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			* Starting to add k-quantization to ggml I think it is better to have quantization separate from ggml. For now just adding the k-quants there, but it would be better to also factor out the existing ggml quantizations. * Adding Q3_K and Q8_K (de)-quantization * Q3_K now working on CUDA and AVX2/scalar CUDA is not ideal - ~50% slower than Q4_0 for single token prediction, about the same in batch mode (perplexity). CPU single token is ~55 ms (on Ryzen 7950X). * Some improvement for Q3_K on CUDA It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0. * Some more CUDA optimizations for Q3_K Single token is now 20.5 ms/token (~20% slower than Q4_0). Perplexity is on par with Q4_0. * Adding Q4_K - scalar, AVX2, CUDA Performance is the same or perhaps very slightly better than Q4_0 on the CPU. On the GPU, single token prediction is ~10% better than Q4_0, batch mode (perplexity is about the same). * Adding Q6_K - scalar, AVX2, CUDA Performance is ~40% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 6-bit model is ~44% larger than the 4-bit. On the GPU, single token prediction is ~6% lower than Q4_0, batch mode (perplexity) is even closer (but still slower). * Adding Q5_K - scalar, AVX2, CUDA Performance is ~20% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 5-bit model is ~22% larger than the 4-bit. On the GPU, single token prediction is about the same as Q4_0 for both, single token and batch prediction. * Per convention, all QX_K quantizations use Q5_K for output.weight * Adding quantization mixes * Quantization mixes: didn't quite get what I wanted in the last commit * Q4_K dot product for ARM_NEON * Q6_K dot product for ARM_NEON * Q5_K dot product for ARM_NEON * Adding Q3_K dot for ARM_NEON It is 22% slower than Q4_K, despite the smaller model size. On x86_64, where we are memory bound, the Q3_K model is quite a bit faster than Q4_K. * A very slightly faster ARM_NEON Q3_K dot * Adding Q2_K - just CUDA for now Token prediction is pretty good - about 15.5 ms on a RTX 4080. Perplexity is about the same as Q4_K. * Adding scalar and AVX2 Q2_K dot * Adding ARM_NEON Q2_K dot About the same performance as Q4_K. * A slightly faster ARM_NEON Q2_K dot Single token prediction is now ~36 ms on M2 Max. The code is much simpler too. * Fixed bug in Q2_K CUDA dot product kernel Stranegly enough, for the few prompts I tried with the 7B model the responses looked perfectly reasonable. Only realized something is not quite right when I tried the larger models and started getting nonse back. In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X box iusing CUDA and model fully loaded on the GPU are ~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B. The max number of layers that fit in VRAM for The 65B is 32. With that, we get ~330 ms per token, which is not that much faster than just running on the CPU (~470 ms per token). * Don't print zeros/NaNs when no count histogram has been collected * A 10% faster CUDA vector dot kernel for Q3_K Q3_K is now running at ~18.5 ms / token on CUDA, so the gap to Q4_0 is only 10%. It seems memory acccess pattern is more important for performance than the amount of computation the kernel does. * A slightly daster Q4_K AVX2 dot product For perplexity, where we are less memory bound, time per pass drops by ~5%. Barely measurable difference for single token prediction. * A slightly faster ARM_NEON A4_K dot product * Minor * Fix quantization error test We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit quantization variants. * Fix docker build I have been sloppy with vector reinterpret casts on ARM_NEON. It seems clang is very forgiving in that regard. * Added forgotten ggml.o dependence on k_quants.h to the Makefile * Had unintentionally committed the Makefile with -Ofast enabled * ggml : rename k_quants -> ggml-quants-k, use lowercase in code --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
		
			
				
	
	
		
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			151 lines
		
	
	
		
			4.4 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| #include "build-info.h"
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| 
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| #include "llama.h"
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| 
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| #include <cstdio>
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| #include <map>
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| #include <string>
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| 
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| static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
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|   {"q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0},
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|   {"q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1},
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|   {"q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0},
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|   {"q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1},
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|   {"q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0},
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|   {"q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K},
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|   {"q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M},
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|   {"q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S},
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|   {"q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M},
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|   {"q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L},
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|   {"q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M},
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|   {"q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S},
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|   {"q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M},
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|   {"q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M},
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|   {"q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S},
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|   {"q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M},
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|   {"q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K},
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| };
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| 
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| bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
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|     auto it = LLAMA_FTYPE_MAP.find(ftype_str);
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|     if (it != LLAMA_FTYPE_MAP.end()) {
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|         ftype = it->second;
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|         ftype_str_out = it->first;
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|         return true;
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|     }
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|     // try to parse as an integer
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|     try {
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|         int ftype_int = std::stoi(ftype_str);
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|         for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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|             if (it->second == ftype_int) {
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|                 ftype = it->second;
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|                 ftype_str_out = it->first;
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|                 return true;
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|             }
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|         }
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|     }
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|     catch (...) {
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|         // stoi failed
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|     }
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|     return false;
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| }
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| 
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| // usage:
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| //  ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
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| //
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| int main(int argc, char ** argv) {
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|     if (argc < 3) {
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|         fprintf(stderr, "usage: %s model-f32.bin [model-quant.bin] type [nthreads]\n", argv[0]);
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|         for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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|             fprintf(stderr, "  type = \"%s\" or %d\n", it->first.c_str(), it->second);
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|         }
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|         return 1;
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|     }
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| 
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|     llama_init_backend();
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| 
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|     // parse command line arguments
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|     const std::string fname_inp = argv[1];
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|     std::string fname_out;
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|     int nthread;
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|     llama_ftype ftype;
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| 
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|     int arg_idx = 2;
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|     std::string ftype_str;
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|     if (try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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|         // argv[2] is the ftype
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|         std::string fpath;
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|         const size_t pos = fname_inp.find_last_of('/');
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|         if (pos != std::string::npos) {
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|             fpath = fname_inp.substr(0, pos + 1);
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|         }
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|         // export as [inp path]/ggml-model-[ftype].bin
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|         fname_out = fpath + "ggml-model-" + ftype_str + ".bin";
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|         arg_idx++;
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|     }
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|     else {
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|         // argv[2] is the output path
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|         fname_out = argv[arg_idx];
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|         arg_idx++;
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| 
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|         if (argc <= arg_idx) {
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|             fprintf(stderr, "%s: missing ftype\n", __func__);
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|             return 1;
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|         }
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|         // argv[3] is the ftype
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|         if (!try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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|             fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
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|             return 1;
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|         }
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|         arg_idx++;
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|     }
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| 
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|     // parse nthreads
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|     if (argc > arg_idx) {
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|         try {
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|             nthread = std::stoi(argv[arg_idx]);
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|         }
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|         catch (const std::exception & e) {
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|             fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
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|             return 1;
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|         }
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|     } else {
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|         nthread = 0;
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|     }
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| 
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|     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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| 
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|     fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
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|     if (nthread > 0) {
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|         fprintf(stderr, " using %d threads", nthread);
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|     }
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|     fprintf(stderr, "\n");
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| 
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|     const int64_t t_main_start_us = llama_time_us();
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| 
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|     int64_t t_quantize_us = 0;
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| 
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|     // load the model
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|     {
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|         const int64_t t_start_us = llama_time_us();
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| 
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|         if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
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|             fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
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|             return 1;
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|         }
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| 
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|         t_quantize_us = llama_time_us() - t_start_us;
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|     }
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| 
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|     // report timing
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|     {
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|         const int64_t t_main_end_us = llama_time_us();
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| 
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|         printf("\n");
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|         printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
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|         printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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|     }
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| 
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|     return 0;
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| }
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