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metal : pad n_ctx by 32 (#6177)

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* metal : require ne00 >= 128 for mat-mat kernels

ggml-ci

* llama : pad n_ctx by 32

ggml-ci
This commit is contained in:
Georgi Gerganov 2024-03-22 09:36:03 +02:00 committed by GitHub
parent 59c17f02de
commit 95d576b48e
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GPG Key ID: B5690EEEBB952194
4 changed files with 14 additions and 2 deletions
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2
examples/batched/batched.cpp
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@ -48,6 +48,8 @@ int main(int argc, char ** argv) {
params.prompt = "Hello my name is"; params.prompt = "Hello my name is";
} }
process_escapes(params.prompt);
// init LLM // init LLM
llama_backend_init(); llama_backend_init();

3
llama.cpp
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@ -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_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; 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 // 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_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); cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);

7
tests/test-backend-ops.cpp
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@ -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_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
for (int n_mats : {2, 4, 8}) { for (int n_mats : {2, 4, 8}) {