#8142: Update sweep tests

tests/sweep_framework/sweeps/eltwise/unary/log10/log10.py

@@ -23,13 +23,22 @@
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
         "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
         "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
@@ -48,8 +57,8 @@ def run(
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
     )(input_shape)
-    torch_output_tensor = torch.log10(torch_input_tensor_a)
-
+    golden_function = ttnn.get_golden_function(ttnn.log10)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,
         dtype=input_a_dtype,

tests/sweep_framework/sweeps/eltwise/unary/log1p/log1p.py

@@ -22,14 +22,23 @@
         "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
-        "input_a_dtype": [ttnn.bfloat16],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
         "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT or test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "Row Major layout and bfloat8_b are not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
@@ -48,7 +57,9 @@ def run(
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
     )(input_shape)
-    torch_output_tensor = torch.log1p(torch_input_tensor_a)
+
+    golden_function = ttnn.get_golden_function(ttnn.log1p)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/log2/log2.py

@@ -23,13 +23,22 @@
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
         "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
         "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
@@ -48,7 +57,8 @@ def run(
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
     )(input_shape)
-    torch_output_tensor = torch.log2(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.log2)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/log_sigmoid/log_sigmoid.py

@@ -23,13 +23,22 @@
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
         "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
         "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
@@ -48,7 +57,8 @@ def run(
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-4, high=10, dtype=torch.float32), input_a_dtype
     )(input_shape)
-    torch_output_tensor = torch.nn.functional.logsigmoid(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.log_sigmoid)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,