feat: first cut at actual token generation and model loading
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62
metal/concat.metal
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62
metal/concat.metal
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// DS4 Metal concat kernel used by the graph.
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struct ds4_metal_args_concat {
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int32_t ne00;
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int32_t ne01;
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int32_t ne02;
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int32_t ne03;
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uint64_t nb00;
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uint64_t nb01;
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uint64_t nb02;
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uint64_t nb03;
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int32_t ne10;
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int32_t ne11;
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int32_t ne12;
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int32_t ne13;
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uint64_t nb10;
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uint64_t nb11;
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uint64_t nb12;
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uint64_t nb13;
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int32_t ne0;
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int32_t ne1;
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int32_t ne2;
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int32_t ne3;
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uint64_t nb0;
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uint64_t nb1;
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uint64_t nb2;
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uint64_t nb3;
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int32_t dim;
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};
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// Concatenates two float tensors along one dimension. In DS4 this is a graph
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// utility for assembling attention inputs with exactly the same tensor layout
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// expected by the downstream kernels.
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kernel void kernel_concat(
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constant ds4_metal_args_concat & args,
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device const char * src0,
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device const char * src1,
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device char * dst,
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uint3 tgpig[[threadgroup_position_in_grid]],
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ushort3 tpitg[[thread_position_in_threadgroup]],
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ushort3 ntg[[threads_per_threadgroup]]) {
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const int i3 = tgpig.z;
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const int i2 = tgpig.y;
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const int i1 = tgpig.x;
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int o[4] = {0, 0, 0, 0};
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o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03));
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device const float * x;
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for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
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if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
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x = (device const float *)(src0 + (i3 )*args.nb03 + (i2 )*args.nb02 + (i1 )*args.nb01 + (i0 )*args.nb00);
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} else {
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x = (device const float *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10);
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}
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device float * y = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
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*y = *x;
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}
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}
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