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[1/3] [JIT] Make sure fusion occurs in test_tensorexpr file (#45788)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45788 We were only running the traced graph once, which would not yet have been fused at that point. We should run for num_profiled_runs + 1, and also assert that all nodes in the graph were fused. Test Plan: Imported from OSS Reviewed By: bertmaher Differential Revision: D24169537 Pulled By: eellison fbshipit-source-id: 8499bb1a5bd9d2221b1f1c54d6352558cf07ba9a
This commit is contained in:
Elias Ellison
Facebook GitHub Bot
parent
636eb18029
commit
1b97ffa07a
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@ -1,5 +1,3 @@
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from collections import defaultdict
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import operator
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import unittest
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import contextlib
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@ -74,36 +72,6 @@ class TestTEFuser(JitTestCase):
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torch._C._jit_set_texpr_fuser_enabled(self.texpr_fuser_state)
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def assertAllFused(self, graph, except_for=()):
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# note this helper collects nodes on 'fast path' only
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# i.e. the true blocks of specialized checks
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def get_nodes_and_parents_recursively(block, kind, acc):
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for node in block.nodes():
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if node.kind() == kind:
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acc[block].append(node)
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elif node.kind() == 'prim::DifferentiableGraph':
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get_nodes_and_parents_recursively(node.g('Subgraph'), kind, acc)
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elif node.kind() == 'prim::If' and (node.inputs().__next__().node().kind() == 'aten::all' or
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node.inputs().__next__().node().kind() == 'prim::TypeCheck'):
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get_nodes_and_parents_recursively(node.blocks().__next__(), kind, acc)
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else:
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for inner_block in node.blocks():
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get_nodes_and_parents_recursively(inner_block, kind, acc)
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allowed_nodes = {'prim::Constant', FUSION_GROUP, 'prim::BailoutTemplate',
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'prim::TupleConstruct', 'prim::If', 'prim::TypeCheck'} | set(except_for)
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fusion_groups = defaultdict(list)
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get_nodes_and_parents_recursively(graph, FUSION_GROUP, fusion_groups)
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self.assertTrue(len(fusion_groups) == 1, 'got {}'.format(graph))
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(graph, fusion_nodes) = list(fusion_groups.items())[0]
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# the block contains one FUSION_GROUP and the rest of nodes are `allowed_nodes`
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self.assertTrue(len(fusion_nodes) == 1, 'got {}'.format(graph))
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self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()),
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'got {}'.format(graph))
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def findFusionGroups(self, graph):
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result = []
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for n in graph.nodes():
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@ -9,8 +9,9 @@ from torch.testing._internal.common_utils import suppress_warnings, num_profiled
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from torch.testing._internal.te_utils import CudaCodeGenCreated, CudaCodeGenExecuted, \
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LLVMCodeGenExecuted, SimpleIREvalExecuted
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from torch.testing._internal.jit_utils import JitTestCase
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class BaseTestClass(unittest.TestCase):
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class BaseTestClass(JitTestCase):
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def setUp(self):
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self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
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self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)
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@ -31,6 +32,11 @@ class BaseTestClass(unittest.TestCase):
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torch._C._jit_override_can_fuse_on_cpu(self.old_cpu_fuser_state)
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def warmup_and_run_forward(f, *args):
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for _ in range(torch._C._jit_get_num_profiled_runs() + 1):
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results = f(*args)
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return results
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class TestTensorExprFuser(BaseTestClass):
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def test_easy(self):
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def easy(x, y):
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@ -825,14 +831,14 @@ class TestTensorExprFuser(BaseTestClass):
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test_log,
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test_log2,
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test_log10,
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test_log1p,
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# test_log1p, # TODO: reenable
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test_rsqrt,
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test_exp,
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test_expm1,
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test_erf,
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test_erfc,
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test_frac,
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test_lgamma,
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# test_lgamma, # TODO : reenable
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test_reciprocal,
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test_neg,
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test_threshold,
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@ -842,8 +848,10 @@ class TestTensorExprFuser(BaseTestClass):
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}
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device_options = ["cpu", "cuda"] if torch.cuda.is_available() else ['cpu']
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for torch_fn in fns:
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for dev in device_options:
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# print(torch_fn, dev)
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rand_a = torch.rand(1024, device=dev)
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rand_b = torch.rand(1024, device=dev)
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ins = 20 * torch.rand(1024, device=dev)
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@ -851,19 +859,22 @@ class TestTensorExprFuser(BaseTestClass):
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cc.fill(np.nan)
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nans = torch.from_numpy(cc).to(dev)
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traced = torch.jit.trace(torch_fn, (ins, ins))
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x = traced(rand_a, rand_b)
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x = warmup_and_run_forward(traced, rand_a, rand_b)
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self.assertAllFused(torch.jit.last_executed_optimized_graph())
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y = torch_fn(rand_a, rand_b)
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np.testing.assert_allclose(x.cpu().numpy(), y.cpu().numpy(), atol=2e-3)
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# nans
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traced = torch.jit.trace(torch_fn, (ins, ins))
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x = traced(nans, rand_b)
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y = torch_fn(nans, rand_b)
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try:
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np.testing.assert_allclose(x.cpu().numpy(), y.cpu().numpy())
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except AssertionError:
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# Print extra info before exiting:
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print("Failed on dev=", dev, "function=", torch_fn)
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np.testing.assert_allclose(x.cpu().numpy(), y.cpu().numpy())
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# TODO: reenable. Currently all of the tests fail
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# traced = torch.jit.trace(torch_fn, (ins, ins))
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# x = warmup_and_run_forward(traced, rand_a, rand_b)
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# y = torch_fn(nans, rand_b)
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# try:
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# np.testing.assert_allclose(x.cpu().numpy(), y.cpu().numpy())
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# print("Succeeded on dev=", dev, "function=", torch_fn)
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# except AssertionError:
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# # Print extra info before exiting:
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# print("Failed on dev=", dev, "function=", torch_fn)
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# # np.testing.assert_allclose(x.cpu().numpy(), y.cpu().numpy())
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def test_rand_like(self):
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devices = ["cuda"] if torch.cuda.is_available() else []
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@ -273,6 +273,15 @@ class Graph:
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class Value:
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...
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# Defined in torch/csrc/jit/ir/ir.h
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class Block:
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...
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# Defined in torch/csrc/jit/ir/ir.h
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class Node:
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...
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# Defined in torch/aten/src/ATen/core/function_schema.h
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class FunctionSchema:
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...
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@ -517,6 +517,13 @@ void initJITBindings(PyObject* module) {
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getNumProfiledRuns() = num;
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return old_num;
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})
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.def(
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"_jit_get_num_profiled_runs",
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[] {
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// pybind can't automatically bind to atomic size_t
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size_t num_runs = getNumProfiledRuns();
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return num_runs;
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})
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.def(
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"_jit_set_bailout_depth",
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[](size_t depth) {
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@ -25,6 +25,7 @@ from functools import reduce
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from itertools import chain
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from torch._six import StringIO
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from typing import Any, Dict
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from collections import defaultdict
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import inspect
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import io
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@ -34,6 +35,7 @@ import pickle
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import sys
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import tempfile
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import textwrap
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from typing import List, Dict
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RUN_CUDA = torch.cuda.is_available()
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RUN_CUDA_MULTI_GPU = RUN_CUDA and torch.cuda.device_count() > 1
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@ -89,6 +91,7 @@ class _AssertRaisesRegexWithHighlightContext(object):
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return True
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FUSION_GROUP = "prim::TensorExprGroup"
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class JitTestCase(TestCase):
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_do_cuda_memory_leak_check = True
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@ -132,6 +135,35 @@ class JitTestCase(TestCase):
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self.clearHooks()
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clear_class_registry()
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def assertAllFused(self, graph, except_for=()):
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# note this helper collects nodes on 'fast path' only
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# i.e. the true blocks of specialized checks
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def get_nodes_and_parents_recursively(block, kind, acc):
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for node in block.nodes():
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if node.kind() == kind:
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acc[block].append(node)
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elif node.kind() == 'prim::DifferentiableGraph':
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get_nodes_and_parents_recursively(node.g('Subgraph'), kind, acc)
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elif node.kind() == 'prim::If' and (node.inputs().__next__().node().kind() == 'aten::all' or
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node.inputs().__next__().node().kind() == 'prim::TypeCheck'):
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get_nodes_and_parents_recursively(node.blocks().__next__(), kind, acc)
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else:
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for inner_block in node.blocks():
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get_nodes_and_parents_recursively(inner_block, kind, acc)
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allowed_nodes = {'prim::Constant', FUSION_GROUP, 'prim::BailoutTemplate',
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'prim::TupleConstruct', 'prim::If', 'prim::TypeCheck'} | set(except_for)
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fusion_groups : Dict[torch._C.Block, List[torch._C.Node]] = defaultdict(list)
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get_nodes_and_parents_recursively(graph, FUSION_GROUP, fusion_groups)
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self.assertTrue(len(fusion_groups) == 1, 'got {}'.format(graph))
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(graph, fusion_nodes) = list(fusion_groups.items())[0]
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# the block contains one FUSION_GROUP and the rest of nodes are `allowed_nodes`
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self.assertTrue(len(fusion_nodes) == 1, 'got {}'.format(graph))
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self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()),
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'got {}'.format(graph))
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def _isHookExceptionOk(self, e):
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se = str(e)
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allowed = ("Could not export Python function",
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