#0: Add fused softplus op

tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_eltwise_unary.py

@@ -1100,3 +1100,36 @@ def test_run_eltwise_unary_comp(
             device,
             test_args,
         )
+
+    @pytest.mark.parametrize("beta", [1.0])
+    @pytest.mark.parametrize("threshold", [20.0])
+    def test_run_eltwise_softplus(
+        self,
+        input_shapes,
+        beta,
+        threshold,
+        device,
+        function_level_defaults,
+        input_mem_config,
+        output_mem_config,
+    ):
+        datagen_func = [
+            generation_funcs.gen_func_with_cast(partial(generation_funcs.gen_rand, low=-100, high=100), torch.bfloat16)
+        ]
+        test_args = generation_funcs.gen_default_dtype_layout_device(input_shapes)[0]
+        test_args.update({"beta": beta, "threshold": threshold})
+        test_args.update(
+            {
+                "input_mem_config": [input_mem_config],
+                "output_mem_config": output_mem_config,
+            }
+        )
+        comparison_func = comparison_funcs.comp_equal
+        run_single_pytorch_test(
+            "eltwise-softplus",
+            input_shapes,
+            datagen_func,
+            comparison_func,
+            device,
+            test_args,
+        )

tt_eager/tt_dnn/op_library/composite/composite_ops.cpp

@@ -110,23 +110,6 @@ Tensor log1p(const Tensor& x, const MemoryConfig& output_mem_config) {
     return operation::decorate_as_composite(__func__, _log1p)(x, output_mem_config);
 }
 
-// softplus[x] =(1/beta) * log[1 + exp[x * beta]]
-// (x*beta) > threshold ==> x
-// use transformation y = log[1+exp[x]] by broadcast
-Tensor _softplus(const Tensor& x, float beta, float threshold, const MemoryConfig& output_mem_config) {
-    float oned_beta = (1 / beta);
-    Tensor x_beta = mul_unary(x, beta, output_mem_config);
-    Tensor exp_x = exp(x_beta, output_mem_config);
-    Tensor result_log1p = log1p(exp_x, output_mem_config);
-    Tensor sp_result = mul_unary(result_log1p,  oned_beta, output_mem_config);
-    sp_result = where(gt(x_beta, full_like(x, threshold, output_mem_config), std::nullopt, output_mem_config), x,
-                where(eqz(full_like(x, beta, output_mem_config), output_mem_config), std::numeric_limits<float>::infinity(), sp_result), output_mem_config);
-    return sp_result;
-}
-Tensor softplus(const Tensor& a, float beta, float threshold, const MemoryConfig& output_mem_config) {
-    return operation::decorate_as_composite(__func__, _softplus)(a, beta, threshold, output_mem_config);
-}
-
 // tanhshrink(x) = x - tanh(x)
 Tensor _tanhshrink(const Tensor& x, const MemoryConfig& output_mem_config) {
     Tensor tan_x = tanh(x, output_mem_config);

tt_eager/tt_dnn/op_library/composite/composite_ops.hpp

@@ -81,10 +81,6 @@ Tensor mac(
 // use transformation y = log(1.0 + x) by broadcast
 Tensor log1p(const Tensor& x, const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
-// softplus[x] = log[1 + exp[x]]
-// use transformation y = log[1+exp[x]] by broadcast
-Tensor softplus(const Tensor& x, float beta=1.0, float threshold=20.0, const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
-
 // mish[x] = x*tanh[softplus[x]]
 // use transformation y = x*tanh[softplus[x]] by broadcast
 // Ref: https://krutikabapat.github.io/Swish-Vs-Mish-Latest-Activation-Functions/

tt_eager/tt_dnn/op_library/eltwise_unary/eltwise_unary_op.cpp

@@ -71,6 +71,9 @@ void update_macro_defines(UnaryOpType op_type, std::map<std::string, std::string
         case UnaryOpType::NEG:
             defines["SFPU_OP_NEG_INCLUDE"] = "1";
             break;
+        case UnaryOpType::SOFTPLUS:
+            defines["SFPU_OP_SOFTPLUS_INCLUDE"] = "1";
+            break;
         default:
             defines["SFPU_OP_COMPUTE_KERNEL_API_INCLUDE"]="1";
             break;
@@ -140,6 +143,16 @@ std::pair<string, string> get_op_init_and_func_parameterized(
         case UnaryOpType::UNARY_NE: op_init_and_name = {"unary_ne_tile_init();", fmt::format("unary_ne_tile({}, {}u);", idst, Converter::to_hex(param0))}; break;
         case UnaryOpType::UNARY_GT: op_init_and_name = {"unary_gt_tile_init();", fmt::format("unary_gt_tile({}, {}u);", idst, Converter::to_hex(param0))}; break;
         case UnaryOpType::UNARY_LT: op_init_and_name = {"unary_lt_tile_init();", fmt::format("unary_lt_tile({}, {}u);", idst, Converter::to_hex(param0))}; break;
+        case UnaryOpType::SOFTPLUS: {
+            TT_ASSERT(params.size() == 2, "Expected softplus to take 2 parameters");
+            float param1 = params[1];
+            op_init_and_name = {"softplus_tile_init();", fmt::format("softplus_tile({}, {}u, {}u, {}u);", idst,
+                    Converter::to_hex(param0),
+                    Converter::to_hex(1.0f / param0), // Pass reciprocal to avoid doing it on device
+                    Converter::to_hex(param1)
+                    )};
+            break;
+        }
         default:
         TT_ASSERT( false && "unexpected parameterized type");
     };

tt_eager/tt_dnn/op_library/eltwise_unary/eltwise_unary_op.hpp

@@ -66,6 +66,7 @@ enum class UnaryOpType {
     RSUB,
     RDIV,
     SILU,
+    SOFTPLUS,
     IDENTITY,
     NEG,
     ADD_UNARY,
@@ -99,6 +100,7 @@ bool is_parametrized_type(T val) {
         case UnaryOpType::RSUB:
         case UnaryOpType::RDIV:
         case UnaryOpType::EXP:
+        case UnaryOpType::SOFTPLUS:
         case UnaryOpType::ADD_UNARY:
         case UnaryOpType::SUB_UNARY:
         case UnaryOpType::MUL_UNARY:
@@ -162,6 +164,8 @@ inline UnaryWithParam string_to_unary_with_param(const std::string& name) {
         return UnaryWithParam(UnaryOpType::SIGN);
     else if (name == "square")
         return UnaryWithParam(UnaryOpType::SQUARE);
+    else if (name == "softplus")
+        return UnaryWithParam(UnaryOpType::SOFTPLUS);
     TT_THROW("Unknown unary op: " + name);
 }
 
@@ -433,6 +437,15 @@ inline Tensor sigmoid_accurate(
         output_mem_config);
 }
 
+inline Tensor softplus(
+    const Tensor& input_tensor,
+    float beta,
+    float threshold,
+    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG) {
+    return run_eltwise_unary(
+        input_tensor, {UnaryWithParam(UnaryOpType::SOFTPLUS, {beta, threshold})}, output_mem_config);
+}
+
 inline Tensor unary_chain(
     const Tensor& input_tensor,
     std::vector<UnaryWithParam> ops_chain,

tt_metal/hw/ckernels/grayskull/metal/llk_api/llk_sfpu/ckernel_sfpu_softplus.h

@@ -0,0 +1,49 @@
+// SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include "ckernel.h"
+#include "ckernel_defs.h"
+#include "ckernel_sfpu_converter.h"
+#include "ckernel_sfpu_exp.h"
+#include "ckernel_sfpu_log.h"
+
+using namespace sfpi;
+
+namespace ckernel {
+namespace sfpu {
+
+template <bool APPROXIMATION_MODE>
+inline void calculate_softplus_body(vFloat beta, vFloat beta_reciprocal, vFloat threshold) {
+    vFloat a = dst_reg[0];
+    v_if(a < threshold) {
+        exp_init<APPROXIMATION_MODE, false>();
+        a = calculate_exponential_body<APPROXIMATION_MODE>(a * beta) + 1.00f;
+
+        log_init<APPROXIMATION_MODE>();
+        dst_reg[0] = a;
+        calculate_log_body<false>(0);
+        a = beta_reciprocal * dst_reg[0];
+    }
+    v_endif;
+    dst_reg[0] = a;
+}
+
+template <bool APPROXIMATION_MODE, int ITERATIONS = 6>
+inline void calculate_softplus(uint param0, uint param1, uint param2) {
+    vFloat beta = Converter::to_float(param0);
+    vFloat beta_reciprocal = Converter::to_float(param1);
+    vFloat threshold = Converter::to_float(param2);
+    for (int d = 0; d < ITERATIONS; d++) {
+        calculate_softplus_body<APPROXIMATION_MODE>(beta, beta_reciprocal, threshold);
+        dst_reg++;
+    }
+}
+
+template <bool APPROXIMATION_MODE>
+void softplus_init() {}
+
+}  // namespace sfpu
+}  // namespace ckernel

tt_metal/hw/ckernels/grayskull/metal/llk_api/llk_sfpu/llk_math_eltwise_unary_sfpu_softplus.h

@@ -0,0 +1,29 @@
+// SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include "ckernel_sfpu_softplus.h"
+#include "llk_math_eltwise_unary_sfpu_3_param.h"
+#include "llk_math_eltwise_unary_sfpu_init.h"
+
+namespace ckernel {
+
+template <bool APPROXIMATE>
+inline void llk_math_eltwise_unary_sfpu_softplus_init() {
+    llk_math_eltwise_unary_sfpu_init<SfpuType::softplus, APPROXIMATE>();
+}
+
+template <bool APPROXIMATE>
+inline void llk_math_eltwise_unary_sfpu_softplus(
+    uint dst_index, uint param0, uint param1, uint param2, int vector_mode = (int)VectorMode::RC) {
+    llk_math_eltwise_unary_sfpu_3_param<APPROXIMATE>(
+        ckernel::sfpu::calculate_softplus<APPROXIMATE>,
+        ckernel::sfpu::calculate_softplus<APPROXIMATE>,
+        dst_index,
+        vector_mode,
+        param0, param1, param2);
+}
+
+}  // namespace ckernel

tt_metal/hw/ckernels/wormhole_b0/metal/llk_api/llk_sfpu/ckernel_sfpu_softplus.h

@@ -0,0 +1,49 @@
+// SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include "ckernel.h"
+#include "ckernel_defs.h"
+#include "ckernel_sfpu_converter.h"
+#include "ckernel_sfpu_exp.h"
+#include "ckernel_sfpu_log.h"
+
+using namespace sfpi;
+
+namespace ckernel {
+namespace sfpu {
+
+template <bool APPROXIMATION_MODE>
+inline void calculate_softplus_body(vFloat beta, vFloat beta_reciprocal, vFloat threshold) {
+    vFloat a = dst_reg[0];
+    v_if(a < threshold) {
+        exp_init<APPROXIMATION_MODE, false>();
+        a = calculate_exponential_body<APPROXIMATION_MODE>(a * beta) + 1.00f;
+
+        log_init<APPROXIMATION_MODE>();
+        dst_reg[0] = a;
+        calculate_log_body<false>(0);
+        a = beta_reciprocal * dst_reg[0];
+    }
+    v_endif;
+    dst_reg[0] = a;
+}
+
+template <bool APPROXIMATION_MODE, int ITERATIONS = 8>
+inline void calculate_softplus(uint param0, uint param1, uint param2) {
+    vFloat beta = Converter::to_float(param0);
+    vFloat beta_reciprocal = Converter::to_float(param1);
+    vFloat threshold = Converter::to_float(param2);
+    for (int d = 0; d < ITERATIONS; d++) {
+        calculate_softplus_body<APPROXIMATION_MODE>(beta, beta_reciprocal, threshold);
+        dst_reg++;
+    }
+}
+
+template <bool APPROXIMATION_MODE>
+void softplus_init() {}
+
+}  // namespace sfpu
+}  // namespace ckernel

tt_metal/hw/ckernels/wormhole_b0/metal/llk_api/llk_sfpu/llk_math_eltwise_unary_sfpu_softplus.h

@@ -0,0 +1,29 @@
+// SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include "ckernel_sfpu_softplus.h"
+#include "llk_math_eltwise_unary_sfpu_3_param.h"
+#include "llk_math_eltwise_unary_sfpu_init.h"
+
+namespace ckernel {
+
+template <bool APPROXIMATE>
+inline void llk_math_eltwise_unary_sfpu_softplus_init() {
+    llk_math_eltwise_unary_sfpu_init<SfpuType::softplus, APPROXIMATE>();
+}
+
+template <bool APPROXIMATE>
+inline void llk_math_eltwise_unary_sfpu_softplus(
+    uint dst_index, uint param0, uint param1, uint param2, int vector_mode = (int)VectorMode::RC) {
+    llk_math_eltwise_unary_sfpu_3_param<APPROXIMATE>(
+        ckernel::sfpu::calculate_softplus<APPROXIMATE>,
+        ckernel::sfpu::calculate_softplus<APPROXIMATE>,
+        dst_index,
+        vector_mode,
+        param0, param1, param2);
+}
+
+}  // namespace ckernel

tt_metal/hw/ckernels/wormhole_b0/metal/llk_api/llk_sfpu_types.h

@@ -73,6 +73,7 @@ enum SfpuType {
     unary_ne,
     unary_gt,
     unary_lt,
+    softplus,
     tiled_prod,
     unused,
 };

tt_metal/include/compute_kernel_api/eltwise_unary/sfpu_split_includes.h

@@ -68,6 +68,10 @@
 #include "compute_kernel_api/eltwise_unary/binop_with_scalar.h"
 #endif
 
+#if SFPU_OP_SOFTPLUS_INCLUDE
+#include "compute_kernel_api/eltwise_unary/softplus.h"
+#endif
+
 #if SFPU_OP_COMPUTE_KERNEL_API_INCLUDE
 #include "compute_kernel_api.h"
 #endif

tt_metal/include/compute_kernel_api/eltwise_unary/softplus.h

@@ -0,0 +1,35 @@
+// SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+
+#include "compute_kernel_api/common_globals.h"
+#ifdef TRISC_MATH
+#include "llk_math_eltwise_unary_sfpu_softplus.h"
+#define MAIN math_main()
+#define MATH(x) x
+#else
+#define MATH(x)
+#endif
+
+
+
+namespace ckernel {
+
+/**
+ * TODO!
+ */
+ALWI void softplus_tile(uint32_t idst, uint32_t param0, uint32_t param1, uint32_t param2) {
+    MATH(( llk_math_eltwise_unary_sfpu_softplus<APPROX>(idst, param0, param1, param2) ));
+}
+
+/**
+ * Please refer to documentation for any_init.
+ */
+ALWI void softplus_tile_init() {
+    MATH(( llk_math_eltwise_unary_sfpu_softplus_init<APPROX>() ));
+}
+
+} // namespace ckernel