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Added support for Q2K, IQ1s, IQ4NL quantization types

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leeetao  2025-03-04 15:22:55 +00:00
parent e2cda4cfa0
commit 2f049b8428
4 changed files with 551 additions and 218 deletions
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190
src/llama.cpp
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@ -3559,16 +3559,22 @@ static bool is_dtype_exist(struct model_params * n_params, enum ggml_type dtype)
case GGML_TYPE_F32:
case GGML_TYPE_F16:
return true;
case GGML_TYPE_Q2_K:
return n_params->layer_q2k > 0 || n_params->output_q2k > 0;
case GGML_TYPE_Q4_K:
return n_params->layer_q4k > 0 || n_params->output_q4k > 0;
case GGML_TYPE_Q5_0:
return n_params->layer_q50 > 0 || n_params->output_q50 > 0;
return n_params->layer_q4k > 0 || n_params->output_q4k > 0;
case GGML_TYPE_Q5_K:
return n_params->layer_q5k > 0 || n_params->output_q5k > 0;
return n_params->layer_q5k > 0 || n_params->output_q5k > 0;
case GGML_TYPE_Q6_K:
return n_params->layer_q6k > 0 || n_params->output_q6k > 0;
return n_params->layer_q6k > 0 || n_params->output_q6k > 0;
case GGML_TYPE_Q5_0:
return n_params->layer_q50 > 0 || n_params->output_q50 > 0;
case GGML_TYPE_Q8_0:
return n_params->layer_q80 > 0 || n_params->output_q80 > 0;
return n_params->layer_q80 > 0 || n_params->output_q80 > 0;
case GGML_TYPE_IQ1_S:
return n_params->layer_iq1s > 0 || n_params->output_iq1s > 0;
case GGML_TYPE_IQ4_NL:
return n_params->layer_iq4nl > 0 || n_params->output_iq4nl > 0;
default:
throw std::runtime_error("Unrecognized data type\n");
}
@ -3649,18 +3655,18 @@ void llama_profile_device(
dev_info->gpu_props.cuda_flops_f16_f32 = device_cuda_flops (model, GGML_TYPE_F16, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q2_K)) {
dev_info->cpu_props.flops_q2k_f32 = device_cpu_flops (model, GGML_TYPE_Q2_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q2k_f32 = device_metal_flops(model, GGML_TYPE_Q2_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q2k_f32 = device_cuda_flops (model, GGML_TYPE_Q2_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q4_K)) {
dev_info->cpu_props.flops_q4k_f32 = device_cpu_flops (model, GGML_TYPE_Q4_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q4k_f32 = device_metal_flops(model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q4k_f32 = device_cuda_flops (model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_0)) {
dev_info->cpu_props.flops_q50_f32 = device_cpu_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q50_f32 = device_metal_flops(model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q50_f32 = device_cuda_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_K)) {
dev_info->cpu_props.flops_q5k_f32 = device_cpu_flops (model, GGML_TYPE_Q5_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q5k_f32 = device_metal_flops(model, GGML_TYPE_Q5_K, GGML_TYPE_F32);
@ -3673,11 +3679,30 @@ void llama_profile_device(
dev_info->gpu_props.cuda_flops_q6k_f32 = device_cuda_flops (model, GGML_TYPE_Q6_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_0)) {
dev_info->cpu_props.flops_q50_f32 = device_cpu_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q50_f32 = device_metal_flops(model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q50_f32 = device_cuda_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q8_0)) {
dev_info->cpu_props.flops_q80_f32 = device_cpu_flops (model, GGML_TYPE_Q8_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q80_f32 = device_metal_flops(model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q80_f32 = device_cuda_flops (model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ1_S)) {
dev_info->cpu_props.flops_iq1s_f32 = device_cpu_flops (model, GGML_TYPE_IQ1_S, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq1s_f32= device_metal_flops(model, GGML_TYPE_IQ1_S, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq1s_f32 = device_cuda_flops (model, GGML_TYPE_IQ1_S, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ4_NL)) {
dev_info->cpu_props.flops_iq4nl_f32 = device_cpu_flops (model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq4nl_f32= device_metal_flops(model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq4nl_f32 = device_cuda_flops (model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32);
}
}
ggml_backend_buffer_type_t llama_dev_buffer_type(struct llama_model * model, int device) {
@ -21029,49 +21054,67 @@ static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, en
case GGML_TYPE_F16:
n_flops->output_f16_f32 += n;
break;
case GGML_TYPE_Q2_K:
n_flops->output_q2k_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->output_q4k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->output_q50_f32 += n;
break;
case GGML_TYPE_Q5_K:
n_flops->output_q5k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->output_q6k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->output_q50_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->output_q80_f32 += n;
break;
case GGML_TYPE_IQ1_S:
n_flops->output_iq1s_f32 += n;
break;
case GGML_TYPE_IQ4_NL:
n_flops->output_iq4nl_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
}
break;
case PROFILER_LAYER_BACKEND:
switch (dtype) {
switch (dtype) {
case GGML_TYPE_F32:
n_flops->layer_f32_f32 += n;
break;
case GGML_TYPE_F16:
n_flops->layer_f16_f32 += n;
break;
case GGML_TYPE_Q2_K:
n_flops->layer_q2k_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->layer_q4k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->layer_q50_f32 += n;
break;
case GGML_TYPE_Q5_K:
n_flops->layer_q5k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->layer_q6k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->layer_q50_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->layer_q80_f32 += n;
break;
case GGML_TYPE_IQ1_S:
n_flops->layer_iq1s_f32 += n;
break;
case GGML_TYPE_IQ4_NL:
n_flops->layer_iq4nl_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
}
@ -21093,21 +21136,30 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case GGML_TYPE_F16:
n_params->input_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->input_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->input_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->input_q50 += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->input_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->input_q6k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->input_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->input_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->input_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->input_iq4nl += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
}
@ -21116,25 +21168,34 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case PROFILER_LAYER_OUTPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_params->output_f32 += n_i64t;
n_params->output_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->output_f16 += n_i64t;
n_params->output_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->output_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->output_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->output_q50 += n_i64t;
n_params->output_q4k += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->output_q5k += n_i64t;
n_params->output_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->output_q6k += n_i64t;
n_params->output_q6k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->output_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->output_q80 += n_i64t;
n_params->output_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->output_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->output_iq4nl += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
@ -21144,25 +21205,34 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case PROFILER_LAYER_BACKEND:
switch (dtype) {
case GGML_TYPE_F32:
n_params->layer_f32 += n_i64t;
n_params->layer_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->layer_f16 += n_i64t;
n_params->layer_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->layer_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->layer_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->layer_q50 += n_i64t;
n_params->layer_q4k += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->layer_q5k += n_i64t;
n_params->layer_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->layer_q6k += n_i64t;
n_params->layer_q6k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->layer_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->layer_q80 += n_i64t;
n_params->layer_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->layer_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->layer_iq4nl += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
@ -21452,23 +21522,29 @@ void llama_model_n_flops(
}
// use average values instead of total values
n_flops->layer_f32_f32 = static_cast<int64_t>((double)n_flops->layer_f32_f32 / (double)n_layer);
n_flops->layer_f16_f32 = static_cast<int64_t>((double)n_flops->layer_f16_f32 / (double)n_layer);
n_flops->layer_q4k_f32 = static_cast<int64_t>((double)n_flops->layer_q4k_f32 / (double)n_layer);
n_flops->layer_q50_f32 = static_cast<int64_t>((double)n_flops->layer_q50_f32 / (double)n_layer);
n_flops->layer_q5k_f32 = static_cast<int64_t>((double)n_flops->layer_q5k_f32 / (double)n_layer);
n_flops->layer_q6k_f32 = static_cast<int64_t>((double)n_flops->layer_q6k_f32 / (double)n_layer);
n_flops->layer_q80_f32 = static_cast<int64_t>((double)n_flops->layer_q80_f32 / (double)n_layer);
n_params->layer_f32 = static_cast<int64_t>((double)n_params->layer_f32 / (double)n_layer);
n_params->layer_f16 = static_cast<int64_t>((double)n_params->layer_f16 / (double)n_layer);
n_params->layer_q4k = static_cast<int64_t>((double)n_params->layer_q4k / (double)n_layer);
n_params->layer_q50 = static_cast<int64_t>((double)n_params->layer_q50 / (double)n_layer);
n_params->layer_q5k = static_cast<int64_t>((double)n_params->layer_q5k / (double)n_layer);
n_params->layer_q6k = static_cast<int64_t>((double)n_params->layer_q6k / (double)n_layer);
n_params->layer_q80 = static_cast<int64_t>((double)n_params->layer_q80 / (double)n_layer);
n_bytes->nb_layer = static_cast<int64_t>((double)n_bytes->nb_layer / (double)n_layer);
n_flops->layer_f32_f32 = static_cast<int64_t>((double)n_flops->layer_f32_f32 / (double)n_layer);
n_flops->layer_f16_f32 = static_cast<int64_t>((double)n_flops->layer_f16_f32 / (double)n_layer);
n_flops->layer_q2k_f32 = static_cast<int64_t>((double)n_flops->layer_q2k_f32 / (double)n_layer);
n_flops->layer_q4k_f32 = static_cast<int64_t>((double)n_flops->layer_q4k_f32 / (double)n_layer);
n_flops->layer_q5k_f32 = static_cast<int64_t>((double)n_flops->layer_q5k_f32 / (double)n_layer);
n_flops->layer_q6k_f32 = static_cast<int64_t>((double)n_flops->layer_q6k_f32 / (double)n_layer);
n_flops->layer_q50_f32 = static_cast<int64_t>((double)n_flops->layer_q50_f32 / (double)n_layer);
n_flops->layer_q80_f32 = static_cast<int64_t>((double)n_flops->layer_q80_f32 / (double)n_layer);
n_flops->layer_iq1s_f32 = static_cast<int64_t>((double)n_flops->layer_iq1s_f32 / (double)n_layer);
n_flops->layer_iq4nl_f32 = static_cast<int64_t>((double)n_flops->layer_iq4nl_f32 / (double)n_layer);
n_params->layer_f32 = static_cast<int64_t>((double)n_params->layer_f32 / (double)n_layer);
n_params->layer_f16 = static_cast<int64_t>((double)n_params->layer_f16 / (double)n_layer);
n_params->layer_q2k = static_cast<int64_t>((double)n_params->layer_q2k / (double)n_layer);
n_params->layer_q4k = static_cast<int64_t>((double)n_params->layer_q4k / (double)n_layer);
n_params->layer_q50 = static_cast<int64_t>((double)n_params->layer_q50 / (double)n_layer);
n_params->layer_q5k = static_cast<int64_t>((double)n_params->layer_q5k / (double)n_layer);
n_params->layer_q6k = static_cast<int64_t>((double)n_params->layer_q6k / (double)n_layer);
n_params->layer_q80 = static_cast<int64_t>((double)n_params->layer_q80 / (double)n_layer);
n_params->layer_iq1s = static_cast<int64_t>((double)n_params->layer_iq1s / (double)n_layer);
n_params->layer_iq4nl = static_cast<int64_t>((double)n_params->layer_iq4nl / (double)n_layer);
n_bytes->nb_layer = static_cast<int64_t>((double)n_bytes->nb_layer / (double)n_layer);
// reset ml, model, and clear contexts
ml->n_created = 0;