ikawrakow/ik_llama.cpp
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Change flash attention and fmoe to be on by default (#863)

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* Change fmoe to be on by default

* Change default fmoe also in llama-bench

* Change flash attention to be on by default

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-10-25 09:37:28 +03:00
committed by GitHub
parent 70c0095e11
commit 41d6c42b96
4 changed files with 26 additions and 26 deletions
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12
common/common.cpp
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@@ -994,8 +994,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.cont_batching = false;
return true;
}
if (arg == "-fa" || arg == "--flash-attn") {
params.flash_attn = true;
if (arg == "-no-fa" || arg == "--no-flash-attn") {
params.flash_attn = false;
return true;
}
if (arg == "-mla" || arg == "--mla-use") {
@@ -1008,8 +1008,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.attn_max_batch = std::stoi(argv[i]);
return true;
}
if (arg == "-fmoe" || arg == "--fused-moe") {
params.fused_moe_up_gate = true;
if (arg == "-no-fmoe" || arg == "--no-fused-moe") {
params.fused_moe_up_gate = false;
return true;
}
if (arg == "-ger" || arg == "--grouped-expert-routing") {
@@ -1804,10 +1804,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-no-fa, --no-flash-attn", "disable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-mla, --mla-use", "enable MLA (default: %d)", params.mla_attn });
options.push_back({ "*", "-amb, --attention-max-batch", "max batch size for attention computations (default: %d)", params.attn_max_batch});
options.push_back({ "*", "-fmoe, --fused-moe", "enable fused MoE (default: %s)", params.fused_moe_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-no-fmoe, --no-fused-moe", "disable fused MoE (default: %s)", params.fused_moe_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-ger, --grouped-expert-routing", "enable grouped expert routing (default: %s)", params.grouped_expert_routing ? "enabled" : "disabled" });
options.push_back({ "*", "-no-fug, --no-fused-up-gate", "disaable fused up-gate (default: %s)", params.fused_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-no-mmad, --no-fused-mul-multiadd", "disaable fused mul-multi_add (default: %s)", params.fused_mmad? "enabled" : "disabled" });

4
common/common.h
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@@ -230,10 +230,10 @@ struct gpt_params {
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool cont_batching = true; // insert new sequences for decoding on-the-fly
bool flash_attn = false; // flash attention
bool flash_attn = true; // flash attention
int mla_attn = 0; // MLA 0: standard attention, 1: MLA with K and transposed V cache, 2: MLA with just K cache
int attn_max_batch = 0; // Max batch size to use when computing attention (only applicable if flash_attn = false)
bool fused_moe_up_gate = false; // fused up*unary(gate) op for MoE models
bool fused_moe_up_gate = true; // fused up*unary(gate) op for MoE models
bool fused_up_gate = true; // fused up*unary(gate) op
bool fused_mmad = true; // fused mul+multi_add op
bool grouped_expert_routing = false; // if to use grouped expert routing (BailingMoeV2 arch)

8
examples/llama-bench/llama-bench.cpp
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@@ -260,7 +260,7 @@ struct cmd_params {
bool verbose;
bool warmup;
bool repack = false;
bool fmoe = false;
bool fmoe = true;
bool ger = false; // ger = Grouped Expert Routing
bool no_fug = false;
bool use_thp = false;
@@ -285,7 +285,7 @@ static const cmd_params cmd_params_defaults = {
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* flash_attn */ {false},
/* flash_attn */ {true},
/* mla_attn */ {0},
/* attn_max_batch */ {0},
/* ser */ {{-1,0.0f}},
@@ -298,7 +298,7 @@ static const cmd_params cmd_params_defaults = {
/* verbose */ false,
/* warmup */ true,
/* repack */ false,
/* fmoe */ false,
/* fmoe */ true,
/* ger */ false,
/* no_fug */ false,
/* use_thp */ false,
@@ -846,7 +846,7 @@ struct cmd_params_instance {
bool use_mmap;
bool embeddings;
bool repack = false;
bool fmoe = false;
bool fmoe = true;
bool ger = false;
bool no_fug = false;
bool use_thp = false;

28
src/llama.cpp
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@@ -3750,10 +3750,10 @@ struct llama_context_params llama_context_default_params() {
/*.logits_all =*/ false,
/*.embeddings =*/ false,
/*.offload_kqv =*/ true,
/*.flash_attn =*/ false,
/*.flash_attn =*/ true,
/*.mla_attn =*/ 0,
/*.attn_max_batch =*/ 0,
/*.fused_moe_up_gate =*/ false,
/*.fused_moe_up_gate =*/ true,
/*.grouped_expert_routing =*/ false,
/*.fused_up_gate =*/ true,
/*.fused_mmad =*/ true,
@@ -4040,19 +4040,19 @@ struct llama_context * llama_new_context_with_model(
cparams.mla_attn = 0;
}
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
LLAMA_LOG_INFO("%s: mla_attn = %d\n", __func__, cparams.mla_attn);
LLAMA_LOG_INFO("%s: attn_max_b = %d\n", __func__, cparams.attn_max_batch);
LLAMA_LOG_INFO("%s: fused_moe = %d\n", __func__, cparams.fused_moe_up_gate);
LLAMA_LOG_INFO("%s: grouped er = %d\n", __func__, cparams.grouped_expert_routing);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
LLAMA_LOG_INFO("%s: mla_attn = %d\n", __func__, cparams.mla_attn);
LLAMA_LOG_INFO("%s: attn_max_b = %d\n", __func__, cparams.attn_max_batch);
LLAMA_LOG_INFO("%s: fused_moe = %d\n", __func__, cparams.fused_moe_up_gate);
LLAMA_LOG_INFO("%s: grouped er = %d\n", __func__, cparams.grouped_expert_routing);
LLAMA_LOG_INFO("%s: fused_up_gate = %d\n", __func__, cparams.fused_up_gate);
LLAMA_LOG_INFO("%s: fused_mmad = %d\n", __func__, cparams.fused_mmad);
LLAMA_LOG_INFO("%s: ser = %d, %g\n", __func__, cparams.min_experts, cparams.thresh_experts);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: fused_mmad = %d\n", __func__, cparams.fused_mmad);
LLAMA_LOG_INFO("%s: ser = %d, %g\n", __func__, cparams.min_experts, cparams.thresh_experts);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
ctx->abort_callback = params.abort_callback;
ctx->abort_callback_data = params.abort_callback_data;