Update strings in Arm unittests (pytorch#4143)

Summary: Update strings in checks for operators. The old checks became invalid when pytorch#4109 was merged. Change-Id: I89e398d4b6467244ce732cafa08850fb8a2c754d Pull Request resolved: pytorch#4143 Reviewed By: kimishpatel Differential Revision: D59327800 Pulled By: digantdesai fbshipit-source-id: 513bcc1d137b67bbf14e55454db99d6749ede857
kirklandsign · Jul 3, 2024 · 28638d7 · 28638d7
1 parent 459bc1d
commit 28638d7
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Showing 3 changed files with 9 additions and 9 deletions.
diff --git a/backends/arm/test/ops/test_linear.py b/backends/arm/test/ops/test_linear.py
@@ -123,7 +123,7 @@ def _test_linear_tosa_MI_pipeline(
                 compile_spec=common.get_tosa_compile_spec(),
             )
             .export()
-            .check_count({"torch.ops.aten.addmm.default": 1})
+            .check_count({"torch.ops.aten.linear.default": 1})
             .check_not(["torch.ops.quantized_decomposed"])
             .to_edge(config=self._edge_compile_config)
             .partition()
@@ -143,7 +143,7 @@ def _test_linear_tosa_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.addmm.default": 1})
+            .check_count({"torch.ops.aten.linear.default": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge(config=self._edge_compile_config)
             .partition()
@@ -163,7 +163,7 @@ def _test_linear_tosa_u55_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.addmm.default": 1})
+            .check_count({"torch.ops.aten.linear.default": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge(config=self._edge_compile_config)
             .partition()

diff --git a/backends/arm/test/ops/test_mean_dim.py b/backends/arm/test/ops/test_mean_dim.py
@@ -58,7 +58,7 @@ def _test_meandim_tosa_MI_pipeline(
                 compile_spec=common.get_tosa_compile_spec(permute_memory_to_nhwc=True),
             )
             .export()
-            .check(["torch.ops.aten.mean.dim"])
+            .check(["torch.ops.aten.adaptive_avg_pool2d.default"])
             .check_not(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()
@@ -79,7 +79,7 @@ def _test_meandim_tosa_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.mean.dim": 1})
+            .check_count({"torch.ops.aten.adaptive_avg_pool2d.default": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()
@@ -100,7 +100,7 @@ def _test_meandim_tosa_u55_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.mean.dim": 1})
+            .check_count({"torch.ops.aten.adaptive_avg_pool2d.default": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()

diff --git a/backends/arm/test/ops/test_softmax.py b/backends/arm/test/ops/test_softmax.py
@@ -46,7 +46,7 @@ def _test_softmax_tosa_MI_pipeline(
                 compile_spec=common.get_tosa_compile_spec(),
             )
             .export()
-            .check(["torch.ops.aten._softmax.default"])
+            .check(["torch.ops.aten.softmax.int"])
             .check_not(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()
@@ -67,7 +67,7 @@ def _test_softmax_tosa_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten._softmax.default": 1})
+            .check_count({"torch.ops.aten.softmax.int": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()
@@ -88,7 +88,7 @@ def _test_softmax_tosa_u55_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten._softmax.default": 1})
+            .check_count({"torch.ops.aten.softmax.int": 1})
             .check(["torch.ops.quantized_decomposed"])
             .to_edge()
             .partition()