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new b0b8746fbb Revert "[Unity] Split DecomposeOpsForTraining into two
steps" (#16442)
b0b8746fbb is described below
commit b0b8746fbb34d6a1e2c2dc78ec3f53febc7b2b31
Author: Tianqi Chen <[email protected]>
AuthorDate: Sun Jan 21 09:37:00 2024 -0500
Revert "[Unity] Split DecomposeOpsForTraining into two steps" (#16442)
---
include/tvm/ir/transform.h | 25 ----
src/ir/transform.cc | 31 ----
src/relax/transform/decompose_ops.cc | 156 +++++++++++----------
tests/python/relax/test_transform_decompose_ops.py | 71 +++++-----
4 files changed, 121 insertions(+), 162 deletions(-)
diff --git a/include/tvm/ir/transform.h b/include/tvm/ir/transform.h
index adf3325250..ec151d9d75 100644
--- a/include/tvm/ir/transform.h
+++ b/include/tvm/ir/transform.h
@@ -525,31 +525,6 @@ TVM_DLL Pass CreateModulePass(
const runtime::TypedPackedFunc<IRModule(IRModule, PassContext)>&
pass_func, int opt_level,
String name, Array<runtime::String> 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 3bae6be9ba..f838120943 100644
--- a/src/ir/transform.cc
+++ b/src/ir/transform.cc
@@ -31,7 +31,6 @@
#include <chrono>
#include <iomanip>
-#include <regex>
#include <stack>
#include <unordered_set>
@@ -532,36 +531,6 @@ Pass CreateModulePass(const
runtime::TypedPackedFunc<IRModule(IRModule, PassCont
return ModulePass(pass_func, pass_info);
}
-Pass ApplyPassToFunction(Pass pass, String func_name_regex,
- bool error_if_no_function_matches_regex) {
- auto pass_name =
- static_cast<const std::stringstream&>(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 1a4cd21625..899c80c1c4 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<Integer>
axes) {
return expand_dims(data, expand_axes);
}
-Tuple DecomposeBatchNorm(const Call& call) {
+Tuple SimplifyBatchNormInference(const Call& call) {
auto attrs = call->attrs.as<BatchNormAttrs>();
ICHECK_NOTNULL(attrs);
@@ -75,18 +75,14 @@ Tuple DecomposeBatchNorm(const Call& call) {
return Tuple({out, call->args[3], call->args[4]});
}
-Expr MutateBatchNormForTraining(Call call) {
+Tuple SimplifyBatchNormTraining(const Call& call) {
auto attrs = call->attrs.as<BatchNormAttrs>();
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<Integer> reduce_axes;
for (int i = 0; i < sinfo->ndim; ++i) {
@@ -96,21 +92,35 @@ Expr MutateBatchNormForTraining(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});
- Expr momentum = MakeConstantScalar(attrs->momentum, sinfo->dtype);
- Expr one_minus_mom = MakeConstantScalar(1 - attrs->momentum, sinfo->dtype);
+ // 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);
- 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));
+ if (attrs->scale) {
+ out = multiply(out, ExpandToMatchInput(gamma, sinfo->ndim, {attrs->axis}));
+ }
+ if (attrs->center) {
+ out = add(out, ExpandToMatchInput(beta, sinfo->ndim, {attrs->axis}));
+ }
- call.CopyOnWrite()->args = {data, gamma, beta, data_mean, data_var};
- // return call;
+ 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 relax::Tuple({TupleGetItem(call, 0), new_moving_mean,
new_moving_var});
+ 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 DecomposeLayerNorm(const Call& call) {
+Expr SimplifyLayerNorm(const Call& call) {
auto attrs = call->attrs.as<LayerNormAttrs>();
ICHECK_NOTNULL(attrs);
@@ -162,92 +172,92 @@ Expr TensorToShape(const Call& call_node, const
BlockBuilder& builder) {
return ShapeExpr(shape_var);
}
-/*! \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_;
+class OpDecomposer : public ExprMutator {
+ public:
+ constexpr static const char* kModeInference = "inference";
+ constexpr static const char* kModeTraining = "training";
- Expr VisitExpr_(const CallNode* call_node) final {
- Call call = Downcast<Call>(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;
- }
+ explicit OpDecomposer(String mode) : ExprMutator(), mode_(mode) {
+ CHECK(mode == kModeInference || mode == kModeTraining)
+ << "The argument mode must be one of the following values:
\"inference\", \"training\".";
}
- /* 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<Call>(VisitExprPostOrder_(call_node));
if (call->op == batch_norm_op_) {
- return DecomposeBatchNorm(call);
+ 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);
} 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");
};
-namespace transform {
+IRModule Decompose(IRModule mod, Optional<String> func_name, String mode) {
+ auto op_decomposer = OpDecomposer(mode);
-Pass MutateOpsForTraining() {
- auto pass_func = [](Function func, IRModule, PassContext) -> Function {
- TrainingOperatorMutator mutator;
- return Downcast<Function>(mutator(func));
- };
- return CreateFunctionPass(/*pass_function=*/pass_func,
- /*opt_level=*/0,
- /*pass_name=*/"MutateOpsForTraining",
- /*required=*/{});
-}
+ IRModuleNode* new_module = mod.CopyOnWrite();
-Pass DecomposeOps() {
- auto pass_func = [](Function func, IRModule, PassContext) -> Function {
- OpDecomposer mutator;
- return Downcast<Function>(mutator(func));
- };
- return CreateFunctionPass(/*pass_function=*/pass_func,
- /*opt_level=*/0,
- /*pass_name=*/"DecomposeOps",
- /*required=*/{});
+ if (!func_name.defined()) { // simplify all functions
+ Map<GlobalVar, BaseFunc> functions = mod->functions;
+ for (const auto& func_pr : functions) {
+ if (const auto* relax_f = func_pr.second.as<FunctionNode>()) {
+ Function f =
Downcast<Function>(op_decomposer(GetRef<Function>(relax_f)));
+ new_module->Update(func_pr.first, f);
+ }
+ }
+ } else { // simplify specified function
+ auto* func_ptr = mod->Lookup(func_name.value()).as<FunctionNode>();
+ CHECK(func_ptr) << func_name.value() << "is not a Relax Function";
+ auto gvar = mod->GetGlobalVar(func_name.value());
+ auto func = GetRef<Function>(func_ptr);
+ func = Downcast<Function>(op_decomposer(func));
+ new_module->Update(gvar, func);
+ }
+
+ return GetRef<IRModule>(new_module);
}
+namespace transform {
Pass DecomposeOpsForInference(Optional<String> func_name) {
- if (func_name) {
- return ApplyPassToFunction(DecomposeOps(), func_name.value());
- } else {
- return DecomposeOps();
- }
+ runtime::TypedPackedFunc<IRModule(IRModule, PassContext)> 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=*/{});
}
Pass DecomposeOpsForTraining(Optional<String> func_name) {
- auto module_pass = tvm::transform::Sequential({MutateOpsForTraining(),
DecomposeOps()},
- "DecomposeOpsForTraining");
- if (func_name) {
- return ApplyPassToFunction(module_pass, func_name.value());
- } else {
- return module_pass;
- }
+ runtime::TypedPackedFunc<IRModule(IRModule, PassContext)> 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=*/{});
}
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 4e5bcb82e9..85657ab245 100644
--- a/tests/python/relax/test_transform_decompose_ops.py
+++ b/tests/python/relax/test_transform_decompose_ops.py
@@ -137,39 +137,44 @@ def test_batch_norm_training():
R.Tensor((64,), dtype="float32"),
):
with R.dataflow():
- # 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]
+ 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]
R.output(gv0, gv1, gv2)
return (gv0, gv1, gv2)
