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ggerganov / llama.cpp

LLM inference in C/C++
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Unexpected embeddings using GritLM #5783

Closed dranger003 closed 4 months ago

dranger003 commented 5 months ago

I reproduced the embeddings sample from GritLM and llama.cpp returns unexpected embedding values. I have been able to get embeddings to work with other models. I verified the tokenization and all seems good (with and without special tokens and bos/eos).

Below is a sample program to reproduce the issue with the unexpected results. The gritlm python inference code can be found here. The model inference supports both text generation and text representation (embeddings) and is based on Mistral 7B.

There is nothing special in this code, and this is based off the embeddings sample in llama.cpp so I'm not sure what is going. Any guidance is appreciated.

Note: It seems HF is in maintenance right now, but I'll add a gguf link when they're back online.

sample source code ``` static float dot_product(const std::vector& v1, const std::vector& v2) { auto dot = 0.0f; for (auto i = 0; i < v1.size(); ++i) dot += v1[i] * v2[i]; return dot; } static float norm(const std::vector& v) { return std::sqrt(dot_product(v, v)); } static float cosine_similarity(const std::vector& v1, const std::vector& v2) { return dot_product(v1, v2) / (norm(v1) * norm(v2)); } static void normalize(std::span in, std::span out) { auto norm = 0.0f; for (auto i = 0; i < in.size(); i++) norm += in[i] * in[i]; norm = std::sqrt(norm); for (auto i = 0; i < out.size(); i++) out[i] = in[i] / norm; } static std::vector> encode(llama_context* ctx, const std::vector& sentences, const std::string& instruction) { auto result = std::vector>{}; auto mdl = llama_get_model(ctx); for (auto i = 0; i < sentences.size(); i++) { auto batch = llama_batch_init(llama_n_batch(ctx), 0, 1); // testing with and without EOS - unexpected embeddings in both cases - GritLM seems to have EOS = "" auto inputs = llama_tokenize(mdl, std::format("{}{}", instruction, sentences[i]), true, false); // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L116 //inputs.push_back(llama_token_eos(mdl)); // debug tokens - these are matching as referenced in their sample so doesn't appear to be a token issue std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) { std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str()); }); std::printf("\n"); for (auto j = 0; j < inputs.size(); j++) llama_batch_add(batch, inputs[j], j, { 0 }, false); // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_seq_rm(ctx, 0, 0, -1); // run model llama_decode(ctx, batch); auto emb_unorm = std::span(llama_get_embeddings_ith(ctx, 0), llama_n_embd(mdl)); auto emb_norm = std::vector(emb_unorm.size()); normalize(emb_unorm, emb_norm); result.push_back(emb_norm); llama_batch_free(batch); } return result; } // ./embeddings -m ggml-gritlm-7b-q8_0.gguf -ngl 33 int main(int argc, char* argv[]) { gpt_params params; if (!gpt_params_parse(argc, argv, params)) return 1; auto mparams = llama_model_params_from_gpt_params(params); auto cparams = llama_context_params_from_gpt_params(params); mparams.progress_callback = [](std::float_t progress, void* state) { std::printf("%s\rLoading model... %u%%\r", std::string(32, ' ').c_str(), static_cast(progress * 100)); return true; }; cparams.embedding = true; llama_backend_init(); auto mdl = llama_load_model_from_file(params.model.c_str(), mparams); auto ctx = llama_new_context_with_model(mdl, cparams); auto bat = llama_batch_init(llama_n_ctx(ctx), 0, 1); // ### Embedding/Representation ### taken sample from here: // https://github.com/ContextualAI/gritlm?tab=readme-ov-file#basic { auto instruction = std::string{ "Given a scientific paper title, retrieve the paper's abstract" }; auto queries = std::vector{ "Bitcoin: A Peer-to-Peer Electronic Cash System", "Generative Representational Instruction Tuning", }; auto documents = std::vector{ "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.", "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.", }; auto gritlm_instruction = [](const std::string& instruction) -> std::string { return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n"; }; // No need to add instruction for retrieval documents auto d_rep = encode(ctx, documents, gritlm_instruction("")); auto q_rep = encode(ctx, queries, gritlm_instruction(instruction)); auto cosine_sim_q0_d0 = 1 - cosine_similarity(q_rep[0], d_rep[0]); auto cosine_sim_q0_d1 = 1 - cosine_similarity(q_rep[0], d_rep[1]); auto cosine_sim_q1_d0 = 1 - cosine_similarity(q_rep[1], d_rep[0]); auto cosine_sim_q1_d1 = 1 - cosine_similarity(q_rep[1], d_rep[1]); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0); std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1); } llama_batch_free(bat); llama_free(ctx); llama_free_model(mdl); llama_backend_free(); return 0; } ```
output ``` ./embeddings -m ggml-gritlm-7b-q8_0.gguf -ngl 33 ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from ggml-gritlm-7b-q8_0.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = GritLM llama_model_loader: - kv 2: llama.context_length u32 = 32768 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 32 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 11: general.file_type u32 = 7 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 1 llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess... llama_model_loader: - kv 23: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q8_0: 226 tensors llm_load_vocab: special tokens definition check successful ( 259/32000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 32768 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 4 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: n_ff = 14336 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 0 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 32768 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: model type = 7B llm_load_print_meta: model ftype = Q8_0 llm_load_print_meta: model params = 7.24 B llm_load_print_meta: model size = 7.17 GiB (8.50 BPW) llm_load_print_meta: general.name = GritLM llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 '' llm_load_print_meta: PAD token = 1 '' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.22 MiB llm_load_tensors: offloading 32 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 33/33 layers to GPU llm_load_tensors: CPU buffer size = 132.81 MiB llm_load_tensors: CUDA0 buffer size = 7205.83 MiB llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 64.00 MiB llama_new_context_with_model: KV self size = 64.00 MiB, K (f16): 32.00 MiB, V (f16): 32.00 MiB llama_new_context_with_model: CUDA_Host input buffer size = 10.01 MiB llama_new_context_with_model: CUDA0 compute buffer size = 73.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 8.00 MiB llama_new_context_with_model: graph splits (measure): 2 [1:][523: <][28766:|][18320:embed][28766:|][28767:>][13: ][28741:A][21690: purely][13669: peer][28733:-][532:to][28733:-][14720:peer][2751: version][302: of][13176: electronic][7877: cash][682: would][1914: allow][3270: online][14923: payments][298: to][347: be][2662: sent][5090: directly][477: from][624: one][4150: party][298: to][1698: another][1671: without][1404: going][1059: through][264: a][5593: financial][16854: institution][28723:.][13770: Digital][1492: sign][2863:atures][3084: provide][744: part][302: of][272: the][5165: solution][28725:,][562: but][272: the][2191: main][7196: benefits][460: are][3654: lost][513: if][264: a][16437: trusted][4008: third][4150: party][349: is][1309: still][3030: required][298: to][5297: prevent][3579: double][28733:-][886:sp][2570:ending][28723:.][816: We][19333: propose][264: a][5165: solution][298: to][272: the][3579: double][28733:-][886:sp][2570:ending][2700: problem][1413: using][264: a][13669: peer][28733:-][532:to][28733:-][14720:peer][3681: network][28723:.][415: The][3681: network][5104: tim][374:est][10991:amps][15852: transactions][486: by][659: has][2299:hing][706: them][778: into][396: an][15260: ongoing][7650: chain][302: of][7135: hash][28733:-][5527:based][7167: proof][28733:-][1009:of][28733:-][1328:work][28725:,][20345: forming][264: a][2395: record][369: that][3573: cannot][347: be][4648: changed][1671: without][312: re][2432:do][288:ing][272: the][7167: proof][28733:-][1009:of][28733:-][1328:work][28723:.][415: The][23397: longest][7650: chain][459: not][865: only][14449: serves][390: as][7167: proof][302: of][272: the][7768: sequence][302: of][3926: events][24385: witnessed][28725:,][562: but][7167: proof][369: that][378: it][1988: came][477: from][272: the][7639: largest][6313: pool][302: of][14865: CPU][1982: power][28723:.][1136: As][1043: long][390: as][264: a][7757: majority][302: of][14865: CPU][1982: power][349: is][12888: controlled][486: by][9249: nodes][369: that][460: are][459: not][18468: cooper][1077:ating][298: to][3517: attack][272: the][3681: network][28725:,][590: they][28742:'][584:ll][8270: generate][272: the][23397: longest][7650: chain][304: and][575: out][2644:pace][3517: attack][404:ers][28723:.][415: The][3681: network][3837: itself][6948: requires][13383: minimal][4693: structure][28723:.][351: M][9251:essages][460: are][11837: broadcast][356: on][264: a][1489: best][4261: effort][6451: basis][28725:,][304: and][9249: nodes][541: can][3530: leave][304: and][312: re][5906:join][272: the][3681: network][438: at][622: will][28725:,][22368: accepting][272: the][23397: longest][7167: proof][28733:-][1009:of][28733:-][1328:work][7650: chain][390: as][7167: proof][302: of][767: what][4243: happened][1312: while][590: they][654: were][4214: gone][28723:.] [1:][523: <][28766:|][18320:embed][28766:|][28767:>][13: ][2595:All][2245: text][28733:-][5527:based][3842: language][4418: problems][541: can][347: be][9397: reduced][298: to][2477: either][8342: generation][442: or][28643: embedding][28723:.][10929: Current][4994: models][865: only][2225: perform][1162: well][438: at][624: one][442: or][272: the][799: other][28723:.][816: We][13097: introduce][1350: gener][1197:ative][2904: represent][1249:ational][13126: instruction][15013: tun][288:ing][325: (][8369:GR][1153:IT][28731:)][970: where][1403:by][264: a][2475: large][3842: language][2229: model][349: is][10898: trained][298: to][4269: handle][1560: both][1350: gener][1197:ative][304: and][28643: embedding][9796: tasks][486: by][11731: distingu][5596:ishing][1444: between][706: them][1059: through][11382: instructions][28723:.][3880: Comp][1327:ared][298: to][799: other][1565: open][4994: models][28725:,][813: our][10503: resulting][420: G][872:rit][27149:LM][28705: ][28787:7][28760:B][6491: sets][264: a][633: new][1665: state][302: of][272: the][1524: art][356: on][272: the][7576: Mass][495:ive][7379: Text][18065: Emb][286:ed][3202:ding][4121: Ben][338:ch][3325:mark][325: (][28755:M][3392:TE][28760:B][28731:)][304: and][575: out][487:per][14367:forms][544: all][4994: models][582: up][298: to][871: its][1669: size][356: on][264: a][2819: range][302: of][1350: gener][1197:ative][9796: tasks][28723:.][2463: By][19903: scaling][582: up][3629: further][28725:,][420: G][872:rit][27149:LM][28705: ][28783:8][28814:X][28787:7][28760:B][575: out][487:per][14367:forms][544: all][1565: open][1350: gener][1197:ative][3842: language][4994: models][369: that][478: we][3851: tried][1312: while][1309: still][1250: being][3352: among][272: the][1489: best][28643: embedding][4994: models][28723:.][2280: Not][1907:ably][28725:,][478: we][1300: find][369: that][19348: GR][1153:IT][9019: matches][4154: training][356: on][865: only][1350: gener][1197:ative][442: or][28643: embedding][1178: data][28725:,][5884: thus][478: we][541: can][521: un][1575:ify][1560: both][438: at][708: no][4397: performance][4320: loss][28723:.][13927: Among][799: other][7196: benefits][28725:,][272: the][521: un][2500:ification][4213: via][19348: GR][1153:IT][27480: speeds][582: up][8337: Ret][10212:riev][282:al][28733:-][21575:Aug][466:ment][286:ed][26802: Generation][325: (][28754:R][2377:AG][28731:)][486: by][876: >][28705: ][28784:6][28734:0][28823:%][354: for][1043: long][10181: documents][28725:,][486: by][708: no][3774: longer][22579: requiring][7681: separate][17913: retriev][282:al][304: and][8342: generation][4994: models][28723:.][3813: Mod][1190:els][28725:,][2696: code][28725:,][4345: etc][28723:.][460: are][21964: freely][2632: available][438: at][4449: https][1508:://][6222:github][28723:.][675:com][28748:/][2083:Context][840:ual][11741:AI][28748:/][820:gr][279:it][24174:lm][28723:.] [1:][523: <][28766:|][1838:user][28766:|][28767:>][13: ][28777:G][5067:iven][264: a][10469: scientific][3830: paper][3941: title][28725:,][20132: retrieve][272: the][3830: paper][28742:'][28713:s][11576: abstract][13: ][28789:<][28766:|][18320:embed][28766:|][28767:>][13: ][8443:Bit][10817:coin][28747::][330: A][3242: Pe][263:er][28733:-][532:to][28733:-][22163:Peer][10394: Elect][7624:ronic][23439: Cash][2135: System] [1:][523: <][28766:|][1838:user][28766:|][28767:>][13: ][28777:G][5067:iven][264: a][10469: scientific][3830: paper][3941: title][28725:,][20132: retrieve][272: the][3830: paper][28742:'][28713:s][11576: abstract][13: ][28789:<][28766:|][18320:embed][28766:|][28767:>][13: ][3602:Gener][1197:ative][17891: Represent][1249:ational][3133: Inst][3112:ruction][22756: Tun][288:ing] Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.551 Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.794 Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.730 Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.803 ```
iamlemec commented 5 months ago

The paper says they use mean pooling for the embeddings, but currently embeddings for generative models like this are hard-coded to last token. If you change the code around llama.cpp:8087 to:

const int64_t embd_pos  = 0;
const int64_t embd_size = n_embd * n_tokens;

It'll pull in embeddings for each token then you can manually do the mean pooling. That said, I just tried it and I'm stilling getting different results. So there must be something else going on as well.

I'm pretty excited about GritLM, especially the document caching tricks they show. Would be cool to get it working here.

dranger003 commented 5 months ago

Thanks, I saw that but don't know what it means (no pun intended). Do I also need to change the way I call llama_decode?

Also, I added some quants on HF.

iamlemec commented 5 months ago

Good news @dranger003! I finally got the numbers to match up exactly. Check it out at iamlemec/llama.cpp:gritlm. Here are the changes that were needed:

  • Mean pooling over token embeddings (with the tweak above, likely possible in master soon)
  • But for the mean token pooling calculation, don't include the instruction tokens
  • Use non-causal attention rather than causal attention (requires another small tweak to llama.cpp)

Also note the slight fix to the cosine similarity (we want similarity, not distance).

dranger003 commented 5 months ago

Thanks, just tested it and it works great! You even included the sample code, amazing. I think there is a lot of potential in using the same model for both representation and generation, especially when resources are scarce.

dranger003 commented 5 months ago

I've been trying to add the text generation piece and I noticed two things:

  • it seems like we need to unload/reload the model with embedding = false and pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED, is that right? I thought we could maybe save having to unload/reload the model but maybe not.
  • text generation gives broken output for some reason (it may be my code)
Text generation code ``` static void generate(llama_context* ctx, const std::string& prompt) { auto mdl = llama_get_model(ctx); auto bat = llama_batch_init(llama_n_batch(ctx), 0, 1); auto inputs = llama_tokenize(mdl, prompt, true, true); auto candidates = std::vector(llama_n_vocab(mdl)); while (true) { llama_batch_clear(bat); for (auto j = 0; j < inputs.size(); j++) { llama_batch_add(bat, inputs[j], j, { 0 }, false); } bat.logits[bat.n_tokens - 1] = true; inputs.clear(); llama_decode(ctx, bat); auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1); for (auto token = 0; token < candidates.size(); token++) candidates[token] = llama_token_data{ token, logits[token], 0.0f }; auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false }; //auto token = llama_sample_token_greedy(ctx, &candidates_p); llama_sample_top_k(ctx, &candidates_p, 40, 1); llama_sample_top_p(ctx, &candidates_p, 0.9f, 1); llama_sample_temp(ctx, &candidates_p, 0.8f); auto token = llama_sample_token(ctx, &candidates_p); if (token == llama_token_eos(mdl)) break; auto piece = llama_token_to_piece(ctx, token); std::printf("%s", piece.c_str()); inputs.push_back(token); } llama_batch_free(bat); } ``` ``` ... // ### Generation ### // # We did not finetune GritLM models with system prompts, as you can just include system-like instructions together with your user instruction { cparams.embedding = false; cparams.pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; std::printf("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", params.n_ctx, params.n_batch, params.n_predict, params.n_keep); auto mdl = llama_load_model_from_file(params.model.c_str(), mparams); auto ctx = llama_new_context_with_model(mdl, cparams); auto prompt = " <|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n"; generate(ctx, prompt); llama_free(ctx); llama_free_model(mdl); } ... ```
dranger003 commented 5 months ago

@iamlemec Am I right in thinking there is a part two for master branch to work? I saw ggerganov's commit yesterday to fix embeddings but gritlm doesn't work in master, so this is why I'm asking.

EDIT: Just saw your v2 branch. If I can help with anything let me know!

iamlemec commented 5 months ago

@dranger003 Yup, that's my attempt at rebasing after the new embedding fixes. Unfortunately it's not giving the right results currently. Trying to figure it out now, but if you spot anything, do tell!

iamlemec commented 5 months ago

@dranger003 Finally figured it out. My gritlm-v2 branch is working now. We switched away from using the KV cache for non-causal BERT models, but Llama type models still use it, so had to tweak the attention mask construction.

dranger003 commented 5 months ago

@iamlemec I added the text generation code to the sample and it works great without reloading the model for both modes. Should we submit a PR?

EDIT: So it looks like we cannot use both embeddings and causal on the same context? The text generation works but the embeddings are different when I set both embeddings and causal to true. Is that expected (i.e. they need to have a separate context)?

iamlemec commented 5 months ago

Just pushed a thing where you can toggle embedding mode with llama_set_embeddings. This has the advantage of not having to manually fiddle with causal_attn, everything goes through embeddings. I haven't tested generation extensively, but the output is sensible at least.

Let's test and think about this for a day or two to see if it's ready for prime time. I might also try to see if I can get the doc caching example working.