Add silu operator python interface and tests.

include/infinicore/ops.hpp

@@ -6,3 +6,4 @@
 #include "ops/ones.hpp"
 #include "ops/rearrange.hpp"
 #include "ops/rms_norm.hpp"
+#include "ops/silu.hpp"

include/infinicore/ops/silu.hpp

+#pragma once
+
+#include "../device.hpp"
+#include "common/op.hpp"
+
+namespace infinicore::op {
+class Silu {
+public:
+    using schema = void (*)(Tensor, Tensor);
+    static void execute(Tensor output, Tensor input);
+    static common::OpDispatcher<schema> &dispatcher();
+};
+
+Tensor silu(Tensor input);
+void silu_(Tensor output, Tensor input);
+} // namespace infinicore::op

python/infinicore/__init__.py

@@ -30,6 +30,7 @@ from infinicore.ops.attention import attention
 from infinicore.ops.matmul import matmul
 from infinicore.ops.rearrange import rearrange
 from infinicore.ops.rms_norm import rms_norm
+from infinicore.ops.silu import silu
 from infinicore.tensor import (
     empty,
     from_blob,
@@ -74,6 +75,7 @@ __all__ = [
     "matmul",
     "rearrange",
     "rms_norm",
+    "silu",
     "empty",
     "from_blob",
     "ones",

python/infinicore/ops/silu.py

+from infinicore.lib import _infinicore
+from infinicore.tensor import Tensor
+
+
+def silu(input, *, out=None):
+    if out is None:
+        return Tensor(_infinicore.silu(input._underlying))
+
+    _infinicore.silu_(out._underlying, input._underlying)

src/infinicore/ops/silu/silu.cc

+#include "infinicore/ops/silu.hpp"
+#include <stdexcept>
+
+namespace infinicore::op {
+
+common::OpDispatcher<Silu::schema> &Silu::dispatcher() {
+    static common::OpDispatcher<Silu::schema> dispatcher_;
+    return dispatcher_;
+};
+
+void Silu::execute(Tensor output, Tensor input) {
+    auto device_type = context::getDevice().getType();
+    auto func = dispatcher().lookup(device_type);
+
+    if (func == nullptr) {
+        throw std::runtime_error("No Silu implementation found for device type: " + std::to_string(static_cast<int>(device_type)));
+    }
+
+    func(output, input);
+}
+
+Tensor silu(Tensor input) {
+    Shape shape = input->shape();
+    auto output = Tensor::empty(shape, input->dtype(), input->device());
+    silu_(output, input);
+    return output;
+}
+
+void silu_(Tensor output, Tensor input) {
+    Silu::execute(output, input);
+}
+} // namespace infinicore::op

src/infinicore/ops/silu/silu_infiniop.cc

+#include "../../utils.hpp"
+#include "infinicore/common/hash.hpp"
+#include "infinicore/ops/common/cache.hpp"
+#include "infinicore/ops/silu.hpp"
+#include <infiniop.h>
+
+namespace infinicore::op::silu_impl::infiniop {
+
+thread_local common::OpCache<size_t, infiniopSiluDescriptor_t> caches(
+    100, // capacity
+    [](infiniopSiluDescriptor_t &desc) {
+        if (desc != nullptr) {
+            INFINICORE_CHECK_ERROR(infiniopDestroySiluDescriptor(desc));
+            desc = nullptr;
+        }
+    });
+
+void calculate(Tensor output, Tensor input) {
+    size_t seed = hash_combine(output, input);
+
+    auto device_type = context::getDevice().getType();
+    auto device_index = context::getDevice().getIndex();
+
+    auto &cache = caches.getCache(device_type, device_index);
+
+    auto desc_opt = cache.get(seed);
+    infiniopSiluDescriptor_t desc = nullptr;
+
+    if (!desc_opt) {
+        INFINICORE_CHECK_ERROR(infiniopCreateSiluDescriptor(
+            context::getInfiniopHandle(), &desc,
+            output->desc(), input->desc()));
+        cache.put(seed, desc);
+    } else {
+        desc = *desc_opt;
+    }
+
+    size_t workspace_size = 0;
+    INFINICORE_CHECK_ERROR(infiniopGetSiluWorkspaceSize(desc, &workspace_size));
+    std::shared_ptr<Memory> workspace = context::allocateMemory(workspace_size);
+
+    INFINICORE_CHECK_ERROR(infiniopSilu(
+        desc, workspace->data(), workspace_size,
+        output->data(), input->data(), context::getStream()));
+}
+
+static bool registered = []() {
+    Silu::dispatcher().registerAll(&calculate, false);
+    return true;
+}();
+
+} // namespace infinicore::op::silu_impl::infiniop

src/infinicore/pybind11/ops.hpp

@@ -7,6 +7,7 @@
 #include "ops/matmul.hpp"
 #include "ops/rearrange.hpp"
 #include "ops/rms_norm.hpp"
+#include "ops/silu.hpp"
 
 namespace py = pybind11;
 
@@ -18,6 +19,7 @@ inline void bind(py::module &m) {
     bind_matmul(m);
     bind_rearrange(m);
     bind_rms_norm(m);
+    bind_silu(m);
 }
 
 } // namespace infinicore::ops

src/infinicore/pybind11/ops/silu.hpp

+#pragma once
+
+#include <pybind11/pybind11.h>
+
+#include "infinicore/ops/silu.hpp"
+
+namespace py = pybind11;
+
+namespace infinicore::ops {
+
+inline void bind_silu(py::module &m) {
+    m.def("silu",
+          &op::silu,
+          py::arg("input"),
+          R"doc(SiLU (Swish) activation function.)doc");
+
+    m.def("silu_",
+          &op::silu_,
+          py::arg("output"),
+          py::arg("input"),
+          R"doc(In-place SiLU (Swish) activation function.)doc");
+}
+
+} // namespace infinicore::ops

test/infinicore/ops/silu.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+
+# ==============================================================================
+# Operator-specific configuration
+# ==============================================================================
+
+# Test cases format: (operation_mode, shape, input_strides, output_strides)
+# SiLU is a single-input activation function: output = input * sigmoid(input)
+_TEST_CASES_DATA = [
+    # Basic 2D SiLU
+    (TestCase.BOTH, (2, 4), None, None),
+    (TestCase.BOTH, (128, 64), None, None),
+    # 3D SiLU
+    (TestCase.BOTH, (2, 4, 8), None, None),
+    (TestCase.BOTH, (4, 48, 6), None, None),
+    # Strided tensors
+    (TestCase.BOTH, (1, 2048), (4096, 1), (4096, 1)),
+    (TestCase.BOTH, (6, 2560), (2048, 1), (2560, 1)),
+    # Mixed cases
+    (TestCase.BOTH, (8, 16, 32), None, None),
+    # Large tensors
+    (TestCase.BOTH, (16, 5632), None, None),
+    (TestCase.BOTH, (4, 4, 5632), None, None),
+]
+
+
+def parse_test_cases(data):
+    """
+    Parse silu test case data according to format:
+    (operation_mode, shape, input_strides, output_strides)
+    """
+    operation_mode = data[0]
+    shape = data[1]
+    input_strides = data[2] if len(data) > 2 else None
+    output_strides = data[3] if len(data) > 3 else None
+
+    # Create input specifications
+    inputs = []
+
+    # Tensor input
+    if input_strides is not None:
+        inputs.append(TensorSpec.from_strided_tensor(shape, input_strides))
+    else:
+        inputs.append(TensorSpec.from_tensor(shape))
+
+    # Output tensor
+    if output_strides is not None:
+        output = TensorSpec.from_strided_tensor(shape, output_strides)
+    else:
+        output = TensorSpec.from_tensor(shape)
+
+    return TestCase(operation_mode, inputs, output)
+
+
+# Parse test cases
+_TEST_CASES = [parse_test_cases(data) for data in _TEST_CASES_DATA]
+
+# Data types
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+# Tolerance
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-3},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-5},
+    infinicore.bfloat16: {"atol": 5e-3, "rtol": 1e-2},
+}
+
+
+class OpTest(BaseOperatorTest):
+    """SiLU test with simplified test case parsing"""
+
+    def __init__(self):
+        super().__init__("SiLU")
+
+    def get_test_cases(self):
+        return _TEST_CASES
+
+    def get_tensor_dtypes(self):
+        return _TENSOR_DTYPES
+
+    def get_tolerance_map(self):
+        return _TOLERANCE_MAP
+
+    def torch_operator(self, input, out=None, **kwargs):
+        # SiLU implementation: input * sigmoid(input)
+        sigmoid_input = torch.sigmoid(input)
+        result = input * sigmoid_input
+        if out is not None:
+            out.copy_(result)
+            return out
+        return result
+
+    def infinicore_operator(self, input, out=None, **kwargs):
+        return infinicore.silu(input, out=out)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()