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# Owner(s): ["module: inductor"]
import copy
import os
import unittest
import torch
from torch import nn
from torch._dynamo.utils import counters, same
from torch._inductor import metrics
from torch._inductor.runtime.benchmarking import benchmarker
from torch._inductor.test_case import TestCase
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU
DO_PERF_TEST = os.environ.get("DO_PERF_TEST") == "1"
class TestScatterOpt(TestCase):
def setUp(self):
super().setUp()
metrics.reset()
counters.clear()
def check_metric(self, val=1):
self.assertEqual(val, metrics.num_matches_for_scatter_upon_const_tensor)
def do_acc_test(self, f, *args):
expect = f(*args)
actual = torch.compile(f)(*args)
self.assertTrue(same(expect, actual, tol=1e-3), f"{expect=}\n{actual=}\n")
def test_3d_tensor(self):
L, M, N = 2, 1024, 2048
def f(x):
y = torch.full([L, M, N], 3.14, dtype=torch.float)
y.scatter_(2, x.unsqueeze(2), 2.718)
return y
x = torch.randint(0, N, (L, M), dtype=torch.int64)
self.do_acc_test(f, x)
expected_num_bytes = (
L * M * N * torch.float.itemsize + L * M * torch.int64.itemsize
)
self.assertEqual(metrics.num_bytes_accessed, expected_num_bytes)
def test_non_last_dim(self):
"""
Test the case that the scatter dimension is not the last one.
"""
M, N = 1024, 2048
def f(x):
y = torch.full([M, N], 3.14, dtype=torch.float)
y.scatter_(0, x.unsqueeze(0), 2.718)
return y
x = torch.randint(0, M, (N,), dtype=torch.int64)
self.do_acc_test(f, x)
expected_num_bytes = M * N * torch.float.itemsize + N * torch.int64.itemsize
self.assertEqual(metrics.num_bytes_accessed, expected_num_bytes)
def test_neg_scatter_dim(self):
M, N = 1024, 2048
def f(x):
y = torch.full([M, N], 3.14, dtype=torch.float)
y.scatter_(-1, x.unsqueeze(1), 2.718)
return y
x = torch.randint(0, N, (M,), dtype=torch.int64)
self.do_acc_test(f, x)
expected_num_bytes = M * N * torch.float.itemsize + M * torch.int64.itemsize
self.assertEqual(metrics.num_bytes_accessed, expected_num_bytes)
def test_shorter_index_tensor(self):
M, N = 1024, 2048
def f(x):
y = torch.full([M, N], 3.14, dtype=torch.float)
y.scatter_(1, x.unsqueeze(1), 2.718)
return y
x = torch.randint(0, N, (M // 2,), dtype=torch.int64)
self.do_acc_test(f, x)
# no match since the index tensor is shorter. May support it in future.
self.assertEqual(0, counters["inductor"]["pattern_matcher_count"])
def test_nonzero_const_tensor(self):
M, N = 1024, 2048
def f(x):
y = torch.full([M, N], 3.14, dtype=torch.float)
y.scatter_(1, x.unsqueeze(1), 2.718)
return y
x = torch.randint(0, N, (M,), dtype=torch.int64)
self.do_acc_test(f, x)
expected_num_bytes = M * N * torch.float.itemsize + M * torch.int64.itemsize
self.assertEqual(metrics.num_bytes_accessed, expected_num_bytes)
def test_can_not_optimize_due_to_dense(self):
M, N = 1024, 2048
def f(x):
y = torch.full([M, N], 0, dtype=torch.float)
y.scatter_(1, x, 0.618)
return y
x = torch.randint(0, N, (M, N // 2), dtype=torch.int64)
self.do_acc_test(f, x)
expected_num_bytes = M * N * torch.float.itemsize + M * (N // 2) * (
torch.int64.itemsize + torch.float.itemsize
)
# Use assertGreaterEqual rather than assertEqual due to the issue related
# to StarDep mentioned here: https://github.com/pytorch/pytorch/pull/129043#discussion_r1651699706
self.assertGreaterEqual(metrics.num_bytes_accessed, expected_num_bytes)
def test_can_not_optimize_due_to_non_const(self):
M, N = 1024, 2048
def f(x, y):
y.scatter_(1, x, 0.618)
return y
x = torch.randint(0, N, (M, 1), dtype=torch.int64)
y = torch.randn([M, N])
self.do_acc_test(f, x, y)
# The generated code is quite in-efficient.
# There are 3 kernels
# 1. copy from arg to buf
# 2. scatter upon buf
# 3. copy buf back to arg
# Link to the wrapper: https://gist.github.com/shunting314/d43b74e680b3e5b514f7c28160c39f40
expected_num_bytes = 4 * M * N * torch.float.itemsize + M * (
torch.int64.itemsize + torch.float.itemsize
)
self.assertGreaterEqual(metrics.num_bytes_accessed, expected_num_bytes)
# the second kernel and third kernel are both mutation kernel. So we
# overestimated the memory accessed
# Update the test once the overestimiation is fixed.
over_estimate = M * torch.float.itemsize + M * N * torch.float.itemsize
self.assertEqual(metrics.num_bytes_accessed, expected_num_bytes + over_estimate)
def test_cross_entropy_loss(self):
"""
Match full+scatter in CEL and replaces it with a pointwise.
Perf data on an A100 GPU:
Without the scatter optimization:
ms=47.340, peak_mem=10.524 GB
With the scatter optimization:
ms=42.768, peak_mem=7.227 GB
"""
B, T, D, V = 32, 1024, 768, 50257
if not DO_PERF_TEST:
# use a smaller V if not doing perf test to avoid OOM
# in CI
V = V // 100
ref_model = nn.Linear(D, V).to(torch.bfloat16)
opt_model = copy.deepcopy(ref_model)
ce = nn.CrossEntropyLoss()
def f(m, x, label):
ce(m(x).view(-1, V), label.view(-1)).backward()
opt_f = torch.compile(f)
x = torch.randn(B, T, D).to(torch.bfloat16)
label = torch.randint(0, V, (B, T)).to(torch.int64)
f(ref_model, x, label)
ref_grad = ref_model.weight.grad
opt_f(opt_model, x, label)
act_grad = opt_model.weight.grad
assert torch.allclose(
ref_grad, act_grad, atol=1e-3, rtol=1e-3
), f"{ref_grad=}\n{act_grad=}"
self.check_metric()
if DO_PERF_TEST:
if GPU_TYPE == "xpu":
raise unittest.SkipTest(
"torch.xpu.reset_peak_memory_stats not implemented."
)
torch.cuda.reset_peak_memory_stats()
for _ in range(3):
opt_f(opt_model, x, label)
ms = benchmarker.benchmark_gpu(lambda: opt_f(opt_model, x, label))
peak_mem = torch.cuda.max_memory_allocated() / 10**9
print(f"{ms=:.3f}, {peak_mem=:.3f} GB")
if HAS_GPU:
torch.set_default_device(GPU_TYPE)
if __name__ == "__main__":
from torch._inductor.test_case import run_tests
if HAS_GPU:
run_tests()
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