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# Owner(s): ["oncall: jit"]
import os
import unittest
import torch
import torch._lazy
import torch._lazy.config
import torch._lazy.ir_cache
import torch._lazy.metrics as metrics
import torch._lazy.ts_backend
from torch.testing._internal.common_utils import IS_WINDOWS, run_tests, TestCase
torch._lazy.ts_backend.init()
torch._lazy.config.set_reuse_ir(True)
def get_test_device():
return "cuda" if "LTC_TS_CUDA" in os.environ else "cpu"
@unittest.skipIf(IS_WINDOWS, "To be fixed")
class TestLazyReuseIr(TestCase):
def testAdd(self):
device = get_test_device()
x = torch.randn(2, 3, 4, device=device)
y = torch.randn(2, 3, 4, device=device)
z = torch.zeros(2, 3, 4, device=device)
device = "lazy"
x_lazy = x.detach().clone().to(device=device)
y_lazy = y.detach().clone().to(device=device)
z_lazy = z.detach().clone().to(device=device)
for i in range(10):
z += x + y
for i in range(10):
z_lazy += x_lazy + y_lazy
torch._lazy.mark_step()
torch.testing.assert_close(z.cpu(), z_lazy.cpu())
assert metrics.counter_value("IrNodeReused_torch::lazy::AddTensor") >= 14
metrics.reset()
torch._lazy.ir_cache.reset()
def testAddSub(self):
device = get_test_device()
x = torch.randn(2, 3, 4, device=device)
y = torch.randn(2, 3, 4, device=device)
z = torch.zeros(2, 3, 4, device=device)
device = "lazy"
x_lazy = x.detach().clone().to(device=device)
y_lazy = y.detach().clone().to(device=device)
z_lazy = z.detach().clone().to(device=device)
for i in range(10):
if i < 5:
z += x + y
else:
z += x - y
for i in range(10):
if i < 5:
z_lazy += x_lazy + y_lazy
else:
z_lazy += x_lazy - y_lazy
torch._lazy.mark_step()
torch.testing.assert_close(z.cpu(), z_lazy.cpu())
assert metrics.counter_value("IrNodeReused_torch::lazy::AddTensor") >= 8
metrics.reset()
torch._lazy.ir_cache.reset()
def testAddSubFallback(self):
torch._lazy.config.set_force_fallback("aten::sub")
device = get_test_device()
x = torch.randn(2, 3, 4, device=device)
y = torch.randn(2, 3, 4, device=device)
z = torch.zeros(2, 3, 4, device=device)
device = "lazy"
x_lazy = x.detach().clone().to(device=device)
y_lazy = y.detach().clone().to(device=device)
z_lazy = z.detach().clone().to(device=device)
for i in range(10):
if i < 5:
z += x + y
else:
z += x - y
for i in range(10):
if i < 5:
z_lazy += x_lazy + y_lazy
else:
z_lazy += x_lazy - y_lazy
torch._lazy.mark_step()
torch.testing.assert_close(z.cpu(), z_lazy.cpu())
assert metrics.counter_value("IrNodeReused_torch::lazy::AddTensor") >= 8
metrics.reset()
torch._lazy.ir_cache.reset()
torch._lazy.config.set_force_fallback("")
def testBatchNorm(self):
device = get_test_device()
x = torch.randn(16, 3, 224, 224, device=device)
weight = torch.randn(3, device=device)
bias = torch.randn(3, device=device)
for i in range(10):
# BatchNorm2d does extra checks on dimensions which SymInts don't support yet
# so we call `torch.ops.aten.native_batch_norm` to bypass the checks.
z, _, _ = torch.ops.aten.native_batch_norm(
x, weight, bias, None, None, True, 0.1, 1e-5
)
z_legit, _, _ = torch.ops.aten._native_batch_norm_legit(
x, weight, bias, True, 0.1, 1e-5
)
device = "lazy"
x_lazy = x.detach().clone().to(device=device)
weight_lazy = weight.detach().clone().to(device=device)
bias_lazy = bias.detach().clone().to(device=device)
for i in range(10):
z_lazy, _, _ = torch.ops.aten.native_batch_norm(
x_lazy, weight_lazy, bias_lazy, None, None, True, 0.1, 1e-5
)
z_legit_lazy, _, _ = torch.ops.aten._native_batch_norm_legit(
x_lazy, weight_lazy, bias_lazy, True, 0.1, 1e-5
)
torch._lazy.mark_step()
torch.testing.assert_close(z.cpu(), z_lazy.cpu())
torch.testing.assert_close(z_legit.cpu(), z_legit_lazy.cpu())
assert metrics.counter_value("IrNodeReused_torch::lazy::NativeBatchNorm") >= 7
metrics.reset()
torch._lazy.ir_cache.reset()
if __name__ == "__main__":
run_tests()
|
