repo-sync-2024-11-01T13:48:20+0800

libspu/dialect/pphlo/IR/fold.cc

@@ -49,6 +49,14 @@ OpFoldResult ReverseOp::fold(FoldAdaptor) {
           dims, [&](int64_t dim) { return shapedType.getDimSize(dim) == 1; })) {
     return input;
   }
+
+  // reverse(reverse(x, dims), dims) = x
+  if (auto prev = input.getDefiningOp<ReverseOp>()) {
+    if (prev.getDimensions() == dims) {
+      return prev.getOperand();
+    }
+  }
+
   return {};
 }
 

libspu/mpc/semi2k/beaver/beaver_impl/trusted_party/trusted_party.cc

@@ -71,7 +71,7 @@ std::vector<NdArrayRef> reduce(ReduceOp op,
             ring_mul_(rs[idx], t);
           }
         } else {
-          SPU_ENFORCE("not supported reduction op");
+          SPU_THROW("not supported reduction op");
         }
       }
     }

libspu/mpc/semi2k/prime_utils.cc

@@ -105,7 +105,8 @@ NdArrayRef MulPriv(KernelEvalContext* ctx, const NdArrayRef& x) {
 
   // P0 sends (x+a) to P1 ; P1 sends (y+b) to P0
   comm->sendAsync(comm->nextRank(), ring_add(a_or_b, x), "(x + a) or (y + b)");
-  xa_or_yb = comm->recv(comm->prevRank(), x.eltype(), "(x + a) or (y + b)");
+  xa_or_yb = comm->recv(comm->prevRank(), x.eltype(), "(x + a) or (y + b)")
+                 .reshape(x.shape());
   // note that our rings are commutative.
   if (comm->getRank() == 0) {
     ring_add_(c, ring_mul(std::move(xa_or_yb), x));
@@ -198,4 +199,4 @@ NdArrayRef ConvMP(KernelEvalContext* ctx, const NdArrayRef& h,
   return x;
 }
 
-}  // namespace spu::mpc::semi2k
+}  // namespace spu::mpc::semi2k

pyrightconfig.json

@@ -0,0 +1,5 @@
+{
+    "executionEnvironments": [
+        {"root": "."}
+    ]
+}

requirements.txt

@@ -1,5 +1,5 @@
 grpcio>=1.42.0,!=1.48.0
-numpy>=1.22.0
+numpy>=1.22.0, <2 # FIXME: for SF compatibility
 protobuf>=4, <5
 cloudpickle>=2.0.0
 multiprocess>=0.70.12.2

sml/linear_model/emulations/quantile_emul.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import time
 
 import jax.numpy as jnp
@@ -48,15 +49,10 @@ def proc(X, y):
     def generate_data():
         from jax import random
 
-        # 设置随机种子
         key = random.PRNGKey(42)
-        # 生成 X 数据
         key, subkey = random.split(key)
         X = random.normal(subkey, (100, 2))
-        # 生成 y 数据
-        y = (
-            5 * X[:, 0] + 2 * X[:, 1] + random.normal(key, (100,)) * 0.1
-        )  # 高相关性，带有小噪声
+        y = 5 * X[:, 0] + 2 * X[:, 1] + random.normal(key, (100,)) * 0.1
         return X, y
 
     try:

sml/linear_model/tests/quantile_test.py

@@ -78,7 +78,7 @@ def generate_data():
         # run
         # Larger max_iter can give higher accuracy, but it will take more time to run
         proc = proc_wrapper(
-            quantile=0.2, alpha=0.1, fit_intercept=True, lr=0.01, max_iter=200
+            quantile=0.2, alpha=0.1, fit_intercept=True, lr=0.01, max_iter=20
         )
         result, coef, intercept = spsim.sim_jax(sim, proc)(X, y)
         rmse_encrpted = jnp.sqrt(jnp.mean((y - result) ** 2))

sml/linear_model/utils/_linprog_simplex.py

@@ -22,7 +22,6 @@ def _pivot_col(T, tol=1e-5):
 
     all_masked = jnp.all(mask)
 
-    # 定义根据最小值选择列的函数
     ma = jnp.where(mask, jnp.inf, T[-1, :-1])
     min_col = jnp.argmin(ma)
 
@@ -44,12 +43,10 @@ def _pivot_row(T, pivcol, phase, tol=1e-5, max_val=1e10):
 
     q = jnp.where(ma >= max_val, jnp.inf, mb / ma)
 
-    # 选择最小比值的行
     min_rows = jnp.nanargmin(q)
     all_masked = jnp.all(mask)
 
     row = min_rows
-    # 处理全被掩盖的情况
     row = jnp.where(all_masked, 0, row)
 
     return ~all_masked, row

spu/tests/BUILD.bazel

@@ -178,7 +178,6 @@ py_binary(
     srcs = ["jnp_debug.py"],
     deps = [
         "//spu:api",
-        "//spu/intrinsic:all_intrinsics",
         "//spu/utils:simulation",
     ],
 )

spu/tests/jnp_debug.py

@@ -15,7 +15,6 @@
 import jax.numpy as jnp
 import numpy as np
 
-import spu.intrinsic as si
 import spu.spu_pb2 as spu_pb2
 import spu.utils.simulation as ppsim
 
@@ -31,9 +30,9 @@
     copts.disable_div_sqrt_rewrite = True
 
     x = np.random.randn(3, 4)
-    y = np.random.randn(5, 6)
-    fn = lambda x, y: si.example_binary(x, y)
-    # fn = lambda x, y: jnp.matmul(x, y)
+    y = np.random.randn(4, 5)
+    fn = lambda x, y: jnp.matmul(x, y)
+
     spu_fn = ppsim.sim_jax(sim, fn, copts=copts)
     z = spu_fn(x, y)