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metal : pad n_ctx by 32 #6177

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merged 2 commits into from Mar 22, 2024
Merged

metal : pad n_ctx by 32 #6177

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712b5d6
metal : require ne00 >= 128 for mat-mat kernels
ggerganov Mar 20, 2024
1d6112b
llama : pad n_ctx by 32
ggerganov Mar 21, 2024
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2 changes: 1 addition & 1 deletion common/common.cpp
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Expand Up @@ -101,7 +101,7 @@ int32_t get_num_physical_cores() {
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}

void process_escapes(std::string& input) {
void process_escapes(std::string & input) {
std::size_t input_len = input.length();
std::size_t output_idx = 0;

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4 changes: 3 additions & 1 deletion examples/batched/batched.cpp
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Expand Up @@ -48,6 +48,8 @@ int main(int argc, char ** argv) {
params.prompt = "Hello my name is";
}

process_escapes(params.prompt);

// init LLM

llama_backend_init();
Expand Down Expand Up @@ -78,7 +80,7 @@ int main(int argc, char ** argv) {
llama_context_params ctx_params = llama_context_default_params();

ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_req;
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_len, n_parallel);
ctx_params.n_seq_max = n_parallel;
ctx_params.n_threads = params.n_threads;
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3 changes: 3 additions & 0 deletions llama.cpp
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Expand Up @@ -13044,6 +13044,9 @@ struct llama_context * llama_new_context_with_model(
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;

// this is necessary due to kv_self.n being padded later during inference
cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32);

// with causal attention, the batch size is limited by the context size
cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
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7 changes: 7 additions & 0 deletions tests/test-backend-ops.cpp
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Expand Up @@ -2091,6 +2091,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
}

test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 128, { 8, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 128, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 64, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 64, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1}));

for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
for (int n_mats : {2, 4, 8}) {
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