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https://github.com/ggerganov/llama.cpp.git
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llama : remove experimental stuff
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@ -925,9 +925,7 @@ void ggml_metal_graph_compute(
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nth1 = 1;
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if (ne11 * ne12 < 4) {
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row];
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//} else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
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} else if (false) {
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// TODO: with ggml_mul_mat_pad this kernel no longer seems to be needed
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} else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_l4];
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nrows = ne11;
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} else {
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44
llama.cpp
44
llama.cpp
@ -438,50 +438,6 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
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ggml_graph_compute(graph, &plan);
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}
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//// EXPERIMENTAL:
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////
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//// faster matrix multiplications for tensors that do not have dimension 0 divisible by "pad"
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//// the idea is to represent the original matrix multiplication:
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////
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//// Z = X @ Y
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////
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//// with the sum of two matrix multiplications:
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////
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//// Z = (X_0 @ Y_0) + (X_1 @ Y_1)
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////
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//// here X_0 and Y_0 are views of X and Y that have dimension 0 divisible by "pad"
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//// and X_1 and Y_1 are the remaining views. X_1 and Y_1 end up being small matrices that can be processed with more
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//// general-purpose kernels
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////
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//static struct ggml_tensor * ggml_mul_mat_pad(struct ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y, int pad = 32) {
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////#if !defined(GGML_USE_METAL)
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//// return ggml_mul_mat(ctx, x, y);
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////#endif
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//
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// // use padding only if dimension 0 is at least 8 times larger than the padding
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// // else we won't get much benefit from the optimization
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// const int n_pad_req = 8;
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//
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// if (x->ne[0] % pad == 0 || x->ne[0] / pad < n_pad_req) {
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// return ggml_mul_mat(ctx, x, y);
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// }
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//
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// struct ggml_tensor * x_0 = ggml_view_3d(ctx, x, (x->ne[0]/pad)*pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], 0);
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// struct ggml_tensor * x_1 = ggml_view_3d(ctx, x, x->ne[0]%pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], x_0->ne[0]*x_0->nb[0]);
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//
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// struct ggml_tensor * y_0 = ggml_view_3d(ctx, y, (y->ne[0]/pad)*pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], 0);
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// struct ggml_tensor * y_1 = ggml_view_3d(ctx, y, y->ne[0]%pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], y_0->ne[0]*y_0->nb[0]);
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//
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// return ggml_add(ctx,
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// ggml_mul_mat(ctx, x_0, y_0),
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// ggml_mul_mat(ctx, x_1, y_1));
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//}
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//
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//// TODO: check if other backends benefit from this and enable for all
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//#if defined(GGML_USE_METAL)
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////#define ggml_mul_mat ggml_mul_mat_pad
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//#endif
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//
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// llama helpers
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//
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