#10071: Move test files to TTNN

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_abs.py → tests/ttnn/unit_tests/operations/backward/test_backward_abs.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import data_gen_with_range, compare_pcc
 
 
 @pytest.mark.parametrize(

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_floor.py → tests/ttnn/unit_tests/operations/backward/test_backward_floor.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import compare_pcc, data_gen_with_range
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import data_gen_with_range, compare_pcc
 
 
 @pytest.mark.parametrize(

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_log.py → tests/ttnn/unit_tests/operations/backward/test_backward_log.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import (
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import (
     data_gen_with_val,
     compare_pcc,
     data_gen_with_range,

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_relu6.py → tests/ttnn/unit_tests/operations/backward/test_backward_relu6.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import compare_pcc, data_gen_with_range
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import data_gen_with_range, compare_pcc
 
 
 @pytest.mark.parametrize(

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_round.py → tests/ttnn/unit_tests/operations/backward/test_backward_round.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import compare_pcc, data_gen_with_range
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import data_gen_with_range, compare_pcc
 
 
 @pytest.mark.parametrize(

tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_selu.py → tests/ttnn/unit_tests/operations/backward/test_backward_selu.py

@@ -5,7 +5,7 @@
 import torch
 import pytest
 import ttnn
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.backward.utility_funcs import data_gen_with_range, compare_pcc
 
 
 @pytest.mark.parametrize(

tests/ttnn/unit_tests/operations/backward/utility_funcs.py

@@ -0,0 +1,123 @@
+# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import torch
+import tt_lib
+from loguru import logger
+from tests.tt_eager.python_api_testing.sweep_tests import (
+    comparison_funcs,
+)
+
+
+def data_gen_pt_tt(input_shapes, device, required_grad=False):
+    torch.manual_seed(213919)
+    pt_tensor = torch.randn(input_shapes, requires_grad=required_grad).bfloat16()
+    tt_tensor = (
+        tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device)
+    )
+    return pt_tensor, tt_tensor
+
+
+def data_gen_with_range(input_shapes, low, high, device, required_grad=False, is_row_major=False):
+    assert high > low, "Incorrect range provided"
+    torch.manual_seed(213919)
+    pt_tensor = torch.rand(input_shapes, requires_grad=required_grad).bfloat16() * (high - low) + low
+    if is_row_major:
+        tt_tensor = (
+            tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16)
+            .to(tt_lib.tensor.Layout.ROW_MAJOR)
+            .to(device)
+        )
+    else:
+        tt_tensor = (
+            tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device)
+        )
+    return pt_tensor, tt_tensor
+
+
+def data_gen_with_val(input_shapes, device, required_grad=False, val=1, is_row_major=False):
+    pt_tensor = (torch.ones(input_shapes, requires_grad=required_grad) * val).bfloat16()
+    if is_row_major:
+        tt_tensor = (
+            tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16)
+            .to(tt_lib.tensor.Layout.ROW_MAJOR)
+            .to(device)
+        )
+    else:
+        tt_tensor = (
+            tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device)
+        )
+    return pt_tensor, tt_tensor
+
+
+def data_gen_pt_tt_prod(input_shapes, device, all_dimensions, dim, required_grad=False):
+    torch.manual_seed(213919)
+    pt_tensor_temp = torch.zeros(input_shapes, requires_grad=required_grad).bfloat16()
+    shape_Required = torch.Size(
+        [
+            input_shapes[0] if (dim != 0 and dim != -4) else 1,
+            input_shapes[1] if (dim != 1 and dim != -3) else 1,
+            input_shapes[2] if (dim != 2 and dim != -2) else 1,
+            input_shapes[3] if (dim != 3 and dim != -1) else 1,
+        ]
+    )
+    if all_dimensions == False and (dim == 1 or dim == 0 or dim == -4 or dim == -3):
+        pt_tensor = torch.randn(shape_Required, requires_grad=required_grad).bfloat16()
+        tt_tensor = (
+            tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device)
+        )
+        return pt_tensor, tt_tensor
+    elif all_dimensions == False:
+        pt_tensor = torch.randn(shape_Required, requires_grad=required_grad).bfloat16()
+        if dim == 3 or dim == -1:
+            pt_tensor_temp[:, :, :, :1] = pt_tensor
+        elif dim == 2 or dim == -2:
+            pt_tensor_temp[:, :, :1, :] = pt_tensor
+    else:
+        shape_Required = torch.Size([1, 1, 1, 1])
+        pt_tensor = torch.randn(shape_Required, requires_grad=required_grad).bfloat16()
+        pt_tensor_temp[:1, :1, :1, :1] = pt_tensor
+    tt_tensor = (
+        tt_lib.tensor.Tensor(pt_tensor_temp, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device)
+    )
+    return pt_tensor, tt_tensor
+
+
+def compare_results(tt_tensor, golden_tensor, pcc=0.99):
+    status = True
+    for i in range(len(tt_tensor)):
+        tt_out_tensor = tt_tensor[i].cpu().to(tt_lib.tensor.Layout.ROW_MAJOR).to_torch()
+        pt_out_tensor = golden_tensor[i]
+        comp_pass, comp_out = comparison_funcs.comp_pcc(pt_out_tensor, tt_out_tensor, pcc=pcc)
+        comp_all, _ = comparison_funcs.comp_allclose(pt_out_tensor, tt_out_tensor, atol=4, rtol=1e-1)
+        logger.debug(comp_pass)
+        logger.debug(comp_all)
+        logger.debug(comp_out)
+        status = status & (comp_pass | comp_all)
+    return status
+
+
+def compare_pcc(tt_tensor, golden_tensor, pcc=0.99):
+    status = True
+    for i in range(len(tt_tensor)):
+        tt_out_tensor = tt_tensor[i].cpu().to(tt_lib.tensor.Layout.ROW_MAJOR).to_torch()
+        pt_out_tensor = golden_tensor[i]
+        comp_pass, comp_out = comparison_funcs.comp_pcc(pt_out_tensor, tt_out_tensor, pcc=pcc)
+        logger.debug(comp_pass)
+        logger.debug(comp_out)
+        status = status & comp_pass
+    return status
+
+
+def compare_all_close(tt_tensor, golden_tensor, atol=4, rtol=1e-1):
+    status = True
+    for i in range(len(tt_tensor)):
+        tt_out_tensor = tt_tensor[i].cpu().to(tt_lib.tensor.Layout.ROW_MAJOR).to_torch()
+        pt_out_tensor = golden_tensor[i]
+        comp_all, comp_out = comparison_funcs.comp_allclose(pt_out_tensor, tt_out_tensor, atol=atol, rtol=rtol)
+        logger.debug(comp_all)
+        logger.debug(comp_out)
+        status = status & comp_all
+    return status