diff --git a/include/tvm/ir/transform.h b/include/tvm/ir/transform.h index ec151d9d7589..adf332525020 100644 --- a/include/tvm/ir/transform.h +++ b/include/tvm/ir/transform.h @@ -525,6 +525,31 @@ TVM_DLL Pass CreateModulePass( const runtime::TypedPackedFunc& pass_func, int opt_level, String name, Array required, bool traceable = false); +/* + * \brief Utility to apply a pass to specific functions in an IRModule + * + * TVM uses IRModule to IRModule transformations at all stages of + * lowering. These transformations may be useful when hand-writing an + * optimized model, or to perform optimizations on specific kernels + * within an IRModule. This utility allows a pass to be applied to a + * specified function, without altering other functions in the module. + * + * \param pass The IRModule to IRModule pass to be applied. + * + * \param func_name_regex A regex used to select the functions to be + * updated. The pass will be applied to all functions whose name + * matches the regex. + * + * \param error_if_no_function_matches_regex Specifies the behavior if + * an IRModule does not contain any function matching the provided + * regex. If true, an error will be raised. If false (default), + * the IRModule will be returned unmodified. + * + * \return The modified IRModule to IRModule pass. + */ +TVM_DLL Pass ApplyPassToFunction(Pass pass, String func_name_regex, + bool error_if_no_function_matches_regex = false); + /*! * \brief A special trace pass that prints the header and IR to LOG(INFO). * \param header The header to be attached to the output. diff --git a/src/ir/transform.cc b/src/ir/transform.cc index f83812094312..3bae6be9ba34 100644 --- a/src/ir/transform.cc +++ b/src/ir/transform.cc @@ -31,6 +31,7 @@ #include #include +#include #include #include @@ -531,6 +532,36 @@ Pass CreateModulePass(const runtime::TypedPackedFunc(std::stringstream() << "ApplyPassTo" << func_name_regex) + .str(); + std::regex regex(func_name_regex.operator std::string()); + + auto pass_func = [pass, regex](IRModule mod, PassContext) -> IRModule { + IRModule subset; + + for (const auto& [gvar, func] : mod->functions) { + std::string name = gvar->name_hint; + if (std::regex_match(name, regex)) { + subset->Add(gvar, func); + } + } + + if (subset->functions.size()) { + IRModule new_subset = pass(subset); + if (!new_subset.same_as(subset)) { + mod.CopyOnWrite()->Update(new_subset); + } + } + + return mod; + }; + + return CreateModulePass(pass_func, 0, pass_name, {}); +} + TVM_REGISTER_NODE_TYPE(PassInfoNode); TVM_REGISTER_GLOBAL("transform.PassInfo") diff --git a/src/relax/transform/decompose_ops.cc b/src/relax/transform/decompose_ops.cc index 899c80c1c454..1a4cd216256b 100644 --- a/src/relax/transform/decompose_ops.cc +++ b/src/relax/transform/decompose_ops.cc @@ -48,7 +48,7 @@ Expr ExpandToMatchInput(Expr data, int ndim, Array axes) { return expand_dims(data, expand_axes); } -Tuple SimplifyBatchNormInference(const Call& call) { +Tuple DecomposeBatchNorm(const Call& call) { auto attrs = call->attrs.as(); ICHECK_NOTNULL(attrs); @@ -75,14 +75,18 @@ Tuple SimplifyBatchNormInference(const Call& call) { return Tuple({out, call->args[3], call->args[4]}); } -Tuple SimplifyBatchNormTraining(const Call& call) { +Expr MutateBatchNormForTraining(Call call) { auto attrs = call->attrs.as(); ICHECK_NOTNULL(attrs); + ICHECK_EQ(call->args.size(), 5); Expr data = call->args[0]; - TensorStructInfo sinfo = MatchTensorStructInfo(data); Expr gamma = call->args[1]; Expr beta = call->args[2]; + Expr moving_mean = call->args[3]; + Expr moving_var = call->args[4]; + + TensorStructInfo sinfo = MatchTensorStructInfo(data); Array reduce_axes; for (int i = 0; i < sinfo->ndim; ++i) { @@ -92,35 +96,21 @@ Tuple SimplifyBatchNormTraining(const Call& call) { } Expr data_mean = mean(data, reduce_axes, false); - Expr data_mean_rs = ExpandToMatchInput(data_mean, sinfo->ndim, {attrs->axis}); Expr data_var = variance(data, reduce_axes, false); - Expr data_var_rs = ExpandToMatchInput(data_var, sinfo->ndim, {attrs->axis}); - - // output = (x - mean) / sqrt(var + epsilon) * gamma + beta - Expr epsilon = MakeConstantScalar(attrs->epsilon, sinfo->dtype); - Expr sqrt_var = sqrt(add(data_var_rs, epsilon)); - Expr out = divide(subtract(data, data_mean_rs), sqrt_var); - if (attrs->scale) { - out = multiply(out, ExpandToMatchInput(gamma, sinfo->ndim, {attrs->axis})); - } - if (attrs->center) { - out = add(out, ExpandToMatchInput(beta, sinfo->ndim, {attrs->axis})); - } - - Expr moving_mean = call->args[3]; - Expr moving_var = call->args[4]; Expr momentum = MakeConstantScalar(attrs->momentum, sinfo->dtype); Expr one_minus_mom = MakeConstantScalar(1 - attrs->momentum, sinfo->dtype); - return Tuple({ - out, - add(multiply(one_minus_mom, moving_mean), multiply(momentum, data_mean)), - add(multiply(one_minus_mom, moving_var), multiply(momentum, data_var)), - }); + Expr new_moving_mean = add(multiply(one_minus_mom, moving_mean), multiply(momentum, data_mean)); + Expr new_moving_var = add(multiply(one_minus_mom, moving_var), multiply(momentum, data_var)); + + call.CopyOnWrite()->args = {data, gamma, beta, data_mean, data_var}; + // return call; + + return relax::Tuple({TupleGetItem(call, 0), new_moving_mean, new_moving_var}); } -Expr SimplifyLayerNorm(const Call& call) { +Expr DecomposeLayerNorm(const Call& call) { auto attrs = call->attrs.as(); ICHECK_NOTNULL(attrs); @@ -172,92 +162,92 @@ Expr TensorToShape(const Call& call_node, const BlockBuilder& builder) { return ShapeExpr(shape_var); } -class OpDecomposer : public ExprMutator { - public: - constexpr static const char* kModeInference = "inference"; - constexpr static const char* kModeTraining = "training"; +/*! \brief Update operators that have a training-specific form + * + * Some operators, such as relax.op.batch_norm, need additional + * processing when being run for training. This mutator applies any mutations required + */ +class TrainingOperatorMutator : public ExprMutator { + private: + using ExprMutator::VisitExpr_; - explicit OpDecomposer(String mode) : ExprMutator(), mode_(mode) { - CHECK(mode == kModeInference || mode == kModeTraining) - << "The argument mode must be one of the following values: \"inference\", \"training\"."; + Expr VisitExpr_(const CallNode* call_node) final { + Call call = Downcast(VisitExprPostOrder_(call_node)); + if (call->op == batch_norm_op_) { + return MutateBatchNormForTraining(call); + } else if (call->op == layer_norm_op_) { + // Here we only decompose LayerNorm in training because it is more efficient as a single op. + // In the future maybe we can also remove this decomposition during training. + return DecomposeLayerNorm(call); + } else { + return call; + } } + /* composite opeartor list */ + const Op& batch_norm_op_ = Op::Get("relax.nn.batch_norm"); + const Op& layer_norm_op_ = Op::Get("relax.nn.layer_norm"); +}; + +class OpDecomposer : public ExprMutator { private: using ExprMutator::VisitExpr_; Expr VisitExpr_(const CallNode* call_node) final { Call call = Downcast(VisitExprPostOrder_(call_node)); if (call->op == batch_norm_op_) { - if (mode_ == kModeInference) { - return SimplifyBatchNormInference(call); - } else { - ICHECK_EQ(mode_, kModeTraining); - return SimplifyBatchNormTraining(call); - } - } else if (call->op == layer_norm_op_ && mode_ == kModeTraining) { - // Here we only decompose LayerNorm in training because it is more efficient as a single op. - // In the future maybe we can also remove this decomposition during training. - return SimplifyLayerNorm(call); + return DecomposeBatchNorm(call); } else if (call->op == tensor_to_shape_op_) { return TensorToShape(call, builder_); } return call; } - const String mode_; - /* composite opeartor list */ const Op& batch_norm_op_ = Op::Get("relax.nn.batch_norm"); - const Op& layer_norm_op_ = Op::Get("relax.nn.layer_norm"); const Op& tensor_to_shape_op_ = Op::Get("relax.tensor_to_shape"); }; -IRModule Decompose(IRModule mod, Optional func_name, String mode) { - auto op_decomposer = OpDecomposer(mode); - - IRModuleNode* new_module = mod.CopyOnWrite(); +namespace transform { - if (!func_name.defined()) { // simplify all functions - Map functions = mod->functions; - for (const auto& func_pr : functions) { - if (const auto* relax_f = func_pr.second.as()) { - Function f = Downcast(op_decomposer(GetRef(relax_f))); - new_module->Update(func_pr.first, f); - } - } - } else { // simplify specified function - auto* func_ptr = mod->Lookup(func_name.value()).as(); - CHECK(func_ptr) << func_name.value() << "is not a Relax Function"; - auto gvar = mod->GetGlobalVar(func_name.value()); - auto func = GetRef(func_ptr); - func = Downcast(op_decomposer(func)); - new_module->Update(gvar, func); - } +Pass MutateOpsForTraining() { + auto pass_func = [](Function func, IRModule, PassContext) -> Function { + TrainingOperatorMutator mutator; + return Downcast(mutator(func)); + }; + return CreateFunctionPass(/*pass_function=*/pass_func, + /*opt_level=*/0, + /*pass_name=*/"MutateOpsForTraining", + /*required=*/{}); +} - return GetRef(new_module); +Pass DecomposeOps() { + auto pass_func = [](Function func, IRModule, PassContext) -> Function { + OpDecomposer mutator; + return Downcast(mutator(func)); + }; + return CreateFunctionPass(/*pass_function=*/pass_func, + /*opt_level=*/0, + /*pass_name=*/"DecomposeOps", + /*required=*/{}); } -namespace transform { Pass DecomposeOpsForInference(Optional func_name) { - runtime::TypedPackedFunc pass_func = [=](IRModule mod, - PassContext pc) { - return Decompose(mod, func_name, OpDecomposer::kModeInference); - }; - return CreateModulePass(/*pass_function=*/pass_func, - /*opt_level=*/0, - /*pass_name=*/"DecomposeOpsForInference", - /*required=*/{}); + if (func_name) { + return ApplyPassToFunction(DecomposeOps(), func_name.value()); + } else { + return DecomposeOps(); + } } Pass DecomposeOpsForTraining(Optional func_name) { - runtime::TypedPackedFunc pass_func = [=](IRModule mod, - PassContext pc) { - return Decompose(mod, func_name, OpDecomposer::kModeTraining); - }; - return CreateModulePass(/*pass_function=*/pass_func, - /*opt_level=*/0, - /*pass_name=*/"DecomposeOpsForTraining", - /*required=*/{}); + auto module_pass = tvm::transform::Sequential({MutateOpsForTraining(), DecomposeOps()}, + "DecomposeOpsForTraining"); + if (func_name) { + return ApplyPassToFunction(module_pass, func_name.value()); + } else { + return module_pass; + } } TVM_REGISTER_GLOBAL("relax.transform.DecomposeOpsForInference") diff --git a/tests/python/relax/test_transform_decompose_ops.py b/tests/python/relax/test_transform_decompose_ops.py index 85657ab245ea..4e5bcb82e979 100644 --- a/tests/python/relax/test_transform_decompose_ops.py +++ b/tests/python/relax/test_transform_decompose_ops.py @@ -137,44 +137,39 @@ def main( R.Tensor((64,), dtype="float32"), ): with R.dataflow(): - lv: R.Tensor((64,), dtype="float32") = R.mean(x, axis=[0, 2, 3], keepdims=False) - lv1: R.Tensor((1, 64, 1, 1), dtype="float32") = R.expand_dims(lv, axis=[0, 2, 3]) - lv2: R.Tensor((1, 64, 112, 112), dtype="float32") = R.subtract(x, lv1) - lv3: R.Tensor((64,), dtype="float32") = R.variance( - x, axis=[0, 2, 3], keepdims=False - ) - lv4: R.Tensor((1, 64, 1, 1), dtype="float32") = R.expand_dims(lv3, axis=[0, 2, 3]) - lv5: R.Tensor((1, 64, 1, 1), dtype="float32") = R.add( - lv4, R.const(9.9999997473787516e-06, "float32") - ) - lv6: R.Tensor((1, 64, 1, 1), dtype="float32") = R.sqrt(lv5) - lv7: R.Tensor((1, 64, 112, 112), dtype="float32") = R.divide(lv2, lv6) - lv8: R.Tensor((1, 64, 1, 1), dtype="float32") = R.expand_dims(gamma, axis=[0, 2, 3]) - lv9: R.Tensor((1, 64, 112, 112), dtype="float32") = R.multiply(lv7, lv8) - lv10: R.Tensor((1, 64, 1, 1), dtype="float32") = R.expand_dims(beta, axis=[0, 2, 3]) - lv11: R.Tensor((1, 64, 112, 112), dtype="float32") = R.add(lv9, lv10) - lv12: R.Tensor((64,), dtype="float32") = R.multiply( - R.const(0.89999997615814209, "float32"), moving_mean - ) - lv13: R.Tensor((64,), dtype="float32") = R.multiply( - R.const(0.10000000149011612, "float32"), lv - ) - lv14: R.Tensor((64,), dtype="float32") = R.add(lv12, lv13) - lv15: R.Tensor((64,), dtype="float32") = R.multiply( - R.const(0.89999997615814209, "float32"), moving_var - ) - lv16: R.Tensor((64,), dtype="float32") = R.multiply( - R.const(0.10000000149011612, "float32"), lv3 - ) - lv17: R.Tensor((64,), dtype="float32") = R.add(lv15, lv16) - bn: R.Tuple( - R.Tensor((1, 64, 112, 112), dtype="float32"), - R.Tensor((64,), dtype="float32"), - R.Tensor((64,), dtype="float32"), - ) = (lv11, lv14, lv17) - gv0: R.Tensor((1, 64, 112, 112), dtype="float32") = bn[0] - gv1: R.Tensor((64,), dtype="float32") = bn[1] - gv2: R.Tensor((64,), dtype="float32") = bn[2] + # This portion is training-specific, computing the + # mean/variance of the dataset. + lv = R.mean(x, axis=[0, 2, 3], keepdims=False) + lv3 = R.variance(x, axis=[0, 2, 3], keepdims=False) + + # This portion is identical to the batch_norm run during inference + lv1 = R.expand_dims(lv, axis=[0, 2, 3]) + lv2 = R.subtract(x, lv1) + lv4 = R.expand_dims(lv3, axis=[0, 2, 3]) + lv5 = R.add(lv4, R.const(9.9999997473787516e-06, "float32")) + lv6 = R.sqrt(lv5) + lv7 = R.divide(lv2, lv6) + lv8 = R.expand_dims(gamma, axis=[0, 2, 3]) + lv9 = R.multiply(lv7, lv8) + lv10 = R.expand_dims(beta, axis=[0, 2, 3]) + lv11 = R.add(lv9, lv10) + inner_tuple = (lv11, lv, lv3) + # This is the result that would be returned from a + # batch_norm at inference. + + # However, at training we need to update the moving + # mean/variance, and to return those updated values. + inner_res = inner_tuple[0] + lv12 = R.multiply(R.const(0.89999997615814209, "float32"), moving_mean) + lv13 = R.multiply(R.const(0.10000000149011612, "float32"), lv) + lv14 = R.add(lv12, lv13) + lv15 = R.multiply(R.const(0.89999997615814209, "float32"), moving_var) + lv16 = R.multiply(R.const(0.10000000149011612, "float32"), lv3) + lv17 = R.add(lv15, lv16) + bn = (inner_res, lv14, lv17) + gv0 = bn[0] + gv1 = bn[1] + gv2 = bn[2] R.output(gv0, gv1, gv2) return (gv0, gv1, gv2)