#9874: Remove clamp_max_bw

tenstorrent · Jul 18, 2024 · 291f12d · 291f12d
1 parent eb29903
commit 291f12d
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Showing 8 changed files with 6 additions and 63 deletions.
diff --git a/docs/source/ttnn/ttnn/api.rst b/docs/source/ttnn/ttnn/api.rst
@@ -169,7 +169,6 @@ Pointwise Unary
    ttnn/threshold
    ttnn/mul_bw
    ttnn/clamp_min_bw
-   ttnn/clamp_max_bw
    ttnn/clamp_bw
    ttnn/hardtanh_bw
    ttnn/threshold_bw

diff --git a/docs/source/ttnn/ttnn/ttnn/clamp_max_bw.rst b/docs/source/ttnn/ttnn/ttnn/clamp_max_bw.rst
diff --git a/tests/ttnn/unit_tests/operations/backward/test_backward_clamp_max.py b/tests/ttnn/unit_tests/operations/backward/test_backward_clamp_max.py
diff --git a/ttnn/cpp/ttnn/operations/eltwise/unary_backward/device/unary_backward_op.cpp b/ttnn/cpp/ttnn/operations/eltwise/unary_backward/device/unary_backward_op.cpp
@@ -38,24 +38,20 @@ std::vector<Tensor> _clamp_min_bw(
     return grad_tensor;
 }
 
-std::vector<Tensor> _clamp_max_bw(
-    const Tensor& grad, const Tensor& input, float max, const MemoryConfig& output_mem_config) {
-    std::vector<Tensor> grad_tensor;
-    Tensor maxT = ttnn::le(input, max, std::nullopt, output_mem_config);
-    Tensor result = ttnn::multiply(grad, maxT, std::nullopt, output_mem_config);
-    grad_tensor.emplace_back(result);
-    return grad_tensor;
-}
+
 
 std::vector<Tensor> _clamp_bw(
     const Tensor& grad, const Tensor& input, std::optional<float> min, std::optional<float> max, const std::optional<MemoryConfig>& output_mem_config) {
     std::vector<Tensor> grad_tensor;
     auto output_memory_config = output_mem_config.value_or(input.memory_config()); //TODO: Remove after ternary forward ops migration is completed
-    TT_FATAL((max.has_value() || min.has_value()) && "Only one of 'min' or 'max' can be None. Please provided atleast one value");
+    TT_FATAL((max.has_value() || min.has_value()) && "Only one of 'min' or 'max' can be None. Please provide atleast one value");
     if (!max.has_value()) {
         return _clamp_min_bw( grad, input, min.value(), output_memory_config);
     }else if(!min.has_value()) {
-        return _clamp_max_bw( grad, input, max.value(), output_memory_config);
+        Tensor maxT = ttnn::le(input, max.value(), std::nullopt, output_mem_config);
+        Tensor result = ttnn::multiply(grad, maxT, std::nullopt, output_mem_config);
+        grad_tensor.emplace_back(result);
+        return grad_tensor;
     }
     Tensor minT = ttnn::ge(input, min.value(), std::nullopt, output_memory_config);
     Tensor maxT = ttnn::le(input, max.value(), std::nullopt, output_memory_config);
@@ -1732,8 +1728,6 @@ std::function<std::vector<ttnn::Tensor>(const Tensor&, const Tensor&, float, con
             return _mul_bw;
         case UnaryBackwardOpType::CLAMP_MIN_BW:
             return _clamp_min_bw;
-        case UnaryBackwardOpType::CLAMP_MAX_BW:
-            return _clamp_max_bw;
         case UnaryBackwardOpType::ADD_BW:
             return _add_bw;
         case UnaryBackwardOpType::EQ_BW:

diff --git a/ttnn/cpp/ttnn/operations/eltwise/unary_backward/device/unary_backward_op.hpp b/ttnn/cpp/ttnn/operations/eltwise/unary_backward/device/unary_backward_op.hpp
@@ -55,7 +55,6 @@ enum class UnaryBackwardOpType {
     NEG_BW,
     RELU_BW,
     LOGIT_BW,
-    CLAMP_MAX_BW,
     HARDSHRINK_BW,
     SOFTSHRINK_BW,
     LEAKY_RELU_BW,

diff --git a/ttnn/cpp/ttnn/operations/eltwise/unary_backward/unary_backward.hpp b/ttnn/cpp/ttnn/operations/eltwise/unary_backward/unary_backward.hpp
@@ -359,7 +359,6 @@ constexpr auto rsqrt_bw = ttnn::register_operation<operations::unary_backward::E
 constexpr auto neg_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::NEG_BW>>("ttnn::neg_bw");
 constexpr auto relu_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::RELU_BW>>("ttnn::relu_bw");
 constexpr auto logit_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::LOGIT_BW>>("ttnn::logit_bw");
-constexpr auto clamp_max_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::CLAMP_MAX_BW>>("ttnn::clamp_max_bw");
 constexpr auto hardshrink_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::HARDSHRINK_BW>>("ttnn::hardshrink_bw");
 constexpr auto softshrink_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::SOFTSHRINK_BW>>("ttnn::softshrink_bw");
 constexpr auto leaky_relu_bw = ttnn::register_operation<operations::unary_backward::ExecuteUnaryBackward<operations::unary_backward::UnaryBackwardOpType::LEAKY_RELU_BW>>("ttnn::leaky_relu_bw");

diff --git a/ttnn/cpp/ttnn/operations/eltwise/unary_backward/unary_backward_pybind.hpp b/ttnn/cpp/ttnn/operations/eltwise/unary_backward/unary_backward_pybind.hpp
@@ -901,11 +901,6 @@ void py_module(py::module& module) {
         ttnn::floor_bw,
         R"doc(Performs backward operations for floor on :attr:`input_tensor` with given :attr:`grad_tensor`)doc");
 
-    detail::bind_unary_backward(
-        module,
-        ttnn::clamp_max_bw,
-        R"doc(Performs backward operations for clamp max value on :attr:`input_tensor`, :attr:`max` with given :attr:`grad_tensor`.)doc");
-
     detail::bind_unary_backward(
         module,
         ttnn::hardshrink_bw,

diff --git a/ttnn/ttnn/operations/unary_backward.py b/ttnn/ttnn/operations/unary_backward.py
@@ -49,7 +49,6 @@ def unary_bw_prod(torch_op, x, grad_data, *args, **kwargs):
     name_to_golden_function = {
         "mul_bw": lambda x, grad_data: unary_bw_with_float(torch.mul, x, grad_data),
         "clamp_min_bw": lambda x, grad_data: unary_bw_with_float(torch.clamp_min, x, grad_data),
-        "clamp_max_bw": lambda x, grad_data: unary_bw_with_float(torch.clamp_max, x, grad_data),
         "add_bw": lambda x, grad_data: unary_bw_with_float(torch.add, x, grad_data),
         "eq_bw": lambda x, grad_data: unary_bw_with_float(torch.eq, x, grad_data),
         "gt_bw": lambda x, grad_data: unary_bw_with_float(torch.gt, x, grad_data),
@@ -138,7 +137,6 @@ def _golden_function(input_tensor: ttnn.Tensor, **_):
 TTNN_ELTWISE_UNARY_BACKWARD_CPP_FUNCTIONS = [
     ttnn.mul_bw,
     ttnn.clamp_min_bw,
-    ttnn.clamp_max_bw,
     ttnn.add_bw,
     ttnn.eq_bw,
     ttnn.gt_bw,