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Remove unused variables (#706)
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@ -24,8 +24,6 @@ void _qmm_t_4_64(
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constexpr int bitmask = (1 << bits) - 1;
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constexpr int pack_factor = 32 / bits;
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constexpr int packs_in_group = group_size / pack_factor;
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const int Kg = K / group_size;
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const int Kw = K / pack_factor;
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for (int m = 0; m < M; m++) {
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const uint32_t* w_local = w;
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@ -410,7 +410,6 @@ void Compiled::eval_cpu(
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// Get the kernel name from the lib
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int ndim = shape.size();
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bool dynamic = ndim >= 8;
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auto kernel_name = kernel_lib_ + (contiguous ? "_contiguous" : "_strided_");
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if (!contiguous) {
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kernel_name += std::to_string(shape.size());
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@ -182,7 +182,6 @@ void implicit_gemm_conv_2D_gpu(
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int implicit_M = conv_params.N * conv_params.oS[0] * conv_params.oS[1];
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int implicit_N = conv_params.O;
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int implicit_K = conv_params.wS[0] * conv_params.wS[1] * conv_params.C;
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size_t grid_dim_x = (implicit_N + bn - 1) / bn;
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size_t grid_dim_y = (implicit_M + bm - 1) / bm;
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@ -167,10 +167,6 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
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auto& upd = inputs.back();
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size_t nthreads = upd.size();
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NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
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if (thread_group_size > nthreads) {
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thread_group_size = nthreads;
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}
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compute_encoder->setComputePipelineState(kernel);
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@ -691,7 +691,6 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
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// organize into grid nkeys x elem_per_key
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MTL::Size grid_dims = MTL::Size(num_keys, half_size + odd, 1);
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NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
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auto nthreads = std::min(num_keys * (half_size + odd), thread_group_size);
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MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
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auto compute_encoder = d.get_command_encoder(s.index);
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compute_encoder->setComputePipelineState(kernel);
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@ -114,7 +114,6 @@ void gguf_load_quantized(
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<< "has incompatible last dim shape: " << shape[shape.size() - 1];
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throw std::runtime_error(msg.str());
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}
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const uint64_t num_blocks = tensor.num_weights / weights_per_block;
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std::vector<int> weights_shape = shape;
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weights_shape.back() /= (weights_per_byte * 4);
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@ -628,7 +628,6 @@ std::vector<array> Convolution::vjp(
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auto& wt = primals[1];
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auto cotan = cotangents[0];
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int N = in.shape(0);
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int O = wt.shape(0);
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// Resolve Padded input shapes and strides
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