correct way to build the amx enabled llama.cpp?

[jzz001]

I just tried to build the amx enabled llama.cpp as follows.

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build -DGGML_AMX_TILE=ON -DGGML_AMX_INT8=ON -DGGML_AMX_BF16=ON
`-- Building for: Visual Studio 17 2022
-- Selecting Windows SDK version 10.0.22621.0 to target Windows 10.0.20348.
-- The C compiler identification is MSVC 19.43.34808.0
-- The CXX compiler identification is MSVC 19.43.34808.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.43.34808/bin/Hostx64/x64/cl.exe - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.43.34808/bin/Hostx64/x64/cl.exe - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Git: C:/Program Files/Git/cmd/git.exe (found version "2.47.1.windows.2")
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - not found
-- Found Threads: TRUE
-- Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF
-- CMAKE_SYSTEM_PROCESSOR: AMD64
-- CMAKE_GENERATOR_PLATFORM:
-- Including CPU backend
-- Found OpenMP_C: -openmp (found version "2.0")
-- Found OpenMP_CXX: -openmp (found version "2.0")
-- Found OpenMP: TRUE (found version "2.0")
-- x86 detected
-- Performing Test HAS_AVX_1
-- Performing Test HAS_AVX_1 - Success
-- Performing Test HAS_AVX2_1
-- Performing Test HAS_AVX2_1 - Success
-- Performing Test HAS_FMA_1
-- Performing Test HAS_FMA_1 - Success
-- Performing Test HAS_AVX512_1
-- Performing Test HAS_AVX512_1 - Success
-- Adding CPU backend variant ggml-cpu: /arch:AVX512 GGML_AVX512;__AMX_TILE__;GGML_AMX_TILE;__AMX_INT8__;GGML_AMX_INT8;__AMX_BF16__;GGML_AMX_BF16
-- Configuring done (15.6s)
-- Generating done (0.6s)
-- Build files have been written to: D:/LLMB/llama.cpp/build`
cmake --build build --config Release
`......
  get-model.cpp
  正在生成代码...
  test-gguf.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-gguf.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-log.cpp
  get-model.cpp
  正在生成代码...
  test-log.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-log.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-model-load-cancel.cpp
  get-model.cpp
  正在生成代码...
  test-model-load-cancel.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-model-load-cancel.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-quantize-fns.cpp
  get-model.cpp
  正在生成代码...
  test-quantize-fns.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-quantize-fns.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-quantize-perf.cpp
  get-model.cpp
  正在生成代码...
  test-quantize-perf.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-quantize-perf.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-rope.cpp
  get-model.cpp
  正在生成代码...
  test-rope.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-rope.exe
  Building Custom Rule D:/LLMB/llama.cpp/tests/CMakeLists.txt
  test-tokenizer-0.cpp
  test-tokenizer-0.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\test-tokenizer-0.exe
  Building Custom Rule D:/LLMB/llama.cpp/CMakeLists.txt
PS D:\LLMB\llama.cpp>`
than run llama-cli.exe -m path to gguf files
PS C:\Users\Administrator> D:\LLMB\llama.cpp\build\bin\Release\llama-cli.exe --cache-type-k q4_0 --threads 16 --mlock --prio 2 --temp 0.6 --ctx-size 4096 --seed 3407 --model G:\DeepSeek-R1-UD-Q2_K_XL\DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf

`build: 4807 (https://github.com/ggml-org/llama.cpp/commit/2679c3b55d1c9c3fed56dea00fea713622e42594) with MSVC 19.43.34808.0 for x64
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_loader: additional 4 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 48 key-value pairs and 1025 tensors from G:\DeepSeek-R1-UD-Q2_K_XL\DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.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 = deepseek2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = DeepSeek R1 BF16
llama_model_loader: - kv 3: general.quantized_by str = Unsloth
llama_model_loader: - kv 4: general.size_label str = 256x20B
llama_model_loader: - kv 5: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 6: deepseek2.block_count u32 = 61
llama_model_loader: - kv 7: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 8: deepseek2.embedding_length u32 = 7168
llama_model_loader: - kv 9: deepseek2.feed_forward_length u32 = 18432
llama_model_loader: - kv 10: deepseek2.attention.head_count u32 = 128
llama_model_loader: - kv 11: deepseek2.attention.head_count_kv u32 = 128
llama_model_loader: - kv 12: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 13: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 14: deepseek2.expert_used_count u32 = 8
llama_model_loader: - kv 15: deepseek2.leading_dense_block_count u32 = 3
llama_model_loader: - kv 16: deepseek2.vocab_size u32 = 129280
llama_model_loader: - kv 17: deepseek2.attention.q_lora_rank u32 = 1536
llama_model_loader: - kv 18: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 19: deepseek2.attention.key_length u32 = 192
llama_model_loader: - kv 20: deepseek2.attention.value_length u32 = 128
llama_model_loader: - kv 21: deepseek2.expert_feed_forward_length u32 = 2048
llama_model_loader: - kv 22: deepseek2.expert_count u32 = 256
llama_model_loader: - kv 23: deepseek2.expert_shared_count u32 = 1
llama_model_loader: - kv 24: deepseek2.expert_weights_scale f32 = 2.500000
llama_model_loader: - kv 25: deepseek2.expert_weights_norm bool = true
llama_model_loader: - kv 26: deepseek2.expert_gating_func u32 = 2
llama_model_loader: - kv 27: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 28: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 29: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 30: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 31: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
llama_model_loader: - kv 32: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 33: tokenizer.ggml.pre str = deepseek-v3
llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,129280] = ["<｜begin▁of▁sentence｜>", "<�...
llama_model_loader: - kv 35: tokenizer.ggml.token_type arr[i32,129280] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 36: tokenizer.ggml.merges arr[str,127741] = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 39: tokenizer.ggml.padding_token_id u32 = 128815
llama_model_loader: - kv 40: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 41: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 42: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 43: general.quantization_version u32 = 2
llama_model_loader: - kv 44: general.file_type u32 = 10
llama_model_loader: - kv 45: split.no u16 = 0
llama_model_loader: - kv 46: split.tensors.count i32 = 1025
llama_model_loader: - kv 47: split.count u16 = 5
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q2_K: 171 tensors
llama_model_loader: - type q3_K: 3 tensors
llama_model_loader: - type q4_K: 306 tensors
llama_model_loader: - type q6_K: 184 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q2_K - Medium
print_info: file size = 211.03 GiB (2.70 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 819
load: token to piece cache size = 0.8223 MB
print_info: arch = deepseek2
print_info: vocab_only = 0
print_info: n_ctx_train = 163840
print_info: n_embd = 7168
print_info: n_layer = 61
print_info: n_head = 128
print_info: n_head_kv = 128
print_info: n_rot = 64
print_info: n_swa = 0
print_info: n_embd_head_k = 192
print_info: n_embd_head_v = 128
print_info: n_gqa = 1
print_info: n_embd_k_gqa = 24576
print_info: n_embd_v_gqa = 16384
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: n_ff = 18432
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = yarn
print_info: freq_base_train = 10000.0
print_info: freq_scale_train = 0.025
print_info: n_ctx_orig_yarn = 4096
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 671B
print_info: model params = 671.03 B
print_info: general.name = DeepSeek R1 BF16
print_info: n_layer_dense_lead = 3
print_info: n_lora_q = 1536
print_info: n_lora_kv = 512
print_info: n_ff_exp = 2048
print_info: n_expert_shared = 1
print_info: expert_weights_scale = 2.5
print_info: expert_weights_norm = 1
print_info: expert_gating_func = sigmoid
print_info: rope_yarn_log_mul = 0.1000
print_info: vocab type = BPE
print_info: n_vocab = 129280
print_info: n_merges = 127741
print_info: BOS token = 0 '<｜begin▁of▁sentence｜>'
print_info: EOS token = 1 '<｜end▁of▁sentence｜>'
print_info: EOT token = 1 '<｜end▁of▁sentence｜>'
print_info: PAD token = 128815 '<｜PAD▁TOKEN｜>'
print_info: LF token = 201 'Ċ'
print_info: FIM PRE token = 128801 '<｜fim▁begin｜>'
print_info: FIM SUF token = 128800 '<｜fim▁hole｜>'
print_info: FIM MID token = 128802 '<｜fim▁end｜>'
print_info: EOG token = 1 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: CPU_Mapped model buffer size = 47485.39 MiB
load_tensors: CPU_Mapped model buffer size = 47681.52 MiB
load_tensors: CPU_Mapped model buffer size = 47681.52 MiB
load_tensors: CPU_Mapped model buffer size = 47681.52 MiB
load_tensors: CPU_Mapped model buffer size = 25569.12 MiB
....................................................................................................
llama_init_from_model: n_seq_max = 1
llama_init_from_model: n_ctx = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch = 2048
llama_init_from_model: n_ubatch = 512
llama_init_from_model: flash_attn = 0
llama_init_from_model: freq_base = 10000.0
llama_init_from_model: freq_scale = 0.025
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'q4_0', type_v = 'f16', n_layer = 61, can_shift = 0
llama_kv_cache_init: CPU KV buffer size = 11102.00 MiB
llama_init_from_model: KV self size = 11102.00 MiB, K (q4_0): 3294.00 MiB, V (f16): 7808.00 MiB
llama_init_from_model: CPU output buffer size = 0.49 MiB
llama_init_from_model: CPU compute buffer size = 1186.01 MiB
llama_init_from_model: graph nodes = 5025
llama_init_from_model: graph splits = 1
common_init_from_params: KV cache shifting is not supported for this model, disabling KV cache shifting
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 16
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
main: chat template example:
You are a helpful assistant
<｜User｜>Hello<｜Assistant｜>Hi there<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>
system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AMX_INT8 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: interactive mode on.
sampler seed: 3407
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.600
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 1
== Running in interactive mode. ==
Press Ctrl+C to interject at any time.
Press Return to return control to the AI.
To return control without starting a new line, end your input with '/'.
If you want to submit another line, end your input with ''.
Not using system message. To change it, set a different value via -sys PROMPT`

I saw only AMX_INT8 = 1 becomes enabled. Is this a correct way to build? seems like AMX dose't take effect, since the TPS had no diffrence with the AVX512 one.
My system environment
CPU: Intel w5-3435X Memory: DDR5 256G
Windows Server 2022 + visual studio 2022

[slaren]

You would need to enable GGML_AVX512_VNNI as well, since that is required by the AMX code. Also, I do not think that MSVC supports AMX at all, you would need to use the clang compiler, or possibly gcc mingw.

[jzz001]

I tried to compile it with Intel mkl, but no sucess. I'm a newbie. Could you please tell me in detail how to compile it with clang or gcc? Thank you very much.
D:\LLMB\llama.cpp>cmake -B build -DGGML_AMX_TILE=ON -DGGML_AMX_INT8=ON -DGGML_AMX_BF16=ON -DGGML_AVX512_VNNI=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON -- Building for: Visual Studio 17 2022 -- The C compiler identification is MSVC 19.43.34808.0 -- The CXX compiler identification is MSVC 19.43.34808.0 -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.43.34808/bin/Hostx64/x64/cl.exe - skipped -- Detecting C compile features -- Detecting C compile features - done -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.43.34808/bin/Hostx64/x64/cl.exe - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done -- Found Git: C:/Program Files/Git/cmd/git.exe (found version "2.47.1.windows.2") -- Performing Test CMAKE_HAVE_LIBC_PTHREAD -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - not found -- Found Threads: TRUE -- Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF -- CMAKE_SYSTEM_PROCESSOR: AMD64 -- CMAKE_GENERATOR_PLATFORM: -- Including CPU backend -- Found OpenMP_C: -openmp (found version "2.0") -- Found OpenMP_CXX: -openmp (found version "2.0") -- Found OpenMP: TRUE (found version "2.0") -- x86 detected -- Performing Test HAS_AVX_1 -- Performing Test HAS_AVX_1 - Success -- Performing Test HAS_AVX2_1 -- Performing Test HAS_AVX2_1 - Success -- Performing Test HAS_FMA_1 -- Performing Test HAS_FMA_1 - Success -- Performing Test HAS_AVX512_1 -- Performing Test HAS_AVX512_1 - Success -- Adding CPU backend variant ggml-cpu: /arch:AVX512 GGML_AVX512;__AVX512VNNI__;GGML_AVX512_VNNI;__AMX_TILE__;GGML_AMX_TILE;__AMX_INT8__;GGML_AMX_INT8;__AMX_BF16__;GGML_AMX_BF16 -- Looking for sgemm_ -- Looking for sgemm_ - found -- Found BLAS: C:/oneAPI/mkl/latest/lib/mkl_intel_lp64_dll.lib;C:/oneAPI/compiler/latest/lib/libiomp5md.lib;C:/oneAPI/mkl/latest/lib/mkl_intel_thread_dll.lib;C:/oneAPI/mkl/latest/lib/mkl_core_dll.lib -- BLAS found, Libraries: C:/oneAPI/mkl/latest/lib/mkl_intel_lp64_dll.lib;C:/oneAPI/compiler/latest/lib/libiomp5md.lib;C:/oneAPI/mkl/latest/lib/mkl_intel_thread_dll.lib;C:/oneAPI/mkl/latest/lib/mkl_core_dll.lib -- Found PkgConfig: C:/msys64/ucrt64/bin/pkg-config.exe (found version "2.3.0") -- Checking for module 'mkl-sdl' -- Found mkl-sdl, version 2025.0.1 -- BLAS found, Includes: C:/oneAPI/mkl/latest/include -- Including BLAS backend -- Configuring done (18.3s) -- Generating done (0.7s) -- Build files have been written to: D:/LLMB/llama.cpp/build

D:\LLMB\llama.cpp>cmake --build build --config Release 适用于 .NET Framework MSBuild 版本 17.13.15+18b3035f6 1>Checking Build System Generating build details from Git -- Found Git: C:/Program Files/Git/cmd/git.exe (found version "2.47.1.windows.2") Building Custom Rule D:/LLMB/llama.cpp/common/CMakeLists.txt build-info.cpp build_info.vcxproj -> D:\LLMB\llama.cpp\build\common\build_info.dir\Release\build_info.lib Building Custom Rule D:/LLMB/llama.cpp/ggml/src/CMakeLists.txt ggml.c ggml-alloc.c D:\LLMB\llama.cpp\ggml\src\ggml.c(1959,26): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(1959,31): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(1959,36): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(1959,41): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(2843,26): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(2843,31): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(2843,36): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(2843,41): warning C4267: “初始化”: 从“size_t”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build \ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3231,22): warning C4244: “=”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml \src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3232,22): warning C4244: “=”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml \src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3233,22): warning C4244: “=”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml \src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3234,22): warning C4244: “=”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml \src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3236,22): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\ src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3237,22): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\ src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3238,22): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\ src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3239,22): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\ src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3878,48): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3910,52): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3984,41): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3984,51): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(3993,51): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] ggml-quants.c D:\LLMB\llama.cpp\ggml\src\ggml.c(3993,65): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4054,35): warning C4244: “return”: 从“float”转换到“int64_t”，可能丢失数据 [D:\LLMB\llama.cpp\bui ld\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4067,54): warning C4244: “函数”: 从“int”转换到“float”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\ src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4104,46): warning C4244: “初始化”: 从“float”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build\ ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4104,50): warning C4244: “初始化”: 从“float”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build\ ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4127,46): warning C4244: “初始化”: 从“float”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build\ ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4127,50): warning C4244: “初始化”: 从“float”转换到“int32_t”，可能丢失数据 [D:\LLMB\llama.cpp\build\ ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4163,47): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4163,72): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4163,93): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4163,103): warning C4244: “函数”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\gg ml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4534,36): warning C4244: “初始化”: 从“int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\bu ild\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(4535,36): warning C4244: “初始化”: 从“int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\bu ild\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(5671,108): warning C4244: “函数”: 从“const int32_t”转换到“float”，可能丢失数据 [D:\LLMB\llama.cpp\ build\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(5671,112): warning C4244: “函数”: 从“const int32_t”转换到“float”，可能丢失数据 [D:\LLMB\llama.cpp\ build\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml.c(5949,29): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggm l\src\ggml-base.vcxproj] C:\Program Files (x86)\Windows Kits\10\Include\10.0.20348.0\ucrt\assert.h(21,9): warning C4005: “static_assert”: 宏重 定义 [ D:\LLMB\llama.cpp\build\ggml\src\ggml-base.vcxproj] (编译源文件“../../../ggml/src/ggml-quants.c”) D:\LLMB\llama.cpp\ggml\src\ggml-common.h(79,9): 参见“static_assert”的前一个定义 正在生成代码... ggml-backend.cpp ggml-opt.cpp D:\LLMB\llama.cpp\ggml\src\ggml-opt.cpp(599,77): warning C4244: “参数”: 从“int64_t”转换到“float”，可能丢失数据 [D:\LLMB\llama.cpp\bu ild\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-opt.cpp(600,77): warning C4244: “参数”: 从“int64_t”转换到“float”，可能丢失数据 [D:\LLMB\llama.cpp\bu ild\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-opt.cpp(810,30): warning C4244: “=”: 从“const int64_t”转换到“int32_t”，可能丢失数 据 [D:\LLMB\llama .cpp\build\ggml\src\ggml-base.vcxproj] ggml-threading.cpp gguf.cpp D:\LLMB\llama.cpp\ggml\src\gguf.cpp(457,48): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama.cpp\build\g gml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\gguf.cpp(457,33): warning C4244: “初始化”: 从“int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\b uild\ggml\src\ggml-base.vcxproj] D:\LLMB\llama.cpp\ggml\src\gguf.cpp(592,21): warning C4267: “参数”: 从“size_t”转换到“long”，可能丢失数据 [D:\LLMB\llama.cpp\build\gg ml\src\ggml-base.vcxproj] 正在生成代码... 正在创建库 D:/LLMB/llama.cpp/build/ggml/src/Release/ggml-base.lib 和对象 D:/LLMB/llama.cpp/build/ggml/src/Release/ggml-base .exp ggml-base.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\ggml-base.dll Building Custom Rule D:/LLMB/llama.cpp/ggml/src/ggml-blas/CMakeLists.txt ggml-blas.cpp D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(147,36): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(146,36): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(145,36): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(144,36): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(144,30): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(144,25): warning C4244: “参数”: 从“const int64_t”转换到“const int”，可能丢失数据 [D:\LLMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(189,11): warning C4244: “初始化”: 从“const int64_t”转换到“int” ，可能丢失数据 [D:\L LMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(190,11): warning C4244: “初始化”: 从“const int64_t”转换到“int” ，可能丢失数据 [D:\L LMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-blas\ggml-blas.cpp(191,11): warning C4244: “初始化”: 从“const int64_t”转换到“int” ，可能丢失数据 [D:\L LMB\llama.cpp\build\ggml\src\ggml-blas\ggml-blas.vcxproj] 正在创建库 D:/LLMB/llama.cpp/build/ggml/src/ggml-blas/Release/ggml-blas.lib 和对象 D:/LLMB/llama.cpp/build/ggml/src/ggml-bl as/Release/ggml-blas.exp ggml-blas.vcxproj -> D:\LLMB\llama.cpp\build\bin\Release\ggml-blas.dll Building Custom Rule D:/LLMB/llama.cpp/ggml/src/CMakeLists.txt ggml-cpu.c ggml-cpu.cpp ggml-cpu-aarch64.cpp ggml-cpu-hbm.cpp ggml-cpu-traits.cpp amx.cpp mmq.cpp D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(429,22): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢失数据 [D:\LLMB\llama .cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(429,18): warning C4244: “初始化”: 从“int64_t”转换到“const int”，可能丢失数据 [D:\LLMB \llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(452,28): error C2668: “anonymous-namespace'::_mm512_reduce_max_ps”: 对重
载函数的调用不明确 [D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(284,14):
可能是“float anonymous-namespace'::_mm512_reduce_max_ps(const __m512)” C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.43.34808\include\zmmintrin.h(1442,24): 或 “float _mm512_reduce_max_ps(__m512)”[使用参数相关的查找找到] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(452,28): 尝试匹配参数列表“(__m512)”时 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(939,44): warning C4244: “=”: 从“const int16_t”转换到“int8_t”，可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(940,44): warning C4244: “=”: 从“const int16_t”转换到“int8_t”，可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2305,31): warning C4244: “初始化”: 从“const int64_t”转换到“int”， 可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2305,17): warning C4244: “初始化”: 从“const int64_t”转换到“const int”，可能丢失数据 [ D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2306,31): warning C4244: “初始化”: 从“const int64_t”转换到“int”， 可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2306,17): warning C4244: “初始化”: 从“const int64_t”转换到“const int”，可能丢失数据 [ D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2310,9): error C2672: “anonymous-namespace'::get_row_size”: 未找到匹 配的重载函数
[D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(254,5):
可能是“int anonymous-namespace'::get_row_size(int)” D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2310,9): 表达式的计算结果不是常数 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2310,9): 因读取超过生命周期的变量而失败 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2310,9): 请参见“this”的用法 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2330,31): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam a.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2330,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2331,31): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam a.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2331,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2333,5): error C2672: “convert_B_packed_format”: 未找到匹配的重载函数 [D:\LLMB\ll ama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(1381,6): 可能是“void anonymous-namespace'::convert_B_packed_format(void *__restrict ,const TB *__restrict ,int,int)”
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2333,5):
表达式的计算结果不是常数
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2333,5):
因读取超过生命周期的变量而失败
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2333,5):
请参见“this”的用法
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2348,28): warning C4244: “初始化”: 从“const int64_t”转换到“int”， 可能丢失数据 [D:\LLM
B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2348,17): warning C4244: “初始化”: 从“const int64_t”转换到“const int”，可能丢失数据 [
D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2349,29): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam
a.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2349,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM
B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2379,28): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam
a.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2379,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM
B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2380,28): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam
a.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2380,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM
B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2381,29): warning C4244: “初始化”: 从“int64_t”转换到“int”，可能丢 失数据 [D:\LLMB\llam
a.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2381,17): warning C4244: “初始化”: 从“int64_t”转换到“const int”， 可能丢失数据 [D:\LLM
B\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2382,32): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据 [D:\LLMB\llama
.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2382,19): warning C4267: “初始化”: 从“size_t”转换到“const int”，可能丢失数据 [D:\LLMB
\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2391,13): error C2975: “BLOCK_K”:“anonymous-namespace'::tinygemm_kerne l_avx”的模板参数无效，应为编译时常量表达式 [D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(1317,80): 参见“BLOCK_K”的声明 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2454,13): error C2975: “BLOCK_K”:“anonymous-namespace'::tinygemm_kerne
l_vnni”的模板参数无效，应为编译时常量表达式 [D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj]
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(1397,80):
参见“BLOCK_K”的声明
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9): error C2672: “anonymous-namespace'::tinygemm_kernel_amx”: 未找到 匹配的重载函数 [D:\LLMB\llama.cpp\build\ggml\src\ggml-cpu.vcxproj] D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2213,6): 可能是“void anonymous-namespace'::tinygemm_kernel_amx(int,int,int,const void *__restrict ,const void *__restrict ,f
loat *__restrict ,int)”
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9):
表达式的计算结果不是常数
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9):
因读取超过生命周期的变量而失败
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9):
请参见“this”的用法
D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2029,6):
或 “void anonymous-namespace'::tinygemm_kernel_amx(int,int,int,const void *__restrict ,const void *__restrict ,TC *__restrict ,int)” D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9): 表达式的计算结果不是常数 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9): 因读取超过生命周期的变量而失败 D:\LLMB\llama.cpp\ggml\src\ggml-cpu\amx\mmq.cpp(2487,9): 请参见“this”的用法 sgemm.cpp 正在生成代码... Building Custom Rule D:/LLMB/llama.cpp/examples/gguf-hash/CMakeLists.txt sha1.c sha1.vcxproj -> D:\LLMB\llama.cpp\build\examples\gguf-hash\sha1.dir\Release\sha1.lib Building Custom Rule D:/LLMB/llama.cpp/examples/gguf-hash/CMakeLists.txt sha256.c sha256.vcxproj -> D:\LLMB\llama.cpp\build\examples\gguf-hash\sha256.dir\Release\sha256.lib Building Custom Rule D:/LLMB/llama.cpp/examples/gguf-hash/CMakeLists.txt xxhash.c xxhash.vcxproj -> D:\LLMB\llama.cpp\build\examples\gguf-hash\xxhash.dir\Release\xxhash.lib

[zhouwg]

I'm also a newbie and know nothing about Windows programming.I strongly agree that https://www.reddit.com/r/vscode/comments/qg3cq3/is_windows_environment_bad_for_programming/ and VS IDE might-be not suitable for this project:

• overdesign and complex/complicated encapsulation for a simple thing, violates the principle of simple is beautiful which comes from the great Unix(a good example is the powerful and helpful test-backend-ops.cpp: there are many modern C++ features in this 4000+ LoC command line program rather than split into many small source files, but the code is concise and clear and it's easy to add new features, everyone knows the intention of test-backend-ops.cpp), it also breaks another principle: make it run, then make it right, then make it fast, finally make it dazzling/shine.
• a very simple thing such as configure CMake in command line mode or script is really not easy on Windows
• lots of mysterious compile errors with MS's compiler and toolchain for a same llam.cpp derived project which can be easily built for Linux and Android
• we don't know how it works except for MS's dev-tools team
  

at the same time, I personally think following three programmers are really great programmers(they build useful/helpful/creative/unique things for other programmers/community):

and it works on my Window10 VM with build llama.cpp for Windows:

[jzz001]

Thank you very much, I'll try it.

[zhouwg]

you are welcome and thanks for your post, I'm also a newbie for this topic.

[jzz001]

No sucess. Because I need to compile the AMX, so I need to install Intel's oneAPI toolkit. But I can't install it using cygwin. Let's see if there is a possible solution recently. Thanks again.