Merge pull request #1693 from gs-olive/convNd_lowering_bugfix

fix: Bugfix in convNd_to_convolution lowering pass

Author: peri044

core/lowering/passes/convNd_to_convolution.cpp

@@ -1,4 +1,5 @@
 #include <torch/csrc/jit/passes/subgraph_rewrite.h>
+#include "torch/csrc/jit/ir/irparser.h"
 
 #include "core/util/prelude.h"
 
@@ -7,78 +8,91 @@ namespace core {
 namespace lowering {
 namespace passes {
 
-void Conv1DToConvolution(std::shared_ptr<torch::jit::Graph>& graph) {
-  std::string conv1d_pattern = R"IR(
-        graph(%x, %w, %b, %s, %p, %d, %g):
-            %4 : Tensor = aten::conv1d(%x, %w, %b, %s, %p, %d, %g)
-            return (%4))IR";
+void replaceConv(
+    torch::jit::Block* block,
+    const std::string& node_kind,
+    const std::string& unwrapped_conv,
+    const size_t num_input_args) {
+  // Iterate through nodes in block, seaching for aten::conv*
+  for (auto it = block->nodes().begin(); it != block->nodes().end(); it++) {
+    auto n = *it;
+
+    // Recursively explore nested blocks, such as those arising from prim::If
+    for (auto nested_block : n->blocks()) {
+      replaceConv(nested_block, node_kind, unwrapped_conv, num_input_args);
+    }
+
+    // If node matches desired kind and number of input arguments, replace it
+    if ((n->kind().toQualString() == node_kind) && (n->inputs().size() == num_input_args)) {
+      // Establish insert point within block
+      torch::jit::WithInsertPoint guard(*it);
+
+      // Initialize new fused subgraph from IR code provided
+      auto fused_g = std::make_shared<torch::jit::Graph>();
+      torch::jit::parseIR(unwrapped_conv, fused_g.get());
+
+      // Insert subgraph in place of aten::conv*, replacing inputs and outputs accordingly
+      torch::jit::Value* new_output = insertGraph(*it->owningGraph(), *fused_g, it->inputs()).at(0);
+      new_output->setType(it->output()->type());
+      it->output()->replaceAllUsesWith(new_output);
+      it.destroyCurrent();
+    }
+  }
+}
 
-  std::string convolution_pattern = R"IR(
+void Conv1DToConvolution(std::shared_ptr<torch::jit::Graph>& graph) {
+  const std::string conv1d_node_kind = "aten::conv1d";
+  const std::string convolution_pattern = R"IR(
         graph(%x, %w, %b, %s, %p, %d, %g):
             %1 : bool = prim::Constant[value=0]()
             %2 : int[] = prim::Constant[value=[0]]()
             %4 : Tensor = aten::_convolution(%x, %w, %b, %s, %p, %d, %1, %2, %g, %1, %1, %1, %1)
             return (%4))IR";
 
-  torch::jit::SubgraphRewriter map_conv1d_to_convolution;
-  map_conv1d_to_convolution.RegisterRewritePattern(conv1d_pattern, convolution_pattern);
-  map_conv1d_to_convolution.runOnGraph(graph);
+  // Schema is aten::conv1d(%x, %w, %b, %s, %p, %d, %g) --> 7 inputs
+  replaceConv(graph->block(), conv1d_node_kind, convolution_pattern, 7);
   LOG_GRAPH("Post map conv1d -> _convolution: " << *graph);
 }
 
 void ConvTransposed1DToConvolution(std::shared_ptr<torch::jit::Graph>& graph) {
-  std::string conv_transpose1d_pattern = R"IR(
-        graph(%x, %w, %b, %s, %p, %o, %g, %d):
-            %4 : Tensor = aten::conv_transpose1d(%x, %w, %b, %s, %p, %o, %g, %d)
-            return (%4))IR";
-  std::string convolution_pattern = R"IR(
+  const std::string conv_transpose1d_node_kind = "aten::conv_transpose1d";
+  const std::string convolution_pattern = R"IR(
         graph(%x, %w, %b, %s, %p, %o, %g, %d):
             %1 : bool = prim::Constant[value=1]()
             %2 : bool = prim::Constant[value=1]()
             %4 : Tensor = aten::_convolution(%x, %w, %b, %s, %p, %d, %1, %o, %g, %2, %2, %2, %2)
             return (%4))IR";
 
-  torch::jit::SubgraphRewriter map_conv_transpose1d_to_convolution;
-  map_conv_transpose1d_to_convolution.RegisterRewritePattern(conv_transpose1d_pattern, convolution_pattern);
-  map_conv_transpose1d_to_convolution.runOnGraph(graph);
+  // Schema is aten::conv_transpose1d(%x, %w, %b, %s, %p, %o, %g, %d) --> 8 inputs
+  replaceConv(graph->block(), conv_transpose1d_node_kind, convolution_pattern, 8);
   LOG_GRAPH("Post map conv_transpose1d -> _convolution: " << *graph);
 }
 
 void Conv2DToConvolution(std::shared_ptr<torch::jit::Graph>& graph) {
-  std::string conv2d_pattern = R"IR(
-        graph(%x, %w, %b, %s, %p, %d, %g):
-            %4 : Tensor = aten::conv2d(%x, %w, %b, %s, %p, %d, %g)
-            return (%4))IR";
-  std::string convolution_pattern = R"IR(
+  const std::string conv2d_node_kind = "aten::conv2d";
+  const std::string convolution_pattern = R"IR(
         graph(%x, %w, %b, %s, %p, %d, %g):
             %1 : bool = prim::Constant[value=0]()
             %2 : int[] = prim::Constant[value=[0, 0]]()
             %4 : Tensor = aten::_convolution(%x, %w, %b, %s, %p, %d, %1, %2, %g, %1, %1, %1, %1)
             return (%4))IR";
 
-  // replace matmul + add pattern to linear
-  torch::jit::SubgraphRewriter map_conv2d_to_convolution;
-  map_conv2d_to_convolution.RegisterRewritePattern(conv2d_pattern, convolution_pattern);
-  map_conv2d_to_convolution.runOnGraph(graph);
+  // Schema is aten::conv2d(%x, %w, %b, %s, %p, %d, %g) --> 7 inputs
+  replaceConv(graph->block(), conv2d_node_kind, convolution_pattern, 7);
   LOG_GRAPH("Post map conv2d -> _convolution: " << *graph);
 }
 
 void Conv3DToConvolution(std::shared_ptr<torch::jit::Graph>& graph) {
-  std::string conv3d_pattern = R"IR(
-        graph(%x, %w, %b, %s, %p, %d, %g):
-            %4 : Tensor = aten::conv3d(%x, %w, %b, %s, %p, %d, %g)
-            return (%4))IR";
-  std::string convolution_pattern = R"IR(
+  const std::string conv3d_node_kind = "aten::conv3d";
+  const std::string convolution_pattern = R"IR(
         graph(%x, %w, %b, %s, %p, %d, %g):
             %1 : bool = prim::Constant[value=0]()
             %2 : int[] = prim::Constant[value=[0, 0, 0]]()
             %4 : Tensor = aten::_convolution(%x, %w, %b, %s, %p, %d, %1, %2, %g, %1, %1, %1, %1)
             return (%4))IR";
 
-  // replace matmul + add pattern to linear
-  torch::jit::SubgraphRewriter map_conv3d_to_convolution;
-  map_conv3d_to_convolution.RegisterRewritePattern(conv3d_pattern, convolution_pattern);
-  map_conv3d_to_convolution.runOnGraph(graph);
+  // Schema is aten::conv3d(%x, %w, %b, %s, %p, %d, %g) --> 7 inputs
+  replaceConv(graph->block(), conv3d_node_kind, convolution_pattern, 7);
   LOG_GRAPH("Post map conv3d -> _convolution: " << *graph);
 }
 

tests/core/lowering/test_conv1d_pass.cpp

@@ -5,6 +5,8 @@
 #include "tests/util/util.h"
 #include "torch/csrc/jit/ir/irparser.h"
 #include "torch/csrc/jit/ir/subgraph_matcher.h"
+#include "torch/csrc/jit/passes/canonicalize.h"
+#include "torch/csrc/jit/passes/constant_pooling.h"
 
 TEST(LoweringPasses, Conv1dCorrectly) {
   const auto source_graph = R"IR(
@@ -117,3 +119,84 @@ TEST(LoweringPasses, ConvTransposed1dCorrectly) {
 
   ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(trt_results_sg[0], trt_results_tg[0], 2e-6));
 }
+
+TEST(LoweringPasses, Conv1dWithConditionalLowersCorrectly) {
+  std::string source_graph = R"IR(
+      graph(%0 : Tensor,
+            %1 : Float(4, 3, 3, strides=[9, 3, 1]),
+            %2 : Float(3)):
+        %4 : int = prim::Constant[value=0]()
+        %5 : int = prim::Constant[value=1]()
+        %6 : int = prim::Constant[value=1]()
+        %stride : int[] = prim::ListConstruct(%6)
+        %padding : int[] = prim::ListConstruct(%4)
+        %dilation : int[] = prim::ListConstruct(%5)
+
+        # Add intentionally-invalid weight tensor to ensure prim::If blocks are respected
+        %true : bool = prim::Constant[value=1]()
+        %invalid_weight : Tensor = aten::transpose(%0, %4, %5)
+        %12 : Tensor = prim::If(%true)
+            block0():
+                %res: Tensor = aten::conv1d(%0, %1, %2, %stride, %padding, %dilation, %6)
+                -> (%res)
+            block1():
+                %res: Tensor = aten::conv1d(%invalid_weight, %1, %2, %stride, %padding, %dilation, %6)
+                -> (%res)
+        return (%12))IR";
+
+  std::string target_graph = R"IR(
+      graph(%0 : Tensor,
+            %1 : Float(4, 3, 3, strides=[9, 3, 1]),
+            %2 : Float(3)):
+        %4 : int = prim::Constant[value=0]()
+        %5 : int = prim::Constant[value=1]()
+        %true : bool = prim::Constant[value=1]()
+        %3 : bool = prim::Constant[value=0]()
+        %output_padding : int[] = prim::Constant[value=[0]]()
+        %6 : int = prim::Constant[value=1]()
+        %stride : int[] = prim::ListConstruct(%6)
+        %padding : int[] = prim::ListConstruct(%4)
+        %dilation : int[] = prim::ListConstruct(%5)
+
+        # Add intentionally-invalid weight tensor to ensure prim::If blocks are respected
+        %invalid_weight : Tensor = aten::transpose(%0, %4, %5)
+        %12 : Tensor = prim::If(%true)
+            block0():
+                %res: Tensor = aten::_convolution(%0, %1, %2, %stride, %padding, %dilation, %3, %output_padding, %6, %3, %3, %3, %3)
+                -> (%res)
+            block1():
+                %res: Tensor = aten::_convolution(%invalid_weight, %1, %2, %stride, %padding, %dilation, %3, %output_padding, %6, %3, %3, %3, %3)
+                -> (%res)
+        return (%12))IR";
+
+  torch_tensorrt::core::util::logging::get_logger().set_reportable_log_level(
+      torch_tensorrt::core::util::logging::LogLevel::kGRAPH);
+  auto sg = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(source_graph, &*sg);
+  torch_tensorrt::core::lowering::passes::Conv1DToConvolution(sg);
+  torch::jit::ConstantPooling(sg);
+  sg = torch::jit::Canonicalize(sg, false);
+
+  auto tg = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(target_graph, &*tg);
+  torch::jit::ConstantPooling(tg);
+  tg = torch::jit::Canonicalize(tg, false);
+
+  // Validate identical graphs after pooling constants and canonicalizing
+  ASSERT_TRUE((tg->toString() == sg->toString()));
+
+  auto in = at::randint(1, 2, {1, 3, 3}, {at::kCUDA});
+  auto w = at::randint(1, 2, {4, 3, 3}, {at::kCUDA});
+  auto b = at::randint(1, 10, {4}, {at::kCUDA});
+
+  auto trt_in = at::clone(in);
+  auto trt_w = at::clone(w);
+  auto trt_b = at::clone(b);
+  auto params = torch_tensorrt::core::ir::get_static_params(sg->inputs(), {trt_w, trt_b});
+  auto trt_results_sg = torch_tensorrt::tests::util::RunGraphEngine(sg, params, {trt_in});
+
+  params = torch_tensorrt::core::ir::get_static_params(tg->inputs(), {trt_w, trt_b});
+  auto trt_results_tg = torch_tensorrt::tests::util::RunGraphEngine(tg, params, {trt_in});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(trt_results_sg[0], trt_results_tg[0], 2e-6));
+}

tests/core/partitioning/test_conditionals.cpp

@@ -35,12 +35,13 @@ TEST(Partitioning, FallbackOnConditionalsCorrectly) {
   auto g = mod.get_method("forward").graph();
   torch_tensorrt::core::CompileSpec cfg(inputs);
   cfg.partitioning_info.enabled = true;
+  cfg.partitioning_info.forced_fallback_operators.push_back("aten::log_sigmoid");
   torch::jit::script::Module new_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
   auto new_g = new_mod.get_method("forward").graph();
 
   auto conditional_engines_count = count_trt_engines_in_conditionals(new_g);
 
-  ASSERT_TRUE(conditional_engines_count == 1);
+  ASSERT_TRUE(conditional_engines_count == 2);
 }
 
 TEST(Partitioning, FallbackInplaceOPInConditionalsCorrectly) {