Metadata agnostic user computation hash (#8550)

test/neuron/run_tests.sh

@@ -6,6 +6,9 @@ MAX_GRAPH_SIZE=500
 GRAPH_CHECK_FREQUENCY=100
 VERBOSITY=2
 
+# Utils file
+source "${CDIR}/utils/run_tests_utils.sh"
+
 # Note [Keep Going]
 #
 # Set the `CONTINUE_ON_ERROR` flag to `true` to make the CI tests continue on error.
@@ -93,16 +96,6 @@ function run_eager_debug {
   XLA_USE_EAGER_DEBUG_MODE=1 run_test "$@"
 }
 
-function run_save_tensor_ir {
-  echo "Running in save tensor file mode: $@"
-  XLA_SAVE_TENSORS_FILE="/tmp/xla_test_save_ir.txt" XLA_SAVE_TENSORS_FMT="text" run_test "$@"
-}
-
-function run_save_tensor_hlo {
-  echo "Running in save tensor file mode: $@"
-  XLA_SAVE_TENSORS_FILE="/tmp/xla_test_save_ir.txt" XLA_SAVE_TENSORS_FMT="hlo" run_test "$@"
-}
-
 function run_pt_xla_debug {
   echo "Running in save tensor file mode: $@"
   PT_XLA_DEBUG=1 PT_XLA_DEBUG_FILE="/tmp/pt_xla_debug.txt" run_test "$@"
@@ -166,16 +159,16 @@ function run_xla_op_tests1 {
   run_test "$CDIR/dynamo/test_num_output.py"
   run_test "$CDIR/dynamo/test_graph_input_matcher.py"
   run_test "$CDIR/dynamo/test_dynamo_config.py"
-  run_save_tensor_ir "$CDIR/dynamo/test_dynamo_graph_dump.py"
+  run_save_tensor_ir run_test "$CDIR/dynamo/test_dynamo_graph_dump.py"
   #run_test "$CDIR/test_data_type.py"
   run_use_bf16 "$CDIR/test_data_type.py"
   run_downcast_bf16 "$CDIR/test_data_type.py"
   #run_test "$CDIR/test_fp8.py"
   run_xla_ir_debug "$CDIR/test_env_var_mapper.py"
   run_xla_hlo_debug "$CDIR/test_env_var_mapper.py"
   run_xla_hlo_debug "$CDIR/stablehlo/test_stablehlo_save_load.py"
-  run_save_tensor_ir "$CDIR/spmd/test_spmd_graph_dump.py"
-  run_save_tensor_hlo "$CDIR/spmd/test_spmd_graph_dump.py"
+  run_save_tensor_ir run_test "$CDIR/spmd/test_spmd_graph_dump.py"
+  run_save_tensor_hlo run_test "$CDIR/spmd/test_spmd_graph_dump.py"
 }
 
 function run_xla_op_tests2 {
@@ -230,6 +223,7 @@ function run_xla_op_tests3 {
   run_torchrun "$CDIR/pjrt/test_torchrun.py"
   run_test "$CDIR/test_persistent_cache.py"
   run_test "$CDIR/test_devices.py"
+  run_xla_ir_hlo_debug run_test "$CDIR/test_user_computation_debug_cache.py"
 
   #python3 examples/data_parallel/train_resnet_xla_ddp.py # compiler error
   #python3 examples/fsdp/train_resnet_fsdp_auto_wrap.py

test/run_tests.sh

@@ -85,11 +85,6 @@ function run_test_without_functionalization {
   XLA_DISABLE_FUNCTIONALIZATION=1 run_test "$@"
 }
 
-function run_xla_ir_debug {
-  echo "Running with XLA_IR_DEBUG: $@"
-  XLA_IR_DEBUG=1 run_test "$@"
-}
-
 function run_use_bf16 {
   echo "Running with XLA_USE_BF16: $@"
   XLA_USE_BF16=1 run_test "$@"
@@ -100,11 +95,6 @@ function run_downcast_bf16 {
   XLA_DOWNCAST_BF16=1 run_test "$@"
 }
 
-function run_xla_hlo_debug {
-  echo "Running with XLA_IR_DEBUG: $@"
-  XLA_HLO_DEBUG=1 run_test "$@"
-}
-
 function run_dynamic {
   echo "Running in DynamicShape mode: $@"
   XLA_EXPERIMENTAL="nonzero:masked_select:masked_scatter:nms" run_test "$@"
@@ -191,9 +181,9 @@ function run_xla_op_tests1 {
   run_use_bf16 "$CDIR/test_data_type.py"
   run_downcast_bf16 "$CDIR/test_data_type.py"
   run_test "$CDIR/test_fp8.py"
-  run_xla_ir_debug "$CDIR/test_env_var_mapper.py"
-  run_xla_hlo_debug "$CDIR/test_env_var_mapper.py"
-  run_xla_hlo_debug "$CDIR/stablehlo/test_stablehlo_save_load.py"
+  run_xla_ir_debug run_test "$CDIR/test_env_var_mapper.py"
+  run_xla_hlo_debug run_test "$CDIR/test_env_var_mapper.py"
+  run_xla_hlo_debug run_test "$CDIR/stablehlo/test_stablehlo_save_load.py"
   run_save_tensor_ir run_test "$CDIR/spmd/test_spmd_graph_dump.py"
   run_save_tensor_hlo run_test "$CDIR/spmd/test_spmd_graph_dump.py"
 }
@@ -224,7 +214,7 @@ function run_xla_op_tests3 {
   run_test "$CDIR/stablehlo/test_composite.py"
   run_test "$CDIR/stablehlo/test_pt2e_qdq.py"
   run_test "$CDIR/stablehlo/test_stablehlo_custom_call.py"
-  run_xla_hlo_debug "$CDIR/stablehlo/test_stablehlo_inference.py"
+  run_xla_hlo_debug run_test "$CDIR/stablehlo/test_stablehlo_inference.py"
   run_test "$CDIR/stablehlo/test_stablehlo_compile.py"
   run_test "$CDIR/stablehlo/test_unbounded_dynamism.py"
   run_test "$CDIR/quantized_ops/test_quantized_matmul.py"
@@ -252,6 +242,7 @@ function run_xla_op_tests3 {
   # NOTE: this line below is testing export and don't care about GPU
   PJRT_DEVICE=CPU CPU_NUM_DEVICES=1 run_coverage "$CDIR/test_core_aten_ops.py"
   run_test "$CDIR/test_pallas.py"
+  run_xla_ir_hlo_debug run_test "$CDIR/test_user_computation_debug_cache.py"
 
   # CUDA tests
   if [ -x "$(command -v nvidia-smi)" ]; then

test/test_user_computation_debug_cache.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import unittest
+
+import torch
+import torch_xla
+import torch_xla.core.xla_builder as xb
+import torch_xla.core.xla_model as xm
+import torch_xla.debug.metrics as met
+
+parent_folder = os.path.dirname(os.path.dirname(__file__))
+sys.path.append(parent_folder)
+
+
+class TestUserComputationDebugCache(unittest.TestCase):
+
+  def setUp(self):
+    self.assertTrue(
+        os.getenv("XLA_IR_DEBUG") == '1' and os.getenv("XLA_HLO_DEBUG") == '1',
+        "XLA_IR_DEBUG and XLA_HLO_DEBUG must be set for this test.",
+    )
+
+  def test_user_computation_debug_cache(self):
+    """
+        Test that user computations with the same IR, but different OpMetadata
+        are cached correctly. The metadata is generated when the environment
+        variables that enable the Python stack trace for the IR nodes, and
+        subsequently, the XLA HLO metadata; `XLA_IR_DEBUG` and `XLA_HLO_DEBUG`
+        respectively.
+        """
+
+    met.clear_all()
+
+    def fn_op(a, b):
+      return xb.Op.tuple([xb.Op.max(a, b) - xb.Op.min(a, b)])
+
+    def input_scope_0(tensor):
+      return [torch.sin(tensor), torch.cos(tensor)]
+
+    def input_scope_1(tensor):
+      return [torch.sin(tensor), torch.cos(tensor)]
+
+    device = xm.xla_device()
+    init_tensor = torch.tensor(10).to(device)
+
+    def create_user_computation(fn):
+      inputs = fn(init_tensor)
+      comp = xb.create_computation("computation", fn_op,
+                                   [xb.tensor_shape(p) for p in inputs])
+      _ = torch_xla._XLAC._xla_user_computation("xla::computation", inputs,
+                                                comp)
+      torch_xla.sync()
+
+    # Create and launch the graph execution with the same IR graph, but with
+    # different input tensor scope. When 'XLA_HLO_DEBUG' and 'XLA_IR_DEBUG' are
+    # enabled, this will generate different OpMetadata for different input
+    # scopes `input_scope_0` and `input_scope_1`, namely `source_line`.
+    create_user_computation(input_scope_0)
+    create_user_computation(input_scope_1)
+
+    # Ensure that we only compile once, and hit the cache the next time. This
+    # is expected as the OpMetadata will not impact the hash of the user
+    # computation, as the compiled executable is semantically the same.
+    self.assertEqual(met.counter_value("UncachedCompile"), 1)
+    self.assertEqual(met.counter_value("CachedCompile"), 1)
+
+
+if __name__ == "__main__":
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)

test/tpu/run_tests.sh

@@ -44,6 +44,7 @@ python3 "$TEST_CDIR/torch_distributed/test_torch_distributed_all_reduce_xla_back
 python3 "$TEST_CDIR/torch_distributed/test_torch_distributed_multi_all_reduce_xla_backend.py"
 python3 "$TEST_CDIR/torch_distributed/test_torch_distributed_reduce_scatter_xla_backend.py"
 python3 "$TEST_CDIR/quantized_ops/test_dot_general.py"
+run_xla_ir_hlo_debug python3 "$TEST_CDIR/test_user_computation_debug_cache.py"
 
 # run examples, each test should takes <2 minutes
 python3 "$TEST_CDIR/../examples/data_parallel/train_resnet_spmd_data_parallel.py"

test/utils/run_tests_utils.sh

@@ -54,3 +54,24 @@ function run_save_tensor_hlo {
   echo "Running in save tensor file mode: $@"
   run_save_tensor "$run_test_func" "hlo" "$@"
 }
+
+function run_xla_ir_debug {
+  local run_test_func="$1"
+  shift
+  echo "Running with XLA_IR_DEBUG: $@"
+  XLA_IR_DEBUG=1 "$run_test_func" "$@"
+}
+
+function run_xla_hlo_debug {
+  local run_test_func="$1"
+  shift
+  echo "Running with XLA_HLO_DEBUG: $@"
+  XLA_HLO_DEBUG=1 "$run_test_func" "$@"
+}
+
+function run_xla_ir_hlo_debug {
+  local run_test_func="$1"
+  shift
+  echo "Running with XLA_IR_DEBUG and XLA_HLO_DEBUG: $@"
+  XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 "$run_test_func" "$@"
+}

torch_xla/csrc/runtime/computation_client.cc

@@ -196,8 +196,13 @@ metrics::Metric* ComputationClient::OutboundDataMetric() {
 }
 
 ::absl::StatusOr<torch::lazy::hash_t>
-ComputationClient::Computation::ComputeHash(const xla::HloModuleProto& proto,
+ComputationClient::Computation::ComputeHash(xla::HloModuleProto proto,
                                             const std::string& name) {
+  for (auto& computation : *proto.mutable_computations()) {
+    for (auto& instruction : *computation.mutable_instructions()) {
+      instruction.mutable_metadata()->Clear();
+    }
+  }
   TF_ASSIGN_OR_RETURN(auto serialized_status,
                       util::GetDeterministicSerializedModuleProto(proto));
   return torch::lazy::MHash(name, serialized_status);

torch_xla/csrc/runtime/computation_client.h

@@ -212,7 +212,7 @@ class ComputationClient {
     // elements during during serialization. The resulting hash combines the
     // serialized module with its computation name.
     static ::absl::StatusOr<torch::lazy::hash_t> ComputeHash(
-        const xla::HloModuleProto& proto, const std::string& name);
+        xla::HloModuleProto proto, const std::string& name);
   };
 
   using ComputationPtr = std::shared_ptr<Computation>;