wehub-resource-sync
343 lines
13 KiB
Python
343 lines
13 KiB
Python
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Grappler Remapper."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from absl.testing import parameterized
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.core.protobuf import rewriter_config_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import meta_graph
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.grappler import tf_optimizer
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import sysconfig as sysconfig_lib
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from tensorflow.python.platform import test
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from tensorflow.python.util import _pywrap_utils
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def _input(shape):
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"""Generates an input of a given shape."""
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return variables.Variable(random_ops.truncated_normal(shape, seed=0))
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def _weight(shape):
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"""Generates a weight of a given shape."""
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# Note that the lambda is needed to allow construction inside loops.
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return variables.Variable(lambda: init_ops.glorot_uniform_initializer(seed=0)
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(shape))
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def _bias(shape):
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"""Generates a bias of a given shape."""
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return constant_op.constant(0.1, shape=shape)
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def _get_config(remapping_on=False):
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"""Returns a CongfigProto with remapper optimizer on/off."""
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rewrite_config = rewriter_config_pb2.RewriterConfig(
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remapping=rewriter_config_pb2.RewriterConfig
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.ON if remapping_on else rewriter_config_pb2.RewriterConfig.OFF)
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rewrite_config.min_graph_nodes = -1
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graph_options = config_pb2.GraphOptions(rewrite_options=rewrite_config)
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config = config_pb2.ConfigProto(graph_options=graph_options)
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return config
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class RemapperTest(test.TestCase, parameterized.TestCase):
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"""Tests the Grappler remapper optimizer."""
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def setUp(self):
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super(RemapperTest, self).setUp()
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# GeluApproximate fusion on GPU requires cublasLt.
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os.environ['TF_USE_CUBLASLT'] = '1'
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os.environ['TF_CUDNN_USE_RUNTIME_FUSION'] = '1'
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def maybe_skip_test(self, mode):
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if mode == 'cuda':
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# It seems the windows os cannot correctly query the cuda_version.
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# TODO(kaixih@nvidia): Remove this when it works.
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if os.name == 'nt':
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self.skipTest("This test doesn't support Windows")
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# The cublaslt matmul with gelu epilog is only supported since cuda 11.4.
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if not test.is_gpu_available(cuda_only=True):
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self.skipTest('This test requires GPU.')
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cuda_version_str = sysconfig_lib.get_build_info().get(
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'cuda_version', '0.0')
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cuda_version = tuple([int(x) for x in cuda_version_str.split('.')])
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if cuda_version < (11, 4):
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self.skipTest('This test requires CUDA >= 11.4.')
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if mode == 'mkl' and not test_util.IsMklEnabled():
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self.skipTest('MKL is not enabled.')
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def _VerifyNoFusion(self, model_fn):
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ops.add_to_collection('train_op', model_fn)
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mg = meta_graph.create_meta_graph_def(graph=model_fn.graph)
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# Compute reference
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config = _get_config(remapping_on=False)
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gdef_ref = tf_optimizer.OptimizeGraph(config, mg)
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# Compute with remapping ON
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config = _get_config(remapping_on=True)
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gdef = tf_optimizer.OptimizeGraph(config, mg)
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self.assertEqual(len(gdef_ref.node), len(gdef.node))
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self.assertAllEqual([n.op for n in gdef_ref.node],
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[n.op for n in gdef.node])
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def _VerifyValues(self, model_fn, use_low_precision, fused_op, epilog_ops):
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run_options = config_pb2.RunOptions(output_partition_graphs=True)
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metadata = config_pb2.RunMetadata()
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# Compute reference value.
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config = _get_config(remapping_on=False)
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with session.Session(config=config) as sess:
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sess.run(variables.global_variables_initializer())
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output_ref = sess.run(
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model_fn, options=run_options, run_metadata=metadata)
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# Compute output with fusion.
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config = _get_config(remapping_on=True)
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with session.Session(config=config) as sess:
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sess.run(variables.global_variables_initializer())
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output_val = sess.run(
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model_fn, options=run_options, run_metadata=metadata)
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graph = metadata.partition_graphs[0]
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# Graph should contain fused op.
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found_fused_op = False
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for node in graph.node:
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if node.op in fused_op:
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fused_ops = node.attr['fused_ops'].list.s
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ops_matched = len(fused_ops) >= 1 and len(fused_ops) == len(epilog_ops)
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for op_a, op_b in zip(fused_ops, epilog_ops):
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if op_a != op_b:
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ops_matched = False
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break
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found_fused_op = ops_matched
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break
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self.assertTrue(found_fused_op)
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# Computed output value should be close to reference value.
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tol = 1e-2 if use_low_precision else 1e-5
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self.assertAllClose(output_ref, output_val, atol=tol, rtol=tol)
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return graph
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@parameterized.parameters(['cuda', 'mkl'])
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@test_util.run_deprecated_v1
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@test_util.disable_xla('This test does not pass with XLA')
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@test_util.run_without_tensor_float_32('Avoid TF32 convs on A100+ GPUs')
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def test_matmul_biasadd_activation_fusion(self, mode):
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"""Test MatMul+BiasAdd+Gelu fusion."""
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self.maybe_skip_test(mode)
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def gelu_approximate(x):
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return nn.gelu(x, approximate=True)
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def gelu_exact(x):
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return nn.gelu(x, approximate=False)
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# Erfc-based implementation of GeluExact from:
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# https://github.com/tensorflow/tensorflow/pull/76174
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def gelu_exact_erfc(x):
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return (
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0.5
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* x
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* math_ops.erfc(-x * math_ops.cast(0.7071067811865476, x.dtype))
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)
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device = '/device:GPU:0' if mode == 'cuda' else '/device:CPU:0'
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config = []
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use_fp16 = True
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if (
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test_util.IsMklEnabled()
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and not _pywrap_utils.IsDataTypeSupportedByOneDNNOnThisCPU(
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dtypes.float16
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)
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):
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use_fp16 = False
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if mode == 'mkl':
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config.append((dtypes.float32, gelu_exact, b'GeluExact'))
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config.append((dtypes.float32, gelu_exact_erfc, b'GeluExact'))
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config.append((dtypes.float32, gelu_approximate, b'GeluApproximate'))
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if _pywrap_utils.IsDataTypeSupportedByOneDNNOnThisCPU(dtypes.bfloat16):
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config.append((dtypes.bfloat16, gelu_approximate, b'GeluApproximate'))
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config.append((dtypes.bfloat16, gelu_exact, b'GeluExact'))
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elif mode == 'cuda':
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config.append((dtypes.float32, gelu_approximate, b'GeluApproximate'))
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if use_fp16:
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config.append((dtypes.float16, gelu_approximate, b'GeluApproximate'))
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# Gelu exact fusion is supported by cuDNN frontend APIs and performant
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# with fp16 and on Ampere GPUs and later.
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if (test_util.is_gpu_available(
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cuda_only=True, min_cuda_compute_capability=(8, 0))):
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config.append((dtypes.float16, gelu_exact, b'GeluExact'))
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config.append((dtypes.float16, math_ops.tanh, b'Tanh'))
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config.append((dtypes.float16, math_ops.sigmoid, b'Sigmoid'))
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m, n, k = (2, 4, 6) # Matrix dimensions
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fused_op = ['_MklNativeFusedMatMul', '_MklFusedMatMul', '_FusedMatMul']
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for precision, act_fn, act_name in config:
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for transpose in (False, True):
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# Create MatMul + BiasAdd + Activation graph
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ops.reset_default_graph()
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with ops.device(device):
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x = _input([k, m] if transpose else [m, k])
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w = _weight([n, k] if transpose else [k, n])
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b = _bias([n])
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x = math_ops.cast(x, precision)
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w = math_ops.cast(w, precision)
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b = math_ops.cast(b, precision)
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y = math_ops.matmul(
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x, w, transpose_a=transpose, transpose_b=transpose)
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z = nn.bias_add(y, b)
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out = act_fn(z)
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if transpose and (device == '/device:CPU:0') and \
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act_name in (b'GeluApproximate', b'GeluExact'):
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if precision == dtypes.bfloat16:
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# No fusion should happen on CPU.
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self._VerifyNoFusion(out)
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continue
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else:
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# Gelu should not get fused, only BiasAdd.
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epilog_ops = [b'BiasAdd']
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else:
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epilog_ops = [b'BiasAdd', act_name]
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graph = self._VerifyValues(out, precision != dtypes.float32, fused_op,
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epilog_ops)
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@test_util.run_deprecated_v1
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@test_util.disable_xla('This test does not pass with XLA')
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@test_util.run_without_tensor_float_32('Avoid TF32 convs on A100+ GPUs')
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def test_conv2d_biasadd_act_fusion(self):
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"""Test Conv2D+BiasAdd+Relu fusion."""
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if not test_util.is_gpu_available():
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self.skipTest('No GPU available')
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if test.is_built_with_rocm():
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self.skipTest('ROCm does not support conv biasadd fusion')
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N, H, W, C = (5, 3, 3, 8) # pylint: disable=invalid-name
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# The runtime fusion requires the output dims to be 32-bit aligned.
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self.assertEqual(C % 2, 0)
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act_fns = [nn.relu]
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act_names = [b'Relu']
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if test_util.is_gpu_available(
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cuda_only=True, min_cuda_compute_capability=(8, 0)):
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act_fns += [nn.elu, nn.relu6, nn.leaky_relu]
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act_names += [b'Elu', b'Relu6', b'LeakyRelu']
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for precision in ('float16', 'float32'):
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for act_fn, act_name in zip(act_fns, act_names):
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use_fp16 = precision == 'float16'
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if (
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test_util.IsMklEnabled()
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and use_fp16
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and not _pywrap_utils.IsDataTypeSupportedByOneDNNOnThisCPU(
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dtypes.float16
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)
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):
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continue
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# The runtime fusion (when the activation is not relu) only supports
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# fp16 at this moment.
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if not use_fp16 and act_name != b'Relu':
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continue
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ops.reset_default_graph()
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x_shape = [N, C, H, W]
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x_format, b_format = ('NCHW', 'NC..')
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if use_fp16:
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x_shape = [N, H, W, C]
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x_format, b_format = ('NHWC', 'N..C')
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x = _input(x_shape)
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w = _weight([2, 2, C, C])
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b = _bias([C])
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if use_fp16:
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x = math_ops.cast(x, dtypes.float16)
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w = math_ops.cast(w, dtypes.float16)
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b = math_ops.cast(b, dtypes.float16)
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y = nn_ops.conv2d(
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x, w, strides=(1, 1), padding='SAME', data_format=x_format)
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z = nn.bias_add(y, b, data_format=b_format)
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out = act_fn(z)
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out = array_ops.identity(out)
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epilog_ops = [b'BiasAdd', act_name]
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fused_op = ['_FusedConv2D']
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graph = self._VerifyValues(out, use_fp16, fused_op, epilog_ops)
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@test_util.run_deprecated_v1
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@test_util.disable_xla('This test does not pass with XLA')
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@test_util.run_without_tensor_float_32('Avoid TF32 convs on A100+ GPUs')
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def test_two_conv2d_fusions(self):
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"""Test two Conv2D patterns and only the second is fusable."""
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if not test_util.is_gpu_available(
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cuda_only=True, min_cuda_compute_capability=(8, 0)):
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self.skipTest('No GPU with compute compatibility >= 8.0 available')
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N, H, W, C = (5, 3, 3, 8) # pylint: disable=invalid-name
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ops.reset_default_graph()
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x_shape = [N, C, H, W]
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x_format, b_format = ('NCHW', 'NC..')
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x = _input(x_shape)
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w = _weight([2, 2, C, C])
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b = _bias([C])
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y = nn_ops.conv2d(
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x, w, strides=(1, 1), padding='SAME', data_format=x_format)
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y = nn.bias_add(y, b, data_format=b_format)
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y = nn.leaky_relu(y)
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y = nn_ops.conv2d(
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y, w, strides=(1, 1), padding='SAME', data_format=x_format)
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y = nn.bias_add(y, b, data_format=b_format)
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y = nn.relu(y)
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out = array_ops.identity(y)
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# The first Conv-BiasAdd-LeakyRelu is not fusable because cuDNN requires
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# fp16 for this pattern. The second Conv-BiasAdd-Relu is fusable.
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epilog_ops = [b'BiasAdd', b'Relu']
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fused_op = ['_FusedConv2D']
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self._VerifyValues(out, False, fused_op, epilog_ops)
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if __name__ == '__main__':
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test.main()
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