From dc1d2adfc0f4de84da7923866d00781ec5c4e666 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 9 May 2025 23:07:07 -0700 Subject: [PATCH 001/421] vulkan: scalar flash attention implementation (#13324) * vulkan: scalar flash attention implementation * vulkan: always use fp32 for scalar flash attention * vulkan: use vector loads in scalar flash attention shader * vulkan: remove PV matrix, helps with register usage * vulkan: reduce register usage in scalar FA, but perf may be slightly worse * vulkan: load each Q value once. optimize O reduction. more tuning * vulkan: support q4_0/q8_0 KV in scalar FA * CI: increase timeout to accommodate newly-supported tests * vulkan: for scalar FA, select between 1 and 8 rows * vulkan: avoid using Float16 capability in scalar FA --- .github/workflows/build.yml | 2 +- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 243 +++++---- .../vulkan-shaders/flash_attn.comp | 483 ++++++++++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 12 +- 4 files changed, 646 insertions(+), 94 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ae066697d..b62720f30 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -307,7 +307,7 @@ jobs: run: | cd build # This is using llvmpipe and runs slower than other backends - ctest -L main --verbose --timeout 2700 + ctest -L main --verbose --timeout 3600 ubuntu-22-cmake-hip: runs-on: ubuntu-22.04 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 2dc2883a7..e2b357fdc 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -275,6 +275,7 @@ struct vk_device_struct { bool prefer_host_memory; bool float_controls_rte_fp16; bool subgroup_add; + bool subgroup_shuffle; bool integer_dot_product; @@ -402,12 +403,20 @@ struct vk_device_struct { vk_pipeline pipeline_conv2d_dw_cwhn_f32; // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned} + vk_pipeline pipeline_flash_attn_f32_f16_D64_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D80_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D96_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D112_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D128_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D256_cm2[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2]; vk_pipeline pipeline_flash_attn_f32_f16_D80[GGML_TYPE_COUNT][2][2][2]; vk_pipeline pipeline_flash_attn_f32_f16_D96[GGML_TYPE_COUNT][2][2][2]; vk_pipeline pipeline_flash_attn_f32_f16_D112[GGML_TYPE_COUNT][2][2][2]; vk_pipeline pipeline_flash_attn_f32_f16_D128[GGML_TYPE_COUNT][2][2][2]; vk_pipeline pipeline_flash_attn_f32_f16_D256[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_split_k_reduce; std::unordered_map pipelines; @@ -1581,13 +1590,29 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events // number of rows/cols for flash attention shader static constexpr uint32_t flash_attention_num_small_rows = 32; -static std::array fa_rows_cols(uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) { +static constexpr uint32_t scalar_flash_attention_num_small_rows = 1; +static constexpr uint32_t scalar_flash_attention_num_large_rows = 8; + +static uint32_t get_fa_num_small_rows(bool scalar) { + return scalar ? scalar_flash_attention_num_small_rows : flash_attention_num_small_rows; +} + +static std::array fa_rows_cols(bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) { GGML_UNUSED(clamp); + if (scalar) { + if (small_rows) { + return {scalar_flash_attention_num_small_rows, 64}; + } else { + return {scalar_flash_attention_num_large_rows, 32}; + } + } + // small rows, large cols if (small_rows) { - return {flash_attention_num_small_rows, 64}; + return {get_fa_num_small_rows(scalar), 32}; } + // small cols to reduce register count if (ggml_is_quantized(type) || D == 256) { return {64, 32}; @@ -1882,65 +1907,66 @@ static void ggml_vk_load_shaders(vk_device& device) { parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size)); }; + auto const &fa_wg_denoms = [&](bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array { + return {fa_rows_cols(scalar, D, clamp, type, small_rows)[0], 1, 1}; + }; + + auto const &fa_spec_constants = [&](bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector { + // For large number of rows, 128 invocations seems to work best. + // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we + // can't use 256 for D==80. + // For scalar, use 128 (arbitrary) + uint32_t wg_size = scalar ? 128 : ((small_rows && (D % 32) == 0) ? 256 : 128); + auto rows_cols = fa_rows_cols(scalar, D, clamp, type, small_rows); + + // D_split can't be larger than a subgroup because we use subgroupShuffle to reduce it. + // D_split can't be larger than the LSB of D divided by 4 due to vectorization in the shader. + const uint32_t D_lsb = D ^ (D & (D-1)); + uint32_t D_split = std::min(std::min(device->subgroup_size, 8u), D_lsb / 4); + + // mask dim1 is padded to 64, we rely on this to avoid clamping mask loads + GGML_ASSERT((GGML_KQ_MASK_PAD % rows_cols[0]) == 0); + return {wg_size, rows_cols[0], rows_cols[1], (D), clamp, D_split}; + }; + +#define CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, D) \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,false), fa_spec_constants(SCALAR, D,1,TYPE,false), 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,false), fa_spec_constants(SCALAR, D,0,TYPE,false), fa_rows_cols(SCALAR,D,0,TYPE,false)[1], true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,false), fa_spec_constants(SCALAR, D,1,TYPE,false), 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,false), fa_spec_constants(SCALAR, D,0,TYPE,false), fa_rows_cols(SCALAR,D,0,TYPE,false)[1], true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,true), fa_spec_constants(SCALAR, D,1,TYPE,true), 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,true), fa_spec_constants(SCALAR, D,0,TYPE,true), fa_rows_cols(SCALAR,D,0,TYPE,true)[1], true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,true), fa_spec_constants(SCALAR, D,1,TYPE,true), 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,true), fa_spec_constants(SCALAR, D,0,TYPE,true), fa_rows_cols(SCALAR,D,0,TYPE,true)[1], true); \ + +#define CREATE_FA(TYPE, NAMELC, SCALAR, SUFFIX) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 64) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 80) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 96) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 112) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 128) \ + CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 256) + + CREATE_FA(GGML_TYPE_F16, f16, true, ) + CREATE_FA(GGML_TYPE_Q4_0, q4_0, true, ) + CREATE_FA(GGML_TYPE_Q8_0, q8_0, true, ) #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) if (device->coopmat2) { - - auto const &fa_wg_denoms = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array { - return {fa_rows_cols(D, clamp, type, small_rows)[0], 1, 1}; - }; - - auto const &fa_spec_constants = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector { - // For large number of rows, 128 invocations seems to work best. - // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we - // can't use 256 for D==80. - uint32_t wg_size = (small_rows && (D % 32) == 0) ? 256 : 128; - auto rows_cols = fa_rows_cols(D, clamp, type, small_rows); - // mask dim1 is padded to 64, we rely on this to avoid clamping mask loads - GGML_ASSERT((GGML_KQ_MASK_PAD % rows_cols[0]) == 0); - return {wg_size, rows_cols[0], rows_cols[1], (D), clamp}; - }; - -#define CREATE_FA2(TYPE, NAMELC, D) \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \ - -#define CREATE_FA(TYPE, NAMELC) \ - CREATE_FA2(TYPE, NAMELC, 64) \ - CREATE_FA2(TYPE, NAMELC, 80) \ - CREATE_FA2(TYPE, NAMELC, 96) \ - CREATE_FA2(TYPE, NAMELC, 112) \ - CREATE_FA2(TYPE, NAMELC, 128) \ - CREATE_FA2(TYPE, NAMELC, 256) - - CREATE_FA(GGML_TYPE_F16, f16) - CREATE_FA(GGML_TYPE_Q4_0, q4_0) - CREATE_FA(GGML_TYPE_Q4_1, q4_1) - CREATE_FA(GGML_TYPE_Q5_0, q5_0) - CREATE_FA(GGML_TYPE_Q5_1, q5_1) - CREATE_FA(GGML_TYPE_Q8_0, q8_0) - // K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently - //CREATE_FA(GGML_TYPE_Q2_K, q2_k) - //CREATE_FA(GGML_TYPE_Q3_K, q3_k) - //CREATE_FA(GGML_TYPE_Q4_K, q4_k) - //CREATE_FA(GGML_TYPE_Q5_K, q5_k) - //CREATE_FA(GGML_TYPE_Q6_K, q6_k) - //CREATE_FA(GGML_TYPE_IQ1_S, iq1_s) - //CREATE_FA(GGML_TYPE_IQ1_M, iq1_m) - //CREATE_FA(GGML_TYPE_IQ2_XXS, iq2_xxs) - //CREATE_FA(GGML_TYPE_IQ2_XS, iq2_xs) - //CREATE_FA(GGML_TYPE_IQ2_S, iq2_s) - //CREATE_FA(GGML_TYPE_IQ3_XXS, iq3_xxs) - //CREATE_FA(GGML_TYPE_IQ3_S, iq3_s) - //CREATE_FA(GGML_TYPE_IQ4_XS, iq4_xs) - CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl) + CREATE_FA(GGML_TYPE_F16, f16, false, _cm2) + CREATE_FA(GGML_TYPE_Q4_0, q4_0, false, _cm2) + CREATE_FA(GGML_TYPE_Q4_1, q4_1, false, _cm2) + CREATE_FA(GGML_TYPE_Q5_0, q5_0, false, _cm2) + CREATE_FA(GGML_TYPE_Q5_1, q5_1, false, _cm2) + CREATE_FA(GGML_TYPE_Q8_0, q8_0, false, _cm2) + CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, false, _cm2) + } +#endif +#undef CREATE_FA2 #undef CREATE_FA +#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (device->coopmat2) { + // Create 6 variants, {s,m,l}x{unaligned,aligned} #define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ @@ -2837,6 +2863,9 @@ static vk_device ggml_vk_get_device(size_t idx) { device->subgroup_add = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eArithmetic); + device->subgroup_shuffle = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eShuffle); + const bool force_disable_f16 = getenv("GGML_VK_DISABLE_F16") != nullptr; device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; @@ -5709,20 +5738,57 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx assert(q->type == GGML_TYPE_F32); assert(k->type == v->type); + bool scalar = !ctx->device->coopmat2; + + uint32_t gqa_ratio = 1; + uint32_t qk_ratio = neq2 / nek2; + uint32_t workgroups_x = (uint32_t)neq1; + uint32_t workgroups_y = (uint32_t)neq2; + uint32_t workgroups_z = (uint32_t)neq3; + + // For scalar FA, we can use the "large" size to accommodate qga. + // For coopmat FA, we always use the small size (which is still pretty large for gqa). + const uint32_t max_gqa = scalar ? scalar_flash_attention_num_large_rows : get_fa_num_small_rows(false); + + if (N == 1 && qk_ratio > 1 && qk_ratio <= max_gqa && + qk_ratio * nek2 == neq2 && nek2 == nev2 && neq3 == 1 && nek3 == 1 && nev3 == 1) { + // grouped query attention - make the N dimension equal to gqa_ratio, reduce + // workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1 + // and change addressing calculations to index Q's dimension 2. + gqa_ratio = qk_ratio; + N = gqa_ratio; + workgroups_y /= N; + } + vk_pipeline *pipelines; // XXX TODO other backends may be changing accumulator precision to default to f32 soon - bool f32acc = dst->op_params[3] == GGML_PREC_F32; - bool small_rows = N <= flash_attention_num_small_rows; - switch (D) { - case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64[k->type][f32acc][small_rows][0]; break; - case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80[k->type][f32acc][small_rows][0]; break; - case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96[k->type][f32acc][small_rows][0]; break; - case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112[k->type][f32acc][small_rows][0]; break; - case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128[k->type][f32acc][small_rows][0]; break; - case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256[k->type][f32acc][small_rows][0]; break; - default: - assert(!"unsupported D value"); - return; + bool f32acc = scalar || dst->op_params[3] == GGML_PREC_F32; + bool small_rows = N <= get_fa_num_small_rows(scalar); + + if (scalar) { + switch (D) { + case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64[k->type][f32acc][small_rows][0]; break; + case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80[k->type][f32acc][small_rows][0]; break; + case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96[k->type][f32acc][small_rows][0]; break; + case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112[k->type][f32acc][small_rows][0]; break; + case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128[k->type][f32acc][small_rows][0]; break; + case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256[k->type][f32acc][small_rows][0]; break; + default: + GGML_ASSERT(!"unsupported D value"); + return; + } + } else { + switch (D) { + case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64_cm2[k->type][f32acc][small_rows][0]; break; + case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80_cm2[k->type][f32acc][small_rows][0]; break; + case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96_cm2[k->type][f32acc][small_rows][0]; break; + case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112_cm2[k->type][f32acc][small_rows][0]; break; + case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128_cm2[k->type][f32acc][small_rows][0]; break; + case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256_cm2[k->type][f32acc][small_rows][0]; break; + default: + GGML_ASSERT(!"unsupported D value"); + return; + } } assert(pipelines); @@ -5740,27 +5806,14 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx vk_pipeline pipeline = pipelines[aligned]; assert(pipeline); - uint32_t gqa_ratio = 1; - uint32_t qk_ratio = neq2 / nek2; - uint32_t workgroups_x = (uint32_t)neq1; - uint32_t workgroups_y = (uint32_t)neq2; - uint32_t workgroups_z = (uint32_t)neq3; - - if (N == 1 && qk_ratio > 1 && gqa_ratio <= flash_attention_num_small_rows && - qk_ratio * nek2 == neq2 && nek2 == nev2 && neq3 == 1 && nek3 == 1 && nev3 == 1) { - // grouped query attention - make the N dimension equal to gqa_ratio, reduce - // workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1 - // and change addressing calculations to index Q's dimension 2. - gqa_ratio = qk_ratio; - N = gqa_ratio; - workgroups_y /= N; - } - uint32_t split_kv = KV; uint32_t split_k = 1; + // Use a placeholder core count if one isn't available. split_k is a big help for perf. + const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count : 16; + // Try to use split_k when KV is large enough to be worth the overhead - if (workgroups_x == 1 && ctx->device->shader_core_count > 0 && KV >= 512) { + if (workgroups_x == 1 && shader_core_count > 0 && KV >= 512) { // Try to run two workgroups per SM. split_k = ctx->device->shader_core_count * 2 / workgroups_y; if (split_k > 1) { @@ -9530,9 +9583,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_FLASH_ATTN_EXT: { ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - if (!ggml_vk_get_device(ctx->device)->coopmat2) { - return false; - } + auto device = ggml_vk_get_device(ctx->device); + bool coopmat2 = device->coopmat2; switch (op->src[0]->ne[0]) { case 64: case 80: @@ -9540,7 +9592,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case 112: case 128: case 256: - case 575: // DeepSeek MLA break; default: return false; @@ -9566,10 +9617,12 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm switch (op->src[1]->type) { case GGML_TYPE_F16: case GGML_TYPE_Q4_0: + case GGML_TYPE_Q8_0: + // supported in scalar and coopmat2 paths + break; case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: // K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently //case GGML_TYPE_Q2_K: //case GGML_TYPE_Q3_K: @@ -9585,10 +9638,18 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm //case GGML_TYPE_IQ3_S: //case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_NL: + // currently supported only in coopmat2 path + if (!coopmat2) { + return false; + } break; default: return false; } + if (!coopmat2 && !device->subgroup_shuffle) { + // scalar FA uses subgroupShuffle + return false; + } return true; } case GGML_OP_GET_ROWS: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp new file mode 100644 index 000000000..e6545160d --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -0,0 +1,483 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#extension GL_KHR_shader_subgroup_shuffle : enable + +#include "types.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 1) const uint32_t Br = 1; +layout (constant_id = 2) const uint32_t Bc = 32; +layout (constant_id = 3) const uint32_t D = 32; + +layout (constant_id = 5) const uint32_t D_split = 16; +const uint32_t D_per_thread = D / D_split; + +const uint32_t cols_per_iter = gl_WorkGroupSize.x / D_split; +const uint32_t cols_per_thread = Bc / cols_per_iter; + +layout (push_constant) uniform parameter { + uint32_t N; + uint32_t KV; + + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + + uint32_t neq2; + uint32_t neq3; + uint32_t nek2; + uint32_t nek3; + uint32_t nev2; + uint32_t nev3; + uint32_t nem1; + + uint32_t nb01; + uint32_t nb02; + uint32_t nb03; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t nb21; + uint32_t nb22; + uint32_t nb23; + uint32_t nb31; + + float scale; + float max_bias; + float logit_softcap; + + uint32_t mask; + uint32_t n_head_log2; + float m0; + float m1; + + uint32_t gqa_ratio; + uint32_t split_kv; + uint32_t k_num; +} p; + +layout (binding = 0) readonly buffer Q {float data_q[];}; +layout (binding = 0) readonly buffer QV4 {vec4 data_qv4[];}; +layout (binding = 1) readonly buffer K {float16_t data_k[];}; +layout (binding = 1) readonly buffer KV4 {f16vec4 data_kv4[];}; +layout (binding = 2) readonly buffer V {float16_t data_v[];}; +layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];}; +layout (binding = 3) readonly buffer M {float16_t data_m[];}; +layout (binding = 4) writeonly buffer O {D_TYPE data_o[];}; + +#if defined(A_TYPE_PACKED16) +#define BINDING_IDX_K 0 +#define BINDING_IDX_V 1 +layout (binding = 1) readonly buffer KV_PACKED16 {A_TYPE_PACKED16 data_packed16[];} kv_packed[2]; +#endif + +#if defined(DATA_A_Q4_0) +#define BLOCK_BYTE_SIZE 18 + +vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { + uint vui_lo = uint(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); + uint vui_hi = uint(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); + uint shift = (iqs & 0x10) >> 2; + vui_lo >>= shift; + vui_hi >>= shift; + + return float(kv_packed[binding_idx].data_packed16[a_offset + ib].d) * (vec4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - 8.0f); +} +#endif + +#if defined(DATA_A_Q8_0) +#define BLOCK_BYTE_SIZE 34 +vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { + const i8vec2 v0 = unpack8(int32_t(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147 + const i8vec2 v1 = unpack8(int32_t(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy; + + return float(kv_packed[binding_idx].data_packed16[a_offset + ib].d) * vec4(v0.x, v0.y, v1.x, v1.y); +} +#endif + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +// Store the output when doing grouped query attention. +// Rows index by Q's dimension 2, and the first N rows are valid. +D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + uint32_t offset = (iq2 + r) * D + c; + data_o[o_offset + offset] = D_TYPE(elem); + return elem; +} + +// Store column zero. This is used to save per-row m and L values for split_k. +ACC_TYPE perElemOpStoreCol0(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + if (r < N && c == 0) { + uint32_t offset = iq2 + r; + data_o[o_offset + offset] = D_TYPE(elem); + } + return elem; +} + +// Load the slope matrix, indexed by Q's dimension 2. +ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2) +{ + const uint32_t h = iq2 + (r % p.gqa_ratio); + + const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1); + const int exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1); + + return ACC_TYPE(pow(base, ACC_TYPE(exph))); +} + +shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x]; +shared vec4 tmpshv4[gl_WorkGroupSize.x]; + +shared float masksh[Bc][Br]; +shared vec4 Qf[Br][D / 4]; + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + const uint32_t tid = gl_LocalInvocationIndex; + const uint32_t N = p.N; + const uint32_t KV = p.KV; + + const uint32_t d_tid = gl_LocalInvocationIndex % D_split; + const uint32_t col_tid = gl_LocalInvocationIndex / D_split; + + uint32_t i = gl_WorkGroupID.x; + uint32_t split_k_index = 0; + + if (p.k_num > 1) { + i = 0; + split_k_index = gl_WorkGroupID.x; + } + + const uint32_t Tr = CEIL_DIV(N, Br); + + const uint32_t start_j = split_k_index * p.split_kv / Bc; + const uint32_t end_j = CEIL_DIV(min(KV, (split_k_index + 1) * p.split_kv), Bc); + + // When not using grouped query attention, all rows share the same iq2, equal to gl_WorkGroupID.y. + // When using grouped query attention, each workgroup does gqa_ratio consecutive values of iq2. + const uint32_t iq2 = gl_WorkGroupID.y * p.gqa_ratio; + const uint32_t iq3 = gl_WorkGroupID.z; + + // broadcast factors + const uint32_t rk2 = p.neq2/p.nek2; + const uint32_t rk3 = p.neq3/p.nek3; + + const uint32_t rv2 = p.neq2/p.nev2; + const uint32_t rv3 = p.neq3/p.nev3; + + // k indices + const uint32_t ik3 = iq3 / rk3; + const uint32_t ik2 = iq2 / rk2; + + // v indices + const uint32_t iv3 = iq3 / rv3; + const uint32_t iv2 = iq2 / rv2; + + // nb?1 are already divided by the type size and are in units of elements. + // When using grouped query attention, Q is indexed by iq2, so the stride + // should be nb02 (which is in bytes). + uint32_t q_stride = p.gqa_ratio > 1 ? (p.nb02 / 4) : p.nb01; + uint32_t k_stride = p.nb11; + uint32_t v_stride = p.nb21; + // When using grouped query attention, all rows use the same mask (stride 0). + // "p.gqa_ratio >> 16" is just a roundabout way of writing zero + // that prevents the compiler from folding the "&" through the select + // and breaking the alignment detection. + uint32_t m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV; + + uint32_t q_offset = (iq2*p.nb02+iq3*p.nb03) / 4; + + [[unroll]] for (uint32_t idx = 0; idx < Br * D / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (D / 4); + uint32_t r = (idx + tid) / (D / 4); + if (r < Br && d < D / 4 && + i * Br + r < N) { + Qf[r][d] = vec4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d]) * p.scale; + } + } + barrier(); + + vec4 Of[Br][D_per_thread / 4]; + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Of[r][d] = vec4(0.0); + } + } + + float Lf[Br], Mf[Br]; + + // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. + const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); + + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Lf[r] = 0; + Mf[r] = NEG_FLT_MAX_OVER_2; + } + + float slope[Br]; + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + slope[r] = 1.0; + } + + // ALiBi + if (p.max_bias > 0.0f) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2); + } + } + +#if BLOCK_SIZE > 1 + uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE; + uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE; +#else + uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2; + uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2; +#endif + + [[dont_unroll]] + for (uint32_t j = start_j; j < end_j; ++j) { + + float Sf[Br][cols_per_thread]; + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Sf[r][c] = 0.0; + } + } + + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + vec4 K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); +#else + vec4 K_Tf = vec4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); +#endif + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Sf[r][c] += dot(Qf[r][d * D_split + d_tid], K_Tf); + } + } + } + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + // Compute sum across the D_split + [[unroll]] for (uint s = D_split / 2; s > 0; s >>= 1) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Sf[r][c] += subgroupShuffleXor(Sf[r][c], s); + } + } + } + + if (p.logit_softcap != 0.0f) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Sf[r][c] = p.logit_softcap * tanh(Sf[r][c]); + } + } + } + + if (p.mask != 0) { + + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) % Bc; + uint32_t r = (idx + tid) / Bc; + if (idx + tid < Bc * Br) { + masksh[c][r] = float(data_m[(i * Br + r) * m_stride + (j * Bc + c)]); + } + } + barrier(); + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + float mvf = masksh[c * cols_per_iter + col_tid][r]; + + Sf[r][c] += slope[r]*mvf; + } + } + barrier(); + } + + float rowmaxf[Br], Pf[Br][cols_per_thread], rowsumf[Br], eMf[Br], Moldf[Br]; + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + rowmaxf[r] = Sf[r][0]; + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + rowmaxf[r] = max(rowmaxf[r], Sf[r][c]); + } + Moldf[r] = Mf[r]; + + // M = max(rowmax, Mold) + // P = e^(S - M) + // eM = e^(Mold - M) + Mf[r] = max(rowmaxf[r], Moldf[r]); + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Pf[r][c] = exp(Sf[r][c] - Mf[r]); + } + eMf[r] = exp(Moldf[r] - Mf[r]); + + // Compute sum across row of P + rowsumf[r] = 0.0; + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + rowsumf[r] += Pf[r][c]; + } + + Lf[r] = eMf[r]*Lf[r] + rowsumf[r]; + } + + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Of[r][d] = eMf[r] * Of[r][d]; + } + } + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + vec4 Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); +#else + vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); +#endif + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Of[r][d] += Pf[r][c] * Vf; + } + } + } + + barrier(); + } + + // reduce across threads + + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + float rowmaxf, eMf; + + tmpsh[tid] = Mf[r]; + // Compute max across the row + barrier(); + [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { + if (tid < s) { + tmpsh[tid] = max(tmpsh[tid], tmpsh[tid + s]); + } + barrier(); + } + rowmaxf = tmpsh[d_tid]; + barrier(); + + float Moldf = Mf[r]; + + // M = max(rowmax, Mold) + // eM = e^(Mold - M) + Mf[r] = max(rowmaxf, Moldf); + eMf = exp(Moldf - Mf[r]); + + Lf[r] = eMf*Lf[r]; + + tmpsh[tid] = Lf[r]; + + // Compute sum across the row + barrier(); + [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { + if (tid < s) { + tmpsh[tid] = tmpsh[tid] + tmpsh[tid + s]; + } + barrier(); + } + Lf[r] = tmpsh[d_tid]; + barrier(); + + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + + Of[r][d] = eMf * Of[r][d]; + tmpshv4[tid] = Of[r][d]; + + barrier(); + [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { + if (tid < s) { + Of[r][d] += tmpshv4[tid + s]; + tmpshv4[tid] = Of[r][d]; + } + barrier(); + } + Of[r][d] = tmpshv4[d_tid]; + barrier(); + } + } + + + // If there is split_k, then the split_k resolve shader does the final + // division by L. Store the intermediate O value and per-row m and L values. + if (p.k_num > 1) { + uint32_t o_offset = D * p.ne1 * split_k_index; + + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + if (r < N) { + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { + perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); + } + } + } + } + + o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2; + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + if (r < N) { + perElemOpStoreCol0(r, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); + perElemOpStoreCol0(r, 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + } + } + + return; + } + + float Lfrcp[Br]; + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Lfrcp[r] = 1.0 / Lf[r]; + } + + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + Of[r][d] *= Lfrcp[r]; + } + } + + uint32_t o_offset = iq3*p.ne2*p.ne1; + + if (p.gqa_ratio > 1) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + if (r < N) { + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { + perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); + } + } + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + if (i * Br + r < N) { + [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { + [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { + data_o[o_offset + iq2 * D + (i * Br + r) * p.ne1 * D + 4*(d * D_split + d_tid) + comp] = D_TYPE(Of[r][d][comp]); + } + } + } + } + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 382f190f2..d196137eb 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -421,7 +421,6 @@ void process_shaders() { #endif } -#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) // flash attention for (const auto& f16acc : {false, true}) { std::string acctype = f16acc ? "float16_t" : "float"; @@ -432,6 +431,7 @@ void process_shaders() { } if (tname == "bf16") continue; +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) if (tname == "f16") { string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, false, true, f16acc); @@ -440,9 +440,17 @@ void process_shaders() { string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, true, f16acc); } +#endif + if (tname == "f16") { + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", + merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, false, false, f16acc); + } else if (tname == "q4_0" || tname == "q8_0") { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", + merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, false, f16acc); + } } } -#endif for (const auto& tname : type_names) { // mul mat vec From 7fef11766cdeb9fa7bbbe3db13580616b7d3d599 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 10 May 2025 08:16:29 +0200 Subject: [PATCH 002/421] arg : add env var to control mmproj (#13416) * arg : add env var to control mmproj * small note about -hf --mmproj --- common/arg.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index f67e0d96d..e0f1d998f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2204,32 +2204,33 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); add_opt(common_arg( {"--mmproj"}, "FILE", - "path to a multimodal projector file. see tools/mtmd/README.md", + "path to a multimodal projector file. see tools/mtmd/README.md\n" + "note: if -hf is used, this argument can be omitted", [](common_params & params, const std::string & value) { params.mmproj.path = value; } - ).set_examples(mmproj_examples)); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ")); add_opt(common_arg( {"--mmproj-url"}, "URL", "URL to a multimodal projector file. see tools/mtmd/README.md", [](common_params & params, const std::string & value) { params.mmproj.url = value; } - ).set_examples(mmproj_examples)); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL")); add_opt(common_arg( {"--no-mmproj"}, "explicitly disable multimodal projector, useful when using -hf", [](common_params & params) { params.no_mmproj = true; } - ).set_examples(mmproj_examples)); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ")); add_opt(common_arg( {"--no-mmproj-offload"}, "do not offload multimodal projector to GPU", [](common_params & params) { params.mmproj_use_gpu = false; } - ).set_examples(mmproj_examples)); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD")); add_opt(common_arg( {"--image"}, "FILE", "path to an image file. use with multimodal models. Specify multiple times for batching", From d8919424f1dee7dc1638349c616f2ef5d2ee16fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 10 May 2025 09:16:52 +0200 Subject: [PATCH 003/421] CUDA: fix FlashAttention on Turing (#13415) --- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 2b6bdc30c..b2f95fa3f 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -546,7 +546,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( const int i0_stop = i0_start + 2*c::nbatch_V2 < DV ? i0_start + 2*c::nbatch_V2 : DV; const int i0_diff = i0_stop - i0_start; - if (nstages == 1) { + if (nstages <= 1) { constexpr bool use_cp_async = nstages == 1; flash_attn_ext_f16_load_tile (V_h2 + k_VKQ_0*stride_V + i0_start/2, tile_V, i0_diff/2, stride_V); From 053367d149f778cdabc356ee3024494e0dd53223 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 10 May 2025 16:26:42 +0200 Subject: [PATCH 004/421] mtmd : support InternVL 2.5 and 3 (#13422) * convert : internvl support * InternVL3-1B working * fix regression * rm mobilevlm from test * fix conversion * add test for internvl * add to list of pre-quant * restore boi/eoi check * add clarify comment for norm eps --- convert_hf_to_gguf.py | 76 +++++++++++++++++- docs/multimodal.md | 8 ++ gguf-py/gguf/constants.py | 7 ++ gguf-py/gguf/tensor_mapping.py | 12 +++ tools/mtmd/README.md | 1 + tools/mtmd/clip-impl.h | 11 +-- tools/mtmd/clip.cpp | 140 ++++++++++++++++++++++++++++----- tools/mtmd/mtmd.cpp | 7 ++ tools/mtmd/tests.sh | 6 +- 9 files changed, 243 insertions(+), 25 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index bf6bc6838..e5c397fee 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -426,7 +426,11 @@ class ModelBase: logger.warning(f"Failed to load model config from {dir_model}: {e}") logger.warning("Trying to load config.json instead") with open(dir_model / "config.json", "r", encoding="utf-8") as f: - return json.load(f) + config = json.load(f) + if "llm_config" in config: + # rename for InternVL + config["text_config"] = config["llm_config"] + return config @classmethod def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: @@ -2606,6 +2610,11 @@ class Qwen2Model(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if self.hf_arch == "Qwen2Model": name = f"model.{name}" # map to Qwen2ForCausalLM tensors + if "language_model." in name: + name = name.replace("language_model.", "") # for InternVL + if name.startswith("mlp") or name.startswith("vision_model"): + # skip visual tensors + return [] yield from super().modify_tensors(data_torch, name, bid) @@ -2709,6 +2718,62 @@ class Qwen2VLVisionModel(VisionModel): return [] # skip other tensors +@ModelBase.register("InternVisionModel") +class InternVisionModel(VisionModel): + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.INTERNVL) + self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) + # hidden_act + if hparams["hidden_act"] == "silu": + self.gguf_writer.add_vision_use_silu(True) + elif hparams["hidden_act"] == "gelu": + self.gguf_writer.add_vision_use_gelu(True) + else: + raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}") + # downsample_ratio + downsample_ratio = self.global_config.get("downsample_ratio") + assert downsample_ratio is not None + self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, name, n_dims # unused + if ".patch_embd." in new_name: + return gguf.GGMLQuantizationType.F16 + if ".position_embd." in new_name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("vision_model") or name.startswith("mlp"): + # process visual tensors + # correct name + if name.startswith("vision_model"): + name = "vision_tower." + name + if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"): + name += ".weight" + # split QKV tensors if needed + if ".qkv." in name: + if data_torch.ndim == 2: # weight + c3, _ = data_torch.shape + else: # bias + c3 = data_torch.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = data_torch[:c] + wk = data_torch[c: c * 2] + wv = data_torch[c * 2:] + return [ + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.q_proj")), wq), + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.k_proj")), wk), + (self.map_tensor_name(name.replace("attn.qkv", "self_attn.v_proj")), wv), + ] + return [(self.map_tensor_name(name), data_torch)] + return [] # skip other tensors + + @ModelBase.register("WavTokenizerDec") class WavTokenizerDecModel(TextModel): model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC @@ -3360,6 +3425,11 @@ class InternLM2Model(TextModel): head_dim = n_embd // num_heads num_groups = num_heads // q_per_kv + name = name.replace("language_model.", "") # InternVL + if name.startswith("mlp") or name.startswith("vision_model"): + # skip visual tensors + return [] + if bid is not None and f"model.layers.{bid}.attention.wqkv" in name: qkv = data_torch @@ -3433,6 +3503,10 @@ class InternLM3Model(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") + name = name.replace("language_model.", "") # InternVL + if name.startswith("mlp") or name.startswith("vision_model"): + # skip visual tensors + return [] if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight", "k_proj.bias")): diff --git a/docs/multimodal.md b/docs/multimodal.md index efed473a3..090583f97 100644 --- a/docs/multimodal.md +++ b/docs/multimodal.md @@ -66,4 +66,12 @@ NOTE: some models may require large context window, for example: `-c 8192` # Mistral Small 3.1 24B (IQ2_M quantization) (tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF + +# InternVL 2.5 and 3 +(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF +(tool_name) -hf ggml-org/InternVL2_5-2B-GGUF +(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-4B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF ``` diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 7dd7bb6d1..ae5ce71ae 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -491,6 +491,8 @@ class MODEL_TENSOR(IntEnum): V_ENC_FFN_UP = auto() V_ENC_FFN_GATE = auto() V_ENC_FFN_DOWN = auto() + V_LAYER_SCALE_1 = auto() + V_LAYER_SCALE_2 = auto() V_PRE_NORM = auto() V_POST_NORM = auto() V_MM_INP_NORM = auto() @@ -748,6 +750,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up", MODEL_TENSOR.V_ENC_FFN_GATE: "v.blk.{bid}.ffn_gate", MODEL_TENSOR.V_ENC_FFN_DOWN: "v.blk.{bid}.ffn_down", + MODEL_TENSOR.V_LAYER_SCALE_1: "v.blk.{bid}.ls1", + MODEL_TENSOR.V_LAYER_SCALE_2: "v.blk.{bid}.ls2", MODEL_TENSOR.V_PRE_NORM: "v.pre_ln", MODEL_TENSOR.V_POST_NORM: "v.post_ln", MODEL_TENSOR.V_MM_INP_PROJ: "mm.input_projection", @@ -786,6 +790,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.V_ENC_FFN_UP, MODEL_TENSOR.V_ENC_FFN_GATE, MODEL_TENSOR.V_ENC_FFN_DOWN, + MODEL_TENSOR.V_LAYER_SCALE_1, + MODEL_TENSOR.V_LAYER_SCALE_2, MODEL_TENSOR.V_PRE_NORM, MODEL_TENSOR.V_POST_NORM, MODEL_TENSOR.V_MM_INP_PROJ, @@ -2167,6 +2173,7 @@ class VisionProjectorType: PIXTRAL = "pixtral" QWEN2VL = "qwen2vl_merger" QWEN25VL = "qwen2.5vl_merger" + INTERNVL = "internvl" # Items here are (block size, type size) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 003b0172c..bf7ec3257 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -905,6 +905,7 @@ class TensorNameMap: MODEL_TENSOR.V_MMPROJ_MLP: ( "model.mm_projector.mlp.mlp.{bid}", + "mlp1.{bid}", # InternVL ), MODEL_TENSOR.V_MMPROJ_PEG: ( @@ -955,6 +956,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_INPUT_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1", + "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral @@ -963,6 +965,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_OUTPUT: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", + "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral @@ -971,6 +974,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_OUTPUT_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", + "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral @@ -1000,6 +1004,14 @@ class TensorNameMap: "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl ), + MODEL_TENSOR.V_LAYER_SCALE_1: ( + "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL + ), + + MODEL_TENSOR.V_LAYER_SCALE_2: ( + "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL + ), + MODEL_TENSOR.V_PRE_NORM: ( "vision_tower.vision_model.pre_layrnorm", "vision_tower.ln_pre", # pixtral diff --git a/tools/mtmd/README.md b/tools/mtmd/README.md index 06e1fd097..ab258ea17 100644 --- a/tools/mtmd/README.md +++ b/tools/mtmd/README.md @@ -48,6 +48,7 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla - [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint - Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen)) - [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) +- InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported) For older models, please refer to the relevant guide for instructions on how to obtain or create them: diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index fb780e9de..e9c8646e1 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -33,9 +33,6 @@ #define KEY_PROJ_TYPE "clip.projector_type" #define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size" -#define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl -#define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl - #define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" #define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" @@ -60,8 +57,10 @@ #define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" #define TN_FFN_UP "%s.blk.%d.ffn_up.%s" #define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" -#define TN_LN_1 "%s.blk.%d.ln1.%s" -#define TN_LN_2 "%s.blk.%d.ln2.%s" +#define TN_LN_1 "%s.blk.%d.ln1.%s" // layer norm +#define TN_LN_2 "%s.blk.%d.ln2.%s" // layer norm +#define TN_LS_1 "%s.blk.%d.ls1.%s" // layer scale +#define TN_LS_2 "%s.blk.%d.ls2.%s" // layer scale #define TN_LN_PRE "%s.pre_ln.%s" #define TN_LN_POST "%s.post_ln.%s" #define TN_LLAVA_PROJ "mm.%d.%s" @@ -105,6 +104,7 @@ enum projector_type { PROJECTOR_TYPE_IDEFICS3, PROJECTOR_TYPE_PIXTRAL, PROJECTOR_TYPE_QWEN25VL, + PROJECTOR_TYPE_INTERNVL, PROJECTOR_TYPE_UNKNOWN, }; @@ -119,6 +119,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_GEMMA3, "gemma3"}, { PROJECTOR_TYPE_IDEFICS3, "idefics3"}, { PROJECTOR_TYPE_PIXTRAL, "pixtral"}, + { PROJECTOR_TYPE_INTERNVL, "internvl"}, }; static projector_type clip_projector_type_from_string(const std::string & str) { diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 1a81c1fcd..dfe7ac91c 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -215,6 +215,10 @@ struct clip_layer { // layernorm 2 ggml_tensor * ln_2_w = nullptr; ggml_tensor * ln_2_b = nullptr; + + // layer scale (no bias) + ggml_tensor * ls_1_w = nullptr; + ggml_tensor * ls_2_w = nullptr; }; struct clip_vision_model { @@ -589,6 +593,9 @@ struct clip_graph { // Qwen2VL and Qwen2.5VL use M-RoPE ggml_cgraph * build_qwen2vl() { + GGML_ASSERT(model.patch_bias == nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + const int batch_size = 1; const bool use_window_attn = hparams.n_wa_pattern > 0; const int n_wa_pattern = hparams.n_wa_pattern; @@ -625,10 +632,6 @@ struct clip_graph { n_embd, n_patches_x * n_patches_y, batch_size); } - if (model.patch_bias) { - inp = ggml_add(ctx0, inp, model.patch_bias); - } - ggml_tensor * inpL = inp; ggml_tensor * window_mask = nullptr; ggml_tensor * window_idx = nullptr; @@ -859,6 +862,67 @@ struct clip_graph { return gf; } + ggml_cgraph * build_internvl() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; + ggml_tensor * inp = build_inp(); + + // add CLS token + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + ggml_tensor * cur = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + model.position_embeddings, + nullptr); + + // remove CLS token + cur = ggml_view_2d(ctx0, cur, + n_embd, n_patches, + ggml_row_size(cur->type, n_embd), 0); + + // pixel shuffle + { + const int scale_factor = model.hparams.proj_scale_factor; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + const int height = n_patches_y; + const int width = n_patches_x; + GGML_ASSERT(scale_factor > 0); + cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur), + n_embd * scale_factor * scale_factor, + height / scale_factor, + width / scale_factor, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // flatten to 2D + cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, cur), + n_embd * scale_factor * scale_factor, + cur->ne[1] * cur->ne[2]); + } + + // projector (always using GELU activation) + { + // projector LayerNorm uses pytorch's default eps = 1e-5 + // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79 + cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1); + cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); + cur = ggml_add(ctx0, cur, model.mm_1_b); + cur = ggml_gelu(ctx0, cur); + cur = ggml_mul_mat(ctx0, model.mm_3_w, cur); + cur = ggml_add(ctx0, cur, model.mm_3_b); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; + } + // this graph is used by llava, granite and glm // due to having embedding_stack (used by granite), we cannot reuse build_vit ggml_cgraph * build_llava() { @@ -890,10 +954,6 @@ struct clip_graph { ggml_tensor * inp = build_inp(); - if (model.patch_bias) { - inp = ggml_add(ctx0, inp, model.patch_bias); - } - // concat class_embeddings and patch_embeddings if (model.class_embedding) { inp = ggml_concat(ctx0, inp, model.class_embedding, 1); @@ -1260,11 +1320,6 @@ private: ggml_tensor * learned_pos_embd, std::function add_pos ) { - if (model.patch_bias) { - inp = ggml_add(ctx0, inp, model.patch_bias); - cb(inp, "patch_bias", -1); - } - if (learned_pos_embd) { inp = ggml_add(ctx0, inp, learned_pos_embd); cb(inp, "pos_embed", -1); @@ -1324,6 +1379,11 @@ private: cb(cur, "attn_out", il); } + if (layer.ls_1_w) { + cur = ggml_mul(ctx0, cur, layer.ls_1_w); + cb(cur, "attn_out_scaled", il); + } + // re-add the layer input, e.g., residual cur = ggml_add(ctx0, cur, inpL); @@ -1344,6 +1404,11 @@ private: cb(cur, "ffn_out", il); + if (layer.ls_2_w) { + cur = ggml_mul(ctx0, cur, layer.ls_2_w); + cb(cur, "ffn_out_scaled", il); + } + // residual 2 cur = ggml_add(ctx0, inpL, cur); cb(cur, "layer_out", il); @@ -1365,6 +1430,10 @@ private: ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); + } return inp; } @@ -1627,6 +1696,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 { res = graph.build_minicpmv(); } break; + case PROJECTOR_TYPE_INTERNVL: + { + res = graph.build_internvl(); + } break; default: { res = graph.build_llava(); @@ -1790,6 +1863,7 @@ struct clip_model_loader { } } break; case PROJECTOR_TYPE_IDEFICS3: + case PROJECTOR_TYPE_INTERNVL: { get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false); } break; @@ -1897,6 +1971,9 @@ struct clip_model_loader { layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight")); layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false); layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false); + layer.ls_1_w = get_tensor(string_format(TN_LS_1, "v", il, "weight"), false); // no bias + layer.ls_2_w = get_tensor(string_format(TN_LS_2, "v", il, "weight"), false); // no bias + layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false); layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false); layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false); @@ -1904,7 +1981,7 @@ struct clip_model_loader { layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false); layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false); - // new naming + // ffn layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight")); layer.ff_up_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false); layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, "v", il, "weight"), false); @@ -2052,6 +2129,15 @@ struct clip_model_loader { vision_model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false); vision_model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false); } break; + case PROJECTOR_TYPE_INTERNVL: + { + vision_model.mm_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight")); + vision_model.mm_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias")); + vision_model.mm_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight")); + vision_model.mm_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias")); + vision_model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight")); + vision_model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias")); + } break; default: GGML_ASSERT(false && "unknown projector type"); } @@ -2838,7 +2924,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str } else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE || ctx->proj_type == PROJECTOR_TYPE_GEMMA3 - || ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { + || ctx->proj_type == PROJECTOR_TYPE_IDEFICS3 + || ctx->proj_type == PROJECTOR_TYPE_INTERNVL // TODO @ngxson : support dynamic resolution + ) { clip_image_u8 resized_image; int sz = params.image_size; image_manipulation::resize_and_pad_image(*img, resized_image, {sz, sz}); @@ -2988,9 +3076,13 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); - if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + if (ctx->proj_type == PROJECTOR_TYPE_LDP + || ctx->proj_type == PROJECTOR_TYPE_LDPV2 + || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { n_patches /= 4; - n_patches += 2; // for BOI and EOI token embeddings + if (ctx->vision_model.mm_glm_tok_boi) { + n_patches += 2; // for BOI and EOI token embeddings + } } else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { if (ctx->minicpmv_version == 2) { n_patches = 96; @@ -3013,7 +3105,8 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im int n_per_side = params.image_size / params.patch_size; int n_per_side_2d_pool = n_per_side / params.proj_scale_factor; n_patches = n_per_side_2d_pool * n_per_side_2d_pool; - } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { + } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3 || ctx->proj_type == PROJECTOR_TYPE_INTERNVL) { + // both W and H are divided by proj_scale_factor n_patches /= (params.proj_scale_factor * params.proj_scale_factor); } else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) { int n_merge = params.spatial_merge_size; @@ -3408,6 +3501,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } break; case PROJECTOR_TYPE_GEMMA3: case PROJECTOR_TYPE_IDEFICS3: + case PROJECTOR_TYPE_INTERNVL: { // do nothing } break; @@ -3434,6 +3528,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // the last node is the embedding tensor ggml_tensor * embeddings = ggml_graph_node(gf, -1); + // sanity check (only support batch size of 1 for now) + const int n_tokens_out = embeddings->ne[1]; + const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get()); + if (n_tokens_out != expected_n_tokens_out) { + LOG_ERR("%s: expected %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out); + GGML_ABORT("Invalid number of output tokens"); + } + // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); @@ -3604,6 +3706,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_input_proj_w->ne[0]; case PROJECTOR_TYPE_IDEFICS3: return ctx->vision_model.projection->ne[1]; + case PROJECTOR_TYPE_INTERNVL: + return ctx->vision_model.mm_3_w->ne[1]; default: GGML_ABORT("Unknown projector type"); } diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 2fecf08a4..f1b957394 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -252,6 +252,13 @@ int32_t mtmd_tokenize(mtmd_context * ctx, } + else if (proj_type == PROJECTOR_TYPE_INTERNVL) { + // ... (image embeddings) ... + marker_modified = "" + ctx->image_marker + ""; + string_replace_all(prompt_modified, ctx->image_marker, marker_modified); + + } + // llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix // for glm-edge, BOI and EOI token's embeddings are not present in the text model diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh index 22c237497..05ac7a04d 100755 --- a/tools/mtmd/tests.sh +++ b/tools/mtmd/tests.sh @@ -40,7 +40,6 @@ add_test "llama-mtmd-cli" "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0" add_test "llama-mtmd-cli" "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M" -add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek" add_test "llama-mtmd-cli" "THUDM/glm-edge-v-5b-gguf:Q4_K_M" add_test "llama-mtmd-cli" "second-state/Llava-v1.5-7B-GGUF:Q2_K" "vicuna" add_test "llama-mtmd-cli" "cjpais/llava-1.6-mistral-7b-gguf:Q3_K" "vicuna" @@ -50,6 +49,8 @@ add_test "llama-mtmd-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K" add_test "llama-mtmd-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0" add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" +add_test "llama-mtmd-cli" "ggml-org/InternVL2_5-1B-GGUF:Q8_0" +add_test "llama-mtmd-cli" "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0" # to test the big models, run: ./tests.sh big if [ "$RUN_BIG_TESTS" = true ]; then @@ -59,6 +60,8 @@ if [ "$RUN_BIG_TESTS" = true ]; then add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M" # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M" # too big fi @@ -70,6 +73,7 @@ fi # this model has broken chat template, not usable # add_test "llama-mtmd-cli" "cmp-nct/Yi-VL-6B-GGUF:Q5_K" +# add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek" ############### From b064a51a4e7246fa43a3307f58e59f65e6be170a Mon Sep 17 00:00:00 2001 From: Thammachart Chinvarapon <1731496+Thammachart@users.noreply.github.com> Date: Sat, 10 May 2025 21:34:48 +0700 Subject: [PATCH 005/421] ci: free_disk_space flag enabled for intel variant (#13426) before cleanup: 20G after cleanup: 44G after all built and pushed: 24G https://github.com/Thammachart/llama.cpp/actions/runs/14945093573/job/41987371245 --- .github/workflows/docker.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index f8e072fb8..2067927be 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -42,8 +42,7 @@ jobs: - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true } - # Note: the intel images are failing due to an out of disk space error - # - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } + - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true } - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false } # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true } From 43dfd741a5da77982e0a94d1e522a2481dfb914d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sat, 10 May 2025 17:19:52 +0200 Subject: [PATCH 006/421] llguidance : set tokenizer slices to default (#13424) --- common/llguidance.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/common/llguidance.cpp b/common/llguidance.cpp index 8bff89ea4..adce620e4 100644 --- a/common/llguidance.cpp +++ b/common/llguidance.cpp @@ -189,6 +189,7 @@ static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) /* .tokenize_fn = */ llama_sampler_llg_tokenize_fn, /* .use_approximate_greedy_tokenize_fn = */ false, /* .tokenize_user_data = */ vocab, + /* .slices = */ nullptr, }; char error_buffer[1024]; From 3b24d26c22b9d92b5aa39930988700c84e524cf4 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 10 May 2025 18:44:49 +0200 Subject: [PATCH 007/421] server : update docs (#13432) --- docs/multimodal.md | 2 +- tools/server/README.md | 54 ++++++++++++++++++++++++++++++------------ 2 files changed, 40 insertions(+), 16 deletions(-) diff --git a/docs/multimodal.md b/docs/multimodal.md index 090583f97..82b9411e4 100644 --- a/docs/multimodal.md +++ b/docs/multimodal.md @@ -6,7 +6,7 @@ llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools To enable it, can use use one of the 2 methods below: -- Use `-hf` option with a [supported model](../../docs/multimodal.md) +- Use `-hf` option with a supported model (see a list of pre-quantized model below) - To load a model using `-hf` while disabling multimodal, use `--no-mmproj` - To load a model using `-hf` while using a custom mmproj file, use `--mmproj local_file.gguf` - Use `-m model.gguf` option with `--mmproj file.gguf` to specify text and multimodal projector respectively diff --git a/tools/server/README.md b/tools/server/README.md index 972ca384e..7b944c35b 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -7,13 +7,15 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp. **Features:** * LLM inference of F16 and quantized models on GPU and CPU * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes - * Reranking endoint (WIP: https://github.com/ggml-org/llama.cpp/pull/9510) + * Reranking endoint (https://github.com/ggml-org/llama.cpp/pull/9510) * Parallel decoding with multi-user support * Continuous batching - * Multimodal (wip) + * Multimodal ([documentation](../../docs/multimodal.md)) / with OpenAI-compatible API support * Monitoring endpoints * Schema-constrained JSON response format * [Function calling](../../docs/function-calling.md) / tool use for ~any model + * Speculative decoding + * Easy-to-use web UI The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggml-org/llama.cpp/issues/4216). @@ -27,6 +29,7 @@ The project is under active development, and we are [looking for feedback and co | -------- | ----------- | | `-h, --help, --usage` | print usage and exit | | `--version` | show version and build info | +| `--completion-bash` | print source-able bash completion script for llama.cpp | | `--verbose-prompt` | print a verbose prompt before generation (default: false) | | `-t, --threads N` | number of threads to use during generation (default: -1)
(env: LLAMA_ARG_THREADS) | | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) | @@ -41,7 +44,7 @@ The project is under active development, and we are [looking for feedback and co | `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)
| | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | | `-c, --ctx-size N` | size of the prompt context (default: 4096, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | -| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
(env: LLAMA_ARG_N_PREDICT) | +| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity)
(env: LLAMA_ARG_N_PREDICT) | | `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | @@ -69,6 +72,7 @@ The project is under active development, and we are [looking for feedback and co | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | +| `--override-tensor, -ot =,...` | override tensor buffer type | | `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM
(env: LLAMA_ARG_N_GPU_LAYERS) | | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE) | | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
(env: LLAMA_ARG_TENSOR_SPLIT) | @@ -82,15 +86,18 @@ The project is under active development, and we are [looking for feedback and co | `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | | `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)
(env: LLAMA_ARG_MODEL) | | `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | -| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)
(env: LLAMA_ARG_HF_REPO) | -| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)
(env: LLAMA_ARG_HF_FILE) | +| `-hf, -hfr, --hf-repo /[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.
mmproj is also downloaded automatically if available. to disable, add --no-mmproj
example: unsloth/phi-4-GGUF:q4_k_m
(default: unused)
(env: LLAMA_ARG_HF_REPO) | +| `-hfd, -hfrd, --hf-repo-draft /[:quant]` | Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_HFD_REPO) | +| `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)
(env: LLAMA_ARG_HF_FILE) | +| `-hfv, -hfrv, --hf-repo-v /[:quant]` | Hugging Face model repository for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_REPO_V) | +| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_FILE_V) | | `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | | `--log-disable` | Log disable | | `--log-file FNAME` | Log to file | | `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | | `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | | `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.
(env: LLAMA_LOG_VERBOSITY) | -| `--log-prefix` | Enable prefx in log messages
(env: LLAMA_LOG_PREFIX) | +| `--log-prefix` | Enable prefix in log messages
(env: LLAMA_LOG_PREFIX) | | `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | @@ -98,9 +105,9 @@ The project is under active development, and we are [looking for feedback and co | Argument | Explanation | | -------- | ----------- | -| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: dry;top_k;typ_p;top_p;min_p;xtc;temperature) | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature) | | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | -| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) | +| `--sampling-seq, --sampler-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | | `--temp N` | temperature (default: 0.8) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled) | @@ -127,22 +134,26 @@ The project is under active development, and we are [looking for feedback and co | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | -| `--jinja` | Enable experimental Jinja templating engine (required for tool use) | -| `--reasoning-format FORMAT` | Controls extraction of model thinking traces and the format / field in which they are returned (default: `deepseek`; allowed values: `deepseek`, `none`; requires `--jinja`). `none` will leave thinking traces inline in `message.content` in a model-specific format, while `deepseek` will return them separately under `message.reasoning_content` | +| `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | + **Example-specific params** | Argument | Explanation | | -------- | ----------- | -| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | +| `--no-context-shift` | disables context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | | `-sp, --special` | special tokens output enabled (default: false) | | `--no-warmup` | skip warming up the model with an empty run | | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | | `-nocb, --no-cont-batching` | disable continuous batching
(env: LLAMA_ARG_NO_CONT_BATCHING) | +| `--mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ) | +| `--mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL) | +| `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf
(env: LLAMA_ARG_NO_MMPROJ) | +| `--no-mmproj-offload` | do not offload multimodal projector to GPU
(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) | | `-a, --alias STRING` | set alias for model name (to be used by REST API)
(env: LLAMA_ARG_ALIAS) | -| `--host HOST` | ip address to listen (default: 127.0.0.1)
(env: LLAMA_ARG_HOST) | +| `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)
(env: LLAMA_ARG_HOST) | | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | | `--no-webui` | Disable the Web UI (default: enabled)
(env: LLAMA_ARG_NO_WEBUI) | @@ -160,16 +171,29 @@ The project is under active development, and we are [looking for feedback and co | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | | `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
list of built-in templates:
chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--jinja` | use jinja template for chat (default: disabled)
(env: LLAMA_ARG_JINJA) | +| `--reasoning-format FORMAT` | reasoning format (default: deepseek; allowed values: deepseek, none)
controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).
only supported for non-streamed responses
(env: LLAMA_ARG_THINK) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | | `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | -| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)
(env: LLAMA_ARG_DRAFT_MIN) | -| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)
(env: LLAMA_ARG_DRAFT_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)
(env: LLAMA_ARG_DRAFT_P_MIN) | | `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | | `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT) | +| `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) | +| `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall | +| `--embd-bge-small-en-default` | use default bge-small-en-v1.5 model (note: can download weights from the internet) | +| `--embd-e5-small-en-default` | use default e5-small-v2 model (note: can download weights from the internet) | +| `--embd-gte-small-default` | use default gte-small model (note: can download weights from the internet) | +| `--fim-qwen-1.5b-default` | use default Qwen 2.5 Coder 1.5B (note: can download weights from the internet) | +| `--fim-qwen-3b-default` | use default Qwen 2.5 Coder 3B (note: can download weights from the internet) | +| `--fim-qwen-7b-default` | use default Qwen 2.5 Coder 7B (note: can download weights from the internet) | +| `--fim-qwen-7b-spec` | use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can download weights from the internet) | +| `--fim-qwen-14b-spec` | use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note: can download weights from the internet) | Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. From 15e6125a397f6086c1dfdf7584acdb7c730313dc Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 10 May 2025 19:57:54 +0200 Subject: [PATCH 008/421] mtmd : add hard limit on image resolution for qwen2vl / qwen2.5vl (#13434) * mtmd : add hard limit on image resolution for qwen2vl / qwen2.5vl * fix typo --- tools/mtmd/clip-impl.h | 3 +++ tools/mtmd/clip.cpp | 35 +++++++++++++++++++++++++++-------- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index e9c8646e1..d7b788bf9 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -92,6 +92,9 @@ #define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" #define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" +// align x to upper multiple of n +#define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) + enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index dfe7ac91c..0ebe81b07 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -174,6 +174,10 @@ struct clip_hparams { int32_t n_layer; int32_t proj_scale_factor = 0; // idefics3 + // for models using dynamic image size, we need to have a smaller image size to warmup + // otherwise, user will get OOM everytime they load the model + int32_t warmup_image_size = 0; + ffn_op_type ffn_op = FFN_GELU; patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; @@ -1796,6 +1800,9 @@ struct clip_model_loader { get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false); get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false); + // default warmup value + hparams.warmup_image_size = hparams.image_size; + ctx_clip.has_llava_projector = ctx_clip.proj_type == PROJECTOR_TYPE_MLP || ctx_clip.proj_type == PROJECTOR_TYPE_MLP_NORM || ctx_clip.proj_type == PROJECTOR_TYPE_LDP @@ -1870,6 +1877,7 @@ struct clip_model_loader { case PROJECTOR_TYPE_PIXTRAL: { hparams.rope_theta = 10000.0f; + hparams.warmup_image_size = hparams.patch_size * 8; get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false); } break; case PROJECTOR_TYPE_GEMMA3: @@ -1880,8 +1888,19 @@ struct clip_model_loader { // test model (tinygemma3) has a different value, we optionally read it get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false); } break; + case PROJECTOR_TYPE_QWEN2VL: + { + // max image size = sqrt(max_pixels) + // https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json + hparams.image_size = 3584; + hparams.warmup_image_size = hparams.patch_size * 8; + } break; case PROJECTOR_TYPE_QWEN25VL: { + // max image size = sqrt(max_pixels) + // https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json + hparams.image_size = 3584; + hparams.warmup_image_size = hparams.patch_size * 8; get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern); } break; default: @@ -2185,13 +2204,14 @@ struct clip_model_loader { // create a fake batch clip_image_f32_batch batch; clip_image_f32_ptr img(clip_image_f32_init()); - img->nx = ctx_clip.vision_model.hparams.image_size; - img->ny = ctx_clip.vision_model.hparams.image_size; + img->nx = ctx_clip.vision_model.hparams.warmup_image_size; + img->ny = ctx_clip.vision_model.hparams.warmup_image_size; img->buf.resize(img->nx * img->ny * 3); batch.entries.push_back(std::move(img)); ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch); ggml_backend_sched_reserve(ctx_clip.sched.get(), gf); + for (size_t i = 0; i < ctx_clip.backend_ptrs.size(); ++i) { ggml_backend_t backend = ctx_clip.backend_ptrs[i]; ggml_backend_buffer_type_t buft = ctx_clip.backend_buft[i]; @@ -2590,8 +2610,8 @@ struct image_manipulation { float target_width_f = static_cast(inp_size.width) * scale; float target_height_f = static_cast(inp_size.height) * scale; - int aligned_width = GGML_PAD((int)target_width_f, align_size); - int aligned_height = GGML_PAD((int)target_height_f, align_size); + int aligned_width = CLIP_ALIGN((int)target_width_f, align_size); + int aligned_height = CLIP_ALIGN((int)target_height_f, align_size); return {aligned_width, aligned_height}; } @@ -2910,10 +2930,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { clip_image_u8 resized; - auto patch_size = clip_get_patch_size(ctx) * 2; - int nx = ceil((float)img->nx / patch_size) * patch_size; - int ny = ceil((float)img->ny / patch_size) * patch_size; - image_manipulation::bicubic_resize(*img, resized, nx, ny); + auto patch_size = params.patch_size * 2; + auto new_size = image_manipulation::calc_size_preserved_ratio(original_size, patch_size, params.image_size); + image_manipulation::bicubic_resize(*img, resized, new_size.width, new_size.height); clip_image_f32_ptr img_f32(clip_image_f32_init()); // clip_image_f32_ptr res(clip_image_f32_init()); From d2a4ef05c60506ee48e7375eb36f2257de7ab0d2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sat, 10 May 2025 22:08:07 +0200 Subject: [PATCH 009/421] vocab : add ByteDance-Seed/Seed-Coder (#13423) --- convert_hf_to_gguf.py | 3 +++ convert_hf_to_gguf_update.py | 1 + include/llama.h | 1 + src/llama-vocab.cpp | 11 +++++++++++ 4 files changed, 16 insertions(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index e5c397fee..a34ba2988 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -798,6 +798,9 @@ class TextModel(ModelBase): if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3": # ref: https://huggingface.co/mistral-community/pixtral-12b res = "pixtral" + if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec": + # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base + res = "seed-coder" if res is None: logger.warning("\n") diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 03a1d8d8c..5993a4c98 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -116,6 +116,7 @@ models = [ {"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", }, {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", }, {"name": "pixtral", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", }, + {"name": "seed-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", }, ] diff --git a/include/llama.h b/include/llama.h index a18f365bf..7d5f9d559 100644 --- a/include/llama.h +++ b/include/llama.h @@ -112,6 +112,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32, LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33, LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34, + LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35, }; enum llama_rope_type { diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 50ded286f..e6b96ecc8 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -415,6 +415,13 @@ struct llm_tokenizer_bpe : llm_tokenizer { "'(?:[sSdDmMtT]|[lL][lL]|[vV][eE]|[rR][eE])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+", }; break; + case LLAMA_VOCAB_PRE_TYPE_SEED_CODER: + regex_exprs = { + // original regex from tokenizer.json + // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1}| ?[^\\s\\p{L}\\p{N}\r\n]+|\\s*[\r\n]+|\\s+(?!\\S)|\\s+" + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1}| ?[^\\s\\p{L}\\p{N}\\r\\n]+|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }; + break; default: // default regex for BPE tokenization pre-processing regex_exprs = { @@ -1634,6 +1641,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "bailingmoe") { pre_type = LLAMA_VOCAB_PRE_TYPE_BAILINGMOE; clean_spaces = false; + } else if ( + tokenizer_pre == "seed-coder") { + pre_type = LLAMA_VOCAB_PRE_TYPE_SEED_CODER; + clean_spaces = false; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } From 0208355f42bdab88a08507ead4a6302790a08323 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 10 May 2025 22:22:48 +0200 Subject: [PATCH 010/421] CUDA: fix race conditions FlashAttention kernels (#13438) --- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 2 ++ ggml/src/ggml-cuda/fattn-vec-f16.cuh | 1 + 2 files changed, 3 insertions(+) diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index b2f95fa3f..9873ea755 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -874,6 +874,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } } + __syncthreads(); + // Write back combined meta data: #pragma unroll for (int imeta = 0; imeta < nmeta; ++imeta) { diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh index ef0addc1d..d96e39212 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh @@ -168,6 +168,7 @@ static __global__ void flash_attn_vec_ext_f16( for (int j = 0; j < ncols; ++j) { KQ[j*D + tid] = -HALF_MAX_HALF; } + __syncthreads(); half2 VKQ[ncols] = {{0.0f, 0.0f}}; From 62d4250e52917dc4d634f054b1e2183ed7dee944 Mon Sep 17 00:00:00 2001 From: Thomas Germer <99991@users.noreply.github.com> Date: Sat, 10 May 2025 22:26:46 +0200 Subject: [PATCH 011/421] docs : Fix typo in InternVL3 model name (#13440) --- docs/multimodal.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/multimodal.md b/docs/multimodal.md index 82b9411e4..6a5d2b342 100644 --- a/docs/multimodal.md +++ b/docs/multimodal.md @@ -69,9 +69,9 @@ NOTE: some models may require large context window, for example: `-c 8192` # InternVL 2.5 and 3 (tool_name) -hf ggml-org/InternVL2_5-1B-GGUF -(tool_name) -hf ggml-org/InternVL2_5-2B-GGUF +(tool_name) -hf ggml-org/InternVL2_5-4B-GGUF (tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF (tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF -(tool_name) -hf ggml-org/InternVL3-4B-Instruct-GGUF +(tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF (tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF ``` From a634d75d1bb4034b8c945042ceea268b5b895730 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sun, 11 May 2025 11:34:23 +0200 Subject: [PATCH 012/421] mtmd : move helpers to dedicated file (#13442) * mtmd : move helpers to dedicated file * fix windows build * rm redundant include --- tools/mtmd/CMakeLists.txt | 1 + tools/mtmd/mtmd-helper.cpp | 310 +++++++++++++++++++++++++++++++++++ tools/mtmd/mtmd.cpp | 324 ++----------------------------------- tools/mtmd/mtmd.h | 1 + 4 files changed, 325 insertions(+), 311 deletions(-) create mode 100644 tools/mtmd/mtmd-helper.cpp diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 27b6d27e5..dfafa9cf8 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -28,6 +28,7 @@ endif() add_library(mtmd OBJECT mtmd.cpp + mtmd-helper.cpp mtmd.h clip.cpp clip.h diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp new file mode 100644 index 000000000..7a3288672 --- /dev/null +++ b/tools/mtmd/mtmd-helper.cpp @@ -0,0 +1,310 @@ +#include "mtmd.h" +#include "llama.h" + +#include +#include +#include + +#define LOG_INF(...) fprintf(stdout, __VA_ARGS__) +#define LOG_ERR(...) fprintf(stderr, __VA_ARGS__) + +size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) { + size_t n_tokens = 0; + for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { + auto chunk = mtmd_input_chunks_get(chunks, i); + auto chunk_type = mtmd_input_chunk_get_type(chunk); + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens_text; + mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); + n_tokens += n_tokens_text; + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); + n_tokens += mtmd_image_tokens_get_n_tokens(tokens_image); + } else { + GGML_ASSERT(false && "chunk type not supported"); + } + } + return n_tokens; +} + +llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks) { + llama_pos n_pos = 0; + for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { + auto chunk = mtmd_input_chunks_get(chunks, i); + auto chunk_type = mtmd_input_chunk_get_type(chunk); + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens_text; + mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); + n_pos += n_tokens_text; + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); + n_pos += mtmd_image_tokens_get_n_pos(tokens_image); + } else { + GGML_ASSERT(false && "chunk type not supported"); + } + } + return n_pos; +} + +// helper struct to make working with embd batch easier +// note: this will be removed after llama_batch_ext refactoring +struct decode_embd_batch { + int n_pos_per_embd; + int n_mmproj_embd; + std::vector pos; + std::vector pos_view; // used by mrope + std::vector n_seq_id; + std::vector seq_id_0; + std::vector seq_ids; + std::vector logits; + llama_batch batch; + decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) { + pos .resize(n_tokens * n_pos_per_embd); + n_seq_id.resize(n_tokens); + seq_ids .resize(n_tokens + 1); + logits .resize(n_tokens); + seq_id_0.resize(1); + seq_ids [n_tokens] = nullptr; + batch = { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ embd, + /*pos =*/ pos.data(), + /*n_seq_id =*/ n_seq_id.data(), + /*seq_id =*/ seq_ids.data(), + /*logits =*/ logits.data(), + }; + } + + void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) { + seq_id_0[0] = seq_id; + for (int i = 0; i < batch.n_tokens; i++) { + batch.pos [i] = pos_0 + i; + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } + + void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) { + GGML_ASSERT(n_pos_per_embd == 4); + seq_id_0[0] = seq_id; + for (int y = 0; y < ny; y++) { + for (int x = 0; x < nx; x++) { + int i = y * nx + x; + pos[i ] = pos_0; + pos[i + batch.n_tokens ] = pos_0 + y; + pos[i + batch.n_tokens * 2] = pos_0 + x; + pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused + } + } + for (int i = 0; i < batch.n_tokens; i++) { + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } + + llama_batch get_view(int offset, int n_tokens) { + llama_pos * pos_ptr; + pos_view.clear(); + pos_view.reserve(n_tokens * n_pos_per_embd); + if (n_pos_per_embd > 1) { + // mrope + // for example, with layout of src: 1234...1234...1234...1234... + // offset 2 will give us dst: 34...34...34...34... + for (int i = 0; i < n_pos_per_embd; i++) { + // assume n_tokens is less than or equal to batch.n_tokens + // batch.n_tokens is number of **total** tokens + // n_tokens is number of viewed token + size_t src_idx = i * batch.n_tokens + offset; + pos_view.insert(pos_view.end(), + pos.data() + src_idx, + pos.data() + src_idx + n_tokens); + } + pos_ptr = pos_view.data(); + } else { + // normal + pos_ptr = pos.data() + offset; + } + return { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ batch.embd + offset * n_mmproj_embd, + /*pos =*/ pos_ptr, + /*n_seq_id =*/ batch.n_seq_id + offset, + /*seq_id =*/ batch.seq_id + offset, + /*logits =*/ batch.logits + offset, + }; + } +}; + +// Helper function for decoding an image whose embeddings have already been calculated +int32_t mtmd_helper_decode_image_chunk( + mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunk * chunk, + float * encoded_embd, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + llama_pos * new_n_past) { + if (mtmd_input_chunk_get_type(chunk) != MTMD_INPUT_CHUNK_TYPE_IMAGE) { + LOG_ERR("failed to decode image chunk: input chunk not of image type\n"); + return -1; + } + const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); + if (!image_tokens) { + LOG_ERR("failed to decode image chunk: image tokens are null\n"); + return -1; + } + + const llama_model * model = llama_get_model(lctx); + int n_mmproj_embd = llama_model_n_embd(model); + int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1; + + int32_t n_tokens = mtmd_image_tokens_get_n_tokens(image_tokens); + int32_t i_batch = 0; + int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; + decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd); + + const int nx = mtmd_image_tokens_get_nx(image_tokens); + const int ny = mtmd_image_tokens_get_ny(image_tokens); + + if (mtmd_decode_use_mrope(ctx)) { + batch_embd.set_position_mrope(n_past, nx, ny, seq_id); + } else { + batch_embd.set_position_normal(n_past, seq_id); + } + + if (mtmd_decode_use_non_causal(ctx)) { + llama_set_causal_attn(lctx, false); + // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image + } + + while (i_batch < n_img_batches) { // split into batches + int pos_offset = i_batch*n_batch; + int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); + llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); + + LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); + + int64_t t1 = ggml_time_ms(); + int32_t ret = llama_decode(lctx, batch_embd_view); + if (ret != 0) { + LOG_ERR("failed to decode image\n"); + llama_set_causal_attn(lctx, true); // restore causal attn + return ret; + } + + LOG_INF("image decoded (batch %d/%d) in %" PRId64 " ms\n", i_batch+1, n_img_batches, ggml_time_ms() - t1); + + i_batch++; + } + + n_past += mtmd_image_tokens_get_n_pos(image_tokens); + *new_n_past = n_past; + + if (mtmd_decode_use_non_causal(ctx)) { + llama_set_causal_attn(lctx, true); + } + return 0; +} + +int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunk * chunk, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past) { + int32_t ret; + llama_batch text_batch = llama_batch_init(n_batch, 0, 1); + auto chunk_type = mtmd_input_chunk_get_type(chunk); + + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens; + const auto tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); + // LOG_INF("decoding text chunk, n_tokens = %zu\n", n_tokens); + size_t i = 0; + while (i < n_tokens) { // split into batches + text_batch.n_tokens = 0; // clear the batch + for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) { + text_batch.n_tokens++; + text_batch.token [i] = tokens[i]; + text_batch.pos [i] = n_past++; + text_batch.n_seq_id[i] = 1; + text_batch.seq_id [i][0] = seq_id; + text_batch.logits [i] = false; + } + bool is_last_token = (i == n_tokens); + if (logits_last && is_last_token) { + text_batch.logits[text_batch.n_tokens - 1] = true; + } + ret = llama_decode(lctx, text_batch); + if (ret != 0) { + LOG_ERR("failed to decode text\n"); + llama_batch_free(text_batch); + return ret; + } + *new_n_past += text_batch.n_tokens; + } + + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); + int64_t t0 = ggml_time_ms(); + + LOG_INF("encoding image or slice...\n"); + + ret = mtmd_encode(ctx, image_tokens); + if (ret != 0) { + LOG_ERR("failed to encode image\n"); + llama_batch_free(text_batch); + return ret; + } + + LOG_INF("image/slice encoded in %" PRId64 " ms\n", ggml_time_ms() - t0); + + float * embd = mtmd_get_output_embd(ctx); + ret = mtmd_helper_decode_image_chunk(ctx, lctx, chunk, embd, n_past, seq_id, n_batch, new_n_past); + if (ret != 0) { + LOG_ERR("failed to decode image\n"); + llama_batch_free(text_batch); + return ret; + } + } else { + GGML_ABORT("chunk type not supported"); + } + + return 0; +} + +int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunks * chunks, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past) { + size_t n_chunks = mtmd_input_chunks_size(chunks); + if (n_chunks == 0) { + LOG_ERR("no chunks to eval\n"); + return 0; + } + + for (size_t i = 0; i < n_chunks; i++) { + bool chunk_logits_last = (i == n_chunks - 1) && logits_last; + auto chunk = mtmd_input_chunks_get(chunks, i); + + int32_t res = mtmd_helper_eval_chunk_single(ctx, lctx, chunk, n_past, seq_id, n_batch, chunk_logits_last, &n_past); + if (res != 0) { + LOG_ERR("failed to eval chunk %zu\n", i); + return res; + } + *new_n_past = n_past; + } + + return 0; +} diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index f1b957394..2a852d9c1 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -461,307 +461,26 @@ float * mtmd_get_output_embd(mtmd_context * ctx) { return ctx->image_embd_v.data(); } -size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) { - size_t n_tokens = 0; - for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { - auto chunk = mtmd_input_chunks_get(chunks, i); - auto chunk_type = mtmd_input_chunk_get_type(chunk); - if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - size_t n_tokens_text; - mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); - n_tokens += n_tokens_text; - } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); - n_tokens += mtmd_image_tokens_get_n_tokens(tokens_image); - } else { - GGML_ASSERT(false && "chunk type not supported"); - } +bool mtmd_decode_use_non_causal(mtmd_context * ctx) { + projector_type proj_type = clip_get_projector_type(ctx->ctx_clip); + if (proj_type == PROJECTOR_TYPE_GEMMA3) { + return true; } - return n_tokens; + return false; } -llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks) { - llama_pos n_pos = 0; - for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { - auto chunk = mtmd_input_chunks_get(chunks, i); - auto chunk_type = mtmd_input_chunk_get_type(chunk); - if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - size_t n_tokens_text; - mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); - n_pos += n_tokens_text; - } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); - n_pos += mtmd_image_tokens_get_n_pos(tokens_image); - } else { - GGML_ASSERT(false && "chunk type not supported"); - } - } - return n_pos; +bool mtmd_decode_use_mrope(mtmd_context * ctx) { + return ctx->use_mrope; } -// helper struct to make working with embd batch easier -// note: this will be removed after llama_batch_ext refactoring -struct decode_embd_batch { - int n_pos_per_embd; - int n_mmproj_embd; - std::vector pos; - std::vector pos_view; // used by mrope - std::vector n_seq_id; - std::vector seq_id_0; - std::vector seq_ids; - std::vector logits; - llama_batch batch; - decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) { - pos .resize(n_tokens * n_pos_per_embd); - n_seq_id.resize(n_tokens); - seq_ids .resize(n_tokens + 1); - logits .resize(n_tokens); - seq_id_0.resize(1); - seq_ids [n_tokens] = nullptr; - batch = { - /*n_tokens =*/ n_tokens, - /*tokens =*/ nullptr, - /*embd =*/ embd, - /*pos =*/ pos.data(), - /*n_seq_id =*/ n_seq_id.data(), - /*seq_id =*/ seq_ids.data(), - /*logits =*/ logits.data(), - }; - } - - void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) { - seq_id_0[0] = seq_id; - for (int i = 0; i < batch.n_tokens; i++) { - batch.pos [i] = pos_0 + i; - batch.n_seq_id[i] = 1; - batch.seq_id [i] = seq_id_0.data(); - batch.logits [i] = false; - } - } - - void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) { - GGML_ASSERT(n_pos_per_embd == 4); - seq_id_0[0] = seq_id; - for (int y = 0; y < ny; y++) { - for (int x = 0; x < nx; x++) { - int i = y * nx + x; - pos[i ] = pos_0; - pos[i + batch.n_tokens ] = pos_0 + y; - pos[i + batch.n_tokens * 2] = pos_0 + x; - pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused - } - } - for (int i = 0; i < batch.n_tokens; i++) { - batch.n_seq_id[i] = 1; - batch.seq_id [i] = seq_id_0.data(); - batch.logits [i] = false; - } - } - - llama_batch get_view(int offset, int n_tokens) { - llama_pos * pos_ptr; - pos_view.clear(); - pos_view.reserve(n_tokens * n_pos_per_embd); - if (n_pos_per_embd > 1) { - // mrope - // for example, with layout of src: 1234...1234...1234...1234... - // offset 2 will give us dst: 34...34...34...34... - for (int i = 0; i < n_pos_per_embd; i++) { - // assume n_tokens is less than or equal to batch.n_tokens - // batch.n_tokens is number of **total** tokens - // n_tokens is number of viewed token - size_t src_idx = i * batch.n_tokens + offset; - pos_view.insert(pos_view.end(), - pos.data() + src_idx, - pos.data() + src_idx + n_tokens); - } - pos_ptr = pos_view.data(); - } else { - // normal - pos_ptr = pos.data() + offset; - } - return { - /*n_tokens =*/ n_tokens, - /*tokens =*/ nullptr, - /*embd =*/ batch.embd + offset * n_mmproj_embd, - /*pos =*/ pos_ptr, - /*n_seq_id =*/ batch.n_seq_id + offset, - /*seq_id =*/ batch.seq_id + offset, - /*logits =*/ batch.logits + offset, - }; - } -}; - -// Helper function for decoding an image whose embeddings have already been calculated -int32_t mtmd_helper_decode_image_chunk( - mtmd_context * ctx, - struct llama_context * lctx, - const mtmd_input_chunk * chunk, - float * encoded_embd, - llama_pos n_past, - llama_seq_id seq_id, - int32_t n_batch, - llama_pos * new_n_past) { - if (mtmd_input_chunk_get_type(chunk) != MTMD_INPUT_CHUNK_TYPE_IMAGE) { - LOG_ERR("failed to decode image chunk: input chunk not of image type\n"); - return -1; - } - const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); - if (!image_tokens) { - LOG_ERR("failed to decode image chunk: image tokens are null\n"); - return -1; - } - - int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip); - int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1; - - int32_t n_tokens = mtmd_image_tokens_get_n_tokens(image_tokens); - int32_t i_batch = 0; - int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; - decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd); - - const int nx = mtmd_image_tokens_get_nx(image_tokens); - const int ny = mtmd_image_tokens_get_ny(image_tokens); - - if (mtmd_decode_use_mrope(ctx)) { - batch_embd.set_position_mrope(n_past, nx, ny, seq_id); - } else { - batch_embd.set_position_normal(n_past, seq_id); - } - - if (mtmd_decode_use_non_causal(ctx)) { - llama_set_causal_attn(lctx, false); - // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image - } - - while (i_batch < n_img_batches) { // split into batches - int pos_offset = i_batch*n_batch; - int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); - llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); - - LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); - - int64_t t1 = ggml_time_ms(); - int32_t ret = llama_decode(lctx, batch_embd_view); - if (ret != 0) { - LOG_ERR("failed to decode image\n"); - llama_set_causal_attn(lctx, true); // restore causal attn - return ret; - } - - if (ctx->print_timings) { - LOG_INF("image decoded (batch %d/%d) in %" PRId64 " ms\n", i_batch+1, n_img_batches, ggml_time_ms() - t1); - } - - i_batch++; - } - - n_past += mtmd_image_tokens_get_n_pos(image_tokens); - *new_n_past = n_past; - - if (mtmd_decode_use_non_causal(ctx)) { - llama_set_causal_attn(lctx, true); - } - return 0; +void mtmd_image_tokens_deleter::operator()(mtmd_image_tokens * val) { + mtmd_image_tokens_free(val); } -int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, - struct llama_context * lctx, - const mtmd_input_chunk * chunk, - llama_pos n_past, - llama_seq_id seq_id, - int32_t n_batch, - bool logits_last, - llama_pos * new_n_past) { - int32_t ret; - llama_batch text_batch = llama_batch_init(n_batch, 0, 1); - auto chunk_type = mtmd_input_chunk_get_type(chunk); - - if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - size_t n_tokens; - const auto tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); - LOG_DBG("decoding text chunk, n_tokens = %zu\n", n_tokens); - size_t i = 0; - while (i < n_tokens) { // split into batches - text_batch.n_tokens = 0; // clear the batch - for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) { - text_batch.n_tokens++; - text_batch.token [i] = tokens[i]; - text_batch.pos [i] = n_past++; - text_batch.n_seq_id[i] = 1; - text_batch.seq_id [i][0] = seq_id; - text_batch.logits [i] = false; - } - bool is_last_token = (i == n_tokens); - if (logits_last && is_last_token) { - text_batch.logits[text_batch.n_tokens - 1] = true; - } - ret = llama_decode(lctx, text_batch); - if (ret != 0) { - LOG_ERR("failed to decode text\n"); - llama_batch_free(text_batch); - return ret; - } - *new_n_past += text_batch.n_tokens; - } - - } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); - int64_t t0 = ggml_time_ms(); - if (ctx->print_timings) { - LOG_INF("encoding image or slice...\n"); - } - ret = mtmd_encode(ctx, image_tokens); - if (ret != 0) { - LOG_ERR("failed to encode image\n"); - llama_batch_free(text_batch); - return ret; - } - if (ctx->print_timings) { - LOG_INF("image/slice encoded in %" PRId64 " ms\n", ggml_time_ms() - t0); - } - float * embd = mtmd_get_output_embd(ctx); - ret = mtmd_helper_decode_image_chunk(ctx, lctx, chunk, embd, n_past, seq_id, n_batch, new_n_past); - if (ret != 0) { - LOG_ERR("failed to decode image\n"); - llama_batch_free(text_batch); - return ret; - } - } else { - GGML_ABORT("chunk type not supported"); - } - - return 0; -} - -int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, - struct llama_context * lctx, - const mtmd_input_chunks * chunks, - llama_pos n_past, - llama_seq_id seq_id, - int32_t n_batch, - bool logits_last, - llama_pos * new_n_past) { - size_t n_chunks = mtmd_input_chunks_size(chunks); - if (n_chunks == 0) { - LOG_WRN("no chunks to eval\n"); - return 0; - } - - for (size_t i = 0; i < n_chunks; i++) { - bool chunk_logits_last = (i == n_chunks - 1) && logits_last; - auto chunk = mtmd_input_chunks_get(chunks, i); - - int32_t res = mtmd_helper_eval_chunk_single(ctx, lctx, chunk, n_past, seq_id, n_batch, chunk_logits_last, &n_past); - if (res != 0) { - LOG_ERR("failed to eval chunk %zu\n", i); - return res; - } - *new_n_past = n_past; - } - - return 0; -} +// these 2 helpers below use internal clip_image_u8_ptr, +// so unfortunately they cannot moved to mtmd-helper.h +// however, in theory, user can decode image file to bitmap using +// whichever library they want, and then use mtmd_bitmap_init() to create bitmap mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len) { clip_image_u8_ptr img_u8(clip_image_u8_init()); @@ -787,23 +506,6 @@ mtmd_bitmap * mtmd_helper_bitmap_init_from_file(const char * fname) { return mtmd_bitmap_init(nx, ny, data); } -bool mtmd_decode_use_non_causal(mtmd_context * ctx) { - projector_type proj_type = clip_get_projector_type(ctx->ctx_clip); - if (proj_type == PROJECTOR_TYPE_GEMMA3) { - return true; - } - return false; -} - -bool mtmd_decode_use_mrope(mtmd_context * ctx) { - return ctx->use_mrope; -} - -void mtmd_image_tokens_deleter::operator()(mtmd_image_tokens * val) { - mtmd_image_tokens_free(val); -} - - // // public API functions // diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h index 54cf481b6..0ada78c90 100644 --- a/tools/mtmd/mtmd.h +++ b/tools/mtmd/mtmd.h @@ -10,6 +10,7 @@ #include #ifdef __cplusplus +#include #include #include #include From 3eac209319a6726fd9687c6188fc6b916b65953d Mon Sep 17 00:00:00 2001 From: City <125218114+city96@users.noreply.github.com> Date: Sun, 11 May 2025 11:35:52 +0200 Subject: [PATCH 013/421] mtmd : support InternVL 3 38B and 78B mmproj (#13443) * Support InternVL 3 38B and 78B mmproj * Swap norms in clip.cpp * Group variables together --- gguf-py/gguf/constants.py | 6 ++++++ gguf-py/gguf/tensor_mapping.py | 8 ++++++++ tools/mtmd/clip-impl.h | 2 ++ tools/mtmd/clip.cpp | 15 +++++++++++++++ 4 files changed, 31 insertions(+) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index ae5ce71ae..0e6226b90 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -483,7 +483,9 @@ class MODEL_TENSOR(IntEnum): V_ENC_EMBD_PATCH = auto() V_ENC_EMBD_POS = auto() V_ENC_ATTN_Q = auto() + V_ENC_ATTN_Q_NORM = auto() V_ENC_ATTN_K = auto() + V_ENC_ATTN_K_NORM = auto() V_ENC_ATTN_V = auto() V_ENC_INPUT_NORM = auto() V_ENC_OUTPUT = auto() @@ -742,7 +744,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.V_ENC_EMBD_PATCH: "v.patch_embd", MODEL_TENSOR.V_ENC_EMBD_POS: "v.position_embd", MODEL_TENSOR.V_ENC_ATTN_Q: "v.blk.{bid}.attn_q", + MODEL_TENSOR.V_ENC_ATTN_Q_NORM: "v.blk.{bid}.attn_q_norm", MODEL_TENSOR.V_ENC_ATTN_K: "v.blk.{bid}.attn_k", + MODEL_TENSOR.V_ENC_ATTN_K_NORM: "v.blk.{bid}.attn_k_norm", MODEL_TENSOR.V_ENC_ATTN_V: "v.blk.{bid}.attn_v", MODEL_TENSOR.V_ENC_INPUT_NORM: "v.blk.{bid}.ln1", MODEL_TENSOR.V_ENC_OUTPUT: "v.blk.{bid}.attn_out", @@ -782,7 +786,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.V_ENC_EMBD_PATCH, MODEL_TENSOR.V_ENC_EMBD_POS, MODEL_TENSOR.V_ENC_ATTN_Q, + MODEL_TENSOR.V_ENC_ATTN_Q_NORM, MODEL_TENSOR.V_ENC_ATTN_K, + MODEL_TENSOR.V_ENC_ATTN_K_NORM, MODEL_TENSOR.V_ENC_ATTN_V, MODEL_TENSOR.V_ENC_INPUT_NORM, MODEL_TENSOR.V_ENC_OUTPUT, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index bf7ec3257..ecf21b2b4 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -938,6 +938,10 @@ class TensorNameMap: "visual.blocks.{bid}.attn.q", # qwen2vl, generated ), + MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( + "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL + ), + MODEL_TENSOR.V_ENC_ATTN_K: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", "vpm.encoder.layers.{bid}.self_attn.k_proj", @@ -946,6 +950,10 @@ class TensorNameMap: "visual.blocks.{bid}.attn.k", # qwen2vl, generated ), + MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( + "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL + ), + MODEL_TENSOR.V_ENC_ATTN_V: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", "vpm.encoder.layers.{bid}.self_attn.v_proj", diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index d7b788bf9..23036ba72 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -53,6 +53,8 @@ #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" #define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" +#define TN_ATTN_K_NORM "%s.blk.%d.attn_k_norm.%s" +#define TN_ATTN_Q_NORM "%s.blk.%d.attn_q_norm.%s" #define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" #define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" #define TN_FFN_UP "%s.blk.%d.ffn_up.%s" diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 0ebe81b07..735dfe7f7 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -205,6 +205,9 @@ struct clip_layer { ggml_tensor * o_w = nullptr; ggml_tensor * o_b = nullptr; + ggml_tensor * k_norm = nullptr; + ggml_tensor * q_norm = nullptr; + // layernorm 1 ggml_tensor * ln_1_w = nullptr; ggml_tensor * ln_1_b = nullptr; @@ -1363,6 +1366,16 @@ private: Vcur = ggml_add(ctx0, Vcur, layer.v_b); } + if (layer.q_norm) { + Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il); + cb(Qcur, "Qcur_norm", il); + } + + if (layer.k_norm) { + Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il); + cb(Kcur, "Kcur_norm", il); + } + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); @@ -1988,6 +2001,8 @@ struct clip_model_loader { layer.q_w = get_tensor(string_format(TN_ATTN_Q, "v", il, "weight")); layer.v_w = get_tensor(string_format(TN_ATTN_V, "v", il, "weight")); layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight")); + layer.k_norm = get_tensor(string_format(TN_ATTN_K_NORM, "v", il, "weight"), false); + layer.q_norm = get_tensor(string_format(TN_ATTN_Q_NORM, "v", il, "weight"), false); layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false); layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false); layer.ls_1_w = get_tensor(string_format(TN_LS_1, "v", il, "weight"), false); // no bias From 7f323a589f8684c0eb722e7309074cb5eac0c8b5 Mon Sep 17 00:00:00 2001 From: David Huang <1969802+hjc4869@users.noreply.github.com> Date: Sun, 11 May 2025 20:18:39 +0800 Subject: [PATCH 014/421] Add `--no-op-offload` to improve `-ot` pp perf in MoE models like llama4 400B (#13386) --- common/arg.cpp | 7 +++++++ common/common.cpp | 1 + common/common.h | 1 + ggml/include/ggml-backend.h | 4 ++-- ggml/src/ggml-backend.cpp | 8 +++++-- include/llama.h | 1 + src/llama-context.cpp | 4 +++- src/llama-cparams.h | 1 + tests/test-opt.cpp | 2 +- tools/llama-bench/llama-bench.cpp | 35 +++++++++++++++++++++++++++++-- tools/mtmd/clip.cpp | 2 +- 11 files changed, 57 insertions(+), 9 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index e0f1d998f..a1fd4c965 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2437,6 +2437,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } )); + add_opt(common_arg( + {"--no-op-offload"}, + string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"), + [](common_params & params) { + params.no_op_offload = true; + } + )); add_opt(common_arg( {"--lora"}, "FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)", diff --git a/common/common.cpp b/common/common.cpp index bd20af233..710bf1fe2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1113,6 +1113,7 @@ struct llama_context_params common_context_params_to_llama(const common_params & cparams.offload_kqv = !params.no_kv_offload; cparams.flash_attn = params.flash_attn; cparams.no_perf = params.no_perf; + cparams.op_offload = !params.no_op_offload; if (params.reranking) { cparams.embeddings = true; diff --git a/common/common.h b/common/common.h index d051d4ec9..e15356b12 100644 --- a/common/common.h +++ b/common/common.h @@ -332,6 +332,7 @@ struct common_params { bool no_kv_offload = false; // disable KV offloading bool warmup = true; // warmup run bool check_tensors = false; // validate tensor data + bool no_op_offload = false; // globally disable offload host tensor operations to device bool single_turn = false; // single turn chat conversation diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index ea2c1a402..778927f68 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -248,7 +248,7 @@ extern "C" { // preferrably to run on the same backend as the buffer ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); - sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false); + sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false, true); // initialize buffers from a max size graph (optional) reserve_graph = build_graph(sched, max_batch_size); @@ -289,7 +289,7 @@ extern "C" { typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); // Initialize a backend scheduler, backends with low index are given priority over backends with high index - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel); + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload); GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index c36b5abfb..6f69d895f 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -674,6 +674,8 @@ struct ggml_backend_sched { char * context_buffer; size_t context_buffer_size; + bool op_offload; + int debug; }; @@ -766,7 +768,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); // check if a backend with higher prio wants to offload the op - if (src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { + if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { for (int b = 0; b < src_backend_id; b++) { if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { SET_CAUSE(tensor, "1.off"); @@ -1452,7 +1454,8 @@ ggml_backend_sched_t ggml_backend_sched_new( ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, - bool parallel) { + bool parallel, + bool op_offload) { GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU); @@ -1497,6 +1500,7 @@ ggml_backend_sched_t ggml_backend_sched_new( } sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends); + sched->op_offload = op_offload; ggml_backend_sched_reset(sched); diff --git a/include/llama.h b/include/llama.h index 7d5f9d559..6c6d377f8 100644 --- a/include/llama.h +++ b/include/llama.h @@ -363,6 +363,7 @@ extern "C" { bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool flash_attn; // whether to use flash attention [EXPERIMENTAL] bool no_perf; // whether to measure performance timings + bool op_offload; // whether to offload host tensor operations to device }; // model quantization parameters diff --git a/src/llama-context.cpp b/src/llama-context.cpp index fd64622b8..a12849f0e 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -93,6 +93,7 @@ llama_context::llama_context( } cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); + cparams.op_offload = params.op_offload; const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max; @@ -243,7 +244,7 @@ llama_context::llama_context( } } - sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel)); + sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel, cparams.op_offload)); if (pipeline_parallel) { LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(sched.get())); @@ -1871,6 +1872,7 @@ llama_context_params llama_context_default_params() { /*.offload_kqv =*/ true, /*.flash_attn =*/ false, /*.no_perf =*/ true, + /*.op_offload =*/ true, }; return result; diff --git a/src/llama-cparams.h b/src/llama-cparams.h index 30e550f02..246fa5777 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -30,6 +30,7 @@ struct llama_cparams { bool flash_attn; bool no_perf; bool warmup; + bool op_offload; enum llama_pooling_type pooling_type; diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index f90c92b4b..1bc160511 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -853,7 +853,7 @@ int main(void) { backends_modded.insert(backends_modded.end(), backends.begin(), backends.end()); ggml_backend_sched_t backend_sched = ggml_backend_sched_new( - backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false); + backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false, true); printf("Backend %zu/%zu: %s\n", i + 1, dev_count, ggml_backend_dev_name(devs[i])); printf(" Device description: %s\n", ggml_backend_dev_description(devs[i])); diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 078659429..5d26b506b 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -219,6 +219,7 @@ struct cmd_params { std::vector> tensor_buft_overrides; std::vector use_mmap; std::vector embeddings; + std::vector no_op_offload; ggml_numa_strategy numa; int reps; ggml_sched_priority prio; @@ -253,6 +254,7 @@ static const cmd_params cmd_params_defaults = { /* tensor_buft_overrides*/ { std::vector{{nullptr,nullptr}} }, /* use_mmap */ { true }, /* embeddings */ { false }, + /* no_op_offload */ { false }, /* numa */ GGML_NUMA_STRATEGY_DISABLED, /* reps */ 5, /* prio */ GGML_SCHED_PRIO_NORMAL, @@ -311,6 +313,7 @@ static void print_usage(int /* argc */, char ** argv) { join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); printf(" -ot --override-tensors =;... (default: disabled)\n"); + printf(" -nopo, --no-op-offload (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio); printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay); @@ -588,6 +591,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = string_split(argv[i], split_delim); params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); + } else if (arg == "-nopo" || arg == "--no-op-offload") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.no_op_offload.insert(params.no_op_offload.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -794,6 +804,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } + if (params.no_op_offload.empty()) { + params.no_op_offload = cmd_params_defaults.no_op_offload; + } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -833,6 +846,7 @@ struct cmd_params_instance { std::vector tensor_buft_overrides; bool use_mmap; bool embeddings; + bool no_op_offload; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -902,6 +916,7 @@ struct cmd_params_instance { cparams.offload_kqv = !no_kv_offload; cparams.flash_attn = flash_attn; cparams.embeddings = embeddings; + cparams.op_offload = !no_op_offload; return cparams; } @@ -921,6 +936,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & ot : params.tensor_buft_overrides) for (const auto & mmp : params.use_mmap) for (const auto & embd : params.embeddings) + for (const auto & nopo : params.no_op_offload) for (const auto & nb : params.n_batch) for (const auto & nub : params.n_ubatch) for (const auto & tk : params.type_k) @@ -959,6 +975,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, + /* .no_op_offload= */ nopo, }; instances.push_back(instance); } @@ -990,6 +1007,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, + /* .no_op_offload= */ nopo, }; instances.push_back(instance); } @@ -1021,6 +1039,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, + /* .no_op_offload= */ nopo, }; instances.push_back(instance); } @@ -1056,6 +1075,7 @@ struct test { std::vector tensor_buft_overrides; bool use_mmap; bool embeddings; + bool no_op_offload; int n_prompt; int n_gen; int n_depth; @@ -1089,6 +1109,7 @@ struct test { tensor_buft_overrides = inst.tensor_buft_overrides; use_mmap = inst.use_mmap; embeddings = inst.embeddings; + no_op_offload = inst.no_op_offload; n_prompt = inst.n_prompt; n_gen = inst.n_gen; n_depth = inst.n_depth; @@ -1134,7 +1155,7 @@ struct test { "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", - "use_mmap", "embeddings", "n_prompt", "n_gen", "n_depth", "test_time", + "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", }; return fields; @@ -1146,7 +1167,7 @@ struct test { if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" || field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "n_depth" || - field == "avg_ns" || field == "stddev_ns") { + field == "avg_ns" || field == "stddev_ns" || field == "no_op_offload") { return INT; } if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" || @@ -1222,6 +1243,7 @@ struct test { tensor_buft_overrides_str, std::to_string(use_mmap), std::to_string(embeddings), + std::to_string(no_op_offload), std::to_string(n_prompt), std::to_string(n_gen), std::to_string(n_depth), @@ -1404,6 +1426,9 @@ struct markdown_printer : public printer { if (field == "test") { return 15; } + if (field == "no_op_offload") { + return 4; + } int width = std::max((int) field.length(), 10); @@ -1435,6 +1460,9 @@ struct markdown_printer : public printer { if (field == "embeddings") { return "embd"; } + if (field == "no_op_offload") { + return "nopo"; + } if (field == "tensor_split") { return "ts"; } @@ -1503,6 +1531,9 @@ struct markdown_printer : public printer { if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { fields.emplace_back("embeddings"); } + if (params.no_op_offload.size() > 1 || params.no_op_offload != cmd_params_defaults.no_op_offload) { + fields.emplace_back("no_op_offload"); + } fields.emplace_back("test"); fields.emplace_back("t/s"); diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 735dfe7f7..3f11c301a 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -383,7 +383,7 @@ struct clip_ctx { backend_buft.push_back(ggml_backend_get_default_buffer_type(backend_cpu)); sched.reset( - ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false) + ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false, true) ); } From 7474e00b34629e9cd8b06bc87ad935584ea30f8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 11 May 2025 16:09:33 +0200 Subject: [PATCH 015/421] CUDA: fix crash with partial offloading of MoE (#13439) --- ggml/src/ggml-cuda/ggml-cuda.cu | 10 ++++++++-- ggml/src/ggml-cuda/mmq.cu | 4 ++-- ggml/src/ggml-cuda/mmvq.cu | 4 ++-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 7643c4b8b..b4b85abcd 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1909,13 +1909,19 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); + // If src0 is a temporary compute buffer it may have some padding that needs to be cleared for mul_mat_vec_q or mul_mat_q. + // But if src0 is also a view of another tensor then this cannot be done safely because it may overwrite valid tensor data. + // Therefore, in such cases use cuBLAS. + const bool bad_padding_clear = ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE + && ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src; + bool use_mul_mat_vec = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && src0->ne[0] % 2 == 0 && src1->ne[1] == 1; - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - bool use_mul_mat_q = ggml_is_quantized(src0->type) + bool use_mul_mat_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; bool any_gpus_with_slow_fp16 = false; diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index b4962e6a5..e1cf843de 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -91,11 +91,11 @@ void ggml_cuda_mul_mat_q( // If src0 is a temporary compute buffer, clear any potential padding. if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { - GGML_ASSERT(ggml_is_contiguously_allocated(src0)); - GGML_ASSERT(!src0->view_src); const size_t size_data = ggml_nbytes(src0); const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); if (size_alloc > size_data) { + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); + GGML_ASSERT(!src0->view_src); CUDA_CHECK(cudaMemsetAsync((char *) src0->data + size_data, 0, size_alloc - size_data, stream)); } } diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 3b313ea29..dc7adf509 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -515,11 +515,11 @@ void ggml_cuda_mul_mat_vec_q( // If src0 is a temporary compute buffer, clear any potential padding. if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { - GGML_ASSERT(ggml_is_contiguously_allocated(src0)); - GGML_ASSERT(!src0->view_src); const size_t size_data = ggml_nbytes(src0); const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); if (size_alloc > size_data) { + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); + GGML_ASSERT(!src0->view_src); CUDA_CHECK(cudaMemsetAsync((char *) src0->data + size_data, 0, size_alloc - size_data, stream)); } } From 09232370fc6426aa5dd9be01a8271b9c28f5af3a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 11 May 2025 16:20:39 +0200 Subject: [PATCH 016/421] scripts : exit compare-llama-bench.py gracefully when there's nothing to compare (#13451) --- scripts/compare-llama-bench.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 8c599cf9e..c32b449f7 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -318,7 +318,7 @@ else: show = [] # Show CPU and/or GPU by default even if the hardware for all results is the same: - if "n_gpu_layers" not in properties_different: + if rows_full and "n_gpu_layers" not in properties_different: ngl = int(rows_full[0][KEY_PROPERTIES.index("n_gpu_layers")]) if ngl != 99 and "cpu_info" not in properties_different: @@ -338,6 +338,10 @@ else: pass rows_show = get_rows(show) +if not rows_show: + logger.error(f"No comparable data was found between {name_baseline} and {name_compare}.\n") + sys.exit(1) + table = [] for row in rows_show: n_prompt = int(row[-5]) From 9a390c4829cd3058d26a2e2c09d16e3fd12bf1b1 Mon Sep 17 00:00:00 2001 From: Anthony Umfer Date: Sun, 11 May 2025 11:08:26 -0400 Subject: [PATCH 017/421] tools : fix uninitialized llama_batch in server (#13436) * add constructor to initialize server_context::batch, preventing destructor's call to llama_batch_free from causing an invalid free() * Update tools/server/server.cpp Co-authored-by: Xuan-Son Nguyen * use C++11 initializer syntax * switch from Copy-list-initialization to Direct-list-initialization --------- Co-authored-by: Xuan-Son Nguyen --- tools/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index de8ded71f..7169ffdce 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -1862,7 +1862,7 @@ struct server_context { llama_context_params cparams_dft; - llama_batch batch; + llama_batch batch {}; bool clean_kv_cache = true; bool add_bos_token = true; From c104023994d36a8e791fc6a43789b84fd552cefc Mon Sep 17 00:00:00 2001 From: City <125218114+city96@users.noreply.github.com> Date: Mon, 12 May 2025 00:39:06 +0200 Subject: [PATCH 018/421] mtmd : Use RMS norm for InternVL 3 38B and 78B mmproj (#13459) --- tools/mtmd/clip.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 3f11c301a..0adf03163 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -879,9 +879,15 @@ struct clip_graph { // add CLS token inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + // The larger models use a different ViT, which uses RMS norm instead of layer norm + // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188 + norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45) + ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B) + : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models) + ggml_tensor * cur = build_vit( inp, n_pos, - NORM_TYPE_NORMAL, + norm_t, hparams.ffn_op, model.position_embeddings, nullptr); From 14492144c286bbf38bb1903128403d9e2ebad54c Mon Sep 17 00:00:00 2001 From: Atharva Dubey Date: Mon, 12 May 2025 06:15:32 +0100 Subject: [PATCH 019/421] enable dpcpp nightly builds with libraries (#13406) --- ggml/src/ggml-sycl/CMakeLists.txt | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index 6699b70ba..231fb71da 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -52,9 +52,8 @@ target_compile_options(ggml-sycl PRIVATE "-Wno-narrowing") find_package(DNNL) set(GGML_SYCL_DNNL 0) if(DNNL_FOUND) - if (DEFINED ENV{ONEAPI_ROOT} AND NOT DEFINED DNNL_GPU_VENDOR) - # Assuming oneDNN packaged with oneapi release is used which - # supports only intel target + if (NOT DEFINED DNNL_GPU_VENDOR) + # default to intel target set(DNNL_GPU_VENDOR "INTEL") if(NOT "${GGML_SYCL_TARGET}" STREQUAL "INTEL") message(WARNING "oneDNN builds bundled with oneapi release only support INTEL target") @@ -108,6 +107,9 @@ endif() if (GGML_SYCL_TARGET STREQUAL "INTEL") # Intel devices use Intel oneMKL directly instead of oneMath to avoid the limitation of linking Intel oneMKL statically # See https://github.com/uxlfoundation/oneMath/issues/654 + if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + set(SYCL_COMPILER ON) + endif() find_package(MKL REQUIRED) target_link_libraries(ggml-sycl PRIVATE MKL::MKL_SYCL::BLAS) target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_USE_INTEL_ONEMKL) From df8491922f0ea4e032338186350dff2fb6b2b4e3 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 12 May 2025 10:29:13 +0200 Subject: [PATCH 020/421] ggml : add mrope kernel for metal (#13457) --- ggml/src/ggml-metal/ggml-metal-impl.h | 4 + ggml/src/ggml-metal/ggml-metal.m | 58 +++++++--- ggml/src/ggml-metal/ggml-metal.metal | 146 ++++++++++++++++++++++++++ 3 files changed, 192 insertions(+), 16 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index ea8cf4586..17eab976f 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -207,6 +207,10 @@ typedef struct { float attn_factor; float beta_fast; float beta_slow; + int32_t sect_0; + int32_t sect_1; + int32_t sect_2; + int32_t sect_3; } ggml_metal_kargs_rope; typedef struct { diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m index 943f0fe72..576f9581b 100644 --- a/ggml/src/ggml-metal/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -332,6 +332,10 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F16, GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, + GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32, + GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16, + GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32, + GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16, GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F16, @@ -1275,6 +1279,10 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F16, mul_mm_id_iq4_xs_f16, has_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32, rope_multi_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16, rope_multi_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32, rope_vision_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16, rope_vision_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); @@ -1637,16 +1645,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_NORM: return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0])); case GGML_OP_ROPE: - { - const int mode = ((const int32_t *) op->op_params)[2]; - if (mode & GGML_ROPE_TYPE_MROPE) { - return false; - } - if (mode & GGML_ROPE_TYPE_VISION) { - return false; - } - return true; - } + return true; case GGML_OP_IM2COL: return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_1D: @@ -3826,6 +3825,7 @@ static bool ggml_metal_encode_node( } break; case GGML_OP_ROPE: { + // make sure we have one or more position id(ne10) per token(ne02) GGML_ASSERT(ne10 % ne02 == 0); GGML_ASSERT(ne10 >= ne02); @@ -3852,20 +3852,42 @@ static bool ggml_metal_encode_node( memcpy(&beta_fast, (const int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (const int32_t *) dst->op_params + 10, sizeof(float)); - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; + + // mrope + const int sect_0 = ((const int32_t *) dst->op_params)[11]; + const int sect_1 = ((const int32_t *) dst->op_params)[12]; + const int sect_2 = ((const int32_t *) dst->op_params)[13]; + const int sect_3 = ((const int32_t *) dst->op_params)[14]; id pipeline = nil; - if (!is_neox) { + if (is_neox) { switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; + default: GGML_ABORT("fatal error"); + }; + } else if (is_mrope && !is_vision) { + GGML_ASSERT(ne10*4 >= ne02); // need at least 4 pos per token + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16].pipeline; break; + default: GGML_ABORT("fatal error"); + }; + } else if (is_vision) { + GGML_ASSERT(ne10*4 >= ne02); // need at least 4 pos per token + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16].pipeline; break; default: GGML_ABORT("fatal error"); }; } else { switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; default: GGML_ABORT("fatal error"); }; } @@ -3896,6 +3918,10 @@ static bool ggml_metal_encode_node( /*.attn_factor =*/ attn_factor, /*.beta_fast =*/ beta_fast, /*.beta_slow =*/ beta_slow, + /* sect_0 =*/ sect_0, + /* sect_1 =*/ sect_1, + /* sect_2 =*/ sect_2, + /* sect_3 =*/ sect_3, }; [encoder setComputePipelineState:pipeline]; diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index a808e79c2..9cfddf450 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -2713,8 +2713,148 @@ kernel void kernel_rope_neox( } } +template +kernel void kernel_rope_multi( + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; + + float corr_dims[2]; + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); + + device const int32_t * pos = (device const int32_t *) src1; + + const float inv_ndims = -1.f/args.n_dims; + + float cos_theta; + float sin_theta; + + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < args.n_dims) { + const int ic = i0/2; + + // mrope theta calculations + // note: the rest is the same as kernel_rope_neox + const int sect_dims = args.sect_0 + args.sect_1 + args.sect_2 + args.sect_3; + const int sec_w01 = args.sect_0 + args.sect_1; // end of section 1 + const int sec_w012 = args.sect_0 + args.sect_1 + args.sect_2; // end of section 2 + const int sector = ic % sect_dims; + + float theta_base; + if (sector < args.sect_0) { + theta_base = (float) pos[i2]; + } else if (sector < sec_w01) { + theta_base = (float) pos[i2 + args.ne02]; + } else if (sector < sec_w012) { + theta_base = (float) pos[i2 + args.ne02 * 2]; + } else { + theta_base = (float) pos[i2 + args.ne02 * 3]; + } + // end of mrope + + const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); + + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); + + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0); + + const float x0 = src[0]; + const float x1 = src[args.n_dims/2]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } +} + +template +kernel void kernel_rope_vision( + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; + + float corr_dims[2]; + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); + + device const int32_t * pos = (device const int32_t *) src1; + + const float inv_ndims = -1.f/args.n_dims; + + float cos_theta; + float sin_theta; + + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < 2*args.n_dims) { // different from kernel_rope_multi + const int ic = i0/2; + + // mrope theta calculations (only support 2 dimensions) + const int sect_dims = args.sect_0 + args.sect_1; + const int sector = ic % sect_dims; + + float p; + float theta_base; + if (sector < args.sect_1) { + p = (float) sector; + theta_base = (float) pos[i2]; + } else { + p = (float) sector - args.sect_0; + theta_base = (float) pos[i2 + args.ne02]; + } + + const float theta = theta_base * pow(args.freq_base, 2.0f * inv_ndims * p); + // end of mrope + + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); + + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0); + + const float x0 = src[0]; + const float x1 = src[args.n_dims]; // different from kernel_rope_multi + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[args.n_dims] = x0*sin_theta + x1*cos_theta; // different from kernel_rope_multi + } else { + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } +} + typedef decltype(kernel_rope_norm) kernel_rope_norm_t; typedef decltype(kernel_rope_neox) kernel_rope_neox_t; +typedef decltype(kernel_rope_multi) kernel_rope_multi_t; +typedef decltype(kernel_rope_vision) kernel_rope_vision_t; template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm; template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm; @@ -2722,6 +2862,12 @@ template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_ template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox; template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox; +template [[host_name("kernel_rope_multi_f32")]] kernel kernel_rope_multi_t kernel_rope_multi; +template [[host_name("kernel_rope_multi_f16")]] kernel kernel_rope_multi_t kernel_rope_multi; + +template [[host_name("kernel_rope_vision_f32")]] kernel kernel_rope_vision_t kernel_rope_vision; +template [[host_name("kernel_rope_vision_f16")]] kernel kernel_rope_vision_t kernel_rope_vision; + typedef void (im2col_t)( device const float * x, device char * dst, From 95e18884fc7ea4031f70f1a518d5d1df616e5717 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 12 May 2025 10:51:21 +0200 Subject: [PATCH 021/421] CUDA: fix misaligned synchronization in FA (#13469) --- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 9873ea755..491780abd 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -895,6 +895,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols; dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs); } + } else if (np > 1) { + // Warps with threadIdx.y % np == 0 execute a __syncthreads() in the if branch. + // Therefore, all other warps also need to execute a __syncthreads(). + // Otherwise the points at which warps synchronize with each other would become misaligned. + __syncthreads(); } #pragma unroll From a71a4075cdb8e81eaaa834e48ffda88b038286bc Mon Sep 17 00:00:00 2001 From: Dan Johansson Date: Mon, 12 May 2025 13:06:19 +0200 Subject: [PATCH 022/421] ggml-cpu: Integrate fp32=bf16xbf16 SME KleidiAI kernel (#13053) * ggml-cpu: Integrate fp32=bf16xbf16 SME KleidiAI kernel Signed-off-by: Dan Johansson * * code review fixes Signed-off-by: Dan Johansson * * adds a comment that clarifies barrier usage Signed-off-by: Dan Johansson --------- Signed-off-by: Dan Johansson Co-authored-by: Charles Xu --- ggml/src/ggml-cpu/CMakeLists.txt | 29 +- ggml/src/ggml-cpu/kleidiai/kernels.cpp | 93 ++++++- ggml/src/ggml-cpu/kleidiai/kernels.h | 58 +++- ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 335 +++++++++++++++++++----- 4 files changed, 416 insertions(+), 99 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 9a3085bef..bdaec2881 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -428,6 +428,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ${KLEIDIAI_SRC}/kai/ukernels/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/ + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/) set(ARCH_FLAGS_TEMP "${ARCH_FLAGS}") @@ -438,17 +439,19 @@ function(ggml_add_cpu_backend_variant_impl tag_name) string(FIND "${ARCH_FLAGS_TEMP}" "+i8mm" I8MM_ENABLED) string(FIND "${ARCH_FLAGS_TEMP}" "+sme" SME_ENABLED) - set(PRIVATE_ARCH_FLAGS ${ARCH_FLAGS}) + set(PRIVATE_ARCH_FLAGS ${ARCH_FLAGS_TEMP}) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c) + list(APPEND GGML_KLEIDIAI_SOURCES + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c) if (NOT DOTPROD_ENABLED MATCHES -1) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.c) + list(APPEND GGML_KLEIDIAI_SOURCES + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.c) endif() if (NOT I8MM_ENABLED MATCHES -1) @@ -456,9 +459,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() if (NOT SME_ENABLED MATCHES -1) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.c) - list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c) - set(PRIVATE_ARCH_FLAGS "${PRIVATE_ARCH_FLAGS}+sve+sve2") + list(APPEND GGML_KLEIDIAI_SOURCES + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c) + set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2") endif() set_source_files_properties(${GGML_KLEIDIAI_SOURCES} PROPERTIES COMPILE_OPTIONS "${PRIVATE_ARCH_FLAGS}") diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index aacc2bb5e..910fd0ee4 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -4,16 +4,22 @@ // KleidiAI micro-kernels #include "kai_matmul_clamp_f32_qsi8d32p_qsi4c32p_interface.h" -#include "kai_lhs_quant_pack_qsi8d32p_f32.h" -#include "kai_lhs_quant_pack_qsi8d32p_f32_neon.h" -#include "kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.h" -#include "kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.h" #include "kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod.h" #include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm.h" #include "kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.h" +#include "kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.h" + +#include "kai_lhs_pack_bf16p2vlx2_f32_sme.h" +#include "kai_lhs_quant_pack_qsi8d32p_f32.h" +#include "kai_lhs_quant_pack_qsi8d32p_f32_neon.h" + +#include "kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.h" +#include "kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.h" +#include "kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.h" + #include "kai_common.h" #include "kernels.h" @@ -61,6 +67,53 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon, }, /* .required_cpu = */ CPU_FEATURE_SME, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q4_0, + /* .op_type = */ GGML_TYPE_F32, + }, + { + /* SME GEMM */ + /* .kern_info = */ { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .run_kernel = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + }, + /* SME GEMV */ + /* .kern_info = */ { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .run_kernel = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + }, + /* .lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme, + /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme, + /* .packed_size = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme, + /* .pack_func = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme, + }, + /* .rhs_info = */ { + /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme, + /* .pack_func = */ kai_run_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme, + }, + /* .required_cpu = */ CPU_FEATURE_SME, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_F16, + /* .op_type = */ GGML_TYPE_F32, }, #endif #if defined(__APPLE__) @@ -105,6 +158,9 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q4_0, + /* .op_type = */ GGML_TYPE_F32, }, #endif #if defined(__ARM_FEATURE_MATMUL_INT8) @@ -148,6 +204,9 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q4_0, + /* .op_type = */ GGML_TYPE_F32, }, #endif #else @@ -192,6 +251,9 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q4_0, + /* .op_type = */ GGML_TYPE_F32, }, #endif #if defined(__ARM_FEATURE_DOTPROD) @@ -235,12 +297,33 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q4_0, + /* .op_type = */ GGML_TYPE_F32, }, #endif #endif }; -ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature features) { +ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, const ggml_tensor * tensor) { + ggml_kleidiai_kernels * kernel = nullptr; + + if (tensor->op == GGML_OP_MUL_MAT && tensor->src[0] != nullptr && tensor->src[1] != nullptr) { + for (size_t i = 0; i < NELEMS(gemm_gemv_kernels); ++i) { + if ((cpu_features & gemm_gemv_kernels[i].required_cpu) == gemm_gemv_kernels[i].required_cpu && + gemm_gemv_kernels[i].lhs_type == tensor->src[1]->type && + gemm_gemv_kernels[i].rhs_type == tensor->src[0]->type && + gemm_gemv_kernels[i].op_type == tensor->type) { + kernel = &gemm_gemv_kernels[i]; + break; + } + } + } + + return kernel; +} + +ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features) { ggml_kleidiai_kernels * kernels = nullptr; for (size_t i = 0; i < NELEMS(gemm_gemv_kernels); ++i) { diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index 2ffe97eb4..5ac02bda7 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -4,6 +4,9 @@ #pragma once +#include +#include "ggml.h" + enum cpu_feature { CPU_FEATURE_NONE = 0, CPU_FEATURE_DOTPROD = 1, @@ -26,26 +29,53 @@ struct kernel_info { size_t (*get_nr)(void); size_t (*get_kr)(void); size_t (*get_sr)(void); - size_t (*get_lhs_offset)(size_t m_idx, size_t k, size_t bl); - size_t (*get_rhs_packed_offset)(size_t n_idx, size_t k, size_t bl); + std::variant< + std::function, + std::function + > get_lhs_offset; + std::variant< + std::function, + std::function + > get_rhs_packed_offset; size_t (*get_dst_offset)(size_t m_idx, size_t n_idx, size_t stride); size_t (*get_dst_size)(size_t m, size_t n); - void (*run_kernel)(size_t m, size_t n, size_t k, size_t bl, const void* lhs_packed, const void* rhs_packed, - float* dst, size_t dst_stride_row, size_t dst_stride_col, float scalar_min, float scalar_max); + std::variant< + std::function, + std::function + > run_kernel; }; struct lhs_packing_info { size_t (*get_offset)(size_t m_idx, size_t lhs_stride); - size_t (*get_packed_offset)(size_t m_idx, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); - size_t (*packed_size)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); - void (*pack_func)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, size_t m_idx_start, const float* lhs, - size_t lhs_stride, void* lhs_packed); + std::variant< + std::function, + std::function + > get_packed_offset; + std::variant< + std::function, + std::function + > packed_size; + std::variant< + std::function, + std::function + > pack_func; }; struct rhs_packing_info { - size_t (*packed_size)(size_t n, size_t k, size_t nr, size_t kr, size_t bl); - void (*pack_func)(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t bl, const uint8_t* rhs, - const float* bias, void* rhs_packed, size_t extra_bytes, const struct kai_rhs_pack_qs4cxs1s0_param* params); + std::variant< + std::function, + std::function + > packed_size; + std::variant< + std::function, + std::function + > pack_func; }; struct ggml_kleidiai_kernels { @@ -55,6 +85,10 @@ struct ggml_kleidiai_kernels { rhs_packing_info rhs_info; cpu_feature required_cpu; + ggml_type lhs_type; + ggml_type rhs_type; + ggml_type op_type; }; -ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features); +ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, const ggml_tensor * tensor); +ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features); diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index 4e89ca0fa..f3dffdd6b 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -34,8 +34,9 @@ #include "ggml-common.h" struct ggml_kleidiai_context { + cpu_feature features; ggml_kleidiai_kernels * kernels; -} static ctx = { NULL }; +} static ctx = { CPU_FEATURE_NONE, NULL }; static void init_kleidiai_context(void) { @@ -47,18 +48,18 @@ static void init_kleidiai_context(void) { const char *env_var = getenv("GGML_KLEIDIAI_SME"); int sme_enabled = 0; - cpu_feature features = (ggml_cpu_has_dotprod() ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) | - (ggml_cpu_has_matmul_int8() ? CPU_FEATURE_I8MM : CPU_FEATURE_NONE) | - (ggml_cpu_has_sve() ? CPU_FEATURE_SVE : CPU_FEATURE_NONE); + ctx.features = (ggml_cpu_has_dotprod() ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) | + (ggml_cpu_has_matmul_int8() ? CPU_FEATURE_I8MM : CPU_FEATURE_NONE) | + (ggml_cpu_has_sve() ? CPU_FEATURE_SVE : CPU_FEATURE_NONE); if (env_var) { sme_enabled = atoi(env_var); } if (sme_enabled != 0) { - features |= ggml_cpu_has_sme() ? CPU_FEATURE_SME : CPU_FEATURE_NONE; + ctx.features |= ggml_cpu_has_sme() ? CPU_FEATURE_SME : CPU_FEATURE_NONE; } - ctx.kernels = ggml_kleidiai_select_kernels(features); + ctx.kernels = ggml_kleidiai_select_kernels_q4_0(ctx.features); } ggml_critical_section_end(); } @@ -68,95 +69,275 @@ static inline int64_t ggml_ne(const ggml_tensor * tensor, int dim) { return tensor->ne[dim]; } +template +static Ret variant_call(const Variant & var, Args&&... args) { + return std::visit([&](auto&& func) -> Ret { + if constexpr (std::is_invocable_r_v) { + return func(std::forward(args)...); + } else { + throw std::runtime_error("Invalid function type in variant_call"); + } + }, var); +} + namespace ggml::cpu::kleidiai { + +static size_t round_down(size_t x, size_t y) { + return y == 0 ? x : x - (x % y); +} + +static void transpose_f32kxn_f16nxk(size_t n, size_t k, float * dst, const uint16_t * src, size_t rhs_stride) { + size_t src_stride = rhs_stride / sizeof(uint16_t); + size_t dst_stride = n; + + for (size_t k_idx = 0; k_idx < k; ++k_idx) { + for (size_t n_idx = 0; n_idx < n; ++n_idx) { + uint16_t v = *(src + k_idx + n_idx * src_stride); + *(dst + n_idx + k_idx * dst_stride) = kai_cast_f32_f16(v); + } + } +} + class tensor_traits : public ggml::cpu::tensor_traits { bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override { - GGML_ASSERT(ctx.kernels); - kernel_info * kernel = op->src[1]->ne[1] == 1 ? &ctx.kernels->gemv : &ctx.kernels->gemm; + ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op); + GGML_ASSERT(kernels); + kernel_info * kernel = op->src[1]->ne[1] == 1 ? &kernels->gemv : &kernels->gemm; size_t k = op->src[0]->ne[0]; + size_t n = op->src[0]->ne[1]; size_t m = op->src[1]->ne[1]; size_t mr = kernel->get_mr(); size_t kr = kernel->get_kr(); size_t sr = kernel->get_sr(); - size = ctx.kernels->lhs_info.packed_size(m, k, QK4_0, mr, kr, sr); + if (kernels->rhs_type == GGML_TYPE_Q4_0) { + size = variant_call(kernels->lhs_info.packed_size, m, k, QK4_0, mr, kr, sr); + } else if (kernels->rhs_type == GGML_TYPE_F16) { + size = variant_call(kernels->lhs_info.packed_size, m, k, mr, kr, sr) + + variant_call(kernels->rhs_info.packed_size, n, k) + + k * n * sizeof(float) + n * sizeof(float); + } else { + GGML_ASSERT(false); + } return true; } + bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * dst) override { if (dst->op == GGML_OP_MUL_MAT) { - const ggml_tensor * src0 = dst->src[0]; - const ggml_tensor * src1 = dst->src[1]; + if (dst->src[0]->type == GGML_TYPE_Q4_0) { + return compute_forward_q4_0(params, dst); + } else if (dst->src[0]->type == GGML_TYPE_F16) { + return compute_forward_kv_cache(params, dst); + } + } + return false; + } - GGML_TENSOR_BINARY_OP_LOCALS + bool compute_forward_kv_cache(ggml_compute_params * params, struct ggml_tensor * dst) { + static std::atomic_flag first_to_arrive = ATOMIC_FLAG_INIT; - GGML_ASSERT(ctx.kernels); - kernel_info * kernel = src1->ne[1] == 1 ? &ctx.kernels->gemv : &ctx.kernels->gemm; - lhs_packing_info * lhs_info = &ctx.kernels->lhs_info; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; - GGML_ASSERT(kernel); + GGML_TENSOR_BINARY_OP_LOCALS - const int ith = params->ith; - const int nth = params->nth; + ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); + GGML_ASSERT(kernels); - const size_t k = ne00; - const size_t m = ne11; - const size_t n = ne01; + kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm; + GGML_ASSERT(kernel); - const size_t n_step = kernel->get_n_step(); - const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step); - const size_t n_start = ith * num_n_per_thread; + const int nth = params->nth; + const int ith = params->ith; - size_t n_to_process = num_n_per_thread; - if ((n_start + n_to_process) > n) { - n_to_process = n - n_start; + const int64_t lhs_batch_size0 = ne12; + const int64_t rhs_batch_size0 = ne02; + const int64_t batch_size = rhs_batch_size0; + + const int64_t r = lhs_batch_size0 / rhs_batch_size0; + + const int64_t m = ne11 * r; + const int64_t n = ne01; + const int64_t k = ne00; + + const size_t lhs_stride = src1->nb[1]; + const size_t rhs_stride = src0->nb[1]; + const size_t dst_stride = dst->nb[1]; + + const int64_t mr = static_cast(kernel->get_mr()); + const int64_t nr = static_cast(kernel->get_nr()); + const int64_t kr = static_cast(kernel->get_kr()); + const int64_t sr = static_cast(kernel->get_sr()); + + const size_t lhs_packed_size = variant_call(kernels->lhs_info.packed_size, m, k, mr, kr, sr); + const size_t rhs_packed_size = variant_call(kernels->rhs_info.packed_size, n, k); + const size_t kxn_size = k * n * sizeof(float); + const size_t bias_size = n * sizeof(float); + + const size_t wsize_required = lhs_packed_size + rhs_packed_size + kxn_size + bias_size; + GGML_ASSERT(wsize_required <= params->wsize); + + uint8_t * lhs_packed = static_cast(params->wdata); + uint8_t * rhs_packed = lhs_packed + lhs_packed_size; + uint8_t * rhs_kxn = rhs_packed + rhs_packed_size; + uint8_t * bias = rhs_kxn + kxn_size; + + for (int64_t batch_idx = 0; batch_idx < batch_size; ++batch_idx) { + const uint8_t * lhs_batch = static_cast(src1->data) + batch_idx * m * lhs_stride; + const uint8_t * rhs_batch = static_cast(src0->data) + batch_idx * n * rhs_stride; + uint8_t * dst_batch = static_cast(dst->data) + batch_idx * m * dst_stride; + + // LHS packing + { + const int64_t m_roundup_mr = kai_roundup(m, mr); + const int64_t num_threads = KAI_MIN(m_roundup_mr / mr, nth); + + if (ith < num_threads) { + const int64_t num_m_per_thread0 = round_down(m_roundup_mr / num_threads, mr); + const int64_t num_m_per_threadN_1 = m - (num_threads - 1) * num_m_per_thread0; + + const int64_t m_start = ith * num_m_per_thread0; + const int64_t num_m_per_thread = (ith == num_threads - 1) ? num_m_per_threadN_1 : num_m_per_thread0; + + const size_t lhs_offset = variant_call(kernels->gemm.get_lhs_offset, m_start, lhs_stride); + const size_t lhs_packed_offset = variant_call(kernels->lhs_info.get_packed_offset, m_start, k, mr, kr, sr); + + const void * src_ptr = static_cast(lhs_batch) + lhs_offset; + void * dst_ptr = static_cast(lhs_packed) + lhs_packed_offset; + + variant_call(kernels->lhs_info.pack_func, num_m_per_thread, k, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr); + } } - const uint8_t * lhs = static_cast(src1->data); - uint8_t * lhs_packed = (uint8_t*)params->wdata; - const uint8_t * rhs_packed = static_cast(src0->data); + // RHS packing + if (first_to_arrive.test_and_set(std::memory_order_acquire) == false) { + // First thread to reach this point handles RHS packing + memset(bias, 0, n * sizeof(float)); + transpose_f32kxn_f16nxk(n, k, reinterpret_cast(rhs_kxn), + reinterpret_cast(rhs_batch), rhs_stride); - size_t mr = kernel->get_mr(); - size_t kr = kernel->get_kr(); - size_t sr = kernel->get_sr(); - - // Calculate number of columns to be processed per thread - const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth; - const size_t m_start = ith * num_m_per_thread; - size_t m_to_process = num_m_per_thread; - if ((m_start + m_to_process) > m) { - m_to_process = m - m_start; - } - - if(m_start < m) { - // Transform LHS - const size_t src_stride = src1->nb[1]; - const float * src_ptr = reinterpret_cast(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1])); - const size_t lhs_packed_offset = lhs_info->get_packed_offset(m_start, k, QK4_0, mr, kr, sr); - void * lhs_packed_ptr = static_cast(lhs_packed + lhs_packed_offset); - - lhs_info->pack_func(m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); + variant_call(kernels->rhs_info.pack_func, 1, n, k, nr, kr, sr, n * sizeof(float), + rhs_kxn, bias, nullptr, rhs_packed, 0, nullptr); } ggml_barrier(params->threadpool); - // Perform the operation - const size_t dst_stride = dst->nb[1]; - const size_t lhs_packed_offset = lhs_info->get_packed_offset(0, k, QK4_0, mr, kr, sr); - const size_t rhs_packed_offset = kernel->get_rhs_packed_offset(n_start, k, QK4_0); - const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride); - const void * rhs_ptr = static_cast(rhs_packed + rhs_packed_offset); - const void* lhs_ptr = (const void*)((const char *)lhs_packed + lhs_packed_offset); - float *dst_ptr = reinterpret_cast(static_cast(dst->data) + dst_offset); + first_to_arrive.clear(std::memory_order_release); - kernel->run_kernel(m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, - dst_stride, sizeof(float), -FLT_MAX, FLT_MAX); - return true; + // Perform the matmul + { + const int64_t m_to_process = m; + const int64_t m_start = 0; + + const int64_t n_step = static_cast(kernel->get_n_step()); + const int64_t num_threads = KAI_MIN(n / n_step, nth); + + if (ith < num_threads) { + const int64_t num_n_per_thread0 = round_down(n / num_threads, n_step); + const int64_t num_n_per_threadN_1 = n - (num_threads - 1) * num_n_per_thread0; + + const int64_t n_start = ith * num_n_per_thread0; + const int64_t n_to_process = (ith == num_threads - 1) ? num_n_per_threadN_1 : num_n_per_thread0; + + const size_t lhs_packed_offset = variant_call(kernel->get_lhs_offset, m_start, k); + const size_t rhs_packed_offset = variant_call(kernel->get_rhs_packed_offset, n_start, k); + const size_t dst_offset = kernel->get_dst_offset(m_start, n_start, dst_stride); + + const void * lhs_ptr = lhs_packed + lhs_packed_offset; + const void * rhs_ptr = rhs_packed + rhs_packed_offset; + float * dst_ptr = reinterpret_cast(dst_batch + dst_offset); + + variant_call(kernel->run_kernel, m_to_process, n_to_process, k, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, sizeof(float), -FLT_MAX, FLT_MAX); + } + } + + if (batch_idx != batch_size - 1) { + // This barrier is necessary when the batch size is larger than 1. While processing a batch, + // the work data buffer (params->wdata) is used as temporary storage which means that only + // a single batch can be processed at any given time. No barrier is needed for the last + // batch since GGML inserts a barrier between the execution of every operator. + ggml_barrier(params->threadpool); + } } - return false; + + return true; + } + + bool compute_forward_q4_0(struct ggml_compute_params * params, struct ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); + GGML_ASSERT(kernels); + + kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm; + lhs_packing_info * lhs_info = &kernels->lhs_info; + + GGML_ASSERT(kernel); + + const int ith = params->ith; + const int nth = params->nth; + + const size_t k = ne00; + const size_t m = ne11; + const size_t n = ne01; + + size_t mr = kernel->get_mr(); + size_t kr = kernel->get_kr(); + size_t sr = kernel->get_sr(); + + const uint8_t * lhs = static_cast(src1->data); + uint8_t * lhs_packed = (uint8_t*)params->wdata; + const uint8_t * rhs_packed = static_cast(src0->data); + + const size_t n_step = kernel->get_n_step(); + const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step); + const size_t n_start = ith * num_n_per_thread; + + size_t n_to_process = num_n_per_thread; + if ((n_start + n_to_process) > n) { + n_to_process = n - n_start; + } + + // Calculate number of columns to be processed per thread + const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth; + const size_t m_start = ith * num_m_per_thread; + size_t m_to_process = num_m_per_thread; + if ((m_start + m_to_process) > m) { + m_to_process = m - m_start; + } + + if (m_start < m) { + // Transform LHS + const size_t src_stride = src1->nb[1]; + const float * src_ptr = reinterpret_cast(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1])); + const size_t lhs_packed_offset = variant_call(lhs_info->get_packed_offset, m_start, k, QK4_0, mr, kr, sr); + void * lhs_packed_ptr = static_cast(lhs_packed + lhs_packed_offset); + + variant_call(lhs_info->pack_func, m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); + } + + ggml_barrier(params->threadpool); + + // Perform the operation + const size_t dst_stride = dst->nb[1]; + const size_t lhs_packed_offset = variant_call(lhs_info->get_packed_offset, 0, k, QK4_0, mr, kr, sr); + const size_t rhs_packed_offset = variant_call(kernel->get_rhs_packed_offset, n_start, k, QK4_0); + const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride); + const void * rhs_ptr = static_cast(rhs_packed + rhs_packed_offset); + const void* lhs_ptr = (const void*)((const char *)lhs_packed + lhs_packed_offset); + float *dst_ptr = reinterpret_cast(static_cast(dst->data) + dst_offset); + + variant_call(kernel->run_kernel, m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, + sizeof(float), -FLT_MAX, FLT_MAX); + + return true; } public: @@ -169,13 +350,13 @@ public: size_t sr = ctx.kernels->gemm.get_sr(); #ifndef NDEBUG - const size_t repacked_size = ctx.kernels->rhs_info.packed_size(n, k, nr, kr, QK4_0); + const size_t repacked_size = variant_call(ctx.kernels->rhs_info.packed_size, n, k, nr, kr, QK4_0); GGML_ASSERT(repacked_size <= data_size && "repacked size larger than the packed size!"); #endif struct kai_rhs_pack_qs4cxs1s0_param params; params.lhs_zero_point = 1; params.rhs_zero_point = 8; - ctx.kernels->rhs_info.pack_func(1, n, k, nr, kr, sr, QK4_0, (const uint8_t *)data, NULL, tensor->data, 0, ¶ms); + variant_call(ctx.kernels->rhs_info.pack_func, 1, n, k, nr, kr, sr, QK4_0, (const uint8_t*)data, nullptr, tensor->data, 0, ¶ms); return 0; @@ -189,7 +370,7 @@ static ggml::cpu::tensor_traits * get_tensor_traits(ggml_backend_buffer_t, struc } } // namespace ggml::cpu::kleidiai -GGML_API enum ggml_status ggml_backend_cpu_kleidiai_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +static enum ggml_status ggml_backend_cpu_kleidiai_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { tensor->extra = (void *) ggml::cpu::kleidiai::get_tensor_traits(buffer, tensor); GGML_UNUSED(buffer); @@ -238,12 +419,11 @@ static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alignment(ggml_backend_b namespace ggml::cpu::kleidiai { class extra_buffer_type : ggml::cpu::extra_buffer_type { bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override { - if ( op->op == GGML_OP_MUL_MAT && - op->src[0]->type == GGML_TYPE_Q4_0 && - op->src[0]->buffer && - (ggml_n_dims(op->src[0]) == 2) && - op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && ctx.kernels - ) { + if (op->op == GGML_OP_MUL_MAT && + op->src[0]->type == GGML_TYPE_Q4_0 && + op->src[0]->buffer && + (ggml_n_dims(op->src[0]) == 2) && + op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && ctx.kernels) { if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) { return false; } @@ -260,6 +440,19 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) { return (ggml::cpu::tensor_traits *) op->src[0]->extra; } + else if (ggml_kleidiai_select_kernels(ctx.features, op) && + op->src[0]->op == GGML_OP_VIEW && + (op->src[1]->op == GGML_OP_PERMUTE || op->src[1]->op == GGML_OP_SOFT_MAX) && + op->src[1]->ne[1] > 1) { + if ((op->src[0]->nb[0] != 2) || + (op->src[1]->nb[0] != 4) || + (op->src[0]->nb[1] * op->src[0]->ne[1] != op->src[0]->nb[2]) || + (op->src[1]->nb[1] * op->src[1]->ne[1] != op->src[1]->nb[2])) { + return nullptr; + } + + return ggml::cpu::kleidiai::get_tensor_traits(NULL, NULL); + } } return nullptr; } From 22cdab343b63edd0906f1c132616746085f81983 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Mon, 12 May 2025 13:08:22 +0200 Subject: [PATCH 023/421] llama-bench : accept ranges for integer parameters (#13410) --- tools/llama-bench/README.md | 28 +- tools/llama-bench/llama-bench.cpp | 721 ++++++++++++++++-------------- 2 files changed, 404 insertions(+), 345 deletions(-) diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md index d6fc77df8..4fb2a24e1 100644 --- a/tools/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -20,10 +20,20 @@ Performance testing tool for llama.cpp. ## Syntax ``` -usage: ./llama-bench [options] +usage: llama-bench [options] options: -h, --help + --numa numa mode (default: disabled) + -r, --repetitions number of times to repeat each test (default: 5) + --prio <0|1|2|3> process/thread priority (default: 0) + --delay <0...N> (seconds) delay between each test (default: 0) + -o, --output output format printed to stdout (default: md) + -oe, --output-err output format printed to stderr (default: none) + -v, --verbose verbose output + --progress print test progress indicators + +test parameters: -m, --model (default: models/7B/ggml-model-q4_0.gguf) -p, --n-prompt (default: 512) -n, --n-gen (default: 128) @@ -33,7 +43,7 @@ options: -ub, --ubatch-size (default: 512) -ctk, --cache-type-k (default: f16) -ctv, --cache-type-v (default: f16) - -t, --threads (default: 8) + -t, --threads (default: 16) -C, --cpu-mask (default: 0x0) --cpu-strict <0|1> (default: 0) --poll <0...100> (default: 50) @@ -44,17 +54,15 @@ options: -nkvo, --no-kv-offload <0|1> (default: 0) -fa, --flash-attn <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - --numa (default: disabled) -embd, --embeddings <0|1> (default: 0) -ts, --tensor-split (default: 0) - -r, --repetitions (default: 5) - --prio <0|1|2|3> (default: 0) - --delay <0...N> (seconds) (default: 0) - -o, --output (default: md) - -oe, --output-err (default: none) - -v, --verbose (default: 0) + -ot --override-tensors =;... + (default: disabled) + -nopo, --no-op-offload <0|1> (default: 0) -Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. +Multiple values can be given for each parameter by separating them with ',' +or by specifying the parameter multiple times. Ranges can be given as +'start-end' or 'start-end+step' or 'start-end*mult'. ``` llama-bench can perform three types of tests: diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 5d26b506b..ca0d0aed5 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -195,6 +195,40 @@ static std::string pair_str(const std::pair & p) { return buf; } +static std::vector parse_int_range(const std::string & s) { + // first[-last[(+|*)step]] + std::regex range_regex(R"(^(\d+)(?:-(\d+)(?:([\+|\*])(\d+))?)?(?:,|$))"); + + std::smatch match; + std::string::const_iterator search_start(s.cbegin()); + std::vector result; + while (std::regex_search(search_start, s.cend(), match, range_regex)) { + int first = std::stoi(match[1]); + int last = match[2].matched ? std::stoi(match[2]) : first; + char op = match[3].matched ? match[3].str()[0] : '+'; + int step = match[4].matched ? std::stoi(match[4]) : 1; + + for (int i = first; i <= last;) { + result.push_back(i); + + if (op == '+') { + i += step; + } else if (op == '*') { + i *= step; + } else { + throw std::invalid_argument("invalid range format"); + } + } + search_start = match.suffix().first; + } + + if (search_start != s.cend()) { + throw std::invalid_argument("invalid range format"); + } + + return result; +} + struct cmd_params { std::vector model; std::vector n_prompt; @@ -251,7 +285,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ { false }, /* flash_attn */ { false }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, - /* tensor_buft_overrides*/ { std::vector{{nullptr,nullptr}} }, + /* tensor_buft_overrides*/ { std::vector{ { nullptr, nullptr } } }, /* use_mmap */ { true }, /* embeddings */ { false }, /* no_op_offload */ { false }, @@ -270,13 +304,29 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); printf("options:\n"); printf(" -h, --help\n"); + printf(" --numa numa mode (default: disabled)\n"); + printf(" -r, --repetitions number of times to repeat each test (default: %d)\n", + cmd_params_defaults.reps); + printf(" --prio <0|1|2|3> process/thread priority (default: %d)\n", + cmd_params_defaults.prio); + printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n", + cmd_params_defaults.delay); + printf(" -o, --output output format printed to stdout (default: %s)\n", + output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err output format printed to stderr (default: %s)\n", + output_format_str(cmd_params_defaults.output_format_stderr)); + printf(" -v, --verbose verbose output\n"); + printf(" --progress print test progress indicators\n"); + printf("\n"); + printf("test parameters:\n"); printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", + join(cmd_params_defaults.n_depth, ",").c_str()); printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); printf(" -ub, --ubatch-size (default: %s)\n", @@ -308,25 +358,17 @@ static void print_usage(int /* argc */, char ** argv) { join(cmd_params_defaults.flash_attn, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" --numa (default: disabled)\n"); printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); - printf(" -ot --override-tensors =;... (default: disabled)\n"); - printf(" -nopo, --no-op-offload (default: 0)\n"); - printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio); - printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay); - printf(" -o, --output (default: %s)\n", - output_format_str(cmd_params_defaults.output_format)); - printf(" -oe, --output-err (default: %s)\n", - output_format_str(cmd_params_defaults.output_format_stderr)); - printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); - printf(" --progress (default: %s)\n", cmd_params_defaults.progress ? "1" : "0"); + printf(" -ot --override-tensors =;...\n"); + printf(" (default: disabled)\n"); + printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); printf("\n"); printf( - "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter " - "multiple times.\n"); + "Multiple values can be given for each parameter by separating them with ','\n" + "or by specifying the parameter multiple times. Ranges can be given as\n" + "'start-end' or 'start-end+step' or 'start-end*mult'.\n"); } static ggml_type ggml_type_from_name(const std::string & s) { @@ -380,186 +422,190 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { std::replace(arg.begin(), arg.end(), '_', '-'); } - if (arg == "-h" || arg == "--help") { - print_usage(argc, argv); - exit(0); - } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.model.insert(params.model.end(), p.begin(), p.end()); - } else if (arg == "-p" || arg == "--n-prompt") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end()); - } else if (arg == "-n" || arg == "--n-gen") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); - } else if (arg == "-pg") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], ','); - if (p.size() != 2) { - invalid_param = true; - break; - } - params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) }); - } else if (arg == "-d" || arg == "--n-depth") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_depth.insert(params.n_depth.end(), p.begin(), p.end()); - } else if (arg == "-b" || arg == "--batch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); - } else if (arg == "-ub" || arg == "--ubatch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); - } else if (arg == "-ctk" || arg == "--cache-type-k") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - std::vector types; - for (const auto & t : p) { - ggml_type gt = ggml_type_from_name(t); - if (gt == GGML_TYPE_COUNT) { + try { + if (arg == "-h" || arg == "--help") { + print_usage(argc, argv); + exit(0); + } else if (arg == "-m" || arg == "--model") { + if (++i >= argc) { invalid_param = true; break; } - types.push_back(gt); - } - if (invalid_param) { - break; - } - params.type_k.insert(params.type_k.end(), types.begin(), types.end()); - } else if (arg == "-ctv" || arg == "--cache-type-v") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - std::vector types; - for (const auto & t : p) { - ggml_type gt = ggml_type_from_name(t); - if (gt == GGML_TYPE_COUNT) { + auto p = string_split(argv[i], split_delim); + params.model.insert(params.model.end(), p.begin(), p.end()); + } else if (arg == "-p" || arg == "--n-prompt") { + if (++i >= argc) { invalid_param = true; break; } - types.push_back(gt); - } - if (invalid_param) { - break; - } - params.type_v.insert(params.type_v.end(), types.begin(), types.end()); - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_threads.insert(params.n_threads.end(), p.begin(), p.end()); - } else if (arg == "-C" || arg == "--cpu-mask") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.cpu_mask.insert(params.cpu_mask.end(), p.begin(), p.end()); - } else if (arg == "--cpu-strict") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.cpu_strict.insert(params.cpu_strict.end(), p.begin(), p.end()); - } else if (arg == "--poll") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.poll.insert(params.poll.end(), p.begin(), p.end()); - } else if (arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); - } else if (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rpc_servers.push_back(argv[i]); - } else if (arg == "-sm" || arg == "--split-mode") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - std::vector modes; - for (const auto & m : p) { - llama_split_mode mode; - if (m == "none") { - mode = LLAMA_SPLIT_MODE_NONE; - } else if (m == "layer") { - mode = LLAMA_SPLIT_MODE_LAYER; - } else if (m == "row") { - mode = LLAMA_SPLIT_MODE_ROW; - } else { + auto p = parse_int_range(argv[i]); + params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end()); + } else if (arg == "-n" || arg == "--n-gen") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); + } else if (arg == "-pg") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], ','); + if (p.size() != 2) { + invalid_param = true; + break; + } + params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) }); + } else if (arg == "-d" || arg == "--n-depth") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_depth.insert(params.n_depth.end(), p.begin(), p.end()); + } else if (arg == "-b" || arg == "--batch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); + } else if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); + } else if (arg == "-ctk" || arg == "--cache-type-k") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + + std::vector types; + for (const auto & t : p) { + ggml_type gt = ggml_type_from_name(t); + if (gt == GGML_TYPE_COUNT) { + invalid_param = true; + break; + } + types.push_back(gt); + } + if (invalid_param) { + break; + } + params.type_k.insert(params.type_k.end(), types.begin(), types.end()); + } else if (arg == "-ctv" || arg == "--cache-type-v") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + + std::vector types; + for (const auto & t : p) { + ggml_type gt = ggml_type_from_name(t); + if (gt == GGML_TYPE_COUNT) { + invalid_param = true; + break; + } + types.push_back(gt); + } + if (invalid_param) { + break; + } + params.type_v.insert(params.type_v.end(), types.begin(), types.end()); + } else if (arg == "-t" || arg == "--threads") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_threads.insert(params.n_threads.end(), p.begin(), p.end()); + } else if (arg == "-C" || arg == "--cpu-mask") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.cpu_mask.insert(params.cpu_mask.end(), p.begin(), p.end()); + } else if (arg == "--cpu-strict") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.cpu_strict.insert(params.cpu_strict.end(), p.begin(), p.end()); + } else if (arg == "--poll") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.poll.insert(params.poll.end(), p.begin(), p.end()); + } else if (arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = parse_int_range(argv[i]); + params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers.push_back(argv[i]); + } else if (arg == "-sm" || arg == "--split-mode") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + + std::vector modes; + for (const auto & m : p) { + llama_split_mode mode; + if (m == "none") { + mode = LLAMA_SPLIT_MODE_NONE; + } else if (m == "layer") { + mode = LLAMA_SPLIT_MODE_LAYER; + } else if (m == "row") { + mode = LLAMA_SPLIT_MODE_ROW; + } else { + invalid_param = true; + break; + } + modes.push_back(mode); + } + if (invalid_param) { + break; + } + params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); + } else if (arg == "-mg" || arg == "--main-gpu") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.main_gpu = parse_int_range(argv[i]); + } else if (arg == "-nkvo" || arg == "--no-kv-offload") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); + } else if (arg == "--numa") { + if (++i >= argc) { invalid_param = true; break; } - modes.push_back(mode); - } - if (invalid_param) { - break; - } - params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); - } else if (arg == "-mg" || arg == "--main-gpu") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.main_gpu = string_split(argv[i], split_delim); - } else if (arg == "-nkvo" || arg == "--no-kv-offload") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); - } else if (arg == "--numa") { - if (++i >= argc) { - invalid_param = true; - break; - } else { std::string value(argv[i]); - /**/ if (value == "distribute" || value == "") { + if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; @@ -569,177 +615,182 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - } - } else if (arg == "-fa" || arg == "--flash-attn") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end()); - } else if (arg == "-mmp" || arg == "--mmap") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); - } else if (arg == "-embd" || arg == "--embeddings") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); - } else if (arg == "-nopo" || arg == "--no-op-offload") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = string_split(argv[i], split_delim); - params.no_op_offload.insert(params.no_op_offload.end(), p.begin(), p.end()); - } else if (arg == "-ts" || arg == "--tensor-split") { - if (++i >= argc) { - invalid_param = true; - break; - } - for (auto ts : string_split(argv[i], split_delim)) { - // split string by ; and / - const std::regex regex{ R"([;/]+)" }; - std::sregex_token_iterator it{ ts.begin(), ts.end(), regex, -1 }; - std::vector split_arg{ it, {} }; - GGML_ASSERT(split_arg.size() <= llama_max_devices()); + } else if (arg == "-fa" || arg == "--flash-attn") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end()); + } else if (arg == "-mmp" || arg == "--mmap") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + } else if (arg == "-embd" || arg == "--embeddings") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); + } else if (arg == "-nopo" || arg == "--no-op-offload") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.no_op_offload.insert(params.no_op_offload.end(), p.begin(), p.end()); + } else if (arg == "-ts" || arg == "--tensor-split") { + if (++i >= argc) { + invalid_param = true; + break; + } + for (auto ts : string_split(argv[i], split_delim)) { + // split string by ; and / + const std::regex regex{ R"([;/]+)" }; + std::sregex_token_iterator it{ ts.begin(), ts.end(), regex, -1 }; + std::vector split_arg{ it, {} }; + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - std::vector tensor_split(llama_max_devices()); - for (size_t i = 0; i < llama_max_devices(); ++i) { - if (i < split_arg.size()) { - tensor_split[i] = std::stof(split_arg[i]); - } else { - tensor_split[i] = 0.0f; + std::vector tensor_split(llama_max_devices()); + for (size_t i = 0; i < llama_max_devices(); ++i) { + if (i < split_arg.size()) { + tensor_split[i] = std::stof(split_arg[i]); + } else { + tensor_split[i] = 0.0f; + } + } + params.tensor_split.push_back(tensor_split); + } + } else if (arg == "-ot" || arg == "--override-tensor") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto value = argv[i]; + /* static */ std::map buft_list; + if (buft_list.empty()) { + // enumerate all the devices and add their buffer types to the list + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + auto * buft = ggml_backend_dev_buffer_type(dev); + if (buft) { + buft_list[ggml_backend_buft_name(buft)] = buft; + } } } - params.tensor_split.push_back(tensor_split); - } - } else if (arg == "-ot" || arg == "--override-tensor") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto value = argv[i]; - /* static */ std::map buft_list; - if (buft_list.empty()) { - // enumerate all the devices and add their buffer types to the list - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - auto * dev = ggml_backend_dev_get(i); - auto * buft = ggml_backend_dev_buffer_type(dev); - if (buft) { - buft_list[ggml_backend_buft_name(buft)] = buft; + auto override_group_span_len = std::strcspn(value, ","); + bool last_group = false; + do { + if (override_group_span_len == 0) { + // Adds an empty override-tensors for an empty span + params.tensor_buft_overrides.push_back({{}}); + if (value[override_group_span_len] == '\0') { + value = &value[override_group_span_len]; + last_group = true; + } else { + value = &value[override_group_span_len + 1]; + override_group_span_len = std::strcspn(value, ","); + } + continue; } - } - } - auto override_group_span_len = std::strcspn(value, ","); - bool last_group = false; - do { - if (override_group_span_len == 0) { - // Adds an empty override-tensors for an empty span - params.tensor_buft_overrides.push_back({{}}); + // Stamps null terminators into the argv + // value for this option to avoid the + // memory leak present in the implementation + // over in arg.cpp. Acceptable because we + // only parse these args once in this program. + auto override_group = value; if (value[override_group_span_len] == '\0') { value = &value[override_group_span_len]; last_group = true; } else { + value[override_group_span_len] = '\0'; value = &value[override_group_span_len + 1]; - override_group_span_len = std::strcspn(value, ","); } - continue; - } - // Stamps null terminators into the argv - // value for this option to avoid the - // memory leak present in the implementation - // over in arg.cpp. Acceptable because we - // only parse these args once in this program. - auto override_group = value; - if (value[override_group_span_len] == '\0') { - value = &value[override_group_span_len]; - last_group = true; - } else { - value[override_group_span_len] = '\0'; - value = &value[override_group_span_len + 1]; - } - std::vector group_tensor_buft_overrides{}; - auto override_span_len = std::strcspn(override_group, ";"); - while (override_span_len > 0) { - auto override = override_group; - if (override_group[override_span_len] != '\0') { - override_group[override_span_len] = '\0'; - override_group = &override_group[override_span_len + 1]; - } else { - override_group = &override_group[override_span_len]; - } - auto tensor_name_span_len = std::strcspn(override, "="); - if (tensor_name_span_len >= override_span_len) { - invalid_param = true; - break; - } - override[tensor_name_span_len] = '\0'; - auto tensor_name = override; - auto buffer_type = &override[tensor_name_span_len + 1]; - if (buft_list.find(buffer_type) == buft_list.end()) { - printf("Available buffer types:\n"); - for (const auto & it : buft_list) { - printf(" %s\n", ggml_backend_buft_name(it.second)); + std::vector group_tensor_buft_overrides{}; + auto override_span_len = std::strcspn(override_group, ";"); + while (override_span_len > 0) { + auto override = override_group; + if (override_group[override_span_len] != '\0') { + override_group[override_span_len] = '\0'; + override_group = &override_group[override_span_len + 1]; + } else { + override_group = &override_group[override_span_len]; } - invalid_param = true; + auto tensor_name_span_len = std::strcspn(override, "="); + if (tensor_name_span_len >= override_span_len) { + invalid_param = true; + break; + } + override[tensor_name_span_len] = '\0'; + auto tensor_name = override; + auto buffer_type = &override[tensor_name_span_len + 1]; + if (buft_list.find(buffer_type) == buft_list.end()) { + printf("Available buffer types:\n"); + for (const auto & it : buft_list) { + printf(" %s\n", ggml_backend_buft_name(it.second)); + } + invalid_param = true; + break; + } + group_tensor_buft_overrides.push_back({tensor_name, buft_list.at(buffer_type)}); + override_span_len = std::strcspn(override_group, ";"); + } + if (invalid_param) { break; } - group_tensor_buft_overrides.push_back({tensor_name, buft_list.at(buffer_type)}); - override_span_len = std::strcspn(override_group, ";"); - } - if (invalid_param) { + group_tensor_buft_overrides.push_back({nullptr,nullptr}); + params.tensor_buft_overrides.push_back(group_tensor_buft_overrides); + override_group_span_len = std::strcspn(value, ","); + } while (!last_group); + } else if (arg == "-r" || arg == "--repetitions") { + if (++i >= argc) { + invalid_param = true; break; } - group_tensor_buft_overrides.push_back({nullptr,nullptr}); - params.tensor_buft_overrides.push_back(group_tensor_buft_overrides); - override_group_span_len = std::strcspn(value, ","); - } while (!last_group); - } else if (arg == "-r" || arg == "--repetitions") { - if (++i >= argc) { + params.reps = std::stoi(argv[i]); + } else if (arg == "--prio") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.prio = (enum ggml_sched_priority) std::stoi(argv[i]); + } else if (arg == "--delay") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.delay = std::stoi(argv[i]); + } else if (arg == "-o" || arg == "--output") { + if (++i >= argc) { + invalid_param = true; + break; + } + invalid_param = !output_format_from_str(argv[i], params.output_format); + } else if (arg == "-oe" || arg == "--output-err") { + if (++i >= argc) { + invalid_param = true; + break; + } + invalid_param = !output_format_from_str(argv[i], params.output_format_stderr); + } else if (arg == "-v" || arg == "--verbose") { + params.verbose = true; + } else if (arg == "--progress") { + params.progress = true; + } else { invalid_param = true; break; } - params.reps = std::stoi(argv[i]); - } else if (arg == "--prio") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.prio = (enum ggml_sched_priority) std::stoi(argv[i]); - } else if (arg == "--delay") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.delay = std::stoi(argv[i]); - } else if (arg == "-o" || arg == "--output") { - if (++i >= argc) { - invalid_param = true; - break; - } - invalid_param = !output_format_from_str(argv[i], params.output_format); - } else if (arg == "-oe" || arg == "--output-err") { - if (++i >= argc) { - invalid_param = true; - break; - } - invalid_param = !output_format_from_str(argv[i], params.output_format_stderr); - } else if (arg == "-v" || arg == "--verbose") { - params.verbose = true; - } else if (arg == "--progress") { - params.progress = true; - } else { + } catch (const std::exception & e) { + fprintf(stderr, "error: %s\n", e.what()); invalid_param = true; break; } } + if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); print_usage(argc, argv); From 91159ee9df84edb72ccf874e353d2414f3a8c60d Mon Sep 17 00:00:00 2001 From: Anudit Nagar Date: Mon, 12 May 2025 18:56:42 +0700 Subject: [PATCH 024/421] server : allow content to be null in oaicompat_completion_params_parse (#13477) --- tools/server/utils.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp index 23163f4fe..b8d140e3f 100644 --- a/tools/server/utils.hpp +++ b/tools/server/utils.hpp @@ -644,7 +644,7 @@ static json oaicompat_completion_params_parse( } for (auto & msg : messages) { json & content = msg.at("content"); - if (content.is_string()) { + if (content.is_string() || content.is_null()) { continue; } From 064cc596ac44308dc326a17c9e3163c34a6f29d1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 May 2025 15:12:27 +0300 Subject: [PATCH 025/421] context : fix state io for memory-less contexts (#13470) ggml-ci --- src/llama-context.cpp | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index a12849f0e..0cb6ebc9f 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1788,10 +1788,13 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { } } - LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__); - llama_kv_cache * kv_self = static_cast(memory.get()); + if (memory) { + LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__); - kv_self->state_read(io); + llama_kv_cache * kv_self = static_cast(memory.get()); + + kv_self->state_read(io); + } return io.n_bytes(); } @@ -1799,9 +1802,11 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); - llama_kv_cache * kv_self = static_cast(memory.get()); + if (memory) { + llama_kv_cache * kv_self = static_cast(memory.get()); - kv_self->state_write(io, seq_id); + kv_self->state_write(io, seq_id); + } return io.n_bytes(); } @@ -1809,9 +1814,11 @@ size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id s size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); - llama_kv_cache * kv_self = static_cast(memory.get()); + if (memory) { + llama_kv_cache * kv_self = static_cast(memory.get()); - kv_self->state_read(io, seq_id); + kv_self->state_read(io, seq_id); + } return io.n_bytes(); } From 10d2af0eaa0aafd7c6577b279dfa5221ff44a63f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 12 May 2025 14:44:49 +0200 Subject: [PATCH 026/421] llama/ggml: add LLM training support (#10544) * llama/ggml: add LLM training support more compact progress bar llama_save_model_to_file llama_opt_param_filter ggml_graph_dup force_grads refactor ggml_opt, fix test-opt * remove logits_all * refactor CUDA implementation for ACC * reset graph at beginning of opt period --- build-xcframework.sh | 1 + common/common.cpp | 17 + common/common.h | 6 + examples/CMakeLists.txt | 1 + examples/training/CMakeLists.txt | 5 + examples/training/README.md | 17 + examples/training/finetune.cpp | 96 ++++++ ggml/include/ggml-opt.h | 75 ++-- ggml/include/ggml.h | 13 +- ggml/src/ggml-backend.cpp | 2 +- ggml/src/ggml-cuda/acc.cu | 66 ++-- ggml/src/ggml-cuda/sum.cu | 2 +- ggml/src/ggml-opt.cpp | 570 ++++++++++++++++++++----------- ggml/src/ggml.c | 41 ++- include/llama.h | 36 ++ src/CMakeLists.txt | 1 + src/llama-context.cpp | 244 ++++++++++++- src/llama-context.h | 30 ++ src/llama-graph.cpp | 1 + src/llama-graph.h | 3 + src/llama-model-loader.cpp | 20 +- src/llama-model-saver.cpp | 281 +++++++++++++++ src/llama-model-saver.h | 37 ++ src/llama-model.cpp | 8 +- src/llama-model.h | 2 + src/llama-quant.cpp | 4 +- src/llama-vocab.cpp | 35 +- src/llama-vocab.h | 6 + src/llama.cpp | 9 + tests/test-backend-ops.cpp | 93 ++--- tests/test-opt.cpp | 52 +-- 31 files changed, 1415 insertions(+), 359 deletions(-) create mode 100644 examples/training/CMakeLists.txt create mode 100644 examples/training/README.md create mode 100644 examples/training/finetune.cpp create mode 100644 src/llama-model-saver.cpp create mode 100644 src/llama-model-saver.h diff --git a/build-xcframework.sh b/build-xcframework.sh index 3c2498b03..a08419a80 100755 --- a/build-xcframework.sh +++ b/build-xcframework.sh @@ -117,6 +117,7 @@ setup_framework_structure() { # Copy all required headers (common for all platforms) cp include/llama.h ${header_path} cp ggml/include/ggml.h ${header_path} + cp ggml/include/ggml-opt.h ${header_path} cp ggml/include/ggml-alloc.h ${header_path} cp ggml/include/ggml-backend.h ${header_path} cp ggml/include/ggml-metal.h ${header_path} diff --git a/common/common.cpp b/common/common.cpp index 710bf1fe2..2b1d8da59 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1565,3 +1565,20 @@ common_control_vector_data common_control_vector_load(const std::vector & tokens, int64_t stride) { + const int64_t ne_datapoint = llama_n_ctx(ctx); + const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride; + ggml_opt_dataset_t result = ggml_opt_dataset_init( + GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1); + + llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data; + llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data; + + for (int64_t idata = 0; idata < ndata; ++idata) { + memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token)); + memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token)); + } + + return result; +} diff --git a/common/common.h b/common/common.h index e15356b12..dea34267c 100644 --- a/common/common.h +++ b/common/common.h @@ -666,3 +666,9 @@ const char * const LLM_KV_SPLIT_COUNT = "split.count"; const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; } + +// +// training utils +// + +ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector & tokens, int64_t stride); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 4ca9230c5..49e4d2cf8 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -32,6 +32,7 @@ else() add_subdirectory(speculative) add_subdirectory(speculative-simple) add_subdirectory(gen-docs) + add_subdirectory(training) if (NOT GGML_BACKEND_DL) add_subdirectory(convert-llama2c-to-ggml) # these examples use the backends directly and cannot be built with dynamic loading diff --git a/examples/training/CMakeLists.txt b/examples/training/CMakeLists.txt new file mode 100644 index 000000000..64afe6ddc --- /dev/null +++ b/examples/training/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-finetune) +add_executable(${TARGET} finetune.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/training/README.md b/examples/training/README.md new file mode 100644 index 000000000..ecdf398f8 --- /dev/null +++ b/examples/training/README.md @@ -0,0 +1,17 @@ +# llama.cpp/examples/training + +This directory contains examples related to language model training using llama.cpp/GGML. +So far finetuning is technically functional (for FP32 models and limited hardware setups) but the code is very much WIP. +Finetuning of Stories 260K and LLaMA 3.2 1b seems to work with 24 GB of memory. +**For CPU training, compile llama.cpp without any additional backends such as CUDA.** +**For CUDA training, use the maximum number of GPU layers.** + +Proof of concept: + +``` sh +export model_name=llama_3.2-1b && export quantization=f32 +./build/bin/finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512 +./build/bin/perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf +``` + +The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs. diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp new file mode 100644 index 000000000..23bede49b --- /dev/null +++ b/examples/training/finetune.cpp @@ -0,0 +1,96 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +int main(int argc, char ** argv) { + common_params params; + + params.escape = false; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) { + return 1; + } + + if (params.use_mmap) { + LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__); + params.use_mmap = false; + } + if (params.cache_type_k != GGML_TYPE_F32) { + LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__); + params.cache_type_k = GGML_TYPE_F32; + } + if (params.cache_type_v != GGML_TYPE_F32) { + LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__); + params.cache_type_v = GGML_TYPE_F32; + } + + common_init(); + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model and apply lora adapter, if any + common_init_result llama_init = common_init_from_params(params); + llama_model_ptr & model = llama_init.model; + llama_context_ptr & ctx = llama_init.context; + + if (model == NULL) { + LOG_ERR("%s: unable to load model\n", __func__); + return 1; + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + } + + constexpr float val_split = 0.05f; + + std::vector tokens = common_tokenize(ctx.get(), params.prompt, true); + ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2); + + struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr); + optimizer_params.adamw.alpha = 1e-7f; // learning rate + + struct llama_opt_params lopt_params { + /*n_ctx_train =*/ 0, + /*param_filter =*/ llama_opt_param_filter_all, + /*param_filter_ud =*/ nullptr, + /*get_opt_pars =*/ ggml_opt_get_constant_optimizer_params, + /*get_opt_pars_ud =*/ &optimizer_params, + }; + llama_opt_init(ctx.get(), model.get(), lopt_params); + + const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split); + + ggml_opt_result_t result_train = ggml_opt_result_init(); + ggml_opt_result_t result_eval = ggml_opt_result_init(); + + for (int epoch = 0; epoch < 2; ++epoch) { + llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split, + ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar); + fprintf(stderr, "\n"); + + ggml_opt_result_reset(result_train); + ggml_opt_result_reset(result_eval); + } + ggml_opt_result_free(result_train); + ggml_opt_result_free(result_eval); + + llama_model_save_to_file(model.get(), "finetuned-model.gguf"); + + llama_backend_free(); + + return 0; +} diff --git a/ggml/include/ggml-opt.h b/ggml/include/ggml-opt.h index eb5eab9de..da0c24b46 100644 --- a/ggml/include/ggml-opt.h +++ b/ggml/include/ggml-opt.h @@ -37,13 +37,16 @@ extern "C" { // ====== Dataset ====== GGML_API ggml_opt_dataset_t ggml_opt_dataset_init( - int64_t ne_datapoint, // number of elements per datapoint - int64_t ne_label, // number of elements per label - int64_t ndata, // total number of datapoints/labels - int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied) + enum ggml_type type_data, // the type for the internal data tensor + enum ggml_type type_label, // the type for the internal labels tensor + int64_t ne_datapoint, // number of elements per datapoint + int64_t ne_label, // number of elements per label + int64_t ndata, // total number of datapoints/labels + int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied) GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset); // get underlying tensors that store the data + GGML_API int64_t ggml_opt_dataset_ndata (ggml_opt_dataset_t dataset); GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata] GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata] @@ -56,13 +59,19 @@ extern "C" { struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch] struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch] int64_t ibatch); + GGML_API void ggml_opt_dataset_get_batch_host( + ggml_opt_dataset_t dataset, + void * data_batch, + size_t nb_data_batch, + void * labels_batch, + int64_t ibatch); // ====== Model / Context ====== enum ggml_opt_build_type { - GGML_OPT_BUILD_TYPE_FORWARD, - GGML_OPT_BUILD_TYPE_GRAD, - GGML_OPT_BUILD_TYPE_OPT, + GGML_OPT_BUILD_TYPE_FORWARD = 10, + GGML_OPT_BUILD_TYPE_GRAD = 20, + GGML_OPT_BUILD_TYPE_OPT = 30, }; // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss @@ -81,20 +90,22 @@ extern "C" { // userdata can be used to pass arbitrary data typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata); - // returns the default optimizer params (constant) + // returns the default optimizer params (constant, hard-coded values) // userdata is not used GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata); + // casts userdata to ggml_opt_optimizer_params and returns it + GGML_API struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata); + // parameters for initializing a new optimization context struct ggml_opt_params { ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs - struct ggml_context * ctx_compute; // created in user code, holds non-static tensors - - // the forward graph is defined by inputs and outputs - // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts - struct ggml_tensor * inputs; - struct ggml_tensor * outputs; + // by default the forward graph needs to be reconstructed for each eval + // if ctx_compute, inputs, and outputs are set the graphs are instead allocated statically + struct ggml_context * ctx_compute; + struct ggml_tensor * inputs; + struct ggml_tensor * outputs; enum ggml_opt_loss_type loss_type; enum ggml_opt_build_type build_type; @@ -107,12 +118,9 @@ extern "C" { // get parameters for an optimization context with defaults set where possible // parameters for which no sensible defaults exist are supplied as arguments to this function - GGML_API ggml_opt_params ggml_opt_default_params( - ggml_backend_sched_t backend_sched, - struct ggml_context * ctx_compute, - struct ggml_tensor * inputs, - struct ggml_tensor * outputs, - enum ggml_opt_loss_type loss_type); + GGML_API struct ggml_opt_params ggml_opt_default_params( + ggml_backend_sched_t backend_sched, + enum ggml_opt_loss_type loss_type); GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params); GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx); @@ -121,6 +129,7 @@ extern "C" { GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer); // get underlying tensors that store data + // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against @@ -128,11 +137,12 @@ extern "C" { GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels + // get the gradient accumulator for a node from the forward graph GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node); // ====== Optimization Result ====== - GGML_API ggml_opt_result_t ggml_opt_result_init(); + GGML_API ggml_opt_result_t ggml_opt_result_init(void); GGML_API void ggml_opt_result_free(ggml_opt_result_t result); GGML_API void ggml_opt_result_reset(ggml_opt_result_t result); @@ -144,11 +154,20 @@ extern "C" { // ====== Computation ====== - // do forward pass, increment result if not NULL - GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + // if not using static graphs, this function must be called prior to ggml_opt_alloc + GGML_API void ggml_opt_prepare_alloc( + ggml_opt_context_t opt_ctx, + struct ggml_context * ctx_compute, + struct ggml_cgraph * gf, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs); - // do forward pass, increment result if not NULL, do backward pass - GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + // allocate the next graph for evaluation, either forward or forward + backward + // must be called exactly once prior to calling ggml_opt_eval + GGML_API void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward); + + // do forward pass, increment result if not NULL, do backward pass if allocated + GGML_API void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); // ############################################################################ // ## The high-level functions start here. They do not depend on any private ## @@ -200,9 +219,9 @@ extern "C" { // fit model defined by inputs and outputs to dataset GGML_API void ggml_opt_fit( ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs - ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs - ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch] - ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used + struct ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs + struct ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch] + struct ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used ggml_opt_dataset_t dataset, // dataset with data and optionally also labels enum ggml_opt_loss_type loss_type, // loss to minimize ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index c518366d5..e91dedf14 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -768,7 +768,7 @@ extern "C" { // Tensor flags GGML_API void ggml_set_input(struct ggml_tensor * tensor); GGML_API void ggml_set_output(struct ggml_tensor * tensor); - GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API void ggml_set_param(struct ggml_tensor * tensor); GGML_API void ggml_set_loss(struct ggml_tensor * tensor); // @@ -938,7 +938,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_repeat_back( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride // concat a and b along dim // used in stable-diffusion @@ -2049,15 +2049,14 @@ extern "C" { GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API void ggml_build_backward_expand( - struct ggml_context * ctx_static, // context for static gradients (loss + gradient accumulation) - struct ggml_context * ctx_compute, // context for gradient computation - struct ggml_cgraph * cgraph, - bool accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static + struct ggml_context * ctx, // context for gradient computation + struct ggml_cgraph * cgraph, + struct ggml_tensor ** grad_accs); // graph allocation in a context GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads); - GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph); + GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads); GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst); GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1 GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph); diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 6f69d895f..b30b4cb38 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -1111,7 +1111,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg const int node_backend_id = tensor_backend_id(node); - assert(node_backend_id != -1); // all nodes should be assigned by now + assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback // check if we should start a new split based on the sources of the current node bool need_new_split = false; diff --git a/ggml/src/ggml-cuda/acc.cu b/ggml/src/ggml-cuda/acc.cu index 96bfe1c9d..e084607c0 100644 --- a/ggml/src/ggml-cuda/acc.cu +++ b/ggml/src/ggml-cuda/acc.cu @@ -1,47 +1,61 @@ #include "acc.cuh" -static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, int offset) { - const int i = blockDim.x * blockIdx.x + threadIdx.x; +static __global__ void acc_f32(const float * x, const float * y, float * dst, const int64_t ne, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, + const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) { + const int64_t i = blockDim.x * blockIdx.x + threadIdx.x; + if (i >= ne) { return; } - int src1_idx = i - offset; - int oz = src1_idx / nb2; - int oy = (src1_idx - (oz * nb2)) / nb1; - int ox = src1_idx % nb1; - if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { - dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; - } else { - dst[i] = x[i]; + + int64_t src1_idx = i - offset; + + int64_t tmp = src1_idx; + const int64_t i13 = tmp / s13; + tmp -= i13 * s13; + const int64_t i12 = tmp / s12; + tmp -= i12 * s12; + const int64_t i11 = tmp / s11; + tmp -= i11 * s11; + const int64_t i10 = tmp; + + float val = x[i]; + if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) { + val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10]; } + dst[i] = val; } -static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, const int offset, cudaStream_t stream) { - int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE; - acc_f32<<>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset); +static void acc_f32_cuda(const float * x, const float * y, float * dst, const int64_t n_elements, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, + const int64_t s1, const int64_t s2, const int64_t s3, const int64_t offset, cudaStream_t stream) { + const int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE; + acc_f32<<>>(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset); } void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const float * src0_d = (const float *)src0->data; - const float * src1_d = (const float *)src1->data; - float * dst_d = (float *)dst->data; + + const float * src0_d = (const float *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(dst->nb[0] == ggml_element_size(dst)); + GGML_ASSERT(ggml_is_contiguously_allocated(dst)); - acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream); + const int64_t s1 = dst->op_params[0] / sizeof(float); + const int64_t s2 = dst->op_params[1] / sizeof(float); + const int64_t s3 = dst->op_params[2] / sizeof(float); + const int64_t offset = dst->op_params[3] / sizeof(float); + + acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], s1, s2, s3, offset, stream); } diff --git a/ggml/src/ggml-cuda/sum.cu b/ggml/src/ggml-cuda/sum.cu index f9589080a..eb3d7cdba 100644 --- a/ggml/src/ggml-cuda/sum.cu +++ b/ggml/src/ggml-cuda/sum.cu @@ -31,7 +31,7 @@ void ggml_cuda_op_sum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); const float * src0_d = (const float *) src0->data; float * dst_d = (float *) dst->data; diff --git a/ggml/src/ggml-opt.cpp b/ggml/src/ggml-opt.cpp index 7c3e24103..58d77578f 100644 --- a/ggml/src/ggml-opt.cpp +++ b/ggml/src/ggml-opt.cpp @@ -28,16 +28,19 @@ struct ggml_opt_dataset { }; struct ggml_opt_context { - ggml_backend_sched_t backend_sched = nullptr; - ggml_cgraph * allocated_graph = nullptr; - ggml_cgraph * allocated_graph_copy = nullptr; - struct ggml_context * ctx_static = nullptr; - struct ggml_context * ctx_static_cpu = nullptr; - struct ggml_context * ctx_compute = nullptr; - struct ggml_context * ctx_copy = nullptr; - ggml_backend_buffer_t buf_static = nullptr; - ggml_backend_buffer_t buf_static_cpu = nullptr; - std::mt19937 rng; + ggml_backend_sched_t backend_sched = nullptr; + ggml_cgraph * allocated_graph = nullptr; + ggml_cgraph * allocated_graph_copy = nullptr; + struct ggml_context * ctx_static = nullptr; + struct ggml_context * ctx_cpu = nullptr; + struct ggml_context * ctx_compute = nullptr; + struct ggml_context * ctx_copy = nullptr; + ggml_backend_buffer_t buf_static = nullptr; + ggml_backend_buffer_t buf_cpu = nullptr; + std::mt19937 rng; + enum ggml_opt_loss_type loss_type; + enum ggml_opt_build_type build_type; + enum ggml_opt_build_type build_type_alloc; struct ggml_tensor * inputs = nullptr; struct ggml_tensor * outputs = nullptr; @@ -50,6 +53,11 @@ struct ggml_opt_context { struct ggml_cgraph * gf = nullptr; struct ggml_cgraph * gb_grad = nullptr; struct ggml_cgraph * gb_opt = nullptr; + bool static_graphs = false; + bool eval_ready = false; + std::vector grad_accs; + std::vector grad_m; + std::vector grad_v; int64_t iter = 1; int32_t opt_period = 1; @@ -73,7 +81,13 @@ struct ggml_opt_result { // ====== Dataset ====== -ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, int64_t ndata, int64_t ndata_shard) { +ggml_opt_dataset_t ggml_opt_dataset_init( + enum ggml_type type_data, + enum ggml_type type_label, + int64_t ne_datapoint, + int64_t ne_label, + int64_t ndata, + int64_t ndata_shard) { GGML_ASSERT(ne_datapoint > 0); GGML_ASSERT(ne_label >= 0); GGML_ASSERT(ndata > 0); @@ -92,11 +106,11 @@ ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, result->ctx = ggml_init(params); } - result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_datapoint, ndata); + result->data = ggml_new_tensor_2d(result->ctx, type_data, ne_datapoint, ndata); result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata; if (ne_label > 0) { - result->labels = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_label, ndata); + result->labels = ggml_new_tensor_2d(result->ctx, type_label, ne_label, ndata); result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata; } else { result->labels = nullptr; @@ -119,6 +133,10 @@ void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) { delete dataset; } +int64_t ggml_opt_dataset_ndata(ggml_opt_dataset_t dataset) { + return dataset->ndata; +} + struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) { return dataset->data; } @@ -144,6 +162,8 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * GGML_ASSERT( data_batch && ggml_is_contiguous(data_batch)); GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch)); GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr)); + GGML_ASSERT( data_batch->type == dataset->data->type); + GGML_ASSERT(!labels_batch || labels_batch->type == dataset->labels->type); const size_t nb_data_batch = ggml_nbytes(data_batch); GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0); @@ -171,6 +191,31 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * } } +void ggml_opt_dataset_get_batch_host(ggml_opt_dataset_t dataset, void * data_batch, size_t nb_data_batch, void * labels_batch, int64_t ibatch) { + GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr)); + GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0); + + const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data; + + GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size())); + + for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) { + const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch]; + + const char * ptr_data = (const char *) dataset->data->data + ishard *dataset->nbs_data; + char * ptr_data_batch = (char *) data_batch + ishard_batch*dataset->nbs_data; + memcpy(ptr_data_batch, ptr_data, dataset->nbs_data); + + if (!labels_batch) { + continue; + } + + const char * ptr_labels = (const char *) dataset->labels->data + ishard *dataset->nbs_labels; + char * ptr_labels_batch = (char *) labels_batch + ishard_batch*dataset->nbs_labels; + memcpy(ptr_labels_batch, ptr_labels, dataset->nbs_labels); + } +} + // ====== Model / Context ====== struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) { @@ -187,17 +232,18 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us return result; } +struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata) { + return *((struct ggml_opt_optimizer_params *) userdata); +} + struct ggml_opt_params ggml_opt_default_params( ggml_backend_sched_t backend_sched, - struct ggml_context * ctx_compute, - struct ggml_tensor * inputs, - struct ggml_tensor * outputs, enum ggml_opt_loss_type loss_type) { return { /*backend_sched =*/ backend_sched, - /*ctx_compute =*/ ctx_compute, - /*inputs =*/ inputs, - /*logits =*/ outputs, + /*ctx_compute =*/ nullptr, + /*inputs =*/ nullptr, + /*logits =*/ nullptr, /*loss_type =*/ loss_type, /*build_type =*/ GGML_OPT_BUILD_TYPE_OPT, /*opt_period =*/ 1, @@ -266,195 +312,246 @@ static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) { return dst; } -static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) { - GGML_ASSERT(graph); - if (opt_ctx->allocated_graph == graph) { - return; - } +static void ggml_opt_build(ggml_opt_context_t opt_ctx) { + GGML_ASSERT(opt_ctx->ctx_compute && "no compute context set, either use static graphs or set one with ggml_opt_prepare_alloc"); + GGML_ASSERT((!opt_ctx->static_graphs || opt_ctx->inputs->data) && "when using static graphs the inputs must be allocated statically"); - ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph + const bool accumulate = opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD && + !(opt_ctx->static_graphs && opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period == 1); - { - ggml_init_params params = { - /*.mem_size =*/ ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE, - /*.mem_buffer =*/ nullptr, - /*.no_alloc =*/ true, - }; - ggml_free(opt_ctx->ctx_copy); - opt_ctx->ctx_copy = ggml_init(params); - } - - opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph); - - ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); - opt_ctx->allocated_graph = graph; -} - -ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) { - ggml_opt_context_t result = new struct ggml_opt_context; - result->backend_sched = params.backend_sched; - result->ctx_compute = params.ctx_compute; - result->inputs = params.inputs; - result->outputs = params.outputs; - result->opt_period = params.opt_period; - result->get_opt_pars = params.get_opt_pars; - result->get_opt_pars_ud = params.get_opt_pars_ud; - - GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically"); - GGML_ASSERT(result->opt_period >= 1); - - const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD || - (params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1); - - ggml_set_input(result->inputs); - ggml_set_output(result->outputs); - - result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass. - ggml_build_forward_expand(result->gf, result->outputs); + ggml_set_input(opt_ctx->inputs); + ggml_set_output(opt_ctx->outputs); int n_param = 0; - for (int i = 0; i < result->gf->n_nodes; ++i) { - if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { + for (int i = 0; i < opt_ctx->gf->n_nodes; ++i) { + const struct ggml_tensor * node = opt_ctx->gf->nodes[i]; + if (node->flags & GGML_TENSOR_FLAG_PARAM) { n_param++; } + GGML_ASSERT(!(node->flags & GGML_TENSOR_FLAG_LOSS) && "support for extra loss terms not implemented"); } - { + if (!opt_ctx->ctx_static) { // The static context is used for: - // - gradients (1 tensor per param if using gradient accumulation) + // - gradients (1 per loss, 1 tensor per param if using gradient accumulation) // - optimizer momenta (2 tensors per param) - // - labels - // - loss + its gradient (up to 5 tensors) - // - pred - // - ncorrect (2 tensors). - const size_t tensors_per_param = (accumulate ? 1 : 0) + (params.build_type == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0); - const size_t size_meta = (tensors_per_param*n_param + 9) * ggml_tensor_overhead(); + // - labels (if using static graphs) + // - loss (if using static graphs, up to 5 tensors) + // - pred (if using static graphs) + // - ncorrect (if using static graphs, 2 tensors). + constexpr size_t n_loss = 1; + const size_t tensors_per_param = (accumulate ? 1 : 0) + + (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0); + const size_t tensors_const = opt_ctx->static_graphs ? 9 : 0; + const size_t size_meta = (n_loss + tensors_per_param*n_param + tensors_const) * ggml_tensor_overhead(); struct ggml_init_params params = { /*.mem_size =*/ size_meta, /*.mem_buffer =*/ nullptr, /*.no_alloc =*/ true, }; - result->ctx_static = ggml_init(params); + opt_ctx->ctx_static = ggml_init(params); } + GGML_ASSERT(opt_ctx->build_type <= opt_ctx->build_type_alloc); + { - // The static cpu context is used for: - // - optimizer parameters (1 for the entire context) + // The cpu context is allocated statically if using static graphs, dynamically otherwise. + // It is used for: + // - optimizer parameters (1 shared for all optimizer invocations) const size_t size_meta = 1 * ggml_tensor_overhead(); struct ggml_init_params params = { /*.mem_size =*/ size_meta, /*.mem_buffer =*/ nullptr, /*.no_alloc =*/ true, }; - result->ctx_static_cpu = ggml_init(params); + ggml_free(opt_ctx->ctx_cpu); + opt_ctx->ctx_cpu = ggml_init(params); + + ggml_backend_buffer_free(opt_ctx->buf_cpu); + opt_ctx->buf_cpu = nullptr; } + struct ggml_context * ctx_results = opt_ctx->static_graphs ? opt_ctx->ctx_static : opt_ctx->ctx_compute; - switch (params.loss_type) { + switch (opt_ctx->loss_type) { case GGML_OPT_LOSS_TYPE_MEAN: { - result->loss = ggml_sum(result->ctx_static, result->outputs); - ggml_set_name(result->loss, "loss_sum"); - const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); - result->loss = ggml_scale(result->ctx_static, result->loss, scale); - ggml_set_name(result->loss, "loss_mean"); - result->loss_per_datapoint = true; + opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs); + ggml_set_name(opt_ctx->loss, "loss_sum"); + const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs)); + opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale); + ggml_set_name(opt_ctx->loss, "loss_mean"); + opt_ctx->loss_per_datapoint = true; break; } case GGML_OPT_LOSS_TYPE_SUM: { - result->loss = ggml_sum(result->ctx_static, result->outputs); - ggml_set_name(result->loss, "loss_sum"); - result->loss_per_datapoint = false; + opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs); + ggml_set_name(opt_ctx->loss, "loss_sum"); + opt_ctx->loss_per_datapoint = false; break; } case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: { - result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); - ggml_set_input(result->labels); - ggml_set_name(result->labels, "labels"); - result->loss = ggml_cross_entropy_loss(result->ctx_static, result->outputs, result->labels); - ggml_set_name(result->loss, "loss_cross_entropy"); - if (result->opt_period > 1) { - result->loss = ggml_scale(result->ctx_static, result->loss, 1.0f / result->opt_period); - ggml_set_name(result->loss, "loss_cross_entropy_scaled"); + opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs); + ggml_set_input(opt_ctx->labels); + ggml_set_name(opt_ctx->labels, "labels"); + opt_ctx->loss = ggml_cross_entropy_loss(ctx_results, opt_ctx->outputs, opt_ctx->labels); + ggml_set_name(opt_ctx->loss, "loss_cross_entropy"); + if (opt_ctx->opt_period > 1) { + opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, 1.0f / opt_ctx->opt_period); + ggml_set_name(opt_ctx->loss, "loss_cross_entropy_scaled"); } - result->loss_per_datapoint = true; + opt_ctx->loss_per_datapoint = true; break; } case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: { - result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); - ggml_set_input(result->labels); - ggml_set_name(result->labels, "labels"); - result->loss = ggml_sub(result->ctx_static, result->outputs, result->labels); - ggml_set_name(result->loss, "loss_error"); - result->loss = ggml_sqr(result->ctx_static, result->loss); - ggml_set_name(result->loss, "loss_squared_error"); - result->loss = ggml_sum(result->ctx_static, result->loss); - ggml_set_name(result->loss, "loss_sum_squared_error"); - const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); - result->loss = ggml_scale(result->ctx_static, result->loss, scale); - ggml_set_name(result->loss, "loss_mean_squared_error"); - result->loss_per_datapoint = true; + opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs); + ggml_set_input(opt_ctx->labels); + ggml_set_name(opt_ctx->labels, "labels"); + opt_ctx->loss = ggml_sub(ctx_results, opt_ctx->outputs, opt_ctx->labels); + ggml_set_name(opt_ctx->loss, "loss_error"); + opt_ctx->loss = ggml_sqr(ctx_results, opt_ctx->loss); + ggml_set_name(opt_ctx->loss, "loss_squared_error"); + opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->loss); + ggml_set_name(opt_ctx->loss, "loss_sum_squared_error"); + const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs)); + opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale); + ggml_set_name(opt_ctx->loss, "loss_mean_squared_error"); + opt_ctx->loss_per_datapoint = true; break; } } - ggml_set_output(result->loss); - ggml_set_loss(result->loss); - ggml_build_forward_expand(result->gf, result->loss); + ggml_set_output(opt_ctx->loss); + ggml_set_loss(opt_ctx->loss); + ggml_build_forward_expand(opt_ctx->gf, opt_ctx->loss); - result->pred = ggml_argmax(result->ctx_static, result->outputs); - ggml_set_name(result->pred, "pred"); - ggml_set_output(result->pred); - ggml_build_forward_expand(result->gf, result->pred); + if (opt_ctx->loss_type == GGML_OPT_LOSS_TYPE_CROSS_ENTROPY) { + opt_ctx->pred = ggml_argmax(ctx_results, opt_ctx->outputs); + ggml_set_name(opt_ctx->pred, "pred"); + ggml_set_output(opt_ctx->pred); + ggml_build_forward_expand(opt_ctx->gf, opt_ctx->pred); - if (result->labels) { - result->ncorrect = ggml_count_equal(result->ctx_static, result->pred, ggml_argmax(result->ctx_static, result->labels)); - ggml_set_name(result->ncorrect, "ncorrect"); - ggml_set_output(result->ncorrect); - ggml_build_forward_expand(result->gf, result->ncorrect); - } else { - result->ncorrect = nullptr; + opt_ctx->ncorrect = ggml_count_equal(ctx_results, opt_ctx->pred, ggml_argmax(ctx_results, opt_ctx->labels)); + ggml_set_name(opt_ctx->ncorrect, "ncorrect"); + ggml_set_output(opt_ctx->ncorrect); + ggml_build_forward_expand(opt_ctx->gf, opt_ctx->ncorrect); } - if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) { - result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); - return result; + if (opt_ctx->buf_static) { + if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_FORWARD) { + return; + } + } else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_FORWARD) { + opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors( + opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0)); + return; } - // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients. - result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf); - ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate); + if (opt_ctx->grad_accs.empty()) { + GGML_ASSERT(opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD); - if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) { - result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); - ggml_graph_reset(result->gb_grad); - return result; - } + const int n_nodes = opt_ctx->gf->n_nodes; + opt_ctx->grad_accs.resize(n_nodes); + for (int i = 0; i < n_nodes; ++i) { + ggml_tensor * node = opt_ctx->gf->nodes[i]; + if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) { + opt_ctx->grad_accs[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne); + } else { + opt_ctx->grad_accs[i] = nullptr; + } + } - GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT); - - // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step. - result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad); - - result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7); - ggml_set_input(result->adamw_params); - ggml_set_name(result->adamw_params, "adamw_params"); - - for (int i = result->gf->n_nodes-1; i >= 0; --i) { - struct ggml_tensor * node = result->gb_opt->nodes[i]; - struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node); - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - struct ggml_tensor * m = ggml_dup_tensor(result->ctx_static, node); - struct ggml_tensor * v = ggml_dup_tensor(result->ctx_static, node); - struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params); - ggml_build_forward_expand(result->gb_opt, opt_step); + if (opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) { + opt_ctx->grad_m.resize(n_nodes); + opt_ctx->grad_v.resize(n_nodes); + for (int i = 0; i < n_nodes; ++i) { + ggml_tensor * node = opt_ctx->gf->nodes[i]; + if (node->flags & GGML_TENSOR_FLAG_PARAM) { + opt_ctx->grad_m[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne); + opt_ctx->grad_v[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne); + } else { + opt_ctx->grad_m[i] = nullptr; + opt_ctx->grad_v[i] = nullptr; + } + } } } - result->buf_static = ggml_backend_alloc_ctx_tensors( - result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients. + opt_ctx->gb_grad = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gf, /*force_grads =*/ true); + ggml_build_backward_expand(opt_ctx->ctx_compute, opt_ctx->gb_grad, opt_ctx->grad_accs.data()); - result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type()); + if (opt_ctx->buf_static) { + if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_GRAD) { + return; + } + } else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_GRAD) { + opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0)); + ggml_graph_reset(opt_ctx->gb_grad); + } - ggml_graph_reset(result->gb_opt); + GGML_ASSERT(opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT); + + // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step. + opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true); + + opt_ctx->adamw_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, 7); + ggml_set_input(opt_ctx->adamw_params); + ggml_set_name(opt_ctx->adamw_params, "adamw_params"); + + for (int i = opt_ctx->gf->n_nodes-1; i >= 0; --i) { + struct ggml_tensor * node = opt_ctx->gb_opt->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(opt_ctx->gb_opt, node); + + if (grad && (node->flags & GGML_TENSOR_FLAG_PARAM)) { + struct ggml_tensor * m = opt_ctx->grad_m[i]; + struct ggml_tensor * v = opt_ctx->grad_v[i]; + struct ggml_tensor * opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, opt_ctx->adamw_params); + + ggml_set_name(m, (std::string("AdamW m for ") + std::string(node->name)).c_str()); + ggml_set_name(v, (std::string("AdamW v for ") + std::string(node->name)).c_str()); + ggml_set_name(opt_step, (std::string("AdamW step for ") + std::string(node->name)).c_str()); + + ggml_build_forward_expand(opt_ctx->gb_opt, opt_step); + } + } + + if (!opt_ctx->buf_static) { + opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors( + opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0)); + ggml_graph_reset(opt_ctx->gb_opt); + } + + opt_ctx->buf_cpu = ggml_backend_alloc_ctx_tensors_from_buft(opt_ctx->ctx_cpu, ggml_backend_cpu_buffer_type()); +} + +ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) { + ggml_opt_context_t result = new struct ggml_opt_context; + result->backend_sched = params.backend_sched; + result->ctx_compute = params.ctx_compute; + result->loss_type = params.loss_type; + result->build_type = params.build_type; + result->build_type_alloc = params.build_type; + result->inputs = params.inputs; + result->outputs = params.outputs; + result->opt_period = params.opt_period; + result->get_opt_pars = params.get_opt_pars; + result->get_opt_pars_ud = params.get_opt_pars_ud; + + GGML_ASSERT(result->opt_period >= 1); + + result->static_graphs = result->ctx_compute; + + if (!result->static_graphs) { + GGML_ASSERT(!result->inputs); + GGML_ASSERT(!result->outputs); + return result; + } + + GGML_ASSERT(result->inputs); + GGML_ASSERT(result->outputs); + + result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass. + ggml_build_forward_expand(result->gf, result->outputs); + + ggml_opt_build(result); return result; } @@ -464,9 +561,9 @@ void ggml_opt_free(ggml_opt_context_t opt_ctx) { return; } ggml_backend_buffer_free(opt_ctx->buf_static); - ggml_backend_buffer_free(opt_ctx->buf_static_cpu); + ggml_backend_buffer_free(opt_ctx->buf_cpu); ggml_free(opt_ctx->ctx_static); - ggml_free(opt_ctx->ctx_static_cpu); + ggml_free(opt_ctx->ctx_cpu); delete opt_ctx; } @@ -582,8 +679,79 @@ void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, doubl // ====== Computation ====== -static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_opt_result * result) { - if (graph != opt_ctx->gf) { +void ggml_opt_prepare_alloc( + ggml_opt_context_t opt_ctx, + struct ggml_context * ctx_compute, + struct ggml_cgraph * gf, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs) { + GGML_ASSERT(!opt_ctx->static_graphs); + opt_ctx->ctx_compute = ctx_compute; + opt_ctx->gf = gf; + opt_ctx->inputs = inputs; + opt_ctx->outputs = outputs; +} + +void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward) { + GGML_ASSERT(!opt_ctx->eval_ready); + if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period > 1 && opt_ctx->opt_i == 0) { + ggml_graph_reset(opt_ctx->gb_grad); + } + if (backward) { + const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period; + opt_ctx->build_type = opt_i_next == 0 ? GGML_OPT_BUILD_TYPE_OPT : GGML_OPT_BUILD_TYPE_GRAD; + } else { + opt_ctx->build_type = GGML_OPT_BUILD_TYPE_FORWARD; + } + + if (!opt_ctx->static_graphs) { + ggml_opt_build(opt_ctx); + } + + struct ggml_cgraph * graph = nullptr; + switch (opt_ctx->build_type) { + case GGML_OPT_BUILD_TYPE_FORWARD: { + graph = opt_ctx->gf; + } break; + case GGML_OPT_BUILD_TYPE_GRAD: { + graph = opt_ctx->gb_grad; + } break; + case GGML_OPT_BUILD_TYPE_OPT: { + graph = opt_ctx->gb_opt; + } break; + } + GGML_ASSERT(graph); + + if (opt_ctx->allocated_graph == graph) { + opt_ctx->eval_ready = true; + return; + } + + ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph + + if (opt_ctx->static_graphs) { + ggml_init_params params = { + /*.mem_size =*/ graph->size*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph->size, graph->grads), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ggml_free(opt_ctx->ctx_copy); + opt_ctx->ctx_copy = ggml_init(params); + + opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph); + } else { + opt_ctx->allocated_graph_copy = graph; + } + + ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); + opt_ctx->allocated_graph = graph; + + opt_ctx->eval_ready = true; +} + +void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result) { + GGML_ASSERT(opt_ctx->eval_ready); + if (opt_ctx->allocated_graph == opt_ctx->gb_opt) { struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud); GGML_ASSERT(opt_pars.adamw.alpha > 0.0f); @@ -609,9 +777,19 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, adamw_par_data[6] = beta2h; } - ggml_opt_alloc_graph(opt_ctx, graph); ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt; + opt_ctx->opt_i = (opt_ctx->opt_i + 1) % opt_ctx->opt_period; + + if (!opt_ctx->static_graphs) { + opt_ctx->gf = nullptr; + opt_ctx->gb_grad = nullptr; + opt_ctx->gb_opt = nullptr; + opt_ctx->allocated_graph = nullptr; + opt_ctx->allocated_graph_copy = nullptr; + } + + opt_ctx->eval_ready = false; if (!result) { return; @@ -635,12 +813,14 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss)); result->loss.push_back(loss); - GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32); - std::vector pred(ndata); - ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred)); - result->pred.insert(result->pred.end(), pred.begin(), pred.end()); + if (opt_ctx->pred) { + GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32); + std::vector pred(ndata); + ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred)); + result->pred.insert(result->pred.end(), pred.begin(), pred.end()); + } - if (!opt_ctx->labels || result->ncorrect < 0) { + if (!opt_ctx->ncorrect || result->ncorrect < 0) { result->ncorrect = -1; return; } @@ -652,26 +832,6 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, result->ncorrect += ncorrect; } -void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { - ggml_opt_eval_graph(opt_ctx, opt_ctx->gf, result); -} - -void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { - if (opt_ctx->opt_period == 1) { - ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); - return; - } - - const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period; - if (opt_i_next == 0) { - ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); - ggml_opt_reset(opt_ctx, /*optimizer =*/ false); - } else { - ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_grad, result); - } - opt_ctx->opt_i = opt_i_next; -} - // ====== High-Level Functions ====== void ggml_opt_epoch( @@ -700,16 +860,18 @@ void ggml_opt_epoch( int64_t ibatch = 0; int64_t t_loop_start = ggml_time_us(); for (; ibatch < ibatch_split; ++ibatch) { + ggml_opt_alloc(opt_ctx, /*backward =*/ true); ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); - ggml_opt_forward_backward(opt_ctx, result_train); + ggml_opt_eval(opt_ctx, result_train); if (callback_train) { callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start); } } t_loop_start = ggml_time_us(); for (; ibatch < nbatches; ++ibatch) { + ggml_opt_alloc(opt_ctx, /*backward =*/ false); ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); - ggml_opt_forward(opt_ctx, result_eval); + ggml_opt_eval(opt_ctx, result_eval); if (callback_eval) { callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start); } @@ -726,13 +888,26 @@ void ggml_opt_epoch_callback_progress_bar( int64_t t_start_us) { fprintf(stderr, "%s[", train ? "train: " : "val: "); - constexpr int64_t bar_length = 25; + // The progress bar consists of partially filled blocks, unicode has 8 separate fill levels. + constexpr int64_t bar_length = 8; + const int64_t ibatch8 = 8 * ibatch; for (int64_t j = 0; j < bar_length; ++j) { - const int64_t ibatch_j = ibatch_max * j/bar_length; - if (ibatch_j < ibatch) { - fprintf(stderr, "="); - } else if (ibatch_max * (j - 1)/bar_length < ibatch) { - fprintf(stderr, ">"); + if (ibatch_max * (8*j + 8) / bar_length < ibatch8) { + fprintf(stderr, "\u2588"); // full block + } else if (ibatch_max * (8*j + 7) / bar_length < ibatch8) { + fprintf(stderr, "\u2589"); // 7/8 filled + } else if (ibatch_max * (8*j + 6) / bar_length < ibatch8) { + fprintf(stderr, "\u258A"); // 6/8 filled + } else if (ibatch_max * (8*j + 5) / bar_length < ibatch8) { + fprintf(stderr, "\u258B"); // 5/8 filled + } else if (ibatch_max * (8*j + 4) / bar_length < ibatch8) { + fprintf(stderr, "\u258C"); // 4/8 filled + } else if (ibatch_max * (8*j + 3) / bar_length < ibatch8) { + fprintf(stderr, "\u258D"); // 3/8 filled + } else if (ibatch_max * (8*j + 2) / bar_length < ibatch8) { + fprintf(stderr, "\u258E"); // 2/8 filled + } else if (ibatch_max * (8*j + 1) / bar_length < ibatch8) { + fprintf(stderr, "\u258F"); // 1/8 filled } else { fprintf(stderr, " "); } @@ -764,8 +939,8 @@ void ggml_opt_epoch_callback_progress_bar( const int64_t t_eta_m = t_eta_s / 60; t_eta_s -= t_eta_m * 60; - fprintf(stderr, "| data=%06" PRId64 "/%06" PRId64 ", loss=%.6lf+-%.6lf, accuracy=%.2lf+-%.2lf%%, " - "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 ", ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 "]\r", + fprintf(stderr, "] data=%07" PRId64 "/%07" PRId64 " loss=%.5lf±%.5lf acc=%.2lf±%.2lf%% " + "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " \r", idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc, t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s); if (ibatch == ibatch_max) { @@ -806,7 +981,10 @@ void ggml_opt_fit( int64_t epoch = 1; - ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); + ggml_opt_params params = ggml_opt_default_params(backend_sched, loss_type); + params.ctx_compute = ctx_compute; + params.inputs = inputs; + params.outputs = outputs; params.opt_period = opt_period; params.get_opt_pars = get_opt_pars; params.get_opt_pars_ud = &epoch; diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index bc673292b..8a6546240 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -5499,7 +5499,7 @@ static void ggml_compute_backward( // tensor = src0 * 1 + src1 * 0 if (src0_needs_grads) { // dsrc0 = dtensor * 1 - ggml_add_or_set(ctx, cgraph, isrc0, grad); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad, src0)); } if (src1_needs_grads) { // dsrc1 = dtensor * 0 -> noop @@ -5780,10 +5780,9 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * } void ggml_build_backward_expand( - struct ggml_context * ctx_static, - struct ggml_context * ctx_compute, - struct ggml_cgraph * cgraph, - bool accumulate) { + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + struct ggml_tensor ** grad_accs) { GGML_ASSERT(cgraph->n_nodes > 0); GGML_ASSERT(cgraph->grads); GGML_ASSERT(cgraph->grad_accs); @@ -5856,21 +5855,24 @@ void ggml_build_backward_expand( GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE); - const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); - GGML_ASSERT(igrad != GGML_HASHSET_FULL); - GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad)); - if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) { - cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node); - cgraph->grads[igrad] = cgraph->grad_accs[igrad]; - ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name); + const size_t ihash = ggml_hash_find(&cgraph->visited_hash_set, node); + GGML_ASSERT(ihash != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, ihash)); + if (grad_accs && grad_accs[i]) { + cgraph->grad_accs[ihash] = grad_accs[i]; + cgraph->grads[ihash] = cgraph->grad_accs[ihash]; + } else if (node->flags & GGML_TENSOR_FLAG_LOSS) { + // loss tensors always need a gradient accumulator + cgraph->grad_accs[ihash] = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne); + cgraph->grads[ihash] = cgraph->grad_accs[ihash]; } - grads_needed[igrad] = true; + grads_needed[ihash] = true; } for (int i = n_nodes_f - 1; i >= 0; --i) { // inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation // use allocator to automatically make inplace operations - ggml_compute_backward(ctx_compute, cgraph, i, grads_needed); + ggml_compute_backward(ctx, cgraph, i, grads_needed); } free(grads_needed); @@ -6016,8 +6018,8 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { } } -struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { - struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL); +struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads) { + struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads || force_grads); ggml_graph_cpy(cgraph, result); return result; } @@ -6036,6 +6038,9 @@ struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { } void ggml_graph_reset(struct ggml_cgraph * cgraph) { + if (!cgraph) { + return; + } GGML_ASSERT(cgraph->grads != NULL); for (int i = 0; i < cgraph->n_nodes; i++) { @@ -6345,8 +6350,8 @@ void ggml_set_output(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; } -void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) { - GGML_UNUSED(ctx); // TODO: remove this parameter +void ggml_set_param(struct ggml_tensor * tensor) { + GGML_ASSERT(tensor->op == GGML_OP_NONE); tensor->flags |= GGML_TENSOR_FLAG_PARAM; } diff --git a/include/llama.h b/include/llama.h index 6c6d377f8..abedebdb7 100644 --- a/include/llama.h +++ b/include/llama.h @@ -4,6 +4,7 @@ #include "ggml.h" #include "ggml-cpu.h" #include "ggml-backend.h" +#include "ggml-opt.h" #include #include @@ -445,6 +446,10 @@ extern "C" { size_t n_paths, struct llama_model_params params); + LLAMA_API void llama_model_save_to_file( + const struct llama_model * model, + const char * path_model); + DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model), "use llama_model_free instead"); @@ -1433,6 +1438,37 @@ extern "C" { LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); + // + // training + // + + // function that returns whether or not a given tensor contains trainable parameters + typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata); + + // always returns true + LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata); + + struct llama_opt_params { + uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0 + + llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters + void * param_filter_ud; // userdata for determining which tensors contain trainable parameters + + ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters + void * get_opt_pars_ud; // userdata for calculating optimizer parameters + }; + + LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params); + + LLAMA_API void llama_opt_epoch( + struct llama_context * lctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval); + #ifdef __cplusplus } #endif diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 1cd316b03..d4bf37b1c 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -23,6 +23,7 @@ add_library(llama llama-memory.cpp llama-mmap.cpp llama-model-loader.cpp + llama-model-saver.cpp llama-model.cpp llama-quant.cpp llama-sampling.cpp diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 0cb6ebc9f..62246c10d 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -359,7 +359,9 @@ llama_context::llama_context( } } -llama_context::~llama_context() = default; +llama_context::~llama_context() { + ggml_opt_free(opt_ctx); +} void llama_context::synchronize() { ggml_backend_sched_synchronize(sched.get()); @@ -1846,6 +1848,215 @@ void llama_context::perf_reset() { t_p_eval_us = n_p_eval = 0; } +// +// training +// + +static void llama_set_param(struct ggml_tensor * tensor, llama_opt_param_filter param_filter, void * userdata) { + if (!tensor || tensor->type != GGML_TYPE_F32) { + return; + } + if (!param_filter(tensor, userdata)) { + return; + } + if (strcmp(tensor->name, "token_embd.weight") == 0) { + return; // FIXME + } + if (strcmp(tensor->name, "rope_freqs.weight") == 0) { + return; // FIXME + } + ggml_set_param(tensor); +} + +void llama_context::opt_init(struct llama_model * model, struct llama_opt_params lopt_params) { + GGML_ASSERT(!opt_ctx); + model->hparams.n_ctx_train = lopt_params.n_ctx_train > 0 ? lopt_params.n_ctx_train : n_ctx(); + const uint32_t n_batch = std::min(this->n_batch(), model->hparams.n_ctx_train); + const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch); + GGML_ASSERT(model->hparams.n_ctx_train % n_batch == 0); + GGML_ASSERT(n_batch % n_ubatch == 0); + + ggml_opt_params opt_params = ggml_opt_default_params(sched.get(), GGML_OPT_LOSS_TYPE_CROSS_ENTROPY); + opt_params.opt_period = n_batch / n_ubatch; + opt_params.get_opt_pars = lopt_params.get_opt_pars; + opt_params.get_opt_pars_ud = lopt_params.get_opt_pars_ud; + + opt_ctx = ggml_opt_init(opt_params); + + llama_opt_param_filter param_filter = lopt_params.param_filter; + void * param_filter_ud = lopt_params.param_filter_ud; + + //llama_set_param(model->tok_embd, param_filter, param_filter_ud); // FIXME + llama_set_param(model->type_embd, param_filter, param_filter_ud); + llama_set_param(model->pos_embd, param_filter, param_filter_ud); + llama_set_param(model->tok_norm, param_filter, param_filter_ud); + llama_set_param(model->tok_norm_b, param_filter, param_filter_ud); + llama_set_param(model->output_norm, param_filter, param_filter_ud); + llama_set_param(model->output_norm_b, param_filter, param_filter_ud); + llama_set_param(model->output, param_filter, param_filter_ud); + llama_set_param(model->output_b, param_filter, param_filter_ud); + llama_set_param(model->output_norm_enc, param_filter, param_filter_ud); + llama_set_param(model->cls, param_filter, param_filter_ud); + llama_set_param(model->cls_b, param_filter, param_filter_ud); + llama_set_param(model->cls_out, param_filter, param_filter_ud); + llama_set_param(model->cls_out_b, param_filter, param_filter_ud); + + for (struct llama_layer & layer : model->layers) { + for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) { + llama_set_param(reinterpret_cast(&layer)[i], param_filter, param_filter_ud); + } + } +} + +void llama_context::opt_epoch_iter( + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + const std::vector & tokens, + const std::vector & labels_sparse, + llama_batch & batch, + ggml_opt_epoch_callback callback, + bool train, + int64_t idata_in_loop, + int64_t ndata_in_loop, + int64_t t_loop_start) { + GGML_ASSERT(opt_ctx); + const uint32_t n_ctx = llama_model_n_ctx_train(&model); + const uint32_t n_batch = std::min(this->n_batch(), n_ctx); + const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch); + + llama_kv_cache * kv_self = static_cast(memory.get()); + + kv_self->clear(); + llama_kv_cache_guard kv_guard(kv_self); + + for (uint32_t pos_ctx = 0; pos_ctx < n_ctx; pos_ctx += n_batch) { + batch.n_tokens = n_batch; + for (uint32_t pos_batch = 0; pos_batch < n_batch; ++pos_batch) { + batch.token [pos_batch] = tokens[pos_ctx + pos_batch]; + batch.pos [pos_batch] = pos_ctx + pos_batch; + batch.n_seq_id[pos_batch] = 1; + batch.seq_id [pos_batch][0] = 0; + batch.logits [pos_batch] = true; + } + + const auto n_tokens_all = batch.n_tokens; + + n_queued_tokens += n_tokens_all; + + // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens + const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE; + + embd_seq.clear(); + + int64_t n_outputs_all = n_tokens_all; + + llama_sbatch sbatch = kv_self->sbatch_init(batch, /*logits_all =*/ true); + + // reserve output buffer + if (output_reserve(n_outputs_all) < n_outputs_all) { + LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all); + GGML_ABORT("TODO: handle this error"); + }; + + for (uint32_t pos_batch = 0; pos_batch < n_batch; pos_batch += n_ubatch) { + llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled); + + n_outputs = ubatch.n_tokens; + + // TODO: not sure if this is needed + if (!kv_self->find_slot(ubatch)) { + LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens); + + GGML_ABORT("TODO: handle this error"); + } + + auto * gf = graph_init(); + auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT); + + struct ggml_context * ctx_compute_opt; + { + const size_t size_gf = ggml_graph_size(gf); + const size_t size_meta = 4*size_gf*ggml_tensor_overhead() + 2*ggml_graph_overhead_custom(size_gf, /*grads = */ true); + struct ggml_init_params params = { + /*.mem_size =*/ size_meta, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_compute_opt = ggml_init(params); + } + ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits()); + ggml_opt_alloc(opt_ctx, train); + res->set_inputs(&ubatch); + { + struct ggml_tensor * labels = ggml_opt_labels(opt_ctx); + GGML_ASSERT(labels->ne[1] == n_ubatch); + ggml_set_zero(labels); + const float onef = 1.0f; + for (uint32_t pos_ubatch = 0; pos_ubatch < n_ubatch; ++pos_ubatch) { + const uint32_t ilabel = pos_ctx + pos_batch + pos_ubatch; + GGML_ASSERT(labels_sparse[ilabel] < labels->ne[0]); + ggml_backend_tensor_set(labels, &onef, (pos_ubatch*labels->ne[0] + labels_sparse[ilabel])*sizeof(float), sizeof(float)); + } + } + ggml_opt_eval(opt_ctx, result); + if (callback) { + callback(train, opt_ctx, dataset, result, idata_in_loop + (pos_ctx + pos_batch)/n_ubatch + 1, ndata_in_loop, t_loop_start); + } + ggml_free(ctx_compute_opt); + } + } + + kv_guard.commit(); +} + +void llama_context::opt_epoch( + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval) { + const uint32_t n_ctx = this->n_ctx(); + const uint32_t n_batch = std::min(cparams.n_batch, n_ctx); + const uint32_t n_ubatch = std::min(cparams.n_ubatch, n_batch); + const int64_t ndata = ggml_opt_dataset_ndata(dataset); + + GGML_ASSERT(idata_split >= 0); + GGML_ASSERT(idata_split <= ndata); + + const uint32_t ubatch_per_ctx = n_ctx / n_ubatch; + + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); + std::vector tokens(n_ctx); + std::vector labels_sparse(n_ctx); + + int64_t idata = 0; + + int64_t t_loop_start = ggml_time_us(); + int64_t ndata_in_loop = idata_split*ubatch_per_ctx; + for (; idata < idata_split; ++idata) { + constexpr bool train = true; + const int64_t idata_in_loop = idata*ubatch_per_ctx; + + ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata); + opt_epoch_iter(dataset, result_train, tokens, labels_sparse, batch, + callback_train, train, idata_in_loop, ndata_in_loop, t_loop_start); + } + + t_loop_start = ggml_time_us(); + ndata_in_loop = (ndata - idata_split)*ubatch_per_ctx; + for (; idata < ndata; ++idata) { + constexpr bool train = false; + const int64_t idata_in_loop = (idata - idata_split)*ubatch_per_ctx; + + ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata); + opt_epoch_iter(dataset, result_eval, tokens, labels_sparse, batch, + callback_eval, train, idata_in_loop, ndata_in_loop, t_loop_start); + } + + llama_batch_free(batch); +} + // // interface implementation // @@ -2464,3 +2675,34 @@ void llama_perf_context_print(const llama_context * ctx) { void llama_perf_context_reset(llama_context * ctx) { ctx->perf_reset(); } + +// +// training +// + +bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata) { + GGML_UNUSED(tensor); + GGML_UNUSED(userdata); + return true; +} + +void llama_opt_init(struct llama_context * ctx, struct llama_model * model, struct llama_opt_params lopt_params) { + ctx->opt_init(model, lopt_params); +} + +void llama_opt_epoch( + struct llama_context * ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval) { + ctx->opt_epoch( + dataset, + result_train, + result_eval, + idata_split, + callback_train, + callback_eval); +} diff --git a/src/llama-context.h b/src/llama-context.h index 5a080e67f..c0ceacb10 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -7,6 +7,7 @@ #include "llama-adapter.h" #include "ggml-cpp.h" +#include "ggml-opt.h" #include #include @@ -133,6 +134,32 @@ struct llama_context { llama_perf_context_data perf_get_data() const; void perf_reset(); + // + // training + // + + void opt_init(struct llama_model * model, struct llama_opt_params lopt_params); + + void opt_epoch( + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval); + + void opt_epoch_iter( + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + const std::vector & tokens, + const std::vector & labels_sparse, + llama_batch & batch, + ggml_opt_epoch_callback callback, + bool train, + int64_t idata_in_loop, + int64_t ndata_in_loop, + int64_t t_loop_start); + private: // // output @@ -212,6 +239,9 @@ private: ggml_context_ptr ctx_compute; + // training + ggml_opt_context_t opt_ctx = nullptr; + ggml_threadpool_t threadpool = nullptr; ggml_threadpool_t threadpool_batch = nullptr; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index a8bb83cc5..b0e3f6359 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -971,6 +971,7 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const { inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens); //cb(inp->tokens, "inp_tokens", -1); ggml_set_input(inp->tokens); + res->t_tokens = inp->tokens; cur = ggml_get_rows(ctx0, tok_embd, inp->tokens); diff --git a/src/llama-graph.h b/src/llama-graph.h index 5b404366d..832a8c09f 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -298,6 +298,7 @@ class llm_graph_result_i { public: virtual ~llm_graph_result_i() = default; + virtual ggml_tensor * get_tokens() = 0; virtual ggml_tensor * get_logits() = 0; virtual ggml_tensor * get_embd() = 0; virtual ggml_tensor * get_embd_pooled() = 0; @@ -312,6 +313,7 @@ class llm_graph_result : public llm_graph_result_i { public: virtual ~llm_graph_result() = default; + ggml_tensor * get_tokens() override { return t_tokens; } ggml_tensor * get_logits() override { return t_logits; } ggml_tensor * get_embd() override { return t_embd; } ggml_tensor * get_embd_pooled() override { return t_embd_pooled; } @@ -328,6 +330,7 @@ public: } // important graph nodes + ggml_tensor * t_tokens = nullptr; ggml_tensor * t_logits = nullptr; ggml_tensor * t_embd = nullptr; ggml_tensor * t_embd_pooled = nullptr; diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 1c8bce385..4cce51668 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -301,12 +301,12 @@ namespace GGUFMeta { GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { - case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; - case GGUF_TYPE_INT32: GGML_ASSERT( - (std::is_same::value) || - (std::is_same::value)); break; + case GGUF_TYPE_UINT32: + case GGUF_TYPE_INT32: GGML_ASSERT((std::is_same::value) || + (std::is_same::value)); break; + case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; default: - throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str())); + throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str())); } result.resize(arr_info.length); @@ -330,12 +330,12 @@ namespace GGUFMeta { GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { - case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; - case GGUF_TYPE_INT32: GGML_ASSERT( - (std::is_same::value) || - (std::is_same::value)); break; + case GGUF_TYPE_UINT32: + case GGUF_TYPE_INT32: GGML_ASSERT((std::is_same::value) || + (std::is_same::value)); break; + case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; default: - throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str())); + throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str())); } if (arr_info.length > N_MAX) { diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp new file mode 100644 index 000000000..a70b98923 --- /dev/null +++ b/src/llama-model-saver.cpp @@ -0,0 +1,281 @@ +#include "llama-model-saver.h" + +#include "gguf.h" + +#include "llama.h" +#include "llama-hparams.h" +#include "llama-model.h" +#include "llama-vocab.h" + +#include + +llama_model_saver::llama_model_saver(const struct llama_model & model) : model(model), llm_kv(model.arch) { + gguf_ctx = gguf_init_empty(); +} + +llama_model_saver::~llama_model_saver() { + gguf_free(gguf_ctx); +} + +void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) { + gguf_set_val_u32(gguf_ctx, llm_kv(key).c_str(), value); +} + +void llama_model_saver::add_kv(const enum llm_kv key, const int32_t value) { + gguf_set_val_i32(gguf_ctx, llm_kv(key).c_str(), value); +} + +void llama_model_saver::add_kv(const enum llm_kv key, const float value) { + gguf_set_val_f32(gguf_ctx, llm_kv(key).c_str(), value); +} + +void llama_model_saver::add_kv(const enum llm_kv key, const bool value) { + gguf_set_val_bool(gguf_ctx, llm_kv(key).c_str(), value); +} + +void llama_model_saver::add_kv(const enum llm_kv key, const char * value) { + gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), value); +} + +[[noreturn]] +void llama_model_saver::add_kv(const enum llm_kv key, const char value) { + GGML_UNUSED(key); + GGML_UNUSED(value); + GGML_ABORT("fatal error"); // this should never be called, only needed to make the template below compile +} + +template +void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) { + const size_t n_values = per_layer ? size_t(model.hparams.n_layer) : value.size(); + GGML_ASSERT(n_values <= value.size()); + + if (n_values == 0) { + return; + } + + if (per_layer) { + bool all_values_the_same = true; + for (size_t i = 1; i < n_values; ++i) { + if (value[i] != value[0]) { + all_values_the_same = false; + break; + } + } + if (all_values_the_same) { + add_kv(key, value[0]); + return; + } + } + + if (std::is_same::value) { + gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT8, value.data(), n_values); + } else if (std::is_same::value) { + gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT8, value.data(), n_values); + } else if (std::is_same::value) { + gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT32, value.data(), n_values); + } else if (std::is_same::value) { + gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT32, value.data(), n_values); + } else if (std::is_same::value) { + gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_FLOAT32, value.data(), n_values); + } else if (std::is_same::value) { + gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), reinterpret_cast(value.data())); + } else { + GGML_ABORT("fatal error"); + } +} + +void llama_model_saver::add_kv(const enum llm_kv key, const std::vector & value) { + std::vector tmp(value.size()); + for (size_t i = 0; i < value.size(); ++i) { + tmp[i] = value[i].c_str(); + } + gguf_set_arr_str(gguf_ctx, llm_kv(key).c_str(), tmp.data(), tmp.size()); +} + +void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) { + if (!tensor) { + return; + } + if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) { + GGML_ASSERT(std::string(tensor->name) == "rope_freqs.weight"); // FIXME + return; + } + gguf_add_tensor(gguf_ctx, tensor); +} + +void llama_model_saver::add_kv_from_model() { + const llama_hparams & hparams = model.hparams; + const llama_vocab & vocab = model.vocab; + + const int32_t n_vocab = vocab.n_tokens(); + std::vector tokens(n_vocab); + std::vector scores(n_vocab); + std::vector token_types(n_vocab); + + for (int32_t id = 0; id < n_vocab; ++id) { + const llama_vocab::token_data & token_data = vocab.get_token_data(id); + + tokens[id] = token_data.text; + scores[id] = token_data.score; + + switch(token_data.attr) { + case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break; + case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break; + case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break; + case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break; + case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break; + case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break; + case LLAMA_TOKEN_ATTR_UNDEFINED: + default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break; + } + } + + // add_kv(LLM_KV_GENERAL_TYPE, ???); + add_kv(LLM_KV_GENERAL_ARCHITECTURE, model.arch_name()); + // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION, ???); + // add_kv(LLM_KV_GENERAL_ALIGNMENT, ???); + add_kv(LLM_KV_GENERAL_NAME, model.name); + // add_kv(LLM_KV_GENERAL_AUTHOR, ???); + // add_kv(LLM_KV_GENERAL_VERSION, ???); + // add_kv(LLM_KV_GENERAL_URL, ???); + // add_kv(LLM_KV_GENERAL_DESCRIPTION, ???); + // add_kv(LLM_KV_GENERAL_LICENSE, ???); + // add_kv(LLM_KV_GENERAL_SOURCE_URL, ???); + // add_kv(LLM_KV_GENERAL_SOURCE_HF_REPO, ???); + + add_kv(LLM_KV_VOCAB_SIZE, vocab.n_tokens()); + add_kv(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); + add_kv(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); + add_kv(LLM_KV_BLOCK_COUNT, hparams.n_layer); + add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + add_kv(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, true); + add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + add_kv(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res); + // add_kv(LLM_KV_TENSOR_DATA_LAYOUT, ???); + add_kv(LLM_KV_EXPERT_COUNT, hparams.n_expert); + add_kv(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used); + add_kv(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + add_kv(LLM_KV_POOLING_TYPE, uint32_t(hparams.pooling_type)); + add_kv(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); + add_kv(LLM_KV_DECODER_START_TOKEN_ID, hparams.dec_start_token_id); + add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping); + add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping); + add_kv(LLM_KV_SWIN_NORM, hparams.swin_norm); + add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers); + add_kv(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim); + add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim); + add_kv(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale); + add_kv(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale); + + add_kv(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, true); + add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true); + add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); + add_kv(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv); + add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k); + add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v); + add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + add_kv(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + add_kv(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + add_kv(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts); + add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + add_kv(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale); + + const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; + + add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot); + add_kv(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train); + // add_kv(LLM_KV_ROPE_SCALE_LINEAR, rope_scaling_factor); // old name + add_kv(LLM_KV_ROPE_SCALING_TYPE, llama_rope_scaling_type_name(hparams.rope_scaling_type_train)); + add_kv(LLM_KV_ROPE_SCALING_FACTOR, rope_scaling_factor); + add_kv(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor); + add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn); + add_kv(LLM_KV_ROPE_SCALING_FINETUNED, hparams.rope_finetuned); + add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul); + + // TODO: implement split file support + // add_kv(LLM_KV_SPLIT_NO, ???); + // add_kv(LLM_KV_SPLIT_COUNT, ???); + // add_kv(LLM_KV_SPLIT_TENSORS_COUNT, ???); + + add_kv(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner); + add_kv(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); + add_kv(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state); + add_kv(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank); + add_kv(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms); + + add_kv(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size); + + add_kv(LLM_KV_TOKENIZER_MODEL, vocab.get_tokenizer_model()); + add_kv(LLM_KV_TOKENIZER_PRE, vocab.get_tokenizer_pre()); + add_kv(LLM_KV_TOKENIZER_LIST, tokens); + add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE, token_types); + add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, vocab.n_token_types()); + add_kv(LLM_KV_TOKENIZER_SCORES, scores); + add_kv(LLM_KV_TOKENIZER_MERGES, vocab.get_bpe_merges()); + // FIXME llama_token is type i32 but when reading in a GGUF file u32 is expected, not an issue for writing though + add_kv(LLM_KV_TOKENIZER_BOS_ID, uint32_t(vocab.token_bos())); + add_kv(LLM_KV_TOKENIZER_EOS_ID, uint32_t(vocab.token_eos())); + add_kv(LLM_KV_TOKENIZER_EOT_ID, uint32_t(vocab.token_eot())); + add_kv(LLM_KV_TOKENIZER_EOM_ID, uint32_t(vocab.token_eom())); + add_kv(LLM_KV_TOKENIZER_UNK_ID, uint32_t(vocab.token_unk())); + add_kv(LLM_KV_TOKENIZER_SEP_ID, uint32_t(vocab.token_sep())); + add_kv(LLM_KV_TOKENIZER_PAD_ID, uint32_t(vocab.token_pad())); + // add_kv(LLM_KV_TOKENIZER_CLS_ID, uint32_t(vocab.token_bos())); // deprecated + // add_kv(LLM_KV_TOKENIZER_MASK_ID, ???); + add_kv(LLM_KV_TOKENIZER_ADD_BOS, vocab.get_add_bos()); + add_kv(LLM_KV_TOKENIZER_ADD_EOS, vocab.get_add_eos()); + add_kv(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.get_add_space_prefix()); + add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.get_remove_extra_whitespaces()); + add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, vocab.get_precompiled_charsmap()); + // add_kv(LLM_KV_TOKENIZER_HF_JSON, ???); + // add_kv(LLM_KV_TOKENIZER_RWKV, ???); + add_kv(LLM_KV_TOKENIZER_FIM_PRE_ID, uint32_t(vocab.token_fim_pre())); + add_kv(LLM_KV_TOKENIZER_FIM_SUF_ID, uint32_t(vocab.token_fim_suf())); + add_kv(LLM_KV_TOKENIZER_FIM_MID_ID, uint32_t(vocab.token_fim_mid())); + add_kv(LLM_KV_TOKENIZER_FIM_PAD_ID, uint32_t(vocab.token_fim_pad())); + add_kv(LLM_KV_TOKENIZER_FIM_REP_ID, uint32_t(vocab.token_fim_rep())); + add_kv(LLM_KV_TOKENIZER_FIM_SEP_ID, uint32_t(vocab.token_fim_sep())); + + // TODO: implement LoRA support + // add_kv(LLM_KV_ADAPTER_TYPE, ???); + // add_kv(LLM_KV_ADAPTER_LORA_ALPHA, ???); + + // deprecated + // add_kv(LLM_KV_TOKENIZER_PREFIX_ID, ???); + // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID, ???); + // add_kv(LLM_KV_TOKENIZER_MIDDLE_ID, ???); +} + +void llama_model_saver::add_tensors_from_model() { + if (std::string(model.output->name) != std::string(model.tok_embd->name)) { + add_tensor(model.tok_embd); // some models use the same tensor for tok_embd and output + } + add_tensor(model.type_embd); + add_tensor(model.pos_embd); + add_tensor(model.tok_norm); + add_tensor(model.tok_norm_b); + add_tensor(model.output_norm); + add_tensor(model.output_norm_b); + add_tensor(model.output); + add_tensor(model.output_b); + add_tensor(model.output_norm_enc); + add_tensor(model.cls); + add_tensor(model.cls_b); + add_tensor(model.cls_out); + add_tensor(model.cls_out_b); + + for (const struct llama_layer & layer : model.layers) { + for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) { + add_tensor(reinterpret_cast(&layer)[i]); + } + } +} + +void llama_model_saver::save(const std::string & path_model) { + gguf_write_to_file(gguf_ctx, path_model.c_str(), false); +} + diff --git a/src/llama-model-saver.h b/src/llama-model-saver.h new file mode 100644 index 000000000..a5a434c30 --- /dev/null +++ b/src/llama-model-saver.h @@ -0,0 +1,37 @@ +#pragma once + +#include "llama.h" +#include "llama-arch.h" + +#include + +struct llama_model_saver { + struct gguf_context * gguf_ctx = nullptr; + const struct llama_model & model; + const struct LLM_KV llm_kv; + + llama_model_saver(const struct llama_model & model); + ~llama_model_saver(); + + void add_kv(enum llm_kv key, uint32_t value); + void add_kv(enum llm_kv key, int32_t value); + void add_kv(enum llm_kv key, float value); + void add_kv(enum llm_kv key, bool value); + void add_kv(enum llm_kv key, const char * value); + + [[noreturn]] + void add_kv(enum llm_kv key, char value); // needed to make the template below compile + + template + void add_kv(enum llm_kv key, const Container & value, bool per_layer = false); + + void add_kv(enum llm_kv key, const std::vector & value); + + void add_tensor(const struct ggml_tensor * tensor); + + void add_kv_from_model(); + + void add_tensors_from_model(); + + void save(const std::string & path_model); +}; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 21b12339a..3a4e72a36 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -117,6 +117,10 @@ static const std::map LLAMA_ROPE_SCALING_ { LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" }, }; +std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type) { + return LLAMA_ROPE_SCALING_TYPES.at(rope_scaling_type); +} + static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { @@ -4264,7 +4268,7 @@ uint64_t llama_model::n_elements() const { } void llama_model::print_info() const { - const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train); auto print_f = [](const std::function & f, uint32_t n) { bool is_var = false; @@ -4325,7 +4329,7 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn); LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); - LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); diff --git a/src/llama-model.h b/src/llama-model.h index 815fa11eb..6bdec263b 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -96,6 +96,8 @@ enum llm_type { LLM_TYPE_235B_A22B, }; +std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type); + struct llama_layer_posnet { // resnet struct ggml_tensor * norm1 = nullptr; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 7dc542276..820d5128e 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -519,7 +519,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: nthread = std::thread::hardware_concurrency(); } - // mmap consistently increases speed Linux, and also increases speed on Windows with + // mmap consistently increases speed on Linux, and also increases speed on Windows with // hot cache. It may cause a slowdown on macOS, possibly related to free memory. #if defined(__linux__) || defined(_WIN32) constexpr bool use_mmap = true; @@ -529,7 +529,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: llama_model_kv_override * kv_overrides = nullptr; if (params->kv_overrides) { - auto v = (std::vector*)params->kv_overrides; + auto * v = (std::vector*)params->kv_overrides; kv_overrides = v->data(); } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index e6b96ecc8..9389ca805 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1,5 +1,7 @@ #include "llama-vocab.h" +#include "ggml.h" +#include "gguf.h" #include "llama-impl.h" #include "llama-model-loader.h" @@ -1234,6 +1236,9 @@ struct fragment_buffer_variant { struct llama_vocab::impl { uint32_t n_token_types = 0; // for BERT-style token types + std::string tokenizer_model; + std::string tokenizer_pre; + enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM; enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT; @@ -1369,9 +1374,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { // determine vocab type { - std::string tokenizer_model; - std::string tokenizer_pre; - ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model); ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false); @@ -1466,7 +1468,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str()); if (precompiled_charsmap_keyidx != -1) { - size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx); + const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx); + GGML_ASSERT(pc_type == GGUF_TYPE_INT8 || pc_type == GGUF_TYPE_UINT8); + + const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx); const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx); precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap); #ifdef IS_BIG_ENDIAN @@ -2789,6 +2794,14 @@ void llama_vocab::load(llama_model_loader & ml, const LLM_KV & kv) { pimpl->load(ml, kv); } +std::string llama_vocab::get_tokenizer_model() const { + return pimpl->tokenizer_model; +} + +std::string llama_vocab::get_tokenizer_pre() const { + return pimpl->tokenizer_pre; +} + enum llama_vocab_type llama_vocab::get_type() const { return pimpl->type; } @@ -3011,6 +3024,20 @@ int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string return it->second; } +std::vector llama_vocab::get_bpe_merges() const { + std::vector result(pimpl->bpe_ranks.size()); + + for (const auto & pair : pimpl->bpe_ranks) { + result[pair.second] = pair.first.first + " " + pair.first.second; + } + + return result; +} + +std::vector llama_vocab::get_precompiled_charsmap() const { + return pimpl->precompiled_charsmap; +} + int32_t llama_vocab::tokenize( const char * text, int32_t text_len, diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 5ce355214..daa6cf308 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -21,6 +21,9 @@ struct llama_vocab { void load(llama_model_loader & ml, const LLM_KV & kv); + std::string get_tokenizer_model() const; + std::string get_tokenizer_pre() const; + enum llama_vocab_type get_type() const; enum llama_vocab_pre_type get_pre_type() const; @@ -80,6 +83,9 @@ struct llama_vocab { int max_token_len() const; int find_bpe_rank(const std::string & token_left, const std::string & token_right) const; + std::vector get_bpe_merges() const; + + std::vector get_precompiled_charsmap() const; int32_t tokenize( const char * text, diff --git a/src/llama.cpp b/src/llama.cpp index d5164720b..9fdddf7b0 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4,6 +4,7 @@ #include "llama-mmap.h" #include "llama-vocab.h" #include "llama-model-loader.h" +#include "llama-model-saver.h" #include "llama-model.h" #include "ggml.h" @@ -253,6 +254,13 @@ struct llama_model * llama_model_load_from_splits( return llama_model_load_from_file_impl(splits.front(), splits, params); } +void llama_model_save_to_file(const struct llama_model * model, const char * path_model) { + llama_model_saver ms(*model); + ms.add_kv_from_model(); + ms.add_tensors_from_model(); + ms.save(path_model); +} + // // chat templates // @@ -338,3 +346,4 @@ const char * llama_print_system_info(void) { return s.c_str(); } + diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 9ec24d9f2..543db9340 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -823,7 +823,7 @@ struct test_case { ggml_build_forward_expand(gf, out); ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx.get(), ctx.get(), gb, false); + ggml_build_backward_expand(ctx.get(), gb, nullptr); if (expect.size() != 1 || expect[0] != 0.0f) { GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf)); for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) { @@ -1026,7 +1026,7 @@ struct test_example : public test_case { // Step 3: return the output tensor. return out; } - // In order to also check the gradients for your op, add calls like ggml_set_param(ctx, a) + // In order to also check the gradients for your op, add calls like ggml_set_param(a) // immediately after you create the tensors. // This is optional and only makes sense if a backward pass has actually been implemented for the new op. }; @@ -1058,7 +1058,7 @@ struct test_unary : public test_case { auto ne = ne_a; ne[0] *= 3; a = ggml_new_tensor(ctx, type, 4, ne.data()); if (grad_supported) { - ggml_set_param(ctx, a); + ggml_set_param(a); } ggml_set_name(a, "a"); @@ -1067,7 +1067,7 @@ struct test_unary : public test_case { } else { a = ggml_new_tensor(ctx, type, 4, ne_a.data()); if (grad_supported) { - ggml_set_param(ctx, a); + ggml_set_param(a); } ggml_set_name(a, "a"); } @@ -1133,7 +1133,7 @@ struct test_get_rows : public test_case { const bool grad_supported = ggml_is_matrix(in) && ggml_is_vector(rows); if (grad_supported) { - ggml_set_param(ctx, in); + ggml_set_param(in); // rows is a constant input -> no gradients } @@ -1322,7 +1322,7 @@ struct test_repeat : public test_case { ggml_set_name(target, "target"); ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, src); + ggml_set_param(src); ggml_set_name(src, "src"); ggml_tensor * out = ggml_repeat(ctx, src, target); @@ -1406,7 +1406,7 @@ struct test_dup : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, src); + ggml_set_param(src); ggml_set_name(src, "src"); if (_use_permute) { @@ -1442,7 +1442,7 @@ struct test_set : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data()); - ggml_set_param(ctx, src); + ggml_set_param(src); ggml_set_name(src, "src"); auto ne_dst = ne; @@ -1450,7 +1450,7 @@ struct test_set : public test_case { ne_dst[i] *= 2; } ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data()); - ggml_set_param(ctx, dst); + ggml_set_param(dst); ggml_set_name(dst, "dst"); size_t offset = 0; @@ -1498,7 +1498,7 @@ struct test_cpy : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data()); - ggml_set_param(ctx, src); + ggml_set_param(src); ggml_set_name(src, "src"); if (_src_use_permute) { @@ -1536,7 +1536,7 @@ struct test_cont : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, src); + ggml_set_param(src); ggml_set_name(src, "src"); src = ggml_transpose(ctx, src); @@ -1583,8 +1583,8 @@ struct test_bin_bcast : public test_case { // The backward pass supports broadcasting only for GGML_ADD: const bool grad_supported = op == ggml_add || ggml_are_same_shape(a, b); if (grad_supported) { - ggml_set_param(ctx, a); - ggml_set_param(ctx, b); + ggml_set_param(a); + ggml_set_param(b); } ggml_tensor * out = op(ctx, a, b); @@ -1632,11 +1632,11 @@ struct test_add1 : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * b = ggml_new_tensor_1d(ctx, type, 1); - // ggml_set_param(ctx, b); // TODO: implement + // ggml_set_param(b); // TODO: implement ggml_set_name(b, "b"); ggml_tensor * out = ggml_add1(ctx, a, b); @@ -1667,7 +1667,7 @@ struct test_scale : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_scale(ctx, a, scale); @@ -1762,7 +1762,7 @@ struct test_rms_norm : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); if (v) { @@ -2028,9 +2028,9 @@ struct test_mul_mat : public test_case { b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]); if (!ggml_is_quantized(type_a)) { if (bs[1] == 1 && nr[1] == 1) { - ggml_set_param(ctx, a); + ggml_set_param(a); } - ggml_set_param(ctx, b); + ggml_set_param(b); } ggml_set_name(a, "a"); ggml_set_name(b, "b"); @@ -2040,22 +2040,29 @@ struct test_mul_mat : public test_case { ggml_set_name(a, "a_permuted"); ggml_set_name(b, "b_permuted"); } else { - if (v) { a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]); b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]); + if (!ggml_is_quantized(type_a)) { + if (bs[1] == 1 && nr[1] == 1) { + ggml_set_param(a); + } + ggml_set_param(b); + } + a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0); b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->nb[3], 0); } else { a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]); b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); - } - if (!ggml_is_quantized(type_a)) { - if (bs[1] == 1 && nr[1] == 1) { - ggml_set_param(ctx, a); + + if (!ggml_is_quantized(type_a)) { + if (bs[1] == 1 && nr[1] == 1) { + ggml_set_param(a); + } + ggml_set_param(b); } - ggml_set_param(ctx, b); } ggml_set_name(a, "a"); ggml_set_name(b, "b"); @@ -2204,7 +2211,7 @@ struct test_sqr : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_sqr(ctx, a); @@ -2233,7 +2240,7 @@ struct test_sqrt : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_sqrt(ctx, a); @@ -2273,7 +2280,7 @@ struct test_log : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_log(ctx, a); @@ -2309,7 +2316,7 @@ struct test_sin : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_sin(ctx, a); @@ -2352,7 +2359,7 @@ struct test_cos : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_cos(ctx, a); @@ -2432,7 +2439,7 @@ struct test_diag_mask_inf : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past); @@ -2471,7 +2478,7 @@ struct test_soft_max : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * mask = nullptr; @@ -2553,7 +2560,7 @@ struct test_rope : public test_case { auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3; a = ggml_new_tensor(ctx, type, 4, ne.data()); if (forward) { - ggml_set_param(ctx, a); + ggml_set_param(a); } ggml_set_name(a, "a"); @@ -2562,7 +2569,7 @@ struct test_rope : public test_case { } else { a = ggml_new_tensor(ctx, type, 4, ne_a.data()); if (forward) { - ggml_set_param(ctx, a); + ggml_set_param(a); } ggml_set_name(a, "a"); } @@ -2676,7 +2683,7 @@ struct test_pool2d : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); - ggml_set_param(ctx, input); + ggml_set_param(input); ggml_set_name(input, "input"); ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1); @@ -2752,7 +2759,7 @@ struct test_im2col : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); - ggml_set_param(ctx, input); + ggml_set_param(input); ggml_set_name(input, "input"); ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data()); @@ -2929,7 +2936,7 @@ struct test_sum : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_sum(ctx, a); @@ -2958,7 +2965,7 @@ struct test_sum_rows : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_sum_rows(ctx, a); @@ -2983,7 +2990,7 @@ struct test_mean : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * out = ggml_mean(ctx, a); @@ -3129,11 +3136,11 @@ struct test_acc : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); - ggml_set_param(ctx, a); + ggml_set_param(a); ggml_set_name(a, "a"); ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data()); - ggml_set_param(ctx, b); + ggml_set_param(b); ggml_set_name(b, "b"); ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]); @@ -3370,7 +3377,7 @@ struct test_cross_entropy_loss : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_set_param(ctx, logits); + ggml_set_param(logits); ggml_set_name(logits, "logits"); ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data()); @@ -3452,7 +3459,7 @@ struct test_opt_step_adamw : public test_case { ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); - ggml_set_param(ctx, a); // Despite tensor a having gradients the output tensor will not. + ggml_set_param(a); // Despite tensor a having gradients the output tensor will not. ggml_set_name(a, "a"); ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index 1bc160511..558f87721 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -57,7 +57,8 @@ static helper_ctx_data helper_get_ctx_data( enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) { std::vector datasets(ndata); for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) { - ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard); + ggml_opt_dataset_t dataset = ggml_opt_dataset_init( + GGML_TYPE_F32, GGML_TYPE_F32, ne_datapoint, ne_label, ndata, ndata_shard); float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); @@ -74,7 +75,8 @@ static helper_ctx_data helper_get_ctx_data( datasets[ndata_shard-1] = dataset; } - ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1); + ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init( + GGML_TYPE_F32, GGML_TYPE_F32, 1, 0, ndata, /*ndata_shard =*/ 1); float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised)); @@ -113,7 +115,7 @@ static helper_ctx_data helper_get_ctx_data( struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); ggml_set_name(weights, "weights"); - ggml_set_param(ctx_static, weights); + ggml_set_param(weights); struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights); @@ -127,8 +129,11 @@ static helper_ctx_data helper_get_ctx_data( GGML_ASSERT(nbatch_logical % nbatch_physical == 0); const int32_t opt_period = nbatch_logical / nbatch_physical; - struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); - opt_params.opt_period = opt_period; + struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, loss_type); + opt_params.ctx_compute = ctx_compute; + opt_params.inputs = inputs; + opt_params.outputs = outputs; + opt_params.opt_period = opt_period; if (!optimizer_defaults) { opt_params.get_opt_pars = helper_get_test_opt_pars; } @@ -264,8 +269,9 @@ static std::pair test_grad(ggml_backend_sched_t backend_sched, ggml_ba for (int idata = 0; idata < ndata; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); - ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float)); } @@ -334,8 +340,9 @@ static std::pair test_forward_backward( } else { for (int idata = 0; idata < ndata; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false); ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); - ggml_opt_forward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); } } @@ -367,7 +374,8 @@ static std::pair test_forward_backward( float w0; ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float)); for (int i = 0; i < 10; ++i) { - ggml_opt_forward_backward(cd.opt_ctx, nullptr); + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); + ggml_opt_eval(cd.opt_ctx, cd.result); } ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float)); @@ -387,8 +395,9 @@ static std::pair test_forward_backward( } else { for (int idata = 0; idata < ndata; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); - ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); } } @@ -492,14 +501,16 @@ static std::pair test_idata_split(ggml_backend_sched_t backend_sched, int idata = 0; for (; idata < idata_split; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); - ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); } for (; idata < ndata; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false); ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); - ggml_opt_forward(cd.opt_ctx, cd.result2); + ggml_opt_eval(cd.opt_ctx, cd.result2); ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); } } @@ -573,7 +584,6 @@ static std::pair test_gradient_accumulation( struct helper_ctx_data cd = helper_get_ctx_data( backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type); - struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); std::vector grad_history(ndata); for (int64_t idata = 0; idata < ndata; ++idata) { @@ -584,15 +594,17 @@ static std::pair test_gradient_accumulation( if (nbatch_physical == 1) { for (int idata = 0; idata < ndata; ++idata) { const float idataf = idata; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float)); - ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float)); } } else if (nbatch_physical == 2) { for (int idata = 0; idata < ndata; idata += 2) { const float idataf[2] = {float(idata + 0), float(idata + 1)}; + ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true); ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float)); - ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_opt_eval(cd.opt_ctx, cd.result); grad_history[idata + 0] = 0.0f; ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float)); @@ -617,7 +629,7 @@ static std::pair test_gradient_accumulation( } subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol); subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol); - subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0, atol); } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) { if (nbatch_physical == 1) { subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol); @@ -630,7 +642,7 @@ static std::pair test_gradient_accumulation( } subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol); subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol); - subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0/ndata, atol); } else { GGML_ASSERT(false); } @@ -692,7 +704,8 @@ static std::pair test_regression(ggml_backend_sched_t backend_sched, g std::mt19937 gen(12345); std::normal_distribution nd{0.0f, 0.1f}; - ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression); + ggml_opt_dataset_t dataset = ggml_opt_dataset_init( + GGML_TYPE_F32, GGML_TYPE_F32, 1, 1, ndata_regression, ndata_regression); float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); @@ -733,15 +746,14 @@ static std::pair test_regression(ggml_backend_sched_t backend_sched, g struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); ggml_set_name(a, "a"); - ggml_set_param(ctx_static, a); + ggml_set_param(a); struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); ggml_set_name(b, "b"); - ggml_set_param(ctx_static, b); + ggml_set_param(b); struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b); ggml_set_name(f, "f"); - ggml_set_param(ctx_static, f); ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend); const float a0 = 1.0f; From de4c07f93783a1a96456a44dc16b9db538ee1618 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 12 May 2025 15:06:51 +0200 Subject: [PATCH 027/421] clip : cap max image size 1024 for qwen vl model (#13478) --- tools/mtmd/clip.cpp | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 0adf03163..41ba45a79 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1909,16 +1909,20 @@ struct clip_model_loader { } break; case PROJECTOR_TYPE_QWEN2VL: { - // max image size = sqrt(max_pixels) - // https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json - hparams.image_size = 3584; + // max image size = sqrt(max_pixels) = 3584 + // ref: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json + // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable + // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10 + hparams.image_size = 1024; hparams.warmup_image_size = hparams.patch_size * 8; } break; case PROJECTOR_TYPE_QWEN25VL: { // max image size = sqrt(max_pixels) // https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json - hparams.image_size = 3584; + // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable + // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10 + hparams.image_size = 1024; hparams.warmup_image_size = hparams.patch_size * 8; get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern); } break; From f0d46ef15717cd609a7b69cf6190edde64d466c8 Mon Sep 17 00:00:00 2001 From: lhez Date: Mon, 12 May 2025 13:13:49 -0700 Subject: [PATCH 028/421] opencl: remove unnecessary assert for `add` (#13257) --- ggml/src/ggml-opencl/ggml-opencl.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 05a2f4e63..586946048 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -4855,8 +4855,6 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor if (!any_on_device) { return false; } - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); func = ggml_cl_add; break; case GGML_OP_MUL: From cf0a43bb6490bd49344775abb22ba26f8047cb54 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Mon, 12 May 2025 15:31:37 -0700 Subject: [PATCH 029/421] llama-bench : add defrag-thold, check for invalid ranges (#13487) --- include/llama.h | 2 +- tools/llama-bench/README.md | 7 ++-- tools/llama-bench/llama-bench.cpp | 55 ++++++++++++++++++++++++------- 3 files changed, 49 insertions(+), 15 deletions(-) diff --git a/include/llama.h b/include/llama.h index abedebdb7..99e5fba24 100644 --- a/include/llama.h +++ b/include/llama.h @@ -345,7 +345,7 @@ extern "C" { float yarn_beta_fast; // YaRN low correction dim float yarn_beta_slow; // YaRN high correction dim uint32_t yarn_orig_ctx; // YaRN original context size - float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default) + float defrag_thold; // defragment the KV cache if holes/size > thold, <= 0 disabled (default) ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md index 4fb2a24e1..0479f81a3 100644 --- a/tools/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -43,12 +43,13 @@ test parameters: -ub, --ubatch-size (default: 512) -ctk, --cache-type-k (default: f16) -ctv, --cache-type-v (default: f16) - -t, --threads (default: 16) + -dt, --defrag-thold (default: -1) + -t, --threads (default: system dependent) -C, --cpu-mask (default: 0x0) --cpu-strict <0|1> (default: 0) --poll <0...100> (default: 50) -ngl, --n-gpu-layers (default: 99) - -rpc, --rpc (default: ) + -rpc, --rpc (default: none) -sm, --split-mode (default: layer) -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) @@ -62,7 +63,7 @@ test parameters: Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. Ranges can be given as -'start-end' or 'start-end+step' or 'start-end*mult'. +'first-last' or 'first-last+step' or 'first-last*mult'. ``` llama-bench can perform three types of tests: diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index ca0d0aed5..9457e6815 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -211,6 +211,8 @@ static std::vector parse_int_range(const std::string & s) { for (int i = first; i <= last;) { result.push_back(i); + int prev_i = i; + if (op == '+') { i += step; } else if (op == '*') { @@ -218,6 +220,10 @@ static std::vector parse_int_range(const std::string & s) { } else { throw std::invalid_argument("invalid range format"); } + + if (i <= prev_i) { + throw std::invalid_argument("invalid range"); + } } search_start = match.suffix().first; } @@ -239,6 +245,7 @@ struct cmd_params { std::vector n_ubatch; std::vector type_k; std::vector type_v; + std::vector defrag_thold; std::vector n_threads; std::vector cpu_mask; std::vector cpu_strict; @@ -274,6 +281,7 @@ static const cmd_params cmd_params_defaults = { /* n_ubatch */ { 512 }, /* type_k */ { GGML_TYPE_F16 }, /* type_v */ { GGML_TYPE_F16 }, + /* defrag_thold */ { -1.0f }, /* n_threads */ { cpu_get_num_math() }, /* cpu_mask */ { "0x0" }, /* cpu_strict */ { false }, @@ -335,6 +343,8 @@ static void print_usage(int /* argc */, char ** argv) { join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -dt, --defrag-thold (default: %s)\n", + join(cmd_params_defaults.defrag_thold, ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -C, --cpu-mask (default: %s)\n", @@ -368,7 +378,7 @@ static void print_usage(int /* argc */, char ** argv) { printf( "Multiple values can be given for each parameter by separating them with ','\n" "or by specifying the parameter multiple times. Ranges can be given as\n" - "'start-end' or 'start-end+step' or 'start-end*mult'.\n"); + "'first-last' or 'first-last+step' or 'first-last*mult'.\n"); } static ggml_type ggml_type_from_name(const std::string & s) { @@ -519,6 +529,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.type_v.insert(params.type_v.end(), types.begin(), types.end()); + } else if (arg == "-dt" || arg == "--defrag-thold") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.defrag_thold.insert(params.defrag_thold.end(), p.begin(), p.end()); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { invalid_param = true; @@ -825,6 +842,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } + if (params.defrag_thold.empty()) { + params.defrag_thold = cmd_params_defaults.defrag_thold; + } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } @@ -883,6 +903,7 @@ struct cmd_params_instance { int n_ubatch; ggml_type type_k; ggml_type type_v; + float defrag_thold; int n_threads; std::string cpu_mask; bool cpu_strict; @@ -959,15 +980,16 @@ struct cmd_params_instance { llama_context_params to_llama_cparams() const { llama_context_params cparams = llama_context_default_params(); - cparams.n_ctx = n_prompt + n_gen + n_depth; - cparams.n_batch = n_batch; - cparams.n_ubatch = n_ubatch; - cparams.type_k = type_k; - cparams.type_v = type_v; - cparams.offload_kqv = !no_kv_offload; - cparams.flash_attn = flash_attn; - cparams.embeddings = embeddings; - cparams.op_offload = !no_op_offload; + cparams.n_ctx = n_prompt + n_gen + n_depth; + cparams.n_batch = n_batch; + cparams.n_ubatch = n_ubatch; + cparams.type_k = type_k; + cparams.type_v = type_v; + cparams.defrag_thold = defrag_thold; + cparams.offload_kqv = !no_kv_offload; + cparams.flash_attn = flash_attn; + cparams.embeddings = embeddings; + cparams.op_offload = !no_op_offload; return cparams; } @@ -992,6 +1014,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nub : params.n_ubatch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) + for (const auto & defrag_thold : params.defrag_thold) for (const auto & nkvo : params.no_kv_offload) for (const auto & fa : params.flash_attn) for (const auto & nt : params.n_threads) @@ -1012,6 +1035,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, + /* .defrag_thold = */ defrag_thold, /* .n_threads = */ nt, /* .cpu_mask = */ cm, /* .cpu_strict = */ cs, @@ -1044,6 +1068,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, + /* .defrag_thold = */ defrag_thold, /* .n_threads = */ nt, /* .cpu_mask = */ cm, /* .cpu_strict = */ cs, @@ -1076,6 +1101,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, + /* .defrag_thold = */ defrag_thold, /* .n_threads = */ nt, /* .cpu_mask = */ cm, /* .cpu_strict = */ cs, @@ -1117,6 +1143,7 @@ struct test { int poll; ggml_type type_k; ggml_type type_v; + float defrag_thold; int n_gpu_layers; llama_split_mode split_mode; int main_gpu; @@ -1151,6 +1178,7 @@ struct test { poll = inst.poll; type_k = inst.type_k; type_v = inst.type_v; + defrag_thold = inst.defrag_thold; n_gpu_layers = inst.n_gpu_layers; split_mode = inst.split_mode; main_gpu = inst.main_gpu; @@ -1206,6 +1234,7 @@ struct test { "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", + "defrag_thold", "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", }; @@ -1225,7 +1254,7 @@ struct test { field == "use_mmap" || field == "embeddings") { return BOOL; } - if (field == "avg_ts" || field == "stddev_ts") { + if (field == "avg_ts" || field == "stddev_ts" || field == "defrag_thold") { return FLOAT; } return STRING; @@ -1292,6 +1321,7 @@ struct test { std::to_string(flash_attn), tensor_split_str, tensor_buft_overrides_str, + std::to_string(defrag_thold), std::to_string(use_mmap), std::to_string(embeddings), std::to_string(no_op_offload), @@ -1558,6 +1588,9 @@ struct markdown_printer : public printer { if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) { fields.emplace_back("type_v"); } + if (params.defrag_thold.size() > 1 || params.defrag_thold != cmd_params_defaults.defrag_thold) { + fields.emplace_back("defrag_thold"); + } if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { fields.emplace_back("main_gpu"); } From 1e2809bc4b5d8db2a9ed12ac872eca832c53f5fd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 May 2025 14:01:45 +0300 Subject: [PATCH 030/421] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 1f7c650c2..ddd884d37 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -b59bddafe278877dfa22a80e53a637513862babb +9b048bb72b811f50b0c30d9e5c84d6ff9f4bf005 From d590cd4c244e5f260c42c290b83a358b9d86d763 Mon Sep 17 00:00:00 2001 From: Gabe Goodhart Date: Tue, 13 May 2025 07:12:01 -0600 Subject: [PATCH 031/421] model : Granite MoE shared (#13269) * feat: Add GGUF conversion for granitemoeshared Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * feat: hparam and arch plumbing for granitemoeshared Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Split MoE fused tensors for shared experts in conversion Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * feat: First WIP cut at model arch in cpp The hparam and architecture plumbing should be correct, but the implementation of the shared experts seems to still be broken. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Cleaner (maybe more correct?) splitting for gate/up Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Fix the input to the shared experts I had misread that the shared experts take the inputs _before_ the standard MoE layer and was feeding the output of the MoE to the shared experts. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Avoid architecture-specific checks for Granite MoE Shared This is a cleaner way that will allow more flexibility in architecture strings going forward. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * refactor: Split granite architectures out of llm_build_llama This helps de-clutter the llama-family graph construction and allows granite to diverge further (in preparation for Granite 4). NOTE: I removed the granite scale factors from llm_build_deci because they appear to only be there as copy-paste from llm_build_llama. The HF config does not seem to set those values: https://huggingface.co/Deci/DeciLM-7B/blob/main/config.json Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Fix compiler warning about uninitialized inp_pos This should not have been reachable, but it warns on some compliers Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Consoladate GraniteMoEShared into GraniteMoE for conversion Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart * fix: Consolidate GraniteMoEShared into GraniteMoE on the c++ side Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart --------- Signed-off-by: Gabe Goodhart --- convert_hf_to_gguf.py | 22 ++- gguf-py/gguf/constants.py | 3 + gguf-py/gguf/tensor_mapping.py | 1 + src/llama-arch.cpp | 3 + src/llama-model.cpp | 241 ++++++++++++++++++++++++++++----- 5 files changed, 235 insertions(+), 35 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index a34ba2988..68b5e8799 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -5746,11 +5746,20 @@ class GraniteModel(LlamaModel): logger.info("gguf: (granite) logits_scale = %s", logits_scale) -@ModelBase.register("GraniteMoeForCausalLM") +@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM") class GraniteMoeModel(GraniteModel): """Conversion for IBM's GraniteMoeForCausalLM""" model_arch = gguf.MODEL_ARCH.GRANITE_MOE + def set_gguf_parameters(self): + """GraniteMoeShared uses GraniteMoe parameters plus the following: + - shared_intermediate_size + """ + super().set_gguf_parameters() + if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"): + self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length) + logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: """In modeling_granitemoe, the JetMoe implementation of parallel experts is used. This essentially merges w1 and w3 into a single tensor with 2x @@ -5761,12 +5770,21 @@ class GraniteMoeModel(GraniteModel): if name.endswith("block_sparse_moe.input_linear.weight"): ffn_dim = self.hparams["intermediate_size"] assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size" - gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :] + gate, up = data_torch.split(ffn_dim, dim=-2) return [ (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate), (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up), ] + if name.endswith("shared_mlp.input_linear.weight"): + ffn_dim = self.hparams["shared_intermediate_size"] + assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size" + gate, up = data_torch.split(ffn_dim, dim=-2) + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), gate), + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), up), + ] + return super().modify_tensors(data_torch, name, bid) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 0e6226b90..21af0a9a2 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -1905,6 +1905,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, ], MODEL_ARCH.CHAMELEON: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index ecf21b2b4..2629b3c1a 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -428,6 +428,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2 "language_model.model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4 + "model.layers.{bid}.shared_mlp.output_linear", # granitemoe ), MODEL_TENSOR.ATTN_Q_NORM: ( diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index f2bc8ca76..abf436ada 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -1481,6 +1481,9 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, }, }, { diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 3a4e72a36..f652f4b86 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1389,6 +1389,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { // Add additional layer/vocab/etc checks here for other model sizes default: type = LLM_TYPE_UNKNOWN; } + + // For Granite MoE Shared + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false); } break; case LLM_ARCH_CHAMELEON: { @@ -1772,6 +1775,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + + // For Granite MoE Shared + if (hparams.n_ff_shexp > 0) { + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0); + } } } } break; @@ -4385,10 +4395,13 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); } - if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) { + if (arch == LLM_ARCH_MINICPM || + arch == LLM_ARCH_GRANITE || + arch == LLM_ARCH_GRANITE_MOE) { LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale); LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale); LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale); + LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); } if (arch == LLM_ARCH_BAILINGMOE) { @@ -4598,11 +4611,6 @@ struct llm_build_llama : public llm_graph_context { inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } - // For Granite architecture - if (hparams.f_residual_scale) { - cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); - } - ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); @@ -4674,11 +4682,6 @@ struct llm_build_llama : public llm_graph_context { cb(cur, "ffn_moe_out", il); } - // For Granite architecture - if (hparams.f_residual_scale) { - cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); - } - cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -4701,11 +4704,6 @@ struct llm_build_llama : public llm_graph_context { // lm_head cur = build_lora_mm(model.output, cur); - // For Granite architecture - if (hparams.f_logit_scale) { - cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale); - } - cb(cur, "result_output", -1); res->t_logits = cur; @@ -4816,11 +4814,6 @@ struct llm_build_deci : public llm_graph_context { continue; } - // For Granite architecture - if (hparams.f_residual_scale) { - cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); - } - // modified to support attention-free layer of Llama-3_1-Nemotron-51B ggml_tensor * ffn_inp = cur; if (n_head > 0) { @@ -4844,11 +4837,6 @@ struct llm_build_deci : public llm_graph_context { cb(cur, "ffn_out", il); } - // For Granite architecture - if (hparams.f_residual_scale) { - cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); - } - cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -4871,11 +4859,6 @@ struct llm_build_deci : public llm_graph_context { // lm_head cur = build_lora_mm(model.output, cur); - // For Granite architecture - if (hparams.f_logit_scale) { - cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale); - } - cb(cur, "result_output", -1); res->t_logits = cur; @@ -12214,6 +12197,195 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { } }; + +struct llm_build_granite : public llm_graph_context { + llm_build_granite( + const llama_model & model, + const llm_graph_params & params, + ggml_cgraph * gf, + const bool use_rope = true) + : llm_graph_context(params) { + + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - built only if rope enabled + ggml_tensor * inp_pos = nullptr; + + auto * inp_attn = build_attn_inp_kv_unified(); + + const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and (optionally) RoPE them + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + if (use_rope) { + + if (!inp_pos) { + inp_pos = build_inp_pos(); + } + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + } + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + // For Granite architectures - scale residual + cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network (non-MoE) + if (model.layers[il].ffn_gate_inp == nullptr) { + + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + } else { + // MoE branch + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, + il); + cb(moe_out, "ffn_moe_out", il); + + // For Granite MoE Shared + if (hparams.n_ff_shexp > 0) { + ggml_tensor * ffn_shexp = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } else { + cur = moe_out; + } + } + + // For Granite architectures - scale residual + cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + // For Granite architectures - scale logits + cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + // ref: https://github.com/facebookresearch/chameleon // based on the original build_llama() function, changes: // * qk-norm @@ -12921,8 +13093,6 @@ llm_graph_result_ptr llama_model::build_graph( case LLM_ARCH_LLAMA: case LLM_ARCH_LLAMA4: case LLM_ARCH_MINICPM: - case LLM_ARCH_GRANITE: - case LLM_ARCH_GRANITE_MOE: { llm = std::make_unique(*this, params, gf); } break; @@ -13153,6 +13323,11 @@ llm_graph_result_ptr llama_model::build_graph( { llm = std::make_unique(*this, params, gf); } break; + case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: + { + llm = std::make_unique(*this, params, gf); + } break; case LLM_ARCH_CHAMELEON: { llm = std::make_unique(*this, params, gf); From bf7937112058f2815fc3825a9ff7b536ecafa3bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 13 May 2025 15:31:12 +0200 Subject: [PATCH 032/421] scripts : support arbitrary input file formats in compare-llama-bench.py (#13455) --- scripts/compare-llama-bench.py | 452 ++++++++++++++++++++++++--------- 1 file changed, 330 insertions(+), 122 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index c32b449f7..fc93bf62a 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -7,6 +7,10 @@ import sys import os from glob import glob import sqlite3 +import json +import csv +from typing import Optional, Union +from collections.abc import Iterator, Sequence try: import git @@ -17,6 +21,28 @@ except ImportError as e: logger = logging.getLogger("compare-llama-bench") +# All llama-bench SQL fields +DB_FIELDS = [ + "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", + "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", + "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", + "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", + "defrag_thold", + "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", + "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", +] + +DB_TYPES = [ + "TEXT", "INTEGER", "TEXT", "TEXT", "TEXT", "TEXT", + "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", + "TEXT", "INTEGER", "INTEGER", "TEXT", "TEXT", "INTEGER", + "TEXT", "INTEGER", "INTEGER", "INTEGER", "TEXT", "TEXT", + "REAL", + "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", + "TEXT", "INTEGER", "INTEGER", "REAL", "REAL", +] +assert len(DB_FIELDS) == len(DB_TYPES) + # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ "cpu_info", "gpu_info", "backends", "n_gpu_layers", "tensor_buft_overrides", "model_filename", "model_type", @@ -42,7 +68,7 @@ DEFAULT_HIDE = ["model_filename"] # Always hide these properties by default. GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "] # Strip prefixes for smaller tables. MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"} -DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux): +DESCRIPTION = """Creates tables from llama-bench data written to multiple JSON/CSV files, a single JSONL file or SQLite database. Example usage (Linux): $ git checkout master $ make clean && make llama-bench @@ -70,12 +96,13 @@ help_c = ( ) parser.add_argument("-c", "--compare", help=help_c) help_i = ( - "Input SQLite file for comparing commits. " + "JSON/JSONL/SQLite/CSV files for comparing commits. " + "Specify multiple times to use multiple input files (JSON/CSV only). " "Defaults to 'llama-bench.sqlite' in the current working directory. " "If no such file is found and there is exactly one .sqlite file in the current directory, " "that file is instead used as input." ) -parser.add_argument("-i", "--input", help=help_i) +parser.add_argument("-i", "--input", action="append", help=help_i) help_o = ( "Output format for the table. " "Defaults to 'pipe' (GitHub compatible). " @@ -110,119 +137,321 @@ if unknown_args: sys.exit(1) input_file = known_args.input -if input_file is None and os.path.exists("./llama-bench.sqlite"): - input_file = "llama-bench.sqlite" -if input_file is None: +if not input_file and os.path.exists("./llama-bench.sqlite"): + input_file = ["llama-bench.sqlite"] +if not input_file: sqlite_files = glob("*.sqlite") if len(sqlite_files) == 1: - input_file = sqlite_files[0] + input_file = sqlite_files -if input_file is None: +if not input_file: logger.error("Cannot find a suitable input file, please provide one.\n") parser.print_help() sys.exit(1) -connection = sqlite3.connect(input_file) -cursor = connection.cursor() -build_len_min: int = cursor.execute("SELECT MIN(LENGTH(build_commit)) from test;").fetchone()[0] -build_len_max: int = cursor.execute("SELECT MAX(LENGTH(build_commit)) from test;").fetchone()[0] +class LlamaBenchData: + repo: Optional[git.Repo] + build_len_min: int + build_len_max: int + build_len: int = 8 + builds: list[str] = [] + check_keys = set(KEY_PROPERTIES + ["build_commit", "test_time", "avg_ts"]) -if build_len_min != build_len_max: - logger.warning(f"{input_file} contains commit hashes of differing lengths. It's possible that the wrong commits will be compared. " - "Try purging the the database of old commits.") - cursor.execute(f"UPDATE test SET build_commit = SUBSTRING(build_commit, 1, {build_len_min});") + def __init__(self): + try: + self.repo = git.Repo(".", search_parent_directories=True) + except git.InvalidGitRepositoryError: + self.repo = None -build_len: int = build_len_min + def _builds_init(self): + self.build_len = self.build_len_min -builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall() -builds = list(map(lambda b: b[0], builds)) # list[tuple[str]] -> list[str] - -if not builds: - raise RuntimeError(f"{input_file} does not contain any builds.") - -try: - repo = git.Repo(".", search_parent_directories=True) -except git.InvalidGitRepositoryError: - repo = None - - -def find_parent_in_data(commit: git.Commit): - """Helper function to find the most recent parent measured in number of commits for which there is data.""" - heap: list[tuple[int, git.Commit]] = [(0, commit)] - seen_hexsha8 = set() - while heap: - depth, current_commit = heapq.heappop(heap) - current_hexsha8 = commit.hexsha[:build_len] - if current_hexsha8 in builds: - return current_hexsha8 - for parent in commit.parents: - parent_hexsha8 = parent.hexsha[:build_len] - if parent_hexsha8 not in seen_hexsha8: - seen_hexsha8.add(parent_hexsha8) - heapq.heappush(heap, (depth + 1, parent)) - return None - - -def get_all_parent_hexsha8s(commit: git.Commit): - """Helper function to recursively get hexsha8 values for all parents of a commit.""" - unvisited = [commit] - visited = [] - - while unvisited: - current_commit = unvisited.pop(0) - visited.append(current_commit.hexsha[:build_len]) - for parent in current_commit.parents: - if parent.hexsha[:build_len] not in visited: - unvisited.append(parent) - - return visited - - -def get_commit_name(hexsha8: str): - """Helper function to find a human-readable name for a commit if possible.""" - if repo is None: - return hexsha8 - for h in repo.heads: - if h.commit.hexsha[:build_len] == hexsha8: - return h.name - for t in repo.tags: - if t.commit.hexsha[:build_len] == hexsha8: - return t.name - return hexsha8 - - -def get_commit_hexsha8(name: str): - """Helper function to search for a commit given a human-readable name.""" - if repo is None: + def _check_keys(self, keys: set) -> Optional[set]: + """Private helper method that checks against required data keys and returns missing ones.""" + if not keys >= self.check_keys: + return self.check_keys - keys return None - for h in repo.heads: - if h.name == name: - return h.commit.hexsha[:build_len] - for t in repo.tags: - if t.name == name: - return t.commit.hexsha[:build_len] - for c in repo.iter_commits("--all"): - if c.hexsha[:build_len] == name[:build_len]: - return c.hexsha[:build_len] - return None + + def find_parent_in_data(self, commit: git.Commit) -> Optional[str]: + """Helper method to find the most recent parent measured in number of commits for which there is data.""" + heap: list[tuple[int, git.Commit]] = [(0, commit)] + seen_hexsha8 = set() + while heap: + depth, current_commit = heapq.heappop(heap) + current_hexsha8 = commit.hexsha[:self.build_len] + if current_hexsha8 in self.builds: + return current_hexsha8 + for parent in commit.parents: + parent_hexsha8 = parent.hexsha[:self.build_len] + if parent_hexsha8 not in seen_hexsha8: + seen_hexsha8.add(parent_hexsha8) + heapq.heappush(heap, (depth + 1, parent)) + return None + + def get_all_parent_hexsha8s(self, commit: git.Commit) -> Sequence[str]: + """Helper method to recursively get hexsha8 values for all parents of a commit.""" + unvisited = [commit] + visited = [] + + while unvisited: + current_commit = unvisited.pop(0) + visited.append(current_commit.hexsha[:self.build_len]) + for parent in current_commit.parents: + if parent.hexsha[:self.build_len] not in visited: + unvisited.append(parent) + + return visited + + def get_commit_name(self, hexsha8: str) -> str: + """Helper method to find a human-readable name for a commit if possible.""" + if self.repo is None: + return hexsha8 + for h in self.repo.heads: + if h.commit.hexsha[:self.build_len] == hexsha8: + return h.name + for t in self.repo.tags: + if t.commit.hexsha[:self.build_len] == hexsha8: + return t.name + return hexsha8 + + def get_commit_hexsha8(self, name: str) -> Optional[str]: + """Helper method to search for a commit given a human-readable name.""" + if self.repo is None: + return None + for h in self.repo.heads: + if h.name == name: + return h.commit.hexsha[:self.build_len] + for t in self.repo.tags: + if t.name == name: + return t.commit.hexsha[:self.build_len] + for c in self.repo.iter_commits("--all"): + if c.hexsha[:self.build_len] == name[:self.build_len]: + return c.hexsha[:self.build_len] + return None + + def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]: + """Helper method that gets rows of (build_commit, test_time) sorted by the latter.""" + return [] + + def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]: + """ + Helper method that gets table rows for some list of properties. + Rows are created by combining those where all provided properties are equal. + The resulting rows are then grouped by the provided properties and the t/s values are averaged. + The returned rows are unique in terms of property combinations. + """ + return [] + + +class LlamaBenchDataSQLite3(LlamaBenchData): + connection: sqlite3.Connection + cursor: sqlite3.Cursor + + def __init__(self): + super().__init__() + self.connection = sqlite3.connect(":memory:") + self.cursor = self.connection.cursor() + self.cursor.execute(f"CREATE TABLE test({', '.join(' '.join(x) for x in zip(DB_FIELDS, DB_TYPES))});") + + def _builds_init(self): + if self.connection: + self.build_len_min = self.cursor.execute("SELECT MIN(LENGTH(build_commit)) from test;").fetchone()[0] + self.build_len_max = self.cursor.execute("SELECT MAX(LENGTH(build_commit)) from test;").fetchone()[0] + + if self.build_len_min != self.build_len_max: + logger.warning("Data contains commit hashes of differing lengths. It's possible that the wrong commits will be compared. " + "Try purging the the database of old commits.") + self.cursor.execute(f"UPDATE test SET build_commit = SUBSTRING(build_commit, 1, {self.build_len_min});") + + builds = self.cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall() + self.builds = list(map(lambda b: b[0], builds)) # list[tuple[str]] -> list[str] + super()._builds_init() + + def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]: + data = self.cursor.execute( + "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall() + return reversed(data) if reverse else data + + def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]: + select_string = ", ".join( + [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"]) + equal_string = " AND ".join( + [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [ + f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"] + ) + group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"]) + query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} " + f"GROUP BY {group_order_string} ORDER BY {group_order_string};") + return self.cursor.execute(query).fetchall() + + +class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3): + def __init__(self, data_file: str): + super().__init__() + + self.connection.close() + self.connection = sqlite3.connect(data_file) + self.cursor = self.connection.cursor() + self._builds_init() + + @staticmethod + def valid_format(data_file: str) -> bool: + connection = sqlite3.connect(data_file) + cursor = connection.cursor() + + try: + if cursor.execute("PRAGMA schema_version;").fetchone()[0] == 0: + raise sqlite3.DatabaseError("The provided input file does not exist or is empty.") + except sqlite3.DatabaseError as e: + logger.debug(f'"{data_file}" is not a valid SQLite3 file.', exc_info=e) + cursor = None + + connection.close() + return True if cursor else False + + +class LlamaBenchDataJSONL(LlamaBenchDataSQLite3): + def __init__(self, data_file: str): + super().__init__() + + with open(data_file, "r", encoding="utf-8") as fp: + for i, line in enumerate(fp): + parsed = json.loads(line) + + for k in parsed.keys() - set(DB_FIELDS): + del parsed[k] + + if (missing_keys := self._check_keys(parsed.keys())): + raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}") + + self.cursor.execute(f"INSERT INTO test({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values())) + + self._builds_init() + + @staticmethod + def valid_format(data_file: str) -> bool: + try: + with open(data_file, "r", encoding="utf-8") as fp: + for line in fp: + json.loads(line) + break + except Exception as e: + logger.debug(f'"{data_file}" is not a valid JSONL file.', exc_info=e) + return False + + return True + + +class LlamaBenchDataJSON(LlamaBenchDataSQLite3): + def __init__(self, data_files: list[str]): + super().__init__() + + for data_file in data_files: + with open(data_file, "r", encoding="utf-8") as fp: + parsed = json.load(fp) + + for i, entry in enumerate(parsed): + for k in entry.keys() - set(DB_FIELDS): + del entry[k] + + if (missing_keys := self._check_keys(entry.keys())): + raise RuntimeError(f"Missing required data key(s) at entry {i + 1}: {', '.join(missing_keys)}") + + self.cursor.execute(f"INSERT INTO test({', '.join(entry.keys())}) VALUES({', '.join('?' * len(entry))});", tuple(entry.values())) + + self._builds_init() + + @staticmethod + def valid_format(data_files: list[str]) -> bool: + if not data_files: + return False + + for data_file in data_files: + try: + with open(data_file, "r", encoding="utf-8") as fp: + json.load(fp) + except Exception as e: + logger.debug(f'"{data_file}" is not a valid JSON file.', exc_info=e) + return False + + return True + + +class LlamaBenchDataCSV(LlamaBenchDataSQLite3): + def __init__(self, data_files: list[str]): + super().__init__() + + for data_file in data_files: + with open(data_file, "r", encoding="utf-8") as fp: + for i, parsed in enumerate(csv.DictReader(fp)): + keys = set(parsed.keys()) + + for k in keys - set(DB_FIELDS): + del parsed[k] + + if (missing_keys := self._check_keys(keys)): + raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}") + + self.cursor.execute(f"INSERT INTO test({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values())) + + self._builds_init() + + @staticmethod + def valid_format(data_files: list[str]) -> bool: + if not data_files: + return False + + for data_file in data_files: + try: + with open(data_file, "r", encoding="utf-8") as fp: + for parsed in csv.DictReader(fp): + break + except Exception as e: + logger.debug(f'"{data_file}" is not a valid CSV file.', exc_info=e) + return False + + return True + + +bench_data = None +if len(input_file) == 1: + if LlamaBenchDataSQLite3File.valid_format(input_file[0]): + bench_data = LlamaBenchDataSQLite3File(input_file[0]) + elif LlamaBenchDataJSON.valid_format(input_file): + bench_data = LlamaBenchDataJSON(input_file) + elif LlamaBenchDataJSONL.valid_format(input_file[0]): + bench_data = LlamaBenchDataJSONL(input_file[0]) + elif LlamaBenchDataCSV.valid_format(input_file): + bench_data = LlamaBenchDataCSV(input_file) +else: + if LlamaBenchDataJSON.valid_format(input_file): + bench_data = LlamaBenchDataJSON(input_file) + elif LlamaBenchDataCSV.valid_format(input_file): + bench_data = LlamaBenchDataCSV(input_file) + +if not bench_data: + raise RuntimeError("No valid (or some invalid) input files found.") + +if not bench_data.builds: + raise RuntimeError(f"{input_file} does not contain any builds.") hexsha8_baseline = name_baseline = None # If the user specified a baseline, try to find a commit for it: if known_args.baseline is not None: - if known_args.baseline in builds: + if known_args.baseline in bench_data.builds: hexsha8_baseline = known_args.baseline if hexsha8_baseline is None: - hexsha8_baseline = get_commit_hexsha8(known_args.baseline) + hexsha8_baseline = bench_data.get_commit_hexsha8(known_args.baseline) name_baseline = known_args.baseline if hexsha8_baseline is None: logger.error(f"cannot find data for baseline={known_args.baseline}.") sys.exit(1) # Otherwise, search for the most recent parent of master for which there is data: -elif repo is not None: - hexsha8_baseline = find_parent_in_data(repo.heads.master.commit) +elif bench_data.repo is not None: + hexsha8_baseline = bench_data.find_parent_in_data(bench_data.repo.heads.master.commit) if hexsha8_baseline is None: logger.error("No baseline was provided and did not find data for any master branch commits.\n") @@ -235,27 +464,25 @@ else: sys.exit(1) -name_baseline = get_commit_name(hexsha8_baseline) +name_baseline = bench_data.get_commit_name(hexsha8_baseline) hexsha8_compare = name_compare = None # If the user has specified a compare value, try to find a corresponding commit: if known_args.compare is not None: - if known_args.compare in builds: + if known_args.compare in bench_data.builds: hexsha8_compare = known_args.compare if hexsha8_compare is None: - hexsha8_compare = get_commit_hexsha8(known_args.compare) + hexsha8_compare = bench_data.get_commit_hexsha8(known_args.compare) name_compare = known_args.compare if hexsha8_compare is None: logger.error(f"cannot find data for compare={known_args.compare}.") sys.exit(1) # Otherwise, search for the commit for llama-bench was most recently run # and that is not a parent of master: -elif repo is not None: - hexsha8s_master = get_all_parent_hexsha8s(repo.heads.master.commit) - builds_timestamp = cursor.execute( - "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall() - for (hexsha8, _) in reversed(builds_timestamp): +elif bench_data.repo is not None: + hexsha8s_master = bench_data.get_all_parent_hexsha8s(bench_data.repo.heads.master.commit) + for (hexsha8, _) in bench_data.builds_timestamp(reverse=True): if hexsha8 not in hexsha8s_master: hexsha8_compare = hexsha8 break @@ -270,26 +497,7 @@ else: parser.print_help() sys.exit(1) -name_compare = get_commit_name(hexsha8_compare) - - -def get_rows(properties): - """ - Helper function that gets table rows for some list of properties. - Rows are created by combining those where all provided properties are equal. - The resulting rows are then grouped by the provided properties and the t/s values are averaged. - The returned rows are unique in terms of property combinations. - """ - select_string = ", ".join( - [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"]) - equal_string = " AND ".join( - [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [ - f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"] - ) - group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"]) - query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} " - f"GROUP BY {group_order_string} ORDER BY {group_order_string};") - return cursor.execute(query).fetchall() +name_compare = bench_data.get_commit_name(hexsha8_compare) # If the user provided columns to group the results by, use them: @@ -303,10 +511,10 @@ if known_args.show is not None: logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}") parser.print_usage() sys.exit(1) - rows_show = get_rows(show) + rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare) # Otherwise, select those columns where the values are not all the same: else: - rows_full = get_rows(KEY_PROPERTIES) + rows_full = bench_data.get_rows(KEY_PROPERTIES, hexsha8_baseline, hexsha8_compare) properties_different = [] for i, kp_i in enumerate(KEY_PROPERTIES): if kp_i in DEFAULT_SHOW or kp_i in ["n_prompt", "n_gen", "n_depth"]: @@ -336,7 +544,7 @@ else: show.remove(prop) except ValueError: pass - rows_show = get_rows(show) + rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare) if not rows_show: logger.error(f"No comparable data was found between {name_baseline} and {name_compare}.\n") From b4726345aca49e2ad62d615e6e370b3dbad6434f Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 13 May 2025 15:33:58 +0200 Subject: [PATCH 033/421] =?UTF-8?q?mtmd=20:=20remove=20libllava,=20remove?= =?UTF-8?q?=20clip-quantize-cli=20(=E2=9A=A0=EF=B8=8F=20breaking=20change)?= =?UTF-8?q?=20(#13460)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * mtmd : remove libllava, remove clip-quantize-cli * rm clip_model_quantize --- tools/mtmd/CMakeLists.txt | 35 -- tools/mtmd/README-quantize.md | 44 -- tools/mtmd/README.md | 7 +- tools/mtmd/android/adb_run.sh | 53 -- tools/mtmd/android/build_64.sh | 8 - tools/mtmd/clip-quantize-cli.cpp | 59 -- tools/mtmd/clip.cpp | 135 ---- .../convert_image_encoder_to_gguf.py | 0 .../glmedge-convert-image-encoder-to-gguf.py | 0 .../{ => legacy-models}/glmedge-surgery.py | 0 .../mtmd/{ => legacy-models}/llava_surgery.py | 0 .../{ => legacy-models}/llava_surgery_v2.py | 0 .../minicpmv-convert-image-encoder-to-gguf.py | 0 .../{ => legacy-models}/minicpmv-surgery.py | 0 tools/mtmd/llava.cpp | 591 ------------------ tools/mtmd/llava.h | 49 -- 16 files changed, 4 insertions(+), 977 deletions(-) delete mode 100644 tools/mtmd/README-quantize.md delete mode 100755 tools/mtmd/android/adb_run.sh delete mode 100755 tools/mtmd/android/build_64.sh delete mode 100644 tools/mtmd/clip-quantize-cli.cpp rename tools/mtmd/{ => legacy-models}/convert_image_encoder_to_gguf.py (100%) rename tools/mtmd/{ => legacy-models}/glmedge-convert-image-encoder-to-gguf.py (100%) rename tools/mtmd/{ => legacy-models}/glmedge-surgery.py (100%) rename tools/mtmd/{ => legacy-models}/llava_surgery.py (100%) rename tools/mtmd/{ => legacy-models}/llava_surgery_v2.py (100%) rename tools/mtmd/{ => legacy-models}/minicpmv-convert-image-encoder-to-gguf.py (100%) rename tools/mtmd/{ => legacy-models}/minicpmv-surgery.py (100%) delete mode 100644 tools/mtmd/llava.cpp delete mode 100644 tools/mtmd/llava.h diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index dfafa9cf8..e7ba23587 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -1,29 +1,3 @@ -# llava (legacy) - -add_library(llava OBJECT - llava.cpp - llava.h - clip.cpp - clip.h - ) - -target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) - -target_include_directories(llava PUBLIC .) -target_include_directories(llava PUBLIC ../..) -target_include_directories(llava PUBLIC ../../common) - -target_compile_features(llava PRIVATE cxx_std_17) - -add_library(llava_static STATIC $) -if (BUILD_SHARED_LIBS) - set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD) - add_library(llava_shared SHARED $) - target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) - install(TARGETS llava_shared LIBRARY) -endif() - # mtmd add_library(mtmd OBJECT @@ -53,12 +27,10 @@ if (BUILD_SHARED_LIBS) endif() if (NOT MSVC) - target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h endif() if(TARGET BUILD_INFO) - add_dependencies(llava BUILD_INFO) add_dependencies(mtmd BUILD_INFO) endif() @@ -73,10 +45,3 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_17) - -set(TARGET llama-llava-clip-quantize-cli) -add_executable(${TARGET} clip-quantize-cli.cpp) -set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/tools/mtmd/README-quantize.md b/tools/mtmd/README-quantize.md deleted file mode 100644 index b931513ab..000000000 --- a/tools/mtmd/README-quantize.md +++ /dev/null @@ -1,44 +0,0 @@ -# Quantizing CLIP Visual Projector - -This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance. - -## Usage - -To quantize a CLIP visual projector model, use the following command: - -```sh -./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf -``` - -After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc). - -### Arguments - -- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format. -- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved. -- ``: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`. - -### Quantization Types - -The following quantization types are supported, based on the `enum ggml_type` definition: - -- `2` - `q4_0`: 4-bit quantization with a single scale value. -- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block. -- `6` - `q5_0`: 5-bit quantization with a single scale value. -- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block. -- `8` - `q8_0`: 8-bit quantization with a single scale value. - -### Example - -To quantize a model using the `q4_0` quantization type, you would run: - -```sh -./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2 -``` - -This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method. - -## Notes - -- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements. -- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments. diff --git a/tools/mtmd/README.md b/tools/mtmd/README.md index ab258ea17..ef31d1957 100644 --- a/tools/mtmd/README.md +++ b/tools/mtmd/README.md @@ -41,8 +41,8 @@ Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advanta Multimodal projector (`mmproj`) files are specific to each model architecture. -For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file: -- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support +For the following models, you can use `convert_hf_to_gguf.py` with `--mmproj` flag to get the `mmproj` file: +- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) ; See the guide [here](../../docs/multimodal/gemma3.md) - Note: 1B variant does not have vision support - SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) - SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) - [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint @@ -52,6 +52,8 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla For older models, please refer to the relevant guide for instructions on how to obtain or create them: +NOTE: conversion scripts are located under `tools/mtmd/legacy-models` + - [LLaVA](../../docs/multimodal/llava.md) - [MobileVLM](../../docs/multimodal/MobileVLM.md) - [GLM-Edge](../../docs/multimodal/glmedge.md) @@ -59,4 +61,3 @@ For older models, please refer to the relevant guide for instructions on how to - [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md) - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md) - [IBM Granite Vision](../../docs/multimodal/granitevision.md) -- [Google Gemma 3](../../docs/multimodal/gemma3.md) diff --git a/tools/mtmd/android/adb_run.sh b/tools/mtmd/android/adb_run.sh deleted file mode 100755 index a24d6787d..000000000 --- a/tools/mtmd/android/adb_run.sh +++ /dev/null @@ -1,53 +0,0 @@ -#!/bin/bash - -model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed" -projector_name="mmproj-model-f16.gguf" -llama_name="ggml-model-q4_k.gguf" -img_dir="/Users/cxt/model/llm" -img_name="demo.jpg" -prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" -# img_name="cat.jpeg" -# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" - -program_dir="build_64/bin" -binName="llama-mtmd-cli" -n_threads=4 - - -deviceDir="/data/local/tmp" -saveDir="output" -if [ ! -d ${saveDir} ]; then - mkdir ${saveDir} -fi - - -function android_run() { - # # copy resource into device - # adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name} - # adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name} - adb push ${img_dir}/${img_name} ${deviceDir}/${img_name} - # copy program into device - adb push ${program_dir}/${binName} ${deviceDir}/${binName} - adb shell "chmod 0777 ${deviceDir}/${binName}" - - # run - adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \ - -m ${deviceDir}/${llama_name} \ - --mmproj ${deviceDir}/${projector_name} \ - -t ${n_threads} \ - --image ${deviceDir}/${img_name} \ - -p \"${prompt}\" \ - > ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt" - adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \ - -m ${deviceDir}/${llama_name} \ - --mmproj ${deviceDir}/${projector_name} \ - -t ${n_threads} \ - --image ${deviceDir}/${img_name} \ - -p \"${prompt}\" \ - >> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1" - adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir} -} - -android_run - -echo "android_run is Done!" diff --git a/tools/mtmd/android/build_64.sh b/tools/mtmd/android/build_64.sh deleted file mode 100755 index 71b6fd3f7..000000000 --- a/tools/mtmd/android/build_64.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -cmake ../../../../ \ --DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ --DCMAKE_BUILD_TYPE=Release \ --DANDROID_ABI="arm64-v8a" \ --DANDROID_PLATFORM=android-23 $1 - -make -j4 diff --git a/tools/mtmd/clip-quantize-cli.cpp b/tools/mtmd/clip-quantize-cli.cpp deleted file mode 100644 index 566506954..000000000 --- a/tools/mtmd/clip-quantize-cli.cpp +++ /dev/null @@ -1,59 +0,0 @@ -#include "arg.h" -#include "base64.hpp" -#include "log.h" -#include "common.h" -#include "sampling.h" -#include "clip.h" -#include "llava.h" -#include "llama.h" -#include "ggml.h" - -static void print_usage(int argc, char ** argv) { - (void) argc; - - fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]); - fprintf(stderr, " type = 2 - q4_0\n"); - fprintf(stderr, " type = 3 - q4_1\n"); - fprintf(stderr, " type = 6 - q5_0\n"); - fprintf(stderr, " type = 7 - q5_1\n"); - fprintf(stderr, " type = 8 - q8_0\n"); -} - -int main(int argc, char ** argv) { - if (argc != 4) { - print_usage(argc, argv); - return 1; - } - - const std::string fname_inp = argv[1]; - const std::string fname_out = argv[2]; - - const int itype = atoi(argv[3]); - - const int64_t t_main_start_us = ggml_time_us(); - - int64_t t_quantize_us = 0; - - // load the model - { - const int64_t t_start_us = ggml_time_us(); - - if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) { - fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str()); - return 1; - } - - t_quantize_us = ggml_time_us() - t_start_us; - } - - // report timing - { - const int64_t t_main_end_us = ggml_time_us(); - - printf("\n"); - printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f); - printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f); - } - - return 0; -} diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 41ba45a79..a0f42e8c4 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -3586,141 +3586,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima return true; } -bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) { - assert(itype < GGML_TYPE_COUNT); - ggml_type type = static_cast(itype); - - auto * ctx_clip = clip_init(fname_inp, clip_context_params{ - /* use_gpu */ false, - /* verbosity */ GGML_LOG_LEVEL_ERROR, - }); - - const auto & ctx_src = ctx_clip->ctx_gguf.get(); - const auto & ctx_data = ctx_clip->ctx_data.get(); - - auto * ctx_out = gguf_init_empty(); - gguf_set_kv(ctx_out, ctx_src); - gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); - gguf_set_val_u32(ctx_out, "general.file_type", itype); - - auto fout = std::ofstream(fname_out, std::ios::binary); - - const int n_tensors = gguf_get_n_tensors(ctx_src); - - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(ctx_src, i); - ggml_tensor * cur = ggml_get_tensor(ctx_data, name); - gguf_add_tensor(ctx_out, cur); - } - - const size_t meta_size = gguf_get_meta_size(ctx_out); - for (size_t i = 0; i < meta_size; ++i) { - fout.put(0); - } - - // regexes of tensor names to be quantized - const std::vector k_names = { - ".*weight", - }; - - std::vector work(512); - std::vector conv_buf(512); - size_t total_size_org = 0; - size_t total_size_new = 0; - - for (int i = 0; i < n_tensors; ++i) { - const std::string name = gguf_get_tensor_name(ctx_src, i); - ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str()); - - enum ggml_type new_type; - void * new_data; - size_t new_size; - - bool quantize = false; - for (const auto & s : k_names) { - if (std::regex_match(name, std::regex(s))) { - quantize = true; - break; - } - } - - // quantize only 2D tensors and bigger than block size - quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type); - - if (quantize) { - new_type = type; - if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) { - new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type - // LOG_ERR("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); - } - const size_t n_elms = ggml_nelements(cur); - float * f32_data; - - switch (cur->type) { - case GGML_TYPE_F32: - f32_data = (float *)cur->data; - break; - case GGML_TYPE_F16: - if (conv_buf.size() < n_elms) { - conv_buf.resize(n_elms); - } - for (size_t j = 0; j < n_elms; ++j) { - conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]); - } - f32_data = (float *)conv_buf.data(); - break; - default: - LOG_ERR("%s: Please use an input file in f32 or f16\n", __func__); - gguf_free(ctx_out); - return false; - } - - if (work.size() < n_elms * 4) { - work.resize(n_elms * 4); - } - new_data = work.data(); - - new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr); - } else { - new_type = cur->type; - new_data = cur->data; - new_size = ggml_nbytes(cur); - } - const size_t orig_size = ggml_nbytes(cur); - total_size_org += orig_size; - total_size_new += new_size; - gguf_set_tensor_type(ctx_out, name.c_str(), new_type); - GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size); - gguf_set_tensor_data(ctx_out, name.c_str(), new_data); - fout.write((const char *)new_data, new_size); - size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size; - for (size_t j = 0; j < pad; ++j) { - fout.put(0); - } - - LOG_INF("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, - orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); - } - - // go back to beginning of file and write the updated metadata - fout.seekp(0, std::ios::beg); - std::vector meta(meta_size); - gguf_get_meta_data(ctx_out, meta.data()); - fout.write((const char *)meta.data(), meta_size); - - fout.close(); - - clip_free(ctx_clip); - gguf_free(ctx_out); - - { - LOG_INF("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - LOG_INF("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); - } - - return true; -} - int clip_n_mmproj_embd(const struct clip_ctx * ctx) { switch (ctx->proj_type) { case PROJECTOR_TYPE_LDP: diff --git a/tools/mtmd/convert_image_encoder_to_gguf.py b/tools/mtmd/legacy-models/convert_image_encoder_to_gguf.py similarity index 100% rename from tools/mtmd/convert_image_encoder_to_gguf.py rename to tools/mtmd/legacy-models/convert_image_encoder_to_gguf.py diff --git a/tools/mtmd/glmedge-convert-image-encoder-to-gguf.py b/tools/mtmd/legacy-models/glmedge-convert-image-encoder-to-gguf.py similarity index 100% rename from tools/mtmd/glmedge-convert-image-encoder-to-gguf.py rename to tools/mtmd/legacy-models/glmedge-convert-image-encoder-to-gguf.py diff --git a/tools/mtmd/glmedge-surgery.py b/tools/mtmd/legacy-models/glmedge-surgery.py similarity index 100% rename from tools/mtmd/glmedge-surgery.py rename to tools/mtmd/legacy-models/glmedge-surgery.py diff --git a/tools/mtmd/llava_surgery.py b/tools/mtmd/legacy-models/llava_surgery.py similarity index 100% rename from tools/mtmd/llava_surgery.py rename to tools/mtmd/legacy-models/llava_surgery.py diff --git a/tools/mtmd/llava_surgery_v2.py b/tools/mtmd/legacy-models/llava_surgery_v2.py similarity index 100% rename from tools/mtmd/llava_surgery_v2.py rename to tools/mtmd/legacy-models/llava_surgery_v2.py diff --git a/tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py b/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py similarity index 100% rename from tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py rename to tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py diff --git a/tools/mtmd/minicpmv-surgery.py b/tools/mtmd/legacy-models/minicpmv-surgery.py similarity index 100% rename from tools/mtmd/minicpmv-surgery.py rename to tools/mtmd/legacy-models/minicpmv-surgery.py diff --git a/tools/mtmd/llava.cpp b/tools/mtmd/llava.cpp deleted file mode 100644 index ebef8b3c1..000000000 --- a/tools/mtmd/llava.cpp +++ /dev/null @@ -1,591 +0,0 @@ -#include "clip.h" -#include "llava.h" - -#include "llama.h" -#include "ggml-cpp.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined(LLAVA_LOG_OFF) -# define LOG_INF(...) -# define LOG_WRN(...) -# define LOG_ERR(...) -# define LOG_DBG(...) -#else // defined(LLAVA_LOG_OFF) -# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#endif // defined(LLAVA_LOG_OFF) - -// RGB uint8 image -struct clip_image_u8 { - int nx; - int ny; - - std::vector buf; -}; - -// RGB float32 image (NHWC) -// Memory layout: RGBRGBRGB... -struct clip_image_f32 { - int nx; - int ny; - - std::vector buf; -}; - -struct clip_image_grid_shape { - int first; - int second; -}; - -// convenience cpp wrapper -struct clip_image_f32_batch_deleter { - void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); } -}; -typedef std::unique_ptr clip_image_f32_batch_ptr; - -struct clip_image_size_deleter { - void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); } -}; -typedef std::unique_ptr clip_image_size_ptr; - -/** - * Selects the best resolution from a list of possible resolutions based on the original size. - * - * @param original_size The original size of the image in the format (width, height). - * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...]. - * @return The best fit resolution in the format (width, height). - */ -static std::pair select_best_resolution(const std::pair& original_size, const std::vector>& possible_resolutions) { - int original_width = original_size.first; - int original_height = original_size.second; - - std::pair best_fit; - int max_effective_resolution = 0; - int min_wasted_resolution = std::numeric_limits::max(); - - for (const auto& resolution : possible_resolutions) { - int width = resolution.first; - int height = resolution.second; - float scale = std::min(static_cast(width) / original_width, static_cast(height) / original_height); - int downscaled_width = static_cast(original_width * scale); - int downscaled_height = static_cast(original_height * scale); - int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); - int wasted_resolution = (width * height) - effective_resolution; - // LOG_DBG("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); - if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { - max_effective_resolution = effective_resolution; - min_wasted_resolution = wasted_resolution; - best_fit = resolution; - } - } - - return best_fit; -} - -/** - * @brief Get the anyres image grid shape object - * - * @param image_size - * @param grid_pinpoints - * @param image_patch_size - * @return - */ -static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair & image_size, const std::vector> & grid_pinpoints, int image_patch_size) { - /** - Conversion from gguf flat array to vector: - std::vector> possible_resolutions; - for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) { - possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]}); - } - */ - auto best_resolution = select_best_resolution(image_size, grid_pinpoints); - return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size}; -} - -// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) -static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) { - struct { - struct ggml_context * ctx; - } model; - - const int32_t image_size = clip_get_image_size(ctx_clip); - const int32_t patch_size = clip_get_patch_size(ctx_clip); - - int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches) - - int num_patches_width = grid_shape.first; // grid 1-4 - int num_patches_height = grid_shape.second; // grid 1-4 - - const size_t num_images = num_patches_width * num_patches_height + 1; - - // TODO: size calculation is not calculated - it's only tens of MB - size_t ctx_size = 0; - - { - ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features - ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32); - } - - struct ggml_init_params params { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, // NOTE: this should be false when using the legacy API - }; - - // Python reference code for full unpad: - /* - base_image_feature = image_feature[0] - image_feature = image_feature[1:] - image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() - image_feature = image_feature.flatten(1, 2).flatten(2, 3) - image_feature = unpad_image(image_feature, image_sizes[image_idx]) - image_feature = torch.cat(( - image_feature, - self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1) - ), dim=-1) - image_feature = image_feature.flatten(1, 2).transpose(0, 1) - image_feature = torch.cat((base_image_feature, image_feature), dim=0) - */ - // We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval. - // In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet. - // Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them. - // Once all images are processed to prepended the base_image_features without any changes. - - // Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling)) - /* - image_feature = image_feature.view(2, 2, 24, 24, 4096) - image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous() - image_feature = image_feature.view(2, 24, 2, 24, 4096) - image_feature = image_feature.flatten(0, 3) - - // Reshape to 4D tensor by merging the last two dimensions - image_feature = image_feature.view(2, 2, 24, 24*4096) - image_feature = image_feature.permute(0, 2, 1, 3).contiguous() - image_feature = image_feature.view(-1, 4096) - */ - - model.ctx = ggml_init(params); - - struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4 - // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); - // fill it with the image embeddings, ignoring the base - for (size_t i = 1; i < num_images; i++) { - size_t offset = (i-1) * clip_embd_nbytes(ctx_clip); - memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip)); - } - - struct ggml_cgraph * gf = ggml_new_graph(model.ctx); - size_t size_ele = ggml_type_size(GGML_TYPE_F32); - - struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features, - num_patches_per_side * clip_n_mmproj_embd(ctx_clip), - num_patches_per_side, - num_patches_width, - num_patches_height, - size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip), - size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side, - size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0); - // ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false); - struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3)); - /** - At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings - image_feature = torch.cat(( - image_feature, - self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device) - ), dim=-1) - * - */ - - // ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false); - struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0); - // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false); - ggml_build_forward_expand(gf, flatten); - - ggml_backend_ptr backend { ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr) }; - GGML_ASSERT(backend != nullptr && "failed to initialize CPU backend"); - ggml_backend_graph_compute(backend.get(), gf); - - struct ggml_tensor* result = ggml_graph_node(gf, -1); - - memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context - // append without newline tokens (default behavior in llava_arch when not using unpad ): - memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches - *n_img_pos_out = static_cast(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input)); - - // Debug: Test single segments - // Current findings: sending base image, sending a segment embedding all works similar to python - // However, permuted embeddings do not work yet (stride issue?) - // memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context - // memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context - // *n_img_pos_out=576; - - ggml_free(model.ctx); - return true; -} - -static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) { - int width = image->nx; - int height = image->ny; - int num_patches = (height / patch_size) * (width / patch_size); - clip_image_f32 * patch = clip_image_f32_init(); - patch->nx = patch_size * num_patches; - patch->ny = patch_size; - patch->buf.resize(3 * patch->nx * patch->ny); - - int patch_index = 0; - - for (int i = 0; i < height; i += patch_size) { - for (int j = 0; j < width; j += patch_size) { - for (int pi = 0; pi < patch_size; ++pi) { - for (int pj = 0; pj < patch_size; ++pj) { - int input_index = ((i + pi) * width + (j + pj)) * 3; - int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3; - patch->buf[output_index] = image->buf[input_index]; - patch->buf[output_index+1] = image->buf[input_index+1]; - patch->buf[output_index+2] = image->buf[input_index+2]; - } - } - patch_index++; - } - } - return patch; -} - -static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { - // std::vector img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336 - clip_image_f32_batch_ptr img_res_v(clip_image_f32_batch_init()); - if (!clip_image_preprocess(ctx_clip, img, img_res_v.get())) { - LOG_ERR("%s: unable to preprocess image\n", __func__); - return false; - } - - const int64_t t_img_enc_start_us = ggml_time_us(); - - const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip); - - const size_t n_imgs = clip_image_f32_batch_n_images(img_res_v.get()); - - if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) { - std::vector image_embd_v; - image_embd_v.resize(n_imgs); - clip_image_size load_image_size; - - for (size_t i = 0; i < n_imgs; i++) { - const int64_t t_img_enc_step_start_us = ggml_time_us(); - int nx = clip_image_f32_batch_nx(img_res_v.get(), i); - int ny = clip_image_f32_batch_ny(img_res_v.get(), i); - image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, nx, ny)); - int patch_size = 14; - load_image_size.width = nx; - load_image_size.height = ny; - clip_add_load_image_size(ctx_clip, &load_image_size); - - bool encoded = false; - clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i); - if (clip_is_qwen2vl(ctx_clip)) { - encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); - } - else { - encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(img_res, patch_size), image_embd_v[i]); - } - - if (!encoded) { - LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs); - return false; - } - const int64_t t_img_enc_steop_batch_us = ggml_time_us(); - LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)n_imgs, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0); - } - const int64_t t_img_enc_batch_us = ggml_time_us(); - LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); - - int n_img_pos_out = 0; - for (size_t i = 0; i < image_embd_v.size(); i++) { - int nx = clip_image_f32_batch_nx(img_res_v.get(), i); - int ny = clip_image_f32_batch_ny(img_res_v.get(), i); - clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i); - std::memcpy( - image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), - image_embd_v[i], - clip_embd_nbytes_by_img(ctx_clip, nx, ny)); - n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res); - } - *n_img_pos = n_img_pos_out; - for (size_t i = 0; i < image_embd_v.size(); i++) { - free(image_embd_v[i]); - } - image_embd_v.clear(); - load_image_size.width = img->nx; - load_image_size.height = img->ny; - clip_add_load_image_size(ctx_clip, &load_image_size); - LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size.width, load_image_size.height); - } - else if (clip_is_glm(ctx_clip)){ - struct clip_image_size * load_image_size = clip_image_size_init(); - load_image_size->width = clip_image_f32_batch_nx(img_res_v.get(), 0); - load_image_size->height = clip_image_f32_batch_ny(img_res_v.get(), 0); - clip_add_load_image_size(ctx_clip, load_image_size); - - clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0); - bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); - int pos = int(load_image_size->width/clip_get_patch_size(ctx_clip)/2); - *n_img_pos = (pos * pos + 2); - if (!encoded){ - LOG_ERR("Unable to encode image \n"); - return false; - } - } - else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { - // flat / default llava-1.5 type embedding - clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0); - *n_img_pos = clip_n_output_tokens(ctx_clip, img_res); - bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096 - if (!encoded) { - LOG_ERR("Unable to encode image\n"); - - return false; - } - } - else { - // spatial_unpad llava-1.6 type embedding - // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working - std::vector image_embd_v; - image_embd_v.resize(n_imgs); - for (size_t i = 0; i < n_imgs; i++) { - clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i); - image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184 - const bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside - if (!encoded) { - LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs); - return false; - } - } - const int64_t t_img_enc_batch_us = ggml_time_us(); - LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); - - const int32_t * image_grid = clip_image_grid(ctx_clip); - const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip); - - std::vector> grid_pinpoints; - for (size_t i = 0; i < num_gridpoints; i += 2) { - grid_pinpoints.push_back({image_grid[i], image_grid[i+1]}); - } - - const int32_t image_size = clip_get_image_size(ctx_clip); - - struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size); - - int n_img_pos_out; - clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0); - clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input); - *n_img_pos = n_img_pos_out; - - for (size_t i = 0; i < image_embd_v.size(); i++) { - free(image_embd_v[i]); - } - image_embd_v.clear(); - - // debug image/segment/normalization content: - // clip_image_u8 * tmp = clip_image_u8_init(); - // clip_image_convert_f32_to_u8(*image_feature, *tmp); - // clip_image_save_to_bmp(*tmp, "image_feature.bmp"); - } - - LOG_INF("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); - - const int64_t t_img_enc_end_us = ggml_time_us(); - float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - - LOG_INF("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); - - return true; -} - -bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) { - // make sure that the correct mmproj was used, i.e., compare apples to apples - int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama)); - auto n_image_embd = clip_n_mmproj_embd(ctx_clip); - if (n_image_embd != n_llama_embd) { - LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); - return false; - } - return true; -} - -bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { - // Granite vision uses up to 10 patches + base patch - int num_max_patches = 11; - if (clip_is_minicpmv(ctx_clip)) { - num_max_patches = 10; - } - if (clip_is_glm(ctx_clip)) { - num_max_patches = 1; - } - float * image_embd; - if (clip_is_qwen2vl(ctx_clip)) { - // qwen2vl don't split image into chunks, so `num_max_patches` is not needed. - image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny)); - } else { - image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model - } - if (!image_embd) { - LOG_ERR("Unable to allocate memory for image embeddings\n"); - return false; - } - - int n_img_pos; - if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { - LOG_ERR("%s: cannot encode image, aborting\n", __func__); - free(image_embd); - return false; - } - *image_embd_out = image_embd; - *n_img_pos_out = n_img_pos; - - return true; -} - -struct llava_embd_batch { - std::vector pos; - std::vector n_seq_id; - std::vector seq_id_0; - std::vector seq_ids; - std::vector logits; - llama_batch batch; - llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { - pos .resize(n_tokens); - n_seq_id.resize(n_tokens); - seq_ids .resize(n_tokens + 1); - logits .resize(n_tokens); - seq_id_0.resize(1); - seq_id_0[0] = seq_id; - seq_ids [n_tokens] = nullptr; - batch = { - /*n_tokens =*/ n_tokens, - /*tokens =*/ nullptr, - /*embd =*/ embd, - /*pos =*/ pos.data(), - /*n_seq_id =*/ n_seq_id.data(), - /*seq_id =*/ seq_ids.data(), - /*logits =*/ logits.data(), - }; - for (int i = 0; i < n_tokens; i++) { - batch.pos [i] = pos_0 + i; - batch.n_seq_id[i] = 1; - batch.seq_id [i] = seq_id_0.data(); - batch.logits [i] = false; - } - } -}; - -bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { - int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); - - for (int i = 0; i < image_embed->n_image_pos; i += n_batch) { - int n_eval = image_embed->n_image_pos - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - float * embd = image_embed->embed+i*n_embd; - llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0); - if (llama_decode(ctx_llama, llava_batch.batch)) { - LOG_ERR("%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - } - return true; -} - -struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { - clip_image_u8 * img = clip_image_u8_init(); - if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { - clip_image_u8_free(img); - LOG_ERR("%s: can't load image from bytes, is it a valid image?", __func__); - return NULL; - } - - float* image_embed = NULL; - int n_image_pos = 0; - bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); - if (!image_embed_result) { - clip_image_u8_free(img); - LOG_ERR("%s: couldn't embed the image\n", __func__); - return NULL; - } - - clip_image_u8_free(img); - auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed)); - result->embed = image_embed; - result->n_image_pos = n_image_pos; - return result; -} - -static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { - auto file = fopen(path, "rb"); - if (file == NULL) { - LOG_ERR("%s: can't read file %s\n", __func__, path); - return false; - } - - fseek(file, 0, SEEK_END); - auto fileSize = ftell(file); - fseek(file, 0, SEEK_SET); - - auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data - if (buffer == NULL) { - LOG_ERR("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); - perror("Memory allocation error"); - fclose(file); - return false; - } - errno = 0; - size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer - if (ferror(file)) { - LOG_ERR("read error: %s", strerror(errno)); - free(buffer); - fclose(file); - return false; - } - if (ret != (size_t) fileSize) { - LOG_ERR("unexpectedly reached end of file"); - free(buffer); - fclose(file); - return false; - } - fclose(file); // Close the file - - *bytesOut = buffer; - *sizeOut = fileSize; - return true; -} - -struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) { - unsigned char* image_bytes; - long image_bytes_length; - auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); - if (!loaded) { - LOG_ERR("%s: failed to load %s\n", __func__, image_path); - return NULL; - } - - llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length); - free(image_bytes); - - return embed; -} - -void llava_image_embed_free(struct llava_image_embed * embed) { - free(embed->embed); - free(embed); -} diff --git a/tools/mtmd/llava.h b/tools/mtmd/llava.h deleted file mode 100644 index b6feb3027..000000000 --- a/tools/mtmd/llava.h +++ /dev/null @@ -1,49 +0,0 @@ -#ifndef LLAVA_H -#define LLAVA_H - -#include "ggml.h" - -#ifdef LLAMA_SHARED -# if defined(_WIN32) && !defined(__MINGW32__) -# ifdef LLAMA_BUILD -# define LLAVA_API __declspec(dllexport) -# else -# define LLAVA_API __declspec(dllimport) -# endif -# else -# define LLAVA_API __attribute__ ((visibility ("default"))) -# endif -#else -# define LLAVA_API -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -struct clip_ctx; -struct llava_image_embed { - float * embed; - int n_image_pos; -}; - -/** sanity check for clip <-> llava embed size match */ -LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip); - -LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); - -/** build an image embed from image file bytes */ -LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); -/** build an image embed from a path to an image filename */ -LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path); -/** free an embedding made with llava_image_embed_make_* */ -LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); - -/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ -LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past); - -#ifdef __cplusplus -} -#endif - -#endif From b89d605a91dee0a518ecd582f8991a07d523e2fa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 May 2025 18:01:53 +0300 Subject: [PATCH 034/421] batched-bench : fix pp batch contents (#13492) --- tools/batched-bench/batched-bench.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp index 0f4019293..119df471b 100644 --- a/tools/batched-bench/batched-bench.cpp +++ b/tools/batched-bench/batched-bench.cpp @@ -123,8 +123,8 @@ int main(int argc, char ** argv) { common_batch_clear(batch); - for (int i = 0; i < pp; ++i) { - for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { + for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { + for (int i = 0; i < pp; ++i) { common_batch_add(batch, 0, i, { j }, false); } } From 4f711afed5e7ef4304b567c8888ee1aa60e868eb Mon Sep 17 00:00:00 2001 From: Dan Johansson Date: Tue, 13 May 2025 17:02:28 +0200 Subject: [PATCH 035/421] ggml-cpu: Update KleidiAI to v1.6 and fix include directives (#13509) Signed-off-by: Dan Johansson --- ggml/src/ggml-cpu/CMakeLists.txt | 4 ++-- ggml/src/ggml-cpu/kleidiai/kernels.h | 1 + ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 2 ++ 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index bdaec2881..1d4259dae 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -385,9 +385,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name) # Fetch KleidiAI sources: include(FetchContent) - set(KLEIDIAI_COMMIT_TAG "v1.5.0") + set(KLEIDIAI_COMMIT_TAG "v1.6.0") set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz") - set(KLEIDIAI_ARCHIVE_MD5 "ea22e1aefb800e9bc8c74d91633cc58e") + set(KLEIDIAI_ARCHIVE_MD5 "75b4ad68f25ab673dcc01065e5a0b05f") if (POLICY CMP0135) cmake_policy(SET CMP0135 NEW) diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index 5ac02bda7..3b268d4a2 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -5,6 +5,7 @@ #pragma once #include +#include #include "ggml.h" enum cpu_feature { diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index f3dffdd6b..15f0cd154 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -3,7 +3,9 @@ // #include #include +#include #include +#include #include #include #if defined(__linux__) From c252e0c4097b34666e5a81db9d0450d71fa3098f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 May 2025 18:04:00 +0300 Subject: [PATCH 036/421] metal : optimize multi-sequence FA vec kernel (#13493) * batched-bench : fix pp batch contents * metal : optimize multi-sequence FA vec kernel ggml-ci --- ggml/src/ggml-metal/ggml-metal.metal | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 9cfddf450..122ae5973 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -3887,6 +3887,11 @@ kernel void kernel_flash_attn_ext_vec( sm[tiisg] = pm[ic + tiisg]; } + // skip -INF blocks + if (simd_max(sm[tiisg]) == -INFINITY) { + continue; + } + // Q*K^T { // each simdgroup processes 1 query and NE (NW/NL) head elements From f0995d28ce3d15095b6845d94ce4465e46575873 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 May 2025 18:04:39 +0300 Subject: [PATCH 037/421] metal : use FA-vec kernel up to batch size 20 (#13496) * batched-bench : fix pp batch contents * metal : optimize multi-sequence FA vec kernel ggml-ci * metal : use FA-vec kernel up to batch size 20 ggml-ci --- ggml/src/ggml-metal/ggml-metal.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m index 576f9581b..f4b3d9cf5 100644 --- a/ggml/src/ggml-metal/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -4358,7 +4358,7 @@ static bool ggml_metal_encode_node( // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0) // for now avoiding mainly to keep the number of templates/kernels a bit lower // these are now trivial to add after: https://github.com/ggml-org/llama.cpp/pull/12612 - if (ne01 >= 4 || (ne00%128 != 0 && ne00 != 96 && ne00 != 192 && ne00 != 576)) { + if (ne01 >= 20 || (ne00%128 != 0 && ne00 != 96 && ne00 != 192 && ne00 != 576)) { switch (src1->type) { case GGML_TYPE_F16: { From 71bdbdb58757d508557e6d8b387f666cdfb25c5e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 13 May 2025 17:07:21 +0200 Subject: [PATCH 038/421] =?UTF-8?q?clip=20:=20clip.h=20become=20private=20?= =?UTF-8?q?API=20(=E2=9A=A0=EF=B8=8F=20breaking=20change)=20(#13510)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tools/mtmd/clip.cpp | 21 -- tools/mtmd/clip.h | 126 +++---- tools/mtmd/qwen2vl-test.cpp | 636 ------------------------------------ 3 files changed, 45 insertions(+), 738 deletions(-) delete mode 100644 tools/mtmd/qwen2vl-test.cpp diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index a0f42e8c4..128a95cc1 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -2309,14 +2309,6 @@ struct clip_model_loader { } }; -// read and create ggml_context containing the tensors and their data -struct clip_ctx * clip_model_load(const char * fname, const int verbosity) { - return clip_init(fname, clip_context_params{ - /* use_gpu */ true, - /* verbosity */ static_cast(verbosity), - }); -} - struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) { g_logger_state.verbosity_thold = ctx_params.verbosity; clip_ctx * ctx_clip = nullptr; @@ -3085,19 +3077,6 @@ size_t get_clip_image_grid_size(const struct clip_ctx * ctx) { return ctx->vision_model.hparams.image_grid_pinpoints.size(); } -// deprecated -int clip_n_patches(const struct clip_ctx * ctx) { - clip_image_f32 img; - img.nx = ctx->vision_model.hparams.image_size; - img.ny = ctx->vision_model.hparams.image_size; - return clip_n_output_tokens(ctx, &img); -} - -// deprecated -int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) { - return clip_n_output_tokens(ctx, img); -} - int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) { const auto & params = ctx->vision_model.hparams; const int n_total = clip_n_output_tokens(ctx, img); diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h index 0b0eb0295..2d70eec94 100644 --- a/tools/mtmd/clip.h +++ b/tools/mtmd/clip.h @@ -1,28 +1,9 @@ -#ifndef CLIP_H -#define CLIP_H +#pragma once #include "ggml.h" #include #include -#ifdef LLAMA_SHARED -# if defined(_WIN32) && !defined(__MINGW32__) -# ifdef LLAMA_BUILD -# define CLIP_API __declspec(dllexport) -# else -# define CLIP_API __declspec(dllimport) -# endif -# else -# define CLIP_API __attribute__ ((visibility ("default"))) -# endif -#else -# define CLIP_API -#endif - -#ifdef __cplusplus -extern "C" { -#endif - struct clip_ctx; struct clip_image_size { @@ -39,97 +20,80 @@ struct clip_context_params { enum ggml_log_level verbosity; }; -// deprecated, use clip_init -CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity); +struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params); -CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params); +void clip_free(struct clip_ctx * ctx); -CLIP_API void clip_free(struct clip_ctx * ctx); +size_t clip_embd_nbytes(const struct clip_ctx * ctx); +size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h); -CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); -CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h); - -CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx); -CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx); -CLIP_API int32_t clip_get_hidden_size(const struct clip_ctx * ctx); +int32_t clip_get_image_size (const struct clip_ctx * ctx); +int32_t clip_get_patch_size (const struct clip_ctx * ctx); +int32_t clip_get_hidden_size(const struct clip_ctx * ctx); // TODO: should be enum, not string -CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx); +const char * clip_patch_merge_type(const struct clip_ctx * ctx); -CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx); -CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx); +const int32_t * clip_image_grid(const struct clip_ctx * ctx); +size_t get_clip_image_grid_size(const struct clip_ctx * ctx); -GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx), - "use clip_n_output_tokens instead"); -GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img), - "use clip_n_output_tokens instead"); - -CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img); +int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img); // for M-RoPE, this will be the number of token positions in X and Y directions // for other models, X will be the total number of tokens and Y will be 1 -CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img); -CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img); +int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img); +int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img); // this should be equal to the embedding dimension of the text model -CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); +int clip_n_mmproj_embd(const struct clip_ctx * ctx); -CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); -CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); -CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip); +int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); +void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); +struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip); -CLIP_API struct clip_image_size * clip_image_size_init(void); -CLIP_API struct clip_image_u8 * clip_image_u8_init (void); -CLIP_API struct clip_image_f32 * clip_image_f32_init(void); -CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava +struct clip_image_size * clip_image_size_init(void); +struct clip_image_u8 * clip_image_u8_init (void); +struct clip_image_f32 * clip_image_f32_init(void); +struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava // nx, ny are the output image dimensions -CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny); +unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny); -CLIP_API void clip_image_size_free (struct clip_image_size * img_size); -CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); -CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); -CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch); -CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch); +void clip_image_size_free (struct clip_image_size * img_size); +void clip_image_u8_free (struct clip_image_u8 * img); +void clip_image_f32_free(struct clip_image_f32 * img); +void clip_image_u8_batch_free (struct clip_image_u8_batch * batch); +void clip_image_f32_batch_free(struct clip_image_f32_batch * batch); // use for accessing underlay data of clip_image_f32_batch -CLIP_API size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size() -CLIP_API size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx -CLIP_API size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny -CLIP_API struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data +size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size() +size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx +size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny +struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data /** * Build image from pixels decoded by other libraries instead of stb_image.h for better performance. * The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes */ -CLIP_API void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img); +void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img); -CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); +bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ -CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); +bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); /** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */ -CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); +bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); -CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); +struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); -CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec); -CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec); +bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec); +bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec); -CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype); +int clip_is_minicpmv(const struct clip_ctx * ctx); +bool clip_is_glm(const struct clip_ctx * ctx); +bool clip_is_qwen2vl(const struct clip_ctx * ctx); +bool clip_is_llava(const struct clip_ctx * ctx); +bool clip_is_gemma3(const struct clip_ctx * ctx); -CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx); -CLIP_API bool clip_is_glm(const struct clip_ctx * ctx); -CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx); -CLIP_API bool clip_is_llava(const struct clip_ctx * ctx); -CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx); - -CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec); - - -#ifdef __cplusplus -} -#endif - -#endif // CLIP_H +bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec); diff --git a/tools/mtmd/qwen2vl-test.cpp b/tools/mtmd/qwen2vl-test.cpp deleted file mode 100644 index 7f9e3dca8..000000000 --- a/tools/mtmd/qwen2vl-test.cpp +++ /dev/null @@ -1,636 +0,0 @@ -#include "arg.h" -#include "base64.hpp" -#include "log.h" -#include "common.h" -#include "sampling.h" -#include "clip.h" -#include "llava.h" -#include "llama.h" -#include "ggml.h" - -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif -#ifdef NDEBUG -#include "ggml-alloc.h" -#include "ggml-backend.h" -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -// THIS FILE IS ONLY USED FOR TESTING THE QWEN2VL MODEL -// IT IS NOT A PRODUCTION CODE - -static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, - int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) { - int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); - const int patch_size = 14 * 2; - const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0); - const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0); - auto img_tokens = image_embed->n_image_pos; - // llama_pos mrope_pos[img_tokens * 4]; - std::vector mrope_pos; - mrope_pos.resize(img_tokens * 4); - - for (int y = 0; y < ph; y++) - { - for (int x = 0; x < pw; x++) - { - int i = y * pw + x; - mrope_pos[i] = *st_pos_id; - mrope_pos[i + img_tokens] = *st_pos_id + y; - mrope_pos[i + img_tokens * 2] = *st_pos_id + x; - mrope_pos[i + img_tokens * 3] = 0; - } - } - *st_pos_id += std::max(pw, ph); - - int processed = 0; - std::vector batch_mrope_pos; - batch_mrope_pos.resize(img_tokens * 4); - - for (int i = 0; i < img_tokens; i += n_batch) { - int n_eval = img_tokens - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - - // llama_pos batch_mrope_pos[n_eval * 4]; - std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0); - memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos)); - memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos)); - memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos)); - memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos)); - - llama_batch batch = { - int32_t(n_eval), // n_tokens - nullptr, // token - (image_embed->embed+i*n_embd), // embed - batch_mrope_pos.data(), // pos - nullptr, // n_seq_id - nullptr, // seq_id - nullptr, // logits - }; - - if (llama_decode(ctx_llama, batch)) { - LOG_ERR("%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - processed += n_eval; - } - return true; -} - - -static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past, int * st_pos_id) { - int N = (int) tokens.size(); - for (int i = 0; i < N; i += n_batch) { - int n_eval = (int) tokens.size() - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - auto batch = llama_batch_get_one(&tokens[i], n_eval); - - if (llama_decode(ctx_llama, batch)) { - LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); - return false; - } - *n_past += n_eval; - *st_pos_id += n_eval; - } - return true; -} - -static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) { - std::vector tokens; - tokens.push_back(id); - return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id); -} - -static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){ - std::string str2 = str; - std::vector embd_inp = common_tokenize(ctx_llama, str2, add_bos, true); - eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id); - return true; -} - -static const char * sample(struct common_sampler * smpl, - struct llama_context * ctx_llama, - int * n_past, int * st_pos_id) { - const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); - common_sampler_accept(smpl, id, true); - - const llama_model * model = llama_get_model(ctx_llama); - const llama_vocab * vocab = llama_model_get_vocab(model); - - static std::string ret; - if (llama_vocab_is_eog(vocab, id)) { - ret = ""; - } else { - ret = common_token_to_piece(ctx_llama, id); - } - eval_id(ctx_llama, id, n_past, st_pos_id); - return ret.c_str(); -} - -static const char* IMG_BASE64_TAG_BEGIN = ""; - -static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) { - begin_out = prompt.find(IMG_BASE64_TAG_BEGIN); - end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out); -} - -static bool prompt_contains_image(const std::string& prompt) { - size_t begin, end; - find_image_tag_in_prompt(prompt, begin, end); - return (begin != std::string::npos); -} - -// replaces the base64 image tag in the prompt with `replacement` -static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) { - size_t img_base64_str_start, img_base64_str_end; - find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); - if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { - LOG_ERR("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); - return NULL; - } - - auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN); - auto base64_bytes_count = img_base64_str_end - base64_bytes_start; - auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count ); - - auto required_bytes = base64::required_encode_size(base64_str.size()); - auto img_bytes = std::vector(required_bytes); - base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin()); - - auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); - if (!embed) { - LOG_ERR("%s: could not load image from base64 string.\n", __func__); - return NULL; - } - - return embed; -} - -static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") { - size_t begin, end; - find_image_tag_in_prompt(prompt, begin, end); - if (begin == std::string::npos || end == std::string::npos) { - return prompt; - } - auto pre = prompt.substr(0, begin); - auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END)); - return pre + replacement + post; -} - -struct llava_context { - struct clip_ctx * ctx_clip = NULL; - struct llama_context * ctx_llama = NULL; - struct llama_model * model = NULL; -}; - -static void print_usage(int, char ** argv) { - LOG("\n example usage:\n"); - LOG("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); -} - -static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) { - - // load and preprocess the image - llava_image_embed * embed = NULL; - auto prompt = params->prompt; - if (prompt_contains_image(prompt)) { - if (!params->image.empty()) { - LOG_INF("using base64 encoded image instead of command line image path\n"); - } - embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt); - if (!embed) { - LOG_ERR("%s: can't load image from prompt\n", __func__); - return NULL; - } - params->prompt = remove_image_from_prompt(prompt); - } else { - embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str()); - if (!embed) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str()); - return NULL; - } - } - - return embed; -} - -static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) { - int n_past = 0; - int cur_pos_id = 0; - - const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; - - std::string system_prompt, user_prompt; - size_t image_pos = prompt.find("<|vision_start|>"); - if (image_pos != std::string::npos) { - // new templating mode: Provide the full prompt including system message and use as a placeholder for the image - system_prompt = prompt.substr(0, image_pos); - user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length()); - LOG_INF("system_prompt: %s\n", system_prompt.c_str()); - if (params->verbose_prompt) { - auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); - for (int i = 0; i < (int) tmp.size(); i++) { - LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); - } - } - LOG_INF("user_prompt: %s\n", user_prompt.c_str()); - if (params->verbose_prompt) { - auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); - for (int i = 0; i < (int) tmp.size(); i++) { - LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); - } - } - } else { - // llava-1.5 native mode - system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>"; - user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n"; - if (params->verbose_prompt) { - auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); - for (int i = 0; i < (int) tmp.size(); i++) { - LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); - } - } - } - - eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true); - if (image_embed != nullptr) { - auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip); - qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size); - } - eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false); - - // generate the response - - LOG("\n"); - - struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling); - if (!smpl) { - LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); - exit(1); - } - - std::string response = ""; - for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id); - response += tmp; - if (strcmp(tmp, "") == 0) break; - if (strstr(tmp, "###")) break; // Yi-VL behavior - LOG("%s", tmp); - if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works) - if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6 - if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6 - - fflush(stdout); - } - - common_sampler_free(smpl); - LOG("\n"); -} - -static struct llama_model * llava_init(common_params * params) { - llama_backend_init(); - llama_numa_init(params->numa); - - llama_model_params model_params = common_model_params_to_llama(*params); - - llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params); - if (model == NULL) { - LOG_ERR("%s: unable to load model\n" , __func__); - return NULL; - } - return model; -} - -static struct llava_context * llava_init_context(common_params * params, llama_model * model) { - const char * clip_path = params->mmproj.path.c_str(); - - auto prompt = params->prompt; - if (prompt.empty()) { - prompt = "describe the image in detail."; - } - - auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO); - - llama_context_params ctx_params = common_context_params_to_llama(*params); - ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings - - llama_context * ctx_llama = llama_init_from_model(model, ctx_params); - - if (ctx_llama == NULL) { - LOG_ERR("%s: failed to create the llama_context\n" , __func__); - return NULL; - } - - auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); - - ctx_llava->ctx_llama = ctx_llama; - ctx_llava->ctx_clip = ctx_clip; - ctx_llava->model = model; - return ctx_llava; -} - -static void llava_free(struct llava_context * ctx_llava) { - if (ctx_llava->ctx_clip) { - clip_free(ctx_llava->ctx_clip); - ctx_llava->ctx_clip = NULL; - } - - llama_free(ctx_llava->ctx_llama); - llama_model_free(ctx_llava->model); - llama_backend_free(); -} - -#ifndef NDEBUG - -static void debug_test_mrope_2d() { - // 1. Initialize backend - ggml_backend_t backend = NULL; - std::string backend_name = ""; -// #ifdef GGML_USE_CUDA -// fprintf(stderr, "%s: using CUDA backend\n", __func__); -// backend = ggml_backend_cuda_init(0); // init device 0 -// backend_name = "cuda"; -// if (!backend) { -// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); -// } -// #endif - // if there aren't GPU Backends fallback to CPU backend - if (!backend) { - backend = ggml_backend_cpu_init(); - backend_name = "cpu"; - } - - // Calculate the size needed to allocate - size_t ctx_size = 0; - ctx_size += 2 * ggml_tensor_overhead(); // tensors - // no need to allocate anything else! - - // 2. Allocate `ggml_context` to store tensor data - struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors() - }; - struct ggml_context * ctx = ggml_init(params); - - struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - - struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4); - ggml_set_name(pos, "pos"); - ggml_set_input(pos); - - std::vector dummy_q; - dummy_q.resize(128 * 12 * 30); - std::fill(dummy_q.begin(), dummy_q.end(), 0.1); - // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw)); - - std::vector pos_id; - pos_id.resize(30 * 4); - for (int i = 0; i < 30; i ++) { - pos_id[i] = i; - pos_id[i + 30] = i + 10; - pos_id[i + 60] = i + 20; - pos_id[i + 90] = i + 30; - } - int sections[4] = {32, 32, 0, 0}; - - // 4. Allocate a `ggml_backend_buffer` to store all tensors - ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); - - // 5. Copy tensor data from main memory (RAM) to backend buffer - ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw)); - ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos)); - - // 6. Create a `ggml_cgraph` for mul_mat operation - struct ggml_cgraph * gf = NULL; - struct ggml_context * ctx_cgraph = NULL; - - // create a temporally context to build the graph - struct ggml_init_params params0 = { - /*.mem_size =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph() - }; - ctx_cgraph = ggml_init(params0); - gf = ggml_new_graph(ctx_cgraph); - - struct ggml_tensor * result0 = ggml_rope_multi( - ctx_cgraph, inp_raw, pos, nullptr, - 128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1, - 0, 1, 32, 1); - - // Add "result" tensor and all of its dependencies to the cgraph - ggml_build_forward_expand(gf, result0); - - // 7. Create a `ggml_gallocr` for cgraph computation - ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend)); - ggml_gallocr_alloc_graph(allocr, gf); - - // 9. Run the computation - int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading - if (ggml_backend_is_cpu(backend)) { - ggml_backend_cpu_set_n_threads(backend, n_threads); - } - ggml_backend_graph_compute(backend, gf); - - // 10. Retrieve results (output tensors) - // in this example, output tensor is always the last tensor in the graph - struct ggml_tensor * result = result0; - // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1]; - float * result_data = (float *)malloc(ggml_nbytes(result)); - // because the tensor data is stored in device buffer, we need to copy it back to RAM - ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result)); - const std::string bin_file = "mrope_2d_" + backend_name +".bin"; - std::ofstream outFile(bin_file, std::ios::binary); - - if (outFile.is_open()) { - outFile.write(reinterpret_cast(result_data), ggml_nbytes(result)); - outFile.close(); - std::cout << "Data successfully written to " + bin_file << std::endl; - } else { - std::cerr << "Error opening file!" << std::endl; - } - - free(result_data); - // 11. Free memory and exit - ggml_free(ctx_cgraph); - ggml_gallocr_free(allocr); - ggml_free(ctx); - ggml_backend_buffer_free(buffer); - ggml_backend_free(backend); -} - -enum model_output_type { - conv3d, - patch_embed, - patch_win_attn_scatter, - first_attn_layer, - last_attn_layer, - attn_softmax, - final_layer, -}; - -static void debug_dump_img_embed(struct llava_context * ctx_llava, model_output_type output_type) { - constexpr int ih = 140; - constexpr int iw = 196; - // constexpr int ih = 56; - // constexpr int iw = 56; - // int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama)); - int n_embd = 1280; - int merge = 1; - if (output_type == model_output_type::final_layer) { - n_embd = 2048; - merge = 2; - } - else if (output_type == model_output_type::attn_softmax) { - merge = 1; - n_embd = (ih/14/merge) * (iw/14/merge) * 16; - } - - int ne = (ih/14/merge) * (iw/14/merge) * n_embd; - float vals[iw * ih * 3]; - // float embd[ne]; - std::vector embd; - embd.resize(ne); - - for (int i = 0; i < iw*ih; i++) - { - for (int c = 0; c < 3; c++) - vals[i * 3 + c] = (float)i / (iw*ih); - } - - clip_encode_float_image(ctx_llava->ctx_clip, 8, vals, ih, iw, embd.data()); - - std::string file_postfix = ""; - switch (output_type) - { - case model_output_type::conv3d: - file_postfix = "conv3d"; - break; - case model_output_type::patch_embed: - file_postfix = "patch_embed"; - break; - case model_output_type::patch_win_attn_scatter: - file_postfix = "scatter"; - break; - case model_output_type::first_attn_layer: - file_postfix = "first_attn"; - break; - case model_output_type::last_attn_layer: - file_postfix = "last_attn"; - break; - case model_output_type::attn_softmax: - file_postfix = "attn_softmax"; - break; - case model_output_type::final_layer: - file_postfix = "final"; - break; - default: - break; - } - auto output_path = "img_embed_" + file_postfix + ".bin"; - - std::ofstream outFile(output_path, std::ios::binary); - if (outFile.is_open()) { - outFile.write(reinterpret_cast(embd.data()), ne * sizeof(float)); - - outFile.close(); - std::cout << "Data successfully written to ::[ " << output_path << std::endl; - } else { - std::cerr << "Error opening file!" << std::endl; - } -} - -#endif - - -int main(int argc, char ** argv) { - ggml_time_init(); - - common_params params; - - if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) { - return 1; - } - - common_init(); - - if (params.mmproj.path.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - print_usage(argc, argv); - return 1; - } - - auto * model = llava_init(¶ms); - if (model == NULL) { - fprintf(stderr, "%s: error: failed to init llava model\n", __func__); - return 1; - } - - if (prompt_contains_image(params.prompt)) { - auto * ctx_llava = llava_init_context(¶ms, model); - - auto * image_embed = load_image(ctx_llava, ¶ms, ""); - - // process the prompt - process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); - - llama_perf_context_print(ctx_llava->ctx_llama); - llava_image_embed_free(image_embed); - ctx_llava->model = NULL; - llava_free(ctx_llava); -#ifndef NDEBUG - } else if (params.image[0].empty()) { - auto ctx_llava = llava_init_context(¶ms, model); - - // debug_test_mrope_2d(); - debug_dump_img_embed(ctx_llava, model_output_type::final_layer); - // debug_dump_img_embed(ctx_llava, model_output_type::last_attn_layer); - - llama_perf_context_print(ctx_llava->ctx_llama); - ctx_llava->model = NULL; - llava_free(ctx_llava); -#endif - } else { - for (auto & image : params.image) { - auto * ctx_llava = llava_init_context(¶ms, model); - - auto * image_embed = load_image(ctx_llava, ¶ms, image); - if (!image_embed) { - LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str()); - return 1; - } - - // process the prompt - process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); - - llama_perf_context_print(ctx_llava->ctx_llama); - llava_image_embed_free(image_embed); - ctx_llava->model = NULL; - llava_free(ctx_llava); - } - } - - llama_model_free(model); - - return 0; -} From e5c834f718a32b7584f142799bbf508fddb9021c Mon Sep 17 00:00:00 2001 From: Ed Addario <29247825+EAddario@users.noreply.github.com> Date: Tue, 13 May 2025 18:12:31 +0100 Subject: [PATCH 039/421] quantize : improve tensor-type pattern matching (#13033) --- src/llama-quant.cpp | 24 +++++----- tools/quantize/quantize.cpp | 89 ++++++------------------------------- 2 files changed, 26 insertions(+), 87 deletions(-) diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 820d5128e..159b1307a 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -14,6 +14,12 @@ #include #include +// Quantization types. Changes to this struct must be replicated in quantize.cpp +struct tensor_quantization { + std::string name; + ggml_type quant = GGML_TYPE_COUNT; +}; + static void zeros(std::ofstream & file, size_t n) { char zero = 0; for (size_t i = 0; i < n; ++i) { @@ -48,12 +54,6 @@ struct quantize_state_impl { {} }; -// changes to this struct must be replicated in quantize.cpp -struct tensor_quantization { - std::string name; - ggml_type quant = GGML_TYPE_COUNT; -}; - static void llama_tensor_dequantize_impl( ggml_tensor * tensor, std::vector> & output, std::vector & workers, const size_t nelements, const int nthread @@ -796,17 +796,19 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: // unless the user specifies a type if (params->tensor_types) { const std::vector & tensor_types = *static_cast *>(params->tensor_types); + const std::string tensor_name(tensor->name); for (const auto & [tname, qtype] : tensor_types) { - if (std::regex pattern(tname); std::regex_search(tensor->name, pattern)) { - if (qtype != new_type) { - LLAMA_LOG_DEBUG("(overriding %s -> %s), ", ggml_type_name(new_type), ggml_type_name(qtype)); + if (std::regex pattern(tname); std::regex_search(tensor_name, pattern)) { + if (qtype != new_type) { + LLAMA_LOG_DEBUG("(overriding %s) ", ggml_type_name(new_type)); + new_type = qtype; + break; // if two or more types are specified for the tensor, first match wins } - new_type = qtype; - break; } } } } + if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) { new_type = params->token_embedding_type; } diff --git a/tools/quantize/quantize.cpp b/tools/quantize/quantize.cpp index 0355311dc..3f54af7c5 100644 --- a/tools/quantize/quantize.cpp +++ b/tools/quantize/quantize.cpp @@ -57,6 +57,12 @@ static const std::vector QUANT_OPTIONS = { { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, }; +// Quantization types. Changes to this struct must be replicated in llama-quantize.cpp +struct tensor_quantization { + std::string name; + ggml_type quant = GGML_TYPE_COUNT; +}; + static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file"; static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset"; static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count"; @@ -244,56 +250,10 @@ static ggml_type parse_ggml_type(const char * arg) { return type; } } - fprintf(stderr, "%s: invalid ggml_type '%s'\n", __func__, arg); + fprintf(stderr, "\n%s: invalid ggml_type '%s'\n\n", __func__, arg); return GGML_TYPE_COUNT; } -// Allowed tensors for arbitrary quantization with --tensor-type option -static const std::vector ALLOWED_TENSOR_TYPE = { - "attn_k", - "attn_kv_a_mqa", - "attn_kv_b", - "attn_o", - "attn_output", - "attn_q", - "attn_q_a", - "attn_q_b", - "attn_qkv", - "attn_v", - "channel_mix_key", - "channel_mix_receptance", - "channel_mix_value", - "cls", - "cls.output", - "cross_attn_k", - "cross_attn_o", - "cross_attn_q", - "cross_attn_v", - "ffn_act", - "ffn_down", - "ffn_down_exps", - "ffn_down_shexp", - "ffn_gate", - "ffn_gate_exps", - "ffn_gate_shexp", - "ffn_up", - "ffn_up_exps", - "ffn_up_shexp", - "ssm_in", - "ssm_out", - "time_mix_gate", - "time_mix_key", - "time_mix_output", - "time_mix_receptance", - "time_mix_value", -}; - -// changes to this struct must be replicated in llama-quant.cpp -struct tensor_quantization { - std::string name; - ggml_type quant = GGML_TYPE_COUNT; -}; - static bool parse_tensor_type(const char * data, std::vector & tensor_type) { const char * sep = strchr(data, '='); if (sep == nullptr) { @@ -306,7 +266,6 @@ static bool parse_tensor_type(const char * data, std::vector Date: Wed, 14 May 2025 13:15:50 +0900 Subject: [PATCH 040/421] vulkan: workaround FA compile failures on macos (#13517) --- ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index e6545160d..168355768 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -12,6 +12,7 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +layout (constant_id = 0) const uint32_t WorkGroupSize = 128; layout (constant_id = 1) const uint32_t Br = 1; layout (constant_id = 2) const uint32_t Bc = 32; layout (constant_id = 3) const uint32_t D = 32; @@ -19,7 +20,7 @@ layout (constant_id = 3) const uint32_t D = 32; layout (constant_id = 5) const uint32_t D_split = 16; const uint32_t D_per_thread = D / D_split; -const uint32_t cols_per_iter = gl_WorkGroupSize.x / D_split; +const uint32_t cols_per_iter = WorkGroupSize / D_split; const uint32_t cols_per_thread = Bc / cols_per_iter; layout (push_constant) uniform parameter { @@ -134,8 +135,8 @@ ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const i return ACC_TYPE(pow(base, ACC_TYPE(exph))); } -shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x]; -shared vec4 tmpshv4[gl_WorkGroupSize.x]; +shared FLOAT_TYPE tmpsh[WorkGroupSize]; +shared vec4 tmpshv4[WorkGroupSize]; shared float masksh[Bc][Br]; shared vec4 Qf[Br][D / 4]; From be1d4a13db26750fac702ceb3af88ae4f39dc9f4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Wed, 14 May 2025 08:41:01 +0200 Subject: [PATCH 041/421] scripts : fix compare-llama-bench.py show parameter (#13514) --- scripts/compare-llama-bench.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index fc93bf62a..a1013c3b7 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -113,7 +113,7 @@ parser.add_argument("-o", "--output", help=help_o, default="pipe") help_s = ( "Columns to add to the table. " "Accepts a comma-separated list of values. " - f"Legal values: {', '.join(KEY_PROPERTIES[:-2])}. " + f"Legal values: {', '.join(KEY_PROPERTIES[:-3])}. " "Defaults to model name (model_type) and CPU and/or GPU name (cpu_info, gpu_info) " "plus any column where not all data points are the same. " "If the columns are manually specified, then the results for each unique combination of the " @@ -505,7 +505,7 @@ if known_args.show is not None: show = known_args.show.split(",") unknown_cols = [] for prop in show: - if prop not in KEY_PROPERTIES[:-2]: # Last two values are n_prompt, n_gen. + if prop not in KEY_PROPERTIES[:-3]: # Last three values are n_prompt, n_gen, n_depth. unknown_cols.append(prop) if unknown_cols: logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}") From 21ca987fba504d273ea28ebc4d3e5b3736a11c8e Mon Sep 17 00:00:00 2001 From: ddpasa <112642920+ddpasa@users.noreply.github.com> Date: Wed, 14 May 2025 09:59:12 +0200 Subject: [PATCH 042/421] docs: Update link to ggml-org in multimodal.md (#13513) * Update multimodal.md Minor change to include the huggingface link * Update docs/multimodal.md --------- Co-authored-by: Xuan-Son Nguyen --- docs/multimodal.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/multimodal.md b/docs/multimodal.md index 6a5d2b342..80014ba1c 100644 --- a/docs/multimodal.md +++ b/docs/multimodal.md @@ -31,7 +31,7 @@ llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload ## Pre-quantized models -These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. +These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/ggml-org Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server` From d486dd3e8ecfba0170f8632a1530540de560f4a3 Mon Sep 17 00:00:00 2001 From: Luca Stefani Date: Wed, 14 May 2025 10:07:31 +0200 Subject: [PATCH 043/421] webui: Allow pasting file from clipboard (#13526) * server: Allow pasting file from clipboard * server: Prevent default action on file paste * update build * format then build combined --------- Co-authored-by: Xuan Son Nguyen --- tools/server/public/index.html.gz | Bin 1290053 -> 1290091 bytes tools/server/webui/package.json | 2 +- .../webui/src/components/ChatScreen.tsx | 13 ++++++++++++- 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index d7363e13eb25ae845b984662a2b8f42ee631f3d5..a2a2ee12473103ab30c9213ecf86d1475d8dfa5d 100644 GIT binary patch delta 897552 zcmV(+K;6H^-cIY@PJn~~gaU*Ev;@THf6p!QcZf3l9F=f4Y8=()?QEi5`~iU>DY+XS z$krQSJ>$u*qz5UfPbe+2(6 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a/tools/server/webui/package.json b/tools/server/webui/package.json index ab1b920bd..b83defdf3 100644 --- a/tools/server/webui/package.json +++ b/tools/server/webui/package.json @@ -5,7 +5,7 @@ "type": "module", "scripts": { "dev": "vite", - "build": "tsc -b && vite build", + "build": "npm run format && tsc -b && vite build", "format": "eslint . && prettier --write .", "lint": "eslint .", "preview": "vite preview" diff --git a/tools/server/webui/src/components/ChatScreen.tsx b/tools/server/webui/src/components/ChatScreen.tsx index b645a494d..7d53fe8ac 100644 --- a/tools/server/webui/src/components/ChatScreen.tsx +++ b/tools/server/webui/src/components/ChatScreen.tsx @@ -1,4 +1,4 @@ -import { useEffect, useMemo, useRef, useState } from 'react'; +import { ClipboardEvent, useEffect, useMemo, useRef, useState } from 'react'; import { CallbackGeneratedChunk, useAppContext } from '../utils/app.context'; import ChatMessage from './ChatMessage'; import { CanvasType, Message, PendingMessage } from '../utils/types'; @@ -328,6 +328,17 @@ function ChatInput({ {({ getRootProps, getInputProps }) => (

) => { + const files = Array.from(e.clipboardData.items) + .filter((item) => item.kind === 'file') + .map((item) => item.getAsFile()) + .filter((file) => file !== null); + + if (files.length > 0) { + e.preventDefault(); + extraContext.onFileAdded(files); + } + }} {...getRootProps()} > {!isGenerating && ( From bb1681fbd532eba26ae4c14cd8be884c8afeb31c Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 14 May 2025 10:26:12 +0200 Subject: [PATCH 044/421] webui : use fflate for more deterministic gzip compress (#13525) * webui : use pako for more deterministic gzip compress * simpler code * use fflate instead of pako --- tools/server/public/index.html.gz | Bin 1290091 -> 1340664 bytes tools/server/webui/package-lock.json | 8 ++++++++ tools/server/webui/package.json | 1 + tools/server/webui/vite.config.ts | 7 ++++--- 4 files changed, 13 insertions(+), 3 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 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