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Backed out changeset 01197e4452
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@ -1205,6 +1205,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>,
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const Index max_coeff_count,
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const DSizes<Index, NumInputDims>& input_slice_sizes,
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DSizes<Index, NumInputDims>* target_input_block_sizes) const {
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typedef typename internal::packet_traits<Scalar>::type Packet;
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typedef internal::BlockReducer<Self, Op> BlockReducer;
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// TODO(andydavis) Compute reducer overhead correctly for the case where
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// we are preserving the inner most dimension, and a single reducer
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@ -1232,6 +1233,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>,
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} else if (!first_preserved_dim_allocated) {
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// TODO(andydavis) Include output block size in this L1 working set
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// calculation.
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const Index allocated = max_coeff_count - coeff_to_allocate;
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const Index alloc_size = numext::maxi(
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static_cast<Index>(1), coeff_to_allocate / reducer_overhead);
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(*target_input_block_sizes)[dim] =
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@ -27,7 +27,7 @@ static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
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dims[i] = internal::random<int>(min_dim, max_dim);
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}
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return dims;
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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@ -117,7 +117,7 @@ static void test_execute_broadcasting(Device d)
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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@ -155,7 +155,7 @@ static void test_execute_chipping_rvalue(Device d) {
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TEST_CHIPPING(5)
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#undef TEST_CHIPPING
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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@ -207,7 +207,7 @@ static void test_execute_chipping_lvalue(Device d) {
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TEST_CHIPPING(5)
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#undef TEST_CHIPPING
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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