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https://github.com/ggerganov/llama.cpp.git
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metal : add f16 support
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parent
d5b111f53d
commit
44f906e853
23
ggml-metal.m
23
ggml-metal.m
@ -47,10 +47,11 @@ struct ggml_metal_context {
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GGML_METAL_DECL_KERNEL(relu);
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GGML_METAL_DECL_KERNEL(soft_max);
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GGML_METAL_DECL_KERNEL(diag_mask_inf);
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GGML_METAL_DECL_KERNEL(get_rows_f16);
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GGML_METAL_DECL_KERNEL(get_rows_q4_0);
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GGML_METAL_DECL_KERNEL(rms_norm);
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GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_DECL_KERNEL(rope);
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GGML_METAL_DECL_KERNEL(cpy_f32_f16);
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GGML_METAL_DECL_KERNEL(cpy_f32_f32);
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@ -130,10 +131,11 @@ struct ggml_metal_context * ggml_metal_init(void) {
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GGML_METAL_ADD_KERNEL(relu);
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GGML_METAL_ADD_KERNEL(soft_max);
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GGML_METAL_ADD_KERNEL(diag_mask_inf);
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GGML_METAL_ADD_KERNEL(get_rows_f16);
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GGML_METAL_ADD_KERNEL(get_rows_q4_0);
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GGML_METAL_ADD_KERNEL(rms_norm);
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GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_ADD_KERNEL(rope);
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GGML_METAL_ADD_KERNEL(cpy_f32_f16);
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GGML_METAL_ADD_KERNEL(cpy_f32_f32);
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@ -498,6 +500,14 @@ void ggml_metal_graph_compute(
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// use custom matrix x vector kernel
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switch (src0t) {
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case GGML_TYPE_F16:
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{
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GGML_ASSERT(ne02 == ne12);
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nth0 = 64;
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nth1 = 1;
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
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} break;
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case GGML_TYPE_Q4_0:
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{
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GGML_ASSERT(ne02 == 1);
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@ -507,14 +517,6 @@ void ggml_metal_graph_compute(
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nth1 = 4;
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
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} break;
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case GGML_TYPE_F16:
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{
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GGML_ASSERT(ne02 == ne12);
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nth0 = 32;
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nth1 = 1;
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
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} break;
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default: GGML_ASSERT(false && "not implemented");
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};
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@ -551,6 +553,7 @@ void ggml_metal_graph_compute(
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}
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switch (src0->type) {
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case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
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case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
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default: GGML_ASSERT(false && "not implemented");
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}
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@ -169,6 +169,22 @@ kernel void kernel_diag_mask_inf(
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}
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}
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kernel void kernel_get_rows_f16(
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device const void * src0,
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device const int * src1,
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device float * dst,
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constant int64_t & ne00,
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constant uint64_t & nb01,
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constant uint64_t & nb1,
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uint tpig[[thread_position_in_grid]]) {
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const int i = tpig;
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const int r = ((device int32_t *) src1)[i];
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for (int j = 0; j < ne00; j++) {
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dst[i*nb1 + j] = ((device half *) ((device char *) src0 + r*nb01))[j];
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}
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}
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kernel void kernel_get_rows_q4_0(
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device const void * src0,
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device const int * src1,
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@ -961,7 +961,6 @@ static void llama_model_load_internal(
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model.hparams = ml->file_loaders.at(0)->hparams;
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llama_file_version file_version = ml->file_loaders.at(0)->file_version;
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auto & hparams = model.hparams;
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uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
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{
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switch (hparams.n_layer) {
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@ -975,6 +974,8 @@ static void llama_model_load_internal(
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hparams.n_ctx = n_ctx;
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}
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const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
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{
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fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
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fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
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