#8142: Add documentation for log alike ops

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

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
 # Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
@@ -49,8 +43,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype

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

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
 # Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
@@ -49,8 +43,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype

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

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
 # Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
@@ -49,8 +43,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype

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

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
 # Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
@@ -49,8 +43,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-4, high=10, dtype=torch.float32), input_a_dtype

ttnn/cpp/ttnn/operations/eltwise/unary/unary_pybind.hpp

@@ -806,7 +806,7 @@ void bind_power(py::module& module, const unary_operation_t& operation) {
 }
 
 template <typename unary_operation_t>
-void bind_unary_composite(py::module& module, const unary_operation_t& operation, const std::string& description, const std::string& range = "") {
+void bind_unary_composite(py::module& module, const unary_operation_t& operation, const std::string& description, const std::string& range = "", const std::string& info_doc = "") {
     auto doc = fmt::format(
         R"doc(
         {2}
@@ -820,14 +820,18 @@ void bind_unary_composite(py::module& module, const unary_operation_t& operation
         Returns:
             ttnn.Tensor: the output tensor.
 
+        Note:
+            {4}
+
         Example:
             >>> tensor = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
             >>> output = {1}(tensor)
         )doc",
         operation.base_name(),
         operation.python_fully_qualified_name(),
         description,
-        range);
+        range,
+        info_doc);
 
     bind_registered_operation(
         module,
@@ -1468,8 +1472,32 @@ void py_module(py::module& module) {
         )doc");
 
     detail::bind_unary_operation(module, ttnn::log, R"doc(\mathrm{{output\_tensor}}_i = log(\mathrm{{input\_tensor}}_i))doc");
-    detail::bind_unary_operation(module, ttnn::log10, R"doc(\mathrm{{output\_tensor}}_i = log10(\mathrm{{input\_tensor}}_i))doc");
-    detail::bind_unary_operation(module, ttnn::log2, R"doc(\mathrm{{output\_tensor}}_i = log2(\mathrm{{input\_tensor}}_i))doc");
+    detail::bind_unary_operation(module, ttnn::log10, R"doc(\mathrm{{output\_tensor}}_i = log10(\mathrm{{input\_tensor}}_i))doc",
+        R"doc(Supported dtypes, layouts, and ranks:
+
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+
+           BFLOAT8_B supported only in WHB0.
+
+        )doc");
+
+    detail::bind_unary_operation(module, ttnn::log2, R"doc(\mathrm{{output\_tensor}}_i = log2(\mathrm{{input\_tensor}}_i))doc",
+        R"doc(Supported dtypes, layouts, and ranks:
+
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+
+           BFLOAT8_B supported only in WHB0.
+
+        )doc");
+
     detail::bind_unary_operation(module, ttnn::logical_not, R"doc(\mathrm{{output\_tensor}}_i = \mathrm{{!input\_tensor_i}})doc", R"doc(Supports bfloat16 dtype and both TILE and ROW_MAJOR layout)doc");
     detail::bind_unary_operation(module, ttnn::ltz, R"doc(\mathrm{{output\_tensor}}_i = (\mathrm{{input\_tensor_i\ < 0}}))doc",
         R"doc(Supported dtypes, layouts, and ranks:
@@ -1513,7 +1541,16 @@ void py_module(py::module& module) {
     detail::bind_unary_operation(module, ttnn::square, R"doc(\mathrm{{output\_tensor}}_i = square(\mathrm{{input\_tensor}}_i))doc");
     detail::bind_unary_operation(module, ttnn::tan, R"doc(\mathrm{{output\_tensor}}_i = tan(\mathrm{{input\_tensor}}_i))doc");
     detail::bind_unary_operation(module, ttnn::tanh, R"doc(\mathrm{{output\_tensor}}_i = tanh(\mathrm{{input\_tensor}}_i))doc");
-    detail::bind_unary_operation(module, ttnn::log_sigmoid, R"doc(\mathrm{{output\_tensor}}_i = \verb|log_sigmoid|(\mathrm{{input\_tensor}}_i))doc");
+    detail::bind_unary_operation(module, ttnn::log_sigmoid, R"doc(\mathrm{{output\_tensor}}_i = \verb|log_sigmoid|(\mathrm{{input\_tensor}}_i))doc",
+        R"doc(Supported dtypes, layouts, and ranks:
+
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+
+        )doc");
     detail::bind_unary_operation(module, ttnn::bitwise_not, R"doc(\mathrm{{output\_tensor}}_i = \verb|bitwise_not|(\mathrm{{input\_tensor}}_i))doc", "Input tensor needs to be in the range [-2147483647, 2147483647], INT32 dtype. Support provided only for Wormhole_B0.");
 
     //  Unaries with fast_and_approximate_mode
@@ -1677,7 +1714,16 @@ void py_module(py::module& module) {
     detail::bind_unary_composite(module, ttnn::cosh, R"doc(Performs cosh function on :attr:`input_tensor`.)doc", "[supported range -9 to 9]");
     detail::bind_unary_composite(module, ttnn::digamma, R"doc(Performs digamma function on :attr:`input_tensor`.)doc", "[supported for value greater than 0]");
     detail::bind_unary_composite(module, ttnn::lgamma, R"doc(Performs lgamma function on :attr:`input_tensor`.)doc", "[supported for value greater than 0]");
-    detail::bind_unary_composite(module, ttnn::log1p, R"doc(Performs log1p function on :attr:`input_tensor`.)doc", "[supported range -1 to 1]");
+    detail::bind_unary_composite(module, ttnn::log1p, R"doc(Performs log1p function on :attr:`input_tensor`.)doc", "[supported range -1 to 1]",
+        R"doc(Supported dtypes, layouts, and ranks:
+
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16                |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+
+        )doc");
     detail::bind_unary_composite(module, ttnn::mish, R"doc(Performs mish function on :attr:`input_tensor`, not supported for grayskull.)doc");
     detail::bind_unary_composite(module, ttnn::multigammaln, R"doc(Performs multigammaln function on :attr:`input_tensor`.)doc", "[supported range 1.6 to inf]");
     detail::bind_unary_composite(module, ttnn::sinh, R"doc(Performs sinh function on :attr:`input_tensor`.)doc", "[supported range -88 to 88]");