xref: /aosp_15_r20/external/pytorch/test/jit/test_profiler.py (revision da0073e96a02ea20f0ac840b70461e3646d07c45) 
1# Owner(s): ["oncall: jit"]
2
3import os
4import sys
5
6import torch
7from torch.testing._internal.common_utils import skipIfTorchDynamo
8
9
10# Make the helper files in test/ importable
11pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
12sys.path.append(pytorch_test_dir)
13from torch.testing._internal.jit_utils import FileCheck, JitTestCase, warmup_backward
14
15
16if __name__ == "__main__":
17    raise RuntimeError(
18        "This test file is not meant to be run directly, use:\n\n"
19        "\tpython test/test_jit.py TESTNAME\n\n"
20        "instead."
21    )
22
23
24@skipIfTorchDynamo()
25class TestProfiler(JitTestCase):
26    def setUp(self):
27        self.prev_exec = torch._C._jit_set_profiling_executor(True)
28        self.prev_profiling = torch._C._get_graph_executor_optimize(True)
29        self.inline_autodiff = torch._C._debug_set_autodiff_subgraph_inlining(False)
30        self.texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
31        self.can_fuse_on_cpu = torch._C._jit_can_fuse_on_cpu()
32        torch._C._jit_set_texpr_fuser_enabled(True)
33        torch._C._jit_override_can_fuse_on_cpu(True)
34        self.default_dtype = torch.get_default_dtype()
35        self.old_reduction_enabled = torch._C._jit_set_texpr_reductions_enabled(True)
36        torch.set_default_dtype(torch.double)
37        self.old_fusion_inlining = torch._C._debug_get_fusion_group_inlining()
38        torch._C._debug_set_fusion_group_inlining(False)
39        self.old_te_must_use_llvm_cpu = torch._C._jit_get_te_must_use_llvm_cpu()
40        torch._C._jit_set_te_must_use_llvm_cpu(False)
41
42    def tearDown(self):
43        torch._C._jit_set_profiling_executor(self.prev_exec)
44        torch._C._get_graph_executor_optimize(self.prev_profiling)
45        torch._C._debug_set_autodiff_subgraph_inlining(self.inline_autodiff)
46        torch._C._jit_set_texpr_fuser_enabled(self.texpr_fuser_state)
47        torch._C._jit_override_can_fuse_on_cpu(self.can_fuse_on_cpu)
48        torch.set_default_dtype(self.default_dtype)
49        torch._C._jit_set_texpr_reductions_enabled(self.old_reduction_enabled)
50        torch._C._debug_set_fusion_group_inlining(self.old_fusion_inlining)
51        torch._C._jit_set_te_must_use_llvm_cpu(self.old_te_must_use_llvm_cpu)
52
53    def test_tensor_type_not_determined_by_inputs(self):
54        @torch.jit.script
55        def scalar_type_input(x, y, z):
56            return x + y + 4 + z.item()
57
58        x = torch.tensor([2, 2])
59        scalar_type_input(x, x, torch.tensor(1))
60        scalar_type_input(x, x, torch.tensor(1))
61        scalar_type_input(x, x, torch.tensor(1.0))
62        g = torch.jit.last_executed_optimized_graph()
63
64        # item & add should not get pulled into the fusion group -
65        # we expect to see Fusion Group (item / add) Fusion Group in ir dump
66        FileCheck().check("TensorExpr").check("Scalar = aten::item").check_next(
67            "Tensor = aten::add"
68        ).check("TensorExpr").run(g)
69
70        @torch.jit.script
71        def non_const_dtype(x, y, cond: bool):
72            dtype = torch.int16 if cond else torch.int32
73            return (x + y + 3).sum(dtype=dtype)
74
75        non_const_dtype(x, x, True)
76        non_const_dtype(x, x, True)
77        g = torch.jit.last_executed_optimized_graph()
78        # because dtype is non-const, sum should not get pulled into the Fusion Group
79        FileCheck().check("TensorExpr").check("TensorExpr").check_not("aten::sum").run(
80            g
81        )
82
83    def test_specialize_backward(self):
84        def test_fuse(a, b):
85            c = a * b
86            d = c * b
87            return d
88
89        test_fuse.__disable_jit_function_caching__ = True
90
91        scripted_f = torch.jit.script(test_fuse)
92        x = torch.ones(1, requires_grad=True)
93        y = torch.ones(1, requires_grad=True)
94        scripted_f(x, y)
95        b = scripted_f(x, y)
96        warmup_backward(b)
97        g = torch.jit.last_executed_optimized_graph()
98        # Backward has an if node guarding specializations,
99        # within the if node true block there is only one if node
100        # that guards a tensorexpr group
101        optimized_block = next(g.findNode("prim::If").blocks())
102        if_nodes = list(optimized_block.findAllNodes("prim::If"))
103
104        self.assertEqual(len(if_nodes), 1)
105        FileCheck().check("Group[Subgraph").run(str(if_nodes[0]))
106        # no broadcasts occurred, sum_to_size have been specialized out
107        self.assertIsNone(optimized_block.findNode("aten::_grad_sum_to_size"))
108
109        broadcast_f = torch.jit.script(test_fuse)
110        x = torch.ones([2, 2], requires_grad=True)
111        y = torch.ones([1], requires_grad=True)
112        broadcast_f(x, y)
113        b = broadcast_f(x, y)
114        b.backward(torch.ones([2, 2], dtype=torch.float), retain_graph=True)
115        b.backward(torch.ones([2, 2], dtype=torch.float))
116        # warmup_backward(b, torch.ones([2, 2], dtype=torch.float))
117        g = torch.jit.last_executed_optimized_graph()
118        optimized_block = next(g.findNode("prim::If").blocks())
119        # broadcasts occurred, currently expect to see aten::_grad_sum_to_size
120        self.assertIsNotNone(optimized_block.findNode("aten::_grad_sum_to_size"))
121
122    def test_specialized_types(self):
123        @torch.jit.script
124        def test_fuse(a, b):
125            c = a * b
126            d = c * b
127            return d
128
129        x = torch.tensor([0.5])
130        for _ in range(3):
131            test_fuse(x, x)
132
133        g = torch.jit.last_executed_optimized_graph()
134        # Types should remain specialized for typecheck outputs & fusion outputs
135        FileCheck().check("Double(").check_same("prim::TypeCheck").check_same(
136            "\n"
137        ).check("Double").check_same("TensorExpr").run(g)
138
139        # other outputs should not be specialized
140        FileCheck().check("Tensor = prim::If").run(g)
141
142    def test_aliasing_merge(self):
143        @torch.jit.script
144        def foo(a, b):
145            c = a * b
146            d = c * b
147            d.add_(b)
148            e = d * b
149            return d + e
150
151        x = torch.ones(1)
152        y = torch.ones(1)
153        foo(x, y)
154        b = foo(x, y)
155        g = torch.jit.last_executed_optimized_graph()
156        self.assertEqual(len(list(g.findAllNodes("prim::TypeCheck"))), 2)
157        FileCheck().check("TensorExpr").check("aten::add_").check("TensorExpr").run(g)
158
159    def test_use_not_profiled(self):
160        def foo(t1, t2, t3, t4, t: float):
161            h = t1 + t2 + t3 + t4
162            if t > 0.5:
163                # Putting a use of t1 in a never-executed conditional prevents
164                return t1 + 1
165            return h
166
167        t = torch.rand(8, dtype=torch.float)
168
169        foo_script = torch.jit.script(foo)
170        for _ in range(torch._C._jit_get_num_profiled_runs() + 1):
171            foo_script(t, t, t, t, 0.1)
172
173        self.assertEqual(foo(t, t, t, t, 0.1), foo_script(t, t, t, t, 0.1))
174        g = torch.jit.last_executed_optimized_graph()
175        # all adds fused
176        FileCheck().check("graph").check_not("aten::add").check("prim::If").run(g)
177
178    def test_not_fusing_scalar_ops(self):
179        @torch.jit.script
180        def foo(x: int, y: int):
181            return x + y + 2 + 4 + 5 + 6
182
183        foo(1, 2)
184        foo(2, 3)
185        g = torch.jit.last_executed_optimized_graph()
186        FileCheck().check_not("TensorExpr").run(g)
187
188    def test_not_optimizing_property(self):
189        @torch.jit.script
190        def foo(x, y):
191            return x + y + 1 + 2 + 3, x.size()
192
193        x = torch.ones(1)
194        foo(x, x)
195        foo(x, x)
196        g = torch.jit.last_executed_optimized_graph()
197        FileCheck().check("aten::size").run(g)
198        x = torch.ones([2, 3, 5])
199        self.assertEqual(foo(x, x), (x + x + 1 + 2 + 3, x.size()))
200
201    def test_fallback_graph_not_specialized(self):
202        @torch.jit.script
203        def foo(a, b):
204            c = a * b
205            d = c * b
206            e = d * b
207            return d + e
208
209        x = torch.ones(1)
210        y = torch.ones(1)
211        foo(x, y)
212        foo(x, y)
213        g = torch.jit.last_executed_optimized_graph()
214        FileCheck().check("CallFunction").check_next("Tensor = prim::TupleUnpack").run(
215            g
216        )
217
218    def test_autograd_fallback_graph(self):
219        @torch.jit.script
220        def foo(a, b):
221            c = a * b
222            d = c * b
223            e = d * b
224            return d + e
225
226        x = torch.ones(1, requires_grad=True)
227        y = torch.ones(1, requires_grad=True)
228        foo(x, y)
229        b = foo(x, y)
230        b.backward(torch.ones([1], dtype=torch.float), retain_graph=True)
231        b.backward(torch.ones([1], dtype=torch.float))
232
233        g = torch.jit.last_executed_optimized_graph()
234        FileCheck().check("fallback_function").check_next("CallFunction").run(g)
235
236    def test_tensor_constant(self):
237        def foo(a, b):
238            return a + b + torch.tensor([2])
239
240        x = torch.ones(1, requires_grad=False)
241        foo_script = torch.jit.script(foo)
242        foo_script(x, x)
243        foo_script(x, x)
244
245        self.assertEqual(foo_script(x, x), foo(x, x))
246        g = torch.jit.last_executed_optimized_graph()
247        FileCheck().check_count("aten::add", 2, exactly=True).run(g)
248
249    def test_local_fusion_strategy(self):
250        @torch.jit.script
251        def foo(x):
252            return x + x + x
253
254        torch.jit.set_fusion_strategy([("STATIC", 1)])
255        for _ in range(3):
256            foo(torch.rand([10]))
257
258        torch.jit.set_fusion_strategy([("STATIC", 10)])
259
260        for i in range(10):
261            foo(torch.rand([i]))
262            foo(torch.rand([i]))
263
264        g = torch.jit.last_executed_optimized_graph()
265        FileCheck().check_count(":TensorExprGroup", 2, exactly=True).run(g)
266
267    def test_iterative_fusion(self):
268        @torch.jit.script
269        def foo(a, b, c, d):
270            a = a + b
271            b.add_(3)
272            c = c + b + d
273            a = a + 1
274            return a, c
275
276        x = torch.ones(1, requires_grad=False)
277        foo(x, x, x, x)
278        foo(x, x, x, x)
279
280        # when we iterate through the block, we will start
281        # by fusing a = a + b with a = a + 1
282        # if we were to continue iteration from that fusion point,
283        # would miss the fusion opportunity of c = c + d + b
284
285        g = torch.jit.last_executed_optimized_graph()
286        self.assertEqual(len(list(g.findAllNodes("prim::TensorExprGroup"))), 2)
287