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7b6d569a2b1a762fd3f2cb90c15242b80a477db4
pytorch/test/cpp/jit/test_concat_opt.cpp
Raghavan Raman 7b6d569a2b [jit] Renamed prim::Concat as prim::VarConcat (#61983) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61983

Trial #2. The previous PR (https://github.com/pytorch/pytorch/pull/61498) was reverted because this caused a failure in `pytorch_linux_backward_compatibility_check_test`. Fixed that now by adding to the exception list in `check_backward_compatibility.py`.

Test Plan: Imported from OSS

Reviewed By: eellison

Differential Revision: D29828830

Pulled By: navahgar

fbshipit-source-id: 947a7b1622ff6e3e575c051b8f34a789e105bcee
2021-07-29 10:28:59 -07:00

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#include <gtest/gtest.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/concat_opt.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/testing/file_check.h>
namespace torch {
namespace jit {
namespace {
void checkOutputs(
const std::vector<at::Tensor>& out1,
const std::vector<at::Tensor>& out2) {
ASSERT_EQ(out1.size(), out2.size());
for (size_t i = 0; i < out1.size(); ++i) {
ASSERT_EQ(out1[i].sizes(), out2[i].sizes());
float max_diff = (out1[i] - out2[i]).abs().max().item<double>();
ASSERT_EQ(max_diff, 0);
}
}
std::vector<at::Tensor> runGraph(
std::shared_ptr<Graph> graph,
const std::vector<at::Tensor> inputs) {
std::vector<IValue> stack = fmap<IValue>(inputs);
Code code(graph, "test");
InterpreterState(code).run(stack);
TORCH_INTERNAL_ASSERT(!stack.empty());
// Graph outputs that are handled below:
// * A list of Tensors.
// * 1 Tensor.
if (stack.front().isTensorList()) {
return stack.front().toTensorVector();
}
TORCH_INTERNAL_ASSERT(stack.front().isTensor());
return {stack.front().toTensor()};
}
} // namespace
TEST(ConcatOptTest, SimpleCommonInputsEliminationPrefix) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%5 : int = prim::Constant[value=0]()
%concat.2 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %5)
%concat.3 : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %2, %5)
%res : Tensor[] = prim::ListConstruct(%concat.2, %concat.3)
return (%res)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(EliminateConcatCommonInputs(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// Graph after EliminateConcatCommonInputs:
// graph(%0 : ...,
// %1 : ...,
// %2 : ...):
// %3 : int = prim::Constant[value=0]()
// %4 : Tensor = prim::VarConcat(%0, %1, %3)
// %7 : Tensor = prim::VarConcat(%4, %2, %3) // UPDATED
// %8 : Tensor[] = prim::ListConstruct(%4, %7)
// return (%8)
testing::FileCheck()
.check_count("= prim::VarConcat(%0, %1, %3)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%4, %2, %3)", 1, /*exactly*/ true)
->check_count("= prim::ListConstruct(%4, %7)", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, SimpleCommonInputsEliminationSuffix) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%5 : int = prim::Constant[value=0]()
%concat.2 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%1, %2, %5)
%concat.3 : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %2, %5)
%res : Tensor[] = prim::ListConstruct(%concat.2, %concat.3)
return (%res)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(EliminateConcatCommonInputs(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// Graph after EliminateConcatCommonInputs:
// graph(%0 : ...,
// %1 : ...,
// %2 : ...):
// %3 : int = prim::Constant[value=0]()
// %4 : Tensor = prim::VarConcat(%1, %2, %3)
// %7 : Tensor = prim::VarConcat(%0, %4, %3) // UPDATED
// %8 : Tensor[] = prim::ListConstruct(%4, %7)
// return (%8)
testing::FileCheck()
.check_count("= prim::VarConcat(%1, %2, %3)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%0, %4, %3)", 1, /*exactly*/ true)
->check_count("= prim::ListConstruct(%4, %7)", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, CommonInputsEliminationWithDifferentOrderInputs) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%5 : int = prim::Constant[value=0]()
#CHECK: prim::VarConcat
%concat.1 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %5)
#CHECK: prim::VarConcat
%concat.2 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%1, %0, %2, %5)
#CHECK: prim::ListConstruct
%res : Tensor[] = prim::ListConstruct(%concat.1, %concat.2)
return (%res)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_FALSE(EliminateConcatCommonInputs(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// No optimizations should have happened in this case since the inputs
// to the `cat` are in different order.
testing::FileCheck().run(input, *graph);
}
TEST(ConcatOptTest, MoreCommonInputsElimination) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%3: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%4: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%5 : int = prim::Constant[value=0]()
%concat.1 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %5)
%concat.2 : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %2, %5)
%concat.3 : Float(160, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %2, %3, %5)
%concat.4 : Float(192, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = prim::VarConcat(%0, %1, %2, %3, %4, %5)
%res : Tensor[] = prim::ListConstruct(%concat.1, %concat.2, %concat.3, %concat.4)
return (%res)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(EliminateConcatCommonInputs(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
testing::FileCheck()
.check_count("= prim::VarConcat(%0, %1, %5)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%6, %2, %5)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%11, %3, %5)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%12, %4, %5)", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, ExpandConcat) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%2 : int = prim::Constant[value=0]()
%3 : float = prim::Constant[value=0.5]()
%4 : Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::clamp_max(%0, %3)
%5 : Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::clamp_max(%1, %3)
%input : Tensor[] = prim::ListConstruct(%4, %5)
%concat : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %2)
return (%concat)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU), at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ExpandConcatAndEliminateRedundancy(graph);
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// After full concat optimization we should have the following graph:
//
// graph(%0 : ...,
// %1 : ...):
// ...
// %4 : Tensor = aten::clamp_max(...)
// %5 : Tensor = aten::clamp_max(...)
// %13 : int[] = prim::ListConstruct(...)
// %14 : Tensor = aten::empty(%13, ...) // concat buffer
// %17 : Tensor = aten::slice(%14, ...) // slice for %4
// %18 : Tensor = aten::copy_(%17, %4)
// %20 : Tensor = aten::slice(%14, ...) // slice for %5
// %21 : Tensor = aten::copy_(%20, %5)
// return (%14)
testing::FileCheck()
.check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= aten::clamp_max(", 2, /*exactly*/ true)
->check_count("= aten::empty(", 1, /*exactly*/ true)
->check_count("= aten::slice(", 1, /*exactly*/ true)
->check_count("= aten::copy_(", 1, /*exactly*/ true)
->check_count("= aten::slice(", 1, /*exactly*/ true)
->check_count("= aten::copy_(", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, ConcatWithoutResultShape) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%2 : int = prim::Constant[value=0]()
%3 : float = prim::Constant[value=0.5]()
# CHECK: clamp_max
%4 : Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::clamp_max(%0, %3)
# CHECK: clamp_max
%5 : Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::clamp_max(%1, %3)
# CHECK: prim::ListConstruct
%6 : Tensor[] = prim::ListConstruct(%4, %5)
# CHECK: aten::cat
%7 : Tensor = aten::cat(%6, %2)
return (%7)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU), at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ExpandConcatAndEliminateRedundancy(graph);
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// No optimizations should have happened in this case since the output
// shape of `aten::cat` is not known.
testing::FileCheck().run(input, *graph);
}
TEST(ConcatOptTest, ConcatWithoutInputShape) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%2 : int = prim::Constant[value=0]()
%3 : float = prim::Constant[value=0.5]()
# CHECK: clamp_max
%4 : Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::clamp_max(%0, %3)
# CHECK: clamp_max
%5 : Tensor = aten::clamp_max(%1, %3)
# CHECK: prim::ListConstruct
%6 : Tensor[] = prim::ListConstruct(%4, %5)
# CHECK: aten::cat
%7 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%6, %2)
return (%7)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU), at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ExpandConcatAndEliminateRedundancy(graph);
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// No optimizations should have happened in this case since the shape of %5,
// which is an input to `aten::cat`, is not known.
testing::FileCheck().run(input, *graph);
}
TEST(ConcatOptTest, UseVariadicCat) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%3: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%4: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%5: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%10 : int = prim::Constant[value=0]()
%input : Tensor[] = prim::ListConstruct(%0, %1, %2, %3, %4, %5)
%concat : Float(224, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
return (%concat)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(UseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// After replacing `aten::cat` with `prim::VarConcat` we should have the
// following graph:
//
// graph(%0 : ...,
// %1 : ...):
// %zero : int = prim:Constant[value=0]()
// %varcat : Tensor = prim::VarConcat(%0, %1, %2, %3, %4, %5, %zero)
// return (%varcat)
testing::FileCheck()
.check_count("= prim::VarConcat(", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(OptimizeConcatTest, UseVariadicCatReplaceMultiple) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%3: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%10 : int = prim::Constant[value=0]()
%input1 : Tensor[] = prim::ListConstruct(%0, %1)
%concat1 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input1, %10)
%input2 : Tensor[] = prim::ListConstruct(%2, %3)
%concat2 : Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input2, %10)
return (%concat1, %concat2)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(UseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// After full concat optimization we should have the following graph:
//
// graph(%0 : ...,
// %1 : ...,
// %2 : ...,
// %3 : ....):
// %zero : int = prim:Constant[value=0]()
// %varcat1 : Tensor = prim::VarConcat(%0, %1, %zero)
// %varcat2 : Tensor = prim::VarConcat(%2, %3, %zero)
// return (%varcat1, %varcat2)
testing::FileCheck()
.check_count("= prim::VarConcat(", 2, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, UseVariadicCatWithMultipleListUses) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%2 : int = prim::Constant[value=0]()
%input : Tensor[] = prim::ListConstruct(%0, %1)
%concat : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %2)
return (%concat, %input)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU), at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(UseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// After replacing `aten::cat` with `prim::VarConcat` we should have the
// following graph:
//
// graph(%0 : ...,
// %1 : ...):
// %zero : int = prim:Constant[value=0]()
// %input : Tensor[] = prim::ListConstruct(%0, %1)
// %varcat : Tensor = prim::VarConcat(%0, %1, %zero)
// return (%varcat, %input)
testing::FileCheck()
.check_count("= prim::ListConstruct(", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, UseVariadicCatWithListMutationAfterCat) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%10 : int = prim::Constant[value=0]()
%input : Tensor[] = prim::ListConstruct(%0, %1)
%concat : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
%11 : Tensor = aten::append(%input, %2)
return (%concat, %input)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(UseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// The input list to `aten::cat` is mutated only after `aten::cat` op. So,
// it should have been replaced with `prim::VarConcat`. The transformed graph
// should look like the following:
//
// graph(%0 : ...,
// %1 : ...,
// %2 : ...):
// %3 : int = prim:Constant[value=0]()
// %4 : Tensor[] = prim::ListConstruct(%0, %1)
// %7 : Tensor = prim::VarConcat(%0, %1, %3)
// %6 : Tensor = aten::append(%4, %2)
// return (%7, %4)
testing::FileCheck()
.check_count("= prim::ListConstruct(", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(ConcatOptTest, UseVariadicCatWithListMutationBeforeCat) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%10 : int = prim::Constant[value=0]()
%input : Tensor[] = prim::ListConstruct(%0, %1)
%11 : Tensor = aten::append(%input, %2)
%concat : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
return (%concat)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
{
ASSERT_FALSE(UseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// No transformation should have happened since the `prim::ListConstruct` is
// mutated before `aten::cat`.
testing::FileCheck()
.check_count("= prim::ListConstruct(", 1, /*exactly*/ true)
->check_count("= aten::cat(", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(", 0, /*exactly*/ true)
->run(*graph);
}
{
ASSERT_TRUE(RemoveListMutationAndUseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// The mutation of the list must be removed and the `aten::cat` op must
// be replaced with the `prim::VarConcat` op in the graph. The transformed
// graph should look like the following:
//
// graph(%0 : ...,
// %1 : ...,
// %2 : ...):
// %3 : int = prim:Constant[value=0]()
// %7 : Tensor = prim::VarConcat(%0, %1, %2, %3)
// return (%7)
testing::FileCheck()
.check_count("= prim::VarConcat(", 1, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
}
TEST(ConcatOptTest, UseVariadicCatWithMultipleListMutations) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%3: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%4: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%10 : int = prim::Constant[value=0]()
%input : Tensor[] = prim::ListConstruct(%0, %1)
%concat.1 : Float(96, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
%11 : Tensor = aten::append(%input, %2)
%concat.2 : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
%12 : Tensor = aten::append(%input, %3)
%concat.3 : Float(160, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
%13 : Tensor = aten::append(%input, %4)
%concat.4 : Float(192, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%input, %10)
return (%concat.1, %concat.2, %concat.3, %concat.4)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(RemoveListMutationAndUseVariadicCat(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// All the mutations of the list must be removed and the `aten::cat` ops must
// be replaced with `prim::VarConcat` ops in the graph. The transformed graph
// should look like the following:
//
// graph(%0 : ...,
// %1 : ...,
// %2 : ...,
// %3 : ...,
// %4 : ...):
// %10 : int = prim:Constant[value=0]()
// %5 : Tensor = prim::VarConcat(%0, %1, %10)
// %6 : Tensor = prim::VarConcat(%0, %1, %2, %10)
// %7 : Tensor = prim::VarConcat(%0, %1, %2, %3, %10)
// %8 : Tensor = prim::VarConcat(%0, %1, %2, %3, %4, %10)
// return (%5, %6, %7, %8)
testing::FileCheck()
.check_count("= prim::VarConcat(", 4, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->run(*graph);
}
TEST(
ConcatOptTest,
RemoveListMutationUseVariadicCatAndCommonInputsElimination) {
auto graph = std::make_shared<Graph>();
const std::string input =
R"IR(
graph(%0: Float(64, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%1: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu),
%2: Float(32, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu)):
%5 : int = prim::Constant[value=0]()
%features.2 : Tensor[] = prim::ListConstruct(%0, %1)
%6 : Tensor [] = aten::append(%features.2, %2)
%concat.2 : Float(128, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%features.2, %5)
%7 : Tensor [] = aten::append(%features.2, %0)
%concat.3 : Float(160, 56, 56, strides=[3136, 56, 1], requires_grad=0, device=cpu) = aten::cat(%features.2, %5)
%res : Tensor[] = prim::ListConstruct(%concat.2, %concat.3)
return (%res)
)IR";
parseIR(input, graph.get());
std::vector<at::Tensor> inputs = {
at::rand({64, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU),
at::rand({32, 56, 56}, at::kCPU)};
auto orig_outputs = runGraph(graph, inputs);
ASSERT_TRUE(RemoveListMutationAndUseVariadicCat(graph));
ASSERT_TRUE(EliminateConcatCommonInputs(graph));
graph->lint();
auto opt_outputs = runGraph(graph, inputs);
checkOutputs(orig_outputs, opt_outputs);
// After performing:
// * Remove list mutation
// * Use variadic cat
// * Eliminate common inputs
// we should have the following graph:
//
// graph(%0 : ...,
// %1 : ...,
// %2 : ...):
// %3 : int = prim::Constant[value=0]()
// %10 : Tensor = prim::VarConcat(%0, %1, %2, %3)
// %12 : Tensor = prim::VarConcat(%10, %0, %3) // UPDATED
// %8 : Tensor[] = prim::ListConstruct(%10, %12)
// return (%8)
testing::FileCheck()
.check_count("= prim::VarConcat(%0, %1, %2, %3)", 1, /*exactly*/ true)
->check_count("= prim::VarConcat(%10, %0, %3)", 1, /*exactly*/ true)
->check_count("= prim::ListConstruct(%10, %12)", 1, /*exactly*/ true)
->check_count("= aten::cat(", 0, /*exactly*/ true)
->check_count("= prim::ListConstruct(", 0, /*exactly*/ true)
->run(*graph);
}
} // namespace jit
} // namespace torch
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