992 lines
40 KiB
Python
992 lines
40 KiB
Python
# Owner(s): ["oncall: jit"]
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import unittest
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import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.testing import FileCheck
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from unittest import skipIf
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from torch.testing._internal.common_utils import run_tests, IS_SANDCASTLE, ProfilingMode, GRAPH_EXECUTOR, \
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enable_profiling_mode_for_profiling_tests, IS_WINDOWS, TemporaryDirectoryName, shell
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from torch.testing._internal.jit_utils import JitTestCase, enable_cpu_fuser, _inline_everything, \
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RUN_CUDA, RUN_CUDA_HALF, RUN_CUDA_MULTI_GPU, warmup_backward
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from textwrap import dedent
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from itertools import product, permutations
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from torch.testing._internal.common_cuda import with_tf32_off
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from test_jit import backward_graph, all_backward_graphs, get_lstm_inputs, get_milstm_inputs, \
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LSTMCellC, LSTMCellF, LSTMCellS, MiLSTMCell
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if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
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torch._C._jit_set_profiling_executor(True)
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torch._C._jit_set_profiling_mode(True)
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def strip_profiling_nodes(nodes):
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profiling_opcodes = {'prim::BailoutTemplate', 'prim::BailOut'}
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return [n for n in nodes if n.kind() not in profiling_opcodes]
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def warmup_forward(f, *args):
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profiling_count = 2
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for i in range(profiling_count):
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results = f(*args)
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return results
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@skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "skip due to SIGIOT failures, #67646")
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class TestFuser(JitTestCase):
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def assertAllFused(self, graph, except_for=()):
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diff_graphs = [n for n in graph.nodes() if n.kind() == 'prim::DifferentiableGraph']
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if len(diff_graphs) > 0:
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self.assertEqual(len(diff_graphs), 1)
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graph = diff_graphs[0].g('Subgraph')
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allowed_nodes = {'prim::Constant', 'prim::FusionGroup', 'prim::BailoutTemplate',
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'prim::BailOut', 'prim::TupleConstruct'} | set(except_for)
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self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()),
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f'got {graph}')
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self.assertTrue([node.kind() for node in graph.nodes()].count('prim::FusionGroup') == 1)
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def _test_fused_abs(self, device='cpu'):
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def func(x):
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return x.abs() * 2
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a = torch.randn(5, device=device)
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scripted = self.checkScript(func, (a,))
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self.assertAllFused(scripted.graph_for(a))
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@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
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@enable_cpu_fuser
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def test_abs_cpu(self):
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self._test_fused_abs()
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@unittest.skipIf(not IS_WINDOWS, "This is meant to be Windows-specific")
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@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
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@enable_cpu_fuser
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def test_abs_cpu_unicode_temp_dir(self):
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with TemporaryDirectoryName(suffix='中文') as dname:
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shell_env = os.environ.copy()
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shell_env['TMP'] = dname
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cmd = [sys.executable, os.path.basename(__file__), type(self).__name__ + '.test_abs_cpu']
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legacy_jit_flag = '--jit-executor=legacy'
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for v in sys.argv:
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if v == legacy_jit_flag:
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cmd.append(legacy_jit_flag)
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return_code = shell(cmd, cwd=os.path.dirname(__file__), env=shell_env)
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self.assertEqual(return_code, 0)
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@unittest.skipIf(not RUN_CUDA, "requires CUDA")
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def test_abs_cuda(self):
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self._test_fused_abs(device="cuda")
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@unittest.skipIf(not RUN_CUDA, "requires CUDA")
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def test_zero_element_tensors(self):
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def decode(sin_t, cos_t):
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theta = torch.atan2(sin_t.float(), cos_t.float())
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return theta
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sin = torch.zeros(0, device="cuda")
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cos = torch.zeros(0, device="cuda")
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inputs = [sin, cos]
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ge = self.checkScript(decode, inputs)
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_arg_configurations_smoke_cuda(self):
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# A smoke test to make sure we won't use the same kernel for contiguous
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# and non-contiguous arguments.
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# TODO: add optionally enabled debug counters to the fuser to verify
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# that we really can tell the difference between configurations
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def f(x, y):
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z1, z2 = (x + y).chunk(2, dim=1)
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return z1 * z2
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x = torch.randn(4, 4, dtype=torch.float, device='cuda')
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y = torch.randn(4, 4, dtype=torch.float, device='cuda')
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traced_f = torch.jit.trace(f, (x, y,))
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self.assertEqual(traced_f(x.t().contiguous(), y), traced_f(x.t(), y))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_broadcast_cuda(self):
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def scaleshift(x, scale, shift):
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return x * scale + shift
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inputs = [
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torch.randn(4, 4, dtype=torch.float, device='cuda'),
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torch.randn(4, dtype=torch.float, device='cuda'),
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torch.randn(4, dtype=torch.float, device='cuda'),
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]
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ge = self.checkTrace(scaleshift, inputs)
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self.assertAllFused(ge.graph_for(*inputs))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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@unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no bfloat support with profiling on")
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def test_cuda_bfloat16(self):
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def foo(x, y):
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return (x + y).relu()
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m = torch.jit.script(foo)
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x = torch.randn(65536).cuda().bfloat16()
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y = torch.randn_like(x)
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self.assertAllFused(m.graph_for(x, y))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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@unittest.skipIf(not RUN_CUDA_HALF, "no half support")
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@unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on")
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def test_cuda_half(self):
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x = torch.randn(4, 4, dtype=torch.half, device='cuda')
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y = torch.randn(4, 4, dtype=torch.half, device='cuda')
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funcs = [
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self.fn_test_comparison_gt_lt,
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self.fn_test_relu,
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self.fn_test_exp
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]
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# Note: Non fused inputs must be float to prevent loss of precision
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inputs = (x.float(), y.float())
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fusion_inputs = (x, y)
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for fn in funcs:
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local_inputs = [t.clone().requires_grad_() for t in inputs]
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local_fusion_inputs = [t.clone().requires_grad_() for t in fusion_inputs]
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# Verifies outputs
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fusion = torch.jit.trace(fn, local_fusion_inputs, check_trace=False)
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outputs = fn(*local_inputs)
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fusion_outputs = fusion(*local_fusion_inputs)
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outputs_half = [t.half() for t in outputs]
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self.assertEqual(outputs_half, fusion_outputs)
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# Verifies gradients
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for output, fusion_output in zip(outputs_half, fusion_outputs):
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grads = torch.autograd.grad(
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output.float().sum(), local_inputs, allow_unused=True, retain_graph=True)
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fusion_grads = torch.autograd.grad(
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fusion_output.sum(), local_fusion_inputs, allow_unused=True, retain_graph=True)
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grads_half = [t.half() for t in grads]
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self.assertEqual(grads_half, fusion_grads)
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_checks_cat_inputs(self):
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# We shouldn't treat cat nodes as broadcasting. All their inputs
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# need to be checked for having the same map size, before we can
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# run the kernel.
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def f(x, y):
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return torch.cat([x + 2 * x + x ** 2, y + 4 * y + y ** 3], dim=0)
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# NOTE: y is broadcastable to x, but output of f(x, y) should have
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# shape 3x4, and not 4x4.
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x = torch.randn(2, 4, dtype=torch.float, device='cuda')
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y = torch.randn(1, 4, dtype=torch.float, device='cuda')
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scripted = self.checkScript(f, (x, y))
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self.assertAllFused(scripted.graph_for(x, y))
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@unittest.skipIf(not RUN_CUDA, "No CUDA")
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def test_remainder_cuda(self):
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def cuda_rem(x, y):
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return 1 + torch.remainder(x, y) - 1
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a = torch.rand([512], dtype=torch.float).cuda()
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b = torch.rand([512], dtype=torch.float).cuda()
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inputs = [a, b]
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ge = self.checkScript(cuda_rem, inputs)
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graph = ge.graph_for(*inputs)
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self.assertAllFused(graph)
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@unittest.skipIf(not RUN_CUDA, "No CUDA")
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def test_chunk_cuda(self):
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def fn(x):
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a, b, c = x.chunk(3, 1)
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return a * b + c
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inputs = [torch.randn(10, 6, dtype=torch.float, device='cuda')]
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ge = self.checkScript(fn, inputs)
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graph = ge.graph_for(*inputs)
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self.assertAllFused(graph)
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FileCheck().check("prim::ConstantChunk[chunks=3, dim=1]").run(str(graph))
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@staticmethod
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def _test_chunk_correctness(self, device='cpu'):
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def chunk_4_0(x):
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x0, x1, x2, x3 = x.chunk(4, 0)
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return x0 + x1 + x2 + x3
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def chunk_4_1(x):
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x0, x1, x2, x3 = x.chunk(4, 1)
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return x0 + x1 + x2 + x3
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def chunk_4_last(x):
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x0, x1, x2, x3 = x.chunk(4, 2)
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return x0 + x1 + x2 + x3
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fns = [chunk_4_0, chunk_4_1, chunk_4_last]
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tensors = [
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# splitSize = 1
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torch.randn(4, 4, 4, dtype=torch.float, device=device),
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# contiguous case
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torch.randn(12, 8, 16, dtype=torch.float, device=device),
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# non-contiguous case
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torch.randn(12, 8, 16, dtype=torch.float, device=device).transpose(1, 2),
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]
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for tensor in tensors:
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for fn in fns:
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self.checkScript(fn, [tensor])
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@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
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@enable_cpu_fuser
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def test_chunk_correctness(self):
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return self._test_chunk_correctness(self, 'cpu')
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@unittest.skipIf(not RUN_CUDA, "No CUDA")
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def test_chunk_correctness_cuda(self):
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return self._test_chunk_correctness(self, 'cuda')
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_chunk_distributes_cuda(self):
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def f(x, y):
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z1, z2 = (x + y).chunk(2, dim=1)
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return z1 * z2
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x = torch.randn(4, 4, dtype=torch.float, device='cuda')
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y = torch.randn(4, 4, dtype=torch.float, device='cuda')
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ge = self.checkTrace(f, (x, y))
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graph = ge.graph_for(x, y)
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FileCheck().check("broadcast_tensors").check('with prim::FusionGroup_') \
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.check_count('ConstantChunk', 2, exactly=True).run(str(graph))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_chunk_motion_deduplicates_inputs(self):
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def func1(x):
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z = x * x
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z0, z1 = z.chunk(2)
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return z0 * z1
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def func2(x):
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z = x * x * x
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z0, z1 = z.chunk(2)
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return z0 * z1
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inputs = [
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torch.tensor([1.1, 1.2], device='cuda', dtype=torch.float),
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]
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for func in [func1, func2]:
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module = self.checkScript(func, inputs)
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forward_graph = module.graph_for(*inputs)
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self.assertGraphContainsExactly(forward_graph, 'prim::FusionGroup', 1)
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fusion_group = list(forward_graph.nodes())[-1]
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self.assertEqual(len(list(fusion_group.inputs())), 1)
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@unittest.skipIf(not RUN_CUDA, "No CUDA")
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def test_chunk_multiple_cuda(self):
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# The arguments are intentionally used out of order as a test to see
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# if the fusion compiler adds extra args in the correct order
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def fn(s, x, y, z):
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z1, z2 = z.chunk(2, 2)
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x1, x2, x3 = x.chunk(3, 1)
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y1, y2 = y.chunk(2, 0)
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return s + x1 + x2 + x3 + y1 + y2 + z1 + z2
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inputs = [
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torch.randn(5, 2, 3, dtype=torch.float, device='cuda'),
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torch.randn(5, 6, 3, dtype=torch.float, device='cuda'),
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torch.randn(10, 2, 3, dtype=torch.float, device='cuda'),
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torch.randn(5, 2, 6, dtype=torch.float, device='cuda'),
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]
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ge = self.checkScript(fn, inputs)
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self.assertAllFused(ge.graph_for(*inputs))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_minmax(self):
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def tmax(a, b):
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return torch.max(2 * a, b)
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def tmin(a, b):
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return torch.min(2 * a, b)
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a = torch.randn(4, 4, dtype=torch.float, device="cuda")
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b = torch.randn(4, 4, dtype=torch.float, device="cuda")
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nan = torch.tensor(float('nan'), dtype=torch.float, device="cuda")
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for f, inputs in product(
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(tmax, tmin),
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([a, b], [a, nan], [b, nan])):
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s = self.checkScript(f, inputs)
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self.assertAllFused(s.graph_for(*inputs))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_clamp(self):
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def func2(a, b):
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return torch.clamp(a + b, min=0, max=2)
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def funcInf(a, b):
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return torch.clamp(a + b, min=0, max=float('inf'))
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def funcOptMin(a, b):
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return torch.clamp(a + b, max=2)
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def funcOptMax(a, b):
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return torch.clamp(a + b, min=0)
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a = torch.randn(4, 4, dtype=torch.float, device='cuda', requires_grad=True)
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b = torch.randn(4, 4, dtype=torch.float, device='cuda')
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nan = torch.tensor(float('nan'), dtype=torch.float, device='cuda')
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funcs = (func2, funcInf, funcOptMin, funcOptMax)
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for f, inputs in product(funcs, [[a, b], [a, nan]]):
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f.__disable_jit_function_caching__ = True
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inp1, inp2 = inputs
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s = self.checkScript(f, (inp1, inp2), profiling=ProfilingMode.PROFILING)
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self.assertAllFused(s.graph_for(inp1, inp2), except_for={'aten::size', 'aten::_size_if_not_equal'})
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c = s(inp1, inp2)
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with enable_profiling_mode_for_profiling_tests():
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warmup_backward(c.sum())
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graph = backward_graph(s)
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self.assertAllFused(graph, except_for={'aten::Float', 'aten::_grad_sum_to_size'})
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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@unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on")
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def test_dropout(self):
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def func(x):
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x = torch.nn.functional.dropout(x)
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return torch.nn.functional.relu(x)
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a = torch.randn(4, 4, dtype=torch.float, device='cuda', requires_grad=True)
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s = torch.jit.script(func)
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c = s(a)
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c = s(a)
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warmup_backward(c.sum())
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# skip_check to skip extra bailout nodes in between
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graph = backward_graph(s, skip_check=True)
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self.assertAllFused(graph, except_for={'aten::div', 'prim::Constant'})
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_comparison_eq_ne(self):
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def f(x, y):
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mask = (x == 0).type_as(x)
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z = x * mask + y
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mask = (x != 0).type_as(x)
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z = z * mask + y
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return z
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x = torch.randn(4, 4, dtype=torch.float, device='cuda')
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y = torch.randn(4, 4, dtype=torch.float, device='cuda')
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ge = self.checkTrace(f, (x, y))
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self.assertAllFused(ge.graph_for(x, y))
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@staticmethod
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def fn_test_comparison_gt_lt(x, y):
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mask = (x > 0).type_as(x)
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z = x * mask + y
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mask = (x < 0).type_as(x)
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z = z * mask + y
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return z
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_comparison_gt_lt_cuda(self):
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x = torch.randn(4, 4, dtype=torch.float, device='cuda')
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y = torch.randn(4, 4, dtype=torch.float, device='cuda')
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ge = self.checkTrace(self.fn_test_comparison_gt_lt, (x, y))
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self.assertAllFused(ge.graph_for(x, y))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_comparison_ge_le_cuda(self):
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def f(x, y):
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mask = (x >= 0).type_as(x)
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z = x * mask + y
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mask = (x <= 0).type_as(x)
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z = z * mask + y
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return z
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x = torch.randn(4, 4, dtype=torch.float, device='cuda')
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y = torch.randn(4, 4, dtype=torch.float, device='cuda')
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ge = self.checkTrace(f, (x, y))
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self.assertAllFused(ge.graph_for(x, y))
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x.requires_grad_(True)
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y.requires_grad_(True)
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self.assertAllFused(ge.graph_for(x, y), except_for=("aten::size", "prim::BroadcastSizes",
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"aten::_size_if_not_equal"))
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@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
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def test_addcmul_cuda(self):
|
|
t = torch.randn(1, 4, dtype=torch.float, device='cuda')
|
|
t1 = torch.randn(4, 1, dtype=torch.float, device='cuda')
|
|
t2 = torch.randn(1, 4, dtype=torch.float, device='cuda')
|
|
|
|
def foo(t, t1, t2):
|
|
return t.addcmul(t + 1, t2, value=0.1)
|
|
|
|
ge = self.checkTrace(foo, (t, t1, t2), allow_unused=True)
|
|
graph = ge.graph_for(t, t1, t2)
|
|
self.assertAllFused(graph)
|
|
|
|
# TODO: We leak CUDA memory here because the traced graph holds onto a
|
|
# constant-ified tensor. Since the Python-global CompilationUnit is alive
|
|
# until the end of the process, the memory is effectively leaked.
|
|
# Removed `_cuda` suffix from this test which disables leak-checking.
|
|
# If this is a real problem, we'll need to revisit Torchscript Function
|
|
# lifetimes in Python.
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_lerp(self):
|
|
start = torch.randn(4, 1, dtype=torch.float, device='cuda')
|
|
end = torch.randn(1, 4, dtype=torch.float, device='cuda')
|
|
weight = torch.tensor(0.5, dtype=torch.float, device='cuda')
|
|
|
|
# scalar weight overload
|
|
def foo_weight_scalar(start, end):
|
|
return torch.lerp(start + 1, end, 0.5)
|
|
|
|
# tensor weight overload
|
|
def foo_weight_tensor(start, end):
|
|
return torch.lerp(start + 1, end, weight)
|
|
|
|
ge_weight_scalar = self.checkTrace(foo_weight_scalar, (start, end))
|
|
graph = ge_weight_scalar.graph_for(start, end)
|
|
self.assertAllFused(graph)
|
|
|
|
ge_weight_tensor = self.checkTrace(foo_weight_tensor, (start, end))
|
|
graph = ge_weight_tensor.graph_for(start, end)
|
|
self.assertAllFused(graph)
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_concat_cuda(self):
|
|
hx = torch.randn(3, 20, dtype=torch.float, device='cuda')
|
|
cx = torch.randn(3, 20, dtype=torch.float, device='cuda')
|
|
|
|
def foo(hx, cx):
|
|
return torch.cat((hx + cx, hx * cx))
|
|
|
|
ge = self.checkTrace(foo, (hx, cx))
|
|
graph = ge.graph_for(hx, cx)
|
|
self.assertAllFused(graph)
|
|
FileCheck().check("FusedConcat").check_next("return").run(str(graph))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_concat_invariant_cuda(self):
|
|
# Invariant: the output of prim::FusedConcat may
|
|
# not be an input to any node inside the FusionGroup.
|
|
def fn(x, y, z):
|
|
x1 = x + y
|
|
y1 = x - y
|
|
w = torch.cat([x1, y1])
|
|
return w + z
|
|
|
|
x = torch.randn(2, 2, dtype=torch.float, device='cuda')
|
|
y = torch.randn(2, 2, dtype=torch.float, device='cuda')
|
|
z = torch.randn(4, 2, dtype=torch.float, device='cuda')
|
|
ge = self.checkTrace(fn, (x, y, z))
|
|
graph = ge.graph_for(x, y, z)
|
|
self.assertAllFused(graph, except_for={'aten::add'})
|
|
FileCheck().check("FusedConcat").check_next("return").run(str(graph))
|
|
|
|
@staticmethod
|
|
def fn_test_exp(x, y):
|
|
return (x + .5 * y).exp()
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_exp_cuda(self):
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
y = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
|
|
ge = self.checkTrace(self.fn_test_exp, (x, y))
|
|
self.assertAllFused(ge.graph_for(x, y))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "broken with profiling on")
|
|
@torch._jit_internal._disable_emit_hooks_decorator
|
|
@_inline_everything
|
|
def test_fuse_decompose_normalization(self):
|
|
class ResLike(torch.jit.ScriptModule):
|
|
def __init__(self, norm_module):
|
|
super().__init__()
|
|
self.nm = norm_module
|
|
|
|
@torch.jit.script_method
|
|
def forward(self, x, y):
|
|
return y + torch.relu(self.nm(x))
|
|
|
|
def test_norm_decompose(nm, in_opt_graph, not_in_opt_graph, in_fusegraph):
|
|
model = ResLike(nm).cuda()
|
|
model_noopt = ResLike(nm).cuda()
|
|
model_noopt.load_state_dict(model.state_dict())
|
|
x = torch.randn(2, 16, 8, 8, device='cuda')
|
|
y = torch.randn(2, 16, 8, 8, device='cuda')
|
|
|
|
# FIXME: We need differentiation for CNNs for this optimization to trigger
|
|
with torch.no_grad():
|
|
out = model(x, y)
|
|
graph = model.graph_for(x, y)
|
|
rep = str(graph)
|
|
|
|
with torch.jit.optimized_execution(False):
|
|
out_noopt = model_noopt(x, y)
|
|
rep_noopt = str(model_noopt.graph_for(x, y))
|
|
self.assertEqual(out, out_noopt, atol=3e-5)
|
|
|
|
# Check that normalization op has really been decomposed
|
|
for node_in_graph in in_opt_graph:
|
|
self.assertIn(node_in_graph, rep)
|
|
|
|
for node_not_in_graph in not_in_opt_graph:
|
|
self.assertNotIn(node_not_in_graph, rep)
|
|
self.assertIn(node_not_in_graph, rep_noopt)
|
|
|
|
fusion_groups = [node for node in graph.nodes() if node.kind() == 'prim::FusionGroup']
|
|
self.assertEqual(len(fusion_groups), 1)
|
|
fused_graph = str(fusion_groups[0].g('Subgraph'))
|
|
for node_in_fusegraph in in_fusegraph:
|
|
self.assertIn(node_in_fusegraph, fused_graph)
|
|
|
|
# test for batchnorm decompose
|
|
bm = nn.BatchNorm2d(16)
|
|
test_norm_decompose(bm, ['aten::batch_norm_update_stats'],
|
|
['aten::batch_norm('], ['aten::sqrt'])
|
|
|
|
# test for layernorm decompose
|
|
lm = nn.LayerNorm(8)
|
|
test_norm_decompose(lm, ['aten::batch_norm_stats'],
|
|
['aten::layer_norm('], ['aten::sub', 'aten::mul', 'aten::add'])
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_threshold(self):
|
|
def f(x):
|
|
return torch.threshold(x, 0, -10) + x + x + x
|
|
|
|
x = torch.tensor([-1, -0.5, 0, 1, 2, 3], device='cuda')
|
|
scripted = self.checkScript(f, (x,))
|
|
self.assertAllFused(scripted.graph_for(x))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_scalar_arg_cuda(self):
|
|
def fn_test_scalar_arg(x: torch.Tensor, p: float) -> torch.Tensor:
|
|
return p * (x * x + x)
|
|
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
p = 3
|
|
scripted = self.checkScript(fn_test_scalar_arg, (x, p))
|
|
self.assertAllFused(scripted.graph_for(x, p))
|
|
|
|
x.requires_grad_(True)
|
|
|
|
# use another function otherwise we will bailout
|
|
# and won't be able to do fused checks
|
|
def fn_test_scalar_arg_requires_grad(x: torch.Tensor, p: float) -> torch.Tensor:
|
|
return p * (x * x + x)
|
|
|
|
scripted = torch.jit.script(fn_test_scalar_arg_requires_grad)
|
|
out = scripted(x, p)
|
|
self.assertAllFused(scripted.graph_for(x, p), except_for=("aten::size", "prim::BroadcastSizes",
|
|
"aten::_size_if_not_equal"))
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
|
|
@unittest.skip("deduplicating introduces aliasing in backward graph's outputs")
|
|
@enable_cpu_fuser
|
|
def test_fuser_deduplication(self):
|
|
# See that fusion kernel outputs are deduplicated when removing _grad_sum_to_size in the fuser's compilation
|
|
# see the discussion in PR #14957.
|
|
def f(x, y):
|
|
return torch.sigmoid(x + y)
|
|
|
|
b = torch.randn(5, 5, requires_grad=True)
|
|
a = torch.randn(5, 5, requires_grad=True)
|
|
s = self.checkScript(f, (a, b))
|
|
self.assertAllFused(s.graph_for(a, b), except_for={
|
|
'aten::size', 'aten::_size_if_not_equal', 'prim::BroadcastSizes'})
|
|
|
|
c = s(a, b)
|
|
results = warmup_backward(c.sum(), [a, b])
|
|
ga2, gb2 = results.pop()
|
|
graph = backward_graph(s)
|
|
self.assertAllFused(graph)
|
|
# check that a, b share storage, i.e. were generated as a single output in the fuser
|
|
self.assertEqual(ga2.data_ptr(), gb2.data_ptr())
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
|
|
@enable_cpu_fuser
|
|
@unittest.skip("temporarily disabled because fusion was restricted in fixing #22833")
|
|
def test_fuser_iou(self):
|
|
# This checks if most of Intersection over Union is fused.
|
|
# In particular, the backward contains many _grad_sum_to_size.
|
|
def iou(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2):
|
|
ltx = torch.max(b1x1, b2x1) # [N,M]
|
|
lty = torch.max(b1y1, b2y1)
|
|
rbx = torch.min(b1x2, b2x2)
|
|
rby = torch.min(b1y2, b2y2)
|
|
|
|
w = (rbx - ltx).clamp(min=0, max=float('inf')) # [N,M]
|
|
h = (rby - lty).clamp(min=0, max=float('inf')) # [N,M]
|
|
inter = w * h # [N,M]
|
|
|
|
area1 = (b1x2 - b1x1) * (b1y2 - b1y2) # [N,1]
|
|
area2 = (b2x2 - b2x1) * (b2y2 - b2y2) # [1,M]
|
|
iou = inter / (area1 + area2 - inter)
|
|
return iou
|
|
|
|
box1 = torch.randn(5, 4, requires_grad=True)
|
|
box2 = torch.randn(5, 4, requires_grad=True)
|
|
# unsqueezing can currently not be fused
|
|
b1x1 = box1[:, 0].unsqueeze(1) # [N,1]
|
|
b1y1 = box1[:, 1].unsqueeze(1)
|
|
b1x2 = box1[:, 2].unsqueeze(1)
|
|
b1y2 = box1[:, 3].unsqueeze(1)
|
|
b2x1 = box2[:, 0].unsqueeze(0) # [1,N]
|
|
b2y1 = box2[:, 1].unsqueeze(0)
|
|
b2x2 = box2[:, 2].unsqueeze(0)
|
|
b2y2 = box2[:, 3].unsqueeze(0)
|
|
|
|
s = self.checkScript(iou, (b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2))
|
|
self.assertAllFused(s.graph_for(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2),
|
|
except_for={'aten::size', 'prim::BroadcastSizes', 'aten::_size_if_not_equal'})
|
|
|
|
with enable_profiling_mode_for_profiling_tests(True):
|
|
c = s(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2)
|
|
warmup_backward(c.sum(), [b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2])
|
|
graph = backward_graph(s)
|
|
self.assertAllFused(graph, except_for={'aten::size', 'prim::BroadcastSizes', 'aten::_size_if_not_equal'})
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device")
|
|
@enable_cpu_fuser
|
|
def test_fusion_reuse_multi_gpu(self):
|
|
def fn(x, y):
|
|
return x * y * x * y
|
|
|
|
inputs_cpu = [
|
|
torch.randn(4, 4, dtype=torch.float),
|
|
torch.randn(4, 4, dtype=torch.float),
|
|
]
|
|
inputs_cuda0 = [x.cuda(0) for x in inputs_cpu]
|
|
inputs_cuda1 = [y.cuda(1) for y in inputs_cpu]
|
|
|
|
# Should not crash; these should compile different kernels.
|
|
ge = self.checkScript(fn, inputs_cpu)
|
|
self.assertAllFused(ge.graph_for(*inputs_cpu))
|
|
ge(*inputs_cuda0)
|
|
ge(*inputs_cuda1)
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device")
|
|
@enable_cpu_fuser
|
|
def test_kernel_cache_multi_gpu(self):
|
|
def not_fusible(x):
|
|
return x
|
|
|
|
def fn(x, y, z):
|
|
x_out = x * x * x * x * x # fusion: lambda x. x * x * x * x * x
|
|
y_out = y * y * y * y * y
|
|
z_out = z * z * z * z * z
|
|
return not_fusible(x_out), not_fusible(y_out), not_fusible(z_out)
|
|
|
|
inputs = [
|
|
torch.randn(4, 4, dtype=torch.float),
|
|
torch.randn(4, 4, dtype=torch.float, device='cuda:0'),
|
|
torch.randn(4, 4, dtype=torch.float, device='cuda:1'),
|
|
]
|
|
|
|
prev_cache_size = torch._C._jit_debug_fuser_num_cached_kernel_specs()
|
|
|
|
# There are 3 FusionGroups. Because they have the same graph, they
|
|
# should reuse the same KernelSpec in the KernelSpec cache.
|
|
ge = self.checkScript(fn, inputs)
|
|
self.assertGraphContainsExactly(
|
|
ge.graph_for(*inputs), 'prim::FusionGroup', 3, True)
|
|
new_cache_size = torch._C._jit_debug_fuser_num_cached_kernel_specs()
|
|
# XXX: This assumes that the same kernel isn't already used by another test
|
|
self.assertEqual(new_cache_size - prev_cache_size, 1)
|
|
|
|
@unittest.skipIf(not RUN_CUDA_MULTI_GPU, "needs non-zero device")
|
|
def test_nonzero_device_cuda(self):
|
|
device = 'cuda:' + str(1)
|
|
x = torch.tensor([0.4], dtype=torch.float, device=device)
|
|
y = torch.tensor([0.7], dtype=torch.float, device=device)
|
|
|
|
def doit(x, y):
|
|
return torch.sigmoid(torch.tanh(x * (x + y) + x))
|
|
|
|
ge = self.checkTrace(doit, (x, y))
|
|
self.assertAllFused(ge.graph_for(x, y))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_lstm_cuda(self):
|
|
inputs = get_lstm_inputs('cuda', training=True)
|
|
module = self.checkScript(LSTMCellS, inputs)
|
|
return
|
|
forward_graph = module.graph_for(*inputs)
|
|
self.assertGraphContainsExactly(
|
|
forward_graph, 'prim::FusionGroup', 1, consider_subgraphs=True)
|
|
self.assertTrue(len(strip_profiling_nodes(forward_graph.nodes())) == 2)
|
|
# Everything is differentiable but TupleConstruct return
|
|
FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \
|
|
.check_next("return").run(str(forward_graph))
|
|
|
|
with enable_profiling_mode_for_profiling_tests(True):
|
|
hy, cy = module(*inputs)
|
|
warmup_backward((hy + cy).sum())
|
|
backward = backward_graph(module)
|
|
self.assertAllFused(backward, except_for=("aten::t", "aten::mm",
|
|
"aten::_grad_sum_to_size"))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
# By default, on Ampere or later GPUs, LSTM computes float tensors at TF32 precision.
|
|
# We want float tensors to be computed at full precision in order to use the default precision
|
|
@with_tf32_off
|
|
def test_lstm_concat_cuda(self):
|
|
inputs = get_lstm_inputs('cuda')
|
|
ge = self.checkTrace(LSTMCellC, inputs)
|
|
graph = ge.graph_for(*inputs)
|
|
FileCheck().check("FusedConcat").check_next("return").run(str(graph))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_lstm_gates_permutations_cuda(self):
|
|
# lstm has gates = x.mm(w_ih.t()) + hx.mm(w_hh.t()) + b_ih + b_hh.
|
|
# Test that any permutation of this will still result in one FusionGroup.
|
|
choices = ['x.mm(w_ih.t())', 'hx.mm(w_hh.t())', 'b_ih', 'b_hh']
|
|
template = dedent('''
|
|
def cell(x, hx, cx, w_ih, w_hh, b_ih, b_hh):
|
|
gates = {} + {} + {} + {}
|
|
ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
|
|
return ingate * forgetgate * cellgate * outgate
|
|
''')
|
|
for permutation in permutations(choices, len(choices)):
|
|
code = template.format(*permutation)
|
|
scope = {}
|
|
exec(code, globals(), scope)
|
|
cu = torch.jit.CompilationUnit(code)
|
|
|
|
inputs = get_lstm_inputs('cuda', training=False)
|
|
self.assertEqual(cu.cell(*inputs), scope['cell'](*inputs))
|
|
forward_graph = cu.cell.graph_for(*inputs)
|
|
self.assertGraphContainsExactly(forward_graph, 'prim::FusionGroup', 1)
|
|
|
|
# TODO: Fuser doesn't work at all when inputs require grad. Fix that
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
# By default, on Ampere or later GPUs, LSTM computes float tensors at TF32 precision.
|
|
# We want float tensors to be computed at full precision in order to use the default precision
|
|
@with_tf32_off
|
|
def test_lstm_traced_cuda(self):
|
|
inputs = get_lstm_inputs('cuda')
|
|
ge = self.checkTrace(LSTMCellF, inputs)
|
|
graph = ge.graph_for(*inputs)
|
|
# .check_not("aten::add") don't get pulled into FusionGroup because of BailOuts
|
|
FileCheck().check_not("Chunk").check_not("aten::sigmoid") \
|
|
.check_not("aten::tanh").check("FusionGroup").check_next("TupleConstruct") \
|
|
.check_next("return").check_not("FusionGroup_2").run(str(graph))
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
|
|
@unittest.skip("Test is flaky, see https://github.com/pytorch/pytorch/issues/8746")
|
|
@enable_cpu_fuser
|
|
def test_lstm_traced_cpu(self):
|
|
inputs = get_lstm_inputs('cpu')
|
|
try:
|
|
ge = self.checkTrace(LSTMCellF, inputs)
|
|
graph = ge.graph_for(*inputs)
|
|
FileCheck.check("FusionGroup").run(str(graph))
|
|
except RuntimeError as e:
|
|
if 'Failed to compile' in e.args[0]:
|
|
warnings.warn('CPU fuser test has failed! This is not a hard failure, '
|
|
'because the kernels sometimes trigger bugs in compilers '
|
|
'(most notably GCC 7.2).')
|
|
raise unittest.SkipTest('Failed to compile') from e
|
|
else:
|
|
raise
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_milstm_cuda(self):
|
|
inputs = get_milstm_inputs('cuda', training=True)
|
|
module = self.checkScript(MiLSTMCell, inputs)
|
|
forward_graph = module.graph_for(*inputs)
|
|
self.assertGraphContainsExactly(
|
|
forward_graph, 'prim::FusionGroup', 1, consider_subgraphs=True)
|
|
FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \
|
|
.check_next("return").check("FusionGroup").run(str(forward_graph))
|
|
hy, cy = module(*inputs)
|
|
warmup_backward((hy + cy).sum())
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "borked on the legacy executor")
|
|
def test_rand_cuda(self):
|
|
class M(torch.jit.ScriptModule):
|
|
__constants__ = ['d']
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.d = torch.device('cuda')
|
|
|
|
@torch.jit.script_method
|
|
def create(self, x):
|
|
return x * x + x + torch.rand_like(x)
|
|
|
|
x = torch.zeros([3, 4, 5], dtype=torch.float, device='cuda')
|
|
m = M()
|
|
out1 = m.create(x)
|
|
out2 = m.create(x)
|
|
self.assertNotEqual(out1, out2)
|
|
self.assertTrue(torch.all(out1 >= 0))
|
|
self.assertTrue(torch.all(out1 < 1))
|
|
self.assertTrue(torch.all(out2 >= 0))
|
|
self.assertTrue(torch.all(out2 < 1))
|
|
self.assertAllFused(m.create.graph_for(x))
|
|
|
|
@staticmethod
|
|
def fn_test_relu(x, y):
|
|
return F.relu(x + .5 * y)
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_relu_cuda(self):
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
y = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
|
|
ge = self.checkTrace(self.fn_test_relu, (x, y))
|
|
self.assertAllFused(ge.graph_for(x, y))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_erf_cuda(self):
|
|
def fn_test_erf(x):
|
|
return F.relu(torch.erf(x) - torch.erfc(x))
|
|
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
ge = self.checkTrace(fn_test_erf, (x,))
|
|
self.assertAllFused(ge.graph_for(x))
|
|
x.requires_grad_(True)
|
|
ge = self.checkTrace(fn_test_erf, (x,))
|
|
self.assertAllFused(ge.graph_for(x), except_for=("aten::size", "prim::BroadcastSizes",
|
|
"aten::_size_if_not_equal"))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(GRAPH_EXECUTOR == ProfilingMode.LEGACY, "borked on the legacy executor")
|
|
def test_rand_broadcast_cuda(self):
|
|
def fn_test_rand(x, y):
|
|
r = torch.rand_like(y)
|
|
return r * x + x
|
|
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
y = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
script_f = torch.jit.script(fn_test_rand)
|
|
out = script_f(x, y)
|
|
self.assertAllFused(script_f.graph_for(x, y))
|
|
x.requires_grad_(True)
|
|
out = script_f(x, y)
|
|
self.assertAllFused(script_f.graph_for(x, y), except_for=("aten::size", "prim::BroadcastSizes",
|
|
"aten::_size_if_not_equal"))
|
|
# test that broadcasting random produces correct results
|
|
x = torch.ones(4, 4, dtype=torch.float, device='cuda')
|
|
y = torch.ones(4, dtype=torch.float, device='cuda')
|
|
out = script_f(x, y)
|
|
self.assertEqual(out[0], out[1])
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
|
|
@enable_cpu_fuser
|
|
def test_scalar(self):
|
|
def fn(x, y):
|
|
return 2 * x + y
|
|
|
|
x = torch.tensor(0.1, dtype=torch.float, device='cpu')
|
|
y = torch.tensor(1, dtype=torch.float, device='cpu')
|
|
ge = self.checkScript(fn, (x, y))
|
|
self.assertAllFused(ge.graph_for(x, y))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_small_constant_cuda(self):
|
|
def fn_test_small_constant(x, y):
|
|
return (1e-8 * x + 5e-9 * y) * 1e8
|
|
x = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
y = torch.randn(4, 4, dtype=torch.float, device='cuda')
|
|
|
|
ge = self.checkTrace(fn_test_small_constant, (x, y))
|
|
self.assertAllFused(ge.graph_for(x, y))
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
def test_tensor_scalar_ops_cuda(self):
|
|
def should_fuse(x):
|
|
z = 3.
|
|
y = x + z
|
|
return x * y
|
|
|
|
# XXX: right now we only support fusing scalars if
|
|
# they're constant (#9940)
|
|
def should_not_fuse(x, z):
|
|
y = x + int(z)
|
|
return x * y
|
|
|
|
inputs = [torch.randn(2, 2, dtype=torch.float, device='cuda')]
|
|
ge = self.checkScript(should_fuse, inputs)
|
|
self.assertAllFused(ge.graph_for(*inputs))
|
|
|
|
inputs = [
|
|
torch.randn(2, 2, dtype=torch.float, device='cuda'),
|
|
torch.tensor(3., dtype=torch.float, device='cuda'),
|
|
]
|
|
ge = self.checkScript(should_not_fuse, inputs)
|
|
self.assertGraphContainsExactly(
|
|
ge.graph_for(*inputs), 'prim::FusionGroup', 0, consider_subgraphs=True)
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "NYI: fuser CPU support for Sandcastle")
|
|
@enable_cpu_fuser
|
|
def test_where_and_typing(self):
|
|
def f(x, y):
|
|
mask = x > y
|
|
res = torch.where(mask, x, y)
|
|
return mask, res
|
|
|
|
x = torch.randn(4, 4, dtype=torch.double)
|
|
y = torch.randn(4, 4, dtype=torch.double)
|
|
|
|
script_f = self.checkScript(f, (x, y))
|
|
self.assertAllFused(script_f.graph_for(x, y), except_for={'prim::TupleConstruct'})
|
|
|
|
@unittest.skipIf(not RUN_CUDA, "fuser requires CUDA")
|
|
@unittest.skipIf(GRAPH_EXECUTOR != ProfilingMode.LEGACY, "no half support with profiling on")
|
|
def test_grad_sum_to_size_elimination(self):
|
|
|
|
def my_broadcasted_cell(a, b, c):
|
|
return (a + b) + c
|
|
|
|
s1 = torch.randn(5, 1, requires_grad=True, device='cuda')
|
|
s2 = torch.randn(5, 5, requires_grad=True, device='cuda')
|
|
|
|
module = self.checkScript(my_broadcasted_cell, (s1, s1, s1), profiling=ProfilingMode.PROFILING)
|
|
forward_graph = module.graph_for(s1, s1, s1)
|
|
self.assertAllFused(forward_graph, except_for=("aten::size", "prim::BroadcastSizes",
|
|
"aten::_size_if_not_equal"))
|
|
|
|
old_plans = set()
|
|
for i in range(3):
|
|
# if we have s2, then the s1 are _grad_sum_to_size'd
|
|
|
|
args = s2 if i < 1 else s1, s2 if i < 2 else s1, s2
|
|
args = [a.detach_().requires_grad_() for a in args]
|
|
# recompile, so we don't trigger bailouts
|
|
module = self.checkScript(my_broadcasted_cell, args, profiling=ProfilingMode.PROFILING)
|
|
res = module(s2 if i < 1 else s1, s2 if i < 2 else s1, s2)
|
|
warmup_backward(res.sum(), args)
|
|
grads = torch.autograd.grad(res.sum(), args)
|
|
for inp, gr in zip(args, grads):
|
|
self.assertEqual(inp.shape, gr.shape)
|
|
backward = None
|
|
# this is a workaround for the backward graphs not being
|
|
# in order for Python 2
|
|
for g in all_backward_graphs(module):
|
|
if str(g) not in old_plans:
|
|
assert backward is None
|
|
backward = g
|
|
old_plans.add(str(backward))
|
|
num_grads = 1 if i > 0 else 0
|
|
self.assertEqual(len([n for n in backward.nodes() if n.kind() == 'aten::_grad_sum_to_size']), num_grads)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|