issue/584-修改变量名,文件名;添加#include<optional>;修改测试代码

include/infinicore/ops/embedding.hpp

+#pragma once
+
+#include "common/op.hpp"
+
+namespace infinicore::op {
+
+Tensor embedding(Tensor input, Tensor weight);
+void embedding_(Tensor out, Tensor input, Tensor weight);
+} // namespace infinicore::op

include/infinicore/ops/linear.hpp

 #pragma once
 
 #include "common/op.hpp"
+#include <optional>
 
 namespace infinicore::op {
 

include/infinicore/ops/rope.hpp

 #pragma once
 
 #include "../device.hpp"
-#include "../tensor.hpp"
 #include "../nn/rope.hpp"
+#include "../tensor.hpp"
 #include "common/op.hpp"
 
 namespace infinicore::op {
 class RoPE {
 public:
     using schema = void (*)(Tensor, const Tensor &, const Tensor &, const Tensor &, const Tensor &, infinicore::nn::RoPE::Algo);
-    static void execute(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo);
+    static void execute(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo);
     static common::OpDispatcher<schema> &dispatcher();
 };
 
 // Internal function
-void rope_(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo);
+void rope_(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_table, infinicore::nn::RoPE::Algo algo);
 
 // Public API that uses infinicore::nn::RoPE::Algo
-Tensor rope(const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo);
+Tensor rope(const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_table, infinicore::nn::RoPE::Algo algo);
 } // namespace infinicore::op

python/infinicore/nn/functional/__init__.py

 from .causal_softmax import causal_softmax
+from .embedding import embedding
 from .linear import linear
 from .random_sample import random_sample
 from .rms_norm import rms_norm
+from .rope import RopeAlgo, rope
 from .silu import silu
 from .swiglu import swiglu
 
-__all__ = ["causal_softmax", "random_sample", "rms_norm", "silu", "swiglu", "linear"]
+__all__ = [
+    "causal_softmax",
+    "random_sample",
+    "rms_norm",
+    "silu",
+    "swiglu",
+    "linear",
+    "embedding",
+    "rope",
+    "RopeAlgo",
+]

python/infinicore/nn/functional/embedding.py

+from infinicore.lib import _infinicore
+from infinicore.tensor import Tensor
+
+__all__ = ["embedding"]
+
+
+def embedding(
+    input: Tensor,
+    weight: Tensor,
+    padding_idx=None,
+    max_norm=None,
+    norm_type=2.0,
+    scale_grad_by_freq=False,
+    sparse=False,
+    *,
+    out=None,
+) -> Tensor:
+    r"""Generate a simple lookup table that looks up embeddings in a fixed dictionary and size."""
+
+    assert (
+        (padding_idx is None)
+        and (max_norm is None)
+        and (scale_grad_by_freq is False)
+        and (sparse is False)
+    ), "Unsupported parameters."
+
+    assert "cpu" == input.device.type, (
+        "The device of 'input' variable must be on the CPU."
+    )
+
+    if out is None:
+        return Tensor(_infinicore.embedding(input._underlying, weight._underlying))
+
+    _infinicore.embedding_(out._underlying, input._underlying, weight._underlying)
+    return out

python/infinicore/nn/functional/rope.py

+from infinicore.lib import _infinicore
+from infinicore.tensor import Tensor
+
+__all__ = ["rope", "RopeAlgo"]
+
+
+class RopeAlgo:
+    r"""Different types of RoPE algorithms."""
+
+    GPT_J = _infinicore.Algo.GPT_J
+    GPT_NEOX = _infinicore.Algo.GPT_NEOX
+
+
+def rope(
+    x: Tensor,
+    pos_ids: Tensor,
+    sin_table: Tensor,
+    cos_table: Tensor,
+    algo: RopeAlgo = RopeAlgo.GPT_NEOX,
+    *,
+    out=None,
+) -> Tensor:
+    r"""Rotary Position Embedding(RoPE)."""
+
+    if out is None:
+        return Tensor(
+            _infinicore.rope(
+                x._underlying,
+                pos_ids._underlying,
+                sin_table._underlying,
+                cos_table._underlying,
+                algo,
+            )
+        )
+
+    _infinicore.rope_(
+        out._underlying,
+        x._underlying,
+        pos_ids._underlying,
+        sin_table._underlying,
+        cos_table._underlying,
+        algo,
+    )
+    return out

src/infinicore/ops/embedding/embedding.cc

+#include "infinicore/ops/embedding.hpp"
+#include "infinicore/context/context.hpp"
+#include <cstring>
+
+namespace infinicore::op {
+
+Tensor embedding(Tensor input, // LongTensor of arbitrary shape containing the indices to extract
+                 Tensor weight // Weight: Embedding matrix of floating point type with shape (V, embedding_dim), where V = maximum index + 1
+) {
+    auto input_shape = input->shape();
+    auto weight_shape = weight->shape();
+    auto vocab_size = weight_shape[0];
+    auto embedding_dim = weight_shape[1];
+
+    // Assign memory to out variables
+    auto output_shape = input_shape;
+    output_shape.push_back(embedding_dim);
+    Tensor inputs_embeds = Tensor::empty(output_shape, weight->dtype(), weight->device());
+
+    embedding_(inputs_embeds, input, weight);
+    return inputs_embeds;
+}
+
+void embedding_(Tensor out, Tensor input, Tensor weight) {
+    assert(infinicore::DataType::I64 == input->dtype() || (infinicore::DataType::I32 == input->dtype()));
+    assert(infinicore::Device::Type::CPU == input->device());
+
+    auto input_shape = input->shape();
+    auto weight_shape = weight->shape();
+    auto vocab_size = weight_shape[0];
+    auto embedding_dim = weight_shape[1];
+
+    // Calculate the number of token
+    Size counts = 1;
+    for (auto &v : input_shape) {
+        counts *= v;
+    }
+
+    // the bytes of one token
+    const Size bytes = dsize(weight->dtype()) * embedding_dim;
+    auto *weight_ptr = weight->data();
+    auto *out_ptr = out->data();
+
+    // copies
+    if (weight->device().getType() == Device::Type::CPU) {
+        if (infinicore::DataType::I64 == input->dtype()) {
+            const int64_t *input_arr = reinterpret_cast<const int64_t *>(input->data());
+            for (Size i = 0; i < counts; ++i) {
+                int64_t idx = input_arr[i];
+                assert((idx >= 0) && (idx < vocab_size));
+                std::memcpy(out_ptr + i * bytes,
+                            weight_ptr + idx * bytes,
+                            bytes);
+            }
+        } else if (infinicore::DataType::I32 == input->dtype()) {
+            const int32_t *input_arr = reinterpret_cast<const int32_t *>(input->data());
+
+            for (Size i = 0; i < counts; ++i) {
+                int32_t idx = input_arr[i];
+                assert((idx >= 0) && (idx < vocab_size));
+                std::memcpy(out_ptr + i * bytes,
+                            weight_ptr + idx * bytes,
+                            bytes);
+            }
+        }
+
+    } else {
+        if (infinicore::DataType::I64 == input->dtype()) {
+            const int64_t *input_arr = reinterpret_cast<const int64_t *>(input->data());
+            for (Size i = 0; i < counts; ++i) {
+                int64_t idx = input_arr[i];
+                assert((idx >= 0) && (idx < vocab_size));
+                context::memcpyD2D(out_ptr + i * bytes,
+                                   weight_ptr + idx * bytes,
+                                   bytes);
+            }
+        } else if (infinicore::DataType::I32 == input->dtype()) {
+            const int32_t *input_arr = reinterpret_cast<const int32_t *>(input->data());
+            for (Size i = 0; i < counts; ++i) {
+                int32_t idx = input_arr[i];
+                assert((idx >= 0) && (idx < vocab_size));
+                context::memcpyD2D(out_ptr + i * bytes,
+                                   weight_ptr + idx * bytes,
+                                   bytes);
+            }
+        }
+    }
+}
+
+} // namespace infinicore::op

src/infinicore/ops/rope/rope.cc

@@ -9,7 +9,7 @@ common::OpDispatcher<RoPE::schema> &RoPE::dispatcher() {
     return dispatcher_;
 };
 
-void RoPE::execute(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo) {
+void RoPE::execute(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_table, infinicore::nn::RoPE::Algo algo) {
     auto device_type = context::getDevice().getType();
     auto func = dispatcher().lookup(device_type);
 
@@ -17,17 +17,17 @@ void RoPE::execute(Tensor x_out, const Tensor &x, const Tensor &pos, const Tenso
         throw std::runtime_error("No RoPE implementation found for device type: " + std::to_string(static_cast<int>(device_type)));
     }
 
-    func(x_out, x, pos, sin_cache, cos_cache, algo);
+    func(x_out, x, pos, sin_table, cos_table, algo);
 }
 
-void rope_(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo) {
-    RoPE::execute(x_out, x, pos, sin_cache, cos_cache, algo);
+void rope_(Tensor x_out, const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_table, infinicore::nn::RoPE::Algo algo) {
+    RoPE::execute(x_out, x, pos, sin_table, cos_table, algo);
 }
 
-Tensor rope(const Tensor &x, const Tensor &pos, const Tensor &sin_cache, const Tensor &cos_cache, infinicore::nn::RoPE::Algo algo) {
+Tensor rope(const Tensor &x, const Tensor &pos, const Tensor &sin_table, const Tensor &cos_table, infinicore::nn::RoPE::Algo algo) {
     Shape shape = x->shape();
     auto x_out = Tensor::empty(shape, x->dtype(), x->device());
-    rope_(x_out, x, pos, sin_cache, cos_cache, algo);
+    rope_(x_out, x, pos, sin_table, cos_table, algo);
     return x_out;
 }
 

src/infinicore/pybind11/ops.hpp

@@ -5,12 +5,14 @@
 #include "ops/add.hpp"
 #include "ops/attention.hpp"
 #include "ops/causal_softmax.hpp"
+#include "ops/embedding.hpp"
 #include "ops/linear.hpp"
 #include "ops/matmul.hpp"
 #include "ops/mul.hpp"
 #include "ops/random_sample.hpp"
 #include "ops/rearrange.hpp"
 #include "ops/rms_norm.hpp"
+#include "ops/rope.hpp"
 #include "ops/silu.hpp"
 #include "ops/swiglu.hpp"
 
@@ -30,6 +32,8 @@ inline void bind(py::module &m) {
     bind_rms_norm(m);
     bind_silu(m);
     bind_swiglu(m);
+    bind_rope(m);
+    bind_embedding(m);
 }
 
 } // namespace infinicore::ops

src/infinicore/pybind11/ops/embedding.hpp

+#pragma once
+
+#include "infinicore/ops/embedding.hpp"
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+namespace infinicore::ops {
+
+inline void bind_embedding(py::module &m) {
+
+    m.def("embedding",
+          &op::embedding,
+          py::arg("input"),
+          py::arg("weight"),
+          R"doc(Generate a simple lookup table that looks up embeddings in a fixed dictionary and size..)doc");
+
+    m.def("embedding_",
+          &op::embedding_,
+          py::arg("out"),
+          py::arg("input"),
+          py::arg("weight"),
+          R"doc(In-place, Generate a simple lookup table that looks up embeddings in a fixed dictionary and size..)doc");
+}
+
+} // namespace infinicore::ops

src/infinicore/pybind11/ops/rope.hpp

+#pragma once
+
+#include <pybind11/pybind11.h>
+
+#include "infinicore/ops/rope.hpp"
+
+namespace py = pybind11;
+
+namespace infinicore::ops {
+
+inline void bind_rope(py::module &m) {
+
+    py::enum_<infinicore::nn::RoPE::Algo>(m, "Algo")
+        .value("GPT_J", infinicore::nn::RoPE::Algo::GPT_J)
+        .value("GPT_NEOX", infinicore::nn::RoPE::Algo::GPT_NEOX);
+
+    m.def("rope",
+          &op::rope,
+          py::arg("x"),
+          py::arg("pos"),
+          py::arg("sin_table"),
+          py::arg("cos_table"),
+          py::arg("algo"),
+          R"doc( Rotary Position Embedding(RoPE).)doc");
+
+    m.def("rope_",
+          &op::rope_,
+          py::arg("x_out"),
+          py::arg("x"),
+          py::arg("pos"),
+          py::arg("sin_table"),
+          py::arg("cos_table"),
+          py::arg("algo"),
+          R"doc(In-place, Rotary Position Embedding(RoPE).)doc");
+}
+
+} // namespace infinicore::ops

test/infinicore/ops/embedding.py

+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.tensor import TensorInitializer
+from framework.utils import (
+    convert_infinicore_to_torch,
+    infinicore_tensor_from_torch,
+    to_torch_dtype,
+)
+
+import infinicore
+
+# ==============================================================================
+# Operator-specific configuration
+# ==============================================================================
+_TEST_CASES_DATA = [
+    # bs, ntok, vocab_size, embedding_dim, type
+    (1, 5, 32000, 4, infinicore.int64),
+    (2, 10, 32000, 2048, infinicore.int32),
+    (1, 5, 10, 10, infinicore.int64),
+]
+
+# Tolerance configuration
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-3},
+    infinicore.bfloat16: {"atol": 0, "rtol": 5e-2},
+}
+
+# Data types to test
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    """
+    Parse test case data and return list of TestCase objects for Embedding operation.
+    Each test case contains all necessary information for execution and validation.
+    """
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        bs, ntok = data[0], data[1]
+        vocab_size, embedding_dim = data[2], data[3]
+        input_type = data[4]
+
+        input_strides = None
+        weight_strides = None
+
+        # Determine shapes
+        input_shape = (bs, ntok)
+
+        weight_shape = (vocab_size, embedding_dim)
+
+        # Check if tensors support in-place operations
+        # Generate test cases for all data types
+        for dtype in _TENSOR_DTYPES:
+            tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+
+            # Create typed tensor specs
+            input_spec = TensorSpec.from_tensor(
+                input_shape,
+                input_strides,
+                input_type,
+                init_mode=TensorInitializer.RANDINT,
+                low=1,
+                high=9,
+            )
+            weight_spec = TensorSpec.from_tensor(weight_shape, weight_strides, dtype)
+
+            # Test Case 1: Out-of-place (return value)
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec, weight_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"Embedding - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Embedding operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Embedding")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, out=None, **kwargs):
+        """PyTorch Embedding implementation"""
+
+        return torch.nn.functional.embedding(*args, **kwargs)
+
+    def infinicore_operator(self, input, weight, out=None, **kwargs):
+        """InfiniCore Embedding implementation"""
+
+        if input.device.type == "cpu":
+            input_cpu = input
+        else:
+            # 将 input的数据 转移到 cpu 上
+            torch_reference = torch.zeros(
+                input.shape,
+                dtype=to_torch_dtype(input.dtype),
+                device="cpu" if "cpu" == input.device.type else "cuda",
+            )
+            torch_reference = convert_infinicore_to_torch(input)
+            torch_reference = torch_reference.contiguous().cpu()
+
+            # 创建cpu的 input
+            input_cpu = infinicore_tensor_from_torch(torch_reference)
+
+        return infinicore.nn.functional.embedding(input_cpu, weight, out=out)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/rope.py

+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import infinicore_tensor_from_torch, is_broadcast
+from infinicore.nn.functional import RopeAlgo
+
+import infinicore
+
+# ==============================================================================
+# Operator-specific configuration
+# ==============================================================================
+
+
+_TEST_CASES_DATA = [
+    # ntok, num, head_dim, Algo
+    (1, 1, 64, RopeAlgo.GPT_NEOX),
+    (5, 32, 64, RopeAlgo.GPT_NEOX),
+    (1, 1, 128, RopeAlgo.GPT_J),
+    (10, 1, 64, RopeAlgo.GPT_J),
+]
+
+# Tolerance configuration
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-2, "rtol": 1e-3},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+# Data types to test
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    """
+    Parse test case data and return list of TestCase objects for Rope operation.
+    Each test case contains all necessary information for execution and validation.
+    """
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        ntok, num, head_dim = data[0], data[1], data[2]
+        algo = data[3]
+
+        # Determine shapes based on batch dimension
+        out_shape = (ntok, num, head_dim)
+        x_shape = (ntok, num, head_dim)
+        sin_table_shape = (ntok, head_dim // 2)
+        cos_table_shape = (ntok, head_dim // 2)
+
+        # Check if tensors support in-place operations
+        c_supports_inplace = not is_broadcast(out_shape)
+
+        # Generate test cases for all data types
+        for dtype in _TENSOR_DTYPES:
+            tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+
+            # Create typed tensor specs
+            out_spec = TensorSpec.from_tensor(out_shape, None, dtype)
+            x_spec = TensorSpec.from_tensor(x_shape, None, dtype)
+            sin_table_spec = TensorSpec.from_tensor(sin_table_shape, None, dtype)
+            cos_table_spec = TensorSpec.from_tensor(cos_table_shape, None, dtype)
+
+            # Test Case 1: Out-of-place (return value)
+            test_cases.append(
+                TestCase(
+                    inputs=[x_spec, sin_table_spec, cos_table_spec],
+                    kwargs={"algo": algo},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"Rope - OUT_OF_PLACE",
+                )
+            )
+
+            # Test Case 2: In-place with explicit output tensor
+            if c_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[x_spec, sin_table_spec, cos_table_spec],
+                        kwargs={"algo": algo},
+                        output_spec=out_spec,  # Specify the output tensor spec
+                        comparison_target="out",
+                        tolerance=tolerance,
+                        description=f"Rope - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+def rotary_embedding(t, sin, cos, algo, *, out=None):
+    def _torch_rope(sin, cos, t1, t2):
+        cos = cos.unsqueeze(1)  # [seq_len, 1, dh // 2]
+        sin = sin.unsqueeze(1)  # [seq_len, 1, dh // 2]
+        t_out_1 = t1 * cos - t2 * sin
+        t_out_2 = t1 * sin + t2 * cos
+
+        return t_out_1, t_out_2
+
+    ans = t.clone()
+
+    dh = t.shape[-1]
+    dt = t.dtype
+    assert dh % 2 == 0, "Embedding dimension must be even."
+
+    if RopeAlgo.GPT_J == algo:
+        t_even = t[..., 0::2]  # [seq_len, n_head, dh // 2]
+        t_odd = t[..., 1::2]  # [seq_len, n_head, dh // 2]
+
+        t_out_even, t_out_odd = _torch_rope(sin, cos, t_even, t_odd)
+
+        ans[..., 0::2] = t_out_even.to(dt)
+        ans[..., 1::2] = t_out_odd.to(dt)
+    elif RopeAlgo.GPT_NEOX == algo:
+        half_dim = dh // 2
+        t_first = t[..., :half_dim]
+        t_second = t[..., half_dim:]
+
+        t_out_first, t_out_second = _torch_rope(sin, cos, t_first, t_second)
+
+        ans[..., :half_dim] = t_out_first.to(dt)
+        ans[..., half_dim:] = t_out_second.to(dt)
+    else:
+        raise KeyError("error Algo ")
+
+    if out is not None:
+        out.copy_(ans)
+        return out
+    return ans
+
+
+class OpTest(BaseOperatorTest):
+    """Rope operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Rope")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        """PyTorch Rope implementation"""
+
+        return rotary_embedding(*args, **kwargs)
+
+    def infinicore_operator(self, x, sin_table, cos_table, algo, out=None, **kwargs):
+        """InfiniCore Rope implementation"""
+
+        ntok = x.shape[0]
+        torch_device = "cpu"
+        if x.device.type != "cpu":
+            torch_device = "cuda"
+
+        # 创建 pos_ids的变量
+        pos_ids_torch = torch.arange(0, ntok, dtype=torch.int32, device=torch_device)
+        pos_ids_ref = infinicore_tensor_from_torch(pos_ids_torch)
+        pos_ids_infini = infinicore.empty(
+            list(pos_ids_ref.shape), dtype=pos_ids_ref.dtype, device=pos_ids_ref.device
+        )
+        pos_ids_infini.copy_(pos_ids_ref)
+
+        # 计算
+        pos_ids = pos_ids_infini
+        return infinicore.nn.functional.rope(
+            x, pos_ids, sin_table, cos_table, algo=algo, out=out
+        )
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()