#10778: Update Argmin with ttnn support

docs/source/ttnn/ttnn/dependencies/tt_lib.rst

@@ -531,9 +531,6 @@ Other Operations
 
 .. autofunction:: tt_lib.tensor.argmax
 
-.. autofunction:: tt_lib.tensor.argmin
-
-
 Loss Functions
 ==============
 

tests/ttnn/profiling/ops_for_profiling.py

@@ -1536,23 +1536,23 @@ def argmax_all(x):
 
 
 def argmin_1(x):
-    tt_lib.tensor.argmin(x, dim=-1)
+    ttnn.argmin(x, dim=-1)
 
 
 def argmin_2(x):
-    tt_lib.tensor.argmin(x, dim=-2)
+    ttnn.argmin(x, dim=-2)
 
 
 def argmin_3(x):
-    tt_lib.tensor.argmin(x, dim=-3)
+    ttnn.argmin(x, dim=-3)
 
 
 def argmin_4(x):
-    tt_lib.tensor.argmin(x, dim=-4)
+    ttnn.argmin(x, dim=-4)
 
 
 def argmin_all(x):
-    tt_lib.tensor.argmin(x, dim=-1, all=True)
+    ttnn.argmin(x, dim=-1, all=True)
 
 
 def primary_moreh_softmax_0(x):
@@ -2284,22 +2284,22 @@ def clone(x):
     },
     {
         "op": argmin_1,
-        "name": "tt_lib.tensor.argmin_dim_3",
+        "name": "ttnn.argmin_dim_3",
         "num_repeats": 2,
     },
     {
         "op": argmin_2,
-        "name": "tt_lib.tensor.argmin_dim_2",
+        "name": "ttnn.argmin_dim_2",
         "num_repeats": 2,
     },
     {
         "op": argmin_3,
-        "name": "tt_lib.tensor.argmin_dim_1",
+        "name": "ttnn.argmin_dim_1",
         "num_repeats": 2,
     },
     {
         "op": argmin_all,
-        "name": "tt_lib.tensor.argmin_all",
+        "name": "ttnn.argmin_all",
         "num_repeats": 2,
     },
     {

ttnn/cpp/ttnn/deprecated/tt_dnn/op_library/composite/composite_ops.cpp

@@ -871,17 +871,6 @@ Tensor argmax(
     return operation::decorate_as_composite(__func__, _argmax)(input_a, dim, all, output_mem_config);
 }
 
-Tensor _argmin(const Tensor& input_a, int64_t _dim, bool all, const MemoryConfig& output_mem_config) {
-    Tensor neg_input = ttnn::neg(input_a, output_mem_config);
-    return (argmax(neg_input, _dim, all, output_mem_config));
-}
-Tensor argmin(
-    const Tensor& input_a,
-    int64_t dim,
-    bool all,
-    const MemoryConfig& output_mem_config /* = operation::DEFAULT_OUTPUT_MEMORY_CONFIG */) {
-    return operation::decorate_as_composite(__func__, _argmin)(input_a, dim, all, output_mem_config);
-}
 }  // namespace tt_metal
 
 }  // namespace tt

ttnn/cpp/ttnn/deprecated/tt_dnn/op_library/composite/composite_ops.hpp

@@ -282,12 +282,6 @@ Tensor argmax(
     bool all = false,
     const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
-Tensor argmin(
-    const Tensor& input_a,
-    int64_t dim = 0,
-    bool all = false,
-    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
-
 }  // namespace tt_metal
 
 }  // namespace tt

ttnn/cpp/ttnn/deprecated/tt_lib/csrc/tt_lib_bindings_tensor_composite_ops.cpp

@@ -142,30 +142,6 @@ void TensorModuleCompositeOPs(py::module& m_tensor) {
                 "output_mem_config", "Layout of tensor in TT Accelerator device memory banks", "MemoryConfig", "Default is interleaved in DRAM", "No"
         )doc");
 
-    m_tensor.def(
-        "argmin",
-        &argmin,
-        py::arg("input").noconvert(),
-        py::arg("dim"),
-        py::arg("all") = false,
-        py::arg("output_mem_config").noconvert() = operation::DEFAULT_OUTPUT_MEMORY_CONFIG,
-        R"doc(
-            Returns the indices of the minimum value of elements in the ``input`` tensor
-            If ``all`` is set to ``true`` irrespective of given dimension it will return the indices of minimum value of all elements in given ``input``
-
-            Input tensor must have BFLOAT16 data type.
-
-            Output tensor will have BFLOAT16 data type.
-
-            .. csv-table::
-                :header: "Argument", "Description", "Data type", "Valid range", "Required"
-
-                "input", "Tensor argmin is applied to", "Tensor", "Tensor of shape [W, Z, Y, X]", "Yes"
-                "dim", "Dimension to perform argmin", "int", "", "Yes"
-                "all", "Consider all dimension (ignores ``dim`` param)", "bool", "default to false", "No"
-                "output_mem_config", "Layout of tensor in TT Accelerator device memory banks", "MemoryConfig", "Default is interleaved in DRAM", "No"
-        )doc");
-
     m_tensor.def(
         "lerp",
         py::overload_cast<const Tensor&, const Tensor&, float, const MemoryConfig&>(&lerp),

ttnn/cpp/ttnn/operations/eltwise/unary_backward/device/unary_backward_op.cpp

@@ -1257,7 +1257,7 @@ std::vector<Tensor> _digamma_bw(const Tensor& grad, const Tensor& input, const s
     auto output_memory_config = output_mem_config.value_or(input.memory_config());
     float t_inf = std::numeric_limits<float>::infinity();
     float t_nan = std::nanf("");
-    Tensor grad_a = ttnn::multiply(grad, polygamma(input, 1, output_memory_config), std::nullopt, output_mem_config);
+    Tensor grad_a = ttnn::multiply(grad, ttnn::polygamma(input, 1, output_mem_config), std::nullopt, output_mem_config);
     grad_a = where(
         ttnn::logical_and(ttnn::eqz(input, output_mem_config), ttnn::eqz(grad, output_mem_config), std::nullopt, output_mem_config),
         t_nan,
@@ -1286,7 +1286,7 @@ std::vector<Tensor> _polygamma_bw(
     if (n == 2 || n == 4 || n == 6 || n == 8 || n == 10) {
         pos_neg = -1.0f;
     }
-    Tensor grad_a = ttnn::multiply(grad, polygamma(input, (n + 1), output_memory_config), std::nullopt, output_mem_config);
+    Tensor grad_a = ttnn::multiply(grad, ttnn::polygamma(input, (n + 1), output_mem_config), std::nullopt, output_mem_config);
     grad_a = where(
         ttnn::logical_and(
             ttnn::le(input, 0.0, std::nullopt, output_mem_config), ttnn::eqz(grad, output_mem_config), std::nullopt, output_mem_config),