Add epilogue functor for residual block fusion (#391)
* Add epilogue functor for residual block fusion * Do not run split-k tests when ActivationOp is not Identity * explain TestSplitK param * return early
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@@ -95,7 +95,8 @@ struct Conv2dWithBroadcastReferenceOp {
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template <
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typename Conv2d,
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typename ReferenceOp = Conv2dWithBroadcastReferenceOp<Conv2d>
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typename ReferenceOp,
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bool AddBroadcastFirst = false
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>
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class TestbedConv2dWithBroadcast {
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public:
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@@ -113,7 +114,8 @@ public:
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using ElementT = typename EpilogueOutputOp::ElementT;
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static cutlass::conv::Operator const kConvolutionalOperator = Conv2d::kConvolutionalOperator;
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static const bool kAddBroadcastFirst = AddBroadcastFirst;
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static const bool kStoreT = EpilogueOutputOp::kStoreT;
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public:
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/// Initialization
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@@ -270,7 +272,7 @@ public:
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cutlass::conv::Conv2dProblemSize const &problem_size,
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cutlass::conv::SplitKMode const &split_k_mode = cutlass::conv::SplitKMode::kSerial,
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ElementCompute alpha = ElementCompute(1),
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ElementCompute beta = ElementCompute(0)) {
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ElementCompute beta = ElementCompute(1)) {
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// Waive test if insufficient CUDA device
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if (!sufficient()) {
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@@ -300,7 +302,7 @@ public:
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{alpha, beta},
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split_k_mode,
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tensor_Broadcast.device_data(),
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tensor_T_computed.device_data(),
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kStoreT ? tensor_T_computed.device_data() : nullptr,
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0, // This must be zero
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implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size).c()
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);
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@@ -338,7 +340,8 @@ public:
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//
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// Reference check
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//
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// When kAddBroadcastFirst is true, add bias on the host
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ElementCompute beta_ref = kAddBroadcastFirst ? ElementCompute(0) : beta;
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#if CUTLASS_CONV_TEST_UNIT_REFERENCE_DEVICE_ENABLED
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cutlass::reference::device::Conv2d<
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@@ -358,7 +361,7 @@ public:
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tensor_C_reference.device_ref(),
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tensor_Y_reference.device_ref(),
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alpha,
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beta);
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beta_ref);
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// sync host (copy device data to host) for dumping error output in case of mismatches
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tensor_Y_reference.sync_host();
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@@ -382,7 +385,7 @@ public:
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tensor_C_reference.host_ref(),
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tensor_Y_reference.host_ref(),
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alpha,
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beta);
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beta_ref);
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#endif
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ReferenceOp reference_op;
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@@ -395,9 +398,16 @@ public:
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ElementZ z;
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ElementT t;
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reference_op(z, t, tensor_Y_reference.at({n, p, q, k}), tensor_Broadcast.at({0, 0, 0, k}));
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ElementCompute accum = tensor_Y_reference.at({n, p, q, k});
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ElementCompute bias = ElementCompute(tensor_Broadcast.at({0, 0, 0, k}));
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if (kAddBroadcastFirst) {
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reference_op(z, t, accum + bias,
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beta * ElementCompute(tensor_C_reference.at({n, p, q, k})));
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} else {
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reference_op(z, t, accum, bias);
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}
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tensor_Z_reference.at({n, p, q, k}) = z;
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tensor_T_reference.at({n, p, q, k}) = t;
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}
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@@ -405,11 +415,11 @@ public:
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}
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}
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passed = cutlass::reference::host::TensorEquals(
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tensor_T_computed.host_view(),
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tensor_T_reference.host_view());
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EXPECT_TRUE(passed);
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if (kStoreT) {
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passed = cutlass::reference::host::TensorEquals(
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tensor_T_computed.host_view(), tensor_T_reference.host_view());
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EXPECT_TRUE(passed);
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}
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passed = cutlass::reference::host::TensorEquals(
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tensor_Z_computed.host_view(),
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@@ -479,10 +489,13 @@ public:
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// Additionaly, each conv2d test can provide conv problem sizes (conv_test_sizes) and blacklist of sizes
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// (conv_blacklist_sizes)
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/////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename ImplicitGemm>
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template <typename ImplicitGemm,
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typename ReferenceOp = Conv2dWithBroadcastReferenceOp<ImplicitGemm>,
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bool AddBroadcastFirst = false,
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bool TestSplitK = true>
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bool TestAllConv2dWithBroadcast(
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const Conv2dProblemVector & conv_test_sizes = Conv2dProblemVector(),
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const Conv2dProblemVector & conv_blacklist_sizes = Conv2dProblemVector()) {
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const Conv2dProblemVector &conv_test_sizes = Conv2dProblemVector(),
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const Conv2dProblemVector &conv_blacklist_sizes = Conv2dProblemVector()) {
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bool passed = true;
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@@ -490,7 +503,7 @@ bool TestAllConv2dWithBroadcast(
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// Testbed object
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//
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TestbedConv2dWithBroadcast<ImplicitGemm> testbed;
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TestbedConv2dWithBroadcast<ImplicitGemm, ReferenceOp, AddBroadcastFirst> testbed;
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//
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// Get conv problem sizes to run conv operator
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@@ -597,6 +610,9 @@ bool TestAllConv2dWithBroadcast(
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return passed;
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}
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if (!TestSplitK)
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return passed;
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// Sweep split-k-slice using serial and prallel reduction with non-unity alpha and non-zero beta for
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// a single conv2d problem size. Convolution unit tests take a long time to run so only sweep parameters
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// which are abolutely neccessary to catch functional bugs. The below code does provide option to sweep
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