[LANG] Added seeded random number generation - philox (#261)

python/test/language/test_random.py

@@ -0,0 +1,198 @@
+import torch
+import triton
+import triton.language as tl
+import pytest
+import scipy.stats
+import numpy as np
+
+from numpy.random import Philox
+
+#####################################
+## Reference Philox Implementation
+#####################################
+
+class PhiloxConfig:
+    def __init__(self, PHILOX_ROUND_A, PHILOX_ROUND_B, PHILOX_KEY_A, PHILOX_KEY_B, DTYPE):
+        self.PHILOX_ROUND_A = np.array(PHILOX_ROUND_A, dtype=DTYPE)
+        self.PHILOX_ROUND_B = np.array(PHILOX_ROUND_B, dtype=DTYPE)
+        self.PHILOX_KEY_A = np.array(PHILOX_KEY_A, dtype=DTYPE)
+        self.PHILOX_KEY_B = np.array(PHILOX_KEY_B, dtype=DTYPE)
+        self.DTYPE = DTYPE
+
+
+# This is better for GPU
+PHILOX_32 = PhiloxConfig(
+    PHILOX_KEY_A=0x9E3779B9,
+    PHILOX_KEY_B=0xBB67AE85,
+    PHILOX_ROUND_A=0xD2511F53,
+    PHILOX_ROUND_B=0xCD9E8D57,
+    DTYPE=np.uint32,
+)
+
+# This is what numpy implements
+PHILOX_64 = PhiloxConfig(
+    PHILOX_KEY_A=0x9E3779B97F4A7C15,
+    PHILOX_KEY_B=0xBB67AE8584CAA73B,
+    PHILOX_ROUND_A=0xD2E7470EE14C6C93,
+    PHILOX_ROUND_B=0xCA5A826395121157,
+    DTYPE=np.uint64,
+)
+
+
+class CustomPhilox4x:
+    def __init__(self, seed, config):
+        self._config = config
+        seed = self._into_pieces(seed)
+        self._key = np.array(seed[:2], dtype=self._dtype)
+        self._counter = np.array((0, 0) + seed[2:], dtype=self._dtype)
+
+    @property
+    def _dtype(self):
+        return self._config.DTYPE
+
+    def _into_pieces(self, n, pad=4):
+        res = []
+        while len(res) < pad:
+            res.append(np.array(n, dtype=self._dtype))
+            n >>= (np.dtype(self._dtype).itemsize * 8)
+        assert n == 0
+        return tuple(res)
+
+    def _multiply_low_high(self, a, b):
+        low = a * b
+        high = int(a) * int(b)
+        high = np.array(high >> (np.dtype(self._dtype).itemsize * 8), dtype=self._dtype)
+        return low, high
+
+    def _single_round(self, counter, key):
+        lo0, hi0 = self._multiply_low_high(self._config.PHILOX_ROUND_A, counter[0])
+        lo1, hi1 = self._multiply_low_high(self._config.PHILOX_ROUND_B, counter[2])
+        ret0 = hi1 ^ counter[1] ^ key[0]
+        ret1 = lo1
+        ret2 = hi0 ^ counter[3] ^ key[1]
+        ret3 = lo0
+        return np.array([ret0, ret1, ret2, ret3], dtype=self._dtype)
+
+    def _raise_key(self, key):
+        ret0 = key[0] + self._config.PHILOX_KEY_A
+        ret1 = key[1] + self._config.PHILOX_KEY_B
+        return np.array([ret0, ret1], dtype=self._dtype)
+
+    def random_raw(self):
+        counter = self._counter
+        key = self._key
+        for _ in range(10):
+            counter = self._single_round(counter, key)
+            key = self._raise_key(key)
+        self.advance(1)
+        return counter
+
+    def advance(self, n_steps):
+        self._counter[0] += n_steps
+        assert self._counter[0] < 2**32, "FIXME: doesn't work for large offsets"
+
+
+class CustomPhilox(CustomPhilox4x):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.buffer = []
+
+    def random_raw(self):
+        if len(self.buffer) == 0:
+            self.buffer = list(super().random_raw())[::-1]
+        return int(self.buffer.pop())
+
+
+#####################################
+## Unit Tests
+#####################################
+
+BLOCK = 1024
+
+# test generation of random uint32
+@pytest.mark.parametrize('size, seed',
+    [(size, seed) for size in ['10', '4,53', '10000']\
+                  for seed in [0, 42, 124, 54]]
+)
+def test_randint(size, seed, device='cuda'):
+    size = list(map(int, size.split(',')))
+    @triton.jit
+    def kernel(X, N, seed):
+        offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
+        rand = tl.randint(seed, offset)
+        tl.store(X + offset, rand, mask=offset < N)
+    # triton result
+    x = torch.empty(size, dtype=torch.int32, device=device)
+    N = x.numel()
+    grid = (triton.cdiv(N, BLOCK),)
+    kernel[grid](x, N, seed)
+    out_tri = x.cpu().numpy().astype(np.uint32).flatten().tolist()
+    # reference result
+    gen = CustomPhilox4x(seed, config=PHILOX_32)
+    out_ref = [gen.random_raw()[0] for _ in out_tri]
+    assert out_tri == out_ref
+
+# test conversion of random uint32 into random float in [0, 1]
+def test_uint32_to_uniform_float():
+    @triton.jit
+    def kernel(SRC, TGT, N, **meta):
+        pid = tl.program_id(0)
+        offset = pid * BLOCK + tl.arange(0, BLOCK)
+        src = tl.load(SRC + offset)
+        tgt = tl.random.uint32_to_uniform_float(src)
+        tl.store(TGT + offset, tgt, mask=offset < N)
+    
+    def run(source):
+        target = -torch.ones(source.shape, dtype=torch.float32, device=source.device)
+        N = source.numel()
+        grid = lambda meta: (triton.cdiv(N, BLOCK),)
+        kernel[grid](source, target, N)
+        return target
+        
+    # check range of edge values
+    n = 100
+    source = torch.tensor(list(range(n)) + list(range(-n, 0)), dtype=torch.int32).cuda()
+    target = run(source).tolist()
+    assert target == sorted(target)
+    assert all(0.0 <= num < 1.0 for num in target)
+    # check distribution is uniform
+    source = torch.randint(-2**31, 2**31 - 1, dtype=torch.int32, size=(100000,)).cuda()
+    target = run(source).tolist()
+    assert scipy.stats.kstest(target, 'uniform', args=(0, 1)).statistic < 0.01
+
+# test uniform PRNG
+@pytest.mark.parametrize('size, seed',
+    [(size, seed) for size in [1000000]\
+                  for seed in [0, 42, 124, 54]]
+)
+def test_rand(size, seed, device='cuda'):
+    @triton.jit
+    def kernel(X, N, seed):
+        offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
+        rand = tl.rand(seed, offset)
+        tl.store(X + offset, rand, mask=offset < N)
+    # triton result
+    x = torch.empty(size, dtype=torch.float32, device=device)
+    N = x.numel()
+    grid = (triton.cdiv(N, BLOCK),)
+    kernel[grid](x, N, seed)
+    assert scipy.stats.kstest(x.tolist(), 'uniform', args=(0, 1)).statistic < 0.01
+
+# test normal PRNG
+@pytest.mark.parametrize('size, seed',
+    [(size, seed) for size in [1000000]\
+                  for seed in [0, 42, 124, 54]]
+)
+def test_randn(size, seed, device='cuda'):
+    @triton.jit
+    def kernel(X, N, seed):
+        offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
+        rand = tl.randn(seed, offset)
+        tl.store(X + offset, rand, mask=offset < N)
+    # triton result
+    x = torch.empty(size, dtype=torch.float32, device=device)
+    N = x.numel()
+    grid = (triton.cdiv(N, BLOCK),)
+    kernel[grid](x, N, seed)
+    assert abs(x.mean()) < 1e-2
+    assert abs(x.std() - 1) < 1e-2