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chore(syntax): Test 仓 R1 + R12 迁移

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R1：T | t.CPtr -> T*（40 文件 / 1121 处；含多级 **、强转位 (T*)(x)、下标内 list[AST*]）；引号前向引用 'Rect' | t.CPtr 26 处按例外保留，T | t.CPtr | t.State 25 处因 T* 后不能接 | 保持原联合写法。

R12：i = 0 + while i < N: + 尾 i += 1 -> or i in range(N):（10 文件 / 42 处；含 <= -> range(EXPR + 1)、带注解初始化 i: int = 0、尾 i = i + 1）。余 20 处经守卫判定不可安全改写（复合条件 1、尾部非自增 8、体内重绑定/continue 5、while-else 2、循环后仍被读取 2、找不到初始化 2）。
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TermiNexus committed 2026-10-05 21:18:37 +08:00
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commit aa5a7e7250
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SimdTest/App/main.vp
+49 -67
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@@ -10,8 +10,8 @@ import testcheck
# 全局内存池
arena: uint8 | t.CPtr = None
pool: memhub.MemPool | t.CPtr = None
arena: uint8* = None
pool: memhub.MemPool* = None
# 外部函数: clock() 用于性能计时
def clock() -> t.CLong | t.CExtern: pass
@@ -25,19 +25,19 @@ define CLOCK_PER_SEC = 1000
def test_avx2_add_sub_mul_div():
testcheck.section("AVX2 add/sub/mul/div 4d")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(2.0)
a.data[2] = double(3.0)
a.data[3] = double(4.0)
b: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
b: numpy.ndarray[double]* = numpy.zeros(pool, 4)
b.data[0] = double(5.0)
b.data[1] = double(6.0)
b.data[2] = double(7.0)
b.data[3] = double(8.0)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out: numpy.ndarray[double]* = numpy.zeros(pool, 4)
# add
vipersimd.simd_add4d(a.data, b.data, out.data)
@@ -70,13 +70,13 @@ def test_avx2_add_sub_mul_div():
def test_avx2_neg_abs_sqrt():
testcheck.section("AVX2 neg/abs/sqrt 4d")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(-4.0)
a.data[2] = double(9.0)
a.data[3] = double(-16.0)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out: numpy.ndarray[double]* = numpy.zeros(pool, 4)
# neg
vipersimd.simd_neg4d(a.data, out.data)
@@ -105,19 +105,19 @@ def test_avx2_neg_abs_sqrt():
def test_avx2_min_max():
testcheck.section("AVX2 min/max 4d")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(8.0)
a.data[2] = double(3.0)
a.data[3] = double(6.0)
b: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
b: numpy.ndarray[double]* = numpy.zeros(pool, 4)
b.data[0] = double(5.0)
b.data[1] = double(2.0)
b.data[2] = double(7.0)
b.data[3] = double(4.0)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out: numpy.ndarray[double]* = numpy.zeros(pool, 4)
vipersimd.simd_min4d(a.data, b.data, out.data)
testcheck.check(out.data[0] == double(1.0), "min[0]==1", "min[0]==1")
@@ -138,16 +138,16 @@ def test_avx2_min_max():
def test_avx2_scalar_ops():
testcheck.section("AVX2 scalar broadcast ops")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(2.0)
a.data[2] = double(3.0)
a.data[3] = double(4.0)
s: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 1)
s: numpy.ndarray[double]* = numpy.zeros(pool, 1)
s.data[0] = double(10.0)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out: numpy.ndarray[double]* = numpy.zeros(pool, 4)
vipersimd.simd_mul_scalar4d(a.data, s.data, out.data)
testcheck.check(out.data[0] == double(10.0), "mul_scalar[0]==10", "mul_scalar[0]==10")
@@ -165,19 +165,19 @@ def test_avx2_scalar_ops():
def test_avx2_hsum_dot():
testcheck.section("AVX2 hsum/dot 4d")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(2.0)
a.data[2] = double(3.0)
a.data[3] = double(4.0)
b: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
b: numpy.ndarray[double]* = numpy.zeros(pool, 4)
b.data[0] = double(5.0)
b.data[1] = double(6.0)
b.data[2] = double(7.0)
b.data[3] = double(8.0)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 1)
out: numpy.ndarray[double]* = numpy.zeros(pool, 1)
vipersimd.simd_hsum4d(a.data, out.data)
testcheck.check(out.data[0] == double(10.0), "hsum==10", "hsum==10")
@@ -192,21 +192,21 @@ def test_avx2_hsum_dot():
def test_avx2_fma():
testcheck.section("AVX2 FMA 4d")
a: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[double]* = numpy.zeros(pool, 4)
a.data[0] = double(1.0)
a.data[1] = double(2.0)
a.data[2] = double(3.0)
a.data[3] = double(4.0)
b: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
b: numpy.ndarray[double]* = numpy.zeros(pool, 4)
b.data[0] = double(5.0)
b.data[1] = double(6.0)
b.data[2] = double(7.0)
b.data[3] = double(8.0)
c_val: numpy.ndarray[double] | t.CPtr = numpy.ones(pool, 4)
c_val: numpy.ndarray[double]* = numpy.ones(pool, 4)
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out: numpy.ndarray[double]* = numpy.zeros(pool, 4)
vipersimd.simd_fma4d(a.data, b.data, c_val.data, out.data)
testcheck.check(out.data[0] == double(6.0), "fma[0]==6", "fma[0]==6")
@@ -227,9 +227,9 @@ def test_avx2_fma():
def test_batch_array_ops():
testcheck.section("Batch array operations")
n: sizet = 16
a: numpy.ndarray[double] | t.CPtr = numpy.arange(pool, double(1.0), double(17.0), double(1.0))
b: numpy.ndarray[double] | t.CPtr = numpy.full(pool, 16, double(2.0))
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 16)
a: numpy.ndarray[double]* = numpy.arange(pool, double(1.0), double(17.0), double(1.0))
b: numpy.ndarray[double]* = numpy.full(pool, 16, double(2.0))
out: numpy.ndarray[double]* = numpy.zeros(pool, 16)
# add array
vipersimd.simd_add_array(a.data, b.data, out.data, n)
@@ -242,18 +242,18 @@ def test_batch_array_ops():
testcheck.check(out.data[3] == double(8.0), "mul_arr[3]==8", "mul_arr[3]==8")
# sqrt array
c: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
c: numpy.ndarray[double]* = numpy.zeros(pool, 4)
c.data[0] = double(4.0)
c.data[1] = double(9.0)
c.data[2] = double(16.0)
c.data[3] = double(25.0)
out4: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
out4: numpy.ndarray[double]* = numpy.zeros(pool, 4)
vipersimd.simd_sqrt_array(c.data, out4.data, sizet(4))
testcheck.check(out4.data[0] == double(2.0), "sqrt_arr[0]==2", "sqrt_arr[0]==2")
testcheck.check(out4.data[3] == double(5.0), "sqrt_arr[3]==5", "sqrt_arr[3]==5")
# abs array
d: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
d: numpy.ndarray[double]* = numpy.zeros(pool, 4)
d.data[0] = double(-1.0)
d.data[1] = double(-2.0)
d.data[2] = double(3.0)
@@ -264,13 +264,13 @@ def test_batch_array_ops():
testcheck.check(out4.data[3] == double(4.0), "abs_arr[3]==4", "abs_arr[3]==4")
# dot array
e: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 4)
e: numpy.ndarray[double]* = numpy.zeros(pool, 4)
e.data[0] = double(1.0)
e.data[1] = double(2.0)
e.data[2] = double(3.0)
e.data[3] = double(4.0)
f: numpy.ndarray[double] | t.CPtr = numpy.ones(pool, 4)
out1: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, 1)
f: numpy.ndarray[double]* = numpy.ones(pool, 4)
out1: numpy.ndarray[double]* = numpy.zeros(pool, 1)
vipersimd.simd_dot_array(e.data, f.data, sizet(4), out1.data)
testcheck.check(out1.data[0] == double(10.0), "dot_arr==10", "dot_arr==10")
@@ -365,53 +365,43 @@ define N_ELEM = 40000
define N_ITER = 50000
def scalar_add_array(a: t.CPtr, b: t.CPtr, out: t.CPtr, n: sizet):
i: sizet = 0
da: double | t.CPtr = a
db: double | t.CPtr = b
dout: double | t.CPtr = out
while i < n:
da: double* = a
db: double* = b
dout: double* = out
for i in range(n):
dout[i] = da[i] + db[i]
i += 1
def scalar_mul_array(a: t.CPtr, b: t.CPtr, out: t.CPtr, n: sizet):
i: sizet = 0
da: double | t.CPtr = a
db: double | t.CPtr = b
dout: double | t.CPtr = out
while i < n:
da: double* = a
db: double* = b
dout: double* = out
for i in range(n):
dout[i] = da[i] * db[i]
i += 1
def scalar_sqrt_array(a: t.CPtr, out: t.CPtr, n: sizet):
i: sizet = 0
da: double | t.CPtr = a
dout: double | t.CPtr = out
while i < n:
da: double* = a
dout: double* = out
for i in range(n):
dout[i] = vipersimd.simd_sqrt(da[i])
i += 1
def benchmark_simd_vs_scalar():
testcheck.section("Benchmark: SIMD vs Scalar")
a: numpy.ndarray[double] | t.CPtr = numpy.full(pool, N_ELEM, double(1.5))
b: numpy.ndarray[double] | t.CPtr = numpy.full(pool, N_ELEM, double(2.5))
out: numpy.ndarray[double] | t.CPtr = numpy.zeros(pool, N_ELEM)
a: numpy.ndarray[double]* = numpy.full(pool, N_ELEM, double(1.5))
b: numpy.ndarray[double]* = numpy.full(pool, N_ELEM, double(2.5))
out: numpy.ndarray[double]* = numpy.zeros(pool, N_ELEM)
# --- SIMD add benchmark ---
t0: t.CLong = clock()
iter: int = 0
while iter < N_ITER:
for iter in range(N_ITER):
vipersimd.simd_add_array(a.data, b.data, out.data, sizet(N_ELEM))
iter += 1
t1: t.CLong = clock()
simd_add_ms: t.CLong = t1 - t0
# --- Scalar add benchmark ---
t0 = clock()
iter = 0
while iter < N_ITER:
for iter in range(N_ITER):
scalar_add_array(a.data, b.data, out.data, sizet(N_ELEM))
iter += 1
t1 = clock()
scalar_add_ms: t.CLong = t1 - t0
@@ -423,19 +413,15 @@ def benchmark_simd_vs_scalar():
# --- SIMD mul benchmark ---
t0 = clock()
iter = 0
while iter < N_ITER:
for iter in range(N_ITER):
vipersimd.simd_mul_array(a.data, b.data, out.data, sizet(N_ELEM))
iter += 1
t1 = clock()
simd_mul_ms: t.CLong = t1 - t0
# --- Scalar mul benchmark ---
t0 = clock()
iter = 0
while iter < N_ITER:
for iter in range(N_ITER):
scalar_mul_array(a.data, b.data, out.data, sizet(N_ELEM))
iter += 1
t1 = clock()
scalar_mul_ms: t.CLong = t1 - t0
@@ -447,19 +433,15 @@ def benchmark_simd_vs_scalar():
# --- SIMD sqrt benchmark ---
t0 = clock()
iter = 0
while iter < N_ITER:
for iter in range(N_ITER):
vipersimd.simd_sqrt_array(a.data, out.data, sizet(N_ELEM))
iter += 1
t1 = clock()
simd_sqrt_ms: t.CLong = t1 - t0
# --- Scalar sqrt benchmark ---
t0 = clock()
iter = 0
while iter < N_ITER:
for iter in range(N_ITER):
scalar_sqrt_array(a.data, out.data, sizet(N_ELEM))
iter += 1
t1 = clock()
scalar_sqrt_ms: t.CLong = t1 - t0