remove batched
This commit is contained in:
Wenjing Yu
parent
715540a77b
commit
4b850f0ce4
6 changed files with 1 additions and 331 deletions
18
Makefile
18
Makefile
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@ -1,7 +1,6 @@
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# Define the default target now so that it is always the first target
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BUILD_TARGETS = \
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libllava.a \
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llama-batched \
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llama-batched-bench \
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llama-bench \
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llama-benchmark-matmult \
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@ -40,7 +39,7 @@ BUILD_TARGETS = \
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# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
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LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \
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simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli \
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simple batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli \
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retrieval speculative infill tokenize benchmark-matmult parallel export-lora lookahead lookup passkey gritlm
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# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them.
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@ -1154,16 +1153,6 @@ llama-tokenize: examples/tokenize/tokenize.cpp \
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-batched: examples/batched/batched.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-batched-bench: examples/batched-bench/batched-bench.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-quantize: examples/quantize/quantize.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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@ -1361,11 +1350,6 @@ llama-llava-cli: examples/llava/llava-cli.cpp \
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$(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS)
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ifeq ($(UNAME_S),Darwin)
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swift: examples/batched.swift
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(cd examples/batched.swift; make build)
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endif
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common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
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@sh scripts/build-info.sh "$(CC)" > $@.tmp
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@if ! cmp -s $@.tmp $@; then \
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@ -14,7 +14,6 @@ if (EMSCRIPTEN)
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else()
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add_subdirectory(cvector-generator)
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add_subdirectory(batched-bench)
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add_subdirectory(batched)
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add_subdirectory(benchmark)
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add_subdirectory(convert-llama2c-to-ggml)
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add_subdirectory(embedding)
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@ -1,5 +0,0 @@
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set(TARGET llama-batched)
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add_executable(${TARGET} batched.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@ -1,44 +0,0 @@
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# llama.cpp/example/batched
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The example demonstrates batched generation from a given prompt
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```bash
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./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4
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...
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main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113
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Hello my name is
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main: generating 4 sequences ...
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main: stream 0 finished
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main: stream 1 finished
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main: stream 2 finished
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main: stream 3 finished
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sequence 0:
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Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b
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sequence 1:
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Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between
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sequence 2:
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Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am
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sequence 3:
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Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and
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main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s
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llama_print_timings: load time = 587.00 ms
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llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second)
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llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second)
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llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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llama_print_timings: total time = 4156.04 ms
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```
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@ -1,259 +0,0 @@
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#include "common.h"
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#include "llama.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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static void print_usage(int argc, char ** argv, const gpt_params & params) {
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gpt_params_print_usage(argc, argv, params);
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
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LOG_TEE("\n");
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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params.prompt = "Hello my name is";
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params.n_predict = 32;
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if (!gpt_params_parse(argc, argv, params)) {
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print_usage(argc, argv, params);
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return 1;
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}
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// number of parallel batches
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int n_parallel = params.n_parallel;
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// total length of the sequences including the prompt
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int n_predict = params.n_predict;
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// init LLM
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llama_backend_init();
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llama_numa_init(params.numa);
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// initialize the model
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llama_model_params model_params = llama_model_params_from_gpt_params(params);
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llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
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if (model == NULL) {
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fprintf(stderr , "%s: error: unable to load model\n" , __func__);
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return 1;
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}
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = ::llama_tokenize(model, params.prompt, true);
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const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
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// initialize the context
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llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
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ctx_params.n_ctx = n_kv_req;
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ctx_params.n_batch = std::max(n_predict, n_parallel);
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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if (ctx == NULL) {
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fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
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return 1;
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}
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const int n_ctx = llama_n_ctx(ctx);
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LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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LOG_TEE("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req);
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LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
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return 1;
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}
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// print the prompt token-by-token
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fprintf(stderr, "\n");
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for (auto id : tokens_list) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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// create a llama_batch
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// we use this object to submit token data for decoding
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llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
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std::vector<llama_seq_id> seq_ids(n_parallel, 0);
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for (int32_t i = 0; i < n_parallel; ++i) {
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seq_ids[i] = i;
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}
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// evaluate the initial prompt
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for (size_t i = 0; i < tokens_list.size(); ++i) {
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llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
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}
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GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
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if (llama_model_has_encoder(model)) {
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if (llama_encode(ctx, batch)) {
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LOG_TEE("%s : failed to eval\n", __func__);
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return 1;
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}
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llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
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if (decoder_start_token_id == -1) {
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decoder_start_token_id = llama_token_bos(model);
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}
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llama_batch_clear(batch);
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llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
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}
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// llama_decode will output logits only for the last token of the prompt
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batch.logits[batch.n_tokens - 1] = true;
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if (llama_decode(ctx, batch) != 0) {
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LOG_TEE("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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//// assign the system KV cache to all parallel sequences
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//// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
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//for (int32_t i = 1; i < n_parallel; ++i) {
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// llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
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//}
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if (n_parallel > 1) {
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LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
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}
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// main loop
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// we will store the parallel decoded sequences in this vector
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std::vector<std::string> streams(n_parallel);
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// remember the batch index of the last token for each parallel sequence
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// we need this to determine which logits to sample from
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std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
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int n_cur = batch.n_tokens;
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int n_decode = 0;
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const auto t_main_start = ggml_time_us();
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while (n_cur <= n_predict) {
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// prepare the next batch
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llama_batch_clear(batch);
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// sample the next token for each parallel sequence / stream
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for (int32_t i = 0; i < n_parallel; ++i) {
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if (i_batch[i] < 0) {
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// the stream has already finished
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continue;
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}
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auto n_vocab = llama_n_vocab(model);
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auto * logits = llama_get_logits_ith(ctx, i_batch[i]);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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const int top_k = 40;
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const float top_p = 0.9f;
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const float temp = 0.4f;
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llama_sample_top_k(ctx, &candidates_p, top_k, 1);
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llama_sample_top_p(ctx, &candidates_p, top_p, 1);
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llama_sample_temp (ctx, &candidates_p, temp);
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const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
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//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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// is it an end of generation? -> mark the stream as finished
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
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i_batch[i] = -1;
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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
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}
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continue;
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}
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// if there is only one stream, we print immediately to stdout
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if (n_parallel == 1) {
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LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
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fflush(stdout);
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}
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streams[i] += llama_token_to_piece(ctx, new_token_id);
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i_batch[i] = batch.n_tokens;
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// push this new token for next evaluation
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llama_batch_add(batch, new_token_id, n_cur, { i }, true);
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n_decode += 1;
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}
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// all streams are finished
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if (batch.n_tokens == 0) {
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break;
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}
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n_cur += 1;
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch)) {
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fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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}
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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("\n");
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for (int32_t i = 0; i < n_parallel; ++i) {
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LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
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}
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}
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const auto t_main_end = ggml_time_us();
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LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
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__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
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llama_print_timings(ctx);
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fprintf(stderr, "\n");
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llama_batch_free(batch);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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@ -183,11 +183,6 @@ if [ "$1" -eq "1" ]; then
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make -j && ./bin/llama-perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
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# batched
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cd /workspace/llama.cpp
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GGML_CUDA=1 make -j && ./llama-batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
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# batched-bench
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cd /workspace/llama.cpp
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