reverse" iterate from backwards indexing - if ( verbose ) { - printf("["); - fflush(stdout); - } for ( auto idx = 0; idx < n_outputs; ++idx ) { // sample ith token auto t = llama_sampler_sample(smpl, ctx, batch.n_tokens - n_outputs + idx); // store token output_tokens.emplace_back(t); - if ( verbose ) { - printf("%i, ", t); - fflush(stdout); - } - } - if ( verbose ) { - printf("]\n"); - fflush(stdout); } + if ( verbose ) print_tokens( output_tokens ); } const auto t_main_end = ggml_time_us(); @@ -771,7 +750,7 @@ int main( int argc, char** argv ) { struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl); if (!ectx) { - printf("%s: error during loading model\n", __func__); + fprintf(stderr, "%s: error during loading model\n", __func__); return 1; } @@ -811,7 +790,7 @@ int main( int argc, char** argv ) { // NAR-len demasking if ( modality == MODALITY_NAR_LEN ) { // inference len - int len = 0; + int len = 75; if ( !len ) { input.task = "len"; output_tokens = generate( ctx, model, smpl_nar, input, io_map, 5, INFERENCE_MODE_LEN ); diff --git a/vall_e.cpp/vall_e.h b/vall_e.cpp/vall_e.h index e31949d..27237fd 100644 --- a/vall_e.cpp/vall_e.h +++ b/vall_e.cpp/vall_e.h @@ -101,10 +101,18 @@ struct io_map_t { ggml_context* ctx = NULL; }; +struct score_t { + int32_t idx; + float value; + + bool operator<( const score_t& that ) const { return this->value < that.value; } +}; + // helper tensor functions std::vector VALL_E_API read_2d_tensor( struct ggml_tensor* tensor ); ggml_tensor* VALL_E_API view_2d_tensor( ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 ); // cringe method to keep in my pocket ggml_tensor* VALL_E_API view_2d_tensor( ggml_context* ctx, ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 ); +void VALL_E_API print_tokens( const std::vector& tokens, const std::string& prefix = "Tokens: " ); std::vector> VALL_E_API map_embeddings( const std::vector& tokens, int n_embd, const float* embds ); std::vector> VALL_E_API sum_embeddings( const std::vector>& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM ); @@ -125,8 +133,4 @@ std::vector VALL_E_API decode_audio( struct encodec_context* ectx, const const io_t& VALL_E_API vall_e_inputs_map_get_embeddings( io_map_t& inputs_map, const std::string& name ); const float* VALL_E_API vall_e_inputs_map_get_embeddings_p( io_map_t& inputs_map, const std::string& name ); int32_t VALL_E_API vall_e_inputs_map_get_classifier_idx( io_map_t& inputs_map, const std::string& name ); -void VALL_E_API vall_e_inputs_map_init( io_map_t&, llama_model* model ); - -struct ggml_tensor * VALL_E_API vall_e_get_prom_embds( llama_vall_e_userdata& userdata, int32_t idx ); -struct ggml_tensor * VALL_E_API vall_e_get_resp_embds( llama_vall_e_userdata& userdata, int32_t idx ); -struct ggml_tensor * VALL_E_API vall_e_get_aux_embds( llama_vall_e_userdata& userdata, int32_t idx ); \ No newline at end of file +void VALL_E_API vall_e_inputs_map_init( io_map_t&, llama_model* model ); \ No newline at end of file